households were randomly selected from each EA, for a total sample size of 3,600 households. Figure 1 illustrates the geographical location of sample cells.

The survey includes detailed information on demographics, resource endowments, social networks, crop production and agricultural practices, households’ perceptions and participation in key markets including those for credit, land and other inputs. The agricultural module covers the first cropping season in 2010 (March to August).<sup>5</sup> Information on loan applications in the past 12 months and respondents’ decisions on credit market participation is used to classify households into being constrained or unconstrained in informal, semi-formal and formal credit markets following the direct elicitation approach.<sup>6</sup> Figure 2 illustrates the procedure followed to do so: First, those who did not apply for a loan in the past 12 months were asked why they did not request for a loan from each source. Households who reported having sufficient resources of their own or being price rationed due to high interest rates were classified as unconstrained. Constrained households fall into three categories, namely (i) risk rationed ones who reported fear of being indebted and possible loss of collateral; (ii) quantity rationed ones who lacked collateral and feared being rejected; and (iii) transaction cost constrained ones who failed to borrow due to a lack of supply in the community and lack of knowledge on how and where to apply for a loan.

Observations were aggregated for all sources in each sector (formal, semi-formal and informal), allowing for multiple types of constraints in each sector per household. However, if households reported that they did not apply because they had enough resources in any of the sources in a particular sector, they were subsequently classified as unconstrained. Second, loan applicants were further classified based on the status of their loan application: applicants whose demand was fully met (unconstrained), applicants whose request was partially or fully rejected (quantity rationed), and applicants who received the full loan amount they requested for but still would have liked to borrow more given the loan terms (constrained, and then subsequently classified based on their stated reason for not applying for more in a similar fashion to those of non-applicants).

# **3.2 Descriptive evidence**

Table 1 presents data on households’ participation in credit markets by asset wealth quartiles.<sup>7</sup> Results point towards a rather inactive formal credit market, in line with the results from the Finscope Rwanda

> 5 While levels of total crop output are similar between season A and B for most crops, there are some differences for sorghum and paddy as well as coffee and fruits (see NISR 2008 for details) but these do not affect our results.

> 6 The informal sector sources include friends or neighbors, family relatives and private money lender. Sources of credit in the semi-formal sector were divided between associations, agricultural input suppliers Roscas, microfinance institutions and NGOs (including faith based). The credit module was intentionally administered to the female respondent (if any) in the household, to avoid responses based on the perceptions of the male respondent only. As a result, 82 percent of the constraint variables are based on female responses.

> 7 Wealth quartiles are computed using a principal component analysis to generate wealth indexes following Filmer and Pritchett (2001). Components include: total size of land owned, whether the household owns the dwelling or not, number of buildings on the plot, binary variables for different types of roofing, exterior wall and flooring materials as well as main source of drinking water and lighting, and number of household asset items (mattress, table, sewing machine, refrigerator, radio, radio cassette, bicycle, motorcycle and mobile phone owned) and livestock

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