measure of the number of years that school-aged children were exposed to the crisis. The range of this exposure variable is therefore between zero (children who were never of school-age during the crisis) and five (children who were of school age during every year between 1988 and 1992). (Details of these exposure calculations can be found in the Data Appendix.) I interpret the coefficients on the measure of exposure as the marginal effect of a year of exposure to the crisis on the years of schooling completed.

In both instances, I report results from a number of specifications. In addition to the survey year dummies (in both the logit and OLS regressions), and the measure of crisis exposure (in the OLS regressions), specification (i) includes strata dummies, gender dummies, and unrestricted age dummies. Information on the years of completed education of both parents (separately) is also available for a sub-sample of children-specifically, those who live in households in which both parents are present and whose relationship to the household head is son or daughter. Specification (ii) therefore adds controls for the education of both parents (separately), unrestricted dummies for the age structure within the household, and a main effect in household size. The LSMS tapes include identifiers with which to map households in the surveys to the districts in which they live.<sup>12</sup> The fixed-effects specification in (iii) limits the analysis to households living in districts wlhich were included in the sample in 1985/86, 1991, and 1997, and includes the variables in specification (ii), as well as a dummy variable for each district. These regressions therefore identify differences in attendance levels, employment levels, and educational attainment from _within-district_ variation across survey years or birth cohorts. (Note that district fixed effects are _not_ a satisfactory solution for the problem of differences in the sample in the rural sierra between the 1985/86 and 1997 surveys on the one hand, and the 1991 survey on the other. The likely problem with the 1991 sample for the rural sierra is that enumerators may not have visited outlying areas _within_ a given district, rather than not visiting outlying districts.) One disadvantage of the estimations which include measures of parental education and those witlh district fixed effects is that both are limited to sub-samples of the data. In the three regions considered in the analysis in this paper, we only have data on the schooling of both parents for 73.9% of 6 to 11 year-olds, and only 77.8% of 6 to 11 year-olds live in districts which were included in the sample in 1985/86, 1991, and 1997. Better controls therefore come at the cost of smaller sample sizes anid less precision.

> 12 There are more than 1800 districts in Peru, and these are the smallest political and administrative division in the country (roughly comparable to counties in the United States). According to the 1993 population census, the average district population was about 12,600 inhabitants. Some, predominantly rural districts have less than 200 inhabitants, whereas a handful of urban districts in Lima have more than I 100,000 inhabitants. See Schady 2000, p. 3.