”-1E5'S Ifljlllllllil III! II WUILIIIMUII “ll! ”III MI “W 293 63 951 1 This is to certify that the thesis entitled THE DISTRIBUTION OF PERSONAL INCOMES AMONG AFRICAN FARMERS--A TWO PERIOD ANALYSIS presented by James Otunola Olukosi has been accepted towards fulfillment of the requirements for PhD degreeinjgricultural EconOmics diql/Im% Dr. Peter J. Matlon Major professor Date July 31, 1979 0-7639 THE DISTRIBUTION OF PERSOXAL INCOMES AMONG AFRICAN FARMERS--A TWO PERIOD ANALYSIS By James Otunola Olukosi A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Agricultural Economics 1979 ABSTRACT THE DISTRIBUTION OF PERSONAL INCOMES AMONG AFRICAN FARMERS——A TWO PERIOD ANALYSIS 33’ James Otunola Olukosi The third National DevelOpment Plan of the Federal Republic of Nigeria has assigned the highest priority to the development of the agricultural sector. This commit- ment is based on equitable distribution of income between sectors, geographic regions and individuals. However, it has been difficult to design income and pricing policies to fulfill equity objectives due to inadequate information on the rural poor and why they are poor. Apart from the design issue, the impacts of income and pricing policies on the rural poor cannot be predicted without full under- standing of the level, distribution, sources, and changes of income among the rural poor. The few available studies which are related to incOme distribution in Nigeria have each used one year's data. Since very little is know about the determinants of income changes over time more than one year's data would greatly improve our understanding of the true nature of incdme distribution. The central objective of this study was to describe and. explain the structure and distribution of income among a sam- ple of rural households in Kwara State, Nigeria. Omu-Aran was chosen by the Kwara State Ministry of Agriculture as the area for an intensive management study conducted in 1969 and repeated in 1974. Within the area, two villages were chosen to reflect differences in ease of communication with marketing centers and to represent two ecological zones-- savannah and forest—-found in the area. A simple random sample of 30 households were drawn in each village in 1969 from the list obtained from the total population enumeration. The same households were included in the sample in 1974 with a few additions to replace those who had died or moved. Input-output data were obtained by interviewing the households twice weekly throughout each survey year. The levels of interpersonal distribution of income were estimated on the basis of these data. A Gini coefficient of 0.35 was found suggesting that income was fairly equitably distributed. Moreover, the Gini coefficient changed little and specific households remained relatively stable in their relative income ranking between the two years. Finally, the data showed that off-farm income tended to reduce income in- equality. An important finding was that the causes of poverty can- not be attributed to one single factor but rather to a combination of factors. Among the resource endowment vari- ables crOpped land was found to be consistently related to income. Operating capital also showed high correlation with incOme pointing to the credit needs of lower income households. Furthermore, two sets of the very poor were distinguished. Some households were land short but worked their land very intensively. The other set of households possessed average land holdings but worked their land at low levels of intensity and thus realized low output levels. Low productivity, however, was common to both poverty groups. Ill health, insects, pests and diseases, poor quality inputs and poor management could be possible causes of low pro- ductivity. This is a critical area for further research be- cause it has important implications for the development of improved technologies which are compatible with the circum- stances of the poor. Due to the wide difierences in results between villages and between years policy makers are cautioned against making blanket applications over wide areas and against placing heavy reliance on the results of a single year's data on incomes. Further research priorities are identified on credit, land tenure, calorie intake, eCOnomic contribution of migrants and causes of inter-year variations in productivity. TO QUEEN BENYA ii ACKNOWLEDGMENTS I owe gratitude to many people who have assisted me in the various stages of this study. Professor David Norman provided direction and support throughout the survey period. He taught me most of what I know in data collection and our association for the past eleven years has grown into more than just being celleagues. Thanks also go to Professor Warren Vincent, my major professor, who was an unfailing source of inspiration, help and advice. Dr. Peter Joseph Matlon, my thesis supervisor, deserves special thanks for his intellece tual and editorial assistance. Without him, there would have been no dissertation because he provided the core ideas on the analysis and write-up. I would also like to thank Dr. Les Manderschied who acted beyond being just a member of my guidance committee to give guidance and direction on the final product. Dr. Carl Liedholm's comments, also a member of my guidance committee, are greatly appreciated. To the entire junior members of the staff of the Department of Agricultural Economics and Rural Sociology at Ahmadu Bello University, Zaria Nigeria, I say "Thank You". In particular, the assistance from Muili Oladejo, Departmental Secretary, is much appreciated. The untiring support of Dr. George Abalu, who has been acting iii as the head of Agricultural EcOnomics and Rural Sociology since the departure of Professor David Norman, is worthy of mention. I should not fail to thank my enumerators who were: Joseph Alabi, Joseph Fatoye, Kolawole Adeoye, Tunji Owa, Sanson Oluwole, Joseph Ibiwoye and Dipo Okunnola. Finally thanks are due to the people whose encduragement gave me much needed support and energy throughout my doctoral program. In particular I thank Samsan and Rebeccah Ogundipe, Bayo and Tolani Oladimeji, Joshua and Laran Afolayan. To my wife and children who have been extremely patient and understanding throughout my study I give my special thanks. iv II. III. IV. VI. TABLE OF CONTENTS IDITRODUCTIOI‘I. o o o o o o o o o o o o o o o o o A. Income Distribution and the DevelOpment Question. . . . . . . . . . . . . . . . . . . B. Problem Statement and Need. . . . . . . C. Objectives of the Study . D. Plan of Study . LITERATURE REVIEW AND CONCEPTUAL FRAMEWORK. . . . A. Brief Review of Other Studies . . . B. Conceptual Framework of the Present Study THE STUDY AREA, SAMPLING PROCEDURE AND DATA. COLLECTIOFI I'IETHODOLOGXr o o o o o o o o o A. Description of Omu-Aran Environment . . . . . B Choice of Villages. . . . . . . . . . . . . . C. Population and Land . . . . . . . . D. Representativeness of the Village . E. Sampling Procedure. . . . . . . . . . . . . . F. Data Collection . . . . . . . . . . . . . . . LEVELS, DISTRIBUTION AND SOURCES OF INCOME A. The Definition of Income. . . . . B. Nean Incomes by Village and Household Sector. C. Size Distribution . . . . . . . . . . D. Summary . . . . . . . . . . . . . . . SOCIO-DEMOGRAPHIC CHARACTERISTICS OF INCOME STRATA. . . . . A. Family Size . . . . . . . . . . . . . B. Family Composition. . . . C. Age of Household Head . . . . . . . . . . . D. Percent Literacy. . . . . . . . . . . . . . . E. Summary . . . . . . . . . . . . . . . . . . . RESOURCE ENDOWMENT AND USE BY INCOME STRATA . . . A. Land Holdings . . . . . . . B. Value of Capital Stock Used in Production . U'IU'ICOH \l .22 .22 .28 .33 .37 .38 .39 .43 .46 .53 .72 .76 .76 .78 .79 .80 .83 .84 VII. VIII. IX. C. Value of Livestock. . . . . . . . . . . . . D 0 Labor Use 0 O O O O O O O O O O O O O O O O E. sumary 0 O O O O O O O O O O O O O O O O O CROPPING PATTERNS AND FARM BUDGETS BY INCOME STRATA. O C O O O O O O O O O O O O O O O C O O A. Crepping Patterns . . . . . The Farm Budget Components Defined. . Technical Efficiency. . . . . . . . . Results of the Budget Analysis. . . . Summary . . . . . . . . . . . . . . O O O O O O O O O O O O O O O O O O O O NUOCD CORREMTES OF INCOIdE O O O O I O O O O O - O O O O . The Variables . . . . . . . . . . . . . Correlation Coefficients. . . . . . . The Results of the Regression Models. . Comparison Between Models and Years . . Summary . . . . . . . . . . . . . . . macaw»- .00.. 0.... INTER-YEAR VARIATION IN INCOME DISTRIBUTION . . A. Characteristics of Households with Large Income Change Between Years . . . . . . . . B. Case Studies. . . . . . C. Family Structure Changes Among Households Whose Relative Income Ranking Decreased By More Than 20 Percentile . . . . . . . . . . D. Summary . . . . . . . . . . . . . . . . . SUMMARY AND CONCLUSIONS . . . . . . . . . . . . A. Distribution of Income. . . . . . . . . . . B correlates of IncOme. . . . . . . . . . . . C. Inter-Year Variation in Income Distribution D Policy Implications . . . . . . . . . . . . BIBLIOGRAPHY 0 O O O O O O O O O O O O O O O O 0 APPENDIX. D O O O O O O O O O O O O O O O O O 0 vi 0 O O O I 92 96 110 112 112 127 129 131 142 144 145 150 150 160 161 163 164 168 175 177 179 179 182 185 187 195 201 LIST OF TABLES Page No. Gini coefficients on net income for nine villages in Sokoto, Zaria and Bauchi Areas. . . . . . . . ll Gini coefficients on distribution of land in nine villages in Sokoto, Zaria and Bauchi areas . . . 12 Gini coefficients on household income for three villages in Kano State, 1974 . . . . . . . . . . 13 Age distribution of total population, Ipetu and Odo-ore, 1969. o o o o o o o o o o o o o o o o o 34 Age distribution of total population, Ipetu and OdO'Ore, 1974. o o o o o a o o o o o c o I o o o :34 Coefficients applied to estimate the number of man-equivalent worker units per household. . . . 35 Components of net household income . . . . . . . 44 Coefficients applied to estimate the number of man-equivalentsconsumer units per household. . . 47 Mean net incomes by village and household, 1969 and 1974 (in naira). . . . . . . . . . . . . . . 48 A cemparison of mean incomes per capita and per households and off—farm income as percent of household income obtained in three Nigerian surveys. . . . . . . . . . . . . . . . . . . . . 50 Average and cumulative incOme, number of residents, and consumer units by net farm income strata, Total Sample, 1969 and 1974. . . . . . . . . . . . . . 55 Average and cumulative net farm incomes, number of residents and consumer units by income strata with- in each village, 1969 and 1974 . . . . . . . . . 57 Three summary measures of the size distribution of income by household and village, 1969. . . . . . 63 vii Three summary measures of the size distribution of incOme by household and village, 1974. . . . . 64 The percent distribution of households within net farm and net household incOme per capita, 1969 and 1974. O O O O O O O O O O O O I 0 O O O O O O 68 Household size and composition by village net farm income per consumer strata, 1969 and 1974 . . . . 77 Age of household head, percent literacy and family composition by net farm income per consumer strata, 1969 and 1974 . . . . . . . . . . . . . . . . . . 81 Cultivated land holdings by village and farm incOme per consumer strata, 1969 and 1974 . . . . 85 Frequency distribution of households among land and net farm income per consumer classes, 1969. . 88 Frequency distribution of households among land and net farm income per consumer classes, 1974. . 90 The value of capital per household, per worker and per consumer by net farm income per consumer strata, 1969 and 1974 (in naira). . . . . . . . . 93 Value of livestock by type, Ipetu and Odo-Ore, 1969 and 1974 0 I O O O O O O O O O O O O O O O O 95 Land and labor use intensities by land and net farm income per consumer classes, 1969 . . . . . . . . 99 Land and labor use intensities by land and net farm income per consumer classes, 1974 . . . . . . . .10l Hired labor per household, per acre by type,by land and net farm income per consumer classes, Ipetu and Odo—ore, 1969 o o o o o o o o o o o o o o o 0105 Hired labor per household and per acre by type, by land and net farm income per consumer classes, Ipetu and OdO‘Ore, 1974 o o o o o o o o o o o o o o o 0107 Adjusted acreage of each major crop.zrown as a per- centage of total acreage, Ipetu and Odo-Ore, 1969. O O O O O O 0 O O O O 0 O O 0 O O O .114 Adjusted acreage of each major crop grown as per- centage of total acreage, Ipetu and Odo-Ore, 19740 O O O O O O O O O O O O O I 0 O O O O 0 O 0117 viii 8.8 CrOpping pattern by crOpped land and net farm income per consumer classes, Ipetu and Odo-Ore, 1.969. O O O I O I O O O O O O O O O O 0 O o O O 122 CrOpping patterns by crOpped land and net farm income per consumer classes, Ipetu and Odo-Ore, 1974. O O O O O O O O l O O O I O O O O O O O O 124 Results of farm production function, fitted to pooled data for both villages, 1969 and 1974. . 132 Costs and returns per acre in cropping enter- prises by land and net farm income per consumer classes, Ipetu and Odo—Ore, 1969. . . . . . . . I35 Costs and returns per acre in crOpping enter- prises by land and net farm income per consumer Classes, Ipetu and Odo-ore, 1974. o o o o o o o .138 Means and standard deviations of variables used in regression models, Ipetu 1969 and 1974 . . . 148 Means and standard deviations of variables used in regression models, Odo-Ore 1969 and 1974 . . 149 Correlation cOefficients of variables used in regression models, Ipetu 1969 . . . . . . . . . 151 Correlation coefficients of variables used in regression models, Ipetu 1974 . . . . . . . . . 152 Correlation coefficients of variables used in regression models, Odo-Ore, 1969. . . . . . . . 153 Correlation coefficients of variables used in regression models, Odo-Ore, 1974. J . . . . . . 154 Regression coefficients and test statistics for two econometric models to examine the influence of all factors on variation in income, Ipetu and Odo-Ore, 1969 and 1974. . . . . . . . . . . . . 156 Marginal productivities and test statistics of variables for two income models, Ipetu and Odo—ore 1969 and 1.974 0 o o o o o o o o o o 157 Percentile changes of household between 1969 and 1974 by village . . . . . . . . . . . . . . 165 Percentage change in levels of variables between 1969 and 1974 (using 1969 as base year), Ipetu. 169 ix A.12 A.13 A.14 Percentage change in levels of variables between 1969 and 1974 (using 1969 as base year), Odo:0re. .170 Changes in household structure between 1969 and 1974, Ipetu and Odo-Ore . . . . . . . . . . . . .174 Retail prices of crops in Omu-Aran market, 1969 and 1974. O O O O O O O O O O O O O O O O O O O O .201 Retail prices of crops in Ora market, 1969 and 1974. O O O O O O O O O O I O O O O O O O O O O O .203 Off-farm wage rates for both villages, 1969 .‘. . .205 Consumer price indices for food at Kaduna, Sokoto- Gusau and IlOI‘in, 1965-1974 9 o o o o c o o o o o .206 Monthly rainfall distribution, Omu-Aran area, 1957—1967 (inches). . . . . . . . . . . . . . . . .207 Average and cumulative incomes,:number of residents and consumer units by net household income strata, Total sample, 1969. ... . . . . . . . . . . . . . .208 Average and cumulative net household incomes, number of residents and consumer units by income strata within each village, 1969 . . . . . . . . . . . . .209 Cultivated land holdings by village and net house— hole income per consumer strata, 1969 . . . . . . .210 Age of household head, percent literacy and family composition by net household income per consumer Strata, 19690 O o O O O O I O O O O 0 O O o O O I .211 The percentage distribution of households within net farm and net household income per consumer, 1969 and 19740 O O O O I O o o O I O O O O l o I O 0 O O o O 212 The percentage distribution of households within net farm and net household income per household, 1969 and 1974. O O O O O I O O O O O O O O O O O O O O O 216 Land and labor use intensities by land and net house- hold inCOme per consumer classes, 1979. . . . . . . 220 Efficiency measures by net household incomes and by land per censumer classes, 1969 . . . . . . . . . . 22 0) 01 Value of each crop as percentage of total, 1969 . . 22 A.15 Value of each crOp as percentage of total value prOdUCt, 1974. O O I O O O O O O O O O O O O O O A.16 Net farm and net household incomes per consumer classes, by total crOpped land per household Strata, 1969 . 0 O I O 0 O O O O O O O O O O O A.17 Net farm incomesby consumer classes by total cropped land per household strata, 1974. . . . . A.18 Net farm income, net house income, off-farm percent of house income by village by land strata O O I O O O O O O O O I O O O O O O C O O A.19 Income rankings of households in both years' sample, Ipetu. . . . . . . . . . . . . . . . . . A.2O IncOme rankings of households in both years'. sample, Odo-ore. o o o o o o o a o o o o o o o o O 121. Figure 2.1 A.6 LIST OF FIGURES Determinants of net farm income per consumer.. . 17 Average monthly rainfall distribution, Omu-Aran 1957-1974. 0 o o o o o o o o o o o o o o o o a o 25 The percentage distribution of households based on net farm income per capita, 1969. . . . . . . 69 Percentage distribution of households based on net household income per capita, 1969. . . . . . 70 Percentage distribution of households based on . net farm income per capita, 1974 . . . . . . . . 71 Illustration of how technical efficiency is calculated. . . . . . . . . . . . . . . . . .130 Percent change in relative ranking between 1969 and 1974 with 1969 as base year. . . . . . . . .166 The percentage distribution of households based on net farm income per consumer, 1969. . . . . .213 The percentage distribution of households based on net heusehold income per censumer, 1969. . . . .214 The percentage distribution of households based on net farm income per consumer, 1974. . . . . .215 The percentage distribution of households based on net farm income per household, 1969 . . . . .217 The percentage distribution of households based on net household income per household, 1969. . .218 The percentage distribution of households based on net farm income per household, 1974 . . . . .219? xii LIST OF MAPS Map 3.1 Detailed map of the two study villages in Kwara State, with inset of Nigeria. . . . . . . . . . 24 xiii CHAPTER I INTRODUCTION A. Income Distribution and the DeveIOpment Question During the 19508 and 19603 development programs in less developed countries focused on stimulating growth in per capita Gross National Product (GNP). However, eXperiences in many develOping countries during this period showed that growth in per capita GNP alone was at best only a rough proxy for deve10pment. As defined later by Seers in "The Meaning of Development", (52), development takes place only when there has been an improvement over time in unemployment, poverty levels and inequality. But the distribution of wealth and income has in most countries become more unequal with time deepite a rapid growth rate in per capita GNP. This is believed to be the case in India (so) and Brazil (21), for example, in which the benefits from economic deve10pment have gone diSprOportionately to the rich. Over time the poorest households might have attained a marginally higher lever of income or wealth but such growth left them still relatively worse off compared with higher income households. Although researchers and development planners have be- come increasingly concerned with income distribution the lack of adequate data has, among other obstacles, restricted 1 2 effective policy action (38, p.l). Needless to say, severe difficulties are encountered by planners when there is a paucity of data. The eXperience of Stolper (54) in pre- paring the First Nigerian Development Plan has been well enumerated in his book entitled, "Planning Without Facts”. The lack of accurate data on income distribution in particu- lar has been eXpressed by Phillips (47) who showed that among feum- African countries studied Nigeria was the poorest with reSpect to the availability of data on interpersonal income distribution. The lack of information on income distribution, however, is only a part of a more general knowledge problem. There is also a general paucity of information on indigeneous farming practices. As an attempt to overcome the lack of accurate data on indigeneous farming practices in Northern Nigeria, D.w. Norman (42) undertook a socio-economic study of three villages in Zaria area during the late 1960's. The importance of such information was emphasized at the Ivory Coast Conference on Agricultural Research Priorities for Economic Development in Africa (56, pp. 139). The results of such studies have proven to be valuable in providing basic information for technical research workers in determining research priorities, and in giving extension workers some idea of what innovations are likely to be most readily adOpted. At the same time that Norman's study was being repeated in Bauchi and Sokoto areas, the Kwara State Ministry of Natural Resources requested that a similar study be carried 3 out in Kwara State. This request was considered by the Institute for Agricultural Research, Ahmadu Bello University, as an Opportunity to replicate Norman's study in a different ecological zone of the then Northern States. The Kwara State Ministry preferred that the study be carried out in two areas Okene, an Igbirra Speaking area, and Omu-Aran, a Yoruba Speaking area. The focus of attention in this study is the Omu-Aran area while the Okene study was conducted by another researcher and will be reported elsewhere. B. Problem Statement and Need The most recent National DevelOpment Plan of the Federal Republic of Nigeria has assigned the highest priority to the development of the agricultural sector. This commitment was framed within the broader objective of distributive equity both between geographic regions of the country and among individuals. It has been argued, however, (38. pp 7—9) that the lack of adequate information on incomes and of a policy relevant theoretical framework pose substantial obstacles for the design and implementation of income policies to Oper- ationalize the interpersonal equity objective. This agrument is summarized in the recent five year plan as follows: Inter-factorial and inter-personal distri- bution of incomes is at the heart of devel- Opment policy. For, while Optimal factor remuneration will ensure rapid growth, an equitable allocation of income among per- sons provides an effective transmission mechanism between growth and deve10pment. Unfortunately, Nigeria has never had an articulate and deliberate incomes policy. One of the main difficulties has been the complete lack of relevant data on the subject (20, p.35). 4 In his address (18) to the nation on domestic and foreign policies, the Head of State stated that an effective income policy will be established in order to curb inflation in Nigeria. However, in order to establish an incomes policy existing distributional information has to be improved. More- over, understanding income in the subsistence rural sector is a pre—requisite to sound income and development policies in Nigeria more generally. As a further step in meeting the objectives of the village studies set up by Norman as well as to provide basic data on the rural income distribution, this study concentrates on income generation of farming households in one area of Kwara State. The need for this study can therefore be summarized as- follows: 1. Priority given to agricultural deve10pment by the Federal Government of Nigeria is based on the ob- jective of an equitable distribution of incomes among sectors, geographic regions and individuals. 2. It is difficult to design income and pricing policies to fulfill the development objective due to inade- quate data with which to identify and describe the rural poor, and to examine factors associated with rural poverty. The design and the impacts of income and pricing policies on the rural poor cannot be predicted without full understanding of the level, distribution, structure, and stability of incomes among the rural pOpulation. C. Objectives of the Study The objectives of this study can be summarized as fol- lows: 1. To describe the levels, sources and distribution of personal incomes in two villages of Kwara State. 2. To describe the structural and behavioral charac- teristics among households in different income groups. The following characteristics will be considered: 1) Demographic make up of the household ii) Asset ownership iii) Cropping patterns iv) Patterns of resource use v) Variation in average returns to factors 3. To identify the most important factors associated with income in two villages in each of two years. 4. To describe and eXplain changes in the levels and pattern of distribution of net farm income in two villages between 1969 and 1974. D. Plan of Study This study contains ten chapters. Chapter II presents a brief review of research pertaining to income distribution in Nigeria. Chapter III describes the data collection method- ology and provides a general review of the ecological and economic characteristics of the study area. The levels, sources and distribution of net farm income and net house- hold incomes are discussed in Chapter IV. Chapter V examines the differences in structural household characteristics among 6 income strata. Chapter VI examines differences in the endowments and use of land, labor and capital among income strata. Average factor productivities and crepping pattern variation are examined in Chapter VII. Chapter VIII sum- marizes the major correlates with net farm income during each year of study through an econometric analysis. The inter-year variation in income is examined through a case study approach in Chapter IX. Finally, Chapter X summarizes the major findings and general conclusions that can be drawn from the results of the study. CHAPTER II LITERATURE REVIEW AND CONCEPTUAL FRAMEWORK A. Brief Review of Other Studies The literature on income inequality is large and has eXpanded rapidly during the past two decades. During the early 1950's Kuznets (34) examined the nature and causes of long term changes in the personal distribution of income. His seminal study addressed the question of whether income inequality increased or decreased in the course of a country's economic growth. Kuznets projected that income inequality would worsen for a period and then improve. Other scholars apart from Kuznets who have been associated with the question of income inequality include Chenery (ll), Ahluwalki (4 ), Adelman and Morris (3 ). These authors using a static com- parison of cross-country data to infer within-country dis- tributional changes over time, have generally confirmed Kuznets' projection. Others like Mincer (40) and Ranis and Fei (49) used within-country data to describe income dis- tributions within the countries concerned. The distribution of income within a country can be con- sidered in terms of three general types of decompositions. Firstly, decomposition by factors of production answers questions as to how much of income inequality can be 7 8 attributed to the existing production technology together with the distribution of labor, land and capital. An example of this approach was that of Gardner (24). Secondly, income inequality can be broken down into economic sectors. This decomposition can answer questions such as how much of total national inequality can be attributed to between and within sector differentials employing an agricultural/non-agricultural or rural/urban breakdown. The study conducted by Yu (60) in Taiwan, Fields (21) and Fishlow (23) in Brazil are examples of this type of approach. A third method of income decom- position is by income generation functions. The studies by Fields (21), Lopez (37) and Patrick and Graber (45) are ex- amples. Such a decomposition makes it possible to determine how much of total inequality can be explained by character- istics of workers and household production systems. The present study utilizes the latter approach. As mentioned in the first Chapter there are very few studies of income distribution in Nigeria. In 1975, Phillips (47) conducted a survey of literature on income distribution in Ghana, Keyna, Tanzania and Nigeria and found that Nigeria had perhaps the poorest distribution data on income. In 1965, the FAO published a detailed agricultural plan named, "Agri— cultural Development in Nigeria 1965-1880." The equity ques- tion as to who would benefit from the plan was not eXplicitly dealt with. Michigan State University's Consortuim for the study of Nigerian Rural DevelOpment 1969-1985 also assumed away the question of inter-personal distribution. The absence 9 of equity treatment in these studies can be associated with at least two factors. First, the authors may not have given priority to the question of equity due to lack of data. Second, income inequality is a politically controversial area which may not be apprOpriate for examination by eXpa- triates. In a country like Nigeria characterized by many internal diversities, indigenes themselves have found that a tOpic on income distribution could not be handled without some political risk. As a result the topic was relegated to the background by researchers and by policy makers in earlier development plans. Of the few previous studies on income distribution pub- lished in Nigeria most have made exclusive use of official secondary data collected for other purposes. Examples are Aboyade (1.) and Philip and Teriba (48). An exception to this more general pattern was the work of Essang. Essang (15) conducted an in-depth study to describe and eXplain patterns of income distribution among southern Nigerian farmers. Although his was the first study on income dis- tribution based on primary data, his analysis considered only incomes generated from a single cash crop, cocoa. At best this is only a crude approximation of household income since the farmers also grew a wide range of food crOps es— pecially cocoyams, yams, maize and cassava which contributed a-major prOportion of household income. Moreover, only a single year's data was used. Essang reported a very skewed distribution for both land and income. The Gini ratio was .68 for the distribution of 10 cocoa holdings, for example, and .79 for cocoa earnings. Moreover, he found a high correlation between political status and the distribution of cocoa earnings. The reason for this was found in the tenure system which gave the tra- ditional rulers custody over communal land. As a result the richer class had priviledged access to land as well as to modern inputs and credit. A second study of inequality was conducted by Hill, in a single village in the then North Central State, now Kaduna State (29). Although Hill made no attempt to es- timate actual levels of income, she classified 171 farming units into four groups delineated on their relative ability to "withstand the shock of an exceedingly poor or late harvest." The subjective classification that Hill developed provided a useful approach to examine factors associated with relative poverty, and to infer causal relationships. She found, for example, that high income households had more working members, more wives and larger farms. Hill's work, however, is not without its limitations. Since only one village was surveyed, it was impossible to ascertain varia- tions in income profiles due to market location and pOpulation density. Her study was devoid of statistical analysis and she was not able to estimate income levels directly. More recently, Norman (42) has summarized the results of nine village studies conducted in the Zaria, Sokoto and Bauchi areas. These studies provide a broad comparative view of the levels and distribution of incomes at the village level in the north. Table 2.1 shows that income distribution in 11 Table 2.1 Gini coefficients on net income for nine villages in Sokoto, Zaria and Bauchi Areas Village Net Mean Year ,Income -a Income of iPer Capita Per Capita Study Sokoto: 111.34 1968/69 "TEEEtuku 0.2648 Kaura Kimba 0.4043 Gidan Karma 0.2990 Zaria: 196.73 1967/68 Hanwa 0.3588 Doka 0.2986 Dan Mahawayi 0.5004 Bauchi: 75.15 1968/69 BIshi 0.3728 ' ‘ Nasarawa 0.3612 Nabayi 0.3873 —_i aNet farm income from creps and livestock excluding taxes.. Source: Norman, D.W. Hausaland," submitted for MSU Rural Development Series, 1979 and Pryor, D.H., "The Small Farmer in 12 Table 2.2 Gini coefficients on distribution of land in nine villages in Sokoto, Zaria and Bauchi areas.a Village Total Acres Cultivated Acres Soxoto: TERatuku 0.1987 0.1990 Kaura Kimba 0.4319 0.4279 Gidan Karma 0.2418 0.2518 Zaria: Hanwa 0.3635 0.3410 Doka 0.3997 0.3050 Dan Mahawayi 0.3568 0.4850 B auchi : Bishi 0.3419 0.3459 Nasarawa 0.3316 0.3486 Nabayi 0.5577 0.2876 a. The Gini coefficients are calculated on the basis of the families possessing the usufructuary rights during the survey years. Source: Norman, D.w. and Pryor, D.H., "The Small Farmer in Hausaland," submitted for MSU Rural Development Series,l979 13 Table 2.3 Gini coefficients on household income for three villages in Kano State, 1974 InCOme Village Gini Measure Coefficient Total Income Barbeji 0.2898 Per Capita Zoza 0.2251 ROgo 0.3034 All 0.2823 Farm Income Barbeji 0.3298 Per'Capitaa Zoza 0.2108 ROgo 0.3504 All 0.3183 Off-Farm Income Barbeji 0.4588 Per Capita Zoza 0.5562 R080 0.5464 All 0.5306 Non-Agricultural Barbeji . 0.5574 IncomebPer Zoza 0.6759 Capita ‘ Rego 0.5775 All 0.6097 Total Income Barbeji 0.3426 Per Household Zoza 0.2624 ROgo 0.3176 All 0.3146 a. Farm income is net farm income obtained from field and tree crOp production b. Non-agricultural income is equal to net off-farm income less earnings obtained through emplOyment asarhired farm laborer, Source: Matlon, P.J., "The Size distribution, structure, and determinants of personal income among farmers in the north of Nigeria", Ph.D. Thesis, Cornell University 1977, p. 77. 14 the nine villages was fairly equitable even though there were substantial differences in average income among regions. Norman defined net income as the net farm income from crOps and livestock. However, he did not include non-farm earnings. Table 2.2 also shows the Gini coefficients on distribution of land in the nine villages again reflecting a low degree of concentration. The studies summarized were of one year duration. Matlon's work (38) provides the most comprehensive study on rural income distribution in Nigeria. Focusing on three villages in Kano State, Matlon estimated household income from all sources Opened to each household, both agricultural and non-agricultural. One of the most unique features of Matlon's work was the generation of data on cash constraints, credit and participation in government programs. Table 2.3 summarizes his findings regarding the distribution of personal incomes which were in line with Norman's but contrasted im- portantly from the wide inequality implied in Essang's re- sults. Off-farm income provided by hired farm labor employ- ment was found to reduce inequality in the lower income house- holds but trading incomes increased inequality among the upper income households. Productivity of land and labor was found to be the most important determinant of income. In summary, the above studies (15,29,38,43 ) gave the following major findings: Hill found that there was a sys- tematic association between demographic factors and income. 15 Essang and Hill further reported close relationship between land holdings and income. Like Essang, Matlon found that access to agricultural extension and modern inputs were closely correlated to the relative income ranking Of a household. Essang asserted that higher income was closely related to political power. This was likely a result Of positive relationship between political status and land holdings, access to commerical sources of credit and to extension services. Hill could not pinpoint the causes of poverty status among the poor households but Matlon found that land and labor pro- ductivity were probably the most important explanatory factors. Matlon concluded that the efficiency of resource use rather than variation in resource endowment was more important in explaining income variation. The studies reviewed above have the following features: 1. Essang's study was on a single cash crop, cocoa, and the place of research was in the South-Western State Of Nigeria. He examined only a single year. 2. Matlon's study was conducted in the far north in an area where groundnut is the dominant cash crOp, though his study covered all farm and non-farm production. However, the fact that only one year's data was used by Matlon makes his results inconclusive as far as knowledge of the stability of income distribution is concerned. 16 3. Norman's study used a land-per-resident stratification to examine production relationships which according to Matlon (38, p. 13) is not an adequate proxy for in- come per resident. Thus behavioral and structural characteristics associated with income strata di- rectly could not be examined. Moreover, while he examined the production of a wide range of crOps, his study was in the north and was only for one year. B. Conceptual Framework of the Present Study Neither farm production nor income studies have been con- ducted in the geographical area in which the present research concentrates, the Nigerian middle-belt. This is a zone where most of the crOps grown in both the south and north of Nigeria can be found but where no single crOp has yet achieved the status Of a cash crop. The present study will therefore have the following unique characteristics: 1. It will provide information on rural income on the middle-belt, a different ecological zone from those of earlier studies. 2. Using the data collected during two survey years (1969 and 1974) it will be possible to see what changes Occur over time in the components, correlates and distribution of income. Examining data for two years also enables one to observe interstrata mobility Of households. Based on the review of earlier findings, income in this study is conceptually viewed(see Figure 2.1) as being a function 17 Land Resource Endowment < Labor 4._____1 Capital t Resource Use i Resource Productivity Management or “—————- 8. . Technical Efficiency 4: Net Farm Income'Qr 92::Family Composition I V Net Farm Income Per Consumer Figure 2.1 Determinants Of net farm income per consumer 18 Of family composition, resource endowment, resource use and resource productivity. 1. Resource Endowment Resource endowment include the stock Of land and capital and the available household labor force. A close relationship between income status and land was found by Essang, Hill and Matlon and between in- come and work force by Hill. Under certeris paribus assumptions if both labor and capital are not limiting it would be eXpected that the greater the size of land holding the greater the income generated. The total number of workers potentially able to work is deter- mined by the size and age/sex composition Of the house- hold. The number Of workers would be eXpected to determine how much acreage a household could endeavor to cultivate in a situation of surplus land and limited Off-farm employment Opportunities. Under the tra- ditional technology in which hoes and cutlasses are the major tools and baskets and calabashes are the major equipment, a close relationship cannot be ex— pected between farm income and capital stock per worker- However, Operating capital might be closely related to income if efficiently used on seeds, fertilizer and hired labor. Since Operating capital is directly re- lated to savings it follows that the previous period's income would be a critical factor determining current income levels. 19 Resource Use The total quantity Of any factor available for use determines, in part, how much can be potentially employed in the production process, but the quantity actually employed will be more closely related to realized income. Thus, the total man-hours input may be an important determinant Of both farm and non-farm income. Given the available work force and complementary inputs, how hard each worker labors (man-hours per worker) is a result Of three variables: output per man_hour, the disutility Of labor, and the utility of income. These, of course, are determined by a range of factors including the worker's income- leisure utility function, age, health status, resource quality, etc. Resource Productivity Hill contended that the higher income groups were more competent farm managers generating higher marginal and average returns to labor. Matlon also found a strong positive relationship between income status and factor productivity. Differences in income per consumer, our welfare measure, could be widened given available resources and use levels if there is sys- tematic variation in factor productivity among house- holds. Factors which affect productivity may vary among households and for individual households over time. 2O Illness generally not only leads to loss of working days but it can lower the efficiency of the worker. Both the loss in working days and efficiency can lead to untimely Operations which can result in reduced yields. Differences in crop mix might result in lower or greater output per acre or per man-hour. The levels Of use Ofi‘nputs can also result in productivity differences. The skill Oflcombining the inputs and conducting timely and apprOpriate operations are also important factors. Finally other factor quality differences such as soil and climiate can affect pro- ductivity. 4. Family Composition ,Family composition in the form of number of consumer units sharing the net farm income determine the size of the net farm income per consumer. The consumer- tO-worker ratio, would be expected to influence the income per consumer in an inverse direction through its influence on the cultivated land per consumer ratio. Interhousehold differences in the above sets Of factors are hypothesized to be the major contributors of income inequality. Through tabular presentation and discussion we identify the relative importance Of each of these sets within each year's data. Through this examination, it is hOped that an understanding is gained as to the basic causes of poverty. 21 Similarly, the movement of households between strata during the two time periods will be examined within the same frame- work tO determine the most important causes Of relative income changes over time. The present chapter was concerned with the review Of literature and the conceptual framework for this study. References will be made in later chapters to the various aspects Of the framework presented here. The next chapter contains the description of the study area, sampling pro— cedure and data collection methodology. CHAPTER III THE STUDY AREA, SAMPLING PROCEDURE AND DATA COLLECTION METHODOLOGY A. Description of Omu-Aran Environment1 The middle-belt of Nigeria lies between the Sahel savannah in the north and the rain forest zone in the south. The middle—belt is characterized by large expanse Of un— cultivated arable land drained by the Niger and Benue rivers. According to the FAO Nigerian Agricultural Development Plan 1965-1980 (16), the middle-belt has perhaps the greatest agricultural potential of any region in Nigeria. Kwara State occupied about a third of this high potential agri- cultural region. Omu-Aran, the study area, is located in the south—central portion of Kwara State. The two villages of Ipetu and Odo-Ore were selected for intensive study within the Omu-Aran area. The criteria for village selection and village characteristics will be discussed later. 1Most of the material in this section has been taken from description Of the land resources area Of the Northern State of Nigeria by K. Klinkenberg, Head of Soil Survey Section, Institute for Agriculturan Research, Ahmadu Bello University, Zaria, Nigeria. (Unpublished work). 22 23 1. Climate and Vegetation The climate Of Omu-Aran can be classified as sub— humid with a severe rainfall deficit from November to March, with rainfall concentrated in the April to October period. Rainfall is bimodal with an average annual level or about 62 inches.1 The first peak occurs in Nay-July and the second higher peak in September-October. The seasonal rainfall dis- tribution is shown in Figure 3.1. Omu—Aran is situated in the Southern Guinea Savannah zone. While this area was originally forested, most large trees have been felled leaving only scattered patches of forest. The area is now a derived savannah zone covered by grasses such as AndrOpogon and Hyparrhenia species. There is a wide range Of crops in the area the most important of which are yams, maize, guinea corn, cowpeas, cassava, vegetables and cotton, groundnuts and cocoa, bananas, plantains and cocoyam. 2. Geology Omu—Aran and the surrounding area is underlain by a mixture of rocks, of which gneisses are the most widespread. The area is dominated by plains separated 1The 62 inches annual rainfall reported was a 8—years' average figure Obtained from the scanty rainfall records Of the Ministry of Natural Resources Igbomina/Ekiti Division Omu-Aran and Omu-Aran Women Teachers' College. 24 [ .Soko . To Ora Zaria 0 10 N Odo-Ore '.Isui ‘f’lgwo Study Area ” OWU Port . .- Kwara State ’ Harcourt: Iji ‘5 To Ilorin ‘Oke-Onigb in Laterite Road Omu-Aran +H+++H4++H+ Railway Lines Miles Nap 3—1 Detailed map of the two study villages in Kwara State, with inset Of Nigeria 25 .ml< canoe.oom.uoon:cm wcaalhmma cmna£pCOE mmmgo>< H.m Oasufim mzezo: com >02 #00 poem .ws< hash case >82 cn< no: new new 4/ a q JII 4 q 4 4 \t 1 a. . L a . a , ; 6801660 when Haeecaem.lurl..ll. , zuccH vsma Hammchmn.;uzl N I o.oH cema semsbbea ..:.I . RN 16664 notes Haeecaem . \ «M.\ . . c.HH momalhmma Hammcamn Ommpo><.llll. I \ M14 .1 La 1 c.ma t u? . K . . o.ma seuour ur IIBJUIEH fituiuom 26 by groups of hills and steep quartzite ridges. Granite hills located near Osi rise to an altitude Of 1300-1800 feet. Osi is located about 12 miles south—east of Omu-Aran. agile Inselbergs and hill masses with shallow soils and rock outcrOps which limit cultivated area are common. The upper lepes Of the plains have 0.50 to 1.5 meters deep sandy clay loams. Locally, under high rainfall or over schists, very deeply weathered pro- files may be found. Nearly all soils contain iron concretions, locally hardened to form an iron pan. The soils are classified as Ferrisols and generally have a moderately low cation exchange capacity and a low base saturation. Soils on amphiholite tend to be richer in plant nutrients. The soils are perhaps most deficient in nitrogen followed by phosphorus and potash. Human and Political Omu-Aran inhibitants are Yoruba speaking. With the creation of states and subsequent creation of admin— istrative division in Kwara State, the area occupied by Igbomina dialect and Ekiti dialect speakers were combined into one division called Igbomina Ekiti Division. Being one Of the Oldest Of the major towns Omu-Aran became the headquarters of the division in 1968. 27 Both study villages possessed a chieftancy institutional arrangement which means that political power in the village lay mostly in the hands of the chief and his subjects. A number of secondary of- ficials assist the chief in carrying out his re- sponsibilities. An Elemesho acts like the public relations Officer and is next in importance to the chief. The Oluode, as the head Of the hunters and of youth, assumes the responsibility of arranging the time and place for the hunting season. He is also responsible for gathering the youth to work on the farm Of the chief whenever the need arises. In modern times there is also a councillor from each village who accompanies the chief to divisional headquarter meetings. He is paid a small fee for his services and is considered the political leader Of the village. There is no landless class because every male and female member of the villages possesses the right to crOp the land. The only exception is forest land for which permission is needed from the clan head owning jurisdiction over it. The land tenure system is purely traditional. This is to imply that there are no sales of land and most of the land is said to be Obtained through allocation and inheritance. The allocation Of land is done by the chief and his subjects but each family can pass 28 down to future generations whatever land has been allocated to it. There is no evidence to suggest that the political and social institutional powers have been used to favor particular classes Of farmers. Occasionally, a farmer can borrow a portion of a land from another friend if it is not under use. For such transfers there was no record of any payment being made to the owner. The market system is also largely traditional being held once in five days. Traders come from Omu-Aran to sell manufactured articles including clothes, lanterns and shoes while some come to pur- chase farm products for resale at Omu-Aran markets. Some petty traders also live in Ipetu and go to Omu- Aran or Ilorin or Oshogbo to purchase their re- tailing wares. Some farmers carry their farm prod- ucts by head load or lorry for sale in Omu-Aran. Similar transactions take place between Ora and Odo- Ore. With less commerical vehicles plying between the two villages of 0ra:mmiOdo-Ore, most Of the goods are moved through head loads. The markets are held at five day intervals in both Omu-Aran and Ora, but Omu-Aran is the larger market. B. Choice of Villages Two villages were studied during the 1969/70 and 1974/75 cropping seasons. The following criteria were taken into account in the final selection of these villages: 29 Experience has shown that the village head is most influential in determining the cooperativeness and attitude of the village toward the survey. Great care was therefore taken to find villages whose heads would be sympathetic tOwards the aim Of the project. It was intended that aerial photographs of the area of the study would be taken to show clearly all field boundaries. Village areas devoid of steep lepes were selected to avoid the need for corrections for slope distortions in field measurements. Limited time and finance was available for the study. Since a census had to be conducted to establish a sampling frame, villages were chosen which had a population below 1,000 inhabitants. TO ensure adequate supervision of the enumerators throughout the year it was considered necessary that even the most isolated village should be ac- cessible, at least by bicycle, during the rainy season. The chosen villages should differ in ease Of com- munication from Omu-Aran. This selection was based on the concept that important differences between villages may arise as a result of differences in market access. Villages were selected to represent two general ecological types. The villages further south and 30 at the border with the Western States of Nigeria are more heavily forested and tend to be wetter. To the north, villages are somewhat drier and more rep- resentative of the derived savannah area. A village was chosen to represent each ecological zone. The two villages were about 24 miles apart with one situated in each climatic zone. The two villages selected were as follows: i) Ipetu was located in Omu-Aran District, four miles southwest of Omu-Aran and sit- uated on one of the best roads in Kwara State linking Kwara to the Western State. The total pOpulation was 768 in 1969 and 864 in 1974. Ipetu represents the forest type village thus rainfall would be ex- pected to be higher in Ipetu. Although rainfall estimates were not available in in 1969 due to lack of rain guages, rain- fall in Ipetu during 1974 was 40 inches in 1974. This figure was only 62 percent of the 8 years' average reported above. Nore— Over it was also unexpectedly lower than in the other village. It should also be noted that rainfall was less well distrib- uted in 1974 compared with the other village. A lower September peak was Obtained (Figure 3.1) and there were no rains at all in 31 February and March as in Odo-Ore, the other village. ii) Odo-Ore is situated in former Ishin Dis— trict about twenty miles north-west Of Omu-Aran town. Situated ten miles Off of the main Ilorinl-Omu—Aran interstate road it is more isolated than Ipetu. It is, however, motorable throughout the rainy season. The total pOpulation was 593 in 1969 and 608 in 1974. Odo—Ore represents the purely derived savannah type. The survey rainfall estimates in Odo- Ore in 1974 was 43 inches, below the 8 years' average Obtained from other sources. Odo-Ore is about nine miles away from Ora, the market outlet for Odo-Ore, while Omu- Aran only four miles away is the village's major market outlet. Thus Odo—Ore has more difficult access to the large external mar- kets. In 1969 the cultivated land per resi— dent ratio was 0.45 acres at Ipetu, while Odo-Ore had 0.38 acres per resident. There was no data on the total acreage crOpped by lIlorin is the Headquarters for Kwara State and about fifty miles away from Omu-Aran. 32 each village in 1974 hence the total land per resident ratio cOuld not be calculated. Based on the 1969 figures, however, it appears that Opulation pressure on land is more acute at Odo—Ore than Ipetu despite its greater distance away from the urban influence. The reason for this situation is mostly due to a large portion of Odo— Ore land which is uncultivable due to rock outcrops. Only minor differences in the types of crops grown characterized the two villages with cocoa, cocoyams, plantains and kola nuts grown at Ipetu, the forest land but not at Odo—Ore the drier village. Neither Ipetu nor Odo—Ore have been importantly influenced by the presence Of an extension worker. The extension agent responsible for the area was stationed at Iwo and was expected to serve Odo-Ore and about fourteen other surrounding villages. Due to lack of transportation and an in- adequate supply Of extension inputs to Iwo itself, the influence Of the extension agent was not felt at all in Odo-Ore. Omu-Aran, being about 4 miles from Ipetu, was the base for the extension agent to serve Ipetu. 33 For reasons similar for Odo-Ore, Ipetu was not importantly influenced by the extension worker. C. POpulation and Land A detailed enumeration Of the pOpulation in the two villages was conducted each year to provide frames Of farmers in each village from which samples could be drawn. Tables 3.1 and 3.2 show age distribution of the population in both villages and for both years. For both villages more than 45 percent of the population was less than 20 years of age and nearly 30 percent were less than 10. These figures are similar to those Obtained by J.C. Gibbs in his Bauchi study (25). The average number Of adult male equivalent worker units1 was about 6 per household. Although the average number of residents per family was about 9 in 1969 and about 7 in 19742, this did not necessarily reflect a reduction in family size between the two years of study. Rather the differences could have arisen from a number of problems which arose in the data collection procedure: 1. There were some definitional problems encountered during the census. In 1969 the farmers did not lWorker units were obtained by assigning weights on the basis Of age/sex to the number of residents found in each household. The coefficients applied to estimate the worker equivalent units are shown in Table 3.3. 2 . . . . . . Th1s is shown in Table 5.1 which contains detailed household size and composition treated in Chapter V. mama >6>43m "oonsom 34 66.6 1 66.664 666 666 466 6666 44< 66.0 00.004 «v.6 we mm 64 6665 go 06 sm.4 mm.mm mh.m em 64 64 mm30m 66.0 mm.6m mv.04 mm mm mm melov mm.o ov.66 m6.m4 me me 6m mmlom OLOIOUO 6m.o m6.m6 mv.mm mmH 66 mm mmION 4m.o mm.4v mm.64 mm em we 64104 m6.o mS.vN mh.wm Sea mm mm mlo 66.6 1 66.664 . 66s . 666 666 6666.44< v0.4 00.004 mm.6 my em mm OLOE no 06 66.0 mm.mm mm.m me 6m 64 mmIOm mm.o 66.66 em.h 46 ow 4m melee mm.o mm.mh om.m4 604 66 mm mmlom succH mm.o No.66 60.64 _ HmH mm mm mm:0m m6.o mm.mw 04.64 mm4 em mm @4104 mm.o om.om 6m.om 6mm 6N4 444 6:0 Oaumm.. Ommpcoonom Hence .Hmuoe 646566 6462. Amnhv om< ow6444> 646866 o>HpmHOEso mo \Oamz OmOpcOopom AODEOZ 6664 .mbonoeo 6:6 24664 .8646646666 46664 66 :64psb4t464e 66< 4.6 64666 35 6660 66>656 “666306 66.6 1 66.664 666 666 666 6666 446 66.0 00.004 40.64 66 66 46 6.4666 no 06 66.0 66.66 66.6 66 mm 44 66:06 66.0 .60.66 66.6 66 66 66 66106 66.0 66.66 66.64 66 66 66 66:06 6601660 66.0 60.66 66.64 66 66 46 66:06 66.0 66.66 66.64 66 66 66 64:04 66.0 66.66 66.66 664 66 66 6:0 .66.6 I 66.664 .666.. 666..,.666. 6666 446. 44.4 00.004 66.6 66 66 66 6.46.: no 06 66.0 66.46 66.6 66 64 64 66106 60.4 66.66 66.6 66 66 mm 66:06 66.6 66.66 46.64 644 66 66 66:66 66664 66.0 64.66 66.64 464 66 66 66106 66.0 66.06 66.46 664 66 66 64:04 66.0 66.66 66.66 666 464 664 610 O4pmm ommpcoopmm 46606 4mpoe 646Eom .6462 46660 cw< 666444> 646566 O>Hpm4SESU Mo \6462 ommpcmoaoa 660502 6664 .666u666 6:6 66664 .6646643666 46666 66 664666466646 666 6.6 64666 36 Table 3.3 Coefficients applied to estimate the number of man-equivalent worker units per household Male Female Age 0-6 7-14 4l5+ O 0.50 1.00 O 0.50 0.75 seem to understand the precise definition of the family as it was explained to them. A family was defined as consisting of the people who were eating and working together. Some households interpreted this to mean peOple who were living together under the same roof even though they could have censisted of more than one family given our definition. Some of the villagers were skeptical about the pur- poses and intent of the study in the first year. Fear was exercised by some that their incOme taxes would be increased if the true number of families in their compound was revealed. At the end of the 1969 survey year each participating household head was given gifts in appreciation for his participation. Compounds which could have been reported to consist of more than one family received less total gifts than would have been the case. Therefore, in 1974 it appeared that more families were willing to be counted separately both because of the gifts and because the 1969 survey had no effect on the income tax. 37 It is important to note that this apparent discrepancy in the family size definition could affect the inter-year comparison of incomes particularly the income per consumer figure, our welfare measure. It would also affect the comparison of household incomes. The average land farmed per household in 1969 was 4.69 acres in Ipetu and 3.41 acres in Odo-Ore. In 1974, however, Ipetu had 3.70 acres and Odo-Ore had 4.36 acres per house- hold. In both villages substantial acreage of upland fields remained in bush fallow. This meant that the farmers could continue to practice shifting cultivation. However, there was little that could be termed virgin land and the fertility of the soil was not importantly improved before the farmers returned to the fallow land. The average fallow period was only about five years with no appreciable difference between villages. There were still some virgin forest lands at Ipetu apart from the upland fields. However, the clans who hold the right to them would not allow them to be develOped without permission. D. Representativehess Loom n hoesmcoo pom m mafiamo gmm paocomsomugom .Amgamc may vsma cam mama .cHozowso: cam mmmHHH> hnlwmsooca poc cams mrv wands 49 mama >o>p3m ”oopsom .LOpmemp ocp mo mcofipmHSOHoo how ml< pcm HI< moanme mom .Lmoz puma mo moma Spa: mgouooo pow oma ocm :quH pom vma wo nonwammp moOHpQ poxnos popswfloz omeHH> on» mean: coprHMCH pom pcsooom on mHo>oH mood cu pmumSmcm coon o>mn mmgzmflm osoocHo .pmoz mums mm swam spa: quad CH mom 5cm mama cfl mma mm: cows: xmecfl oofigc possmcoo cHgoHH wcflms coapmfiwca pom ucsooom on mHo>oH momH ow popmzmpd coon o>d£ mopsmfim oEooch .oEoocH Hoqu 0p oEoocw Shawlmmo mo oHpmh ecu pmosfiocfl ma mamocpcohmd CH0 .m.v manna Ca pocHMpcoo who .mpcoEmpfisoog oHLono mmeHKOLQQm wcfiucomogdoh .mpcwHoB one .xmm cam 0mm m.compoo mSp mo mammn map :0 com lama come on magmaos wCHhaaom kn oopSQEoo who: mucoam>azconcwe poESmQOU .mpcoaw>fi:cm Isms poEDmcoo >9 moEoocH um: opmwonmmm mcfipfi>fip ho oocHMuno ohm LoESmcoo Loo moEoocHn .paocmmsoc comm CH mcowpoo mo LooEsc HGHOH >9 moEoocfi uoc opmwohmmm MCHUH>flp an pocflmpno mam opfidmo Loo moEooch AU.pCOov m.v ®HQGB 50 Table 4.4 A.comparison of mean incOmes per capita and per household and off-farm inCOme as percent of household income obtained in three Nigerian surveys. Mean Household Hean Off-Farm Income Place Income Per Household Percent of and Capita Income House Income Yearcxf (Haira) (Haira) Study 1 a a N Norman 28 (23) 206 (169) 22a Zaria 1967 Matlon2 52 (l9)b 350 (129)0 28% Kano 1974 ciukosi3 45 (34)C 337 (255)C 19% {waral969 Figures in bracket are deflated values. We are reducing each figure to 1957 constant prices using the urban con- sumer price index. aConsumer Price Index at Kaduna was 122 in 1967 with 1957 100 (See Table A-4) 0Consumer Price Index at Kaduna was 271 in 1974 with 1957 100 (See Table A-4) CConsumer Price Index at Ilorin was 132 in 1969 with 1957 100 (See Table A-4) Sources: lNorman, D.H., ”An Economic Survey of Three Villages in Zarior Province: Input-Output Study," Vol. 2. Samaru Misc. Paper 38, 1972. 2Matlon, P.J., ”The Size Distribution, Structure, and Determinants of Personal Income Among Farmers in the horth of Nigeria," Ph.D. Thesis, Cornell University, 1977 3s survey Data 51 percent of net household income. In Norman's study off-farm income was about 22 percent of net household income while Matlon estimated the prOportion to be 28 percent. The present study found 19 percent. Net household incomes per capita were H28 and 352 for Norman and Matlon, respectively, while the present study observed H45. It is likely that differences shown in these figures can be attributed largely to differences in agroclimatic conditions and to price changes. Table 4.3 shows that in 1969 Ipetu, the forest—type village located closer to the major market, had sub- stantially higher figures in all nominal income measures than did Odo-Ore, the more isolated village located in the savannah zone. Ipetu also had a slightly higher figure of off-farm income both as a percent of total net household income and in absolute terms. This might be due to proximity to 0mu-Aran, the large market center which permitted greater access to off-farm opportunities. In 1974 there was no difference between net farm incomes of the two villages, however, Odo-Ore had a higher net farm income per capita and per consumer. The reason for this reversed situation between years is due in part to better rainfall distribution.in Odo-Ore in 1974.1 1Figure 3.1 and Table A-5 show that Ipetu had no rainfall recorded in February and Harch unlike Odo-Ore which also had a higher September peak and higher rainfall in August. 52 In the whole of Kwara State, Ilorin is the only town where the Federal Office of Statistics gather price data to estimate consumer price indices. In 1969 the consumer price index for all foods was 132 and in 1974 it was 302, with 1957 as the base year (17, p.116). If 1969 is used as base year (i.e. 1969 = 100), the 1974 price index was approximately 229. This means that between 1969 and 1974 in Ilorin prices of food have increased by 129 percent. However, since Omu—Aran is less urbanized than Ilorin, it would be expected to have a.less dramatic increase in relative prices between 1969 and 1974. This is because Ilorin as the State headquarters would be expected to experience a faster population growth rate which would result in increased demand for food products. At a nearly con- stant level of food‘supply retail prices of food crops would be expected to rise. For this reason a second price index was calculated from the survey price data. For crops which were grown in each of the two years and for which price information was obtained, the prices in each year were weighted by multiplying the price by the value of each crop as a percentage of total value of all crops grown in all households during the year of study. These weighted values were summed for each year and the difference between them is expressed as a percentage of that of 1969 (the base year). Using this method the percentage increase in price between 1969 and 1974 was 53 94 at Ipetu (Omu—Aran market) and 90 at Odo-Ore (Ora market)} The two price indices were used to deflate 1974 income figures shown in Table 4.3. The average net farm income per household in 1969 for both villages was H274 increasing to H463 in 1974. In nominal terms this gives a 69 percent increase over 1969. But in real terms (after deflating) the house- hold incomes in fact decreaseciin 1974 using both price indices. Considering each village separately, however, average incomes in Odo-Ore increased between 1969 and 1974 using the weighted market price deflator. This increase in Odo—Ore is observed for income per household, per capita and per consumer. In short, while real in- comes decreased in Ipetu during the period, a real in— crease in incomes was experienced in Odo-Ore. Size Distribution It was stated in the first chapter that the distributional impacts of alternative policies in develOping countries is receiving greater attention. But since many types of distributions can occur substantial measurement problems 1' Appendix Table A - 3 shows that in 1969 prices were higher in Omu-Aran market for 6 out of 9 major crops. On average,prices in Omu-Aran were 34 percent higher than Ora market. In 1974 prices were higher in Omu-Aran for 8 out of 9 crOps with an average difference of about 41 percent. It would be recalled that Ora is the nearer market to Odo- Ore and Ipetu is located more closely to Omu-Aran. These figures also show that intervillage income differences are in part due to prices. 54 have been encountered in uniquely quantifying changes in distribution. Champernowne (10, pp.787-816), for example, has tested six inequality measures and found that the standard deviation of the log of income and the harmonic mean formulation were the most sensitive for ranking distributions characterized by differences in the extreme low income range. The coefficient of variation was found to be most sensitive in discrimi— nating distributions with extreme inequality in the high income range; while the Gini coefficient was more sensi- tive to transfers in the middle income range. Due to the unique sensitivities of these measures, three approaches have been used in this study in order to describe the underlying distributions. Tables 4.5 and 4.6 present the size distribution of net farm in- come per household, per capita and per consumer unit for each stratum in the total sample for each year and each village. Similar information for net household income is in the Appendix. For the village net incomes per consumer strata, the households in each village sample were arrayed according to the size of their in- come per consumer. The poorest third was allocated into the low income stratum, the second third into the medium and the richest third into the high income stratum. A similar array and allocation was used for the incomes per capita and per household stratifications. Allocating households into the combined or total sample strata for oc.vm mo.mm mc.vnv awe: Amsmnv nm.cm mm.cm Acemmv Ha.moa cc: mm.ma vm.aa mm.nm seq otnma mv.an mo.mv Ho.aov swam Acacmv ms.om me.mw Aceemv am.mca cc: om.ma mn.m mo.mv sou neema 5 ma o.ooa mc.m o.ooa nm.m ms.mca vm.aaa o.oo~ mm.aam swam 5 ma o.vc mm.m n.m© sn.n vm.m© sm.om v.vm mm.mcm cc: esma ma m.mm av.v m.mm sm.c mc.om ma.mm m.n om.m0a sou ma o.ooH mv.v o.ooH 5H.© oa.mm mH.mn oo.ooa um.aom .nwam ma c.sn mv.s n.ms mm.aa om.av om.mm o.mm ca.cmm cc: acma ma c.ov vH.m m.mm mm.aa sm.ma vm.aa c.aa mm.ma sou 6H0: Annamzv pmm> mLmESm nomson mcom paon Angamzv Annamzv oEoo paonmmsos wpao: Icoo mo \mamESm lama no Immson spasm mumamo lam no Log 0800 mmpmaum Immson R m>Hp Icoo & m>Hp \mcomamd.¢00\osoo ppm @500 & m>Hp ICH summ msoocH mo .02 ImHSESU oc.m>< Inaseso .oc.o>< ICH .o>< ICH .o>< Imaseso ”tfla.m>< 59mm #02 .vsma can acma .caaeem fiance .cpmspm oeooca Emmy no: kn mums: nmszmcoo pom .mpCmpHmmh mo homes: .mEoocH m>HpmHDEzo ocm mwmam><.m.v manna 56 puma >o>hzm "oopsom .LOpmHHmp mnp Ho mcoHuanono now mn< pcm Hn< mmHnme omm .pmmz ammo mm momH csz OQOIOUO mom omH pcm SumaH pom va Ho gownHHop anHLo pmxnms pmustoz mmmHHH> any wchs coHpmHmcH mom pczooom on mHo>oH momH on ponSnpm coon m>m£ amasmHH mEOOCHo .pmmh ammo mm smmH 59H: vsmH :H mom pan momH CH mmH mm: :Ost xmch oOHLQ meSmcoo choHH wch: coHpmHmcH pom pcsooom ow mHm>mH momH ow pcpmshpn can: o>m£ mopsmHm msoocHo .Esumgpm cmHs mnp oucH pAan amazon.H any can sapmgpm EdemE mm» on pAan pcoomm any .ESHwnum 30H can on pHocmmson on» no paHnu pmmgooa an» mCHHMQOHHm can gmszmcoo Ama oEoocH Sham pm: on mchhooom mpHonmmson any mchcmnpw an posmHHdEooom mm3 numnum osoocH cch mpHocmmson Ho pcoscmHmmm oxen Ac.ucocv m.c wanes 57 o.ooa oo.s me.ema cs.mma o.ocH sv.maa a swan m.vo no.5 om.Hm mH.©m m.mm os.mnm a EDHpmz mnouopo o.mm mm.v no.0v u©.mm m.© Hv.mm m 304 . . . . . . van o.ooH or m mm omH om.mm. 0.00H we.oom 0H cmHm v.ac ca.m oo.oc mm.nv H.mm ma.ccm ca co: spodH H.mm om.u vm.ov om.mH w.m Hw.HmH 0H 304 o.ooH mm.m >¢.mm. ma.um o.ooH_.Hm.HHv m anm. ©.©s oo.m mo.mm Hm.mm m.mm vm.u©H s cm: mnouopo n.mv mm.mH ms.mH Om.m m.mH mv.mn m 30A . . . . . . , mmmH o OOH on.m hm mHH .oo.om 0.00H aw an 0H anm. 5.0m mn.HH mv.0m mo.mm O.Hv om.mcm m pm: anodH m.vv Om.mH VH.HN ov.mH v.vH mm.mmH OH 304 AmAanv amm> AmaHmzv AmnHmzv 0H0: mHCoUHmmu whosdmcoo prQmo mEoo nomson . mucmpHmmn mo Log hon ICH mo hog mpHo: Ho & amass: msoocH msoocH R m>Hp msoocH ummso: ®>HumHssno mmnum>< mwmnm>< mmnam>< umHssso mwnno>< Ho .02 ESHMme mmmHHH> «smH pcm mme .mmmHHH> some CanHz muwppm oEoocH ho mHHCS LmESmcoo pan mpcopHmmn mo amnesc .meoocH Spam pm: m>HumHSESO 6cm «mapm><©.v oHowB 00m .00H H OOOH :pH3 onouopc pom OOH Oocuoe mmoesa poxhos OmpmemB OmmHHa2 0:4 damn >m>nsm "oopsom ncoHH0430Hno 0:4 pom mu< Ocm Hu< noHcma Ocm SpocH pom VOH O0 xoch cm m>mw 50H23 mch: OmumHHOO 0903 monsmHH-oeoocH VBOHH 4 . , .004 u smoa spa: enoa :H mom OOOH CH mmH n63 50H53 HLO CHLoHH mchs OopmHOmO who: monszm mEQOCH numHm nn uu mm.4m sn.0c nu om.nms m swam uu nu mm.mv mn.mm nu m4.mm4 O no: mnouopo nu nu mv.vw HO.mH nn sm.Om m 304 uu nn om.sm om.mv nn nm.mcs 04 swam cvsm4 nu nu Om.4m so.mm un mv.OmH OH pm: DquH uu nu OH.VN 54.04 un cm.mO OH 304 % nu un mm.sc mo.nm nu mm.¢ov a swan nu uu mm.Ov Os.Vm nu Hm.mO4 m cm: mnouopo nu un bm.Om ON.HH uu OO.HV m 304 nmwvnm4 un nn sm.sm cm.4v nn mm.cam ca swam nn nu ov.cm mo.0m nu «4.484 04 cc: datum uu nu mm.Om H0.0 un OO.mm OH 304 AmnHmzv AmnHmzv AQLHQZVHHTia mucmpHmmn nmesmcoo mpHamo mEoo nomsoc mucmonOL mo nma Lou ucH mo and mpHon . m0 § Amossz msoocH osoocH & m>Hp msoocH umnson m>HumHSEDO mmwpm>< mm¢pw>< mmmnm>< umHssso mmmn0>< H0 .02 sapmnpm ommHHH> A0.pco0v O.v mHnt 59 the three income measures was achieved in the same man— ner. Because of intervillage income differences there- fore, it is possible for a household to belong to dif- ferent strata in the village and total groupings. Table 4.5 shows that during 1969 the low income group on average earned only 511.34 net farm income per capita compared with H72.l9 for the high income group. The data also showed that during 1969 family size and income per capita varied inversely, with house— holds in the lowest income group oomposed.of‘about 12 persons per household cOmpared with only 6 among the richest households. In 1974, undeflated net farm in- 00me per capita of the poorest third was 322.15 com- pared with Hlll.54 for the richest third. Moreover in cOntrast to the earlier pattern household size varied directly with income. While the poorest third on average had 6 persons per household, the middle and richest third had 7 and 8 persons, respectively. For the two village combined sample the real incomes of the lowest and medium income classes did not change appreciably between 1969 and 1974. However, a 20 per- cent decrease in real per capita incomes was observed for the richest class. Thus, inequality in per capita incomes decreased during the period. For the individual villages, however, the decrease in inequalities arose for different reasons. In Ipetu, for example, the poor- est class experienced the smaller decrease in real per 6O capita incomes, 24 percent compared with 30 and 38 for the medium and high classes respectively. In Odo-Ore, on the other hand, the poorest class had the greatest percentage increase in real incomes, 43 compared with 18 and 5 percent respectively for the medium and high income classes. The ratio of income per capita between low and high income strata in Ipetu and Odo—Ore was 1:5.9 and 1:6.1 respectively in 1969. It appeared that in 1969, Ipetu with the easier access to the market showed less income inequality than the more isolated Odo-Ore village. In 1974 the ratio of income per capita between low and high income strata in Ipetu was 1:4.9 and 1:5.1 at Odo-Ore. It would be recalled that the above figures rep- resent only farm incomes. The effect of non-farm in- comes on both relative inequality and the absolute in- come differences between strata can be identified by examining the distribution of total household incomes per capita among strata. This is done in the Appendix. The data show that the low income group on average earned 317.66 net household income per capita compared to H80.87 for the high income group in 1969. The cummulative percentages of incomes and residents of of total sample in 1969 reveal that the poorest third of households (42 percent of the pOpulation included in the low stratum) obtained only 19.7 percent of net house- hold income while the richest third of households (21.30 61 percent of the population included in the high stratum) obtained 41.8 percent.1 In 1969 the addition of the off-farm income thus increased the share of the poorest third from 11.5 (Table 4.5) for net farm income to 19.7 percent for the net household income (Table.A—6). The share of the richest third also decreased from 61 per- cent (Table 4.5) to 41.8 percent. Thus relative in-' equality was reduced with the addition of off-farm in- come, although the absolute income gap widened slightly between strata. Tables 4.7 and 4.8 present three summary measures of size distribution of net farm income and net house— hold income per household, per capita and per consumer for 1969. The three measures used are: The Gini coefficient, defined as: n u 2 , Ian u) s: : u‘xi-le n i=1 3:1 The coefficient of variation defined as: V u The Standard Deviation of the Natural Logarithm of income, defined as: LyELog (5*):‘21‘ (y) dy \ 1The above results fall in line with the figures reported tn? Ilatlon. Matlon found that the poorest third of the house- he’1ds earned about 18.6 percent compared with the richest ttkird which earned 46.“ percent. 62 Where for the three measures: V standard deviation of income u = arithmetic mean of income u*=-harmonic mean of income = an income observation V y 2 income of observation 1 Y i j= income of observation j — = maximum income observed n = number of individual observations Two values are given for the coefficient of varia- 5 tion and standard deviation of natural log of income. The first is the absolute value of the coefficient while the figure in parenthesis is a standardized measure such that zero equals perfect equality and a value of 1 equals perfect inequality. The conversion1 follows after Champernowne (10, pp.787-816). The Gini coefficient is already standardized. The Gini coefficient for the net household income per capita for the total sample is .3482 in 1969 ranging between .3246 in Ipetu to .3749 in Odo-Ore. In comparison 1The standardized value have been calculated as follows: Coefficient of variation 2 2 V / V (u) (u) + 1 Standard deviation of natural logarith of income (VLnY)2 / (VLnY)2 + 1 where V = standard deviation; u = mean income; Y = income Table 4.7 63 Three summary measures of the size distribution of income by household and village, 1969 Income Village Gini Co- Coefficient Standard Devia- Measure efficient Variation tion of Natural Logarithm of Income Net Farm Ipetu 0.3842 0.9146 (0a4555) 0.2599 (0.0633) Income Odo-Ore 0.4257 0.8006 (0.3906) 0.2998 (0.0825) per fA11 0.4027 0.8950 (0.4448) 0.2789 (0.0722) Capita Net Farm Ipetu 0.3648 0.7885 (0.3834) 0.2245 (0.0480) Income Odo-Ore 0.4157 0.7853 (0.3815) 0.2617 (0.0641) per A11 0.3951 0.8250 (0.4050) 0.2463 (0.0572) Consumer Net Farm Ipetu 0.3275' 0.6219 (0.2789) 0.1289 (0.0163) Income Odo—Ore 0.4185 0.8402 (0.4138) 0.1658 (0.0268) per All 0.3772 0.7199 (0.3414) 0.1508 (0.0222) Household Net House Ipetu 0.3246 0.7793 (0.3778) 0.1865 (0.0836) hold In- Odo-Ore 0.3749 0.7212 (0.3422) 0.2068 (0.0410) come per.A11 0.3482 0.7769 (0.3764) 0.1982 (0.0378) Capita Net House Ipetu 0.3041 0.6657 (0.3071) 0.1562 (0.0238) hold In- Odo—Ore 0.3598 0.6998 (0.3287) 0.1798 (0.0313) come per All 0.3390 0.7141 (0.3377) 0.1744 (0.0295) Consumer Net House Ipetu 0.2711 0.5083 (0.2053) 0.0900 (0.0010) hold Inc- Odo-Ore 0.3541 0.6812 (0.3170) 0.1201 (0.0142) come per All 0.3146 0.5985 (0.2637) 0.1107 (0.0121) Household Source: Survey Data 64 Table 4.8 Three summary measures of the size distribution of income by household and village, 1974 Income Village Gini Co- Coefficient Standard Devia- Measure efficient Variation tion of Natural .Logarithm of. Income Net Farm Ipetu 0.3492 0.6769 (0.3142) 0.2127 (0.0433) Income Odo-0rd 0.3609 0.7538 (0.3623) 0.2091 (0.0419) Per.’ All 0.3818 0.7380 (0.3526) 0.2116 (0.0492) Capita Net Farm Ipetu 0.3548 0.6786 (0.3153) 0.1872 (0.0339) Income Odo-Ore 0.3802 0.7731 (0.3741) 0.1958 (0.0369) per All 0.3879 0.7396 (0.3536) 0.1948 (0.0366) Consumer Net Farm Ipetu 0.3969 0.7831 (0.3881) 0.1599 (0.0249) Income Odo-Ore 0.5456 0.8472 (0.4179) 0.1876 (0.0340) per All 0.4871 0.8068 (0.3943) 0.1716 (0.0287) Household Source: Survey Data 65 Matlon (38, p.77) reported a Gini coefficient of .2823 for his overall sample and Norman (41) reported .2867 for the Sokoto study, .3501 for Zaria and .3190 for the Bauchi study. Thus incdme inequality was somewhat greater in the present Kwara State study area. Comparing Gini ratios calculated for net household in00me per capita with net farm income per capita in 1969 it is apparent that off-farm income importantly reduced inequality. Thus, for the overall sample, the Gini coefficient for the net household incOme per capita is .3482 compared with .4027 for the net farm income per capita. There are also notable differences between the Gini coefficients for the two villages. Ipetu the large vil- lage located on a major road and nearest to Omu-Aran 00n- sistently showed less inequality than Odo-Ore using the Gini measure. This pattern is apparent for both years and for both farm and household incdme measures in 1969. There are, however, some differences between years even though the relative village differences remain. Since data on non-farm incomes were not obtained in 1974, how- ever, it is not possible to compare the distribution of total household incomes between villages in the latter year. Differences in the Gini cOefficients calculated for the income per capita, per consumer and per household measures should also be noted. In 1969 the Gini 66 coefficient for income per capita is greater than that for income per cOnsumer and both are greater than the Gini coefficient for income per household. The con— clusion might then be that the income is more equitably distributed among households than among individuals. However, in 1974, the Gini coefficients depict a re- verse order. These changes are due in part to the reversed relationship between income per capita and family size between 1969 and 1974. It is recalled that households in the higher income group were on average smaller than lower income households in 1969 but larger in 1974. Differences in village rankings between the co- efficient of variation and the standard deviation of the logarithm of income measures also merit mention. As stated earlier, the coefficient of variation is more sensitive to distributions with inequality in the relative high income range while the standard deviation of the log of income is more sensitive to extreme lower income inequality. From the point of view of net household income per capita and income per consumer, Ipetu during 1969 had greater inequality using the coefficient of variation measure but less inequality using the standard deviation of natural log of income. This shows that the larger village of Ipetu was characterized in 1969 by greater inequality within the high income range, while Odo-Ore displayed greater 67 inequality attributable to extreme relative inequality in the lower income range. In 1974, however, these patterns reversed with Odo-Ore displaying a relatively greater inequality at high income level and Ipetu dis- playing a relatively greater inequality at the extreme poverty level. In short, due to interyear variation, it is not possible to characterize either type of in- equality as representative of either village. More- over, due to distinct village distributions, it is not possible to characterize either village as either more or less equitable. The distribution patterns within each village and for different income measures are presented graphically in Table 4.9 and Figures 4.1 — 4.3 and also in the Appendix,Tables A-10 and A-11 and Figures A-l - Aa6. Both villages display distributions which are negatively skewed to right. As pointed out by Matlon (38, p.73), this is typical of most income distributions and par- ticularly expected in a pOpulation where mean earnings do not greatly exceed a minimum subsistence level. The net farm income per capita is more skewed in Ipetu in 1969 than in Odo-Ore. This implies that Ipetu was characterized in 1969 by greater income inequality within the high range in support of the coefficient of variation results. However, it is clear that the patterns changed between years. Note the concentration of a small set of high income households in Odo-Ore 68 mama zo>a5m "monsom .OOH 3O .OOH Om .OOH mm Hmuoe OOJH H O O Ov.O H +HvH OO.H H OO.v H O O OVHIHNH Os.O N O O O0.0 m OmHuHOH O0.0 O OO.v H O0.0 m OOHnHO mHHomO Hv.s v O0.0 m O0.0 m OOnHO nod no.3m OH O0.0N O mm.sm O OOqu oEoocH OH.OO OH O0.00 O Ov.vm OH oqum oHosomsom sm.Om HH O0.0m s Oh.OH v OmuO Hmz .OOH hm .OOH hm .OOH OO .OOH vm .OOH ON .OOH Om Hmuoe HO.s w HH.HH m O0.0 H OO.H H O O Ov.O H +H¢H Om.m m He.s m mm.O H . o o o o o c oeHnHNH. H0.0 m Oh.m H O0.0 H O0.0 O 00.3 H O0.0 m OmHnHOH O0.0H O Hv.s m V0.0H v OO.H H O O Ov.O H OOHuHO mpHomO O0.0H O HH.HH O O0.0H O O0.0 O O0.0. m OV.O H OOuHO sod N0.0m OH V0.00 O O0.0N O mm.mm OH O0.0H v mm.sm O OOqu osoocH HO.NN OH HO.vH v O0.00 m O0.00 OH O0.00 O OO.HO O oqum Emma 30.vH O HH.HH O s0.0H m O0.0N OH O0.00 O vH.vm s ONuO 402 .3 .oz 8 . ..oz. &lu .oz K .02 (R. .402 a,u.oz AmnHmzv opsmmoz wH< m,aouopo SpmaH HH< onouooo :uomH mpHomO mEoocH poo vsmH OOOH meoocH moHonomoo: Ho omcmm eaoconaos so: new snag pm: CHSpfis meHog .4434 see a©a4 .epaaeo sea omzoc we coHpanapmHo scoot QEOOCfi 0Q $38 9.? QHDQP 69 40' 31.03 7 59 30 ' . IPETU 24.14 I 20 10 ' 6.90 3.45 3.45 3 4- l i I 40 36 36. 6 H 30 O E ODO-ORE U1 3 20 2 16 CH O 10 8 4S 4,, 8 I I $3 a 40 33.33, 29.6; " ' 30 22.22. BOTH VILLAGES 20 10 . 5.56 5.56 1.85 1.85 r—-+j 0 21 41 61 81 101 121 141 to 20 40 60 80 100 120 140 above Net farm income per capita (in naira) F‘Iigure 4.1 The percentage distribution of households based on net farm incOme per capita, 1969 7O 40 , 34.48 IPETU 30 27.59 20 13.79 10 6.9 6.9 6.9 3.45 40 36 3 H 30 28 E W (D g 20 20 O .C‘. "H 010 8 5 4 4 ' ' I 8’. 40 u 35.19 30 BOTH VILLAGES 20.39 4-0 20» 10 7.41 .70 3 85 O 21 41 61 81 101 '121 141 to 20 40 60 80 100 120 140 above E‘iigure'4.2 Net household in00me per capita (in Naira) Percentage distribution of households based on net household income per capita, 1969 4O 30 20 10 4O 30 20 10 Percent of households 40 30 20 10 F igure 4.3 71 30.00 IPETU 0.00 °*67 13.34 10.00 3.33.3.33 3.33 l L 7 3.34 ODD-ORE 11.1114'81 ,11-11 11.11 7 41 7.41 [3.70 26.32 22.81 BOTH VILLAGES 14.04 0-53 10.53 26 7.01 3.51 0 21 41 61 81 101 121 141 to 20 40 60 80 100 120 140 above Net farm incOme per capita (in Naira) Percentage distribution of households based on net farm income per capita, 1974. 72 in 1974 c0mpared with Ipetu, the larger village. This confirms the results shown earlier for the higher co— efficient of variation in Odo-Ore during 1974 i.e. indicating higher income inequality in the high income range. Similar patterns are obtained for incOme per consumer and per household shown in the Appendix. For the total sample, cOmparing Figure A-6 with Figure 4.1 it is evident that in 1969 the net farm income per household is more skewed than net farm income per capita. For the same total sample in 1974, however, the comparison between Figures A-4 and 4.3 revealed that the net farm income per household is less skewed than net farm income per capita. This confirms the results of the Gini c06fficients and c0efficient of variation which showed that income per household was more equitable in the year 1969 than income per capita while the reverse occurred in 1974. Summary The discussion in this chapter can be summarized as follows: 1. The overall mean net farm and net household in- comes were 3274 and H337 respectively in 1969. Ipetu, the larger village closer to Omu-Aran and on the better road, had the higher net farm in- come of about 3320 in 1969 while Odo—Ore had H222. Ipetu also had a higher off-farm income in 1969 of 377 as compared to £47 in Odo-Ore, the more isolated 73 village. In 1974, however, the net farm income was about 3463 in both villages. The per consumer net farm income was also higher in Ipetu (362) than in Odo-Ore (H40) in 1969. Aver-- age income per consumer in 1974 decreased by 33 per- cent (using the weighted price deflator) in Ipetu below that of 1969. On the other hand in the smaller, more isolated village, Odo-Ore, per capita farm in- comes increased by 29 percent. The increase in per consumer income experienced in Odo—Ore was due in large part to better rainfall level and distribution. The different equity measures applied to the data indicated that in general the distribution of in- comes per capita is relatively equitably distributed as shown by the Gini coefficients of 0.3 82 on net household income per capita in 1969. The distri- bution of net farm income per capita was relatively stable between years showing a slight decline in the Gini coefficient from 0.4027 in 1969 to 0.3813 in 1974. Each measure of inequality reflected this same decrease in inequality between years and in both villages. Net household incomes were more equitably distributed than the net farm income. That is, off-farm incomes tended to reduce inequality during he one year for which data were available for such off-farm earnings. 74 4. Due to interyear variation it was not possible to distinguish either village as displaying greater or lesser inequality nor was either village con- sistently characterized by a particular type of inequality. a. Ipetu, the larger village situated on the better road and closer to Omu-Aran, displayed. greater income inequality within the high income range in 1969. The Gini coefficient calculated on the net farm income per capita was 0.3842. b. Odo-Ore, the smaller village representing the savannah type village and more isolated from Omu—Aran, displayedflgreater'income inequality at the middle and low income levels in 1969. The Gini coefficient was 0.4257 for the net farm income per capita. c. However, in 1974 Odo—Ore showed a somewhat greater income inequality compared with Ipetu within the higher income range. The Gini co- efficient for the net farm income per capita was 0.3609. d. Ipetu, on the other hand, in 1974 showed greater income inequality at the low income levels. The net farm income per capita Gini coefficient was 0.3492. 75 These changes in the type of inequality in each village between years demonstrate the caution with which the results from one year's study on income distribution should be used. CHAPTER V SOCIO-DEMOGRAPHIC CHARACTERISTICS OF INCOME STRATA The present chapter examines the demographic makeup of households within each income stratum. These include a description of family size, age/sex compesition, and edu- cation. These determine in part both the production ca- pacity of the households and its demand for income. Within the conceptual framework set out earlier the resource en- dowment of land and capital are considered in the nex chapter while this chapter is concerned with the labour endowment treated under the various demographic character- istics. .A. Family Size Statistics describing variation in household size and cOmposition among inc0me strata are shown in Table 5.1. The size of the household has been presented as the number of residents, consumer equivalents and worker equivalents. These data show that in both villages in 1969 the poorest households had larger families on average than richer households. How- ever the data show a reversed pattern in 1974 with poorest households smaller in size than the high 76 77 than >o>p5m "condom .zhomopmo x0m\omm map so comma came pfisow :3 mo away 0p popmcsoo poQEoE zafismh HwSUH>HUCH am now zpfiomdmo mcfixpoa 05p mm mosamoo coon mm: u:oa¢>fisoolcme poxpozm wH.H HH.H mH.H mH.H HH.H mH.H OH.H mH.H HH< oprp mH.H mo.a mo.H OH.H no.H mo.a mo.H mo.H .QLOIOpo poxpos 0p wH.H mH.H wa.a oa.a mH.H Hm.a mH.H mH.H :podH posSmcou m.v H.m m.m ®.v H.v m.© m.b m.m HH< dwmucoam>asuo N.v m.m v.m m.v. v.v m.© m.m m.o o,HOIOUO ICME m.m o.m m.v m.v v.m H.m H.s m.m :uoaH waxwos o.m ©.m v.¢ m.m m.v H.s m.m. s.o .HH4 mucuam>aseo m.v h.m ©.@ 5.? h.v 9.0 m.m m.© o.HOIOUO acme m.© m.m m.v h.m m.m v.5 m.m w.© squH aoESmcoo _v.m h.u V40 m.u me N.HH @JHH. o.ma. .HH<. 0.5 5.5 m.v m.© m.m 0.0 m.ma o.m phonopo nymppfimop 5.3 H.m m.s 3.x s.m m.HH «.ma m.oH sumaH go wmnasz swam . pm: .304 . saw: 4 swam ..poz..._304 .cwmz. mmmao oEoocH ommHHa> mmmao oEoocH omeHH> vsma mama - owmaaa> manwaam> quad cam mama .mpmwpm meSmCOO pod oEoocH Spam #0: 0mmaafi> ho coduamOQEoo pcm oNHm paonomsom H.m magma 78 income households. In general family size was greater in 1969 than 1974. The apparent reverse in the associ- ation between income and family size between the two years and the apparent larger family size in 1969 are largely due to data collection problems explained earlier. The 1974 figures appeared to be the more realisticestimates because of the greater trust and openess on the part of the farmers during the later year. Family Composition The consumer-to-worker ratios are also shown in Table 5.1. The consumer-to-worker ratio as a measure of dependency has been calculated by dividing the number of consumer man-equivalents by the number of worker man-equivalents (See Tables 4.2 and 3.3 earlier). The data shows that the c0nsumer-to-worker ratio was stable between years at 1.13 for the overall sample. It was hypothesized that under the traditional farming system with abundant land and capital stock not being a limiting factor, income per consumer would be determined in part by the size of the household's work force relative to consumer requirements. That is, one could expect that households with a higher depend- encyratio would generally be poorer. However, the data show that there were no consistent relationships between incomes and dependency ratio which suggests 79 that interhousehold differences in composition may not be an important factor affecting relative inc0mes. What might be more important is the intensity with which each worker works, the quantity and quality of cOmplementary factors, and the resulting productivity differentials. In a polygamous society like the one with which we are concerned, the possession of many wives in a household cduld be an asset in boosting the labor force. 0n the other hand, the possession of many wives can be a reflection of income status. For the overall sample, the mean number of wives per household was approximately 1.9 during both years. Moreover, there was no consistent association with incOme. In 1969 there was an inverse relationship between the number of wives per household and income in Odo-Ore but with no clear relationship in Ipetu. In 1974, this was reversed with direct relationship evident in Ipetu but no pattern in Odo-Ore. The mean number of wives was greater in Ipetu during both years. Age or Household Head1 Management quality in farming could be expected to be related to the age and experience of the farm manager. 1The actual age figures should be used with some caution because birth records were generally absent in the study area. Despite the effort made in COllecting the information on age, lack of accurate knowledge c0upled with social prestige as- sociated with age in Yorubaland, the reported age figures were only approximate. 80 Other factors which might contribute to a life cycle incOme pattern are acoumulation of land and other assets, changing dependency ratio and the size of household. The age of the household head was there- fore broken down by incOme groups. The results are shown in Table 5.2. The mean age of the household head was 53 in 1969 and 61 in 1974 for the total sam- ple. The age figures ranged between 35 and 80 years in 1969 and between 35 and 85 years in 1974 for the total sample. There was no consistent relationship with income. We further considered the variation in income per c0nsumer across household head age groups but found no consistent pattern. In short, there was no evidence to suggest a life cycle pattern in earnings. PerCent Literacy Percent literacy is defined as the percentage of family members who either could read or write at least in Yoruba and/or those going to school. Field's study ( 21 ) in Brazil has shown that educational attainment is an important contributing factor to wage differen- tials and thus to income status. In general, the higher the level of education the greater the expected in- c0me. Within the present setting characterized by self-employment one could expect that literacy might widen the horizon of the individual farmer and could ....u..... c.... :.. ...._ . _ u..~......_.y >_ _.:...._ —..:.. >.n.....~.;‘~ — H ._.—.v..v.~w.:~ n~.....mv_~ m.~fi:~w~uu~::~ New reva< ...... ¢~e~..~ . I .\ 1 t.. 81 mung >0>psm "mopsom 0mm 50 mhmom ma Scamp mponEoE zaasmm onEom use made mm pocHhoo 0pm cohoafinom mm.a Hm.m mm.a mm.H 5m.a mm.a mm.m oo.m HH< .. mm.H mm.a NN.H om.a vm.a 5o.a H5.H HH.m ououopo .w0>H3 om.m om.m om.m om.a om.a oH.H m5.m om.H :quH 56 emeezz mv.a 5m.a mv.H 5v.a 0m.w va.o 5H.m om.~ HH< acmeeaaco oo.a HH.H HH.H .m5.o oo.a 35.0 em.a mm.m euoroeo ,maeseu om.H om.H om.H om.a m5.m oo.H mm.m om.e :meH 56 eonesz mo.H mv.m mm.a HH.H no.m mm.H eo.m mm.H HH< ©N.H em.a m5.H. ev.o em.a oo.a va.m. 56.H ewotoeo maeeeaaco mama 5o.m oo.m oo.a om.a mv.m ov.H om.m om.m speeH 56 565552 em.m mm.w oa.m 5v.m vo.m ev.m ev.m mm.v HH< v5.m m5.m mm.m .em.m em.m mm.m. oo.m Ha.m. eworoeo moaeseu.pazea mm.m oo.m ov.m ov.m em.m om.H oo.m Q5.m speeH mo eoeasz em.H we.H. mo.H mm.H 5m.m om.a 6m.m oo.m HH< . mm.H om.a mw.a HH.H vo.m m5.a oo.m mm.m. PMoroeo modes.ufizna 5m.a om.H om.a om.a va.m ov.H mm.m O5.m speeH mo 562822 ca.HH HH.¢H V5.m me.m mm.5 cm.e He.m 6m.m Ha< eaocemzon cm ma.m m5.oH 5©.o# oo.m oo.m 5e.m em.m mm.m ohonooo. somaouaa 5m.ma om.oa oo.oH oH.mH wm.oH 05.5 mm.ma om.aa nemeH pcmoeea m5.oe mm.Ho mm.mo mo.mm 65.5m He.om mm.mm m5.5m HH< new: mm.me 5e.He mm.5e 55.mo oo.He HH.oo wa.mo 5o.om enouooo eaonomsoc mm.5m om.ao oo.mm om.5m me.Vm om.om mm.mm om.vm spoaH mo om< HH< swam no: 264 HH< cw“: e6: 364 eweaaa> maeeawew ¢5ma mama possmcoo 90m oEoocH summ.poz .V5mH paw mama .MpMme noszmcoo god oEoocH spam no: hp coauHmOQEoo zaflsmm 0cm zomgouaa pcooaod .pmoc paonomsoc no ow< m.m oaome 82 enlighten him as to the existence of modern inputsl thereby facilitating their use. While neither village had its own school, schooling was available in villages within three to four miles of each village in the years of study. Due to the recent establishment of the schools, household heads could not have been educated. With the exception of two household heads in Ipetu none of the household heads could read or write even in Yoruba. Only about 8 percent in Ipetu and 4 percent in Odo-Ore of the household members on the average were in school ir11969, and 13 and 9 percent, respectively, in Ipetu and Odo- Ore in 1974. Since only children were literate, their influence on farming decisions was most likely negli— gible. Thus the literacy figures may more likely reflect the effect of income on education rather than vice versa. Tab1e5.2 shows no clear relationship between the percent literacy and inc0me. In 1974 overall literacy had increased and a slight positive relationship with incOme was evident indicating that with growing aware- ness, higher incdme households may have begun to take lModern inputs like fertilizers, seed dressing, im- proved seed varieties, etc. were not a common feature to observe in the study villages during both years. E0 83 somewhat greater advantage of the available 0p- portunity. Summary In summary, it has been found that income strata do not divide themselves into distinct family types. In general, demographic factors do not appear to be associated in a consistent manner with income as shown by family size and composition or percent literacy. Age was also not found to be importantly related to inc0me status. Moreover since income did not vary with age we concluded that life-cycle factors affecting inc0mes were negligible. Most of the factors described above show conflicting patterns With income between years. Again this shows that heavy reliance on one year's data might be misleading. The next chapter takes us into the consideration of the sec0nd part of our conceptual framework. There we c0nsider the resource endowment factors which determine production capacity. CHAPTER VI RESOURCE ENDOWMENT AND USE BY INCOME STRATA It has been shown in the previous chapter that demo— graphic factors do not appear to contribute significantly to income differentials as shown by the dependency ratio, family size, and composition, percent literacy or number of wives. Moreover, there was no significant variation between the two villages as far as these demographic fac4 tors are concenned, we found that the results agree with other studies in one year but not in the other, sug— gesting possibly wide changes between years. Having considered the endowment of labor this chapter examines the endowment of land and capidal. The levels of use of land, labor and capital as they relate to income are also considered. A. Land Holdings Information on total land holdings was not available hence the crOpped land has been USBd as an ap- proximation of the endowment of land. Under ceteris paribus assumptions, the size of land holding would be expected to vary directly with gross farm income. Table 6.1 shows the cultivated acreage per household, 84 85 0000 >o>030 ”mopsom .0000 0000000 00000 00 0:00000 0 mm pmmmmpdxmm 0 00.0 50.0 00.0 05.0 00.0 00.0 05.0 000 0 0 0 0 00.0 0 0 00.0 000-000 0000 0 00.0 50.0 00.0 00.0 00.0 05.0 00.0 20000 0M0000 00.0. 00.0 00.0 00.0 50.0 05.0 05.0 00.0 00< 0 0 .0. .0. .0. 0 . 0 0 0001000 05.0 00.0 05.0 00.0 00.0 00.0 00.0 00.0 20000 00000000 05.00 00.00 00.00 00.50 05.00 00.00 50.00 00.00 00< .1 00.000 00.000 00.000 00.000 00.00 00.000 00.000 00.00 0000000 00.50 00.00 00.00 00.00 00.00 00.00 00.00 05.00 20000 000000: 50.0 00.0 00.0 00.0 00.0 00.0 00.0 05.0 - 000 00020000 00.0 50.0 00.0. 00.0 00.0 00.0. .00.0, 00.0 0001000 000 00000 50.0 00.0 00.0 00.0 00.0 05.0 00.0 00.0 20000 0000000 00.0 00.0 00.0 00.0 00.0 05.0 00.0 00.0 00< 000000 05.0 05.0 00.0 50.0 00.0 00.0 .00.0 00.0 000-000 000 00000 50.0 00.0 0000 55.0 05.0 00.0 00.0 50.0 20000 0000000 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00< 000000200 00.5 00.0 00.0 ,00.0 00.0. . 00.0 00.0 ,00.0 0000000 000.00000 00.0 50.0 00.0 05.0 00.0 .00.0 55.0 00.0 20000 0000000 0000 000 300 0002 000m 002, 300 000: 000000> 0000000> . 0000000 000000>, mmmmwao 0&00CH 0000000 mEooCH WhmH .0000. . 0500 0:0 0000 0000000 005:0:00 000 osoocfi E000 000 000000> 00 0000000: 0000 0000>00050 0.0 00009 86 per worker and per consumer. It is recalled that in Chapter V an inverse relationship was found between income per consumer and the number of residents and workers in 1969. In 1974, however, a direct relation- ship was obtained. During both years, in cOntract with family size, a direct relationship was found be— tween cropped land per cOnsumer and income per cOn- sumer. This means that in 1969 and 1974 the poorest third was characterized by substantially lower cropped acres,per consumer. As would be expected (See Appendix Table A-8) the association was not as strong between land holding and net household income as with farm in- come. As a result of the observed positive relationship between income and size of land holding, some of the variables under consideratoion in this and subsequent chapters are broken down both by cropped land classes and by income classes. This has been done to partially control for the individual effect of land on the de- pendent variables as they vary across income classes. There are three land types in this study area namely upland (0000), lowland (EEE£2)9 and forest (igbg) type. Lowland fields are usually more productive than upland because they are well-watered all the year 87 round and are used for growing a wider range of crops, A especially maize and yams. Forest land could be more productive than upland, especially during the first few years it is put into production of food crops. Tree crops like kolanuts and cocoa, cocoyam, and plantains are grown in the forest land. The per- centage of each of these three land types has been broken down by income strata and the results are shown in Table 6.1 Lowland soils were only found in Ipetu where they constituted about 4 percent of total cropped land during both years. An examination of land holdings among income strata in that village showed no associ- ation between land holdings by type and incOme. Tables 6.2 and 6.3 show the frequency distribution of households with incOme and land classes for 1969 and 1974, respectively. It is clear that the average net farm incOme per consumer increased as one moves from low to higher land classes. Nevertheless a sub- stantial proportion of the poorest households fall into middle and high land strata indicating that land alone is not a cOmpletely determining factor. Vaifié of Capital Stock Used in Production Capital, the third major factor of producation is de- fined here to include the value of tools and equipment. Tools include hoes and cutlasses, and equipment 88 00.00 00.0 00.0 00.0 00.0 .00020000\0000.0>< 00 0 5 0 00.0:00.0w “_000. 00.00 0 0 0 I 00.0:00.0 0000 00.00 5 0 0 0 00.0:00.0 002 05.50 0 u 0 0 00.0:00.0 300 0001000 50.00 00.0 00.0 05.0 00.0 00E20000\0000.0>< 00_ 00 0. 00.. 05.0:00.o,. 00¢ 000000 00.000 00 0 0 u 05.0:00.0 0000 00020000 00.00 0 0 0 0 05.0:00.0 00: 000 0000 50.00 00 u 0 0 00.0:00.0 :00 20000 0000000 0000020000 00: 00.0 00.0 00.0 00.0 00x003\0000.0>< 00 0 .5 0 00.0r00.o 000 0 5 0 0 00.0:05.0 000: 5 0 0 0 05.0:00.0 00: 0 0 0 0 50.0:00.0 300 0001000 0000020000 000 50.0 05.0 00.0 00.0 00x003\0000 .0>< 00 O0 0 Q0 00.0000.o. 004. 000000 00 0 0 : 00.0:00.0 000: 00x00: 0 0 0 0 00.0-05.0 00: 000 0000 00 u 0 0 00.0:00.0 :00 20000 0000000 mesmcoo 00a. Hauoml! cwHE so: 300 onmm 00000m om0000> 00n0000> mEoOC0 E000 , r , .000o< 0c00 no: mm000>< mommmav mEOOCH 0000 .0000000 LoESmcoo 0cm oEooCH E000 no: new pcwa mcosm 0000:0050: mo :oHuSQHmeHU hocmzqopmgwxw 00nme 89 0p0m >0>nsm "000500 00.00 00.0 00.0 00.0 00.0 00000002050000.0000 00 0 5 0 05.00u05.0 000 00.00 0 0 0 0 05.00u50.0 0000 00.00 5 0 0 0 50.0:00.0 000 00.00 0 0 0 0 00.0:05.0 3o0 000:000 50.00 00.0 00.0 00.0 55.0 .000000200\0000 .0>< 00 .00 0‘ 00 50.0a00.0. 000 000000 00.00 00 0 0 0 50.0:50.0 0000 000000200 00.00 0 n 0 0 00.0:00.0 00: 000 0000 00.50 00 0 0 0 00.0:00.0 300 20000 0000000 00020000 000 00000 0000 002 :00 00000 000000 000000> 00000000. . . .UEOOCw .Ehmw . m®90< UCGA um: 0m000>< mmmmmHU @EOOCH 00.00000 0.0 00000 90 om.o mm.H no.0 V©.o passmcoo \ccmH.o>< mm.um mm m m m .n¢.H«oH.o. HH<. mo.moH m n H H mv.Humo.o an: vH.ou m m w m mo.oavv.o um: va.ov m r v m om.o:oH.o 30H mgonono no.0 mmJO mmao mv.3 poeswcoo\ccmHam><. Hm.mu om 0H 0H 0H mu.mn©m.o HH< mumppm mm.HHH OH 5 m H mh.mnoo.H :mH: goesmcoo mo.mm 0H m w m ou.ouom.o to: Log ccmH mH.mm OH I w o cm.onom.o 304 :pmaH cmaaogo cmpmHSono no: no.H mu.H mu.o mm.o pmxgoz\ccmH.o>< . mm m mA m mm.HLHH.o. .HH¢ m h H H mm.Hnmu.o :me m H v v mu.o-vv.o am: a H v v um.ouHH.o 304 ogouooo ompmHsono Ho: 55.0 5H.H mo.o av.o poxpoz\ncmH.m>< om oH 0H 0H mm.mpmm.o. HH<. mpmgpm 0H m m H mm.mnoH,H :mHm pmxpoz oH v v m om.onHu.o no: awn ccmH OH I m b O©.O|mm.o .304 :meH Umaaoao LmESmcoo.th. Haves Qwfim vmz 304. omcmm wpmppm ommaafi> manmapw> mEoocH spam pmc mmmpo>< mmwmmao mEOOCH mopo< Gama .vumH .mmmmmao awesmcoo 9mm meoocw Epwm am: new Gama mcosm mUHozmmso: Mo coHpSQHmeHU zocmSUmgm m.© mHnme mama >m>95m "mopzom 91 om.v om.n vm.m mH.m nHozomsoc\ocmH.o>< um.um mm m a . m vu.mHumm.o HH< mv.VmH m o m . : cu.mHuho.m :mHm om.Hm m m w m um.mumv.m cm: uo.ov m H m o ow.mnmu.o 30H mpouooo on.m mm.m um.m mm.H oHozmmzon\ncmH.o>< .Hm.wu. om oH AoH oH .um.mrmv.H HH< mumppm mm.omH 0H m m u um.muuo.m :mH: cHocomsoc oo.oo oH N v w mm.mumm.m um: awn ocmH vm.ov oH I v o om.mumv.H 30H :meH umagogo LmESmcoo Ham, Hmuoe swam cm: 304 mmcmm Mumapm ommHHH> manmfigm> ._osoocH sum“ . m H ‘ 1+;4w ‘ mopo<‘ ncmq pmc mmmpm>< mammmfio mEOOCH An.ucoov m.o «Hams 92 includes items like baskets, calabashes and knives. Table 6.4 relates the value of capital stock to in- cOme class. The average capital per household was about H7 in 1969 and 320 in 1974. The deflated value for 1974 is 310.401. Both capital per worker and per c0nsumer varied directly with income in 1969 in both villages. In contrast no consistent pattern was evident in 1974 in either village. Operating capital will be considered in a later chapter. C. Value of Livestock As a final aspect of resource endowment we examined the distribution of wealth among the sample households. While wealth is a partial reflection of past incomes, it can directly influence current farming decisions in several critical respects. For example, house- holds which possess more wealth would presumably be able to assume more risk and would be less likely to fall below meeting subsistence needs during bad years. within the study area wealth could be best represented by the number of livestock (especially cattle) by types of transportation (such as bicycles, motor-cycles or motor vehicles) and by the value of dwelling places. 1The weighted village market price deflator was used. These were 194 in Ipetu and 190 in Odo-Ore with 1969 as base year. The third estimating procedure is shown in Appendix Table A-2. 93 _-. I. r“, Table 6J1 The value of capital per household, per worker and per consumer by net farm income per consumer strata, 1969 and 1974 (in naira) Net Farmflncome Per Consumer Strata Capital Ipetu , . . Odo—Ore Year Per Low Med High All Low Hed High All Worker 0.87 1.46 2.17 1.50 0.97 1.60 2.83 1.81 1969 Consumer0.73 1.20 1.90 1.28 0.87 1.48 2.64 1.68 House— . hold 6.14 8.21 6.80 7.01 5.59 8.12 8.88 7.48 Worker 3.88 4.63 4.06 4.19 4.63 3.09 6.06 4.59 1974 Consumer3.34 4.08 3.40 3.60 4.20 2.81 5.47 4.16 House-p . . hold 14.28 22.92 21.11 19.43 14.31 14.21 24.99 17.84 19748 Worker- 2.02 2.41 2.11 2.18 2.45 1.64 3.21 2.43 Consumer1.74 2.12 1.77 1.87 2.23 1.49 2.90 2.20 House- hold 7.43 11.92 10.98 10.10 7.58 7.53 13.24 9.46 aDeflated using the weighted village market prices deflated Source: Survey Data 94 Since data collected for these items were not available for present analysis it is not possible to analyze the wealth aspect fully. For our consideration, the value of livestock would be used as a rough partial measure of wealth. Livestock is a minor concern in these villages. This does not mean that livestock is not an important item in the overall economy of the study area. In fact it is important. The owners of the cattle, however, belong to another tribe, the Fulanis. Although some villagers purchase cows as a form of savings these are typically kept away from the vil- lages in the care of the Fulanis. Usually cows are purchased by well-to-do farmers out of savings from farming Operations, from trading, or in other enter- prises.l The purpose is to see if there are any strong relationships between the value of livestock reported by the households and income status. Table 6.5 contains the value of livestock dis- aggregated by income class. The mean value of live— stock per household in 1969 was about $15 increasing to about H48 in 1974. The deflated value for 1974 1Some Fulanis move to other unknown areas without informing the cow owners and some lie that the cows have died or that they fail to reproduce. As a result of such fraudulent acts only a few farmers now take the risk of keeping their cattle with the Fulanis. mama mo>p5m "oopsom mm.mm mo.em ew.mH mk.em In I: I: u: ‘HH4. pepeHeea ooflpm % ea.kH mH.mv mo.mH kH.km r: I: In I: eaouoeo emmHHH> . nepcmHez Hk.em mo.HH km.0m am.mm n: In I: :: speaH an eepmHoea m.em Ho.mm .mm.em mm.kv NH.mH mm.oH we.0m me.qH .HH< xoouwm>Ha m.qm mm.mm mo.em em.Hm om.mH no.aH me.mm om.mm eaouoeo oo 6.65 ee.mm em.mm Hm.ve no.6 ae.c mo.m mm.k speaH oaHm> Hmpoe csz em: 304. see: cmHm no: 304 see: emmHHH> eHemHam> omeHH> ‘ omeHm>. vemH . aeaH wompmm LoEDmcoo pom oEoocH Ehmm p02. ..... . vamH ocm momH .mponooo ocm SpooH .oazo mo xoowwo>fla mo msHm> ma;w oHome 96 was about N25. A substantial part gfvthis in- crease is likely due to the increased cpeness and trust on the part of the farmers in 1974. Important interyear variation is evident with respect to income strata. In 1969 it was unex- pectedly found that the poorest households appeared to possess more livestock with this relationship reversing in 1974. These results probably reflect the effect of an underenumeration for cattle, with underenumeration greatest among higher income house— holds. The higher income households would have tended to understate their holdings more than the poor in- come households for fear of increased personal income tax arising from the study. Finally an examination of the types of animals kept showed that there was nothing to suggest that the composition of livestock holdings varied by income group. Labor Use The endowment of labor was considered in Chapter V and in earlier sections of this chapter land and cap- ital use have been taken as proxies for factor en- dowments. This leaves us with labor use to be con- sidered in the present section. As noted earlier in Chapter III, there are about four months, November- February, in which there is no rainfall. As a result, farming activities are mostly concentrated in the 97 remaining 7-8 month growing season. The distinct seasonality of farm labor underlies the low labor in- puts reported in Table 6.6. For example, the average level of farm labor for adult males was 658 man—hours per year in Ipetu and only 417 man-hours in Odo-Ore in 1969. On the average, a female adult worked only 42 hours in farm labor in 1969 in Ipetu and 9 hours in Odo-Ore. The low hours of female farm labor is particularly significant in View of the fact that there are no social restrictions on women as regards farm work.1 It was evident, however, that certain Operations like ridging and weeding were done mostly by men while women were considered better in harvesting operations, particularly for crops such as maize, guinea corn, cotton and cowpeas. Both males and females worked more hours on the average in Ipetu than in Odo-Ore in 1969. This might be due to the greater acreage per household in Ipetu (4.69 compared to 3.41 acres in Odo-Ore). Substantially higher employment levels were recorded in 1974 how- ever, and relative employment levels switched between 1This contrasts with the Moslem dominated areas of the north where women are usually kept in seclusion so that they cannot contribute to on-farm work. Although there are Moslems in these survey villages there was no strict ob- servance of this rule hence it cannot be asserted that wo- men's participation on the farm are hindered by religious beliefs. 98 villages in that year. For example, an adult male in Ipetu worked an average of 745 man-hours compared with 1048 man-hours in Odo—Ore. The increase over the 1969 figure for adult male farm labor input in Odo-Ore was 150 percent. The cause of this substantial increase is not clear, but part of the reason might be due to increased average land holding per household in 1974 above that of 1969 (Table 6.1). However, in addition the increases in land and labor reported could also be a function of the greater trust and cpeness on the part of the farmers during the later year. In con- trast to the pattern for males, there was a decrease in both villages in 1974 in the adult female farm labor input. The layout of Tables 6.6 and 6.7 permits us to examine separately the relation between land and labor, income and labor, and their interaction. It is clear that these figures should be used with caution, however, because the number of observations per cell are very small. In both years and in both villages, the highest levels of employment were recorded among high income males, but at least in 1974 this was due Primarily ‘to land holding differentials. After cOntrolling for land in 1969, the data Show that miles in ~ high income strata generally worked more 99 mHm moHH. mHm wow mmeH emeH evaH mHmH HH< . _ . “weeHci. cmHH memH man u- onH HmoH beam 1... lawnisHHemev meson eve Hme mam oem mmmH seem oeaH moeH ems ems cH goemH evm -.. emu ka aevH u-.. memH mmeH 304 same Hmpoa vmm emm mmV. mmH mom cam eHm mam HH< pHaem eHmeee mmv new new 1: mmm smm mHm |.I cwH: pod mason emH meH mmH on mHm eoH mmH mmm ems cme Eamonoeo .vmm -.. moo 4mm mmm .. omH «em 304 pHaee oHeEea ..mH oH Hm 5 ea mm emH .1 co HH< pHaee eHeE Hm mH em. .. we me em. --.. awH: eea.maso: 5H m cm mH qu cm Hmm me we: Eemoueoo H -n. 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Hw w wH HHe pHaew estew wH eH ow e eH Hw m 11 :wH: eea eaoewH ww we wH. wH wH wH eH HH we: seem eHHeww Ha II ma OH OH mm b Om BOA mason cm: weoH. ewHH wHHH oew eee www How wee HHe eHaew eHwe HwHH wwHH eweH WHw www. oww. oww 11. :wH: wea.aaoewH eeHH eww weHH wowH wee awe wwe eHw eez seem wHaeww www 11 www eww wa ewe www www sou mesa: ems- HH< cme. pm: 304 HH< swam no: 304 mommeo oHoprw> l myoppm hmEDmcoo mom mEoocH Ehmm poz ooanoao eewH mommmao LoEzmcoo god oEooce Enme pm: ocm ocma >9 mowpwmcopcfl on: Aonma one ocmq e.© mHome 102 mama >m>o5m “mogzom ew w w w {ow oH oH oH HHe HH w H w w w w 11 swam weHonewaon HH H w e w w w w we: we w 11 w w eH H w w 303 emeeaz .HDODGH ww ww ew ww ow ww ww ew HHe sewn.kuoa ww ww ww ooH ww ww ww 11 swam we acme ww ew ww ow Hw ww Hw ew we: 1aea esoan ww 11 cm ww ww ww ww Hw 303 eHHeww HH< swam om: 304 HH< nmwm cm: 304 mommmeo mHanLm> oucvooo .DummH pmesmcoo nod ocmH mpmhpm ,LmESmCOU .Hmm QEOOCH 89mm #02 UmQQOcHO Ao.ucoov 5.0 manms 103 hours than males in the low-incOme classes. Among households with medium land holdings, for example, 1035 man-hours per worker was recOrded in the high- incOme class, cOmpared with only 407 man-hours in the low-income class in Ipetu in 1969. In 1974, however, the reverse of the above result was obtained. For the medium land class of 1974, for example, 917 man-hours per male worker was recorded for the low income class but 689 man-hours for the high income class. The data also show that greater off-farm hours were cdntributed by females than by males. While on average male adults worked 98 man-hours per year in off-farm activities in Ipetu and about 13 man-hours in Odo-Ore, female adults averaged 264 in Ipetu and 334 in Odo-Ore. As mentioned earlier the off-farm data was available for only 1969, hence no comparison between years is possible. Farm employment was earlier shown to be lowest among the land—short poor households during both years and in both villages. In 1974, there was no clear pattern in either villages as to labor inputs per acre. But in 1969 labor inputs per acre were in fact lowest among the lowest incOme households with medium land holdings. For example, in Ipetu in 1969 the lowest inoOme households with medium land holdings had 343 man-hours per acre (Table 6.6) 104 compared with 528 for the highest income households with medium land holdings. Also in Odo-Ore the former group had 205 man-hours per acre COmpared with 334 man-hours per acre for the latter group. The 1969 labor data therefore tended to suggest that there were two types of poverty households (1) those households who were short of land but who worked their land very intensively, and (2) households with adequate land who farmed at very low levels of intensity and as a result realized low returns to land. Family labor as a proportion of total man-hours averaged 87 percent in Ipetu and 95 percent in Odo- Ore in 1969. In 1974 these figures were 90 in Ipetu and 96 in Odo-Ore. A particularly interesting but unexpected result is the finding that family labor as a percent of total hours in farm work increased with crOpped land per consumer and also with income. That is, poorer households tended to use a greater proportion of hired labor relative to their total labor inputs. Moreover the data show that in 1969 the poorest third hired the greatest amounts of labor per acre and per household in both villages. 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He.H, ew.H o. 11 amaze 1cee.ca wH.H o o ew.w ow.H ow.e o c we: epee pea ww.w 11 ew.w we.w ew.eH ww.H ew.HH wH.ew so: poaeH ezo ww.w wH.e wH.o ww.w ew.wH ww.e wH.wH we.wH HHe epao: ww.w ew.e Hm.o o ww.oH Hw.w ww.Hw, 11 swam 1ame.aH ew.w Hw.aw o ww.w ww.wH we.eH Hw.ww ww.w we: epew pea mm.m 11 c ww.w mo.mH ww.e mo.m om.ma 304 poomH 0p< HH< swam pm: 304 HH< swam pm: 304 mmmmmHo eHanpm> epo1owo speaH. pesswaoo. .mumhum amszmcoo hma.mEoocH.Epmm #02,. non ocmq . pmaaopo eema .mp01opo pom damaH .mmmmmHo pessmcoo pea mEoocH Eamm pm: ocm mama NAo max» mo mpom pea pcm oHonmmzon pea pooma topaz m.© manme 108 mum: >m>p5m "mopsom ew a. w w ow 0H OH OH HHe HH m H m. w. w. m 11 swam mpHozemsoc HH H w e w w w w we: we w 11 w w eH H w w 30: peaesz ew.Hw ww.ww ww.ee we.ww ew.on ew.wwH ee.ww oH.ww HHeepaoace: ca we.ee we.Hw oo.e o ww.eeH ew.wwH oo.HOw 11 swam..wHa:ewpoa ew.ew ww.ww ew.w ww.eHH He.ee ew.eeH ww.ww ww.we we: pea poaeH oo.we 11 ew.weH oo.oH ee.ww ww.we ew.ew ww.on pzwawepa: Hepoe ww.ee ee.ww oodbe ww.we ee.we ew.ew oe.eH ow.oe Haeepaocpe: ca He.ee ww.ww o o ew.ew eH.ww ww.ww 11 awe: wHopewao: ow.oe ww.e ew.w we.ww ww.we ee.ww ee.wH oo.ew we: pea poaeH ww.Hw 11 ew.ewH o He.ww 00.0w ew.HH ww.we 30: opeawe Heme ew.w code ww.w ew.wH ew.wH oo.HH oH.ww HHe epsocce: ee.w wH.e o o ww.w ww.HH o. 11 swam GH.wHa: ew w o o oo.oH ww.w ew.wH o a we: 1eeao: pea ew.w 11 ee.wH ww.w ww.ww ee.w ee.ww ww.we 30: poaeH e20 HHe .swa: we: :04 HHe :wH: we: so: eeeeeHo eHeere> .ep01awo ‘ .speaH pesaeaao_ mumpum pmesmcoo pea mEoocH Emma pmz pom ocmq omaaopo Aw.pcooV:w;w eHnme 109 Hired labor consisted of three types, namely £39, 933 and ACbaro. Agg was a reciprocal arrange- ment between two farmers in which they worked to- gether on each other's farm on alternate days. The cost incurred was usually in the meals provided by the host. 9 g was an arrangement whereby a farmer invited all other farmers in the village or all the youth to perform a particular task, like ridging, on his farm. The cost to him would be the feast which followed in his house. Agbaro was an arrangement in which the farmer employed people either within or from outside the village to work on his farm. The employer would supply the day's meals and usually also provided some payment in cash. There wasaatendency to use Agbaro labor for harder Operations like ridging and weeding. It would be expected that the higheincome class hired more agboro labor because it is expected to be more productive while the low-income class would use more gag and Egg because the form of payment for the latter two depended on the farmer's discretion thus enabling him to pay laborers in whatever product was meet COnvenient, like yams or maize.' The data show, however, that there were no patterns evident between use of these hired labor types and income. E. 110 Summary 1. Among the three resources considered--1and, labor and capital--on1y land seemed to show a strong and consistent relationship with income status. Off-farm activities were found to be earried out mostly by women in 1969 while the farm labor input was mostly by men. Off-farm labor input was greater in Ipetu, the larger village closer to Omu-Aran. Two types of poverty households were evident on examining the relationship of labor input per acre with land and income classes in 1969. The data suggest that some households were poor pri- marily because of limited land use. Another group of households, however, were found to be poor not because they were land short but due in part to low labor use on available land and consequently low output. In 1969 the poorest third hired the greatest amounts of labor per acre and per houSehold in both villages. However, in the later year, the poorest third hired more labor on a per acre basis in both villages, but on per household basis the richest third hired more in both villages. The value of livestock has been used as an ap- proximation for wealth but there was conflicting 111 evidence between the two years as to whether our measure of wealth was positively or negatively re- lated to incOme. In the next chapter analysis is focussed on the relationship between income and the choice of crop enterprises. The degree of intercrOpping is also examined to see whether the land short house; holds tended to maximize returns to land through intercrOpping. CHAPTER VII CROPPING PATTERNS AND FARM BUDGETS BY INCOME STRATA The previous two chapters were respectively concerned with a description of household characteristics and resources use by income classes. Having considered patterns of re- source endowment and use within our earlier stated conceptual framework, the next step is to examine resource productivity. In this chapter crOpping patterns are examined to deter- mine systematic variation among income classes. Farm bud- gets are also constructed to identify variation in productiv- ity among income and land classes. A. Cropping Patterns As mentioned previously in Chapter I, the study area is located within an ecologically heterogeneous zone within which both annual and perennial crops are grown. The major crOps include yams, maize, guinea corn, cotton, cowpeas, cassave, okra, spinach and roselle. The food staples are the root crOps yams and cassava and grains in the form of maize and guinea corn. In spite of the minor ecological variation there were no major differ- ences in the growing of these staples between the savannah and forest type villages. There was, however, 112 113 some variation among other crops. For example, cOtton was more common in the drier Odo-Ore area, whereas in the forest land of Ipetu, cotton was replaced by tree crops like cocoa, bananas, plantains, kolanut and by cocoyams. The perennial crops such as cocoa and kolanut can be considered the main cash crop of Ipetu while cOtton was the only non-food crOp grown for the market and for home use in Odo—Ore. Every farmer grew yam and maize in Ipetu, whereas in Odo-Ore every farmer grew guinea corn in addition to yams and maize. Guinea corn appeared more common in Odo-Ore than Ipetu because the farafara variety grown in this study area requires a drier cOndition than that which prevailed at Ipetu.l As a result of the lowland in Ipetu, the farmers grew more vegetables like okra, gygyg, spinach and amukan. The distribution of the major crOps grown is shown in Tables 7.1 and 7.2. Most crops were grown in mixtures which means that more than one crop grows on the same plot at the same time. In order to obtain a rough estimate of the acreage under each crop, it was necessary to find the adjusted acreage by dividing the size of the plot by the number of crops grown on it. For example, in a two- crop mixture field Of two acres maize and guinea corn, 1When the Ipetu farmers were asked why they were not growing as much guinea as at Odo-Ore, they replied that their soil was too wet and heavy for guinea corn and instead they grew more maize. 114 we.w wH.w oe.e ww.w ww.HH ww.HH wH.HH ww.wH HHe we.w ww.w 11 11 ew.wH. we.HH ww.w. .11.. :wH:... ew.w o ww.w ww.w wH.wH ee.wH ew.wH ww.wH we: ww.e 11 He.w Hw.w wH.w 11 ww.e eH.oH 20H weeazow ww.ww Hw.Hw ww.Hw ww.wH ww.H ww.H ew.H ww.e HHe we.Hw ee.wH ww.ww 11. ww.H ew.H 11 11 swam ww.ww ww.ew Hw.ww ww.wH ww.H 11 Hw.w ww.e we: epow OH.mH 11 we.wH Hw.Hm 11 11 11 11 BOA monesw Hw.eH we.wH ww.wH Hw.HH eo.eH we.wH wH.wH Hw.eH HH< wH.wH ew.wH ww.w 11 we.eH ww.wH oH.oH 11 :wH: we.wH ww.wH we.w Hw.wH ew.eH wH.wH we.wH ww.HH we: eHwe: ww.eH 11 ee.ww Hw.eH we.wH 11 we.e we.eH 30H ewe: ew.eH He.wH ew.ew Hw.eH ww.ww ww.ww ew.Hw ew.ew HHe ww.wH ww.wH ww.ww 11. ww.ww ew.ew we.ee 11 .awH: ww.ww eH.ww ww.ww ee.wH ww.ww ww.w ww.ww HH.Hw we: eNHe: ew.eH 11 ew.eH ww.eH ew.wH 11 we.Hw ew.wH 30: :Hpe: ww.w we.e ee.w ew.w Hw.e ww.w we.e we.w HHe . m©.e. ww.e ww.w. 11 .ve.m mm.m Hw.w 11 .zmH: owmcpom ee.w ew.w wH.w ww.H we.e wH.e we.w ww.e we: weewawwe ww.w 11 ww.w ww.w ew.w 11 ew.e wH.w so: Hepoe HHe asz we: 30: HHe wwH: we: 30: meeeeHe Hepop po , ma 1 ma amESmcoo mmmucmoamd o ooo_. .Sv H pea ocmH aoao comm ... . . . . . . ,. .......... . omnaoao 90.0mmmpom mommmao poesmcoo pom mEoocH Emma pez ompmsho< wwwH .epo1owo wee apeaH .ommepom Hmpou mo mwmpcmopma m mm GBOpw aoao pOnmE Some mo mmmmpom pennanw< H.\. mHomE 115 o o o o o o o o HH< mmOpmuom o o o 11 o o o 11 :wH: Heezw o o o o o o o e we: w o 11 o o o 11 o Q 304 smhoooo He.wH ww.wH ww.w we.eH ww.w ww.w we.e ww.w HHe ww.eH eH.wH ww.HH 11 ww.w oe.e o 11 :wH: we.wH ww.w Hw.e ww.ww ee.w ew.ww He.w we.w we: He.wH 11 ww.e ww.HH He.e 11 ww.wH Hw.e 30: w>eeweo ww.wH ww.wH we.wH we.ow ew.ew ww.ww ew.Hw ww.ew HHe ww.wH He.eH Hw.ww 11 ww.ww ee.ww ew.ww 11. wwH: we.eH ww.wH ww.HH ww.ww ew.ew ww.ww Hw.ew ew.ww we: ww.wH 11 ww.0w ww.wH ww.ww 11 He.ww ew.ew 30H 5w: ew.o .o c ww.w ww.e ww.H o ww.e HHe . o o. o .11 aw.H ew.H o, 11 :wH: o o o o eo.o ew.w o c we: we.w 11 wo.e ww.e 11 o ee.w 30H pacwpaopw oe.c wH.H o .o wo.H ee.w ww.e He.e HHe wH.e wH.e o 11 ew.w ww.w ww.w 11 :wH: o o o o a o o o we: 0 11 o c Hw.e 11 o ww.e 30H ceea ewe HH< Emmi pm: 30; HH< Smmm cm: 304 mmwmmao HmQOH mo ep01owo. :peaH peESmcoo emmpceopea poa mama ache comm mmmmmHo pmesmcoo pea mEoocH Emma pmz omaaoao Mo mmmmhom wepwanwe Ao.pcoov H.e oHnt 116 mumo >m>asm “mopsom ww w e a aw oH .1m w HHe _ . ... w e H 11 0H m. 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O HH< 11 Hw.wH o 11 o e e 11 :wH: ew.w ww.w ww.w ww.w e e e e we: ww.w 11 mquH ee.w o 11 o O 30H coupoo ww.H ww.e ew.o ew.w ww.w ee.w wH.w. ww.w HHe ww.e He.e .e .1- .ww.w ee.w ww.e .11 ewH: ww.e e .o ww.e ew.w HH.w ww.w ee.w we: eeHeeeewe> ww.w 11 ww.e ww.e wH.w 11 ee.w ww.e 3eH HHe HHe :wH: we: :eH HHe ewH: we: 39: eeeeeHe Heeep we .. . wep01ewo _. aueaH . pesaweee eweeaeepea 113 90a pcmH aoao comm mmmmmHo pmesmcoo pea esoocH Epmm umz omaaopo mo ommmpom weweanwe AwLeeev H.e eHeep. 117 Hw.w He.w we.w ew.w ww.w ew.w wH.w ww.H HHe ew.w ww.w ww.w ww.w ew.H ww.e ewee 11. :wH: Hw.w ew.ww HH.e ww.e we.w ww.w ee.w e we: ww.w 11 e we.w wH.w o Hw.w ww.H 3e: weeasee ww.wH ee.wH wH.wH we.wH ww.w ww.w .ww.w ww.e HHe He.wH ew.wH wH.wH we.Hw we.w ww.eH ww.w .11 :me. ew.eH ew.wH Hw.wH we.ew ww.w ee.w ww.e ew.wH we: cpeo ww.eH 11 ww.eH ww.w ww.w we.w ee.wH ww.w so: eecHae ew.wH ew.e we.wH ww.e ww.wH ww.wH ee.wH we.wH HHe ew.w ww.w ee.eH we.wH -Ho.wH wH.eH ee.wH 11 awH: ww.HH o ww.eH wH.w ww.eH wH.ew wH.wH we.Hw we: ewHe: we.wH 11 ee.ww ww.w we.ew we.ww ee.ww eH.wH 2e: epeH ww.wH ew.eH ww.eH we.eH we.Hw wH.wH ww.ew ew.ew HHe ww.wH we.eH wH.wH we.w ew.wH ew.Hw ew.wH 11 eme ww.wH ww.wH ww.eH ew.wH ww.ww ew.wH ew.ew ee.ww we: ewHe: ww.wH ww.eH ew.w ew.HH ew.Hw ww.wH we.eH ew.ww 3e: :Hpew ww.e ww.e ew.w wH.w ee.w ew.w ww.w ww.H HHe ee.w. eH.w. ww.e Hw.w ww.w eH.e ww.w 11 :wH: eepee Hw.w eH.H ew.w Hw.w ww.w ww.e we.w ww.w we: weeeanwe ww.w o ww.w ww.H wH.w He.e Hw.w ew.H 3e: Heeee HHm swam we: 30H HH< swam, pm: 304 mmmmmHo mHanpm> .thIOUO DquH LQESmCOO hmnm mmmmmHo meDmcoo pea mEOOcH Emma pmz -1 ocmq omdaoao . eewH.»ep01ewe wee :pmaH .mwmmpom Hmuoev mo owmpcmopoa m mm Esoaw aoao acnmE some .Ho mmmmpom pmumsnSwK mHnma. 118 0000 COO 0000 0000 of) l\ LOCO (DCUO‘D 0000 0000 HHe :wH: we: 30A Opmpoapmmzm ocm Emhoooo ww.ww ww.wH mv.hm ww.ww ww.wH ww.wH O ww.ww mO.HH hm.om Hm.mH ww.ww Hw.ew H0.0m V0.0v wH.w Nm.h wH.w no.0 ww.w mm.m ww.w mm.© ww.w ew.wH wH.w b©.© HHe awH: we: BOA m>mmmmo ww.ww ww.ww. HH.®H ww.ww ww.ww ww.Hw ww.Hw Om.mH wH.HN mo.©H Oh.om b©.NN OO.NN mm.ba OV.®N no.mm vo.mv om.om ww.ww mo.av ww.ww ww.ww h©.Nm mm.am hm.¢¢ ww.ww Vb.mm HHe eme we: 304 EN? 0000 I000 OOOO COCO COOO HHe :wH: we: 30A unconsopu 0000 000 O<3c>c CNDCJQ OCDCDO 0000 0000 0000 0000 HHe :wH: we: 30H ammo em» HH< :wH: U02 3 o -H HHe :mH: Um: mpOIOUO meaH mommmHo meSmcoo pea mEoocH Emma pmz mommmHo HmESmGOO. pea ocmq pmaaoao eHeere> AU.pCoov N.n oHQmB 119 mama >m>p5m "mopsom ew .w a we ow 0H 0H 0H HHe HH w H w w w w. 11 ewH: ewHeweeae: HH H w e w w w w we: we w 11 w w eH H w w 3e: peeps: o e o e He.H o ww.e wH.H HHe o o c .o o. o o 11 :wH: o e e o ee.w o ww.w 11 we: 0 11 O o ww.e J1 o ww.H 304 mHchmpma e e e e ww.e ww.H ww.e e HHe e e e e wH.H ew.w ww.H 11 ewH: e e e e ee.w ew.w e e we: 0 O O .O O 11 o o 304 ooomnoe wH.w He.w ew.H wH.w e o e .1 e HHe we.w we.w we.eH c. e. e o 11. eme ww.H e ww.e ww.e e e o e we: 11 11 o o e .e e e 3e: powwow ww.H ww.w Hw.e ww.H ww.e ww.H ww.e Ww.e HHe ew.w we.w e e ee.w ww.H e. 11 ewa: ew.H e we.e ew.w ww.H ww.H ww.e ww.w we: e 11 e o ww.e ew.w HH.H ww.e 3e: eeHeeeewe> HHe :wH: we: 3eH HHe wwH: we: go: eeeeeHo eHeere> maelooo . :meH peezmmmw mommmao meSmcoo pea mEoocH Emma #02 ocmq pmaaoao Hw.eweev w.e eHeee 120 the adjusted acreage under maize is taken as one acre and the adjusted acreage under guinea corn is similarly taken as one acre. The total adjusted acreage under maize for the household is the sum of the adjusted acreages under maize from all the fields on which maize was grown fOr that year. In an effort to identify ppssible differ- ences among incOme and land classes in the crOpping pat- terns, the adjusted acreage of each crOp has been further disaggregated by both income and land classes and by villages. It can be-seen that crOpping patterns were strikingly similar among all households regardless of land or income status. The value of each group as a percentage of total was also broken by incOme and land classes as a further step in establishing differences in cropping patterns (Appendix Tables A-14 and A-15) and again there were no clear changes in cropping patterns across either the land or income strata. Yam commanded the greater per- centage of value of all crops grown in both villages and years, representing as much as 70 percent of the total harvest. Maize was next in importance with about 13 percent followed by guinea corn in Odo-Ore with about 11 percent. Mixed cropping, as mentioned earlier, was a pOpular practice. Norman (4l,pp.87~101) has reported that net returns per acre are higher in crop mixtures than sole 121 cropped fields. The scope of the present study cannot examine this aspect of sole crops versus mixtures, but Tables 7.3 and 7.4 show how the percentages of sole crOp- ped acreage and the various combinations of crap mixtures varied by income and land classes. In Ipetu in 1969 ap- proximately 46 percent of th? land was sole grOpped cOmpared with the 18 percent in Odogore.;,In the same year Ipetu farmers devoted about 30, 20, 2 and 2 percent of their land to two-crOp, three-crOp, four and five-crop mixtures respectively. In Odo-Ore, as much as 38 percent of cultivated land was put into two-crOp mixtures, followed by 29 percent for threeecrOp, 11 and 4 for four and five- crop mixtures respectively.1 In 1974, the pattern was similar except that the two—crop mixture had the greater share in both villages. We set out the hypothesis that land short households would try to maximize returns to land through inter- crOpping. However, careful examination of Tables 7.3 and 7.4 show that in all but Ipetu in 1969 the highest sole cropping was among the lowest income class. In 1969 in Odo-Ore 24 percent of the crOpped land was sole crOpped by the low income households compared to 12 per- cent by the highest income households. In 1974 in Odo-Ore 1For his one year study, Norman (4l,p.73) found that about 23 percent was sole crOpped while about 77 percent of the crOpped land was in mixtures. 122 pcma pmmmopo wH.HH we.wH ww.e ew.w ew.H ew.w o we.e pHe Hepep we oo.HH ew.wH o 11 em.o em.o o 11 swam pcmopma m Hw.e mo.Hm ww.w o o o o o om: mm mpprHE wH.HH 11 ww.eH ww.HH we.w 11 e we.w 3eH aepe1peea mama ommmOpo ww.ww we.ww ww.eH ew.ew ew.ew ww.ww ww.ew ww.wH HHe. Hepea.ae wH.ew ew.ew ew.ew 11 ww.ew ww.ww He.eH 11 ewH: peeepea e we.wH ee.e ww.eH ew.ew ww.ew Hw.w we.ew ee.wH we: we epepre ee.ew 11 ww.w ww.ww ee.wH 11 ww.Hw wH.HH 3e: aepe1eepee . 1 weeH we.ww we.ee ew.we He.ww ww.ew ww.ww wH.Hw ww.ww. HHe weaaepe.Heaea we.ww Hw.ew ee.ww 11 ew.Hw .ew.Hw ww.wH 11 pawnipe Heeepea m ew.we we.we ww.ww we.ww ww.ww ww.ww we.ww ww.Hw we:. we eepeexHe ew.we 11 ww.we ee.ww ew.ew 11 ww.ww ee.ww 3eH aepe1e3e ww.eH ww.HH ww.ww Hw.ew ww.we Hw.ee wH.ee we.we HHe. weeH wemmepe we.wH ew.wH ww.ww 11 wH.ee we.we ww.ww 11 ewH: deuce we ueee ee.e ww.w ee.e ww.eH we.ee eH.ww we.Hw ew.He axzepea e we weeH wH.ww 11 wH.ew ww.ww ew.we 11 ww.we we.we 3e: weaaepe eHew HHe :mHm1, em: zea, HHe :wp: we:. 3eH weeeeHe eHeere> hmEDmCOO ®L0.0UO 3¥®QH -1 .HQQ mumppm LmEDmCOO pom mEoocH Emma pmz ccma pmaQOpo wwwH .epO1ewo wee DquH .mmmmmao mezmcoo pea mEoocH Epmm pm: ocm ocmH ooaaoao mamcpmppma mcHaaoao mfie. mHnme 123 mama >m>p3m "mopsom mm m e m mm OH OH m HH< w .e. H w OH w H. 11 ewH: wwHeeeeae: h N N m Ha H m m cos mo OH 1 v o m 11 e v 30H pmoEDZ ocmH omQQOpO ew.w we.H ee.w eH.H ww.H o‘ e ww.e HHe Heaea.ae mm.H mm.m o 11 o o o 11 swam unmopma omm ww.w O Hw.wH .O wH.w O O wH.w no: logom mthxHE NO.H 11 o He.H O 11 .O 3 BOA omaQOLOIm>Hm HHe ewH: we: 3eH HHe ewH: we: 3eH eeeweHe eHeere> ep 10 s a peasmeeo O CO . no H . (Hmm mumppm meDmcoo pea mEoocH Epmm pmz ocmq .. pmaaoao Ae.pcoov w.e mHome 124 ocmH omamomo wH.w .ww.oH we.e o He.w ww.e we.e ew.w. HHe Heeew we we.w mm.mH ww.w 11 wH.w 0m.v 0 11 swam pcmowma m 0 0 0 0 Elm Hw.w mm.0 ew.e. p02. mm mmpprwE Hw.w 11 ww.eH 0 0m.m 0 mv.0 ww.w 304 QOLOstom - ocmH pmmmowo 0e.0m me.0m . ww.ww wH.wH ew.wH He.wH ww.Hw we.HH HH<. Hmp0p.wo we.wH we.eH ew.ee 11 ww.wH ww.wH ew.e 11 ewH: peeepea e ww.ew H0.ee m0.0m ww.wH ew.wH ww.wH ww.HH wH.wH om: mfiw mewsust Hw.w 11 o ew.HH ee.wH ww.HH ew.ww wH.eH 3e: aepe1eepew 11. ocma omamowo ww.ww ww.ew e0.ee mm.0m ww.we ww.we ww.we ww.ww HH< HmHOp.wo ee.ww ww.ww ww.wH me.0m ew.Hm we.ww mm.0m 11 swam pcmopma m ww.ww ww.ww e0.me wH.Hm mm.0m wH.Hm ww.we ew.we ops. map mmwsuxwe ww.ww 11 ew.we ww.wH ew.we 0H.00 ww.ew em.em BOA a0.31039 0H.mm 00.2.. eH.0m ww.ew we.ww m0.mm ee.ww ww.we HH.w. ocmH pmmaowo we.Hw , ww.Hw . .0. . ww.we wH.ee. 00.9» we.ww 11. anm. Hmuou .wo paeo ww.ww, 0 ww.ww 00.0w 0m.mm ww.ww ww.we ww.eH pm: 1.39 m mm ocmH ww.we 11 0m.m m0.0e mm.mm ww.wH wH.0H ew.we 304 pmooowo mHom HH< swam no: 304 HH< Ema: pm: 304 mommmHo mHomwpm> meSmcoo mwOIOUO SmeH won mumwum wmesmcoo pea mEoocH swma umz ocmH omaaowo eemH .mp010e0 wem zpeaH .mmmmmHo meDmcoo wma mEoocH ewmw pm: ocm mama omaaowo an mcwmpuma mewaaowo aefiu mHnt mpma >m>wsm "mowsom 125 ew a w w ew 6H oH ‘ .. OH HH< . . HH m H .w m. 0 w. 11 zmwz noHonmmsoc HH H 0 w w m m N no: mo m 11 m m vH H m m BOA wmnEDz ocmH pumaowo w0.H wH.w. 0 0 0e.H o we.w. 0 HH<. Hence we ew.w ew.w e e ww.w e ww.ww 11 :3: peeepea m 0 .0 0 0 0 . 0 0. 0 no: mm mmwsuxHE 0 11 0 0 He.0 0 eH.H 0 304 . QowOIm>Ha HH< nmwz om: BOA HH< cmH: om: 304 mommmHo mHomem> . . 1 . _ peesweeo. 0:0 000 SummH pea mumppm peEpmcoo pea eEoocH Epma pez wcmH omaqowo AU.pCoov w.n mHnt 126 again the low incOme households devoted as much as 57 percent of their land to sole crOpping while the high income group devoted 30 percent. Similarly in Ipetu in 1974 the low incOme group devoted 43 percent to sole crOpping verSus 36 percent devoted by the high- est incdme households. Examination of the cells show that the percentage of sole cropping was highest among land short-low income households. As high as 34 percent was reported in 1969 in Odo-Ore for the land-short lowest income households compared with 14 percent for the land rich-highest income households. Also in 1974 the land- short low income households devoted 70 percent to sole cropping in Odo-Ore cOmpared with 32 percent for the land-rich highest income households. Similar figures for Ipetu in 1974 were about 50 to 45 percent respectively for the land-short lowest incOme households and land-rich highest income households respectively. {Hue apparent tendency of low income households to follow a management practice shown to reduce returns to land should be kept in mind as we examine productivity differences in the next section. During 1969 Ipetu had a greater percentage in sole cropping than Odo-Ore which may reflect the fact that land pressure was more acute in Odo-Ore than in Ipetu. Moreover, guinea corn is grown mostly in Odo-Ore and this is usually grown in mixture with early maize. 0n 127 the other hand, at Ipetu as a result of the agroclimatic' conditions guinea cOrn is not grown extensively with the result that early maize tended to be grown in sole stands. B. The Farm Budget Components Defined In earlier chapters we have considered the resource en- dowment and resource use cOmponents of our conceptual framework. The following section cOnsiders returns to factors and how they differ among income classes. This is acoomplished through an analysis of farm budgets. We will begin by defining the components of the budgets. The value of output is defined as the value of the total yield of each crOp. Variable cOsts are the sum of the value of seed, fertilizer and hired labor. With the exception of one farmer, farmers used neither organic nor inorganic fertilizer.l As much as 80 percent of the seeds were saved from the previous year. Moreover, yam accOunted for nearly 80 percent of cost of the seed. The seeds were valued at the average market price. Hired labor was generally paid in kind.2 1For the single farmer who used fertilizer, inorganic fertilizer was valued at the 1969 price of HO.9O per cwt.for superphosphate and 31.10 for sulphate of ammonia, after gov- ernment subsidy. 2Items paid in kind were valued in each village at the mean market price applied to the harvest (See Appendix Tables A-1 and A—2, Heals were valued at the cost of 30.05 and H0.10 depending on the type. 128 Gross margin was defined as gross farm incOme less total variable costs. Return to household land, manage- ment and labor per hour was found by deducting the fixed cOsts from the gross margin and dividing it by the total number of man-hours input.l Fixed costs have been cal- culated as an estimate of the annual service charge on the capital stock. 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HHe .we.e wH.e. ee.e1 11 we.e1 we.e Hw.e1 11 ewH: xeweH ee.e1 ww.e we.e ew.e1 ee.e we.e ee.e ww.e 1 we: :eeeHewae He.e1 11 ww.e ww.e1 ee.e 11 ew.e ww.e 1 30H HeeHeeeew.e ww.Hw we.we ew.we ee.ew ww.ee wH.Hw wH.ww we.we HHe .,ww.ww ee.oe we.ew 11 ‘ wH.ww ww.ee ww.we 11 ewH: epee pea we.ww ww.ew Hw.ww He.ew ww.ww ew.eeH ew.we ww.ee. we: eeeeeH we.ww 11 ew.we ew.ew we.ee 11 we.wwH we.we sew spew we:.w ew.e ww.e ew.e wH.e Hw.e ew.e ww.e HH.e .HHe .ew.o ww.e we.e 11 ww.e ww.e. HH.e 11 ewH: pee: pea Hw.e Hw.e Hw.e wH.e wH.e Hw.e wH.e wH.e we: eeeeeH Hw.e 11 ew.e wH.e ww.e 11 we.e wH.e 39: spew we:.w .HH: eww: we: .zeH ,HHe ewH: . we: so: weeHe eHeere> . MLCIOGO «5qu HoEDmGOo mpmwum.wmssncoo.wma meoocH.Epmm.umz :1 ppm ocmH cmaaowo COLCOOV w.e. mHome 136 the high and low incOme households was about H36 in both Ipetu and Odo-Ore in 1969. In 1974 the average product Of land was 3195 and H151 in Ipetu and Odo—Ore respectively. The respective labor input per acre was 397 and 483 man-hours in Ipetu and Odo-Ore the same year. Thus, Odo-Ore households on the average expended more labor per acre but generated lower returns to land. The difference in the average product of land between the high and low income households was 588 (a 59 percent difference) in Ipetu and $101 (a 111 percent difference) in Odo-Ore in 1974. In short, Odo-Ore high income households displayed a relatively wider margin in land productivity than Ipetu. The net farm income per acre shows similar relationships across income classes. Average Product of Labor The average product of labor was approximately H0329 in Ipetu and 30.31 in Ode-Ore in 1969. In 1974 the values were 30.51 and $0.35 in Ipetu and Odo-Ore respectively. Reasons for the low average product of labor in Odo-Ore are not clear. The average product of labor varied be- tween HO.20 and £0.34 (a 70 percent difference) in Ipetu and between 50.18 and H0.4O (a 122 percent difference) in Odo-Ore between the low and high income classes in 1969. 137 And in 1974 these differences were 111 percent in Ipetu and 172 percent in Odo-Ore. In short the data show that in both years and both villages, the average product of labor reflected a strOng direct relationship with income. More im- portantly, this positive relationship also holds when land is cOntrolled implying that land alone is not the only factor limiting labor's productivity. For ex- ample, examing the middle land strata we can see that the low income households had an average product of labor of 30.22 in Ipetu cOmpared with 30.27 for the high income class in 1969. In the same year in Odo-Ore, among the middle land strata the low incOme households had an average product of labor of H0.22 cOmpared with 30.38 for high income households. Gross Margin Per Acre The data in Tables 7.6 and 7.7 also show that the high-incOme class achieved greater gross margins per acre both years and in both villages. In 1969 gross margins per acre for the high income households was 80 percent greater than that of low income households in Ipetu and 85 percent greater in Odo-Ore. In 1974, the percentage differences were even wider at 117 in Ipetu and 143 in Odo-Ore. Despite the fact that with- in each income class Odo-Ore farmers had lower gross 138 we.w ee.e ew.H eH.e ee.w we.e wH.w ew.wH HHe we.w ww.w wH.e e ww.w ww.w ww.e 11 ewH: ww.e ee.eH ww.e we.w ee.w we.w ew.e ww.e we: peeeH Hwnw 11 ww.w HH.H ww.w.1. ee.w we.w we.wH sew wepH:.e ww.we ww.ew ww.He He.ww we.ww ew.ew eH.ww we.ww HHe ee.we ww.ww ee.ew ee.ew ee.ww ww.ww we.ew 11 ewH: ee.ww we.ww eH.ww ww.ww ee.ww we.ee we.ee ww.Hw we: ww.ew 11 ee.ww ww.ew ww.ww we.we ee.ew ww.ww 3eH weew.e mmeO mHflmHeHw> .v. 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This may be a partial factor explaining the results shown earlier that inequality as measured by the Gini coefficient was greater in Odo-Ore than in Ipetu. Technical Efficiency The data show that households in the high income class had consistently greater technical efficiency. Holding land constant, the technical efficiency tended to in- crease with income status during both years and in both villages. At each income level, the technical efficiency tended to decrease with greater land holdings except for the high income households in Odo-Ore in 1974. This implies that in general technical efficiency is a strong correlate of income. Average Returns to Land and Management The average return to land and management was on average 355 in Ipetu in 1969 and H45 in Odo-Ore. Similar re— turn figures were about 536 in each village in 1974. These return figures varied directly with income sta- tus during both years. In fact, low income house- holds in 1974 had negative returns to management and land in both villages. Return to management and 142 land and per man—hour of labor were-on average 30.07 in Ipetu in 1969 and No.08 in Odo-Ore. In 1974 sim- ilar figures were H0.l2 in Ipetu and 30.07 in Odo-Ore, with a strong direct association with income class. Variation in factor use and variable costs. In an effort to explain these differences in factor returns among incOme classes, we examined varia— tion in factor use. Separating the effects of face tor use among income classes enabled us to see what caused the variation in factor returns. For example, while total variable cost~per acre remained fairly stable as one moves from low to high income classes in 1969, the gross farm income per acre increases rapidly. The net effect is that gross margin per acre increases as one moves from low to high income groups as a result of higher crop yields among the high in- come households. Returns to factors all follow along the same pattern. In 1974, however, the total variable cOsts per acre increased steadily but the gross farm incOme per acre increased even more rapidly as one moves from low to high income classes. The net effect again is increasing returns to factors as one moves from low to high income classes due to greater yields per acre among the high incOme households. E. 143 Summary Thus far we have considered all the aspects in our earlier conceptual framework. The use of land and labor have been found to be highly correlated with income status whereas capital stock did not show such a close cOr- relation. A strong direct relationship has also been observed between income and factor returns regardless of land use. The low incOme households were found to devote a greater prOportion of their land to sole crOps than the high incOme households. Under traditional technology s it has been found that returns to factors are greater for crOp mixtures than sole crOps. Hence, the practice of sole crOpping might be one of the reasons for low productivity among the low incOme households. These productivity differences might also be due to other management quality differences; either reflected in allocative or technical inefficiencies. Since data is not available to investigate these further, we will attempt in the next chapter to measure the relative im- portance of each set of factors set out in the con- ceptual framework as income determinants. CHAPTER VIII CORRELATES OF INCOME In earlier chapters it has been demonstrated that crOpped land is closely correlated with income. Similarly labor use generally tended to be positively associated with incOme both in terms of labor use per unit of land and per worker. Sub- stantial productivity differences not entirely related to levels of factor use were also found among income classes. Moreover, no substantial differences were found in the crOp- ping techniques or in crOpping pattern except that the poor income households appeared to devote a greater percentage of their land to sole crOpping. It was clear in the preceeding descriptive analysis, however, that a number of household variables were intercorrelated thereby restricting inferences of more fundamental association with income. This chapter aims at separating the interactions of in- dividual factors by fitting several econometric models to the data in order to measure the partial correlation of different factors with gross farm income. The analysis was cOnducted separately for each year and for each village to test the stability of the income correlates over time and between vil- lages. 144 145 The Variables The dependent variable in each model was gross farm incOme. Gross household income could not be used because there was no information on capital (a key independent variable) for off-farm occupation. Moreover, in 1974 data on off- farm activities was not cOllected making gross farm income the only measure that affords cOmparison between the two years. Our inability to use gross household incOme limits our conclusions to farm earnings only which, though a major part of the household income,permits only qualified con- clusions“ The set of independent variables considered were: Xl - Lowland (all of which was mixed cropped) in acres, X2 - Upland plus forest land which was devoted to 2 or more crOp mixtures in acres, K3 - Upland plus forest land devoted to sole crops in acres, X4 - Number of adult male worker equivalents, X5 - Value of capital stock (tools and equipment) in naira, X6 - Total manhours per household X7 - Family labor per worker, and X8 - Non labor variable costs in farming in Naira. It is expected that the greater the total crOpped land the greater would be both gross and net farm income. Breaking land which was devoted to crop mixtures into 146 X1 and X2 is to separate land quality differences. It is eXpected that the lowland, being more productive due to greater natural fertility and soil moisture would show a larger coefficient than upland devoted to crop mixtures. Invariably all lowland fields were devoted to crOp mixtures.1 The acreage under sole crOps versus acreage under mixtures have been incOrporated as a partial measure of management. It is expected that both upland under crOp mixtureszmui under $016 crOps would show positive relationship with incOme but the size of the coefficient for the sole crOpped land would be expected to be smaller. Labour availability (the work force endowment of the household) has been measured in terms of the number of adult male worker equivalents. Again it is expected that the relationship between the worker equivalents and incOme would be positive both because of the larger work force and due to greater consumption requirements. The value of capital may or may not be an important cor- relate of income given the traditional technology but the relationship would be eXpected to be positive. Comparing the two study villages, Ipetu has closer access to Omu—Aran market and is expected to receive lNorman ( 41 ) found that returns to factors were greater with crop mixtures than sole crops. 147 heavier rainfall. However, our survey data for 1974 showed that Ipetu received less rainfall than Odo—Ore hence making it difficult to predict whether the cOnstants obtained for Ipetu models would be expected to be greater than those obtained for Odo-Ore. The variables reflecting resource endowment X to 1 X5 and resource use variables X6 to X8 were combined in the equation in two forms. Variable X (total manhours) 6 was entered separately with Al, X2, X4 and x10 simply to determine marginal effect of labor on gross farm in- cOme. Variables X4 and X9 were entered together in sub- sequent equations to separate labor effects into two possible components, the number of workers (family com— position) and hours per worker (intensity of labor effort). Each of these variables is eXpected to be positively re- lated to income. Non-labor variable costs reflect the intensity of land use and was expected to show a positive relationship with income. The means and standard deviations of the variables used in the regression models are reported in Tables 8.1 and 8.2.1 1Worker quality was introduced into earlier models as the age of the household head. The quadratic form was introduced to test the hypothesis that management and physical strength first increases and decreases with age. Thus age was ex— pected to have a positive cOeffioient and the age square to have a negative coefficient. However, since age and age squared gave Opposite signs and were generally not significant they were eliminated from the models. sumo >o>h3m “mopsom hm.o we.w ww.H mm.o we.w ww.H mpficalpoxgo: ww.e ©5.mmm ww.w mo.o hc.m®m ww.w Loxhoz Log gonad hafiEML V5.0 mo.me Hw.w m©.O ww.HOH we.w momcoaxm Ehmm LOQMAICOZ ww.e ww.wH ww.w mo.o ww.w ww.H xoOpm Hmpadmo mo 03Ho> % ww.e ww.H Hw.e ww.e ww.H ww.e Aooxflev mogom tapas: l mO.H wH.O Oh.HI mm.o VH.O ww.HI Acmxasv mmLo.m UCwHZCA no.0 om.o we.e: ww.e ww.H ew.e Aofiomv mobom ncmfias ww.e no.HHNH OH.5 om.o mH.mmVH Hm.h mgsoscmz HMpOB ew.e hm.mmm ww.w ww.e Ho.bwm ww.w oEooCa Enos mmopu cofipmfiboo moqlfipc< Ecuapmmoq cofluma>om moqlauc< Eswahmwoq AESpfigmmoq opoocmpm coo: tamocopm coo: assaumzv shad coma mofinmapm> whoa ecu mama :uocH .mHoUoE scammogmog Ca pom: moanmfipm> mo mcoflpmfi>ot pamocwum ocm mcmos H.w canoe 149 mama >o>hsm "oopsom mm.0 05.0 mm.H 00.0 wH.w V0.H mpfiCDIhoxLo: vm.0 Hw.wwm ww.w 00.0 mm.mma mo.m LoxLoB pom gonad kHHEmm 00.0 mh.mmH Hw.w H5.0 Hm.mv 00.0 momcoqu Ehmh LOSMAICOZ Hb.o ww.wH ww.w 0m.0 ww.w ww.H zoopm Hmpfidoo ew.e wH.w m>.0 m©.0 ww.w mm.0 Aooxfiev mogom ocoaa: ww.e mH.H wH.e ww.e mm.o ww.e: Amaomv motes scmfia: H0.0 mm.mmmH mm.b mv.0 mH.bmm 05.0 mhzozcmz Hmpoe no.0 v0.0mv HH.w 05.0 0m.mam mm.m oEoocH Sham mmopu coHpmH>oQ moqlapc< sapapmmoq cofipmfi>om moqlapc< snpfinmwoq AESprmmoA opmocmum cmo: Unmocmpm coo: Hogspmzv vuma mood moaomago> tha pcm moma ohoeooo .mHoUOE :onmopmoL CH pom: moanoflpm> mo mcofiuma>mo Upmtcmpw ocm mcwoz N.w oazme 150 B. Correlation Coefficients In Tables 8.3 to 8.6 are presented the correlation co- efficients for all variables. It is clear that some of them were quite high indicating likely problems of cOllinearity. In particular we should note the high cOrrelation (greater than 0.6) between cropped upland, the non-labor variable costs and total man-hours in both villages and for both years. With such a high degree of multicolinearity of course the estimates of the regression coefficients may be highly imprecise be- cause of the large variances of the least square es- timates. This implies that the absolute values of the regression coefficients should be used with caution. C. The Results of the Regression Models Several forms of income generating functions have been used in the literature. For our purposes in this chapter the Cobb-Douglas form has been reported because it gave a better fit than the linear form and because it displays diminishing returns to factors, an expected prOperty of . l the present farming system. 1The same functional form was used in Chapter VII for the whole farm production function fitted for the purpose of estimating the technical efficiency index. 151 spam >o>93m "oopsom hV.I 0H. 0m. 0m. 00. Va. cm. VH. muHCD Loxhoz sq. mm. mm. ma. 5H. mm. as. waste; was gonna hafleom vm. V0. Vm. HV. 50. mm. noncooxm Esau poanIco: mm. «m. ma. mm. mm. xooum Hmuwnmo no msam> 0m. ©0.I V0. m0. Apoxfiev mosom pcmHQD H0.l 5V. 0N. AfimxHEv mogoo UCQHZOA mm. NV. Aoaomv moses pcde: mo. mason ICoE Hmpoe Loxgoz momcodxo xOOQm Apowflev Apoxasv Aoaomv mpsoc oEoocH AESuHmmmoq sziponoa Epmm Hopfidmo mopom mopom mogom 1cm: 59mm HmLSumzv haaemm Lonmalcoz mo 02Hm> panda: ocmazoq panda: Hmuoa mmOLU moanmahm> mama squH .maopos :onmopmop as pom: moacmwpm> go mucofloflmyooo coflumaoppoo m.m manna mama >o>h5m "oopsom 152 mm.| mm. mm. mm. mo. 00. mV. mm. mafia: poxso: NV. mm. VV. oa.l 5H. ms. aV. Loxnos pod gonna haflsms 0V. mm. 00.: mm. mm. on. mongoose Epmw nonoqlco: 0V. mm.1 50. mm. 0V. zooum Hapflsmo mo oSHm> vo.u mo. as. as. Anmxwev mosom pcmaoa m0. 0a.: H0.I ApoxHEv monom panazoq ma. cm. Aoaomv mopom ocmac: 0w. mason lame Hauos Loxsoz momcoawm xooum ApoxHE%AooxHEV Aoaomv mason oEoocH Aanuapmwoq mom Show Hana wonoo mosom monom Isms Esau ampspmzv ponmq gonad Idmo Mo pcmfioa ccmazoq sedan: Hmpoe mmopw moHQmeo> haaemm Ico: 03Hm> Vhoa DuooH .mHoUOE cowmmoawmp CH tom: moanmflsm> mo mucofloammooo cofipmamgpoo V.c oHQsB 153 spam >o>nsm “oopsom mm.| Vm. V0. mm. 0V. mV. om. muHCD poxno: Va. mm.l ma. mH.I om. ma. soxpo: sod gonna hawsmm Hm. ms. VV. 05. mm. noncoax Emmy ponmqlco: hm. m0.l mm. VV. zooum Hmpacmo wo oSHm> Ha. mm. mm. ApoxHEV meson pcmac: um. um. Aoaomv mopom ocmac: we. masoccms Hmpoe poxpo3 noncomxo god Spam AooxHEv Aoaomv oEooCH AESHHpmwog ponma ponmq xOOpm mogom woaom mssoncs: esmm Hapzpmzv hafiemh Icoz Hmufidmo panama UCQHQ: Hmpos mmono oHQmHLm> .mHoUOE scammopmop CH poms noHQmHLmS .Ho memfl.mnououo mpcowofiggooo coflpmdohhoo m.w wands __ mama >o>psm noonsom om. Vm. mm. Hm. 0V. be. we. muss: posse: mV. mm. mm. 0H. mm. HV. Loxpoz pod gsoomq hafiemm mm. on. mm. Vw. mm. momcodxo Epmm aonmqtcoz mm. m0. 5V. 0V. xoovm prHQQU mo 03Hm> 4 m en. as. ea. Aooxnsv mosom pcmao: mm. mm. Amfiomv mopom pcmHQD mm. masoccme Hmuoe Loxpoz momcomwo , nod Spam XOOpm AooxHEV Aoaomv QEOOCH Aezpfipmmoq gonna ponmq Hmufidoo mohom mohom assoccmz Esau HmaSpmzv kHHEmm Icoz mo.o3Hm> pcmHQD panama Hmpoe mmonu moHQmHLm> tha .oLOIooo waoooE conmopwoa CH pom: moanmflam> wo mucoHOHmmooo coflpmaoggoo ©.w oHnms 155 Various models were specified using several c0mbi~ nations of the sets of independent variables. The models reported in Table 8.7 are of this form: 1. Log Y = Log A + B Log V + B Log Y + B Log X + l “l 2 ‘2 3 3 ' V U K K BsLog x5 + B6 Log ‘6 + B8 Log 8 8 = . . D X 2. Lob Y Log A + Bl Log ‘1 + b2 Log X2 + 83 Log 3 + B4 Log A4 + BSLog X5 + B7 Log A7 + B8 Log X8 where Y is gross farm income and A is the constant (the intercept) and B1 to B8 are the beta coefficients which in the Cobb-Douglas form represent production elasticities. The marginal productivitiesl and their standard errors shown in Table 8.8 are derived from the elasticity figures of Table 8.7. We shall first present the results of the regressions for each factor and then examine the interaction of in- dependent variables in the various models. 1. Lowland Soils It will be recalled that lowland soils existed only in Ipetu. For both years and both models in Ipetu, the ocefficient- on lowland was not significantly different from zero. In model 2 for both years low- land gave the expected positive cOefficient while model 1 gave an unexpected negative sign. This 1See Heady and Dillon ( 28 p.228-230) auaa >o>usm "oouzom ~o>o~ unwound a an unscauucmam .... ~c>oH uioosoc m ad assauuucuuu so. ~o>o~ ucoonoa 0H ad udaoqudcuuw o. Ho>o~ ucoouoc Cw an Accenuucuum. too. to coo o 0.00 LCLLM ..o‘ -.0 sa.o ew.e 00.0 50.0 HH.e .Cum ma.cV No.0 V0.0 «v.0 Ha.0 mV.o m~.cn 00.0 «v.0 ew.H .uucoo tha a a ton. Lee—Lu .oi. Hw.e «.0 mm. eH.e -.0 0m.0 .Vum .n.s“ 00.6 we.e am.0 anCI 50.0! na.0 wH.e 00.0 wH.w .uucoo mcafi N to... .0. o u .0... L97...» OLOIOmd a... 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LCLL',“ SuQLH ...: wH.e H~.0 wH.e 00.0 b~.0 00.0 .cun nm.mn mi.L ma.o wb.o pm.o mc.0| ~0.0n HH.0u 00.0: wH.H: .00030 th~ to on LCLLH ...: o~.0 CN.O ma.0 n10 NN.0 010 ...um n.w ww.e mc.o V~.c 00.: 00.0: 0m.0 «v.0 no.0: ms.m .90000 000“ a cowuaczvm ox o h 1 . u uzmancoc :ouH Leer noses: oua~fiah .3 - a m nausea: . x ..x «x J. ax c3325 .......C.........m n N :30..— mhsoz .3320: so; ex 200.3“ Auger; Cognac; 20.33:; noon; IcoE Lego: caco~c>uzcm uauacmo mouoo mono: nopoc ncoml, chokl >~Hznm chLo: no ozmo> 0cc~cz scams: tamazo; Vsmfi cc: awed ononooo 0:0 :uccn .oscoc« :w ceasedgm> co mucuocu -m we oo:o:~uc« oz» ozuecxo o» mfiotoc ounuccccooo 039 no; nodamguoun amok 0cm m»:o«o«huooc couscogco: 5.0 canme sumo zo>ham «ochDOm 157 Hc>ou ucoouoq H as unauduucmumuco. Ho>o~ unwound m as assuauucwum... Ho>om ucoonoa 0n no acaoauucuumoc doze” accepoa 0N um accouuacmumo a... to out o tutu LOLLH ew.e cw. ww.ww we.w om.s~ sm.n~ .oum mmma 0m.0\\ ~0.mm mm.VI ww.Hw ww.ww ww.e .oopm.muo: thn o i too. LOLLH cm.o ww.e we.eH No.8 ww.ew ww.w“ .cum ww.H so.on sm.~n ww.w .s~.mo mo.mo mo.-.oogm.uta: mom” m a... to. a 0 too. LOLLM OBOIOUO ew.e we.e we.w om.s~ ew.Hw .csm ew.H m~.o mm.vu ms.am H~.os mm.~ .coum.utm: vsma a t not. LOLLW ew.e ee.e we.w ov.mv o~.m~ .cum ww.H Ho.ou ww.w am.Os oo.so . sv.cm.uopm.mta: mama H 00.. at. not LOLLH ao.o ww.e sm.vm we.w .om.ov ww.ew m~.mma .aum wH.w oo.H ou.mu ww.e: mm.sH s~.mmu nm.vm~ we.e vogm.uba: vsma an to LOLLw ms.o ew.e m~.m~ cm.m we.ww 8H.mv om.os~ .cum ew.e wH.e ee.w ww.w mm.mw cc.~w ew.w ww.ww uoum.mna: mama m o... to. J LOLLW SaflQH ww.e o~.o us.v om.ov ww.ew ma.mma .cum sm.m ww.e uwm.on ms.mn ew.ew- ww.ewH- mm.o uoum.uta: vsmu not to LOLLE ww.e we.e om.m wH.ew mm.mv cc.os~ .cum ww.ew News ww.w- ww.ww we.ww mo.s~n ~44mm.mcwcoua.wgc: mama - H . OHAuC< EOuH Lao> hung—LS: WHGH~d> as A30 ox sx ex mxxzv mx mx ax ucasucoo coagascm momcoaxm mason Amusoncmsv Apogeszv xooum Aoaomv Aoostv. Aooxasv chum Icoe Lasso: nod ucoam>«=cm Hmuacoo moped moped woaoo Lozasnco: Haves gonna sausam passes «0 osaa> sedan: usages: cccfizos V>oH 0:0 000” vacuoto csd :uomw .naocoe ofipuoaocooo one on» Ca moancano> ho mouuwaucum umou can moauu>fiuozoonm Hazaugaz 0.0 ofiome 158 implies that lowland acreage was not a strong cor- relate of income. Upland Soils Devoted to Crgp Mixtures Upland soils devoted to crOp mixtures showed a significantly positive association with incOme in 1969 and a highly significant association in OdofOre during both years. In Ipetu, however, this variable gave an unexpected negative coefficient which was not significant.- Upland Soils Devoted to Sole CrOps For both villages in 1969 upland acreage devoted to sole crops was found to be significantly and positively related to income. In 1974, however, the pattern was not very clear. For the first model in Ipetu the 1974 result showed a negative effect on incOme but a positive effect in the second model. Moreover, neither cOefficient was significant. In Odo-Ore a positive effect was shown in both models but was only significant in the first model. It is particularly important to note that in 1969 in Ipetu and in 1974 in Odo-Ore upland soils devoted to crOp mixtures gave significantly higher marginal productivity figures than upland devoted to sole crOps. On the other hand, inconsistent though insignificant results were ob- tained in the other year. As would be recalled these 159 two variables X2 and X3 were incorporated as a partial reflection of management. But as those results in- dicate whether planting crOps in mixtures give higher productivity or not depends on the year and also on the location. 252122: Because the marginal product of total manhours was significant in both villages only in 1974 the separation of labor correlation with incOme into its two component parts appears to be meaningful only in that year. The results for that year in both villages indicate that the number of workers per household was more closely correlated with incOme than the intensity of family labor use. This implies that family structure is perhaps a better explanation of income than the duration of effort. Although the number of workers and family hours per worker are negatively correlated the correlation is not high enough to affect values of the coefficients. Capital Stock With the exception of Odo-Ore in 1974 none of the capital stock coefficients were significant and in five of the equations reported the sign was negative. It is important to recall that given the traditional technology there were no major qualitative differ- ences in the capital stock. 160 6. :on—Labor Farm Egpenses Operating capital, unlike capital stock, showed a high cOrrelation with income and a stable co- efficient value of at least 0.22 in 1974. In Ipetu in 1974 about 32.28 gross farm income could be generated from an additional naira of non-labor farm variable costs. This represents an approximate annual return to capital of 128 percent. In Odo-Ore in 1974 an additional naira expenditure on variable costs cOuld generate an additional $1.50 gross farm incOme. This represents approximately 50 percent annual return to capital. During 1969, however, the association was not significant in Ipetu and only significant at the 20 percent level in Odo-Ore. Comparison Between Models and Years In general, the models display high R2 for cross-sectional data. The high R2 can largely be attributed to high cor- relation between gross farm income and 2932 land devoted to crop mixtures (0.62 in Ipetu and 0.85 in Odo-Ore in 1969, 0.79 in Ipetu and 0.86 in Odo-Ore in 1974). There is no appreciable difference between the R2 for either model, however, in both villages 1974 data gave much higher 32 values. An important result was that there are notable inter— year variations both in terms of the magnitude of the E. 161 coefficients and in the significance of the variables. In terms of the size of the coefficients the variables were fairly stable between models each year given a particular village. But the coefficients varied widely between villages for each model. For example, the re- sults indicated that an acre of upland in mixture could generate an additional HB3 gross farm income for Ipetu in 1969 and B66 in Odo-Ore with these relatives reversing in 1974. The labor variables also gave substantial inter- year variation. Summary In conclusion, this analysis shows that: 1. Use of upland soils in both years is consistently and highly cOrrelated with income while lowland gave no significant results. 2. Having used acreage under mixed cropping versus sole crOpping as a partial proxy for management, the re- sults indicate that the superiority of crop mixtures over sole crOpping as cOrrelates of income is highly variable accOrding to time (year) and village lo- cation. 8. Operating capital, in cOntrast to value of capital stock appeared to be a high correlate of gross farm incOme. The rate of return on pperating capital is high indicating that if cash shortage is experienced 162 by low income groups especially at critical pro- duction periods, the effect on income can be significant. When labor is separated into its two components, number of workers (family composition) seems to be more clearly cOrrelated with income than intensity of labor use. The substantial differences observed for different years appears to suggest that analysis of a single year alone for determining factors associated with income may give unreliable results. Different fac- tors or combinations of factors may be critical in determining the distribution of income in any given year. This points to the necessity of making long- itudinal studies for the purpose of deriving firm conclusions on incOme determinants. The fact that one year’s study cannot be expected to give generalizable results, leads us to the exam- ination of inter-year variation in the next chapter. Chapter IX examines the movement of households be— tween income strata and describes which of the fac- tors stipulated in our conceptual framework are responsible for relative gain or loss in income sta- tus between the two survey years. CHAPTER IX INTEL-YEAR VARIATION IN INCOME DISTRIBUTION Among the 54 households interviewed in 1969 and 1974 a total of 42 households remained in the sample both years. Twenty of these were in Ipetu and 22 in Odo-Ore. A unique aspect of this study is that two years of data were available for examination of income changes over time. The change in rankings of households between 1969 and 1974 is examined in this chapter. Due to a range of factors, the underlying income distribution might differ between years and households may move substantially with respect to their relative income standing. The characteristics of the house- holds which display wide inter-year changes are therefore examined to determine possible causes of movement. The 42 households with data for both years were ranked according to each year's incOme per consumer within their respective villages and within the total sample. The change in their rankings were found by subtracting the 1974 ranking from that of 1969. The percentile change was found by di- viding the change in rank by the total two year sample size in Odo-Ore (22) and in Ipetu (20) and multiplying the result 164 by 100 (see Appendix Tables A-19 and .A.-20). The results are shown in Table 9.1 and in Figure 9.1. The relative stability of the interhousehold distri- bution is clear in Figure 9.1. Within each village 60 per- cent of the households changed by 20 percentile or less. Examining the pooled sample, that is both villages, about 47 percent of the households changed by 20 percentile or less. A. Characteristics of Households with Large IncOme Changes Between Years Two groups of households were singled out for more de- tailed analysis, those whose relative incOme ranking improved by more than 20 percentile between years, and those whose relative ranking declined by more than 20 percentile. In an attempt to understand what is as- sociated with these changes, the characteristics of the households cOncerned were examined as case studies (Table 9.2 and 9.3). l. Households Whose Relative Income Ranking Increased by Greater Than 20 Percentile Four households in Ipetu and 5 in Odo-Ore experienced more than 20 percentile increase in their relative income rankings between 1969 and 1974. The data suggests that productivity improvement was the most cOmmon contributing factor. All of the 9 households in this group experienced an increase in gross farm income per hour and all but one had an increase in 165 Table 9.1 Changes in relative income status of households between 1969 and 1974 by village Households Positive Change Negative Change Per-- Percent fiercent centile of house- of house- Village Change Number holds Number holds Ipetu O 2 10 -- -- .1-20 5 25 5 25 21-40 1 5 0 0 41-60 1 5 3 15 61-80 1 5 l 5 81-100 1 5 0 0 Odo-Ore 0 l 5 -- -- 1-20 5 23 7 32 21—40 3 l4 1 5 41-60 1 5 3 14 61-80 1 5 __ -_ 81-100 -- -- -- —- Source: Survey Data. Calculated from Appendix Tables A-19 and A-20 . Percent of households 40.- 32 CLO-On; 30- 23 2o_ 14 14 10. 5 a 5 5 to81 ‘61 41 21 1 o 1 21 41 61 81 40f 100 80 60 40 20 2o 40 60 80 100 - + 30» IPETU 25 25 20" — + 15 10p 10 a .5 5 s 5 , o to 81 61 41 21 1 o 1 21 41 61 81 40F 100 80 60 4o 20 20 4o 60 80 100 30» 26 BOTH VILLAGES — + Q 20. 17 1- 14 10- 5 7 2 2 2 2 2 I 1 8i’ 61 41 21 1 o 1 21 41 61 81 to 100 :80 60 40 2o 20 40 6o 80 100 Percentile change between 1969 and 1974 Figure 9.1 Percent change in relative ranking between 1969 and 1974 with 1969 as base year 167 gross farm incOme per acre. Finally all but one of them experienced an increase in technical efficiency. The data, however, suggest that changes in family structure may also have played a key role. For ex- ample, all but one of these households had a decrease in the number of consumers, and 5 out of 9 had a de- crease in dependency ratio. Moreover in 6 out of 9 of these cases changes in land holdings were such that crOpped land per cOnsumer also increased. This might have contributed to the increase in labor productivity, but since land productivity and technical efficiency also increased a moregeneral improvement in pro- duction efficiency must have been experienced by this set of households. Thus no single cause, but rather a set of fac- tors underlies the interyear improvements in ranking with the greatest consistency in the productivity measures. This again emphasizes the need for a bet- ter understanding of factors contributing to pro- duction efficiency both in a static and dynamic frame. Households Whose Relative Income Ranking Decreased by Greater Than 20 Percentile There were a total of 8 households, 4 in each village, that experienced a decrease of more than 20 percentile in their relative income ranking. All 8 households in this group had a decline in gross farm income per hour while all but one of them had a decline in gross 168 farm income per acre. Finally all of them suffered a decrease in productivity. This implies that a decline in production efficiency was closely associ- ated with decline in relative income status. Four out of the 8 households also had a worsening in their dependency ratio, two had no change,while only two had an improvement in their dependency ratio. These results again tend to show that changes in family structure probably cOntributes to the fall in incOme status even over relatively brief periods of only five years. Finally,in 5 out of 8 households whose positions deteriorated crOpped land decreased and in 6 out of 8 cropped land per cOnsumer also fell. In short, again no single factor would appear to explain the decline in income status of these households over time. Of the factors considered, however, productivity changes again show the most consistent results. Ease Studies The immediately preceeding analysis (Section A) has dem- onstrated that productivity changes as well as family structure were closely associated with inter-year income variation. The following section examines the nature of households structural changes and possible factors con- tributing to changes in farm productivity. Each of the households in Table 9.2 and Table 9.3 are examined in depth on a case by case basis. 169 case ho>psm "oonsom mcuxsaa 0200:“ :« oncogene :uuz eoumuooamo agouoau :« omcazvll meaxcah oaoo:« c«.ouaopo:« sud! nouauoouna opoaomu :« omcmzc o om .om mm: mm a“. a ma“ huh“ awn ms: m- «an o . mu NH em «a. .ov . on am. em omcssc m 1 mm“. .mml pr“ saw .mma . .nll mm mm: 1 Eh mm.“ pl... Mel. so- Law 2... a .3 me- .nn E: a... E 2 $563.; me: as: we: mm: on .mvw .vm .8 s .88. av- «m: on: me Aav «a c>aasuea enmw .OOH 09‘ 0mm V I HH.H RN.“ on I cam! NIH I on mm any Nm owcmzu .32 L: .3 .3 on own 8.. :1. I .91 m.. .Fu 2... ms :0 cm s: com .00 tub cam“ mu! than the. on I .07. com um: ..N no any mm 10:00.7: .000 omvm ohm" cwhw a I am: tub tmul com: cums 0V! own no Auv Ba o>wuumou Lao; ono< once onus noxnoz oaumu mgossmcoo 90:36:00 posse: noao< ~o=~¢>v hop—cauduum Lawn no? Hench Lon uaoo so...— none.“ hon son—8.2320: o» «0 son 0:04 mo anemone omcozo oeooca oEoo: hossocou nonmmk madden: homes: Humor . can bones: ¢.oMCn£0 ogsuozsuw ucoszoocm lucoohom cue: cad .I+ hua>auoseoam (III on: consonoz Unocomaom .oogaoamm oeoocw Jonson lucoogon :uoaH .230» camp no 33 9.33 :2 can mean coerce: «033.32, no 3.53 5 00523 093.320.. ~.u 033. 170 made >o>nam "condom ucaxsaa osooc« :« ousonooc sud: nouauoomna nhouoau :u omcmzoll mcaxcma osooca :« oaaohocu sud: couauooana unauoau a“ omcmco . «I. .1 e 1 mm- mm- .8 .8 m. Wm mm- .mm o? 8 80 2 3326 EH mm“. cm: mm: a .3: .2: o m .m‘ o .8 o... a: m ...: K]. we... .. Eu 8.. .mem .v: .3- a 9.3.. .3 em: 8 A: a $583 «8.. co. co: 3.. 3.. .2 .9: o o .2 o .3 on. A: a “3388.5 cm. .3 a- .8 8. an .83 m .9... .E 8.. :8 8 :0 o .2: L3 .2: .8" mm ....m 1. .N: 8 mm- . 8 .mm 3 A: 8 .3is6 .8... .63 1. .3. 3 .88: .mom P. .2: LS 9r... o? mm 3. om 6: tow-u? tom ohm tmm mml .th. Oman ml 050.. town ”NI omV Om adv V luv-0000“» .m3 .8” .c ...: w... mu .8». 3 .mm- ..on we- .03 2. :0 3 3336.1 93o: ouo< 09mm one: LoxLo: cages ascend Lossmcoo nnoxaoz neso< Aozua>v >0:o«oC«m mom has Huge son uuoo you .392 non 9096.23.33 on Icon «0 god 053 .00 0259.0 098.8 3505 uncuczooi csoochegdhlnuOLUIvl_ canuuua>, AHaEah >HHEMh Lossmcou 909652. condosui,nons:2 Hmwmw. cad banana—nqomcmzo h onsuoznum ueoezoecu tucooaom one: cuuu uu>uu0300hm on: couscous vaozouso: condone: osooan .Ioaaoz :coouon .Aaaoh omen nu mmmu unuunv 0901000 «had one moan coozuon noanaaua> no a~o>o~ cu oucano ouaueoosom n.a canes 171 In order to thoroughly analyse the nature of the inter—year changes in the household structure, complete census data for the two years are needed. Unfortunately, complete census data for only one year was at hand while the other years census data was available only in summary form. The analysis that follows makes use of the availa- ble scanty data. 1. Causes of Inter:year Changes in Household Structure A. Outmigration During the 1950's there was a mass movement of people out of Igbomina/Ekiti Division into the Western State of Nigeria in quest for forest land to grow cocoa trees.l Ipetu and Odo-Ore was no exception for the outmigration to the Western State. Farmers were purchasing land for the production of cocoa, residing on their cocoa farms and returning to their home villages on special occasions only once or twice a year. During the census surveys each household head was asked to enumerate all members of their household who were living outside of their village. These included the following categories of people: 1The author was born in this study area and knows this to be true though there is no written work to cite as refer- ence. 172 a. outmigrants to the cocoa farms in the Western State. b. educated persons working in other cities like Ibadan, Lagos, Kano, etc. c. traders in the cities and other towns like Ilorin. d. those who were gone to other places in search of employment. e. females who were married to men outside of Odo-Ore and Ipetu In 1969 the census survey data showed that about 33 percent of the enumerated household mem- bers in Ipetu lived outside Ipetu village and approximately 55 percent of the Odo-Ore pOpu- lation were similarly reported living outside of that village. By 1974 the census survey data showed that the outmigration had increased--the percentage of Ipetu population living outside was 45 while 64 percent was reported in Odo-Ore. It is expected that the change in the composition of the outmigration between the two years would affect the structure of the households in the village samples. Due to lack of the age/sex breakdown of the outmigrants for both years the impact of the change in the composition of the outmigration on the households in the villages 173 cannot be examined directly. However, it should be noted that the analysis show that there was no apparent correlation between outmigration changes and the dependency ratio nor between positive or negative percentile changes in in- cOme ranking. Family Structure Changes Among Households Whose Relative Income Ranking Increased by More Than 20 Percentile It was noted earlier that 5 out of the 9 house— holds which exoerienced a greater than 20 per- centile increase in income rank experienced a decrease in the cOnsumer-to-worker ratio. Ex- amination of the change in the number of children for each of those 5 households reveals that 3 of them--27 (1), 10 (l) and 32 (l) in Ipetu-- experienced a decline in the number of children reported between 1969 and 1974 using 1969 as the base. For these 3 households the decrease in the dependency ratio may be partly due to the children joining the work force. This is further substantiated by the average age of the 3 house- hold heads which was 65 years in 1969. As noted at the bottom of Table 9.4 the average age of head of households which had a decrease in their number of children was 64 years compared to 48 years for those whose number of children increased. 174 apnea 0V mm: compaflzo mo popes: commosocfl 50H: mama >m>nsm ”oopsom mpmoc paocomsoz mo own ommmo>

Hpmmoc Spas mums: paonmmso: no 008 mmmpo>Hpamoa cpwz mpmo: paonowson mo owm owmmo>wpmmmz m + 000+ :z NH+ 0 05 Adv 0 . . m I am: mz m I . o as Amy am 0 H0I mmz mal 0 m0 AHV 0m 8 + as: can Han . H1 mm Adv V _ . ma+ mm+ 6m+ bH+ H+ XII mV mavrkmw, msonomo . m I com+ om: m+ m1 1 m0 Aav mm _ esoocH m I 00+ mnl V+ 0L m0 AHV 0H .. sHmeCmso. 0 I 0m+ 0 H+ 0 00 AHV mm 0*Hucmopom man as: com: on at me Adv an enema m>wuamoa . F capes wucmsmas newsmano mpssswms assesses asses an senesz Immsaaw>. . meoocH secs nuso Ipso seem :4 use: :H Ipcmaoo Upon 0H0: Iomsom 1 omcmno Iomsoc . Amman mm m0mav Amman mm m0mav mo om< .1 mowcmcv ommwcoosod Mo meESZ Cm,om:m:0.- .mpOIOUO pcm :poQH .Vsma 0cm a0mH cooapon ossuosmpm paocomson CH mowcm£0.V.m wanna 175 Hence it appears that these 3 household heads had stOpped bearing children between 1969 and 1974, and persons reported to be nonworking children in 1969 were active workers in 1974. Family Structure Changes Among Households Whose Relative Income Ranking Decreased 9y Hore Than 20 Percentile It was mentioned above that children Joining the work force could be a possible cause for the decreased dependency ratio among households which had greater than 20 percentile increase in their income ranking. It was also observed that 4 of the 8 households_with greater than 20 percentiles decline in incOme ranking, experienced an in- crease in their dependency ratio. Three of these 4 households (17 (2), 9 (l) and 26 (2) ) in Ipetu each had a 50 percent increase in the number of children. The average age of the household heads was 48 years for the three households implying that they were still probably bearing children between 1969 and 1974. It would also appear that because the number of children increased the number of consumers increased relatively more than workers hence contributing to lower incomes per consumer. Causes of Inter-year Changes in Productivity Measures The importance of changes in productivity measures to the inter-year income variation has been emphasized 176 repeatedly in previous sections. Factors which could be hypothesized to cause inter-year variations in household productivity measures among others in- clude the following: a. quality of inputs like seeds, land and labor b. timely performance of Operations d. the incidence of insects, pests and crOp dis- d. changes in crOpping pattern Differences between years in the quality of iné puts like seed can result in important changes in § productivity. The fertility of the farmers fields have been assumed uniform. However, factors like erosion, slope and soil texture would affect the rate at which any field losses its fertility over time. Horeover, substantial micro-variation in soil fertility unrelated to previous use was also un- doubtedly present. Changes between two years in the health of household workers can also be expected to cause inter-year productivity changes. Untimely performance of operations such as weeding due to illness or poor time allocation in a year can result in obtaining less than maximum yield. Insects, pests and diseases can cause partial or complete loss of h rvests during any year. Differences in damage caused by such occurrences over time can, of course, 177 lead to productivity changes. Lastly, the cropping patterns may differ between years. Earlier we looked at crOpping pattern variation among income groups but found no significance difference between years. How- ever, the presence or absence of some high yielding crOps in the crOp rotation system in a certain year could be expected to cause interyear productivity changes. The above factors need to be carefully examined in order to be able to pinpoint causes of inter- year productivity changes. Unfortunately, the data at hand cannot be used to explore such causes in the interyear productivity measures. Detailed data is required on (a) changes in time allocation be- tween the years and (b) the incidences of cr0p failure (c) germination rates, plant population densities (d) records of illnesses and number of working days lost as a result of the illnesses. These are essential tOpics for further research. sums! This chapter has examined possible causes of inter- year income changes. No single factor has been found to explain all changes, but productivity improvement was the most consistent. Changes in family structure were also found to be important. Furthermore the same set of factors appear to be 178 responsible for both increases and decreases in the household's income status between years. However, it was not possible with the available data to identify with certainty the underlying factors which contributed to changes in either farm productivity or family structure. The next chapter cOntains a summary of results, concluding remarks, policy im- plications of the study and suggestions for further research. CHAPTER X SUMMARY AND CONCLUSIONS The primary purpose of this study has been to document the distribution of personal incomes among farmers in Kwara State, Nigeria. The study has been designed to examine factors associated with the level and distribution of income within rural areas. Factors correlated with income have been placed in a framework encompassing inter-relationships among resource endowments, resource use and resource productivity. Through this model it has been possible to examine separate factors contributing to income variation among households. Two years of data enabled us to examine inter-year differences in the levels and distribution of income and factors associ- ated with variation in income. A. Distribution of Income The mean net household income in 1969 was 3337 for both villages, £397 in Ipetu and 5269 in Odo-Ore. Off-farm income data was not collected in 1974 hence net household income could not be reported. However, net farm income was obtained for both years. In 1969 the mean net farm income per family was 3274 for both villages, H320 in Ipetu and H222 in Odo—Ore. In 1974, the undeflated mean 179 180 net farm income was about H463 in each village while the deflated figures (1969 as base year) were 3239 in Ipetu, H244 in Odo-Ore and 3241 for both villages. The lower incOmes in 1974 were due in large part to lower rainfall compared with 1969 especially in Ipetu. Average per capita net farm incOme was $37 in 1969 for both villages and H62 in 1974 in nominal terms and H32 in constant 1969 naira. There were also differences between villages. in the net farm incOme per capita which was 343 in Ipetu and 331 in Odo-Ore in 1974. The deflated values of net farm income per capita in 1974 were H28 in Ipetu and 337 in Odo-Ore, again expressed in constant 1969 value. Several measures of income distribution were pres- ented and showed that incomes were in general equitably distributed. A Gini ocefficient of 0.40 on net farm income per capita was found in 1969 while the net house- hold incOme after off—farm income has been added had a Gini coefficient of 0.35. While the poorest third of the sample pOpulation obtained only 11.6 percent of net incOme generated in farming enterprises, with the addi- tion of off-farm income the share of the poorest third increased to 19.7 percent while the share of the richest third drOpped from 61 to 42 percent. Thus it was found that off-farm incOme tended to reduce inequality since lower incOme households allocated a greater percentage of their time to off-farm employment. 181 The mean net farm income per capita for households in the poorest third in 1969 was 311 and H72 for the richest third. In 1974 the poorest third had 322 net farm income per capita compared to 3111 for the richest third. When the 1974 incOme figures were deflated to constant 1969 prices, the poorest third had a mean net farm income per capita of 312 and the richest third had 358. Hence in real times, income decreased between 1969 and 1974 for the high income class. Some inter-village and inter-year differences in the distribution of income were observed. In 1969 Ipetu showed a Gini coefficient of 0.38 on net farm income per capita while Odo-Ore had 0.43. These coefficients imply that inequality was generally higher in Odo-Ore, In 1974, however, Ipetu had a Gini coefficient of 0.35 on net farm income per capita while Odo-Ore had 0.36. When examining the other inequality cOefficients it was revealed that Ipetu the larger village situated on the better road and closer to Omu-Aran, displayed a rela» tively greater income inequality at the low levels while Odo-Ore showed higher inequality at the higher and middle income levels. Factors underlying the differences in distribution between villages and between years were not able to be identified. 182 Correlates of Income Through tabular and regression analysis the effects of five sets of variables on incOme were examined. The explanatory factors included (1) village location (2) resource endowment (3) worker quality (4) resource use and (5) resource productivity. These variables were examined across net farm incOme per consumer strata and the following relationships were found. 1. Income classes have not been found to divide them- selves into distinct family types. An analysis of the dependency ratio, family size and composition, percent literacy and number of wives showed that these demographic factors did not appear to be con- sistently related to income differentials. The size of the household shown in terms of number of residents, consumer equivalents and worker equivalents showed conflicting patterns with income in the two survey years. In 1969 the poorest households were larger than average but in 1974 they were smaller. Family size reported on the whole was greater in 1969 than in 1974. The larger family size reported in 1969 and the apparent reversal in the association between family size and income may have been due to errors in reporting family size as discussed in Chapter III. 183 The dependency ratio, a measure of the house- hold's work force relative to consumer requirements, was relatively stable between years. Contrary to expectations, however, the dependency ratio did not show a strong or consistent association with incOme. It was assumed that management quality in farming is related to the age and experience of the farm manager. The mean age of household heads was 58 in 1969 and 61 in 1974 but there was no consistent relationship between age and incOme. The data also showed no association between percent literacy and incOme. The percent literacy was defined as the percentage of family members who either cOuld read or write at least in Yoruba and/or those going to school. Overall literacy was low. The lack of correlation was not suprising given the fact that only the children in the households were educated and their influence on farming decisions were negligible. Among the resource endowment variables land, labor and value of capital stock, only crOpped land was found to be cOnsistently related to income. An analysis of the distribution of three land types akuro (lowland), igbo (forest) and odan (upland) 184 revealed that there was no significant differences in the prOportion of either type held among income classes. The intensity of resource use was examined through the analyses of family labor per worker and non- labor farm variable costs per acre. Operating capital (non-labor variable costs) showed a cOn- sistently high cOrrelation with incOme. Regression analysis showed that the annual return to Operating capital was approximately 128 percent in Ipetu and 50 percent in Odo-Ore. The results suggest that 1the liquidity positiOn of the household during key production periods may critically affect farm income generation. This points to the credit needs of lower income farmers. In general males were found to work more hours on the farm while the females worked more on Off- farm activities. The low incOme group generally spent a greater percent of time in the off-farm activities and they in turn farmed their land less intensively than the high income group when exam- ined within the same land category. As expected farm employment was low both years and in both villages among the land short poor households. But some low incOme households which had medium land holdings still had low farm employment. This suggests that there are two types of poverty households (1) those land short house- holds who worked their land very intensively and (2) households with adequate land who for reasons which were not identified did not work particularly hard--the latter had very low hours per acre. 4. The last and the most important explanatory variable in our conceptual framework was variation in resource productivity. Ho matter what other factors were associated with poverty, low productivity was a common feature to all poor households. Ill health, insects, pests and diseases, poor quality input like seeds and poor management and micro-climatic' differences might be possible causes Of low pro- ductivity among the poor income classes. Regression analysis results showed that the superiority Of crop mixtures over sole crOpping as a correlate of income depends on the year and village location. Inter-Year Variation in Income Distribution The incOme distribution was found to be fairly stable between the five year period as shown by the Gini coefficients of 0.40 in 1969 and 0.38 in 1974 for net farm income per capita. moreover, within each village 186 more than 60 percent of the households changed their relative income position by 20 percentile or less. The households which changed their relative positions in their village rankings by more than 20 percentile were examined to determine possible causes. Productivity changes appear to be most critical in overall income ranking. Those households whose income ranking in- creased by greater than 20 percentile had all experienced an improvement in their farm productivity. Similarly households whose income ranking had decreased by greater than 20 percentile had generally experienced a decrease in productivity. The data also suggested that changes in family structure affecting the dependency ratio may also have played a key role in income rank changes. Households which had an increase in their income rankings also tended to have experienced a decrease in their dependency ratio and vice versa. Case studies of the households whose income ranking changed by more than 20 percentile were also carried out to determine possible causes. Among households which experienced improvement in incOme ranking a decrease in the dependency ratio was associated with a decrease in othe number of children between the two survey years. These children probably joined the work force. House- holds which experienced a decline in income ranking experienced an increase in the dependency ratio, 187 probably due to an increase in number of children relative to workers. It appeared that household heads for this group were in the child bearing. agewhich would further substantiate the observation. Causes Of inter-year changes in productivity were also examined. Several factors which were speculated as possible causes of productivity change included: (1) quality Of input like seeds, land and labor; (2) timely and untimely Operations; (3) insects, pests and disease and; (4) the crOpping pattern. However, the available data prevented further analysis of these relationships. Poligy Implications 1. Implications of the Income Levels The income levels obtained in the present study are comparable with those found by other studies of traditional farming systems. The income levels, however, are low cOmpared with wages in urban areas. By 1974 the unskilled labourer employed by the Nigerian government was earning at least H720 per annum while undeflated mean net farm income for both villages was only N463 in 1974. ,Even the high income rural households would be considered re- latively low income by national standards. The implication of this for policy design is that policies which will raise farmers' income level must be pursued. In order to raise farm income levels in general, 188 improved technology is a must in the form of im- proved seed varieties, fertilizer use, technology to alleviate the weeding bottleneck and curb insect, pests and disease damage. Implications of the Income Distribution The fact that the income distribution among farmers at the rural level has; been found to be relatively equitable does not imply policy designers should not be cOncerned with income distribution. Ex- perience in other countries, has shown that income disparity generally worsens with the introduction Of technical change. Thus the policy approaches suggested here are for the purposes of minimizing the potential widening Of incOme inequalities as a result of technical changes. As mentioned earlier improved technology is essential for raising the general level of incomes. Introduction of improved technology like fertilizer use, improved seed varieties, etc. is a way of im- proving farm productivity. However, the situation is not that simple because this study also shows 'mun:production efficienty was lower among the low income classes given the traditional technology; If this is a reflection of poorer management quality on 189 the part of the poorer households given the tra- ditional technology, it would be expected that productivity may alsoi be lower among the low income classes even under the improved technology. Untimely and poorer application of the improved techniques cOuld lead to lower production efficiency among the low income households in the future. We have, however, not been able to examine the factors causing variations in the technical ef- ficiency, given the traditional technology. If these factors are known we would be able to suggest ways to improve on the technical efficiency among the poor income classes under the traditional tech— nology. It could be expected, however, that the same factors causing variations in technical ef- ficiency under the traditional system may prevail or even magnify under the improved technology. This points to the need for further research on the causes of productivity variations. The policy implication of the income distri- bution results imply that the extension of improved technology cannot be done among farmers as a blanket application. It has to be done in such a way as to consider the circumstances peculiar to lower income households. At a minimum this requires making available improved inputs to the poor farmers in 190 sufficient quantities and at the right time. The inputs should not only be made available, poor farmers should also be properly instructed and guided on their use to maximize results. COnstraints to the Low Income Farmers The above discussion on the implications of the in- cOme distribution ties in closely with the con- straints faced by low incOme farmers. Operating capital may be a limiting constraint for low income farmers. The liquidity position Of the low income households especially during the production periods can critically affect farm income generation. While the improved inputs can be made available on credit to the poor farmers, the richest farmers should be made to pay for theirs. This, however, could be a politically sensitive issue and must be used with caution. Lower interest rates would be helpful to the poorer farmers as well as making the time and the form of payment as flexible as possible. .For example, payment in kind rather than cash could be more helpful to the poorer households. The potential difficulties, however, should be noted. The results Of this study also suggested that there were two poverty groups. Low productivity was common to both but in addition one of the groups seemed to be constrained by limited access to 191 crOpped land which they worked very intensively. The other group seemed to possess adequate crOpped land but applied low labor per acre. It is clear that different policy measures would be needed to meet the needs of each of them. Given the existing traditional land tenure system, where every male had access to farm land, and the fact that there was sufficient unused arable land in the study villages, it would appear that the first group could increase their land holdings without much friction. However, this calls for more study of land tenure relations to see if lower income farmers were being discriminated against. Lack of Operating capital may be one rea- son why they did not crOp more land hence pointing to their credit needs. The second group, however, which had a medium land holdings but low intensity of labor use poses a different policy question. The reasons for the low labor intensity could be diverse. Inadequate calorie intake, sickness and lack of motivation cOuld be possible reasons. Again this calls for consumption and nutrition related studies and provision of adequate health care sys- tem as well as adequate extension system. 192 Implications of the Inter-Village and Inter-Year Variations . Important differences have been found between villages in several factors like labor use, family size, correlation of variables with income and productivity. This points to the danger of making blanket applications or recommendations over wide geographic areas. It has also been noted at several points in this study the danger of placing heavy reliance on results of one year's data. Even given the medium- term span of five years between the two surveys, wide differences have been observed in effects of variables included in our conceptual framework. Implications for Further Research Priorities a. 0f utmost importance is the recognition of the necessity for further research to fully under- stand reasons for productivity variation. Face tors causing variation in production efficiency both between income groups and over long periods of time have to be known in order to develOp improved technical packages which are truly cOmpatible with the circumstances of poor households. Moreover, acceptable improved tech- nological packages need to be develOped through an inter-disciplinary approach comprising of both social and technical scientists. 193 As a follow-up of the finding that some poor households had limited access to crOpped land, further studies of land tenure institutions are needed. Answers could be found in the insti- tutional class structure as to which clans have right over what type of land. If the poor house- holds are discriminated against studies should be conducted to determine how to minimize the discrimination. If those poor households de- cided to crop low acreages out of choice, man- agement issues will need to be addressed. Low labor use among some poor households point to the need for consumption and nutrition studies to determine the adequacy of colorie intake among poor households. There is also a need to determine the adequacy ofand ways to improve rural health care systems to meet the needs of poor households in particular. Further analysis is needed of the relationship between household structural changes and income status. The contribution of outmigrants to the ecOnomic position of households was not docu- mented in this study. It could be found that some poor households were, in fact, not relatively poor if all remittances from outmigrants were added to net farm incOme. 194 e. Finally, the strong association between Operating capital and income points to the credit needs of the poor households. There has been no study done on credit in the survey area. ”The actual amount, conditions of repayment, time to extend credit, etc. are possible questions that need to be addressed. BIBLIOGRAPHY D.) 10. BIBLIOGRAPHY Aboyade, 0., "IncOmes Profile," University of Ibadan Inaugural Lectures. Ibadan, Nigeria, 1972-73. "IncOme Structure and Economic Power." Presidential Address, 14th Annual Conference on Nigerian Economic Society, Ibadan, Nigeria, 1974. Adelman, I. and Morris, C.T., EcOnomic Growth and Social Eqpipy in Developing Countries, Palo Alto, Sanford Press, 1973. Ahluwalia, M.S., "Income Inequality: Some Dimensions of the Problem,"in Redistribution With Growth. Ed. by Chenery 23 g; Oxford, 1974, pp.3-37. "IncOme Distribution and DevelOpment: Some Stylized Facts," American Economic Review Pro- ceedings, Vol. 66, No. 2, 1976. Atkinson, A.B., "On the Measurement of Inequality," Wealth; Income and Inequality, ed. A.B. Atkinson Penguin Education, London, 1973. 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Net Household Income Per Consumer Strata Variable Village Low Med High ' All ' Age of -Ipetu 54.50 59.44 49.90 54.45 household . ,0do-0re 56.67 62.14 66.11 61.60 head (years) All 58.89 57.78 56.61 57.76 Percent Ipetu 8.00 15.44 8.10 10.34 literacy .0donre 3.33 3.86 3.67 3.60 in household All 5.39 9.78 6.50 7.22 Number of Ipetu 2.60 2.56 1.30 2.14 adult Odo—Ore 3.22 3.00 1.78 2.64 males A11 2.83 2.72 1.56 2.37 Number of Ipetu 3.70 3.33 1.60 2.86 adult Odo-Ore 5.11 2.00 2.33 3.24 females All 4.28 ‘ 2.39 2.44 3.04 Number of Ipetu 2.60 3.44 1.40 2.45 male .Odo Ore 1.67 2.14 1.00 1.56 children All 2.22 2.67 1.22 2.04 Number of Ipetu 4.00 3.56 0.70 2.72 children All 2.83 2.83 0.94 2.20 Number Ipetu 2.40 2.11 1.20 1.90 Of OdO’Ore 2011 1071 1.67 1.84 wives All 2.00 2.28 1.33 1.87 Source: Survey Data epee sm>eam ”oceaom .ooH em .ooH mm .ooH am ww.H H o o mv.m H +60m o o o o o o OONLHwH o o o o o .o omHnHeH ew.e m 00.4 H ms.mH v oeHquH emeamcoo o o o o o o oanHmH tea ee.w m ee.w H mv.m H cmHnHoH eeoocH Hw.e v 66.8 m ee.e m ooHuHm eHor ee.wH a ee.wH m ee.ew e omnHe [mason we.ww 6H ee.ew s we.Hm a ceaHv was ww.ww NH ee.ww s ww.eH m ovnHm ew.e m ee.wH v we.w H cmuo .ooH em .ooH em .ooH om .ooH em .ooH mm .ooH 6 Hence ee.w m ww.eH v mm.m H ww.H H o o ww.w H +Hcm ew.w m He.e m ww.w H o o o c c o ooNerH o o o o o o o. o o o o o omHnHoH m 55.8 m o o ee.wH m o o o o o o oeHuHeH amesmcoo 2 ms.H H os.m H o o ew.e m 60.4 H ae.mH v oanHmH tea Hw.w m os.m H mm.m H o o o o o o cmHuHoH meoocH ww.wH s mm.mH m ee.e m He.e v 60.8 m 06.6 m ooHnHm same mo.Hm NH ww.wH m ww.ww s Hw.eH m ee.wH m ww.eH m omnHe uoz ee.wH a Hw.wH e ee.wH m ee.wH a ee.wH m ee.ew e oequ ww.wH e Hw.e m ee.wH m ee.ww 4H ee.ww s wH.ew s ovnHm ww.eH o HH.HH m oo.oH m ee.ew mH ee.ew a as.mH v cmuo & .oz 1% .oz & .02 fi .02 !& .02 fi .02 z mpwdmo madmmoz HH< osoroco suede HH< oobnooo .mpomH soc msoocH vsmH . aeaH meoocH moHozmmDo: mo owcmm tha UCQ mood .poesmcoo you oEooCH oHocomson no: ocm EsmM nos Canvas moHosomdo: M0 soapsoahumflo ommpCoosod ocaodlonome 213 40, 30 24.14 IPETU 20 69 20 ° 17.24 _ 13.79 13°79 10 6'9 3.45 m 40r A s 36.0% H O c 28.00 5 e 20 CH 0 12.00 12.00 g 10 8.00 8 4.00 8 [ I Q—c 40 30 BOTH VILLAuLS 25.93 24.0 20 16'67 14.81 10 7.41 1 9.26 1.85 . . 1 r———*1 0 21 41 61 81 101 121 141 161 181 Above to 20 41 60 80 100 120 140 160 180 200 201 Net farm incOme per consumer (in naira) Figure A-l The percentage distribution of households based on net farm incOme per consumer, 1969 214 40, Q 30 31'0‘ IPETU 20.69 20 17.24 13.79 10 6.9 3'45 3.45; 3,45} 40, CD (330 .2 $201 3 16.00 5 ' 12.00 4..) 4.00 4.00 CI . '8 ————7 F_—_T # S... z$40i 30 29.63 BOTH VILLAGES _____ Q 20 2_.22 16.67 10 9.26 7.41 9.26 .3119? 1.55 1. r'—t3 0 21 41 61 81 101 121 141 161 181 Above to 20 40 60 80 100 120 140 160 180 200 201 Net household income per consumer (in naira) Figure A-2 The percentage distribution of households based on net household income per c0nsumer, 1969 215 40.. IPETU 30.. 20 ‘3 ‘ 16.671667 16.67 10 10 L6.67 .31321 F1§§1§1§§1 4OF' S ODD-ORE ,_q 230- “—"' '22 320— 18.5218.52 ..C m 11.11 14'81 $3123. 010 p 7.41 7.41 g 3.70 3.70 g I ‘_1 3)40 1 F 30m BOTH VILLAGES 21.05 ‘ 20” 15.79 12 28 2 28 ' 0.54 ' 8.77 R77 4 lo 5.26 7"‘7 0 21 41 61 81 101 121 141 161181 Above to 20 40 60 80 100 120 140 160 180 200 201 Net farm income per consumer (in naira) Figure A - 3 The percentage distribution of households based on net farm income per consumer, 1974 216 mums >m>05m ”mopsow oo.ooH 66 oo.ooH 6m oo.ooH mm HH< o o o o o o +HoHH o o o o o o ooHHnHooH o o o c o o oooHuHoa 66.6 m 66.6 H 66.6 m oomnHow o o o o o o oomuHos He.e v o o 68.6H e oosuH66 Hw.e v 66.6 H vm.oH m 666IH66 He.e v 66.6 m 66.6 m 6661Hov bHocmmso: em.6m HH oo.6H v wH.ew s oovnHom 666 ww.ww NH ee.ew 6 66.6m 6 oomuHom osoocH 66.6m 6H 66.66 m ww.eH 6 OONIHoH 6Honmmso: 66.6 m 66.6 m o o ooHuo poz oo.ooH s6 oo.ooH em oo.ooH om oo.ooH 66 oo.ooH 6 oo.ooH am HH< 6m.6 m os.m H 56.6 , m o o o o o o +HoHH 6m.6 6 He.» .6 66.6 H o o o o o o ooHHnHooH 66.6 m He.e N 66.6 H o o o o o c oooHuHom 6m.6 m o o oo.oH m 66.H H 66.6 H o o oomsHow m6.m m 66.6 H 66.6 H 68.6 m o o 66.6 m oomuHos 6m.6 m He.e m 66.6 H 68.6 m o o 66.6 m oosaHo6 ss.6 6 He.e m oo.oH m 66.H H o o 66.6 H 6661H66 ee.w 6 HH.HH m s6.6 m HH.HH 6 ee.wH m ww.eH m cemuHov 6Hosomao: ee.w 6 He.e m oo.oH m ee.wH s ee.wH m 68.6H v ooquom 666 66.6H 6; HH.HH m oo.oH m 66.6H oH oo.6H e 66.66 6 oomuHoN bsoocH «6.6H oH H6.vH e 66.66 6 66.6m 6H ee.ww 6 66.8m 6 cemuHoH 2666 66.6H a m6.6H 6 em.mH w, s6.6H 6 66.6m 6 wmuoH m ooHso 662 fi .02 R .02 & 02 R .02 fi .02 1X .02 Amesmcoo oLSmMoz HH< wmeouobo aubmH HHe 666-666 spumH 666 oeoocH . wsmH 666H I. beoocH mUHocomsom . 90 omcmm .vuma Ucm mama .oHonomson god osoocH béocomsoc um: new 5969 um: CHSpHB moHosmmson mo COHpsoprmHo emancoopod 059.9140Hn69 217 40. 3O IPETU 27.59 ---' 2O 20. 13.79 10 0.34 10-3. 6.9 609 3.45 40 3 32.00 060 ORE 6 30 .__;:___ 6 24.09 U1 8 S 20 16.00 . ‘8 712.001200 10 E 4.00 {—7 Q) m 40 30 29,53 BOTH VILLAGES “‘— 20 16.6% 18-52 7* 12.97 10 ‘ 111.11 . 3 70 3 70 1.85- ° ' 1.85 A ‘ 1 1————-1 0 101 201 301 401 501 601 701 801 901 to 100 200 300 400 500 600 700 800 900 1000 Net farm incOme per household (in naira) Figure A-4 The percentage distribution of households based on net farm income per household, 1969 218 Percent of households 401 30 - IPETU 24.14 20__ 20.6 1 I 17.24 10 10.3413°79 P 6.9 6.9. 40F 36.00 30 ODO-ORE 24.00 20. 16.00 10’8.OO 8.00 4.00 14,0p 40 F 30 80TH VILLAGES 25.93 22.22 20 . 20 V ‘ 10 . 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Change Income House- in Percentile hold , income Change number 1969 1974 ranking tve ~ve 4 (2) 13 12 1 ’5 7 (1) l 4 -3 15 8 (l) 16 18 -2 10 9 (l) 5 16 -11 55 10 (1) 15 6 9 45 14 (3) 2 2 O O O 15 (1) 12 9 3 15 16 (1) 17 14 3 15 17 (l) 8 17 -9 45 17 (2) 4 13 -9 45 23 (l) 6 10 —4 2O 26 (1) 7 3 4 20 26 (2) 3 19 -16 8O 27 (1) 18 1 17 85 29 (1) 2O 7 . 13 65 30 (1) , 9 -8 l 5 32 (1) 10 5 5 25 33 (1) ll 11 O O O 41 (2) 14 15 -l 5 43 (3) 19 20 -1 5 Source: Survey Data Table.A-2OIncome rankings of households in both years, sample, 236 Odo-Ore Change ‘fncome House- in Percentile hold . income Change number 1969 1974 rank tve —ve 1 (1) 13 21 -8 35 3 (1) 15 2o -4 3 4 (1) 21 10 11 50 4 (2) 1 2 -1 5 4 (3) 15 12 3 14 5 (1) 7 8 -1 5 5 (1) 5 1 5 23 7 (1) 2 13 -11 50 8 (1) 12 14 -2 9 9 (1) 5 15 -11 50 11 (1) 11 9 2 9 12 (1) 2o 22 -2 9 12 (2) 18 17 1 5 12 (3) 4 15 -11 50 13 (1) 9 7 2 9 15 (1) 14 18 -4 18 17 (1) 22 5 17 77 19 (1) 8 4 4 18 2o (1) 10 3 7 32 21 (1) 3 5 —3 14 22 (1) 19 19 o o o 31 (1) 17 11 5 27 Source: Survey Data