metadata: authors: 0: author: Sinha,Rishabh
count: Central America
docna: 0: docna: Talent Allocation and Post-Reform Growth in
            Central America
docty: Policy Research Working Paper
subtopic: Labor Markets,Rural Labor Markets,Human Rights,Educational Sciences
teratopic: Social Protections and Labor,Rural Development,Law and Development,Education
abstracts: cdata!: This paper examines the post-reform
            economic growth in three Central American economies -- Costa
            Rica, El Salvador, and Panama. From 1995 to 2015, each
            economy witnessed phenomenal shifts in labor market
            participation and occupational distribution of women. If the
            innate talent for a job did not change differently across
            genders, the occupational changes suggest that many talented
            women in the mid-1990s were in professions that did not
            conform to their comparative advantage. The paper studies
            the evolution of the occupational distribution using a model
            of occupational choice in which three forces create
            frictions to efficient allocation -- discrimination in labor
            markets, obstacles to human capital accumulation, and
            preferences (or social norms). The analysis shows that the
            underlying improvement in talent allocation over the past
            two decades had a quantitative impact on growth in Costa
            Rica and Panama. Decomposing the aggregate effects reveals
            that the gains were driven by declines in obstacles to human
            capital accumulation. In contrast, shifts in labor market
            discrimination created headwinds for expansion. The
            aggregate effects in El Salvador are relatively mild and
            noisy to the extent that the qualitative effect is difficult
            to pin down. Nonetheless, the analysis finds that the
            preference for market work has increased sharply in El
            Salvador for both genders and has proved to be a drag on growth.
repnme: repnme: Talent Allocation and Post-Reform Growth in
            Central America
display_title: Talent Allocation and Post-Reform
            Growth in Central America
listing_relative_url: /research/2020/09/32374891/talent-allocation-post-reform-growth-central-america
topicv3: Labor Markets m1326488 855,Education m1327048 470,Jobs m1503931 2670,Labor Force Participation m1504050 2746,Discrimination m1505707 3379
publishtoextweb_dt: 2020-09-02T00:00:00Z
historic_topic: Social Protections and Labor,Rural Development,Law and Development,Education
origu: DECRG: Macroeconomics & Growth (DECMG)
no_of_pages: 50
author_keys: 0: author_key: 1411342
model_extractions: Policy Research Working Paper 9380 Talent Allocation and Post-Reform Growth in Central America _Rishabh Sinha_ Development Economics Development Research Group August 2020
Policy Research Working Paper 9380

# **Abstract**

This paper examines the post-reform economic growth in three Central American economies—Costa Rica, El Salvador, and Panama. From 1995 to 2015, each economy witnessed phenomenal shifts in labor market participation and occupational distribution of women. If the innate talent for a job did not change differently across genders, the occupational changes suggest that many talented women in the mid-1990s were in professions that did not conform to their comparative advantage. The paper studies the evolution of the occupational distribution using a model of occupational choice in which three forces create frictions to efficient allocation—discrimination in labor markets, obstacles to human capital accumulation, and preferences (or social norms). The analysis shows that the underlying improvement in talent allocation over the past two decades had a quantitative impact on growth in Costa Rica and Panama. Decomposing the aggregate effects reveals that the gains were driven by declines in obstacles to human capital accumulation. In contrast, shifts in labor market discrimination created headwinds for expansion. The aggregate effects in El Salvador are relatively mild and noisy to the extent that the qualitative effect is difficult to pin down. Nonetheless, the analysis finds that the preference for market work has increased sharply in El Salvador for both genders and has proved to be a drag on growth.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at rishabhsinha@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Talent Allocation and Post-Reform Growth in Central America Rishabh Sinha<sup>§</sup> The World Bank

# **JEL classification:** O47; J24; J71

**Keywords:** Economic growth; misallocation; gender; labor market discrimination; barriers to human capital accumulation; preferences and norms; Central America; Costa Rica; El Salvador; Panama

> § 1818 H St NW, Washington, DC 20433, USA. Email: rishabhsinha@worldbank.org. Tel: +1-(202) 458 0770. Thanks to Charles Jones for his valuable comments and suggestions. Luis Diego provided excellent research assistance. The findings, interpretations, and conclusions expressed in this paper are entirely those of the author. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Most recent version available here.
# **1   Introduction**

Guided by the principles of the Washington Consensus, many Central American (CENAM) countries started on a path of structural reforms beginning in the late 1980s.<sup>1</sup> Since then, the region has recorded considerable gains in multiple dimensions of reforms outpacing those of many fast-growing economies during the period (Swiston and Barrot, 2011). The growth experiences of regional economies in the pre- and post-reform periods varies markedly (Figure 1(a)). At one extreme the growth took off after 1990 in El Salvador and Panama while remaining near trend in Costa Rica, Honduras, and Guatemala. Nicaragua stands out as the outlier where the real GDP per capita dropped drastically during the 1980s following the Sandinistas’ ascension to power. The country has been on a path of slow recovery since then.

Given their unique experience, the discussion of growth in CENAMs since 1990 has primarily concentrated on the role of reforms in delivering it. Yet, there is a lack of consensus on whether reforms promote growth. Instead, the reform-growth link is found to be highly context-specific (Easterly and Levine, 2003; Rodrik, 2005; Prati _et al._ , 2013).<sup>2,3</sup> Moreover, studies concentrating on CENAMs have not found definite evidence of reform-led growth in the region (Ferreira and Harrison, 2012; Caceres, 2017; Roquez-Diaz and Escot, 2018). At the same time, some CENAMs have experienced healthy economic growth over the last few decades. Figure 1(b) highlights the relative performance of three CENAMs – Costa Rica, El Salvador, and Panama, that are the focus of this paper.<sup>4</sup> In terms of real GDP per capita expansion, Costa Rica and El Salvador lie above the 60<sup>th</sup> and 70<sup>th</sup> percentiles of the global distribution. Their relative standing is robust to the exclusion of high-income economies.<sup>5</sup> Panama lies above the 90<sup>th</sup> percentile of the global sample and 80<sup>th</sup> percentile of the sample that excludes high-income countries.

If reforms were not effective, what other factors can explain the immense gains observed over this period? In this paper, I turn the lens towards the phenomenal changes in the labor markets in the last two decades. Specifically, I investigate the shifts in the participation and occupational decisions of men and women. Table 1 provides a snapshot of these changes.

> 1 Williamson (1990, 2000) lists the set of reforms that constitute the development policies prescribed by the Washington Consensus.

> 2 Studies that concentrate on specific reforms have also not yielded a consistent takeaway. For example, see Sachs _et al._ (1995), Dollar and Kraay (2004) and Berg and Krueger (2003) for evidence on trade liberalization. Also see Juhn _et al._ (2014) and Gaddis and Pieters (2017) who report differential impact of trade liberalization on labor market outcomes of men and women in Brazil and Mexico.

> 3 Reform literature often exploits cross-country variation to investigate the reform-growth link. This leaves little room for the individual country analysis. In the context of CENAMs, Ferreira and Harrison (2012) and Roquez-Diaz and Escot (2018) are notable exceptions. The former finds that export diversification policies that replaced import substitution regime did not deliver long-run growth in Costa Rica. The latter considers the effects of trade liberalization in four CENAMs and finds no evidence of reform-led growth in any except Nicaragua.

> 4 The exclusion of other CENAMs is not because of their growth performance. Instead, the unavailability of data restricts their inclusion.

> 5 I follow the World Bank’s income definitions (The World Bank, 2020). For the fiscal year 2020, the World Bank classifies countries with GNI per capita of USD 12,376 or more in 2018 (as per the World Bank Atlas method) as highincome.

2
<!-- Start of picture text --> st<br>PAN<br>7°<br>B SLv<br>S<br>eo CRI<br>8 N CIM<br>oy NIC<br>o HND<br>a)<br>oO<br>2<br>a<br>°<br>1950 1960 1970 1980 1990 2000 2010 2020<br><!-- End of picture text --> <!-- Start of picture text --> Worldorld (60p)(60)<br>World ex-HI (60p)<br>Costa Rica<br>World (70p)<br>World ex-HI (70p)<br>El Salvador<br>World (80p)<br>World ex-HI (80p)<br>World (90p)<br>Panama<br>World ex-HI (90p)<br>0 1 2 3 4<br><!-- End of picture text -->
than of men. Though the extension realized by women in El Salvador was muted (80 bp), it is noteworthy given that men's representation shrunk by 2 pp.

How can these changes bear an effect on the aggregate economic performance? What links occupational convergence to aggregate gains is the notion that innate talent in an occupation within a group is likely to be time invariant. A recent study analyzes the occupational convergence in the United States (between race and gender groups) and finds that 20-40 percent of the growth in market output per capita during 1960-2010 can be attributed to the underlying improved allocation of talent (Hsieh _et al._ , 2019). Likewise, the occupational distribution of 1995 in CENAMs suggests that many talented women with a comparative advantage in high-income jobs – or in the labor market in general, were engaged elsewhere. A better allocation of talent over the decades could have potentially boosted growth in CENAMs too.

To analyze the labor market changes and their aggregate consequences, I employ the occupational choice model in Hsieh _et al._ (2019). The model extends the classic Roy (1951) model in which a worker chooses an occupation to maximize her utility. Three forces in the model can lead to workers choosing occupations that do not correspond to their comparative advantage. First, discrimination in the labor markets arises in the form of occupation-specific wedges between wages and marginal products (Altonji and Blank, 1999). Second, workers face barriers to human capital accumulation that raise the costs of acquiring occupation-specific education and training.<sup>6</sup> Finally, preferences and social norms can drive workers away from their most suited occupations. For instance, rigid social norms can deter women from entering the workforce or from pursuing high-skilled occupations (Altonji and Blank, 1999; Bertrand, 2011). The channel is broad enough to encompass shifts in preferences for children or the ability to alter fertility decisions that are closely tied to market work (Costa, 2000; Blau _et al._ , 2013).<sup>7</sup> Besides evaluating the aggregate effects, the analysis sheds light on how the three forces have evolved over the years. I find that it became easier for women to attain human capital in most occupations. More importantly, these gains are also evident in high-income occupations. However, there is considerable heterogeneity across CENAMs when it comes to labor market discrimination. In Costa Rica and Panama, discrimination became more severe in all occupations barring a couple. The trend extends to high-income jobs. As such, the two forces acted counter to each other in many cases. In contrast, discrimination declined more broadly in El Salvador.

The intertemporal trends in preferences offer valuable insights. Departing from Hsieh _et al._ (2019), I use a less restrictive identifying assumption that allows men's preferences to vary across occupations and time. Women's and men's preferences have shifted away from home-based work to market occupations in Costa Rica and Panama. However, changes in preferences and norms have veered towards working at home in El Salvador. The heterogeneity across CENAMs has implications that are

> 6 These barriers include bias in the allocation of household resources in favor of boys. Acosta (2011) shows that increases in remittances in El Salvador led to an uptick in the school attendance of girls but not of boys. This suggests that financial constraints concerning educational investments are more binding for girls. Similarly, highlighting the importance of intra-household allocation, Li _et al._ (2017) find that an additional sibling reduces the likelihood of secondary education of girls relatively more in the country.

> 7 In the context of CENAMs, Kasy and Ramos-Chaves (2014) conclude that child support legislation and mandatory DNA testing led to changes in the family structure, creating opportunities for women to enter the labor market in Costa Rica.

4
critical from a policy standpoint. Reforms need to focus more on the labor market discrimination in Costa Rica and Panama. On the other hand, El Salvador will benefit from human capital interventions and policies that affect preferences and social norms.

The model shows that the changes in the occupational distribution had a substantial impact on aggregate output. Market output per capita in 2015 drops by 23-40 percent in Costa Rica and by 75-94 percent in Panama when I replace the present level of barriers and preferences with those from 1995. For the former, the lower bound translates to roughly half of the actual change in real GDP per capita. For Panama, the conservative estimate surpasses the actual growth by 16 percent.<sup>8</sup> The aggregate implications are milder in El Salvador. Additionally, it is difficult to determine whether the shifts in barriers and preferences aid or constrict growth. Depending on the choice of parameters, market output per capita in 2015 could have been either 13 percent lower or 6 percent higher if past levels persisted.

Decomposition reveals that gains in Costa Rica and Panama are driven by changes in obstacles to human capital accumulation. Shifts in labor market discrimination created headwinds in both countries. Though less powerful than the human capital channel, declines in discrimination support growth in the United States (Hsieh _et al._ , 2019). The exercise contributes to the literature by showing that the aggregate effects of shifts in preferences can be quantitatively significant. Unlike the US study, my identification strategy allows men’s preferences to change over time. Over the decades, both women's and men's preferences in El Salvador have shifted away from market work. Market output per capita in 2015 would have been 5-16 percent higher if the country had the preference profile as in 1995. This indicates 10-34 percent of the actual growth over the period. In contrast, preferences do not exert any meaningful impact on growth in the United States.

Finally, the paper complements the literature that has investigated the barriers to occupational decisions in CENAMs by comparing how the situation has evolved over the last two decades.<sup>9</sup> A novelty of the approach here is that the occupational choice model adjusts for the fact that workers self-select into occupations based on their idiosyncratic occupation-specific innate talent. Under self-selection, occupational frictions are more robustly associated with occupational shares than with wage gaps (Hsieh _et al._ , 2019). The average talent of women will be higher in an occupation in which they are not well represented as only the most talented can pursue it. In the absence of the above three forces, women earn more than men to compensate for talent differences. Davila and Pagan (1999) make a similar point while comparing gender wage gaps in Costa Rica and El Salvador. In the presence of selection effects, the analysis of frictions should jointly consider the occupational distribution and wage gaps.

Following the identification strategy in Hsieh _et al._ , (2019), I measure occupational frictions faced by women relative to men. This amounts to assuming that men do not face discrimination in the labor markets, nor do they encounter obstacles to human capital formation. The assumption is restrictive as barriers can arise for both genders. Indeed, Dahbura (2018) and Kalsi (2018) find that boys are more prone to criminal victimization in El Salvador which affects their schooling. The identification, however, does not

> 8 In the results section, I explain why real labor earnings match more transparently to data relative to GDP per capita. Though quantitatively different, the significance of the three forces remains substantial when using labor earnings as a yardstick. At a minimum, the three channels jointly account for 25 percent of the actual gain in real earnings per capita in Costa Rica. For Panama, their contribution ranges between 147-185 percent of the actual change.

> 9 Some of the earlier studies include Arends (1992a; 1992b), Winter and Gindling (1992), Gindling (1993) and Funkhouser (1997).

5
constrain women to always remain at a disadvantage. In case women are favored, it will amount to them receiving _subsidies_ as wages or in acquiring human capital. The baseline estimates rule out this possibility in all three CENAMs when both barriers are considered jointly. There is some evidence that suggests that women receive _subsidies_ in human capital formation in Costa Rica and Panama in certain occupations. However, they are dwarfed by labor market discrimination.

The rest of the paper is organized as follows. The next section lays out the occupational choice model. Following, I discuss the details of the data used in the quantitative analysis. Section 4 covers the country-wise calibration of the model. Section 5 describes how the barriers and preferences have evolved over the last two decades, and section 6 reports the aggregate implications of the changes.

# **2   Model**

To analyze the occupational choices of workers, I employ the occupational choice model developed in Hsieh _et al._ (2019). The model extends the set-up of a standard Roy (1951) model in which workers are endowed with idiosyncratic innate talent in each occupation. More importantly, the model introduces three forces that are critical in choosing occupations. First, workers face idiosyncratic discrimination in the labor market. Second, the cost of acquiring human capital varies across workers effectively creating obstacles to human capital accumulation. Third, workers prefer some occupations over others. In what follows, I often refer to the first two forces together as barriers to occupational choice.

## **2.1   Workers**

Each worker’s lifetime consists of three periods – young, middle and old, during which she works. This implies that in each period there are three cohorts of workers. The worker chooses her occupation and human capital investment in an initial pre-period which remains fixed throughout the three working periods. There is a total of occupations, one of which involves working in the home sector. In addition, each worker belongs to a group which influences occupational preferences and barriers. The groups II correspond to the gender of the workers. In any period , the lifetime utility i of a young worker from gg yy group who decides to work in occupation follows the following specification ii tt UU gg ii i ii+2 i (1) yy yy ii ii ii log UU = ββ�log CC + log(1 −ss) + log zz where is the consumption in period ii and is the time spent on accumulating human capital in the preperiod. The total time endowment in the pre-period is set to 1. Hence, captures the trade-off between ii CC tt ss lifetime consumption and leisure in the pre-period. ββ Preferences can drive workers away from their comparative advantage. The parameter i is the yy utility that a young worker in period derives from working in occupation . There are various reasons ii zz why preferences may change over time and across groups. For instance, they may be shifts in preferences tt ii towards fertility and marriage which might shift preferences for working in the home sector for women but not men. In general, evolving social norms can also alter preferences of men and women in complex fashion.

6
<!-- Start of picture text --> = ( )w (1 +T ) (3 )<br>e (1<br>(x<br>)<br>exp (-Le ) (4)<br><!-- End of picture text -->
The above specification implies that workers have different levels of innate talent across occupations, and it is possible for a worker to have a much higher talent in one occupation but not the others. The above set-up implies that the amount of time spent in school by young workers in period depends on the elasticity of human capital to education expenditures , consumption-leisure trade-off tt parameter , and the occupation-specific elasticity to schooling i ηη 1 = ββ φφ iyy∗ 1 +<sup>1 −</sup><sup><u>η</u></sup> (5) i ss The above expression indicates that all variation in schooling time across time and occupations is driven 3ββφφ by i . More importantly, the choice of is independent of a worker’s group . At this point, it is also useful to introduce a notation for _composite_ barrier i which combines i and i and is given φφ ss gg cc cc cc <u>�</u> i <u>(h)�</u> ii ii(h) ii(ww) by cc ηη ~~.~~ The composite barriers will feature repeatedly in aspects related to the optimization ττ ττ ττ 1+ττ i ii cc problem of the workers. 1−ττ ii(ww) **Occupational choice:** A worker chooses an occupation that provides her with the highest utility i . As yy the talent is drawn from the Fréchet distribution, it follows that the utility from choosing an occupation ii UU also follows the same distribution. Thus, it is possible to analytically solve for the occupational propensities at the group level. **Proposition 1:** The propensity of a young worker from group to choose an occupation in any period is given by gg ii tt <u>ji</u> = i θθ (6) yy ww� � ii θθ pp yy jj jji 1−η ∑�ww� where i = i iyy �φφi <u>�</u> iyy iyy <u>�</u> 3ββ ~~.~~ yy ww �ss �1−ssi �zz ii cc ww� ii ττ ii Note that the occupational choices made when young follow throughout the lifetime of workers, i.e., i i i . The above proposition illustrates the forces that govern the occupational yy mm oo sorting. ii Equation (5) ,ii+1 shows that the choice of schooling time ,ii+2 i is invariant across groups. Given this, the pp = pp = pp yy parameters that vary across groups and lead to variations in occupational distribution across groups are ss preferences i and the composite frictions i . The latter increases with a rise in barriers in either the yy yy labor market or in human capital accumulation. Hence, if a group has a higher fraction of workers in some ii ii zz ττ occupation relative to some other group, then either that group has a higher preference or faces steeper barriers in that occupation, or both. The above equation also pins down the labor force participation rate as home-sector is one of the occupations. An interesting feature of the model is that it allows preferences to change in groups who otherwise may or may not face any significant barriers. For example, to use the exposition in Hsieh et al. (2019), the LFPR could have declined for men in the United States because of the 8
**Average wages:** The model also allows to see which forces determine the variation in average occupational wages across groups. The principal takeaway is that in the presence of the selection effect due to variations in innate ability across occupations, the wage gaps across groups are a biased measure of barriers faced. The following proposition gives the analytical expression for the average wages earned by workers from different groups in any occupation. **Proposition 3:** The average wage w�������i in any occupation earned by workers from any group and cohort in any period is given by cc ii ggee ii gg cc tt

9
-rn(Se3 )) (w: ) ~~——-~~

((-s )e ) ~~Oi~~
o ~~r~~ (H ) -(r-DDdeow |-(ETNe | ay

“Tle --r Ye - Ys
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.

12
individuals not in the labor force work to be engaged in the home sector. Table 2 lists the 1-digit occupations together with some examples of narrower sub-groups that constitute them.<sup>11</sup> I make two more adjustments to the data. First, I classify the bottom five percentile of workers in terms of their hours worked in the market occupations to be engaged in the home sector. Depending on the country and the year, the cut-off for being classified in the marker occupations ranges from 21–24 hours. Second, I drop the bottom five percentile of workers in terms of their incomes when using such data. The income of a worker includes earnings from supplying labor in market occupations together with business and farm income in the previous year. A useful feature of the SEDLAC data is that the data on income are converted to real income in local currency with base year in 2011. Finally, I use sampling weights provided by the surveys to aggregate variables at the group-occupation-cohort level, or any combination thereof.

The next section explains how I calibrate the model using the survey data to recover the parameters of the model including the barriers operating in the labor markets and in the accumulation of human capital. Obtaining the estimates of barriers allows me to conduct counterfactual experiments that quantify their aggregate impact.

|Tab|le 2: Occupational classification<br>|
|---|---|
|Occupation|Sub-groups|
|Managers (MNG)|Chief executives, senior officials and legislators; Administrative and<br>commercial managers; Production and specialized services managers<br>etc.|
|Professionals (PRF)|Science and engineering professionals; Health professionals; Teaching<br>professionals etc.|
|Technicians (TCH)|Science and engineering associate professionals; Health associate<br>professionals; Business and administration associate professionals<br>etc.|
|Clerks (CLR)|General and keyboard clerks; Customer services clerks; Numerical and<br>material recording clerks etc.|
|Sales and personal services workers (SSR)<br>Skilled agricultural workers (AGR)|Personal services workers; Sales workers; Personal care workers etc.<br>Market-oriented skilled agricultural workers; Market-oriented skilled<br>forestry, fishery and hunting workers; Subsistence farmers, fishers,<br>hunters and gatherers|
|Craft workers (CRF)|Building and related trade workers; Metal, machinery and related<br>trade workers; Handicraft and printing workers etc.|
|Machine operators (MOP)|Stationary plant and machine operators; Assemblers; Drivers and<br>mobile plant operators etc.|
|Elementary workers (ELM)|Cleaners and helpers; Agricultural, forestry and fishery laborers;<br>Laborers in mining, construction, manufacturing and transport etc.|
|Home sector workers (HSW)|_Not in labor force_<sup>1</sup>|

1Individuals not in any occupation and not looking for work. For more information on the ISCO-88, see ILO (2012).

> 11 The 1-digit occupations (major groups) in ISCO-88 are comparable to those in ISCO-88. New occupations are introduced in the latter, together with some merging and splitting of older categories. Yet, the changes are restricted at the sub-group or finer levels (ILO, 2012).

13
# **4   Calibration**

I now move on to the quantitative analysis and begin by discussing the calibration of the model.

## **4.1   Calibration of time-invariant parameters**

First, let me consider the parameters of the model that remain invariant over the decades. There are seven such parameters. Barring the elasticity of substitution across occupation-specific labor inputs , all time-invariant parameters are calibrated at the country level. For the baseline results, I assume 4 and report the responsiveness of the results by considering other values of in the appendix. σσ σσ= The preference parameter captures the trade-off between consumption and time dedicated in σσ accumulating human capital. I calibrate to match the Mincerian returns to schooling at the occupationββ group level after adding gender controls. ββ To calibrate the elasticity of human capital with respect to associated expenditures, I use the share of GDP (adjusted for the share attributed to labor) spent on education. The education expenditure ηη share includes both private and public spending. The World Development Indicators database (World Bank, 2019) contains annual series on the government expenditure on education as a share of GDP for many years since 1970. The average of this series serves as one component of the total education expenditure.<sup>12</sup> The OECD Indicators database (OECD, 2019) comprises some information on the private education spending across countries. However, the data are sparse both in terms of country and time coverage. The only CENAM for which the private education expenditures are available is Costa Rica. Moreover, this data is available only for 2013. The private education spending as a share of GDP stood at 2 percent in 2013 and I use this as a baseline for all countries.<sup>13</sup> Nonetheless, I note that this value is higher relative to many countries in the OECD database and it is likely that the actual values are lower. Finally, I use a labor income share of 0.65 to adjust the education expenditure share for all countries. There is a wide range of estimates of this parameter with many well-known measurement concerns. The chosen value lies within the range of the estimates and near the median and the average across countries and years (Gollin (2002), Guerriero (2019)).

The Fréchet shape parameter governs the skill dispersion and an increase in its value reduces the variability of innate talent. To estimate this parameter, I fit the residuals obtained after regressing the log of hourly wages on occupation-group-dummies to estimate in an intermediate step. The value of is recovered given the estimate of via the process discussed previously. θθ(1 −ηη) θθ The only time-invariant parameters that need calibration are the experience-related returns to ηη human capital . The parameters 0 1 2 relate to the young, middle and old cohorts and I normalize 0 to 1. To isolate the experience-related human capital gain for the other cohorts, I target the wage cc γγ γγ , γγ , γγ growth of young men over their lifetime after controlling for occupational choices. The average across all γγ occupations serve as the baseline for each country. Table 3 reports the time-invariant parameters for each country. The calibrated values of El Salvador are close to that of Panama. The preferences structure of

> 12 Another strategy is to use a long-term average considering data from the mid-1990s onward only. However, this truncated average is very close to the baseline considered here for each country.

> 13 The private education share consists of early childhood spending which is approximately 0.2 percent of GDP.

14
Costa Rica reveals that households in the country put a lower weight on consumption relative to time in school compared to the other two countries. Moreover, the country allocates a higher share of its income on education and realizes lower variability of workers’ income together with a higher return to experience.

**Table 3: Calibration of time-invariant parameters**

||||Country<br>||
|---|---|---|---|---|
|Parameters<br><br>|Target|Costa Rica|El Salvador|Panama|
|β<br><br>|Mincerian returns|0.175|0.218|0.234|
|η<br><br>|Expenditure share of education|0.117|0.085|0.089|
|θ<br> <br>|Dispersion of hourly wages|6.46|4.71|4.99|
|γy, γm, γo<br>|Wage growth of young men|1, 1.40, 1.90|1, 1.24, 1.42|1, 1.28, 1.33|
|<br>σ|Arbitrary|3|3|3|

# **4.2   Parameters that vary over time**

Remaining parameters of the model vary over time. These parameters also vary across occupations, and often also across gender groups and cohorts. I discuss each set of parameters separately following closely the chronology of actual steps undertaken. **Occupation-specific time elasticity of human capital** i **:** The parameter captures the fact that time allotted to human capital accumulation varies across occupations and time. The calibration focuses on the φφ young men who by assumption do not face any obstacles. Using the worker optimization condition, the elasticity is estimated using equation (5) given estimates of and from before. The schooling i relates to the mean schooling of young men in occupation in period . yy ηη ββ ss **Taste parameters** i : The parameters capture preference and social norms that apply to women and ii tt cc men individually. I normalize the parameters so that the preferences for occupations are measured ii zz relative to the home sector ( ). Next, in contrast to Hsieh _et al._ (2019), I assume the preference for only agricultural work, and not every occupation, equals unity for men hoommoo,icc = 1) . zz = 1 ∀ gg, cc, tt yy Note that preferences of a worker are invariant over time.<sup>14</sup> The first step is to impute average wages in aaiiaa,mmoomm,ii (zz the home sector for young men given average wages in other market occupations. I also assume that women do not face barriers of either sort in the home sector. This allows inferring average wages in home sector for women given data on occupational propensity of both groups. Next, I apply wage equation (8) to back out taste parameters of both groups. **Labor market frictions** i **and barriers to human capital accumulation** i **:** As stated before, I cc yy assume that men do not face any barriers ii(ww) = 0� . The barriers are estimated in ii(hh) ττ ττ cc cc two steps. In the first step, the composite barriers are identified using propensity and wage data according ii,mmoomm,ii(ww) iimmoomm,ii(h) �ττ = ττ to equation (9). Following, the composite is decomposed into the two constituent barriers based on the variation in timing and impact across cohorts which varies for the two barriers. Barriers to human capital accumulation are only faced by young cohorts when they are choosing occupations and the level of

> 14 i i i . yy mm oo ii ,ii+1 ,ii+2 zz = zz = zz ∀ ii, gg, tt

15
schooling. Thus, any variation in wage growth of a cohort across men and women controlling for occupation and human capital differences when young is entirely due to changes in labor marker barriers. Finally, the difference in the timing- and cohort-impact allows for disentangling the change in the two barriers over time. To obtain the levels, I assume an initial split with Cobb-Douglas share of 0.5 on either barrier.<sup>15</sup> As this split is arbitrary, I consider alternative initial splits by loading nearly all composite effect on either barrier in turn to check the sensitivity of the results. **Occupation-specific productivity** i **:** The only parameters left to be calibrated are the productivity parameters and equilibrium conditions are used to pin them down. For a given set of efficiency wages i AA<sup>16</sup> which are used to update labor supply till convergence is achieved.

This concludes the discussion of the calibration strategy. Table 4 summarizes the identifying assumptions and normalization employed in the model calibration. Further details are provided in the appendix.

**Table 4: Identifying assumptions and normalizations**

|Parameter<br><br><br>|Definition|Nature of<br>identification|Value|
|---|---|---|---|
|τi,mom,i(h)<br>c<br><br><br><br>|Human capital barriers: men|Assumption|0|
|τi,mom,i(w)<br>c<br><br><br><br>|Labor market barriers: men|Assumption|0|
|τhomo,i,i(h)<br>c<br><br><br><br>|Human capital barriers: home-sector, men and women|Assumption|0|
|τhomo,i,i(w)<br>c<br><br><br><br>|Labor market barriers: home-sector, men and women|Assumption|0|
|zhomo,i,i<br>y<br><br><br>|Preferences: home-sector, men and women<br>|Normalization|1|
|zaia,mom,i<br>y<br>|Preferences: agriculture, men<br>|Assumption|1|
|α|Initial CD split across barriers,αon labor markets|Assumption|0.5|

# **5   Barriers and preferences**

The calibrated model allows for making counterfactual changes on barriers and preferences, and quantitatively evaluate their aggregate impact. However, the analysis also yields a rich set of estimates on barriers and preference which offers insights for policy action. I discuss them before moving on to the counterfactual exercises in the next section.

> 15 Specifically, i � = 1 and i � i � at 16 The calibration procedure outlined till now also allows for backing out �ττcc ii αα 1−ττicc h(ww) �ττcc ii 1−αα = �1 + ττcc ii(h) ηη tt= 1995.i from the data. The only set of unknowns are the productivity terms for which a starting guess is required. See further details of the calibration exercise in the appendix. ww

16
# **5.1   Changes in barriers over the last two decades**

Recall that there are nine market occupations in addition to the home-sector. Neither men nor women face any barriers in the home-sector. The calibration identifies the levels of two barriers for young cohorts in each occupation across all periods. Thus, it is possible to analyze whether it has become easier for women to participate in the market work because of declining barriers. Specifically, I can check in which of the nine market occupations have these gains been realized and if there are any in which the barriers have increased over time. The model puts no restriction on the barriers other than the assumptions and normalizations pertaining to the home-sector. Therefore, it is possible that the estimated barriers faced by women are negative (or gross barriers being lower than unity). A negative value in an occupation will indicate that instead of being at a disadvantage, women hold an edge over men in that occupation. Figure 2 plots the barriers faced by young women in the labor market (panel (a)) 1 and human capital accumulation (panel (b)). The figures correspond to the gross levels: ~~y~~ and � i ~~�~~ 1−ττ hii(ww) i � for labor market and human capital barriers respectively. The 1995 values of both barriers ηη yy are normalized to unity for each occupation to facilitate inter-temporal comparison. ii(h) �1 + ττ El Salvador is the only economy in which there has been a general decline in labor market discrimination faced by women (figure 2.2(a)). Only three occupations in Costa Rica (figure 2.1(a)) and two in Panama (figure 2.3(a)) recorded a decline in barriers. While none of the education-intensive occupations see a decline in the two countries, two such occupations undergo a weakening of obstacles in El Salvador. Still, the median decline in labor market barriers for the country stands at a meager 2 percent. In contrast, the median occupation in the other two countries register a growth of 12-21 percent.

Unlike barriers in labor markets, the barriers affecting human capital accumulation have gone down more generally across occupations in each of the three countries. Six of the nine occupations in Costa Rica (figure 2.1(b)) and El Salvador (figure 2.2(b)) have undergone a contraction whereas in Panama (figure 2.3(b)) this trend is visible in seven. The decline has also been quantitatively meaningful in many cases. The median occupation in Costa Rica and Panama has seen its gross barrier shrink by 18 and 26 percent respectively. For El Salvador, the median drop in the human capital barrier is close to that in the labor market.

Considering the joint effect of the two barriers reveals that the changes over time have benefitted women in most occupations in each of the three Central American economies. While the composite barriers declined in five occupations in Costa Rica, all but two recorded a drop in the other two countries. All three education-intensive occupations followed the general trend in El Salvador and Panama. The median fall in composite barriers for both countries is close and ranges between 10-12 percent. In contrast, the quantitative joint impact is much lower in Costa Rica where the median contraction was just 0.4 percent.

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In summary, women in all three countries are finding it easier to accumulate human capital. Nonetheless, the trends in the labor markets are not as encouraging. The labor market barriers have risen in all three education-intensive occupations in Costa Rica and Panama. Thus, the findings call for reforms that correct for barriers women face post their educational attainment in the market for jobs. The discussion until now focused on the changes in barriers over the last two decades. Moving on, I show where the occupations stand with respect to the level of barriers in 2015.

# **5.2   Current level of barriers**

Figure 3 plots the occupation-wise distribution of the gross barriers for the three economies in 1 2015. Figures (a) and (b) correspond to the gross barriers in labor market ~~y~~ and human � ~~�~~ 1−ττii,w iiw ,2015(ww) capital accumulation respectively. Recall that the gross barriers are not � � ηη yy bounded below by 1 in either case. Thus, an absolute value of less than unity implies that women receive ii,wwoommoomm,2015(h) ��1 + ττ a net subsidy and are an advantageous position relative to men. Like previously, I discuss the current standing of barriers in each country separately.

Wage discrimination against women is prevalent in Costa Rica (figure 3.1(a)) and Panama (figure 3.3(a)). Barring a couple of cases – none of which include any education-intensive professions, the gross labor market barriers are above unity in almost all occupations. El Salvador fares better in this regard (figure 3.2(a)). Women hold an edge over men in five occupations, including two education-intensive occupations of managerial and technical work. The situation reverses when it comes to human capital accumulation. Barring clerical jobs, women face obstacles in obtaining skills to pursue all other occupations in El Salvador (figure 3.2(b)). In contrast, women in Panama receive favorable treatment in all but two occupations (figure 3.3(b)). Costa Rica lies in between the two cases where the barriers act against women in four of the nine market occupations (figure 3.1(b)). Moreover, women hold an advantage over men in all three education-intensive professions in the latter two countries. Women encounter barriers acting against them in all occupations in each country with clerical jobs in Panama coming out as the only exception to this rule.

Comparing the prevalence of barriers across countries shows intriguing variations that are relevant for policy. Moving on, I now highlight the disparities in the magnitude of the barriers. Note that both Costa Rica and Panama appear close when considering the labor market barriers. Wage discrimination is prevalent in both countries. Nonetheless, the gross barrier for the median occupation in the latter (1.43) surpasses that of the former (1.30) by 10 percent. Furthermore, the median barrier among the education-intensive occupations transcends the overall median in both countries. Still, the extension is more notable in Panama. In contrast, parity holds across the gender line for the median occupation in El Salvador. Nonetheless, the level of barriers is relatively low even in professions in which women face discrimination with the median gross barrier standing at 1.10 for such occupations.

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The challenges women face in human capital accumulation also vary across countries. In Costa Rica and Panama, an advantage in acquiring skills partially offsets the negative effects of wage discrimination in many occupations. However, the strength of support varies across the two countries. The median occupation lies very close to parity in the former while sitting firmly in favor of women in Panama. However, not only do women in both countries enjoy a favorable situation in all three educationintensive occupations, the variation in the median barrier among such jobs is negligible across the two Central American economies. The prevalence of barriers in El Salvador is not innocuous with the median gross barrier standing at 1.35 for all occupations and at 1.22 for the education-intensive ones.

**Table 5: Cross-country comparison of barriers in 2015**

||Costa|El<br>||
|---|---|---|---|
||Rica|Salvador|Panama|
||Panel A:|Barriers in labor|markets|
|No. of occupations in which women are at a disadvantage|8|4|7|
|– of which how many are education-intensive|3|1|3|
|Median gross barrier||||
|All occupations|1.30|1.00|1.43|
|Occupations in which women are at a disadvantage|1.32|1.10|1.50|
|Education-intensive occupations|1.33|1.00|1.50|
||Panel B:|Barriers in huma|n capital|
|No. of occupations in which women are at a disadvantage|4|8|2|
|– of which how many are education-intensive|0|3|0|
|Median gross barrier||||
|All occupations|0.97|1.35|0.82|
|Occupations in which women are at a disadvantage|1.32|1.35|2.41|
|Education-intensive occupations|0.78|1.22|0.76|
||Panel|C: Composite ba|rriers|
|No. of occupations in which women are at a disadvantage|9|9|8|
|– of which how many are education-intensive|3|3|3|
|Median gross barrier||||
|All occupations|1.26|1.18|1.24|
|Occupations in which women are at a disadvantage|1.26|1.18|1.24|
|Education-intensive occupations|1.13|1.14|1.13|

Finally, consider the composite barriers. As I discussed earlier, there is not much difference in the prevalence of obstacles faced by women across the three countries. The combined effect almost always works against women. However, three notable observations stand out when I consider magnitudes. First, 21
the quantitative estimates of barriers are not notional with the median estimate lying firmly above unity in each country. Second, the median is relatively lower in El Salvador than in the other two countries, across which it varies little. Third, the median barrier among the three education-intensive occupations is invariant across the three countries. The above discussion highlights that there are similarities in the incidence of barriers. But more importantly, there are deviations which are crucial for policy. For instance, reforms need to focus more on the labor markets in Costa Rica and Panama whereas El Salvador can benefit from education and training interventions. Table 5 provides a summary of the cross-country comparison of the barriers in 2015.

# **5.3   Preference for market occupations**

Apart from barriers operating in the labor markets and in human capital accumulation, preferences i also affect occupational choices. The calibration strategy normalizes preference for yy home-sector to unity and measures preference for market occupations relative to that for the homeii sector. Unlike the identification in zz Hsieh _et al._ (2019), the calibration strategy does not impose = 1 for all non-market occupations. Agriculture is the only market occupation for which I invoke this yy ii,mmoomm,ii zz restriction. Thus, it is possible for men to favor certain occupations over the home-sector, and vice-versa. Moreover, like for women, the identification strategy admits dynamic preferences for men as well. As such, men can also find certain occupations to become relatively more desirable over time. First, let me consider the changes in preferences over time. Figure 4 plots i for women (panel (a)) and men (panel yy (b)) after normalizing preferences in 1995 to unity to facilitate comparison. ii zz Figure 4.1(a) plots the change in women’s preferences in Costa Rica. Women increasingly prefer to work in market occupations compared to working at home. Except for managerial occupations, all categories have seen a relative rise over the last two decades. The median change in is 15 yy percent with skilled-agricultural occupations leading the charge. Figure 4.1(b) charts the developments in ii,wwoommoomm,ii zz men preferences. Though the relative preferences have risen for most market occupations, the trend is less general compared to women. Preference to stay in the home-sector has gone up relative to working in three market occupations. Two of these are intensive in human capital. In fact, the sharpest contraction has occurred for professional jobs. The median growth in nears 5 percent. yy Figure 4.2(a) and 4.2(b) show how the relative preferences of women and men have changed over zzii,mmoomm,ii time in El Salvador. In contrast to Costa Rica (and Panama as I show later), the preferences of both women and men have shifted away from the market occupations towards the home-sector. The swing has been general, and every occupation has become relatively less preferable. This widespread weakening in labor market participation of both genders appears alarming. The median decline in i over the period for yy both groups is similar and stands at approximately 35 percent. Preference for professional jobs has taken ii zz the largest hit for both women and men.

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<!-- Start of picture text --> 2<br>AGR<br>+<br>SSR<br>eetSBP<br>SRR<br>a MNG<br>2<br>1995 2005 2015<br><!-- End of picture text --> <!-- Start of picture text --> AGR<br>©<br>MNG<br>CRF<br>oO* HOPFePRF<br>1995 2005 2015<br><!-- End of picture text --> <!-- Start of picture text --> Nn AGR<br>N<br>N<br>st<br>TCH<br>MARE<br>- Br<br>© ELM<br>RF<br>2<br>1995 2005 2015<br><!-- End of picture text --> <!-- Start of picture text --> 2<br>+<br>~ SSR<br>fee<br>TCH<br>~ oe<br>PRE<br>2<br>1995 2005 2015<br><!-- End of picture text --> <!-- Start of picture text --> ~~<br>- AGR<br>2<br>ea CLRaPRF<br>1995 2005 2015<br><!-- End of picture text --> <!-- Start of picture text --> q<br>Nn<br>«©<br>2<br>+<br>- MNG<br>CLR<br>- ioeELM<br>«<br>2<br>1995 2005 2015<br><!-- End of picture text -->
The evolution of women preferences in Panama (figure 4.3(a)) resembles that in Costa Rica in many respects. There is general shift towards market occupations. Barring craft-related and elementary occupations, preference for working in every other market occupation has increased relative to the home sector. More importantly, this shift is recorded in all three education-intensive occupations. However, the largest change has been in skilled-agricultural work. The median change in is lower compared yy to Costa Rica and is just over 5 percent. The shift towards working in market occupations has traversed ii,wwoommoomm,ii zz gender lines (figure 4.3(b)). Barring elementary occupations, relative preferences in each market occupation in 2015 are higher than in 1995. The quantitative gains for men are muted compared to those for women. The median change in is 4 percent with managerial occupations experiencing the most yy extensive gains. ii,mmoomm,ii zz To summarize, I find that preferences have shifted towards working in market occupations in Costa Rica and Panama. This transformation applies to both genders, though the spread is a bit restricted for Costa Rican men. In sharp contrast, both men and women are finding it less appealing to work in market occupations in El Salvador with the quantitative decline in preferences being considerable too. Next, I show where the relative preferences stand in 2015. Figure 5 plots the estimates i in 2015 for both women (panel (a)) and men (panel (b)) in the yy three Central American economies. A value greater than unity for an occupation indicates is preference ii zz over the home-sector. To reiterate, I assume that men are indifferent towards working in skilledagriculture compared to working in the home-sector.

As seen earlier, preferences have moved towards the market occupations in Costa Rica (figure 5.1). The shift has been more widespread for women. Figure 5.1(a) further shows that not only have women’s preferences moved away from the home-sector, they prefer working in seven occupations relative to working at home on an absolute basis as well. Skilled-agricultural work is the most preferred occupation. The preference standing does not extend to education-intensive occupations. Somewhat surprisingly, working in the home-sector is still favored relative to taking managerial and technician positions. Men prefer every market occupation to skilled-agricultural work implying they also favor them to working in the home-sector (figure 5.1(b)). The median stands at 1.15 with clerical and yy elementary occupations being the most preferred. Surprisingly, managerial and technical jobs are among ii,mmoomm,2015 zz the least preferred jobs.

In a break from the general trend, both women and men are increasingly finding it preferable to work in the home-sector compared to other occupations over time in El Salvador. Figure 5.2(a) and 5.2(b) indicate that every market occupation is less preferred than the home-sector in 2015. The median i ,2015 yy for women is 0.77 while being closer to parity at 0.95 for men. The median among the education-intensive zz occupations is closer to the overall median for both women and men. Nonetheless, both groups absolutely prefer any occupation over managerial jobs.

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Despite the shift in preferences towards the market occupations in Panama, the home-sector remains the occupation of choice for women in 2015 (figure 5.3(a)). The median in Panama yy is even lower than in El Salvador and stands at 0.72. Managerial and technical jobs are among the three ii,wwoommoomm,2015 zz least preferred occupations. The situation is not very different for men. Barring clerical positions, men prefer home-sector work over all other occupations (figure 5.3(b)). Like women, men find managerial and technical trades to be particularly less preferable. The median in Panama is 0.88 which is lower yy than in El Salvador. However, the country might move closer in the future if the individual time trends in ii,mmoomm,2015 zz preferences hold.

This concludes the discussion on the quantitative aspects of barriers and preferences. In the next section, I quantify the aggregate impact of these exogenous variables in a series of counterfactual experiments.

# **6   Aggregate implications of barriers and preferences**

The calibrated model allows quantifying the effect of barriers and preferences on aggregate output. This section presents the counterfactual change in output when I replace the present exogenous conditions with those in 1995 and 2005.<sup>17</sup> There are three points to note here. First, the counterfactuals relate to the labor market barriers faced by all cohorts. Still, it is only the young workers who realize a shock in preferences and barriers to human capital accumulation. Hence, the exercises cause a change in the occupational choices of only young workers. However, this does not mean that the non-young cohorts are immune to counterfactual changes. Changes in labor market barriers i in period affect all cohorts and bear a direct influence on wages (equation (8)). Similarly, changes in cc ii(ww)i and i apply only to the young cohort but these ττ tt yy yy changes have general equilibrium effects that have implications for other cohorts. Second, in all the ii ii ττ zz counterfactuals, I keep occupation specific TFPs i fixed to focus on the effect borne by the barriers and preferences. Finally, I consider only the share of output that is produced in the market occupations so that it maps to real GDP following the method in Hsieh AA _et al._ (2019).

**6.1   Aggregate output and labor earnings** Let me begin by investigating the change in market output when I replace i , i ,2015(h) and i ,2015 with their prior values. A decline in market output will indicate that the changes contributed cc ,2015(ww) yy ττ ττ yy to economic growth and vice versa.<sup>18</sup> Thus, this exercise assists in answering two vital questions. First, zz which of the three channels is critical in explaining the aggregate growth over the last two decades? Second, has any channel provided headwinds to economic expansion? If so, how large has been this drag?

> 17 The robustness checks on the baseline results are relegated to the appendix.

> 18 A more intuitive approach will be to plug the 2015 values of barriers and preferences into a model calibrated to earlier periods. However, calibrating the model for prior periods requires information on wages and occupational data from the pre-1985 years. Unfortunately, the surveys do not go as far back for any country.

26
**Table 6: Counterfactual change in market output and labor earnings: Using barriers and preferences from 1995, 2005**

||Labor ma<br>|rket only<br>|Human c<br>|apital only<br>|Preferen<br>|ces only<br>|A<br>|ll<br>|Da<br>|ta<br>|
|---|---|---|---|---|---|---|---|---|---|---|
||1995|2005|1995|2005|1995|2005|1995|2005|1995|2005|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|**Panel A: Market o**|**utput, relative t**|**o 2015**|||||||||
|Costa Rica|1.468|1.008|0.587|0.798|0.981|1.064|0.689|0.855|0.584|0.700|
|El Salvador|1.013|1.001|0.971|1.017|1.074|1.034|1.038|1.067|0.522|0.762|
|Panama|2.999|1.754|0.146|0.298|0.718|0.819|0.149|0.385|0.354|0.575|
|**Panel B: Labor ear**|**nings, relative t**|**o 2015**|||||||||
|Costa Rica|1.445|1.007|0.599|0.804|0.988|1.065|0.703|0.863|0.139|0.424|
|El Salvador|1.014|1.001|0.973|1.016|1.050|1.017|1.019|1.047|0.659|0.886|
|Panama|2.987|1.750|0.148|0.300|0.719|0.819|0.151|0.386|0.494|0.576|

27
Panel A of table 6 reports the results of the exercise. Columns (1)-(8) report the counterfactual market output relative to the baseline calibrated value as I make exogenous changes in i , i ,2015(h) and i ,2015 . In columns (1)-(6), I make changes in one set of parameters while keeping the cc ,2015(ww) ττ yy yy others fixed at the 2015 levels. For example, columns (1) and (2) corresponds to changes when I replace ττ zz i with i and i respectively. Columns (7) and (8) report the changes when I replace both barriers and preferences jointly. To gauge how large these variations are, I report the relative cc cc cc ,2015(ww) ,1995(ww) ,2005(ww) ττ ττ ττ real GDP per capita in 1995 and 2005 in columns (9) and (10) respectively.

All figures in columns (1) and (2) exceed unity. Hence, changes in labor market discrimination have dragged economic expansion in all the economies. The impact is most pronounced in Panama. Market output increases by 200 and 75 percent when I supplant barriers from 1995 and 2005 respectively. Replacing barriers from 1995 causes output to rise considerably in Costa Rica as well. The counterfactual expansion is expected given the earlier evidence of rising labor market barriers in the two countries (figures 2.1(a) and 2.3(a)). The predicted shift is marginal for El Salvador where the impact from rising discrimination in some occupations is offset by declining barriers in others (figure 2.2(a)).

Columns (3) and (4) report the aggregate effect of changes in human capital barriers. Recall that all the countries were generally successful in reducing these distortions (figure 2, panel (b)). Hence, it is no surprise that the counterfactual output usually diminishes when past barriers replace the present ones. Like in the case of labor market barriers, the channel is quantitatively most influential in Panama. Output contracts by 70-85 percent relative to the present level. Though comparatively much weaker, the channel is quantitatively relevant for Costa Rica too. The aggregate implications for El Salvador are again trivial.

Columns (5) and (6) quantify the impact of the preference channel. The model estimates a net expansionary effect for Panama. Past preferences exert a qualitatively variable influence in Costa Rica. Substituting the preference profile from 1995 indicates a minor economic loss. However, market output expands when I use preferences from a decade ago. Preferences in El Salvador have evolved somewhat peculiarly over the years. Relative to past decades, both women and men find it preferable to work in the home-sector compared to the market occupations (figures 4.2(a) and 4.2(b)). In line with this, I find that the output in 2015 will increase by 7.5 and 3.5 percent if the preferences of the young workers are replaced with the preferences of the old and middle cohorts respectively. Interestingly, the channel is the strongest among the three considered.

Finally, columns (7) and (8) report relative output associated with the collective effect of the three channels. In Costa Rica and Panama, human capital and preference channels are strong enough to offset the impact of the labor market channel. In El Salvador, the impact of lower human capital barriers relative to 1995 is not enough to compensate for the change exerted by the other two channels. In conclusion, if there were no changes in barriers and preferences over the decades, the market output would be lower in Costa Rica (15-31 percent) and Panama (62-85 percent). In contrast, the output would be marginally higher (4-7 percent) in El Salvador.

Columns (9) and (10) provide perspective on whether these effects are notable compared to actual changes in GDP per capita. GDP per capita is not an exact benchmark for model output. The latter is derived using information from labor surveys. Moreover, the lack of data before 1995 does not allow me to solve the model for any period other than 2015. Even so, the change in real GDP per capita can serve as a first yardstick to assess the strength of the three channels. In general, the three channels –
independently and jointly, are relatively consequential. I discuss this in more detail by comparing the changes in real labor earnings. Unlike GDP per capita, real labor earnings in the model match transparently to the data.

The calibration of the model ensures that the labor earnings of all cohorts match the data in 2015. Besides, the earnings of the young in all years and those of the middle cohorts except in 1995 in the model are the same as in the data. Panel B of table 6 reports the relative labor earnings under the various counterfactual scenarios and in the data.

The figures in columns (1)-(8) of panel B are close to those in panel A. However, the actual figures in columns (9) and (10) vary considerably across the two panels highlighting the survey nature of the household data. The discrepancies are particularly substantial for Costa Rica. Like GDP per capita, labor earnings have grown substantially over the decades. Since 1995, the growth ranges from 50 percent in El Salvador to more than 600 percent in Costa Rica. Though smaller, the shift over the last decade is also not trivial.

First, let me consider the joint effect of the three channels. Together they provided support for earnings expansion in two of the three economies. For Costa Rica, this represents 24-35 of the actual increase. The channels are even more active in Panama. The thrust they generate accounts for more than the actual change. In contrast, the changes in barriers and preferences over time have led to a fall in earnings. The negative impact boils down to 6 and 41 percent of the actual change since 1995 and 2005 respectively.

Now, let's look at the channels individually. The findings are in line with the previous discussion. In Costa Rica, the human capital channel overpowers the negative effects generally spawned by the others. The labor market channel generates significant drag on earnings in Panama. However, the other channels more than offset it. The human capital channel is factors more effective than the preference mechanism. With one exception – changes in human capital barriers since 1995, all channels drag earnings down in El Salvador. The relative importance of the channels has evolved. The preference channel dominates the labor market barriers in producing the aggregate drag since 1995. However, the latter has proved innocuous in the last decade while the other two have been equally influential.

To summarize, there are two key insights from the above exercises. First, in general, changes in human capital barriers have aided economic expansion. On the other hand, changes in labor market barriers have cut into these gains. The impact of the preference channel varies across countries and periods. Second, the aggregate effects of the three channels are quantitatively relevant relative to the actual shifts.

# **6.2   Labor force participation**

The model also provides insights on how labor force participation rates (LFPR) respond to exogenous changes in barriers and preferences. Development policy often targets the rates specifically. Table 7 reports the counterfactual changes in the participation rates of young women (panel A) and men (panel B). Recall that the counterfactual exercises can only affect the occupational decisions of the young. All figures are in percentage points (pp) and correspond to changes over the 2015 level. Thus, a positive number indicates an increase over the actual rate.

29
**Table 7: Counterfactual change in labor force participation rate: Using barriers and preferences from 1995, 2005**

||Labor ma<br>|rket only<br>|Human<br>on<br>|capital<br>ly<br>|Preferen<br>|ces only<br>|A<br>|ll<br>|D<br>|ata<br>|
|---|---|---|---|---|---|---|---|---|---|---|
||1995|2005|1995|2005|1995|2005|1995|2005|1995|2005|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|**Panel A: Labor for**|**ce participation**|**rate, differen**|**ce over 201**|**5: women a**|**ged 25-34 (p**|**p)**|||||
|Costa Rica|15.3|2.4|3.7|2.1|-23.4|-12.1|-15.9|-14.0|-22.8|-10.8|
|El Salvador|-6.5|-1.2|-0.3|-0.2|38.9|34.1|35.3|32.3|-3.9|0.0|
|Panama|20.1|7.9|12.1|9.8|5.4|9.7|18.8|17.2|-18.2|-8.5|
|**Panel B: Labor for**<br>|**ce participation**<br>|**rate, differen**<br>|**ce over 201**<br>|**5: men aged**<br>|**25-34 (pp)**<br>||||||
|Costa Rica|-2.8|-0.5|2.4|1.5|-0.8|-2.0|1.1|-0.1|1.9|2.2|
|El Salvador|0.3|0.1|0.2|0.0|7.3|6.3|7.4|6.3|2.7|-0.3|
|Panama|-2.5|-1.7|2.9|2.4|1.0|1.0|2.7|2.4|-0.8|-0.3|
The LFPR rate of young women in the three economies varies over a 6 pp range – from 61 percent in Panama to 55 percent in El Salvador. Columns (7) and (8) show that the LFPR in Costa Rica declines by 14-16 pp when I supplant the present barriers and preferences with their past values. The effect is significant from a historical perspective. Columns (9) and (10) report the actual change over the decades that ranges between 11-23 pp. A quantitatively opposite change materializes in the other two countries. According to the model, the same exercise leads to a 32-35 pp and 17-19 pp expansion in LFPR of El Salvador and Panama respectively. The preference channel is instrumental in driving the aggregate change in Costa Rica and El Salvador. Its effect is also non-trivial in Panama. The dominance of the channel contrasts with the earlier output exercises where it played a secondary role.

The magnitude of change in the LFPR of young men is much smaller. The joint effect of the three channels lifts the rates by 0-1 pp, 6-7 pp, and 2-3 pp in Costa Rica, El Salvador, and Panama respectively. The human capital channel drives the shifts in Costa Rica and Panama. However, like for women, it is the changes in preferences that generate the maximum impact in LFPR of men in El Salvador.

Factors that lead to an increase in the participation of women might reduce that of men. An increase in women's labor supply will cause the wage rate in market occupations to fall relative to that of the home sector. The talent endowment of many men will not be able to support participation under the new wage structure. Hence, men with sufficiently high talents in the home sector will exit the labor force. The results indicate that such general equilibrium effects are offset by changes elsewhere.

# **6.3   Robustness checks**

|Recall tha<br> <br><br><br> <br>|t the bench<br> <br><br>|mark calibr<br> <br>|ation uses<br> <br>|an arbitrar<br> <br>c<br>|y assumpti<br><br> <br>|on regardin<br> <br>|g the Cob<br> <br>|b-Douglas<br> <br>|
|---|---|---|---|---|---|---|---|---|
|parameterαt<br><br><br> <br><br>|at splits t<br><br><br>|he comp<br><br>|osite barri<br><br>|ersτi,19<br><br><br>|95<br>equally<br><br>|into its<br><br>|two co<br><br>|nstituents<br><br>|
|�τi,1995(w)<br>c<br>, τi,19<br>c<br>the first, I assume<br>Alternatively, I se<br>Table 8reports th|95(h)<br>�. I che<br>that almost<br>tαto 0.95<br>e results of t<br>**Table 8: C**<br>Labor|ck the sens<br>all the init<br>which impli<br>he robustn<br>**ounterfactu**<br>market|itivity of th<br>ial weight f<br>es that lab<br>ess exercis<br>**al change in**<br>Human|e results b<br>alls on the<br>or markets<br>e.<br>**market out**<br>capital|y consideri<br>human ca<br>constitute<br> **put: Varyin**<br>Prefer|ng two extr<br>pital barrier<br>the bulk of<br>**g CD split**α<br>ences|eme valu<br>s and set<br>the initia<br>A|es ofα. In<br>αto 0.05.<br>l barriers.<br>ll|
||on<br>|ly<br>|<br>on<br>|<br>ly<br>|on<br>|ly<br>|||
||1995|2005|1995|2005|1995|2005|1995|2005|
||(1)<br>|(2)<br>|(3)<br>|(4)|(5)|(6)|(7)|(8)|
|**Panel A: Market o**|**utput, relativ**|**e to 2015**(α|= 0. 0)||||||
|Costa Rica|1.399|<br>0.985|<br>0.663|0.850|1.013|1.081|0.772|0.914|
|El Salvador|1.018|<br>1.009|<br>0.993|1.038|1.046|1.012|1.058|1.080|
|Panama<br>|2.975<br>|1.644<br>|0.249<br>|0.391|0.651|0.774|0.249|0.491|
|**Panel B: Market o**|**utput, relativ**|**e to 2015**(α|= 0. 90)||||||
|Costa Rica|1.524|<br>1.041|<br>0.514|0.736|0.939|1.036|0.598|0.782|
|El Salvador|1.016|<br>0.971|<br>0.810|0.912|1.161|1.132|0.866|0.997|
|Panama|2.227|1.781|0.059|0.134|1.163|1.074|0.062|0.158|
The exercise helps set bounds on the aggregate effects. Reading results in tables 6 and 8 together allows me to arrive at some general conclusions on a country basis. The aggregate effect of changes in barriers and preferences on output has been expansionary in Costa Rica and Panama. Replacing the present values with those from 1995 and 2005 in the former implies a contraction of 23-40 and 9-20 percent respectively. The analogous figures for Panama stand at 75-94 and 51-84 percent. Shifts in barriers to human capital accumulation drive these economic gains. On the other hand, intertemporal variations in labor market barriers have usually proved a hindrance to growth. The qualitative role of the preference channel is difficult to pin down as it is acutely sensitive to the choice of . Taken together, the qualitative effect of the three channels on aggregate output in El Salvador is αα Taken together, the qualitative effect of the three channels on aggregate output in El Salvador is inconclusive. Moreover, the joint impact is relatively mild compared to Costa Rica and Panama. Still, it is possible to ascertain the role of some individual channels. Over the decades, both women and men are finding it preferable to stay in the home sector rather than working in market occupations. These changes are dragging the economy down. Replacing the present preferences with those in 1995 and 2005 can increase output by 5-16 and 1-13 percent respectively. The analysis also shows that shifts in human capital barriers since 1995 have been growth-enhancing. However, the estimated impact varies over a broad range. Going back to 1995 levels will lead to a compression that can be either trivial (1 percent) or sizable (19 percent).

# **7   Conclusion**

Costa Rica, El Salvador, and Panama have seen considerable economic growth over the last two decades. During the period, there has been a phenomenal shift in the occupational choices of women in these countries. Do these changes imply declines in barriers that women face in labor markets and human capital accumulation? And how important are these trends in explaining the accompanying economic growth? Building on recent literature that ties occupational distribution to the allocation of talent, I find that a better allocation of talent in Costa Rica and Panama provided substantial tailwinds for economic expansion. Conservative estimates suggest that the channel can account for almost half of the actual growth in market output per capita and a quarter of the increase in real labor earnings in Costa Rica. In Panama, the channel is even more potent and explains more than the actual growth in both variables. The aggregate implications are milder in El Salvador. Still, I find that shifts in social norms created a notable drag on growth.

I conclude by making a couple of points that have policy implications and require further consideration. First, the shift in social norms in El Salvador is surprising given the trend in other countries. Why have the preferences to be engaged in the home sector, as opposed to participating in market work, increased in the last two decades? Variation in crime and violence might be one of the factors driving the trend. For instance, the surveys will record men who join gangs as being occupied in the home sector to the extent they are not engaged in any other occupation. The income earned due to criminal activity is likely to go unreported as well. The model will interpret the two outcomes as an increase in preference for the home sector. Additionally, workers of both genders who participate in market occupations may be incrementally more exposed to victimization. If the differential risks do not lead to a loss of wages or 32
schooling, the model will reconcile them with shifts in preferences. Separation of such effects from traditional sources of change, such as fertility decisions, is essential for policy intervention.

Second, the identification of barriers is only a first step towards successful policy intervention. Removing them in one sphere can have unintended consequences, raising barriers elsewhere. For instance, boys are more prone to victimization due to gang activity in El Salvador. As expected, the educational outcomes of boys improved due to a decline in homicide rates. At the same time, it also led to a decline in attendance rates of girls. This suggests that intra-household allocation of resources that favors boys kicked in raising obstacles for girls, as a result of external barriers for boys going down (Dahbura, 2018). The unintended consequences of policies aimed at reducing barriers can also spill over to unrelated spheres. For example, Chant (2000) reports that the improvements in the labor market outcomes of women in Guanacaste (Costa Rica) are not without their side effects. The ensuing loss of power held by men increased the stress among the family members bound by marital associations. Hence, corrective policies need to be cognizant of a range of inter-related factors and should be carefully implemented.

# **References**

**Acosta, Pablo.** “School attendance, child labour, and remittances from international migration in El Salvador.” _Journal of Development Studies_ 47.6 (2011): 913-936.

**Altonji, Joseph G., and Rebecca M. Blank.** “Race and gender in the labor market.” _Handbook of labor economics_ 3 (1999): 3143-3259.

**Arends, Mary.** “Female labor force participation and earnings in Guatemala.” _Case Studies on Women’s Employment and Pay in Latin America_ . Washington, DC, United States: World Bank (1992a).

**Arends, Mary.** “Female labor force participation and wages: A case study of Panama.” _Case Studies on Women’s Employment and Pay in Latin America_ . Washington, DC, United States: World Bank (1992b).

**Becker, Gary.** “The economics of discrimination.” _The University of Chicago Press: Chicago, IL_ (1957).

**Berg, Andrew, and Anne O. Krueger.** “Trade, growth, and poverty: a selective survey.” In _Annual World Bank Conference on Development Economics_ (2003): 47-91, World Bank.

**Bertrand, Marianne.** “New perspectives on gender.” In _Handbook of Labor Economics_ , vol. 4, pp. 15431590. Elsevier, 2011.

**Blau, Francine D., Marianne A. Ferber, and Anne E. Winkler.** “The economics of women, men and work.” _Prentice-Hall Englewood Cliffs, NJ_ (2013).

**Caceres, Luis Rene.** “Economic openness and stagnation in Central America.” _Journal of Developing Areas_ 51.1 (2017): 373-389.

**CEDLAS, and the World Bank.** “Socio-economic database for Latin America and the Caribbean _.” Centro de Estudios Distributivos, Laborales y Sociales,_ Facultad de Ciencias Economicas, Universidad Nacional de La Plata, La Plata, Argentina; _the World Bank,_ Washington DC (2019).

**Chant, Sylvia.** “Men in crisis? Reflections on masculinities, work and family in north-west Costa Rica.” _European Journal of Development Research_ 12, no. 2 (2000): 199-218.

33
**Costa, Dora L.** “From mill town to board room: The rise of women's paid labor.” _Journal of Economic Perspectives_ 14, no. 4 (2000): 101-122.

**Dahbura, Juan Nelson Martínez.** “The Short-Term Impact of Crime on School Enrollment and School Choice: Evidence from El Salvador.” _Economía_ 18.2 (2018): 121-145.

**Dávila, Alberto, and José A. Pagán.** “Gender Pay and Occupational-Attainment Gaps in Costa Rica and El Salvador: A Relative Comparison of the Late 1980s.” _Review of Development Economics_ 3, no. 2 (1999): 215-230.

**Dollar, David, and Aart Kraay.** “Trade, growth, and poverty.” _Economic Journal_ 114, no. 493 (2004): F22F49.

**Easterly, William, and Ross Levine.** "Tropics, germs, and crops: How endowments influence economic development." _Journal of Monetary Economics_ 50.1 (2003): 3-39.

**Eaton, Jonathan, and Samuel Kortum.** “Technology, geography, and trade.” _Econometrica_ 70, no. 5 (2002): 1741-1779.

**Feenstra, Robert C., Robert Inklaar, and Marcel P. Timmer.** “The next generation of the Penn World Table.” _American Economic Review_ , no. 105(10) (2015): 3150-3182, available for download at <u>www.ggdc.net/pwt.</u>

**Ferreira, Gustavo FC, and R. Wes Harrison.** “From coffee beans to microchips: Export diversification and economic growth in Costa Rica.” _Journal of Agricultural and Applied Economics_ 44, no. 4 (2012): 517-531.

**Funkhouser, Edward.** “Mobility and labor market segmentation: The urban labor market in El Salvador.” _Economic Development and Cultural Change_ 46, no. 1 (1997): 123-153.

**Gaddis, Isis, and Janneke Pieters.** “The gendered labor market impacts of trade liberalization evidence from Brazil.” _Journal of Human Resources_ 52, no. 2 (2017): 457-490.

**Gindling, Thomas H.** “Women's wages and economic crisis in Costa Rica.” _Economic Development and Cultural Change_ 41, no. 2 (1993): 277-297.

**Gollin, Douglas.** "Getting income shares right." _Journal of Political Economy_ 110, no. 2 (2002): 458-474.

**Guerriero, Marta.** “The labor share of income around the world: Evidence from a panel dataset.” _ADBI Working Paper 920_ , Asian Development Bank Institute, Tokyo (2019).

**Hsieh, Chang-Tai, Erik Hurst, Charles I. Jones, and Peter J. Klenow.** “The allocation of talent and US economic growth.” _Econometrica_ 87, no. 5 (2019): 1439-1474.

**International Labor Organization.** “International standard classification of occupations: Structure, group definitions and correspondence tables.” _International Labor Office, Geneva_ (2012).

**Juhn, Chinhui, Gergely Ujhelyi, and Carolina Villegas-Sanchez.** “Men, women, and machines: How trade impacts gender inequality.” _Journal of Development Economics_ 106 (2014): 179-193.

**Kalsi, Priti.** “The impact of US deportation of criminals on gang development and education in El Salvador.” _Journal of Development Economics_ 135 (2018): 433-448.

**Kasy, Maximilian, and Alvaro Ramos-Chaves.** “The Impact of Changing Family Structures on the Income Distribution among Costa Rican Women 1993–2009.” _Feminist Economics_ 20, no. 2 (2014): 122-144.

34
**Li, Jing, William H. Dow, and Luis Rosero-Bixby.** “Education gains attributable to fertility decline: Patterns by gender, period, and country in Latin America and Asia.” _Demography_ 54.4 (2017): 1353-1373.

**McFadden, Daniel.** “Conditional logit analysis of qualitative choice behavior.” In P. Zarembka, ed., _Frontiers of Econometrics,_ New York, NY: Academic Press (1973): 105-142.

**OECD.** “Education at a glance 2019: OECD Indicators” _OECD Publishing_ , Paris (2019).

**Prati, Alessandro, Massimiliano Gaetano Onorato, and Chris Papageorgiou.** “Which reforms work and under what institutional environment? Evidence from a new data set on structural reforms.” _Review of Economics and Statistics 95.3_ (2013): 946-968.

**Rodrik, Dani.** “Growth strategies.” _Handbook of Economic Growth_ 1 (2005): 967-1014.

**Roquez-Diaz, Adolfo, and Lorenzo Escot.** “Relationship between trade openness and economic growth in Latin America: A causality analysis with heterogeneous panel data.” _Review of Development Economics_ 22.2 (2018): 658-684.

**Roy, Andrew Donald.** "Some thoughts on the distribution of earnings." _Oxford Economic Papers 3_ , no. 2 (1951): 135-146.

**Sachs, Jeffrey D., Andrew Warner, Anders Aslund, and Stanley Fischer.** “Economic reform and the process of global integration.” Brookings Papers on Economic Activity 1995, no. 1 (1995): 1-118.

**Swiston, Andrew J., and Luis-Diego Barrot.** “The role of structural reforms in raising economic growth in Central America.” _IMF Working Papers_ (2011): 1-20.

**Williamson, John.** “What Washington means by policy reform.” In John Williamson, ed., _Latin American Adjustment: How Much Has Happened?_ , Washington DC: Institute for International Economics (1990)

**Williamson, John.** “What should the World Bank think about the Washington Consensus?.” _World Bank Research Observer 15.2_ (2000): 251-264.

**Winter, Carolyn, and T. H. Gindling.** “Women's labor force participation and earnings in Honduras.” Case Studies on Women’s Employment and Pay in Latin America. Washington, DC, United States: World Bank (1992).

**World Bank.** “World development indicators.” The World Bank, Washington DC (2019).

**World Bank.** “World Bank country and lending groups.” The World Bank, Washington DC (2020).

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# **Online Appendix**

## **A1. Data**

Household surveys are retrieved from the SEDLAC database (CEDLAS and the World Bank, 2019). The data repository covers the period from 1974 to 2015. However, for most countries, the surveys are only available since the 1990s.

To add power to the analysis, I pool data from the adjacent year surveys to the reference years whenever possible. Table A1 lists the mapping of survey years to model year for each country.

**Table A1: Surveys constituting model years**

|Model Year||Survey years included||
|---|---|---|---|
||Costa Rica|El Salvador|Panama|
|1995|1995, 1996|1995, 1996|1995, 1997|
|2005|2004, 2005, 2006|2004, 2005, 2006|2004, 2005, 2006|
|2015|2013, 2014, 2015|2013, 2014, 2015|2013, 2014, 2015|

The sample selection of workers is discussed in detail in the main paper. For the sample, I extract the following variables from the survey – sex, age, years of schooling, occupation, hours worked and labor income.

**Generalities:** The occupational taxonomy assigns workers into 10 professions which correspond to the major groups of the International Standard Classification of Occupations, 1988 (ISCO-88) (ILO, 2012). Following Hsieh _et al._ (2019), the workers engaged in armed forces are dropped. Respondents not in the labor force are assumed to be employed in the home sector. Hence, together with the home sector, a worker can choose from a set of 10 occupations. For Costa Rica and Panama, some recoding is needed to map workers to the same occupational taxonomy.

Besides the number of workers in each _cohort-gender-occupation_ category, I also need the years of schooling, labor income, and hours worked associated with them. Hours worked in the main occupation are reported weekly in each survey, while income is reported on a monthly basis. Years of schooling and labor income are coded by the SEDLAC. The labor income is deflated using the consumer price index (2011=100). Beyond this general transformation, each country presents its own idiosyncrasies, which are discussed below.

**Costa Rica:** There are two different surveys available for the country in SEDLAC. For 1990-2009, the repository contains the surveys from _Encuesta de Hogares de Propósitos Múltiples_ (NISC, 2009) and from 2010 onward the surveys correspond to _Encuesta Nacional de Hogares_ (NISC, 2020). The sampling methodology varies a bit across the two surveys which raises the concern of consistency. However, in practice there are no unusual jumps in the time series of variables around 2010. The yearly change from 2009 to 2010 in the series appears reasonable given the past variations.

Beginning in 2001, the household surveys in Costa Rica adopted the ISCO-88 occupational taxonomy. Before 2001, a national classification was employed. The system was based on the second 36
revision of the International Standard Classification of Occupations (ISCO-68). To make the classification consistent, I first convert the national classification into the ISCO-68. Following, I remap the ISCO-68 occupations to ISCO-88 following the scheme in Ganzeboom and Treiman (1996). Again, I do not find any unusual kinks in the share of occupations as a result of these transformations.

**El Salvador:** The country dollarized its economy on January 1, 2001. Hence, labor income in the preceding survey years, was divided by the exchange rate to convert it to dollars.

**Panama:** From 2011 to 2013 occupations in the national surveys match the major groups of the ISCO-88. For previous years, Panama has its own classification of occupations (NCO-2000). The system follows the ISCO-88 taxonomy and it is straightforward to construct correspondence between the two.  The NCO2000 classification was used from 2001 to 2010. Before 2001, a different classification was in use (NCO90). The working manual from the National Institute of Statistics and Census (1999) provides the correspondence tables between the NCO-1990 and the NCO-2000 taxonomies. Using these, I harmonize the occupations across the survey years.

# **A2. Calibration details**

**Parameters invariant over time:** There are seven parameters in the model that are invariant over time. Barring the elasticity of substitution across occupation-specific labor inputs , all time-invariant parameters are calibrated for each country separately. For the baseline results, I assume and report the responsiveness of the results by considering other values of . σσ σσ= 4 **a.   Preference parameter :** The parameter captures the trade-off between consumption and time σσ dedicated in accumulating human capital. To calibrate the parameter, I match the Mincerian returns to ββ schooling. The first step is to collect for year using the following regression ������� ii ln�wψψ i tt i i where variables under bars represent the average for a group ggee ii�= ααii + ψψii ss̅ ii + DDii in any occupation + εε ii and are group

As a worker’s lifetime endowment of time is 1 spread over 25 years of that in the data, the model 1 equivalent of 1 year is = 0.04 . The average estimates of across all years serves as the time-invariant 25 measure used in the quantitative analysis. ii ββ 37
**b.   Elasticity of human capita** To calibrate the elasticity of human capital with respect to associated expenditures, I use the share of GDP (adjusted for the share attributed to labor) spent on education. ηη: Whenever data availability permits, the education share includes both private and public spending. Specifically,<sup>sshwwaaee oGGDDGG ssppeesstt ooss eeddeeccwwttiiooss</sup> **c.   Talent distribution parameter** ηη= **:** The parameter directs the skill dispersion and an increase in its value llwwllooaa iissccoommee sshwwaaee o GGDDGG reduces the variability. The first step in estimating the parameter is to collect the residuals after regressing θθ the log of hourly wage of a worker on occupation-group-cohort dummies i cc wwi ii cc wwi ccii In the next step, a generalized extreme value distribution (GEV) is fit across the collected residuals. The ln�wwhooeeaa �= DD + εε −1 estimate of GEV parameter equals . Given , the relationship yields the value of the shape parameter . ξξ �θθ(1 −ηη)� ηη **d.   Experience-related human capital accumulation** θθ **:** The human capital of workers increases with experience and middle and old cohorts have higher human capital compared to when they were 00 11 22 young. The parameters 0 1 2 are related to the young, middle and old cohorts and γγ , γγ , γγ 0 is normalized to 1. To pin down the experience-related human capital gain for the other cohorts, I target the wage γγ , γγ , γγ γγ growth of the group that faces no frictions in labor markets and in human capital accumulation after controlling for the occupational choice. I consider wage growth of young workers in all later life periods for which data are available and then average across them to arrive at a figure for a country (see more details on the calibration of in the following pages where I discuss the estimation of taste parameters i ). jj γγ cc ii **Parameters that vary over time:** zz Remaining parameters of the model vary over time. These parameters also vary across occupations, and often also across groups and cohorts. Their order below roughly follows the chronology needed to estimate them. **a.   Occupation-specific time elasticity of human capital** i **:** The parameter captures the effect that time allotted to human capital accumulation varies across occupations and time. Hence, a higher observed φφ schooling intensity in some occupation can be due to its high elasticity. Similarly, an intertemporal shift in elasticity will also lead to shifts in amount of time spent in school. Note that workers make their occupational and schooling choices when young, and their decisions depend only on the elasticity they observe when young. The time elasticity when they are in middle and old cohort is orthogonal to their decision-making. The calibration focuses on the young cohort of the reference group which does not face any frictions. Using the worker optimization condition, the elasticity is estimated using aa i i = yy ~~y~~ iii ss̅ 1 −ηη φφ 1 −ss̅ ii 3ββ

38
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Finally, the barriers to human capital accumulation in any period can be decomposed from the composite frictions once the labor market frictions have been identified. **d. Occupation-specific productivity** i **:** The only parameters left to be calibrated are the productivity parameters and equilibrium conditions are used to pin them down. Given schooling choice i , relative propensities i , frictions i and AAi , and efficiency wages i , the average quality of workers is cc yy cc cc ss obtained. The average quality then pins down the labor supply i in each occupation and starting with ii (ii) ii(h) pp ττ ττ ww some initial guess of productivities, the total output is determined using the production technology. LLSS Once, total output is known, the productivities consistent with firm optimization are given by ii YY 1 i i = �<sup>LLSSi</sup> �σσ−1 σσ which are used to update labor supply till convergence is achieved. AA ii ww YY

# **A3.   Adjustments to the wage data**

As mentioned previously, I drop workers who earn less than threshold income and when data permit work fewer than threshold number of weeks. There are two additional model related adjustments that I make to the wage data.

**a.   Adjusting for changes in LFPR across periods for a cohort:** In the model, the LFPR of a cohort is

41
re ~~y~~ e ~~te)~~ = Prob(U #i b ~ )vj #i )=exp(Se ) ~~a~~ exp(@e_ )de ~~=~~ | are

x( )
6) @ Jel) ~~(Ja ey~~ El(s_) (€ ) elchoose|=(s_) ~~(a) af~~ t |

-r ~~(—~~ = exp[-) cvsm(-5( ~~-)~~ ) ) = exp (- ~~(—)~~ ; ) (from proposition 1) E(e = | € -| ( ~~—)~~ e(-( ~~—)~~ s )e - ~~(—}~~ [ <u>~ewesas</u> [retainer = ~~(—~~ < ) ~~jee~~
~~(0~~ E|(s ) (e ) €| choose i|

=(1-t by ~~<u>=)</u> p—~~ q ~~r(~~ <u>—</u> =A =) | ~~__| el~~
<u>y(a-r ) "</u> [ a- JI ~~—~~ —, oy ~~mf) mt)~~ ald ~~(5 ny (7)~~ | ~~——(~~ t =ovj) =A(o ~~—} —~~ = A(1 ~~-——) —~~
# **A6.   Supplementary results**

The following tables report results and robustness checks not included in the main paper for brevity.

brevity. **A6.1 Robustness: Choice of initial Cobb-Douglas split** αα

**Table A2: Counterfactual change in women’s LFPR: Using barriers and preferences from 1995, 2005**

||Labor<br>on|market<br>ly|Human<br>on|capital<br>ly|Preferen|ces only|A|ll|
|---|---|---|---|---|---|---|---|---|
||1995|2005|1995|2005|1995|2005|1995|2005|
||(1)<br>|(2)<br>|(3)<br>|(4)<br>|(5)<br>|(6)<br>|(7)<br>|(8)|
|**Panel A: Labor for**<br>|**ce participatio**<br>|**n rate, diffe**<br>|**rence over**|**2015: wom**<br>|**en aged 25-**<br>|**34 (pp)**(α<br>|= 0. 0)<br>||
|Costa Rica|16.7|3.6|4.5|0.8|-24.2|<br>-12.7|<br>-19.0|-16.8|
|El Salvador|-6.7|-1.4|-0.5|-0.5|39.2|<br>34.4|<br>35.2|32.3|
|Panama<br>|20.6<br>|9.7<br>|9.4<br>|6.1<br>|8.3<br>|10.8<br>|10.0<br>|11.3|
|**Panel B: Labor for**|**ce participatio**|**n rate, diffe**|**rence over**|**2015: wom**|**en aged 25-**|**34 (pp)**(α=|0. 90)||
|Costa Rica|14.2|1.7|0.3|3.1|-22.6|<br>-11.6|<br>-12.3|-11.1|
|El Salvador|-6.5|-1.0|0.6|0.7|38.4|<br>33.4|<br>36.0|32.4|
|Panama|20.4|7.4|15.8|17.0|4.4|9.1|32.7|30.8|

**Table A3: Counterfactual change in men’s LFPR: Using barriers and preferences from 1995, 2005**

||Labor<br>on|market<br>ly|Human<br>on|capital<br>ly|Prefere|nces only|A|ll|
|---|---|---|---|---|---|---|---|---|
||1995|2005|1995|2005|1995|2005|1995|2005|
||(1)<br>|(2)<br>|(3)<br>|(4)<br>|(5)<br>|(6)<br>|(7)<br>|(8)|
|**Panel A: Labor for**<br>|**ce participatio**<br>|**n rate, diffe**<br>|**rence over**<br>|**2015: wom**<br>|**en aged 25-**<br>|**34 (pp)**(α=<br>|0. 0)<br>||
|Costa Rica|-2.7|-0.5|1.7|1.0|-1.0|<br>-2.2|<br>0.4|-0.6|
|El Salvador|0.2|0.0|0.1|-0.1|7.3|<br>6.4|<br>7.4|6.3|
|Panama<br>|-2.3<br>|-1.4<br>|2.4<br>|1.6<br>|0.9<br>|1.0<br>|2.2<br>|1.7|
|**Panel B: Labor forc**|**e participatio**|**n rate, diffe**|**rence over**|**2015: wom**|**en aged 25-**|**34 (pp)**(α=|0. 90)||
|Costa Rica|-2.9|-0.4|3.3|2.1|-0.5|<br>-1.9|<br>1.7|0.5|
|El Salvador|0.4|0.2|1.5|0.5|7.2|<br>6.2|<br>7.5|6.3|
|Panama|-2.4|-1.8|3.3|3.2|1.1|1.0|3.3|3.2|

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# **A6.2 Robustness: Choice of elasticity** σσ

**Table A4: Counterfactual change in market output: Varying elasticity** σσ

||Labor ma|rket only|Human<br>on|capital<br>ly|Preferen|ces only|A|ll|
|---|---|---|---|---|---|---|---|---|
||1995<br>(1)<br>|2005<br>(2)<br>|1995<br>(3)<br>|2005<br>(4)|1995<br>(5)|2005<br>(6)|1995<br>(7)|2005<br>(8)|
|**Panel A: Market**|**output, relati**|**ve to 2015**(σ|= 2)||||||
|Costa Rica|1.388|<br>1.011|<br>0.604|0.810|0.983|1.049|0.701|0.858|
|El Salvador|1.012|<br>1.001|<br>0.972|1.014|1.065|1.030|1.031|1.054|
|Panama<br>|2.602<br>|1.624<br>|0.146<br>|0.328|0.727|0.826|0.150|0.403|
|**Panel B: Market**|**output, relati**|**ve to 2015**(σ|= 4)||||||
|Costa Rica|1.529|<br>1.006|<br>0.576|0.790|0.980|1.075|0.680|0.852|
|El Salvador|1.014|<br>1.001|<br>0.970|1.020|1.080|1.037|1.043|1.076|
|Panama|3.261|1.852|0.147|0.279|0.712|0.814|0.148|0.371|

**Table A5: Counterfactual change in women’s LFPR: Varying elasticity** Labor market σσ

||Labor<br>on|market<br>ly|Human<br>o|capital<br>nly|Preferen|ces only|A|ll|
|---|---|---|---|---|---|---|---|---|
||1995<br>(1)<br>|2005<br>(2)<br>|1995<br>(3)<br>|2005<br>(4)<br>|1995<br>(5)<br>|2005<br>(6)<br>|1995<br>(7)<br>|2005<br>(8)|
|**Panel A: Labor for**<br>|**ce participati**|**on rate, diff**|**erence over**|**2015: wom**|**en aged 25-**|**34 (pp)**(σ|= 2)||
|Costa Rica|12.1|1.3|4.8|3.3|-21.1|<br>-10.7|<br>-10.6|-10.6|
|El Salvador|-6.4|-1.2|-0.2|-0.3|37.7|<br>32.5|<br>33.8|30.4|
|Panama<br>|13.5<br>|4.9<br>|19.8<br>|15.8<br>|6.4<br>|10.0<br>|30.0<br>|22.6|
|**Panel B: Labor for**|**ce participati**|**on rate, diff**|**erence over**|**2015: wom**|**en aged 25-**|**34 (pp)**(σ=|4)||
|Costa Rica|17.6|3.4|3.3|1.3|-24.9|<br>-12.9|<br>-19.2|-16.4|
|El Salvador|-6.6|-1.3|-0.4|-0.2|39.5|<br>34.9|<br>36.1|33.3|
|Panama|24.6|10.0|8.4|6.1|5.3|9.7|11.0|13.8|

47
**Table A6: Counterfactual change in men’s LFPR: Varying elasticity** Labor market σσ

||Labor<br>on|market<br>ly|Human<br>on|capital<br>ly|Preferen|ces only|A|ll|
|---|---|---|---|---|---|---|---|---|
||1995|2005|1995|2005|1995|2005|1995|2005|
||(1)<br>|(2)<br>|(3)<br>|(4)<br>|(5)<br>|(6)<br>|(7)|(8)|
|**Panel A: Labor for**<br>|**ce participatio**|**n rate, diff**|**erence over**|**2015: men**|**aged 25-34**|**(pp)**(σ= 2)|||
|Costa Rica|-3.3|-0.5|3.1|1.9|-0.1|<br>-1.5|2.0|0.7|
|El Salvador|0.4|0.1|0.3|0.0|7.1|<br>6.0|7.2|6.0|
|Panama<br>|-3.3<br>|-2.2<br>|3.2<br>|2.8<br>|1.6<br>|1.2<br>|3.1|2.7|
|**Panel B: Labor for**<br>|**ce participatio**<br>|**n rate, diff**<br>|**erence over**<br>|**2015: men**<br>|**aged 25-34**<br>|**(pp)**(σ= 4)<br>|||
|Costa Rica|-2.4|-0.4|2.0|1.3|-1.3|<br>-2.4|0.4|-0.6|
|El Salvador|0.2|0.1|0.2|0.0|7.4|<br>6.5|7.5|6.5|
|Panama|-2.0|-1.4|2.5|2.1|0.7|0.9|2.4|2.1|

# **References**

**CEDLAS, and the World Bank.** “Socio-economic database for Latin America and the Caribbean _.” Centro de Estudios Distributivos, Laborales y Sociales,_ Facultad de Ciencias Economicas, Universidad Nacional de La Plata, La Plata, Argentina; _the World Bank,_ Washington DC (2019).

**Ganzeboom, Harry BG, and Donald J. Treiman.** “Internationally comparable measures of occupational status for the 1988 International Standard Classification of Occupations.” _Social Science Research_ 25, no. 3 (1996): 201-239.

**Hsieh, Chang-Tai, Erik Hurst, Charles I. Jones, and Peter J. Klenow.** “The allocation of talent and US economic growth.” _Econometrica_ 87, no. 5 (2019): 1439-1474.

**International Labor Organization.** “International standard classification of occupations: Structure, group definitions and correspondence tables.” _International Labor Office, Geneva_ (2012).

**National Institute of Statistics and Censuses.** “Encuesta de Hogares de Propósitos Múltiples.” San Jose, Costa Rica (2009).

**National Institute of Statistics and Censuses.** “Encuesta Nacional de Hogares.” San Jose, Costa Rica (2020).

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