rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 6832

# **Abstract**

This paper presents findings from the impact evaluation of the Economic Empowerment of Adolescent Girls and Young Women (EPAG) project in Liberia. The EPAG project was launched by the Liberian Ministry of Gender and Development in 2009 with the goal of increasing the employment and income of 2,500 young Liberian women by providing livelihood and life skills training and facilitating their transition to productive work. The analysis in this paper is based on data collected during two rounds of quantitative surveys in 2010 and 2011, the second of which was conducted six months after the classroom-based phase of the training program ended. Strong impacts are found on the employment and earnings outcomes of program participants, relative to a control group of non-participants. The EPAG program increased employment by 47 percent and earnings by 80 percent. In addition, the impact evaluation documents positive effects on a variety of empowerment measures, including access to money, self-confidence, and anxiety about circumstances and the future. The evaluation finds no net impact on fertility or sexual behavior. At the household level, there is evidence of improved food security and shifting attitudes toward gender norms. These results reinforce the highly positive feedback received from focus group discussions with program participants. Finally, preliminary cost-benefit analysis indicates that the budgetary cost of the EPAG business development training for young women is equivalent to the value of three years of the increase in income among program beneficiaries. These preliminary results provide strong evidence for further investment and research into young women’s livelihood programs in Liberia.

This paper is joint product of the Poverty Reduction and Economic Management Unit, Africa Region and the Social Protection and Labor Unit, Human Development Network. . 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://econ.worldbank.org. The authors may be contacted at schakravarty@ 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
curve lies below the solid reference curve. This indicates that Engel curves have drifted to the left, a signal that the national CPIs in these countries may overstate the increase in cost of living. In Ghana, Mozambique, and Uganda the direction of the CPI bias does not seem consistent across the income distribution. In Ghana, for example, the CPIs appear downward-biased among those people at the lower end of the income distribution, while they appear biased in the opposite direction among people at the upper end of the distribution. The pattern is reversed in Mozambique and Uganda.

While the illustrations in Figure 1 show a clear drift in the Engel curves for the countries in our study, movements in food budget shares could also be attributed to changes in relative prices, household characteristics, etc. Thus, we use regression analysis to condition on these potentially confounding influences and assess the actual drift in the food Engel curve.

Table 4 reports the results of ordinary least square (OLS) regressions of the Engel functions. All regressions are weighted by population-level sampling weights (as discussed later in this section, unweighted regressions are run as a robustness check). As explained in section III, we apply equation (4) to Ethiopia, Ghana, Mozambique, and Uganda (linked to the availability of crosssectional price data) and equation (10) to the rest of the countries. We also limit our sample to urban populations for all countries except Mauritius, Nigeria, and Rwanda.

Across all countries, the estimated regression coefficients for CPI-deflated total expenditure are negative and statistically significant. These results are consistent with Engel’s Law that food budget shares decline as households become richer. The coefficients of the various household characteristics show that, in general, food shares are higher among households of larger size, with younger children, and with heads who are married or older. Food shares decline among households in which the heads have higher educational attainment. There is no clear pattern across countries between the gender of the household head and the food budget share.

The signs of the time dummy variables in Table 4 are consistent with the shifts in the food Engel curves illustrated in Figure 1. They suggest a drift to the left in the food Engel curves in Cameroon, Côte d’Ivoire, the Democratic Republic of Congo, Ethiopia, Madagascar, Mauritius, Mozambique, Nigeria, Rwanda, Senegal, South Africa, Tanzania, and Togo. This drift suggests that growth in real consumption is underestimated. Meanwhile, the food Engel curves in Burkina Faso, Ghana, 15
1.4 data

17 each year, and does not predict how firm productivity changes year to year. Because I do not have a variable time dimension in my econometric model, the effects of explanatory regressors are on an equilibrium level of FDI. This is better reflected by FDI stock because it is far less volatile on an annual basis than FDI flows. Importantly, the extensive margin of FDI measured by whether multinational firms from country i operate in country j can only be estimated by FDI stock, not flows.

For the main explanatory variables, I use the new IAB indicators of FDI regulation across 87 economies, prepared by the World Bank Group in 2010. With 21 countries from Sub-Saharan Africa, 20 from Eastern Europe and Central Asia, 14 from Latin America and Caribbean, 10 from East Asia and Pacific, five from South Asia, and five from the Middle East and North Africa, this sample of 87 countries can be considered random, and a fair representation of the actual world. In 200708, they constituted 87.1 percent of global population and 77.9 percent of global output (Table 15). The sample includes 15 high-income OECD countries.

As shown in Figure 4, in terms of market size, the mean and standard deviation of the sample of all countries<sup>19</sup> in the world and the subsample of 87 IAB countries are almost identical. However, within the IAB sample the distribution of the subgroup with zero FDI observations is remarkably different from the subgroup with non-zero observations. The sample selection bias is, therefore, likely to be more prominent within the IAB sample.

The 23 IAB indicators measure, among others, openness to FDI through equity ownership permitted in 11 sectors (11 indicators); time, procedures and regulations for starting a foreign business (three indicators); arbitrating commercial disputes (three indicators); and accessing industrial land (6 indicators). The data are compiled from detailed surveys filled out by over 2350 local experts from leading law and accounting firms, chambers of commerce, and investment promotion 19 Excludes countries with total population of less than one million in 2008.
The obtained variable, now denoted _g_Statusst_ for each regions _s_ and year _t_ , becomes the dependent variable in our estimation framework, which we regress on the fraction of migrant workers in the working-age population in region _s_ and year _t_ (fst), together with region and time fixed effects. The baseline equation we estimate is given therefore by the following: S S s = γ s s (6) ss ss gg_S + αα + ττ + εε In this specification, _g_Status_ st is the adjusted measure of risk exposure among the working-age population of natives in NUTS2 region _s_ and year _t_ ; τt are the time fixed effects that account for common, time-varying technological parameters; αs are region fixed effects that account for variation due to time-invariant unobserved characteristics of the population in that region; and fst is the share of foreign workers in each region and year. Therefore, γ is the main parameter of interest in the estimation. From the EU Labour Force Survey, our sample consists of 123 NUTS regions in 14 EU countries observed with yearly frequency from 2011 to 2018.

Estimating Equation 6 using a standard OLS approach can raise endogeneity concerns, even after including fixed effects to control for unobserved regional characteristics and time patterns. In particular, OLS estimates for γ may be inconsistent if the share of immigrants in EU regions is correlated with the unobservable term in equation 6 ( st|fst] ≠0). This might arise, for instance, when migration flows to a given region are driven by unexpected contemporaneous ii. ee. EE[ε region-specific shocks. Further, OLS estimates could suffer from reverse causality if migrants choose to locate in areas where natives are employed in occupations that complement migrants’ skills. These occupations, in turn, may differ in their degree of exposure to economic and health risks due to COVID-19. Similarly, unobserved technology and demand shocks, which may differ across EU regions due to sectoral variation, may simultaneously affect the productivity of (and demand for) certain occupations as well as the demand for immigrant workers. A large body of literature indeed shows that migrants’ location choices are endogenous with respect to local labor market conditions, including native workers’ characteristics and labor market outcomes (World Bank, 2018). Therefore, OLS estimates of the relationship between the vulnerability of the jobs that natives hold to COVID-19 and the degree of presence of immigrants in the region may not reflect true causal effects.

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unavailable for a small subset of exporting ...rms, which are therefore excluded from the ...nal sample used in the empirical analysis.

Importing countries. We have supplemented the ...rm data with information on each importing country, namely its real GDP (measured at PPP), GDP per worker, and the distance between its most populated city and Lisbon (measured in Kms). The source of the information on distance is CEPII. The remaining variables come from the World Development Indicators (WDI) of the World Bank. Whenever WDI data were reported missing, we have used instead information from the CIA factbook. These data are available for 199 destination markets, including the Portuguese-speaking countries listed in Table 1.

Portuguese emigrants. We have further added information on the number of Portuguese emigrants in each importing country. These data come from the Global Migrant Origin Database (GMOD) from the the University of Sussex’s Development Research Centre on Migration, Globalization and Poverty. The stocks refer to the year 2000, and are available for 193 of the 199 countries mentioned above. The full GMOD data set consists of a 226x226 matrix of origin-destination stocks. The data are obtained by disaggregating the information on migrant stocks in each destination country as given in the 2000 round of its population census.

The ...nal data set used in the estimation gathers information on 14,782 exporting ...rms and 193 destination markets. Table A1 provides summary statistics on these data.<sup>8</sup>

# 5 Networks and trade at the country level

Table 2 reports descriptive statistics on the distribution of exporters across destinations. As can be seen, on average each ...rm exported to 3.4 countries. However, the mean hides signi...cant ...rm heterogeneity. More than one-half of all exporters sell to only one destination (54.2%), but they tend to be relatively small exporters, accounting for only 6.8% of the total export value. By contrast, only 7% of ...rms export to more than 10 countries, but they account for 60.2% of the total export value.

We proceed by presenting preliminary evidence based on aggregate measures of the extensive and intensive margins of bilateral exports. To do so we decompose the shipments of Portugal to each importing country into two di¤erent terms: to preserve con...dentiality.

> 8 The ...nal data set used in this paper is an extended version of that described in Bastos and Silva (2010), supplemented with information on Portuguese emigrants and a wider set of ...rm characteristics.

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# **III Data**

We collected three pieces of data: baseline individual data, post-intervention individual data, and post-intervention clinic data. The baseline and post-intervention interviews took place at each respondent’s house, and the post-intervention clinic interviews were carried out at health clinics only among respondents who visited the clinics.

## **Study Area and Sampling**

Our study was conducted in the Jada local government area of Adamawa state in the northeastern region of Nigeria.<sup>29</sup> The experiment was implemented in March-May 2013.

We employed the following three-stage sampling. First, 10 main health clinics in all 9 rural wards in Jada were selected.<sup>30</sup> Second, we selected a total of 80 villages which are situated within one of the catchment areas of the 10 health clinics in the sample.<sup>31</sup> All villages with more than 10 households located within the catchment area of each health clinic were selected, unless the total number of villages in the same catchment area was more than 15; if it exceeded 15, the priority was given to villages far from the health clinic to ensure enough variation in the distance to health clinic.

Third, in each of the 80 villages in the sample, we selected one woman from each household, who was aged 15 to 35 and had not received a tetanus vaccine less than six months prior to the baseline survey.<sup>32</sup> In the case where there was more than one eligible woman in the household, the priority was given to the following woman in the following order: a pregnant woman, a nonpregnant woman who had never received tetanus vaccine, and non-pregnant women who had not received the tetanus vaccine in the past six months.<sup>33</sup> If more than one woman with the same priority was eligible, then we selected the first one in the alphabetical order of their first name.<sup>34</sup> The sample covers 2,530 women in 80 villages in total. On average, a health clinic covers 305 women (range: 80-439) in 9.6 villages (range: 6-22), and a village covers 50.1 women (range: 9-189).<sup>35</sup> 11
Strategic Interdependence in the East-West Gas Tmade Gris & Zheng Table 2

# Strategic Interdepundence with a Nested CES Welfare Function for the Inporter

|Supplier's Price<br>S. pplier's P-ice|pit|_S+eRPR_<br>_7 PR)-ST(PR) +eRC_<br>7<br>_1EPR)+eRPR_<br>_71PR)+eRPE_<br>**_R_**_PRC T(PR)+eRPRR_<br>_OaVRPR+ SI_<br>_aTPR)-_<br>|
|---|---|---|
|Transitree|T|_T-S_<br>_=_<br>_[a(T+e[rRP)I_I<br>_eRPR_+_S_<br>_-1_|
|Demand for<br>Russian Gas|G|-G=<br>_D_[<br>_(T+eRPpl_<br>\<br>G<br>_T+e=_<br>_(1,_<br>_e13P0_|
|mporer'sPrice|PC.|_eD_<br>_(T_<br>_P_<br>_e4tPR)_|
|Damand for<br>'Oter'<br>Gas|0|_O-F(Y-PGG)_|
|Importers<br>Welfare Index|U|_U=.CE5[G,O,a,o.=(a_<br>_G0+(I-a)O_<br>_[_|
|Unit Rent|R|_R = eDPG - S - eRC_|
|Russia'sProfit|if'|_=(PR_- _C)G_<br>_FR_|
|Transiter's Profit|_fr|I<br>_= (T-S)G_<br>_- FT_|
||R|_T_<br>evenue<br>~~~~~~T+<br>_eRPR_|
|Shamof<br>_R_<br>Transiter|_"3R_<br>-|<br>_T+eP-_<br>_S-eRC_|
|||_FH+eRlIt_|

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# **3.1 Measuring Disease with Infant Mortality**

Table 2 verifies the quantitative robustness and statistical significance of our main empirical result: men who were born in district-years with higher infant mortality have lower adult wages, on average. The regression coefficients imply that a 1 percentage point increase in infant mortality (10 IMR deaths per 1,000) would be associated with a decline in adult wages of 1.7 percent or more.<sup>9</sup> The result is notably stable across regression specifications. In particular, similar results are found if IMR is interpolated between survey rounds linearly, as in Panel A, or if linear interpolation is instrumented for with log-linear interpolation, as in Panel B, to reduce measurement error.

Adding a long vector of regression controls fails to importantly change the coefficient estimate. In particular, column 2 controls for state-specific linear year-of-birth trends (age gradients), separate urban indicators for each state, and separate religious and caste indicators for each state; the coefficient remains essentially identical, and if anything slightly increases. Columns 3 and 4 go further by controlling in a flexible semi-parametric way for variables which may well be in part _consequences_ of early-life health and human capital accumulation: job categories and education, measured as school-grade indicators interacted with literacy. Neither of these coefficients importantly changes the coefficient on early-life IMR exposure. This suggests both that education and job categories are not omitted variables that are spuriously responsible for our main result and that early-life health does not appear to have its effect on wages through the mechanism of sorting into these categories.

# **3.2 Measuring Disease with Sanitation**

Table 3 reports regression results substituting sanitation coverage for infant mortality as the independent variable. The sample is smaller in Table 3 than in the IMR analysis because historical sanitation data are not available as early as are historical IMR data; this smaller sample will decrease the precision of coefficient estimates. Insofar as results using sanitation

> 9Cameron and Miller (2013) propose a strategy for two-dimensional clustering of standard errors: in our case, by districts, and by age cohorts. When we implement this procedure, the standard error in IMR _decreases_ , although the number of birth cohorts in our study may be too small for credible asymptotic estimates. We will not further discuss multi-dimensional clustering, and note here merely that this procedure gives no reason to suspect that our standard errors are too small.

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<mark>possible transition. For instance, it would allow to assess the eligibility of the poor households that are considered non-vulnerable for the humanitarian assistance and thus, not included in their database. Moreover, perceived ISIS affiliation, fear of persecution, and other</mark> grievances may mean not all eligible households will be willing to be referred to the formal government social protection. <mark>The drawback, however, is that it comes at a great cost, as the door-to-door survey sweeps are very expensive. O</mark> f course, if funding permits, all vulnerable IDP households should be referred to government support. The approach which identifies all eligible households is to conduct a very large and costly new data collection effort, visiting all households. Which could be the preferred choice for policy makers depending on their capacity, budget, and appetite for the uncertainty of eligibility. However, in the absence of resources to conduct a new data collection (common in many places), the ranking approach of the PPMT model could serve as an acceptable substitute.

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the positive economic impacts may be limited to those households with educated workers. The fact that the benefits reached the more educated workforce implies that investment in education and skills training is important for the local labor force and could serve as a poverty reduction tool. Investigating whether such distributional impacts can be resolved over time requires further studies with longer-term observations upon offshore oil discovery.

There is large potential to create economic benefits from the oil development locally through transfers of oil revenue from the central government to the district governments. It can support investment in education and infrastructure. Such investment can strengthen the positive impacts of oil development and reduce poverty. Thus, efficient public spending needs to be given a high priority on the policy agenda.

The quality of governance and institutions has tremendous impacts on the allocation of revenue (Anderson, Francois, and Kotwal, 2015; Besley and Burgess, 2002; Bhattacharyya and Hodler, 2010; Brollo et al., 2013; Lizzeri and Persico, 2001). Empirical evidence from other countries suggests that oil revenue can bring about corruption and rent seeking behavior. Transparency of revenue collection and allocation is critical to prevent the misuse of the revenue.

# **References**

- Ades, Alberto, and Rafael Di Tella. 1999. "Rents, competition, and corruption." _American economic review_ 89 (4):982-993.

- Alexeev, Michael, and Robert Conrad. 2009. "The Elusive Curse of Oil." _The Review of Economics and Statistics_ 91 (3):586-598. doi: 10.1162/rest.91.3.586.

- Allcott, Hunt, and Daniel Keniston. 2017. "Dutch disease or agglomeration? The local economic effects of natural resource booms in modern America." _The Review of Economic Studies_ 85 (2):695-731.

- Anderson, Siwan, Patrick Francois, and Ashok Kotwal. 2015. "Clientelism in Indian villages." _American Economic Review_ 105 (6):1780-1816.

- Angrist, Joshua D, and Adriana D Kugler. 2008. "Rural windfall or a new resource curse? Coca, income, and civil conflict in Colombia." _The Review of Economics and Statistics_ 90 (2):191-215.

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