rafmacalaba/data-use-mentions-extended huggingface dataset:
of Mexico’s _Oportunidades_ CCT program investigate the extent to which the urban component of _Oportunidades_ affects children’s outcomes and household consumption. Angelucci and Attanasio (2009) estimate a linear probability model of program participation among eligible households in treatment areas that incorporates second-degree polynomials for the poverty level as well as household income and consumption variables. They find that a household in the 75th percentile of the poverty distribution is 69 percent more likely to be a program participant than a household in the 25th percentile. They also conclude that participation is inversely related to both consumption and income. In addition to data on household income, consumption, and poverty level, their model includes information on transitory shocks and the local availability of schools and health centers. However, their model has some data limitations because it does not include key determinants of household decisions to participate in _Oportunidades_ , such as proxies for the relative price of schooling, parental preferences, and the opportunity costs of participating. Although their results do not directly support the outline presented in this paper, they conclude

> 8 Heinrich (2007) constructed an index of need using 20 measures from base data that include: dependents, household head occupation, whether household head is pregnant, type of home/tenancy/living conditions, distance to school, years of education of all household members, student hours worked outside/inside home, student age-grade difference, illness or disability, and household income.

6
_Ingo Borchert and Mattia Di Ubaldo_ of mutually exclusive binary variables that proxy for the depth of services PTAs. Depth can be captured either by a count measure tallying the number of substantial provisions included in services chapters<sup>15</sup> , or by indicator variables for the presence of specific policy configurations of key provisions in a certain policy area. The construction of these latter variables will be explained in detail in section 4.2 below. At the most disaggregated level, **SPolij** _,_ **t** could stand for individual PTA provisions.<sup>16</sup> Because of its particular significance in internal services market liberalization, we control for (time-varying) EU membership in all models.

We include a demanding set of fixed effects in all models, namely at the exporter-year, importer-year and country-pair level. This represents current best practice in the estimation of structural gravity models (Baier et al., 2019), and addresses several endogeneity concerns.

The country-pair fixed effects _λ_ **ij** account for all bilateral time-invariant factors that could affect bilateral trade (or VA sourcing) or the existence and content of a services PTA: conventional gravity variables such as distance, similarity of legal systems etc. Importantly, the use of country-pair fixed effects also addresses self-selection into signing a PTA. If there were any (economic or political) characteristics of country pairs that provided an incentive of conclude a services PTA, then the dyadic fixed effects _λ_ **ij** will absorb these factors as long as such features are time-invariant (Baier and Bergstrand,

> are plurilateral ones since “mixed agreements” require ratification by individual EU member states. At the same time, the Commission negotiates on behalf of member states, which _de facto_ renders these agreements akin to bilateral ones. To resolve these competing considerations, we keep this set of agreements separate.

> 15Following the approach adopted in other papers that exploit the WB-DTA 2.0 data, we approximate vertical depth
above combinations, although the gradual increase in both areas between 2006 and 2012 is statistically significant at conventional levels (95 percent) for math but not for science (90 percent only).

_Table 1: Changes in Learning Outcomes, by Year and Subject Area_

|**Reading**|**2006**|**2009**|**2012**|
|---|---|---|---|
|2000|-44.53 (-3.41)|-19.99 (-1.68)|-22.27 (-1.85)|
|2006|-|24.54 (2.63)|22.26 (2.30)|
|2009|-|-|-2.28 (-0.36)|
|**Mathematics**||||
|2006|-|6.81 (0.89)|7.18 (2.30)|
|2009|-|-|4.79 (-0.36)|
|**Science**||||
|2006|-|9.59 (1.20)|14.39 (1.81)|
|2009|-.|-|4.79(0.76)|

_Source_ : Authors’ calculations based on PISA-OECD. T-statistics included in parenthesis.

Although the evolution in average test scores is an important indicator of the quality of education services, equally important is the variation or dispersion of test scores. Given its linkages with future labor market outcomes, the inequalities that we observe today in test scores is a good predictor of the inequalities in incomes that will prevail in the future (Checchi and van de Werfhorst, 2014). Table 2 shows the evolution in dispersion in test scores for all three subjects, over the period 2000 to 2012. The dispersion measure presented in Table 2 is
# _E. Determinants of aggregate productivity (TFPQ)_

Table 3 suggests that all three of the predictions of the Melitz‐Ottaviano model about the determination of aggregate industry productivity apply to the production of cinder blocks but not to any of the three food processing industries. The table displays estimates of parameters of the Arellano‐Bond specification of productivity in a plant as an augmented AR (1) process of TFPQ in log units by industry. In the table, market size measured by nighttime luminosity of the city where the establishment is located. Estimates of parameters the model for cinder blocks production are presented in the last column of the table, where TFPQ rises as market size increases but declines with any rise in sunk costs of entry or in transport costs. The decline in TFPQ that an increase in license fees would imply per the column is consistent with the model’s prediction that productivity would fall with any increase in sunk costs of entry. The model would explain this outcome in terms of rising sunk costs having the effect of reducing entry and exit rates in the industry. Similarly, the negative elasticity coefficients of the terms in transport costs is consistent with the prediction of the model that rising transport costs would reduce aggregate productivity. The model would explain this outcome by higher transport costs reducing final demand product substitution possibilities for customers of the industry.

But results contrast sharply with what we see in data on grain mills and bakeries as shown in column 3 (for grain mills) and column 4 (for bakeries) of the same table. In each of these cases TFPQ declines as the market expands but rises if sunk costs of entry or transport costs increase.<sup>6</sup> Broadly speaking, these findings concur with what is reported in Table 4, where we apply the same estimator to the Arellano‐Bond specification but this time while measuring market size by the population size of the city of location of the plant rather than by the city’s nighttime luminosity. In Table 4, all
Policy Research Working Paper 9723

# **Abstract**

A large body of evidence suggests sizeable improvements in cognitive and social-emotional skills and subsequent educational attainment following preprimary education interventions as well as increases in earnings later in life. Yet, while the world has nearly reached universal primary education, coverage of early childhood education is still low in many countries. This study uses a novel global dataset of - effect sizes from more than 50 studies conducted in 19 coun tries to examine measures of school participation, cognitive skills, social-emotional skills, and behavior, both during and after preprimary ages. Estimates from meta-regression analysis suggest both strong demand for preprimary services when offered and significant improvements in children’s cognitive skills (0.15 sd) and their executive functioning, social-emotional learning, and behavior (0.12 sd) during the pre-primary period. Moreover, our meta-analytic results indicate statistically significant persistent advantages (0.07 sd) in each type of skill beyond the preprimary period. Pooled heterogeneous treatment effects also suggest higher gains for disadvantaged children. Lastly, cost-benefit analysis using studies from low- and middle-income countries implies benefit-to-cost ratios ranging between 1.7 and 14.2, suggesting high returns to preprimary investments even in contexts with limited state capacity.

This paper is a product of the Development Research Group, the Education Global Practice, and the Development Impact Evaluation Group. 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 aholla@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.
included in the table, only Thailand, Morocco, Korea and India apply tariff rates above 20 per cent. Total estimated tariff revenue, therefore, adds up only to about US$ 732 million for the world as a whole8 The EU and Korea are estimated to collect close to half of the total. No other country collects more than US$ 100 million, and many below US$ 10 million.

> 7 The estimates are reasonably reliable for the most important categories where trade and tariff data were available for the most important countries. A few data problems persist as volume data for some products facing specific tariffs were not available, sometimes the tariff rate was not provided, and applied tariff rates for some of the smaller countries were not available. For an independent attempt to measure the revenue implications of electronic commerce, see Teltshcher (2000).

> 8 Not counting China, for which estimates did not seem reliable.
what was believed, providing access to credit to all will not make an entrepreneur out of everybody.

A closely related study is by Crépon et al. (2011) in rural Morocco. This intervention involved randomly offering microcredit to one out of two villages in 81 matched pairs. They find that providing access to microcredit did not lead to new business creation, only to an expansion in the scale of existing businesses. Households with no existing business at baseline merely increased consumption once they got access to credit. This again reminds us that credit, by itself, cannot spawn entrepreneurs. Like Pitt and Khandker (1998), they also find that treated households decreased their wage employment and increased their consumption of leisure, offsetting the income gains realized from the scale expansion of existing businesses. If this finding has external validity, it not only casts a shadow on the income generating potential of microcredit, but also raises longer term questions about the borrowers‟ ability to repay their loans and/or the possibility of chronic indebtedness.

Kaboski and Townsend (2011) also utilize village level differentials in access to credit, albeit through an entirely different channel. They study the impact of the Thai Million Baht Village Fund program, under which the government of Thailand provided a million baht to each of the country‟s 80,000 villages to start a rural bank. The exogenous variation stems from the fact that all villages got the same amount, irrespective of their population. As a result, there was a huge variation in the per capita expansion of credit across villages. The authors construct a structural model and use a panel of 960 households from 64 villages for estimation. They find significant increases in consumption, but no impact on average investment. Similar to the preceding two papers, this one also finds heterogeneous effects, with a small subset of households choosing to increase its investment spending. However, this increase is more than 5
positive impact of technological innovation on productivity (log of sales per employee) for all countries except Costa Rica. Moreover, they find that the magnitude of the results is very heterogeneous. Crespi, Tacsir, and Vargas (2016) used 2010 World Bank Enterprise Survey firm-level data to analyze 17 Latin American countries. Results show that investment in R&D per worker increases the probability that the firm will innovate, and that this translates into a strong increase in labor productivity (measured as log of sales per employee). These results are robust to five different measures of innovation: innovation of product or process, product innovation, process innovation, innovative sales (share of sales of new products), and filing for intellectual property rights. In Argentina, the CDM framework was first used by Chudnovsky, López, and Pupato (2004) and then by Arza and López (2010). Both papers show that investment in R&D boosts firms’ labor productivity.

7
**Figure 1: Key financial ratios of the International Finance Corporation.** Values of assets prior to 2001 are not directly comparable with those after due to a change in accounting standards requiring derivatives to be held at fair rather than book value (FAS No. 133).

<!-- Start of picture text --> Panel A<br>1960 1970 1980 1990 2000 2010 2020<br>Panel B<br>1960 1970 1980 1990 2000 2010 2020<br>Panel C<br>1960 1970 1980 1990 2000 2010 2020<br>.2<br>.15<br>.1<br>.05<br>0<br>Net Income/Capital<br>7<br>5<br>3<br>Assets/Capital<br>1<br>.03<br>.02<br>.01<br>Non-Interest Expense/Assets<br><!-- End of picture text --> _Source:_ The IFC annual reports. _Note:_ Source data reported in Table A1.
As the sampling frames for the NSSO surveys are based on the decennial population census, we use population census data to approximate the actual number of women in the labor force across rounds. Since the census and NSSO survey years do not match, we first project the population figures for NSSO survey years from the census for compatibility. Population figures from the 1991, 2001 and 2011 censuses for various categories, including rural males, rural females, urban males and urban females are interpolated for the years viz. 1993, 1994, 1999, 2000, 2004, 2005, 2007, 2008, 2009, 2010, and 2012 using compound annual growth rates. Next, we take a simple average of the two years that correspond to each NSSO survey period viz., 1993-94; 1999-00; 2004-05; 2007-08; 2009-10 and 2011-12. The projected population figures are given in Table 3.

**Table 3: Trend in Female Labor Force between 1993-94 and 2011-12**

||Total female<br>|population<br>|Female p<br>above<br>|opulation<br>15 years<br>|FL<br>|FP (Based on UPS<br>|S)<br>|
|---|---|---|---|---|---|---|---|
|Round|Rural|Urban|Rural|Urban|Rural|Urban|Total FLFP|
|1993-94|317,673,031|110,054,315|202,964,444|73,677,754|99,518,541|17,432,031|116,950,573|
|1999-00|351,946,619|129,962,760|224,652,419|89,170,854|103,383,673|18,704,086|122,087,759|
|2004-05|375,871,197
Dating of crisis periods is not straightforward, however. One approach is to let the data on exchange market pressure (the sum of exchange rate changes and reserve changes, appropriately weighted) identify crisis periods. A recent application is Kaminsky (2003), and we use the crisis dates she identifies (Kaminsky, 2003, table 2), but instead of assuming that the duration of each crisis is 24 months, we use a much smaller window, six months following the crisis date in her list—except for Argentina. In practice, the period of very high spreads has been limited to a few months, when a resolution has been in sight. Argentina’s 2002 default is an exception, and we make the crisis dummy continue to the end of our sample. We also add the Russian crisis, starting in August 1998, since Russia is not in her sample.

# **IV. Estimation Results**

We first describe the long-run (static) regressions of the log of spreads on both push and pull factors. Of especial interest is the relative importance of the two sets, and evidence of non-linearity in the relationship. Table 1 gives the coefficient estimates, with the sample divided into crisis and non-crisis periods, with the latter dominating in our sample (1497 months versus only 54 crisis periods<sup>14</sup> ).

Country specific variables seem to dominate US interest rates in influence over emerging market spreads. In particular, trade openness has a strong negative effect on spreads, which is plausible since more open countries are better able to adjust their

> 14 As noted above, our choice of a small crisis window (6 months) limits the number of crisis periods. In addition, our reliance on Kaminsky (2003) for the crisis dates no doubt misses some crises, since not all the countries in our sample are on her list.

15
, which is difficult to employ in most developing countries. Instead the approach described here links the behavioral model to sectoral and aggregate macroeconomic data for Iraq, and extrapolates the microeconomic snapshot of a future scenario from this projection.<sup>9</sup> This approach has been extended to explicitly take into account large scale population movements, which in the case of Iraq, is the internal forced displacement of people from the IS‐affected provinces (or governorates).

This micro‐simulation model accounts for multiple transmission mechanisms and captures impacts at the micro level across the income and consumption distribution. In particular, the model can take into account large changes in population over time and space; labor market adjustments in employment and earnings or a combination of both; non‐labor incomes including public and private transfers; and price changes (including variations in food and non‐food prices). These changes can be positive or negative, depending on the trends outlined by the macroeconomic inputs.

In the following subsections, we present a brief discussion about the micro‐simulation model and each step in which this method is based ‐i.e. baseline, simulation and assessment‐ and we end the section with a discussion of the limitations and assumptions of this methodological approach.

> 8 World Bank (2015)

> 9This approach was conceptualized, refined and tested in a diverse mix of countries not only during the financial crisis (such as Bangladesh, The Philippines, Mexico, Poland, and Mongolia) but also after the crisis (such as Costa Rica, Panama, Uruguay, Serbia, Armenia, Belarus, Kyrgyz Republic, Moldova, Poland, Romania, and Ukraine).

4
33

# **Appendix A:  Checking the Robustness of Findings Using Observed Expenditure**

# **Data**

The purpose of this appendix is to see if the poverty and inequality findings of Tables 6 and 7 of this paper are robust for different ways of defining household income (expenditure). Therefore, in this appendix all of the calculations are based on observed – rather than predicted – data. In other words, all calculations in this section are based on observed per capita household expenditures, and the income contribution of the migrant in the excluding remittances situation is assumed to be zero.

Using the observed data on per capita household expenditures, Appendix Table 1 replicates Table 6 in the text. With only a few exceptions, all of the main results are as before. For example, the three poverty measures in Appendix Table 1 show that the inclusion of remittances – either internal or international – in household expenditure reduces the level, depth and severity of poverty in Ghana. Moreover, just as in Table 6, the size of the poverty reduction depends very much on the type of remittances (internal or international) received, and how poverty is being measured. At first glance, results for the poverty headcount and poverty gap measures in Appendix Table 1 suggest that poverty falls more with the inclusion of internal, as opposed to international, remittances. However, just as in Table 6, the results for the most sensitive poverty measure – squared poverty gap – shows that poverty actually falls more with the inclusion of international remittances. The results in columns (5) and (6) for the squared poverty gap show that including international remittances in household expenditure reduces the severity of poverty by 11.1 percent, while including internal remittances in such expenditure reduces the severity of poverty by only 5.8 percent.
the years; for Carmelo, Dolores, Paso de los Toros, Río Branco, San Ramón, Sarandí del Yí, and Sarandí Grande in 17 of the years; and for Young in 18 of the years. Therefore, we have a total of 735 observations.<sup>14</sup> Some cities began to appear in the ECH because of their rapid population growth; therefore, we checked the robustness of results to a smaller subsample in terms of the time span from 1993 to 2009.

Two observations must be made. First, there are far more control cities than treatment cities; hence, we also estimate our model restricting the number of controls to capital cities alone. The sanitation data for each capital city are available in every year of the full period; hence, in this instance, 12
reduced job insecurity, may have increased the leverage of workers to demand that the labor contract be formalized. The second driver has to do with a more active role of two Brazilian institutions, the Brazilian Public Prosecutor’s Office, or PPO ( _Ministério Público_ ), and the Ministry of Labor and Employment (MLE). Corseuil, Almeida and Carneiro (2012) present evidence of increased enforcement from these two institutions. They document a causal relationship between changes in the frequency and effectiveness of labor inspections and increases in formal employment from 1996 to 2006, using municipality-level data on labor inspection intensity and job flows.

Whatever the exact causes for the increase in formalization, the fact is that the proportion of workers employed under formal contracts increased by almost a fifth, from 48 percent to 57 percent, over the complete period, as shown in Table 1. This came at the expense of both informal employees and self-employment. Interestingly, Table 1 also shows that the increase in the proportion of workers earning below the minimum wage during 19952003 occurred despite this rising formalization: doubling the real level of the minimum wage proved to be too large an increase to sustain coverage, even as informality as a whole was in retreat.

The third group of factors affecting earnings inequality is related to the demographic composition of the labor force. As shown in Table 1, there was a substantial increase in female labor force participation between 1995 and 2012, as a result of which the proportion of female workers increased by 10 percent, from 38 percent to 42 percent. This trend is associated with increases in women’s educational attainment levels but, more recently, there were also large increases in the provision of public childcare. From 1991 to 2007, the proportion of 0-6 year old children attending childcare increased from 27% to 44.5%, according to the IBGE.<sup>14</sup> It is likely that such a large increase might also have had an enabling impact on female labor force participation. Table 1 also shows