rafmacalaba/datause-encoder-data huggingface dataset:
Young Lives surveys will allow researchers to examine the links between labor earnings, schooling, and skills.<sup>6</sup> The studies from low- and middle-income countries closest to our work are Glewwe, Huang, and Park (2017) and Glewwe, Song, and Zou (2022), who follow a sample of children in rural China. In 2017, they found no correlation between labor earnings and cognitive or SEM skills. By 2022, earnings were correlated with cognitive skills after controlling for years of schooling, but SEM skills were not. This scholarship complements ours by offering insights from a different context. Yet, the studies also differ from ours in their ability to track students over time: Glewwe, Huang, and Park (2022) have some data on 67% of their original sample and face-to-face interviews on just under 50% (compared to 84.5% and 75.1% in our case). In addition, they focus less explicitly on the (potential) heterogeneity in returns by migration status.

It is in this context that our contribution adds value. We first show that our lengthy tool development process produced measures of SEM skills in low-income countries that satisfied desirable measurement properties. We then document the associations between these skills and labor earnings, highlighting the critical role of migration in a sample that started in rural areas but moved to multiple locations over 15 years. As other longitudinal studies either do not (yet) observe labor market outcomes (e.g., Young Lives) or do not include well-constructed measures of SEM skills, this is only the second long-term research project to establish a pattern of correlations between labor earnings and specific cognitive and SEM skills, in a less-developed country. We hope this descriptive evidence helps resurrect the fundamental question raised by Bowles and Gintis (1976), this time for low-income countries: What are the skills that children learn in schools that are then valued in the labor market?

# **I. SAMPLE**

# **I.1. SAMPLE SELECTION**

The data come from the Learning and Education Achievement in Punjab Schools (LEAPS) project, a longitudinal study of education in Pakistan (Andrabi, Das, and Khwaja 2022). In 2003, the LEAPS project randomly sampled 112 villages from three districts in the province of Punjab (from a list frame

> 6 Using the Young Lives data, Mitchell et al. (2020) find that individuals with higher task effectiveness skills are less likely to engage in risky behavior. Singh et al. (2018) show that psychosocial skills are positively correlated with progression through school. Das, Singh, and Yi Chang (2022) show that test scores at age 12 predict years of schooling at age 22 but still leave a substantial role for socioeconomic status.

7
Several recent papers make significant progress in addressing these issues. In terms of classification, most papers had simply used an indicator variable measuring the presence of labor clauses, regardless of the content of the clauses. Using such an indicator, Kamata (2014) finds no statistically significant effects of RTAs with or without labor clauses. Using the “Labor Provisions in Trade Agreements” (LABPTA) dataset created by Raess and Sari (2018), Carrere et al. (2017) extend this work by differentiating labor clauses as those with and without enforcement and labor clauses designed to foster deep cooperation. Using a sample covering the 1995-2004 period, restricted to countries with positive trade flows, and by differentiating trade flows from low-income to high-income countries, they find that enforcement has no effect on trade flows, but clauses with “deep” cooperation are associated with more trade.

Extending the analysis to include a legal-based classification of labor clauses, including different forms of enforcement, LeClercq et al. (2020) find that labor provisions, defined in their paper as a combination of standards and enforcement clauses, are associated with lower trade flows but they are accompanied by clauses the promote trade, so that the overall effect of labor clauses on trade is positive.

This paper offers three extensions to the current literature. The main question asked by this paper is whether the application of new techniques and incorporating other key characteristics of trade agreements affects the estimate of the relationship between labor clauses and trade flows. The first contribution is to apply the Pseudo-Poisson Maximum Likelihood (PPML) estimation with country-pair and importer and exporter time effects to the estimate the relationship between trade flows and labor clauses. This approach is widely accepted in the gravity model literature, but has not been applied in most papers assessing the labor clause-trade flow relationship.

The second contribution is to use the depth of the Handbook of Deep Trade Agreements (HDTA) (Mattoo et al. 2020) to estimate the correlation between labor clauses and other clauses, including tariff preference margins. The effects of trade agreements on trade volumes could be due to a wide range of factors that, if not included, could bias the estimation of labor clauses on trade. For example, if agreements with larger tariff preference margins (often associated with larger tariff reductions) are more likely to include labor clauses, then estimating the effect of labor clauses without the tariff preference margins could generate positive results that are, in fact, driven by omitted variable bias. Similarly, tariffs or other “deep” clauses could pg. 3
Table 1: Non-Hydrocarbon Government Revenue, 1992-2012

||Tax revenue as a pe|rcentage of GDP|Percentage of total revenue|
|---|---|---|---|
||Non-hydrocarbon|Total revenue|Non-hydrocarbon|
|Norway|41.87|54.78|76.83|
|Russian Federation|28.57|36.97|77.63|
|Vietnam|21.50|23.89|88.79|
|Ecuador|18.90|24.55|80.46|
|Uganda|17.34|17.34|100.00|
|Trinidad and Tobago|17.25|27.22|64.27|
|Kazakhstan|16.41|25.07|66.58|
|Kuwait|16.17|61.05|27.26|
|Mexico|15.13|19.79|77.54|
|Azerbaijan|15.03|30.84|53.66|
|Syrian Arab Republic|14.62|26.36|55.67|
|Qatar|14.17|37.64|37.52|
|Venezuela, RB|13.43|30.10|45.19|
|Cameroon|12.91|20.49|68.43|
|Gabon|12.53|27.09|46.71|
|Indonesia|11.79|17.61|67.02|
|Libya|11.74|44.03|31.98|
|Algeria|11.22|34.90|33.12|
|United Arab Emirates|11.21|29.14|39.56|
|Yemen, Rep.|10.59|30.52|36.00|
|Chad|10.28|15.07|78.85|
|Iran, Islamic Rep.|10.28|23.44|45.36|
|Oman|9.27|42.52|22.18|
|Angola|8.94|43.81|21.05|
|Bahrain|8.46|29.20|29.40|
|Republic of Congo|8.20|34.42|25.39|
|Equatorial Guinea|7.89|35.39|23.96|
|Sudan|7.83|15.35|59.63|
|Nigeria|7.44|32.00|24.56|
|Brunei|6.60|43.04|16.70|
|Saudi Arabia|5.53|42.54|13.73|
|**Min**|**_5.53_**|**_15.07_**|**_13.73_**|
|**Max**|**_41.87_**|**_61.05_**|**_88.79_**|
|**Median**|**_11.77_**|**_30.31_**|**_45.28_**|

Source: International Monetary Fund, authors’ calculations. Uganda is excluded for the min, max and median calculations. Countries are sorted based on their non-hydrocarbon tax revenue as a percentage of GDP.

as a function of revenue from hydrocarbon products, _R_<sup>_H_</sup> , normalized by GDP, _Y_ , and a series of control variables as follows: 6
# **1. Introduction**

An issue of particular relevance for economic growth is how the development of markets and institutions influences the growth of firms. This issue is even more important in developing countries, where markets and institutional infrastructure are less developed. A related concern is whether an adverse business environment affects the efficiency and the dynamism of the business sector.

This paper uses firm- level data on more than 56,000 enterprises in 85 developing countries and 5 high-income economies to assess the effects of the broader business environment on employment growth by firms, focusing on differences across firm size. This paper is related to two strands of literature. First it relates to recent studies (Restuccia and Rogerson (2008), Hsieh and Klenow (2008)) that show that there is considerable resource misallocation across firms, which is driven by firm specific distortions. While these studies are silent about the nature of the distortions, they show such distortions lead to substantial aggregate productivity losses, and that differences in the degree of distortions would explain a significant portion of why India and China exhibit lower manufacturing productivity than the United States. This paper examines differences in business environment across firm size, and therefore it helps to provide a better understanding of which dimensions of the business environment matter and the heterogeneity of distortions across firms.

This paper is also related to a number of studies that assess the effect of different dimensions of the business environment on firms’ performance. Several studies in the literature examine the effect of employment regulations on firms’ adjustment and on labor market outcomes more generally.<sup>1</sup> Others look at the effect of regulations of entry of firms on firm creation and growth.<sup>2</sup> A number of them investigate the importance of access to finance for firm development and growth.<sup>3</sup> Overall, these studies indicate that regulations on labor, entry barriers for firms, and financing conditions can have a direct impact on firms’

> 1 A few recent examples are: Botero et al. (2004), Besley and Burgess (2004), Almeida and Carneiro (2009), Haltiwanger, Scarpetta and Schweiger (2006), Micco and Pagés (2006), Petrin and Sivadasan (2006) and Autor, Kerr and Kugler (2007).

> 2 For example: Djankov et al (2002) and  Klapper, Laeven and Rajan (2004).

> 3 For example: Demirgüç-Kunt  and Maksimovic (1998), Rajan and Zingales (1998), Beck, Demirgüç-Kunt and Maksimovic (2005), Galindo and Micco (2007).

2
Harding, D., and A. Pagan. 2002. “Dissecting the Cycle: A Methodological Investigation.” Journal of Monetary Economics 49(2): 365-381.

Harding, D., and A. Pagan. 2016. The Econometric Analysis of Recurrent Events in Macroeconomics and Finance. Princeton: Princeton University Press.

Hatzius, J., and S. J. Stehn. 2018. “The Case for a Financial Conditions Index.” Global Economic Paper, July 16, Goldman Sachs, New York.

Hausmann, R., F. Rodríguez, and R. Wagner. 2006. “Growth Collapses.” CID Working Paper 136, Center for International Development, Harvard University, Cambridge.

Helbling, T., R. Huidrom, M. A. Kose, and C. Otrok. 2011. “Do Credit Shocks Matter? A Global Perspective.” European Economic Review 55(3): 340-353.

Herman, A., D. Igan, and J. Solé. 2017. “The Macroeconomic Relevance of Bank and Nonbank Credit: An Exploration of U.S. Data.” Journal of Financial Stability 32: 124-141.

Hoshi, T., and A. K. Kashyap. 2015. “Will the U.S. and Europe Avoid a Lost Decade? Lessons from Japan’s Postcrisis Experience.” IMF Economic Review 63(1): 110-163.

Huidrom, R., M. A. Kose, H. Matsuoka, and F. L. Ohnsorge. 2019. “How Important are Spillovers from Major Emerging Markets?” International Finance, forthcoming.

Imbs, J. 2010. “The First Global Recession in Decades.” IMF Economic Review 58(2): 327-354.

IMF (International Monetary Fund). 2019. Fiscal Monitor: Curbing Corruption. April. Washington, DC: International Monetary Fund.

Knoop, T. A. 2004. Recessions and Depressions: Understanding Business Cycles. Westport: Praeger.

Koepke, R. 2019. “What Drives Capital Flows to Emerging Markets? A Survey of the Empirical Literature.” Journal of Economic Surveys 33(2): 516-540.

Kose, M. A., S. Kurlat, F. Ohnsorge, and N. Sugawara. 2017. “A Cross-Country Database of Fiscal Space.” Policy Research Working Paper 8157, World Bank, Washington, DC.

Kose, M. A., P. Nagle, F. Ohnsorge, and N. Sugawara. 2020. Global Waves of Debt: Causes and Consequences. Washington, DC: World Bank.

Kose, M. A., and F. Ohnsorge, eds. 2019. A Decade After the Global Recession: Lessons and Challenges for Emerging and Developing Economies. Washington, DC: World Bank.

Kose, M. A., F. Ohnsorge, and N. Sugawara. 2020. “Global Growth Next Decade: Optimistic Expectations, Disappointing Outcomes.” Policy Research Working Paper, World Bank, Washington, DC.

Kose, M. A., C. Otrok, and E. Prasad. 2012. “Global Business Cycles: Convergence or Decoupling?” International Economic Review 53(2): 511-538.

Kose, M. A., C. Otrok, and C. H. Whiteman. 2003. “International Business Cycles: World, Region, and Country-Specific Factors.” American Economic Review 93(4): 1216-1239.

Kose, M. A., C. Otrok, and C. H. Whiteman. 2008. “Understanding the Evolution of World Business Cycles.” Journal of International Economics 75(1): 110-130.

Kose, M. A., and E. S. Prasad. 2010. Emerging Markets: Resilience and Growth amid Global Turmoil. Washington, DC: Brookings Institution Press.

27
# **_CASE: Addressing LGBT health needs and discrimination in Brazil and India_**<sup>**_21_**</sup>

Gender and sexual minorities face some of the most severe and most diverse forms of exclusion among any group worldwide. These groups have far worse health outcomes than heterosexual people. Factors like low rates of health insurance coverage, high rates of stress due to systematic harassment and discrimination, and a lack of cultural relevancy and competency in health care systems, leave gender and sexual minorities at a higher risk for health problems such as mental illnesses, cancers and other diseases.

Some governments have taken steps in breaking the stigma and discrimination that contribute to the social exclusion of lesbian, gay, bisexual, and transgender (LGBT) groups. Often these involve integrated strategies that involve changes in policy, better targeting and more attention to needs in the provision of health services, along with social marketing campaigns to tackle stigma among the wider population.

In Brazil, the government adopted policies that would assist the adaptation of healthcare to the needs of gender and sexual minorities. This complements a long history of cumulative progress on gender and sexual minority issues such as the promulgation of anti-discrimination laws targeting the actions of businesses and citizens, the legalization of same-sex unions, and widespread public education campaigns to combat prejudice among the general population and in schools.

Tamil Nadu state in south India has put in place a series of official measures to address the exclusion of transgender people. The state established a transgender welfare board in 2008, with the social welfare minister as the president. The Board undertook an enumeration of the transgender population in all districts along with the issuance of identity cards with a third gender identity. With these identity cards, the state government has also been able to start issuing ration cards (for buying food and other items from government-run fair-price shops) to transgender people. The Board also addresses a range of critical issues that contribute to the exclusion of transgender people such as education, income generation and social security. Government health services have also been adapted to better meet the needs of transgender individuals.

# **Tackling exclusion in informal institutions**

Informal institutions are often more salient and may yield a very different set of values and associated norms than formal institutions. Tackling the prejudices in operation in informal institutions is often thought of as a long-term endeavor as they are highly resistant to change (North 1990). This is not always true of formal institutions and organizations, many of which can, literally, be changed overnight via policy and administrative reforms. There are a number of ways in which change may be brought about, possibly quite rapidly. Thus, we highlight the importance of understanding the inter-play 21 Sources: Institute of Medicine (2011), Ottosson (2009), Beyrer, Wirtz, Walker, Johns, Sifarkis and Baral, (2011), UNDP (2010) and Krehley (2009).

31
<!-- Start of picture text --> 8<br>6<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>-10<br>Metro Rail and Bullet train Refineries New<br>Waterways Heat processes<br>Integration<br>MtCO2<br><!-- End of picture text -->

**Figure 5:** Bias from short-term (2020) time horizon compared with optimal longterm (2030) strategy. On the positive side, measures are used excessively in the 2020 strategy, compared to what they should be used to reach the 2030 target; on the negative side, measures are used insufficiently. Only the 5 larger absolute differences are shown.

measures, the least-cost strategy is to implement them as late as possible, to benefit from the discount rate. This means that under an emission-reduction target in one point in time, such as -30% by 2030, the two-phase penetration pictured in Fig. 3 is not optimal for positive-cost measures. A better solution is to delay the implementation such that the maximum potential is reached just in time, when the target needs to be achieved.<sup>5</sup>

# _2.4. Strategy with a 2020 objective_

The optimal emission reduction pathway to achieve 223 MtCO2 in 2030 leads to 127 MtCO2 of emission reductions in 2020. To investigate how focusing on short-term targets may lead to suboptimal outcomes, we run a second simulation with the only constraint of reducing emissions by 127 MtCO2 in 2020, and we investigate the “optimal” solution provided by our model in this case (Tab. 3).

In line with Vogt-Schilb and Hallegatte (2014), the least-cost strategy for 2010-2020 uses different emission-reduction options, depending on whether the strategy aims at a short-term target (127 MtCO2 in 2020) or at a longer-term one (223 MtCO2 in 2030). The simulation that ends in 2020 uses notably less investment in metro and other clean transportation infrastructure, and more heat integration and other marginal improvements in existing refineries (Fig. 5). All the measures that are underused in this scenario are characterized by a huge abatement potential, which cannot be exhausted before 2030. In the long-term target scenario, those are implemented as fast as possible. In the short-term target scenario for 2020, they are replaced by cheaper and faster to implement measures (see also Fig. 6).

> 5 If the climate mitigation target is expressed in terms of a carbon budget (i.e. if it limits cumulative emissions instead of emissions at one point in time), then the two-phase penetration target may be optimal (Vogt-Schilb and Hallegatte, 2014).

10
past week. The response rate averaged 79% for firms interested in Agruppa (81% in treatment blocks, 77% in control blocks), and 69% for not-interested firms (70% in treatment blocks, 68% in control blocks). Tables A4.1 and A4.2 break this down by survey round, showing a tendency for response rates to fall over time, reflecting some survey fatigue.<sup>13</sup> We then collected two longer surveys at six-months and 12 months after the launch of Agruppa in a block. In addition to the information collected in the high-frequency surveys, these questionnaires also asked about business opening hours, sales of some other products, pricing strategies, crime, record-keeping, and work-life balance. The response rates for interested firms were 78% at six months (80% in treatment blocks, 75% in control blocks), and 76% at 12 months (77% in treatment blocks, and 74% in control blocks), and were again lower for uninterested firms (Table A4.1). We were able to collect at least sales prices in most stores interviewed (Table A4.2), but item non-response rates were very high for sales and profits (Table A4.3), so that profits and sales are only available for 35 percent of interested firms on average (similar by treatment status). In our regressions we control for block pair fixed effects (which controls for issues like overall insecurity that affect response rates) and for the baseline values of the outcome of interest (adjusting for an imbalance on initial outcomes caused by attrition).

# **4. Impacts on Firms**

We analyze the impacts of this intervention by assessing the extent to which it was able to reduce travel times and travel costs to Corabastos and improve life quality for firm owners, whether it changed their input costs and sale prices, whether this resulted in changes in sales and profits, and how competing firms were then affected.

# **4.1.Estimation Methods**

Randomization at the block level enables us to estimate the intention-to-treat (ITT) effect of offering Agruppa to interested firms by comparing outcomes for interested firms in treatment blocks to those for interested firms in control blocks. To maximize power, we use an Ancova specification which controls for the baseline level of the outcome of interest, and pool together

> 13 Scratch-and-win cards with airtime incentives were offered as an incentive to get firms to respond.

14
**Policy, Research, and External Affairs** - **I** **_it_** _-rl_

**Trade Policy**

WPS 625 This paper -a product of the Trade Policy Division, Country Economics Department -is part of a research effort in PRE, Trade Reform and SustainabiFty (RPO 675-32). Copies are available ICre from **tlhc** World Bank, 1818 H Street NW, Washington DC 20433. Please contact Karla Cabana, room N 10-035, extension 37946 (30 pages).

Lopez provides cross-country empirical evidencc on the relationship between trade and macroeconomic policy and economic growth. He finds that: Countries that follow sustainable strategies perfonn better than those following unsustainable strateg. . Indeed, unsustainable policies hurt growth. Sustainable policies (as in Korea, Taiwan, Singapore, Hong Kong, Thailand, and Malaysia) promote exports and lead to real exchange rates that are either fully aligned or even undervalued for prolonged periods of time but arc relatively stable. Unsustainable policies (more common in developing countries) include policies that tax exports and overvalue exchange rates for extended periods, leading to periodic balance of paymenits crises anid a hiighily unstablc real exchange rate.

Export promotion policies generate faster growth than policies that remove import restrictions.

* Economic instability and foreign debt are key determinants of capital growth.

* Contrary to conventional belief, capital accumulation appears to be stimulated by direct export restrictions and does not seem to be directly affected by economic instability.

**Thc** PRE Working Papcr Scrics disseminates dte findings of work under way in the Bank's Policy. Rcsearchi, and External AffairsComplcx. An objectiveof thcserics is to get thesc findings out quickly, even if'presentations are lcss thlan fully lx)lished. Thc findings, interpretations, and conclusions in thcsc papers do not necessarily represent official B1ank policy.

I'roduccd by the IPRE Dissemination Ccnter
The Impact of Secondary Schooling in Kenya: A Regression Discontinuity Analysis<sup>_∗_</sup> Owen Ozier<sup>_†_</sup> Development Research Group The World Bank

> _∗_ I am indebted to Lori Beaman, Blastus Bwire, Pascaline Dupas, Esther Duflo, Steve Fazzari, Frederico Finan, Sebastian Galiani, Justin Gallagher, Francois Gerard, Erick Gong, Joan Hamory Hicks, Guido Imbens, Gerald Ipapa, Pamela Jakiela, Seema Jayachandran, Pat Kline, Michael Kremer, Ashley Langer, Karen Levy, Isaac Mbiti, Jamie McCasland, Justin McCrary, Edward Miguel, Salvador Navarro, Carol Nekesa, Rohini Pande, Bruce Petersen, Robert Pollak, Jon Robinson, Alex Rothenberg, and Kevin Stange, as well as conference and seminar participants, for their helpful comments. All errors are my own.

> _†_ Please direct correspondence to oozier@worldbank.org.