rafmacalaba/datause-encoder-data huggingface dataset:
Section 3.1 of this paper, Cuberes and Teignier (2014a) calculate the macroeconomic eects of gender inequality in the labor market using data from the International Labor Organization for a large sample of countries, but they do not include any of the Balkan countries and they study the case of Turkey only in 2010.

The rest of the paper is organized as follows. Section 2 documents several gender gaps in the labor market of the eight countries considered here. In Section 3 we sketch the general equilibrium occupational choice model proposed by Cuberes and Teignier (2014a) and calculate the static costs associated with the gaps in two dierent years, which vary across countries due to data availability. Section 4 carries out this exercise by dierent age groups and, nally Section 5 concludes the paper.

# 2 Gender Gaps in the Balkans and Turkey

## Data Description

In this section we provide some descriptive statistics of the labor market in Albania, Bosnia-Herzegovina, Croatia, Kosovo, Macedonia, Montenegro, Serbia, and Turkey for two recent years<sup>3</sup> . Table 1 lists the countries and years used in the paper, along with the number of available observations and their data sources.<sup>4</sup> The data come from household surveys and we only consider individuals at working age i.e. between the ages of 15 and 65.<sup>5</sup> Figures 1 and 2 and tables 2-4 display the evolution of the gender gaps in labor force participation rates, self-employed, and employers for each country over the two years of available data. In terms of Labor Force Participation, the most salient feature is that there is a very large gender gap in labor force participation, with women being underrepresented in the labor market in all countries. This gap is largest in Kosovo, where only 31 women participate in the labor market for every 100 men that do so in 2012. Second, this gap increases in all countries over time, with the exception of Turkey, where it dramatically dropped by ten percentage points, from 59% to 49%.

> 3We choose two years that are four to six years apart to have some sense of how thse gaps have changed over time. We acknowledge the fact that this time interval overlaps in some cases with the so-called Great Recession that started in 2007 and that these may somewhat distort our calculations.

> 4All the variables used in the analysis contain data for the two years specied in this table. The only exception is Montenegro, for which we do not have information on self-employment for the year 2011. LFS stands for Labor force survey and ECAPOV stands for World Bank Database of household survey for Eastern Europe and Central Asia.

> 5In some cases we also consider the population above 65 years-old, although they represent a very small fraction of the observations.

3
rate in Argentina during the last decade was always greater than 10 percent, making this experience a great case study to investigate the impacts of agro-manufactured exports in the presence of adjustment costs in labor markets.

I set up a maximum likelihood model of trade, wages and unemployment. Based on their market and reservation wages, workers decide whether to be active (i.e., to participate in the labor market) or inactive. Firms make job offers to active workers; workers who receive an offer are employed and those who do not become unemployed. Thus, there are three regression functions in my model: a wage equation, a reservation wage equation, and a job offer equation.

I capture the role of trade with the international price of agro-manufactured exports and I make both wages and job offers a function of these prices. To estimate the model, I combine labor force data on wages, employment, and unemployment at an individual level with export and import price data at an aggregate level. The method takes advantage of the time variability in prices and the time variability of household surveys to identify the parameters of the model. Similar methods have been utilized by Deaton (1997), Goldberg and Tracy (2003), Porto (2006), Ravallion (1990), and Wolak (1996).

The maximum likelihood model estimates parameters that measure how market wages, reservation wages, and job offers react to a change in the prices of agro-manufactured exports. These parameter estimates can be used to predict the probability of actively participating in the labor market, the probability of getting a job offer, and the probability of employment and unemployment. In addition, the estimated regression function for wages can be used to predict expected wages. Armed with these estimates, I investigate the impacts of higher 2
seen as adding to the evidence on such quality mattering for economic performance (Hausman et al. 2007).

Another possibility is that final exports of sophisticated goods by a country may reflect merely its comparative advantage in the final “assembly” stage rather than a deeper sophistication in its production processes. For example, a significant proportion of China’s uphill exports of sophisticated goods contain imports of sophisticated components from rich countries. On the one hand, this could indicate that we are mismeasuring sophistication. On the other hand, our measure could capture the extent of a country’s participation in modern global production chains which confer benefits in terms of knowledge of markets, just-in-time capability, improved production technology etc. Thus, what we capture—imperfect though it undoubtedly is—may provide clues about an additional channel through which the impact of global integration is felt. As noted in the introduction, there is now increasing evidence supporting these channels. (Javorcik, 2006).

In principle, these benefits can also arise from sales to foreign firms located abroad. Recently, de Loecker (2007), working with micro data of Slovenian firms, has demonstrated that productivity gains are higher for firms exporting towards high income regions. Moreover, exports of goods to high income destinations are frequently associated with being part of global production chains that confer important benefits (Hoekman and Javorcik, 2007.) Further, uphill flows could affect growth through induced changes in economy-wide skill acquisition and hence in long run endowments, creating a self-reinforcing and virtuous cycle. Again a relevant example is India. Educational attainment in India, especially at the primary and secondary levels, was disappointing until the early 1990s. In the last fifteen years, though, educational indicators have improved markedly. While greater government attention has been important, a key change has been the increased the demand for education due to the higher returns to human capital which in turn is a consequence of increased skill-intensive and uphill specialization (the derived demand for skills and hence education is arguably a function not just of what is sold but also to whom it is sold). This demand has elicited a supply response, largely from the private sector leading to a more rapid spread of education and skills (Kremer et al., 2005).

Finally, if there are benefits from uphill flows, in some circumstances, significant development benefits might derive not from deifying comparative advantage but from defying it.

11
Calvo-Gonzalez, O., B. Cunha and R. Trezzi (2017). When Winners Feel Like Losers: Evidence from an Energy Subsidy Reform. The World Bank Economic Review, 31(2): 329–350.

Coady, D., I. Parry, L. Sears, and B. Shang (2015). How Large Are Global Energy Subsidies? IMF Working Paper, WP/15/105. International Monetary Fund, Washington, DC.

del Granado, A. F. J., D. Coady, and R. Gillingham (2012). The Unequal Benefits of Fuel Subsidies: A Review of Evidence for Developing Countries. World Development. doi:10.1016/j.worlddev.2012.05.005.

Dorsch Consulting (2012). Implementation and Financing Plan for Gas Sector Development of Bangladesh, Petrobangla. Dhaka.

Elshennawy, A. (2014). The implications of phasing out energy subsidies in Egypt. Journal of Policy Modeling 36: 855–866.

Gelan, A.U. (2018). Kuwait's energy subsidy reduction: Examining economic and CO2 emission effects with or without compensation. Energy Economics 71: 186–200.

Gharibnavaz, M.R. and R. Waschik (2015), Food and energy subsidy reforms in Iran: A general equilibrium analysis. Journal of Policy Modeling 37: 726–741.

Glomm, G. and J. Jung (2015). A Macroeconomic Analysis of Energy Subsidies in a Small Open Economy. Economic Inquiry 53(4): 1783–1806.

Hong, L., D. Liang and W. Di (2013). Economic and environmental gains of China’s fossil energy subsidies reform: A rebound effect case study with EIMO model. Energy Policy 54:335– 342.

Inchauste, G. and D. G. Victor (eds.) (2017). The Political Economy of Energy Subsidy Reform. World Bank Group, Washington, DC.

Inchauste, G., Y. Mansur and U. Serajuddin (2017). Jordan: Reform amid Turmoil. Inchauste, G. and D. G. Victor (eds.) The Political Economy of Energy Subsidy Reform. World Bank Group, Washington, DC.

Jiang, Z. and J. Tan (2013). How the removal of energy subsidy affects general price in China: A study based on input–output model. Energy Policy 63:599–606.

- Jiang, Z., X. Ouyang and G. Huang (2015). The distributional impacts of removing energy subsidies in China. China Economic Review 33: 111–122.

- Jiang, Z., X. Ouyang, and G. Huang (2015). The distributional impacts of removing energy subsidies in China. China Economic Review, 33, 111-122.

31
_i_ . The income is spent on aggregate private consumption _YC_ , on exogenous investment (savings) demand _I_<sup> ̄</sup> , and on exogenous government demand ( _G_<sup> ̄</sup> ).

Production _Yi_ of commodity _i_ by representative firm _i_ is given as a nested constantelasticity-of-substitution (CES) function that captures price-responsive substitution possibilities between factor and intermediate inputs (see Appendix B Figures 9 – 12). The choice of inputs, in particular the amount and composition of energy carriers, implicitly defines _μ_ for the firm in each sector. Production of final demand commodities enters final demands of the representative agent (private consumption _YC_ , investment demand _YI_ , and government demand _YG_ ). All other domestically produced goods are split subject to a constant-elasticityof-transformation (CET) function between export demand _Xi_ and input demand for the production of the Armington good _Ai_ . Armington production for each good _i_ is based on a CES technology that combines the domestically produced good and imports _Mi_ . Armington outputs _Ai_ in turn serve as intermediate inputs to the production _Yi_ of all commodities including final demands. The Armington assumption of product heterogeneity distinguishes goods by origin (Armington, 1969). This accommodates both imports and exports of the same commodity reflecting empirical evidence on the crosshauling of trade flows.

In international markets, our country of interest (in this case, the United States) is assumed to be a price taker, meaning changes in the country’s import and export volumes have no influence on international prices. In other words, export and import prices in foreign currency – the so-called terms of trade – are made exogenous. A balance-of-payment constraint requires that the total value of exports equal the total value of imports, accounting for an initial trade deficit or surplus.

As is customary in applied general equilibrium analysis, base-year data together with exogenous elasticities determine the free parameters of the functional forms. Our central case model parameterization deploys the most recent Global Trade Analysis Project (GTAP) data (version 10) for the US economy, which includes detailed balanced accounts of production, consumption, trade, and CO2 emissions together with key elasticities for the base year 2014 (Aguiar et al., 2019). We do not include preexisting taxes to abstract from second-best effects such that the simulation results should adhere closely in qualitative terms to the theoretical predictions outlined above, while capturing the quantitative differences between 23
The study appears well-balanced; Table A2 shows balance using the household data, including all TASAF members, and incorporating dummies for Early TASAF treatment and Training, as well as the UCT and UCT*Training interactions as above. Across all primary outcomes we find only 1 out of 40 comparisons significant at the 5% level, slightly less than what we would expect by random chance. Because the TASAF groups do not exist at baseline we do not test for balance on the group data.

# **4. OBSERVATIONAL EVOLUTION OF THE TASAF GROUPS OVER TIME**

A starting point in understanding what the TASAF investment did is a simple descriptive analysis of the evolution of enterprise outcomes. In 92% of cases the TASAF program created the group enterprises from nothing, and so business outcomes such as business assets and profits are typically zero prior to treatment (meaning the baseline for the Early group, and the baseline, the rapid resurvey (RR) at six months, and the midline at one year for the Late group).<sup>13</sup> Using our three post treatment surveys we can construct four different durations since treatment: 6 months (RR), 12 months (R2), and 36 months (R3) for the Early treatment group, and 24 months (R3) for the Late-treatment group. To avoid conflating the effects of the UCT with the normal operating of the TASAF groups, which are only relevant for R3 outcomes and presented later in this section, we only include the groups that received no UCTs.

Table 1 provides basic summary statistics of group outcomes (first six columns) and individual contributions to/earnings from groups (remaining columns) across the four rounds of the survey. It illustrates the way the pipeline design worked, with no activity in any group at baseline, the Early groups in full swing by the 6- month RR, and then despite some initial stirrings

> 13 Six of the 60 groups in the delayed treatment arm had positive total stock values at the time of R2 data collection with average stock values much smaller (USD 653) than the groups in the TASAF early treatment arm (USD 5,633).

17
contact information was recorded incorrectly at the time of the third round of BEEPS. Further, the classification treats firms that have shut down temporarily the same as firms that have closed permanently. Finally, some firms that were still operating but were not interviewed might have filed for insolvency or bankruptcy—although as shown in Table 2 this might not be a major concern given that only 26 of 1696 operating firms that were interviewed were in bankruptcy or liquidation. Despite these minor limitations, this is the most reasonable breakdown possible given the available information. The firms that we identify as no longer operating almost certainly struggled more during the early phase of the crisis than others.

The dependent variable in the survival analysis is a dummy indicating that the firm was not operating or was in the process of closing down at the time of the second survey—either because the firm had closed permanently, had filed for bankruptcy or insolvency, or had been temporarily shut down. This was the case for 296 of 2,501 firms interviewed in these countries during the third round of BEEPS (see Table 2).

# **IV. Estimation**

As noted, we use the data from BEEPS and the FCS to look at the effect of the crisis on different firms in the region focusing on firm and country characteristics. The first part of the analysis focuses on whether firms were still operating in mid-2009 using data from the FCS. We refer to this analysis as the ̳survival analysis.‘ The second exploits variation over time in firms‘ reported financial constraints for a balanced panel of respondents to the 2002, 2005, and 2008-2009 BEEPS. We refer to this as the analysis of financial constraints.

For both analyses, the basic regression is: In the survival analysis, Y is a dummy variable that takes the value 1 if the firm was not operating at the time of the FCS survey—but was operating at the time of the earlier BEEPS survey. Because the survival analysis is cross-sectional—we only have information on whether a firm survived at only one point in time (mid-2009)—the subscript ―t‖ is relevant only for the second analysis of financial constraints.

14
Policy Research Working Paper 10348

# **Abstract**

Reliable small-area estimates of economic welfare significantly inform the design and evaluation of development policies. This paper compares the accuracy of wealth estimates obtained from the empirical best predictor (EBP) of a linear nested error model, Cubist regression, extreme gradient boosting, and boosted regression forests. The evaluation draws two-stage samples from unit-level household census data in seven developing countries, combines them with publicly available geospatial indicators to generate small area estimates of assets for all seven countries and poverty for two, and evaluates these estimates against census-derived benchmarks. Extreme gradient boosting and Cubist regression generally produce more accurate predictions than traditional EBP models. A proposed two-stage residual bootstrap procedure slightly underestimates confidence intervals, but leads to higher coverage rates than the parametric bootstrap approach used for EBP predictions. These results demonstrate that, given a sufficiently large sample of enumeration areas, predictions from extreme gradient boosting or Cubist regression with a two-stage residual block bootstrap generally provide more accurate point and uncertainty estimates for generating small-area welfare estimates.

This paper is a product of the Development Data 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 authors may be contacted at dnewhouse@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
Banking is also very expensive for deposit customers, as reflected by very high minimum balance requirements and annual fees not only for checking customers but even for savings account holders in many African countries (see Figure 7).<sup>10</sup> Moreover, these high costs alone can explain why less than 20 percent of the population in many African countries has a bank account. Using simple back-of-the-envelope calculations, Beck et al. show that 54 percent of the population in Cameroon, 81 percent in Kenya, 40 percent in Madagascar, 94 percent in Malawi, 89 percent in Sierra Leone, and 93 percent in Uganda cannot afford the fees for checking accounts, given their annual income and the assumption that they cannot spend more than 2 percent of household income on financial transaction account charges.<sup>11</sup> High fees, however, are only one factor behind the limited outreach of Africa’s financial systems. High documentation requirements to open an account—that is, the need to present several documents of identification, such as passport, pay slip, utility bill, and so on, also represent significant barriers given that large parts of the population live and work in the informal sector. Africa again stands out along this dimension, as becomes obvious from Figure 8. Similarly, physical access is limited, as the low bank branch and ATM penetration numbers for Africa illustrate.

The gap between Africa and other regions of the world is even starker in other parts of the financial systems. Only a third of countries in the region have stock markets, which are mostly small and illiquid (Figure 9). The actual float of the listed companies is low. The low transaction volume in both primary and secondary markets is self-enforcing, as it deters new

> 10 Beck et al. 2008; World Bank, 2008.

> 11 Beck et al. 2008. The 2 percent assumption is based on Genesis 2005. Fees on savings accounts are typically lower and many of these countries also have (postal) savings banks that offer basic accounts, but these accounts typically do not have the same functionality and many savings banks offer services of limited quality—that is, long queues and limited access to funds.

6
> Retirement", Journal of Public Economics, Vol. 10, pp. 295-336

(1982), "Social Insurance with Variable Retirement and Private Savings", MIT Working Paper (1986a), "Payroll-Tax Financed Social Insurance with Variable Retirement", Scandinavian Journal of Economics, Vol. 88 (1), pp. 25 - 50 (1986b), "Insurance Aspects of Pensions" in David A. Wise, Ed., _Pensions, Labor and Individual Choice,_ NBER, University of Chicago Press

> DIAMOND, Peter A. (1993), "Privatization of Social Security: Lessons from Chile", NBER Working Paper No. 4510

(1998), "The Economics of Social Security Reform", NBER Working Paper No. 6719 DONKERS, Bas & Arthur van Soest, (1999), "Subjective Measures of Household Preferences and Financial Decisions", _Journal of Economic Psychology_ 20, pp 613-642 EDWARDS, Sabastian and Alejandra Cox Edwards, (2000b), "Economic Reforms and Labor Markets: Policy Issues and Lessons from Chile," Economic Policy, April, Great Britain EDWARDS, Sabastian and Alejandra Cox-Edwards, (2000a): "Economic Reforms and Labor Markets: Policy Issues and Lessons from Chile" NBER Working Paper, No. 7646 EDWARDS, Sebastian & Nora Lustig, (1997) Labor Markets in Latin America: Combining Social Protection with Market Flexibilitv. Washington DC: Brookings Institute Press EHRLICH, Isaac & Hiroyuki Chuma, (1990), "A Model of the Demand for Longevity and the Value of Life Extension", Journal of Political Economy, Vol. 98, Issue 4, pp. 761-782 EHRLICH, Isaac, and Gary Becker (1972), "Market Insurance, Self-Insurance and SelfProtection." Journal of Political Economy 80: pp. 623-648 FAFCHAMPS, Marcel and S. Lund, (2000), "Risk-sharing Networks in Rural Philippines", mimeo, Oxford University FIORITO, Riccardo & Flavio Padrini, (2001), "Distortionary Taxation and Labor Market Perforrnance", Oxford Bulletin of Economics and Statistics, Vol. 63, No. 2., pp. 173-196 FUCHS, Victor R., 1986, The Health Economy. Harvard University Press, Cambridge, MA 1986 GENICOT, G. and D. Ray (2000), "Endogenous Group Formation in Risk Sharing Arrangements", mimeo, University of California and New York University GETLER, Paul, (2000), "Insuring the Economic Costs of Illness" in Nora Lustig (ed.), _Shielding the Poor: Social Protection in the Developing World,_ Washington D.C., Brookings Institutions and the Inter-American Development Bank GILL, Indermit, and Nadeem Ilahi, (2000), "Economic Insecurity, Individual Behavior, and Social Policy." Office of the Chief Economist, Latin America and Caribbean Region. Washington D.C.: World Bank GRUBER, Jonathan, (1995), "The Incidence of Payroll Taxation: Evidence from Chile", NBER Working Paper 5053.

HECKMAN, James, (1979), "Sample Selection Bias as a Specification Error" Econometrica, January, pp. 153 - 161 35