within these EAs. An important feature of these surveys is that households are georeferenced, which facilitates the mapping of each household’s geographic location to its respective agricultural zone. This enables the integration of information on zonal agricultural season cycles with households’ self-reported agricultural cycle data.

For our individual level analysis, we further restrict the sample to keep middle and high school students who are currently enrolled in school. That is, our sample consists of students enrolled in 5<sup>_th_</sup> through 12<sup>_th_</sup> grades. To account for potential spatial correlation within EAs, we cluster the standard errors of all our regressions at the EA level.

# **4 Empirical Framework**

We estimate the effects of temperature during the exam year on standardized total test scores of each student using the following regression specification.

where the outcome variable _Yisjt_ is the standardized test score for student _i_ , in stream _s_ , in school _j_ , and in year _t_ . The variables _Tjt_<sup>_m_denotethenumberofdaysinschool</sup><sup>_j_and</sup> year _t_ where the daily maximum temperature was in the _m_<sup>_th_</sup> of the seven bins used in our analysis.<sup>12</sup> Following the literature, we set 18-21<sup>_◦_</sup> C to be the reference bin. Similarly, variables _Prec_<sup>_n_</sup> _jt_<sup>denotethenumberofdaysinschool</sup><sup>_j_andyear</sup><sup>_t_wherethedailytotal</sup> precipitation was in the _n_<sup>_th_</sup> of the four bins used in our analysis.<sup>13</sup> We estimate several versions of this specification, dividing the year into school days and non-school days as well as growing season and non-growing season days.

The variable _Female_ is a dummy which takes the value of 1 if the student is female, and 0 otherwise. We include a female control because the level of female students’ performance is lower than that of male students, on average. _μs_ , _γj_ and _αt_ are the stream, school, and year fixed effects, respectively. We include stream fixed effects as the subjects that students are tested on differ by stream and as do the distribution of scores and the passing cutoff scores. School fixed effects are added to control for school infrastructure and other unobservables which might be location specific. Year fixed effects help to control for year specific shocks as well as curriculum changes or changes in the exams over the years.

We are interested in the semi-elasticity or the _β_<sup>_m_</sup> coefficients which capture the marginal effect of an additional day during the year in which the temperature is in bin _m_ instead of being in the reference temperature bin. The key assumption in this approach is that daily temperature variation is orthogonal to the unobserved determinants of learning outcomes (Deschenes, Greenstone, & Guryan, 2009).

> 12We use the following set of temperature bins in our analysis _{≤_ 18 _◦_ C, (18-21] _◦_ C, (21-24] _◦_ C, (24-27] _◦_ C, (27-30]<sup>_◦_</sup> C, (30-33]<sup>_◦_</sup> C, _>_ 33<sup>_◦_</sup> C _}_ .

> 13 We use the following set of precipitation bins in our analysis _{_ 0 m, (0-0.01] m, (0.01-0.02] m, _>_ 0.02 m _}_ .

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