rafmacalaba/datause-encoder-data huggingface dataset:
Table 5 below shows the resulting estimated agglomeration wage premiums using the same approaches to delineating metro areas for which we showed results in Table 2.<sup>39</sup> Columns [1] – [4] show that all results for our main city size measure, i.e., the population within 10 km of the average person in the metro area/district, are close to those we found before. The main difference with our baseline results in Table 2 is a generally slightly smaller estimated agglomeration wage premium.

**Table 5. Estimated agglomeration wage premium using Ordinary Least Squares regressions after** **<u>controlling for overall human capital, sectoral specialization, and market access</u>**

||**[1]**|**[2]**|**[3]**|**[4]**|
|---|---|---|---|---|
|**District sample:**|**All**|**All**|**Metro**|**Metro**|
|**Worker sample:**|**All**|**Urban**|**All**|**Urban**|
|**Metro definition:**||**Weighted**|**population**||
|District|0.039***<br>[0.013]|0.051***<br>[0.017]|||
|Duranton: 10 %|0.036***|0.045***|0.156***|0.164***|
||[0.013]|[0.016]|[0.048]|[0.054]|
|Duranton: 7 %|0.034**|0.043**|0.065|0.068|
||[0.017]|[0.021]|[0.054]|[0.057]|
|NTL: 80<sup>th</sup>percentile|0.048**|0.060**|0.308**|0.345***|
||[0.019]|[0.023]|[0.095]|[0.097]|
|NTL: 25<sup>th</sup>percentile|0.046**|0.054**|0.132*|0.154*|
||[0.018]|[0.022]|[0.069]|[0.073]|
|AI|0.038**|0.046**|0.128***|0.123***|
||[0.018]|[0.022]|[0.034]|[0.033]|
|Cluster algorithm|0.040**|0.052**|0.337***|0.364***|
|(High density)|[0.019]|[0.023]|[0.068]|[0.074]|
|Cluster algorithm|0.039*|0.047*|0.180**|0.206*|
|(Urban cluster)|[0.020]|[0.025]|[0.082]|[0.098]|
|n|47,513|33,243|13,610|11,018|

**_Notes:_** The dependent variable in all columns is the natural log of hourly wage. Standard errors clustered at the metro level are reported in brackets. ***, **, *, denotes significance at the 1 percent, 5 percent and 10 percent levels respectively. All results include the full set of job and worker characteristics discussed in Section 4.1.1, as well as the share of skilled workers in the metro area’s workforce, the share of workers in the metro area’s workforce employed in the same sector as the worker him/herself, as well as the natural log of a district’s market access to all other Indonesian districts. See Section 4.1.1 for a detailed definition of these variables. The number of observations varies by metro area definition used. To give an idea of the relative sample size, the reported number of observations denotes the number of observations using the “Duranton: 10 %” approach to define metro areas.

> 39 Results for the three additionally included agglomeration variables are available upon request. Generally, we find that a district’s market access to other Indonesian districts, if significant, negatively affects nominal wages, and, if significant, a positive effect of a metro area’s share of skilled people in the workforce, and a mostly insignificant negative effect of a metro area’s degree of sectoral specialization.

26
This paper contributes in two aspects. First, it shows that geographical targeting using woreda levels of undernutrition is a viable strategy, as it explains a large share of the variation in undernutrition levels, even after controlling for other known correlates at the household level that could be used as a targeting mechanism. Second, almost all available undernutrition studies on Ethiopia note the importance of location in identifying, understanding and addressing child undernutrition. The paper addresses this shortage of updated evidence on levels of undernutrition at the woreda level by estimating levels for 2014, using SAE.

The rest of the paper is organized as follows: Section 2 describes the data, Section 3 presents the SAE methodology, Section 4 shows the results, while Section 5 concludes.

# **2. Data**

The primary data source of undernutrition in this study is the Ethiopia Mini Demographic and Health Survey (EMDHS) 2014 [2]. The EMDHS 2014 is a stratified nationally representative survey, including 5,579 <mark>children under five years of age.</mark> The survey follows the Demographic Health Surveys [23] standard survey design, although some sections, such as HIV and immunization, were not included. Detailed information about the sampling is available in the survey’s report [2].

Undernutrition is measured as share of children moderately undernourished (below two standard deviations from the mean) based on z-scores for stunting (height for age), underweight (weight for age) and wasting (weight for height). The z-scores were calculated using EMDHS and the 2006 WHO growth standards [24].

To obtain estimates of undernutrition rates at the woreda level, EMDHS is combined, through SAE, with the 2007 Census. The 2007 census has two formats – a long and a short format. The long format is richer in terms of data and, in addition to demographic information, it includes information on assets, housing characteristics, education, fertility, and mortality. A randomly selected 20 percent of the households received the long form, while the other 80 percent received the short form that only covered basic demographics.

The EMDHS and a 10 percent sample of the 2007 census are available on request from the Central Statistical Agency [25].

4
# **1. Introduction**

It is not easy to reach _a priori_ conclusions about the net impact of parental migration on the children that are left behind in rural areas. Parental migration helps increase household income, which can lead to more resources being invested in children’s education, but it also entails parental absence, which can result in lack of the parental supervision or support much needed by children in their formative years. Which effect dominates can depend on a host of context-specific factors; for example, if the remittances sent by parents are not put to good use, parental migration would likely result in lower education outcomes for their left-behind children (LBCs hereafter). On the contrary, if LBCs are taken good care of by their guardian(s), improved household resources and parental social capital at the destination area may benefit them over the long term. Evidence actually exists for both the positive and negative effects of parental migration on LBCs in different countries.<sup>1</sup> A better understanding of the interwoven connections between parental migration and child migration thus seems as relevant to guiding policy advice as studying the effects of parental migration.

We make several conceptual and empirical contributions in this paper. Our conceptual contribution is to investigate the effects of school fees on migrant parents’ decision over whether or not they should bring their children with them. We explicitly model in our theoretical framework the role that school fees play in the migrant household’s utility maximization problem. To identify the causal impacts of school fees, we employ in our empirical analysis a novel instrument—unexpected shocks to public spending on education. Our paper straddles two distinct literatures on developing countries: one related to education

> 1 For example, parental migration is found to have positive effects on children left behind in terms of more education and reduced child labor in Mexico (Alcaraz, Chiquiar, Salcedo, 2012) and in the Philippines (Yang, 2008), lower infant mortality rates and higher birth weights in Mexico (Hildebrandt and McKenzie, 2005), and better cognitive and nutrition outcomes in Nicaragua (Macours and Vakis, 2010). Other studies, on the contrary, find that parental migration has negative effects on child education in Mexico (McKenzie and Rapoport, 2011) and on child health in Tonga (Gibson et al, 2011). See Lall et al. (2006) for a detailed survey of internal migration in developing countries and Antman (2013) for a review of the literature on the impacts of migration on family members left behind.

2
# **PRIVATE EDUCATION PROVISION AND PUBLIC FINANCE:**

# **THE NETHERLANDS**

## HARRY ANTHONY PATRINOS<sup>∗</sup>

## WORLD BANK

JEL Classification Codes: I72, I22, L33 Keywords: School choice, vouchers, education, human capital

> ∗ I have benefited greatly from discussions with and comments from Husein Abdul-Hamid, Eric Bettinger, Wim Bos, Cypriaan Brom, Eamonn Butler, Erik Canton, A. Clement, H. Davids, Frans de Vijlder, AnneBert Dijkstra, Henri Geerling, Martyn Haeser, Sjoerd Karsten, Gwang-Jo Kim, Michael Latham, Henry Levin, Jesse Levin, Daniel Levy, Neil McIntosh, Hessel Oosterbeek, Tjeerd Plomp, George Psacharopoulos, Jo Ritzen, Ton Rolvink, R. Schouten, J.W. Schraven, Christine Teelken, Roelien Timmerman, R. van der Horst, J. van Dommelen, Nico van Zuylen, Ayesha Vawda, Richard Venniker, Adrie Visscher, Dinand Webbink, and participants at seminars at the American Economic Association, the World Bank, and the National Center on School Choice, Vanderbilt University.  I thank Vicente Garcia-Moreno for able research assistance.  Support from the World Bank’s Human Development Network Professional Development Grant is gratefully acknowledged.  I thank the editors and anonymous referees for comments.  The views expressed here are those of the author and should not be attributed to the World Bank Group. hpatrinos@worldbank.org.
# **Introduction**

Averting the Old Aye Crisis: Policies to Protect the Old and Promote Growth is the third in a series of major Policy Research Reports. In contrast to its predecessors, The East Asian Miracle and A4justment in Africa does not concentrate on a specific region but rather, focuses on the topic of income security for old age. More than two years of research were required to gather data, review theoretical literature, examine empirical evidence and write the book which today represents the World Bank's most important study of the issue. This Annex gives a detailed explanation of the data sources, concepts and definitions used in the book and provides additional information.

The "Technical Annex to Averting the Old Age Crisis" consists of four major sections, several appendices and a page of errata. The first section briefly describes the demographic data used in the report. Section II discusses data related to publicly-managed pension schemes around the world, giving specific sources for individual countries. Section III covers privately managed schemes while Section IV presents miscellaneous other data. All tables and figures mentioned here refer to those in the book, unless designated as Annex tables and figures.

In addition to providing detailed data tables in the Annex, an attempt has been made to cross reference the data available on STARS diskettes, which can be downloaded and analyzed in most database or statistical software packages. The STARS diskettes do not include extensive notes on the data and generally refer the user to the Technical Annex for further explanation of concepts, definitions and sources. Appendix IV lists the pages where variables appearing in the STARS system are described in the Annex.

v
Figure **A9.1:** Regional disparity trends in **China** <!-- Start of picture text -->
1.800<br>1.600 -<br>1.400 - -:  N  . - ~  .0,''  .<br>21.200  - 44<.:a-  -.) : -~> .;.-<br>E1  1  ooo00=<br>c  0.800  :<br>0.400<br>0.200  _,  ____C__  __  _<br><!-- End of picture text -->

1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 Year Simple coefficient of variation (CVu) ->- Weighted coefficient of variation (CVw) -- Relative mean Deviation (Rw) -Unweighted gini index (Gu) + - Weighted gini index (Gw) - Theil index (T)

# **Figure A9.2:** Regional disparity trends **irn** China (new)

<!-- Start of picture text -->
1.200<br>1.000 -<br>0.800<br>1 0s800 ^ s. Simple coefficient of variation o t * e -;a (CVu)<br>a *A______________________ ;-+; _ 0 W eighted c oefficient of variation (CVw)<br>.- ;=. eff-fX .- .Relative mean Deviation (Rw)-ti , ;- * *- -<br>~.0.600<br>2 Unweighted gini index (Gu)<br>g z .i D Q t " ' ^ 0 > ^-+-b W X > * F > eighted gini -,~-: ndex "- 0', (Gw) vrl <.<br>c 0.400 0.400 - - '-;. . n . aiS .: :ff ; - Theil index (T)<br>i |I .t 0. ' ;<br>0.200<br>0.000 I<br>1993 1994 1995 1996 1997 1998 1999<br>Year<br><!-- End of picture text -->

44
Table C3: Debriefing Survey on Comprehension Issues with ACASI Module

||(1)|(2)|
|---|---|---|
||Mean|(=1 if yes)|
||Malaw|i<br>Liberia|
|Was it easy for you to remember the meaning of pictures?|0.90|0.90|
|Was it easy for you to choose answers on the screen?|0.91|0.88|
|Was it easy for you to move between questions on the screen?|0.92|0.88|
|Observations|866|616|

Note: Questions were asked only to those in the ACASI treatment group (i.e., the FTFI group did not get these questions).

Table C4: Relationship between Reporting Comprehension and Passing Screening

||(1)|(2)<br>=1|(3)<br> if passed|(4)<br> SI screeni|(5)<br>ng<sup>a</sup>|(6)|
|---|---|---|---|---|---|---|
|||Malawi|||Liberia||
|=1 if said:|||||||
|easy to remember the meaning of pictures|0.019|||0.136**|||
||(0.055)|||(0.065)|||
|easy to choose answers on screen||-0.001|||0.074||
|||(0.057)|||(0.059)||
|easy to move between questions on screen|||-0.003|||0.074|
||||(0.060)|||(0.058)|
|R-square|0.000|0.000|0.000|0.008|0.003|0.003|
|Outcome mean when said no|0.60|0.62|0.62|0.59|0.64|0.64|
|Observations|866|865|866|616|616|616|

Note: Standard errors clustered at individual level are in parentheses. ***, **, and * represent significance at 1%, 5%, and 10%, respectively. a Passing threshold is choosing “yes” for the first five questions in Table 2.

34
more efficiently lobby for rents. Thus rent seeking activities may attract labor from different sectors of the economy, potentially depriving the economy of potential entrepreneurs. (iii) Increases in private subsidies can come at the cost of spending on public goods such as education and health. The positive correlation between education attainment and entrepreneurship has well been established (Parker, 2004). Thus private subsidies may crowd out the government budget at the cost of other types of spending that may encourage entrepreneurship.

Our empirical strategy is to exploit individual level variation in entrepreneurship activity and cross country variation in government spending. To achieve this we combine cross-country micro-economic panels from the Global Entrepreneurship Monitor with country-specific government spending data. Our data cover 43 countries, spanning 2001-2009 for about 360,000 observations<sup>1</sup> . Concerns about simultaneity bias are ameliorated in our use of aggregate country-level explanatory variables because the individual decision of a potential entrepreneur should not affect country-level factors. However, endogeneity may arise because the mean country-level individual entrepreneurship outcome may affect some of the country-level variables, so we lag all our macroeconomic and institutional variables by one year.

# 2. Empirical Methods

We obtain individual level data from the Global Entrepreneurship Monitor (GEM). The data are generated through stratified samples of 2,000 individuals surveyed per country. We use two definitions of entrepreneurship. The first definition is the standard definition by Reynolds et al. (2005) and is already available in the GEM data set. An individual is considered a nascent entrepreneurs if he or she is between

> 1 List of countries include: Algeria, Argentina, Australia, Bolivia, Chile, China, Croatia, Czech Republic, Denmark, Egypt, Arab Rep., Finland, Greece, Guatemala, Hungary, India, Iran, Islamic Rep., Italy, Jordan, Kazakhstan, Latvia, Lebanon, Malaysia, New Zealand, Norway, Philippines, Poland, Portugal, Romania, Russian Federation, Serbia, Singapore, Slovenia, South Africa, Spain, Sweden, Switzerland, Syrian Arab Republic, Thailand, Uganda, United Kingdom, United States, Uruguay, Venezuela RB.

3
select households from one municipality (Shanghai) and 6 provinces (Guangdong, Sichuan, Jilin, Shandong, Shanxi, and Ningxia). They scatter across China in the southeast, south, southwest, northeast, east, middle and north, respectively. As migration across dierent provinces was not common in China by 1991, each province can be regarded as a separate marriage market. Another important dimension that cuts across areas is the urban-rural distinction. The rigid Hukou system eectively blocked people from migrating between cities and countryside at the time of the survey. Furthermore, although our data consist of married couples only, we do not face much selection in divorce. The divorce rate around our sample period, 1990, was only 0.71 per 1000 couples, far below the corresponding numbers in many countries in 1995, which are 4.44 in the U.S. and 1.59 in Japan (Zeng and Wu 2000). SSCCW interviewed husband and wife separately. Here we focus on the male sample because a Chinese couple tends to live with the husband's parents by tradition (if they live with any parent at all), and therefore the paternal parents have clearer incentives to value a marriage candidate's ability in economic and home production. Our sample thus consists of husbands. Wife characteristics will be examined as dependent variables, as they are the result of the choice of the husband (and his parents if they were involved in the search process).

# 3.2 Old Age Support From Adult Children

A key assumption in our theoretical framework section is that children, especially those in rural areas, are key providers of old age support, and parents may prefer certain types of daughter-in-laws to deliver them old age support. We now provide some suggestive evidence from SSCCW to validate our assumption.

Based on a few questions on old age support, a signicant share of residents, especially the rural ones, rely more on their children for old age support. On the question of what you expect to get from your son when you grow old, 4.6% of urban husbands answered nancial support, 43.5% answered home services, 38.2% answered emotional support, and 13.2% answered nothing. In comparison, rural husbands expected more old age support from their sons: 19.8% of them expected nancial support, 67% expected home services, 9.1% expected emotional support, and only 3.8% expected nothing. Similar patterns occur on a parallel question of what you expect to get from your daughter when you are old, but both urban and rural husbands expected more emotional support from their daughters (41.5% in urban and 29.8% in rural), and less nancial support (2.4% urban, 11.5% rural) than from sons. On home services, urban husbands expected about same home services from daughters (44%) as from sons (43.5%), while rural husbands expected less home services 15
L. ∆Prefik ≡0, i.e., we assume there is no change in the lagged bilateral level of import protection.17 We also summarize the complete characterization of the key explanatory variable in Table 2.<sup>18</sup>

# **3.2 Econometric Methodology, IV Estimation, and Data**

The earlier literature has identified a number of econometric issues that arise when estimating models of the relationship between changes in levels of import protection that a country applies to PTA insiders and PTA outsiders. We briefly present them here; for a more in-depth discussion see Estevadeordal et al. (2008), as we essentially adopt their motivation and approach to dealing with these concerns. In particular, we focus on a subset of their 10 country sample and analyze the policies of Argentina and Brazil, the two largest MERCOSUR economies, over the period of 1990-2001.

First consider the measures of import protection. We lag ∆Prefik one year since the bilateral tariff component is scheduled by the terms of the MERCOSUR agreement, and thus predetermined relative to MFN tariffs (or TTBs). In addition, adopting a lag helps lower simultaneity bias. Second, we define the preferential tariff in industry _i_ in a given year as the minimum of the preferential tariff that the country applies on sector _i_ in that year against any of its MERCOSUR partners. Finally, in the estimation, we drop the observations for which the MFN tariffs are set at zero. If the MFN tariff is zero, the preferential tariff would also have to be set at zero and that may bias the results.

The preferential tariff data component in ∆Prefik comes from the MERCOSUR tariff schedules, which establish how tariffs are to be reduced over time in each country and product. The tariffs are aggregated as simple averages into roughly one hundred four-digit ISIC industries.<sup>19</sup> In the period preceding the year in which a country grants the first preference in a given sector, the preferential tariff 17 Implicitly this assumes that the size of the ad valorem equivalent to the AD/SG import restriction is equal to the size of the preferential tariff cut. In some instances, countries impose TTBs that are much larger than the size of preferential tariff cuts, which would indicate that this assumption is conservative. On the other hand, these TTBs are typically applied on only a subset of products within an ISIC 4-digit industry, for which case the assumption would be stringent.

18 In theory it is also possible for a third category to exist, whereby a country increases its applied levels of bilateral tariff. However, there are no instances in our data for which the bilateral tariff (only) actually increases during the sample; it either decreases or remains unchanged.

19 We thank Estevadeordal, Freund and Ornelas for sharing their data. As explained in Estevadeordal et al. (2008), the preferential tariff data had to be converted into a common nomenclature using the 4-digit ISIC classification, because the PTAs negotiated during the sample period used different tariff nomenclatures (e.g. NANDINA, NALADISA, HS) and tables for conversion were only available for ISIC codes.

12
<mark>1] i]</mark> <!-- Start of picture text --> South Asia Current Account Deficits<br>9.0<br>gol<br>7.0 |<br>3 40. CdRR<br>530) | dCOd I<br>5 2.0 | |<br>o 1.0<br>2.0 a ed<br>-4,.0<br>2002 2003 2004 2005 2006 2007 2008<br>Source: Country Authorities Wi Bangladesh§§ India§§ NepalJJ Pakistan§§ SriLanka<br><!-- End of picture text -->