One possible reason for losses to be inflated would be if the two proxies were not complementary but overlapped instead. The accuracy of the loss estimate crucially hinges on farmers being able to clearly differentiate between planted area- and plant productivity losses. For example, the differentiation between planted area and plant productivity losses may not always be as clear cut as in theory and may result in farmers not treating the questions on the share of planted area harvested and the percentage of damage on the crop as independent, complementary questions. As a result, there may be some overlap between both questions which would lead to our loss estimate being inflated. Another potential source of error is heterogenous plant productivity reductions across the plot. The question for productivity reductions we are using relies on farmers’ ability to accurately report the _average_ plant productivity loss in harvested areas. However, productivity reductions may be very heterogenous affecting some plants more and others less, especially when the plot is large. Forming an accurate estimate of the average percentage share of productivity reductions is thus arguably difficult. As a result, farmers may struggle to report the average plant productivity reductions accurately and rather satisfice.

_<mark>Table 4</mark>_ <mark>: Yield regression with area and plant productivity loss measures.</mark>

|**ETHIOPIA: Yield regression**|**s with area- and**<br>|**productivi**<br>|**ty-based los**<br>|**ses - Excl. Fu**<br>|**ll Losses**<br>||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|VARIABLES|ESS13<br>Maize|ESS15<br>Maize|ESS18<br>Maize|ESS13<br>Sorghum|ESS15<br>Sorghum|ESS18<br>Sorghum|
|Planted area harvested (%)|0.00567**|0.00359*|0.00813***|0.00713|0.00329|0.00415*|
||(0.00238)|(0.00213)|(0.00292)|(0.00551)|(0.00252)|(0.00224)|
|Damage on crop (%)|-0.00862***|-0.0114***|-0.00708***|-0.00838***|-0.0168***|-0.00541**|
||(0.00169)|(0.00170)|(0.00265)|(0.00201)|(0.00267)|(0.00266)|
|Constant|4.659***|4.640***|4.944***|4.776***|5.182***|5.406***|
||(0.352)|(0.284)|(0.431)|(0.604)|(0.371)|(0.313)|
|Observations|2,853|2,846|1,586|2,370|2,291|1,230|
|Adjusted R-squared|0.370|0.346|0.190|0.284|0.391|0.318|
|District FE|YES|YES|YES|YES|YES|YES|
|Controls<br>Note: Regression of log yield on loss<br>dummy, log seed quantity per hectar<br>Household Characteristics - depende<br>Manager Characteristics - female du<br>and a number of district fixed effects|FULL<br>proxies. Control varia<br>e, improved seed du<br>ncy ratio, dummy for<br>mmy, age, dummy for<br>. Robust standard err|FULL<br>bles: Inputs - lo<br>mmy, log inorga<br>any HH membe<br>at least primar<br>ors in parenthe|FULL<br>g plot area, log t<br>nic fertilizer qua<br>r with at least pr<br>y education; Plot<br>ses. *** p<0.01,|FULL<br>otal labor on the<br>ntity per hectar<br>imary education<br>- intercropping<br>** p<0.05, * p<0.1|FULL<br>plot per hecta<br>e, organic fertil<br>, agricultural as<br>dummy, soil fe<br>"|FULL<br>re, hired labor<br>izer dummy;<br>set index;<br>rtility index;|

While this paper cannot determine the degree of overlap and inaccuracy with certainty, it is possible to explore whether both proxies independently add explanatory power to a yield regression and carry the expected signs. This is confirmed in Table 4, however, the increase in explanatory power when controlling for productivity reductions on top of area reductions is relatively small. Furthermore, the actual loss estimate carries less predictive power than the percentage-based area and productivity reduction variables (not reported). Therefore, while the area and productivity measure seem somewhat complementary and serve as suitable proxies for 18