They therefore do not explain much of the remaining variance when already controlling for the attainable harvest through expectations.<sup>46</sup> _Table 8_ : Malawi - Regressions of self-reported harvest realizations on expected harvest and a range of controls.

# **Malawi - Maize Yield**

||(1)<br>IHP<br>|(2)<br>S19<br>|(3)<br>IHP<br>|(4)<br>S16<br>|(5)<br>IHP<br>|(6)<br>S13<br>|(7)<br>IH<br>|(8)<br>PS10<br>|
|---|---|---|---|---|---|---|---|---|
|VARIABLES|Bivariate|Multiv.|Bivariate|Multiv.|Bivariate|Multiv.|Bivariate|Multiv.|
|Expected maize yield (kg/ha)|0.388***<br>(0.0768)|0.214**<br>(0.0826)|0.536***<br>(0.0546)|0.398***<br>(0.0472)|0.687***<br>(0.0883)|0.315***<br>(0.0991)|0.136***<br>(0.0239)|0.0618***<br>(0.0196)|
|Planted maize area harvested (%)||6.385***<br>(1.642)|||||||
|Constant|784.3***|783.3|359.7***|-1,836|894.4***|10,025|1,252***|3,271***|
||(115.0)|(1,684)|(70.56)|(1,344)|(264.2)|(7,343)|(86.41)|(934.1)|
|Observations|568|544|493|490|442|442|842|842|
|Adjusted R-squared|0.373|0.509|0.385|0.448|0.468|0.556|0.090|0.333|
|Controls|NO|YES|NO|YES|NO|YES|NO|YES|

Note: Regression of self-reported realized maize yield on expected maize yield, planted area loss (only IHPS19), and a number of plot, manager-, and household controls as well as inputs and district fixed effects. Controls: Inputs - Plot area, total labor p. ha, any hired labor dummy, seed quantity p. ha, improved variety dummy (only IHPS19 and IHPS15), inorganic fertilizer quantity p. ha, any organic fertilizer dummy; Household - dependency ratio, head employed for wage dummy, any HH member owns non-ag enterprise dummy, any HH member has primary education dummy, agricultural asset index; Manager - female dummy, age, primary education dummy; Plot - intercropped dummy, precipitation of wettest month, soil fertility index; Zone fixed effects. Clustered standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

As a final exercise, Table 9 and Table 10 investigate the factors that underlie farmers’ expectation building. Neither a set of respondent characteristics nor past shock exposure or other factors potentially associated with the process of forming an expected harvest estimate are significant predictors of the expectation gap across crops and countries.<sup>47</sup> We gain some explanatory power by constraining the sample to those plots where expectations exceeded realized harvest, i.e. those 46 In Ethiopia, we also have data from crop cut harvests for a sub-sample of plots. This allows us to verify whether expectations are also strong predictors of what is commonly taken as the “gold standard” measure of realized harvest. We find that expectations do not seem to be a reliable predictor for crop cut harvest, and in particular crop cut yields. Furthermore, this does not seem to be due to bias only inherent in self-reported expectations as there is only a slightly stronger association even between self-reported harvest _realizations_ and the crop cut measure. We thus conclude that expectations only provide a fair proxy of the attainable harvest when also using self-reported harvest realizations. The reason for this is likely that both share a similar underlying mental model and thus similar biases that cancel out when comparing them.

47 The fact that we do not find a robust effect of past shock exposure, measured either by community reports for the past two years, on expectation building is somewhat surprising as there is strong evidence in the literature that past shock exposure affects risk attitudes and expectations about the likelihood of future shocks. This may be because the shock proxies used are not specific enough, i.e. they do not necessarily measure adverse experiences with past _agricultural shocks_ and may not capture individual exposure.

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