Agricultural Diversification in Mali: the Case of the Cotton Zone of Koutiala

AGRICULTURAL DIVERSIFICATION IN MALI: THE CASE OF THE COTTON ZONE OF KOUTIALA

Mariam Sako Thiam

A THESIS

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

Agricultural, Food, and Resource Economics – Master of Science

2014

ABSTRACT

AGRICULTURAL DIVERSIFICATION IN MALI: THE CASE OF THE COTTON ZONE OF KOUTIALA

Mariam Sako Thiam

Cotton production plays a central role in the economy and the livelihood of cotton growers in the

Koutiala area of Mali. Despite all the investment made in the cotton zones, the cotton farmers in

Koutiala suffer substantially from uncertainties in the cotton subsector linked to prices, timely payment, and to the future structure of the industry. This study analyzes empirically how cotton growers with different agricultural characteristics coped with these uncertainties over the period

2006-2010. The data used in this study were collected during the survey that covered 150 households in the Koutiala area during three cropping seasons: 2006-07, 2008-09 and 2009-

10.The results show that despite income diversification among the households surveyed in

Koutiala, agricultural production remains the main source of income. The findings also show that the farmers who continued to grow cotton during the three years of the survey and those who started producing cotton after year one diversified within the agricultural sector by producing more peanuts and cowpeas while the farmers who dropped out of cotton production after year one of the survey diversified toward non-farm activities such as commerce and self. We also found that the non-cotton growers are the poorest group of farmers, with less agricultural equipment and labor as well as less overall wealth, limiting their potential to invest in farm activities and start an off-farm business.

ACKNOWLEDGMENTS

I would like to express my sincere gratitude to those who helped and supported me in completing my Master’s program. I am particularly grateful to Pr. John Staatz, my major professor and thesis advisor, for his supervision, support and patience throughout my program. I would also like to express my special thanks to my guidance committee members, Pr. Valerie

Kelly and Pr. Timothy J Vogelsang for their comments on my thesis, their support and advice. I would also like to say thank you to Dr. Abdoul Murekezi and my colleague Mr. Abdrahamane

Berthe for providing me with important documents necessary to complete this thesis. I also express my thanks to the US Agency for International Development Mali mission, whose support, via the Food Security III Cooperative Agreement with Michigan State University

(MSU), provided a graduate research assistantship that allowed me to undertake my studies at

MSU and co-financed the research (along with the Bill and Melinda Gates foundation) that led to this thesis. Thank you also to my friends and colleagues in the AFRE department for their friendship and support. Last but not least, I am very grateful to my parents, Mahamadou Sako and Dienebou Sanogo as well as my sister Fatimata Sako, my brothers Lassana Sako, Sidiki

Sako and my husband for their unconditional love, prayers and encouragements. I could not accomplish this achievement without them.

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TABLE OF CONTENTS

LIST OF TABLES ...... vi

LIST OF FIGURES ...... viii

CHAPTER 1: INTRODUCTION AND PROBLEM STATEMENT ...... 1 1.1 Problem statement ...... 6 1.2. Objectives and organization of the thesis ...... 8 CHAPTER 2: CONCEPTUAL FRAMEWORK AND RESEARCH HYPOTHESES ...... 10 2.1. Factors affecting cotton production...... 11 2.1.1. On-farm conditions affecting household decision-making ...... 11 2.1.2. Off-farm factors household decision-making ...... 12 2.2. Hypotheses ...... 13 CHAPTER 3: DATA ...... 15

CHAPTER 4: BACKGROUND INFORMATION ON THE AGRICULTURAL SECTOR IN THE KOUTIALA DISTRICT ...... 20 4.1 Physical environment of the Koutiala zone...... 20 4.1.1 Soil ...... 22 4.2 Principal crops ...... 22 4.3. Livestock ...... 29 4.4. Land Tenure ...... 30 4.5 Farmers’ responses to price regulation reforms in the Malian cotton zone ...... 31 4.5.1 Brief overview of reforms in the Malian cotton sector ...... 31 4.5.2 The constraints farmers face that prevent them from responding to opportunities to substitute other crops for cotton ...... 34 4.5.2.1 Peanuts ...... 34 4.5.2.2 Cowpeas ...... 35 4.5.2.3 Sesame ...... 35 CHAPTER 5: DESCRIPTIVE ANALYSIS ...... 36

CHAPTER 6: ECONOMETRIC ANALYSIS...... 54 6.1 Results ...... 58 6.2. Discussion ...... 61 CHAPTER 7: CONCLUSIONS ...... 64

APPENDIX ...... 67

REFERENCES ...... 78

LIST OF TABLES

Table 1: Characteristics of Sample Villages in the Koutiala Zone ...... 17

Table 2: Rainfall in Koutiala (mm) over the period 2000-2010 ...... 19

Table 3 Evolution of the Area per Person in CMDT Sector of Koutiala 1988/2002 (ha/person) 31

Table 4: Description of Group Means for Different Characteristics of the Households ...... 39

Table 5: Agriculture Equipment (%) among sample households in Koutiala from 2006 to 2009 40

Table 6: Median Commodity Producers Price (FCFA/kg) ...... 41

Table 7: Average Coarse Grain Production (kg) by Group per Household (hh) and Adult Equivalent (AE) 2006-09 ...... 43

Table 8: Average Coarse Grain Net Sales (kg) by Group per Household (hh) and Adult Equivalent (AE), 2006-09 ...... 44

Table 9: Average Household Production of Rice, Peanut, Cowpea, Cotton, Fonio, and Sesame (in kg) by Group, 2006-09 ...... 45

Table 10: Average Cultivated Land (ha) by Crop, Group and Year, 2006-09 ...... 46

Table 11: Average Yield (kg/ha) by Crop, Group and Year; 2006-09 ...... 47

Table 12 Average Rainfall in Koutiala per Month in Millimeter May-October 2004-2010 ...... 48

Table 13: Total Income Share by Source ...... 50

Table 14: Average Total Income (FCFA) by Group per Household (hh) and Adult Equivalent (AE) over the 2006-09 Period ...... 51

Table 15:Multinmial Probit Model Output Using The Study Data ...... 58

Table 16: Marginal Effects of Variables by Group 2-4 ...... 59

Table 17: Total Number of Households per Group and Village ...... 68

Table 18: STATA Significance T- Test Output ...... 69

Table 19: Average Off-Farm Income by Group per Household (hh) and Adult Equivalent (AE) over the 2006-09 Period ...... 72

Table 20: Average On-Farm Income by Group per Household (hh) and Adult Equivalent (AE) over the 2006-09 period ...... 73

Table 21: Average Non Agricultural Wage Earning (in FCFA) by Group per Household (hh) and Adult Equivalent (AE) over the 2006-09 Period...... 74

Table 22 : Average Revenue from Self Employment (in FCFA) by Group per Household (hh) and Adult Equivalent (AE) over the 2006-09 Period...... 75

Table 23: Average Agricultural Wage Earnings by Group per Household (hh) and Adult Equivalent (AE) over 2006-09 Period ...... 76

Table 24: Average Livestock Revenue (in FCFA) by Group per Household (hh) and Adult Equivalent (AE) over the 2006-09 Period ...... 77

vii

LIST OF FIGURES

Figure 1: CMDT Zones ...... 3

Figure 2: The Evolution of Seed Cotton Prices, Seed Cotton Production and Adjusted World Price in Mali, 1994/95 - 2008/09 ...... 4

Figure 3: Factors Affecting Farm Household Production Decision ...... 12

Figure 4: Koutiala District ...... 21

Figure 5: Evolution of Grain Production in Sikasso Region, 1990 - 2010 (Quantities in Metric Tons) ...... 23

Figure 6: Evolution of Cultivated Land Area of Cereals and Cotton (in Hectares) in the Sikasso Region, 1999/2000 - 2009/2010 ...... 24

Figure 7: Evolution of Cereal Yields (kg/ha) in the Sikasso Region; 1999/2000- 2009/2010 ..... 25

Figure 8: Evolution of Cotton Production of Sikasso Region (in metric tons) and National Cotton Production from 1990 to 2008 ...... 26

Figure 9: Evolution of Fonio, Peanut, Sesame and Cowpea Production (in tons) from 1999/2000 to 2008/09 ...... 27

Figure 10: Evolution of Peanut, Cowpea, Sesame and Fonio Land in the Sikasso Region between 1999/2000 and 2009/2010 ...... 28

Figure 11: Evolution of Peanut, Cowpea, Sesame and Fonio Yields in the Sikasso Region between 1999/2000 and 2009/2010 ...... 28

Figure 12: Evolution of Land Allocated to Cotton, Maize and Peanuts from 1999 to 2009; Unit = hectares ...... 29

viii

KEY TO ABBREVIATIONS

CEPIA: Centre International pour le Développement de l’Elevage en Afrique (International Centre for Livestock Development in Africa) CFDT: Compagnie Française pour le Développement des fibres Textiles (French Company for the Development of Textile) CIRAD: Centre de coopération internationale en recherche agronomique pour le développement (Center for International Cooperation in Agronomic Research for Development) CMDT: Compagnie Malienne pour le Developpement des Textiles (Malian Company for the Development of Textile) CSPP: Lettre de Politique de Développement du Secteur Coton. (Cotton Sector Policy Paper)

FAFPA: Fonds d’Appui à la Formation Professionnelle et à l’Apprentissage (Support Fund for Vocational Training)

GIE: Groupement d’Interêt Economique (Group of Economic Interest)

GTZ: Organisme d'Aide Etrangère du Gouvernement Allemand (German Government’s Foreign Assistance Agency) IER: Institut d’Economie Rurale (Rural Economics Institute) IFAD: International Fund for Agricultural Development MRSC: Mission de Restructuration du secteur du coton (Mission for the Restructuring the Cotton Sector) MSU: Michigan State UniversityOHVN: Office de la Haute Vallée du Niger (Upper Niger River Valley Development Authority) OP: Organisations Paysannes (Farmers’ Organizations)

SYCOV: Syndicat des Paysans Cotonniers et Vivriers (Union of Cotton and Food Farmers)

CHAPTER 1: INTRODUCTION AND PROBLEM STATEMENT

Many West African countries, including Mali, have included cotton as a pillar of their development strategies for many years. But now volatility in world prices, combined with reforms in domestic marketing systems, have exposed cotton farmers to much greater uncertainty about the cotton sector than in the past. While the acreage, cotton producers’ annual revenue and the overall economy are argued to be affected by the cotton price volatility and the structure of the cotton industry, there is very little information about how these effects vary depending on the characteristics of cotton producers . Understanding better how different classes of farmers respond to such uncertainty will be critical in determining what role cotton can play as part of

Mali’s agricultural development strategy in the future.

World cotton production increased from 17 million tons in 1961 to 48 million tons in

2011 (FAOSTAT, 2012). World production is dominated by China (28%), the United States

(17%) and India (12%). West Africa is the fifth largest cotton producer, but contributes only 5% of the global production. However, West Africa represents the third largest cotton exporter and produces 15% of the global cotton fiber traded. Despite this performance, since the end of the

1990s the cotton sector in West Africa faced periods of major socio-economic and financial crises, including accumulated debts due to input subsidies, low credit repayment rates from farmers, corruption and poor management of the financial resources of parastatal cotton companies.

A vast landlocked country located in western Africa, Mali covers over 1,240,000 km², with a population of 14.5 million, roughly 75% of which lives in rural areas (FAOSTAT, 2009).

Only 4.1 million people are considered to be in the labor force, with 80 percent of those 1

dedicated primarily to agriculture. The Malian economy is dominated by the agriculture sector, which represents 37% of the country’s GDP (Gross Domestic Product), accounts for the main source of income for 80% of Mali’s labor force and 28% of the country’s export revenues (CSA,

2009). The agricultural production within the country is oriented toward cereals. Millet, sorghum, maize and rice represent 72% of Mali’s cultivable area (CSA, 2009). Other agricultural products of Mali, in addition to cotton, include vegetables, peanuts, fruit, fonio, and livestock.

Considered as the key cash crop in Mali, cotton production is important both in terms of its contribution to agricultural development and poverty reduction within the country.

Representing the main source of income for households in the CMDT(Compagnie Malienne pour le Developpement des Textiles) zone, which stretches over Dioila in the Koulikoro region, Baroueli, Bla and San in the Segou region, the entire region of Sikasso, and Kita in the

Kayes region, the cotton sector supports much of the rural economy in Southern Mali (Figure 1).

Eighty-five to 123 billion FCFA (188 to 270 million USD) of annual gross revenues generated by the cotton sector is distributed in the cotton zone.

Figure 1: CMDT Zones

Source: CMDT, 2010

The parastatal cotton company (CMDT) employs 4,000 permanent and

temporary workers, and cotton represents the source of income for 3.7 million people (Samaké et

al, 2007). Cotton revenues have also played a key role in financing farmers’ purchases of

agricultural equipment and livestock for animal traction, which are used for cereal production

and cotton production. Consequently, one can argue that cotton production is directly related to

coarse grain production and thus to food security. Furthermore, at the household level, cotton

revenue finances education, health care, weddings and other social events. At the community

level, cotton revenues have financed much of the infrastructure (e.g., feeder roads) in the cotton

zone.

Although Mali has long been regarded as one of the largest cotton producers in West

Africa, Mali’s cotton production decreased significantly from 2003/04 through 2010/11 due to numerous internal and external difficulties faced by the cotton sector. The fluctuation of the world price is one of those difficulties.

Figure 2: The Evolution of Seed Cotton Prices, Seed Cotton Production and Adjusted World Price in Mali, 1994/95 - 2008 /09 1

Production (1000 tonnes) Prix co ton graine (Fcfa/kg) Prix mondial ajus té (en Fcfa/kg )

5 00 70 0 Fc fa/ kg 1 000 4 50 60 0 4 00

3 50 50 0

3 00 40 0 2 50 30 0 2 00

1 50 20 0

1 00 10 0 50 0 0 94/95 95/96 96/97 97/98 98/99 99/00 00/01 01/02 02/03 03/04 04/05 05/06 06/07 07/08* 08/09* Source : données CM DT pour production et prix coton g raine, OCDE/BA fD, 2006 p ou r prix mond ial . 07/09 e s timé

Cotton production continuously increased, from 200,000 metric tons (mt) in 1994/95 to

350,000 mt in 1998/99. It sharply fell in 2000/01 because 60% of farmers stopped producing cotton after the government announcement in 1999 of a drop in price during the marketing period, while the farmers had been told during the growing period that the price would not drop.

As a result of this boycott, the production fell to 150,000 mt in 2000/01. Between 2000/01 and

2004/05, the producer price increased as well as cotton production. Like the other West African countries, Mali does not play a major role in the world cotton market; it therefore must adapt to international cotton price volatility. The decline in the world cotton price led to the adoption in January 2005 of a new mechanism for determining the purchase price of seed cotton paid to

Malian farmers. The new formula resulted in a decrease from 210 FCFA/kg for cotton graded as

"first choice" in 2004 to a range between 160 and 175 FCFA/kg in 2005. Under the new mechanism, the world price is used to calculate the farm-level price, while production cost determined the minimum guaranteed price under the old mechanism (Samaké et al., 2008). The producer price remained nearly constant between 2004/05 and 2007/08.

CMDT and farmer organizations (OPs-Organisations Paysannes) are the main providers of purchased inputs, mainly cotton seed and fertilizer, whose costs are paid by farmers only after harvest. CMDT directly subtracts the input cost from the farmers’ cotton revenue before payment. From 1994 to 2000, the CMDT, in agreement with other actors, ensured the regular supply to producers of agricultural inputs for both cotton and key cereal crops.

CMDT agricultural advisors were also present in the villages to give advice on production techniques and crop intensification on cotton as well as cereals to ensure the good quality of the output; but in 2004, the World Bank pressured CMDT to scale back its activities and focus on cotton. As a result, the supply of cotton inputs was provided by the CMDT, while cereal producers’ organizations supplied farmers with cereals inputs (Ministere de l’Agriculture du Mali, 2004).

Because some organizational and financial problems impeded the OPs in their role of supplying inputs for cereals, cotton was the main channel farmers used to get access to purchased inputs used in coarse grain production. Consequently, a joint venture, GIE (Groupement d’Interêt

Economique) was created in 2008 by CMDT, OHVN (the Upper Niger River Valley

Development Authority) and representatives of farmer organizations. This new organization now procures inputs for both cotton and cereals.

1.1 Problem statement

This thesis examines how different classes of farmers in the cotton zone of Koutiala have reacted to fluctuations in the farm-level price of cotton and the changing structure of the cotton sector in the period 2006-2010. This analysis differs from previous analyses, which have focused on aggregate supply response, but less on the variability of responses shown by different types of farm households in the cotton zone to these uncertainties.

The cotton sector in Mali has gone through numerous reforms designed to help increase cotton producers’ revenue. These have ranged from the introduction of cotton in Mali during the colonial period under CFDT (Compagnie française pour le développement des fibres textiles), a

French company that had a monopoly power over Malian cotton purchases and exports, input supply, marketing, transportation, technical support and training for farmers, to the recent attempts to privatize the CMDT (the successor to the CFDT).

Numerous studies have been carried out on the institutional reforms, their effect on cotton production efficiency, the competiveness of Malian cotton, and the determinants of income diversification amongst households in southern Mali. The effect of agricultural policies in rural areas, in particular in the cotton zone, has been studied by many researchers in the past. For example, Theriault (2010) quantitatively analyzed cotton growers’ supply responsiveness to both price and non-price variables using an augmented supply model. Using a balanced panel dataset for six cotton regions over the period 1998- 2008, she estimated cotton grower supply responsiveness to prices and institutional arrangements. She found that delays in payment to producers and bad credit recovery negatively affected acreage decisions and production levels.

Coulibaly et al. (2011) showed that the Malian government’s 2011 policy to increase the farm-

gate cotton price in response to world cotton price increases enhanced farm income but had less impact on cotton than on maize production. They analyzed the change in production and land devoted to maize and sorghum under different cotton price scenarios. According to these authors, at a cotton price below 200 FCFA/kg ($0.44/kg), small areas of cash crops are raised to satisfy pressing household expenditures. The low cotton price constrains the amount of fertilizer that will be obtained on credit to grow maize and cotton. Thus, little area is put into these crops.

Farmers are more oriented to the production of subsistence crops, sorghum and millet, for home consumption and sales. But they also purchase maize to meet their subsistence goal. However, these authors found that at a price above 200 FCFA/kg, farmers had a greater response from expansion of maize area than that of cotton because the marginal returns from increasing maize production are greater than those from increasing cotton production. These types of research identify neither those farmers who are better off nor those who suffer from the price change policies. In general such research focuses on sensitivity (change in yield, cultivated area, income, health, etc.) of the sample as a whole to policy changes and misses much of the adaptive capacity of individuals or groups of farm households. Consequently, an analysis of the unequal degree of vulnerability to changes in cotton prices among different classes of households with different agricultural and socio-economic characteristics needs to be developed. This thesis aims to contribute to the literature on agricultural diversification by analyzing the strategies to cope with the cotton crisis during the period 2006-2009 of different types of farm households who face uncertainties about the cotton price, the payment schedule as well as the future of the cotton sector in the Koutiala area (the heart of Mali’s cotton belt). More precisely, the paper will address the following research questions:

• What factors drove farm households’ decision to either produce or not produce cotton in

Koutiala during the years 2006-07, 2008-09 and 2009-10?

• What strategies did households in the Koutiala area develop to cope with the cotton crisis

over this period?

Answering these questions involves understanding why farm households make the choice to stop or continue producing the main cash crop available to them in a moment of crisis. It will also involve not only an analysis of the change in quantity produced, the acreage and the yield for each crop of the sample as a whole, but also will break down the sample into categories of farmers (which are presented below) for a better understanding of their adaptive capability and crisis coping strategies.

1.2. Objectives and organization of the thesis

This thesis’s objective is to analyze how different groups of farmers adapted to changing cotton prices and other uncertainties in the cotton subsector over the study period of 2006-09.

Specifically, the study will analyze the effects of these changes in cotton production on the production of grain and other food and cash crops of farm households in the Koutiala region of

Mali. It will also examine the strategies they adopted in response to cotton price fluctuations to meet their food and monetary needs, by analyzing the evolution of their crop, livestock and non- farm income.

The thesis first outlines, in Chapter 2, the conceptual framework to be used in analyzing the income diversification pattern in Koutiala. Chapter 3 will then discuss the data used in this study. Chapter 4 will present a brief description of the economy of Mali in general and of the cotton area of Koutiala in particular by presenting the geographical, economic, social and 8

institutional environment of Koutiala. This chapter will also outline the reforms that took place in the CMDT zone and then will present previous researchers’ findings regarding farmers’ response to them in terms of production mix change and income diversification. Another point in this chapter will focus on the constraints farmers may face that prevent them from responding to opportunities to substitute other crops for cotton in their production plans, such as sesame, soy, peanuts, and sunflowers. Chapter 5 will focus on a descriptive and statistical analysis. This analysis helps identify farmers’ coping patterns in response to the cotton crisis by analyzing the change in production, net sales, yields and amount of land allocated for each crop, using cross- tabulations. Chapter 6 then presents a Multinomial Probit model that analyzes what drove those patterns. Finally, Chapter 7 draws conclusions with respect to the reasons behind the changes in production mix, acreage and farm labor allocation in response to cotton price fluctuations.

CHAPTER 2: CONCEPTUAL FRAMEWORK AND RESEARCH HYPOTHESES

The main conceptual tool to be used in this study is based on the general economic framework of the farm household decision-making model. This tool is used in research such as price policy analysis, technology adoption, migration, deforestation and biodiversity (Taylor and

Adelman, 2002). Recently, the model had been used to analyze the influence of household- specific transaction costs on the effect of an exogenous policy or price shift in rural areas.

In developing countries, farm households produce both for sale and home consumption.

They combine two entities: the household and the firm, which were traditionally studied separately (Singh and Strauss, 1986). If perfect markets for all goods, including labor, existed, the household would be indifferent between consuming own-produced and market-purchased goods; however, the absence of a well-structured market, which assures farm households access to food on the market any time, constrains farm households to combine their production and consumption decisions. Consequently, in developing economies, any study of the consumption or labor supply of agricultural households must assume that the household does not separate its production decision from it consumption choice (Taylor and Adelman, 2002).

In this thesis, the model is based on the assumption that the households maximize their utility, which is function of agricultural goods, market-purchased goods and leisure. The utility is maximized subject to a set of constraints: cash income, family time and endowments of fixed productive assets, production technologies and prices of inputs and outputs (Taylor and

Alderman, 2002). Even though much of the cash income is earned from cotton sales for the majority of households in Koutiala, farmers will grow cotton only if the utility derived from it is higher than not planting it. When the cotton price is high, farmers have more incentive to grow

cotton and buy consumption goods with the cash they earn from cotton sales. However, the household may decide to grow as much as possible of the staple food it might need when the price of that commodity climbs later in the year. Additionally, coarse grain market volatility coupled with the weather uncertainty can cause the market supply for the staple good to fall.

Consequently, farm cotton production decisions are affected by the cotton price and the grain market. The volatility of the cotton price in recent years has put cotton growers at risk of not being able to meet their cash needs. To reduce that risk, it will be rational for farmers to diversify their production and sources of cash income subject to their asset and budget constraints. In this diversification process, the household will allocate the labor to each activity with respect to its marginal utility. If the market wage increases, the family may decide to engage more family labor in off-farm activities. Others factors that influence cotton production decisions are discussed below.

2.1. Factors affecting cotton production

2.1.1. On-farm conditions affecting household decision-making

On-farm conditions affecting decision-making are broadly divided into socioeconomic and biophysical factors. The principal factor affecting production is widely documented as being the household's factor endowment, which includes available land, the size of the household work force, and access to capital. Agro-climatic conditions, such as rainfall and weather conditions and the risk of insect infestations and disease, are also considered as part of the environment that highly influences the household production decisions. Finally, the personal traits of the head of the farm household or decision-maker are non-negligible factors and must be taken into consideration when examining the production and decision-making process. Among others, the

level of management skills, age, education and gender may all directly or indirectly influence the

production decision.

2.1.2. Off-farm factors household decision-making

Off-farm factors have an influence on farm household decision-making, as represented in Figure

Figure 3: Factors Affecting Farm Household Production Decision

Market Support services Local, National Credit, input

Extension

Farm

Household

Technical Policies, Rules and Information Regulation

Source: http://www.fao.org/DOCREP/X0266E/X0266E00.HTM

Those factors include access to credit, inputs, and markets for both products purchased and sold, input and output prices, the levels and volatility of other food and cash-crop prices, and access to research and extension through technical training sessions (covering general education,

production techniques, quality and safety control, etc.). In the case of this study, cotton price would not be considered as stochastic since CMDT announces the output price at the planting season; however, input and other cash-crop and grain prices are uncertain. Cotton production is further influenced by local institutions (for example, belonging to a local OP or other village association that facilitates access to information, inputs and credit).

2.2. Hypotheses

1) Although cotton prices are announced before planting season, farmers are differentially

sensitive to price changes. When the price dropped in 2001, the production of cotton was

cut in half; consequently, we assume that cotton prices are positively correlated with the

likelihood to grow cotton. A Multinomial Probit (MNP) Model will be used in Chapter 5

to test this hypothesis, with cotton price relative to maize price as an independent

variable. The coefficient of relative cotton price in the model will determine the

probability that a farmer will choose to be in one group (with respect to the decision on

whether to produce cotton) rather than the other when cotton price changes.

2) The amount of land allocated to other types of cash crops such as peanuts, cowpeas,

sesame and soy increases when the price of cotton falls. We expect non-cotton growers to

be affected by the drop in cotton price because of the important role played by cotton in

supporting the economy in Koutiala. Assuming non-cotton producers supply cotton

producers with labor, the drop in cotton price will decrease the demand for labor; this will

constrain non-cotton producers to develop an alternative source of income. Since the

largest cotton growers are net coarse grain sellers, non-cotton growers will orient their

production towards alternative cash crops such as peanuts, soy and sesame, as the supply

of coarse grain increases in response to low cotton prices.

3) The absolute number of the household work force working in the nonagricultural sector

increased when the cotton price fell. A low cotton price makes its production less

profitable; thus, we expect that it will create a push of farm labor toward the

nonagricultural sector.

CHAPTER 3: DATA

The data used in this study were collected within the framework of a broad study, which consisted of three phases:

Phase I: The study entitled "Structural Implications of Liberalization for Agriculture and

Development" (RuralStruc) was financed by the World Bank in partnership with the French technical cooperation agency CIRAD and the International Fund for Agricultural

Development (IFAD). It was designed to increase knowledge about the effects of liberalization and economic integration on the agricultural system, structural changes, and the living standards of rural populations. This study was implemented in Kenya, Madagascar, Mali, Morocco,

Mexico, Nicaragua, and Senegal. Conducted in 2006/07, Phase I reexamined previous studies and secondary data about the importance of agriculture in the Malian economy and how the market structure changed over time. It also looked at the process of structural differentiation and farmers’ responses to policy reforms. In Mali, the study was conducted by CEPIA (Centre

International pour le Développement de l’Elevage en Afrique), in collaboration with CIRAD.

The information produced in this phase contributed to the literature review of the thesis.

In Phase II study, conducted by the Malian Institut d’Economie Rurale (IER), CIRAD, and MSU (Michigan State University), 750 households were surveyed in Diéma (an international migration zone); Tominian (a zone mainly oriented toward rainfed agriculture, which has received little government investment in rural development); Macina (a zone in the large irrigated agricultural area in the Office du Niger; Mali’s largest irrigated rice area) and Koutiala

(a cotton zone) in order to have a better understanding of the income sources and the

diversification of economic activities by farm households. Although the farm-level surveys began in 2008, the information collected was about the 2006/07 production year.

In Phase III, MSU and IER extended the phase II farm-level surveys for an additional two years (2008/09 and 2009/10) in three of the four zones (Tominian, Macina and Koutiala).

Combining these two years with the phase 2 data yields a three-year farm panel, but with a one- year gap between the first year (2006/07) and the second year (2008/09).

Looking only at the Koutiala case, which will be the focus of this thesis, the farm household survey was carried out in six villages. The availability of arable land was the main criterion for selection of the survey villages. Among the selected villages, three had high pressure on land while the other three had average pressure on land. 1 Since involvement in cotton production could not be used as a selection criterion because the majority of the households grow cotton, access to market was another criterion used to choose the villages. The access to market was determined by the state of the roads and the distance from major markets. Access to major markets influences the farm household’s development opportunities. Among the three villages with high pressure on land, one had a relatively difficult market access and two had easy access. For the three villages with average pressure on land, two had difficult access and one had easy access to market (Table 1).

1 High pressure = less reserve land (no land to be cleared and very few short fallows). Average pressure = have some land reserves in the form of long and short fallows. 16

Table 1: Characteristics of Sample Villages in the Koutiala Zone

Village Name Selection Criteria Commune Population 1998

Average land pressure and Nampala II Fagui 982 difficult access to market Average land pressure and Tonon Sinkolo 286 difficult access to market High land pressure and easy Kaniko Sincina 1735 access to market High land pressure and easy Try I Sincina 864 access to market Average land pressure and Signe Koutiala 1005 easy access to market High land pressure and Gantiesso Mpessoba 3219 difficult access to market Source: Program RuralStruc Phase II, 2008 Within the villages, households were randomly selected: 150 households were retained after data cleaning. The data consist of information on households’ demographic characteristics, land assets, total production of different crops, sales, gifts, input use and purchases of all crops and livestock, equipment assets, migration and revenue from on- and off-farm activities. Unlike all the other variables, input data were collected differently in the 2006/07 period from the

2008/09- 2009/10 periods. In 2006/07 (round 1), the total household input use was collected, while in 2008/09 (round 2) and 2009/10 (round 3), input data were collected by individual crops instead. Additionally, during rounds 1 and 2, the data were collected during a single visit to the farmers; they were interviewed twice during round 3 instead. The production data were collected during the first visit, then the marketing data were collected during a second visit.

In analyzing the comparisons of production levels presented later in this thesis, it is important to bear in mind how production conditions (in addition to prices) varied across the three survey years. Although there were some cases of flooding in the Sikasso region during the 17

first two weeks of August in 2006, the production conditions in terms of rainfall in 2006/07 were in general satisfactory in the region. A few cases of aphids on sorghum were reported in the

Koutiala area; however with the government pest control program underway, there was no major damage to the crops. The rainfall in 2008/09 was less than 80% of that of 2006/07, and minor crop damage from grasshoppers, caterpillars, aphids and termites was reported in Koutiala.

Following a short drought in September 2009, some crops in the 2009/10 crop year were attacked by caterpillars, and other harmful events were reported in Sikasso region: diseases of sorghum, millet, rice and groundnuts. However, the rainfall level was higher than in the previous year (see Table 2)

Table 2: Rainfall in Koutiala (mm) over the period 2000-2010

Rainfall Year (mm) 1999/00 745 2000/01 914 2001/02 633 2002/3 1,003 2003/4 1,024 2004/5 827 2005/6 992 2006/07 922 2007/08 913 2008/09 730 2009/10 992 Source: http://power.larc.nasa.gov

CHAPTER 4: BACKGROUND INFORMATION ON THE AGRICULTURAL SECTOR IN THE KOUTIALA DISTRICT

Outlining the principal characteristics of agriculture in Koutiala helps set the context for the analysis of farm household decisions concerning coarse grain and cotton production. Much of the data used in this section come from the MSU food security websites

(http://aec.msu.edu/fs2 and http://angel.msu.edu). Because the production data for Koutiala are not available, data for the entire region of Sikasso, which includes the zone of Koutiala, are used in this section.

4.1 Physical environment of the Koutiala zone

Koutiala District is in the heart of the old cotton basin and occupies the western part of

Sikasso region. It is bounded on the north by San District (cercle), northwest by Bla, southwest by the Dioïla District, to the south by the district of Sikasso and the Republic of Burkina Faso and to the east by the District of Yorosso (see Figure 4).

Figure 4: Koutiala District

Source: http://www.google.com/imgres

The Koutiala district covers an area of 8,740 km 2 and includes the Central District and part of the district of Zangasso. The climate is tropical sub-Saharan and characterized by two seasons in a year: a dry season from November to April and a rainy season from May to

October. The rainfall in Koutiala ranges from 750 to 1000 mm per year (Table 2). The district has neither a river nor large lakes; yet we can distinguish surface water and wells, generally fed by rainwater.

4.1.1 Soil

The topography of Koutiala involves plateaus, sloping lands, and lowlands. The soil textures are predominantly clay, sandy loam and sandy soils. Sandy soils have very low organic matter and low infiltration capacity. Due to their poor level of fertility and poor water retention capacity, sandy soils are mainly favorable to millet production, which tolerates low fertility and water scarcity. Cotton, sorghum and maize are grown in loam sandy soils and clay because of the higher quality of these soils (Coulibaly et al. 2011).

4.2 Principal crops

Koutiala is one of the largest cereal production zones in the country. Millet, sorghum and maize are the main staple foods produced in Koutiala. Rice is also grown in the zone. These crops serve for home consumption as well as being marketed.

Both cereals and cotton are produced under rain-fed conditions. Koutiala is the main cotton- growing area, with production of 200,000 tons during the CMDT season of 2008/2009. Cereals are grown in rotation with cotton, which allows them to benefit from the residual effect of fertilizers used in cotton. Figure 5 shows the production evolution of the four main cereals grown in the Sikasso region, where Koutiala is located.

Figure 5: Evolution of Grain Production in Sikasso Region, 1990 - 2010 (Quantities in Metric Tons) 600,000

500,000

400,000

300,000 millet sorghum 200,000 maize 100,000 rice

1990/1991 1991/1992 1992/1993 1993/1994 1994/1995 1995/1996 1996/1997 1997/1998 1998/1999 1999/2000 2000/2001 2001/2002 2002/2003 2003/2004 2004/2005 2005/2006 2006/2007 2007/2008 2008/2009 2009/2010 Source: CPS/MA, 2010 Prior to 1994, cereal production in Sikasso was dominated by sorghum, followed by maize, millet and rice. Starting in 1993/1994 and continuing to the present, maize has gained the highest share of the total production, growing at an exponential rate. In 1994, 200,000 tons of maize were produced in Koutiala. In 2010, this number had almost tripled to 570,000 tons.

Sorghum has seen a more modest growth rate, increasing by 12% between 1994 and 2010.

However, as shown in Figure 6, the increase in cultivated land for sorghum was much bigger than that of maize, implying that much of the increase in maize production came from yield increases, while sorghum’s production expanded mainly through expansion of area planted. The production of millet and rice has also increased, but at a more modest growth rate. The improvement of cereal production during the1990-2010 period is partly due to the liberalization policy initiated by the Malian government in 1982 and intensified in 1991 through the complete removal of price controls (Coulibaly et al. 2011). The devaluation of the CFA franc, Mali’s currency, in 1994, increased agricultural prices (since they are mostly tradable commodities) and

thus also had a positive effect on inducing increased cereal production. Furthermore, according to Raja (2007), the introduction of improved varieties of seeds coupled with favorable rainfall contributed as well to the increase in cereal production in Mali.

Figure 6: Evolution of Cultivated Land Area of Cereals and Cotton (in Hectares) in the Sikasso Region, 1999/2000 - 2009/2010 500000 450000 400000 350000 300000 millet 250000 sorghum 200000 maize 150000 100000 rice 50000 cotton 0

Source: CPS/MA, 2010 In 1999, maize accounted for the largest amount of cultivated land devoted to cereals in the region, followed by sorghum, millet and rice. From 2001 to 2009, the land area for sorghum and maize went up rapidly, with the land allocated to sorghum dominating in most years the area devoted to maize. The land allocated to rice and millet also increased over the period. Over the

2006- 2008 period, the cultivated land devoted to millet was comparable to that of maize, but by

2009/2010, land allocated to millet exceeded maize land area. Over the past decade, the production of maize has increased by 143%, followed by sorghum, 18%, in the cotton zone.

Lower cotton prices may have encouraged such diversification, but we will investigate this further in the empirical analysis in Chapters 5 and 6.

Figure 7: Evolution of Cereal Yields (kg/ha) in the Sikasso Region; 1999/2000- 2009/2010 3,000

2,500

2,000 Millet 1,500 Sorghum 1,000 Maize

500 Rice

Source: CPS/MA, 2010 In Figure 7, we can see that over the last 10 years, only rice and maize have seen their yields significantly increase. Yields of millet and sorghum stagnated across most years. The increase in maize yields is partly due to maize being grown in rotation with cotton; consequently, it benefits from the fertilizer used on cotton. Also the cotton fertilizers are diverted to maize.

This is due to the increase in maize demand in response to the expansion of poultry sector, as maize is used to feed the birds (Diallo, 2011).

Figure 8: Evolution of Cotton Production of Sikasso Region (in metric tons) and National Cotton Production from 1990 to 2008 700000

600000

500000

400000 Production (in tons) – 300000 Sikasso Region

200000 National production (in tons) 100000

Source: CPS/MA, 2010

Since its introduction, numerous varieties of cotton have been grown in Mali, mostly of foreign origin. The most commonly grown varieties (and the percentage of total production attributable to them) are: Satm 59-A (65%), Stam 279-A (10%), N’TA -90-5 (14%) and G440

(2%). Even though the acreage and prices declined between 2001 and 2009, cotton remains the principal cash crop in southern Mali. The Sikasso region produces more than half of the total national production. Despite the Malian government’s efforts to help cotton farmers through input subsidies and the implementation of a minimum price, cotton production both in Sikasso and at the national level experienced a continuous decline from 2003-04 to 2007-08. It passed from 400,000 tons in the 2003-04 season to nearly 150,000 tons in 2007-08. This decrease can be attributed to a low and volatile cotton price, high production costs and the new price-fixing

mechanism introduced by the government in 2005, which, according to Oxfam (2007), left farmers worse off. 2

Figure 9: Evolution of Fonio, Peanut, Sesame and Cowpea Production (in tons) from 1999/2000 to 2008/09 100000 90000 80000 70000 60000 peanut 50000 40000 cowpea 30000 sesame 20000 fonio 10000 0

Source: CPS/MA, 2010 Possible alternative sources of cash income to farmers other than cotton include cultivation of peanuts, fonio, cowpeas and sesame (Figure 9). The lack of data makes it difficult to assess to evolution of sesame production, which is in high demand in regional markets for its cosmetic and nutritional attributes. Fonio and cowpea quantities produced are declining, unlike peanuts. While cotton in 2008 was at its lowest level of production, peanut production, on the other hand, nearly tripled between 2002 and 2008. It passed from 34,000 tons on 2002 to 90,000 tons in 2008.

2 Theriault (2010) found late payments to farmers to be a major factor affecting farmers’ willingness to grow cotton. However information about farmers’ payment schedule was not collected during the survey which data we are using in this thesis which prevents us from arguing whether or not the drop in cotton production between 2006 and 08 in Koutiala was partly due to late payment to farmers.

Figure 10: Evolution of Peanut, Cowpea, Sesame and Fonio Land in the Sikasso Region between 1999/2000 and 2009/2010 90000 80000 70000 60000 50000 peanut 40000 cowpea 30000 sesame 20000 fonio 10000 0

Source: CPS/MA, 2010

Figure 11: Evolution of Peanut, Cowpea, Sesame and Fonio Yields in the Sikasso Region between 1999/2000 and 2009/2010 1,400 1,200 1,000 800 Peanut 600 Cowpea 400 Sesame 200 Fonio 0

Source: CPS/MA, 2010

As illustrated in Figures 9 and 10, unlike cowpeas, the production and area of peanuts, sesame and fonio remained low between 2000 and 2005. In 2006, we observe a sharp expansion

of both the land and the production for those crops. While the production and area increased over the past five years, the yields remained constant over the past decade (Figure 11).

Figure 12: Evolution of Land Allocated to Cotton, Maize and Peanuts from 1999 to 2009; Unit = hectares 400000

h 350000 e 300000 c 250000

t 200000 maize a 150000 cotton r 100000 peanut e 50000 0

Source: CPS/MA, 2010

Figure 12 shows that except the year 2000/2001, when the land allocated to cotton, maize and peanuts decreased from the previous year; and the allocated land to maize and peanuts increased when cotton land went down over the past 10 years. This supports Coulibaly et al.’s

(2011) argument that maize production becomes relatively more profitable when the cotton price is low. Whether peanuts and maize are replacing cotton at the Koutiala level will be discussed in

Chapter 5.

4.3. Livestock

Sikasso is now Mali’s second largest livestock producing region after the Mopti region.

The abundance of wells in Sikasso and its proximity to some of Mali’s neighboring countries that serve as important markets (e.g. Cote d’Ivoire) favor livestock production in the region.

During the cotton crisis, livestock represents an important source of income diversification in the

Sikasso region. In southern Mali, cotton producers are more likely to be involved in the livestock sector; the cotton revenue serves to finance cattle purchases over time, which farmers use as collateral to access credit or sell the by-products such as manure or hides and skins to invest in non-farm activities such as commerce (Abdulai and CroleRees, 2001). Bessan et al. (2009) report that farmers who have the flexibility to stop growing cotton when the price drops choose livestock, among other activities, to diversify their income. In the analytical part of this thesis, we will examine households’ livestock revenues over time to investigate this issue further.

According to Oxfam (2007), when the cotton revenue decreased as a result of low cotton prices, farmers were forced to sell the livestock they accumulated for years to be able to pay their debts and cover their food costs. The poorest farmers, who own a very small amount of livestock if any, had to sell their farming equipment, which further lowered their income. In Chapter 5, we will analyze in depth how livestock income changed as the cotton price varied.

Other important sources of income in Koutiala zone are commerce and handcrafts. The proximity of the zone to Cote d’Ivoire coupled with the support of GTZ (the German government’s foreign assistance agency) and FAFPA (Fonds d’Appui à la Formation

Professionnelle et à l’Apprentissage) have had an important influence on the expansion of these sectors. These partners help entrepreneurs in the trade and the handicraft sectors to organize into associations. They provide training and help them finance their activities.

4.4. Land Tenure

Since 1980s, land pressure in Koutiala has increased as a result of population growth.

According to Bodnar (2005), the arable land available per person fell from 1.15 ha to 0.87 ha between 1988 and 2002 in the CMDT sector of Koutiala (Table 3).

Table 3 Evolution of the Area per Person in CMDT Sector of Koutiala 1988/2002 (ha/person) 1988 2002 Total Land per Person 3.85 2.90 Cultivable Land per Person 1.15 0.87 Cultivated Land per Person 0.61 0.72 Source : Bodnar, 2005 Between 1998 and 2002, the total land and cultivable land per person dropped while the cultivated land per person increased. This increase suggests that there has been an elimination or reduction of fallow periods or an agricultural intensification to meet the growing population’s food needs. Under the traditional land tenure arrangements in the zone, only the head of the household may temporarily give up some lands to outside people. The land is not sold but borrowed; this land use is not subject to a rental fee. The head of the village is responsible for providing new families with land for agricultural activities and home construction as well for settling land-tenure disputes.

4.5 Farmers’ responses to price regulation reforms in the Malian cotton zone

4.5.1 Brief overview of reforms in the Malian cotton sector

In its effort to reduce poverty, the Malian government has implemented numerous programs and projects to improve farmers’ standards of living. Because the cotton sector has been the engine of rural development in southeastern Mali, it is one of the agricultural sectors that received large-scale investment from the Malian government. From the 1980s to today, the cotton sector has seen major institutional reforms designed to liberalize crop marketing and agricultural input procurement, decrease bureaucratic control of agriculture, change the form and function of farmer organizations and make crop production more competitive in the world market.

From its creation in 1944 to 1988, CFDT (Compagnie française pour le développement des fibres textiles) and its successor CMDT served both an economic function (purchasing and ginning of cotton) and social functions (education, adult literacy and heath) in the Malian cotton zone (Dougnon et al., 2010). The first institutional reform that took place in the cotton sector was the establishment of “contract plans” between the state and CMDT in 1988. Through this reform, the CMDT moved from being a parastatal to a limited liability company jointly owned by the state and private actors; its capital was 60% owned by the Malian government and 40% by the

CFDT, a French multinational owned in part by the French government. The “contract-plans” set performance objectives for the CMDT, which determined in theory the remuneration received by the company.

In 1991 after the end of the Moussa Traoré government, farmers decided to play an active role in the cotton sector; they thus created the Syndicat des Paysans Cotonniers et Vivriers, or

SYCOV which became the interlocutor between CMDT and the state. The new contract plans, which defined the missions and role of each actor of the sector, were created. In particular, they defined the management of the sector’s income and profit as well as the mechanism for cotton price setting (Giraudy, 1996).

After the cotton strike in 2001 that led to the drop of cotton production by half, the different agents of the cotton sector held a consultative assembly in 2002. CMDT decided to offer a higher price, which led to an increase in production as well as a financial deficit for the

CMDT. Consequently, in that same year, 2002, the Malian government decided to re-examine the sector and to draft the “Lettre de Politique de Développement du Secteur Coton “ (Cotton

Sector Development Policy Paper, or CSPP). The objectives of this policy were to reduce cotton production costs, withdraw the government from extension, input supply and transport, liberalize

the price and marketing of seed cotton and an eventually privatize the CMDT, with the government keeping only 20% of the ownership (Dougnon et al, 2010). In 2001, the government launched the Initiative for Restructuring the Cotton Sector (MRSC: Mission de restructuration du secteur coton) that built on the previous reforms through the privatization of CMDT, with a mandate to evaluate reform options and monitor implementation of the reform program. To implement this reform successfully, the strategies were to liberalize the cotton and oilseed industry, refocus CMDT activities on the cotton system, promote the participation of producers and the private sector in the industry, and open stock ownership of the company to producers and private operators. Accordingly, CMDT would be split into four full-fledged companies: the northeast affiliate; the southern affiliate; the central affiliate and the western affiliate, each with its own accounting. The shares in each company will be assigned as follows:

• 61 percent to a private buyer, the majority shareholder;

• 17 percent to the State;

• 2 percent to CMDT employees;

• 20 percent to producers (Serra, 2012).

The CMDT privatization scheduled for 2008 was postponed due to delay in the creation of the four subsidiaries and the draft of the privatization bill. Among the six international companies and one Malian Group that made some pre-offers in February 2010, only the Chinese company Yue Mei, which is negotiating to purchase two subsidiaries (the southern affiliate in

Sikasso-Bougouni and the western affiliate in Kita), met the required criteria. The privatization process is still on hold as of today (Serra, 2012).

4.5.2 The constraints farmers face that prevent them from responding to opportunities to substitute other crops for cotton

Peanuts, cowpeas, sesame, sunflowers and soy are the major crops in addition to maize that can be used as an alternative cash crop to cotton. Many of the activities around these cash crops are performed in a small-scale manner and operated by private agents.

4.5.2.1 Peanuts

Peanuts represent an important alternative for income diversification at the farm level in the cotton zone, which also happens to be the largest peanut production zone in Mali. Peanuts are processed into shelled nuts, butter and oil by private processing plants that are located in the cotton zone. The shelling is mostly performed in a traditional manner, but mechanical shelling is slowly growing. The “Groupe Tomota”, which in 2005 bought HUICOMA (the cottonseed processing company formerly owned by the CMDT and which had the monopoly on cottonseed oil processing in Mali), initiated a project to increase the volume of peanut and sunflower production that will be used as feedstock in its vegetable-oil processing plant. Despite these opportunities, peanut production expansion is impeded by aflatoxin (a toxin produced by mold that can damage the liver and may lead to liver cancer). The existence of this toxin eliminates all chances for Malian peanuts to be exported to European and American markets (Samaké et al,

2008). Promoting training of peanut producers in improved storage techniques will help farmers keep peanuts for an extended period without facing the risk of aflatoxin infection or insects destroying the grain. The difficulty of preserving peanuts is a disadvantage to its production because producers lack knowledge and equipment to keep insects away from the seeds.

4.5.2.2 Cowpeas

Like peanuts, cowpeas are another crop that has interested many farmers during this cotton-crisis period. There are three main products produced from cowpeas: the grain for human consumption and the shell and the hay for livestock feeding. Though cowpeas can, at a certain time of the year, reach a price higher than that of rice, preserving the grain from insects is often a difficult task for farmers due to lack of technical training in cowpea storage. In addition, some practices used by farmers for cowpea storage could cause food-borne illness. For example, since

CMDT has stopped its adult literacy training programs, some farmers may not know how to read the label of an insecticide. Consequently, the dosage used to protect stored grain is not likely to be respected, which could put the consumer’s life in danger. Promoting cowpea hay production could be an alternative to costly animal feed, particularly in the cotton zone where animal traction equipment is widely used in crop production. Moreover, a decrease in cotton production will further increase the price of animal feed produced from cottonseed. The information about the cowpea value chain in Koutiala is limited, so at this point It is difficult to assess the opportunities that cowpea production may offer to farmers, or the possibility of industrial production either for human or animal foods.

4.5.2.3 Sesame

Sesame does not enjoy heavy government support, but the CMDT and OHVN have started to give attention to the plant as an option for crop diversification programs. Sesame cultivation does not require a lot of cost, as no fertilizer is used by the farmer; consequently, the yield remains relatively low. There are not adequate data about this crop to help us assess its role in income generation and its ability to represent a potential substitute to cotton.

CHAPTER 5: DESCRIPTIVE ANALYSIS

Data analysis will first focus on a descriptive analysis, followed by econometric modeling in Chapter 6 that will be used to test the hypotheses of this study.

In the following analysis, the sample households are divided into four groups:

Group 1: Those who continuously grew cotton throughout all the three years of the survey.

Group 2:Those who grew cotton in the first year and then abandoned it in either year 2 or 3.

Group 3:Those who did not grow cotton in year 1 and started its production after year 1.

Group 4: Those who never grew cotton during the three rounds.

The descriptive analysis will focus on the following variables, by household group over the three-year study period (2006-07= round 1, 2008-09=round 2, 2009-10= round 3), using a cross- tabulation procedure:

• Change in total production of coarse grains (millet, sorghum and maize), other crops and

cotton;

• Change in net coarse grain sales;

• The yields of each crop produced;

• The change in off-farm and on-farm income. 3

We will also examine whether income diversification is oriented toward high- or low- return non-farm activities. To do this, we will separate off-farm activities into sectors

(commerce, livestock, industry, handcrafts, migration, etc.). We will then will look at the amount, change in absolute amount and share of income generated from each sector. Using the

3 On-farm income = crop value-added + livestock value added + processed products production value- added + annual income from fishing, hunting and gathering + rents received from renting land + rents received from renting agricultural equipment - wages of external workers - rents paid for rented land - rents paid for rented equipment. The crop and livestock value-added are the difference between the total production valued at the purchase price minus input cost . 36

above described classification, there are 60 households in Group1, Group 2 = 53 households,

Group 3 = 13 households and Group 4 = 24 households. The characteristics of the groups are shown in Tables 4 and 5.

The average number of household members is the largest in Group 1.The majority of the households in Groups 1 and 3 have difficult access to market and high land pressure while more than half of the households in Groups 2 and 4 have easy access to market ( see Table 17). The dependency ratio 4 in Group1 is the lowest except for Group 2 in 2008 and 2009. Even though the households in Group 4 own about as much land as the rest of the groups, the relatively low amount of cultivated land and low level of agricultural equipment coupled with a high dependency ratio in Group 4 suggest that their lack of family labor prevents households in this group from exploiting a large amount of land to feed the family. Table 5 shows that few households in Group 1 do not own a complete set of animal traction equipment and oxen compared to the rest of the groups; however, from 2008 to 2010, the number of households owning more than two sets of animal traction equipment and oxen is the largest in Group 3.

Group 2, however, had the largest number of households owning at least one set of animal traction equipment and oxen. After running a statistical significance test, we failed to reject that the difference mean of the dependency ratio between Group 2 and Group1 is zero at the 5% significance level; however, the means of land owned, equipment index and durable goods in

Group 2 are statistically different (at the 5% level) from their means in Group 1(See Table 18).

The level of equipment index, labor and durable goods is the largest in Group 1 compared to

4 Dependency Ratio: measured by the household’s ratio of workers to non-workers. Workers are the adults between the age of 15 and 60 and non-workers are the children (below age 15) and the elderly (over age 60).

Group 2 (see Table 18). Between Group 1 and Group 3, the difference in equipment index and durable goods means is not statistically significant; yet the means of land and labor are significantly different at the 5% level (see Table 18). Comparing Group1 to Group4, the means of Land owned, labor, equipment index and durable goods are significantly different between the two groups ( see Table 18). Group 1 has more equipment than Group 3 and 4; however, the mean difference of durable goods between Group 1 and 3 is negative.

Table 4: Description of Group Means for Different Characteristics of the Households 2006 2008 2009 Group Number of households 1: Continuous Cotton 60 60 60 2: Cotton in Year 1 then no cotton 53 53 53 3: No cotton year 1 then uptake 13 13 13 4: No cotton entire period 24 24 24 Average Number of Household Members 1 19 20 21 2 14 15 16 3 15 16 18 4 13 14 15 Dependency Ratio 1 2.2 2.1 2.1 2 2.2 2.1 2.1 3 2.5 2.3 2.3 4 2.6 2.2 2.3 Average Land Owned (ha) 1 15.6 15.6 15.6 2 12.3 12.3 12.3 3 13.0 13.0 13.0 4 13.3 13.3 13.3 Average Land Owned Per Adult Equivalent (ha) 1 1.1 1.0 1.0 2 1.3 1.0 1.0 3 1.1 1.1 0.9 4 1.1 1.3 1.3 Average Area Planted (ha) 1 12 11 12 2 10 8 9 3 10 9 10 4 7 6 6 Average Area Planted Per Adult Equivalent(ha) 1 0.8 0.7 0.7 2 0.9 0.7 0.7 3 0.8 0.7 0.6 4 0.7 0.6 0.5 Source: Computation of author using the survey data

Table 5: Agriculture Equipment (%) among sample households in Koutiala from 2006 to 2009 Group 1 2 3 4 Do not own a complete set of animal traction equipment and oxen 5 2% 17% 15% 46% Own at least one set of animal traction equipment and oxen 57% 55% 46% 50% Own at least two sets of animal traction equipment 2006 and oxen 42% 28% 38% 4% Do not own a complete set of animal traction equipment and oxen 13% 21% 23% 54% Own at least one set of animal traction equipment and oxen 50% 58% 38% 46% Own at least two sets of animal traction equipment 2008 and oxen 37% 21% 38% 0% Do not own a complete set of animal traction equipment and oxen 17% 21% 23% 50% Own at least one set of animal traction equipment and oxen 43% 58% 31% 46% Own at least two sets of animal traction equipment 2009 and oxen 40% 21% 46% 4% Source: Computation of author using the survey data

Table 5 also shows that the ownership of a set of animal traction equipment and oxen decreased from 2006 to 2009 across all groups except Group 2, where there were more people owning at least one set of animal traction equipment and oxen in 2008 compared to the previous year. Although some of the equipment was stolen or given away because it was not functional,

35% of the farmers who had sold their equipment told the interviewer that they had to decapitalize to repay their debts. Another reason why they had sold their equipment was to invest in another activity to generate cash or to pay for inputs used in their crop production.

5 A complete set of animal traction includes: at least 2 plows, 1 multiculteur, 1 drill, 1 wagon and at least 4 oxen.

Commodity prices are year-specific variables that affect farmers’ production decisions.

The commodity prices shown in Table 6 are zone median producers’ prices received by the sample group for their sales collected during the survey.

Table 6: Median Commodity Producers Price (FCFA/kg) 2006 2008 2009 Maize 75 110 100 Sorghum 88 100 100 Millet 98 110 115 Rice 126 180 142 Fonio 160 113 100 Peanut 165 157 117 Cowpea 98 175 150 Sesame 178 215 250 Cotton 160 200 170 Source: Computation of author using the survey data

As previously mentioned, the cotton sector drives the economy in Koutiala and all the other cotton zones. The cotton price increased by 25% from 2006 to 2008 then dropped in 2009.

The increase in the cotton price in 2008 coincided with an increase in coarse grain prices and the price of the other major crops grown in Koutiala except fonio and peanuts, whose prices fell. As the cotton price goes up, farmers allocate more land to cotton, reducing land allocated to coarse grain production, thereby reducing the coarse grain supply and driving up grain prices The increase in the coarse grain price in 2008 could also result from the relatively low rainfall (see

Table 2) and the insect attack that were reported in Koutiala in 2008 as well as the increase in global grain prices, some of which was transmitted to Malian markets. An increase in the cotton price will increase the cash income earned by non- cotton growers who rely on grain sales to generate cash income. We will use the cotton price relative to the maize price in our model to determine its impact on cotton production decisions in Koutiala.

The households in Group 1 were the largest coarse grain producers in the sample in 2008 and 2009 and the second-largest producers (after Group 3) in 2006 (Table 7). In 2006, when the cotton price was at its lowest level (160 CFA/kg) and rainfall was adequate, the production and sale of coarse grains were at their highest levels for all the groups except Group 4, which produced more coarse grains when the cotton price increased in 2008 (despite the drop in rainfall shown in Table 2). Coarse grain production continuously decreased for Groups 2 and 3 in 2008 and 2009. The decrease in maize production explains the decline in total coarse grain production.

While Group 1 had the largest coarse grain production volume compared to the rest of the groups, its production per adult equivalent in 2006 was smaller than that of Groups 2 and 3. The production per Adult-Equivalent continuously decreased for each group over time, with Group 3 having the largest production per Adult-equivalent in 2006 and 2008. In 2008 when the cotton price increased, the production of peanuts and cowpeas increased (Table 9). This result supports our hypothesis that the land allocated to cowpeas and peanuts will increase when the cotton sector uncertainties increase. Much of the peanuts, cowpeas and rice were produced by Group 1.

Fonio and sesame were also produced in the zone but at a smaller scale.

A possible reason for Group 3’s decline in coarse grain production could be that households in this group shifted land from coarse grain to cotton, as their cotton area planted passed from 0 hectares in 2006 to an average of 2.4 ha in 2008 (Table 10). The drop in maize production in 2008 corresponds also with the drop in area planted to cotton for Group 2, which corresponds to the argument that access to cotton inputs is important for maize production. The areas planted and the yield of some crops such as sorghum slightly increased (Tables 10 and 11), suggesting that households may have shifted more to these coarse grains for their household needs as they cut back on maize production. An analysis of Table 10 shows that the cultivated

area of maize did not change by much between 2006 and 2008; but the cotton area dropped by more than one hectare in Groups 1 and 2. This finding suggests that even though the households in Group 1 kept growing cotton despite its price volatility, they may have diversified their production mix by introducing or increasing the share of cowpeas and peanuts to their production choice. Moreover, in 2008, the rainfall started normal but decreased toward the middle of the cropping season (Table 12), which shows that the decrease in cotton area could be more influenced by factors other than weather effect (which would have only affected area planted if the rains started late).

Table 7: Average Coarse Grain Production (kg) by Group per Household (hh) and Adult Equivalent (AE) 2006-09 Group 2006 2008 2009 Pc=160; Pc=200; Pc=170; RF=922* RF=730* RF=992* hh AE hh AE hh AE 1 7,912 544 6,776 438 7301 444 (563) (33) (550) (32) (517) (29) 2 5,869 574 5,259 462 4,915 407 (465) (42) (529) (43) (403) (32) 3 8,328 676 6,613 518 4,904 341 (1,580) (78) (1,764) (101) (795) (37) 4 3924 422 4,249 397 3,655 321 (608) (62) (828) (69) (515) (38) Source: Computation of author using the survey data. The numbers in parenthesis are the standard deviations

* Pc=Cotton price in FCFA/kg; RF=annual rainfall in mm

Table 8: Average Coarse Grain Net Sales (kg) by Group per Household (hh) and Adult Equivalent (AE), 2006-09 2006 2008 2009 Group hh AE hh AE hh AE 1 4120 272 695 45 870 59 (511) (24) (151) (6) (181) (10) 2 2770 266 847 70 642 66 (391) (29) (240) (15) (121) (10) 3 4552 387 533 54 835 78 (1057) (72) (199) (18) (124) (13) 4 2198 209 612 68 1335 118 (548) (46) (205) (20) (828) (22) Source: Computation of author using the survey data. Figures in parentheses are standard deviations.

Table 9: Average Household Production of Rice, Peanut, Cowpea, Cotton, Fonio, and Sesame (in kg) by Group, 2006-09 Group 1 2 3 4 Rice 2006 483 533 660 200 2008 550 300 220 381 2009 485 728 933 850 Peanut 2006 507 336 940 450 2008 996 629 802 346 2009 550 445 410 355 Cowpea 2006 183 191 216 125 2008 367 170 170 172 2009 651 130 107 107 Cotton 2006 2474 1760 0 0 2008 2633 1 748 2660 0 2009 2845 1222 2480 0 Cotton per AE 2006 164 172 0 0 2008 186 4 34 0 2009 173 91 152 0 Fonio 2006 50 200 0 0 2008 0 55 0 0 2009 100 0 0 0 Sesame 2006 30 250 230 50 2008 895 320 50 0 2009 327 550 0 218 Source: Computation of author using the survey data

Table 10: Average Cultivated Land (ha) by Crop, Group and Year, 2006-09

Group Rice Millet Sorghum Maize Fonio Cotton peanut cowpea Sesame 2006 1 0.8 3.3 3.5 1.5 0.5 3.3 0.8 0.6 0.3 2 0.7 2.7 3.5 1.2 0.3 2.2 0.7 0.7 0.3 3 0.8 2.8 3.8 1.2 0.0 0.0 1.9 0.5 0.5 4 0.4 2.6 3.2 1.3 0.0 0.0 0.8 0.5 0.5 2008 1 1.1 2.8 3.4 1.3 0.0 2.2 1.0 1.0 0.8 2 0.6 2.7 3.8 1.0 0.0 1.5 0.8 1.5 1.0

3 0.8 2.0 3.7 1.0 0.2 2.4 1.0 1.7 0.3 4 0.5 2.6 2.3 0.9 0.0 0.0 0.6 1.4 0.0 2009 1 1.0 3.2 3.7 1.4 0.3 2.8 0.8 1.5 0.6 2 1.1 2.4 3.0 1.0 0.0 1.9 0.7 1.4 0.7 3 1.0 2.5 3.0 1.2 0.0 2.3 0.7 1.3 0.0 4 1.0 2.4 2.3 0.8 0.0 0.0 0.6 1.2 0.6

Source: Computation of author using the survey data

Table 11: Average Yield (kg/ha) by Crop, Group and Year; 2006-09 Group 1 2 3 4 Maize 2006 1,757 1,540 1,976 926 2008 1,748 1,338 1,378 1,208 2009 1,838 1,430 1,373 1,295 Sorghum 2006 821 720 706 744 2008 837 738 708 777 2009 890 750 743 804 Millet 2006 804 780 697 678 2008 930 839 885 619 2009 917 765 893 672 Rice 2006 587 550 809 500 2008 951 985 735 762 2009 916 720 700 850 Peanut 2006 700 559 474 650 2008 1166 755 969 662 2009 757 803 867 733 Cowpea 2006 451 314 335 318 2008 425 400 450 498 2009 447 377 200 466 Cotton 2006 808 793 0 0 2008 1233 1165 1164 0 2009 1030 1125 1045 0 Source: computation of author using survey data

Table 12 Average Rainfall in Koutiala per Month in Millimeter May-October 2004-2010

Year May June July August September October November 2004/05 8.3 93.8 219.5 0.0 2005/06 35.6 117.9 175.7 296.9 108.8 26.1 0.0 2006/07 42.1 115.9 175.7 296.0 116.0 36.2 0.0 2007/08 25.5 115.0 269.9 282.5 219.3 35.5 0.0 2008/09 60.7 174.0 124.0 183.6 98.8 72.0 0.0 2009/10 79.9 100.9 225.0 159.0 250.6 83.1 0.0 Source NASA 2011

The decrease in cotton and maize cultivated area between 2006 and 2008, when the cotton price rose from 170 FCFA/kg to 200 FCFA/kg, is inconsistent with Coulibaly et al.’s

(2011) finding that farmers allocate less land to cotton and maize and increase other cash crops areas to meet the household cash needs when the cotton price falls below 200 FCFA/Kg.

Furthermore, it is contrary to the expectation that cotton production would increase when the price of cotton increased. The explanation of this larger area devoted to cotton and maize in

2006 compared to 2008 is that some farmers would grow cotton at a low output price to get access to fertilizer that will be used in the grain fields; moreover, maize is considered a cash crop even though farmers are not comfortable replacing cotton with maize because of the high volatility of its price; hence maize revenue is not as guaranteed as is cotton (Serra, 2012). The payment schedule is another factor that heavily influences the cotton acreage decision.

According to Serra, (2012), a survey conducted by the APPP Cotton Sector Project in 2009 revealed that the main reason why farmers decreased the cotton acreage in 2009 was delay in payment, not the low cotton price.

Agricultural activities remain the main sources of income in Koutiala. They represent

80% of the total household income in all rounds except for Group 2, which lowered its on-farm income to 65% of total income in 2009 (Table 13). From 2006 to 2008, the total income dropped

for all the groups except Group 4; however, in 2009, the total income of Groups 2 and 3 increased from the previous year by 19% and 20%, respectively. On-farm income represents the main source of income for all the groups, but some of the groups have seen an important change in their off-farm incomes (Tables 19 and 20). The Off-farm Income for Group 2 continuously increased, passing from about 100,000 FCFA in 2006 to 400,000 FCFA in 2009. Group 4 also increased its off-farm income by over 100,000 FCFA between 2006 and 2009. Groups 3 and 1 followed a different pattern from Groups 2 and 4; their off-farm income dropped in 2008 then went back up again in 2009 when the cotton price fell. This result suggests that the diversification among households in Groups 2 and 4 is oriented toward off-farm activities while

Groups 1 and 3 chose to earn more income by growing cotton, peanuts and cowpeas. Much of the off-farm income created between 2006 and 2009 came from three main sectors: nonagricultural wage-earning activities (Table 21), which consist of small-scale industry and handicrafts; self-employment (commerce) (Table 22), and wages earned from selling family labor on the agricultural labor market (Table 23). The increase in income earned from migration and transfer payments was also important for Group 2 in 2009 compared to the previous year.

Unlike Group 2, for which livestock revenue increased in 2008 then drastically dropped from

100,000 FCFA to 26,000 FCFA in 2009 (Table 24), the revenue from livestock continuously decreased for all the other groups. This suggests that even though the magnitude of livestock revenue is larger than that of agricultural wages, none of groups relied on livestock as a major instrument of additional source of income diversification as cotton prices became more variable.

Table 13: Total Income Share by Source On-Farm Income Off-Farm Income Total Agricultural Non Income Crop Livestock Wage Agricultural Self- %change Income Income Income Wage Employment Migration 6 Transfers7 2006 1 - 82% 6% 1% 0% 9% 1% 1% 2 - 81% 9% 0% 1% 5% 2% 2% 3 - 73% 11% 0% 0% 8% 4% 4% 4 - 78% 14% 1% 2% 3% 1% 2% 2008 1 -11% 84% 5% 0% 1% 6% 1% 3% 2 -13% 64% 12% 1% 3% 12% 2% 5% 3 -30% 81% 5% 0% 0% 9% 1% 3% 4 5% 72% 3% 1% 6% 14% 1% 3% 2009 1 -3% 83% 4% 1% 1% 6% 2% 3% 2 19% 63% 2% 1% 1% 9% 11% 12% 3 20% 76% 7% 1% 0% 10% 1% 6% 4 -8% 72% 2% 1% 5% 13% 2% 5% Source: Computation of author using the survey data

6 Migration= Share of income earned by household members who migrated for long and or short term

7 Transfers= monetary value of private and public donations received by the household

Table 14: Average Total Income (FCFA) by Group per Household (hh) and Adult Equivalent (AE) over the 2006-09 Period

Group 2006 2008 2009

hh AE hh AE hh AE Group 1 FCFA 1,599,228 107,245 1,427,577 93,150 1,383,275 92,988 (111,857) (5,481) (95,942) (5,697) (109,985) (4,683) US$ 8 3,250 218 3,018 197 2,783 187

Group 2 FCFA 1,177,165 112,117 1,024,334 81,956 1,213,572 98,468 (90,476) (7,393) (148,764) (7,083) (220,532) (6,733)

US$ 2,393 228 2,166 173 2,442 198 Group 3 FCFA 1,357,447 110,218 954,403 71,763 1,141,924 90,401 (256,986) (14,927) (222,289) (12,463) (229,320) (12,735) US$ 2,759 224 2,018 152 2,298 182

Group 4 FCFA 672,431 71,690 714,441 61,998 647,850 62,522 (91,107) (12,365) (112,147) (8,705) (79,166) (10,975)

US$ 1,367 146 1,510 131 1,304 126

Source: Computation of author using the survey data. Figures in parentheses are standard deviations.

8 exchange rate= the year average exchange rate; source: www.oanda.com

The increase in average agricultural wage earnings in Groups 2 and 4 (Table 23) over the survey period supports our hypothesis that those households who did not grow cotton all three years of the survey and those who did not grow cotton at all are affected by the cotton price volatility. When the cotton price goes up, poor farmers supply cotton growers with labor; consequently, these agricultural laborers start planting their own crops after the cotton grower, which has a negative impact on their yields and the total area they can plant (Keita and Nubukpo,

2005). Looking at our data, coarse grain area planted except for millet decreased while the agricultural wage earnings in Group 4 increased in 2008, when the cotton price increased. This suggests that poor farmers make a tradeoff between earning cash from supplying family labor to the market and starting to grow their own crops at the beginning of the planting season. Having less equipment and cash to invest in production inputs, low-income farmers may earn a higher marginal revenue from selling their labor than allocating it to crop production. An alternative explanation is that they may have cash needs that they can only meet from engaging in wage- labor.

The descriptive analysis shows that the households in Group1 have the advantage of owning more production factors in terms of family labor, agricultural equipment and land compared to the other groups; therefore, they produce on average more coarse grains than the other groups. Groups 2 and 3 have about the same endowment of factors of production, and from the tables, the households in Group 4 (those who have never produced cotton) appear to be the poorest. They have on average the largest dependency ratio and the smallest amount cultivated land, farm equipment and total income. Despite the inequality in terms of production factors and income between the groups, Group 1 seems to be as much dependent on agriculture as the other groups and also looks to have adopted a cotton-crisis coping strategy in terms of income

diversification different from the other groups’ strategies. Both the total income and on-farm income continuously decreased in Group 1 during the study years. At a low cotton price (in

2006), the households in Group 1 compensated for the lost cotton income by selling coarse grains, peanuts and cowpeas. While Group 1 stayed in cotton production, Groups 2 and 3 developed a crisis-coping strategy that allowed them to diversify their incomes using off-farm activities. Groups 2 and 3 showed a capacity to enter and exit the cotton sector. They were able to increase their incomes in 2009 after they initially dropped in 2008.The households in Group 2 and Group 4 made self-employment and agricultural wages the main sources of their income diversification. Also, in 2009, Group 2 relied heavily on earnings from migration and transfers.

They more than doubled the income earned from those sectors over the three survey years.

However, the households that stayed in cotton (Group 1) or got back into cotton after being away from it (Group 3) relied more on agricultural incomes. While the descriptive analysis helps to identify crisis-coping patterns by farmers in the cotton zone, the econometric modeling will help identify what drove those patterns.

CHAPTER 6: ECONOMETRIC ANALYSIS

In this chapter, we will use a Multinomial Probit Model (MNP) to examine the factors influencing the decision of farm households to cultivate cotton. Also called the Multivariate

Probit Model, MNP is used to estimate jointly several correlated binary outcomes. It is the most popular of multivariate models with limited dependent variables because it is less restrictive; it relaxes the Independence of the Irrelevant Alternative (IIA)9 assumption that the Multivariate

Logit Model makes. However, one disadvantage of the MNP model is the difficulty of computing the multidimensional normal integral, which represents the choice probability when the number of outcomes is greater than or equal to three. The choice probability Pn(i) associated with the alternative I, i Є Cn, chosen by individual n can be computed as an mn- dimensional integral of the form:

∞ ∞ …. ∞ nγn, φin dγn

where nγn, φin is a multivariate normal density with mean zero and φin is a covariance

matrix. To compute Pn (i) using a numerical integration technique, mn must be small. In recent years, many simulation techniques such as the GHK Simulator have been proven to easily compute the choice probability (Bolduc, 2004).

This model is relevant to this thesis because it allows us to determine what factors differentiate the groups of producers during the three years. The coefficients of the independent variables of the model will tell us how a marginal change in a given independent variable will

9 The IIA assumption states that the odds of preferring one class over another do not depend on the presence or absence of other "irrelevant" alternatives, Source : Wikipedia

change the probability of a household’s being in group X rather than group Y, Y being the base group. This type of coefficient, for example, will help us determine how sensitive each group of farmers is to a 1 unit increase in the relative price of cotton to maize. To run the model, we will set Group 1 as the pivot group; this choice is arbitrary. The independent variables are:

• Labor: measured by the number of household members between the age of 15 and 60,

considered to be the working-age adults.

• Land assets: total land owned.

• Agricultural equipment assets: measured by the equipment index.10

• Wealth: measured by computing the monetary value of the durable goods owned by the

household.

• Dependency Ratio: measured by the household’s ratio of workers to non-workers.

Workers are the adults between the age of 15 and 60 and non-workers are the children

(below age 15) and the elderly (over age 60).

• Cotton price relative to maize price: cotton price divided by maize price. The maize price

we used was estimated using the village median producers’ maize price of each year.

The cotton price is the panterritorial price announced each year by the CMDT.

• Year dummy.

• Access to market dummy: 1 if the village has easy access to a major market and 0

otherwise.

• Land pressure dummy: 1 if the village has high land pressure and 0 otherwise.

Equipment Index=EQih = Sum of (1-Pi) with Pi = ni / n and where EQih = 1 if the household owns the equipment i, Pi = the probability of this farm equipment i ni = number of households that have this equipment i, n = total number of households. In Koutiala, the following materiel were used to compute agricultural equipment index: set of animal traction equipment and oxen, tractor, or a motor traction equipment and a seeder.

• Social Capital variable: measured by the number of farmers’ organizations the household

was a member of at the beginning of the survey in 2006.

We used 2006 data for all the years to avoid the problem of endogeneity for the following

variables: labor, land owned, dependency ratio, equipment index, social capital and durable

goods.

We will use the coefficients of cotton/maize price to test our hypothesis:

1 Relative cotton/maize prices are positively correlated with the likelihood to grow cotton.

The model is presented as follows:

Yit = Xit β +Ziα + D2008 t γ1 + D2009 t γ 2 + uit

Y = is the outcome; it represents the choice probability of a household to be in one of the groups

Xit represents the dependent variables that vary over time and across households. The variables in Xit are: Cotton price relative to maize price, year dummy, land pressure dummy and access to market dummy.

Zi includes the dependent variables that do not vary over time but do vary across households.

The variables in Z i are: labor, land owned, dependency ratio, equipment index, social capital and durable goods. i= individual household t = time period (year of survey)

β, α and γ= coefficient u= error term

After running the model using STATA, we have the different groups defined as outcomes. We will focus on the signs of the variables’ coefficients in each outcome because

unlike a linear regression or a logit regression, the interpretation of the coefficient of

Multinomial Probit regression is not straightforward. In a Multinomial Probit Model, the change in the probability resulting from a one unit increase of a given predictor depends not only on the starting point of the predictor, but also on the other predictors in the model. To determine the marginal effects of the independent variables, we run a MNP post-estimate command called mfx.

6.1 Results Table 15:Multinmial Probit Model Output Using The Study Data group Coef. Std. Err. z P>z [95%Conf. Interval] (base outcome 1) Outcome 2 Labor Owned -.1067700 . 0441865 -2.42 0.016 -0.19337 -0.02017 Land Owned .0134862 .0182074 0.74 0.459 -0.02220 0.04917 Equipment index -.1630420 .3089135 -0.53 0.598 -0.76850 0.44241 Dependency ratio -.2335840 .1821709 -1.28 0.200 -0.59063 0.12346 OP Membership -.3072790 .1027743 -2.99 0.003 -0.50871 -0.10585 Durable Goods -9.09e-07 3.45e07 -2.64 0.008 -1.58E06 -2.33E-07 Cotton/Maize Price .7081828 .7227284 0.98 0.327 -0.70834 2.12470 Year 2008 Dummy .1338778 .2794584 0.48 0.632 -0.41385 0.68160 Year 2009 Dummy .2534419 .3519771 0.72 0.471 -0.43642 0.94330 Market Dummy .8413578 .2388231 3.52 0.000 0.37327 1.30944 Land Pressure Dummy -.7054519 .222989 -3.16 0.002 -1.14250 -0.26840 Outcome 3 Labor Owned -.1414277 .0677804 -2.09 0.037 -0.27427 -0.00858 Land Owned .0052924 .0249516 0.21 0.832 -0.04361 0.05419 Equipment index -.0447345 .4142093 -0.11 0.914 -0.85657 0.76710 Dependency ratio -.0060144 .2349347 -0.03 0.980 -0.46648 0.45444 OP Membership -.0111900 .1234742 -0.09 0.928 -0.25319 0.23081 Durable Goods 3.50e-07 2.95e-07 1.19 0.235 -2.28E07 9.28E-07 Cotton/Maize Price -.3914105 1.0009740 -0.39 0.696 -2.35328 1.57046 Year 2008 Dummy -.0884121 .3822764 -0.23 0.817 -0.83766 0.66083 Year 2009 Dummy -.1344674 .4705899 -0.29 0.775 -1.05681 0.78787 Market Dummy -.0721916 .3348014 -0.22 0.829 -0.72839 0.58400 Land Pressure Dummy -.8453456 .2918270 -2.90 0.004 -1.41732 -0.27338 Outcome4 Labor Owned -.0284073 .0650459 -0.44 0.662 -0.15589 0.09908 Land Owned .0369643 .0233889 1.58 0.114 -0.00888 0.08280 Equipment index -2.7109070 .5549237 -4.89 0.000 -3.79854 -1.62328 Dependency ratio .3793326 .2364531 1.60 0.109 -0.08411 0.84277 OP Membership -.9723056 .2347073 -4.14 0.000 -1.43232 -0.51229 Durable Goods -6.63e-07 6.70e-07 -0.99 0.322 -1.98E06 6.50E-07 Cotton/Maize Price -.9133536 1.0665830 -0.86 0.392 -3.00382 1.17711 Year 2008 Dummy -.2206970 .4132010 -0.53 0.593 -1.03056 0.58916 Year 2009 Dummy -.3320800 .5060821 -0.66 0.512 -1.32398 0.65982 Market Dummy 1.2658270 .3397467 3.73 0.000 0.59993 1.93171 Land Pressure Dummy .4012036 .3176436 1.26 0.207 -0.22137 1.02377

Table 16: Marginal Effects of Variables by Group 11 2-4 variable dy/dx Std. Err. z P>z [ 95% C.I. ] X Outcome 2 Labor Owned -0.02373 0.01219 -1.95 0.052 -0.04763 0.00017 7.27083 Land Owned 0.0027807 0.00495 0.56 0.574 -0.00692 0.012484 13.4294 Equipment index 0.0246827 0.08628 0.29 0.775 -0.14443 0.193794 0.678168 Dependency ratio -0.079352 0.04835 -1.64 0.101 -0.17412 0.015413 2.28187 OP Membership -0.065674 0.02904 -2.26 0.024 -0.12259 -0.00876 2.04861 Durable Goods -2.72E-07 0 -2.84 0.005 -4.60E07 -8.40E08 370216 Cotton/Maize Price 0.2558132 0.19237 1.33 0.184 -0.12123 0.632851 1.88966 Year 2008 Dummy 0.0500879 0.07564 0.66 0.508 -0.09817 0.198348 0.333333 Year 2009 Dummy 0.0907311 0.09507 0.95 0.34 -0.0956 0.277059 0.333333 Market Dummy 0.2151439 0.06216 3.46 0.001 0.093312 0.336976 0.486111 Land Pressure Dummy -0.174231 0.05907 -2.95 0.003 -0.29 -0.05846 0.5 Outcome3 Labor Owned -0.010788 0.0074 -1.46 0.145 -0.02529 0.003709 7.27083 Land Owned -0.00039 0.00277 -0.14 0.888 -0.00583 0.005048 13.4294 Equipment index 0.0257155 0.04551 0.57 0.572 -0.06349 0.114919 0.678168 Dependency ratio 0.0083752 0.02482 0.34 0.736 -0.04027 0.057018 2.28187 OP Membership 0.0228109 0.01379 1.65 0.098 -0.00422 0.049841 2.04861 Durable Goods 9.42E-08 0 2.55 0.011 2.20E-08 1.70E-07 370216 Cotton/Maize Price -0.07546 0.10836 -0.7 0.486 -0.28784 0.136924 1.88966 Year 2008 Dummy -0.01534 0.03957 -0.39 0.698 -0.0929 0.062222 0.333333 Year 2009 Dummy -0.025629 0.04677 -0.55 0.584 -0.11731 0.066047 0.333333 Market Dummy -0.061441 0.03396 -1.81 0.07 -0.12799 0.005111 0.486111 Land Pressure Dummy -0.064809 0.03169 -2.04 0.041 -0.12693 -0.00269 0.5 Outcome4 Labor Owned 0.0021832 0.00383 0.57 0.568 -0.00532 0.009686 7.27083 Land Owned 0.0019248 0.00137 1.41 0.159 -0.00076 0.004605 13.4294 Equipment index -0.165642 0.05308 -3.12 0.002 -0.26967 -0.06161 0.678168 Dependency ratio 0.0299961 0.01418 2.12 0.034 0.002204 0.057789 2.28187 OP Membership -0.052932 0.01521 -3.48 0 -0.08274 -0.02313 2.04861 Durable Goods -2.07E-08 0 -0.51 0.608 -1.00E07 5.90E-08 370216 Cotton/Maize Price -0.072683 0.06163 -1.18 0.238 -0.19347 0.048107 1.88966 Year 2008 Dummy -0.015695 0.02109 -0.74 0.457 -0.05702 0.025633 0.333333 Year 2009 Dummy -0.02414 0.02444 -0.99 0.323 -0.07204 0.023759 0.333333 Market Dummy 0.0600069 0.0266 2.26 0.024 0.007878 0.112136 0.486111 Land Pressure Dummy 0.0512926 0.02191 2.34 0.019 0.008351 0.094234 0.5

11 Outcome= Group

In Table 15 showing the multinomial results, the coefficient of labor is statistically significant in Groups 2 and 3 at the .05 level and has a negative sign, meaning that as the amount of labor increases, the households would more likely be in Group 1 rather than Groups 2 or 3.

This result likely reflects the high labor requirements for growing cotton. Households in Groups

2 and 3 do not appear to be different from the ones in Group 1 with respect to land and agricultural equipment ownership because even though the latter variables’ coefficients have the signs we expected (positive for land and negative for equipment), they are not statistically different from zero. Similarly, the coefficient for land, while positive for Group 4, is also not statistically different from zero. A one unit increase in the dependency ratio is associated with an

8% decrease in the probability of a household falling into Group 2 relative to Group 1. This difference between the dependency ratios ofGroups 1 and 2 is statistically significant at the 10% level and that between Groups 1 and 4 is significant at the 5% level. The more OPs (Farmers’

Organizations) a household was initially a member of in 2006, the more likely it is to be in

Group 1 rather than in Groups 2 or 4. However, the coefficient of OP membership was positive and significant for Group 3. The heavier involvement of Group 3 farm households in OP may have helped them enter cotton production during the time of the survey when others (e.g., in

Group 2) were leaving cotton production. The social capital variable’s coefficient is statistically different from zero for Groups 2 and 4 at the 5% level and at the 10% level for Group 3.

Looking at the households’ wealth, the coefficient of the durable goods variable is negative for Groups 2 and 4 and positive for Group 3; yet it is statistically significant only for

Groups 2 and 3. Like membership in OPs, greater wealth may have facilitated Group 3 households entering the cotton sector. The estimated results also show that if the price of cotton relative to maize increases, the probability of households falling into Group 1 increases relative

to the rest of the groups; however, the cotton/maize variable is statistically significant only for

Group 2 at the 10% level.

The probability of a household being in Group 2 rather than Group 1 increases if the household has easy access to the market. The same thing goes for Group 4, but if the access to market is easy, the probability of a household being in Group 3 instead of Group 1 decreases.

The coefficient of market access is statistically different from zero for all the groups. This suggests that market access facilitates doing something other than cotton, and the decision to enter cotton production during the survey period (Group 3 households) may have been influenced by the lack of easy access to other market alternatives.

The land pressure variable has a positive sign for Group 4 and negative for Groups 2 and 3. It is statistically significant at the 5% level for all the Groups (see Table 16).

6.2. Discussion

The above findings show that each group of farmers responded differently to a one unit increase of each variable used in the model. It shows how different or similar these groups of households are, which in turn helps us understand their cotton-crisis coping strategies. We learn from the model that households in Group 2 (those who grew cotton in the first year and then abandoned it in either year 2 or 3) have about as much agricultural equipment and land as do the households in Group 1 (those who continuously grew cotton throughout all the three years of the survey). They also have a low dependency ratio, belong to fewer OPs and have a lower endowment of durable goods compared to the farmers in Group 1. However, they also have less labor than Group 1. Moreover, the positive (although not statistically significant) sign of the coefficient of the relative cotton/maize price variable suggests that the reason why the farmers in

Group 2 dropped out of cotton sector could be due to lack of labor rather than low cotton price.

We notice that the access to market is easy for these households, which favors the diversification toward non-farm activities. The proximity to a major market provides them with opportunities to be engaged in economic activities such as commerce; it also helps them sell their agricultural commodities at a low transport cost since they do not have to travel a long distance to the market. Having low land pressure is another factor that encourages diversification by the households in Group 2. As more land becomes available, the household can cultivate multiple crops at a larger scale.

Although all the other variables have the signs we would expect them to have, we cannot state with confidence that the households in Group 3 (those who did not grow cotton in year 1 and started its production after year 1) are different from the ones in Group 1 because only the coefficients of access to market, land pressure, and OP membership are statistically significant at the 5%, 10% and 10% levels, respectively. Yet, we must take into consideration that the sample size in Group 3 was small, 13 households, which may be the reason why we did not see more statistically significant differences between the farmers in Groups 3 and 1.

Despite the fact that the sample size was also small in Group 4 (24 households), we can say that the households in Group 4 are the poorest in this sample. They have least agricultural equipment, smallest amount of family labor, highest dependency ratio and are members of fewest OPs. The regression results suggest that the households in Group 4 (those who never grew cotton during the three rounds) have fewer durable goods and face high land pressure, yet the coefficient of the durable goods variable is not statistically different from zero. We can argue that having the lowest level of agricultural equipment level, farm households in Group 4 require not only more agricultural equipment to improve their production and productivity, but also more

cash to be able hire non-farm workers since they have the highest dependency ratio and the lowest amount of cultivated land compared to the rest of the groups.

Cotton growers (Groups 1 and 3) are more likely to own more durable goods. For a farmer to enter the cotton sector, he must have some wealth to start with, which he will use to buy inputs and equipment because cotton is very input-intensive. Social capital also plays an important role in farmers’ income diversification. Farmers who are oriented toward on-farm diversification are more likely to be part of many OPs. The majority of farmers responded to the interviewers that the reason they joined the OP was to be able to get access to inputs and credit and to sell their harvested crops.

Beside on-farm factors, the access to market plays a crucial role in farmers’ income diversification. Those who stopped cotton production, Group 2, had the advantage of being close to a market, which makes it easier to practice commerce and sell their labor. It also makes it easier to transport their crops to the market.

Finally, land pressure is also an important determinant of income diversification. As the pressure on land increases, farmers will face the land allocation constraint, which will push them to grow the crop that has a more guaranteed revenue instead of diversifying their production choice.

CHAPTER 7: CONCLUSIONS

The cotton sector generates 85 to 123 billion FCFA (188 to 270 million USD) of annual gross revenues and represents the source of income for 3.7 million people in Mali

(Samaké et al, 2007). Cotton production in Mali and many other West African countries significantly decreased from 2006 through 2010 due to the volatility in world price, accumulated debts caused by input subsidies, low credit repayment rates from farmers, and corruption and poor management of the financial resources of parastatal cotton companies. Much research has been done to evaluate the impact of cotton price volatility on farmers’ revenue and the overall economy. However, the question that was left unanswered was how the effects of cotton price volatility (and more generally, those of uncertainty in the cotton sector) vary across groups of cotton producers with different agricultural characteristics . Understanding how different classes of farmers respond to such volatility and general uncertainty in the cotton sector will be critical in determining what key role cotton can play as part of Mali’s poverty reduction strategy in rural areas in the future.

This thesis’s objective is to respond to that unanswered question by analyzing the cotton crisis coping strategy developed by different groups of farmers to adapt to changing cotton prices and uncertainty in the sector in the Koutiala zone using data from a three-year panel collected by

MSU/IER/CIRAD. To do so, we divided the farmers interviewed during the survey into four groups: those who continuously grew cotton throughout all the three years of the survey, those who grew cotton in the first year and then abandoned it in either year 2 or 3, those who did not grow cotton in year 1 and started its production after year 1, and those who never grew cotton during the three rounds. The findings show that the cotton grower farmers are more likely to have access to more family labor and agricultural equipment to perform cotton production tasks.

They also are members of more OPs and have a high level of durable goods (a measure of the farm household wealth), which helps them enter into cotton production (Group 3) or stay in it

(Group 1). Being a member of an OP and the ownership of durable goods also provides farmers with opportunities to engage in more farm and non-farm activities in order to diversify their income.

We also found that cotton growers are more likely to be confronted by high land pressure. As land pressure increases, farmers are better off applying the scarce resource to the crop that has the most guaranteed revenue, which in Koutiala is cotton. The descriptive analysis showed that those farmers in Group 1 diversify their income by producing more peanuts and cowpeas in response to low cotton price.

While farmers in Group 1 diversify within the agricultural production domain, farmers in

Group 2 oriented themselves towards non-farm activities after they stopped producing cotton.

The farm households in Group 2 have on average less family labor and a lower value of durable goods compared to households in Group 1; however, Group 2 households also have a lower dependency ratio, less pressure on land and have and are located closer to major markets in the region, which promotes non-farm activities such as commerce and handicrafts. They also more heavily relied on earnings from migration and transfers over the survey period than did the other groups. We were unable to demonstrate statistical differences in key characteristics of farmers in

Group 3 and those in Group 1. As noted earlier, only OP membership and durable goods coefficients for Group 3 are significant at the 10% level. Indeed, we would expect them to have some similarities in terms of the production factors endowment because households in Group 3 need to have developed about the same agricultural characteristics as the farmers in Group 1 in order for them to enter cotton production after year 1.

Moreover, we showed that the households in Group 4, which never grew cotton, are on average poorer than other groups in the sample. They have less labor, less production equipment, high dependency ratio and small durable goods values compared to the rest of the farmers.

Finally, we showed that the cotton price relative to the price of maize which we used as an alternative cash crop in our study was not the main factor affecting whether farmers decided to produce cotton. In order to create incentive toward cotton production, farmers need to have more equipment and an easier access to market. The proximity to a major market can also help farmers diversify their income by engaging in non-farm activities. We also showed that the more money farmers can get from off-farm activities, the less likely they are to grow cotton in all years.. The cotton-crisis coping strategy developed by Group 2 showed that endowing farmers with the resource needed and by creating a business environment that promotes self- employment, commerce, handicrafts, cotton producers could use those activities to cope with the uncertainty in the cotton sector.

APPENDIX

Table 17: Total Number of Households per Group and Village Number of households per Group

land market Village pressure access Group1 Group2 Group3 Group4 Nampalla average difficult 11 7 5 2 Tonon average difficult 6 11 4 3 Kaniko high easy 14 5 0 6 Try 1 high easy 3 10 1 10 Signe average easy 7 15 1 2

Gantiesso high difficult 19 5 2 1 Source: Computation of author using the survey data. Figures in parentheses are standard deviations.

Table 18: STATA Significance T- Test Output

Group 1 and 2 Land diff = mean(1) - mean(2) = 3.481061 t = 4.0075 Ho: diff = 0 degrees of freedom = 337 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 1.0000 Pr(T > t) = 0.0001 Pr(T > t) = 0.0000 Dependency ratio diff = mean(1) - mean(2) = -.0064786 t = -0.1024 Ho: diff = 0 degrees of freedom = 337 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.4593 Pr(T > t) = 0.9185 Pr(T > t) = 0.5407 Equipment Index diff = mean(1) - mean(2) = .1756493 t = 3.9553 Ho: diff = 0 degrees of freedom = 337 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 1.0000 Pr(T > t) = 0.0001 Pr(T > t) = 0.0000 Durable Good diff = mean(1) - mean(2) = 292885.6 t = 6.4121 Ho: diff = 0 degrees of freedom = 337 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 1.0000 Pr(T > t) = 0.0000 Pr(T > t) = 0.0000 Labor diff = mean(1) - mean(2) = 2.545912 t = 6.1139 Ho: diff = 0 degrees of freedom = 337 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 1.0000 Pr(T > t) = 0.0000 Pr(T > t) = 0.0000

Table 18 (cont’d) group 1 and 3 Land diff = mean(1) - mean(3) = 2.790393 t = 1.6925 Ho: diff = 0 degrees of freedom = 217 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.9540 Pr(T > t) = 0.0920 Pr(T > t) = 0.0460 Dependency Ratio diff = mean(1) - mean(3)= -.2723335 t = -3.0082 Ho: diff = 0 degrees of freedom = 217 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.0015 Pr(T > t) = 0.0029 Pr(T > t) = 0.9985 Equipment Index diff = mean(1) - mean(3) = .0761012 t = 1.0097 Ho: diff = 0 degrees of freedom = 217 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.8431 Pr(T > t) = 0.3138 Pr(T > t) = 0.1569 Durable Good diff = mean(1) - mean(3) = -17148.65 t = -0.1652 Ho: diff = 0 degrees of freedom = 217 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.4345 Pr(T > t) = 0.8690 Pr(T > t) = 0.5655 Labor diff = mean(1) - mean(3)= 2.558974 t = 3.3078 Ho: diff = 0 degrees of freedom = 217 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.9994 Pr(T > t) = 0.0011 Pr(T > t) = 0.0006

Table 18 (cont’d) group 1 and 4 Land diff = mean(1) - mean(4) = 2.478556 t = 1.6568 Ho: diff = 0 degrees of freedom = 250 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.9506 Pr(T > t) = 0.0988 Pr(T > t) = 0.0494 Dependency Ratio diff = mean(1) - mean(4) = -.4221254 t = -4.0998 Ho: diff = 0 degrees of freedom = 250 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.0000 Pr(T > t) = 0.0001 Pr(T > t) = 1.0000 Equipment Index diff = mean(1) - mean(4)= .4923887 t = 9.1669 Ho: diff = 0 degrees of freedom = 250 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 1.0000 Pr(T > t) = 0.0000 Pr(T > t) = 0.0000 Durable Good diff = mean(1) - mean(4) = 374247 t = 5.5399 Ho: diff = 0 degrees of freedom = 241 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 1.0000 Pr(T > t) = 0.0000 Pr(T > t) = 0.0000 Labor diff = mean(1) - mean(4) = 3.783333 t = 6.6558 Ho: diff = 0 degrees of freedom = 250 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 1.0000 Pr(T > t) = 0.0000 Pr(T > t) = 0.0000 Source: Computation of author using the survey data. Figures in parentheses are standard deviations.

Table 19: Average Off-Farm Income by Group per Household (hh) and Adult Equivalent (AE) over the 2006-09 Period

Group 2006 2008 2009

hh AE hh AE Hh AE 1 195,313 11,702 162,349 10,286 182,435 10,271 (57,010) (2,917) (22,742) (1,478) (21,965) (2,584) 2 118,394 10,779 241,046 17,823 418,144 9,568 (21,952) (2,007) (42,005) (2,436) (197,781) (1,829) 3 209,139 17,651 129,751 8,554 197,583 14,794 (78,189) (7,175) (37,129) (2,388) (55,359) (6,187) 4 50,764 6,166 180,196 16,765 166,379 4,987 (16,467) (2,218) (41,239) (3,886) (38,712) (1,621) Source: Computation of author using the survey data. Figures in parentheses are standard deviations.

Table 20: Average On-Farm Income by Group per Household (hh) and Adult Equivalent (AE) over the 2006-09 period 2006 2008 2009 Group HH AE HH AE HH AE 1,403,915 95,543 1,265,228 82,863 1,200,839 82,717 1 (88,586) (4,593) (87,152) (5,467) (102,452) (3,859) 1,045,756 101,338 783,288 64,133 795,428 88,900 2 (81,736) (6,990) (115,752) (6,259) (86,368) (6,264) 1,148,309 92,568 824,653 63,208 944,342 75,606 3 (224,099) (11,061) (206,193) (12,270) (211,193) (9,351) 576,209 65,524 493,634 45,233 481,471 57,535 4 (96,534) (12,682) (97,957) (7,554) (59,001) (11,239) Source: Computation of author using the survey data. Figures in parentheses are standard deviations.

Table 21: Average Non Agricultural Wage Earning (in FCFA) by Group per Household (hh) and Adult Equivalent (AE) over the 2006-09 Period

Group 2006 2008 2009 Hh AE Hh AE hh AE 1 3,967 236 19,725 1320 19,458 1237 (2,817) (172) (7,557) (542) (7,178) (472) 2 14,615 1,128 29,096 1875 15,481 795 (8,564) (644) (11,196) (664) (8,811) (430) 3 0 0 1,538 71 0 0 0 0 (1538) (71) 0 0 4 10,909 1,506 43,045 3687 33,636 2,293 (10,909) (1,506) (32,689) (2671) (31,784) (2,133) Source: Computation of author using the survey data. Figures in parentheses are standard deviations.

Table 22 : Average Revenue from Self Employment (in FCFA) by Group per Household (hh) and Adult Equivalent (AE) over the 2006-09 Period Group 2006 2008 2009 HH AE HH AE HH AE 143,628 8,235 82,054 5,293 81,139 5,280 1 (49,396) (2,572) (14,985) (975) (13,965) (962) 54,024 4,935 127,887 8,155 116,251 7,274 2 (13,469) (1,319) (34,749) (1,599) (40,164) (1,813) 110,600 8,918 85,563 5,704 111,639 6,730 3 (63,261) (5,157) (34,170) (2,415) (36,716) (2,291) 15,918 1,874 99,528 9,415 86,350 8,224 4 (9,838) (888) (24,618) (2,491) (25,724) (3,238) Source: Computation of author using the survey data. Figures in parentheses are standard deviations.

Table 23: Average Agricultural Wage Earnings by Group per Household (hh) and Adult Equivalent (AE) over 2006-09 Period Group 2006 2008 2009 hh AE Hh AE hh AE 1 9,238 761 5,742 381 7,200 494 (3,011) (250) (2,314) (153) (2,210) (159) 2 5,019 566 10,241 1002 14,061 1,209 (2,482) (261) (3,054) (252) (6,224) (372) 3 0 0 2,154 127 8,231 613 0 0 (1,609) (104) (7,818) (557) 4 3,580 425 5,143 477 6,434 1,080 (1,503) (148) (3,627) (219) (1,792) (410) Source: Computation of author using the survey data. Figures in parentheses are standard deviations.

Table 24: Average Livestock Revenue (in FCFA) by Group per Household (hh) and Adult Equivalent (AE) over the 2006-09 Period Group 2006 2008 2009 hh AE Hh AE hh AE 1 90,470 6,472 64,443 4,136 54,377 2,376 (17,533) (1482) (16,796) (1,112) (25,734) (1,102) 2 104,261 9,270 128,578 8,551 26,288 1,724 (30,432) (2,325) (67,923) (3,086) (10,294) (667) 3 155,333 11,608 49,276 3,900 75,398 5,249 (56,487) (4,462) (22,608) (1,729) (38,840) (2,724) 4 93,968 12,544 22,640 2,112 15,499 2,096 (69,194) (10,029) (22,391) (2,216) (13,644) (1,624) Source: Computation of author using the survey data. Figures in parentheses are standard deviations.

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