intensively, but soil quality is better and farmers have higher years of schooling.

At the quintile level, estimating the model linearly shows that a 1 percent increase in urea use increases yields by 0.31-0.49 percent on Java and 0.16 percent mostly off-Java. Within JavaBali, quintiles 1-4 show differences in the impact of urea use on yields and a much larger effect on the yields of smaller farms. Similarly, the urea consumption of the bigger farms in quintile 5, mostly off-Java farms, reflect the lowest boost in yields for the sample. This suggests that other deteminants (land size, irrigation, labor) are more significant drivers of yields than fertilizer for the larger producers, and it is not surprising that, since they are less credit constrained and have access to better information, other determinants are better able to explain their variation in rice yields.

Most control variables behave as expected: land size is negatively associated with yields, while agricultural inputs (labor and irrigation) are positively associated. Land size is negatively associated with rice yields, as anticipated given that smaller plots are farmed more intensively. The dummy for Java-Bali, which is introduced to proxy for different soil qualities, shows that on-Java more urea is consumed compared with off-Java (a positive and significant effect). Nonetheless, one would expect to find a positive relationship between the dummy for JavaBali and yields, to reflect the better soil quality in these islands. As such, the significant and negative relationship in most quintiles is surprising (with the exception of quintile 5 where it is inconclusive),<sup>13</sup> and signals that the dummy may not be such a good proxy or the fact that other variables in the model (irrigation, fertilizer) are already explaining much of the impact from soil quality. Increased labor (including non-wage labor or unpaid family members) is also positively associated with higher rice yields, and this is particularly true in larger farms (quintiles 4 and 5), where the effect is greater in magnitude and significance. As expected the findings show that the effect on rice yields from using diverse inputs varied between small and larger farmers.<sup>14</sup> Further research is required to capture the relationship between irrigation and rice yields. While the impact from the dummy for irrigation is positive and significant (and greater in magnitude for the larger farmers), the survey data did not allow capturing factors such as: maintenance, the quality of the irrigation network, or the extent to which existing infrastructure is operational, which are the true drivers of the relationship with yields. Therefore, further research may shed light on the impact of these factors in determining rice production.<sup>15</sup> Not surprisingly, the urea price is negatively associated with urea use, although there are significant differences by farm size (Table 4.3). The consumption of urea in the smallest farmers (quintile 1) is the least affected by the urea price. However, urea consumption of the largest farmers mostly off-Java (quintile 5) is highly sensitive to changes in fertilizer subsidies, as well as quintile 2 farmers that are mostly composed of Javanese farmers.

> 13 The fact that the dummy for Java-Bali is not significant in the quintile 5 suggests that for these larger farms, which are mostly located off-Java, soil quality is not a driver of yields and that soil characteristics are more homogenous.

> 14 One production input that could be controlled for is capital inputs. There is limited information from both surveys to control for investments that farmers may be doing in their fields.

> 15 There may be measurement error in the irrigation variable. The surveys show that about 50 percent of all rice farmers grow rice in irrigated fields ( _sawah_ ), both in Java and outside Java. This is much lower than widespread perceptions and data published by BPS, which show that over 90 percent of rice produced in Indonesia is grown in irrigated fields.

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