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This is to certify that the

thesis entitled

An Analysis of the Performance of the Sugar Cane
Industry in the Dominican Republic

presented by

Wagner Alexi Mendez Herasme

has been accepted towards fulfillment
of the requirements for

M. S . degree in Agricultural Economics

M E/JIAM

MMrofessor

Date 12 November 1992

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MSU is An Affirmative Action/Equal Opportunity Institution
6mm:

AN ANALYSIS OF THE PERFORMANCE OF THE SUGAR CANE
INDUSTRY IN THE DOMINICAN REPUBLIC

BY

Wagner Alexi Méndez Herasme

A THESIS

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

MASTER OF SCIENCE

Department of Agricultural Economics

1992

ABSTRACT

AN ANALYSIS OF THE PERFORMANCE OF THE SUGAR CANE
INDUSTRY IN THE DOMINICAN REPUBLIC

BY

Wagner Alexi Mendez Herasme

Agriculture is the most important sector in the Dominican economy.
Within the Agricultural sector, the sugar cane industry is the most important
sub-sector. However, the sub-sector’s share of the Gross Domestic Product,
employment, and income generation has decreased substantially due to
external and internal problems.

The purpose of this research is to analyze the performance of the sugar
cane industry in the Dominican Republic. An econometric model using time
series from 1970 to 1990 was developed to achieve this objective. The model
shows some of the factors affecting sugar supply and demand. Some supply
and demand elasticities, such as income and own price elasticities are
estimated. Moreover, forecasts of supply and demand for the domestic and
export market are made for the near future.

The model shows a decline of supply and export of sugar from the
Dominican Republic. This trend in the sugar industry in the Dominican Republic
leads the author to believe that in the near future, the country might have to

import sugar to fulfill its domestic needs.

A mis padres y hermanos
por haber soportado estos dos anos y medio sin mi.

To Audra

You mean much more to me than you can imagine.
I’m sorry Audra, you're very smart.

ACKNOWLEDGEMENT

If I try to mention all the people who have helped and encouraged me

during my studies at Michigan State University, maybe I have to write another
volume similar to this paper. However, it is not possible to do so at this time.
i would like to especially thank Dr. Lindon Robison and Dr. John Ferris. To Dr.
Robison, I want to thank him for his guidance throughout this Master’s
program, for serving as my major professor, and also for his friendship. To Dr.
Ferris, i want to thank him for his valuable help in carrying out this piece of
research, which was one of the main learning tools in the whole Master’s
program. I am also grateful to Dr. Scott Witter for his valuable 'comment on this
research and his willingness to help me'whenever I needed it.

Special thanks to Audra Vifince for using part of your time to help me edit
this paper and for that time you had to take a nap in the computer room while
waiting for me and providing me with the energy I needed to continue working
and to finish this paper on time. Thanks to my parents, brothers and sisters.
From a distance, you all encouraged me to work hard to, finally, be with you
again. Thanks to all the people in the Fellowship of Internationals of East
Lansing Trinity Church for their friendship, and all their prayers. Furthermore, I
would also like to thank the Instituto Superior de Agriculture (ISA) and the
Agency for the lntemational Development (AID) for their support and

sponsorship.

Finally, and most important, I would like to thank "...the Mighty God, the
Everlasting Father, the Prince of Peace...“ for his unconditional love for people

and for listening to all our prayers.

TABLE OF CONTENTS

LIST OF TABLES ......................................... ix

LIST OF FIGURES ........................................ xi

CHAPTER I

INTRODUCTION .......................................... 1
1.1 Problem Statement ................................. 1
1.2 Objective of the Study ............................... 3

CHAPTER II

RESEARCH METHODOLOGY ................................. 4
2.1 Collection of Data ................................... 4
2.2 Analysis of Data .................................... 4

CHAPTER III

THE AGRICULTURAL SECTOR IN THE DOMINICAN REPUBLIC ...... 6
3.1 Economic and Social Importance of Agriculture ............. 6
3.2 Structure of the Agricultural Sector ...................... 8
3.2.1 Land Use and Availability ....................... 8
3.2.2 Number and Size of the Farms ................... 9
3.2.3 Land Tenure ............................... 12
3.3 institutions Related to the Agricultural Sector in the Dominican
Republic ...................................... 13
CHAPTER N
A PROFILE OF THE SUGAR INDUSTRY IN THE DOMINICAN
REPUBLIC ......................................... 16
4.1 Economic and Social Importance of the Sugar Industry ....... 16
4.2- A Historical Overview of the Sugar Cane Industry in the
Dominican Republic ................................ 17
4.3- Structure and Organization of the Sugar Cane Industry in the
Dominican Republic ................................ 20
4.4- Description of Some of the Institutions Dealing with the
Sugar Cane Industry in the Dominican Republic ............ 22
4.4.1 The National Sugar Council (CEA) . . . . . . .' ......... 22
4.4.2 The Dominican Sugar Institute (INAZUCAR) ......... 23

vi

4.5 Commercialization of the Dominican Sugar ................ 23

4.5.1 The Domestic Market ......................... 23
4.5.2 The Export Market ........................... 25
4.6- Problems Affecting the Dominican Sugar Industry .......... 29
4.6.1 External Problems ........................... 29
4.6.2- internal Problems ........................... 31
4.7- Future Trends in the Dominican Sugar Industry ............ 32
CHAPTER V
THE MODEL: SUPPLY AND DEMAND OF SUGAR IN THE
DOMINICAN REPUBLIC ............................... 36
5.1 Objectives of the Model ............................. 36
5.2 The Supply Model ................................. 36
5.1.1 Specification of the Model ...................... 36
5.1.2 Results ................................... 39
5.2 The Demand Model ................................ 41
5.2.1 Specification of the Model ...................... 41
5.2.1.1 The Domestic Demand Model ............. 42
5.2.1.2 The Export to the US. Market Model ....... 45
5.2.1.3 The Export to the World Market Model ...... 46
5.2.2 Results ................................... 49
5.2.2.1 The Domestic Demand Model ............. 49
5.2.2.2- The Export to the US Market Model ....... 52
5.2.2.3— The Export to the World Market Model ...... 53
5.3- The Supply and Demand Model ....................... 56
5.3.1 Formulation of the General Model ................ 56
5.3.2 Evaluation of the General Model ................. 57
5.4- Elasticity Estimates ................................ 65
CHAPTER VI
SUPPLY AND DEMAND FORECASTS .......................... 68
6.1- Forecast of Exogenous Variables ...................... 68
6.1.1 Projection of Real Expected Gross Return per Hectare
(REGRPH) ................................ 69
6.1.2 Projection of Deflated per Capita National Disposable
Income (DPNDI) ............................ 69
6.1.3 Projection of the Deflated Retail Price of Refined Sugar
(DREI’PR) ................................ 70

6.1.4 Projection of the Pressure to Export sugar from the
World Excluding the United States and the
Dominican Republic (WPRESS) . . . . . . . .' ......... 70

6.1.5 Projection of Beginning Stock (BSTOCK) ........... 71

vii

6.2- Forecast of Endogenous Variables ..................... 71

CHAPTER VII
CONCLUSION AND SUGGESTIONS FOR FURTHER STUDIES ....... 79
7.1 Conclusion ...................................... 79
7.2 Suggestions for Further Studies ........................ 80
LIST OF REFERENCES .................................... 81
APPENDICES
APPENDIX l: Edit File for the Supply and Demand Model ............. 84
APPENDIX Ii: Alphabetical Order and Description of the
Variables used in the Model ........................... 85
APPENDIX lil: Evaluation Turning-point Errors ..................... 87
APPENDIX IV: List of Exogenous Variables Used in the Model . ......... 92

APPENDIX v: Actual and Forecast Variables for the Endogenous Variables 95

APPENDIX VI: Computer Output of the Regression Equations .......... 99

viii

LIST OF TABLES

Table 1: Participation of the Agricultural Sector in the GDP in the Dominican

Republic ............................................ 7
Table 2: Number, Area, and Size of the Farms in the Dominican Republic.
1971 and 1981 Censuses. ............................. 10
Table 3: Stratification of the Farms in the Dominican Republic. 1981
Census. .......................................... 11
Table 4: Land Tenure in the Dominican Republic. 1971 Census ........ 13
Table 5: Daily Processing Capacity of Dominican Sugar Mills. .......... 21
Table 6: Share of Sugar Export in Total Export from the Dominican
Republic. ......................................... 26
Table 7: Profitability of Some Export Crops. .............. . ......... 34
Table 8: Selected Results of the Regression on the Area Harvested
equation to Determine Sugar Supply ...................... 39
Table 9: Sugar Export from the Dominican Republic ................. 42
Table 10: Selected Results on The Domestic Demand Equation ........ 50

Table 11: Selected Results on the Regression to Determine Sugar
Export from the Dominican Republic to the United States ...... 52

Table 12: Selected Results on the Regression to Determine Sugar Export
from the Rest of the World Excluding the United States and the
Dominican Republic ................................. 54
Table 13: Selected Results on the Regression to Determine the Ratio of Export
from the Dominican Republic to Export from the Rest of the World
Excluding the Unites States and the Dominican Republic ...... 55
Table 14: Summary of Turning-point Errors for the Behavioral Equations . . 58
Table 15: Estimated Supply and Demand Elasticities ................ 65

Table 16: Evaluating Turning-point errors (AREAHAR) ............... 87

ix

Table 17: Evaluating Turning-point Errors (PCONS) ................. 88

Table 18: Evaluating Turning-point Errors (WEXPT RES) .............. 89
Table 19: Evaluating Turning-point Error (EXPTUS) ................. 90
Table 20: Evaluating Turning-point Errors (REXPT) .................. 91

LIST OF FIGURES

Figure 1: Export to the United States Equation: Actual and Fitted Values

for the Sample Period ............................... 59
Figure 2: Area Harvested Equation: Actual and Fitted Value for the

Sample Period .................................... 60
Figure 3: Per Capita Consumption: Actual and Fitted Values for the

Sample Period .................................... 61
Figure 4: Export from the Rest of the World: Actual and Fitted Values

for the Sample Period ................................. 62

Figure 5: Ratio of Export from the DR and the Rest of the World: Actual

and Fitted Values for the Sample Period .................. 63
Figure 6: Area Harvested: Actual and Forecast Values ............... 74
Figure 7: Sugar Supply in the Dominican Republic: Actual and Forecast

Value ........................................... 75
Figure 8: Export to the United States: Actual and Forecast Values ....... 76

Figure 9: Export from the Dominican Republic to the Rest of the World:
Actual and Forecast Values ........................... 77

Figure 10: Domestic Consumption per Capita: Actual and Forecast
Values .......................................... 78

CHAPTER I
INTRODUCTION

 

1.1 Problem Statement

Despite a decline in Agriculture’s role in the Gross Domestic Product
(GDP), exports, and employment generation in the Dominican Republic, it
continues to be the most important sector in the country's economy.
Agriculture is not only the main source of foreign currency received by the
country in international markets, but also generates employment for a majority
of the labor force. For example, in 1986 the country exported agricultural
goods valued at US$ 425.6 million, representing 60% of the value of all. exports
made by the country during that year. As far as employment generation is
concerned, Perez-Luna (1984), cited by Rivas (1988), estimated that 58% of the
labor force in the Dominican Republic is devoted to activities related to
Agriculture.

Within the agricultural sector in the Dominican Republic, the sugar cane
industry is ranked as the foremost cash crop. Fluctuation in sugar output and
prices are important determinants of economic conditions in the Dominican
Republic. Increase in world sugar prices greatly contribute to generating

foreign exchange. Conversely, fluctuations in earnings from the sugar sector

2

have been a destabilizing force in the economy in the Dominican Republic
(World Bank, 1977). However, the GNP contribution of the sugar cane industry
to the Dominican economy has declined so sharply that much of the land
previously cropped to sugar cane production is being diverted to the production
of other agricultural products. It is important to understand the domestic and
international supply and demand linkages as the US. is the major export
market for sugar cane.

The decline of the sugar cane industry is primarily due to external factors
such as low world market prices and the dependence of the Dominican
Republic on the US. market for exports. Thus, it is important for policy makers
in the country to obtain a good understanding of the variables affecting supply
and demand for sugar in the Dominican Republic. The development of an
econometric model is a valuable tool for policy decision makers. This model
can describe the past performance of the sugar cane industry in the Dominican
Republic and form the basis for predicting future trends. Moreover, this study is
relevant to the instituto Superior de Agriculture’s (ISA)1 long-range goals of

helping improve the performance of the Dominican public sector.

 

‘ The institute Superior de Agriculture is the main agricultural university in the Dominican Republic and the
reeearcher worke for it.

1 .2 Objective of the Study

The purpose of this paper is to analyze the performance and current
status of the sugar cane industry in the Dominican Republic. To achieve this
purpose, the following activities are proposed:

1- To develop an econometric model to analyze the supply and demand
trends for sugar in'the Dominican Republic.

2- To estimate price and income elasticities for sugar consumption based
on econometric modeling.

3- To forecast supply and demand of sugar in the Dominican Republic
based on past trends and the current situation.

4- To analyze how the general macroeconomic policy in the country has

helped or hindered the sugar cane industry.

CHAPTER II
RESEARCH METHODOLOGY

 

2.1 Collection of Data

The data for this paper consist mainly of time series trends of production,
consumption, and prices as they relate related to the sugar industry both in the
Dominican Republic, in the United States, and an aggregate of the rest of the
world. Moreover, some economic variables are included as exogenous
variables to help explain supply and demand trends. The period covered by the
time series is from 1970 to 1990. The kind of data to be used will be annual
time series data. Furthermore, this paper includes relevant data on the
agricultural sector in the Dominican Republic as the sugar cane industry is the

main agricultural activity.

2.2 Analysis of Data

A linear multiple regression analysis was used to determine the
relationship among variables. Both supply and demand equations were
modelled using this method. This analysis was carried out using the MicroTSP
computer software package. This computer software solves the system of
equations simultaneously using the Gauss-Seidei algorithm. Projections of the

endogenous variables were made for the years 1991 to 1995. '

5

In the process of developing the model, many variables that theoretically
affects supply and demand were tested. Some of them were dropped because
they were not significant. For example, producer price is supposed to affect
area of sugar cane harvested in the Dominican Republic. However, when it was

tested, it was not significant.

CHAPTER III
THE AGRICULTURAL SECTOR IN THE DOMINICAN REPUBLIC

 

3.1 Economic and Social Importance of Agriculture

Agriculture continues to be the most important sector in the Dominican
Republic. its contribution to the economy can be measured in terms of foreign
exchange generated, employment, and contribution to the GDP.

The share of the agricultural sector in the total GDP in the Dominican
Republic is shown in Table 1. From 1975 to 1991, the total value of agricultural
production at 1970 constant prices has ranged from RD$ 262.80 million in 1970
to RD$ 330.60 million in 1983. Although these are the minimum and maximum
values of agricultural production, they do not represent the lowest and the
highest percentages of the total GDP. The lowest percentage (8.17 96) and the
highest percent (11.74) ,were obtained in 1990 and 1976, respectively (Central
Bank, various years). Traditional export crops such as sugar, coffee, cocoa,
and tobacco are the ones that have contributed more to the total value of
agricultural production. Some other important crops include cereals,
vegetables, roots, and tubers.

In spite of the low price of sugar, coffee, and cocoa, and the reduction in

the sugar quota by the U.S. market, agriculture continues to be the most

7
Table 1: Participation of the Agricultural Sector In the GDP In the

Dominican Republic

 

 

Total GDP Share of Agriculture Percentage
Year (million not) (million R03)
(1970 prices) (1970 prices)

1975 2,288.90 262.80 11.48
1976 2,442.90 286.80 11.74
1977 2,564.60 286.30 11.16
1978 2,619.50 293.70 11.21
1979 2,738.20 287.90 10.51
1980 2,903.90 297.50 10.24
1981 3,021.90 312.10 10.33
1982 3,069.20 323.80 . 10.55
1983 3,209.40 330.60 10.30
1984 3,218.10 329.00 10.22
1985 3,134.90 314.80 10.04
1986 3,234.00 312.10 9.65
1987 3,488.60 323.20 9.26
1988 3,512.70 315.10 8.97
1989 3,655.20 317.90 8.70
1990 3,468.40 283.20 8.17
1991 3,441.00 289.70 8.42

 

Source: Central Bank. Monthly Bulletin.
Dominican Republic. Various years (1975-1991)

important source of foreign currency for the Dominican Republic
(Jacc/RD, 1989). According to the Jacc/RD (1989), the export of non-

traditional agricultural products such as vegetables, plantain, cassava, yam, and

8

fruit have compensated, in part, for the decline in foreign currency previously
generated by the export of sugar.

The social importance of agriculture in the Dominican Republic can be
explained by the number of people who benefit from it. These are mainly
people in the rural areas. In 1987, the rural population, that was estimated to
be 3.06 million people, benefited directly or indirectly from agricultural activities
(Jacc/RD, 1989). In addition to the rural population, many people in the urban
areas benefited from agriculture through commercialization, transport, and agro-
industries.

Therefore, agriculture, despite a slow growth and reduction of some
crops, continues to be the most important sector to generate foreign currency
and to produce food for the Dominican population as well as the main source of

employment.

3.2 Structure of the Agricultural Sector
3.2.1 Land Use and Availability

The Dominican Republic has a total area of 48,442.23 squared kilometers
( 18,710.79 squared miles). This is equal to approximately 77,120,141 tareasz.
According to the national census carried out in 1981, 27.2 96 of the total land,
which is approximately 20,958,000 tareas, are used for agricultural activities,

24.7 % (19,036,000 tareas) are used for pasture, and the rest is mountain,

 

’ 1 tarea (ta) is approximately 1/16 of a Hectare (Ha).

9
forest, and other. Out of the total agricultural land, 76.5 % (16,039 ta) is in use,

13.6 % (2,843,000 ta) is fallow, and 9.9 % (2,075,000) is idle (Jacc/RD, 1989).

A study carried out by FAO in 1988 on the agricultural sector shows that
there are 14,803,345 ta (931.028 Ha) monocropped and 8,357,771 ta (525,646
Ha) intercropped. This makes a total of 23,161,116 ta (1,456,674 He). FAO
(1980) also states that the crops that use more land in the Dominican Republic
are sugar cane, coffee, and cocoa. They use 3,640,225 ta (228, 945 Ha),
2,396,607 ta (150,730 He), and 1,991,221 ta (125,234 Ha), respectively. These
crops are followed by rice, which uses 1,510,500 ta (95,000 Ha) . Some other
important crops are beans, peanuts, cotton, plantain-bananas, coconuts, maize,

sorghum, and roots and tubers.

3.2.2 Number and Size of the Farms

The integration that exists between agriculture and animal production in
small but numerous subsistence farms in the Dominican Republic makes it
difficult to differentiate between these two activities (Jacc/RD, 1989). For this
reason, the agricultural census of 1981 groups these two activities by number
and size of farms. However, the census separates the area dedicated to each
activity.

Between 1971 and 1981, the period between the two national censuses,
the number of farms changed from 304,820 to 385,060. . This means an

increase of 26.3 %. Most of these farms are located in the northern part of the

10

country. The number of farms in the north is 201,911. Out of this total,

106,420 are located in the northcentral area. in descending order, the
southeastern region is located in second place with 104,448 farms, and the
southwestern region is in third place with 78,701 farms. Table No. 2 shows a
summary of these pieces of information.

Table 2: Number, Area, and Size of the Farms In the Dominican Republic.

1971 and 1981 Censuses.

 

 

 

 

 

 

 

 

 

Region lulber ot tar-I Area Average also
(tarea) (terse)

1971 1981 1971 1981 1971 1981

Total 304,820 385,060 43,508,888 42,559,639 143 111

North 159,144 201,911 20,191,334 19,064,513 127 94

Northcentral 82,560 106,420 9,618,975 9,922,646 117 93

Northeast 53,889 62,785 7,466,183 5,890,667 139 94

Northwest 22,695 32,706 3,106,176 3,251,200 137 99

Southwest 59,601 78,701 5,404,868 5,565,136 91 71

Southeast 86,075 104,448 17,912,686 17,929,990 208 172
Source: Adapted from Jacc/RD, 1989

According to the 1971 and 1981 censuses, between this period, the land
used for agriculture and animal production decreased by 949,249 tareas.
However, if only the area used for cropping is considered, there is an increase

in 2.93 million tareas. This means that cropping, in fact, was actually growing.

11

Table 2 also shows that a tendency to small farms has been observed in
the last few years. In 1971, the average size of a farm was 143 ta, but in 1981,

it decreased to 111 ta.

Table 3 presents the distribution of farms according to their size. 0f the

Table 3: Stratification of the Farms In the Dominican Republic. 1981

 

 

Census.
Size (tareas) luber Percentage Area Percentage Average area
of fer-e (taree) (tarea)
Less than 8 61,670 16.00 185,994 0.90 3.00
From 0 to 79 252,995 55.70 4,175,710 19.90 15.50
From 00 to 159 32,543 0.50 2,470,420 11.00 ‘ 75.20
From 150 to 799 30,015 0.00 4,503,301 21.90 140.70
From 000 to 1,599 4,001 1.10 1,512,700 7.20 370.70
From 1,500 to 3,199 1,025 0.50 1,310,399 5.30 722.40
From 3,200 to 7,999 705 0.20 1,200,470 5.70 1,527.30
From 0,000 to 15,999 104 0.00 555,124 3.20 3,520.20
15,000 or more 151 0.00 4,035,444 23.10 30,040.00
100.1 and average 305,050 20,957,542 54.43

 

Source: Jacc/RD, 1989.

terms, 81.7 96 have less than 80 ta, 16.4 96 have between 80 and 800 ta, and
only 1.9 96 have more than 800 ta. However, farms with less than 80 ta
represent only 20.8 96 of the total area used for agriculture. The farms between
80 and 800 ta represent 33.7 96, and 1.9 96 of the farms with more than 800 ta

represent 45.5 96 of the agricultural land.

12

The average farm size is 54.4 ta, but 81 96 of the farms have 13.9 ta,
which are too small to sustain a rural Dominican family (ONE, 1983). it is
worthwhile to mention that within the last category, there are 345 farms with
more than 8000 ta, with a total area of 5.50 million ta that belong to government
institutions such as the National Sugar Council (CEA), the Land Reform Institute

(IAD), and private sugar cane enterprises.

3.2.3 Land Tenure

Recent land tenure information could not be found. The land tenure
structure, according to the 1971 census, is shown in Table 4. The 1971 census
indicates that 70.81 96 of the land used for agriculture had titles. This total
included all government and private land. In a study done by Development
Associates in 1985, it was estimated that the CEA had titles on 2,750,000 ta
(173,000 Ha). In addition, 281,430 ta (17,700 Ha) had been invaded illegally by
farmers. The National Cotton Institute (INDA) has 79,500 ta (5,000 Ha). Other
government institutions have 69,006 ta (4,340 He) for a total of 182,340 Ha
(2,899,206 ta)

Government land used illegally by farmers is 3,600,428 ta (226,442 Ha).
Moreover, the IAD has 6,232,800 ta (392,000 Ha). Therefore, the government is

the owner of a large portion of the agricultural land in the Dominican Republic.

13
Table 4: Land TORUI‘O In the DOI'I‘III'IICBD HBPUDIIC. 1971 census

 

 

Area

Tenure (Hectares) Tareas Percentage
With titles 1,938.5 30,822.1 70.81
Rented . 84.0 1,335.6 3.07
Agrarian Reform 48.1 764.8 1.76
Illegally occupied 208.7 3,318.3 7.62
Combined tenure 400.8 6,372.7 14.64
Others 57.5 . 914.3 . 2.10
Total 2,737.6 43,527.8 100.00

 

Source: Adapted from Development Associates, 1985.

3.3 Institutions Related to the Agricultural Sector In the Dominican
Republic
The objective of the different Institutions, related to the agricultural sector
in the Dominican Republic, is to create the necessary conditions for the
production of the food products demanded by the population. Moreover, they

try to provide the services needed by producers during the production process.

14

These institutions, since they were founded, have tried to adapt to the changing
conditions in the Dominican Republic to try to solve all agricultural challenges
(Jacc/RD, 1989).

The most important institutions within the Dominican agricultural sector

are the following:

1- The Ministry of Agriculture (SEA)

This is considered the most important institution dealing with the
agricultural sector in the DominiCan Republic. The SEA is in charge of creating
the necessary conditions to ensure a good supply of food products to the
population. All the other institutions, though independent, are linked to SEA

because of the nature of this institution.

2- The Agricultural Bank (BAGRICOLA)
This is the main national institution in charge of providing financial

assistance to small and medium sized farmers.

3- The Dominican Agrarian Institute (IAD)
This organization is in charge of controlling the land concentration and

distribution. it tries to benefit landless people in the rural areas.

15
4- The National Water Resources Institute (INDRHI)

This institution is in charge of managing the whole irrigation system in the
Dominican Republic. All laws and regulations dictated by this institution tend to

favor all agricultural producers in the country.

5- The National Institute for Price Stabilization (INESPRE)

This is the official institution in charge of regulating all agricultural
marketing and commercialization activities. It is specifically oriented toward
protecting small and medium sized farmers in marketing and commercializing
their agricultural products. Its main objective is to regulate prices and to avoid
speculation with agricultural products. The INESPRE sets the buying and

selling price of some agricultural and animal products.

6- Center for the Administration of the National Forest (DGF)
The objective of this institution is to manage and preserve the national
forest. In the beginning, it functioned as a dependent of the Ministry of

Agriculture. However, it is now a dependent of the military.

CHAPTER IV
A PROFILE OF THE SUGAR INDUSTRY IN THE
DOMINICAN REPUBLIC

 

4.1 Economic and Social importance of the Sugar industry

More than any other country, the sugar cane industry is the backbone of
the Dominican economy. Within the agricultural sector, sugar cane is the most
important subsector. Garcia (1990) states that the sugar agro-industry has
been the most important industrial activity in the Dominican Republic for a long
time. It generates most of the employment, income, and exports.

Sugar exports from the Dominican Republic have fluctuated through time.
Sugar export from 1980 to 1989 reached a maximum level of 956,174 metric
tons in 1983. It decreased to 481,473 in 1986, and recovered again in 1987
when the export level reached 587,358 metric tons and 521,356 metric tons in
1989 (Garcia, 1990). .

According to Garcia (1990), the relative importance of sugar exports
compared with total exports had also changed during that decade. in 1982,
sugar exports represented 40 96 of the total exports. In 1983, this percentage
decreased to 23 96 and it continued decreasing until 1988.

Despite the decrease in its relative importance, the sugar cane industry

continues to be very important for the Dominican economy. It continues to be

16

17

a great source of foreign currency generation and it employs more than 80,000
people annually directly in the fields and sugar enterprises plus around 70,000
more people employed by sugar "colonos." The labor force used by the sugar
industry reaches 50 96 of the labor force used by the manufacturing industry
(Garcia, 1990). Moreover, there are many more people in the economy that

depend indirectly from the sugar cane industry.

4.2- A Historical Overview of the Sugar Cane Industry in the Dominican
Republic

Sugar cane was introduced to America through the Dominican Republic.
It was introduced by Christopher Columbus in his second trip to the New World
in 1493 (CRC, undated and Garcia, 1990). In the beginning, it was planted in
household gardens. it was planted at a commercial level in 1506. The industry
was favored by the import of black slaves from Africa to the Dominican Republic
(Diaz, 1986). Sugar was produced for the first time on the island in 1509
(INAZUCAR, 1977). Sugar plantations were growing and the Dominican sugar
industry was becoming so important that in 1516 the Dominican Republic
provided sugar to the Old World. Around 1550, sugar cane was already one of
the most valuable crops in the Hispaniola island, which is today the Dominican
Republic and Haiti. However, it is not until the end of the XIX century when the

sugar industry was truly developed.

18

During colonial times, despite the excellent soil and climate conditions in
the Santo Domingo island, sugar production in the oriental part of the island,
which today is known as the Dominican Republic, did not develop very well.
The industry declined at the end of XVII century. In Cuba and Haiti, however,
the sugar industry developed very well.

The Dominican sugar industry was restored around 1870 and the basis
for its future development was set. This new development was supported by
the emigration of Cubans after the independence war in their country (CEA,
1991). At that time, sugar mills such as "La Fe“, “La Esperanza" (in 1874), and
"La Caridad“ were established around the capital city, and the “Angelina“ was
established in the oriental region close'to the Higuamo River. This last one is
the only one that still remains today.

At the beginning of this century, some foreign companies dedicated to
sugar production were established in the Dominican Republic. This fact further
contributed to the Development of the industry. These companies sent their
production to foreign markets, in higher amounts each time (CEA, 1991).

In this stage, the development of the sugar industry was accelerated.
Then, the sugar industry became the main national economic activity. Sugar
production increased from 4,380 tons in 1880 to 53,000 tons in 1900, and
178,558 tons in 1920. At that time, the national sugar industry had all the
conditions that characterized the Dominican Republic as a “natural sugar

exporting" country.

19
After World War I, the sugar industry greatly influenced the Dominican

economy. In 1920, the value of the national exports suddenly increased to
nearly US$ 50 million from an average of less than US$ 25 million in the
previous five years. This increase in the value of exports was due to an
increase in the world price of sugar to more than 22 cents per pound. At that
time, sugar cane was named ”The Green Gold” of the Dominican Republic.
However, the following year, 1921, the disequilibrium in the world supply and
demand led to a sharp decrease in price from 22 cents to less than 2 cents per
pound. This price decrease greatly affected the Dominican economy that
depended almost exclusively on sugar export for its subsistence.

In 1950, a new stage began for the national sugar industry. The dictator
Rafael Leonidas Trujillo decided to participate directly in the sugar industry.
Trujillo anticipated large benefits as a result of the recovery of the world
economy after World War II. He constructed two more sugar mills and
acquired two others for his ownership. With this new expansion policy, sugar
production increased to more than one million tons annually (Gomez, 1988).

After the dictator was assassinated, the Government took all the
properties belonging to the Trujillo family, including 12 sugar mills that produced
more than 60 96 of the sugar produced in the Dominican Republic. This .
expropriation divided the industry in two sectors: the public and the private
sector. The private sector was represented by the Vicini group and the Gulf

and Western Americas Corporation - Central Romana Division. These last two

20
groups contributed to 28 96 and 8 96 of the sugar production, respectively. This

is the basis for the current structure of the sugar industry in the Dominican

Republic.

4.3- Structure and Organization of the Sugar Cane Industry In the
Dominican Republic

Within the Dominican sugar industry, there are three main sectors.
These sectors are the sugar mills, the institutions dealing with the
commercialization process, and the "colonos" (Diaz, 1986). These "colonos" are
individual sugar cane producers that have a contract to sell their production to
one of the sugar mills. They also receive technical and financial assistance in
exchange. Sometimes they own their land, but in some cases, the land is
provided to them by the Government.

In 1990, sugar cane was planted in a total area of 200 to 250 thousand
Ha (Garcia, 1990). At that time, there were 16 sugar mills in the country. In the
two last years, two of those sugar mills were closed, leaving 14. These mills
were closed due to low profitability. Moreover, the total area devoted to sugar
cane has also decreased. The CEA thought it was more profitable to produce
some other agricultural products such as pineapple, oranges, and vegetables.

Ten of the 14 sugar mills are administrated by the CEA and the
remaining four are private property. Three of the private ones belong to the

Vicini group and the other one belong to the Central Romana Corporation,

21

which is a foreign company established in the country. The distribution of the

sugar mills in the Dominican Republic is presented in Table 5.

Table 5: Daily Processing Capacity of Dominican Sugar Mills.

 

 

 

 

 

Supt mt Processing
(Silent Tons)
Cass Vlcini
Cristobal Colon 1,760
Cael' 2,300
Angelins' 1,850 '
Sub-total 5,900
Central Romans Co.
Central Romans 15,000
Sub-total 15,000
Dominican Sugar Council (CEA) '
Consuelo 4,000
Gui-quor- 3.000
Bsrahons 5.000
Boca Chla 3.6!)
He Hslns 13,700
Catarey' 2,200
Porvenlr 35m
Santa Fe 3.00:)
Ozsms 3,500
Monte Llano 2,200
Esp-mu“ 1,500
Amlstsd ' 5m
Sub-total 40.500
Total , 67.400

 

* Sugar mills currently closed
Source: Adapted from World Bank, 1988.

In the country, there are also two sugar refineries (Garcia, 1990). One of
them belongs to the 'Porvenir" sugar mill, which has a nominal capacity of 70
thousand tons of refined sugar. The other belongs to the Central Romana,

which has a total capacity of 33 thousand tons.

22

The sugar enterprises in the Dominican Republic transport the sugar
cane by two means: rail and roads. For the latter means, they use oxen and
tractors. The rail system has a length of around 1,500 Km (932 miles) (Garcia,
1990).

Garcia (1990) also states that in the country there are also 6 ports for
sugar export. These ports are the following: Barahona, Haina, Andres (in Boca

Chica), San Pedro de Macoris, La Romans, and Puerto Plata.

4.4- Descrlptlon of Some of the Institutions Dealing with the Sugar Cane
Industry In the Dominican Republic
4.4.1 The National Sugar Council (CEA)

The CEA was created on August 20, 1966 through the Law No. 7. This
is one of the most important institutions dealing with the sugar industry in the
Dominican Republic. It is also very important for the country as it provides a
large portion of the national income and employment (CEA, 1991). This
institution is in charge of administrating the sugar mills expropriated to the
Trujillo family by the Dominican Government in 1961. Today these land and

mills constitute the public sugar sector.

The main responsibilities of the CEA are the following:
1) To dictate all the regulations related to the intemal organization of the sugar

mills.

23

2) To set the policies related to production, commercialization, and employment
3) To coordinate all the activities related to the study and provision of land to

investors, and to periodically visit all development projects.

4.4.2 The Dominican Sugar institute (INAZUCAR)

The INAZUCAR was created February 16, 1965 through the Law
No. 616. This institution is in charge of making recommendations to the
Executive Power about the regulations concerning the national sugar policy,
following up on these regulations, and conducting product and marketing
research.

Through the Law No. 616 and Some other regulations, the INAZUCAR
can assign production quotas to the different sugar companies and the
placement of sugar in the different markets to which the Dominican Republic

has access.

4.5 Commercialization of the Dominican Sugar
4.5.1 The Domestic Market

Approximately 88 96 of the domestic raw sugar supply is consumed
directly by households. Of the total supply of refined sugar, 39 96 is used by
industries mainly for the production of non-alcoholic beverages and candy

production (Diaz, 1986).

24

The main government institutions dealing with the domestic market are

the following:

a) The National Institute for Price Stabilization (INESPRE)

This institution is in charge of the commercialization and distribution of
sugar for domestic consumption.

In the first half of 1986, the domestic quota system established by
INESPRE to distribute sugar to wholesalers and retailers was eliminated and
private marketing firms were free to purchase sugar directly from the sugar

mills.

b) The General Direction for Price Control (DGCP)
This institution belongs to the Ministry of Industry and Commerce. it,
along with the iNAZUCAR, sets the price for raw and refined sugar in the

domestic market.

c) The Dominican Sugar Institute (INAZUCAR)

This institution sets the national policy related to both the domestic and
the export market.

As far as domestic price is concerned, in 1985, the INESPRE faced
severe liquidity problems and was delayed in paying CEA for its purchases. In
October 1985, the retail price of refined sugar increased about 66 96 which

25

resulted in widespread consumer protests, especially in Santo Domingo, the
capital city. However, the consumer price of unrefined sugar actually
decreased at that time (World Bank, 1988).

For the national sugar industry to stay as a productive activity, it has
been necessary to adjust the price of the different kinds of sugar in the
domestic market. Due to this fact, there has been a price increase in the last
five years. From 1988 to 1991, the prices were as follows: In the second half
of 1988, the price for 100 pounds of raw sugar was set at RD$ 50.00 from the
producer to the consumer. In 1989, the price increased to RD$ 75.00 and then
decreased to RD$ 57.00. This last decrease was ordered by the President.

The highest prices from producer to consumer for 1991 were set as follows

(CEA, 1991):
119.191.5993: W
Raw RD$ 190.00
Powdered (afinada) 219.00
Refined 277.00

4.5.2 The Export Market
The export market is the most important market for the Dominican sugar.
Most of the sugar produced in the Dominican Republic is for export. About 76

96 of the domestic production was exported (Diaz, 1986). Moreover, sugar

26

export has always represented a high percent of the total exports from the

Dominican Republic ranging from 20.09 96 in 1991 to 55.09 96 in 1971 (Table 6).

Table 8: Share of Sugar Export In Total Export from the Dominican

 

 

Republic.
Year Total exports Sugar exports Percentage
(million RDS) (million RDS)
1970 213.60 103.60 48.45
1971 243.00 133.88 55.09
1972 347.60 159.70 45.94
1973 442.10 187.08 42.32
1974 636.80 324.12 50.90
1975 893.80 561.04 62.77
1976 716.40 , 263.91 35.44
1977 780.50 218.59 28.01
1978 675.50 172.04 25.47
1979 868.60 190.93 21.98
1980 961.90 290.20 30.17
1981 1188.0 513.25 43.20
1982 767.70 265.51 34.59
1983 785.20 263.56 33.57
1984 868.10 271.89 31.32
1985 738.50 158.48 21.46
1986 722.10 133.85 18.54
1987 711.30 127.09 17.87
1988 889.70 123.20 13.85
1989 924.40 157.09 16.99
1990 734.50 142.68 19.43
1991 658.30 132.28 20.09

 

Source: Central Bank. Monthly Bulletin. Dominican Republic. Various Years
(1970-1991).

The export market for the Dominican Sugar is comprised of the U.S.

market, which is called the preferential market, and the world market. The U.S.

27

market works through the quota system. This means that each year the
Dominican Republic is assigned a sugar quota to export to the U.S. through the
preferential market. The Dominican Republic cannot export all the sugar it
wants through this market.

The Dominican Republic depends on the U.S. market to survive (G & W,
1977). Due to this fact, the US sugar policy is a very important factor in
helping or hindering the Dominican economy. Most of the sugar exports from
the Dominican Republic to the U.S. are through this market. The U.S. market
offers a higher price compared with the world market (Gomez, 1987). In 1985,
73 96 of the sugar exports were directed to this market. Moreover, the price
paid in 1985 by the U.S. market for imported sugar was US$ 20.00/100 lb,
whereas the price paid by the world market was only US$ 5.00/ 100 lb (Santana
and Ferreiras, 1987).

The U.S. market was called the traditional market for the Dominican
Republic because of the linkage that the Dominican Republic had with the U.S.
In 1963, the Dominican Republic was a reliable sugar supplier for the U.S. in
that year, the Dominican Republic exported 894,000 short tons of sugar. During
the 1976 to 1981 period, the Dominican Republic exported an average of
819,500 short tons per year. However, exports from the Dominican Republic to
the U.S. were interrupted when the quota system was established in 1982
(Despradel, 1984). Since that time, exports to the U.S. havebeen decreasing

due to a decrease in the sugar quota each year, except in 1983-1984.

28
The world market is characterized by high price fluctuation. This high

price fluctuation affects both producing and consuming countries. Prices in the
U.S. market are more stable (Santana and Ferreiras, 1987). However, in the
last few years, this market has been so restricted that it has affected both
production and exports in the Dominican Republic (Diaz, 1986). Because sugar
export is one of the main economic activities in the Dominican Republic, the
whole economy has also been affected.

In spite of the instability in the world market price and the restrictions in
the U.S. market in the last few years, these two markets continue to be the
most important for the Dominican Republic. Until 1984, around 73 96 of the
Dominican sugar export were to the preferential market and 27 96 to the world
market (Diaz, 1900).

The reason why prices in the U.S. and the world market are different is
because in the world market, prices are set by the supply and demand forces,
whereas in the U.S., market prices are set by the U.S. according to its
requirements. This price in the U.S. is set such that domestic producers in the
U.S. can compete with imported sugar. This is one of the reasons why prices
in the U.S. market are higher than in the world market (Diaz, 1986).

The year 1974 is a special case for export prices. In this year, the world
market price was USS 29.98/100 lb. This price was higher than the U.S. price
which was USS 29.49/100 lb. This was because negotiations in the world

29

market were carried out under the pressure of a high supply due to an unfulfill
demand in 1973 (Cerro, 1984).

The low prices in the 1980’s made the world market an unattractive
market. Price decreased from 18.9 U.S. cents per pound in 1980 to 4.09 in
1985 (CEA, 1991). This price was below the lowest cost of production of any
producer in the world. in the following years, prices increased slightly to 6.07
cents in 1986, 6.71 cents in 1987, 10.16 cents in 1988, and 12.79 in 1989. In
1990, the average price of raw sugar was around 13.72 cents per pound and
17.31 cents for refined sugar. Notice that none of these price increase reached

the price level in 1980.

4.6- Problems Affecting the Dominican Sugar Industry
There are many problems affecting the Dominican sugar industry. These
problems are both external, which are very difficult for the country to solve, and

internal, which can be controlled directly by the Dominican Republic.

4.6.1 External Problems

The extemai problem affecting the Dominican sugar industry are basically
marketing problems. The Dominican sugar industry is having problems with
both the US and the world market. One of the main problems with the US
market is the quota system. One of the main problems in the world market is
the price instability (JACC/RD, 1909).

30
The main problem with the US market is the reduction in the export

quota. One of the reasons for the U.S. to reduce the sugar quote is because it
wants to protect its domestic producers (Cerro, 1987). This protectionist policy
tries to keep the price high enough to domestic producers. Moreover,
developed countries like the U.S. are increasing the sources of sweeteners like
beet sugar and corn syrup, which makes the country less dependent on cane
sugar. Furthermore, it is worth mentioning that people in the U.S. are
becoming more health conscious, which tends to decrease their sugar
consumption.

As far as the world market is concerned, at the beginning of the 1970’s,
the world market price was increasing and reached high levels. However, in the
1980’s, this price began to decrease, and in 1984 it went down to USS
4.00/100 pound. This fact shows the instability in the world market price, which
greatly affects developing-sugar-exportlng countries like the Dominican
Republic.

One of the main reasons for a low world market price is the surplus of
production over consumption at the world level. For example, in 1984-1985, the
world production was estimated to be 100,251 million metric tons raw value,
whereas the consumption was only 97,957 million metric tons raw value (Cerro,
1987)

As sugar export is one of the main economic activities in the Dominican

Republic, a price change in the world market has a big impact on the

31

Dominican economy. When price go up, the economic situation in the
Dominican Republic improves, but when price go down, the economic situation

WOI’SBI‘IS.

4.6.2- Internal Problems

In addition to external factors, there are many intemal factors affecting
the Dominican sugar cane industry. These problems rage from production to
the final stage of commercialization. These domestic problems make the
Dominican sugar industry less competitive compared with other countries.

Most of the studies done on the sugar cane industry in the Dominican
Republic are related to production process. However, they do not contribute
much to problem solutions. The different companies comprising the sugar
subsector carry out separate studies and most of the times, the results of these
studies are not published (Gil, 1987).

Producing sugar in the Dominican Republic is becoming too expensive
compared with the price received. One of the reasons is the high input prices.
Therefore, the industry is not so profitable. The cost of production is very high,
mainly for the CEA, which produces the highest amount of sugar. In addition to
the high cost of production, the CEA has some other problems like obsolete
machinery and equipment as well as some administrative problems (Gomez,
1988). Moreover, a large portion of the land planted to sugar cane is the worst

land in the country. The only alternative use it is for pasture (Gil, 1987). Gil

.—
i4

32

(1987) also states that the Dominican Republic has the worst conditions for
harvest, piling, and transportation to the factory of the world.

The Dominican sugar industry is also characterized by its low
technology. The cane cutting process is not mechanized. One of the reasons
for this is possibly because of the high labor supply. However, Dominicans
hate to cut cane. Most of the labor comes from Haiti. Many times it is also
difficult to contract with Haitians. Currently, because of the political situation in
Haiti, Haitians are not participating in the cane cutting process as they used to
do. Moreover, the Dominican'Repubiic has been punished by international
organizations because of the way Haitians have been treated in the Dominican
Republic (CEA, 1991). According to these international organizations, Haitians
are treated like slaves in the Dominican Republic.

On the marketing size, the Dominican Republic does not apply a
marketing concept for the sugar industry. People dealing with the sugar
industry in the Dominican Republic think that marketing is only to try to sell what
is already produced. They do not think that it is much better to try to produce

just what can be sold or try to have a secure market before producing.

4.7- Future Trends In the Dominican Sugar Industry
Due to all the problems that the Dominican sugar industry is facing, the
Dominican Republic is looking for new alternatives. One of the most obvious

trends for the sugar industry is ”Diversification.“ Beginning in 1979, the main

33

objectives of the diversification process were to produce food to be sold to the
employees and to increase profitability (Gomez, 1988). At this time,
diversification has two meanings. One of the meanings is to try to use some
resource, such as land, previously used for sugar production, to produce other
more profitable crops, such as oranges, pineapples, sorghum, maize, cassava,
beans, and vegetables. Some of these crops can also be exported. A study
done by the Dominican Center for Exports Promotion (CEDOPEX) in 1987
showed that sugar cane generates less income that many other export crops
(Rodriguez, 1987). These crops are shown in Table 7.

The other meaning of diversification is to try to produce some other
products such as alcohol for auto fuel, fiber, and cane juice to feed animals, in
addition to sugar. The Central Romana and the Vicini group are currently
increasing the animal production capacity as one of the diversification activities.
In that sense, they are producing cane juice to feed their animals.

The diversification process has been created due to the U.S. policy
toward the Dominican Republic. Because the U.S. is the major market for the
Dominican sugar, the US sugar policy has a profound effect on the Dominican
sugar industry and, consequently, on the whole economy (09 Castro, 1992).

De Castro (1992) points out that according to Kryzanek and Marda
(1988), in the mid 1980’s, due to the U.S. self-sufficiency in sugar, pressure by
the sugar beet growers, and the fact that the Reagan administration wanted to

force the Dominican Republic to diversify its traditionally one-crop economy, the

34
Table 7: Profitability of Some Export Crops.

 

 

Crop Return/tarea Hours/Person/Tarea
(USS)

Sugar cane 103.0 5.80
Eggplant 763.2 19.75
Green pepper 400.0 20.33
Cucumber 430.0 43.33
Okra 4 1,294.0 40.00
Salad tomato 1,204.0 53.33
Cabbage 480.0 23.17
Fresh pineapple 878.0 20.00
Melon . 1 ,283.0 20.00
Papaya 1 ,502.0 20.00

 

Source: CEDOPEX, 1987

U.S. drastically cut the sugar quota to the Dominican Republic. De Castro
(1992) says that this drastic reduction in the sugar quota was one of the major
shocks to the Dominican economy that contributed to impulse agricultural

diversification strategies.

35

The diversification process is aimed at establishing a stable sugar
production according to requirements. it is not aimed at eliminating the sugar
cane industry. The sugar cane industry has been and will continue to be one of
the main sources of foreign currency and employment for the country

(Morales, 1986).

CHAPTER V
THE MODEL: SUPPLY AND DEMAND OF SUGAR IN THE
DOMINICAN REPUBLIC

 

5.1 Objectives ofthe Model

The objective of this chapter is to understand the behavior of the sugar
cane industry in the Dominican Republic by analyzing the factors affecting
supply and demand for sugar in the country. The results of the model analyzed
here may be used to make forecasts regarding the effects of these factors on
the sugar cane industry in the near future. These results could be very useful in
providing information to decision makers about future actions related to this

sector of the economy.

5.2 The Supply Model
5.1.1 Specification of the Model

According to economic theory, the factors affecting the supply of a
commodity include the expected price of the commodity, the expected price of
other commodities competing for the same resources or the same consumers,
production costs, availability of land and other natural resources, area planted
the previous year, changes in technology, institutional constraints (government

policy, for example), and weather (T omek and Robinson 1990). Because there

36

37

is a lack of data on factors, such as data related to other commodities
competing for the same resources like land, they will not be included.

The equations included in this supply model are as follows:

1) AREAHAR = f( c, AREAHAR(-1), REGRPH)
2) CANEPROD = (AREAHAR * YiELD)/1000
3) EYIELD = SMOOTHED YIELD

4) SUGAPROD = CANEPROD * SUYIELD

5) TSUPPLY = SUGAPROD + IMPT + BSTOCK

Where variables’ names and descriptions are as follows:

AREAHAR = Area of sugar cane harvested (1000 Ha)

C = The constant term

AREAHAR (-1) = Area of sugar cane harvested the previous year (1000
Ha)

REGRPH = Real expected gross return per hectare3 (RDS)

CANEPROD = Total cane sugar production (1000 MT)

 

’RedExpecbdGrossReMmpumewuodculsudbymuMplylngmewondmuketpnoe (UScents/ib)
laggedtwoyearstlmestheexchsngerste (US$zRDS) lsggedtwoyesrs timestheexpeoted sugarcaneyleld (Kg/Ha)

timestheconverslonfeotorfromosnetorswsugsr. ThenthstresuitwssdlvldedbyCPiet 1Mconstsntprices
The formula was as follows:
REGRPH- [(WPRICE(-2) ' EXRATE ' (EYIELD‘2.2) " SUYIELD)/100] / CPI
The EYIELD variable was derived by double exponential smoothing the YIELD variable. Notice in this

equation that EYIELD was multiplied times 2.2 to convert Kg/Ha to Lb/Hs. Moreover, the result was divided by 100 to
transform RD cents to RDS.

38
YIELD = Actual sugar cane yield (Kg/Ha)

SUGAPROD = Total sugar production (1000 MT raw value)‘

EYIELD = Expected sugar cane yield (Kg/Ha)

SUYIELD = Conversion factor of cane to raw sugar an decimal)

TSUPPLY = Total sugar supply in the Dominican Republic (1000 MT raw
value)

lMPT = Total sugar import in the Dominican Republic (1000 MT raw
value)

BSTOCK = Beginning stock (1000 MT raw value)

The first equation is a behavioral equation which states that the area
harvested depends on the area harvested the previous year and the real
expected gross return per hectare. Equations 2 to 4 are identities to calculate
the total sugar supply in the Dominican Republic. Some other variables that
theoretically explain sugar supply, such as producer’s price, and domestic retail
prices were tested. However, these variables were not significant. Another
variable that could have been included in the model was area planted to sugar
cane. This variable was not included in the model due to a lack of data.

The area of sugar cane harvested in the Dominican Republic is expected
to respond positively to the area harvested in the previous year. That is, an

increase in the area harvested last year causes an increase in the area

 

‘AscsnbeseenintheCANEPRODequstion.thlslsdivldedby1000. Thlswssdonetooonverttheresulttolooo
MTandtohsveslivolumevsrlsbIeslnthessmeunIts. NoticethsttheYlELDvsrlsblelsexpressedln Kg/Ha.

z... .2}:

39
harvested this year. Thus, the sign of the variable AREAHAR(-1) is expected to

be positive. This variable carries the effect of producer response in previous
years and represents, along with REGRPH, a geometric lag. In the same way,
an increase in the real expected gross return per hectare is supposed to cause

an increase in the area of sugar cane harvested. Therefore, the sign is also

 

expected to be positive.

5.1.2 Results
The results of the regression on the area harvested equation to determine

total sugar supply in the Dominican Republic are presented in Table 8. The

Table 8: Selected Results of the Regression on the Area Harvested

equation to Determine Sugar Supply

 

 

 

 

 

 

 

—
VIIIAILIB IlfiULIS OI IIIIVIDUAL VARIABLES RESULTS 0' III IIBIIBSIOI
COEFF. STD. T-SIAT Z-TAIL R? ADJ. R? PROD. D-HAISON
ERROR SIG.
P-STAI.
AREAHAR 0.88 0.86 0.00 2.46
C 0.89 15.66 0.56 0.5782
AREAHAR('1) 0.94 0.09 10.63 0.0000
REGRPH 0.16 0.07 2.23 0.0407

 

 

 

 

 

 

 

 

 

 

 

Source: Constructed by the Author

40
AREAHAR(-1) is highly significant at less than a 1 percent level. The variable

REGRPH was also significant at less than a 5 percent level. The signs of this
two variables are as expected. For example, the positive sign of the coefficient
of the area harvested the previous year indicates that the area harvested in a
specific year responds positively to changes in the area harvested the previous
year. In the same way, the positive sign of the real expected gross return per
hectare indicates that the higher this return the higher the area harvested as it
was expected.

The coefficient of determination (R2) of 88 percent is very high, indicating
a good statistical fit. Thus, if the theoretical specification is correct, it can be
deduced that the set of independent variables in the equation explains a high
proportion of the changes in the dependent variable. The adjusted R2 of 86
percent is also very high, which indicates that the number of independent
variables is adequate for the time series data used (19 observations). The F
statistic is also highly significant at much less than a 1 percent level of
significance, which means that the joint hypothesis of all the parameters’
coefficients being zero can be rejected with a margin of error of less than 1
percent.

Due to the inclusion of a lagged dependent variable as one of the
explanatory variables the Durbin-Watson statistics is not useful in this case.

However, a regression of the error term on the error term lagged one year

41

shows that the error term is not autocorrelated. Therefore, the null hypothesis

of no autocorrelation assumed by least square can be accepted.

5.2 The Demand Model
5.2.1 Specification of the Model

In the Dominican Republic, sugar demand has two main components.
The first main component consists of demand for domestic consumption. This
demand for domestic consumption has almost no influence on the demand for
sugar in the Dominican Republic. However, along with production, it has to be
analyzed very carefully in order to determine the potential of the country in
fulfilling its domestic needs for sugar; The second main component is the
demand for exports. An average of than 64 percent of the total sugar supply in
the Dominican Republic is exported (T able 9). The demand for exports is also
subdivided into two other components, which are U.S. demand and the rest of
the world. Table 9 also shows that an average of more than 68 percent of the
sugar exported goes to the United States.

it is worth mentioning that the export market in the Dominican Republic
has priority over the domestic market. There is a trade off between the export
and the domestic market. The domestic market receives only the sugar left
over after exports. For this reason, if the export market is good in a specific
year and total supply is not so high, it is likely that the domestic supply is going

to be low. In this case, Dominican people have to pay the consequences ofa

42
Table 9: Sugar Export from the Dominican Republic

 

 

 

 

 

 

 

 

 

 

Year IDEAL SUPPLY IOIAL 2320!! 2320!! ID ill 03
(1000 HI)

Vblu-e Percent Vblule Percent of
(1000 II!) (1000 It!) total export
1971 1,355.00 1,011.00 74.61 665.04 65.78
1972 1,410.00 1,141.00 80.92 692.61 60.70
1973 1,288.00 1,069.00 83.00 677.40 63.37
1974 1,262.00 1,055.00 83.60 742.63 70.39
1975 1,514.00 1,030.00 68.03 703.20 68.27
1976 1,488.00 1,180.00 79.30 881.15 74.67
1977 1,507.00 1,040.00 69.01 884.31 85.03
1978 1,319.00 1,020.00 77.33 665.44 65.24
1979 1,245.00 760.00 61.04 741.14 97.52
1980 1,099.00 900.00 81.89 558.24 62.03
1981 1,198.00 790.00 65.94 690.37 87.39
1982 1,457.00 930.00 63.83 347.64 37.38
1983 1,468.00 900.00 61.31 440.32 48.92
1984 1,427.00 760.00 53.26 610.24 80.30
1985 1,193.00 470.00 39.40 463.09 98.53
1986 1,152.00 570.00 49.48 343.62 60.28
1987 1,113.55 520.00 46.70 312.13 60.02
1988 1,004.78 550.00 54.74 212.14 38.57
1989 913.00 400.00 43.81 286.13 71.53
1990 795.66 435.00 54.67 311.38 71.58
Average 64.59 68.38

 

 

Source: Constructed by the Author

good export market. Sometimes, even with a high production for a specific

year, the Dominican Republic has to import sugar to meet the domestic

demand.

5.2.1.1 The Domestic Demand Model

Some of the economic factors affecting the demand for a commodity are

population size and its distribution by age, sex, or geographic area, consumer

income and distribution, the availability of other commodities and services, and

consumer tastes and preferences (T omek and Robinson, 1990). According to

Tomek and Robinson (1990),

sometimes prices and quantities are

‘—

 

43

simultaneously determined in the market. However, this is not the case in the
Dominican Republic. Domestic price does not have much influence on
production decisions in the Dominican Republic.

An important factor that is difficult to include in the model but that one
has to be aware of when dealing with domestic demand for sugar and other
food products in the Dominican Republic is that lately, although food
consumption per capita has remained constant, the statistics show an increase
in consumption. This fact is mainly due to two factors. First, there is a large
increase in the number of illegal Haitians in the Dominican Republic. Food
consumed by these people is considered as consumed by Dominicans.
Second, due to the higher prices received by retailers in cities adjacent to Haiti,
there is an illegal traffic of sugar from the Dominican Republic to Haiti through
the border. These problems might overestimate domestic demand for sugar in
the Dominican Republic. However, it is very difficult to take account of these

factors when formulating the demand equation.

The domestic demand equation included in this model is the following:

PCONS = f( c, DPNDI, DRETPR, PCONS(-1), ovsa, ovso)

Where variables’ names and description are as follows: .

C = The constant term

44

PCONS = Total sugar consumption per capita in the Dominican Republic
(K9)

DPNDI = National Disposable income per capita in the Dominican
Republic deflated by the CPI at 1980 prices (RD$)

DRETPR= Retail price per pound of refined sugar in the Dominican
Republic deflated by the CPI at 1980 prices (DR cents/lb)

PCONS(-1) = Sugar consumption per capita in the Dominican Republic
lagged one year (Kg/Ha)

DV88 = Dummy variable equal to 1 in 1988 and 0 otherwise

DV90 = Dummy variable equal to 1 in 1990 and 0 otherwise

As can be seen, two dummy variables were used in the demand
equation to correct for the occurrence of unusual events. These two dummy
variables were used for 1988 and 1990. These two years were periods in which
the Dominican economy was very unstable. Inflation rate was very high and
prices were very volatile. in those periods, wholesalers accumulated large
inventories for speculation. Moreover, sugar was very scarce in retail
establishments. Most of the time, sugar was not sold unless people purchased
some of the slow moving items in the store.

All the variables on the right hand side of the equation, except
PCONS(-1) which is a lagged dependent variable, are considered exogenous to

the model. They help to forecast the dependent endogenous variables. Each

45

of these exogenous variables were forecast individually, making some

assumptions about their future trend by extrapolating over past values.

5.2.1.2 The Export to the U.S. Market Model

The U.S. market is controlled by the quota system, which is imposed
according to U.S. requirements. Due to the quota system and some other
regulations, this market is so controlled that many of the economic relationships
do not work in the way they are supposed to.

In the beginning, some relationships that were trying to include some
U.S. market variables to forecast sugar export from the Dominican Republic to
the United States were tested. However, they did not work in the way they
were expected. Finally, sugar export from the Dominican Republic to the United

States was determined using the following equation:

EXPTUS =lf(C, DOMPRESS, DVQUOTA)

Where variables’ names and descriptions are as follows:
EXPTUS = Total sugar export from the OR. to the U.S. (1000 MT raw
value)

C = Constant term

'In‘.‘l '1';

1""—

45
DOMPRESS = Pressure to export from the Dominican Republic5

DVQUOTA = A dummy variable for the quota system imposed by the
U.S. in 1981 equal to zero for the period before 1982 and one

aftenivards

The pressure to export from the Dominican Republic is expected to be
positively related to export to the United States. The higher the pressure to
export from the Dominican Republic, the higher the export to the United States
should be. However, the dummy variable for the quota system is expected to
be negatively related to export to the United States, which is the same as
sugar import in the United States from the Dominican Republic. The quota
system was imposed by the United States to control sugar import and to
protect domestic producers. Therefore, this restriction reduces U.S. sugar

import.

5.2.1.3 The Export to the World Market Model

Different from the U.S. market, the world market is controlled by the
supply and demand forces. Although, price is one of the main variables to
consider when analyzing supply and demand, in this specific case, the world

market price is not endogenous because the Dominican sugar production and

 

' The pressure to export from the Dominican Republic was calculated by subtracting the consumption trend In the
Dominican Republic as a three year moving average from the total sugar supply. This was done as follows:

DOMPRESS - TSUPPLY -[(CONS(-1) + CONS(-2) + CONS(-3)]/3

é Ont-ax.

IL
fl

47

export are very low compared with the total world production and exports. The
Dominican Republic has to adapt to existing market conditions. Furthermore,
the world market price was tested and it was not significant. Instead of the
world market price, some other variables were included in the model.

The equations included in this model to determine sugar export from the
Dominican Republic to the rest of the world excluding the United States are as

follows:

1) WEXPTRES = «C, WPRESS, DVQUOTA, owe, AR(1))
2) REXPT = no, spaces, ows)
3) EXPT = REXPT * WEXPTRES

4) EXPTREST = EXPT - EXPTUS

Where variables’ names and descriptions are as follows:

EXPT = Total sugar export from the Dominican Republic (1000 MT raw
value)

REXPT = Ratio of sugar export from the DR to the rest of the world
excluding the U.S.

WEXPTRES = Sugar export by exporting countries excluding the U.S.
and the DR. (1000 MT raw value)

C = The constant term in the behavioral equation

48

WPRESS = Pressure to export sugar by producing countries excluding
the U.S. and the DR.“

DVQUOTA = A dummy variable for the quota system imposed by the
U.S. in 1981 equal to zero for the period before 1982 and one
afterwards.

DV75 = A dummy variable equal to 1 in 1975 an 0 othenivise7

AR(1) = Autoregressive error specification assuming first order
autocorrelation

RPRODS = Ratio of sugar production in the Dominican Republic to
sugar production in the rest of the world excluding the United

States and the Dominican Republic

EXPT REST = Sugar export from the Dominican Republic to the rest of

the world excluding the U.S. (1000 MT raw value)

Total world sugar export is expected to be positively related to both

pressure to export and DVQUOTA. Therefore, the sign of the coefficients of

 

‘Thepressuretoexportwsscalcuistsdbysubtrsctlngsoneyesrlaggedthrseyearmovlngsverageofworld
consumption from world production excluding the United States and the Dominican Republic. This was done as
follows:

WPRESS - (WPROD-USPROD-SUGAPROD)-TCONSRES

iMiere TCONSRES is the one year lagged three year moving average of world consumption

’ This dummy variable Is to take Into account that 1974 was the only time the world market price was higher than
the U.S. price. This fact might have distorted the market the following year.

49

these variables is expected to be positive. The rationale for the positive sign of
DVQUOTA in this case, is that if the quota system reduces export to the United
States, it increases export to the rest of the world.

In the third equation, the ratio of export from the Dominican Republic to
export from the rest of the world excluding the United States and the Dominican
Republic is expected to be positively related to the production ratio. This
means that the higher the production ratio, the higher the export from the
Dominican Republic compared with the rest of the world. It is worthwhile to
mention that some other variables, such as the beginning stock ratio and the
total supply ratio were tested in the third equation. However, they were

dropped because they were not significant.

5.2.2 Results
5.2.2.1 The Domestic Demand Model

The results of the regression on the domestic demand equation are
presented in Table 10. All variables are highly significant at less than 1 percent
level. The signs of all the variables are as expected. For example, the positive
sign of the coefficient on DRETPR variable supports the economic theory that
states that keeping the other factors constant, the higher the price the lower the
amount demanded.

The negative sign on the coefficient of DPNDI variable indicates that

sugar consumption in the Dominican Republic decreases when real income per

 

l
l
l
l

50
Table 10: SOIBCtBO HBSUNS OR The DOI‘I‘IOStIC Demand EQUB‘IIOI’I

 

 

 

 

 

 

 

 

 

vmanus assume a mrvmuar. VARIABLE mus C! m -11.
CORP? . STD . T-S‘I’A‘l’ Z-TAIL R‘ ADJ . R‘ PROD . D-HATSON
m 816. P'STA‘I'.
PCONS 0.88 0.84 0.0000 2.36
C 55.96 16.36 3.42 0.0041
DPNDI '3.25 1.01 '3.21 0.0063
DRETPR '7042.44 2015.79 '3.49 0.0036
PCONSI'I) 0.91 0.13 7.08 0.0000
DV88 '22.9 2.98 '7.68 0.0000
DV90 -13.48 2.66 '5.08 0.0002

 

 

 

 

 

 

 

 

 

 

 

Source: Constructed by the Author

capita decreases, which indicates that, apparently, sugar in the Dominican
Republic is an inferior good. This fact is different from what happens in most
countries. However, it has a logical explanation. in the Dominican Republic,
there are many kinds of food consumed by the lower income population that
require sugar for their preparation, including different kinds of flours, and
including lemonade. These kinds of food are replaced by more expensive kinds
when people can afford to buy them. Moreover, these kinds of food are
consumed more often by low income people in the rural areas trying to fulfill
their energy requirements. Furthermore, the higher income population in the
Dominican Republic is more health conscious and prefers to consume less

sugar. in addition to this, Geene and Roe (1989, p. 130) show that low income

121.341"

51

households in the Dominican Republic spend a higher share of their income in
sugar and sweeteners than high income households.

The coefficient of determination (R2) of 88 percent is high, indicating a
good statistical fit. Thus, if the theoretical specification is correct, it can be
deduced that the set of independent variables in the equation explain a high
proportion of the changes in the dependent variable. The adjusted R2 of 84
percent is also very high, indicating that the number of independent variables is
adequate for the time series data used (20 observations). The F statistic is also
highly significant at less than 1 percent level of significance, which denotes that
the joint hypothesis that all the parameter coefficients are zero can be rejected
with a margin of error of less than 1 percent.

Due to the inclusion of a lagged dependent variable as one of its
explanatory variables, the Durbin-Watson statistics is not applicable in this case.
However, a regression of the error term on the error term lagged one year
showed that the error term is not autocorrelated. Therefore, the null hypothesis
of no autocorrelation assumed by least square can be accepted.

From the results of this domestic demand equation, it can be concluded
that consumption per capita of sugar in the Dominican Republic tends to
decrease as income increases. The estimated income elasticity of demand is -
0.91 (Table 15). This means that a percent increase in real income per capita

decreases the amount of sugar demanded by 0.91 percent, The retail price

52

elasticity of demand is -0.45. This implies that an increase in price of one

percent decreases the amount of sugar demanded by 0.45 percent.

5.2.2.2- The Export to the U.S. Market Model
The results of the regression on the export to the United States are
presented in Table 11. The variables pressure to export from the Dominican

Republic (DOMPRESS) and the dummy variable for the quota system

Table 11: Selected Results on the Regression to Determine Sugar Export

from the Dominican Republic to the United States

 

 

.IL
VARIABLES INDEPENDENT VARIABLES REGRESSIOR
COEFP. STD. T'STAI. 2-TAIL R? ADJ.RF PROD. D-HATSOR
ERROR SIG. F-STAT.

 

EXPTUS 0.87 0.84 0.0000 1.89

 

C 337.89 109.66 3.08 0.0068

 

DOMPRESS 0.32 0.09 3.56 0.0024

 

 

 

 

 

 

 

 

 

 

 

DVQUOTA '264.09 43.96 '6.01 0.0000

 

Source: Constructed by the Author

(DVQUOTA) are highly significant at a less than one percent level. The sign of
the DOMPRESS variable is expected to be positive. The higher the pressure to
export sugar from the Dominican Republic, the higher the export to the United

States should be. However, the coefficient of the DVQUOTA variable is

 

53

expected to be negative because the quota system was established by the
United States to control sugar import and to protect domestic producers.

The coefficient of determination (R2) of 86 percent is high, indicating a
good statistical fit. Thus, if the theoretical specification is correct, it can be
deduced that, as in the previous equations, the set of independent variables in
the equation explains a very high proportion of the changes in the dependent
variable. The adjusted R2 of 84 percent is also very high, indicating that the
number of independent variables is adequate for the time series data used (20
observations). The F statistic is also highly significant at a less than 1 percent
significance level, which implies that the joint hypothesis that all the parameter
coefficients are zero can be rejected with a margin of error of less than 1
percent. With the Durbin-Watson statistics equal to 1.6, there is evidence to
accept the null hypothesis that the error term is not autocorrelated. Moreover,
a regression of the error term on the error term lagged one year shows no

autocorrelation.

5.2.2.3- The Export to the World Market Model

As shown in section 5.2.1.3, the export to the world market model is
comprised of two behavioral equations (equations 1 and 2) and two identities
(equation 3 and 4). The results of the first and second equations are presented

in Table 12 and 13, respectively.

 

54
Table 12: Selected Results on the Regression to Determine Sugar Export

from the Rest of the World Excluding the United States and the

Dominican Republic

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

VARIABLE
VARIABLE
COEFP. STD. ‘r-S'I'A‘l'. Z-IAIL R' ADJ .R‘ PROS. D-RATSON
ERROR SIG. F-STA‘I‘.
WEXPTRES 0.89 0.86 0.0000 1.99
C 25036.4 2496.34 10.03 0.0000
“PRESS 0.17 0.08 2.13 0.0500
DVQUOTA 2569.68 1626. 99 1.58 0.0135
DV75 -4212. 75 1634.9 -2. 58 0.0210
AR(1) 0.83 0.13 6.19 0.0000
Source: Constructed by the Author

Table 12 shows that the variable pressure to export from the rest of the
world excluding the United States and the Dominican Republic (WPRESS) is
significant at five percent level. Moreover, the sign of the coefficient is positive
as expected. The dummy variable for the quota system established by the
United States (DVQUOTA) is not significant at a five percent level, but at 13
percent. However, this variable was left in the equation because it is known
that it greatly affects exports to the world market. Restriction in the U.S.
markets, which reduces U.S. sugar import, contributes to increasing export to
the rest of the world. The coefficients of the dummy variable for 1975 (DV75) is
significant at a 5 percent confidence level.

Furthermore, as Table 13 shows,

this equation was estimated assuming first order serial correlation. The

55

Table 13: Selected Results on the Regression to Determine the Ratio of
Export from the Dominican Republic to Export from the Rest of the

World Excluding the Unites States and the Dominican Republic

 

 

 

 

 

VARIABLE m VARIANB
CORP? . STD . T-STAT . 2-1'AIL R’ ADJ .R‘ PROD . D-WA‘I‘SON
ERROR SIG. F'STAT .
REXPT 0.88 0.86 0.0000 1.23

 

C ‘0.01 0.0042 '2.68 0.0151

 

RPRODS 3.39 0.3104 10.93 0.0000

 

DV75 0.01 0.0050 2.82 0.0114

 

 

 

 

 

 

 

 

 

 

 

Source: Constructed by the Author

coefficient for the correction for serial correlation [AR(1)] is also significant at a
less than 1 percent confidence level.

The coefficient of determination (R2) of 89 percent is very high, indicating
that the statistical fit of the equation is very good. Thus, if the theoretical
specification is correct, it can be inferred that, as in the previous equations, the
set of independent variables in the equation explains a very high proportion of
the changes in the dependent variable. The adjusted R2 of 86 percent is also
very high, which means that the number of independent variables is adequate
for the time series data used (20 observations). The F statistic is also highly

significant at less than 1 percent significance level, which implies that the joint

 

56

hypothesis that all the parameter coefficients are zero can be rejected with a
margin of error of less than 1 percent.

Table 13 shows that the ratio of production in the Dominican Republic to
production in the rest of the world excluding the United States and the
Dominican Republic (RPRODS) is highly significant at a less than one percent
level. Moreover, the sign of the coefficient is positive as expected. The dummy
variable for 1975 (DV75) is also significant at a one percent level.

The coefficient of determination (R2) of 88 percent is very high, indicating
that the statistical fit of the equation is very good. Thus, if the theoretical
specification is correct, it can be deduced that, as in the previous equations, the
set of independent variables in the equation explains a very high proportion of
the changes in the dependent variable. The adjusted R2 of 86 percent is also
very high, which means that the number of independent variables is adequate
for the time series data used (21 observations). The F statistic is also highly
significant at less than a 1 percent significance level, which implies that the joint
hypothesis that all of the parameter coefficients are zero can be rejected with a

margin of error of less than 1 percent.

5.3- The Supply and Demand Model
5.3.1 Formulation ot the General Model
The supply and demand equations were combined in .a general model

which used historical data from 1970 to 1990. The model was simultaneously

57

solved using the MicroTSP computer software package, which uses the Gauss-
Seidel algorithm. The edit file for the supply and demand model is presented in

Appendix i.

The different equations comprising the general model are the following:

1) AREAHAR = f(C, AREAHAR(-1), REGRPH)
2) CANEPROD = AREAHAR * YIELD

3) EYIELD = SMOOTHED YIELD

4) SUGAPROD = CANEPROD * SUYIELD

5) TSUPPLY = SUGAPROD + lMPT + BSTOCK

6) PCONS = «c, DPNDI, DRETPR, PCONS(—1), ovaa, DV90)
7) EXPTREST = REXPT * WEXPTRES

8) WEXPTRES = no, WPRESS, DVQUOTA, DV75, AR(1)}
9) REXPT = f(C, RPRODS, DV75)

10) EXPTUS = no, DOMPRESS, DVQUOTA)

5.3.2 Evaluation of the General Model

One of the objectives of building this econometric model was to make
forecasts of supply and demand for sugar in the Dominican Republic.
Therefore, after building the general model, it has to be evaluated to determine
how good its forecasting ability is. This section tries to evaluate the forecasting

ability of the general model. The evaluation criteria used are analysis of turning-

58

point errors and the percent of the square root of the mean of the squared
error (Percent RMSE). The percent RMSE was calculated based on the mean
of the dependent variable.
Table 14 shows a summary of the number of turning- point error and
percent RMSE for the supply and demand behavioral equations. Moreover, ,1

Figures 1 to 5 show the actual and forecast values for the sample period.

Table 14: Summary of Turning-point Errors for the Behavioral

 

 

 

 

 

 

 

 

 

 

Equations
EQUATIONS VALID TURNING-POINT Percent
OBSERVATIONS ERROR RMSE
AREAHAR 19 7 4.37
PCONS 19 4 15.77
EXPTUS 18 3 18.78
WEXPTRES 19 4 1 1 .35
REXPT 19 3 18.69
_—_l—__—

 

 

Source: Constructed by the Author

59

 

900

\
I \
" I \
\
'''''

700- ‘

600 «

FORECAST
500 ~ .

Volume (1000 MT)

4 00 - ‘ “sacrum.

300 -

 

 

200 I l I l I l r l I l I l I l I l I

1972 1974 1976 1978 1980 1988 1984 1986 1988 1990
Years

Figure 1: Export to the United States Equation: Actual and Fitted Values for the

 

Sample Period

190

 

 

180-

170-

160‘

Area Harvested (1000 Ha)

 

 

 

140

 

Years

Figure 2: Area Harvested Equation: Actual and fitted Value for the Sample
Period

61

 

55

Volume (Kg)

 

 

 

 

25 l ‘ i 1 l r T T l
72 74 76 78 80 82 84 86 88 90
Years

Figure 3: Per Capita Consumption: Actual and Fitted Values for the Sample

Period

 

62

 

32500

30000.

’l\‘
s
.....

27500-

  
    

I
I
l

,: FORECAST
25000-

Volume (1000 MT)

22500-

 

20000

   

 

 

70 72 74 76 78 80 82 84 86 88

Years

Figure 4: Export from the Rest of the World: Actual and Fitted Values

63

 

  
   

 

 

 

0.06
ACTUAL
0.05~ ‘
0.04 . “,‘PORECAST
.9. “
ti
o:
0.03‘
0.02-
0'01 I T r I I r I I I I I I I r I I r‘
1972 1974 1976 1978 1980 1982 1984 1986 1988 1990
Years

Figure 5: Ratio of Export from the DR and the Rest of the World: Actual and

fitted Values

 

64

To evaluate turning-point errors, some specific conventions should be
adopted. This analysis of turning-point errors follows the convention adopted
by Tomek and Robinson (1990). This convention assumes that one-step-ahead
forecasts are being evaluated and uses the current predicted value relative to
the previous actual value. In this case, a turning-point error occurs if the sign in
the second column is different from the sign in the first column.

As Table 14 shows, the number of turning-point errors is higher for the
AREAHAR equation. This could be explained by the fact that, the Dominican
Republic did not have very good policies on how to control sugar production.
The Dominican Republic just produces sugar because it is the main export
crop. People dealing with the sugar cane industry in the Dominican Republic
were more interested in the production aspects of the industry. They did not
pay much attention to determining how external conditions were changing and
how those conditions could affect production decisions. However, in the last
few years, the Dominican Republic has been more interested in diversifying its
one-crop economy.

The percent RMSE is higher for the EXPTUS and REXPT equations,
many theoretical economic relationships, such as supply and stock ratio were
tested. However, they were not significant. The only significant one was the
production ratio. Logically, this is not the only variable affecting export from the
Dominican Republic. The rest of the equations seemed to track better over the

historical period. In spite of these few turning-point errors in the equations, the

65

model seems to accurately forecast what is currently happening with the sugar
cane industry in the Dominican Republic in terms of sugar production, supply,
and demand. The model forecasts the declining production trend very well and,

as a result, the declining export.

5.4- Elasticity Estimates
Elasticities are very important factors for economic inferences and policy
analysis. After estimating the supply and demand equations, elasticities were

computed at the mean values for some of the independent variables in the

Table 15: Estimated Supply and Demand Elasticities

 

 

 

 

fl TYPE OF ELASTICITY ESTIMATED ELASTICITY ‘
fl Own price elasticity of demand -0.450
II income elasticity of demand 0312

Gross return elasticity 0.018

World price elasticity 0.016
J.______=_______=

 

 

 

model. The results of the estimated elasticities are presented in Table 15.
These elasticities represent the percent change of the dependent variable due

to a percent change in the independent variable assuming that all the other

66

factors are constant. For example, the -O.45 price elasticity of demand means
that an increase in price of one percent decreases the quantity of sugar
demanded by 0.45 percent.

Due to the lack of data for some variables which theoretically affect
supply and demand, such as sugar substitutes and complements, cross price
elasticities cannot be calculated. The only elasticities calculated were income
and price elasticities for domestic demand and gross return and world price
elasticities for the area of sugar cane harvested. Because the world price was
not included directly in the supply equation, this elasticity was calculated
indirectly. This was derived by calculating the effect that the world price lagged
two years has on the real expected gross return per hectare (REGRPH) and
then calculating the effect of this variable on the area harvested.

To determine the precision of these estimated elasticities, they should be
compared to some other estimates. However, no study about sugar industry in
the Dominican Republic could be found to compare with these estimated
elasticities.

As said previously, elasticities should be very important factors for policy
decision making. The results of a specific policy depend on how this affects
people’s decision. if the government policy is to increase income to the sugar
industry by increasing the sugar price, it would be an appropriate policy in the
domestic market. The quantity demanded dOes not decrease as much as the

price increases.

67

The estimated gross return and world price elasticity show that
Dominican’s decision makers seem to ignore changes in price and return when
making production decisions. If the country is to maximize income or to be
efficient in the use of its resources, it has to consider how profitability is
changing. However, they do not appear to respond to changes in price and
return.

Sugar production is one of the main economic activities in the Dominican
Republic and the export market is the major market. However, if the world
price is not so favorable, sugar cane can be replaced by some other more
profitable crops. This can improve the efficiency in the use of the scarce

resources in the Dominican Republic.

 

CHAPTER VI
SUPPLY AND DEMAND FORECASTS

 

6.1- Forecast of Exogenous Variables

Forecasts of supply and demand from the model require some
assumptions concerning the rate of change of exogenous variables included in
the model. These forecasted values were then inserted into the estimated
equations. The system of equations is then solved simultaneously to give the
forecasted values of the endogenous variables. The actual and forecast values
for the exogenous variables are presented in Appendix II.

in addition to the dummy variables, which represent unusual situations
affecting some other variables, the model has eight exogenous variables whose
values for the forecast period have to be supplied to the model. Moreover,
some of these variables are composed of some other exogenous variables.
The values for these variables also have to be forecast outside the model.
Some of the variables were forecast using a double exponential smoothing
method, and some others were forecast taking into account their average
growth over a certain period of time. The criterion chosen depended on which

one was the most representative of the actual trend.

68

69
6.1.1 Projection of Real Expected Gross Return per Hectare (REGRPH)

As stated previously, the REGRPH is composed of world price
(WPRICE), exchange rate (EXRATE), expected sugar cane yield per hectare
(EYIELD), the conversion factor of cane to raw sugar (SUYIELD), and the
Consumer Price 'lndex (CPI). All these variables were forecast independently.
Then, the REGRPH was calculated out of these forecasts.

The WPRICE, YIELD, and SUYIELD variables were forecast using the
double exponential smoothing method over the whole sample period (1970-
1990). The EXRATE variable was also smoothed in the same way as the world
price. However, because the official exchange rate was fixed at US$ 1.00 to
RD$° 1.00 before 1985, the range used to smooth it was from 1985 to 1992.
As can be seen, only three years needed to be forecast. On the other hand,
the CPI was forecast using the average percent increase from 1970 to 1990,

which is 14.77%.

6.1.2 Projection of Deflated per Capita National Disposable income
(DPNDD
This variable is equal to the current National Disposable income (NDl)
divided by the population (POP) and then by the CPI. The NPl variable was

forecast using the same smoothing technique explained previously over the

 

' ROS is the currency sign for the Dominican Peso.

70

whole sample period. The POP variable was forecast assuming a 2.8 percent

increase, which is the average percent increase from 1970 to 1990.

6.1.3 Projection of the Deflated Retail Price of Refined Sugar (DRETPR)
Although the deflated retail price is the retail price divided by the CPI, this
variable was smoothed by itself as a whole. The reason for this is because
smoothing the retail price by itself and then dividing by the CPI overestimates it.
in 1991, the average current price of refined sugar in the Dominican Republic
was RD$ 3.51. A forecast of the current retail price by itself shows that for
1995, the current retail price would be RD$ 8.46. The price increase is not very

likely considering that currently, the Dominican economy is quite stable.

6.1.4 Projection of the Pressure to Export sugar from the World Excluding
the United States and the Dominican Republic (WPRESS)

The pressure to export sugar from the world excluding the United States
and the Dominican Republic is equal to production in the rest of the world
(WPRODRE) minus the consumption trend in the rest of the world
(T CONSRES). As the consumption trend is a three year moving average of
consumption in the rest of the world (CONSRES), the consumption trend was
generated by smoothing consumption and then calculating the moving average.

The WPRODRE was smoothed in the same way as consumption.

71
6.1.5 Projection of Beginning Stock (BSTOCK)

The beginning stock variable was supposed to be endogenous. in the
beginning it was treated in this way. However, because of data problems, the
results were not as expected. To solve this problem, the beginning stock was
considered an exogenous variable.

The beginning stock variable is the same as the ending stock lagged one
year. in the same way, the beginning stock forecast was calculating by
smoothing the ending stock and lagging it one year. The beginning stock could
have been calculated and smoothed directly, but the results of the approach

used here were more realistic.

6.2- Forecast of Endogenous Variables

After forecasting the exogenous variables, the model was solved for a
period beyond the sample period to forecast the endogenous variables. The
forecast of the endogenous variables along with actual values for the supply
and demand variables are presented in Appendix ill. The actual and forecasted
values for total supply, domestic demand, and export demand are presented
graphically in Figures 6 to 10.

Figure 6 shows that the area of sugar cane harvested in the Dominican
Republic trended upward until 1982. It stayed constant until 1984. After 1984

the area of sugar harvested began to decline. According to the model this

72

trend is to continue in the future unless there is a major external change that
helps to restore the Dominican sugar industry.

Logically, a decline in sugar cane production means a decline in total
sugar supply as shown in Figure 7. Moreover, sugar supply also declines due
to a decline in the conversion factor from cane to raw sugar. This decline in the
conversion factor might be due to obsolete machinery and equipment and the
low technology used.

The are many reasons for the decline of sugar cane production in the
Dominican Republic. One of the reason is the effect of the quota system
established by the United States in 1981. The decline did not began in 1981 or
1982 because of the lag between the event and the time when the planting
decision is made. Another reason is the United States policy toward the
Dominican Republic. The Reagan administration wanted the Dominican
Republic to diversify its one-crop economy. Furthermore, the Dominican
Republic realized that there are many other crops that can be planted in the
sugar cane land and are actually more profitable than sugar cane.

Figure 8 and 9 also show a declining trend for export from the Dominican
Republic to the United States and the rest of the world. The declining trend to
the rest of the world is steeper than the decline to the United States. This is
logical because the United States is the preferential market. Moreover the U.S.

price has always been higher than the world market price except in 1974.

73

If the trend continues, it is likely that, instead of exporting sugar to the
world market, the Dominican Republic is going to export sugar only to the
United States to take advantage of the preferential price and import sugar from
the world market to meet the domestic consumption requirements. This is
exemplified by the fact that the Dominican Republic had to import sugar in 1990
and 1991.

For the domestic consumption side, Figure 10 shows an increasing trend
in consumption per capita until 1987. In 1987, consumption per capita dropped
sharply. The consumption per capita forecast for the future does not have a
specific trend. It increases in the beginning and declines at the end of the

forecast period.

 

74

190

 

180 ~

ACTUAL
17o . FORECAST

160 -

150 -

AREA HARVESTED (1000 Ha)

 

 

140

 

 

T r I I fl r T I

do 82 84 86'8‘8'90152'9'4
YEARS
Figure 6: Area Harvested: Actual and Forecast Values

IIrIIIIT
7072747678

75

1600

 

1500~
1400-
1300-
1200 - . ACTUAL FORECAST
1100 -
10004

900-

800 q /\

700 l'l’lTlTT'ITI'lrl'fl'l
72 74 7e 78 80-82 84 86 88 90 92 94

Years

Volume (1000 MT raw value)

 

 

 

 

Figure 7: Sugar Supply In the Dominican Republic: Actual and Forecast

Vflflue

76

 

900
800«
700-
600-
500« ,ACTUAL FORECAST

400-

200 fi I I I I I I I I I I I I I I I I I I I I I I
70 72 74 76 78 80 82 84 86 88 90 92 94
Years

Figure 8: Export to the United States: Actual and Forecast Values

Volume (1000 MT raw value)

 

 

 

 

77

 

600
500 ~
400 ~

ACTUAL FORECAST

300 -

 

200 .

100 4

Volume (1000 MT raw value)

 

0

 

 

 

I I I I I I I I I I I I I I I I I I I I
70 72 74 76 78 so 82 84 86 88 90 92 94
Years

Figure 9: Export from the Dominican Republic to the Rest of the World:

Actual and Forecast Values

78

 

55

50-

45~

4 0 " ACTUAL FORECAST

Volume (Kg)

35-)

30~

 

 

 

 

85 I I I I I I I I I I I I I I I I I f I I I I I I I
70 72 74 76 78 80 82 84 86 88 90 92 94
Years

Figure 10: Domestic Consumption per Capita: Actual and Forecast Values

CHAPTER VII
CONCLUSION AND SUGGESTIONS FOR FURTHER STUDIES

 

7.1 Conclusion

This paper has tried to analyze the performance of the sugar cane
industry in the Dominican Republic. The analysis was carried out by developing
an econometric model of supply and demand for sugar. This paper showed
that the declining trend of the sugar cane industry in the Dominican Republic in
recent years is likely to continue. The results can provide policy decision
makers in the Dominican Republic with useful information about the different
factors affecting the sugar cane industry.

The factors affecting the sugar are both internal and external. The
paper showed that the quota system established by the United States and the
low market price of sugar are two of the main factors affecting the Dominican
sugar cane industry. The internal problems are somewhat related to the
external ones. Policy makers related to the sugar cane industry in the
Dominican Republic seem not to be aware of those problems as shown by the
low response of production to change in the world price and, as a result,
change in the profitability of the sugar industry.

Fortunately, the Dominican Republic is now trying to diversify its one-crop

economy by replacing sugar plantations by some other more profitable crops.

79

80

Different from sugar cane production, the production decision of some of these
crops like vegetables can be more easily changed if the market conditions
change. This flexibility can improve the general economic situation of the

Dominican Republic.

7.2 Suggestions tOr Further Studies

To try to predict what is going to happen with the sugar cane industry in
a country like the Dominican Republic is really a very challenging task. In
addition to the data used in this model, much more data, both quantitative and
qualitative, as well as time and economic resources are needed. The variables
used in this model are not the only factors affecting supply and demand for
sugar in the Dominican Republic.

To try to forecast exports to the rest of the world, what is going on in the
different countries which import sugar from the Dominican Republic has to be
considered. In the case of export to the United States, sugar import from the
Dominican Republic depends on many other factors like sugar and other
sweeteners produced in the United States that were not actually included in this

study. Research on that scale was beyond the scope of this study.

LIST OF REFERENCES

LIST OF REFERENCES

Banco Central de la Republica Dominicana. Monthly Bulletin. Santo Domingo,
D.R. Various years.

Central Romana Corporation (CRC). Historia del Azucar. La Romana, D.R.
Undated.

Cerro, Jose A. El Proteccionismo en el Mercado Azucarero International.
INAZUCAR. Year 9, No. 40, April-June 1984. Santo Domingo, DR.
1984.

Cerro, Jose A. Situacion Azucarera Mundial. INAZUCAR. Santo Domingo
D.N. February, 1987.

Consejo Estatai del Azucar (CEA). Estructura, Organizacion e lnformacion
Basica Sobre la lndustria Azucarera Estatai. Fouth Edition. Santo
Domingo, D.N. August, 1991.

De Castro, Monica. The Dominican Orange Juice Industry: an Economic
Analysis of its Development and Prospects for the U.S. Market (Thesis).
Cornell University. January, 1992.

Despradel, Carlos. La Politica lnternacional del Azucar y la Republica
Dominicana. lNAZUCAR. Year 9, No. 41, July-December 1984.
Santo Domingo, DR 1984.

Development Associates. Agriculture Sector Profile. Dominican Republic.
1985.

De Vries, Jos. The World Sugar Economy. Economic Analysis for Long-Term
development. World Bank Staff Commodity Working Paper Number 5.
Washington, DC. October, 1984.

Diaz, Jhonnys O. Aspectos Tecnicos y Economicos del Ingenio Esperanza
(Thesis). PUCMM. Santiago, D.R. January, 1986.

Food and Agriculture Organization. FAO Production Yearbook. Various years.

81

82

Food and Agriculture Organization. Republica Dominicana: Diagnostico del
Sector Aagropecuario y Acuacultura. Santo Domingo, DR. 1988.

Garcia, Jimmy. La Estructura de la Agroindustria Azucarera en la
Republica Dominicana. INAZUCAR. Santo Domingo, D.R. June,
1990.

Greene, Duty D. and Terry L. Roe. Trade, Exchange Rate, and Agricultural
Pricing Policy in the Dominican Republic. World Bank, Vol.l. 1989.

Gil, Carlos E. Origen Primario de la Crisis Azucarera Dominicana. Santo
Domingo, D.N. February, 1987.

Gomez, Jose L. and Frank F. Escalante. La Pine W como
Alternativa de Diversificacion Agricola en el Ingenio Catarey (Thesis).
PUCMM. Santiago, D.R. January, 1988.

Gulf and Western (G & W). Aaniisis de la prioridad que tiene la Republica
Dominicana como abatecedor de azucar a EU con relacion a otros
paises, on base a criterios y factores establecidos por el congreso
americano para determinar la distribucion entre paises de las cuotas de
azucar importada. Santo Domingo, DR. 1977.

lnstituto Dominicano del Azucar. El Azucar y su Historia iNAZUCAR. Year 2,
No. 10, February-March 1977. Santo Domingo, DR 1977.

international Monetary Fund. international Financial Statistics. Various years

Johnson, D. Gale. The Sugar Program. American Enterprise for Public Policy
and Research. Washington DC, 1974.

Junta Agroempresarial de Consultoria y Coinversion, inc. (JACC/RD).
Diagnostico del Sector Agropecuario. Santo Domingo, D.N. February,
1989.

Oficina Nacional de Estadisticas. Republica Dominicana en Cifras. Santo
Domingo, DR 1983.

Pindyck, Robert S. and Daniel L. Rubinfeld. Econometric Models and Economic
Forecasts. Second Edition. McGraw-Hill Book Company. New York,
1981.

83

Robert R. Nathan Associates, Inc. Cane Sugar Refining in the United States.
Its Economic Importance. Washington, DC, 1971.

Rodriguez, Pablo. La Diversificacion Azucarera y la Promocion de
Exportaciones. CEDOPEX. February, 1987.

Santana, Juan M. and Francisco Ferreiras. El arroz (911mm como
Alternativa de Diversificacion Agricola en el Ingenio Esperanza (Thesis).
PUCMM. January, 1987.

Tomek, William G. and Kenneth L Robinson. Agricultural Product Prices. Third
Edition. Cornell University Press. Ithatca, 1990.

United Nations. National Account Statistics: Main Aggregates. Various years.

United States Department of Agriculture. Sugar and Sweeteners Situation and
Outlook. Various years.

United States Department of Agriculture. U.S. Sugar Statistics Compendium.
1990.

        
     
 

- ,

-. . Fersan

\fiyella, F. .. 11:: ll . -. ‘4 _
arch, 1987. Santo

Z. 0' . ‘ e : :_ -ll
lnforma. Vol. 10 No.39. pp 37-40. January- M
Domingo, D.R. March, 1987.

       

-
4'.

World Bank. The Dominican Republic. its Main Economic Development
Problems. Washington, DC, 1977.

World Bank. World Tables. ‘lhe Johns Hopkins University Press. Baltimore.
Various years.

APPENDICES

 

 

5
3.
I44:

 

 

APPENDIX I

EDIT FILE FOR THE SUPPLY AND DEMAND MODEL

1:
2:

84
APPENDIX I

AREAHARF=8.888024+.9361912‘!REAHARF(-1)+.15823560REGRPH
PO0N3F=55.95877-3.249521‘0PNDI-7042.442‘DRETPRfi.91138480PCON3F(-1)-22.89895‘D

V88-13.48383.DV90

3: EXPTU3F=337.8882+.3239224000MPRESF-264.0893‘DVQUOTA
c: wEXPrREF=zsose.40o.torszttupness+2sss.ovvocovouorA-4212.Isratovrs+ran(i)=.ezs
1211]

5: REXPTF=-.011199243.39221720RPR003F6.0340920¢DV75

s: CANEPROF=(AREAHARFtVIELoiltOOO

7: SUGAPROF=CANEPROFOSUYIELD

a: TSUPPLYF=3UGAPROF+IHPT+BSTOCK

9: cousr=(PCONsrtpop)/iooo

10: exprr=nexprrtwexprner

ll: DOMPRESF=TSUPPLYF-(CON3F(-1)+CON8F(-2)+CON8F(-3))/3
12: RPRODSF=SUGAPROFIHPRODRE

13:

EXPTRESF:EXPTF-EXPTUSF

APPENDIX II

ALPHABETICAL ORDER AND DESCRIPTION OF THE VARIABLES USED IN
THE MODEL

85
APPENDIX Il

AREAHAR = Area of sugar cane harvested (1000 Ha)

AREAHAR (-1) = Area of sugar cane harvested the previous year
(1000 Ha)

AR(1) = Autoregressive error specification assuming first order
autocorrelation

BSTOCK = Beginning stock (1000 MT raw value)

C = The constant term

CANEPROD = Total cane sugar production (1000 MT)
DOMPRESS = Pressure to export from the Dominican Republic

DPNDI = National Disposable Income per capita in the Dominican
Republic deflated by the CPI at 1980 prices (RD$)

DRETPR= Retail price per pound of refined sugar in the Dominican
Republic deflated by the CPI at 1980 prices (DR cents/lb)

DVQUOTA= A dummy variable for the quota system imposed by the
U. S. in 1981 equal to zero for the period before 1982 and one
afterwards

DV75 s A dummy variable equal to 1 in 1975 an 0 otherwise

DV88 = Dummy variable equal to 1 in 1988 and 0 otherwise

DV90 = Dummy variable equal to 1 in 1990 and 0 otherwise

EXPT = Total sugar export from the Dominican Republic (1000 MT raw
value

 

 

 

86

APPENDIX II
(Cont’d)

EXPTREST = Sugar export from the Dominican Republic to the rest of
the world excluding the U.S. (1000 MT raw value)

PCONS = Total sugar consumption per capita in the Dominican Republic
(K9) '

PCONS(-1) = Sugar consumption per capita in the Dominican Republic
lagged one year (Kg/Ha)

REGRPH = Real expected gross return per hectare (RDS)

REXPT = Ratio of sugar export from the OR to the rest of the world
excluding the U.S.

RPRODS = Ratio of sugar production in the Dominican Republic to
sugar production in the rest of the world excluding the United
States and the Dominican Republic

SUGAPROD = Total sugar production (1000 MT raw value)

SUYIELD = Conversion factor of cane to raw sugar 0n decimal)

TSUPPLY = Total sugar supply in the Dominican Republic (1000 MT raw
value)

WEXPTRES = Sugar export by exporting countries excluding the U.S.
and the DR. (1000 MT raw value)

WPRESS = Pressure to export sugar by producing countries excluding
the U.S. and the DR

YIELD = Actual sugar cane yield (Kg/Ha)

 

APPENDIX Ill

EVALUATING TURNING-POINT ERRORS

87
APPENDIX Ill

Table 16: Evaluating Turning-point errors (AREAHAR)

   

88

APPENDIX Ill

(Cont’d)

Table 17: Evaluating Turning-point Errors (PCONS)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

PCONS
YEARS Actual-Actual(-1) Forecast-Actual(-1)
1971 1.99 1.99
1972 1.28 -0.09
1973 4.54 1.46
1974 —O.72 -O.17
1975 -2.38 -2.43
1976 -0.84 -1.03
1977 0.96 1.24
1978 -0.05 2.94
1979 -0.33 4.45
1980 3.25 8.12
1981 -1.35 3.37
1982 1.68 4.78
1983 0.35 5.14
1984 2.20 4.83
1985 6.16 5.25
1986 -2.61 -0.85
1987 7.48 5.89
L 1988 -19.77 -19.77
I 1989 2.84 2.84
L1990 -7.07 -7.07 E

 

 

 

Source: Constructed by the Author

 

 

APPENDIX Ill
(Cont’d)

89

Table 18: Evaluating Turning-point Errors (WEXPTRES)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

WEXPTRES
YEARS Actual-Actual( 1) Forecast-Actual(- 1)
1971 -686. 54 5628. 52
1972 655. 82 5700. 49
1973 1086. 43 5640.11
1974 1461.21 4352. 23
1975 -2295. 25 1436. 33
1976 304. 43 5019. 52
1977 3569. 57 5298. 00
1978 2242. 82 2645.13
1979 -1025. 75 -281.24
1980 -248. 00 -932. 32
1981 450. 79 -577. 07
1982 4249.07 3218. 63
1983 -1036. 50 -950. 36
1984 -1157. 21 -1015.36 II
1985 602. 75 415. 46 II
L 1986 -588.14 -963.68
K 1987 -1527.82 -14.01
H 1988 -356.29 1118.06
I 1989 1178.11 1425.10

 

 

 

_ 645.43

 

312.32

 

90

APPENDIX ill
(Cont’d)

Table 19: Evaluating Turning-point Error (EXPTUS)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

EXPTUS
YEARS Actual-Actuai(-1) Forecast-Actual(-1)

1972 27.57 65.76
1973 -15.21 51.45
1974 65.23 38.56
1975 -39.43 75.45
1976 177.95 53.56
1977 3.16 -111.25
1978 -218.86 -171.74
1979 75.69 47.07
1980 -182.89 -50.32
1981 132.13 110.00
1982 -342.73 -295.17
1983 92.67 75.96
1984 169.93 1 1 .39
1985 -147.16 -184.38
1986 -1 19.47 49.39
1987 -31.49 -13.60
1988 -99.98 -19.22
1989 73.99 70.98

 

 

25.25

Source: Constructed by the Author

 

 

-18.33

 

91

APPENDIX III

(Cont’d)

Table 20: Evaluating Turning-point Errors (REXPT)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

REXPT
YEARS Actual-Actual(-1) Forecast-Actual(-1)

1972 0.00 0.00
1973 40.01 0.01
1974 0.00 0.00
1975 0.00 0.01

I. 1976 0.01 0.00

II 1977 A -0.01 0.01
1978 0.00 -0.01
1979 -0.01 0.00 ||
1980 0.01 0.01 I
1981 0.00 0.00
1982 0.00 0.00

n 1983 0.00 0.00

l 1984 0.00 0.00
1985 -0.01 0.00

l 1986 0.00 0.01

i 1987 0.00 0.00
1988 0.00 0.00 l
1989 0.01 0.01 I

 

 

0.00

 

0.00

 

Source: Constructed by the Author

 

APPENDIX N

LIST OF THE EXOGENOUS VARIABLES USED IN THE MODEL

92
APPENDIX IV

List of Exogenous Variables Used In the Model

 

IEAI. ' has EYIELD IIILD DIP! RIG!!! DEIDI
1970 11.332.942 66.730.875 61.866.000 0.000 NA 9.099
1971 9.271.582 64.644.133 68.122.000 0.000 NA 9.506
1972 5.603.056 65,072,426 69.893.000 0.000 15.539 10.133
1973 9.157.444 66,011.883 64.564.000 0.000 16.991 9.983
1974 7.954.902 65,090,719 64,562.000 0.000 24.036 8.576
1975 24.418.902 64,423.879 60,610.000 0.000 27.193 11.169
1976 6.959.546 62,705,656 66.700.000 0:000 77.211 11.056
1977 10.439.173 63.359.785 64.766.000 0.000 43.495 10.974
1978 15.911.275 63,295,738 63.683.000 0.000 22.337 10.726
1979 11.830.953 62,944,785 57.823.000 0.000 14.020 11.416
1980 1.821.249 60.862.516 50.309.000 0.000 9.930 11.414
1981 2.461.450 56.935.816 52.049.000 0.000 11.359 11.252
1982 12.471.046 54.536.426 62.793.000 0.000 40.280 11.187
1983 12.949.053 56.168.641 61.277.000 0.000 19.910 10.800
1984 6.373.780 57.012.094 54.633.000 0.000 7.511 10.092
1985 8.007.057 55.616.883 48.111.000 15.000 7.680 9.997
1986 3.372.481 52,542,086 42.750.000 0.000 7.052 ‘ 9.786
1987 5.530.525 48.557.395 48,733.000 0.000 5.260 10.229
1988 3.168.156 47,478.234 49.265.000 0.000 9.107 10.214
1989 2.874.192 46.912.625 46.212.000 0.000 6.772 10.006
1990 3.264.135 45.605.555 41.176.000 25.000 5.931 9.931
1991 3.799.164 43.102.563 43.102.563 25.000 5.589 10.469
1992 4.604.991 41.888.820 41,888.820 0.000 7.004 11.036
1993 4.376.140 40.675.074 40.675.074 0.000 6.376 11.633
1994 4.147.289 39.461.328 39.461.328 0.000 5.796 12.264
1995 3.918.438 38.247.582 38.247.582 0.000 5.193 12.927

 

93

APPENDIX IV
(Cont'd)

 

 

YEAR DRETPR. SUYIELD BSTOCK WPRODRE “PRICE BIBLE!
1970 0.003 0.109 NA 68.042.570 3.680 1.000
1971 0.003 0.111 224.000 68.475.289 4.520 1.000
1972 0.003 0.115 209.000 67.085.609 7.430 1.000
1973 0.003 0.127 95.000 72.576.680 9.610 1.000
1974 0.003 0.124 48.000 73.460.500 29.990 1.000
1975 0.003 0.132 280.000 91.533.961 20.490 1.000
1976 0.003 0.118 201.000 74.848.180 11.580 1.000
1977 0.002 0.113 249.000 79.423.570 8.110 1.000
1978 0.002 0.108 120.000 86.164.141 7.820 1.000
1979 0.002 0.116 45.000 85.201.750 9.660 1.000
1980 0.002 0.115 60.000 78.272.680 29.010 1.000
1981 0.002 0.110 135.000 81.799.539 16.930 1.000
1982 0.002 0.116 83.000 93.256.430 8.420 1.000
1983 0.002 0.106 249.000 94.782.109 4.490 1.000
1984 0.003 0.114 253.000 89.788.430 5.180 1.000
1985 0.002 0.109 257.000 94.013.391 4.040 3.120
1986 0.003 0.116 257.000 91.389.539 6.050 2.890
1987 0.002 0.093 298.000 95.759.914 6.710 3.510
1988 0.002 0.093 228.000 96.102.047 10.170 5.810
1989 0.002 0.088 220.000 98.624.070 12.790 6.350
1990 0.002 0.084 181.000 100.989.375 12.550 8.650
1991 0.002 0.100 141.000 102.391.000 11.035 12.740
1992 0.002 0.100 121.000 106.216.227 11.166 12.750
1993 0.002 0.100 130.523 107.946.875 11.296 14.782
1994 0.002 0.100 117.907 109.677.531 11.427 16.518
1995 0.002 0.100 105.291 111.408.180 11.558 18.254

 

94

 

APPENDIX iv
(Cont’d)

YEAR CPI- EDP IDI IDDISIIB IEIEIHET
1970 37.100 4.060.000 1.370.500 56.709.629 61.515.699
1971 38.700 4.180.000 1.537.700 59.203.703 63,604.699
1972 41.800 4.300.000 1.821.300 61.482.551 65.137.301
1973 48.100 4.430.000 2.127.300 63.419.234 67,774.797
1974 54.400 4.560.000 2.127.300 65,505.598 68,433.102
1975 62.300 4.700.000 3.270.300 67.115.063 67.458.000
1976 67.100 4.840.000 '3.590.600 67,888.633 71.062.102
1977 75.800 4.980.000 4.142.600 68.984.398 72.238.500
1978 78.500 5.120.000 4.310.800 70,252.867 76.811.797
1979 85.700 5.305.000 5.190.199 73.370.797 80,304.000
1980 100.000 5.443.000 6.212.500 76.451.430 80.898.461
1981 107.500 5.581.000 6.750.901 79,338,086 81.153.688
1982 115.800 5.744.000 7.440.900 80.785.383 83,447.023
1983 121.300 6.123.000 8.021.099 81.833.055 85,643.227
1984 154.000 6.269.000 9.742.800 83.414.648 88.928.766
1985 211.200 6.416.000 13.546.000 86,006,336 89.479.172
1986 232.200 6.565.000 14.917.100 88,017,055 92,280,234
1987 268.700 6.716.000 18,459.400 90.229.391 97.042.273
1988 388.000 6.867.000 27.213.699 92.933.891 97,927.125
1989 564.300 6.903.000 38.975.949 95.749.875 98.206.328
1990 1.131.900 7.119.000 80.020.406 97.725.242 99.642.047
1991 1.299.105 7.318.000 99.525.625 98.591.836 101.261.000
1992 1.491.009 7.523.000 123.785.305 99.703.125 103.806.820
1993 1.711.261 7.734.000 153.958.359 101.569.953 105,739.039
1994 1.964.049 7.950.000 191.486.188 103.602.289 107,671.258
1995 2.254.179 8.173.000 238.161.547 105,739.039 109,603,477

 

 

APPENDIX V

ACTUAL AND FORECAST VALUES OF THE ENDOGENOUS VARIABLES
USED IN THE MODEL

95

APPENDIX V
Actual and Forecast Values for the Endogenous Variables

 

 

YEARS AREAHAR. AREAHAR! PCOIB EDDIE? EXPTUS EXPTUS?
1970 151.00 151.00 30.616 30.616 660.17
1971 150.00 150.00 32.608 32.608 665.04
1972 150.00 151.74 33.884 32.513 692.61 730.81
1973 145.00 153.60 38.420 35.341 677.40 744.07
1974 152.00 156.45 37.697 38.246 742.63 715.97
1975 154.00 159.60 35.319 35.265 703.20 818.08
1976 164.00 170.37 34.483 34.292 881.15 756.76
1977 172.00 175.18 35.442 35.722 884.31 769.91
1978 174.00 176.38 35.391 38.379 665.44 712.57
1979 178.00 176.20 35.061 39.840 741.14 712.52
1980 180.00 175.40 38.313 43.182 558.24 690.81
1981 185.00 174.87 36.966 41.681 690.37 668.24
1982 188.00 178.89 38.646 41.749 347.64 395.20
1983 188.00 179.48 38.996 43.788 440.32 423.61
1984 188.00 178.09 41.192 43.827 610.24 451.71
1985 175.00 176.81 47.355 46.445 463.09 425.86
1986 180.00 175.52 44.747 46.503 343.62 413.70
1987 180.00 174.03 52.222 50.633 312.13 330.01
1988 170.00 170.00 32.456 32.456 212.14 292.90
1989 170.00 170.00 35.299 35.299 286.13 283.13
1990 170.00 170.00 28.228 28.228 311.38 267.79
1991 168.91 32.617 291.33
1992 168.12 35.000 267.33
1993 167.27 35.456 260.57
1994 166.39 34.047 240.87
1995 165.48 30.832 225.65

96

 

 

APPENDIX v
(Cont’d)

YEARS CAIIPIDD ICAIEPIDP TSUPPLY TSUPPLY? 51.8 CHIS?
1970 9313.0 124.30 124.30
1971 10200 1355.0 136.30 131.27
1972 10463 9874.4 1410.0 1342.4 145.70 139.80
1973 9372.0 10140 1288.0 1385.7 170.20 156.56
1974 9798.0 10183 1262.0 1309.7 171.90 174.40
1975 9334.0 10282 1514.0 1639.3 166.00 165.75
1976 10932 10683 1488.0 1458.7 166.90 165.97
1977 11140 11099 1507.0 1502.4 176.50 177.90
1978 11094 11164 1319.0 1326.6 181.20 196.50
1979 10304 11091 1245.0 1336.7 186.00 211.35
1980 9056.0 10675 1099.0 1284.8 208.54 235.04
1981 9629.0 9956.4 1198.0 1234.1 206.31 232.62
1982 11805 9756.2 1457.0 1218.5 221.98 239.81
1983 11520 10081 1468.0 1315.7 238.77 268.11
1984 10271 10153 1427.0 1413.5 258.23 274.75
1985 8419.0 9833.7 1193.0 1347.8 303.83 297.99
1986 7695.0 9222.1 1152.0 1329.6 293.76 305.29
1987 8772.0 8450.4 1113.5 1083.6 350.72 340.05
1988 8375.0 8224.9 1004.8 990.85 222.87 222.87
1989 7856.0 8075.0 913.00 932.32 243.67 243.67
1990 7000.0 7796.7 795.66 862.77 200.96 200.96
1991 7280.6 766.00 894.06 235.00 238.69
1992 7042.3 825.23 263.30
1993 6803.9 810.91 274.22
1994 6566.1 774.52 270.67
1995 6329.1 738.20 251 99

 

577

APPENDIX V
(Cont’d)

 

 

YEARS I!!! ll!!! EXPIRES! lllrlflflil
1970 793.00 132.83
1971 1011.0 345.96
1972 1141.0 1197.8 448.39 466.99
1973 1069.0 1305.4 391.60 561.31
1974 1055.0 1241.0 312.37 525.07
1975 1030.0 1327.2 326.80 509.10
1976 1180.0 1200.1 298.85 443.33
1977 1040.0 1133.9 155.69 364.04
1978 1020.0 1005.7 354.56 293.08
1979 760.00 1066.9 18.864 374.35
1980 900.00 1061.3 341.76 370.51
1981 790.00 874.98 99.630 206.74
1982 930 00 894.01 582.36 498.82
1983 900.00 803.30 459.68 379.69
1984 760.00 936.07 149.76 484.36
1985 470.00 799.43 6.9118 373.57
1986 570.00 806.10 226.38 392.40
1987 520.00 474.55 207.87 144.54
1988 550.00 442.54 337.86 149.64
1989 400.00 373.60 113.87 90.475
1990 435.00 305.78 123.62 37.989
1991 410.00 364.94 73.603
1992 320.43 53.100
1993 288.55 27.978
1994 257.80 16.927
1995 228.13 2.4791

98

APPENDIX V
(Cont’d)

 

 

YEARS oaurnnss nonrnzsr wzxrrnzs wzxrtnzr nzxrr azxprr
1970 20961 26935 0.03783

1971 1224.4 20275 26590 0.04987

1972 1278.9 1213.0 20930 25975 0.05451 0.04611
1973 1152.6 1253.9 22017 26570 0.04855 0.04913
1974 1111.3 1167.2 23478 26369 0.04494 0.04706
1975 1351.4 1482.4 21183 24914 0.04862 0.05327
1976 1318.6 1293.1 21487. 26202 0.05492 0.04580
1977 1338.7 1333.7 25057 26785 0.04151 0.04233
1978 1149.2 1156.7 27300 27702 0.03736 0.03630
1979 1070.1 1156.5 26274 27018 0.02893 0.04023
1980 917.77 1089.5 26026 25342 0.03458 0.04188
1981 1006.1 1019.8 26477 25449 0.02984 0.03438
1982 1256.7 992.20 30726 29695 0.03027 0.03011
1983 1255.7 1079.9 29689 29775 0.03031 0.02698
1984 1204.6 1166.7 28532 28674 0.02664 0.03265
1985 953.34 1086.9 29135 28947 0.01613 0.02762
1986 885.05 1049.3 28547 28171 0.01997 0.02861
1987 828.27 790.97 27019 28533 0.01925 0.01663
1988 688.67 676.41 26662 28137 0.02063 0.01573
1989 623.88 646.23 27841 28088 0.01437 0.01330
1990 523.24 598.89 28486 28153 0.01527 0.01086
1991 671.56 26928 28243 0.01523 0.01292
1992 597.45 28378 0.01129
1993 576.60 28339 0.01018
1994 515.78 28301 0.00911
1995 468.80 28263 0.00807

APPENDIX VI

COMPUTER OUTPUT OF THE REGRESSION EQUATIONS

99
APPENDIX VI

Ls // Dependent Variable 1e AREAHAR

Date: 11-1141992 / Tine:

suPL range: 1972

Number of obeervatione:

11:49
1990
19

 

VARIABLE

c

AREAHAR(-1)
REGRPH

R-equared
Adjusted R-equared
3.6. of regreeeion

Log 11ke11hood
Ourbin-Hateon stat

COEFFICIENT

8.8880242
0.9381912
0.1582357

0.880023
0.885025
4.890818
~55.48831
2.483722

870.

ERROR T-STAT.
15.859885
0.0880725
0.0701703

0.5875871
10.829778
2.2285222

Mean of dependent var
8.0. of dependent var
Sun of squared reoid
F-etatietic
Prob(F-etatietic)

2-TIIL 8I0.
0.5782
0.0000
0.0407

171.7388
13.31182
382.8903
58.87917
0.000000

—.

Residual P1ot

RESIDUAL

ACTUAt

FITTED

 

§

. .- a. .- -. -o o- o. o. u. 0. a».

o- o. Q- Q. n- a. u. 0. O. O. -- 0- -- 0- a- O- o

 

 

-1.74441
-8.97128
3.80890
-1.43753
-1.12484
2.78117
0.59732
4.02433
2.91850
5.82281
-0.37883
-0.00280
1.93447
-11.0919
8.17887
1.77570
-8.82527
0.90149
1.03290

-- o. a- o. a. a. o- o- .- 0. a. a- -. o- -- o- c- -- a.
d
3
‘

150.000
145.000
152.000
154.000
184.000
172.000
174.000
178.000
180.000
185.000
188.000
188.000
188.000
175.000
180.000
180.000
170.000
170.000
170.000

151.744
151.971
148.391
155.438
185.125
189.219
173.403
173.978
177.081
179.177
188.377
188.003
188.088
188.092
173.823
178.224
178.825
189.099
188.987

 

——"=X=

100

 

 

 

 

 

 

 

 

APPENDIX Vl
(Cont’d)
L8 // Dependent VarIabIe 1e PCONS
Date: 11-11-1992 / T1ae: 11:52
8NPL range: 1971 - 1990
Nueber of obeervatione: 20
VARIADLE COEFFICIENT 8T0. ERROR T-8TAT. 2-TAIL 8I0.
c 55.958771 18.358188 3.4208458 0.0041
DPNDI -3.2495217 1.0129293 -3.2080441 0.0083
DRETPR —7042.4428 2015.7872 -3.4938438 0.0038
PCONSI-1) 0.9113849 0.1288858 7.0833582 0.0000
DV88 -22.898953 2.9819295 -7.8785894 0.0000
DV90 -13.483838 2.8588514 -5.0755005 0.0002
R-equared 0.881879 Mean of dependent var 37.83828
Adjusted R-equared 0.839893 8.0. of dependent var 5;481741
9.E. of regreeeion 2.188792 3un of equared ree1d 88.94881
Log 11ke11hood -40.48o73 F-etatIetic 20.90451
0urb1n-Natson etat 2.383954 Prob(F-etatietic) 0.000005
Ree1dua1 PIOt ope RE8IDUAL ACTUAL FITTED
I : I t : I 1971 1.20445 32.8077 31.4032
I I t : I 1972 1.37100 33.8837 32.5127
I I 4: I 1973 1.82977 38.4199 38.5901
I ‘ I I 1974 -3.35535 37.8974 41.0527
I I I 1975 0.55447 35.3191 34.7847
I I I 1978 0.14259 34.4835 34.3409
I : 9 I : I 1977 -0.45551 35.4418 35.8973
I 9 : I I 1978 -2.73287 35.3908 38.1233
I 9 I : I 1979 -2.05539 35.0813 37.1188
I 9 I I 1980 -0.51341 38.3127 38.8281
I ‘I I 1981 -0.27888 38.9883 37.2432
I 2 I 9 I 1982 1.19291 38.8455 37.4528
I :* I I 1983 -1.98328 38.9981 40.9593
I I t I 1984 1.73224 41.1921 39.4598
I : I I 1985 3.31138 47.3552 44.0438
I * I I 1988 -2.58513 44.7470 47.3321
I I I 1987 3.18902 52.2222 49.0331
I 9 I 1988 7.1E-15 32.4557 32.4557
I 9 I I 1989 -0.59025 35.2993 35.8895
I t I 1990 5.35-15 28.2283

28.2283

 

II
II
I
I
II
I
I
I
ll

101

APPENDIX VI

(Cont’d)

L3 ll Dependent VariabIe is EXPTUS

Date:

SNPL range: 1971

11-11-1992 / Tine:
1990
Nuaber of observations: 20

11:53

 

VARIA8LE
C

DOMPRESS

DVQUOTA

R-squared

Adjusted R-squared
8.6. of regression
Log 1ike1ihood
Durbin-Nateon stat

COEFFICIENT

337.88828
0.3239224
-284.08931

0.858804
0.839734
82.29484
-114.9597

1.894840

8T0.

ERROR
109.85712
0.0909488
43.958737

T-STAT.

3:.-

3.0813180
3.5818774
-8.0078838

E‘-

Nean of dependent var
9.0. of dependent var
Sun of squared rssid
F-statistic

Prob(F-statistic)

2-TAIL 910.

0.0088
0.0024
0.0000

581.4111
205.5858
115130.9
50.77833

0.000000

 

ResiduaI PIOt

obs

RESIDUAL

ACTUAL

FITTED

 

II
II
II
I
ll

R

O. I- O. C. .. O. C- O. C. .. O. -. O. O. .. O. .- O. C. O.

1971
1972
1973
1974
1975
1978
1977
1978
1979
1980
1981
1982
1983
1984
1985
1988
1987
1988
1989
1990

-89.4475
~59.5359
-33.8283
44.7778
-72.4391
118.129
112.774
-44.8981
58.8077
-78.9293
28.5878
-133.234
-40.2393
148.233
80.4817
-18.8717
-29.9899
-84.7310
10.2393
88.0921

885.041
892.812
877.402
742.830
703.198
881.152
884.308
885.444
741.138
558.244
890.370
347.844
440.317
810.244
483.088
343.818
312.125
212.143
288.128
311.379

734.489
752.148
711.230
897.852
775.837
785.023
771.534
710.140
884.528
835.173
883.783
480.878
480.558
484.011
382.808
380.488
342.095
298.874
275.889
243.287

 

Date:

8NPL range: 1971

Nuaber of observations:

11-11-1992 I TIIO:

APPENDIX VI

102

(Cont’d)

L8 // Dependent VariabIe is NEXPTREs

11:55

20

Convergence achieved after 5 iterations

VARIADLE

COEFFICIENT

9T0. ERROR

T-STAT.

2-TAIL 810.

 

C
NPRESS
DVQUOTA
DV75

25038.405
0.1875209
2589.8770

-4212.7513

2498.3393
0.0785792
1828.9888
1834.9003

10.029247
2.1318731
1.5794088
-2.5787833

0.0000
0.0500
0.1351
0.0210

 

AR(1)
R-squared
Adjusted R-squared
8.6. of regression
Log 1ike1ihood
Durbin-Natson stat

0.8281211

8.1918791

0.0000

0.894148
0.885919
1177.458
-188.9242
1.988329

0.1334201

x

Nean of dependent var
3.0. of dependent var
Sun of squared resid
F-statistic

Prob(F-statistic)

25858.90
3215.598
20798111
31.87828
0.000000

Rssidua1 P10t

O

4.

 

obs RESIDUAL ACTUAL
-1379.94
172.322
~388.170
870.877
-1343.25
~1832.38
2188.81
1025.59
-412.183
1299.38
482.527
181.299
-937.405
-70.8953
304.478
220.811
-1824.04
~223.759
970.994
537.157

20274.5
20930.4
22018.8

21182.8
21487.2
25058.8
27299.8
28273.8
28025.8
28478.8
30725.7
29889.2
28532.0
29134.7
28548.8
27018.8
28882.5
27840.8
28488.0

-e
O
O
O

23478.0

FITTED

21854.5
20758.0
22403.0
22807.1
22528.0
23118.5
22889.9
28274.0
28888.0
24728.4
28014.1
30544.4
30828.8
28802.7
28830.2
28325.8
28842.8
28888.2
28889.8
27948.8

103

APPENDIX VI
(Cont’d)

L3 // Dependent Variab1e is REXPT
Date: 11-11-1992 / Tine: 11:58
3NPL range: 1370 - 1990

Nueber of observations: 21

VARIA8LE COEFFICIENT 8T0. ERROR

T-9TAT.

2-TAIL SIC.

 

C -0.0111992
RPR008 3.3922172
DV75 0.0140920

0.0041898
0.3104155
0.0049997

0.877942
0.884380
0.004877
83.80983
1.232410

R-squared

Adjusted R-squared
3.5. of regression
Log 11ke1ihood
Durbin-Natson stat

Residua1 P10t

1970
1971
1972
1973
1974
1975
1978
1977
1978
1979
1980
1981

* . o. o- o- o.

1983
1984
1985
1988
1987
1988
1989
1990

-2.8857508
10.927988
2.8185888

RESIDUAL

—0.00152
0.00504
0.00498
0.00399
7.5E-05
0.00000
0.00779

-0.00102
0.00138

-0.00785
0.00075

-0.00305

-0.00851

-0.00211

-0.00852

-0.00590

-0.00205
0.00155
0.00441
0.00173
0.00888

 

 

Nean of dependent var
3.0. of dependent var
Sun of squared resid

F-statistic
Prob(F-statistic)

ACTUAL

0.03783
0.04987
0.05451
0.04855
0.04494
0.04882
0.05492
0.04151
0.03738
0.02893
0.03458
0.02984
0.03027
0.03031
0.02884
0.01813
0.01997
0.01925
0.02083
0.01437
0.01527

--=

0.0151
0.0000
0.0114

0.033537
0.013242
0.000428
84.73518
0.000000

FITTED

0.03935
0.04483
0.04953
0.04458
0.04488
0.04882
0.04713
0.04253
0.03800
0.03858
0.03383
0.03288
0.03878
0.03243
0.03315
0.02203
0.02202
0.01789
0.01822
0.01284
0.00881

MICHIGAN STATE U IV

N . LIBRRRIES
HI! VIII IHI HHI IIIHIHIWIHIIWI
3726

3129300910