rafmacalaba/data-use-mentions-extended huggingface dataset:
2016; Schneider 2012).

- Analyses relying primarily on nightlight data capture the concentration of human activities in an unprecedented manner. However, problems such as blooming lead to biased estimates of the urban extent (Elvidge and others 2007; Elvidge and others 2009; Li and Zhou 2017). As access to electricity becomes more common and reliable, the pace of urbanization could be misinterpreted as well.

- When using gridded population data, one fundamental issue is the lack of reliable information on disaggregated administrative boundaries. Open‐source boundaries are available mostly at the district or subdistrict level (GADM 2012). Population census data are at best georeferenced to these levels of administrative boundaries. And both population data and administrative boundaries tend to be out of date by several years.

To address these limitations, we develop a tractable method for identifying urban areas and illustrate its potential by applying it to India. The proposed method differs from the previous literature in three important ways:

- Instead of cells, we use _cities, towns and villages_ , as officially defined, as our units of analysis. Because the available boundaries of administrative units tend to be unavailable, out of date, or erroneous, we rely on newly available georeferenced and digitized boundaries of administrative units at all levels, based on the India’s _Administrative Atlas_  _2011_ (ORGI 2011b) **.** Although the analysis covers the entire universe of cities, towns, and villages, an in‐depth assessment is conducted for a stratified sample of them. The sample design results in roughly three‐quarters of the units being administratively urban and the rest administratively rural.

3
(pg. 56) and refer to 1995. Unemployment figure, for 1994, is from CIA Factbook 1995. Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and is for 1990. Local Govemment figures are from Civil Service Reform in the Philippines (David Stiedman), p. 44, and data are for December 31, 1994. It includes permanent and non-permanent staff. Health figure includes both those personnel still working for the National Govemment in Health and also those devolved to local governments. Military employment data do not include personnel enrolled in paramilitary units, e.g., Philippine National Police (40,500), whose responsibility is divided between Local Government and the Ministry of Interior, the Coast Guard (2,000) and the Citizen Armed Force Geographical units (CAFGU), a part-time militia which can be called up for extended periods (60,000).

GDP and Consolidated Central Government wages and salaries are for 1993 and are taken from IMF Government Finance Statistics Yearbook, 1995. Data on wages in manufacturing is taken from the united Nations' Statistical Yearbook for Asia and the Pacific 1995 and refer to 1990.

Singapore Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1994. Numbers relating to Central, Local, Education and Health employment are drawn from Singapore Statistical Office web page, off the Intemet, and relate to 1994. Military employment data include conscripts (33,800). GDP and Consolidated Central Government wages and salaries are for 1992 and are taken from IMF Government Finance Statistics Yearbook, 1995.

# Solomon Islands

GDP is from the UN Statistical Yearbook for Asia and the Pacific. 1995 and is for 1991._Wages and Salaries of Consolidated Central Government are from IMF Government Finance Statistics Yearbook 1995 and are for 1991.

Sri Lanka Data on unemployment rate and number of unemployed people are drawn from Unemplovment in Sri Lanka: sources and solutions. IDP 154 of 07
have usually either repeated a grade or entered school later than others. The ethnic composition controls have some predictive power. Program impact estimates are similar if these explanatory variables are excluded, although estimates are less precise.<sup>13</sup> Estimates are largely unchanged when individual demographic controls collected in the 2002 student questionnaire – including parent education and household asset ownership – are included as explanatory variables,<sup>14</sup> and interactions of the program indicator with these household socioeconomic proxies are not statistically significant at traditional confidence levels (regressions not shown), suggesting that better-off residents do not gain disproportionately.

Test score effects in Teso district are near zero and statistically insignificant (Table 5, Panel B, regression 2). This is consistent with the hypothesis that winning a scholarship was less desirable in Teso due to mistrust of the NGO or lack of social prestige associated with winning the award, and also with the hypothesized bias due to sample attrition in Teso. It remains unclear whether there might have been larger program effects had there been no lightning tragedy in 2001, or how common the problems of program implementation and sample attrition encountered in Teso would be in other settings.<sup>15</sup> The scholarship program not only significantly raised test scores when it was first introduced in 2001, but also continued to increase scores of cohort 1 girls in program schools during 2002: the point estimate in year two is 0.24 (standard error 0.08, Table 5, Panel C regression 1) for the restricted sample, 13 For instance, the program impact for Busia cohort 1 girls is still exactly 0.27 standard deviations in this case, but the standard error rises to 0.19, while the program impact for cohort 2 rises to 0.31 with standard error 0.18 (significantly different than zero at 90 percent confidence) – regressions not shown. Standard errors fall considerably when disturbance terms are not clustered at the school level; for instance, the
significant effect of the satisfaction with the government on the propensity to work without contract in Estonia, Latvia, Romania, Russia, Norway, Austria, the UK, Ireland, and Israel (these results are available on request).

# **6 Conclusion**

In this paper we have compared the prevalence of informal employment in 30 European countries using data from the European Social Survey. Overall size of informal employment decreases from the South to West to East to North, but the median prevalence of dependent informal employment is higher in the East than in the West. Yet there is a strong heterogeneity within these geographical areas. In particular, Western Europe is split into highly informal part (Ireland, the UK, and Austria) on one hand and the continental part (without Austria), where work without contract is quite rare, although informal selfemployment is more prevalent than in most of Eastern Europe. We have found a substantial within-country regional variation in informality in all countries except for Nordic countries, Hungary, Romania and Slovenia.

In all European regions, the dependent informality rate is inversely related to skills (measured in terms of either schooling or occupation). Both in Southern and in Western Europe, the highest dependent informality rate is found among immigrants from CEE and former Soviet Union, whilst in Eastern Europe this group is second after local born minorities without immigrant background. In Southern Europe (especially in Spain, Portugal, and Greece) immigrants not covered by the ―free movement of labor‖ provisions are more likely to work without contract than both natives and covered immigrants. Both in Eastern and Southern Europe, as well as in highly informal part of Western Europe, these not covered immigrants have significantly higher propensity to work without contracts also after controlling for individual characteristics. Students and persons with permanent health problems also are more likely to work informally, other things equal.

Our findings lend support to theoretical arguments that apart from low productivity, informality drivers include ―informal‖ social capital, low value placed on job security
34 and developed nations, its coverage of developing nations is less detailed than in our preferred sources. The number of seats won by parties is more often omitted, as is the identification of Parties as affiliated with or opposed to the government. Details on the electoral systems are also more often absent, as are vote percentages for presidential systems. Our sources provided information on seats, parties, and election results more consistently in developing nations. The reporting in Statesman’s also lagged events by a few years, especially in developing nations or nations emerging from Communism. For example, election results in countries such as Kazakhstan or Kyrgisztan are not covered until 1995, compared to 1992 in our preferred sources. Finally, Statesman’s rarely provided lists or descriptions of active parties. Another possible source for this database is Keesing’s Record Of World Events. This source was rejected because it was primarily focused on news rather than permanent institutions; and because it is organized by month, creating significant compilation costs - since relevant data would have to be scoured from every month’s report. Various other sources (CIA Handbook ofthe Nations, OECD Political Data Handbook edited by Lane, et al., the Year Book of World Affairs, produced by the London Institute of World Affairs) were rejected for reasons of coverage and focus.

~Despite the use of comprehensive sources and detailed cross-checking procedures, there are several variables which need to be treated with some caution because of the fundamental limitations of cross-country sources such as those upon which we relied. Party orientation is one of these, since we were unable to rely on systematic listings of party platforms or legislative positions. However, since our first-cut source (the party name/description in the ~ handbooks) and our default source (the Agora website) disagreed infrequently, we believe these variables can be used with confidence.
# **1. Introduction**

Traditionally, trade performance and diversification are studied from a product perspective. This perspective lost focus due to large-scale offshoring trends with countries trading tasks rather than products, carrying out different activities in global value chains (Grossman and Rossi-Hansberg 2008; Baldwin and Robert-Nicoud 2014; Antrás and Chor 2022). The aim of this paper is to explore patterns of export specialization from an activity perspective. To aid intuition, table 1 provides an illustrative example. It shows the distribution of activities in exports of a particular product class, textiles, for four countries at different levels of development. Activities are identified in the data by cross classifying the occupational class of workers and their industry of work. It shows that activities of machine operators in the textile industry make up more than twothirds of domestic value-added exports in Türkiye. Instead, in Italy much more value is added by managers, engineers and other professionals outside the textile industry. In Pakistan agricultural workers provide most value in textile exports as cotton is a major input in domestic textile production, whereas in Vietnam workers in wholesale trade services are major contributors. Activities in textiles exporting thus differ widely across countries, suggesting potential for new insights when using an activity perspective on trade.<sup>1</sup> Exploiting a new panel data set on export activities covering countries at a wide range of income levels, we document four main empirical findings. First, production activities typically account for the majority of export value at low levels of GDP per capita. As countries grow richer, incomes from engineering, managerial, and services support activities grow and eventually account for the majority of export value. Second, poorer countries specialize along the extensive margin, shifting export activity across industries within occupational classes. Advanced countries mostly specialize along the intensive margin, shifting export activity between occupational classes within industries. Third, the probability that a country gains new comparative advantage in a particular activity is positively related to the proximity of this activity to the initial export basket. Proximity
to the fact that a smaller _fraction_ of _post-ultrasound_ births preceded by a firstborn girl are now female, where as the conception effect is driven by the change in the number of births that are preceded by a firstborn girl.

The number of excess postnatal female deaths in a year equals: where _EFMFG_ refers to the difference between the probability of death by age 5 among children preceded by a firstborn girl and a firstborn boy.

The change in the number of excess postnatal female deaths can thus be written as: The change in the number of excess postnatal female deaths can also be decomposed into three components: the “behavioral effect” refers to the EFM decline due to, say, better postnatal health investments in girls in the post-ultrasound period; the “mechanical sex-selection effect” is driven by the decrease in the _fraction_ of _post-ultrasound_ births that are preceded by a firstborn girl; and the “conception effect” that reflects the change in the _number_ of births that are preceded by a firstborn girl itself.

The table below uses the aforementioned formulae to calculate the number of births preceded by a firstborn girl during the pre-ultrasound (in column (1)), during the late diffusion period (in column (2)), and the change in the number of missing girls as the difference between columns (1) and (2) in column (3). _EFMFG_ in column (1) is the coefficient of _Firstborn girl * Female_ and in column (2) is the coefficient of _Firstborn girl * Female * Post2_ from column (4) in panel B of Table 3. During the pre-ultrasound period, the fraction of females in births preceded by a firstborn girl was 47.9 percent. Ultrasound access decreased this fraction by 1.8 p.p..<sup>64</sup> We calculate<sup>_<u>N</u>_</sup> _N_<sup>_<u>F G</u>_</sup> from the
# **Table 1: Mincer Regressions, Estimates**

|Covariates|||Wage-employe|d workers, ages 1<br>|5–45<br>||
|---|---|---|---|---|---|---|
|||All||Men|Women|All, private<br>sector|
||OLS|OLS|Semi<br>parametric|Semi<br>parametric|Semi<br>parametric|Semi<br>parametric|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Intercept||4.792***|||||
|_N_||4,291|4,291|2,886|1,405|3,276|
|_R_-squared statistic||0.207|0.097|0.106|0.109|0.104|

## _Source_ : Authors’ estimations based on data from the 2016/17 GLSS.

_Note_ : OLS = ordinary least squares. Samples for all regressions are restricted to wage-employed workers ages 15–45. Reference region is Greater Accra. Reference category for father’s and mother’s schooling level is none. “Postsecondary, nonuniversity” includes teacher training, nurse training, and a variety of technical training programs ranging from one to three years of study. Robust standard errors reported in parentheses. Significance level: * = 10 percent, ** = 5 percent, *** = 1 percent.

20
MEBF staff also supported applicants to comprehensively define their needs in terms of technical assistance and trainings for their businesses as well as investments in equipment or inventory that they could buy in case they were offered a cash grant. In total, 2,279 business plans were submitted to MEBF.

**Phase 3 - Selection of applicants.** By May 2018, MEBF staff and staff of Bagrépole short-listed 1,612 of the 2,279 applicants. Each applicant was scored based on a predefined set of eligibility criteria. These included, for example, the cogency of the justifications on the needs expressed by the applicant, the relevance of the applicant’s personal experience in relation to the proposed business, and the potential of the proposed project to create and consolidate employment. Then, MEBF conducted individual interviews with all preselected applicants to double-check their level of commitment to their proposed business plan.

**Phase 4 - Training of all selected applicants.** MEBF invited all the preselected 1,612 applicants for a training based on a simplified module of the ‘Start and Improve Your Business Training’ (SIYB) developed by the International Labour Organization (ILO). As in McKenzie (2017), the intention of the training was to provide all applicants with the basics of business management, including accounting and financial and business planning, to help grant recipients finalize and implement their business plan. Of the preselected applicants, 1,575 attended the training. Using another round of face-to-face interviews, by August 2018, MEBF then selected, again based on performance and quality of the proposed project, a final list of 1,300 applicants, of these 1,300 the top 1,200 ranked firms were then selected for the lottery and the other 100 were put on a reserve list in case some applicants would drop out of the experiment before the implementation starts. A baseline survey with all 1,300 firms was conducted in November/December 2018. This survey was implemented with Innovations for Poverty Action (IPA)
# **1 Introduction**

Barriers to schooling have been one of the main obstacles to development across the globe. While the motivation for expanding access to schooling often relies on labor productivity gains, there are reasons to believe that a large portion of returns to such policies materialize within families and across generations. Estimating these spillover effects of education is particularly relevant not only because it affects the accounting of social returns to public investments in education, but because it may influence the design of contemporaneous policies targeting early childhood development, like those focusing on parenting and school readiness. The literature on intergenerational impacts of education is large and very active (see reviews in Holmlund, Lindahl, and Plug (2011) and Haveman and Wolfe (1995)),<sup>1</sup> but also relatively scarce for developing-country contexts where educational attainment levels are far lower.<sup>2</sup> In this paper we take steps towards filling this gap by examining the intergenerational impacts of one of the most prominent schooling-expansion interventions in the developing world, the Indonesia _Sekolah Dasar_ INPRES program of the 1970s.

We focus on the children of women originally targeted by the INPRES program and who reside in rural areas of Indonesia by the early 2010s. We focus on the expansion of maternal education given the gendered division of labor towards childrearing often found in developing country settings like those of Indonesia. Our analyses capitalize on a novel and comprehensive data set on early childhood development that is representative of rural Indonesia, allowing us to examine the impacts of the INPRES program over multiple developmental outcomes for children born to women targeted by the school construction program. Our outcomes encompass effects over health as well as cognitive and socio-emotional skills. In this way we complement and expand on re-

> 1The increased accessibility to Scandinavian registry data has led to an explosion of articles on the topic. Some of the most influential work includes the seminal article by S. E. Black, Devereux, and Salvanes (2005) and more recent iterations which exploit
Figure 2: Estimates of Poverty Evolution in Indonesia during<sup>the Crisis</sup> <!-- Start of picture text --> 300<br>264<br>250 24S 248<br>234 S<br>_M ",218<br>5"<br>200 199<br>188<br>1MYV<br>172<br>148 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~153m<br>} _ lso tr / ~~~~~~~~~~~~~~~~~~~ 149M M<br>115/<br>100C X<br>Data Sources:<br>so = Susenas Consumption Modulb<br>C =Sussas Core<br>M = Mini Susenas<br>V 100 Village SurVey<br>I Indonesian Family Ufe Survey<br>0<br>Feb-96 May-96 Aug-96 Nov-96 Feb-97 May-97 Aug-97 Nov-97 Feb-98 May-98 Aug-98 Nov-98 Feb-99 May-99 Aug-99 NOV-99<br><!-- End of picture text -->

> Fourth, after the peak point, the poverty rate has started to decline<sup>again.</sup>

Nevertheless, by August 1999, two years after the crisis started,<sup>the poverty rate</sup> was still substantially higher, i.e. around 50 percent, than<sup>its pre-crisis level.</sup> If we allow a line to _not_ pass through the anomalous point in December<sup>1998,</sup> this series of poverty estimates paints a very reasonable picture,<sup>around which the</sup> data show a striking consensus, as it neatly tracks known<sup>events (e.g. inflation</sup> stabilization, rice prices). This still leaves a
.<sup>4</sup> Figure 1a shows that the literature associates almost twice as often income changes with adverse rather than positive effects on conflict, suggesting a possible asymmetric research focus on the link between income and violence. In Figure 1b, we adjust the _t_ -students so that the _ex-ante_ assumed effects all work in the same direction.<sup>5</sup> It reveals that finding a result in line with _ex-ante_ expectations is twice more frequent (66.8 % of estimates) than finding one with an unexpected signs (33.2 % of counter-intuitive or null effects). This coincidence between the obtained and expected results may suggest the presence of genuine effects or manipulations by researchers, namely, publication selection bias (i.e., to prefer results with signs consistent with their expectations or prior understanding, or results with higher statistical significance).

> 3This effect disappears when spatial units are countries, suggesting a local nature of the explanatory mechanism.

> 4Figure 1 only presents the sample of direct estimates from the MRA database of this study.

> 5 We reverse the sign of _t_ -statistics for regressions focusing on theoretically pacifying mechanisms as they are the only channels with an _ex-ante_ expectation of reducing risks of conflicts.

3
Our paper contributes to the literature on curative health care for children in low-income countries, aided by novel daily panel data. Research on policies intended to improve acute care is still fairly rare, with some exceptions, such as Powell-Jackson et al. (2014).<sup>4</sup> The problem of underuse is known to be severe for preventive goods, where the benefits are nearly universal, but also often abstract and far off. Our results show that underuse is similarly pervasive at the extensive margin of acute care, that is, the decision to visit a provider. We show that subsidies can substantially improve utilization at this margin. These results contrast with Cohen et al. (2015) and Lopez et al. (2022), who examine the effect of subsidies for malaria drugs at the point of purchase. Both papers document that there are significant rates of overtreatment that increase with subsidization. Similarly, patient audit studies have shown low-quality diagnostic and treatment choices, which often result in non-indicated or mis-targeted treatment, in a range of low and low middle income contexts Das et al. (2016); Currie et al. (2014). The reasons for overtreatment range from financial incentives for providers to patients demanding powerful medicines. This points to a challenging twin problem, where underuse at the extensive margin, in the form of too few contacts with the health care system, occurs simultaneously with overuse at the intensive margin, in the form of overmedication conditional on such a contact. Subsidies that increase access are more likely to induce overuse at the intensive margin when there are fewer cost controls such as those implemented by Mali Health. Thus, efforts to reduce the cost of care likely require accompanying measures to limit non-indicated or overly expensive treatment.<sup>5</sup> An innovation in this paper is to collect illness spell data at home and apply hazard models to estimate the effects of the interventions on care seeking conditional on health status. In section 5.4, we discuss the