Mathews Madola University of Greenwich Natural Resources Institute

Outline Background and Motivation Problem Statement Key Research Questions Conceptual Framework (Institutional Analysis) Methodology and Data Collection Preliminary Results Emerging Conclusions

Background There is a growing trend towards the promotion of farmer organisations as a poverty reduction strategy to improve smallholders access to inputs, extension services and output markets. It is therefore important understand benefits and effects of this institutional arrangement. Most studies have concentrated on the effects of contract farming

Problem Statement Farmers no longer assured of ready markets for their products. Face volatile market prices in place of a previous system of stable markets (state-guaranteed prices) Decreased access to credit and inputs. Negatively affecting output and productivity

Research Questions The Key research questions are: What are the determinants of participation in farmer organisations? What is the impact of participation on household income (performance ) ?

Conceptual Framework and Institutional Analysis We develop the conceptual framework following Williamson (1991) and link it to an institutional analysis to identify the factors determining the current organisational form of production and marketing in the cotton sub-sector in Malawi. The analytical model follows the ones used in applied work in transaction costs (e.g. Doward, 2001) The likelihood of observing a particular market institution is a function of certain properties of the underlying transaction Can be expressed as Y=Ω[X], where Y is a vector of alternative marketing arrangements i.e. spot marketing, contract marketing and collective marketing (farmer organisations)

Conceptual Framework (cont’d) X is a vector of transaction characteristics that affect transaction costs i.e. asset specificity, uncertainty, complexity and frequency of transactions. The level of X is influenced by production, marketing characteristics, and the economic and political environment. Framework the used to explain how these factors affect transaction costs and choice of organisational form

Transaction Costs and Marketing Institutional Arrangements FactorEffect on Transaction Costs Type of Marketing Institutional Arrangement Most Favoured Spot MarketingContract Marketing Collective Marketing Production Characteristics Economies of scale High returns to inputs Requires high initial investment and high cash flow & not feasible for smallholders Requires effective research and extension and timely availability of inputs XXXX XXXX Marketing/Processing characteristics High economies of scale in processing High Quality standards Many potential buyers The need for complimentarily creates a strong incentive for a stable supply of raw materials through more coordinated arrangements Increases returns to close vertical coordination Increases costs and risk of default (side selling) X XXXX XXXX Endogenous economic & political factors Poorly integrated output markets Missing input/factor markets Poor communication Low literacy levels/education levels among farmers Weak contract enforcement Increases the costs of procurement and marketing. Increases returns to coordination. Increases returns to vertical coordination Raises the costs of vertical coordination Raises the costs of ensuring the adoption of new technologies and raises the costs of collective action Increases uncertainty and increases risk of default XXXXXX XXXX XXXXXXXX

Methodology & Data Collection Used a structured questionnaire Interviewed smallholder farmers growing cotton 170 respondents (83 participants and 87 non participants). Also did some key informant interviews

Distribution of the Sample DistrictEPA/ChaptersMACSVillagesHouseholds Balaka Bazale 3340 Utale 2335 Ulongwe 3340 Ntcheu Manjawira -330 Bilira -325

Methodology : Determinants of Participation We will estimate the following probit model : m PART i = φ 0 + ∑φ j x j +e 2 j=1 x j is a vector of exogenous variables assumed to influence the participation decision; φ j s represents estimated marginal effects of the determinants of participation; PART is a dummy variable that takes a value of one or zero

Impact of Participation on Incomes (Performance) We will use propensity score matching methods applied in programme evaluation. Use four matching methods to enhance the robustness of our comparisons Nearest Neighbor Radius Kernel Stratification matching Our aim is to determine whether participating households have significantly higher crop and household incomes than non-participants.

Endogenous Switching Regression We also estimate the endogenous switching regression model to take into account selection bias We use this model to examine how farmers characteristics affect their decisions to participate in a farmer organisations and their income (performance) with or without the farmer organisation. We will also compare farmers expected performance (income) with the farmer organisation and without the farmer organisation The following model describes farmers’ choices about participating in a farmer organisations and their performance with and without a farmer organisation

Impact of Participation (cont’d) If δZ i + u t > 0, farmer i chooses to join a farmer organisation, described by I i =1 (A) If δZ i + u t ≤ 0, farmer i chooses not to join a farmer organisation, described by I i =0 Then income (performance) equations associated with each alternative can be expressed as Participants :ln y 1i = x 1 ß 1 + e t0 if j = 1 (B) Non-participants:ln y 0i = x 0 ß 0 + e t1 if j = 0 (C) Z i is a vector of farmers characteristics that affect decisions to participate in farmer organisation ln y 01 and ln y 0i are natural logs of income (performance) for participants and non- participants, respectively; δ, ß’s are unknown parameters Assume that u t, e t0 and e t1 are three random terms that follow a trivariate normal distribution The methodology involves estimating system of equations (A), (B) and (C) equations by FIML as suggested by (Loskin and Sajaia, 2004)

Results-Comparison of Means Selected VariableType of Farmer ParticipantsNon-Participants Demographic Variables Female Headed households (%) Education Household Head Age of the Household Head Household Size Dependency Ratio Farm Assets Total Area (Acres) Value of Manual tools (MK) Use of Hired Labour Permanent Labour (% using) Casual labour (% using) Income Diversification (%) Livestock Ownership Grows other cash crops Small business Household Income (MK) Net Household Income Net Agricultural Incomes Net Cotton Incomes Business Income Livestock Income Social Capital Past Group Experience (1) Friends in a FO (1) * , * * ,874.40** 35,608.72*** 26,665.12** 3, ,966.27*** 0.928*** 0.940*** * , * * ,935.28** 25,875.52*** 18,361.71** 4, *** Number of Observations 8287

Initial Results from Probit Model The availability of alternative sources of income reduces the likelihood of participation. Other cash crops income that are more lucrative and business income are negatively related to the likelihood of participation although the business income is not statistically significant Household size is positively related to participation in a farmer organisation Households with more educated household heads are less likely to participate in farmer organisations. The ownership of assets value of agricultural tools is positively related to probability of participation while land is negatively related to participation. Having a friend who is a member of a farmer organisation (social capital), the more likely the household will participate.

Emerging Conclusions Initial analysis indicates that FO leads to higher income (performance of participants). Other benefits of participation include reliable markets, stable prices and reduced uncertainty The social capital in farmer organisations has reduced the problems of writing, enforcing contracts prevalent in Malawi (culture of wilful default)

Thank you for your attention!