the other hand, SR yields are underestimated vis-`a-vis CC yields in each of the first four quintiles of quantity of seeds per hectare and days of household labor per hectare and are conversely overestimated in the top quintile.

**Table 11:** Comparison of plot-level measures of sorghum yield and harvest in EACI 2017

||**Y**<br>All|**ields (Kg/**<br>Pure-stand|**Ha)**<br> Intercropped|All|**Harvest (K**<br>Pure-stand|**g)**<br> Intercropped|
|---|---|---|---|---|---|---|
|_Means_|||||||
|SR|574.6|561.1|687.1|970.6|950.8|1135.1|
||(27.2)|(28.0)|(101.0)|(43.6)|(44.1)|(174.3)|
|CC|757.0|760.2|730.5|2037.1|2052.9|1906.5|
||(14.5)|(15.4)|(42.5)|(85.9)|(91.9)|(237.6)|
|_Diference in Means_|||||||
|SR vs CC|-182.4***|-199.1***|-43.4|-1066.5***|-1102.0***|-771.4***|
||(29.1)|(30.3)|(99.5)|(77.9)|(84.1)|(193.9)|
|_Diference in Distribution_<br>SR vs CC|***|***|***|***|***|***|

_Notes:_ Distributional differences are assessed using the Kolmogorov-Smirnov two-sample test Significance levels: *p _<_ 0.1; **p _<_ 0.05; ***p _<_ 0.01 The differences in the graphs based on ERIVaS versus EACI samples are due, in part at least, to the differences in the distributions of the input variables. For example, the mean SR-CC difference in the third quintile of plot area in EACI is equivalent to the mean difference in the fourth quintile in ERIVaS, so the underestimation trend that we observe in ERIVaS starts earlier in the distribution for EACI 2017. However, the direction of the correlation between the SR-CC difference and the quantity of inputs per hectare are the same in both ERIVaS and EACI samples. As such, it is not surprising to see in Table 12 that the coefficients of the bivariate and multivariate regressions of yields and inputs change significantly based on whether we use SR versus CC yields in the EACI data, but that the changes are fully consistent with the findings based on the data from the methodological experiment.

Furthermore, while we do not have concerns regarding measurement errors in CC yields in the ERIVaS data set due to (i) strict supervision of activities related to crop cut sub-plot placement, harvesting and weighing, and (ii) comprehensive quality control measures applied to the incoming data throughout the fieldwork, there is reason to believe that the EACI crop cutting operations were not implemented as successfully. Figure 6 shows a scatterplot of SR and CC yields in the EACI data set and there is evidence of rounding errors in CC yields. Assuming that these errors are not correlated with the measures of inputs,<sup>18</sup> this would mostly create an attenuation bias in the coefficients for the respective inputs in the regressions of CC yields and could, therefore, explain in part the zero coefficients in Table 12. This finding underscores the need of quality (but still scalable) digital measurement devices when objective measurement is the goal.<sup>19</sup>

> 18We created a variable indicating whether an observation was rounded and it appears not to be associated with the value of the key inputs we study here. A close look at this issue is left to future work.

> 19In fact, discussion with CPS/SDR confirmed that during this round, the survey enumerators relied on scales which lacked accuracy and were scheduled to be replaced.

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