rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 10492

# **Abstract**

This paper aims to provide global evidence on whether and what attributes of laws governing the provision of childcare services affect women’s labor market outcomes. It merges country-year-level data from the World Bank’s Women, Business and the Law database, which documents childcare laws across countries, with data on women’s labor force participation from ILOSTAT. Using a difference-in-difference estimation framework, the analysis finds that the enactment of childcare laws increases women’s labor force participation by 2 percent, on average. Moreover, the effect increases over time, reaching up to 4 percent five years after an enactment. This effect is driven by women who are married, have completed less than primary education, and are between the ages of 35 and 44. Lastly, regulation of the availability and affordability of childcare has a similar impact on female labor force participation, whereas the effect of quality regulation is smaller.

This paper is a product of the Development Research 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 sanukriti@worldbank.org and ldinartediaz@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
|**GRACE**|no|The supply and demand is simulated by<br>nested CES functions.|GHGs include CO2, methane (CH4),<br>nitrous oxide (N2O), fluorinated gases<br>(FGAS)|
|---|---|---|---|
|**Ifs**|The capital/output (capital/production)<br>ratios for all fuel types decline over<br>time due to technological<br>improvements at rates determined by<br>two user controllableparameters.|Energy system cost minimization|Fossil fuel consumption generates carbon<br>emissions specific to coal, oil, and natural<br>gas use. Net deforestation generates<br>additional carbon emissions or removal.|
|**PHOENIX**|Electricity-generating technologies are<br>placed into three nests: base load,<br>intermediate load, and peak load|Energy system cost minimization|GHG and other pollutants|
|**WorldScan2**||Nested CES (constant elasticity of<br>substitution) function|GHG and other pollutants|

**Table 5.b Representation of renewable energy, substitutions and emissions for Partial Equilibrium**

|**Model**|**Renewables and Intermittency**|**Functional Structure for Energy**<br>**System Substitution**|**Emissions**|
|---|---|---|---|
|**DNE21+**|Electricity demand and supply are formulated<br>for 4 time periods; Useful capacity of wind and<br>solar power depends on time period and share<br>of wind and solar power is limited; Electricity<br>storage options can mitigate limitations|Bottom-up energy system model with<br>substitution calculated endogenously by<br>minimizing total energy system cost|GHG and other pollutants|
|**global TIMES**|The temporal resolution is determined by<br>three seasons, summer, winter and<br>intermediate. Each of the season accounts for a<br>third of the whole year or 4 months. These<br>time slices are again split into night and day,<br>where day represents 16 hours and night 8<br>hours|Renewable production can be<br>constrained through annual bounds on<br>capacity and growth constraints.|Pollutants and non-GHG forcing agents<br>are not explicitly modeled, however<br>additional forcing factor are included in<br>the climate module.|
|**global ETP-TIMES**|Expansion and decline constraints system<br>integration constraints|Lowest cost with adjustment penalties.<br>Discrete technology choices with<br>mostly high substitutability in some<br>sectors and mostly low substitutability<br>inothersectors|GHG|
|**COFFEE**|COFFEE describes energy conversion<br>technologies based on discrete techniques with<br>pre-defined technological (size, lead time,<br>efficiency, availability, etc.) and economic<br>(overnight costs, fixed and variable O&M costs,<br>contingency factors etc.) variables, thus<br>capturing technological deployment over time<br>in a least cost approach.|Energy system cost minimization|GHG and selected other pollutants|
|**AIM-Enduse**|Requires additional storage for intermittent<br>renewables|Energy system cost minimization|Energy and non energy GHG|
|**GCAM**|Splits the load duration curve into four distinct<br>pieces in the USA; Requires backup for<br>intermittent renewables in all regions|Logit sharing|GHG and other pollutants|

31
15 members of farm households to competitive pressure (which may take the form of declining income), employees in SOEs are still relatively protected through incomeinsurance arrangements and in some cases (although much less than earlier) also by direct job protection.

Turning to the last dimension in Figure 1, the pronounced openness of the Chinese economy is one of the most remarkable features of the reforms. Naturally, the large export share – about 30 percent of GDP in 2005 as measured by the official exchange rate – is only _one_ among several measures of the high degree of internationalization of the Chinese economy. Another is that fixed investment by firms registered outside mainland China has amounted to 4-5 percent of GDP during the last decade, or about 10 percent of investment in real capital assets.<sup>11</sup> While these investments, to begin with, mainly took the form of joint ventures, they have recently occurred more often in non-mainland owned firms. Indeed there is a close relation between investment by firms owned by agents outside mainland China and the export performance of the country, since about half of China’s export emanates from such firms.<sup>12</sup> This development has been facilitated by the important role of entrepreneurs of Chinese origin living outside mainland China, mainly in Hong Kong, Macau, Taiwan/China and Singapore. However, some of the financing of these investments, in fact, originates in mainland China. Agents in other areas have then simply functioned as financial intermediaries for “roundtrips” of capital flows (Garnaut and Song, 1999), perhaps because of tax considerations or ambitions to balance political risks.

Most likely, the internationalization of the Chinese economy, which has been more important in industry than in agriculture, results not only in traditional static (text-book type) gains from trade and international capital mobility, but also in efficiency-enhancing international competition and greater opportunities to learn from foreign firms.<sup>13</sup> It is also likely that China’s interaction with the rest of the world influences the values and life styles of the domestic population. We may expect the emergence of more individualistic

> 11 These figures can be derived from official statistics on investment by non-mainland firms, assuming an aggregate investment ratio of 43 percent of GDP. (Statistical Yearbook of China, 2005, Table 6.1). 12 Chow’s (2006c) estimate is 60 percent. Moreover, in 2003 about 27 percent of value-added in industrial enterprises with annual sales revenues in excess of 5m yuan (USD 0.6m) within China was produced by firms funded outside mainland China (Holz, 2005b).

> 13 Indeed, there is empirical evidence of such learning among Chinese firms (Li et al., 2006).
i ) ∗ln (I ) would indicate a stronger increase in imports from countries with larger population size in the pre-earthquake period. ii jj II(H HHh SSha aa J JJ 2011 IIp aaIIHHIInn IIa Information on country and country-pair characteristics come from different sources. Bilateral distance in kilometers and an indicator variable that captures if two countries share a border are from the CEPII’s GeoDist database (Mayer and Zignago, 2011). Country characteristics related to population and GDP per capita are from the World Bank World Developments Indicators (WDI). Countries’ vulnerability to natural disasters is constructed based on the World Risk Index (Bündnis Entwicklung Hilft, 2011) and it is defined equal to one if the index is greater than 63.3 (i.e., very high risk). The real exchange rate is constructed based on data from the Penn World Tables version 9.1 (Feenstra et al., 2015).<sup>16</sup> We use trade data from WITS to construct an indicator variable equal to one if the exporter was among the top 4 suppliers of importer in HS 6-digit product in the pre-shock period and to compute exporter’s revealed comparative advantage (RCA) index. Data on Free Trade Areas (FTA) are from Mario Larch’s Regional Trade HH kk Agreements Database from Egger and Larch (2008). Finally, we use World Bank’s regional classification to construct an indicator variable equal to one if two countries are located in the same region.<sup>17</sup> To reduce potential endogeneity concerns, we use averages based on the pre-shock period for all the time varying variables, except for the real exchange rate which is lagged.

_Table 4: Country-specific fundamentals_

||(1)<br>|(2)<br>|(3)<br>|(4)|(5)<br>|(6)<br>|
|---|---|---|---|---|---|---|
||A|ll<br>|Auto<br>||Elec<br>|tronics<br>|
||Auto|Electronics|Intermediate|Final|Intermediate|Final|
||log of<br>imports|log of<br>imports|log of<br>imports|log of<br>imports|log of<br>imports|logof imports|
|(Dummy share 2004-2010 > 10)|0.188***|0.103***|0.135***|0.119|0.197***|0.079***|
||(0.033)|(0.010)|(0.038)|(0.074)|(0.019)|(0.012)|
|(Dummy share) * ...|||||||
|... (log GDP pc pre)|-0.061***|-0.063***|-0.065***|-0.007|-0.069***|-0.060***|
||(0.015)|(0.006)|(0.016)|(0.029)|(0.011)|(0.007)|
|... (log of pop. Pre)|0.019**|0.019***|0.030***|0.006|0.018***|0.017***|
||(0.009)<br>|(0.004)<br>|(0.010)<br>|(0.021)<br>|(0.007)<br>|(0.004)<br>|
|16We follow Rodrik (2008)<br>|and define t<br>|he log of the re<br>|al effective rate a<br>|sln(RRi <br>|) = ln(XRXTi<br>⁄<br>|JJi)<br><br>.<br>|
|Data on exchange rates (X|RXT) and p|ower parity co|nversion factors|<br>(JJ) com|<br>e from the Pen|n World|
|Tables.|||||||

> 17 We modify World Bank’s regional classification and classify Mexico as part of North America.

17
_Figure 8: Share of officials agreeing with statements on gender equality._ a. “Schools should try to promote gender equality.” b. “Girls face additional challenges in accessing and completing their education compared with boys in this country.” <!-- Start of picture text --> 100%<br>100%<br>50% 50%<br>0% 0%<br>IDN LAO MNG PHL VNM Non-EAP IDN LAO MNG PHL VNM Non-EAP<br>Strongly agrees Agrees Strongly agrees Agrees<br>Disagrees Strongly disagrees Disagrees Strongly disagrees<br>Does not know Does not know<br><!-- End of picture text --> Source: Calculations from CDG–World Bank 2022 microdata.

- 4.4 Officials favoring gender equity tend to promote access for children with disabilities.

Policy makers’ support for statements associated with gender equity is correlated with their support for statements of inclusion of children with disabilities.<sup>10</sup> The survey includes several questions measuring beliefs using a Likert scale.<sup>11</sup> Some are linked to beliefs about gender, views about children with disabilities or about EdTech and the effectiveness of more funding for education. We use factor analysis to identify potential latent variable measuring views about equity.<sup>12</sup> While there are large cross-country disparities in this index, respondents from EAP countries overall tend to have higher scores on the index than those from non-EAP countries (Figure 9). The index average is statistically higher in the EAP than in the non-EAP group.<sup>13</sup> Compared with non-EAP respondents, officials in EAP strongly support both gender equity and inclusion when surveyed. This index provides support for a range of beliefs and values on both topics that are meaningful for the World Bank and other development partners to be aware of when engaging with public officials in EAP countries. We find no statistically significant link between this index and per capita GDP.<sup>14</sup> 10 This correlation is evidenced by the KMO statistics (the Kaiser–Meyer–Olkin (or KMO) test is a statistical measure to determine how suited data are for factor analysis), which are above 0.6 as seen in Table 4 in the Annex. The correlation coefficient of typical questions such as “schools should try to promote gender equality” and “Children with disabilities deserve the same level of access to public school” is about 0.25.

11 Officials were asked to rate statements using a discrete scale: “Strongly agree”, “Agree”, “Disagree”, “Strongly disagree”, “Do not know”.

12 The sample appears inadequate according to the KMO test if we only analyze gender views, meaning that the items are not correlated enough to reflect an underlying trait. We therefore combined the views on gender with other measures of inclusion in this discussion. See Table 4 in the Annex for details of the analysis.

13 The probability that the averages are different is about 4 percent in a t-test.

14 As evidenced by the p-value of the F test, which is about 0.35.

12
we construct the Frankel and Romer predicted trade measures by industry. Second, we do it over time. And ...nally, rather than looking at total trade, we look solely at exports.

Armed with a working model for predicting exports to GDP in each industry, it is straightforward to construct the instrument for external ...nancing need of exports, based on predicted export patterns rather than actual ones. That is, our instrument will be, in a manner identical to equation (11): Here, the predicted share of exports in industry i, in country c and time t, !b<sup>X</sup> ict<sup>,isconstructed</sup> from the predicted exports to GDP ratios, X<sup>b</sup> ict in a straightforward manner: Note that even though X<sup>b</sup> ict is exports in industry i normalized by a country’s GDP, every sector is normalized by the same GDP, and thus they cancel out when we take the predicted export share.

We proceed by describing the data sources in the next section. We provide a snapshot of our data, focusing on the patterns of external ...nancing needs of exports that we obtain. Then, in the following section we document stages of constructing our instrument, and present OLS and 2SLS regression results for both a cross-section of countries and a panel of 5-year averages going back to the 1970’s.

# 4 Data Description

International trade ‡ows come from the World Trade Database described in Feenstra et al. (2005). This database contains bilateral trade ‡ows between some 150 countries, accounting for 98% of world trade. Trade ‡ows are reported using the 4-digit SITC Revision 2 classi...cation. Since our variable of interest, EFNXct, is constructed using information on total exports from each country in each industry, we ...rst aggregate bilateral ‡ows across countries to obtain total exports for each country and manufacturing sector. We then convert the trade ‡ows from SITC to 3-digit ISIC Revision 2 classi...cation.<sup>8</sup> This allows us to combine the trade data with the information on external dependence from Rajan and Zingales.

For the purposes of estimating the gravity equation (13), we retain information on bilateral trade, converting it once again into the 3-digit ISIC Rev. 2 classi...cation. We merge bilateral trade data with geography variables taken from Centre d’Etudes Prospectives et d’Informations Internationales

> 8 The conversion is based on the concordance found on the International Trade Resources website maintained by Jon D. Haveman: http://www.haveman.org.

11
Self-Enforcing Trade Agreements: Evidence from Time-Varying Trade Policy Chad P. Bown Meredith A. Crowley<sup>_∗_</sup> The World Bank Federal Reserve Bank of Chicago This version: May 2012

**Abstract**

The Bagwell and Staiger (1990) theory of cooperative trade agreements predicts new tariffs (i) increase with imports, (ii) increase with the inverse of the sum of the import demand and export supply elasticities, and (iii) decrease with the variance of imports. We find US import policy during 1997-2006 to be consistent with this theory. A one standard deviation increase in import growth, the inverse of the sum of the import demand and export supply elasticity, and the standard deviation of import growth changes the probability that the US imposes an antidumping tariff by 35%, by 88%, and by -76%, respectively.

> _∗_ Bown: The World Bank, Development Research Group, Trade and International Integration, 1818 H Street NW, Mailstop: MC3-303, Washington, DC 20433, cbown@worldbank.org. Crowley: Federal Reserve Bank of Chicago, Economic Research, 11th floor, 230 S. LaSalle, Chicago, IL 60604, meredith.crowley@chi.frb.org. For helpful feedback, we thank three anonymous referees, Pol Antras, Kyle Bagwell, Emily Blanchard, Marc Busch, Robert Feinberg, James Harrigan, Bernard Hoekman, Doug Irwin, Nuno Lim ̃ao, Rod Ludema, Giovanni Maggi, Petros Mavroidis, Rachel McCulloch, Bob Staiger, and seminar participants at American, Brandeis, BLS, Chicago Fed, ETSG, Georgetown, SEA Meetings, UVA, and the World Bank. Aksel Erbahar, Adam Hogan, Xi Luo, and Christine Ostrowski provided excellent research assistance. Any opinions expressed in this paper are those of the authors and do not necessarily reflect those of the World Bank, the Federal Reserve Bank of Chicago, or the Federal Reserve System. All remaining errors are our own.

JEL Codes: F12, F13 Keywords: trade agreements, terms of trade, anti-dumping, safeguards, WTO
**Table 5. Returns to Education by Gender (percentage)**

|EducationalLevel|Men|Women|
|---|---|---|
|Primary|20.1|12.8|
|Secondary|13.9|18.4|
|Higher|11.0|10.8|
|Overall|8.7|9.8|

Source: Table A3.

<!-- Start of picture text --> 35/ E5 Private E Social<br>30 26.6<br>25 18.9 190.<br>204~~~~~1.<br>e 1. Retuns to ivestmen in eduatbn bybvel,bestye 10.8<br>15 1<br>0<br>Primary Secondary Higher<br><!-- End of picture text --> Figure 1. Returns to investmrent in education **by level,** latest year _1 5_
Policy Research Working Paper 10510

# **Abstract**

This paper analyzes the effects of power outages and constraints on manufacturing firms’ revenue-based total factor productivity in developing countries. The empirical analysis is based on the World Bank Enterprise Surveys dataset for 84 countries over 2006–2019. The paper starts by showing statistically that firms facing power outages differ and operate in very different environments compared to firms not facing power outages, underlining a potential nonrandom issue of the treatment variable. The matching-based approach (entropy balancing) is designed to contain this type of bias. It shows that power outages negatively and significantly affect firm-level revenue-based total factor productivity, with a 9 percent lower unconditional average productivity for exposed firms compared to nonexposed firms. Moreover, the estimates suggest a connection between the severity of self-reported power constraints or obstacles by firms and the magnitude of revenue-based total factor productivity loss. The results also indicate that the effect of power outages on firm-level revenue-based total factor productivity could be influenced by the stage of economic development (low-income countries, lower-middle-income countries, upper-middle-income countries), and the ability of firms to engage in research and development and purchase backup generators. These findings suggest that to ensure economic development, the government should provide a stable power supply that can mitigate the negative shocks faced by manufacturing firms and enhance their productivity and competitiveness, allowing them to drive economic growth.

This paper is a product of the Development Research 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 aly6@worldbank.org or aapeti@ifc.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
Appendix Table 19 Firm Indebtedness and Risk among Credit Program Users, Over Sales (2020)

||∆Guarante|ed Debt /|∆Non-guara|nteed Debt /|
|---|---|---|---|---|
||Sales <br>|(2020)<br>|Sales <br>|(2020)<br>|
||(1)<br>With<br>Previous<br>Loans|(2)<br>Without<br>Previous<br>Loans|(3)<br>With<br>Previous<br>Loans|(4)<br>Without<br>Previous<br>Loans|
|Risk|0.143|0.222|-0.083|0.097|
||(0.014)|(0.038)|(0.017)|(0.027)|
|Increase in Sales Dummy|-0.118|-0.138|-0.026|-0.077|
||(0.007)|(0.013)|(0.010)|(0.011)|
|Decrease in Sales Dummy|-0.095|-0.110|-0.028|-0.074|
||(0.007)|(0.013)|(0.010)|(0.011)|
|Use Employment Program|0.018|0.005|-0.003|-0.006|
||(0.002)|(0.004)|(0.003)|(0.003)|
|Dependent Variable Mean|0.172|0.161|-0.012|0.027|
|Dependent Variable Std. Dev.|0.150|0.165|0.192|0.113|
|Number of Firms|31,648|9,030|31,648|9,030|
|R<sup>2</sup>|0.092|0.126|0.024|0.083|
|Industry FE and Municipality FE|Yes|Yes|Yes|Yes|

This table reports linear regressions of the change in firms’ guaranteed and non-guaranteed debt on a set of firmlevel characteristics, separately for firms with and without previous loans, for the credit program users sample. Firms with previous loans are those that either have bank credit outstanding in December 2019 or receive a bank loan over the period 2012–2019. Conversely, firms without previous loans are those with no credit records in the banking system throughout the same period. The dependent variable in Columns 1–2 (3–4) is the change in the stock of public guaranteed (non-guaranteed) debt between December 2020 and December 2019, relative to 2020 sales. Risk corresponds to the fitted values of the regression specifications reported in Columns 4 and 8 of Table 1 for firms with and without previous loans, respectively. Increase (decrease) in sales dummy is equal to one if sales growth is greater (lower) than or equal to 2% (-2%), and zero if sales growth is between -2% and 2%. Use employment program is a dummy variable equal to one for employment program participation and is zero otherwise. All columns include industry and municipality fixed effects. Bootstrapped standard errors, shown in parentheses, are clustered at the industry and municipality level. For change in guaranteed debt over 2020 sales, observations in the top and bottom 1% of the distribution are winsorized. Change in non-guaranteed debt over sales is constructed as the difference between change in debt outstanding and the change in guaranteed debt, over 2020 sales.

37