This concludes the discussion of the theory of occupational choice which I use to study the evolution of occupational change during the last two decades in Costa Rica, El Salvador and Panama. In the next section, I provide details on the data that I use to perform the quantitative analysis for the three Central American economies.

# **3   Data**

The main source of data is the country household surveys. The Socio-Economic Database for Latin America and the Caribbean (SEDLAC) (CEDLAS and the World Bank, 2019) is a repository of the major household surveys conducted in the Latin American and Caribbean countries. For most countries, the available surveys start appearing since the late 1980s. Given that the focus of the analysis is on primeaged workers (25-54 years), the SEDLAC potentially offers lifetime information of the oldest cohort (4554 years) in the present decade. For example, it is possible to know the occupational distribution of this cohort in the 1990s when it was classified as young. Thus, the availability of data over last two decades or so allows me to extract comprehensive information on all current cohorts. The three years that serve as reference points are 1995, 2005 and 2015 because the latter is the latest year for which data are available. The surveys do not correspond to actual panel. Rather, I construct a synthetic panel for each cohort to foster analysis.

The repository harmonizes the data at the country level to make them comparable across years. Still, due to several reasons many surveys remain non-comparable. To fulfill the basic requirement of having data on at least three years separated by a decade, I try bringing non-comparable surveys under analysis subject to them passing quality checks so that they are suitable for use in the study. This effort helps extend coverage to Costa Rica and Panama, as comparable surveys spanning a 20-year span are only available for El Salvador. Furthermore, to add power to the analysis, I pool data from the adjacent year surveys to the reference years whenever possible.<sup>10</sup> More details are listed in the data appendix.

The sample restrictions follow the procedure adopted by Hsieh _et al._ (2019). The model assumes that the workers make their occupational decisions after completing their school. Thus, considering only workers within the ages of 25-54 keeps focus on post-schooling choices. Moreover, the model does not feature the transitory state of unemployment. Hence, I drop those individuals from the analysis who report being unemployed. Finally, I exclude all individuals in the armed forces. Gender lines partition the workers into two groups.

In addition to cohort and gender classification, I need information on three important worker characteristics – occupational choice, level of schooling and income. The occupational classification of workers follows the structure of International Standard Classification of Occupations (ISCO-88) (International Labor Organization, 2012) at the 1-digit (major group) level. There are nine occupations at this level which vary considerably in terms of skills requirements and educational intensity. Apart from the standard market occupations, the set of possible occupations includes the home sector. I assume

> 10 Table A1 in the appendix lists the survey years associated with each reference year for each country.

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