rafmacalaba/datause-encoder-data huggingface dataset:
(a) CDF of pre-program change in literacy, by whether TSC started by 2003 <!-- Start of picture text --> 1<br>.8<br>.6<br>.4<br>.2<br>0<br>0 10 20 30 40<br>change in district literacy (percentage points), 1991 to 2001 census<br>no TSC by 2003 some TSC by 2003<br>empirical cumulative probability function<br><!-- End of picture text -->

- (b) pre-program change in literacy and TSC latrines per capita in 2003

<!-- Start of picture text --> 0 .02 .04 .06 .08 .1<br>TSC latrines per capita, 2003<br>40<br>30<br>20<br>10<br>change in district literacy, 1991 to 2001 census<br>0<br><!-- End of picture text --> Figure 5: TSC implementation uncorrelated with pre-program literacy trends, 1991 & 2001 census Observations are Indian districts, to match the district-level variation in the TSC. Panel (b) uses the restricted sample that omits two districts with implausibly high reported TSC intensity, to avoid stretching 39 the horizontal axis.
# How Do Government Transfer Payments Affect Retail Prices and Welfare? Evidence from SNAP<sup>*</sup>

Justin H. Leung<sup></sup> Hee Kwon Seo<sup></sup> National University of Singapore World Bank

**Keywords:** Consumption, SNAP, food stamps, incidence, prices **JEL Classification Numbers:** E31, H53, I38

> * We are indebted to Marianne Bertrand, Michael Greenstone, Neale Mahoney, and Canice Prendergast for their invaluable advice and support. We also thank Chiman Cheung, Jos ́e Ignacio Cuesta, Peter Ganong, Austan Goolsbee, G ̈unter Hitsch, Erik Hurst, Aart Kraay, Magne Mogstad, Krisztina Orb ́an, Jung Sakong, Jesse Shapiro, Chad Syverson, Joseph Vavra, Michael Weber, Thomas Wollmann, Michael Wong, and anonymous referees for helpful comments. We thank Arthur Middlebrooks and Katie Bell at the Kilts Marketing Center; John Kirlin at the Economic Research Service; Anita Singh, RPMD HQ-Webmaster and Barbara Murphy at the Food and Nutrition Service; Karen Cunnyngham and Kelsey Farson Gray at Mathematica Policy Research; Philip Cho, Judith Denver and Beth Berendsen at the City of Chicago Department of Law and Department of Family & Support Services; and Timothy Beggs at Econometrica, Inc. for responses to questions and data requests. We thank friends at the House of Prayer for All Nations Church for sharing anecdotes about food stamps (Proverbs 31:8-9). Views in this paper do not represent those of the acknowledged institutions. Researcher(s) own analyses calculated (or derived) based in part on data from The Nielsen Company (US), LLC and marketing databases provided through the Nielsen Datasets at the Kilts Center for Marketing Data Center at The University of Chicago Booth School of Business. The conclusions drawn from the Nielsen data are those of the researcher(s) and do not reflect the views of Nielsen. Nielsen is not responsible for, had no role in, and was not involved in analyzing and preparing the results reported herein. Errors are ours alone.

>  Leung: National University of Singapore (e-mail: jleung@uchicago.edu).

>  Seo: Development Impact Evaluation (DIME), World Bank (e-mail: hseo@worldbank.org).
facing stronger likelihood of promotion that would be enhanced by better economic performance coming with privatization, would be more eager to seek privatizing SOEs under their oversight.

# **3. Data**

Our data set is the Annual Survey of Industrial Firms (ASIF) from 1998 to 2009 collected by China’s National Bureau of Statistics. It includes _all_ SOEs and all non-state firms with sales above five million yuan in the industrial sectors, accounting for about 90% of total industrial output value in China.<sup>10</sup> Since our goal is to study the incentives of local politicians (i.e., politicians at the prefectural city level or below),<sup>11</sup> we only keep SOEs that are affiliated with local governments (defined as those SOEs with the share of local government ownership exceeding 50 percent) in our baseline regressions. In constructing our panel, we follow the best practices in fixing some identification inconsistencies.<sup>12</sup> We construct a dummy variable, _Privatized_ , that equals one for a firm for the privatization year and the subsequent years if it is privatized during the sample years and 0 otherwise. An SOE is classified as being privatized in two ways: staying in the database with the state share dropping below 50 percent; exiting the data set, which implies privatization, and its sales being below five million yuan or being merged with small private firms. Out of 50,030 SOEs in our sample, 40,557 had been privatized by the end of our sample period.

We match the ASIF data of SOEs with a data set on local politicians in China, i.e., party secretaries in the prefectural-level cities. The politician data set has details of their personal information (age, gender, and education), as well as their career path (the appointment date, the next deputation, and promotions/demotions). We use provincial yearbooks for the names of city-level party secretaries, and search their resumes to identify their personal characteristics and career trajectory.<sup>13</sup> Macroeconomic variables including the city-level GDP and the

> 10 The industrial sector in this data set includes mining and manufacturing.

> 11 Whenever referring to “local” we mean the prefectural-level city or below, which includes the county-level.

> 12 A common issue that must be addressed when constructing a panel drawn on the ASIF data is that a small proportion of firms occasionally receives a new identification code due to restructuring, merger, or acquisition. See Appendix A for details on how we follow best practice in dealing with this issue.

> 13 The primary internet sources are resumes of the politicians posted on the government website as well as Baidu Baike (http://baike.baidu.com/), a Chinese version of Wikipedia.

``` 11 ```
|||Distribution of students in the<br>classrooms.||
|---|---|---|---|
|SUPERVISION<br>QUALITY|**CALI_S_1:**Quantitative<br>indicator of supervision's<br>quality.|Includes annual frequency of<br>visits, duration, occupations of<br>interviewed people and themes<br>discussed.|0-100|
|PARENTS'<br>PARTICIPATION|**APF6:**Quantitative<br>indicator of parents'<br>participation in the school<br>process.|This indicator weighs the<br>attitudes of parents with respect<br>to teachers' attendance, parents'<br>participation in school activities<br>and relevance of parents<br>associations in the school.|0-100|
|UNIT COST|**Unit cost per pupil**|Presents the fixed unit cost per<br>pupil.||
|CHILD'S PART<br>ACADEMIC RECORD|**HIST_ESC:**Index of<br>historical academic record<br>of the student.|Total years in pre-school, total<br>repetition and dropout years.|0-100|
|DUMMY FOR RURAL|**DUMMYR**|Dummy variable: If DUMMYR<br>= 1 then the observation is of<br>rural areas. DUMMYR= 0 for<br>|0 & 1|
|||other cases.||
|DUMMY FOR NATIVE|**DUMMYI**|Dummy variable: If DUMMYR<br>= 1 then the observation is of<br>native areas. DUMMYR= 0 for|0 & I|
|||other cases.||
|FACTOR -||It's a compound index||
|CHARACTERISTICS OF|**FACI_14**|constructed by principal||
|COMMUNITY AND<br>FAMILY||components method. It includes<br>the characteristics of the<br>demand such as family and<br>community background,<br>parental attitude towards and<br>involvement in schooling,<br>academic history, self-esteem,<br>etc.||
|FACTOR-||It's a compound index||
|CHARACTERISTICS OF<br>SCHOOL AND SYSTEM|**FACI_15**|constructed by principal<br>components method. It includes<br>the characteristics of the supply<br>such as teachers, principals and<br>supervisors background,<br>perfornance, attitudes,<br>assiduity, pay, etc., as well as<br>characteristics of the school<br>infrastructure and availability of||

21
# Table of Contents

|||Page|
|---|---|---|
|I|Introduction|1|
|II|The EC and Hungary as<sup>Trade Partners</sup>|1|
||A.<br>The present EC-Hungary trading<sup>pattern</sup><br>B.<br>Exports of services: tourism<br>C.<br>Imports|3<br>7<br>7|
|III|Trade Policy of the EC<sup>Applied on Hungarian Exports</sup>|9|
||A. Tariffs|9<br>|
||B. Non-tariff barriers|11|
|IV|Hungary's Recent Trade Relations<sup>with the EC</sup>|15|
|V|Three Challenges to Hungary's<sup>Exports</sup>|16|
||A.<br>First challenge: The progressive<br>integration of<br>Spain and Portugal into the<sup>European system of</sup><br>division of labor<br>|17<br>|
||B.<br>Secor.d challenge:<br>The integration of the<sup>GDR in the EC</sup><br>C.<br>Third challenge: Hungary and<sup>"1992"</sup>|18<br>19|
|VI|Hungary's Future Trade Regime<sup>with Western Europe</sup>|25|
|VII|Simulations of the Possible<sup>Effects of Changes in</sup><br>the EC Merchandise<br>Trade Regime|27|
|VIII|Conclusions|35|
||Bibliography|36|
||Statistical Annex|38|
(intra-regional trade) is greater in terms of percentage change than to the world, because of the intensified trade relations among the APEC economies. The comparison between the two cases reminds us of the higher elasticities of substitution in the raw material, because Case 1 indicates greater impacts.

There is a contrast between the two estimates with a similar and corresponding scenario, namely our estimates in Case 2 – APEC and that by Helble, Shepherd and Wilson (2007). In the estimates of the former, the expansion in exports of the reforming economies generally surpasses that of other APEC economies, while in the latter, the tendency reverses. This is a natural result, because the simulation of HSW measures the impacts to bilateral trade flows by applying shocks to the importers transparency index (ITI) in the reforming economies only. In the simulation, therefore, only the imports of reforming economies are affected. This is why the impact on imports of non-reforming economies in APEC is nil in Table 2. Accordingly, the simulated exports just reflect the increase in imports of the reforming economies in APEC, and the reforming economies result in receiving just average impacts in APEC.

# _Macroeconomic Benefits: Welfare and Real Gross Domestic Product_

The merits of adopting a CGE model include the ability to explicitly estimate the macroeconomic benefits of reform, such as potential gains to welfare and real gross domestic product (GDP). The GTAP model contains Equivalent Variation (EV), an income measure of the welfare improvement, as a standard variable for the presentation purpose. Real GDP (the variable _rgnp_ in the percentage change terms) is also an indicator often referred to in GTAP simulations, which focuses on the impacts to macroeconomic production<sup>19</sup> . One of the policy shocks of our simulation, _ams_ that is a kind of technical progress, should directly bring about expansionary effects, as well as efficiency improvements. The other policy shock, the reduction of irregular payments also contributes to the efficiency improvements we find by expanding lower cost imports<sup>20</sup> . A technical progress, enabling the economy to produce more with fixed

> 19 This reference indicator corresponds to the weighted sum of the sector base output in the economy.

> 20 The welfare improvement from the removal of irregular payments should be smaller than that

24
transmission capacity is about 11 GW; it is expected to almost double to 20 GW by 2025, and it is assumed to stay at the same level thereafter until the end of the study horizon (2035).

**Figure 1. Existing and planned cross-border transmission interconnections (MW)**

<!-- Start of picture text --> (2022)<br>1200<br>Lebanon Syria<br>470 227<br>(2025) 350 1000<br>(2025)200 West Bank & 800 (2025) 1000<br>Gaza Iraq (2027)<br>Morocco 300 Tunisia 25 40 (2025)500 (2025)500<br>Kuwait<br>1000<br>1100<br>(2025)1000 400 1000 1000 (2025)450 Jordan (2026)1200<br>Algeria (2025) Egypt<br>(2030) 500 Bahrain<br>(2025) 600<br>3000<br>Libya (2022<br>180 1200 Qatar<br>1200<br>200 (2025) KSA<br>Sudan 1200<br>U.A.E<br>Existing Interconnection 500 1200<br>(2025)<br>Under construction/ 1000 400<br>Planned Interconnection (2027)<br>Yemen Oman<br>Number in parenthesis - Year<br><!-- End of picture text --> <!-- Start of picture text --> Existing Interconnection<br>Under construction/<br>Planned Interconnection<br>Number in parenthesis - Year<br><!-- End of picture text --> Note: Numbers refers to transmission capacity in MW, years when the transmission lines will be commissioned are in parenthesis Source: World Bank (2020)

# **5. Scenarios Analyzed**

This study considers the six scenarios or cases as defined in Table 5 for the analysis. Under Case 0, each country independently meets its projected electricity demand by expanding the required capacity within its border. Although some of the existing interconnections are in operation and cross-border trade occurs, for the simplicity of the modeling, we assumed there would be no cross-border trade in Case 0. The same is true in Cases 2 and 4. Under Case 1, the existing and planned (or under construction) cross-border interconnections capacity will be utilized to facilitate cross-border electricity trade. Case 2 and Case 3 represent cases of subsidy removal, respectively, 16
## education.

Table 5: Differences between Natives and Non-natives after Controlling for Education

||Precarious<br>housing<br>(1)|Ownership land<br>& house<br>(2)|Use<br>computer [14+)<br>(3)|Legally<br>married [18+)<br>(4)|Economically<br>active [18-65]<br>(5)|
|---|---|---|---|---|---|
|Natives|0.078<sup>_∗∗∗_</sup>|-0.040<sup>_∗∗∗_</sup>|0.019<sup>_∗∗∗_</sup>|-0.073<sup>_∗∗∗_</sup>|-0.000|
||(0.013)|(0.006)|(0.005)|(0.009)|(0.006)|
|Years of Educ.|-0.010<sup>_∗∗∗_</sup>|0.001<sup>_∗∗_</sup>|0.064<sup>_∗∗∗_</sup>|0.005<sup>_∗∗∗_</sup>|0.016<sup>_∗∗∗_</sup>|
||(0.000)|(0.001)|(0.000)|(0.001)|(0.000)|
|Observations|16,641,852|16,640,667|12,277,326|7,032,487|9,565,399|
|R-squared|0.029|0.000|0.294|0.003|0.023|
||Formal<br>employm. [18-65]<br>(6)|Unemploy-<br>ment [18-65]<br>(7)|Health<br>insurance<br>(8)|Number<br>of Disabilities<br>(9)|Teen<br>Pregnancy<br>(10)|
|Natives|-0.022<sup>_∗∗∗_</sup>|0.011<sup>_∗∗∗_</sup>|-0.093<sup>_∗∗∗_</sup>|0.025<sup>_∗∗∗_</sup>|0.011<sup>_∗_</sup>|
||(0.005)|(0.002)|(0.010)|(0.004)|(0.006)|
|Years of Educ.|0.027<sup>_∗∗∗_</sup>|-0.002<sup>_∗∗∗_</sup>|0.030<sup>_∗∗∗_</sup>|-0.026<sup>_∗∗∗_</sup>|-0.007<sup>_∗∗∗_</sup>|
||(0.001)|(0.000)|(0.001)|(0.000)|(0.000)|
|Observations|5,493,000|6,884,064|16,641,852|16,641,852|944,534|
|R-squared|0.062|0.001|0.069|0.034|0.008|

_Source_ : REDATAM INDEC Census Argentina 2010. _Notes_ : Standard errors clustered at the department level in parentheses. * _p <_ 0.10, ** _p <_ 0.05, *** _p <_ 0.01.

There are several reasons why differences in outcomes may survive controlling for years of education. Firstly, there may be other personal characteristics or attributes that may affect outcomes which we do not observe and may differ between Natives and nonNatives. For example, there may be differences in wealth and social capital. Secondly, there may be differences on the quality of education available to the two groups, which may make “years of education” an imperfect measure of human capital. Unfortunately, we lack a method to assess the of the education available to Natives and non- quality Natives. And thirdly, labor market discrimination may result in different returns to education for the two groups, but further research is needed to confirm this potential channel in this setting.<sup>8</sup>

# **3.4 The transmission of education**

We study the transmission of education across generations and whether this differs between Natives and non-Natives. To do so, we focus on the more than 700,000 households

> 8 On discrimination by race in Latin America, see Chong and  ̃Nopo (2008),  ̃Nopo et al. (2010), Arceo-Gomez and Campos-Vazquez (2014), and Gerard et al. (2021).

12
|ISCO-08 major groups|Skill level|
|---|---|
|1 Managers|3+4|
|2 Professionals|4|
|3 Technicians and Associate Professionals|3|
|4 Clerical Support Workers|2|
|5 Services and Sales Workers||
|6 Skilled Agricultural, Forestryand Fishery Workers||
|7 Craft and Related Trades Workers||
|8 Plant and Machine Operators, and Assemblers||
|9 Elementary Occupations|1|
|0ArmedForcesOccupations|1+2+4|
wholesale price in this market averaged $76/MWh between 2014 and 2016, compared to an average wholesale price of $35/MWh in the United States in 2015 (Figure 1). However, electricity traded through SAPP represented only 0.14 percent of total generation in member countries in 2015.<sup>9</sup> This means that the wholesale prices may understate the marginal cost of generation, especially in areas with binding transmission constraints.

In most SSA countries, electricity consumption for households with grid connections is very low (Figure 2). In Uganda in 2011, median household electricity consumption, for gridconnected households, was 40 kWh per month. Such low consumption makes it difficult to recover the fixed costs of providing a grid connection using volumetric charges alone.

Regulated retail electricity tari↵s in many countries have a nonlinear structure, based on either increasing block tari↵s or volume-di↵erentiated tari↵s. In either case, households with low usage pay a low marginal price for an additional unit consumed (Figure 3). For eight countries in SSA, the average price for consumption of 50 kWh per month is below the SAPP wholesale price. Electricity retailers in these countries would lose money from supplying these customers, even before considering the cost of providing the connection. Conversely, a small number of countries have very high retail electricity prices (in one case over 50 cents per kWh), likely reflecting the high cost of small-scale diesel generation.

A simple calculation demonstrates that, given the low consumption, high wholesale prices, and low retail tari↵s, adding a residential customer would be unprofitable for most electricity distributors in Sub-Saharan Africa. Based on the above averages, suppose the median new user has electricity consumption of 40 kWh per month, and the wholesale cost of electricity is US$76 per MWh. Assume that transmission and distribution losses are 15 percent. Apart from the wholesale cost of electricity, there is assumed to be a fixed annual cost of US$41 per user for administration, billing, and network maintenance. The before-tax retail price in each country is the average shown in Figure 3. Distribution utilities are assumed to have an annual discount rate of 5 percent.

With these assumptions, the distribution utilities in 15 countries would lose money from adding one more user, before considering any connection costs or charges (Figure 4). Assuming an upfront cost of US$200 to provide a connection, adding consumers would be unprofitable in all but ten countries. Distribution utilities in all of the remaining countries would need to set a high access charge for it to be profitable to add a user.

> 9Calculation based on data from the SAPP 2015 Annual Report. Total generation in member countries in 2015 was 363 TWh (page 38). Trading volume in the market was 0.5 TWh (page 24).

7
# **<mark>1. Introduction</mark>**

Women earn less income and are less likely to participate in the labor market, especially in low- and middle-income countries (World Bank, 2012). There are several ways to increase women’s involvement in economic activities, such as micro-credit program <mark>s, self-help groups, or programs that are specially designed to help</mark> improve their access to infrastructure and technology.<sup>1</sup> In this paper, we study a simple but important factor—childcare—that can release women from domestic work and encourage them to participate in the labor market. Specifically, we examine the impacts on women’s labor market outcomes of pre-school (age 1-5) childcare in Vietnam.

Analyzing the most recent household surveys for Vietnam in the past decade, we find that childcare (attendance) has a strong effect on women’s participation in the labor market, as well as the probability of their working in a formal wage-earning job. <mark>Childcare also helps increase women’s total annual wages and household income per capita and reduces poverty. The effect of childcare is larger for younger children than older ones. For younger children, moreover, a medium-term effect exists after 2 years, increasing the probability that women will have a wage-earning job by 38 percentage points and reducing self-employed farm work. We also find heterogeneous effects of childcare on maternal employment, where its impact on the probability of having a wage job is larger for younger and highly-educated women. Interestingly, childcare positively impacts the probability of men having a wage-earning job as well.</mark> A key challenge in measuring the effects of childcare is the endogeneity issue. Women who send children to childcare services differ from those who do not (e.g., they may be richer or simply have less time for childcare). We address this problem by using the threshold in the birth months of children—which is exogenously given—as an instrumental variable for childcare attendance. In Vietnam, children’s enrollment in kindergartens or primary schools is based on the running age (or current age) instead of the full age (or completed age), and the school year for public kindergarten and primary school starts in September. We can thus compare labor outcomes for women whose children were born in adjacent months in two contiguous, but different, years. In particular, a child who was born

> 1 F <mark>or recent reviews,</mark> see <mark>Brody et al. (2017) and Winther et al. (2017).</mark>

2
accommodate these changes in the survey data, larger weights have been assigned to older people than those assigned to younger individuals. Next, new sets of population shares by age and education subgroups are projected and household sampling weights are re-scaled according to the demographic and educational changes above (larger box in the middle of Figure 1). In a second step, the demographic changes will impact labor supply growth by age and skill groups. These changes are incorporated into the CGE model to simulate overall economic growth, growth in relative incomes by education groups and sector reallocation of labor (link between the middle and bottom rectangles).<sup>12</sup> Finally, consistent with an overall growth rate of real income per capita, changes in labor remuneration by skill level and sector are applied to each worker in the sample depending on their education and sector of employment. The number of workers changing sector of occupation and the growth differential in labor remuneration which are use to "shock" the micro-data are consistent with the results of the global computable general equilibrium (CGE) model (bottom link in Figure 1). In reality these changes take place simultaneously, but in the GIDD's simplified framework they are accommodated in a sequential fashion.

The sequential changes described above reshape national income distribution under a set of strong assumptions. In particular, income inequality within population subgroups formed by age, skills, and sector of employment is assumed to be constant over the period. Moreover, data limitations affect estimates of the initial inequality and its evolution. Although consumption expenditure is a more reliable welfare measure than income, and its distribution is normally more equal than the distribution of income, consumption data are not available for all countries' surveys. To get a global picture, the present study had to include in the same dataset countries with consumption information and countries for which only income data were available.

> 12 The GIDD uses the World Bank’s LINKAGE global CGE model. LINKAGE is a relatively standard CGE model with may neoclassical features currently based on the Global Trade Analysis Project (GTAP) Release 6.3 with a 2001 base year (for the full details, see van der Mensbrugghe (2006)).

8