for the former), possible due to other market imperfections (such as labor market imperfection at household and community levels) that might prevent households from using their land endowment optimally.

We obtain similar results when all types of credit constraints are considered (columns 4-6 of Table 5). Male family labor does have a positive and significant effect on crop productivity only when the credit constraint is binding. It is highly likely that unconstrained households could use hired labor to compensate for any shortage in family labor as shown in the descriptive statistics. On the other hand, constrained households will find it difficult to adjust through participation in the labor market as they may lack the necessary resources to do so. The negative relationship between land size and the value of output per hectare remains robust irrespective of the definition of being credit constrained in the semi-formal sector. The negative relationship between land size and agricultural productivity is consistent with evidence based on Rwandan data from 1990 by Byiringiro and Reardon (1996). While imperfections in land, labor or other input markets could explain it, descriptive statistics suggest that the intensity of labor and other inputs decreases significantly with land size.<sup>16</sup> This suggests that having access to credit markets offsets only part of the effects of imperfection in the other markets.

To check the robustness of our results, we estimate two other specifications. First, we define households as credit constrained if they have no option to borrow in both informal and semi-formal credit sectors. Secondly, we use the logarithm of household land endowment instead of a simple linear specification. Results, presented in Table 6, are consistent with earlier findings both in terms of the estimation strategy and the avenues through which credit constraints affect economic outcomes.

To quantify efficiency losses arising from binding credit constraints in the semi-formal sector, we use the results from columns 4-6 in Table 5 and, following the standard literature on treatment effects, define the potential impact of exogenously removing binding constraints in the semi-formal credit sector as follows Using the broader definition of being credit constrained that includes quantity constraints but also risk and transaction costs, we estimate that removing the constraint will result in an increase of households’ total value of output from USD 272 to USD 326 (or by 17 percentage points).<sup>17</sup> This predicted effect is large and could potentially be explained by the large scope for more intensive use of inputs including labor by

> 16 Refer to Ali and Deininger (2012) for a detailed study of the negative relationship between land and productivity using the same data. Analysis points towards the translation of labor market imperfections into low labor opportunity cost for small farm holders.

> 17 Note that using the prediction of the model when the definition of constraints only accounts for quantity constrained households, we estimate a threefold increase in yield per ha. The difference between the two estimations can potentially be the result of systematic differences between quantity rationed households and other types of rationing. In addition, the selection regime considering quantity rationed households only relies on the assumption that these households do not face any other demand side binding constraints (risk aversion or transaction costs).

13