Abstract
Access to agricultural credit contributes to rural development by allowing farmers to carry out profit-maximising investments that increase productivity and income, underlining the importance of exploring ways to increase access to this resource. This paper analyses the role of Rural Producer Organisations (RPOs) in easing access to formal agricultural credit. We build an original dataset comprising 15,000 municipality-year observations of RPO creation and credit allocation in Colombia to estimate a fixed effects model. We show that when the number of RPOs increases in a municipality, aggregate access to credit increases. This positive relation also holds at the individual level, with RPO membership increasing both the likelihood of a farmer requesting credit and of receiving the requested credit. We discuss demand and supply-side mechanisms that plausibly explain these results, and we further show that the relation between RPOs and access to credit is heterogeneous according to the source of credit (public vs. private bank) and the type of farmer to whom it is allocated (low-wealth, mid-wealth or high-wealth farmers). Our results point to the potential of RPOs to improve access not only to input and output markets but also to financial markets.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
Datasets are available upon request.
Acknowledgements
We are thankful to Maria del Pilar Lopez, Janne Tukianen, Joachim Wehner, and Jose Antonio Ocampo for their comments. We thank the participants of the Stanford-LSE-Uniandes conference on long-range development in Latin America, the workshop on development in Latin America and Africa—South Africa, as well as institutions in Colombia including Universidad ICESI, CEARN, RIMISP, and FINAGRO.
Notes
2 The existence of aggregate effects also implies that the total amount of (real) resources allocated would increase, as we show was the case. See .
3 Through qualitative research we conducted in parallel to writing this paper, we identified different demand and supply side mechanisms explaining the relation between RPOs and access to agricultural credit. The mechanisms and the qualitative methodology employed are discussed in detail in a working paper still not published. In the present paper, we summarise the mechanisms as part of the conceptual framework.
4 In related fieldwork, we found more than one case in which RPO members mentioned other members having lent them money after family emergencies and other shocks, demonstrating that RPO communities can act as safety nets for RPO members.
5 Commercial banks in Colombia are forced by law to invest a fixed share of their checking and savings accounts in TDAs (Agricultural Development Titles). These resources are managed by Finagro, a second-level bank, and are transferred to the public bank to finance its own credit allocations. Commercial banks have the alternative of granting agricultural credit directly, which substitute for the forced investment requirements.
6 Regulations require that agricultural credit be used for productive purposes, not consumption or debt repayment.
7 Formally created in the sense that their creation was registered with a Chamber of Commerce. It is impossible to estimate how many non-registered organisations there are, although this number is probably small, considering that organisations need to be registered to access public support, sign contracts with buyers, or access credit. Furthermore, the registry process is not costly, requiring only to fill out forms and establish their own statutes.
8 There can be underreporting of cancellations. However, RPOs are legally required (by Decree 019 of 2012) to update their registry annually, and thus we have information on the last update date, allowing us to identify whether RPOs are active.
9 Census data does not allow classifying farmers based on the value of their assets.
10 From the Unique Economic and Social Registry (RUES), in which social organisations are required to register and update their register annually. RPOs have incentives to do this, despite the cost, as public programs, clients, and banks require organisations to be registered.
11 Note that the official terms used in Colombian laws are ‘small, medium and large-scale farmers’.
12 Census data refers to agricultural productive units—UPA, defined as the unit of organisation for production managed under one producer. 96% of UPA are composed by one household and managed by one producer. Thus, for simplicity, we use the term producer or farmer instead of UPA.
13 In Colombia, 'ethnic minority’ includes afro-Colombians, indigenous populations, and Roma Gypsies
15 We are able to estimate these additional dependent variables as we have the municipality-year numbers and value of credits allocated by source and recipient. We do not have municipality-year information on the total number of farmers by wealth levels, but we do have municipality-year information on municipalities’ rural populations. This is why we use per capita measures.
16 We express the variable in this way rather than per capita terms so that its scale is easier to understand. It does not have econometric implications vis a vis dividing by total population (RPO per capita).
17 The only policy intervention that took place during the period was a law creating a new public agency and modifying requirements for creating and registering social organisations. But this was a national level change affecting all municipalities. As such, it did not generate exogenous variation across time and municipality groups that could be analysed, for instance, in a differences-in-differences approach.
18 Over time, regulators broadened the legal definition of agricultural credit to include various rural activities, and even credit to supermarkets and restaurants, so inflating the credit count.
19 The psacalc command running Oster’s (Citation2019) test of unobservable selection and coefficient stability indicates that selection on unobservables would have to be too high to drive the RPO coefficient to zero. When we estimate the regression with no controls, the RPO coefficient is 7.365. With the full set of 23 controls, the coefficient is 8.152, while the R-squared increases from 0.011 to 0.490. This shows that the coefficient’s significance survives the barrage of controls.
20 Value estimated using as exponent the coefficient on RPO membership (0.933) in specification (1), which has a significantly larger N.
21 This can be the case when the supply of credit is not fixed, and thus the total amount of resources allocated can increase, as occurred in this case.