rafmacalaba/datause-encoder-data huggingface dataset:
occupation and 9 for the highest-ranked occupation.<sup>25</sup> In Table 8, our independent variable of interest is the number of hours of work/week. Accounting for the triple selection into emigration, legal status, and return, Table 7 shows that migrants’ legal status does not have any significant effect on occupations upon return. Even though illegal migrants seem to have lower ranked occupations compared to non-migrants upon return, the results show that the effect is not statistically significant. Table 8 reports the results on labor supply. Likewise, the results in Table 8 show that migrants’ legal status does not have any significant effect on labor supply. Indeed, we find that undocumented migrants have lower labor supply compared to non-migrants, while documented migrants seem to work more than non-migrants. However, these effects are not statistically significant.

Finally, in Table 9, we examine the effect of migrants’ legal status on monthly wages upon return. We estimate equation (1b), unconditional on return migration. Our main outcome of interest is the logarithm of monthly wages upon return to Egypt. We account for the various layers of selection in columns (1) to (3), and in column (4), we report the results from our structural equation. The results in Table 9 confirm that undocumented migrants witness a wage penalty with respect to both documented migrants and non-migrants. In terms of monthly wages, we find that undocumented migrants witness a wages penalty of 28% compared to non-migrants, while documented migrants witness a wage premium of 29% relative to non-migrants. In terms of monthly wages, the estimated penalty for undocumented migrants is larger in magnitude compared to the estimates presented in section 5.1.1 in terms of hourly wages. On the other hand, the wage premium of documented migrants remains of the same magnitude.

# **5.2 Underlying Mechanisms: Does the Legal Status of Migrants Matter while Abroad?**

The results in section 5.1 suggest that undocumented migrants fare worse upon return to Egypt compared to documented migrants but also compared to non-migrants. While documented migrants benefit from their migration experience abroad and witness a positive wage premium compared to non-migrants, undocumented migrants witness a wage penalty associated with their experience overseas. In this section, we investigate several underlying mechanisms.

In the ELMPS, individuals are asked about the reasons for return. Interestingly, we find that the primary motives for return migration differ between return migrants who had a documented migration versus those who had an undocumented migration. For example, while the most important cause of return migration is the end of the work contract for documented migrants (26%), the primary reason for return for undocu-

> 25 In Table A.5 in the Online Appendix, we compute the occupational indices following Sicherman and Galor (1990) and Carletto and Kilic (2011).

21
In summary, the comparison income that is most consistent with the data on national poverty lines is the rank-weighted income in (10). We find that 70% of an increase in the Ginidiscounted mean is passed onto the poverty line. Statistically, this model outperforms past methods of measuring relative poverty when judged against national poverty lines.

# **6. Implications for global poverty measures**

Building on the results so far, we now provide lower and upper bounds to the true welfareconsistent schedule of poverty lines. To recap, our lower bound (<sup>_z_</sup> _Lj_<sup>) is the World Bank’s $1.90</sup> absolute line at 2011 PPP while the upper bound is given by our schedule of weakly-relative lines based on (18). With some rounding off, the upper bound is: _zUj_  $.190  max(7.0 _m_ * _j_  $.100, 0) (20) (Notice that this approximation is equally valid whether or not one includes the OECD.) We find * 13 countries (11% of the non-OECD sample) for which $1.90 is binding (i.e., _m j_  $.143 ). The * elasticity of the upper bound to _m j_ goes from zero to a value close to unity (0.97) in the country * with the highest _m j_ (Norway). The mean (and median) elasticity in the OECD countries is 0.96. So the upper bound is very close to being a strongly relative measure in the OECD.

In our empirical implementation we will confine attention to the headcount index of poverty (the proportion of the population living below the poverty line), so we will not need to calculate a distribution of equivalent incomes. All we need to know is whether income is above or below the relevant poverty line. Table 2 gives the headcount index for both the lower and upper bounds over 1990-2013. The regional classification in the upper panel is for all low and middle-income countries and a few high-income countries that are eligible for World Bank loans or recently graduated to the Bank’s “high-income” category. We also give a separate row for all high-income countries (excluding those elsewhere classified to avoid double-counting). Countries stay in their base-year category over time. For developing countries we use _PovcalNet_ while for other countries we use the _Luxembourg Income Study_ (LIS).<sup>49</sup> For about two-thirds of countries, we use a comprehensive measure of consumption, which we take to be the preferred

> 49 The assumptions made in constructing the _PovcalNet_ data set are described in Chen and Ravallion (2010, 2013). While LIS is clearly the best available option for developed countries, it has some inadequacies for the purpose of measuring global poverty as discussed in Ravallion (2015).

27
The next three proposed scenarios have lower estimated impacts ranging from 5.86 percent to 9 percent; and for all three cases, the _ex post_ ITT estimator falls in the 95 percent confidence interval of the _ex ante_ ITT estimator. For these scenarios, the average transfer per household ranges from US$9.8 to US$15.95, regardless of whether the US$15 ceiling is taken into consideration. As a consequence, the estimated cost of the program never extrapolates to the cost in scenario 1 of US$15 per household. For example, this result suggests that we would have observed higher program effects (without affecting the overall cost of the program) by making individual, smaller transfers of US$7.5 per child, so that the amount of transfer per household ranges from US$7.5 to US$45 depending on the size of the household.

For the reasons described earlier, we cannot calculate _ex post_ results for the BDH program in a way that would allow for a direct comparison with _ex ante_ predictions as we have done for the case of _PROGRESA_ . However, the prediction power of the BFL model is reasonable and consistent with our expectations conditional on the assumption made about the allocation of the household transfer among beneficiaries. As we would expect, considering each child in the sample to be a household under the BFL definition (and receiving US$15) overestimates the impact of the BDH. Keeping the average transfer per household close to aor above US$15 and using smaller amounts of transfer per child, the BFL model estimates program effects on enrollment rate that are not statistically different (at 5 percent level of significance) from _ex post_ results reported by SA for all simulated ITT estimators.

# **VI. Conclusion**

The results of our paper suggest that _ex ante_ and _ex post_ methods for estimating impacts can effectively complement each other to inform the design and evaluation of social programs. In principle, combining an _ex ante_ simulation exercise using a structural model of schooling choices with an experimental evaluation design would help policymakers to assess the implications of alternative choices for program design, potential impacts and cost implications upfront, and rely on _ex post_ evaluation results to validate and identify the actual impacts of the program. For _ex ante_ models to be used this way, however, their results need to be credible in terms of accuracy or predictive power. Our findings make some progress in this direction. Our results suggest that _ex ante_ predictions of certain impacts of CCT programs using the BFL model match up well with 29
# (b) <u>Implementation</u>

Packages were prepared in July 2015, at GRADE premises (in Lima). Each ‘GYM package’ was personalized for each school based on the number of classrooms in grades seven and eight, as reported in the 2014 Peru School Census.<sup>16</sup> Once packages were ready, a private courier company was hired to deliver them to all schools in the treated group. Each package contained an official letter from MINEDU (signed by the Director of the Secondary Level) addressing the head teacher of the school. This letter was placed on the outside of the package so that it was visible to the person who received it.

To locate schools, the courier was given the name of the institution, full address and (when available) contact phone numbers, as reported in the publicly available information system of MINEDU called ESCALE. The initial aim was to deliver all packages during August 2015. However, there were problems with some of the deliveries because the information given to the courier company was not detailed enough and/or was not accurate. For this reason, for a group of schools the courier company was given a physical map to locate the district (elaborated with Google Maps, using the geo-referenced location from the school) and the most up to date information about phone numbers (which was not necessarily information that was publicly available at that time). This information was obtained from the data division of the Ministry ( _Sistema de Información de Apoyo a la Gestión de la Institución Educativa_ , SIAGIE). Based on this strategy, the delivery of packages was completed between August and September 2015. As part of the protocol, each school that received the package signed a delivery receipt.

# (c) <u>Monitoring delivery and compliance</u>

A small tracking team, composed of two individuals with experience in telemarketing, was trained to monitor–through phone calls–all schools that had received the ‘GYM package’ according to the courier. The tracking exercise had multiple purposes. First, to make sure that the ‘GYM package’ had been received by tutor teachers in each school. Second, to ask if there were any doubts about the nature of the sessions and/or if the material they received was incomplete. Third, to monitor when sessions were going to take place and/or to ask why

> 16 If the number of sections was uneven, we rounded it to the next even number (e.g., 4 packages were sent to schools with 3 sections). We assumed each section had 30 students.

10
# I. INTRODUCTION

The notion of human capital – resources imbedded in people – was alluded to as early as 1776 by Adam Smith and formalized two centuries later by Becker (1962). Ever since, the literature has explored the role of human capital in economic development. For decades, this literature proxied human capital and education with measures of schooling.<sup>2</sup> This applies even to the most prominent index of human capital to date, the United Nation’s Human Development Index (HDI).

However, proxying human capital with schooling assumes that being in school translates into learning. Evidence suggests that this is often not the case (Pritchett 2013). A recent analysis reveals that six out of ten adolescents worldwide cannot meet basic proficiency levels in math and reading (UNESCO 2017). The gap between schooling and learning is acute in developing countries. In Kenya, Tanzania, and Uganda three-quarters of grade 3 students cannot read a basic sentence such as “the name of the dog is Puppy.” In rural India, half of grade 3 students cannot solve a two-digit subtraction problem such as 46 minus 17 (World Bank, World Development Report 2018).

These stylized facts demonstrate a gap in human capital formation: students are in school but learning is limited.<sup>3</sup> Closing this gap presents a significant margin to drive economic development. Several papers have argued that when human capital is measured by schooling it fails to deliver the returns predicted by growth models. However, when measured by learning, human capital is more strongly associated with growth<sup>4</sup> .

To date, much of the analysis of human capital when measured by learning has focused on advanced economies. This is due to the absence of comparable measures of learning in developing countries. This excludes a significant portion of the global distribution, in particular countries with the most potential to gain from human capital accumulation.

In this paper, we bridge this gap. We introduce a database of globally comparable learning outcomes for 164 countries covering 98 percent of the global population from 2000 to 2017.<sup>5</sup> This is the largest and most current global learning database, one of the first to disaggregate learning results by gender, and introduces methodological improvements, such as inclusion of standard errors to quantify uncertainty. The database will be updated at regular intervals and made available

> 2 Examples include Mincer (1984), Mankiw, Romer, and Weil (1992), and Lutz and Samir (2011).

> 3 We refer to ‘schooling’ as measured by enrollment or average years of school and ‘learning’ measured by the stock of cognitive skills on basic proficiencies including mathematics, reading and science.

> 4 See related papers by Krueger and Lindahl (2001); Pritchett (2006); Hanushek and Woessmann (2012a).

> 5 There are two administrative regions: Hong Kong and Macao, which we refer to as countries for simplicity.

2
<!-- Start of picture text --> TACNA TALARA<br>140 600<br>120 500<br>100 400<br>80 300<br>60 200<br>40<br>100<br>20<br>0<br>0<br>-100<br>-20 may-48 oct-53 abr-59 oct-64 mar-70 sep-75 mar-81 ago-86 feb-92 ago-97 feb-03<br>may-48 oct-53 abr-59 oct-64 mar-70 sep-75 mar-81 ago-86 feb-92 ago-97 feb-03<br>TRUJILLO TUMBES<br>300 1000<br>250 800<br>200<br>600<br>150<br>400<br>100<br>200<br>50<br>0<br>0<br>-200<br>-50<br>may-48 oct-53 abr-59 oct-64 mar-70 sep-75 mar-81 ago-86 feb-92 ago-97 feb-03<br>may-48 oct-53 abr-59 oct-64 mar-70 sep-75 mar-81 ago-86 feb-92 ago-97 feb-03<br>average for 1960-1990 (Milimeters)<br>Precipitation Anomalies compared to<br>average for 1960-1990 (Milimeters)<br>Precipitation Anomalies compared to<br>average for 1960-1990 (Milimeters)<br>average for 1960-1990 (Milimeters) Precipitation Anomalies compared to<br>Precipitation Anomalies compared to<br><!-- End of picture text -->

# _<u>Mountain stations</u>_

<!-- Start of picture text --> AREQUIPA AUGUSTO WEBERBAUER<br>350 200<br>300<br>150<br>250<br>200 100<br>150<br>50<br>100<br>50 0<br>0<br>-50<br>-50<br>-100 -100<br>may-48 oct-53 abr-59 oct-64 mar-70 sep-75 mar-81 ago-86 feb-92 ago-97 feb-03 may-48 oct-53 abr-59 oct-64 mar-70 sep-75 mar-81 ago-86 feb-92 ago-97 feb-03<br>Precipitation Anomalies compared to average for 1960-1990 (Milimeters) Precipitation Anomales compared to average for 1960-1990 (Milimeters)<br><!-- End of picture text --> 32
ANGOLA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE prevents surplus power in the north being wheeled to the center and south of the country. This problem of regional imbalances in power supply and demand will only become more accentuated as new generation capacity comes on stream, underscoring the importance of improving the transmission network.

Power production costs, at $0.16 per kWh, are relatively high by the standards of neighboring countries in southern Africa (figure 4). The higher costs in Angola, particularly when compared to its neighbors, are partly explained by the country’s reliance on oil-based generation for about 40 percent of its production, at a cost of around $0.30 per kWh.

**Figure 4. The costs of power production in Angola are relatively high**

<!-- Start of picture text --> 45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Power Production costs (US cents per KWh)<br>Mali Burkina Faso Congo Benin Cape Verde Cameroon Rwanda Niger Angola Madagascar Kenya Tanzania Botswana Chad Ghana Senegal Namibia Cote d'Ivoire Lesotho Uganda Nigeria Malawi Mozambique Ethiopia DRC Zambia South Africa<br><!-- End of picture text --> Source: Derived from Briceño-Garmendia and Shkaratan (2010-); based on data from 2005–06. Angola costs derived based on IFC staff estimates and are for 2010.

_Note:_ DRC = Democratic Republic of Congo; kWh = kilowatt-hour.

Meanwhile, tariffs, at $0.042 per kWh, are among the lowest in Africa, covering only a small fraction of costs (figure 5). Power tariffs in Angola are low even by the standards of other hydropower-dependent countries, whose power tariffs typically stand closer to $0.10 per kWh. Angola has not revised its power tariffs since 2004. These low power prices, although meant to benefit the poor, largely subsidize the better-off minority that live in larger cities covered by the grid, while the poor remain unconnected.

13
63 Minneapolis: University of Minnesota Press, 1987, pp. 26-47.

- Jaffee, Dwight and Russell, Thomas. "Imperfect Information, Uncertainty, and Credit Rationing." Ouarterly Joumal of Economics, November 1976, 2Q(4), pp. 651-66.

- James, Christopher. "Some Evidence on the Uniqueness of Bank Loans," Journal of Financial Economics, December 1987, 12(2), pp. 217-36.

- James, Christopher and Wier, Peggy. "Borrowing Relationships, Intermediation, and the Cost of Issuing Public Securities, Journal of Financial Economics, November-December 1990, 21(1/2), pp. 149-171.

- Jappelli, Tullio and Pagano, Marco, "Saving, Growth, and Liquidity Constraints," Quarterly Journal of Economics, February, 1994, 109(1), pp. 83-109.

- Jaramillo, Fidel, Schiantarelli, Fabio and Weiss, Andrew. "Capital Market Imperfections, Financial Constraints and Investment: Econometric Evidence from Panel Data for Ecuador," Journal of Development Economics, 1996, forthcoming.

- Jayaratne, Jith and Strahan, Philip E. "The Finance-Growth nexus: Evidence from Bank Branch Deregulation," Federal Reserve Bank of New York Research Paper No. 9513, 1995.

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Jevons, W. Stanley. Money and the Mechanism of Exchange, London: Appleton, 1875.

- Jung, Woo S. "Financial Development and Economic Growth: International Evidence," Economic Development and Cultural Change, January 1986, 3A(2), pp. 333-46.

- Kadlec, Gregory B. and McConnell, John J. "The Effect of Market Segmentation and Illiquidity on Asset Prices: Evidence from Exchange Listings," Journal of Finance, June 1994, 42(2), pp. 611-36.

- Kerr, Andrew.W. History of Banking in Scotland Glasgow, 1884 (3rd ed. London: A&C Black, 1918).

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journals, we included the first four principal components (PC) for the dummies, which accounted for two-thirds of the variation embodied in the journal dummies. The loadings indicate that PC1 (27% of the variation) represents a reading pattern that emphasizes the 4 best-established journals ( _AJAE_ , _ERE_ , _JEEM_ , _Land Economics_ ), while PC2 (15%) represents a pattern that emphasizes two newer journals that tend to publish more papers on developing country topics ( _EDE_ , _Ecological Economics_ ). PC3 (14%) represents a pattern that emphasizes the two journals that have made the most effort to target a policy audience ( _EDE_ , _REEP_ ). Finally, PC4 (12%) represents a pattern that emphasizes miscellaneous other journals.

**Table 8. Effects of respondent characteristics on mean and median WTP.** Dependent variables: ln(1 + mean WTP) and ln(1 + median WTP). Observations: individual respondents in all models. Dummies are used to control for regional differences in the models in the first two columns and country differences in the models in the last two columns.

||Include inco|me, exclude FE|Exclude inco|me, include FE|
|---|---|---|---|---|
|Variables|Mean|Median|Mean|Median|
|ln(GDP per capita, PPP)|0.820***|0.829***|||
||(0.237)|(0.191)|||
|Dummy: plan A|0.287***|0.297***|0.347***|0.333***|
||(0.0283)|(0.0301)|(0.0243)|(0.0188)|
|Dummy: Asia|0.293|0.676|||
||(0.486)|(0.416)|||
|Dummy: North America|0.530**|0.755***|||
||(0.262)|(0.248)|||
|Dummy: Oceania|-0.363|-0.294|||
||(0.366)|(0.452)|||
|Accuracy of Delphi prediction|-0.0493|-0.0149|0.0409|0.0638*|
||(0.0540)|(0.0515)|(0.0330)|(0.0357)|
|Difficulty of CV study|-0.0470|-0.0525|-0.0673***|-0.0658***|
||(0.0473)|(0.0459)|(0.0213)|(0.0209)|
|No. CV surveys|-0.0119|-0.0203*|-0.00961|-0.0199***|
||(0.0120)|(0.0123)|(0.00696)|(0.00770)|
|No. CE surveys|-0.0179|-0.0188|-0.0411*|-0.0417*|
||(0.0260)|(0.0240)|(0.0219)|(0.0224)|
|No. SP surveys (biodiversity)|-0.0140|0.000274|-0.0114|-0.00155|
||(0.0177)|(0.0190)|(0.0236)|(0.0242)|
|No. benefit transfer exercises|0.0153|0.0168|-0.00358|4.57e-05|
||(0.0136)|(0.0114)|(0.0183)|(0.0159)|
|Journals: PC 1|0.124|0.133*|0.142***|0.138***|
||(0.0766)|(0.0798)|(0.0516)|(0.0437)|
|Journals: PC 2|0.103|0.0987|-0.0497|-0.0426|
||(0.123)|(0.127)|(0.116)|(0.0980)|
|Journals: PC 3|-0.0592|-0.0863|-0.127**|-0.0766|

20
# **Table A9**

Impact of new road on population growth, age distribution and gender ratios _Panel A. Population_ _<u>growth (2001-2011)</u>_

||Log|Level|
|---|---|---|
|New road|-0.027|-13.539|
||(0.030)|(20.627)|
|Control group mean|6.43|652.79|
|N|11474|11474|
|R2|0.78|0.83|

_Panel B. Age_ _<u>group</u> share_

||11-20|21-30|31-40|41-50|51-60|
|---|---|---|---|---|---|
|New road|-0.003|-0.005|0.004|-0.002|0.002|
||(0.005)|(0.005)|(0.004)|(0.004)|(0.003)|
|Control group mean|0.24|0.19|0.15|0.11|0.07|
|N|11474|11474|11474|11474|11474|
|R2|0.22|0.19|0.26|0.38|0.40|

_Panel C. Male share by age_ _<u>group</u>_

||11-20|21-30|31-40|41-50|51-60|
|---|---|---|---|---|---|
|New road|-0.010|0.002|0.004|-0.004|0.018|
||(0.009)|(0.008)|(0.008)|(0.010)|(0.013)|
|Control group mean|0.52|0.52|0.51|0.52|0.51|
|N|11474|11474|11474|11474|11474|
|R2|0.13|0.20|0.10|0.08|0.06|

_<u>∗ ∗∗ ∗∗∗</u> p <_ 0 _._ 10 _, p <_ 0 _._ 05 _, p <_ 0 _._ 01 Notes: This table presents regression discontinuity estimates from the main estimating equation of the effect of PMGSY treatment on village demographics. Panel A presents results on 2011 village population, both in log and level. Panel B presents results on the share of the village population in ten-year age bins. Panel C presents results on the share of the population in each age bin that is male. Dependent variables in Panels B and C are generated from the SECC microdata. For each regression, the outcome mean for the control group (villages with population below the threshold) is also shown. The specification includes baseline village-level controls for amenities and economic indicators, as well as district-cutoff fixed effects (see Section V for details). Heteroskedasticity robust standard errors are reported below point estimates.

53