To analyze the impacts of weather on child outcomes we estimate equation (3). We use the standardized height-for-age z-score for children 36-months or younger as our measure of health.<sup>29</sup> Unlike weight-for-age, height-for-age is not as sensitive to very short-term and immediate scarcities or illness, but would capture more chronic conditions.<sup>30</sup> Given that all the data were collected during the 1999 dry season, the year and season terms drop out. Given that the weather data are at the municipal level, we use state level fixed effects to control for the time invariant characteristics, such as the agro-climatic conditions at the state level.

Besides our measures of weather shocks, we also include information on household composition (the number of children, the number of adult males, and the number of adult females), on mother‘s characteristics (mother‘s education, height, and whether she speaks an indigenous language), information about the child (gender, if the child has an older sibling who was born alive within 2 years of the child‘s birth, multiple birth, the birth order of the child, whether the child was characterized as very small at birth, and the age of the child at the time anthropometric measurement was taken), an asset index,<sup>31</sup> housing characteristics (presence of indoor toilet, tap water, type of floor), and information about the child‘s locality (altitude) as regressors in the analyses.<sup>32</sup> Table 10 describes the variables used in the analyses. Given the differences in the average climate in the North and in the Centre/South regions, we also analyze the children in each region separately.

There are 1,995 rural children less than 36 months in the ENN dataset. Our sample consists of only 1,540 children.<sup>33</sup> We only include those children whose mother has not moved in the past year to ensure that the weather shocks match what the child experienced. Of the excluded children, there are 138 children with missing height information and 91 children with improbable z-scores,<sup>34</sup> suggesting data entry errors. The other excluded children have incomplete information on the covariates. The children measured (and with probable z-scores) are statistically significantly older than those who are not measured. This poses a problem

> 29 We use, WHO Anthro for personal computers, version 3, 2009: Software for assessing growth and development of the world's children. Geneva: WHO, 2009 (http://www.who.int/childgrowth/software/en/ ) for calculating the standardized height-for-age scores.

> 30 The measure does not capture any differences in mortality from unusual weather.

> 31 The asset index is based on the principal factor analysis of  the household‘s ownership of a radio, a television, a VCR, a telephone, a computer, a refrigerator, a washing machine, a stove, a heater, and motor vehicle.

> 32 Only when analyzing the effects by participation in a nutritional program, we also include nutritional program participation as a regressor.

> 33 This is the pre- _canícula_ sample without nutritional supplement program variables and access to health care included as explanatory variables.

> 34 That is, their height-for-age z-scores are less than -6 or more than 6.

23