THE ECONOMIC IMPACT OF IMPROVED BEAN VARIETIES AND
DETERMINANTS OF MARKET PARTICIPATION:
EVIDENCE FROM LATIN AMERICA AND ANGOLA
By
Byron Alejandro Reyes

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
Agricultural, Food and Resource Economics
2012

ABSTRACT
THE ECONOMIC IMPACT OF IMPROVED BEAN VARIETIES AND DETERMINANTS OF
MARKET PARTICIPATION:
EVIDENCE FROM LATIN AMERICA AND ANGOLA
By
Byron Alejandro Reyes
This dissertation consists of two essays. The first essay studies the economic impact of
improved bean varieties (IVs) in Costa Rica, El Salvador, Honduras, Nicaragua, and Ecuador. In
these countries, the National Agricultural Research Systems (NARS), in collaboration with
private and public institutions, have actively generated and promoted IVs over the past 20 years.
There are two types of yield gains derived from the use of IVs: Type I gains in areas where IVs
replace traditional varieties, and Type II gains in areas where new IVs replace old IVs. Previous
studies have only estimated Type I benefits in Honduras and northern Ecuador. This study
estimated the Type II yield gains associated with varietal development of small red and red
mottled bean varieties over time in Central America, Honduras, and northern Ecuador, using
experimental yield data. Further, it provided estimates of current total adoption rates of IVs in
each country, using bean expert opinions. The economic impact of bean IVs was estimated by
combining the Type I and Type II yield gains. The results suggest that the Type II yield gains
from small red varieties averaged 0.49% per year for Central American countries and 0.56% per
year for Honduras. Similarly, the Type II yield gains from red mottled varieties averaged 1.68%
per year for Ecuador. Breeders estimated that adoption rates for 2010 ranged from 46% in
Honduras to 82% in Nicaragua. Amadeus 77 was the most widely adopted small red IV in
Central America and accounted for an estimated 49.7% of the total bean area. Similarly, Portilla,
the most widely planted red mottled IV in northern Ecuador and accounted for an estimated 43%

of the red mottled bean area in northern Ecuador. Ex post benefit/cost analysis for the period
1991-2015 indicate that returns to investments in bean research have been negative in Costa Rica
and positive in all other countries, with a regional net present value of $358 million and a
regional IRR of 32%. The surplus per hectare per year was estimated at $74/ha/yr in the region.
The second essay studies the factors affecting farmers’ marketing decisions in the rural
highlands of Angola, focusing on potatoes, beans, and onions. This essay uses single equation
ordinary least squares regressions for analysis of factors affecting production of potatoes, beans,
and onions in the central highlands of Angola. Furthermore, it implements double hurdle (DH)
regressions to study the factors associated with farmers’ marketing decisions among potato, bean
and onion growers, focusing on gender of the household head, asset ownership, and transaction
costs, while controlling for potentially endogenous variables. The DH regression results suggest
that the factors associated with marketing decisions depend on the crop analyzed and on whether
marketing decisions are analyzed conditionally (i.e., probability of selling and, conditional on
selling, quantity sold) or unconditionally (i.e., unconditional quantity sold). The results also
suggest that boosting sales would be a challenge for the government of Angola, donors, and
organizations working with farmers in this region since, due to Angola’s strong currency,
overcoming the limiting factors found in this study may require large financial and human
resources.

Dedicated to Elena, Alexander, Adrián, our baby boy to be named, and to my parents Teresa and
Alejandro

iv

ACKNOWLEDGMENTS

I would like to thank my mentor and major professor, Richard Bernsten, for his extremely
valuable comments and guidance provided during these past several years. I would also like to
thank the feedback and assistance provided by the rest of my examination committee: Mywish
Maredia, Cynthia Donovan, Eric Crawford, and James Kelly. I appreciate the assistance provided
by the faculty and staff of the Department of Agricultural, Food, and Resource Economics at
MSU, especially Songqing Jin, Debbie Conway, Janet Munn, and Nancy Creed. Especial thanks
go to my friends and colleagues at MSU, especially Ricardo Hernandez, Alexandra Peralta,
Nicky Mason, Wolfgang Pejuan, Sonja Perakis, Tim Komarek, Jake Ricker-Gilbert, and
Guilherme Signorini. I am very grateful for the help provided by Juan Carlos Rosas from
Zamorano’s bean breeding program in Honduras, and by Eduardo Peralta from National Program
on Food Legumes and Andean Grains’ bean breeding program in Ecuador, during the fieldwork
data collection and analysis. Furthermore, I’m also grateful for the help provided by key
informants during my fieldwork in Honduras, El Salvador, Nicaragua, Costa Rica, and Ecuador.
I’m very thankful for the assistance provided by PRORENDA staff in Angola, especially
Deodato Guilherme and Sarah Grindle. While conducting this research, I was fortunate to
receive the support of the Dry Grain Pulses Collaborative Research Support Program with
funding from the United States Agency for International Development (Cooperative Agreement
No. EDH-A-00-07-00005-00). Additional funding was provided by the Bill and Melinda Gates
Foundation through its World Vision’s ProRenda project, and by the Department of Agricultural,
Food, and Resource Economics at MSU. Any opinions and errors are solely my own.

v

TABLE OF CONTENTS

LIST OF TABLES ....................................................................................................................... viii
LIST OF FIGURES ...................................................................................................................... xii
KEY TO ABBREVIATIONS ...................................................................................................... xiii
CHAPTER ONE. INTRODUCTION ..............................................................................................1
1.1 Introduction .............................................................................................................................1
1.2 Objectives ...............................................................................................................................4
CHAPTER TWO. THE ECONOMIC IMPACT OF IMPROVED BEAN VARIETIES IN
LATIN AMERICA: A SURPLUS ANALYSIS ..................................................................5
2.1 Introduction .............................................................................................................................5
2.2 Research Gap ..........................................................................................................................9
2.3 Research Questions ...............................................................................................................14
2.4 Conceptual Framework .........................................................................................................14
2.4.1 Research Benefits .......................................................................................................14
2.4.1.1 Estimation of Potential Genetic Yield Gains .............................................................18
2.4.1.2 Total Adoption Rates..................................................................................................22
2.4.2 Research Costs ...........................................................................................................23
2.4.3 Measures of Project Worth .........................................................................................25
2.4.4 Sensitivity Analysis ....................................................................................................27
2.5 Data Description ...................................................................................................................28
2.5.1 Bean Research Programs ............................................................................................29
2.5.2 Bean Varieties Released between 1990-2010 ............................................................35
2.5.3 Research Benefits .......................................................................................................38
2.5.3.1 Estimating Yield Gains ..............................................................................................38
2.5.3.2 Estimating Adoption Rates .........................................................................................42
2.5.4 Research Costs ...........................................................................................................45
2.5.5 Additional Parameters ................................................................................................49
2.6 Results ......................................................................................................................................52
2.6.1 Adoption of Improved Bean Varieties ..........................................................................52
2.6.2 Estimated Rates of Diffusion ........................................................................................55
2.6.3 Bean Yield Gains Estimation Using Experimental Yield Data .....................................57
2.6.3.1 Descriptive Results .....................................................................................................57
2.6.3.2 Econometric Results ...................................................................................................60
2.6.4 Net Present Value and Internal Rate of Return .............................................................65
2.7 Chapter Summary and Policy Recommendations....................................................................69
2.7.1 Chapter Summary ..........................................................................................................69
2.7.2 Policy Recommendations ..............................................................................................71

vi

CHAPTER THREE. DETERMINANTS OF MARKET PARTICIPATION DECISIONS:
EVIDENCE FROM THE CENTRAL HIGHLANDS OF ANGOLA ...............................74
3.1 Introduction ...........................................................................................................................74
3.2 Research Gap ........................................................................................................................77
3.3 Research Questions ...............................................................................................................82
3.4 Conceptual Framework .........................................................................................................83
3.4.1 Economic Model ........................................................................................................83
3.4.2 Econometric Estimation .............................................................................................87
3.5 Data Used ..............................................................................................................................93
3.6 Results .................................................................................................................................100
3.6.1 Descriptive Statistics ................................................................................................100
3.6.2 Wealth of the Households ........................................................................................109
3.6.3 Households’ Gross Margins .....................................................................................112
3.6.4 OLS Regression Results of Factors Influencing Production ....................................114
3.6.4.1 Potato OLS Results ..................................................................................................115
3.6.4.2 Bean OLS Results ....................................................................................................120
3.6.4.3 Onion OLS Results ...................................................................................................123
3.6.5 Double Hurdle Regression Results of Factors Affecting Marketing Decisions .......126
3.6.5.1 Potato DH Results ....................................................................................................127
3.6.5.2 Bean DH Results ......................................................................................................133
3.6.5.3 Onion DH Results ....................................................................................................139
3.7 Chapter Summary and Policy Recommendations...............................................................145
3.7.1 Chapter Summary .....................................................................................................145
3.7.2 Policy Recommendations .........................................................................................151
APPENDIX ..................................................................................................................................154
REFERENCES ............................................................................................................................195

vii

LIST OF TABLES

Table 2.1. Red bean varieties included in the red ECAR trial data. Zamorano, Honduras,
1999-2009. .........................................................................................................................40
Table 2.2. Improved bean varieties included in the PRUEBA trial data. PRONALEG-GA /
INIAP, Ecuador, 2003-2010. .............................................................................................42
Table 2.3. Bean research investments ($) devoted to the development of red beans (Central
America) and red mottled beans (Ecuador). 1990-2010. ...................................................48
Table 2.4. Estimations of adoption rates (%) of improved small reds and red mottled bean
varieties for 1996 and 2010................................................................................................53
Table 2.5. Estimated mean yields (kg/ha) and other statistics of red bean varieties based on
the experimental trial data. Central America, 1999-2009. .................................................59
Table 2.6. Estimated mean yields (kg/ha) and other statistics of red mottled bean varieties
based on the experimental trial data. Ecuador, 2003-2010. ...............................................59
Table 2.7. Linear regression results of the vintage model using experimental yields of small
red bean varieties released in Central America. 1999-2009. .............................................61
Table 2.8. Linear regression results of the vintage model using experimental yields of small
red bean varieties released in Honduras. 1999-2009. ........................................................63
Table 2.9. Linear regression results of the vintage model using experimental yields of red
mottled bean varieties released in Ecuador. 2003-2010. ...................................................64
Table 2.10. Summary of net present value (NPV) and internal rates of return (IRR)
estimations of investments on bean research in Central America and Ecuador. 19912015....................................................................................................................................66
Table 3.5.1. Independent variables included in the production and marketing decision
regressions. Angola, 2009. .................................................................................................97
Table 3.6.1. Percentage of households growing key crops, per economic status index and
gender of household head (HHH). Central Highlands of Angola, 2009. .........................101
Table 3.6.2. Descriptive statistics of the variables used in the Double Hurdle analysis. Central
Highlands of Angola, 2009. .............................................................................................103
Table 3.6.3. Scoring factors and means per gender of household head (HHH) and crop grown
for asset indicators entering the computation of the first principal component (asset
ownership)........................................................................................................................111

viii

Table 3.6.4. Average receipts, costs and margins of households selling key crops,1 per
economic status index and gender of household head (HHH). Central Highlands of
Angola, 2009. ...................................................................................................................113
Table 3.6.5. Linear regression models of factors influencing potato, bean and onion quantity
produced (kg). Central Highlands of Angola, 2009. ........................................................116
Table 3.6.6. Double-Hurdle model of factors influencing potato marketing decisions. Central
Highlands of Angola, 2009. .............................................................................................128
Table 3.6.7. Double-Hurdle model of factors influencing bean marketing decisions. Central
Highlands of Angola, 2009. .............................................................................................134
Table 3.6.8. Double-Hurdle model of factors influencing onion marketing decisions. Central
Highlands of Angola, 2009. .............................................................................................140
Table A 2.1. Bean trials planted in Central America and Ecuador. 1999-2010. .........................155
Table A 2.2. Costa Rica: Improved bean varieties released. 1990-2010. ....................................156
Table A 2.3. El Salvador: Improved bean varieties released. 1990-2010....................................158
Table A 2.4. Honduras: Improved bean varieties released. 1990-2010. ......................................159
Table A 2.5. Nicaragua: Improved bean varieties released. 1990-2010. .....................................161
Table A 2.6. Ecuador: Improved bean varieties released. 1990-2010. ........................................162
Table A 2.7. Quantity (MT) of seed of improved bean varieties sold or distributed by
government programs in 2010. ........................................................................................164
Table A 2.8. Estimated average yearly salaries ($) of bean breeding programs' permanent
staff for Costa Rica, El Salvador, Honduras (DICTA only) and Nicaragua by
education level. 2010. ......................................................................................................165
Table A 2.9. Parameters α and β used for the estimation of logistic diffusion curves in the
countries of interest. .........................................................................................................166
Table A 2.10. Base scenario: Estimations of total adoption rates (%) of improved bean
varieties using a logistic diffusion curve. 1990-2015. .....................................................167
Table A 2.11. Scenario A: Estimations of total adoption rates (%) of improved bean varieties
using a logistic diffusion curve. 1990-2015. ....................................................................168
Table A 2.12. Scenario B: Estimations of total adoption rates (%) of improved bean varieties
using a logistic diffusion curve. 1990-2015. ....................................................................169
Table A 2.13. Linear regression results of factors influencing experimental yields of small
red bean varieties released in Central America. 1999-2009. ...........................................170
ix

Table A 2.14. Linear regression results of factors influencing experimental yields of small
red bean varieties released in Honduras. 1999-2009. ......................................................171
Table A 2.15. Linear regression results of factors influencing experimental yields of red
mottled bean varieties released in Ecuador. 2003-2010. .................................................172
Table A 2.16. Costa Rica: Base scenario Net Present Value (NPV) and Internal Rate of
Return (IRR) calculations for improved small red bean varieties. 1991-2015. ...............173
Table A 2.17. El Salvador: Base scenario Net Present Value (NPV) and Internal Rate of
Return (IRR) calculations for improved small red bean varieties. 1991-2015. ...............174
Table A 2.18. Honduras: Base scenario Net Present Value (NPV) and Internal Rate of Return
(IRR) calculations for improved small red bean varieties. 1991-2015. ...........................175
Table A 2.19. Nicaragua: Base scenario Net Present Value (NPV) and Internal Rate of
Return (IRR) calculations for improved small red bean varieties. 1991-2015. ...............176
Table A 2.20. Ecuador: Base scenario Net Present Value (NPV) and Internal Rate of Return
(IRR) calculations for improved red mottled bean varieties in northern Ecuador.
1991-2015. .......................................................................................................................177
Table A 2.21. Notes for Table A 2.16 through Table A 2.20. .....................................................178
Table A 3.1. Additional demographic characteristics of farm households (HH). Central
Highlands of Angola, 2009. .............................................................................................179
Table A 3.2. Major sources of crop and non-crop household incomes by market participation.
Central Highlands of Angola, 2009. ................................................................................181
Table A 3.3. Scoring factors, summary statistics, and per tercile means for asset indicators
entering the computation of the first principal component (asset ownership). ................182
Table A 3.4. Means per market participation (seller) for asset indicators entering the
computation of the first principal component (asset ownership). ....................................183
Table A 3.5. Potato sellers: Average receipts, costs, margins and percentage of production
sold, per economic status index and gender of household head (HHH). .........................184
Table A 3.6. Bean sellers: Average receipts, costs, margins and percentage of production
sold, per economic status index and gender of household head (HHH). .........................185
Table A 3.7. Onion sellers: Average receipts, costs, margins and percentage of production
sold, per economic status index and gender of household head (HHH). .........................186
Table A 3.8. Descriptive statistics of factors influencing potato, bean and onion production.
Central Highlands of Angola, 2009. ................................................................................187

x

Table A 3.9. Unconditional average partial effects of factors influencing potato sales. Central
Highlands of Angola, 2009. .............................................................................................189
Table A 3.10. Unconditional average partial effects of factors influencing bean sales. Central
Highlands of Angola, 2009. .............................................................................................190
Table A 3.11. Unconditional average partial effects of factors influencing onion sales.
Central Highlands of Angola, 2009. ................................................................................191

xi

LIST OF FIGURES

Figure 2.1. National bean yields (kg/ha) in Costa Rica, Ecuador, El Salvador, Honduras, and
Nicaragua. FAOSTAT 1990-2009. ......................................................................................8
Figure 2.2. Research-induced supply shift in a small open economy set up. ................................16
Figure 2.3. Flow of breeding materials in the regional and national nurseries in Central
America. For interpretation of the references to color in this and all other figures, the
reader is referred to the electronic version of this dissertation. .........................................32
Figure 2.4. Base scenario: Total adoption rates of improved bean varieties. 1990-2015. .............56
Figure 3.5.1. Distribution of villages included in the ProRenda 2009 survey. The text in the
figure is not meant to be readable but is for visual reference only. ...................................95
Figure 3.6.1. Cumulative distribution of asset index by gender of household head. Central
Highlands of Angola, 2009. .............................................................................................112
Figure A 3.1. Cumulative distribution of asset index by potato growers and non-growers.
Central Highlands of Angola, 2009. ................................................................................192
Figure A 3.2. Cumulative distribution of asset index by onion growers and non-growers.
Central Highlands of Angola, 2009. ................................................................................193
Figure A 3.3. Cumulative distribution of asset index by bean growers and non-growers.
Central Highlands of Angola, 2009. ................................................................................194

xii

KEY TO ABBREVIATIONS

ACDI/VOCA

Agricultural Cooperative Development International / Volunteers in
Overseas Cooperative Assistance

APE

Average Partial Effect

AY

Agricultural Year

B/C CRSP

Bean Cowpea Collaborative Research Support Program

bgm-1

Gene of resistance to BGYMV

BGYMV

Bean Golden Yellow Mosaic Virus

BNF

Biologic Nitrogen Fixation

BTE

Bean Time Equivalents

CAFTA

Central American Free Trade Agreement

CDF

Cumulative Distribution Function

CENTA

Centro Nacional de Tecnología Agropecuaria y Forestal, El Salvador

CIAL

Local Agricultural Research Committee

CIAT

International Center for Tropical Agriculture

CIDA

Canadian International Development Agency

COSUDE

Swiss Agency for Development and Cooperation

COVA

Comprobación de Variedades, a national nursery

CPI

Consumer Price Index

DGP CRSP

Dry Grain Pulses Collaborative Research Support Program

DH

Double-Hurdle

DICTA

Dirección de Ciencia y Tecnología Agropecuaria, Honduras

DNA

Deoxyribonucleic Acid

ECAR

Ensayo Centroamericano de Adaptación y Rendimiento, a regional
nursery
xiii

ESPAC

Encuesta de Superficie y Producción Agropecuaria Continua, Ecuador

FAO

Food and Agriculture Organization

FC

Fixed Costs

Fi

Filial Generation i; i=1,2,…

FO

Farmer Organization

GARB

Gross Annual Research Benefits

GDP

Gross Domestic Product

IDA

Angolan Institute for Agrarian Development

IMR

Inverse Mills Ratio

INEC

Instituto Nacional de Estadísticas y Censos, Ecuador

INIAP

Instituto Nacional Autónomo de Investigaciones Agropeciarias, Ecuador

INTA

Instituto Nicaragüense de Tecnología Agropecuaria, Nicaragua

INTA-CR

Instituto Nacional de Innovación y Transferencia en Tecnología, Costa
Rica

IRR

Internal Rate of Return

IV

Improved Variety

MINADER

Ministério da Agricultura e do Desenvolvimento Rural, the Ministry of
Agriculture of Angola

MLE

Maximum Likelihood Estimator

MPLA

Popular Movement for the Liberation of Angola

MSU

Michigan State University

MT

Metric Tones

NARS

National Agricultural Research Systems

NGO

Non-Governmental Organization

NPV

Net Present Value

NSO

National Statistical Office, one for each country
xiv

OLS

Ordinary Least Squares

PE

Partial Effect

PIF

Programa de Investigaciones en Frijol, Zamorano, Honduras

PITTA-Frijol

Bean research network of Costa Rica

PPB

Participatory Plant Breeding

PROFRIJOL

Programa Cooperativo Regional de Frijol para Centro América, México y
El Caribe

PRONALEG-GA

Programa Nacional de Leguminosas y Granos Andinos, Ecuador

PVS

Participatory Varietal Selection

R&D

Research and Development

SCARs

Sequence Characterized Amplified Regions

SIECA

Secretariat of Economic Integration of Central America

SIMPAH

Honduran Agricultural Price Information System

TLU

Tropical Livestock Units

TNR

Truncated Normal Regression

UNITA

National Union for the Total Independence of Angola

UPR

University of Puerto Rico

USAID

United States Agency for International Development

VC

Variable Costs

VIDAC

Vivero de Adaptación Centroamericana, a regional nursery

Zamorano

Escuela Agrícola Panamericana, Zamorano

xv

CHAPTER ONE. INTRODUCTION

1.1

Introduction
The goal of any development project is to positively impact people’s life (Maredia 2009).

In agriculture, reducing poverty, hunger, and food insecurity is of primary interest. To achieve
this, international donors and national governments support a wide range of projects that could
assist producers. While many projects focus on generating new technologies (e.g. improved
varieties) that offer farmers an opportunity to increase their output, agricultural income, and food
security, other projects assist farmers during the production stage by providing technical
assistance, access to inputs, etc., or during the post-harvest stage by providing technical
assistance regarding storage techniques, better access to markets and information, or assisting
farmers to add value to their products.
As agricultural research funding becomes increasingly scarce, the demand for impact
assessment studies has increased (Alston et al. 2000a) because international donors and national
governments must decide where to invest their money and efforts. This increased demand for
impact assessment has led to a growing focus and emphasis on a more formal, systematic, and
rigorous approach to assessing impacts (Maredia 2009). Although the literature on impact
assessment is overwhelming (Feder et al. 1985) and most studies have demonstrated that returns
to investments are high (Alston et al. 2000a), impact assessment studies are (and will be) an
important and necessary tool to evaluate investments.
Similarly, studying the factors associated with farmers’ decisions of whether to engage in
marketing activities is important because these studies can provide valuable information about
the constraints farmers’ face to participate in markets and increase their agricultural income.

1

Donors and governments can use this information to efficiently allocate scarce resources into
constraining areas to boost market participation and positively affect farmers’ welfare.
This study focuses on two areas: (1) the economic impact of new bean varieties used by
farmers as inputs in their production process in five countries of Latin America, and (2) studying
the factors associated with farmers’ marketing decisions in output markets in the Central
Highlands of Angola.
To estimate the economic impact of new bean varieties in the region, first, experimental
yield data were used to econometrically estimate the yield gains associated with the use of new
improved bean varieties, compared to old improved bean varieties. This was done because of the
time and financial limitations for collecting farm-level data in each country. Furthermore, since
the breeding programs in the region continuously release new improved varieties (IVs), farmers
can replace old IVs with new IVs and benefit from gains in yields (if any). Thus, the
methodology implemented in this study only measures the economic impact derived from these
gains. Second, key informants associated with the bean subsector were interviewed to collect
information related to adoption rates of improved varieties (IVs), research costs, variety-specific
information, etc. Third, additional data were collected from secondary sources.
Fourth, to evaluate the economic impact of new bean varieties in Latin America,
economic surplus concepts were used. This methodology is commonly used to assess the impact
of agricultural projects. Although several studies have evaluated the impact of bean research in
the region in the past, there was a need to generate updated information, especially for Costa
Rica, El Salvador, and Nicaragua, where no economic impact studies have been carried out to
date. To assess the economic impact of bean research, the net present value (NPV) and the
internal rate of return (IRR) were estimated for Honduras, El Salvador, Nicaragua, Costa Rica,

2

and Ecuador for a base scenario. Finally, sensitivity analysis was conducted to evaluate the
robustness of the results.
Since bean research programs in the region have received financial support from donors
for several years, the information generated from this study can be used by donors to make
decisions of whether financial support should be continued and/or which areas require additional
work to augment the economic impact of bean research. Furthermore, government officials can
use this information to generate policies targeted at increasing the benefits of bean research to
farmers.
To study the factors associated with farmers’ marketing decisions in Angola, first,
household-level data were used to estimate factors influencing the production of potatoes, beans,
and onions by ordinary least squares (OLS). The household-level data were collected by World
Visions’ ProRenda project staff in 2009 and included 40 communities across three provinces in
the central highlands of Angola. Second, the residuals from these regressions were estimated.
Third, for each crop, a double hurdle regression was estimated to determine which factors affect
farmers’ marketing decisions, while controlling for self-selection and potential endogeneity
problems. Fourth, the average partial effects (APEs) of the variables of interest were estimated
via bootstrapping. The APEs were used to measure the unconditional (on market participation)
effect of the variables of interest on the total quantity sold.
The ProRenda project’s main objective was to increase smallholders’ income through the
establishment of competitive value chains of several crops. Because of this, the sampling
methodology implemented by this project was out of our control. This caused limitations in our
analysis because the research questions included in the present study were not considered during
the sampling and questionnaire development.

3

1.2

Objectives
The general objective of the study is to generate information that can be used by

stakeholders to efficiently allocate resources as to maximize farmers’ welfare from using new
improved bean varieties in Latin America and from participating in output markets in Angola.
Specifically, the study aims to:
1.

Estimate the economic impact derived from smallholder farmers’ use of improved bean
varieties released between 1996 and 2010 in five countries of Latin America.

2.

Determine the factors that affect smallholder farmers’ marketing decisions in the potato,
bean, and onion markets of rural Angola.
The document is divided into three chapters. Chapter 2 presents information about the

motivation and research questions, conceptual framework, data used, and results of the economic
impact of new bean varieties in five countries of Latin America. Chapter 3 presents information
about the motivation and research questions, conceptual framework, data used, and results of the
factors associated with smallholders’ marketing decisions in rural Angola.

4

CHAPTER TWO. THE ECONOMIC IMPACT OF IMPROVED BEAN VARIETIES IN
LATIN AMERICA: A SURPLUS ANALYSIS

2.1

Introduction
The most common approach for analyzing welfare effects of agricultural research in a

partial-equilibrium framework is the use of economic surplus analysis (Alston et al. 1998). The
economic surplus concept can be applied to ex-ante and/or ex-post evaluation of agricultural
projects. Norton and Davis (1981) review the major research techniques used to evaluate returns
to agricultural research. They found that ex post studies usually use (1) consumer and producer
surplus analyses (i.e. average rates of return) and (2) production function analyses (i.e. marginal
rates of return) to evaluate returns to agricultural research.
Griliches (1958) first used ex-post surplus analysis to estimate the realized social rate of
return of private and public investments in research and development of hybrid corn in the US.
Since then, hundreds of studies have reported measures of the returns to agricultural research and
development (R&D). Alston et al. (2000a) assemble all the available evidence on the returns to
investments in agricultural R&D for the period 1953-1999. The fact that they assembled 292
studies reporting a total of 1,886 rates of return estimates demonstrate how wide surplus
concepts are used for impact evaluation of agricultural research.
Despite its wide use, the surplus approach has been criticized from several perspectives,
including: its normativeness (what it should be vs. what is); its potential for measurement error
(will surplus measures provide an accurate indicator of changes in social welfare?); the partialwelfare nature of the analysis (it ignores the complex interrelationships with other product and
factor markets in the economy); the exclusion from the analysis of (a) externalities and free

5

riders and (b) transaction costs and incomplete risk markets; and its policy irrelevance because
(a) of the problems stated previously and (b) important assumptions and variables are usually not
explained in the calculations (Alston et al. 1998).
However, some of these criticisms (e.g. exclusion of transaction costs and externalities,
measurement errors) can be addressed, at least partially, by making refinements to the measure
of benefits and costs (Alston et al. 1998). In addition, the policy relevance criticism could be
tackled by clearly explaining the implications of the results (Alston et al. 1998).
There are several alternatives to using surplus analysis for impact evaluation. Alston et
al. (1998) suggest the use of cost-benefit analysis (although this type of analysis is usually
complementary to surplus analysis), econometric models (the most commonly used alternative),
domestic resource costs models (with the appeal that they provide a simple measure of the social
value of inputs used to generate a unit of net output valued at its true social value), and the
congruence rule (where resources are invested in relation to the value of output they provide).
However, it is very common to find a mix of methods in the literature. For example, Mather et
al. (2003) used both econometric and surplus methods to estimate the economic impact of bean
research in Honduras. More recently, Mooney (2007) followed a similar approach to estimate the
economic impact of bean breeding investments in Ecuador. Both Mather et al. (2003) and
Mooney (2007) estimated that investments in bean breeding were profitable, a very common
finding across the literature.
Despite these criticisms, surplus analysis is a useful methodology because it is common
used in all methods for estimating research benefits (Alston et al. 1998). This study uses both
econometric and surplus concepts to estimate the economic impact of bean breeding research in
four countries of Central America and one in South America.

6

The study focuses on Honduras, El Salvador, Nicaragua, Costa Rica, and northern
Ecuador because the National Agricultural Research Systems (NARS) of these countries, in
collaboration with private and public institutions, have actively generated and promoted
improved bean varieties (IVs) over the past 20 years.

1

Recent aggregate yield data show that yields have been variable over time (Figure 2.1).
2

For the period of 1990-2009, yields averaged 581 kg/ha (CV =0.20) in Costa Rica; 479 kg/ha
(CV=0.33) in Ecuador;

3

860 kg/ha (CV=0.11) in El Salvador; 717 kg/ha (CV=0.15) in

Honduras; and 713 kg/ha (CV=0.12) in Nicaragua. Therefore, yields varied the most in Ecuador
and Costa Rica, and were less variable in El Salvador and Nicaragua. In addition, while yields
showed a slightly increasing trend in El Salvador, Nicaragua and Costa Rica, the yield trend is
constant in Honduras and decreasing in Ecuador (Figure 2.1).
Although most of the variation in yields have been due to weather-related factors (KII,
2010a), the decreasing yield trend in Ecuador is because (1) INIAP’s (the national research
institute) food legume breeding program, El Programa Nacional de Leguminosas y Granos
Andinos (PRONALEG-GA) primarily focuses on developing bush-type beans targeted for and
4

adopted by farmers in the northern region (KII, 2010a) and (2) FAOSTAT’s dry-bean data does
not distinguish between monocropped and intercropped beans, the latter type with lower yields.

1

Although Guatemala was initially considered for this study, due to the fact that no bean
varieties were released between 1998 and 2009, it was not possible to estimate bean yield gains
from breeding research (since no varieties were released). Thus, this country was excluded from
the analysis.
2
CV = coefficient of variation. Estimated by dividing the standard deviation by the mean.
3
4

For Ecuador, FAOSTAT yield data combines monocropped and intercropped beans.
Mainly the provinces of Carchi and Imbabura.
7

Figure 2.1. National bean yields (kg/ha) in Costa Rica, Ecuador, El Salvador, Honduras,
and Nicaragua. FAOSTAT 1990-2009.

5

The national statistical institute of Ecuador (INEC) reports detailed bean data since 2002. At the
national level, INEC and FAOSTAT report the same yield levels for 2002-2009. However, dry
bean yields for monocropped beans (estimated from INEC data) averaged 655 kg/ha and dry
bean yields for intercropped beans averaged 194 kg/ha. This suggests that intercropped beans
may be driving the low average national yields observed in Figure 2.1 for Ecuador. Although it is
not clear why yields sharply declined after 2001, it is possible that this may be a medium-term
negative effect of the country’s 2000 dollarization, which reduced farmers’ purchase power for
inputs.

5

The data is published annually in the Encuesta de Superficie y Producción Agropecuaria
Continua, ESPAC.
8

The fact that the yield trend has been constant in some countries does not suggest that
bean research has not had a positive effect on (aggregate) production. Morris and Heisey (2003)
note that, over time, most successful crop breeding programs generate genetic gains in yields.
However, genetic yield gains have two components: (a) increased yield potential, which is
observable because yields are higher, and (b) increased biotic and abiotic stress resistance, which
is aimed at avoiding losses from stresses (yields may not be higher; instead, losses are averted in
the presence of stresses). Therefore, without bean research, it is possible that in these countries,
yields could have been much lower. Because of this, it is important to empirically estimate
whether improved bean varieties released over time show genetic yield gains and what has been
the economic impact of investments in bean research in the past two decades.

2.2

Research Gap
Much of the returns-to-research literature has dealt with varietal improvement research

(Pardey et al. 2006). In his pioneering work, Griliches (1958) estimated the realized social rate of
return on public and private funds invested in hybrid-corn research. For this, he estimated the
6

annual gross social returns and subtracted the cost of producing hybrid seed to obtain an annual
flow of net social returns. In addition, he estimated yearly private and public research
expenditures. He then brought forward the net returns and expenditures to the year of interest
(i.e. 1955) and estimated that at least 700 percent per year was earned (in 1955) on the average
dollar invested in hybrid-corn research in the U.S..
Byerlee and Traxler (1995) used total economic surplus concepts to estimate the benefits
of wheat improvement research. Although the concepts used were similar to the ones

6

Also called the gross annual research benefits (GARB) by other authors.
9

implemented by Griliches (1958), they estimated IVs’ yield advantage from (a) IVs replacing
traditional varieties (TVs) and (b) new IVs replacing old IVs, by using experimental yield data.
These authors found that the ex post rate of return of investments in wheat improvement research
during the post-green revolution period was above 50%, and projected that the return on future
investments would be between 37% and 48%.
Pardey et al. (2006) used examples of varietal improvement research in Brazil, focusing
on rice, edible beans, and soybeans, to attribute the benefits of varietal improvements to different
research institutions. They used the total gross annual research benefits (GARB) approach first
implemented by Griliches (1958) for this purpose. Contrary to Griliches (1958), Pardey et al.
(2006) used experimental yield data to generate indexes of varietal improvement to estimate the
7

change in yield from the use of new crop varieties. Furthermore, they used the last-cross rule
8

and the geometric rule to attribute the credit (of the benefits) to different research institutions.
These authors found that, when the benefits are attributed to different institutions, the benefitcost ratio fall from 78:1 when no distinction is made to 16:1 for the institution of interest (the
Brazilian public research corporation Embrapa in their case).
In a similar fashion, Maredia et al. (2010) used the GARB approach to attribute the
benefits of bean research to different research institutions in Michigan, U.S. Following Pardey et
al. (2006), they used experimental yield data to estimate yield gain indexes from the use of IVs
and attributed benefits to the different institutions working on bean research in Michigan. These

7

While both Byerlee and Traxler (1995) and Pardey et al. (2006) used experimental yield data to
estimate benefits, the former used total economic surplus analysis (instead of GARB).
8
While the last-cross rule attributes all the credit to the breeder institution that produced the
variety, the geometric rule attributes the credit to different institutions depending on the share of
the genetic material coming from each institution in the variety.
10

authors found that bean research investments made by a public institution (Michigan State
University in their case) generated benefit-cost ratios between 0.7:1 and 2.2:1.
Marasas et al. (2003) used net present value (NPV), internal rate of return (IRR), and
benefit-cost ratios to estimate the economic impact of efforts made by the International Maize
and Wheat Improvement Center (CIMMYT) to generate disease-resistant wheat varieties in
developing countries. Their study differs from Byerlee and Traxler (1995) in that they estimated
the benefits of research in terms of yield losses avoided with the use of IVs, instead of yield
gains. These authors found that the ex post IRR of investments made to generate disease-resistant
wheat varieties was 41% and that the benefit-cost ratio was 27:1.
In the countries of interest, several studies have been carried out to estimate adoption of
improved varieties and the economic impact of varietal improvement research. Mather et al.
(2003) used econometric methods to estimate adoption rates of bean IVs in two major beanproducing regions of Honduras and the yield loss averted from the use of bean IVs. They
estimated that 41-46% of bean farmers had adopted IVs, planting them in 22-37% of the bean
9

area. Additionally, they found that IV adopters gained the equivalent of 7-16% in bean income
from the yield loss averted through the use of IVs. These authors then used surplus concepts to
estimate the profitability of bean research investments and found that the ex post internal rate of
return to bean research was 41.2% (from 1984-2010).
Hernández and Elizondo (2006) used descriptive statistics to estimate adoption of IVs in
one of the largest bean-producing regions of Costa Rica, the Brunca Region. They estimated that,
in 2004, IVs were planted in approximately 70% of the bean area. Although these authors

9

The area planted to IVs varied by season and region.
11

provided detailed information about adoption rates, the study did not include an estimate of the
economic benefits derived from adoption of IVs in Costa Rica.
Similar to Mather et al. (2003), Mooney (2007) used econometric and surplus analysis to
estimate the impact of bean research in northern Ecuador. He estimated that, when diseases are
present, IV adopters enjoy 40% higher yields and 20% lower per-unit production costs than nonadopters. Furthermore, he estimated that bean research investments (in red mottled beans) have
an ex post internal rate of return of 29% (from 1982-2006).
In contrast, CENTA (2004) documented the advantages and disadvantages of one bean
IV (CENTA San Andrés) developed by the breeding program of El Salvador. Although it
reported detailed information about the characteristics of this variety and its acceptability (by
farmers), the study did not include an analysis about the economic benefits from the use of this
IV. Similarly, there are no studies that estimate the returns to bean improvement research in
Nicaragua. Thus, estimating the economic impact of bean research investments in the countries
of interest will provide valuable information that could be used by stakeholders. This study is a
step in that direction.
Many studies use surplus methods to estimate the economic impact of agricultural
research; however, they differ in the way they estimate the benefits of research. For example,
while Griliches (1958), Byerlee and Traxler (1995), Pardey et al. (2006), and Maredia et al.
(2010) estimate the benefits of agricultural research investments in terms of yield gains, Marasas
et al. (2003), Mather et al. (2003) and Mooney (2007) estimate the benefits in terms of yield loss
averted by the use of IVs.
Within the studies that estimate benefits in terms of yield gains, there are differences in
the way these yield gains are estimated. While Griliches (1958) use experts’ estimates, Byerlee

12

and Traxler (1995), Pardey et al. (2006), and Maredia et al. (2010) use experimental data to
estimate these gains. Similarly, there are differences in the way yield loss averted is estimated.
While Marasas et al. (2003) use experimental data to estimate the yield loss averted by the use of
IVs, Mather et al. (2003) and Mooney (2007) use a combination of farm-level data and
experimental data to estimate these parameters. This study implements a combination of expert
opinions and experimental yield data to estimate the economic benefits from the use of bean IVs.
As noted, several studies demonstrate that investments in bean breeding have been
profitable. Despite this, it is necessary to generate additional and updated information about the
economic benefits of bean research in the countries mentioned above. For countries such as El
Salvador, Nicaragua and Costa Rica, this information will be useful for learning whether
investments in bean improvement research have been profitable, since no such information
currently exists.
The contribution of this study is two-fold. First, it provides estimates of adoption rates
and yield gains of IVs released over time for four Central American countries and one South
American country, using expert opinions and experimental yield data. Second, it estimates the
economic impact of bean improvement research in these countries and provides policy
recommendations based on these results.
Within the countries included in the study, several NARS collaborate to develop bean
IVs, mostly using the same genetic materials to develop bean IVs.

10

Therefore, attributing

benefits to each research institutions is not pursued in this study.

10

The exception is the Ecuadorian breeding program because it does not collaborate with NARS
from the other countries included in this study. Although the International Center for Tropical
Agriculture (CIAT) played an important role in the 1980s and 1990s, during the last decade its
contribution has drastically decreased.
13

2.3

Research Questions
Although the study’s main objective is to generate information about the economic

impact of bean research in Latin America, it also attempts to answer the following research
questions:


What were the adoption rates of improved varieties in the 2009/2010 agricultural year and
which IVs were most widely planted in each country?



What is the cumulative adoption rate of IVs over time?



What are the estimated yield gains from IVs released during the last decade?



What is the economic effect of bean improvement research in the countries of interest?



What policy recommendations can be provided based on these results?

2.4

Conceptual Framework
This section presents the economic rationale to estimate research benefits, costs and

measure of project worth. Although the methodology is based in surplus concepts, the analysis is
tailored to the situation faced in each of the five countries of interest.

2.4.1 Research Benefits
The rationale for the use of economic surplus models is straightforward and a large body
of literature explains this topic (see for example Alston et al. 1998). Although the researchinduced technical changes in the bean sub-sector could affect different sectors of the economy
(e.g. labor markets), it is assumed that these secondary effects are exogenous and were not
addressed in the analysis.

14

For each country, a small open economy surplus model was used (Alston et al. 1998).
The assumption of an open economy is appropriate because the countries of interest trade (export
and import) beans with each other and with other countries in the world. Similarly, the term
small is fitting because the bean supply of each country does not influence international prices.
While it is common that Central American countries trade beans freely

11

with each other,

Ecuador’s main bean-trade partner is Colombia, its northern neighbor.
In the small open economy set up (where quantity supplied does not affect world prices),
the demand curve is perfectly elastic and all of the benefits accrue to producers because there is
no research-induced reduction in price (Alston et al. 1998). Therefore, the change in total surplus
(Δ TS) equals the change in producer surplus (Δ PS).
Following Maredia and Byerlee (2000), Mather et al. (2003) and Mooney (2007), it is
assumed that the supply curve is linear and that its shift (due to technological change) is parallel.
One potential problem of this assumption is that the benefits from a parallel shift may be
overestimated (almost twice) if the supply shift is indeed pivotal (Alston et al. 1998). However,
assuming parallel shifts is appropriate in this context because (a) previous studies have shown
that adoption of IVs is scale-neutral (Mather et al. 2003) and (b) the production technology of
bean producers is relatively homogeneous (Mooney 2007).
Figure 2.2 illustrates the economic benefits derived from the technological changes.
Typically, the curves in Figure 2.2 are defined as annual flows (Alston et al. 1998). The original
supply curve (before research investments) is represented by S0 and the equilibrium price and
quantity under this technology are represented by P0 and Q0, respectively. As farmers adopt the

11

The Central American Free Trade Agreement (CAFTA) allows Central American countries to
export and import beans freely.
15

Figure 2.2. Research-induced supply shift in a small open economy set up.

new technology (i.e. IVs), the original supply curve shifts outwards to S1 and the new
equilibrium quantity shifts from Q0 to Q1. Given that the demand curve (D) is perfectly elastic,
the price remains constant at P0.
The total benefit from research-induced supply shift is equal to the area beneath the
demand curve, D, and between the two supply curves, S0 and S1 (area eabd in Figure 2.2). This
benefit is given by the sum of (a) the cost savings on the original quantity produced (area eacd)
and (b) the economic surplus from incremental production (area abc) (Alston et al. 1998).
16

Empirically, in the case of a small open-economy, the formula for estimating research
benefits from a parallel shift in the supply curve is given by:
(2.1)

Δ TS = Δ PS = P0 × Q0 × Kt (1 + 0.5 Kt ε)

where P0 is the exogenous market price for beans, Q0 is the initial quantity produced before bean
research, Kt represents the shift in the supply curve for each year, and ε represents the supply
elasticity (Alston et al. 1998).
The most critical variable in Equation (2.1) is the supply-shift parameter Kt (Maredia et
al. 2010). This parameter can be estimated in different ways. For example, for each IV, Mather
et al. (2003) estimated Kt by multiplying the change in net bean income due to yield loss (due to
disease pressure) averted from adopting an IV by the adoption rate. Similarly, Mooney (2007)
estimated Kt by multiplying the proportional change in unit cost (from IV adoption) by the
probability of disease pressure and the cumulative adoption rate. These authors used
experimental and farm-level data in their estimations.
In contrast, Byerlee and Traxler (1995), Pardey et al. (2006), and Maredia et al. (2010)
used experimental yield data to estimate yield gains by generating yield gain indexes and then
used this information to estimate the supply-shift parameter Kt. In this study, experimental yield
data were used to estimate yield gains from new bean varieties released over time in the
countries of interest. Thus, these gains reflect the gains obtained by farmers “with” bean research
vs. “without” bean research during the period of evaluation. The supply-shift parameter Kt is
represented by:
(2.2)

Kt = At * kt
17

where At is the share of the bean area planted to improved bean varieties in year t and kt is the
research-induced yield advantage of new bean varieties; that is, the yield gains from new IVs
over old IVs. The methodology for estimating Kt is detailed below.

2.4.1.1 Estimation of Potential Genetic Yield Gains
There are two types of yield gains derived from the use of improved varieties: Type I,
which occurs in areas where improved varieties are replacing traditional varieties (i.e. new
adopters of IVs), and Type II, which occurs in areas where new improved varieties are replacing
old improved varieties (i.e. current adopters replace old IVs with new IVs) (Byerlee and Traxler
1995). Although an ad-hoc estimation of Type I yield gains was conducted, the main focus of the
study was on estimating Type II yield gains (explained below). Therefore, benefits from bean
research shown in the base scenario may be underestimated.
Experimental yield data were used to estimate the Type II yield gains of the commercially
successful bean varieties released to date in the countries of interest. The advantage of using
experimental yield data is that most variables that influence yields are deliberately held constant;
hence, the differences in yields reflect the effect of the variety per se (Pardey et al. 2006). The
disadvantage of using experimental data is that experimental yields are usually higher than
farmers’ yields. However, Pardey et al. (2006) noted that using experimental yields may be
appropriate because farmers’ yields are affected by many factors (e.g. weather, change in relative
price of inputs and outputs) and, although experimental treatments (e.g. fertilizer levels) may
change over time and among locations, this variability is smaller than the variability of farmers’
yields. Additionally, it is yield gains, not yield levels that are relevant in this study.

18

Following Maredia et al. (2010), for each country, the yields of variety i in location j and
year t, Yijt, was estimated by least squares using the following regression model:
Yijt   

(2.3a)

T 1

I 1

J 1

R 1

t 1

i 1

j 1

r 1

  t Dt    i Di 

 j D j 

  r Dr   t

where Dt are the dummy variables for each year, Di are the dummy variables for each variety
included in the dataset (i.e. equal to one if Yijt corresponds to yields of variety i; zero otherwise),
Dj are dummy variables for each location included in the dataset within each country r,

12

Dr are

dummy variables for each country, ut are error terms, and α, βt, γi, δj, and πr are the estimated
coefficients.
The model in Equation (2.3a) can be estimated with a complete dataset. However, the
dataset used in this study was not complete. That is, yield information for every variety for every
location within a country and in every year was not available because the trials were not included
in the same locations each year (Table A 2). Therefore, following Maredia et al. (2010), the
model in Equation (2.3a) was modified to:
(2.3b)

Yit   

T 1

I 1

R 1

t 1

i 1

r 1

  t D t    i Di 

  r Dr   t

where now Yit is the yield of variety i (averaged across all locations within a country) in year t.
Although averaging across locations does not allow us to estimate the effect of the genotype by
environment interaction, each year the breeding programs usually use the same format (i.e.
average yields across locations) to report their results; therefore, averaging yields across
12

In Central America, the variety trials are evaluated in several locations across several
countries. In Ecuador the trials are only evaluated within the country.
19

locations is plausible. For Ecuador, dummies for countries were not included because the trials
are not conducted in other countries. Therefore, for Ecuador, Equation (2.3b) now becomes:
Yit   

(2.3c)

T 1

I 1

t 1

i 1

  t D t    i Di   t

Since the models in Equations (2.3b and 2.3c) were estimated with an intercept, and to
avoid the “dummy” variable trap, one dummy variable for each year, each variety, and each
country (except Ecuador) was dropped from the regression. Once the parameters were estimated,
the fitted values (Ŷit) for the experimental yields of each variety for every year were computed.
Using these (fitted) values provided more accurate estimates of the yield effect because they take
into account the year effect on variety i; that is, they adjust the mean upwards or downwards to
reflect the fact that variety i may have not been tested in high- or low- yielding years (Maredia et
al. 2010).
The predicted yields from Equations (2.3b and 2.3c) were used to estimate the effect of a
vintage variable Vi on yield gains, using the following simplified “vintage”

13

models (adapted

from Maredia et al. 2010):
(2.4a)

(2.4b)

ˆ
ln (Yit )   

ˆ
ln (Yit )   

T 1

R 1

t 1

r 1

  t D t    r D r   Vi   t

for Central America

T 1

  t D t  Vi   t

for Ecuador

t 1

where Vi is the year in which variable i was released (e.g. 1996) and ln (Ŷit) is the natural log of
the fitted values from Equation (2.3b) for the Central American data and from Equation (2.3c)
13

The term vintage refers to the year when the variety was released. Thus, a vintage model is a
model that includes the year of release as an explanatory variable.
20

for the Ecuadorian data.

14

Therefore, the relative (percent) per year yield increase is given by

100 dln (Ŷit)/dVi = 100 λ.
Once the per year yield gain (λ) was estimated using Equations (2.4a and 2.4b), it was
necessary to reflect the impact of these benefits on farmers who had adopted IVs. For this, the
15

research-induced yield advantage was weighted by the yearly cumulative adoption rate of IVs.

Furthermore, the research-induced yield advantage was assumed to grow at a compound rate; i.e.
s

kt = (1 + λ) , where λ is the yield gains from new bean IVs obtained from Equation (2.4) and s =
(t – 1996). Therefore, research-induced Type II yield gains were given by:
II

s

K t = At-1 * [(1 + λ) – 1]

(2.5)
II

where K t measures the benefit from new bean IVs released over time and adopted by farmers
who were already adopters in previous time period (At-1). Following Equation (2.1), the total
Type II benefits from varietal improvement in country r at time t are given by:
(2.6)

II

II

Type II Δ PSrt = Prt × Qrt × K t (1 + 0.5 K t ε)
Type I benefits were estimated using available data from previous research conducted in

the region. For this (ad-hoc) estimation, Equation (2.5) was modified to:
(2.7)

K tI   At  Ab  * k I
I

where At is the adoption rate at time t, Ab is the adoption rate in the base year (i.e. 1996), and k

is the yield gain associated with replacing traditional varieties with improved varieties, obtained
14

In practice, 2.4a and 2.4b are the same models, except for Ecuador, where there are no dummy
variables for countries where the trials were conducted.
15
Total adoption through time was estimated using the logistic diffusion curve (explained in
Section 2.4.1.2 below).
21

from previous research and assumed to be constant through time. Following Equation (2.1), the
total Type I benefits from varietal improvement in country r at time t are given by:
I

I

Type I Δ PSrt = Prt × Qrt × K t (1 + 0.5 K t ε)

(2.8)

Thus, Equation (2.8) assesses the economic benefit for farmers who replace their traditional
varieties with improved varieties (i.e. new adopters). Following Byerlee and Traxler (1995), the
total benefits from varietal improvement in country r at time t are given by the sum of Type I and
Type II benefits; that is, the sum of Equations (2.6) and (2.8).

2.4.1.2 Total Adoption Rates
To estimate Equations (2.5) and (2.7), it was necessary to obtain estimations of adoption
rates of IVs for each year. For this, a logistic diffusion curve was estimated for each country
using total adoption rates of IVs at two points in time (i.e. 1996 and 2010). The logistic diffusion
curves for each country r were estimated using the following formula (from Alston et al. 1998,
pg. 357-358):
Art 

(2.9)

MAX
Ar

1  e ( r   r *t )

where Art is the total adoption rate of IVs in country r and time t (i.e. observed adoption rates),
MAX

Ar

is the maximum adoption rate (adoption ceiling) in country r, and αr and βr are

parameters that define the path of the adoption rate of IVs that asymptotically approaches its
ceiling. The practicality of this formula is that the curve can be generated with as little as three
MAX

parameters: Ar

, αr, and βr.

The expression above can be rearranged and written as:

22


Art
ln 
 A MAX  A
rt
 r

(2.10)


    *t
r
r



From this equation, αr, and βr can easily be estimated because, for each country r, we know
MAX

Ar

and two combinations of Art and t.

16

Although Equation (2.9) does not allow for the possibility of disadoption of the variety,
this is not expected to be a problem because all current breeding programs are mature (i.e.
constantly releasing new varieties); therefore, it is expected that cumulative adoption is
increasing.

17

2.4.2 Research Costs
Constructing the time-series data of research costs can be difficult and time consuming
(Alston et al. 1998). Several considerations must be taken into account to accurately estimate
research costs. First, one must define the duration and scope of the research that will be
evaluated (Mooney 2007). Second, it is necessary to develop a clear understanding of the
institutional history of the research project (Alston et al. 1998) in order to be able to collect
accurate cost data.
Alston et al. (1998) indicate that there can be three possible sources of funding: the core
funds (usually from the government and used to cover routine or core expenditures such as
salaries, consumables, etc.); other government funds (used for non-core activities such as
16

That is, Equation (2.10) needs to be estimated two times using: (1) adoption in the base year
and (2) adoption at the time of the study. Then, these two equations can be set equal to each
other to obtain αr and βr.
17

As will be explained below, only in Costa Rica, adoption of IVs has decreased over time. This
formula is still valid to estimate the decreasing adoption rate in this case.
23

publications, equipment, etc.) that usually are more volatile than the core funds; and donor funds
and grants (from public or private institutions). In the countries of interest, most breeding
programs receive core funds and some receive both core and donor funds. In this study, both core
and donor funds were considered.
Additionally, all expenditures incurred prior to the research program were not considered
because, as Belli et al. (2001) point out, costs incurred in the past are sunk costs that cannot be
avoided; therefore, they should be (and were) ignored from the analysis. Furthermore, extension
expenditures that would have been spent regardless of the current research program were also
excluded from analysis. However, in two of the countries of interest (Honduras and Ecuador),
the research programs provide financial and technical assistance to local agricultural research
committees (CIAL),

18

which complement the work of plant breeders through participatory

varietal selection and/or participatory plant breeding. For these countries, these costs were
included.
Once the duration and scope of the research are defined, it is important to disaggregate
costs in a way that only the costs of the project are reflected (i.e. costs are not overestimated).
For example, a breeder may provide additional services that are not related to the research itself,
such as teaching. Once the disaggregation categories have been specified, knowledgeable
individuals (such as program leaders) can be asked to provide estimates of total program
expenditures and the share of total expenditures devoted to the program of interest (Alston et al.
1998). In this study, this approach was followed.

18

A CIAL is a village-based farmer research group that, among other research activities,
conducts varietal selection to develop new varieties (Ashby et al. 2000).
24

Finally, in agricultural research, it is common to observe a lag between commencing a
19

research activity and generating the new technology (i.e. new varieties) (Alston et al. 1998).

It

is assumed that this lag will be of six years because bean-breeding programs usually take five to
seven years to develop and release a new bean variety, and multiply and distribute seed. During
this period, only expenses are generated.

2.4.3 Measures of Project Worth
After the stream of program benefits and costs are estimated, it is necessary to estimate
the returns to research in each country. For this, two economic measures were used: Net Present
Value (NPV) and Internal Rate of Return (IRR). These measures are useful because they
compress the annual flows of benefits and costs into a summary statistic by aggregating the
flows over time, which allows comparison and evaluation of alternative investments (Alston et
al. 1998).
The NPV is commonly used for ex ante research evaluation; however, in this study it was
used to estimate ex post research benefits. NPV estimation combines the stream of program
benefits and costs over the period of the research. The decision rule is simple: a program is
profitable if NPV > 0. A NPV greater than zero means that the initial investment plus the
opportunity cost of capital are recovered (AEC-865 2008). The formula for calculating NPV is:
(2.11)

T
B  Ct
NPV   t
t
t 1 (1  r )

19

Lags are also observed between generating the new technology and seeing it adopted (Alston
et al. 1998). However, the estimation of logistic adoption curves takes this into consideration.
25

where T is the total number of years under consideration, Bt is the calculated (from Equation
(2.8)) value for annual research benefits in year t, Ct is the program cost in year t, and r is the
discount rate. The numerator in Equation (2.11) is the net benefit.
The IRR is commonly used for ex post research evaluation. The decision rule is also
simple: a program is profitable if the IRR is greater than the opportunity cost of capital. The IRR
is estimated by setting the NPV from Equation (2.11) equal to zero and solving for r; that is:
T

(2.12)

Bt  Ct
t
t 1 (1  IRR )

0

Therefore, the IRR is the rate of return that will make the present value of benefits equal to the
present value of costs (Alston et al. 1998). The IRR is usually estimated by “trial and error,”
although available software easily does this.
Although useful and relatively easy to estimate, both NPV and IRR have their advantages
and disadvantages (AEC-865 2008). Although NPV reflects the size of the investment (i.e.
shows the absolute magnitude of incremental net benefits) and the decision rule is simple (i.e. a
project is profitable when NPV > 0), it requires explicit specification of the discount rate and
implicitly assumes that profits are reinvested at a rate equal to the chosen discount rate. In
contrast, the advantage of IRR is that it does not require knowledge of the exact opportunity cost
of capital; however, it does not reflect the size of the investment. Although projects could be
ranked by either NPV or IRR, NPV is usually preferred because it considers the size (i.e.
magnitude) of the benefits and investments (Alston et al. 1998). In the literature, it is a common
practice to estimate both economic measures of project worth. Therefore, in this study both
measures were estimated.

26

Finally, it is worth mentioning that, when estimating NPV, the higher the discount rate,
the less weight is placed on future benefits. Therefore, one must be careful in deciding what
discount rate to use. Often, high discount rates are used when estimating NPV of programs
located in developing countries (vs. programs in developed countries) because the risk of
investing in developing countries is higher (AEC-865 2008). However, recent literature (Alston
et al. 1998; Maredia et al. 2010) suggests using a lower (e.g. 3-5%) real discount rate (adjusted
for inflation). Using real discount rates is especially common when evaluating the profitability of
medium- to long-term, risk-free projects (Alston et al. 1998; Bazelon and Smetters 2001). In this
study, a real discount rate of 4% (average of the range mentioned above) was used.

2.4.4 Sensitivity Analysis
Sensitivity analysis is commonly used to assess the robustness of the results (Alston et al.
1998). These authors point out that with this type of analysis, it is important to recognize the fact
that the parameters are mutually dependent (e.g. adoption rates likely depend on yields).
Therefore, varying each parameter and considering all combinations is not adequate. In this
study, NPV and IRR were estimated for a base scenario in which breeders’ estimations of
adoption rates, econometric estimations of yield gains, and a 4% real discount rate (among other
parameters) were used. To test the robustness of results, the NPV and IRR were also estimated
using a 10% discount rate (as was used by Mather et al. 2003 and Mooney 2007). Thus, for the
sensitivity analysis, the following parameters from the base scenario were modified as follow:
1.

Scenario A: Type II yield gains and 2010 adoption rates were modified to reflect a +10%
difference from the base scenario. All other parameters were held constant.

27

2.

Scenario B: Type II yield gains and 2010 adoption rates were modified to reflect a -10%
difference from the base scenario. All other parameters were held constant.
Finally, the minimum Type II yield gains and 2010 adoption rates needed to recover

investment (i.e. when NPV=0, or break even values) were estimated separately (e.g. Type II
yield gains were changed until NPV=0, while holding all other variables constant).

2.5

Data Description
The data used in this study were obtained from different sources, including: experimental

trials yield data, expert opinion estimates, secondary sources, and parameters from previous
studies conducted in the region. The experimental yield data was obtained from the following
bean breeding programs: (1) the Programa de Investigaciones en Frijol (PIF) of Zamorano in
Honduras and (2) the Programa Nacional de Leguminosas y Granos Andinos (PRONALEG-GA)
in Ecuador. Before explaining the data, it is important to provide general information about the
breeding process in the countries of interest. For this, first, the breeding programs of Central
America

20

and Ecuador are described. Second, a review of the outcomes of these programs

during the past two decades is provided. Finally, the data used to estimate benefits and costs are
described.

20

Which include Honduras, Guatemala, El Salvador, Nicaragua and Costa Rica. Although
Guatemala is excluded from the analysis, it has collaborated in testing lines in the last decade.
28

2.5.1 Bean Research Programs
Central America

21

Profrijol, a regional bean research network established in 1981 by CIAT and supported
by the Swiss Agency for Development and Cooperation (COSUDE) was the only bean research
network conducting bean research in Central America during the 1980s and 1990s. This network
included researchers from the national agricultural research systems (NARS) of Mexico,
Guatemala, Honduras, El Salvador, Nicaragua, Costa Rica, Panama, and the Caribbean countries
of Haiti, the Dominican Republic, Cuba, and Puerto Rico (Rosas 2010a). During this period,
Zamorano, a private university located in Honduras, had little participation in this network
because only NARS conducted research to generate new varieties.
In 1983, the Bean/Cowpea Collaborative Research Support Program (B/C CRSP) became
involved in bean research in the region; however, its impact was small. In 1990, although
Zamorano and the University of Puerto Rico (UPR) began participating in Profrijol, they did not
generate breeding materials through crosses.

22

In addition, these institutions received only

limited funding from Profrijol (Rosas 2010a).
In 1996 Zamorano, using funds from the B/C CRSP and Profrijol, was given a mandate
to lead efforts to breed small red beans for the region.

23

In 1999, Zamorano also became

responsible for breeding small black beans for Central America (mostly for Guatemala). In 2002,
COSUDE’s funding to Profrijol ended and CIAT’s participation in the region was drastically

21

The information in this section comes from Rosas (2010a).

22

These institutions were in charge of selecting bean varieties with heat tolerance and biologic
nitrogen fixation (BNF) properties only.
23
In the same year, UPR took the lead of the breeding process of Andean beans for Panama and
the Caribbean region.
29

reduced (Rosas 2010a). However, given that the network (established with Profrijol) provided a
great advantage for testing and disseminating breeding materials, Profrijol members continued to
collaborate and the B/C CRSP became the major supporter of this (now informal) network
(Rosas 2010a).

24

Since then, Zamorano’s bean program has provided leadership to the region’s

bean research network, which currently includes NARS from Guatemala, El Salvador, Honduras,
Nicaragua, Costa Rica, Puerto Rico, and Haiti.
25

In 2004, CIAT, in collaboration with this regional network, implemented Agrosalud,

a

biofortification research project designed to benefit Central American and Caribbean (Cuba and
Haiti) countries through the development, promotion, and dissemination of biofortified crops
(including beans). By 2007, the Dry Grain Pulses CRSP (follow up to the B/C CRSP)
unofficially became the major supporter of the regional network through funds provided to
Zamorano (Rosas 2010a).
One of the major contributions of Profrijol was the establishment of regional bean
nurseries (or trials) in which lines from different breeding programs were put together in
nurseries that were distributed to collaborators for testing. These nurseries generated information
needed to select materials

26

adapted to a wide range of environments. Currently, Zamorano’s

bean program is responsible for supplying breeding material which is included in nurseries
throughout the region, including: VIDAC (Central American Adaptation Nursery) and ECAR
(Central American Adaptation and Yield Trial).
24

Although the B/C CRSP never provided funds directly to Profrijol, it provided funds to
Zamorano’s breeding program. Therefore, it has indirectly supported the bean network because
Zamorano’s bean breeding materials are used by NARS throughout the region.
25
This project is coordinated by CIAT and funded by the Canadian International Development
Agency (CIDA).
26
The words “materials” and “lines” are used interchangeable in this document.
30

Figure 2.3 illustrates the sequence of steps required to generate a new bean variety in
Central America. As the figure illustrates, Zamorano’s bean program makes crosses and puts
together the regional nurseries of homogeneous materials that are distributed to collaborators.
These nurseries also contain materials from CIAT and UPR (Rosas 2010a).
From the regional nurseries, the NARS select materials to include in their own national
nurseries.

27

Over the breeding process, while the number of lines decreases in each nursery, the

plot size per line increases. These (regional and national) nurseries are used to select material
that is used to develop new bean varieties.

27

Sometimes, a particular program (from other country) requests Zamorano to make crosses of
specific lines that they want to improve. After the crosses are made, Zamorano sends segregating
lines to that particular program for breeding. This case is not illustrated in Figure 2.3.
31

Figure 2.3. Flow of breeding materials in the regional and national nurseries in Central
America. For interpretation of the references to color in this and all other figures,
the reader is referred to the electronic version of this dissertation.

One key step is that, together with the regional nurseries, Zamorano provides a
spreadsheet for NARS to collect data from these trials and a copy of the data is returned to
Zamorano for further analysis. Each year, approximately 50% of the datasets (i.e. half the
number of nurseries) are returned to Zamorano. Zamorano uses the information from the regional
nurseries to select the best materials until a variety is released. Without this collaboration, testing
lines would be limited and more expensive (Rosas 2010a).

32

Ecuador
Ecuador’s national program on food legumes and Andean grains, El Programa Nacional
de Leguminosas y Granos Andinos (PRONALEG-GA) is in charge of conducting bean-breeding
activities in the country. PRONALEG-GA is located at the Santa Catalina Research Station in
Quito, Ecuador and is part of the country’s national agricultural research institute, El Instituto
Nacional Autónomo de Investigaciones Agropecuarias (INIAP). PRONALEG-GA consolidated
its activities in 1990 and, since 1994 it has supported other experimental stations throughout the
country (INIAP 2009).
In the 1990s, the PRONALEG-GA program collaborated with Profriza, an Andean bean
research network established by CIAT and supported by COSUDE.

28

During this period,

PRONALEG-GA depended on CIAT to generate new varieties (i.e. no crosses were made in
Ecuador). Although PREDUZA, a Dutch organization provided funding to PRONALEG-GA
from 2000-2004 (Mooney 2007), since 2003, the CRSP has been the major external supporter of
bean research in Ecuador. Through this collaboration, PRONALEG-GA has been able to make
its own bean crosses, which has reduced its germplasm-dependence on other institutions (Peralta
2010).
Although several bean varieties were released prior 1990, the varieties that have had the
largest impact were developed post 1990. In addition, in 2000 PRONALEG-GA implemented
participatory research methods to develop and disseminate new varieties (INIAP 2009).
Although farmers are involved in the breeding process, segregating materials are evaluated at the
Santa Catalina Research Station.

28

Profriza was established following the Central American model of Profrijol. Both programs
were coordinated by CIAT and supported by COSUDE.
33

Although most of its research is done on bush-type beans, the PRONALEG-GA program
also conducts research on climbing beans and other legumes. Currently, PRONALEG-GA
collaborates with international institutions (e.g. CIAT, Michigan State University), and with
local NGOs and local farmers groups (CIALs). Its current collaboration with Michigan State
University (MSU) has allowed it to supply black and red mottled bean lines to Rwanda’s bean
program, the first time that this program has supplied materials to another continent (KII, 2010a).
PRONALEG-GA has three main nurseries for testing advanced lines: (1) Prueba, (2)
Comprobación and (3) Producción. These nurseries generally include different market classes
and are tested in different locations under farmer conditions. Similar to the Central American
case, while the number of lines decrease from one nursery to another, the plot size per line
increases. The Producción nursery is the last step before releasing a variety.
Participatory Research: Participatory plant breeding (PPB) vs. participatory varietal selection
(PVS)
Participatory research is a methodology through which farmers are involved in the
breeding process by providing them with either early or late generation materials to select from
(Ceccarelli et al., 2000). In principle, farmers can be provided with a set of segregating (i.e.
>F3)

29

or homogeneous (i.e. >F6) materials to select from and, assisted by scientists, release a

variety. There are two major types of participatory research: participatory plant breeding (PPB)
and participatory varietal selection (PVS). While PPB is the process where farmers are given a
set of segregating materials to select from, PVS is the process where farmers are given a set of
homogeneous materials to select from (Ceccarelli et al., 2000).

29

In the breeding literature, Fi is the ith generation after crosses were made. Thus, F3 is the
third generation, etc.
34

Within the countries of interest, all countries are currently implementing participatory
breeding approaches (KII, 2010a). However, only in Honduras, Nicaragua, Costa Rica and
Ecuador, have new bean varieties been released using this approach. While participatory
breeding was implemented in 2006 in El Salvador, no bean varieties have yet been released using
this methodology (KII 2010a). Furthermore, this methodology is implemented differently across
countries. The major differences lie in the degree of farmer participation (i.e. PPB vs. PVS) and
the type of group of farmers included in the process. In Costa Rica, Ecuador, El Salvador, and
Nicaragua, farmers participate in the breeding process by evaluating and selecting advanced (i.e.
>F6) materials.

30

In contrast, in Honduras, farmers evaluate both segregating (i.e. >F3) and
31

advanced (i.e. >F6) materials.

Regarding the types of group of farmers participating in plant breeding, in Honduras and
Ecuador, the process is implemented in collaboration with farmers organized in CIALs. In
contrast, in Costa Rica, El Salvador, and Nicaragua, this process is implemented with farmers
organized in cooperatives or associations. The major difference between the CIALs and the
cooperatives is that the former are smaller groups of farmers set up to conduct research locally
and are usually located in niche (marginal) environments.

2.5.2 Bean Varieties Released between 1990-2010
Between 1990 and 2010, a total of 90 bean varieties (all market classes) were released in
the five countries of interest. However, some of the varieties released in Central America were

30

However, in Costa Rica and Ecuador, farmers sometimes evaluate segregating materials at
experimental stations.
31
The farmers’ group implements either PPB or PVS. That is, the same group does not
implement both methods at the same time.
35

released in several countries, usually with a different name in each country. The varieties
released in more than one country are: Dorado and Amadeus 77 (four countries each), Tío
Canela 75 (three countries), and Deorho, Carrizalito, DOR 390 and Don Silvio (two countries
each). Hence, 78 unique varieties were released in all five countries. From these, most varieties
were small reds or reds (46 of 78), followed by red mottled (10 of 78), and blacks (7 of 78).
Furthermore, at least 44 of the 78 varieties were developed using (direct or indirect) CRSP
funding (Table A 2.2-Table A 2.6).
In Costa Rica, as many as 18 improved bean varieties have been released in the last two
decades, 56% of them since 2000. The PITTA-Frijol network released all of these varieties.
From these varieties, three have Tío Canela 75 (Line ID: MD 3075), a variety released in
Honduras in 1996 with resistance to BGYMV, as a parent. Surprisingly, although black beans
are most widely consumed in Costa Rica, the last black variety was released in 2000 (UCR 55)
and it was not widely adopted (Table A 2.2).
In El Salvador, only nine improved varieties have been released in the last two decades,
five since 2000. Furthermore, all varieties released over the past 20 years are small red varieties
and were released by CENTA, the national center in charge of bean research (Table A 2.3). Five
of the nine varieties were developed using indirect funding provided by the CRSP (through
germplasm provided by Zamorano), all five in the last decade.
In Honduras, as of 2010, 21 bean IVs have been released in the last two decades, 76% in
the last decade and 57% (12 out of 21 IVs) were developed using participatory methods (Table A
2.4). From these varieties, four have Amadeus 77 germplasm (Line ID: EAP 9510-77), a variety
released in the country in 2003, which is resistant to BGYMV and has a light red seed color, as a
parent. Amadeus 77 was developed using Tío Canela 75 as one of its parents. Moreover, three

36

other varieties had Tío Canela 75 as a parent, which brings the total number of varieties with
genetic share of Tío Canela 75 to eight. Similar to El Salvador, all varieties released to date are
small reds. In addition, 81% of the varieties released over the last 20 years were developed using
CRSP funding. Three institutions supported the development of varieties using participatory
methods: CIALs, NGOs assisting farmers, and Zamorano.
In Nicaragua, 16 bean IVs have been released in the last two decades, less than half were
released since 2000 and all varieties were developed using participatory methods (Table A 2.5).
Three of these varieties have Tío Canela 75 as one of its parents. In Nicaragua, most varieties
released to date are small reds. Although most varieties were released by INTA, one of these
varieties (INTA Pueblo Nuevo JM) was released in collaboration with CIPRES, an NGO.
Among all of the countries, Ecuador has released the highest number of bean varieties in
the past two decades--26 varieties in total. From these, 17 varieties were developed using direct
CRSP funding, 16 varieties were released in the last decade, and 12 varieties were developed
through participatory methods (Table A 2.6). Although the CRSP is currently the main external
supporter of this program, CIAT played an important role in the 1990s, when it was the main
supplier of germplasm to the program.

32

In contrast to all other countries, PRONALEG-GA’s

efforts have focused on developing varieties of several market classes (Table A 2.6). However,
the three main market classes are: red mottle (38% of varieties belong to this market class),
yellow (23% of varieties) and white (12%). Furthermore, both INIAP and CIAL groups are
credited with the release of most varieties developed through participatory breeding, with the
exception of Canario Guarandeño, Libertador, and Canario Siete Colinas, credited only to
INIAP.

32

For example, Paragachi Andino, released in 2009, came from a CIAT cross.
37

2.5.3 Research Benefits
In order to estimate the value of research benefits, yield gains and adoption rates were
needed. The data used to estimate these parameters are explained below.
2.5.3.1 Estimating Yield Gains
There are two types of yield gains derived from adopting IVs: Type I, in areas where
farmers replace traditional varieties with IVs, and Type II in areas where farmers replace old IVs
with new IVs. Type I gains were obtained from previous studies and are detailed in section 2.6.4.
To estimate Type II gains from new bean varieties released through time (i.e. Equations 2.3b,
2.3c, 2.4a and 2.4b), experimental yield data were used. The rest of this sub-section explains the
Central American and Ecuadorian data used for estimations of Type II yield gains and the market
classes analyzed.
Central America
As explained above, the experimental yield data were obtained from Zamorano’s bean
breeding program. These data were used to estimate λ for red beans.

33

There were two nurseries

from which data could be drawn: VIDAC and ECAR. The ECAR data were used because it had
an adequate number of observations (not as many as the VIDAC), it had three repetitions per line
evaluated (vs. no repetitions in the VIDAC), and it was planted in several countries each year
(Table A 2.1).
Although the ECAR nursery has been implemented since the 1990s, data on red varieties
were only available for the period 1999-2009. The ECAR included 14 advanced lines plus one
local check (usually a traditional variety) plus one universal check (Dorado, one of the first IVs

33

Although there is a similar nursery for black beans, it was not possible to estimate λ for this
market class because none of the few black varieties released were in the dataset (since they were
released in 2001 or before, thus they likely were evaluated before 1999).
38

released in the region), for a total of 16 lines per nursery. Each of the (16) lines had three
repetitions (Table A 2.1). The data were averaged across repetitions. Although the trial dataset
contained information for other countries outside the region of interest, only information from
Costa Rica, El Salvador, Guatemala, Honduras, and Nicaragua was used in the analysis since we
were interested in the effect of IVs in this region.

34

On average, the ECAR nursery was planted in six locations across four Central American
countries each year (Table A 2.1). The data were averaged across locations to obtain one yield
observation per country per year. As explained above, this would control for the fact that in some
years, the trial was not planted in every location. Thus, the data used reflected average yields at
the country level.
Although many varieties were released in the last decade in the region, the ECAR dataset
did not contain yield information for all varieties. Table 2.1 summarizes which varieties were
included in the dataset. As expected, data for varieties released in Honduras were included the
most. In contrast, the dataset contained information for only two of the varieties released in
Nicaragua, for example.
Ecuador
Similar to Central America, there were three nurseries from which data could be drawn
for the analysis: Prueba, Comprobación and Producción. The Prueba data were used because of
similar reasons to the ones stated above: the number of lines was large, there were several
repetitions per nursery (although the repetitions were in different locations, not in the same
location), and it was the most common nursery for which information was available.

34

Although Guatemala is not included in the study, the trial data from this country was included
in the dataset because of its proximity with El Salvador and Honduras and to increase the number
of observations.
39

Table 2.1. Red bean varieties included in the red ECAR trial data. Zamorano,
Honduras, 1999-2009.
Country(ies) where
1
2
Year of release
released
Variety Name
Line ID in trial data
1989, 1990, 1992, ES, HND, GUA, CR, Dorado
DOR 364
1992, 1993
NIC
2002, 2002, 2003, ES, NIC, HND, CR Amadeus 77
EAP 9510-77
2003
2003
HND
Cedron
PTC 9557-10
2003
HND
Cayetana 85
PRF 9653-16B-2A
2003, 2004
HND, CR
Carrizalito
EAP 9510-1
2005
ES
CENTA Pipil
PRF 9653-16B-3
2007
HND
Don Cristobal
SRC 1-12-1-8
2007
CR
Tongibe
BCH 9901-14
2007
HND
Cardenal
MER 2226-41
2007, 2008
HND, ES
Deorho
SRC 2-18-1
2008
ES
CENTA C.P.C.
PPB 11-20-MC
2009
HND
Briyo AM
IBC 306-95
2009
HND
La Majada
IBC 301-182
Source: Programa de Investigaciones en Frijol Metadata, Zamorano, Honduras.
1

When more than one year of release, the first year corresponds to the first country
listed in the column to the right; the second year corresponds to the second country, etc.
2
CR = Costa Rica; ES = El Salvador; GUA = Guatemala, HND = Honduras; NIC =
Nicaragua.

This dataset contained information for the period 2003-2010. It included an average of
13 advanced lines plus several checks (Table A 2.1) that were not included in the dataset because
these were different for each market class and these varied across locations and years. As with
the Central American dataset, the data were averaged across repetitions (when available) and
across locations.
In contrast to Central America, this nursery was planted in the same location for 1-2
years. This was because, once farmers tested the Prueba trial for one or two seasons, they

40

selected the best lines and advanced them to the next nursery (i.e. Comprobación) and they did
not plant the prior nursery until a new breeding cycle began (generally several years later).
Similar to the Central American case, although many varieties have been released in the
last decade, the dataset did not contain yield information for all varieties. Table 2.2 summarizes
which varieties were included in the dataset. As expected, data for red mottled varieties were the
most common. Therefore, due to limitations in the number of observations, λ was estimated only
for red mottled bean varieties developed by Ecuador’s breeding program. Thus, Equation (2.4)
was estimated for (a) small red bean varieties in Central America and (b) red mottled bean
varieties in Ecuador.

41

Table 2.2. Improved bean varieties included in the PRUEBA trial data. PRONALEG-GA
/ INIAP, Ecuador, 2003-2010.
Year of Variety
1
release
ID Variety Name
1996
I427 Blanco Imbabura
2003
I422 Blanco Belen
2003
I423 Canario
2004
I414 Yunguilla
2004
I424 La Concepcion
2004
I424 La Concepcion
2004
I425 Blanco Fanesquero
2004
I425 Blanco Fanesquero
2005
I420 Canario del Chota
2005
I420 Canario del Chota
2007
I427 Libertador
2007
I428 Canario Guarandeno
2009
I429 Paragachi Andino
2009
I429 Paragachi Andino
2009
I430 Portilla
2009
I430 Portilla
2009
I480 Rocha
2009
2010
2010
2011

I480
I481
I482
I483

Rocha
Rojo del Valle
Afroandino
InTag

Market Class
White
White
Yellow
Red mottled
Purple mottled
Purple mottled
White
White
Yellow
Yellow
Red mottled
Yellow
Red mottled
Red mottled
Red mottled
Red mottled
Yellow
Yellow
Red mottled
Black
Purple mottled

Line ID in trial data
Blanco Imbabura
I-Blanco Belen
I423 Canario
I414 Yunguilla
I424 Concepcion
MIL UNO
ABE4
I-Blanco Fanesquero
ACE1
I420 Canario del Chota
I-Libertador
I-Guarandeno
AND 1005
I429 Paragachi Andino
I430 Portilla
Yunguilla X Mil Uno, S23
ACE1 x (Cocacho x San Antonio)
s26 p1
I480 Rocha
TP6
A55
(Concepcion x (G916 x
Concepcion))-1

Source: INIAP/PRONALEG-GA Metadata, Ecuador.
1

Sometimes two names shown because the variety had two line IDs. Thus, information in the
last column refers to the same variety.

2.5.3.2 Estimating Adoption Rates
In order to estimate the logistic diffusion curves for IVs for each country, three
parameters were needed: (1) the current adoption rates, At, (2) the base year adoption rates, Ab,
MAX

and (3) the maximum adoption rates, A

.

42

(1) Current adoption rates of IVs, At. The current total (i.e. for all IVs) adoption rates
were obtained from estimations provided by bean breeders in each country. Breeders generally
estimate adoption rates taking into consideration farmers’ re-use of grain as seed and parameters
from previous studies. Although Maredia et al. (2010) used bean seed sales data to estimate
adoption rates in Michigan, U.S., this approach was not appropriate in the Latin American
context since most farmers do not purchase seed.
Current adoption rates reflect 2010 levels of adoption of IVs in each country, for which
bean breeders were asked to estimate adoption rates of IVs in 2010. In most countries of Central
America farmers grow only one market class. Thus, the adoption rates reflect the same market
class. However, in Costa Rica and Ecuador, farmers plant several market classes. Therefore, in
these two countries, experts were asked to specify the share of adoption of IVs to each market
class.
In addition, in Ecuador both bush and climbing beans are produced. Since our interest is
only on bush beans, climbing bean estimations of adoption were not collected.

35

Furthermore,

the analysis only focused on the northern provinces of Carchi and Imbabura since this is where
most of PRONALEG-GA’s legume breeding effort is targeted. Thus, adoption rates only refer to
adoption of bush beans in northern Ecuador.
Although seed distribution/sales data were collected, these data were used only to
demonstrate the strength of the seed systems in each country. Seed data suggest that the seed
systems may be stronger in Central America than in Ecuador (Table A 2.7). However, in all
Central American countries with the exception of Costa Rica, governments have implemented

35

This may underestimate the economic benefit from bean research in Ecuador since a few
improved climbing beans were released by PRONALEG-GA.
43

programs that distribute free or subsidized seed to farmers. Therefore, the apparent strong seed
system in these countries highly depends on the continuity of the government-funded seed
programs. Key informants related to the bean subsector suggested that without these government
programs, seed production will most likely drastically decline because (a) most farmers do not
purchase seed and (b) the estimated market price of the seed distributed by these programs (if it
were sold at full price) is too high (KII 2010a; KII 2010c; KII 2010d).
(2) Base year adoption rates of IVs, Ab. To estimate the diffusion curve, the adoption
rates for 1996 were obtained from previous research conducted in the region. The year 1996 was
used as the base year because, in most countries, many new varieties were released after this
year. The adoption rates for 1996 and 2010 were used together to estimate the diffusion curves
(i.e. total adoption rates over time) for all countries.
MAX

(3) Maximum adoption rate of IVs, A

. Since most bean programs are mature, it is

expected that 2010 adoption rates are approaching the maximum levels of adoption. Thus, it was
assumed that the maximum adoption rate is two percentage points above the current adoption
rate.

36

Given that many breeders reported high adoption rates, this assumption is reasonable.
These three parameters of adoption rates provided the setting to evaluate the economic

effect of bean IVs under bean breeders’ estimations of adoption, referred to as the ‘base
scenario’ from now on. For Scenarios A and B in the sensitivity analysis, while the adoption
rates for 2010 (hence maximum adoption rates) were modified to reflect a +10% difference from
the base scenario, the adoption rates for 1996 were held constant (i.e. logistic curve had a pivotal
shift).

36

Except for Costa Rica, where adoption of IVs has decreased.
44

2.5.4 Research Costs
As mentioned in Section 2.4.2, obtaining cost information is often difficult and requires
knowledge about the institutional history of the breeding programs. The history of the breeding
programs was explained above. Since Zamorano’s breeding program supplies breeding materials
to all NARS in Central America, the costs of generating these materials were imputed to
Zamorano. Although this overestimates Zamorano’s costs, it is impossible to attribute these costs
to the different programs. Furthermore, costs incurred by donors in their respective countries
(e.g. U.S. costs for the DGP CRSP) were not included in these cost estimations.
Bean breeding program leaders were asked to identify their 2010 external sources of
funding and how much they received from each source. In addition, they were asked to estimate
the amount of funding they received during the last ten years from large donors. Funding
provided by large donors for several years was easily accessible (e.g. DGP CRSP). However,
program leaders found it difficult to estimate their annual core budget (i.e. from their own
37

institution).

To estimate the core budget of each program, program leaders were asked how many
staff members their programs employed in 2010 and the share of their time devoted to beanrelated activities (to estimate their bean time equivalent, BTE). Furthermore, they were asked to
classify their staff by education level and state whether the number of staff has
increased/decreased/remained constant over the last decade. Over the past ten years, the number
of staff has remained constant across all education levels in Nicaragua and Honduras (Zamorano
only). In contrast, the number of staff has decreased across all education levels at DICTA in

37

Except for PRONALEG-GA (Ecuador) and Zamorano (Honduras), where this information
was reported without distinction in the total budget and/or was available in the literature (see
Mather 2003 and Mooney 2007).
45

Honduras. However, in Costa Rica, while the number of staff holding a Ph.D. degree has
remained constant, the number of staff holding a M.Sc., B.S., or <Technical degrees has
decreased in the last decade. Similarly, in El Salvador, while the number staff holding a M.Sc.
has remained constant, the number of staff holding a B.S. has decreased over time. Likewise, in
Ecuador, while the number of staff holding a M.Sc. or a Technical degree has remained constant,
the number of staff holding a B.S. degree has increased in the last decade.
The following average monthly salaries
(Table A 2.8):

39

38

were used to estimate the 2010 core budget

Ph.D = $2,500 in Costa Rica (KII 2010a); M.Sc. = $2,250 in El Salvador and

Nicaragua (Mejia 2012)

40

and M.Sc. = $2,000 in Costa Rica (KII 2010a); B.S. = $1,250 in

Honduras, El Salvador, and Costa Rica and B.S. = $875 (70% of Honduras' salary) in Nicaragua
(Mejia 2012); Technician = $1,000 in Honduras (Mejia 2012); and <Technician = $500 in Costa
Rica (KII 2010a). Although salary costs only refer to 2010, it was assumed that for previous
years the costs of salaries were at the 2010 level for El Salvador, Nicaragua, and Costa Rica.
However, Mather (2003) reported DICTA’s budget (salaries + operational) for years prior to
1994. Therefore, DICTA’s budget (salaries + operational) for 1995-2009 was estimated
assuming a proportional (linear) change over time using the 1994 and 2010 values (DICTA’s
operational budget for 2010 was reported at $5,992). While using constant values for salaries

38

Zamorano (Honduras) and PRONALEG-GA (Ecuador) were excluded from this estimation
since the budget provided by program leaders already included salaries.
39
This only reflects expenses associated with bean research. In addition, all program leaders
were able to provide operational budgets (not shown), which were separated from salaries.
40
Personal communication. Mejia, O. works for Swisscontact in Honduras and is in charge of
Monitoring and Evaluation activities. Swisscontact collaborates with the Ministry of Agriculture
(MoA). The salaries reported by Mejia refer to salaries paid by the MoA in Honduras. Since
most salaries were not available for other countries, the salaries for Honduras were used, except
where indicated otherwise.
46

across time may overestimate/underestimate costs (especially in years prior to 2000), data were
not available to estimate salary costs in earlier years.
Among all programs for which salaries were estimated, INTA’s bean program from
Nicaragua had the highest expenses for salaries (Table A 2.8). The bean program of Nicaragua is
a special case because, while segregating lines are evaluated at a central experimental station,
advanced lines are evaluated at five research regions,

41

each with an average of 40 testing sites.

This is possible because each research region receives government funding (and have their own
staff) for this purpose. Thus, this explains the high number of staff reported by the program
leader (although their time devoted to bean activities was low).
After total research costs were estimated, these costs were weighted by the small red IVs’
share of the bean varieties released in Costa Rica in the last two decades (using the information
in Table A 2.2), and the share of costs devoted to developing red mottled IVs in Ecuador
estimated by Michigan State University’s bean breeder (who is the U.S. Principal Investigator
for the MSU-PRONALEG-GA program). Therefore, the costs in Table 2.3 for Costa Rica and
Ecuador only reflect the cost associated with developing the market classes of interest in these
countries. In all other countries, total costs were not weighted since either no other market
classes have been released during the period of evaluation or almost the entire bean production
corresponds to small reds. As this table shows, Nicaragua had the highest average annual real
cost associated with the development of red bean varieties ($376,101/year) and Ecuador had the
lowest average annual real cost associated with the development of red mottled bean varieties
($39,116/year). As explained above, most of the costs reported in Nicaragua relate to salaries
(87% on average).

41

Different lines go to different regions (KII, 2010a).
47

Table 2.3. Bean research investments ($) devoted to the development of red beans
(Central America) and red mottled beans (Ecuador). 1990-2010.
1

Total Bean Research Costs (real $, 2009=100) per country
Year
Costa Rica
El Salvador
Honduras
Nicaragua
Ecuador
1990
165,294
204,360
196,998
510,997
16,034
1991
158,619
196,108
258,564
490,362
22,827
1992
153,984
190,377
211,207
476,032
21,763
1993
149,508
184,843
197,282
462,196
28,098
1994
145,776
180,228
171,351
450,657
37,864
1995
141,758
179,485
125,563
438,237
31,751
1996
137,692
174,337
111,262
425,668
29,341
1997
134,604
170,427
122,252
416,120
28,903
1998
132,540
163,864
129,429
409,738
27,246
1999
129,676
160,323
135,488
400,884
23,406
2000
132,136
155,110
164,462
387,847
7,480
2001
129,454
150,818
168,242
377,116
12,295
2002
96,711
109,713
155,937
323,952
13,626
2003
123,918
107,269
160,481
317,584
44,755
2004
121,616
104,486
156,797
309,346
57,762
2005
118,514
101,062
169,698
299,208
57,749
2006
115,666
97,904
164,844
289,858
55,944
2007
113,295
95,193
212,451
281,831
61,120
2008
109,907
91,673
205,016
271,410
131,810
2009
86,725
92,000
155,897
271,650
57,375
2010
88,094
107,438
187,248
287,435
54,294
Average
127,880
143,667
169,546
376,101
39,116
Source: CRSP (2011); KII (2010a); Mather (2003); Mooney (2007); Zbinden (2005).
1
Costs reflect the share of total bean research costs devoted to small red beans (Central
America) and red mottled beans (Ecuador). Since in Costa Rica, 12 of the 18 IVs
released between 1990-2010 were red beans, Costa Rica's costs reflect 67% of total
bean research costs. For El Salvador, Honduras, and Nicaragua, costs reflect 100% of
total bean research costs because either all IVs released in these countries are small
reds or almost the entire bean production corresponds to small reds. Finally, for
Ecuador, MSU’s bean breeder estimated the share of the costs devoted to developing
red mottled varieties at 65%. This estimation was used since it was judged more
accurate than using the share of red mottled IVs released since 1990.

Cost data used for NPV and IRR estimations reflect a 6-year lag between when breeding
started and when a variety was released. Therefore, cost are only included for the period 19912009, while benefits are accounted from 1997 until 2015. This gives an 18-year period for which
48

benefits are evaluated, which is consistent with the period of evaluation used in previous
research. Finally, although the governments of Honduras, Nicaragua, and El Salvador currently
are implementing (free or subsidized) seed-distribution programs, these costs were not included
in the economic analysis since they were not available. Therefore, the returns to investments only
reflect returns to investments made directly on bean research, which may overestimate benefits.

2.5.5 Additional Parameters
To estimate the change in total surplus (Equation (2.8)), three additional parameters were
needed: yearly bean price, yearly quantity produced, and estimations of supply elasticity. The
sources for this information are explained below.
Bean Prices
In this study, average yearly wholesale prices for the period 1997-2010 were used. The
bean

market

prices

for

all

Central

American

countries

were

collected

from

MERCANET/Consejo Nacional de Producción, a Costa Rican organization that compiles yearly
market prices for all countries in Central America and that provides historic data in U.S. Dollars.
For each year, price data were averaged across countries and a unique price was used for all
Central American countries. For Ecuador, prices were collected from previous research (reported
as real prices) and from the Central Bank of Ecuador.
Prices were adjusted to reflect 2009 real prices by using the U.S. consumer price index
(CPI) because the prices were reported in U.S. Dollars. The CPI came from the U.S. Department
of Labor, Bureau of Labor Statistics from 1990-2010. The advantage of using real prices is that
we can compare prices over time. The prices were adjusted using the following formula:
Real price in year t = ( CPIB / CPIt ) * Nominal price in year t

49

where CPI is the consumer price index, t is the year of interest and B stands for the base year (i.e.
2009). The average real price for 1997-2010 was highest for Central American countries
($984/MT) and lowest for Ecuador ($673/MT). While average real price ranged from $518/MT
to $1,412/MT for Central American countries, real price ranged from $566/MT to $1,039/MT for
Ecuador. For years after 2010, the price was assumed as the average of the previous five years
(i.e. 2006-2010).
Quantity Produced
Yearly production data for the period 1990-2009 were obtained from FAOSTAT.
Although data from each national statistical office (NSO) were collected, these data were not
complete for the period of interest. However, for years when both FAOSTAT and NSO data
were available, the data were compared and the differences between these two sources were
small. Therefore, only FAOSTAT data were used.
FAOSTAT reports dry bean data for all countries. However, while dry bean data refers to
bush beans in Central America, these data refers to both bush and climbing beans in Ecuador.
Since our interest is only on bush beans, the NSO data from Ecuador

42

were used (for available

years) to estimate the share of dry bean production to each bean type. It was estimated that,
between 2002-2009, approximately 51% of total dry bean production corresponds to bush beans.
Furthermore, besides bush beans, in Ecuador, we were only interested in bean production in the
northern region (i.e. provinces of Carchi and Imbabura) since this is the main focus area of the
bean program of Ecuador. Thus, using the national data, it was estimated that approximately
39% of total dry (bush) bean production came from these provinces. These shares were used to
estimate bush-bean production in northern Ecuador from FAOSTAT data.

42

ESPAC data published by INEC only available for 2002-2009.
50

Finally, in all Central American countries both red and black beans are produced. Key
informants were asked to estimate the share of production to red and black beans.

43

While 97%

of El Salvador’s and 95% of Honduras’ bean production corresponds to red beans, 85-90% of
production corresponds to this market class in Nicaragua and only 20-30% in Costa Rica (KII,
2010a). Furthermore, Mooney (2007) estimated that in northern Ecuador, 68.4% of the bean area
is planted to red mottled varieties (traditional and improved). These shares were used to estimate
red/red mottled bean production in Central America and northern Ecuador from FAOSTAT data.
Elasticity and Discount Rate
The supply elasticity parameter, ε, was assumed equal to 0.7. Since no primary research
on supply elasticity exists for the countries of interest, this parameter was assumed identical to
that used by Mather et al. (2003). Mooney (2007) also used an identical elasticity parameter to
that of Mather et al. (2003) in his bean study in Ecuador. In general, the short-run and
intermediate supply responses of a semi-subsistence crop like beans are generally assumed
inelastic (Mather et al. 2003). Therefore, assuming an elasticity parameter equal to 0.7 is
appropriate in this context.
In this study, a 4% real discount rate was used. Recent literature (Alston et al. 1998;
Bazelon and Smetters 2001; Maredia et al. 2010) suggests that using real discount rates (adjusted
for inflation) is appropriate when evaluating long-term profitability of projects and suggest
discount rates in the 3-5% range. Maredia et al. (2010) used a 4% discount rate to evaluate the
benefits of bean research in Michigan. Thus, the discount rate used is comparable to previous
studies. Furthermore, the results were re-estimated using a 10% discount rate to assess their
robustness.
43

Although in Costa Rica white beans are also produced, due to the small quantities produced,
this market class was not expected to account for a significant share of production (KII, 2010a).
51

2.6 Results
This section contains the results of the analysis and is divided into five sub-sections.
First, estimations of adoption of IVs for 1996 and 2010 are provided. While adoption rates were
drawn from previous research for 1996, these parameters were estimated using bean expert
opinions for 2010. The second sub-section includes the diffusion curves estimated from the
logistic function for all countries for the base scenario. Third, the econometric results of bean
yield gain estimations are provided. Fourth, estimations of NPV and IRR are provided for each
country for the base scenario. Finally, the sensitivity analysis results are shown.

2.6.1 Adoption of Improved Bean Varieties
Adoption rates for the base year (i.e. 1996) were obtained from previous research
conducted in the region. In order to estimate adoption of IVs in 2010, bean breeders were asked
to provide the names of the most widely planted bean IVs and to estimate their respective
adoption rates. Although total adoption could be disaggregated by variety for 2010, this was not
possible for 1996.
Adoption rates in 1996
Most adoption rates for the base year (i.e. 1996) for Central American countries came
from a CIAT publication (CIAT 2001b) and PROFRIJOL’s web page.

44

However, for

Honduras, CIAT estimates of adoption for 1996 were not used. This was because Mather et al.
(2003) provided an econometric estimation of adoption rates in Honduras for 1996, based on a
random sample of bean farmers distributed throughout the country. While CIAT’s rates suggest

44

Both sources report the same estimations of adoption rates. For PROFRIJOL’s web page, see:
http://www.guate.net/profrijol/aportes.htm
52

that 46% of the bean area was planted to IVs,

45

Mather’s rates suggest that only 31% of the area

was planted to red IVs in 1996 (Table 2.4). To be conservative, we used Mather’s rates. For
Ecuador, Mooney’s estimations of adoption rates for red mottled beans in northern Ecuador were
used (Mooney 2007).
Breeders’ estimates of adoption for 2010
In each country, bean breeders were asked to estimate adoption rates for the most widely
planted varieties (all market classes). This information was used to estimate total adoption rates
for the market classes of interest. Breeders estimated that in their country, the one or two IVs of

Table 2.4. Estimations of adoption rates (%) of improved small reds and red mottled bean
varieties for 1996 and 2010.

Country
Costa Rica
El Salvador
Honduras
Nicaragua

Adoption rates for 1996 (from previous research)
Adoption rate
1
CIAT Mather et al. Mooney
used
85
n.a.
n.a.
85
25
n.a.
n.a.
25
46
31
n.a.
31
30
n.a.
n.a.
30

Adoption rates for
2010 (breeders'
3
estimations)
80
60
46
82

2

Ecuador
n.a.
n.a.
12
12
50
Source: CIAT (2001b); KII (2010a, 2010d); Mather et al. (2003); Mooney (2007).
1

For Costa Rica, CIAT's rate combines black and red beans. It was not possible to separate
between these market classes for 1996. For all other countries, adoption rates refer to small red
beans.
2

For Ecuador, adoption rates refer to red mottled bush-beans grown in northern Ecuador, not at
the country level.
3

These adoption rates refer to the share of the small red/red mottled bean area planted to small
red/red mottled improved varieties.
n.a. = not applicable.

45

Although black beans are planted in 5% of the area, it is assumed that this rate only refers to
red beans since no black bean varieties have been released in Honduras since 1990.
53

the market classes of interest planted most widely in 2010 were: (a) Honduras--Deorho (23% of
the small red bean area) and Amadeus 77 (16% of the small red bean area), (b) El Salvador-CENTA San Andres (40%) and CENTA Pipil (14%), (c) Nicaragua--INTA Rojo (70%) and INTA
Masatepe (8%), (d) Costa Rica--Cabecar (80% of the small red bean area), and (e) northern
Ecuador--Portilla (43% of the red mottled bean area) and Paragachi Andino (7%). Furthermore,
Amadeus 77, a variety developed by NARS through Bean/Cowpea and Pulse CRSP partial
support and released under a different name in each country, was widely planted across all
Central American countries in this study, was the most widely adopted small red IV in 2010, and
accounted for an estimated 49.7% of the total area harvested to beans (235,028 ha).
As expected, adoption rates in 2010 varied greatly across countries (Table 2.4). Bean
breeders’ estimations suggest that, on average, 67% of the area harvested to small red beans in
Central America and 50% of the area harvested to red mottled beans in northern Ecuador was
planted to IVs. Furthermore, these estimates suggest that adoption rates were highest in
Nicaragua (82%) and lowest in Honduras (46%).
Although estimates of small red bean adoption in Costa Rica were available from
breeders for 2010, data were not available to estimate adoption rates of small red beans for 1996
(since the adoption rates reported by CIAT and PROFRIJOL were not detailed by market class).
Therefore, the total adoption rate (i.e. small reds and black beans) was used for 1996 to estimate
the diffusion curve for Costa Rica. Using the adoption rates for 1996 and 2010 allows estimating
the diffusion curves (i.e. total IV adoption rates over time) for each country.

54

2.6.2 Estimated Rates of Diffusion
The methodology used to estimate the diffusion curves was explained in section 2.4.1.2.
As that section explains, for each country, two parameters were needed to estimate the path of
adoption of IVs over time (i.e. diffusion curves): α and β. Table A 2.9 contains this information
and is not further discussed here.
Although the parameters were estimated for the scenarios previously explained, only the
results for the base scenario are discussed here. For most countries, total adoption of improved
bean varieties has increased since 1996 (Figure 2.4). In contrast, adoption rates of bean IVs in
Costa Rica may have slightly decreased over time. There are a few reasons that could explain
this. First, in 1996, the Consejo Nacional de Producción (CNP), then the government unit in
charge of grain purchases stopped regulating market prices. Because of this, many farmers may
have reduced the bean area they plant (KII, 2010a), imports drastically increased, and is likely
that many farmers may have shifted to produce crops other than beans.
Second, adoption rates may have decreased due to the strong consumer preference for
light red beans, which makes it more difficult to sell dark red beans (most red IVs have darker
seed compared to landraces) (KII 2010a). In Costa Rica, the supply chain works differently than
in other countries in the region. Currently, most farmers sell beans to large packers/processors
who then sell beans through supermarkets, where final consumers purchase them (KII 2010e).
Since farmers mainly sell to bean packers/processors, these packers highly influence which
varieties farmers grow. When some of these packers/processors were asked about the market
classes they prefer, they mentioned that, for red beans, they only buy light reds because that is
the market class consumers prefer. Further, they do not give price discounts (as in other countries
in the region) for dark red beans; instead, they do not buy them (KII 2010e). This suggests that to

55

Costa Rica

El Salvador

Nicaragua

2014

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

Cumulative adoption (%)

1.00
0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00

Honduras

Ecuador

Figure 2.4. Base scenario: Total adoption rates of improved bean varieties. 1990-2015.

increase adoption of red IVs, the bean program should continue improving the market value (i.e.
color) of new red IVs. Third, since CIAT (CIAT 2001b) used expert opinion to estimate adoption
rates for 1996, these experts may have overestimated adoption for that year.
While adoption of IVs has increased most rapidly (i.e. slope of the curve is steeper) in
Nicaragua, Ecuador and El Salvador, IVs have been adopted at a slower rate in Honduras (Figure
2.4). However, in 1990, adoption was higher in Honduras compared to all other countries with
increasing diffusion curves.
Table A 2.10 contains the adoption rate values for each country for each year. To
estimate the values for Scenarios A and B, adoption rates for 2010 (provided by bean breeders)
were adjusted by plus/minus 10% and the diffusion curves were re-estimated. The adoption rate
values for these two scenarios are included in Table A 2.11 and Table A 2.12.

56

2.6.3 Bean Yield Gains Estimation Using Experimental Yield Data
Bean yield gain results are presented as follows. First, descriptive statistics of the
experimental yields are briefly discussed. Then, the econometric results from the vintage model
are explained. In the descriptive statistics section, results for the Central American countries
(combined) and Ecuador (separately) are presented. In contrast, in the econometric results
section, results for the Central American countries (combined), Honduras (separately) and
Ecuador (separately) are presented. The reasons for this are explained in each section below.

2.6.3.1 Descriptive Results
In this section, descriptive statistics of the experimental data for the Central American
countries (combined) and Ecuador (separately) are presented.

46

In Central America, while only

13 of the 45 varieties released between 1990-2010 were reported in the experimental trial data,
12 of the 13 varieties were released post 1999 (year when data are available) and they represent
35% of the small red IVs released since 1999 (see Table A 2.2 through Table A 2.5 for a list of
varieties). This was expected because (a) it is likely that national programs also released varieties
tested in different (national) nurseries, which were not included in the ECAR nursery and (b) the
data were only available since 1999; thus, it is likely that all varieties released prior to 2002 were
evaluated in this trial before 1999.

47

There were a total of 108 observations

46

48

to use in the

Although results for Honduras (separately) are discussed in the econometric results section,
descriptive statistics are only discussed for Central America as a region. It is expected that these
statistics are also valid for Honduras.
47
Dorado is the exception. Although this variety was released in late 1980s-early 1990s, it was
used as a universal check in this nursery. Thus, this allowed comparing yield gains of new
varieties (post 2001) to this old variety.
48
The original number of observations was higher. However, yields of each IV were averaged
across locations, which reduced the number of observations for analysis.
57

regressions and the average experimental yield was 2,125 kg/ha. As expected, experimental
yields were higher than yields obtained by farmers (i.e. reported by FAOSTAT). However, since
we were interested in yield gains between old and new IVs, the value of this variable is not
critical. Furthermore, yields were highly variable and ranged from an average of 1,400 kg/ha to
3,140 kg/ha (Table 2.5).
In Ecuador, five of the 10 red mottled varieties released between 1990-2010 were
reported in the experimental trial data (Table 2.6). However, all five varieties represent 100% of
the red mottled varieties released since 2004 (see Table A 2.6 for a list of varieties), which
allowed comparing yield gains from varieties released since 2004 (i.e. very recent varieties).
There were a total of 26 observations

49

to use in the regressions and the average experimental

yield was 1,252 kg/ha. As expected, experimental yields were much higher than yields obtained
by farmers (i.e. reported by FAOSTAT). Furthermore, yields were highly variable, ranging from
an average of 702 kg/ha to 1,815 kg/ha.

49

Similar to Central America, the original number of observations was higher.
58

Table 2.5. Estimated mean yields (kg/ha) and other statistics of red bean varieties based
on the experimental trial data. Central America, 1999-2009.
Year of
Mean yield Std.
Std.
release Variety Name
N
(kg/ha)
Dev.
Err.
Minimum Maximum
Dorado (DOR
1989
364)
35
2,011
786
133
523
3,954
2002
Amadeus 77
5
1,963
237
106
1,704
2,292
2003
Carrizalito
10
2,216
839
265
912
4,118
2003
Cayetana 85
3
1,922
90
52
1,863
2,026
2003
Cedron
11
1,972
720
217
1,057
3,534
2005
CENTA Pipil
12
2,248
823
237
1,179
3,658
2007
Tongibe
5
1,983
590
264
1,303
2,895
2007
Cardenal
2
2,005
324
229
1,776
2,235
2007
Don Cristobal
4
2,490
1,122
561
1,634
4,010
2007
Deorho
9
2,272
764
255
1,446
3,460
2008
CENTA C.P.C.
3
1,890
352
203
1,683
2,297
2009
La Majada
3
2,357
678
391
1,744
3,086
2009
Briyo AM
6
2,294
726
297
1,383
3,257
Average
2,125
619
247
1,400
3,140
Source: Programa de Investigaciones en Frijol Metadata, Zamorano, Honduras.

Table 2.6. Estimated mean yields (kg/ha) and other statistics of red mottled bean
varieties based on the experimental trial data. Ecuador, 2003-2010.
Year of
Mean yield
release Variety Name
N (kg/ha) Std. Dev. Std. Err. Minimum Maximum
2004 Yunguilla
7
1,138
578
219
531
1,979
2007 Libertador
2
1,305
127
90
1,215
1,395
2009 Paragachi Andino 6
1,102
432
176
495
1,716
2009 Portilla
8
1,256
564
199
551
2,052
2010 Rojo del Valle
3
1,460
649
374
720
1,932
Average
1,252
470
212
702
1,815
Source: INIAP/PRONALEG-GA Metadata, Ecuador.

59

2.6.3.2 Econometric Results
Central America
Due to sample size limitations, it was not possible to estimate yield gains for each
country separately, except for Honduras. Therefore, the experimental data from the four Central
American countries were used to estimate yield gains from small red IVs released over time in
50

the region.

That is, Equations (2.3b) and (2.4a) were estimated using data from the four

Central American countries. Consequently, the results from the vintage model are interpreted as
the yield gains of small red IVs released over time in the region as a whole.
The results of the ordinary least squares (OLS) estimation of Equation (2.3b) are
presented in Table A 2.13. While yields were significantly higher in 2001, 2003 and 2008
compared to 1999 (all statistically significant at least at the 10% level), yields in all other Central
American countries were significantly lower compared to Honduras (1% level). Furthermore,
while most IVs yielded more than Dorado, only Cardenal showed statistically significant (5%
level) higher yields than Dorado.
The results of the vintage model (i.e. Equation 2.4a) suggest that the gain in yield
potential from varieties released from 1989 to 2009 in the region averaged 0.49% (Table 2.7).
This is consistent with previous research that estimated yield gains in Michigan (Maredia et al.
2010). However, these yield gains were not used for Honduras. Because there were enough
observations to obtain reliable results (N=88), it was possible to estimate the vintage model for
Honduras separately.

50

From the 108 observations available for analysis, 88 observations came from trials conducted
in Honduras. Thus, the data from Honduras was also included in the regional estimations;
otherwise, the number of observations for the regional analysis would be very small.
60

Table 2.7. Linear regression results of the vintage model using experimental yields
of small red bean varieties released in Central America. 1999-2009.
N = 108
Prob > F = 0.000
R-squared = 0.9565
Coefficient
p-value
Variables
Year dummy variables (1=Yes):
2000
0.09
***0.000
2001
0.38
***0.000
2002
-0.03
0.227
2003
0.36
***0.000
2004
0.12
***0.000
2005
-0.10
***0.006
2006
0.14
***0.000
2007
0.12
***0.000
2008
0.34
***0.000
2009
0.21
***0.000
Country dummy variables (1=Yes):
Costa Rica
-0.42
***0.000
El Salvador
-0.33
***0.000
Guatemala
-0.91
***0.000
Nicaragua
-0.31
***0.000
0.0049
***0.000
Vintage variable (year of release)
Constant
-2.11
0.124
*, **, *** indicates the corresponding coefficients are significant at the 10%, 5%, and
1% levels, respectively. Year 1999 and country Honduras were excluded to avoid the
dummy trap. Robust standard errors used to estimate p-values because variances are
not equal (Prob > Chi2 = 0.0451).
Source: Programa de Investigaciones en Frijol Metadata, Zamorano, Honduras.

The yield gain results, combined with the information in Table 2.5, indicates that, in Central
America, the gain in yield potential averaged roughly 10 kg/ha/year.

51

This number was

obtained by multiplying the mean yields of each year by the yield gains, and averaging across
years.
51

Is likely that farmers obtained lower yield gains since their yields are generally lower than
experimental yields. This fact was accounted for when estimating the economic benefits by using
FAOSTAT yields (for the base year) instead of experimental yields.
61

Honduras
In Honduras, the breeding program of Zamorano uses yield information from trials
conducted in the region (i.e. all countries) to decide which lines to advance to the next stage of
breeding. Therefore, trial data from the region were used to estimate yield gains for Honduras.
The difference with respect to the estimations discussed above, is that while the estimations for
Central America included all IVs released in all Central American countries, the estimations for
Honduras only included varieties released in Honduras.
The results of the OLS estimation of Equation (2.3b) for Honduras are presented in Table
A 2.14. As these results are similar to the ones discussed for Central America as a whole they are
not discussed further. The results of the vintage model suggest that the gain in yield potential
from varieties released in Honduras from 1989 to 2009 averaged 0.56% (Table 2.8), which is
slightly higher than for Central American countries as a region. Therefore, the vintage results
suggest that, in Honduras, the gain in yield potential averaged roughly 12 kg/ha/year, which is
slightly higher than in Central American countries as a region.

62

Table 2.8. Linear regression results of the vintage model using experimental yields
of small red bean varieties released in Honduras. 1999-2009.
N = 88
Prob > F = 0.000
Adj. R-squared = 0.9616
Coefficient
p-value
Variables
Year dummy variables (1=Yes):
2000
0.08
***0.000
2001
0.39
***0.000
2002
-0.07
***0.005
2003
0.38
***0.000
2004
0.13
***0.000
2005
-0.06
**0.028
2006
0.15
***0.000
2007
0.11
***0.000
2008
0.34
***0.000
2009
0.20
***0.000
Country dummy variables (1=Yes):
Costa Rica
-0.46
***0.000
El Salvador
-0.30
***0.000
Guatemala
-0.92
***0.000
Nicaragua
-0.30
***0.000
0.0056
***0.000
Vintage variable (year of release)
Constant
-3.48
**0.012
*, **, *** indicates the corresponding coefficients are significant at the 10%, 5%, and
1% levels, respectively. Year 1999 and country Honduras were excluded to avoid the
dummy trap.
Source: Programa de Investigaciones en Frijol Metadata, Zamorano, Honduras.

Ecuador
In Ecuador, the OLS estimation of Equation (2.3c) suggests that yields were significantly
higher (at least at the 10% level) in most years compared to 2003. Although most IVs yielded
more than Yunguilla (except for Paragachi Andino), none of these differences were statistically
significant (Table A 2.15).

63

The results of the estimation of Equation (2.4b) suggest that the gain in yield potential
from red mottled varieties released in Ecuador from 2004 to 2010 averaged 1.68% (Table 2.9),
which is slightly higher than expected and much higher than the gains found for Central America
and Honduras. Therefore, this result together with the information in Table 2.6 suggests that in
Ecuador the gain in yield potential averaged roughly 21 kg/ha/year, much higher than in all other
countries in this study.

Table 2.9. Linear regression results of the vintage model using experimental yields
of red mottled bean varieties released in Ecuador. 2003-2010.
N = 26
Prob > F = 0.000
R-squared = 0.9491
Coefficient
p-value
Variables
Year dummy variables (1=Yes):
2004
0.58
***0.000
2005
1.28
***0.000
2006
0.57
***0.000
2007
0.87
***0.000
2008
0.35
***0.000
2009
1.19
***0.000
2010
0.81
***0.000
0.0168
*0.051
Vintage variable (year of release)
Constant
-27.46
0.106
*, **, *** indicates the corresponding coefficients are significant at the 10%, 5%, and
1% levels, respectively. Year 2003 excluded to avoid the dummy trap. Robust standard
errors used to estimate p-values because variances are not equal (Prob > Chi2 = 0.094).
Source: INIAP/PRONALEG-GA Metadata, Ecuador.

64

2.6.4 Net Present Value and Internal Rate of Return
The estimates of bean yield gains, in combination with estimations of adoption rates,
annual bean prices, elasticity of supply, and the annual quantity of bean produced of the market
classes of interest were used to estimate the value of benefits realized at the farm level for the
period of 1997-2015 for each country of interest. Once this information was obtained, annual
research costs and a real discount rate of 4% were used to estimate the net present value and the
internal rate of return to bean research investments in each country, assuming a six-year lag
period between the start of research and the time when farmers start using the varieties.
One more point is worth clarifying before discussing the results. As previously explained,
Type II gains were obtained by estimating λ from equations 2.4. In contrast, Type I gains were
obtained from the literature. In Honduras, Mather et al. (2003) estimated that adopters gain the
equivalent to 7-16% of bean income from yield loss averted from the use of IVs. Thus, for
Honduras, the Type I yield gains were assumed to be 11.5%, the average of the values reported
by Mather et al. (2003). Since no other studies have reported yield gains at the farm level

52

for

other Central American countries, Mather’s values were used for these countries. In Ecuador,
Mooney (2007) estimated that adopters enjoy 18.4% lower unit costs when planting IVs in
northern Ecuador. Thus, for this country, the Type I yield gains were assumed to be 18.4%.
A summary of the NPV and IRR findings is presented in Table 2.10. Results from the
base scenario suggest that in all countries except Costa Rica, investments in bean research have
been profitable and provided a return well above the assumed opportunity cost of capital because
the NPV is positive and the IRR is greater than the discount rate used. When the discount rate

52

CENTA (2004) provides estimations of Type I yield gains for El Salvador. However, its
estimations are for test trials in farmers’ fields, which generally are much higher than yields
obtained by farmers. Thus, since these were judged to be too high, Mather’s gains were used.
65

was increased to 10%, NPV was also greater than zero for all countries except Costa Rica,
suggesting that the results are not greatly affected by the discount rate. The net losses found for
Costa Rica are due to the fact that (a) the area planted to beans has decreased since 1996 (and
only the red-bean share was included in the estimations) and (b) the adoption rates between 1996
and 2010 have also decreased. Therefore, net losses were expected. Although this is true for
small red beans, it is possible that positive gains could be found for black beans because (a) most
farmers have adopted the black bean IVs Brunca (released in 1982) and Guaymi (released in
1996) and (b) the area planted to black IVs is more than twice the area planted to red beans (KII
2010a, 2010d). However, estimating the economic impact of black beans for Costa Rica was not
possible because only a few varieties have been released recently and available experimental
data did not include yield information for these varieties.
Table 2.10. Summary of net present value (NPV) and internal rates of return (IRR)
estimations of investments on bean research in Central America and Ecuador.
1991-2015.
Base
NPV($)
-2,016,054
77,510,816
58,250,437
214,002,964
10,920,047

Scenario (in constant 2009 US$)
Scenario A
Scenario B
IRR
NPV($)
IRR
NPV($)
IRR
-5%
-1,610,978 -3%
n.e.
n.e.
40%
93,170,299 43%
62,688,130 37%
34%
73,724,174 37%
43,698,030 31%
42%
254,621,317 45%
175,583,202 39%
37%
13,216,135 39%
8,832,204 35%

For 1997-2015
Producer surplus

Country
per ha per year
Costa Rica
26
El Salvador
84
Honduras
63
Nicaragua
73
Ecuador
196
Central
American
countries
347,748,163 32%
419,904,813 35%
281,969,362 32%
72
All countries
358,668,210 32%
433,120,948 35%
290,801,566 32%
74
Source: Generated by the Author.
NOTES: n.e. = not estimated.
Scenario A assumes a 10% increase over estimations of Type II yield gains and 2010 adoption rates
simultaneously.
Scenario B assumes a 10% decrease over estimations of Type II yield gains and 2010 adoption rates
simultaneously.
Surplus per hectare per year estimated by dividing each year's total surplus (base scenario) by the area
planted with IVs.

66

For El Salvador, the NPV ranged from $63 million (Scenario B) to $93 million (Scenario
A) and NPV was estimated at $78 million for the base scenario, which represents a surplus of
$84 per hectare planted with IVs per year.

53

Similarly, for Honduras, the NPV ranged from $44

million to $74 million and NPV was estimated at $58 million for the base scenario, which
represents a surplus of $63 per hectare planted with IVs per year.
For Nicaragua, the NPV under the base scenario was estimated at more than $214
million, which represents a surplus of $73 per hectare planted with IVs per year (Table 2.10).
There are two reasons for this: (1) the area planted to beans has more than doubled since 1996
and (2) adoption of improved varieties has greatly increased since 1996 due to investments made
by donors and the government, especially after hurricane MITCH in 1998.
Although the economic benefits are more modest in northern Ecuador than in Central
America, the results suggest that investments in bean research have been profitable under all
scenarios, with NPV ranging from $9 million to $13 million and IRR ranging from 35% to 39%.
The NPV was estimated at $11 million for the base scenario, which represents a surplus of $196
per hectare planted with IVs in northern Ecuador per year, the largest surplus among all
countries.
As a region (i.e. all countries), investments in bean research were profitable, generating a
net present value of more than $358 million, most of which came from Central American
countries, particularly Nicaragua. This is due to the fact that Nicaragua is the largest bean
producer in the region and the adoption rates in this country were relatively high in 2010.
Further, the governments of Nicaragua, Honduras, and El Salvador have implemented (free or

53

See last data-column of Table 2.10.
67

subsidized) seed distribution programs that most likely have contributed to the observed (and
large) economic benefits.
In contrast, the economic impact was small in Ecuador because the area of impact was
relatively small (i.e. concentrated in the north region) and the deficient formal seed system in
Ecuador has limited the access to high-quality bean seed. To overcome this limitation,
PRONALEG-GA is promoting alternative ways to produce and sell high-quality, low-cost bean
seed through seed producers located in villages across different regions. However, PRONALEGGA’s efforts have most likely had limited impact because the amount of seed produced and sold
has been relatively small and its limited resources have not allowed to scale up these initiatives.
The estimated IRR for the region was 32%, which more than offsets the opportunity cost
of capital. Details about estimations of NPV and IRR for the base scenario for each country are
presented in Table A 2.16 to Table A 2.20. Although estimations for scenarios A and B were
made for all countries, only the estimations for the base scenario are included as appendices
since the estimation procedures for scenarios A and B were similar to the procedures described
above.
Although there were no Type I benefits in Costa Rica because farmers have disadopted
IVs through time (thus there are no new adopters), the realized Type II yield gains were not
enough to recover investments in bean research. In Costa Rica, the NPV would be zero if the
value of λ were 114% higher than estimated.
While the benefits derived from Type II gains accounted for less than one-half of the
change in total surplus in El Salvador (42%) and Nicaragua (39%), Type II benefits accounted
for more than 50% of the change in total surplus in Honduras (61%) and Ecuador (53%). These
values were obtained using the change in surplus values in Table A 2.17 through Table A 2.20.

68

Finally, although break-even values (i.e. values that would make NPV=0) of λ and
adoption rates were separately estimated using the parameters from the base scenario, the results
suggest that for all countries except Costa Rica, even if λ=0 (hence Type II gains = 0), the NPV
would be positive. This is because adoption of IVs is assumed to increase (at the base scenario
rate) over time, generating enough Type I benefits (from new adopters) to realize positive NPV
values. Similarly, it was not possible to estimate break-even values of 2010 adoption rates for
these countries since, even if the 2010 adoption rate was only 1% above the 1996 adoption rate
(e.g. 26% in El Salvador in 2010 instead of 60%), NPV would still be positive. This was because
even with a small increase in adoption of IVs over time, both Type I and Type II benefits are
realized and these are large enough to offset the cost of research. In addition, as the 2010
adoption rate decreases (and gets closer to the 1996 adoption rate), the share of the benefits
derived from Type I gains become smaller and that of Type II gains become larger.

2.7 Chapter Summary and Policy Recommendations
2.7.1 Chapter Summary
There are two types of yield gains derived from the use of improved varieties: Type I
gains in areas where improved varieties replace traditional varieties (i.e. new adopters), and Type
II gains in areas where new improved varieties replace old improved varieties (i.e. current
adopters). This essay estimated the Type II yield gains associated with varietal development of
small red and red mottled bean varieties over time in Central America, Honduras, and northern
Ecuador separately. Furthermore, it provided estimates of current total adoption rates of IVs in
each country. The adoption rates were estimated using bean expert opinions. A base scenario was
constructed using the information from econometric regressions, key informants, and secondary

69

sources, and sensitivity analysis was carried out under two additional scenarios: Scenario A
where Type II yield gains and 2010 adoption rates were assumed to simultaneously be 10%
higher than in the base scenario, and Scenario B where Type II yield gains and 2010 adoption
rates were assumed to simultaneously be 10% lower than in the base scenario.
OLS regressions using vintage models were conducted to estimate the Type II yield gains
from IVs released over time. Results from the vintage model regressions suggest that the gain in
yield potential from small red varieties released over time averaged 0.49% per year for Central
American countries and 0.56% per year for Honduras. Similarly, the gain in yield potential from
red mottled varieties released over time averaged 1.68% per year for Ecuador.
Breeders estimated that adoption rates for 2010 ranged from 46% in Honduras to 82% in
Nicaragua. Furthermore, breeders estimated that Amadeus 77, the most widely adopted small red
IV, accounted for an estimated 49.7% of the total area harvested with beans across the Central
American countries (235,028 ha). Similarly, Portilla, the most widely planted red mottled IV in
northern Ecuador, accounted for an estimated 43% of the red mottled bean area harvested in
northern Ecuador.
Ex post benefit/cost analysis of improved small red and red mottled bean varieties for the
period 1991-2015 indicate that returns to investments in bean research have been negative in
Costa Rica and positive in all other countries. In Costa Rica, negative net gains were observed
because both the total bean area and the area planted to small red IVs have decreased over time.
Furthermore, small red beans represent a much smaller proportion of total land area planted to
beans, compared to black beans. In addition, in contrast to all other Central American countries
in this study, Costa Rican farmers do not receive price discounts for dark red beans; instead, they
just cannot sell them. Therefore, developing small red varieties that farmers will adopt is more

70

challenging. An analysis to estimate the economic impact of black varieties could not be carried
out due to data limitations.
Among the countries with positive NPV, under the base scenario, the results indicate a
range of IRR values from 34% in Honduras to 42% in Nicaragua. Although this range was
different under Scenario A and Scenario B, the countries had the same rank under all scenarios.
Furthermore, the estimated NPVs for investments in bean research under the base scenario
ranged from $11 million in northern Ecuador to $214 million in Nicaragua. The disparity of
benefits is due in part to the limited area of impact and deficient seed system in Ecuador, and the
high adoption rates and increased government intervention through seed distribution programs in
Nicaragua.
Under the base scenario, the NPV was over $347 million among Central American
countries and over $358 million across all countries. The regional IRR (i.e. all countries) was
estimated at 32%. While the surplus per hectare per year was highest in northern Ecuador
($196/ha/yr) and lowest in Costa Rica ($26/ha/yr), the regional surplus averaged $74/ha/yr.

2.7.2 Policy Recommendations
The results of the study support the following policy recommendations:


In Costa Rica, to increase adoption rates, future bean research on small red varieties should
give priority to developing varieties that are more acceptable to farmers (i.e. with better
market value). Furthermore, since black beans are the most widely produced market class in
Costa Rica, increased efforts should be devoted to developing new black varieties.



In Ecuador, additional efforts should be devoted to increasing seed production. As briefly
discussed, the seed system in Ecuador is the most deficient among all five countries. In order

71

to increase seed production, financial assistance would likely be required because
PRONALEG-GA’s human and financial resources are stretched thin. Financial support from
the Government of Ecuador and donors will be key to find alternatives to increase seed
production in Ecuador. Although the quantity of seed distributed/sold in Honduras, El
Salvador, and Nicaragua is large, the seed provided to farmers is either subsidized or free,
which is not sustainable in the long term. Therefore, alternative ways to produce and
commercialize low-cost high-quality seed is key to develop a sustainable seed system.


In all countries except Costa Rica, investments in bean research have been profitable.
Because of this, donors and governments should continue funding bean research programs in
these countries. Although investments in bean research were negative in Costa Rica, this
does not imply that funding should be cut in this country. Instead, the bean program of Costa
Rica needs to devote efforts to develop better small red varieties and new black bean
varieties.



Most research centers in the countries included in the study are highly dependent on external
assistance to conduct their activities. A continuous supply of funds from donors is necessary
to support varietal development research activities.



The bean programs of Central America collaborate with each other in their breeding
activities (through supply of germplasm, data, etc.) This research model has proven
successful. Therefore, this model could be implemented in other regions of the world to
successfully conduct breeding activities and make a positive economic impact on producers.
However, one weakness of the model is that the breeding programs in this region highly
depend on Zamorano’s bean program to obtain segregating lines to breed. Thus, if
Zamorano’s program in Honduras could no longer provide segregating lines, other bean

72

programs would likely struggle to generate new varieties until finding another source of
segregating lines.

73

CHAPTER THREE. DETERMINANTS OF MARKET PARTICIPATION DECISIONS:
EVIDENCE FROM THE CENTRAL HIGHLANDS OF ANGOLA

3.1

Introduction
Agricultural households can be classified into three categories based on their net position
54

relative to the market: net buyers, net sellers, or autarkic (non-participants).

Market

participation is both a cause and a consequence of development (Boughton et al. 2007; Barrett
2008). Markets provide households the opportunity to benefit from trade; i.e. they can sell their
surpluses and purchase goods and services they need, according to their comparative advantage
(Boughton et al. 2007; Barrett 2008). Further, as a household’s income increases, its demand for
goods and services also increases, hence stimulating development (Boughton et al. 2007).
However, the net position of the households not only depends on market prices; it also depends
on households’ access to productive technologies (e.g. improved varieties, inputs, etc). and
adequate private and public goods (Barrett 2008) and services.
To date, price-based, top-down macro and trade policy interventions have not been
enough to stimulate smallholder market participation and agricultural and rural transformation
(Barrett 2008). However, understanding the impact of these policies on smallholder farmers’
market participation is important. The fact that market participation is heterogeneous has
important implications when studying households’ response to governmental policy
interventions and should be considered in policy response estimation (Key et al. 2000).

54

Goetz (1992) called this classification the household trichotomy.
74

Farm households are typically located in environments characterized by a number of
market failures

55

(Sadoulet and de Janvry 1995, ch. 6, pg. 9). These authors point out that any

market could fail for a particular household when the margin between the low price at which the
household could sell a commodity and the high price at which it could buy it is large; hence the
household may be better off by being autarkic. This introduces us to the concept of price bands,
which are widely described in the literature (De Janvry et al. 1991; Sadoulet and de Janvry 1995;
Key et al. 2000). The price band refers to the difference between the effective price paid by
buyers and received by sellers (both market participants), which determines the household’s net
market position.
To boost market participation, one of the government and private sector’s goals could be
to target investments at reducing the magnitude of the price band. This magnitude could be
affected by transaction costs, the existence of shallow local markets,

56

and price risks and risk

aversion (Sadoulet and de Janvry 1995). This paper analyzes the effect of gender of head,
transaction costs, and productive assets on household’s marketing decisions, using crosssectional data from three provinces of Angola.
Angola ended its 27-year long civil war in 2002. During the war period, each of the two
combatant sides controlled the two major commodities of the country: oil and diamonds. While
the Popular Movement for the Liberation of Angola (MPLA) monopolized oil exports and
revenue, the National Union for the Total Independence of Angola (UNITA) controlled much of

55

De Janvry et al. (1991) demonstrated that market failure was household, not commodity
specific.
56
For details see Sadoulet and de Janvry (1995, ch. 6, pg. 9). The idea is that there is a negative
covariation between household supply and prices because when the harvest is good and surplus
could be traded, the price falls because all other households also have good harvests, widening
the price band (the opposite is also true).
75

the diamond wealth of the north and eastern interior (Munslow 1999). Although these two
commodities provided significant revenues to the country,

57

during the war, these revenues were

used to sustain Angola’s rival armies (Munslow 1999). The war had a large impact in the
country’s infrastructure

58

and caused the demise of the rural economy and the subsequent sharp

rise of the urban population (World Bank 2007). Land mines and conflict made it dangerous to
stay in rural areas and to farm.
Other effects were the loss of life of over 1 million people and migration (rural to urban,
but also to neighboring countries) (World Bank 2007). Since the Peace Accords, continued rural
to urban migration, urbanization, high population growth rate, and increasing household incomes
have contributed to an increase in the demand for food in the major cities of the country. For
example, the estimated 2005 annual demand for potatoes, onions, carrots, and dry beans in
Luanda (the capital city) was a little over 197,000 MT, 61% of which was imported from
neighboring countries, especially South Africa (World Vision 2008).
Although expenditures in 2003 in energy, agriculture, mining, and transportation were
high (10.2% of GDP; US$1.4 billion), by 2005, expenditure in these areas was drastically
reduced to only 2.2% of GDP (or US$734 million) (World Bank et al. 2007), suggesting that
rural households may still face many limitations to actively participate in markets and reduce
import requirements.

57

In 1997, crude oil exports were estimated at over US $4.5 billion, while diamond production
in 1998 was estimated to be worth over US $0.5 billion (Munslow 1999). However, oil
production was expected to peak in 2010, after which, it is expected to decrease (World Bank et
al. 2007) suggesting that the share of other sectors in the economy will grow.
58
It is estimated that US $4 billion will be necessary just to restore the road and bridge network
of the country (World Bank 2007).
76

In addition to the country’s transition from war to peace, the country went from a
centralized market to a free market (Munslow 1999). However, food aid and assistance programs
undermine private sector investments and government control has resulted in a poorly developed
trading network (World Bank 2007). Furthermore, Angola has been cut off from agricultural
59

technological advances (e.g. new crop varieties), and increasing farmers’ productivity

still

remains a challenge because of the disadvantages of Angola’s strong currency and high
transportation costs (World Bank 2007).
Understanding which factors are associated with farmers’ marketing decisions is
important to target government and private donors’ resources, boost crop sales, and increase
farmers’ incomes and their food security. Because of this, the study focuses on estimating the
determinants of market participation and the quantity of food traded, including the effect of
gender, transaction costs and productive asset endowments on marketing behavior, following
Boughton et al. (2007), Barrett (2008), and Bellemare and Barrett (2006). Thus, this study will
provide the government and private donors the information necessary to better target their
assistance and improve smallholder farmers’ livelihoods in rural Angola.

3.2

Research Gap
Many studies related to the analysis of market participation by agricultural households

have focused on (1) dealing with potential problems of sample selection bias when testing
hypotheses about market participation and (2) understanding the role of transaction costs and
market failures on households’ marketing decisions.

59

Although Angola enjoys better rainfall than many of its neighbors, crop yields are much
lower.
77

Heckman (1979) discussed sample selection bias as a specification error and provided a
technique that allowed for the use of simple regression to estimate behavioral functions free of
selection bias in the case of a censored sample. Examples of censored samples include data
related to market wages, earnings of trainees, or earnings from selling in the market.

60

Sample

selection bias arise because (1) individuals may self-select themselves (i.e. they choose whether
to participate in the activity) or (2) analysts decide to use only a subset of a random sample
obtained from a population (which works in the same way as self-selection). The problem with
censored samples is that the functions estimated (e.g. wage or earnings) on selected samples
generally do not estimate population (i.e. random sample) parameters. For example, if we are
interested in estimating the effect of training on earnings and we only use trainees’ earnings, the
parameter of interest (i.e. training) may be biased because it may confound the effect of the
probability of receiving the training and the effect of the training itself.

61

The solution proposed by Heckman (1979) to obtain unbiased estimators was simple.

62

First, he demonstrated that the bias that results from using (non-randomly) selected samples
could arise from a problem of omitted variables. Second, he proposed that, for the full sample
(e.g. trainees and non-trainees), a probit analysis could be used to estimate the probability that an
individual may be in the selected sample (e.g. will participate in training). Third, he

60

For example, the earnings of trainees generally do not provide reliable estimates of what nontrainees would have earned had they decided to participate in the training. Gujarati (2003)
explains that censored samples are samples in which information on the dependent variable is
available only for some observations.
61
A similar problem happens with truncated samples where data is observed only if a certain
event is true.
62
See Heckman (1979) for a detailed explanation.
78

demonstrated that by using this probability as a regressor in the equation of interest (e.g.
trainees’ earnings) one could obtain unbiased estimators.
In his widely cited work, Goetz (1992) modeled the agricultural household’s discrete
decision of whether to participate in markets separately from the continuous decision of how
much to trade, conditional on market participation;

63

an innovation in market participation

analysis at the time. Elaborating on the groundbreaking work of Goetz (1992), Key et al. (2000)
studied the effect of proportional and fixed transaction costs on household supply response. They
implicitly modeled the household as making the discrete market participation choice
simultaneously with the continuous decision of how much to trade.

64

In constructing their

agricultural structural household model, they separated the structural supply functions from the
production threshold functions. By estimating this model, they were able to separately identify
the effect of proportional and fixed transaction costs on supply response, while avoiding the
problem of selection bias described by Heckman (1979).
As noted, some authors assume households make marketing decisions sequentially, while
other assume they make these decisions simultaneously. Bellemare and Barrett (2006) developed
a two-stage econometric method that allowed them to test whether rural households in
developing countries make market participation and volume decisions simultaneously or
sequentially. Using household data from Kenyan and Ethiopian livestock markets, they found
evidence in favor of sequential decision making. The major implication of this finding is that
households that make sequential marketing decisions are more price-responsive and less
vulnerable to trader exploitation.
63

That is, he assumed households make sequential choices: they first decide whether or not to
participate in the market; then, conditional on participation, they decide how much to trade.
64
In contrast to Goetz (1992) who assumed households make sequential marketing choices.
79

Although many recent studies have focused on the effect of transaction costs; farmers’
assets and wealth also affect marketing decisions. Boughton et al. (2007) took an asset-based
approach to analyze smallholder market participation in Mozambique. They assumed households
made sequential marketing decisions

65

and developed a simple structural model of the

household’s choice problem, facing two constraints: budget and asset allocation constraints.
Fafchamps and Vargas-Hill (2005) analyzed the factors associated with coffee producers’
decision to sell at the market vs. at the farmgate. Although their study did not focus on the
decision to participate in the market,

66

it provides insights about why farmers choose different

places for their sales. They constructed a simple model of farmers’ form of sale choice, focusing
on the relationship between wealth and farmgate sales. They estimated the model with and
without access to public transportation.
Barrett (2008) provides a detailed literature review about evidence on smallholder market
participation in eastern and southern Africa, focusing on staple foodgrains markets. He found
that the empirical evidence suggests that most smallholders do not participate as sellers

67

because they face two basic classes of barriers to entry; (1) at the micro-level, households have
insufficient access to productive assets, financing, and new production technologies and (2) at
the macro-level, especially in remote areas, high transaction costs limit household’s market
access, market-level spatial price transmission, and trader competition. Another reason not
mentioned by Barrett (2008) is that farmers may not have surpluses they could sell.

65

Similar to Goetz (1992).

66

All coffee producers are sellers because coffee is a cash crop. Therefore, household
consumption may be very small, if any.
67
At least not at any significant scale.
80

Markets rarely work perfectly. Market failures are an important consideration for policy
analysis because when markets fail, the household’s ability to respond to price incentives is
constrained. Household modeling under missing markets is well explained by Sadoulet and de
Janvry (1995). De Janvry et al. (1991) analyzed the effect of missing markets on farmers’ supply
response. They developed a model of household behavior under various conditions of labor and
food market failures and empirically tested their model using simulations. They found that
programs directed at reducing the incidence of market failures

68

are very important to increasing

the supply elasticity of households--hence increasing household’s response to price incentives.
The contribution of this study is as follows. First, it uses a double hurdle approach to
control for self-selection bias and provides unconditional effects of variables of interest on the
quantity sold. Second, besides focusing on transaction costs, this study also analyzes the effect of
productive assets on marketing decisions, following Boughton et al. (2007),
gender of household head on marketing decisions.

70

69

and the effect of

Third, it provides new empirical results to

the rather limited literature on market participation in Africa, especially in Angola, by looking at
farmers’ participation in three crop markets in Angola: potatoes, beans and onions.
This paper focuses on potatoes, beans, and onions because:

71

(1) these crops, especially

potatoes, are very important in the country’s agricultural sector because of their high potential to
generate profits to smallholder farmers; (2) there is a strong unmet demand for these crops in

68

Infrastructure investments (which reduce transaction costs), better circulation of information
on prices, and access to credit markets (an indirect source of market failure) for example.
69
Bellemare and Barrett (2006) did not explicitly study the effect of productive assets on
marketing behavior.
70
Donors are interested in learning about the role of gender on household decisions, especially
because after the war, many households are led by females.
71
A detailed explanation is provided in Section 3.5 below.
81

large cities of Angola that currently is satisfied by imports from neighboring countries; and (3)
recent private and public investments targeted at improving supply chains in rural Angola are
focusing on these crops (World Vision, 2008). Therefore, generating information about the
factors affecting smallholder farmers’ marketing decisions will be valuable to target assistance to
farmers.

3.3

Research Questions
Although the study’s main objective is to generate information about the factors affecting

smallholder farmers’ marketing decisions, it also attempts to answer the following research
questions:


What are the characteristics of farmers who trade potatoes, beans, and onions in the central
highlands of Angola, compared to non-traders?



For each crop, what factors affect production?



For each crop, what factors are influencing farmers’ decision of whether to sell their
surpluses?



Conditional on market participation, what factors affect the quantity of food traded by
farmers?



What is the unconditional effect of gender, productive assets, and transaction costs on supply
of each crop?



What policies could the government and private sector participants implement, based on the
empirical evidence, to boost market participation?

82

3.4

Conceptual Framework
In this section, first, the economic rationale for analyzing household’s marketing

decisions is explained. Then, an econometric framework is presented to empirically estimate the
economic model while addressing the econometric challenges of the analysis.

3.4.1 Economic Model
To analyze the factors associated with farmer’s marketing decisions, following Boughton
et al. 2007 and Barrett 2008, a simple model of household choice is developed. It is assumed that
C

households will maximize their utility U, by consuming a vector of agricultural commodities, s ,
for c = 1, 2, 3 crops, and a Hicksian composite of other tradables, x. It earns income from
production and possibly sale of any or all crops, and possibly off-farm income, Y, which could be
earned or unearned.
Crop production is determined by a crop-specific production technology,

72

f

C

C

(A , G),

which depends on the flow of inputs (e.g. fertilizer, pesticides, seed, labor) and services provided
by privately held quasi-fixed productive assets, represented by the vector A. This function is also
affected by the availability of public good and services, G, such as extension services, and farmer
associations, because farmers may have access to price information, receive inputs or technical
assistance, among other benefits that may affect output.
The vector M represents farmer’s choice of whether or not to participate in each market
cv

cb

as a seller, represented by the vector M , or as a buyer, represented by the vector M . The
cv

vector M

72

cv

takes value 1 for every crop c the farmer decides to sell and M

= 0 for crops not

Due to the nature of the crops, each one could be produced using different technologies.
83

cb

sold. Similarly, the vector M
cb

M

= 0 for crops not bought.
cv

positive if and only if M

takes value 1 for every crop c the farmer decides to buy and

73

Net sales of a particular crop, NS
cb

= 1 and negative if and only if M

C

º f C (AC, G) - sC, are

= 1. However, due to data

availability, the focus of this paper is restricted to comparing farmers’ choice as to whether or
not to participate in each market as a seller.
The parametric market price each household faces, p
C

cm

, is affected by crop-and-

C

household-specific transaction costs, τ (A, G, Y, Z, NS ). That is, the household faces wide
price margins (i.e. a price band) between the low price at which it could sell a crop and the high
price at which it could buy that crop (Sadoulet and de Janvry 1995).

74

These transaction costs

create a kinked price schedule, which leads some households to self-select out of the market for
some crops (de Janvry et al. 1991; Sadoulet and de Janvry 1995; Boughton et al. 2007; Barrett
2008). Following Boughton et al. (2007) and Barrett (2008), transaction costs are assumed to be
a function of household’s productive assets, A, access to public good and services, G (e.g. good
roads and/or participating in farmer organizations may reduce transaction costs), liquidity from
off-farm income, Y, household-specific characteristics, Z, and amount traded, NS.
The household’s choice can be represented by the following optimization problem:

Max

sc , x, Ac , M ci

U (sc , x)

73

As mentioned by Boughton et al. (2007) and Barrett (2008), households will not both buy and
sell the same crop in this one-period model because of the price wedge created by transaction
CV
CB
costs. Therefore, there exists a complementary slackness condition, M
* M
= 0, at any
optimum.
74
As mentioned above, shallow local markets and price risks and risk aversion also affect the
magnitude of the price band (Sadoulet and de Janvry 1995).
84

Subject to the liquidity constraint

Y  pxx 

 p c* M cv  M cb  f c Ac , G  s c   0
3

c 1

And equilibrium conditions for non-tradables
3

A

 Ac

c 1



 

f c Ac , G  s c 1  M cb



for c = 1, 2, 3

With each household-specific crop price determined by the household’s net market position:





if M





if M

p c*  p cm   c A,G,Y ,Z ,NS c
p c*  p cm   c A,G,Y ,Z ,NS c

p c*  p a

if M

CB

CV

CB

= 1 (i.e. net buyer)
= 1 (i.e. net seller)
CV

=M

= 0 (i.e. autarkic)

Where pa is the autarkic (i.e. non-tradable) shadow price that equates household supply and
demand. The second equilibrium condition for non-tradables implies that, if the household does
not purchase crop c (i.e. M

cb

= 0), production must be greater than or equal to the quantity of

crop c consumed (may be a net seller) and, if the household does purchase crop c (i.e. M

cb

= 1),

production must be greater than or equal to zero (may produce crop c or not; regardless of which,
the household is a net buyer).
To find the optimal solution, two steps are necessary. First, the system must be solved for
the optimal solution, conditional on the participation regime (i.e. net seller, net buyer, or
autarkic). Then, the market participation regime that yields the highest utility level is chosen
C

C

(Key et al. 2000). That is, the optimal choices of {s , A , x} must be replaced into the utility
85

function to obtain the indirect utility function, V. This indirect utility function must be evaluated
cv

under each feasible combination of M
cv

cb 75

and M

to identify the market participation vectors

cb

{M , M } that yield the highest level of V (Key et al. 2000; Barrett 2008).
Based on the structural model described above, the reduced form of each choice variable
cm

can be represented as a function of observable (exogenous) variables A, G, Y, Z, p
76

This structural model assumes non-separability

x

, and p .

in household’s production and consumption

decisions because the parametric prices are endogenous (because of transaction costs). Because
of this, production and consumption behaviors are estimated simultaneously (Sadoulet and de
Janvry 1995) in this maximization problem.
It is expected that both market participation and quantity traded will be positively
affected by asset endowment and access to public goods and services, and negatively affected by
transaction costs. In this study, transaction costs include all costs associated with a transaction
(e.g. transportation of output to the market, fees paid). Smallholder farmers in rural Angola
generally sell their surpluses to itinerant traders at low prices (World Vision 2008). Although this
suggests that there may be low barriers to participate in the market, due to high transaction costs
(e.g. obtaining price information) farmers’ per unit returns will be small. Therefore,
understanding what factors affect smallholder market participation decisions will be useful in
designing policies regarding public and private investments oriented to boost market
participation by smallholder farmers in rural Angola.

75

There are 27 possible combinations to evaluate for.

76

This implies that production decisions are made as if the household was maximizing profits,
while consumption decisions are made as if the household was maximizing utility. For further
reading see de Janvry et al. (1991) and Sadoulet and de Janvry (1995).
86

3.4.2 Econometric Estimation
As mentioned above, this study attempts to estimate the factors associated with
households’ marketing decisions, focusing on households who sell potatoes, beans and/or onions
in rural Angola. Given that sales are only observed for a subset of the sampled population
because farmers who did not sell any or all of these crops reported zero sales, the function
estimated (i.e. quantity traded) on the selected sample may not estimate the population (i.e.
random sample) function (Heckman 1979) due to self-selection problems.

77

Therefore, if the

parameters were estimated by least squares, they would be biased and inconsistent (Wooldridge
2009).
There are at least three alternatives to least squares to estimate unbiased, consistent, and
efficient parameters. The first alternative is to estimate the parameters using the standard
78

Heckman sample selection model (two step version ) used by Goetz (1992), Benfica et al.
(2006), and Boughton et al. (2007). With Heckman two-step approach, one first estimates a
probit model of market participation; then, in the second step, one fits a regression of quantity
traded (regression equation below) by least squares, conditional on market participation
(Wooldridge 2003). From the probit, one could derive the Inverse Mills Ratio (IMR) and include
it as a regressor into the second equation to control for selection bias and obtain unbiased,
consistent, and efficient estimators using OLS (for details, see Wooldridge 2003, p. 560-562).
A Heckman selection approach would be appropriate in this context because many
households reported zero sales. However, the Heckman regression is designed for incidental
truncation, where the zeros are unobserved values (e.g. as with wage rate models where the
77

Self-selection arises due to transaction costs, which are reflected in the endogenous market
prices faced by farmers.
78
Heckman could also be solved by full maximum likelihood (StataCorp 2009).
87

sample includes unemployed persons) (Ricker-Gilbert et al. 2011). Therefore, a corner solution
model is more appropriate in this context because, due to market and agronomic conditions, the
zeros in the data reflect farmers’ optimal choice rather than a missing value (as with Heckman).
The second and third alternatives to least squares (both corner solution models) are the
Tobit estimator proposed by Tobin (1958) and the double hurdle (DH) proposed by Cragg
(1971),

79

respectively. Although the Tobit model could be used to model farmers’ marketing

decisions, its major drawback is that it requires that the decision to sell a particular crop and the
decision about how much of that crop to sell be determined by the same process (i.e. the same
80

variables), which makes it fairly restrictive (Wooldridge 2003 and Ricker-Gilbert et al. 2011).

In addition, in a Tobit model, the partial effects of a particular variable, xj, on the probability that
the farmer will sell and on the expected value of the quantity traded, conditional on participation,
have the same signs (Wooldridge 2008).
The DH model is a more flexible alternative than the Tobit because it allows for the
possibility that factors influencing the decision to sell a crop are different than factors affecting
the decision of how much to sell. Therefore, the DH model proposed by Cragg (1971) is utilized
in this paper.
In the DH model,

81

the first hurdle estimates the decision of whether or not to participate

in the market (i.e. to sell a crop) and, conditional on market participation, the second hurdle
estimates the quantity traded (i.e. quantity sold). Conceptually, a simple corner-solution model is
where:

79
80
81

He proposed a double-hurdle model that nests the usual Tobit model.
For details about the Tobit model, see Wooldridge (2003), pg. 540-546.
Also called two-tiered model.
88

yi = s yi *

if yi * > 0

yi = 0

if yi * < 0

where
yi * = α + Xi β + εi
s=1

if participates in the market; s = 0 otherwise

In this model, yi is the quantity traded by farmer i, α is the intercept, β is a vector of
coefficients, Xi is a vector of explanatory variables, and εi is the error term.
The binary variable, s, is used to estimate the maximum likelihood estimator (MLE) of
the first hurdle and is assumed to follow a probit model. Therefore, the probability of a farmer
choosing not to participate in the market is given by:
(3.1)

P(s = 0 | x1) = P(y = 0 | x1) = 1 - Φ (x1 γ)

where Φ is the standard normal CDF and γ is the vector of coefficients of x1.
In the second hurdle, the continuous variable, y (i.e. quantity traded), is assumed to
follow a truncated normal distribution. Therefore, the MLE is obtained by fitting a truncated
normal regression model
(3.2)

82

to the quantity traded (Cragg 1971 and Burke 2009):

f (y | x1, x2) = [Φ (x1 γ) (2π)
/ Φ (x2 β / σ)

−1/2

σ

−1

2

2

exp { − (y − x2 β) / 2 σ } ]

for y > 0

82

The model is called truncated because the distribution of y is truncated at zero to guarantee
non-negativity (Cragg 1971).
89

2

where f is the probability density function of positive values of y and σ is the variance of the
distribution. Therefore, Cragg’s model integrates Equations (3.1) and (3.2) to obtain:
(3.3)

f (s, y | x1, x2) = [1 - Φ (x1 γ)]
2

1(s=0)

[Φ (x1 γ) (2π)

2

exp { − (y − x2 β) / 2 σ } / Φ (x2 β / σ) ]

−1/2

σ

−1

1(s=1)

where, as mentioned above, s is a binary indicator equal to 1 if y is positive and 0 otherwise.
Equation (3.3) demonstrates that the probability of market participation (i.e. y > 0) and the
analysis of quantity traded (i.e. value of y), conditional on market participation, could be
determined by different factors (the vectors γ and β, respectively) (Burke 2009). The model also
puts no restrictions in the variables included in x1 and x2, implying that these could be the same
(i.e. x1 = x2) or have several or no variables (i.e. x1 ≠ x2) in common. Furthermore, Equation
(3.3) yields the standard Tobit density when γ = β / σ and x1 = x2 (Wooldridge 2002 and Burke
2009).
In order to fit Cragg’s model to the data, it is necessary to assume that s and y* are
independent, conditional on explanatory variables X (Wooldridge 2008); that is:
D(y* | s, X) = D(y* | X)
where D is the distribution of the latent variable y*. The above equation implies that the expected
value of y conditional on X and s is:
E(y | X, s) = s  E(y* | X, s) = s  E(y* | X)
From Cragg’s model, one could estimate the same probabilities and expected values as
with Tobit. However, Cragg’s model uses an updated functional form (Equation (3.3) above).
The probabilities regarding market participation (i.e. whether y is positive) are:

90

(3.4)

P(yi = 0 | x1i) = 1 - Φ (x1i γ)

i.e. no market participation

(3.5)

P(yi > 0 | x1i) = Φ (x1i γ)

83

i.e. market participation

The expected value of y, conditional on market participation (i.e. y > 0) is:
(3.6)

E(yi | yi > 0, x2i) = x2i β + σ × λ (x2i β / σ)

where λ (x2i β / σ) is the Inverse Mills Ratio (IMR)
λ (x2i β / σ) = Φ (x2i β / σ) / Φ (x2i β / σ)
where Φ is the standard normal probability distribution function. The “unconditional”

84

expected value y of is:
(3.7)

E(yi | x1i, x2i) = Φ (x1i γ) [x2i β + σ × λ (x2i β / σ)]
From the DH model above, one could estimate the “unconditional” partial effect (PE) of

a particular variable, xj, for each observation i. Using these PE, one could estimate the average
partial effect (APE) of the variable of interest (i.e. xj) by averaging the PE across all observations
in the dataset. However, the standard deviation reported with the APE should not be used as a
standard error for inference about the population because it describes only the data. Instead, two
alternatives could be used: (a) standard deviations could be re-estimated by bootstrapping or (b)
standard errors could be approximated by the delta method (i.e. Taylor expansion around the data
mean) (Burke 2009). Burke (2009) provides the Stata programming necessary to make

83

Equations (3.4) to (3.7) were taken from Burke (2009).

84

“Unconditional” refers to market participation (i.e. random population) since all expectations
are “conditional” on the explanatory variables.
91

inferences on an “unconditional” APE using methods (a) and (b) above. In this paper,
bootstrapping at 500 repetitions was used.
Key et al. (2000) showed that while market participation (i.e. household’s decision of
whether or not to participate in the market) depends on both fixed and proportional transactions
costs, the quantity supplied, conditional on participation, is only affected by proportional
transactions costs. The DH model described above allows for different factors to affect the first
and second hurdles. However, the variables used as proxies for fixed costs (i.e. distance to
market and quality of the road) were included in both the market-participation and the quantitytraded regressions to test whether fixed costs only affect the first hurdle among Angolan farmers.
Although the independent variables included in the regressions are explained in the next
section, the quantity harvested (included in both hurdles) is worth mentioning here. Quantity
harvested is potentially endogenous to the decision of whether or not to participate in the market
as a seller and on the decision of how much to sell. For instance, if a farmer produces a crop with
the intention of selling his/her surplus, whether he/she participates in the market will depend on
how much he/she harvests--i.e. if the quantity harvested is small, he/she might decide not to sell
or to sell a smaller amount. Furthermore, market conditions will influence the amount a farmer
produces because if the farmer perceives that he/she could sell in the market, he/she may decide
to produce more for this purpose. Because of these factors, there may be correlation between the
error term in a reduced equation of quantity harvested and the error term of the probability of
participation and quantity traded.
To deal with this potentially endogenous variable, an OLS regression will be estimated
on the quantity produced of each crop. Then, the residuals from these OLS regressions will be
estimated and included in both the probit and truncated normal regressions as an additional

92

explanatory variable. This allows determining if the quantity produced is truly endogenous (i.e. if
the coefficient of this variable is statistically significant, the quantity produced is endogenous).
Although several variables included in the OLS regressions are also included in the DH
regressions, the former includes additional variables that are not expected to directly affect
marketing participation decisions.

3.5

Data Used
Data used in this study come from the cross sectional household- and village-level survey

implemented by World Vision’s ProRenda project in Angola in 2009. World Vision, in
collaboration with ACDI/VOCA,

85

the Ministry of Agriculture and Rural Development of

Angola, and the Angolan NGO HORIZONTE are implementing a four-year project

86

targeted at

increasing smallholder-farming families’ annual income from non-perishable crops (World
Vision 2008). The ProRenda project attempts to increase smallholder’s incomes, especially those
of women, by establishing competitive value chains for potatoes, beans, onions, and other highvalue crops. Michigan State University has been contracted to conduct impact assessment of this
project.
The baseline survey was implemented from January through April of 2009 and collected
data about the latest harvest between September 2007 and December 2008. In Angola, the
agricultural year goes from September through May of the following year (MINADER and FAO
2003). Therefore, the data collected refers to the 2007-2008 agricultural year and the first season
of the 2008-2009 agricultural year.
85

Agricultural Cooperative Development International/Volunteers in Overseas Cooperative
Assistance.
86
The ProRenda Project, which is financed by the Bill and Melinda Gates Foundation.
93

The survey was implemented in three provinces of the central highlands of Angola:
Huambo, Bie, and Benguela. These provinces have the most productive lands within the
highland region (World Vision 2008) because of good rainfall distribution and environmental
conditions; however, yields are usually low (MINADER and FAO 2003). The major crops
produced in the highlands are: corn, wheat, rice, potatoes, sweet potatoes, beans, cassava,
sugarcane, peanuts, sunflower, sesame, tobacco, and vegetables (MINADER and FAO 2003).
The survey included a total of 656 households

87

across 40 villages (Figure 3.5.1). The

households were selected using a clustered sampling methodology. This means that the villages
were selected first from a listing of all the potential villages within the action area of the
ProRenda project. The villages were selected systematically using probability proportional to
size for three categories of villages: 1) primary villages for ProRenda project activities; 2)
secondary villages for ProRenda project activities and 3) control villages. After selection of the
villages in each category, a household listing for each village was used to identify the
classification of each household, based on four categories: male head, female head, and
participation or no participation in a farmer organization at the time of the listing. Within each

87

Although 656 households were surveyed, only 620 surveys were valid and were used for
analysis.
94

Source: ProRenda Survey.

Figure 3.5.1. Distribution of villages included in the ProRenda 2009 survey. The text in
the figure is not meant to be readable but is for visual reference only.

category a random systematic sample of households were selected.

88

In order for the sample

estimates to be representative of the population covered by the survey, sampling weights were
used. The basic weight for each sampled household is the inverse of its probability of selection
(see Reyes et al. 2010 for details).

88

Details about the sampling methodology and weight estimation can be found in Reyes et al.
(2010).
95

The household-level survey collected information about household socioeconomic
characteristics, productive and non-productive assets, participation in farmer organizations, and
production and marketing information of beans, potatoes, onions, carrots, and cabbages. The
village-level survey collected information regarding the distance between the village and the
main commercial town, availability of public services (e.g. local markets, agricultural extension
services) and public transportation, and quality of the road between the village and the main
commercial town.
The independent variables included in the regressions were classified into five categories:
(1) household characteristics, (2) private assets, (3) public assets and quasi-fixed factors, (4)
production- and marketing-related variables, and (5) squared and interaction terms (Table 3.5.1).
These variables were included because they were theoretically expected to affect production and
marketing decisions. A total of 34 independent variables were used in various combinations to
estimate the three models proposed in the previous section: (a) linear regression model of
quantity produced, (b) probit model of market participation, and (c) truncated normal regression
model of quantity traded.
Although most variables are self-explanatory, a brief explanation of key variables is
provided next. The dependency ratio was estimated by dividing the number of people younger
than or equal to 17 by the household size. Having a household member participating in a farmer
organization (FO) refers to any member of the household who participated in a FO within the
previous 12 months. Adult literacy refers to members older than 17 who can read and write (selfdeclared, not tested). The number of tropical livestock units was estimated using FAO
conversion factors for South Africa where, for example, one cattle equals 0.70 livestock units
and one sheep equals 0.10 livestock units (FAO 2010).

96

Table 3.5.1. Independent variables included in the production and marketing decision regressions. Angola, 2009.
Model where
1
included
No. Variable
Description of variable
Dependent:
Quantity produced (kg)
1
Quantity produced of each crop.
Market participation (1=yes)
2
Whether or not participates in the market as a seller.
Quantity sold (kg)
3
How much was sold.
Household (HH) Characteristics:
1
Age of HH head (yr)
1, 2, 3
2
Gender of HH head (1=male)
1, 2, 3
3
Dependency ratio
1, 2, 3
Number of dependants (<17 yr) divided by HH size.
4
HH member is in a farmer organization (1=yes)
1, 2, 3
5
No. adults who can read & write
1, 2, 3
Adults refer to people older than 17 yr of age.
2
6
1, 2, 3
Includes oxen, cattle, goats, sheep, pigs, chicken, and
Number of TLU owned
rabbits.
7
HH has zinc roof (1=yes)
1
8
Asset Index
2, 3
Estimated using principal component analysis for 9-11
types of assets owned by the HH.
Private Assets (1=yes):
9
Own plow
1
Plow used to prepare the soil.
10
Own backpack sprayer
1
Sprayer used to apply pesticides.
11
Own motorcycle
2, 3
12
Own bicycle
2, 3
Public Assets and Quasi-fixed Factors:
13
IDA office in the village (1=yes)
1, 2, 3
IDA is the government's Institute for Agrarian
Development.
14
Public market available in the village (1=yes)
1, 2, 3
Farmers could buy/sell food in the public market.
15-21 Seven dummy variables for municipalities (1=yes)
1, 2, 3
Although eight municipalities were surveyed, only seven
dummies were included to avoid the dummy variable
trap.

97

Table 3.5.1 (cont’d).
No.
22
23

24
25
26
27
28
29
30
31
32
33
34

1
2

Variable
Distance from village to commercial town (km)
Road between village and commercial town in poor
condition (1=yes)
Production- and Marketing-related Variables:
Seed used (kg)
Type of plot (1=rainfed plot)
Planted intercropped (1=yes)
Planted seed of local variety (1=yes)
Used fertilizer (1=yes)
Used pesticides (1=yes)
Reported production costs (Kw/kg)

Model where
included1
Description of variable
2, 3
2, 3
Poor condition means the road is a clay road, not
rehabilitated (i.e. without maintenance).
1
1
1
1
1
1
1

HH reports lower harvest (1=yes)
1
Seller sought price information prior to sales (1=yes) 3
Reported marketing costs (Kw/kg)
3
Quantity produced (kg)
Squared terms:
Age of HH head squared
Dependency ratio squared
No. adults who read & write squared
No. TLU squared
Seed used squared
Interaction of 30 * 31

2, 3

Rainfed plots are the most commonly used plots.
Only beans could be planted intercropped.
Local varieties are usually low-quality seed.

Includes expenses on seed, pesticides, labor, transport
from field to home
Obtained by comparing current year with a normal year.
Includes expenses on bags, transportation, load/unload,
taxes
The predicted residuals are also included in models 2, 3

1
1
1
1
1
1

1 = Ordinary Least Squares for production; 2 = Probit for market participation; 3 = Truncated Normal Regression for quantity sold.
TLU = Tropical Livestock Units, calculated using FAO conversion tables.

98

An asset index was estimated as a proxy for household wealth. This index was estimated
using principal component analysis. Details about this index are included in Section 3.6.2 below.
However, several asset variables were included in the regressions separately from the asset index
(e.g. having a home with zinc roof, owning a motorcycle). The quasi-fixed variables included
seven dummies for the municipalities where the households were located to control for variations
in environment and marketing conditions faced by farmers (at the macro-level).

89

Transaction

costs (TC) included the distance between the village and the main commercial town and the
quality of the road between these two places.
The production-related variables are self-explanatory except for ‘type of plot.’ Angolan
farmers in these provinces could cultivate in one (or several) of four possible types of plots:
nacas, ombandas, otchumbo, and lavras.

90

Nacas are irrigated lowland areas located close to

river deltas, used during the dry season (by exploiting residual moisture), and account for 4% of
the cultivated area. Ombandas are medium-level lands with access to gravity-fed irrigation, used
in all seasons, and account for 15% of the cultivated area. Otchumbo are small areas close to the
homestead, intensively cultivated all year round, and account for 4% of the cultivated area.
Finally, lavras are upland areas used for rainfed agriculture and account for 77% of the
cultivated area (World Vision 2008). Lavras are the most commonly used types of plots, thus a
dummy variable was created to account for whether the crop was produced in this type of plot.
Unit production costs were estimated by adding reported expenditures on fertilizers, seed,
pesticides, hired labor, and transport from the field to the home and dividing this by the total
quantity produced. Family labor contributions were not valued. Similarly, unit marketing costs
89

Although it would have been ideal to include dummy variables for each village, this was not
practical because there were 40 villages.
90
These are Portuguese names with no English translation.
99

were estimated by adding farmers’ reported expenditures for bags, sewing of these bags,
transportation costs, loading and unloading of the output, and taxes and fees paid at the market
and dividing this by the total quantity sold. The squared terms were included to allow for nonlinear relationships between independent and dependent variables.
Finally, the land area owned by each household and the area planted of each crop were
not included the analysis because the instrument used to collect the household-level information
did not include these questions. To control for this potential bias, two variables were included as
explanatory variables: the asset index to compensate for the omission of land owned and the
amount of seed used to compensate for the omission of area planted.

3.6

Results
This section is divided into five subsections. The first subsection describes the sample

and provides the socioeconomic characteristics of farm families, focusing on the variables of
interest for the double hurdle analysis. For each crop, the results are disaggregated by market
participation. The second subsection briefly describes households’ wealth (economic status
index). The third subsection discusses household-level receipts and margins for households
selling key crops (i.e. potatoes, beans and onions). Subsection four presents the OLS regression
results of the quantity produced per crop. The last subsection details the double hurdle regression
results.

3.6.1 Descriptive Statistics
Beans were planted by the highest number of farmers, followed by potatoes and onions.
While almost three out of four farmers planted beans, 55% of farmers planted potatoes, and 46%

100

of farmers planted onions (Table 3.6.1). Further, less than 10% of farmers planted other
vegetables, which was expected since farmers in this region mostly depend on maize, bean, and
potato production. While 27% of farmers planted both potatoes and onions, 29% of farmers
planted beans and onions, and 32% planted potatoes and beans, the most common combination.
Regarding sales, approximately 71% of farmers producing potatoes sold part of their harvest,
69% of farmers producing beans were sellers, and 68% of farmers producing onions sold a share
of their harvest. In contrast, close to 90% of farmers growing a combination of crops sold at least
one of the crops they produced.

Table 3.6.1. Percentage of households growing key crops, per economic status index and
gender of household head (HHH). Central Highlands of Angola, 2009.
1

1

Economic Status Index by tercile
Gender of HHH
Lowest
Middle
Highest
Male Female
(% of households)
45% a
49% a
64% b
58%
48%
**
77%
73%
68%
69%
75%
33% a
49% b
54% b
51%
37% ***

Crop
Potatoes
Beans
Onions
2

Other vegetables
Potatoes and onions
Potatoes and beans
Beans and onions
Number of
observations

3%
23%
24%
23%
164

a
a
a

5%
20%
30%
31%

a
a
ab

184

17%
36%
40%
34%
130

b
b
b

Total
55%
71%
46%

11%
30%
35%
32%

4%
22%
26%
23%

*** 9%
* 27%
** 32%
** 29%

276

250

526

NOTES: All variables are binary (0=NO, 1=YES). Number of observations in Economic Status
Index smaller than in last column because of missing values in this variable. Estimates weighted
to reflect population (except number of observations).
1
Bonferroni test of difference between means: for Economic Status Index, different letters
imply differences are significant at 10%; for Gender of HHH, * significant at 10%; **
significant at 5%; *** significant at 1%. Economic Status Index estimated using principal
component analysis.
2

Other vegetables only include carrots and cabbages.
Source: ProRenda survey, Angola, 2009.

101

Cultivating potatoes, onions, other vegetables, or a combination of potatoes and onions or
91

potatoes and beans was more common among richer farmers (as classified by their asset index )
and male-headed households. In contrast, most households planted beans--across different
economic strata and regardless of the gender of the head (Table 3.6.1). This was expected since,
though beans are generally produced for home consumption, they can easily be sold in the
markets and are an important source of income for rural households. Further, a higher share of
male-headed households planted both beans and onions, compared to female-headed households.
Table 3.6.2 reports the descriptive statistics of households in the Central Highlands of
Angola, disaggregated by crop and their market participation (i.e. non-sellers vs. sellers).
Although most farmers planted beans (Table 3.6.1), they marketed a larger quantity of potatoes
than beans or onions. On average, each farmer supplied 200 kg of potatoes, 96 kg of beans, and
only 50 kg of onions, which corresponds to roughly 77%, 49%, and 68% of total (i.e. nonsellers’ and sellers’) potato, bean, and onion production, respectively (Table 3.6.2). Furthermore,
farmers who did not sell produced less than sellers.
While farmers who sold onions were, on average, five years younger than non-sellers
(10% significance level, SL), the differences in age between sellers and non-sellers of potato and
beans were not statistically significant at the 10% level (Table 3.6.2). Furthermore, as expected,
more male- than female-headed households sold their surpluses. Although the proportion of
male-headed households participating and not participating in the market (i.e. sellers vs. nonsellers) were similar for beans, among potato and onion producers, the share of male-headed
households selling their output was much larger than that of female-headed households (1% SL;
Table 3.6.2).

91

Asset index and economic status index are used interchangeable in this section.
102

Table 3.6.2. Descriptive statistics of the variables used in the Double Hurdle analysis. Central Highlands of Angola, 2009.
Potato
Non-sellers
Sellers
Demographics
Quantity sold (kg)
Household Characteristics
Age of head (years)
Gender of head (% male)
Dependency ratio

2

1

Mean S.E. Mean S.E. MT
n.a.
200 22.36 -42 3.858
52 0.283

39
78

0.411
0.195 ***

0.50 0.018 0.58 0.022 **
3

HH member is in FO (% yes)
Family members older than 17
4
who are literate
No. TLU

Bean
Non-sellers
Sellers

4

0.025

0.054

*

44
70

0.810
0.268

0.53 0.007
10

0.026

1

Mean S.E. MT
96 19.49 -43
68

0.400
0.274

0.56 0.020
1

Mean S.E.
n.a.
47
61

5.412
0.345

0.54 0.021

1

Mean S.E. MT
50 5.49
-42
82

0.811 *
0.157 ***

0.55 0.015

0.006 ***

0.7 0.006

8

0.044

*

0.139

0.7 0.085

0.6 0.082

0.9 0.103 ***

0.8 0.126

1.0

0.47 0.121 0.36 0.083

5

0.9 0.198

11

Mean S.E.
n.a.

Onion
Non-sellers
Sellers

0.38 0.125

0.44 0.083

0.22 0.039

0.45 0.068 ***

0.04 0.250
11 0.051
14 0.050

0.07 0.239
10 0.026
20 0.064

0.37 0.400
12 0.061
17 0.067

0.22 0.126
7 0.044
26 0.058

6

Modified Asset Index
-0.14 0.284 0.37 0.212 ***
Owns motorcycle (% yes)
4 0.022 10 0.026
Owns bicycle (% yes)
25 0.155 29 0.061
Public Assets and Quasi-fixed factors
7

IDA office in village (% yes)
Public market available in
village (% yes)

17 0.052

26

0.040

8

0.078

20

0.028 ***

17

0.084

17

0.024

19 0.052

16

0.052

6

0.045

19

0.023 ***

11

0.035

11

0.045

8

Mean sales price, local market
(kw/kg)
Percent of HH in following
municipalities:
Caala
Ekunha
Bailundo
Londuimbali

88.4 5.246 75.1 2.995 ***

23
1
21
35

0.065
0.008
0.062
0.054

11
2
19
15

0.020 **
0.017
0.053
0.030 ***

64.4 1.140

12
6
46
16
103

0.030
0.035
0.048
0.019

67.8 0.899 **

5
3
53
28

0.010 **
0.020
0.040
0.024 **

83.0 6.155

15
0.6
44
30

0.031
0.007
0.040
0.096

97.8 6.167

6
2
37
23

0.021
0.014
0.069
0.017

**

*

Table 3.6.2 (cont’d).
Potato
Non-sellers
Sellers
Mean S.E.
Demographics
Katchiungo
4 0.014
Tchicalachuluanga
7 0.029
Chiguar
9 0.041
Babaera
0.6 0.005
Distance from village to
commercial town (km)
10.3 0.990
Road between village and
commercial town in poor
9
condition (% yes)
66 0.100
Production and Marketing variables
Quantity produced (kg):
30 8.85
In Caala
50 16.02
In Ekunha
66 0.00
In Bailundo
12 1.91
In Londuimbali
30 15.44
In Katchiungo
20 11.34
In Tchicalachuluanga
16 2.66
In Chiguar
37 11.36
In Babaera
12 3.12

Mean
15
2
36
0.2

S.E.
0.021
0.016
0.059
0.002

Bean
Non-sellers
Sellers

Onion
Non-sellers
Sellers

1

1

1

MT Mean S.E. Mean S.E. MT Mean S.E. Mean S.E. MT
**
11 0.051
3 0.008 ***
2 0.019
9 0.024
**
6 0.038
2 0.010 **
2 0.012
4 0.022
***
4 0.039
6 0.038
6 0.052 20 0.059 **
0.5 0.004 0.2 0.002
0.1 0.001
0
n.a.
--

11.4 0.908

8.0 1.009

10.0 0.405

81

0.029 **

80

0.067

73

29.72 ***
59.52 -54.89 -11.47 -26.64 -51.70 -15.89 -61.55 -9.64 --

53 12.95
26 4.42
26 5.85
41 4.03
149 45.39
35 0.00
23 8.77
12 0.89
9
1.40

143
41
176
137
201
41
71
72
38

25.41
3.47
47.20
16.31
51.86
8.74
9.52
14.59
11.57

63

***
---------

8.0

0.945

53

0.065

230
359
417
42
172
224
136
312
92

9.7 2.238

0.052

81

0.048 ***

16
24
75
16
17
2
5
6
5

3.25
5.37
0.00
5.85
9.63
0.00
0.45
0.52
0.00

58
103
109
29
102
53
44
50
0

5.66
21.27
26.76
2.49
39.15
48.29
24.96
19.16
n.a.

**
---------

Seller sought price information
prior to sales (% yes)
n.a.
Reported marketing costs
(Kw/kg)
n.a.

0.045

--

n.a.

71

0.008 --

n.a.

63

0.068

--

2.9 0.249

--

n.a.

3.0 0.703 --

n.a.

3.1

0.435

--

Number of observations

165

89

216

49

125

75

104

Table 3.6.2 (cont’d).
1

MT = test of difference between means: *significant at 10%; **significant at 5%; ***significant at 1%; -- not tested; n.a. = not
applicable.
2
Dependency ratio estimated by dividing the number of people 17 years or younger by the household size. A high dependency ratio
means more dependants in the household.
3
FO = Farmer organization.
4
5

Literacy refers to people who can read and write (self-declared, not confirmed).
TLU = Tropical Livestock Units (estimated using FAO conversion factors).

6

Modified Asset Index excludes owning a motorcycle and owning a bicycle to be able to estimate the effect of these variables on
marketing decisions separately from other assets included in the index.
7
IDA = Government's Institute for Agrarian Development.
8

For farmers who sold in local markets, their reported price was averaged per community. Communities with missing prices use
average price per the next political division (i.e. town, municipality). This price was imputed to non-sellers.
9
Poor condition means the road is a clay road, not rehabilitated (i.e. without maintenance).
Source: ProRenda survey, Angola, 2009. Estimates weighted to reflect population.

105

On average, there were slightly more than one dependent for every two adults in the
household (the average dependency ratio was 0.52). Furthermore, potato sellers had more
dependents than non-sellers (5% SL; Table 3.6.2). As Table A 3.1 shows, potato sellers had 1.5
children younger than five vs. only one among non-sellers (1% SL).
Less than 11% of households had at least one member who participated in a farmer
organization (FO) in the year prior to the interview (Table 3.6.2). A higher percent of households
selling potatoes and onions, compared to non-sellers, reported having a member participating in
a FO (10% SL). In contrast, a higher percent of households producing beans solely for
consumption (i.e. non-sellers) reported having a household member participating in a FO (1%
SL; Table 3.6.2), which was unexpected. Given that bean non-sellers reported other crops as
their major source of crop income and that the large majority of bean sellers reported beans as
their major source of crop income (Table A 3.2), it is likely that non-sellers participate in FO
related to these other crops, thus explaining this finding.
Among bean producers, households selling beans had more literate adults (i.e. older than
17) living at home (1% SL). While the differences in the number of tropical livestock units
(TLU) owned between sellers and non-sellers were not statistically significant at the 10% level
for potatoes and beans, onion sellers reported more TLU than non-sellers (1% SL; Table 3.6.2).
In this study, an asset index, which was estimated using 11 assets, was used as a proxy
for household wealth. Details about its estimation and interpretation are included in Section
3.6.2. Households selling potatoes were wealthier than non-sellers (1% SL). While there were no
statistically significant differences (at the 10% level) in the asset index between bean and onion
sellers vs. non-sellers, it was surprising to find that onion non-sellers had a higher asset index
than onion sellers (Table 3.6.2). Since a higher percent of onion non-sellers (20% vs. 2% sellers)

106

reported remittances and other transfers as their major source of non-crop income (Table A 3.2),
it is likely that non-sellers invest part of these transfers on improving their home or purchasing
assets; thus, explaining this finding.
Most of the differences (between sellers and non-sellers) regarding access to public assets
and quasi-fixed factors were statistically significant for potato and bean producers (Table 3.6.2).
While 19% of bean sellers reported having access to a public market for purchasing food in their
villages, only six percent of non-sellers reported having access to this public good (1%SL).
Similarly, access to the government’s Institute for Agrarian Development (IDA) office (which
provides extension services) was more common among bean sellers (20% vs. 8% non-sellers, 1%
SL) (Table 3.6.2).
As previously explained, sales prices were collected for farmers who sold at least part of
their output. Farmers reported selling their output in different places, including at their farm,
their home, local markets, and other markets. To control for (potential) endogeneity problems in
market prices, for farmers who reported selling at local markets, the average sale price was
estimated for each crop. However, in some villages, none of the farmers who sold their output
did so in local markets; thus, the average price could not be estimated. In these cases, the average
price of the next political division (i.e. town, municipality) was estimated and used. Although
this information is presented in Table 3.6.2, it was excluded from the double hurdle analysis
because it was judged to be inaccurate. In villages with no sellers, prices collected at the next
political division (e.g. municipality) were imputed to non-sellers in these villages. It is likely that
these non-sellers were imputed too high of a price, thus offsetting any positive effect of this
variable.

92

92

Furthermore, as one would expect, current prices are likely endogenous.
107

The average distance between the villages and their main commercial town was 9.6 km
(Table 3.6.2). Among potato and bean producers, a higher percent of sellers than non-sellers
were located in villages farther from the main commercial town (although the differences were
not statistically significant); thus, the average distance from their villages to their main
commercial town was higher for sellers. Furthermore, a higher share of potato and onion sellers
was located in villages with poor quality road between the village and the main commercial town
(5% and 1% SL, respectively).
Regarding the variables used to control for macro-level environmental and market
conditions, potato and onion production was highest in Ekunha municipality among both sellers
and non-sellers. In contrast, bean production was highest in Londuimbali (Table 3.6.2). Since
Londuimbali accounted for the largest percentage of beans produced, the municipality
coefficients from the regression results (of all crops) were compared to this municipality.

93

Furthermore, since no farmers in Babaera municipality sold onions, the binary variable for this
municipality was excluded from the double hurdle regressions for this crop.
Finally, close to two-thirds of the farmers who sold their output obtained price
information before selling their crop and sellers reported an average marketing cost of three
Kwanzas

94

per kilogram sold, with the highest marketing costs reported by onion sellers (Table

3.6.2).

93

Although there was one dummy variable for each municipality, the variable for Londuimbali
was excluded from the regressions to avoid the dummy variable trap.
94
The exchange rate at the time of the survey was 75 Angolan Kwanzas per US$.
108

3.6.2 Wealth of the Households
To analyze wealth across various types of assets, researchers have developed asset
indices that take into account the relative importance of the asset in the sample (i.e. giving less
weight to assets commonly owned and more weight to assets owned by a few). In this study,
following Filmer and Pritchett (2001), McKenzie (2005) and Reyes et al. (2010), principal
component analysis was used to estimate an economic status (or asset) index, based on asset
ownership. Reyes et al. (2010) details the index estimation process, using the same dataset used
for this study; thus, interested readers can refer to these authors for more details on the theory
and the construction of the index.
In constructing the index, ownership of the following assets was considered: tractors,
trucks, cars, plows, carts, backpack sprayers, motorcycles, bicycles, cell phones, radio, and
televisions. The index also considered ownership of water storage facilities and a latrine at the
homestead, and whether the roof was made of zinc or ‘lusalite,’ both considered improved
materials. From the 14 indicators, tractors, trucks, and cars were excluded because no household
in the sample owned these items. A high value for the index indicates a higher level of ownership
of these assets, implying greater wealth (Reyes et al., 2010).
Households were sorted according to their wealth index and the population was split into
three groups or terciles (Table A 3.3). These terciles were used to analyze households’ crop
margins in the next subsection. The consistency of the index is reflected in the terciles
percentages: only 5% of the poorest tercile (lowest 33% of the population) had water storage
facilities at home, whereas 19% of the middle tercile and 42% of the highest tercile had this
facility at home (Table A 3.3). The mean value of the index (by construction) is zero and its
standard deviation is 1.5. The poorest tercile households had an average index of -1.26 while the

109

richest tercile had an average index of 1.72, a difference of 2.98 units (Table A 3.3). One
example of a combination of assets that would produce this difference is having a plow (1.11)
and a cart (1.87).
The wealth index suggests that male-headed households are richer than female-headed
ones (Table 3.6.3). Similarly, farmers who grew potatoes and onions are richer than farmers who
did not grow these crops (5% and 1% SL, respectively). The differences in the index between
bean growers and non-growers were not statistically significant at the 10% level. These results
are confirmed by a graphical analysis of the cumulative distribution of the index by gender and
crop grown (Figure 3.6.1 and Figure A 3.1 to Figure A 3.3).
While potato sellers were wealthier (as per their asset index) than non-sellers (1% SL),
farmers who did not sell onions were wealthier than onion sellers (10% SL, Table A 3.4). As
explained above, a larger share of onion non-sellers had zinc roof (with a “weight” in the asset
index of 0.79, see first data column on Table 3.6.3) and, as Table A 3.4 shows, a higher share of
non-sellers owned a motorcycle (with a “weight of 1.71) and a television (“weight” of 1.98, the
highest among all assets). Although there were slight differences in the index between bean
sellers and non-sellers (sellers had a higher index), these were not statistically significant at the
10% level, suggesting that bean growers have similar wealth, regardless of their market
orientation.

110

Table 3.6.3. Scoring factors and means per gender of household head (HHH) and crop grown for asset indicators entering the
computation of the first principal component (asset ownership).
Percentage of households owning the asset
Scoring
Gender of HHH
Potato grower
Bean grower
Onion grower
Factor /
Asset indicators
Std. Dev. Male Female MT
No
Yes MT
No
Yes MT
No
Yes MT
Own plow
1.11
13%
2%
-7%
11%
-14% 7%
-9%
9%
-Own cart
1.87
0%
0%
-0%
1%
-1%
0%
-0%
1%
-Own backpack sprayer
1.65
3%
0%
-2%
3%
-4%
1%
-1%
4%
-Own motorcycle
1.71
10%
3%
-7%
8%
-5%
9%
-5%
11%
-Own bicycle
0.82
30%
4%
-18% 25%
-30% 18%
-20% 24%
-Own cell phone
1.27
9%
3%
-5%
9%
-2%
9%
-6%
8%
-Have water storage at home
0.66
25%
19%
-26% 20%
-30% 20%
-19% 27%
-Have latrine in the house
0.23
95%
70%
-87% 87%
-85% 88%
-87% 88%
-Have lusalite or zinc roof
0.79
50%
32%
-38% 50%
-51% 42%
-46% 43%
-Own radio
0.71
54%
19%
-37% 48%
-44% 43%
-30% 58%
-Own television
1.98
2%
1%
-2%
2%
-1%
2%
-2%
2%
-Mean by group
Economic Status Index
0.512 -0.625 *** -0.037 0.269 **
0.270 0.070
-0.081 0.364 ***
Number of observations
478
242
236
202
276
130
348
289
189
Notes: Three of the 14 indicators were dropped because they had zero variance. Scoring Factor is the "weight" assigned to each
indicator or eigenvector in the linear combination of the variables that constitute the first principal component. The percentage of the
covariance explained by the first principal component is 21.06%. The first eigenvalue is 2.32. Data provided in the last eight columns
were estimated with weights to reflect population (except number of observations). MT = Bonferroni test of difference between
means: * significant at 10%; ** significant at 5%; *** significant at 1%; -- not tested.
Source: ProRenda survey, Angola, 2009.

111

Figure 3.6.1. Cumulative distribution of asset index by gender of household head.
Central Highlands of Angola, 2009.

3.6.3 Households’ Gross Margins
In this study, households’ gross margins equal receipts from sales, which are based only
on marketed quantities of potatoes, beans and onions, minus cash expenditures on production
and marketing activities. Table 3.6.4 presents households’ margins disaggregated by asset index
terciles and gender of the household head. While the richest households and male-headed
households reported the highest receipts, these households also reported the highest costs.
Regardless of the high costs, households’ margins were higher for households in the middle and
highest terciles (10% SL) and for male-headed households (1% SL).

112

1

Table 3.6.4. Average receipts, costs and margins of households selling key crops, per
economic status index and gender of household head (HHH). Central Highlands
of Angola, 2009.
Detail

Economic Status Index by tercile
Lowest
Middle
Highest

2

Gender of HHH
Male Female

2

Total

Receipts (Kw)
5,142 a
9,283 a 19,021 b 14,625 5,103 *** 12,150
Total Costs (Kw) 3,143 a
4,945 a 11,436 b
8,487
3,229 *** 7,121
Margins (Kw)
2,178 a
4,573 ab 8,170 b
6,420
2,337 *** 5,359
Number of
observations
119
143
114
241
178
419
NOTES: Kw = Kwanzas. Costs include purchased inputs, hired labor, and reported
marketing costs. Variables are at the household level. Number of observations in Economic
Status Index smaller than in last column because of missing values in this variable.
Estimates weighted to reflect population.
1

Key crops include potato, onion, and bean sales.

2

Bonferroni test of difference between means: for Economic Status Index, different letters
imply differences are significant at 10%; for Gender of HHH, * significant at 10%; **
significant at 5%; *** significant at 1%.
Source: ProRenda survey, Angola, 2009.

Margins were also analyzed in detail for each crop (Table A 3.5 to Table A 3.7).
Although the highest receipts were obtained from potato sales, followed by bean and onion sales,
farmers who sold potatoes also reported the highest costs. In contrast, farmers selling beans
reported the lowest costs. This was expected since, as is common in developing countries where
beans are an important staple, bean producers seldom apply purchased inputs (e.g. fertilizer) to
their crop (Reyes, 2011). In our sample, while 66% of potato producers and 49% of onion
producers applied fertilizers, only 3% of bean producers applied fertilizer (Table A 3.8). Because
of this, bean sellers obtained the highest margins. Furthermore, both richest farmers and maleheaded households obtained approximately 2.6 and 3.3 times higher margins from their bean
sales (10% and 1% SL) than their counterparts, respectively (Table A 3.6).
113

One additional finding is worth mentioning here. As Table A 3.7 shows, on average,
onion sellers in the lowest and highest terciles reported losses, mainly due to the high per unit
production costs reported. However, the differences in margins across terciles and gender of
household head were not statistically significant across onion sellers.

3.6.4 OLS Regression Results of Factors Influencing Production
As explained in the previous section, it was suspected that production could be an
endogenous covariate in the double hurdle analysis. Thus, for each crop, linear regression (OLS)
estimation was used to determine which factors were affecting production. Then, the residuals of
these regressions were included as an additional explanatory variable in the double hurdle
analysis and tested for endogeneity. This subsection first discusses the descriptive statistics of
factors affecting crop production. Second, for each crop, it presents the results of the OLS
regressions on quantity produced.
The descriptive results of the factors influencing production are included in Table A 3.8.
On average, farmers produced almost 170 kg of potatoes, 90 kg of beans, and 46 kg of onions.
Potato producers were slightly younger than both bean and onion producers, had slightly more
dependents, more had members of their household participating in a FO, and more had homes
with zinc roof (Table A 3.8). In contrast, more female-headed households produced beans and
bean producers had more TLUs than potato and onion producers. Furthermore, onion producers
had slightly more literate adults in the household (Table A 3.8).
Regarding productive assets, a slightly higher share of potato producers owned plows and
backpack sprayers (compared to bean and onion producers), which they used to prepare their

114

fields and apply pesticides. Additionally, potato producers had more access to the government’s
IDA offices (Table A 3.8).
Farmers were asked how much seed they used and whether the seed was a local or
imported (usually improved) variety. Potato producers used more seed. However, since the
planting rate is higher for potatoes than for beans, the estimated area planted to potatoes was
smaller than the area planted to beans (0.015 ha vs. 0.393 ha, respectively).

95

Furthermore, it

was more common for potato producers to use improved varieties--almost all bean producers and
more than 90% of onion producers reported using local varieties (Table A 3.8).
While almost 95% of bean producers planted in rainfed plots (i.e. lavras), less than onehalf of potato producers (44%) and 57% of onion producers planted in this type of field (Table A
3.8).

96

Furthermore, almost 60% of bean producers planted beans as an intercrop, which was

expected. Not surprisingly, the use of fertilizer and pesticides was more common among farmers
producing potatoes and onions; therefore, these farmers also reported higher production costs
(Table A 3.8). Finally, more than 54% of farmers reported lower harvests, compared to a normal
year.

3.6.4.1 Potato OLS Results
The econometric results of the OLS regression for potatoes are presented in Table 3.6.5.
The model had a R-squared of 0.49. The results show that, although male-headed households
produced more than female-headed households, these differences were not statistically

95

Potato planting rate = 2,750 kg/ha. Bean planting rate = 60 kg/ha. For onion producers, it was
not possible to estimate the area planted using seed data.
96
Although most farmers produced in Lavras, a high percentage of farmers produced potatoes
(38%) and onions (25%) in irrigated lowland plots located close to river deltas (i.e. Nacas).
115

Table 3.6.5. Linear regression models of factors influencing potato, bean and onion quantity produced (kg). Central
Highlands of Angola, 2009.
Potato
Bean
Onion
N = 281
N = 380
N = 162
R-squared = 0.4947
R-squared = 0.6757
R-squared = 0.4417
Coefficient p-value
Coefficient
p-value
Coefficient p-value
Independent variables
Household (HH) Characteristics
Age of HH head (Years)
10.37
0.198
2.05
**0.031
-1.27
0.572
Gender of HH head (1=Male)
36.61
0.198
19.57
0.155
22.87
*0.057
1

Dependency ratio
HH member is in farmer organization (1=Yes)
2

No. adults (>17 yr) literate
No. of Tropical Livestock Units
Home has zinc roof (1=Yes)
Productive Assets Ownership (1=Yes)
Owns a plow
Owns a backpack sprayer
Public Assets and Quasi-fixed Factors (1=Yes)
IDA office in village
Public market in village
HH in Caala Municipality
HH in Ekunha Municipality
HH in Bailundo Municipality
HH in Katchiungo Municipality
HH in Tchicalachuluanga Municipality
HH in Chiguar Municipality
HH in Babaera Municipality

-265.46
181.33

0.229
0.143

-23.54
16.27

0.609
0.249

-408.42
16.26

*0.069
0.485

13.37
-16.64
68.26

0.739
0.755
**0.032

40.75
33.55
-20.34

**0.018
*0.084
0.109

-37.52
-1.74
65.43

**0.017
0.946
*0.053

186.16
53.22

0.275
0.587

53.10
20.19

***0.004
0.105

6.33
-60.90

0.818
0.142

52.83
-34.88
101.77
125.13
33.43
63.16
40.11
123.56
-96.35

0.290
0.439
0.115
0.157
0.468
0.434
0.406
**0.020
0.358

23.78
0.62
-24.96
-9.61
1.58
-3.82
10.12
-11.36
-44.83

*0.058
0.894
***0.001
0.455
0.544
0.762
*0.083
0.277
***0.010

-46.06
-22.78
-63.57
-76.34
-56.44
-47.08
-112.46
-43.99
-28.10

**0.037
0.214
0.146
*0.085
*0.078
0.377
**0.028
0.351
0.368

116

Table 3.6.5 (cont’d).
Potato
N = 281
R-squared = 0.4947
Coefficient p-value

Bean
N = 380
R-squared = 0.6757
Coefficient
p-value

Onion
N = 162
R-squared = 0.4417
Coefficient p-value

Independent variables
Production-related variables
Total seed used (kg)
3.36
**0.012
3.08
***0.000
-47.85
0.663
Planted in rainfed plot (1=Yes)
25.88
*0.056
3.80
0.563
-18.06
0.406
Planted intercropped (1=Yes)
n.a.
-3.94
0.531
n.a.
Planted local variety (1=Yes)
-80.79
0.157
-33.75
**0.012
-22.45
0.594
Used fertilizer (1=Yes)
30.14
**0.027
-6.71
0.604
51.99
***0.001
Used pesticides (1=Yes)
85.55
**0.013
43.48
***0.007
49.74
0.351
Reported production costs (Kw/kg)
-0.96
0.200
-0.75
**0.014
-0.26
0.191
HH reported lower harvest (1=Yes)
-101.04
0.110
-41.67
**0.029
-6.30
0.791
Squared and interaction terms
Age squared
-0.15
0.113
-0.03
**0.035
-0.02
0.393
Dependency ratio squared
234.79
0.444
30.52
0.654
329.71
*0.052
No. adults literate squared
-13.68
0.451
-12.69
**0.016
11.83
**0.015
Tropical Livestock Units squared
-5.84
0.600
-9.55
**0.030
2.55
0.507
Total seed used squared
-0.003
**0.011
-0.005
***0.000
129.256
**0.028
Production costs * HH reported lower harvest
0.76
0.348
0.79
**0.016
0.12
0.457
Constant
-96.33
0.140
13.04
0.384
296.44
*0.052
Notes: *, **, *** indicates the corresponding coefficients are significant at the 10%, 5%, and 1% levels, respectively. n.a. = not
applicable. All municipalities compared to Londuimbali municipality.
1

Dependency ratio estimated by dividing No. members <17 yr by household size.

2

Literacy refers to adults who can read and write.
Source: ProRenda survey, Angola, 2009. Estimates weighted to reflect population.

117

significant at the 10% level. Thus, providing technical assistance (related to production) to potato
producers will likely generate the same outcome regardless of the gender of the head. However,
since female-headed households are poorer, it may be appropriate to devote additional efforts to
assist female-headed households. Surprisingly, having members of the household participating in
a farmer organization had no statistically significant effect on potato production.
Production was positively associated with owning a home made with improved roof
materials--farmers who owed a home with a zinc roof produced, on average, 68 kg more potatoes
than their counterparts (5% SL). As previously discussed, farmers who produced potatoes were
relatively wealthy (see Table 3.6.3); thus, this finding was not a surprise.
Although owning productive assets had a positive effect on potato production, the
differences between farmers who owned productive assets vs. farmers who did not own these
assets were not statistically significant at the 10% level.
The variables related to different municipalities were included to control for variations in
environmental and market characteristics. Farmers producing potatoes in the Chinguar
municipality produced approximately 124 kg more potatoes (5% SL) than farmers in the
Londuimbali municipality. The differences in production between all other municipalities and
Londuimbali were not statistically significant at the 10% level.
Most production-related variables had statistically significant effects on production
(Table 3.6.5). Since the dependent variable in these models was production (not yield), it was
expected that, as seed use increased, quantity produced would increase (at a decreasing rate).
This was true for potatoes (5% SL). Surprisingly, farmers who planted potatoes in rainfed fields
obtained higher production (10% SL) than farmers planting in other types of fields. This was
because although most farmers (44%) produced potatoes in Lavras or upland rainfed fields, a

118

large share of farmers (38%) produced potatoes in Nacas or irrigated lowlands fields close to
river deltas, generally with poorer soil quality compared to Lavras. Furthermore, during the
period of analysis, rainfall may have been sufficiently abundant to achieve a good harvest on
upland fields. Potato producers who reported obtaining lower harvests (compared to a normal
year) stated that the main cause of this was the little or no use of fertilizer (60% of farmers
reported this) rather than weather-related problems, thus confirming this finding.
Although farmers planting local varieties obtained lower production, the differences
between farmers who used local varieties and farmers who used improved varieties were not
statistically significant at the 10% level. Since 26% of potato farmers reported using IVs, this
suggests that available IVs do not perform better than traditional varieties; thus, efforts to
generate better IVs will likely benefit farmers.
Potato producers who used fertilizer obtained higher production (5% SL) than farmers
who did not apply fertilizer to their fields. Similarly, potato producers who applied pesticides
obtained higher production (5% SL) than farmers who did not apply pesticides (Table 3.6.5). The
results do not suggest whether farmers did not have access to these inputs or could not afford
them (i.e. due to high price). However, given that fertilizer accounted for the largest share of
production costs and that more than 65% of farmers applied fertilizer, is likely that most farmers
could not afford to purchase the required amounts of fertilizer.
Finally, as per unit production cost increased, quantity produced decreased. Similarly,
farmers who reported lower harvest during this period (compared to a normal year) obtained
lower production. However, the differences in these two variables were not statistically
significant at the 10% level (Table 3.6.5).

119

3.6.4.2 Bean OLS Results
The econometric results of the OLS regression for beans are presented in Table 3.6.5.
The model had a R-squared of 0.68. The results show that age of the head of the household was
positively (at a decreasing rate) associated with bean production. Among bean producers, age
97

was positively associated with production until farmers become 34 years old.

After this, age

was negatively associated with the bean quantity produced.
Similar to potato, although male-headed households produced more than female-headed
households, the differences were not statistically significant at the 10% level (Table 3.6.5).
Thus, providing technical assistance (related to production) to bean producers will likely
generate the same outcome regardless of the gender of the head. However, since female-headed
households in general and bean growers in particular are poorer, it may be appropriate to devote
additional efforts to assist female-headed households and households producing beans.
Bean production was positively associated with the number of literate adults in the
household (5% SL) and with the number of TLU owned by the household (10% SL). The
number of literate adults in the household had a positive effect on production until there were
1.61 literate adults at home, after which its effect becomes negative, suggesting that more
educated households may depend less on the bean crop. Bean producers who owned one
additional TLU produced, on average, 14.45 kg more beans (Table 3.6.5).

98

Since medium to

small animals can easily be sold to relief cash constraints faced by poor bean growers, these
farmers are likely using their TLUs to purchase inputs (even in small quantities) for bean

97

Setting the derivative of the bean production model with respect to the age variable equal to
zero and solving for the age variable allows us to find this value.
98
This amount was obtained by evaluating the derivative of the bean production model with
respect to the TLU variable at one TLU.
120

production. Since this variable was only significant for farmers producing beans (i.e. not for
richer farmers producing potatoes or onions), increasing bean farmers’ access to agricultural
credit would greatly benefit them.
Similarly, bean production was positively associated with owning a productive asset.
Farmers who owned a plow produced, on average, 53 kg of beans more than farmers who did not
own this implement (Table 3.6.5). Since plows are used to prepare fields for planting, thus
allowing the plants to grow better, it was expected that owning a plow would positively affect
production. Since less than 1% of farmers producing beans owned a backpack sprayer (see Table
A 3.8), it is not surprising that there was no statistically significant effect of this variable on bean
production.
Although having access to public markets in the villages had no statistically significant
effect on production, the presence of the government’s extension office in the village had a
statistically significant positive effect on bean production (10% SL; Table 3.6.5). Farmers
located in villages with an IDA office obtained, on average, 24 kg more beans than their
counterparts, suggesting that extension agents are helping these relatively poor farmers overcome
some of their production constraints.
Bean producers in the Tchicalachuluanga municipality produced, on average, 10 kg more
beans (10% SL) than farmers in Londuimbali. In contrast, farmers in Caala and Babaera
municipalities produced fewer beans (1% SL) than farmers in Londuimbali. However, the
differences in production between all other municipalities and Londuimbali were not statistically
significant at the 10% level. Since bean production is concentrated in Londuimbali, it was
expected that production would be the same or lower in other municipalities.

121

As with potatoes, most production-related variables had statistically significant effects on
production (Table 3.6.5). Since the dependent variable in this model was production (not yield),
it was expected that, as seed use increased, quantity produced would increase (at a decreasing
rate). This was true for beans (1% SL). In contrast to potatoes (and onions), planting a local
(traditional) bean variety negatively affected production (5% SL). Bean producers who planted
local varieties obtained, on average, 34 kg fewer beans than farmers using improved varieties.
This result is consistent with the literature. For example, Reyes (2011) found that bean producers
in Honduras obtained lower yields when using local varieties versus using improved varieties.
Furthermore, since less than two percent of bean growers planted IVs (Table A 3.8), efforts
should be devoted at developing and promoting the use of IVs in these regions. Making low cost,
high quality seed of IVs available to farmers could also greatly benefit them.
As expected, bean production was positively associated with use of pesticides. Bean
producers who applied pesticides obtained, on average, 43 kg more beans than farmers who did
not apply pesticides. Furthermore, as per unit production cost increased, quantity produced
decreased (5% SL). Farmers reported that the largest share of their production costs was due to
expenses of purchasing seed (most likely grain), followed by payments for services (e.g. labor).
Thus, this result suggests that the seed used by farmers may have poor quality (e.g. low
germination rates), which directly affects the quantity produced. This suggests that providing
farmers with better (low-cost) seeds may greatly benefit them.
Farmers who reported lower harvest during this period (compared to a normal year)
obtained lower bean production (5% SL). Farmers who reported harvest losses said that the main
reason for these losses was weather related (50%), followed by pest and disease incidence (25%)

122

and lack of fertilizer (21%). This suggests that breeding programs need to develop bean IVs
tolerant to abiotic (e.g. droughts) and biotic (e.g. diseases) stresses.
The interaction term between per unit production costs and a household reporting lower
harvest had a statistically significant positive effect on production (5% SL). However, the overall
effect of an increase in production costs would depend on whether farmers reported lower
harvest during the period (the opposite is also true). Thus, for farmers who did not report harvest
losses during the period (i.e. normal year), as production costs increased, quantity produced
decreased. Although for farmers who reported harvest losses during the period, an increase in
production costs would positively affect quantity produced, the magnitude of this effect was
small--0.04 kg.

99

3.6.4.3 Onion OLS Results
The econometric results of the OLS regression for onions are presented in Table 3.6.5.
The model had a R-squared of 0.45. The results show that male-headed households produced
more onions than female-headed households (10% SL)--male-headed households produced, on
average, 23 kg more onions than female-headed ones. Therefore, targeting technical assistance to
households led by a woman will greatly benefit them since, in addition to them being poorer than
male-headed households,

100

they produce fewer onions.

Furthermore, the number of dependents in the household was negatively associated with
the quantity produced (10% SL), perhaps because households with more dependents typically

99

This value is found by evaluating the derivative of the bean production model with respect to
the production cost variable at ‘reported lower harvest’ = YES.
100
The mean asset index values among onion producers were -0.21 and 0.44 for female- and
male-headed households, respectively.
123

have many young children who cannot work in the field. Households with 0.54 dependents
(mean dependency rate value) produced approximately 52 kg

101

less. However, the dependency

ratio was negatively associated with production until the number of dependents becomes
102

0.62.

After this, the dependency ratio was positively associated with onion production.

Onion production was negatively associated with the number of literate adults living in
the household (5% SL; Table 3.6.5). Since the average number of literate adults was higher
among households producing onion, compared to households producing beans or potatoes, this
finding was not surprising since more educated households may be less dependent on
103

agricultural outputs, including onions.

In addition, onion producers owning a home with

improved roof materials produced more onions (10% SL). As previously discussed, farmers who
produced potatoes and onions were wealthier than farmers producing beans (see Table 3.6.3);
thus, this finding was not a surprise.
Onion production was not statistically significantly associated with owning productive
assets (Table 3.6.5). This may have been because, in general, farmers plant a smaller area with
onions (compared to potatoes and beans), thus reducing the need to own (or rent) a plow to
prepare the soil. Furthermore, only a very small percent (4%; Table A 3.8) of onion farmers
applied pesticides to their crop, thus making owning a backpack sprayer too expensive to afford.
Although having access to public markets in the village had no statistically significant
effect on production of onions, the presence of the government’s extension office in the village
101

This value is found by evaluating the derivative of the onion production model with respect
to the dependency ratio variable at the mean value.
102
Setting the derivative of the onion production model with respect to the dependency ratio
variable equal to zero and solving for the dependency ratio variable allows us to find this value.
103
This variable has a negative effect on onion production until there are 1.59 literate adults at
home, then, its effect becomes positive.
124

was negatively associated with onion production (5% SL; Table 3.6.5). The latter suggests that
the government’s extension office perhaps provides greater assistance to farmers producing
beans (a staple) than to farmers producing onions. Whether to provide greater assistance to
farmers producing onions would depend on many factors including the extension agent’s training
and available time, the relative importance of onions compared to staple crops, etc.
While farmers producing onions in Ekunha, Bailundo, and Tchicalachuluanga produced
fewer onions (1% SL, 1% SL, and 5% SL) than farmers in Londuimbali (Table 3.6.5), the
differences in production between all other municipalities and Londuimbali were not statistically
significant at the 10% level.
Regarding the production-related variables, only the use of fertilizer had a statistically
significant effect on onion production (Table 3.6.5). Onion producers who used fertilizer
obtained, on average, 52 kg more onions (1% SL) than farmers who did not apply fertilizer to
their onion crop. Although the results do not suggest whether farmers did not have access to
fertilizers or could not afford them (i.e. due to high price), given that the largest share of
production costs were due to expenses in fertilizer and that 49% of farmers applied fertilizer, is
likely that most farmers could not afford to purchase the required amounts of fertilizer.
Although farmers were asked how much onion seed they used, given that this amount is
generally reported in grams, farmers had difficulty in estimating/recalling how many grams they
used. This may explain the unexpected (albeit not statistically significant) sign of this variable in
the regression since is likely that there were errors in measuring this variable.

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3.6.5 Double Hurdle Regression Results of Factors Affecting Marketing Decisions
This subsection presents the double-hurdle regression results for each crop separately.
The descriptive statistics for the variables included in these models were already discussed at the
beginning of this section (see Table 3.6.2); therefore, this subsection focuses on the double
hurdle (DH) regression results.
Two additional points are worth discussing here. First, the asset index discussed in
section 3.6.2 was re-estimated without two variables: owing a motorcycle and owning a bicycle.
This was done to be able to estimate the effect of these variables on marketing decisions
separately from other assets included in the index. As expected, the magnitude of the new index
was different.

104

Although the results were similar for most comparisons (e.g. male-headed

households still were richer than female-headed households) when re-estimated, the differences
in the index between bean growers vs. non-growers became statistically significant (10% SL),
suggesting that households growing beans were poorer than non-growers. The descriptive
statistics discussed at the beginning of the results section refers to this re-estimated index.
Second, the coefficient of the OLS regression residuals (estimated from the regressions in
subsection 3.6.4) was not statistically significant in any of the two hurdle regressions for potatoes
(p-value=0.723 for hurdle 1 and p-value=0.183 for hurdle 2) and beans (p-value=0.272 for
hurdle 1 and p-value=0.267 for hurdle 2). Similarly, the onion OLS regression residuals variable
was not statistically significant in the first hurdle of the onion regressions (p-value=0.577). These
results suggest that quantity produced was not endogenous; therefore, this variable was excluded
from both hurdles in the potato and bean regressions and from the first hurdle in the onion
104

The economic status index became: 0.4 and -0.58 (1% SL) for male- and female-headed
households, respectively; -0.07 and 0.22 (5% SL) for potato non-growers and growers,
respectively; 0.25 and 0.02 (10% SL) for bean non-growers and growers, respectively; and -0.09
and 0.29 (1% SL) for onion non-growers and growers, respectively.
126

regressions. However, the residuals variable was included in the truncated normal regression for
onions (second hurdle) since its coefficient was highly significant (p-value=0.002), suggesting
that quantity produced was endogenous in this regression.
The DH results are presented for each crop separately. Further, for each crop, the
discussion is divided into conditional and unconditional (on market participation) results.

3.6.5.1 Potato DH Results
Conditional results
The double hurdle regression results for potatoes are presented in Table 3.6.6. While maleheaded households were more likely to participate in the market as sellers (5% SL), once the
market participation decision has been made, gender of the head had no statistically significant
effect on the quantity of potatoes sold. Therefore, targeting assistance to female heads may be
necessary to increase their participation in the potato market as sellers, which would benefit them
due to increased income from potato sales.
In contrast, as the number of literate adults in the household increase, farmers were less
likely to sell potatoes (5% SL). However, after the participation decision has been made, having
more literate adults in the household had no statistically significant effect on the amount of
potatoes sold.
As discussed above, the asset index was used as a proxy for household wealth. Wealthier
households were more likely to sell potatoes (1% SL); however, conditional on selling potatoes,
richer households sold fewer potatoes (5% SL; Table 3.6.6). These results suggest that, although
richer households are more likely to participate in the market as sellers, they sell fewer potatoes.

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Table 3.6.6. Double-Hurdle model of factors influencing potato marketing decisions.
Central Highlands of Angola, 2009.
HURDLE 1
HURDLE 2
Probability of selling
Quantity sold (kg)
Truncated Normal
Probit Estimator
Regression Estimator
N = 240
N = 159
Independent variables: the coefficients
Pseudo R2 = 0.5006 Prob > Chi2 = 0.000
displayed are the conditional average partial
effects (APEs).
Coefficient
p-value Coefficient p-value
Age of HH head (Years)
-0.001
0.506
-0.299
0.532
Gender of HH head (1=Male)
0.125 **0.023
21.726
0.139
Dependency ratio
0.179
0.103
14.564
0.655
HH member is in farmer organization (1=Yes)
0.078
0.110
18.430
0.347
No. adults (>17 yr) literate
-0.065 **0.024
5.492
0.484
No. of Tropical Livestock Units
-0.073
0.114
8.776
0.222
Asset Index
0.051 ***0.009
-10.771 **0.021
Owns motorcycle (1=Yes)
-0.043
0.700
17.110
0.689
Owns bicycle (1=Yes)
-0.073
0.251
37.889 **0.024
IDA office in village (1=Yes)
0.110
*0.090
44.309 **0.012
Public market in village (1=Yes)
-0.185 **0.018
-16.755
0.263
HH in Caala Municipality (1=Yes)
-0.162
0.162
73.411 *0.083
HH in Ekunha Municipality (1=Yes)
-0.063
0.605
83.047 **0.024
HH in Bailundo Municipality (1=Yes)
0.183 **0.016
-100.143 **0.043
HH in Katchiungo Municipality (1=Yes)
0.201 ***0.004
32.464
0.195
HH in Tchicalachuluanga Municipality (1=Yes)
-0.039
0.685
-5.838
0.797
HH in Chiguar Municipality (1=Yes)
0.180 **0.031
-3.190
0.904
HH in Babaera Municipality (1=Yes)
-0.086
0.529
-29.850
0.289
Distance from village to sede (km)
0.003
0.336
-1.046
0.414
Road between village and sede in poor condition
(1=Yes)
-0.016
0.789
-68.381 **0.015
Seller sought price information prior to sales
(1=Yes)
n.a.
-3.418
0.814
Reported marketing costs (Kw/kg)
n.a.
-0.529
0.577
Total potato production (kg)
0.002 ***0.000
0.543 ***0.000
Notes: *, **, *** indicates the corresponding coefficients are significant at the 10%, 5%, and
1% levels, respectively. Coefficients and p-values obtained using the margins command in
Stata.
Dependency ratio estimated by dividing No. members <17 yr by household (HH) size. Literacy
refers to adults who can read and write.
n.a. = not applicable because variable was not included in the regression.
Source: ProRenda survey, Angola, 2009. Estimates weighted to reflect population.

128

It was expected that owning vehicles would be positively associated with marketing
decisions. While owning a bicycle was not associated with the probability of selling potatoes,
owning this type of vehicle (conditional on market participation) was positively associated with
the quantity of potatoes sold (5% SL). This was perhaps due to the fact that a bicycle could
easily be used to transport potatoes to local markets or other places for sale.
Having a government’s extension office in the village had a statistically significant effect
on both market participation and quantity traded (10% SL and 5% SL, respectively). In contrast,
having access to public markets for purchasing food/selling outputs in the village was a
significantly negative factor in market participation (Table 3.6.6), which was unexpected. The
main reason for this may be the fact that a higher share of non-sellers reported that public
markets were available in their villages (19% vs. 16% sellers). Once the market participation
decision has been made, this factor had no statistically significant effect on the quantity of
potatoes sold.
In contrast to Key et al. (2000) the results suggest that one of the proxies for fixed costs
(i.e. road quality) had a statistically significant (5% SL) negative effect on the quantity of
potatoes sold. Farmers located in villages with poor road quality between the village and the
main commercial town sold fewer potatoes. Although only 33% of farmers reported selling at
least one of their outputs in other markets (i.e. outside the village, for whom road quality may be
important), these farmers sold more than double the amount sold by farmers selling at home or in
the local market (308 kg vs. 145 kg, 1% SL). Thus, investing in improving roads could be an
important factor to boost potato sales.
Finally, production was a significantly positive factor on the probability of market
participation and quantity traded. This was expected since farmers who have greater production

129

have more surpluses they could sell. Although the magnitude on farmers’ market participation
decision was small, farmers producing one extra kilogram (above the mean) sold approximately
0.54 kg more potatoes.
In summary, these conditional results suggest that, to increase the likelihood that a
smallholder farmer in the central highlands of Angola would become a seller, investments are
needed to (a) assist female-headed households since this type of households are less likely to sell
potatoes, (b) support households with fewer literate adults since adult literacy was negatively
associated with the probability of selling potatoes, (c) assist (e.g. production, marketing) poorer
potato producers (as classified by the asset index) since these are less likely to sell potatoes, and
(d) support farmers with low access to extension services and farmers located in villages without
public markets. Conditional on being a seller, the quantity sold would increase if investments are
made to (a) assist (e.g. production, marketing) poorer farmers (as per their asset index) and
farmers with low access to extension services, and (b) improve the infrastructure, especially the
quality of the roads. Further, both the likelihood of a farmer becoming a seller and the
conditional quantity sold would be positively affected if farmers increase their production.
Therefore, investments are needed to help farmers increase their production (e.g. access to
inputs, farm credit, better crop management).
Unconditional results
The unconditional (on market participation) average partial effects (APE) of all variables
are included in Table A 3.9. The APE incorporates the partial effect of both hurdles, which
allows making unconditional inferences about the factors affecting the quantity of potatoes sold.
Although male-headed households sold more potatoes, the differences between these households
and female-headed households were not statistically significant at the 10% level. Thus, the

130

unconditional quantity of potatoes sold was gender neutral. This may be explained by the fact
that 41% of female-headed households reported their (male) spouses as the ones responsible for
sales (vs. 26% of male-headed households reporting female spouses as responsible for sales).
This result suggests that households led by (married) females rely on their (male) spouses for
marketing-related decisions; therefore, explaining why the differences in the quantity sold were
not statistically significant.
In contrast to the conditional quantity of potatoes sold, having a member of the household
participating in a farmer organization was positively associated (10% SL) with the unconditional
quantity of potatoes sold (Table A 3.9). Thus, promoting participation in these organizations or
establishing farmer organizations in villages without them could boost potato sales.
Furthermore, the asset index was negatively associated with the unconditional quantity of
potatoes sold (10% SL). However, the magnitude of this effect was very small--an increase of
one unit in the asset index would reduce the quantity sold by approximately 7 kg. The finding
that richer households sell fewer potatoes may be explained by the fact that a lower percent
(39%) of richer potato producers reported potatoes as the major source of crop income
(compared to 43% of farmers in the poorest tercile) and because a larger percent (30%) of richer
potato producers reported services as the main source of non-crop income (compared to 1% of
farmers in the poorest tercile). Thus, richer farmers have diverse sources of income, which make
them less dependent on potato sales.
Owning a bicycle was positively correlated with the unconditional quantity of potatoes
sold (1% SL). As previously discussed, a bicycle could easily be used to transport potatoes for
sale. Similarly, the presence of an IDA office in the village was positively correlated with the
unconditional quantity of potato sold (Table A 3.9). Farmers in villages with IDA offices sold,

131

on average, 43 kg more potatoes than farmers in villages without IDA offices. Thus, providing
farmers with extension services could contribute to increase potato sales.
Not surprisingly, farmers located in villages with poor road quality between the village
and the main commercial town sold fewer potatoes (1% SL). Lastly, farmers producing one extra
kilogram (above the mean) sold approximately 0.58 kg more potatoes (1% SL). Therefore,
investing in public infrastructure (i.e. improving roads) and helping farmers increase their
production could positively affect the unconditional quantity of potatoes sold.
In summary, these unconditional results suggest that, to boost the unconditional quantity
of potatoes sold by smallholder farmers in the central highlands of Angola, investments are
needed to (a) promote farmer participation in organizations and/or establish farmer organizations
in villages without them, (b) provide assistance (e.g. production, marketing) to poorer potato
producers (as classified by the asset index); however, since this crop requires investments, this
assistance cannot focus on farmers who are too poor, (c) support farmers with low access to
extension services, (d) improve the infrastructure, especially the quality of the roads, and (e) help
farmers increase their potato production, which can be done by making inputs more affordable
(e.g. establish credit programs) and/or available. Thus, boosting potato sales would be a
challenge for the government of Angola and donors since, due to its strong currency, overcoming
these limiting factors would need financial and human resources.

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3.6.5.2 Bean DH Results
Conditional results
The double hurdle regression results for beans are presented in Table 3.6.7. In contrast to
potato production, the gender of the head had no statistically significant effect on the market
participation decision or the quantity sold. Although households with a member participating in a
farmer organization were less likely to participate in the bean market (1% SL), once the
participation decision has been made, having a member in a farmer organization had no
statistically significant effect on the amount of beans sold. Having a member in a farmer
organization was expected to have no positive effect on marketing decisions because only a
small share (1%) of bean sellers reported having a member of the family participating in a farmer
organization (vs. 10% non-sellers).
In contrast to potato producers, marketing decisions were not associated with the
household’s wealth. This may be explained by the fact that bean producers were the poorest,
with an average asset index of 0.07 (vs. 0.269 for potato and 0.364 for onion producers; see
Table 3.6.3).
Owning a transportation vehicle had a statistically significant effect on marketing
decisions. As expected, owning a motorcycle was negatively associated with both the probability
of becoming a seller and, conditional on being a seller, the quantity of beans sold. This is
because, owning a motorcycle is also an indicative of wealth; hence, farmers who own this type
of vehicle may be too rich and less dependent on the bean crop as a source of crop income. This
is confirmed by the fact that, while 63% of bean sellers reported the bean crop as their major
source of crop income, only 29% of non-sellers reported beans as their major source of crop

133

Table 3.6.7. Double-Hurdle model of factors influencing bean marketing decisions.
Central Highlands of Angola, 2009.
HURDLE 1
HURDLE 2
Probability of selling
Quantity sold (kg)
Truncated Normal
Probit Estimator
Regression Estimator
N = 305
N = 206
Independent variables: the coefficients
Pseudo R2 = 0.2452
Prob > Chi2 = 0.000
displayed are the conditional average partial
effects (APEs).
Coefficient p-value Coefficient p-value
Age of HH head (Years)
-0.002
0.428
-0.435
0.167
Gender of HH head (1=Male)
-0.054
0.413
9.242
0.236
Dependency ratio
-0.068
0.593
-9.951
0.469
HH member is in farmer organization (1=Yes)
-0.409 ***0.000
17.282
0.237
No. adults (>17 yr) literate
0.063
0.326
4.359
0.308
No. of Tropical Livestock Units
-0.045
0.491
5.268
0.328
Asset Index
-0.004
0.904
1.471
0.684
Owns motorcycle (1=Yes)
-0.312 *0.060
-31.898 **0.012
Owns bicycle (1=Yes)
-0.114
0.226
26.971 **0.026
IDA office in village (1=Yes)
-0.018
0.889
2.216
0.866
Public market in village (1=Yes)
0.115
0.207
-12.830
0.207
HH in Caala Municipality (1=Yes)
-0.232 *0.091
-14.464
0.362
HH in Ekunha Municipality (1=Yes)
-0.147
0.204
24.203
0.184
HH in Bailundo Municipality (1=Yes)
-0.066
0.421
19.040 *0.071
HH in Katchiungo Municipality (1=Yes)
-0.076
0.622
-18.010
0.107
HH in Tchicalachuluanga Municipality (1=Yes)
-0.115
0.316
-12.267
0.250
HH in Chiguar Municipality (1=Yes)
-0.030
0.833
-1.614
0.914
HH in Babaera Municipality (1=Yes)
0.196 **0.037
-41.862 ***0.003
Distance from village to sede (km)
0.011 ***0.009
0.149
0.691
Road between village and sede in poor
condition (1=Yes)
0.153 *0.095
-4.850
0.659
Seller sought price information prior to sales
(1=Yes)
n.a.
-12.430
0.200
Reported marketing costs (Kw/kg)
n.a.
-0.428
0.374
Total bean production (kg)
0.003 ***0.002
0.499 ***0.000
Notes: *, **, *** indicates the corresponding coefficients are significant at the 10%, 5%, and
1% levels, respectively. Coefficients and p-values obtained using the margins command in
Stata.
Dependency ratio estimated by dividing No. members <17 yr by household (HH) size. Literacy
refers to adults who can read and write.
n.a. = not applicable because variable was not included in the regression.
Source: ProRenda survey, Angola, 2009. Estimates weighted to reflect population.

134

income (see Table A 3.2). Furthermore, farmers selling beans also depended on selling their
labor in other farms as a source of non-crop income, which suggest that these farmers may have
limited resources (e.g. land, capital) to diversify the crops they plant.
In contrast, having a cheaper transportation vehicle (i.e. a bicycle) was positively
associated with the amount of beans sold (5% SL). This was no surprise since approximately
15% of farmers in the region owned a bicycle (vs. only 7% owning a motorcycle; see Table A
3.3). Furthermore, a bicycle could easily be used to transport bean surpluses to the place of sale.
Having a government’s extension office in the village had no statistically significant
effect on marketing decisions (i.e. both hurdles). However, having an IDA office in the village
was positively associated with bean production (see Table 3.6.5). These suggest that extension
agents may be providing more assistance related to production techniques than to marketing
strategies.
Both the distance between the village and the main commercial town, and having a poor
condition road between the village and the main commercial town oddly had a positive effect on
the probability of selling beans. The fact that farmers located farther away were more likely to
sell beans highlights that constrained farmers generally produce and sell beans, regardless of how
far they are from the main commercial town. This is perhaps because beans could easily be
stored and sold at any time after harvest.
Several reasons can help explain why poor road quality had a positive effect on becoming
a bean seller. First, 76% of farmers reported selling at least one of their outputs at home or in
local markets (for whom distance to commercial town and road quality may not be important).
Second, although these farmers sold, on average, close to one-half of the amount sold by farmers
selling in other markets (77 kg vs. 151 kg, 1% SL), the aggregated volume sold by them was

135

much higher. Third, most farmers selling at home or in local markets reported convenience
(58%) and lack of transportation (21%) as the main reasons for selling locally. Fourth, since
beans are less perishable and could easily be stored, it is a good crop for farmers to grow when
roads are bad since they could sell the crop through time either in local or distant markets (by
transporting small quantities over time). Therefore, since bean trade is likely concentrated in
local markets, is possible that poor quality roads incentivize participation in local markets.
Finally, bean production was a positively associated with the probability of market
participation and the conditional quantity traded. As with potatoes, this was expected since
farmers who produce more have more surpluses they could sell. Furthermore, although the
magnitude on farmers’ market participation decision was small, farmers producing one extra
kilogram (above the mean) sold approximately 0.50 kg more beans.
In summary, these conditional results suggest that, to increase the likelihood that a
smallholder farmer would become a bean seller, investments are needed to (a) assist households
with no members participating in farmer organizations since having a member of the family in a
FO was negatively associated with selling beans, (b) support farmers with no transportation
vehicles, perhaps on alternative ways to market their surpluses, and (c) assist (e.g. production,
marketing) farmers located in villages farther away from and with poor road quality to the main
commercial town. Conditional on being a seller, the quantity sold would increase if investments
were made to assist farmers with no or low-cost transportation vehicles (e.g. marketing
alternatives). Since gender of the household head had no statistical effect on both hurdles, any
assistance should be targeted to both male- and female-headed households producing beans.
Further, both the likelihood of a farmer becoming a seller and the conditional quantity sold

136

would be positively affected if farmers increase their production. Therefore, investments are
needed to help farmers increase their production.
Unconditional results
The unconditional (on market participation) average partial effects of all variables are
included in Table A 3.10. Similar to the conditional results, the unconditional APEs suggest that
bean sales were gender neutral. This may be explained by the fact that 40% of female-headed
households reported their (male) spouses as the ones responsible for sales (vs. 20% of maleheaded households reporting female spouses as responsible for sales). Thus, as with potatoes,
households led by (married) females rely on their (male) spouses for marketing-related decisions.
In contrast, unconditional bean sales were positively correlated (10% SL) with the number of
literate adults living in the household (albeit in a very small magnitude), which suggests that
teaching family members to read and write would increase sales perhaps due to the fact that
literate adults can make better informed decisions related to marketing activities.
Owning transportation vehicles had the same statistical effect on the unconditional sales
of beans (Table A 3.10). While owning a motorcycle was negatively associated (1% SL) with the
amount of beans sold, owning a bicycle was positively associated (1% SL) with the quantity of
beans sold. Furthermore, distance between the village and the main commercial town oddly had
a positive effect on the unconditional quantity of beans sold. However, the magnitude of this
effect was small. As explained above, this highlights the fact that constrained farmers generally
produce and sell beans, regardless of how far they are from the main commercial town.
While seeking price information prior to sales was negatively associated with the
unconditional quantity of beans sold (1% SL), the magnitude of this effect was small (Table A
3.10). The reason for this could be related to the quality of the information received by farmers,

137

which may have been questionable. Among farmers who sought price information prior to sales,
48% reported receiving this information from a fellow friend (most likely another farmer) and
43% from a trader. It is possible that farmers who sought price information did not trust their
source of information or perhaps were expecting a better (higher) price. Thus, after learning
about the price, they decided to sell fewer beans. This suggests that investments to establish
marketing information systems may be needed to provide farmers with reliable market
information so they can make better marketing decisions.

105

Lastly, farmers producing one extra kilogram (above the mean) sold approximately 0.48
kg more beans. Similar to potatoes, this finding suggests that investing in activities targeted at
helping farmers increase their bean production could greatly boost bean sales.
In summary, these unconditional results suggest that, to boost the unconditional quantity
of beans sold by smallholder farmers in the central highlands of Angola, investments are needed
to (a) provide assistance (e.g. production, marketing) to younger farmers, (b) teach farmers (and
educate children) how to read and write so they can make better informed decisions related to
marketing activities, (c) establish marketing information systems to provide farmers with reliable
market information, especially about prices, and (d) help farmers increase their bean production,
which can be done by providing them with access to education, productive assets, extension
services, improved varieties, inputs, and agricultural credit. Thus, as with potatoes, boosting bean
sales would be a challenge for the government of Angola and donors.

105

Although there may be another possible explanation related to the existence of shallow
markets, proving this is more difficult and outside the scope of this study.
138

3.6.5.3 Onion DH Results
Conditional results
Table 3.6.8 shows the double hurdle regression results for onions. While older farmers
were less likely to sell onions (10% SL), conditional on selling, older farmers sold more onions
than younger farmers (5% SL) perhaps due to the fact that older farmers may be better connected
with traders (i.e. social capital) and may have more marketing experience.
Similar to bean production, the gender of the head had no statistically significant effect on the
market participation decision or the quantity sold, suggesting that marketing decisions are gender
neutral. Although households with more dependents were less likely to sell onions (10% SL),
once the marketing participation decision has been made, households with more dependents sold
more onions (5% SL). In contrast to bean production, households with a member participating in
a farmer organization were more likely to sell onions; however, this factor had no statistically
significant effect on the quantity sold. Thus, establishing farmer organizations in villages without
them would contribute to increase market participation.
The number of TLUs was positively associated (5% SL) with the probability of selling
onions. However, conditional on market participation, this factor was negatively associated (5%
SL) with the quantity of onion sold. In contrast, the asset index (proxy for household wealth) was
negatively associated (5% SL) with the probability of selling onions. This was expected since
non-sellers were richer (as per their asset index; see Table A 3.4) than onion sellers.
Furthermore, non-sellers reported having additional sources of non-crop income, including
transfers and remittances, and other activities (see Table A 3.2), which may be invested in

139

Table 3.6.8. Double-Hurdle model of factors influencing onion marketing decisions.
Central Highlands of Angola, 2009.
HURDLE 1
HURDLE 2
Probability of selling
Quantity sold (kg)
Truncated Normal
Probit Estimator
Regression Estimator
N = 174
N = 103
Independent variables: the coefficients
Pseudo R2 = 0.3767
Prob > Chi2 = 0.000
displayed are the conditional average partial
effects (APEs).
Coefficient p-value Coefficient
p-value
Age of HH head (Years)
-0.005 *0.092
0.286 **0.039
Gender of HH head (1=Male)
0.141
0.132
0.346
0.968
Dependency ratio
-0.323 *0.061
16.448 **0.018
HH member is in farmer organization (1=Yes)
0.177 ***0.005
-2.438
0.667
No. adults (>17 yr) literate
0.053
0.164
2.228
0.235
No. of Tropical Livestock Units
0.281 **0.012
-7.777 **0.030
Asset Index
-0.087 **0.015
-1.596
0.454
Owns motorcycle (1=Yes)
-0.199
0.201
3.766
0.765
Owns bicycle (1=Yes)
0.105
0.257
-0.987
0.881
IDA office in village (1=Yes)
-0.024
0.803
3.737
0.702
Public market in village (1=Yes)
0.142 *0.057
1.551
0.804
HH in Caala Municipality (1=Yes)
-0.082
0.604
2.857
0.844
HH in Ekunha Municipality (1=Yes)
-0.192
0.407
6.849
0.494
HH in Bailundo Municipality (1=Yes)
-0.093
0.361
0.776
0.945
HH in Katchiungo Municipality (1=Yes)
0.055
0.693
8.279
0.459
HH in Tchicalachuluanga Municipality (1=Yes)
-0.125
0.339
0.269
0.976
HH in Chiguar Municipality (1=Yes)
0.200 *0.082
-0.036
0.996
Distance from village to sede (km)
-0.014 **0.011
-0.509
0.310
Road between village and sede in poor
condition (1=Yes)
0.253 **0.021
7.166
0.352
Seller sought price information prior to sales
(1=Yes)
n.a.
2.626
0.244
Reported marketing costs (Kw/kg)
n.a.
-0.192
0.285
Total onion production (kg)
0.003 **0.044
0.810 ***0.000
Residual from onion production equation
n.a.
-0.107 **0.025
Notes: *, **, *** indicates the corresponding coefficients are significant at the 10%, 5%, and
1% levels, respectively; p-values obtained via bootstrapping at 500 repetitions in hurdle 2;
coefficients in both hurdles along with p-values in hurdle 1 obtained using the margins
command in Stata.
Dependency ratio estimated by dividing No. members <17 yr by household (HH) size. Literacy
refers to adults who can read and write.
n.a. = not applicable because variable was not included in the regression.
Source: ProRenda survey, Angola, 2009. Estimates weighted to reflect population.
140

improving the home or purchasing household assets, both reflected in the asset index. These
results suggest that non-sellers were less dependent on onions since they had diverse sources of
income.
While having a public market in the village was positively associated (10% SL) with the
likelihood of selling onions, once this decision has been made, this factor was not statistically
associated with the quantity of onions sold. In contrast, onion producers located farther away
from the main commercial town were less likely to sell onions (5% SL). However, once this
decision has been made, the distance between the village and the main commercial town had no
statistical effect on the quantity of onions sold perhaps due to the small amounts sold by onion
sellers (50 kg; see Table 3.6.2).
Strangely, having a poor quality road between the community and the main commercial
town was positively associated (5% SL) with the probability of selling onions. This may be given
by the fact that 81% of farmers selling onions were located in villages with poor road quality
between the village and main commercial town (vs. only 53% of non-sellers; see Table 3.6.2).
Additionally, since 74% of farmers sold at least one of their harvests either at home or in local
markets and since the differences in the quantity sold by farmers selling in local markets
compared to farmers selling in other markets were not statistically significant, it is possible that
poor road quality incentivized local sales (as also suggested by the effect of having a public
market in the village), reflecting sales targeted at meeting local demand. Once the market
participation decision has been made, the quality of the road had no statistically significant effect
on the amount sold.
Finally, onion production was a significantly positive factor on the probability of market
participation and the quantity traded. Even after controlling for endogeneity in the second hurdle,

141

quantity produced was a highly statistically significant positive factor in the quantity traded.
Although the magnitude on farmers’ market participation decision was small, farmers producing
one extra kilogram (above the mean) sold approximately 0.81 kg more onions.
In summary, these conditional results suggest that, to increase the likelihood that a
smallholder farmer in the central highlands of Angola would become an onion seller,
investments are needed to (a) assist (e.g. production, marketing, storage) younger farmers and
poorer farmers, (b) promote or establish farmer organizations where farmers could participate in
(and perhaps find alternative ways to market their surpluses) and public markets where farmers
could trade their surpluses, (c) mitigate cash constraints (e.g. agricultural credit) affecting onion
producers since the number of tropical livestock units (proxy for cash availability) was positively
associated with being a seller, and (d) assist (e.g. production, marketing, storage) farmers located
in villages closer from and with poor road quality to the main commercial town. Conditional on
being a seller, the quantity sold would increase if assistance (e.g. production, marketing, storage)
is provided to (a) older farmers, (b) households with more dependents, and (c) farmers facing
cash constraints (as per the number of tropical livestock units owned). Since gender of the
household head had no statistical effect on both hurdles, any assistance should be targeted to
both male- and female-headed households producing onions. Further, both the likelihood of a
farmer becoming a seller and the conditional quantity sold would be positively affected if
farmers increase their onion production. Therefore, as with potatoes and beans, investments are
needed to help farmers increase their onion production (e.g. access to inputs, farm credit, better
crop management).

142

Unconditional results
The unconditional APEs of all variables are included in Table A 3.11. Similar to potato
and beans, the quantity of onions sold was gender neutral. Since 34% of female-headed
households reported their (male) spouses as the ones responsible for sales and 32% of maleheaded households reported their (female) spouses as responsible for sales, this finding was not
surprising.
In contrast, the unconditional quantity of onion sold was positively associated with the
number of literate adults in the household, suggesting that teaching family members to read and
write would increase sales perhaps due to the fact that literate adults can make better informed
decisions related to marketing activities.
Richer onion producers owning more assets (as indicated by the asset index) would sell
fewer onions (1% SL). This was expected since richer farmers may be less dependent on the
onion crop as a source of income. Although the presence of an IDA office in the village had no
statistically significant (at the 10% level) effect on onion sales, having a public market in the
village would positively affect sales (Table A 3.11).
Similar to the conditional results, the distance between the village and the main
commercial town was negatively associated (5% SL) with the unconditional quantity of onions
sold (Table A 3.11). Surprisingly, having a poor quality road between the village and the main
commercial town positively affect sales. As explained above, the lack of alternative places for
sale and the fact that most transactions occur in local markets may help explaining this finding.
Furthermore, since the quantity traded is relatively small, it is perhaps not worth for farmers to
travel to other (distant) markets to sell their outputs.

143

Not surprisingly, as per unit marketing costs increased, the unconditional quantity sold
decreased (10% SL; Table A 3.11). Since most of the marketing costs were due to transportation
costs, this also helps explain why farmers located farther away would sell less. Finally, farmers
producing one extra kilogram (above the mean) sold approximately 0.68 kg more onions. As
with potatoes and beans, this finding suggests that investing in activities targeted at helping
farmers increase their onion production will greatly boost onion sales.
In summary, these unconditional results suggest that, to boost the unconditional quantity
of onions sold by smallholder farmers in the central highlands of Angola, investments are needed
to (a) teach farmers (and educate children) how to read and write so they can make better
informed decisions related to marketing activities, (b) provide assistance to poorer onion
producers (as classified by the asset index); however, since the onion crop requires investments,
this assistance should not focus on farmers who are extremely poor, (c) promote or establish
public markets within villages, (d) reduce transaction costs related to marketing activities,
especially transportation costs, and (e) help farmers increase their onion production, which can
be done by providing them with access to agricultural inputs.

144

3.7

Chapter Summary and Policy Recommendations

3.7.1 Chapter Summary
This essay uses single equation ordinary least squares regressions for analysis of factors
affecting production of potatoes, beans, and onions in the central highlands of Angola.
Furthermore, it implements double hurdle regressions to study the factors associated with
marketing decisions among potato, bean and onion growers, focusing on gender of the household
head, asset ownership, and transaction costs. The data used in this study came from the cross
sectional household- and village-level survey implemented by World Vision’s ProRenda project
in 2009, which collected information from 656 households distributed across 40 villages and
three provinces in the central highlands of Angola. The results suggest that the quantity produced
is exogenous in potato, bean, and onion models for market participation (first hurdle) and in
potato and bean models for quantity sold (second hurdle). In contrast, in the onion model for
quantity sold, the amount of onion produced is found to be endogenous.
The wealth analysis using the asset index determined that male-headed households had
more assets than female-headed households and that bean growers had the lowest economic
status index among farmers growing potatoes, beans or onions. Furthermore, while potato and
onion growers were richer than non-growers, there were no statistically significant differences in
wealth between bean growers and non-growers. In contrast, while potato sellers were richer than
their counterparts, onion sellers were poorer than non-sellers and there were no statistically
significant differences in wealth between bean sellers and non-sellers. The latter finding was no
surprise since beans are a staple crop grown both for consumption and sale by farmers across all
wealth categories. The assets with more ‘weight’ (i.e., more important) in the estimation of the
asset index included owning a television, a cart, a motorcycle, and a backpack sprayer.

145

The OLS regression results suggest that potato production was gender neutral.
Furthermore, potato production was positively associated with owning a home with improved
roof materials (i.e. zinc roofs). Surprisingly, owning productive assets had no statistically
significant effect on potato production. Similarly, although using local varieties negatively
affected production, the effect was not statistically significant at the 10% level. Further analysis
in this area may be valuable to learn about the effects of using local vs. improved potato
varieties. As expected, use of agricultural inputs (i.e., fertilizer and pesticides) positively affected
production. The results suggest that, although inputs were available, is likely that farmers could
not afford them, which is understandable since Angola’s strong currency makes inputs very
expensive.
Regarding bean production, the OLS regression results suggest that older farmers
produced more beans perhaps because they have better production experience. Similar to potato
production, bean production was gender neutral. Furthermore, having more literate adults at
home and owning more tropical livestock units (a quick source of cash if needed) were positively
associated with bean production. Likewise, owning a productive asset and having a government
extension office in the village positively affected production.
Not surprisingly, using local varieties had a statistically significant negative effect on
bean production. Making available to farmers low-cost, high-quality seed of improved bean
varieties would greatly benefit them because (a) farmers who reported lower harvest said that the
main reasons for this were weather- and pest and disease-related, which highlights the need to
develop/provide IVs with resistance to diseases and tolerance to abiotic factors (e.g. droughts),
(b) only a small share of bean producers used IVs, which highlights the need to promote existing
bean IVs, and (c) the largest share of production costs was incurred in purchasing seed (most

146

likely grain), which suggest that the quality of the seed used by farmers is low. Furthermore, the
use of pesticides was positively associated with the quantity of beans produced. As with
potatoes, affording pesticides appears to be the limiting factor.
The OLS regression results suggest that, in contrast to potatoes and beans, onion
production was higher among male-headed households. Thus, assisting households led by
females would contribute to reduce poverty and food insecurity since these households are at
disadvantage. Furthermore, having more dependents and more literate adults at home was
negatively associated with onion production. In contrast, onion production was positively
associated with owning a home with improved roof materials (i.e. zinc roofs). Surprisingly, the
presence of a government extension office in the village was negatively associated (5% SL) with
onion production, perhaps because extension services may be targeted at farmers producing
staple crops (e.g. beans) instead of farmers producing high-value crops (e.g. onions). As
expected, use of agricultural inputs (i.e., fertilizer) positively affected production. As with
potatoes and beans, it appears that farmers cannot afford purchasing fertilizers.
The double hurdle regression results suggest that the factors associated with marketing
decisions depend on the crop analyzed and on whether marketing decisions are analyzed
conditionally (i.e., probability of selling and, conditional on selling, quantity sold) or
unconditionally (i.e., unconditional quantity sold).
For potatoes, the conditional results suggest that male-headed households were more
likely to sell, and that households with more literate adults and having access to a public market
in the village were less likely to sell potatoes. However, once the market participation decision
has been made, gender of the head, number of literate adults, or having a public market in the
village had no statistically significant effect on the quantity of potatoes sold.

147

In contrast, although richer households (as per their asset index) were more likely to
participate in the market as sellers, once this decision has been made, richer households sold
fewer potatoes. Furthermore, while owning a bicycle was not associated with the probability of
selling potatoes, owning a bicycle was positively associated with the conditional quantity of
potatoes sold. Moreover, although the quality of the road between the village and the main
commercial town was not associated with the probability of selling potatoes, poor quality road
was negatively associated with the amount of potatoes sold.
Both having a government’s extension office in the village and the amount of potato
produced had statistically significant positive effects on both the likelihood of being a seller and,
conditional on selling, the quantity sold. Although the magnitude of the latter on the market
participation decision was small, approximately 54% of increased production, conditional on
market participation, would be sold.
The unconditional analysis suggests that potato sales were gender neutral. Furthermore,
having a household member participating in a farmer organization, owning a bicycle, the
presence of an IDA office in the village, and quantity produced all positively influenced the
unconditional quantity of potatoes sold. For each additional kilogram produced, approximately
58% would end up being sold. In contrast, the asset index and having a poor quality road
between the village and the main commercial town both negatively affected the unconditional
quantity sold.
For beans, the conditional results suggest that marketing decisions were gender neutral.
Furthermore, while households with a household member participating in a farmer organization
were less likely to sell beans, households located farther away from the main commercial town
or with poor road quality between the village and main commercial town oddly were more likely

148

to sell beans. The fact that farmers located farther away or with poor road quality were more
likely to sell beans highlights the fact that constrained farmers generally produce and sell beans,
regardless of how far they are from the main commercial town or the quality of the road between
these two places perhaps because beans are an important source of cash. Once the market
participation decision has been made, participating in a farmer organization, distance to main
commercial town, or road quality had no statistical effect on the conditional quantity sold.
While owning a motorcycle was a significantly negative factor on both the probability of
becoming a seller and, conditional on being a seller, the quantity of beans sold, owning a bicycle
was positively associated only with the conditional quantity of beans sold. Furthermore, the
amount of beans produced positively affected both the likelihood of being a seller and,
conditional on selling, the quantity sold. Approximately 50% of increased production,
conditional on market participation, would be sold.
The unconditional analysis suggests that bean sales also were gender neutral.
Furthermore, the number of literate adults living in the household, owning a bicycle, distance to
main commercial town, and quantity produced all positively influenced the unconditional
quantity of beans sold. For each additional kilogram produced, approximately 48% would end up
being sold. In contrast, owning a motorcycle and seeking price information prior to sales both
negatively affected the unconditional quantity sold.
Lastly, for onions, the conditional results suggest that the likelihood of selling onions was
negatively affected by age of the household head and the dependency ratio. However,
conditional on selling, these factors positively affected quantity sold. As with beans, marketing
decisions were gender neutral. In contrast, households with a household member participating in
a farmer organization, with access to a public market in the village, or reporting poor quality

149

road between the village and main commercial town were more likely to sell beans. However,
once the market participation decision has been made, participating in a farmer organization,
access to a public market in the village, or road quality had no statistical effect on the conditional
quantity sold.
Richer households (as per their asset index) and households located farther away from the
main commercial town were less likely to sell onions. Once the market participation decision has
been made, these factors had no statistical effect on the quantity sold. Furthermore, while the
probability of selling onions was positively associated with the number of tropical livestock units
owned, conditional on selling, quantity sold was negatively associated with the number of TLUs
owned. As expected, the amount of onions produced positively affected both the likelihood of
being a seller and, conditional on selling, the quantity sold. Approximately 81% of increased
production, conditional on market participation, would be sold.
The unconditional analysis suggests that onion sales also were gender neutral.
Furthermore, the number of literate adults living in the household, having a public market in the
village, having a poor quality road between the village and the main commercial town, and
quantity produced all positively influenced the unconditional quantity of onions sold. For each
additional kilogram produced, approximately 68% would end up being sold. In contrast, the asset
index, distance from village to main commercial town, and per unit marketing costs all
negatively affected the unconditional quantity of onions sold.

150

3.7.2 Policy Recommendations
The results suggest that different policies would be needed to increase the participation of
farmers in crop-markets as sellers and the quantity they sell. The specific policies would depend
on the crop being analyzed.
Thus, for potatoes, the following policy recommendations are proposed:
A. To boost the participation of farmers in the potato market as sellers, the government of
Angola, donors, and organizations that work with farmers should target their assistance to
female-headed households (because they are less likely to sell potatoes), households
composed by a high number of illiterate adults, and households without access to local public
markets. Furthermore, since potato production requires the use of inputs and since the
likelihood of selling potatoes was positively associated with the asset index, a larger impact
could be achieved by assisting farmers who are not extremely poor. Additionally, providing
extension services would also increase market participation.
B. To increase the unconditional quantity of potatoes sold by smallholder farmers in the central
highlands of Angola, investments are needed to (1) promote farmer participation in
organizations and/or establish farmer organizations in villages without them, (2) provide
assistance (e.g. production) to poorer potato producers (as classified by the asset index);
however, since this crop requires investments, this assistance should not focus on farmers
who are extremely poor, (3) provide farmers extension services related to both production
and marketing aspects, and (4) improve the infrastructure, especially the quality of the roads.
For beans, the following policy recommendations are proposed:
A. To encourage the participation of farmers in the bean market as sellers, the government of
Angola, donors, and organizations that work with farmers should target their assistance to

151

both male- and female-headed households, households with no members participating in
farmer organizations, and households without transportation vehicles. Furthermore,
assistance should be provided to farmers located in distant markets and with poor road
access.
B. To boost the unconditional quantity of beans sold by smallholder farmers in the central
highlands of Angola, investments are needed to (a) provide assistance to younger farmers, (b)
teach farmers (and educate children) how to read and write so they can make better informed
decisions related to marketing activities, and (c) establish marketing information systems to
provide farmers with reliable market information, especially about prices.
For onions, the following policy recommendations are proposed:
A. To boost the participation of farmers in the onion market as sellers, assistance should target
both female- and male-headed households, households led by young heads, households
composed by fewer dependents for every adult, households that are asset-constrained, and
households located closer to main commercial towns. Furthermore, although it was not
possible to separate cause from effect, promoting farmer participation in organizations and/or
establish farmer organizations in villages without them would most likely increase market
participation.
B. To boost the unconditional quantity of onions sold by smallholder farmers in the central
highlands of Angola, investments are needed to (a) teach farmers (and educate children) how
to read and write so they can make better informed decisions related to marketing activities,
(b) provide assistance to poorer onion producers (as classified by the asset index); however,
since the onion crop requires investments, this assistance should not focus on farmers who

152

are extremely poor, (c) promote or establish public markets within villages, and (d) reduce
transaction costs related to marketing activities, especially transportation costs.
Finally, although the quantity sold of each of the three crops was gender neutral, given
that female-headed households were poorer than their counterparts, there is a need for special
programs targeted at female-headed households to help them move out from poverty.
Furthermore, since the quantity sold of each of the three crops would increase if farmers’ outputs
were increased, investments should be made to help farmers increase their outputs. For potatoes,
this could be achieved by making inputs more affordable and/or available on credit. For beans,
providing farmers with access to education, productive assets, extension services, improved
varieties, agricultural inputs, and agricultural credit would contribute to increase their
production. Finally, for onions, providing farmers with access to agricultural inputs (perhaps
through credit) would positively affect their production. Thus, boosting sales would be a
challenge for the government of Angola, donors, and organizations working with farmers since,
due to its strong currency, overcoming these limiting factors may require large financial and
human resources.

153

APPENDIX

154

Table A 2.1. Bean trials planted in Central America and Ecuador. 1999-2010.
Year
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Mean

ECAR details
1

Central America: "ECAR trials"
# trials included in analysis
# countries where planted
2

3

# lines per trial
# replications per line
Ecuador: "Prueba Trials"
2
# locations where planted
Average # lines per trial

3

9
3

9
5

5
3

7
4

6
3

5
3

10
4

8
4

6
3

5
3

n.a.
n.a.

7.2
3.5

7

9

9

4

7

6

3

8

6

4

5

n.a.

6.2

16
3

# locations where planted

9
3

16
3

16
3

16
3

16
3

16
3

16
3

16
3

16
3

16
3

16
3

n.a.
n.a.

16
3

n.a.

n.a.

n.a.

n.a.

2

2

1

3

11

1

5

1

3

n.a.

n.a.

n.a.

n.a.

17

18

18

8

13

11

12

10

13

Source: Programa de Investigaciones en Frijol Metadata, Zamorano, Honduras. n.a. = not available.
1

ECAR = Ensayo Centroamericano de Adaptacion y Rendimiento.

2

For Central America: sometimes, the same location is used twice per year (i.e. two seasons). Therefore, the number of locations
may be different (less) than the number of trials planted. For Ecuador: # locations is the same as the number of trials included in the
study.
3

For Central America: from the 16 lines, one is a local check and one is an universal check. Therefore, this nursery contains only 14
advanced lines. For Ecuador: number only includes advanced lines and is the average of the number of lines planted in each location
(the number of lines varied per location).

155

Table A 2.2. Costa Rica: Improved bean varieties released. 1990-2010.
Year of Variety
No. release Name
1 2009 Disquis
2 2009
3 2007
4 2006
5
6
7
8
9

2006
2006
2004
2003
2000

10 2000
11 1996
12 1996
13 1995
14 1995
15 1994
16 1993

Used Developed Ever
Market CRSP through widely
class funds? PPB? planted?
Red
YES
YES
n.a.

1

Genealogy
(SEA 15 x MD 2324) F1 x (MD 3075 x G
21212) F1 / MC-6P-MQ-MC-IIC-MC-MC
Suru
MEB 2232-29
PAN 68 x MD 2324
White
Tongibe
BCH 9901-14
MD 3075 // SRC 1-1-18 / SRC 1-12-1
Red
Changuena MR 13652-39
(BRIBRI x (VAX 1 x RABA 655)) F1 / (NN)
Red
Q-(NN)-C
Curre
MPCR 202-26-1 Selection from MPCR 202
Red
Gibre
MPCR 202-30-2 Selection from MPCR 202
Red
Telire
EAP 9510-1
MD 3075 / DICTA 105
Small red
Cabécar
EAP 9510-77
MD 3075 / DICTA 105
Small red
UCR 55
NJBC-20601-1- NAB 44 // ROS 24 / G 13689
Black
CM(71)
Bribri
MD 2324
(RAB 310 x XAN 155) x (DOR 391 x
Small red
Pompadour G)
Guaymí
MUS 106
XAN 176 x IN 63
Black
Maleku
RAB 572
(MUS70 x RAO27) x (SEL960 x (RAO29 x Small red
(RAB58 x (DOR 164 x IN 199))))
CIAT 95
MUS 181
(XAN 226 x MUS 46) x (G 18252 x (G 13920 Black
x (G 13920 x (G 13920 x G2333))))
Chirripo Rojo DOR 489
DOR 367 x (DOR 364 x BAT 1298)
Small red
UCR 52
DOR 390
(DOR 364 x G 18521) x (DOR 365 x LM
Black
30630)
UCR 51
DOR 474
DOR 367 x (DOR 364 x BAT 1298)
Small red
Line ID
MR 14215-9

156

YES
YES
YES

YES
YES
YES

n.a.
n.a.
n.a.

YES
YES
YES
YES
n.a.

YES
YES
YES
YES
NO

n.a.
n.a.
n.a.
n.a.
NO

YES

YES

YES

n.a.
n.a.

NO
NO

n.a.
n.a.

n.a.

NO

n.a.

n.a.
NO

NO
NO

n.a.
n.a.

n.a.

NO

n.a.

Table A 2.2 (cont’d).
Used Developed Ever
Year of Variety
Market CRSP through widely
1
Genealogy
No. release Name
Line ID
class funds? PPB? planted?
17 1993 Puricise
BAT 76
(G 1741 x G 2045) x (G 4792 x G 5694)
Black
n.a.
NO
n.a.
18 1992 UCR 50
DOR 364
BAT 1215 x (RAB 166 x DOR 125)
Small red NO
NO
n.a.
Source: CIAT (2001a), INTA and UCR (2005), Hernandez (2010), Martinez (2003), and KII (2010a). n.a. = not available.
1

Same pedigree implies that the lines are sisters, i.e. they come from the same parents.
NOTES: Varieties Bribri, Changuena, Curre, and Gibre came from crosses made at the breeding program of Zamorano, which
receives CRSP funds.

157

Table A 2.3. El Salvador: Improved bean varieties released. 1990-2010.
Year of
release Variety Name
1 2008 CENTA Nahuat
2 2008 CENTA C.P.C.

1

3
4

No.

2005
2002

CENTA Pipil
CENTA San Andres

Genealogy
SRC 1-12-1 / MD 3075
Concha Rosada / SRC 1-1-18
/ SRC 1-2-12
PRF 9653-16B-3 Bribri / MD 3037 // RS 3
EAP 9510-77
CENTA 2000 x DICTA 105

5

2000

CENTA 2000

MD 3075

Line ID
SRC 2-18-1
PPB 11-20 MC

DOR 483 x (DOR 391 x
Pompadour J)
6 1997 ROJO Salvadoreño 1 DOR 482
DOR 367 x (DOR 364 x LM
30649)
7 1995 CENTA Costeño
DOR 585
DOR 364 x SEL 1079
8 1993 DOR 582
DOR 582
n.a.
9 1989 CENTA Cuscatleco DOR 364
BAT1215 x (RAB 166 x
DOR 125)
Source: CENTA (2005), CIAT (2001a), Martinez (2003), Reyes (2010), and KII (2010a).
1
Same pedigree implies that the lines are sisters, i.e. they come from the same parents.

158

Market
class
Small red
Small red

Used Developed Ever
CRSP through widely
funds?
PPB? planted?
NO
n.a.
YES
YES
NO
n.a.

Small red
Small red

YES
YES

NO
NO

YES
YES

Small red

YES

NO

NO

Small red

NO

NO

NO

Small red
Small red
Small red

NO
NO
NO

NO
NO
NO

YES
NO
YES

n.a. = not available.

Table A 2.4. Honduras: Improved bean varieties released. 1990-2010.

No.
1
2
3
4
5
6
7
8
9

Year of
release Variety Name
2009 Quebradeño
2009 La Majada AF
2009 Briyo AM
2009 Milagrito
2007 Cardenal
2007 Deorho
2007 Victoria
2007 Don Cristóbal
2007 Conan 33

10 2005 Palmichal 1
11 2005 Nueva
Esperanza 01
12 2004 Macuzalito
13 2003 Amadeus 77
14 2003 Carrizalito
15 2003 Cedrón
16 2003 Cayetana 85

17 1997 DICTA 113

Line ID
IBC307-7
IBC301-182
IBC306-95
F0243
MER 2226-41
SRC 2-18-1
SRS56-3
SRC1-12-1-8
PRF9653-25B-1

PRF 9707-36
DICZA 9801

1

Genealogy
TC75//TC75/Cincuenteño
Amadeus77//Amadeus77/Paraisito
Amadeus77//Amadeus77/Rojo de Seda
Mass selection from landrace
SRC 1-12-1-47 / Amadeus 77
SRC 1-12-1 / MD 3075
Amadeus77/SEA5
DOR476//XAN155/DOR364
EAP 9503 / RS3 // Bribri / MD 30-37 ////
EAP 9503 / RS3 // A429 / K2 /// V8025 /
XR 16492 // APN83 / CNC
UPR 9356-26 / TC-75 // EAP 9507 / AL12
UPR 9606-2-2 / MD 30-37

PPB 9911-44-513M
EAP 9510-77
EAP 9510-1
PTC 9557-10

Concha Rosada // SRC 1-1-18 / SRC 1-121
MD 3075 / DICTA 105
MD 3075 / DICTA 105
EAP 9021 / Bribri // UPR 9356-26 / UPR
9438-129
PRF 9653-16B- EAP 9503 / RS3 // Bribri / MD 30-37 ////
2A
EAP 9503 / RS3 // A429 / K2 /// V8025 /
XR 16492 // APN83 / CNC
DICTA 113
DOR 364 x APN 83

159

Market
class
Small red
Small red
Small red
Small red
Small red
Small red
Small red
Small red
Small red

Used Developed Ever
CRSP through widely
funds? PPB? planted?
YES
YES
NO
YES
YES
NO
YES
YES
NO
YES
YES
NO
YES
NO
NO
YES
NO
YES
YES
YES
NO
YES
YES
NO
YES
YES
NO

Small red
Small red

YES
YES

YES
YES

NO
NO

Small red

YES

YES

NO

Small red
Small red
Small red

YES
YES
YES

NO
NO
YES

YES
YES
NO

Small red

YES

YES

NO

Small red

NO

NO

NO

Table A 2.4 (cont’d).
Year of
No. release Variety Name
18 1997 DICTA 122
19 1996 Tío Canela 75
20 1992 Don Silvio
21 1990 Dorado

Line ID
DICTA 122
MD 3075
DOR 482
DOR 364

1

Genealogy
DOR 364 x APN 83
DOR 483 // DOR 391 / Pompadour J
(DOR 367 x (DOR 364 x LM 30649))
(BAT 1215 x (RAB 166 x DOR 125))

Used Developed Ever
Market CRSP through widely
class
funds? PPB? planted?
Small red
NO
NO
NO
Small red YES
NO
YES
Small red
NO
NO
NO
Small red
NO
NO
YES

Source: CIAT (2001a), DICTA (1987, 1998), Escoto (2000, 2006), Martel-Lagos (1995), Pejuan (2005), PIF/EAP and DICTA/SAG
(2002a, 2002b), PIF/EAP (2003), ASOCIAL Yorito-Sulaco-Victoria et al. (2004), ASOCIALAYO (2005a, 2005b), PIF/EAP and
DICTA/SAG (2005a, 2005b), Rosas (2006), and KII (2010a).
1

Same pedigree implies that the lines are sisters, i.e. they come from the same parents.
n.a. = not available

160

Table A 2.5. Nicaragua: Improved bean varieties released. 1990-2010.
Year of
No. release Variety Name
Line ID
1 2009 INTA Fuerte Sequia SX 14825-7-1
2 2006 INTA Precoz
SRC 2-18
3 2006 INTA Pueblo Nuevo MR 13046-28JM
SM4
4 2002 INTA Rojo
EAP 9510-77
5 2001 INTA Nueva Guinea DOR 390

Genealogy
n.a.
Rojo Nacional // Bribri / MD 3075
(VAX 3 x Catrachita) x MD 3075

Red

NO

YES

NO

Light red
Black

YES
NO

YES
YES

YES
NO

Black

NO

YES

NO

Small red
Small red
Small red
Small red
Small red
Small red
Small red
Small red
Dark red
Red

YES
NO
NO
NO
NO
NO
NO
NO
NO
NO

YES
YES
YES
YES
YES
YES
YES
YES
YES
YES

YES
NO
NO
NO
YES
NO
NO
NO
YES
NO

1

MD 3075 x DICTA 105
(DOR 364 x G 18521) x (DOR 365 x
LM 30 630)
6 2001 INTA Cardenas
DOR 500
(DOR 364 x G 18521) x (DOR 365 x
IN 100)
7 2001 INTA Canela
MD 3075
DOR 483 // DOR 391 / Pompadour J
8 1996 COMPAÑIA
RAB 463
G 18244 x MUS 6
9 1994 CNIGB 93
DOR 391
DOR 367 x(DOR 364 x LM 30649)
10 1993 COMPAÑIA 93
PVA 692
G 14013 x(G 13352 x G 21720)
11 1993 DOR 364
DOR 364
BAT 1215 x (RAB 166 x DOR 125)
12 1990 ESTELI 90A
CNIGB 1-90
Orgulloso x BAT 1654
13 1990 ESTELI 90B
CNIGB 2-90
Orgulloso x BAT 1836
14 1990 ESTELI 150
CNIGB 3-90
Chile Rojo x RAO 36
15 1990 INTA Masatepe
DOR 582
n.a.
16 1990 INTA Estelí
CM-12214-25 n.a.
Source: CIAT (2001a), INTA (2006), Martinez (2003), and KII (2010a).
1

Used Developed Ever
CRSP through widely
funds?
PPB? planted?
YES
YES
n.a.
YES
YES
n.a.

Market
class
Dark red
Red

Same pedigree implies that the lines are sisters, i.e. they come from the same parents.

161

n.a. = not available

Table A 2.6. Ecuador: Improved bean varieties released. 1990-2010.
Year of
No. release Variety Name
1 2010 Rojo del Valle
2 2010 Afroandino
3 2009 Paragachi Andino
4 2009 Portilla

Line ID
INIAP-481
INIAP-482
INIAP-429
INIAP-430

5 2009 Rocha
INIAP-480
6 2007 Libertador
INIAP-427
7 2007 Canario
INIAP-428
Guarandeno
8 2005 Canario del Chota INIAP-420
9 2004 Yunguilla
INIAP-414

1

Genealogy
SEL1308/Red Hawk // JeMa/3/Paragachi
Selection of CIAT A55
SUG 26 x CAL 82
INIAP 414 x INIAP 424

Used Developed Ever
Market
CRSP through widely
class
funds? PPB? planted?
Red Mottle YES
YES
n.a.
Black
YES
YES
n.a.
Red Mottle YES
YES
YES
Red Mottle YES
YES
YES

INIAP 420 x (Cocacho x San Antonio), s26 p1 Yellow
G 12722 x G 21720
Red Mottle
Selection of local variety
Yellow

YES
YES
YES

YES
YES
YES

YES
n.a.
n.a.

CAP 9 x Canario Bola
ICA 24 x ICA 10009 x Mulato Gordo

Yellow
Red Mottle

YES
YES

YES
YES

n.a.
NO

YES

YES

YES

YES

YES

NO

10 2004 La Concepción

INIAP-424 Selection of local variety Mil Uno

11 2004 Blanco
Fanesquero
12 2004 Canario Siete
Colinas**
13 2003 Blanco Belen
14 2003 Canario
15 2003 Boliche
16 2003 Doralisa
17 1999 Bolívar**
18 1998 Chaupeño

INIAP-425 SUG 55 x INIAP 417

Purple
Mottled
White

INIAP-426 TIB 3042 X G 11732

Yellow

YES

YES

n.a.

White
Yellow
Red Kidney
Red Mottle
Red
Cream
(Bayo)

YES
YES
YES
YES
YES
NO

NO
NO
NO
NO
NO
NO

n.a.
n.a.
n.a.
n.a.
n.a.
n.a.

INIAP-422
INIAP-423
INIAP-473
INIAP-474
INIAP-421
INIAP-419

WAF 82 x INIAP 417
CAP 9 x Canario Bola
AFR 298
AFR 722
G 12670 x G 12488
S 24990 x A 197

162

Table A 2.6 (cont’d).
Year of
No. release Variety Name
19 1996 Blanco Imbabura
20 1996 Je.Ma.
21 1995 Canario
22 1993 Toa**

Used Developed Ever
CRSP through widely
Market
class
funds? PPB? planted?
White
NO
NO
n.a.
Red Mottle NO
NO
n.a.
Yellow
NO
NO
n.a.
Red Mottle NO
NO
n.a.

1

Genealogy
n.a.
G 12722 x G 21720
Selection of G 11780F
(L 38 x Cargamanto) x (Mortiño x Diacol
Calima)
23 1993 Vilcabamba
INIAP-413 ICA 15423 x BAT 1620
Cranberry
NO
NO
n.a.
24 1993 Yunguilla
INIAP-414 G 13922 x (G 21721 x G 6474)
Red Mottle NO
NO
n.a.
25 1991 Imbabello
INIAP-411 Selection of local variety Cargabello
Red Mottle NO
NO
n.a.
26 1990 Colorado
INIAP-472 G 13922 x A 195
Red Kidney NO
NO
n.a.
Source: Mooney (2007) [Who used the following sources: Lepiz (1996); INIAP (1991a, 1991b, 1996a, 1996b, 2004a, 2004b, 2004c,
and 2005)]; CIAT (2001a); INIAP (2004a, 2004b, 2004c, 2004d, 2005, 2007a, 2007b, 2009a, 2009b, 2009c, 2010); Peralta et al.
(2009), and KII (2010a).
Line ID
INIAP-417
INIAP-418
INIAP-416
INIAP-412

1

Same pedigree implies that the lines are sisters, i.e. they come from the same parents.
n.a. = not available.
NOTE: most varieties are developed for the northern region. Yellow varieties are developed for the central region and climbing
varieties (denoted by **) are targeted for the southern region.

163

Table A 2.7. Quantity (MT) of seed of improved bean varieties sold or distributed by government programs in
2010.
Share (%) of total seed per market class
Total bean
1
area (ha)
Country
(A)
Costa Rica
3,944
El Salvador
100,940
Honduras
98,856
Nicaragua
217,518
Ecuador
3,288
Source: KII (2010a, 2010d).

Total seed
2
(MT)
(B)
286
1,265
1,818
1,717
16

Red
(C)
31
100
100
100
n.a.

Black
(D)
70
n.a.
n.a.
n.a.
n.a.

Red mottled
(E)
n.a.
n.a.
n.a.
n.a.
57

Purple mottled
(F)
n.a.
n.a.
n.a.
n.a.
22

Yellow
(G)
n.a.
n.a.
n.a.
n.a.
22

1

In Central America, area refers to red beans only. In Ecuador, area refers to bush-type, red-mottled beans in Carchi
and Imbabura only.
2

In Costa Rica, seed data refers to both black and red beans. In El Salvador, Honduras and Nicaragua, seed refers to
red beans only (seed of black beans was not distributed/sold). In Ecuador, seed refers to red mottled, purple mottled
and yellow beans.
n.a. = not applicable

164

Table A 2.8. Estimated average yearly salaries ($) of bean breeding programs' permanent staff for Costa Rica, El Salvador,
Honduras (DICTA only) and Nicaragua by education level. 2010.
Ph.D.

M.Sc.

B.S.

Technicians

< Technician

Total
2
3
BTE Salary
BTE Salary
BTE Salary
BTE Salary
BTE Salary salaries
1
Country Institution No. (%) ($/yr) No. (%) ($/yr) No. (%) ($/yr) No. (%) ($/yr) No. (%) ($/yr) ($/yr)
Costa Rica PITTAFrijol
1
15
4,500
4
44 42,240
3 70 31,500
0 n.a. n.a.
6
80 28,800 107,040
El Salvador CENTA
0
n.a.
n.a.
1 100 27,000
2 100 30,000
0 n.a. n.a.
0 n.a.
n.a.
57,000
Honduras DICTA
0
n.a.
n.a.
0 n.a.
n.a.
1 80 12,000
1
50 6,000
0 n.a.
n.a.
18,000
Nicaragua INTA
0
n.a.
n.a.
3
90 72,900 50 35 183,750 0 n.a. n.a.
0 n.a.
n.a.
256,650
Source: The Author, using information provided by KII (2010a) and Mejia (2012, Personal Communication).
1
Zamorano (Honduras) and PRONALEG-GA (Ecuador) were not included because the budget provided by program leaders already includes
salaries. PITTA-Frijol includes INTA-CR's and UCR's permanent staff only.
2
BTE = bean time equivalent (i.e. share of staff's time devoted to bean research).
3
Salaries expressed in nominal US$ and only reflect the time devoted to bean-related activities (i.e. monthly salary x 12 x BTE).
NOTES: Yearly salaries estimated using the following monthly salaries: Ph.D = $2,750 in Honduras, El Salvador, and Nicaragua and Ph.D. =
$2,500 in Costa Rica; M.Sc. = $2,250 in Honduras, El Salvador, and Nicaragua and M.Sc. = $2,000 in Costa Rica; B.S. = $1,250 in Honduras,
El Salvador, and Costa Rica and B.S. = $875 (70% of Honduras' salary) in Nicaragua; Technician = $1,000 in all countries listed; and
<Technician = $442 in Honduras, El Salvador, and Nicaragua and <Technician = $500 in Costa Rica. Salaries exclude temporary workers. Over
the past ten years, the number of staff has: (a) remained constant in Nicaragua, (b) remained constant or decreased in Costa Rica and El
Salvador, and (c) decreased in Honduras (DICTA only). Thus, yearly salaries are likely underestimated for all countries except Nicaragua.
n.a. = not applicable

165

Table A 2.9. Parameters α and β used for the estimation of logistic diffusion
curves in the countries of interest.
Scenario
Base
Scenario A
Scenario B
Country
α
β
α
β
α
β
Costa Rica
4.406
-0.094
2.358
0.068
4.840 -0.156
El Salvador
-2.289
0.271
-2.562
0.288
-1.971 0.251
Honduras
-0.667
0.181
-1.073
0.205
-0.141 0.151
Nicaragua
-2.738
0.307
-2.992
0.324
-2.447 0.288
Ecuador
-3.415
0.316
-3.640
0.331
-3.162 0.299
Source: Generated by the Author.
Scenario A assumes a 10% increase over the 2010 estimation of adoption rate and
Scenario B assumes a 10% decrease over the 2010 estimation of adoption rate.

166

Table A 2.10. Base scenario: Estimations of total adoption rates (%) of improved
bean varieties using a logistic diffusion curve. 1990-2015.
Year
Costa Rica
El Salvador
Honduras
Nicaragua
Ecuador
1990
0.86
0.07
0.18
0.07
0.02
1991
0.86
0.09
0.20
0.09
0.03
1992
0.86
0.12
0.23
0.12
0.04
1993
0.85
0.14
0.25
0.15
0.05
1994
0.85
0.17
0.27
0.19
0.07
1995
0.85
0.21
0.29
0.24
0.09
1996
0.85
0.25
0.31
0.30
0.12
1997
0.85
0.29
0.33
0.36
0.15
1998
0.85
0.33
0.35
0.43
0.19
1999
0.84
0.37
0.36
0.49
0.23
2000
0.84
0.41
0.38
0.55
0.27
2001
0.84
0.45
0.39
0.61
0.31
2002
0.84
0.48
0.41
0.65
0.35
2003
0.83
0.51
0.42
0.69
0.38
2004
0.83
0.53
0.43
0.73
0.41
2005
0.82
0.55
0.43
0.75
0.44
2006
0.82
0.56
0.44
0.78
0.46
2007
0.82
0.58
0.45
0.79
0.47
2008
0.81
0.59
0.45
0.80
0.48
2009
0.81
0.59
0.46
0.81
0.49
2010
0.80
0.60
0.46
0.82
0.50
2011
0.79
0.60
0.46
0.83
0.51
2012
0.79
0.61
0.47
0.83
0.51
2013
0.78
0.61
0.47
0.83
0.51
2014
0.77
0.61
0.47
0.83
0.51
2015
0.76
0.61
0.47
0.84
0.52
Source: Generated by the Author.
Numbers in bold were used to estimate all other rates using the logistic diffusion curve
formula.

167

Table A 2.11. Scenario A: Estimations of total adoption rates (%) of improved
bean varieties using a logistic diffusion curve. 1990-2015.
Year
Costa Rica
El Salvador
Honduras
Nicaragua
Ecuador
1990
0.83
0.06
0.16
0.06
0.02
1991
0.83
0.08
0.18
0.08
0.03
1992
0.84
0.11
0.20
0.11
0.04
1993
0.84
0.13
0.23
0.14
0.05
1994
0.84
0.17
0.26
0.19
0.07
1995
0.85
0.21
0.28
0.24
0.09
1996
0.85
0.25
0.31
0.30
0.12
1997
0.85
0.30
0.34
0.37
0.15
1998
0.86
0.35
0.36
0.44
0.19
1999
0.86
0.39
0.38
0.52
0.24
2000
0.86
0.44
0.40
0.59
0.29
2001
0.86
0.48
0.42
0.65
0.33
2002
0.87
0.52
0.44
0.71
0.38
2003
0.87
0.55
0.45
0.76
0.42
2004
0.87
0.58
0.46
0.80
0.45
2005
0.87
0.60
0.47
0.83
0.48
2006
0.87
0.62
0.48
0.85
0.50
2007
0.88
0.63
0.49
0.87
0.52
2008
0.88
0.65
0.50
0.88
0.53
2009
0.88
0.65
0.50
0.89
0.54
2010
0.88
0.66
0.51
0.90
0.55
2011
0.88
0.66
0.51
0.91
0.56
2012
0.88
0.67
0.51
0.91
0.56
2013
0.88
0.67
0.51
0.91
0.56
2014
0.88
0.67
0.52
0.91
0.56
2015
0.89
0.68
0.52
0.92
0.57
Source: Generated by the Author.
Numbers in bold were used to estimate all other rates using the logistic diffusion curve
formula.
This scenario assumes a 10% increase over the 2010 estimation of adoption rate.

168

Table A 2.12. Scenario B: Estimations of total adoption rates (%) of improved
bean varieties using a logistic diffusion curve. 1990-2015.
Year
Costa Rica
El Salvador
Honduras
Nicaragua
Ecuador
1990
0.86
0.09
0.22
0.08
0.03
1991
0.86
0.10
0.23
0.10
0.03
1992
0.86
0.13
0.25
0.13
0.04
1993
0.86
0.15
0.27
0.16
0.06
1994
0.86
0.18
0.28
0.20
0.07
1995
0.85
0.22
0.30
0.25
0.10
1996
0.85
0.25
0.31
0.30
0.12
1997
0.85
0.28
0.32
0.35
0.15
1998
0.84
0.32
0.33
0.41
0.18
1999
0.84
0.35
0.35
0.46
0.21
2000
0.83
0.38
0.36
0.51
0.25
2001
0.83
0.41
0.37
0.56
0.28
2002
0.82
0.44
0.37
0.60
0.32
2003
0.81
0.46
0.38
0.63
0.35
2004
0.80
0.48
0.39
0.66
0.37
2005
0.79
0.50
0.39
0.68
0.39
2006
0.78
0.51
0.40
0.70
0.41
2007
0.77
0.52
0.40
0.71
0.42
2008
0.75
0.53
0.41
0.73
0.43
2009
0.74
0.53
0.41
0.73
0.44
2010
0.72
0.54
0.41
0.74
0.45
2011
0.70
0.54
0.42
0.74
0.46
2012
0.68
0.55
0.42
0.75
0.46
2013
0.65
0.55
0.42
0.75
0.46
2014
0.63
0.55
0.42
0.75
0.46
2015
0.60
0.55
0.42
0.76
0.47
Source: Generated by the Author.
Numbers in bold were used to estimate all other rates using the logistic diffusion curve
formula.
This scenario assumes a 10% decrease over the 2010 estimation of adoption rate.

169

Table A 2.13. Linear regression results of factors influencing experimental
yields of small red bean varieties released in Central America. 1999-2009.
N = 108
Prob > F = 0.000
R-squared = 0.4249
Coefficient
p-value
Variables
Year dummy variables (1=Yes):
2000
234.87
0.367
2001
868.32
***0.010
2002
34.56
0.903
2003
805.44
**0.028
2004
310.90
0.283
2005
-242.98
0.421
2006
304.78
0.612
2007
156.74
0.645
2008
704.55
*0.072
2009
313.31
0.262
1

Released varieties dummy variables (1=Yes):
Carrizalito
Amadeus 77
Cedron
Cayetana
CENTA Pipil
Don Cristobal
Deorho
CENTA C.P.C.
Tongibe
Cardenal
Briyo
La Majada
Country dummy variables (1=Yes):
Costa Rica
El Salvador
Guatemala
Nicaragua
Constant

177.13
82.80
-21.85
-2.13
110.34
114.73
203.86
-110.28
161.09
451.34
369.04
354.66

0.583
0.809
0.934
0.995
0.681
0.852
0.451
0.502
0.368
**0.025
0.141
0.134

-944.76
-716.81
-1791.46
-681.14
2155.46

***0.002
***0.000
***0.000
***0.000
***0.000

*, **, *** indicates the corresponding coefficients are significant at the 10%, 5%, and 1%
levels, respectively. Year 1999, variety Dorado and country Honduras were excluded to
avoid the dummy trap. Robust standard errors used to estimate p-values because variances
are not equal (Prob > Chi2 = 0.0167).
1
Carrizalito, Amadeus 77 and Deorho were released in more than one country.
Source: Programa de Investigaciones en Frijol Metadata, Zamorano, Honduras.

170

Table A 2.14. Linear regression results of factors influencing experimental yields
of small red bean varieties released in Honduras. 1999-2009.
N = 88
Prob > F = 0.000
R-squared = 0.3986
Coefficient
p-value
Variables
Year dummy variables (1=Yes):
2000
213.83
0.401
2001
877.40
**0.020
2002
-43.75
0.882
2003
876.77
**0.041
2004
266.93
0.377
2005
-158.29
0.619
2006
322.25
0.593
2007
146.81
0.674
2008
694.62
*0.085
2009
303.38
0.281
Released varieties dummy variables (1=Yes):
Carrizalito
186.44
0.586
Amadeus 77
91.92
0.786
Cedron
4.15
0.988
Cayetana
8.98
0.979
Don Cristobal
60.88
0.923
Deorho
170.72
0.536
Cardenal
358.86
*0.078
Briyo
369.04
0.154
La Majada
354.66
0.148
Country dummy variables (1=Yes):
Costa Rica
-1017.94
***0.005
El Salvador
-648.21
***0.002
Guatemala
-1787.99
***0.000
Nicaragua
-640.41
***0.000
Constant
2128.95
***0.000
*, **, *** indicates the corresponding coefficients are significant at the 10%, 5%, and
1% levels, respectively. Year 1999, variety Dorado and country Honduras were
excluded to avoid the dummy trap. Robust standard errors used to estimate p-values
because variances are not equal (Prob > Chi2 = 0.0135).
Source: Programa de Investigaciones en Frijol Metadata, Zamorano, Honduras.

171

Table A 2.15. Linear regression results of factors influencing experimental yields
of red mottled bean varieties released in Ecuador. 2003-2010.
N = 26
Prob > F = 0.001
Adj. R-squared = 0.6984
Coefficient
p-value
Variables
Year dummy variables (1=Yes):
2004
418.75
0.152
2005
1392.00
***0.000
2006
493.01
*0.070
2007
794.09
***0.006
2008
230.74
0.364
2009
1262.50
***0.000
2010
660.47
*0.078
Released varieties dummy variables (1=Yes):
INIAP 427 Libertador
75.91
0.758
INIAP 429 Paragachi Andino
-58.61
0.733
INIAP 430 Portilla
117.07
0.442
INIAP 481 Rojo del Valle
214.60
0.314
Constant
482.47
**0.037
*, **, *** indicates the corresponding coefficients are significant at the 10%, 5%, and
1% levels, respectively. Year 2003 and variety INIAP 414 Yunguilla were excluded to
avoid the dummy trap.
Source: INIAP/PRONALEG-GA Metadata, Ecuador.

172

Table A 2.16. Costa Rica: Base scenario Net Present Value (NPV) and Internal Rate of Return (IRR) calculations for
improved small red bean varieties. 1991-2015.
Area
Adoption
λ
Supply
Real
Research
Kt
Change in TS
harvested Production
rate
growth
Elasticity Price
Costs Net Benefit
Year Period
(ha)
(mt)
(%)
(%) Type I Type II
e
($/mt) Type I
Type II ($, real)
($)
1991 -5
17,395
0.86
0
0 158,619
-158,619
1992 -4
15,790
0.86
0
0 153,984
-153,984
1993 -3
14,758
0.85
0
0 149,508
-149,508
1994 -2
14,217
0.85
0
0 145,776
-145,776
1995 -1
14,081
0.85
0
0 141,758
-141,758
1996 Base
8,119
4,063
0.85
0
0 137,692
-137,692
1997 1
11,040
5,524
0.85
0.0049 0.000 0.004
0.7
1,412
0
32,530 134,604
-102,074
1998 2
9,280
4,643
0.85
0.0098 0.000 0.008
0.7
1,212
0
47,026 132,540
-85,513
1999 3
9,063
4,535
0.84
0.0148 0.000 0.012
0.7
1,152
0
65,598 129,676
-64,078
2000 4
7,707
3,856
0.84
0.0197 0.000 0.017
0.7
960
0
62,027 132,136
-70,110
2001 5
5,828
2,916
0.84
0.0247 0.000 0.021
0.7
867
0
52,980 129,454
-76,474
2002 6
5,522
2,763
0.84
0.0298 0.000 0.025
0.7
857
0
59,643
96,711
-37,068
2003 7
5,212
2,608
0.83
0.0348 0.000 0.029
0.7
518
0
39,709 123,918
-84,209
2004 8
4,087
2,045
0.83
0.0399 0.000 0.033
0.7
804
0
55,228 121,616
-66,389
2005 9
4,087
2,045
0.82
0.0450 0.000 0.037
0.7
880
0
67,993 118,514
-50,521
2006 10
3,509
1,756
0.82
0.0501 0.000 0.041
0.7
728
0
53,576 115,666
-62,091
2007 11
3,004
1,503
0.82
0.0552 0.000 0.045
0.7
901
0
62,410 113,295
-50,885
2008 12
2,757
1,379
0.81
0.0604 0.000 0.049
0.7
1,411
0
97,578 109,907
-12,329
2009 13
3,944
1,974
0.81
0.0656 0.000 0.053
0.7
963
0
103,044
86,725
16,319
2010 14
3,460
1,731
0.80
0.0708 0.000 0.057
0.7
1,109
0
111,747
111,747
2011 15
3,460
1,731
0.79
0.0761 0.000 0.061
0.7
1,022
0
110,027
110,027
2012 16
3,460
1,731
0.79
0.0813 0.000 0.065
0.7
1,022
0
116,877
116,877
2013 17
3,460
1,731
0.78
0.0866 0.000 0.068
0.7
1,022
0
123,579
123,579
2014 18
3,460
1,731
0.77
0.0920 0.000 0.072
0.7
1,022
0
130,109
130,109
2015 19
3,460
1,731
0.76
0.0973 0.000 0.075
0.7
1,022
0
136,444
136,444
In constant 1991 US$:
NPV =
-956,905
In constant 2009 US$:
NPV =
-2,016,054
IRR =
-5%
Source: Estimations made by The Author. See Table Notes in Table A 2.21.

173

Table A 2.17. El Salvador: Base scenario Net Present Value (NPV) and Internal Rate of Return (IRR) calculations for
improved small red bean varieties. 1991-2015.
Area
Adoption
harvested Production
rate
Year Period
(ha)
(mt)
(%)
1991 -5
75,097
0.09
1992 -4
76,795
0.12
1993 -3
72,110
0.14
1994 -2
72,042
0.17
1995 -1
58,801
0.21
1996 Base
65,659
51,920
0.25
1997 1
80,495
63,652
0.29
1998 2
75,709
59,866
0.33
1999 3
72,178
57,074
0.37
2000 4
76,659
60,618
0.41
2001 5
82,624
65,335
0.45
2002 6
80,707
63,819
0.48
2003 7
81,362
64,337
0.51
2004 8
84,432
66,764
0.53
2005 9
82,989
65,624
0.55
2006 10
84,758
67,022
0.56
2007 11
91,785
72,579
0.58
2008 12
102,529
81,075
0.59
2009 13
100,940
79,818
0.59
2010 14
92,600
73,224
0.60
2011 15
92,600
73,224
0.60
2012 16
92,600
73,224
0.61
2013 17
92,600
73,224
0.61
2014 18
92,600
73,224
0.61
2015 19
92,600
73,224
0.61
In constant 1991 US$:

Kt
λ
Supply
Real
growth
Elasticity Price
(%) Type I Type II
e
($/mt)

0.0049
0.0098
0.0148
0.0197
0.0247
0.0298
0.0348
0.0399
0.0450
0.0501
0.0552
0.0604
0.0656
0.0708
0.0761
0.0813
0.0866
0.0920
0.0973
NPV =

0.005 0.001
0.010 0.003
0.014 0.005
0.019 0.007
0.023 0.010
0.026 0.013
0.030 0.017
0.032 0.020
0.034 0.024
0.036 0.028
0.038 0.031
0.039 0.035
0.040 0.038
0.040 0.042
0.041 0.046
0.041 0.049
0.042 0.053
0.042 0.056
0.042 0.060
36,789,922

Source: Estimations made by The Author. See Table Notes in Table A 2.21.

174

0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7

Change in TS

Research
Costs Net Benefit
Type I
Type II ($, real)
($)
0
0 196,108
-196,108
0
0 190,377
-190,377
0
0 184,843
-184,843
0
0 180,228
-180,228
0
0 179,485
-179,485
0
0 174,337
-174,337
1,412
427,014
110,126 170,427
366,714
1,212
696,425
207,818 163,864
740,379
1,152
944,756
324,292 160,323 1,108,724
960 1,098,834
431,117 155,110 1,374,841
867 1,303,870
580,788 150,818 1,733,841
857 1,461,830
734,146 109,713 2,086,264
518
996,931
560,563 107,269 1,450,226
804 1,749,924 1,094,180 104,486 2,739,618
880 2,011,168 1,389,285 101,062 3,299,391
728 1,786,251 1,354,845
97,904 3,043,192
901 2,490,189 2,062,075
95,193 4,457,072
1,411 4,483,899 4,032,474
91,673 8,424,700
963 3,083,689 2,997,390
92,000 5,989,079
1,109 3,313,509 3,465,898
6,779,408
1,022 3,096,535 3,471,581
6,568,116
1,022 3,128,228 3,745,398
6,873,627
1,022 3,152,659 4,017,819
7,170,478
1,022 3,171,444 4,289,200
7,460,644
1,022 3,185,860 4,559,919
7,745,779
In constant 2009 US$:
NPV =
77,510,816
IRR =
40%

Table A 2.18. Honduras: Base scenario Net Present Value (NPV) and Internal Rate of Return (IRR) calculations for improved
small red bean varieties. 1991-2015.
Area
Adoption
λ
Supply
Real
Research
Kt
Change in TS
harvested Production
rate
growth
Elasticity Price
Costs Net Benefit
Year Period
(ha)
(mt)
(%)
(%) Type I Type II
e
($/mt)
Type I
Type II ($, real)
($)
1991 -5
104,272
0.20
0
0 258,564
-258,564
1992 -4
67,996
0.23
0
0 211,207
-211,207
1993 -3
79,202
0.25
0
0 197,282
-197,282
1994 -2
111,700
0.27
0
0 171,351
-171,351
1995 -1
64,859
0.29
0
0 125,563
-125,563
1996 Base
79,043
57,718
0.31
0
0 111,262
-111,262
1997
1
78,850
57,577
0.33
0.0056 0.002 0.002
0.7
1,412 180,694 141,196 122,252
199,638
1998
2
74,881
54,679
0.35
0.0112 0.004 0.004
0.7
1,212 285,269 245,432 129,429
401,272
1999
3
106,064
77,449
0.36
0.0169 0.006 0.006
0.7
1,152 555,813 524,885 135,488
945,209
2000
4
114,671
83,734
0.38
0.0226 0.008 0.008
0.7
960 641,461 662,847 164,462 1,139,846
2001
5
72,568
52,990
0.39
0.0283 0.010 0.011
0.7
867 439,057 494,904 168,242
765,718
2002
6
132,661
96,870
0.41
0.0341 0.011 0.013
0.7
857 911,342 1,117,124 155,937 1,872,529
2003
7
99,005
72,295
0.42
0.0399 0.012 0.016
0.7
518 457,807 608,424 160,481
905,751
2004
8
98,347
71,814
0.43
0.0457 0.013 0.019
0.7
804 768,922 1,104,652 156,797 1,716,777
2005
9
111,916
81,722
0.43
0.0515 0.014 0.022
0.7
880 1,026,258 1,589,168 169,698 2,445,728
2006 10
121,600
88,794
0.44
0.0574 0.015 0.025
0.7
728 975,038 1,622,924 164,844 2,433,117
2007 11
133,000
97,118
0.45
0.0634 0.016 0.028
0.7
901 1,382,603 2,467,031 212,451 3,637,183
2008 12
133,000
97,118
0.45
0.0693 0.016 0.031
0.7
1,411 2,246,392 4,285,946 205,016 6,327,322
2009 13
98,856
72,186
0.46
0.0753 0.017 0.034
0.7
963 1,176,031 2,393,291 155,897 3,413,425
2010 14
119,674
87,388
0.46
0.0813 0.017 0.037
0.7
1,109 1,681,314 3,640,993
5,322,306
2011 15
119,674
87,388
0.46
0.0874 0.018 0.040
0.7
1,022 1,583,663 3,641,281
5,224,944
2012 16
119,674
87,388
0.47
0.0935 0.018 0.043
0.7
1,022 1,611,759 3,926,310
5,538,070
2013 17
119,674
87,388
0.47
0.0996 0.018 0.046
0.7
1,022 1,635,459 4,212,450
5,847,909
2014 18
119,674
87,388
0.47
0.1057 0.018 0.050
0.7
1,022 1,655,416 4,499,600
6,155,016
2015 19
119,674
87,388
0.47
0.1119 0.019 0.053
0.7
1,022 1,672,195 4,787,722
6,459,917
In constant 1991 US$:
NPV =
27,648,128
In constant 2009 US$:
NPV =
58,250,437
IRR =
34%
Source: Estimations made by The Author. See Table Notes in Table A 2.21.

175

Table A 2.19. Nicaragua: Base scenario Net Present Value (NPV) and Internal Rate of Return (IRR) calculations for
improved small red bean varieties. 1991-2015.
Area
Adoption
harvested Production
rate
Year Period
(ha)
(mt)
(%)
1991 -5
98,368
0.09
1992 -4
88,139
0.12
1993 -3
100,300
0.15
1994 -2
98,786
0.19
1995 -1
120,677
0.24
1996 Base
104,509
67,653
0.30
1997 1
117,694
76,187
0.36
1998 2
165,025
106,827
0.43
1999 3
180,351
116,748
0.49
2000 4
194,773
126,084
0.55
2001 5
201,338
130,333
0.61
2002 6
218,311
141,321
0.65
2003 7
252,934
163,733
0.69
2004 8
202,692
131,210
0.73
2005 9
236,473
153,078
0.75
2006 10
199,953
129,437
0.78
2007 11
202,613
131,159
0.79
2008 12
209,269
135,468
0.80
2009 13
217,518
140,808
0.81
2010 14
213,165
137,990
0.82
2011 15
213,165
137,990
0.83
2012 16
213,165
137,990
0.83
2013 17
213,165
137,990
0.83
2014 18
213,165
137,990
0.83
2015 19
213,165
137,990
0.84
In constant 1991 US$:

Kt
λ
Supply Real
growth
Elasticity Price
(%) Type I Type II
e
($/mt)

0.0049
0.0098
0.0148
0.0197
0.0247
0.0298
0.0348
0.0399
0.0450
0.0501
0.0552
0.0604
0.0656
0.0708
0.0761
0.0813
0.0866
0.0920
0.0973
NPV =

0.007 0.001
0.014 0.004
0.022 0.006
0.029 0.010
0.035 0.014
0.041 0.018
0.045 0.023
0.049 0.028
0.052 0.033
0.055 0.038
0.057 0.043
0.058 0.048
0.059 0.053
0.060 0.058
0.060 0.062
0.061 0.067
0.061 0.072
0.061 0.077
0.062 0.081
101,574,886

Source: Estimations made by The Author. See Table Notes in Table A 2.21.

176

0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7

Change in TS

Research
Costs Net Benefit
Type I
Type II
($, real)
($)
0
0 490,362
-490,362
0
0 476,032
-476,032
0
0 462,196
-462,196
0
0 450,657
-450,657
0
0 438,237
-438,237
0
0 425,668
-425,668
1,412
762,195
158,191 416,120
504,266
1,212 1,879,670
460,346 409,738
1,930,278
1,152 2,954,150
847,761 400,884
3,401,027
960 3,517,663 1,173,653 387,847
4,303,468
867 4,016,351 1,544,826 377,116
5,184,060
857 4,999,838 2,197,405 323,952
6,873,290
518 3,910,834 1,946,442 317,584
5,539,692
804 5,282,203 2,951,336 309,346
7,924,193
880 7,173,491 4,462,568 299,208 11,336,851
728 5,249,426 3,608,318 289,858
8,567,886
901 6,814,754 5,140,042 281,831 11,672,965
1,411 11,294,234 9,289,222 271,410 20,312,046
963 8,166,618 7,283,424 271,650 15,178,392
1,109 9,339,858 8,987,250
18,327,108
1,022 8,700,616 8,992,551
17,693,167
1,022 8,765,976 9,692,501
18,458,477
1,022 8,814,450 10,388,869
19,203,319
1,022 8,850,324 11,083,067
19,933,391
1,022 8,876,828 11,776,409
20,653,237
In constant 2009 US$:
NPV =
214,002,964
IRR =
42%

Table A 2.20. Ecuador: Base scenario Net Present Value (NPV) and Internal Rate of Return (IRR) calculations for improved
red mottled bean varieties in northern Ecuador. 1991-2015.
Kt
Change in TS
Area
Adoption
λ
Supply
Real
Research
harvested Production
rate
growth
Elasticity Price
Costs Net Benefit
Year Period
(ha)
(mt)
(%)
(%) Type I Type II
e
($/mt) Type I Type II ($, real)
($)
1991 -5
6,983
0.03
0
0
22,827
-22,827
1992 -4
6,970
0.04
0
0
21,763
-21,763
1993 -3
7,185
0.05
0
0
28,098
-28,098
1994 -2
8,599
0.07
0
0
37,864
-37,864
1995 -1
7,759
0.09
0
0
31,751
-31,751
1996 Base
8,489
7,287
0.12
0
0
29,341
-29,341
1997
1
8,534
7,326
0.15
0.0168 0.006 0.002
0.7
600 25,598
8,868
28,903
5,562
1998
2
7,533
6,467
0.19
0.0339 0.012 0.005
0.7
600 48,464 19,965
27,246
41,183
1999
3
7,762
6,663
0.23
0.0513 0.020 0.010
0.7
600 79,158 38,566
23,406
94,318
2000
4
6,427
5,517
0.27
0.0689 0.027 0.016
0.7
600 90,857 52,038
7,480
135,415
2001
5
7,207
6,187
0.31
0.0869 0.035 0.023
0.7
600 130,154 87,051
12,295
204,910
2002
6
8,333
7,153
0.35
0.1051 0.042 0.032
0.7
600 181,461 140,667
13,626
308,501
2003
7
7,996
6,864
0.38
0.1237 0.048 0.043
0.7
600 201,042 179,159
44,755
335,446
2004
8
7,036
6,040
0.41
0.1426 0.053 0.054
0.7
659 216,854 220,264
57,762
379,355
2005
9
8,455
7,258
0.44
0.1618 0.058 0.066
0.7
740 318,152 365,169
57,749
625,572
2006 10
7,353
6,312
0.46
0.1813 0.062 0.079
0.7
753 299,596 385,360
55,944
629,012
2007 11
6,607
5,671
0.47
0.2011 0.065 0.092
0.7
626 234,840 335,885
61,120
509,605
2008 12
6,106
5,241
0.48
0.2213 0.067 0.104
0.7
566 203,029 320,610 131,810
391,828
2009 13
6,085
5,223
0.49
0.2418 0.069 0.117
0.7
839 308,033 533,634
57,375
784,292
2010 14
6,921
5,941
0.50
0.2627 0.070 0.129
0.7
1,039 442,028 835,322
1,277,350
2011 15
6,921
5,941
0.51
0.2839 0.071 0.142
0.7
765 329,964 676,782
1,006,747
2012 16
6,921
5,941
0.51
0.3055 0.072 0.154
0.7
765 333,394 738,908
1,072,302
2013 17
6,921
5,941
0.51
0.3274 0.072 0.167
0.7
765 335,929 801,361
1,137,290
2014 18
6,921
5,941
0.51
0.3497 0.073 0.179
0.7
765 337,796 864,319
1,202,115
2015 19
6,921
5,941
0.52
0.3724 0.073 0.191
0.7
765 339,168 927,959
1,267,126
In constant 1991 US$:
NPV =
5,183,118
In constant 2009 US$: NPV =
10,920,047
IRR =
37%
Source: Estimations made by The Author. See Table Notes in Table A 2.21.

177

Table A 2.21. Notes for Table A 2.16 through Table A 2.20.
Country
Notes
Costa Rica
Area harvested is 25% of total area harvested to reflect only red bean
production (i.e. excludes other market classes). Yield for 1996 estimated as
the average yields of 1994-1998, using FAOSTAT data. λ=0.0049 (Type II
gains); Type I gains=11.5% (from Mather et al. 2003). Price is the average
price for Central American countries.
El Salvador

Area harvested is 97% of total area harvested to reflect only red bean
production (i.e. excludes other market classes). Yield for 1996 estimated as
the average yields of 1994-1998, using FAOSTAT data. λ=0.0049 (Type II
gains); Type I gains=11.5% (from Mather et al. 2003). Price is the average
price for Central American countries.

Honduras

Area harvested is 95% of total area harvested to reflect only red bean
production (i.e. excludes other market classes). Yield for 1996 estimated as
the average yields of 1994-1998, using FAOSTAT data. λ=0.0056 (Type II
gains); Type I gains=11.5% (from Mather et al. 2003). Price is the average
price for Central American countries.

Nicaragua

Area harvested is 87.5% of total area harvested to reflect only red bean
production (i.e. excludes other market classes). Yield for 1996 estimated as
the average yields of 1994-1998, using FAOSTAT data. λ=0.0049 (Type II
gains); Type I gains=11.5% (from Mather et al. 2003). Price is the average
price for Central American countries.

Ecuador

Area harvested is 13.5% of total of dry bean area harvested in the country to
reflect only red mottled (i.e. excludes other market classes) bush-bean
production in the provinces of Carchi and Imbabura. Yields in northern
Ecuador are 35% higher (estimated from ESPAC data) than country-level
yields. Thus, FAOSTAT yields data were multiplied by 1.35 to reflect yields
in northern Ecuador and yield for 1996 estimated as the average of 19941998. λ=0.0168 (Type II gains); Type I gains=18.4% (from Mooney 2007).

For all
countries

For 2010-2015, area harvested assumed as the average of the previous five
years (i.e. 2005-2009).
For 2011-2015, price assumed as the average of the previous five years (i.e.
2006-2010).
Discount rate=4%.
Production estimated by multiplying area harvested in each year times the
base year (i.e. 1996) yields.

178

Table A 3.1. Additional demographic characteristics of farm households (HH). Central Highlands of Angola, 2009.
Potato
Bean
Onion
Non-sellers
Sellers
Non-sellers
Sellers
Non-sellers
Sellers
Mean S.E.

Demographics
Marital status of head (%):
Married
Single
Widow
Separated
Household size
No. males >17 yrs
No. females >17 yrs
No. children <5 yrs
No. boys 5-17 yrs
No. girls 5-17 yrs

1

Mean S.E. MT

66
2
20
12
4.9
0.8
1.1
1.0
0.9
1.1

0.195
0.016
0.106
0.082
0.281
0.176
0.026
0.118
0.077
0.078

87
1
9
4
5.5
1.0
1.1
1.5
1.1
0.8

0.113
0.012
0.072
0.034
0.109
0.115
0.035
0.088
0.135
0.181

69

0.137

54

23

0.165

11

Mean S.E.

***

1

Mean S.E. MT

Mean S.E.

1

Mean S.E. MT

79
1
13
7
5.4
1.0
1.1
1.4
1.1
0.9

0.184
0.012
0.111
0.063
0.478
0.254
0.017
0.189
0.104
0.110

79
2
13
6
5.6
1.0
1.1
1.3
1.2
1.0

0.156
0.019
0.094
0.046
0.334
0.119
0.027
0.124
0.196
0.099

79
2
12
7
5.1
0.9
1.1
0.9
1.3
1.0

0.183
0.016
0.118
0.059
0.253
0.168
0.011
0.173
0.328
0.100

88
1
9
2
5.6
1.2
1.1
1.5
1.0
0.9

0.094
0.014
0.061
0.022
0.126
0.056 ***
0.017
0.118 ***
0.035
0.090

0.135

74

0.164

50

0.230

74

0.174

68

0.127

0.068

6

0.052

26

0.080

26

0.191

14

0.092

0.7 0.169
0.3 0.044
46 0.090

0.5 0.051 **
0.2 0.020
34 0.022 **

0.4
0.2
24

0.107
0.035
0.036

0.6 0.073 **
0.3 0.033 **
42 0.044 ***

0.5
0.4
39

0.053
0.054
0.036

0.7 0.060 **
0.2 0.051 *
38 0.049

88

183

101

223

60

***
***
**
**
***
**

2

If HH member is in FO :
Received assistance about
3
production (% yes)
Received assistance about
3
marketing (% yes)
Family members >17 literate:
No. males >17 literate
No. females >17 literate
Share of all adults (%)
Number of observations

*

4

179

134

Table A 3.1 (cont’d).
1
2
MT = test of difference between means: * significant at 10%; ** significant at 5%; *** significant at 1%. FO = Farmer
3

4

organization. Asked to member of FO and refers to assistance over past 12 months. Literacy refers to people who can read and
write.
Source: ProRenda survey, Angola, 2009. Estimates weighted to reflect population.

180

Table A 3.2. Major sources of crop and non-crop household incomes by market participation. Central Highlands of Angola,
2009.
Potato
Bean
Onion
Non-sellers
Sellers
Non-sellers
Sellers
Non-sellers
Sellers
Households' (HH) heads
1

declaring
Crops below as their major source
of crop income (%):
Potatoes
Corn
Beans
Onions
Other crops
Activities below as their major
source of non-crop income (%):
Commerce
Services
Farm labor
Gifts, retirements, transfers,
remittances
Handcrafts, processed products
None
Other activities
Number of observations
1

Mean S.E.

2

Mean S.E. MT

Mean S.E.

2

Mean S.E. MT

Mean S.E.

2

Mean S.E. MT

18
30
33
5
14

0.046
0.046
0.018
0.023
0.026

47
14
19
7
12

0.067 ***
0.039 ***
0.019 **
0.030
0.035

15
23
29
8
24

0.065
0.058
0.034
0.035
0.083

14
14
63
3
6

0.015
0.067 *
0.086 ***
0.010 *
0.012 ***

11
11
58
9
11

0.037
0.027
0.103
0.066
0.028

31
13
29
15
12

28
3
27

0.120
0.028
0.084

18
23
38

0.060 *
0.079 ***
0.017 *

25
20
21

0.043
0.090
0.114

24
13
32

0.019
0.054
0.039

19
11
22

0.012
0.044
0.087

32 0.040
17 0.068
29 0.021

8
4
28
1
76

0.034
0.025
0.119
0.007

3
10
6
3
186

0.007 **
0.013
0.027 ***
0.015

2
12
14
7
85

0.017
0.030
0.026
0.036

7
5
15
4
225

0.008
0.022
0.017
0.017

20
8
10
11
47

0.105
0.024
0.104
0.063

2
7
12
1
125

*
*
*

Column sum within sources may not add to 100% due to rounding.

2

MT = test of difference between means: * significant at 10%; ** significant at 5%; *** significant at 1%.
Source: ProRenda survey, Angola, 2009. Estimates weighted to reflect population.

181

0.026 ***
0.016
0.026 ***
0.027
0.013

0.003 ***
0.015
0.029
0.010 ***

Table A 3.3. Scoring factors, summary statistics, and per tercile means for asset indicators
entering the computation of the first principal component (asset ownership).
Percentage of households
owning the asset
Total sample
Scoring
Asset indicators
Factors
Own plow
0.326
Own cart
0.121
Own backpack sprayer
0.224
Own motorcycle
0.434
Own bicycle
0.297
Own cell phone
0.299
Have water storage at home 0.261
Have latrine in the house
0.094
Have lusalite or zinc roof
0.393
Own radio
0.347
Own television
0.335

Std. Scor. Fac.
Dev. / Std. Dev.
0.292
1.11
0.065
1.87
0.136
1.65
0.254
1.71
0.360
0.82
0.235
1.27
0.398
0.66
0.409
0.23
0.499
0.79
0.485
0.71
0.169
1.98

Mean
0.094
0.004
0.019
0.069
0.153
0.059
0.197
0.789
0.467
0.377
0.029

Economic Status Index
Number of observations

0.000 1.522
478

Lowest Middle Highest
tercile tercile tercile
0%
4%
23%
0%
0%
1%
0%
0%
6%
0%
0%
23%
0%
5%
58%
0%
0%
21%
5%
19%
42%
71%
93%
94%
0%
46%
79%
2%
55%
62%
0%
0%
6%
Mean by tercile
-1.259 -0.277 1.719
164
184
130

Notes: Three of the 14 indicators were dropped because they had zero variance. Scoring Factor
is the "weight" assigned to each indicator or eigenvector (normalized by its mean and standard
deviation) in the linear combination of the variables that constitute the first principal
component. The percentage of the covariance explained by the first principal component is
21.06%. The first eigenvalue is 2.32. Data provided in the last three columns were estimated
with weights to reflect population (except number of observations).
Source: ProRenda survey, Angola, 2009.

182

Table A 3.4. Means per market participation (seller) for asset indicators entering the
computation of the first principal component (asset ownership).
Percentage of households owning the asset
Potato seller
Bean seller
Onion seller
Asset indicators
No
Yes MT
No
Yes MT
No
Yes MT
Own plow
16% 9%
-4%
9%
-7% 11% -Own cart
0%
1%
-0%
0%
-2%
0%
-Own backpack sprayer
0%
4%
-0%
1%
-0%
4%
-Own motorcycle
3% 10% -8%
9%
-21% 7%
-Own bicycle
20% 27% -17% 19% -22% 25% -Own cell phone
7% 10% -5% 11% -10% 8%
-Have water storage at home 19% 21% -23% 19% -44% 20% -Have latrine in the house
81% 90% -86% 89% -80% 91% -Have lusalite or zinc roof
37% 56% -38% 43% -63% 33% -Own radio
35% 54% -48% 40% -47% 63% -Own television
0%
2%
-0%
3%
-2%
1%
-Mean by group
Economic Status Index
-0.118 0.432 ***
-0.086 0.121
0.651 0.224 *
Number of observations
97
175
115
232
57
130
Notes: Three of the 14 indicators were dropped because they had zero variance. The percentage
of the covariance explained by the first principal component is 21.06%. The first eigenvalue is
2.32. Estimates weighted to reflect population (except last row). MT = Bonferroni test of
difference between means: * significant at 10%; ** significant at 5%; *** significant at 1%; -not tested.
Source: ProRenda survey, Angola, 2009.

183

Table A 3.5. Potato sellers: Average receipts, costs, margins and percentage of production sold, per economic status index
and gender of household head (HHH).
1

Detail
Receipts (Kw)
Price per kg sold (Kw/kg)

1

Economic Status Index by tercile
Gender of HHH
Lowest
Middle
Highest
Male
Female
(mean values)
7,705
a
12,630 ab
17,795
b
15,761
7,287
***
79
-86
-89
-85
71
--

Total Costs (Kw)
Production costs per kg produced
(Kw/kg)
Marketing costs per kg sold
(Kw/kg)

5,544

8,864

Margins (Kw)

2,160

3,766

Share sold (% of total production)

77%

12,796

83%

Total
14,087
82

11,379

5,388

**

10,195

46.5

--

43.1

--

61.3

--

51.9

43.4

--

50.2

1.8

--

2.7

--

2.9

--

2.4

2.0

--

2.4

4,999

4,382

1,900

83%

84%

77%

3,892
*

82%

Number of observations
39
68
67
127
74
201
NOTES: Kw = Kwanzas. Costs include purchased inputs, hired labor, and reported marketing costs. Variables are at the household
level. Number of observations in Economic Status Index smaller than in last column because of missing values in this variable.
1
Bonferroni test of difference between means: for Economic Status Index, different letters imply differences are significant at 10%;
for Gender of HHH, * significant at 10%; ** significant at 5%; *** significant at 1%. -- = mean differences not tested.
Source: ProRenda survey, Angola, 2009. Estimates weighted to reflect population.

184

Table A 3.6. Bean sellers: Average receipts, costs, margins and percentage of production sold, per economic status index and
gender of household head (HHH).
1

Detail
Receipts (Kw)
Price per kg sold (Kw/kg)
Total Costs (Kw)
Production costs per kg produced
(Kw/kg)
Marketing costs per kg sold
(Kw/kg)

1

Economic Status Index by tercile
Gender of HHH
Lowest
Middle
Highest
Male
Female
(mean values)
3,193
a
4,792
a
11,625
b
8,447
3,039
***
74
-65
-74
-72
69
-947

987

1,867

1,443

887

Total
6,785
71
1,272

8.8

--

9.6

--

9.3

--

8.2

11.5

--

9.2

5.0

--

3.0

--

0.8

--

3.2

2.0

--

2.8

Margins (Kw)

2,247

a

3,805

a

9,758

b

7,004

2,152

***

5,513

Share sold (% of total production)

60%

64%

57%

**

62%

66%

61%

Number of observations
87
79
64
142
113
255
NOTES: Kw = Kwanzas. Costs include purchased inputs, hired labor, and reported marketing costs. Variables are at the household
level. Number of observations in Economic Status Index smaller than in last column because of missing values in this variable.
1
Bonferroni test of difference between means: for Economic Status Index, different letters imply differences are significant at 10%;
for Gender of HHH, * significant at 10%; ** significant at 5%; *** significant at 1%. -- = mean differences not tested.
Source: ProRenda survey, Angola, 2009. Estimates weighted to reflect population.

185

Table A 3.7. Onion sellers: Average receipts, costs, margins and percentage of production sold, per economic status index
and gender of household head (HHH).
1

Detail
Receipts (Kw)
Price per kg sold (Kw/kg)

1

Economic Status Index by tercile
Gender of HHH
Lowest
Middle
Highest
Male
Female
(mean values)
1,375
5,135
5,294
5,300
1,883
**
117
-86
-107
-98
114
--

Total Costs (Kw)
Production costs per kg produced
(Kw/kg)
Marketing costs per kg sold
(Kw/kg)

1,671

2,386

Margins (Kw)

-296

2,750

Share sold (% of total production)

74%

80%

5,873

Total
4,708
101

4,284

1,096

**

3,732

95.7

--

42.7

--

114.3

--

90

43

--

82

2.9

--

3.6

--

2.4

--

2.9

3.8

--

3.0

-579

1,016

787

976

77%

80%

77%

79%

Number of observations
34
50
45
89
52
141
NOTES: Kw = Kwanzas. Costs include purchased inputs, hired labor, and reported marketing costs. Variables are at the household
level. Number of observations in Economic Status Index smaller than in last column because of missing values in this variable.
1
Bonferroni test of difference between means: for Economic Status Index, different letters imply differences are significant at 10%;
for Gender of HHH, * significant at 10%; ** significant at 5%; *** significant at 1%. -- = mean differences not tested.
Source: ProRenda survey, Angola, 2009. Estimates weighted to reflect population.

186

Table A 3.8. Descriptive statistics of factors influencing potato, bean and onion production. Central Highlands of Angola,
2009.
Potato
Bean
Onion
N = 281
N = 380
N = 162
Mean
Std. Err.
Mean Std. Err. Mean Std. Err.
Variables
Dependent Variable
Quantity produced (kg)
169.49
29.120
89.05
17.083
46.28
7.686
Independent Variables
Household (HH) Characteristics
Age of HH head (Years)
39.71
0.819
43.71
0.396
43.84
0.919
Gender of HH head (% Male)
74.03
0.214
68.75
0.263
76.51
0.208
1

Dependency ratio
HH member is in farmer organization (% yes)

0.59
9.37

0.011
0.010

0.54
6.39

0.012
0.034

0.059
0.092
0.048

0.88
0.51
46.58

0.096
0.094
0.077

1.05
0.41
43.50

0.104
0.049
0.040

13.53
4.44

0.027
0.012

11.78
0.75

0.031
0.005

9.40
2.81

0.010
0.021

26.45
17.33
16.62
1.60
15.97
17.11
12.45

187

0.56
2.18

0.80
0.38
56.97

2

No. adults (>17 yr) literate
No. of Tropical Livestock Units
Home has zinc roof (% yes)
Productive Assets Ownership (% yes)
Owns a plow
Owns a backpack sprayer
Public Assets and Quasi-fixed Factors (% yes)
IDA office in village
Public market in village
HH in Caala Municipality
HH in Ekunha Municipality
HH in Bailundo Municipality
HH in Londuimbali Municipality
HH in Katchiungo Municipality

0.010
0.046

0.054
0.045
0.021
0.013
0.038
0.038
0.022

19.25
18.97
7.06
3.35
50.19
29.70
3.79

0.039
0.032
0.013
0.023
0.040
0.034
0.004

19.03
12.79
10.45
1.27
34.93
24.06
6.09

0.019
0.043
0.040
0.011
0.049
0.051
0.010

Table A 3.8 (cont’d).
Potato
N = 281
Mean
Std. Err.
2.83
0.018
33.17
0.031
0.25
0.002

Variables
HH in Tchicalachuluanga Municipality
HH in Chiguar Municipality
HH in Babaera Municipality
Production-related variables
Total seed used (kg)
41.16
Planted in rainfed plot (% yes)
44.03
Planted intercropped (% yes)
n.a.
Planted local variety (% yes)
73.75
Used fertilizer (% yes)
65.62
Used pesticides (% yes)
10.66
Reported production costs (Kw/kg)
62.54
HH reported lower harvest (% yes)
66.75
Notes: n.a. = not applicable since only beans may be planted intercropped.
1

6.562
0.014
0.046
0.028
0.054
9.583
0.026

Dependency ratio estimated by dividing No. members <17 yr by household size.

2

Literacy refers to adults who can read and write.
Source: ProRenda survey, Angola, 2009. Estimates weighted to reflect population.

188

Bean
N = 380
Mean Std. Err.
2.36
0.013
3.32
0.022
0.23
0.002

Onion
N = 162
Mean Std. Err.
3.52
0.019
19.66
0.044
0.02
0.000

23.59
94.20
59.97
98.10
2.55
0.11
10.93
57.92

0.03
56.57
n.a.
92.90
48.57
3.57
75.66
54.65

3.273
0.016
0.017
0.004
0.006
0.001
2.109
0.020

0.013
0.040
0.031
0.072
0.014
7.912
0.035

Table A 3.9. Unconditional average partial effects of factors influencing potato sales.
Central Highlands of Angola, 2009.
Quantity sold (kg)
Independent variables: the coefficients displayed are the
unconditional average partial effects (APEs).
Coefficient
p-value
Age of HH head (Years)
-0.309
0.124
Gender of HH head (1=Male)
23.994
0.353
Dependency ratio
20.491
0.702
HH member is in farmer organization (1=Yes)
19.389
*0.091
No. adults (>17 yr) literate
1.822
0.666
No. of Tropical Livestock Units
4.275
0.571
Asset Index
-6.980
*0.056
Owns motorcycle (1=Yes)
12.298
0.717
Owns bicycle (1=Yes)
28.198 ***0.000
IDA office in village (1=Yes)
43.242 **0.041
Public market in village (1=Yes)
-23.283
0.111
HH in Caala Municipality (1=Yes)
49.531
0.175
HH in Ekunha Municipality (1=Yes)
65.411 **0.042
HH in Bailundo Municipality (1=Yes)
-86.079
*0.066
HH in Katchiungo Municipality (1=Yes)
36.192 **0.018
HH in Tchicalachuluanga Municipality (1=Yes)
-6.738
0.769
HH in Chiguar Municipality (1=Yes)
4.081
0.805
HH in Babaera Municipality (1=Yes)
-29.095
0.378
Distance from village to sede (km)
-0.773
0.240
Road between village and sede in poor condition (1=Yes)
-59.345 ***0.000
Seller sought price information prior to sales (1=Yes)
-2.937
0.761
Reported marketing costs (Kw/kg)
-0.454
0.683
Total potato production (kg)
0.577 ***0.000
Notes: *, **, *** indicates the corresponding coefficients are significant at the 10%, 5%,
and 1% levels, respectively.
Coefficients and p-values obtained via bootstrapping at 500 repetitions.
Dependency ratio estimated by dividing No. members <17 yr by household (HH) size.
Literacy refers to adults who can read and write.
n.a. = not applicable because variable was not included in the regression.
Source: ProRenda survey, Angola, 2009. Estimates weighted to reflect population.

189

Table A 3.10. Unconditional average partial effects of factors influencing bean sales.
Central Highlands of Angola, 2009.
Quantity sold (kg)
Independent variables: the coefficients displayed are the
unconditional average partial effects (APEs).
Coefficient
p-value
Age of HH head (Years)
-0.400 **0.016
Gender of HH head (1=Male)
5.296
0.502
Dependency ratio
-10.200
0.188
HH member is in farmer organization (1=Yes)
-12.154
0.127
No. adults (>17 yr) literate
5.717
*0.055
No. of Tropical Livestock Units
2.383
0.170
Asset Index
0.971
0.668
Owns motorcycle (1=Yes)
-35.318 ***0.008
Owns bicycle (1=Yes)
14.703 ***0.000
IDA office in village (1=Yes)
1.023
0.958
Public market in village (1=Yes)
-6.534
0.760
HH in Caala Municipality (1=Yes)
-19.680 ***0.000
HH in Ekunha Municipality (1=Yes)
10.260
0.216
HH in Bailundo Municipality (1=Yes)
12.255 ***0.000
HH in Katchiungo Municipality (1=Yes)
-16.531 ***0.000
HH in Tchicalachuluanga Municipality (1=Yes)
-13.475
0.216
HH in Chiguar Municipality (1=Yes)
-2.358
0.910
HH in Babaera Municipality (1=Yes)
-32.637 ***0.001
Distance from village to sede (km)
0.538 ***0.003
Road between village and sede in poor condition (1=Yes)
2.300
0.709
Seller sought price information prior to sales (1=Yes)
-9.474 ***0.002
Reported marketing costs (Kw/kg)
-0.331
0.313
Total bean production (kg)
0.483 ***0.000
Notes: *, **, *** indicates the corresponding coefficients are significant at the 10%, 5%,
and 1% levels, respectively.
Coefficients and p-values obtained via bootstrapping at 500 repetitions.
Dependency ratio estimated by dividing No. members <17 yr by household (HH) size.
Literacy refers to adults who can read and write.
Source: ProRenda survey, Angola, 2009. Estimates weighted to reflect population.

190

Table A 3.11. Unconditional average partial effects of factors influencing onion sales.
Central Highlands of Angola, 2009.
Quantity sold (kg)
Independent variables: the coefficients displayed are the
unconditional average partial effects (APEs).
Coefficient
p-value
Age of HH head (Years)
0.112
0.550
Gender of HH head (1=Male)
3.530
0.211
Dependency ratio
5.954
0.147
HH member is in farmer organization (1=Yes)
0.850
0.851
No. adults (>17 yr) literate
2.795 ***0.000
No. of Tropical Livestock Units
-0.201
0.961
Asset Index
-2.997 ***0.000
Owns motorcycle (1=Yes)
-2.391
0.761
Owns bicycle (1=Yes)
1.157
0.788
IDA office in village (1=Yes)
2.316
0.679
Public market in village (1=Yes)
3.847
*0.065
HH in Caala Municipality (1=Yes)
0.276
0.963
HH in Ekunha Municipality (1=Yes)
-0.561
0.852
HH in Bailundo Municipality (1=Yes)
-1.466
0.702
HH in Katchiungo Municipality (1=Yes)
7.706
0.105
HH in Tchicalachuluanga Municipality (1=Yes)
-2.762
0.410
HH in Chiguar Municipality (1=Yes)
3.635
0.485
Distance from village to sede (km)
-0.681 **0.019
Road between village and sede in poor condition (1=Yes)
10.130 ***0.000
Seller sought price information prior to sales (1=Yes)
1.997
0.267
Reported marketing costs (Kw/kg)
-0.147
*0.078
Total onion production (kg)
0.675 ***0.000
Residual from onion production equation
-0.082 **0.028
Notes: *, **, *** indicates the corresponding coefficients are significant at the 10%, 5%,
and 1% levels, respectively.
Coefficients and p-values obtained via bootstrapping at 500 repetitions.
Dependency ratio estimated by dividing No. members <17 yr by household (HH) size.
Literacy refers to adults who can read and write.
Source: ProRenda survey, Angola, 2009. Estimates weighted to reflect population.

191

Figure A 3.1. Cumulative distribution of asset index by potato growers and nongrowers. Central Highlands of Angola, 2009.

192

Figure A 3.2. Cumulative distribution of asset index by onion growers and non-growers.
Central Highlands of Angola, 2009.

193

Figure A 3.3. Cumulative distribution of asset index by bean growers and non-growers.
Central Highlands of Angola, 2009.

194

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