rafmacalaba/data-use-mentions-extended huggingface dataset:
In the second part of the paper we propose a dynamic overlapping generations model with non-altruistic parents. After describing the environment we impose a social norm that requires intergenerational transfers and show that its sustainability is incentivecompatible across generations. Those transfers give the dynamic link between investment in schooling and future consumption for the parents, who otherwise would have no incentive to invest in their children’s schooling. This dynamic model also allows us to characterize the steady-state level of human capital in the economy. The dynamic effects of the existence of child labor are then discussed. After discussing the relevant issues for child labor in Latin America, we move on to analyze the effect of the conditional cash transfer program, PROGRESA (now OPORTUNIDADES), on child labor among indigenous and non-indigenous children in Mexico, using data from the evaluation database.

There have been several attempts to evaluate the impact of PROGRESA in several dimensions, though close to nothing has been done in order to test the differential impact of the program on indigenous versus non-indigenous people (for a summary, see IFPRI 2000). Perhaps the only attempt in this respect is Parker _et al_ . (2003), which provides an empirical estimation of the impact of language barriers in determining school achievement and the potential ameliorating role of bilingual education. By using large household data sets from PROGRESA, the authors find that, controlling for family resources and school quality, language represents an important barrier for indigenous schoolchildren. In particular, it is shown that there is a large gap in the educational performance of monolingual indigenous children relative to bilingual indigenous children and that bilingual primary schools narrow this gap. The results in the latter paper are rather consistent with our findings below.

# **2. Child Labor and Indigenous Peoples in Latin America**

The indigenous peoples of Latin America live in extreme poverty. There are over 40 million indigenous peoples in Latin America, or about 8 percent of the region’s population. Mexico has the largest indigenous population in the Americas, at over 12 million, or
Policy Research Working Paper 8923

# **Abstract**

The adoption of the value-added tax the Democratic Republic of Congo in 2012 led to price increases that are thought to adversely affect the welfare of most Congolese households. To date, research has not yet examined the poverty and distributional impacts of this tax reform. Using data from the 2012 Living Standards Measurement Survey, this paper investigates whether the current value-added tax regime, with its exemptions, is progressive. Relying on the Quadratic Almost Ideal Demand System and several welfare measures, the analysis finds that the adoption of the value-added tax erodes the purchasing power of all Congolese households by a factor of 10 to 12 percent. Yet, the value-added tax appears to be highly progressive. Households in the top food expenditure quintile bears approximately 40 percent of the welfare loss compared with less than 10 percent among households in the bottom food expenditure quintile. Other inequality measures, such as the Gini coefficient, further support this finding that the value-added tax is progressive. Finally, the study finds that the adoption of the value-added tax leads to a worsening of the food poverty headcount by approximately 1.2 percentage points.

This paper is a product of the Poverty and Equity Global Practice. 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 fadoho@worldbank.org and rgansey@sas.upenn.edu.

_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
was not a causal by-product of the Colombian conflict, but an extremely common strategy of war used by illegal armed groups to weaken the enemy’s popular support, clear regions for illegal crop growing and drug trafficking, and expropriate lands and natural resources (Ib ́a ̃nez and V ́elez, 2008). According to Ib ́a ̃nez, Moya, and Vel ́asquez (2006), for instance, between 1993 and 2002 displaced individuals lost 1.2 million hectares of land.

# **II.1 Characterizing internally displaced migrants**

The annual cumulative displaced population from 1995 to 2010, according to the _Registro Unico_<sup>_ ́_</sup> _de V ́ıctimas_ (RUV) from the Colombian Human Rights Observatory is presented in Figure I.<sup>7</sup> The figure shows that the number of displaced individuals began to increase dramatically between 1996 and 2002 in Colombia, when the internal conflict was most intense, as shown by the evolution of conflict-related variables such as armed actions and clashes between armed groups (see panel b of Figure II). Since then, forced displacement has been decreasing slowly in conjunction with a softening of conflict intensity and violence.<sup>8</sup> Data from RUV also suggests that the cumulative population of forced migrants is balanced in terms of gender (51 percent women) and that most are of working age. Colombia’s forced migrants are also young. In particular, 39 percent of forcefully displaced individuals were 15 years old or younger at the time of displacement, this percentage is disproportionately larger than this age group within the population of Colombia as a whole (28 percent). Indeed, 15.5 percent of forced migrants were younger than 5 years of age at the time of migration. Households also tend to be bigger as several members of the extended family tend to live together to save on housing costs (Unidad para la Atenci ́on y Reparaci ́on de V ́
defined to include “informal wage” workers and “self-employed”. The impacts of foreign ownership on income for self-employed are not considered in this paper due to the lack of wage data.

> 6 See Anderson and Dimon (1999) for Mexico, Fu and Balasubramanyam (2005) for China, Glick and Roubaud (2006) for Madagascar. For instance, applying the Smith-Myint model of “vent for surplus” to China, Fu and Balasubramanyam (2005) find that the expansion of exports from labor-intensive manufacturing, assisted by FDI, accelerated the transfer of surplus labor from the agricultural to the export sector. For Mexico, Anderson and Dimon (1999) report evidence that export oriented production in _maquiladoras_ (across-border export processing plants) created formal sector job opportunities for single women.

> 7 The VHLSSs include data for a category of workers who reported as “work for other households,” which are used as a proxy for the “informal” wage workers.

4
|E - Water supply, sewerage, and waste|16|17|1|17|1|
|---|---|---|---|---|---|
|F - Construction|195|198|3|203|9|
|G - Wholesale and retail trade|402|431|29|430|28|
|H - Transportation and storage|86|94|9|89|4|
|I - Accommodation and food services|202|204|2|215|14|
|J - Information and communication|51|51|0|53|2|
|L - Real estate activities|60|61|1|64|4|
|M - Professional, scientific, and technical|108|127|19|112|4|
|R - Arts, entertainment, and recreation|47|48|1|51|5|
|Other|416|446|30|437|21|
|**Total**|**2,032**|**2,213**|**181**|**2,160**|**128**|

Source: Authors based on AECM survey; European Central Bank.

**Our analysis has several limitations.** With respect to the transition risk assessment, our transition risk score is based on direct GHG emission in the respective sectors. This data does not encompass effects along the supply chain, such as cost pass-through (see Fabra and Reguant 2013). With respect to our physical risk assessment, the main drawback of our analysis is that not all CGSs in our survey were able to provide exposure data at the NUTS 2 level. This could lead to an overall bias in our spatial assessment, given that 90 percent of the exposures in our NUTS 2 sample are obtained from CGSs in Germany, Poland, Portugal, Spain and the
reducing the chance of asylum in Europe, coupled with the effects of border closures and a reduction in the desire to migrate due to the COVID-19 pandemic in the second half of our follow-up period (Bah et al, 2021). The consequence was that, despite focusing geographically on the highest migration regions and then studying the demographic group most likely to migrate, only 1.1 percent of our control group attempted backway migration in the 18month follow-up period. This limited the scope for our interventions to reduce irregular migration, since ex post there was very little to reduce.

> 4 Frontex data may underestimate the total amount of irregular migration by not collecting data on migrants who are not intercepted by border patrol, those who change their country of origin, or who do not file for asylum status upon arrival. However, even if the levels are an understatement, we believe they capture the trend in migration well. Our survey data questions on migration over time closely track the Frontex trends.

5
17

# POLICY RELEVANCE OF THE FINDINGS

The above findings differ in their specificity to particular countries and the extent to which they probably cover both the individual and community (external) effects of the three dimensions of development. To examine the policy proscriptions implied by the findings, I have calculated the costs of births averted directly through family planning programs in a variety of countries and through improved female education, improved infant mortality and a national pension program. Such simplistic measures of impact have obvious limitation. (See Cochrane and Zachariah, 1983 for an outline of the methodology and its limitations). In particular they represent average rather than marginal costs in most cases and do not allow for expected changes in these costs over time. More importantly perhaps they do not show how various interventions to reduce fertility might best be used in combination. This objective would require further research on interaction effects of the various policies. Despite these limitations, it is useful to compare the alternative programs to understand the feasibility in the first instance of various indirect policies to reduce fertility.

The most direct policy to reduce fertility is to support family planning programs. Using data on costs of programs, contraceptive usage rates and age specific fertility rates for various countries around 1980, the costs of a birth averted through family planning were calculated for 16 developing countries. Two different estimate of births averted per user were made depending on the assumption about age specific fertility in the absence of family planning. The difference between rates are small in the low usage
the financial crisis―and 2017, when unemployment rates returned to pre-crisis levels. Again, we use the second quarter data to avoid seasonal effects.

ENOE questionnaires include broad thematic information, such as labor and socio-demographic characteristics of respondents, their current economic condition, type of work, industry, and earnings. The ENOE also includes information on the respondent’s educational attainment. Based on questions regarding the number of school years and whether the respondent had concluded each grade, we constructed four categories of formal education: illiterate and primary, middle school, high school, and college/university.

Urban households are those in localities with more than 2,500 inhabitants. We computed household real income as the sum of all members’ labor earnings, and then deflated these earnings using the official Mexican Consumer Price Index (INPC). The ENOE also provides the number of hours worked by each individual. We defined people working fewer than 35 hours per week as part-time workers. Also, we calculated average wages for different industries using the North American Industry Classification System (NAICS).

We built a “childcare facilities availability” variable using data from population censuses and the national childcare facilities directories. This variable is defined as the number of childcare facilities per 1,000 people in each municipality. First, we used the National Childcare Facilities Directory for Working Mothers of the Social Development Ministry (SEDESOL)—the largest childcare provider in Mexico—to calculate the number of public childcare facilities in each municipality.

We used the December 2008 and December 2016 directories as they provide the closest data available to 2007 and 2017, respectively. Second, we use the Population and Housing Censuses to calculate each municipality’s population size. We use the Census data instead of the ENOE since 20
# **Figure 2. United States Share of Mexican Non-petroleum Exports and Imports**

<!-- Start of picture text --> US Share Mexican Imports US Share Mexican Exports<br>.9<br>.8<br>.7<br>.6<br>.5<br>.4<br>2001 2005 2010<br>Year<br><!-- End of picture text -->

**Notes:** Series calculated by authors using data from http://dgcnesyp.inegi.gob.mx/cgiwin/bdieintsi.exe/Consultar#.

36
Fritsch, Michael and Monika Meschede (2001), “Product Innovation, Process Innovation, and Size,” Review of International Organization, 19:335-350.

Goldberg, Pinelopi, Amit Khandelwal, Nina Pavcnik, and Petia Topalova (2010a), "Multiproduct Firms and Product Turnover in the Developing World: Evidence from India," Review of Economics and Statistics, 92(4): 1042-1049.

–– (2010b), "Imported Intermediate Inputs and Domestic Product Growth: Evidence from India," Quarterly Journal of Economics, 125(4): 1727-1767.

Griffith, Rachel, Elena Huergo, Jacques Mairesse, Bettina Peters (2006), “Innovation and Productivity across Four European Countries,” Oxford Review of Economics Policy, 22(4): 483-498.

Griffith, Rachel, Stephen Redding, and Jon Van Reenen (2004), “Mapping the Two Faces of R&D: Productivity Growth in a Panel of OECD Industries,” Review of Economics and Statistics, 86(4): 883-895.

Grossman, G. M. and Helpman, E., 1991, _Innovation and growth in the global economy_ . Cambridge, Massachusetts, MIT Press.

Haddad, Mona and Ann Harrison (1993), "Are There Positive Spillovers from Direct Foreign Investment? Evidence from Panel Data for Morocco," Journal of Development Economics, 42: 51-74.

Hsieh, Chang-Tai and Peter J. Klenow (2009), “Misallocation and Manufacturing TFP in China and India,” Quarterly Journal of Economics, 124(4): 1403-1448.

Katayama, Hajime, Shihua Lu, James R. Tybout (2009), “Firm Level Productivity Studies: Illusions and a Solution,” International Journal of Industrial Organization, 27(3): 403-413.

Keller, Wolfgang (2004), “International Technology Diffusion,” Journal of Economic Literature, 42(3): 752-782.

Klette, Tor J. and Samuel Kortum (2004), “Innovating Firms and Aggregate Innovation,” Journal of Political
the category of supply-side energy efficiency projects. Geothermal and hydro-electricity projects are the largest and second largest among the renewable resource based electricity generation projects. In particular, electricity generation projects that have the capacity of generating more than one million CERs per year are in hydro, biogas, landfill gas, coal bed/mine methane capture, cement, fugitive, and energy efficiency supply-side and own generation sub-categories.

The UNEP Risoe Center reports initial capital investments in 4,418 of the projects in the pipeline. Annual operation and maintenance cost data for 122 projects are obtained from the PDDs with the help of Climate Solutions (2008). See Annex I for details on how operation and maintenance costs are 12
impact the educational market, and/or if the private sector responds to rank conditions or factors associated with it in a similar fashion. We believe this to be unlikely, since the historical record shows that PCs are used only as revenue collection units, while political representation, and with it the delivery of public services, is centered at the Union-Council level, an alternate and non-overlapping classication.

Three empirical tests, in the spirit of Altonji et al. (2005), provide further support for the exclusion restriction. First, village socio-demographic characteristics are uncorrelated with the eligibility status of the village: Eligible and non-eligible villages do not dier along any observable dimensions other than those (population and area) on which the instrument is based. Second, the instrument does not predict the construction of any other type of public school nor any of a range of other public investments. Third, only local population rank corresponding to the ten-kilometer guideline matters. An analogous but more expanded local rank measure (top rank at the next higher administrative level which has a radius three times that of a PC's) does not predict girls secondary school construction. If entrepreneurs are more likely to pick locally top-ranked villages, we would expect this result to hold for the slightly more expanded top-rank measure as well. Furthermore, in a falsication exercise, we conrm that our instrument has no impact on private school location decisions in administrative units where there was no GSS construction. Thus, local population rank on its own does not aect private sector location decisions.

One natural question is whether this increase in the supply of teachers has led to an increase in educational provision or a sectoral shift from public to private schools. There are several reasons to think that the growth of private schools has had a positive impact on educational outcomes, both in terms of enrollment and learning outcomes. In a representative sample of households in the country (the Pakistan Integrated Household Survey 1998), overall enrollment is higher for villages with private schools
formal stores. We run a product-level version of specification (2) with goods fixed effects and compute an average goods-level estimate of _β_ for each country.<sup>19</sup> We gradually consider variations within narrower goods categories: we first consider food vs non-food (column

> 16Household size controls for economies of scale across households of different size which could affect where households choose to shop - see Deaton and Paxson (1998).

> 17The survey block is the most granular location information and contains on average 74 households in our surveys. The median survey block is representative on average of 52,900 households.

> 18The informal budget share is on average 67% in rural areas versus 52% in urban areas and IEC slopes are steeper in urban locations. Figure A7 shows the IECs separately for rural and urban areas.

> 19Formally we run the regression: _Share In f ormalig_ = _β ∗ ln_ ( _expenditureg_ ) + _αg_ + Γ _Xi_ + _εig_ where _Share In f ormalig_ is the share of household _i_ ’s informal expenditure on good _g_ and _αg_ are goods fixed effects. Each observation is weighted by household survey weights and goods expenditure shares.

12
# **Identifying Skills Needs in Vietnam: The Survey of Detailed Skills**<sup>**1**</sup>

Julia Granata, Harry Moroz, and Nga Thi Nguyen<sup>2</sup> The World Bank JEL code: J24, O15, I25 Keywords: Socioemotional skills, digital skills, routine skills, interpersonal skills, remote work, employment, Vietnam, O*NET, PIAAC, STEP 1 We are grateful to the Second Australia-World Bank Group Strategic Partnership (ABP2) in Vietnam for financing part this work. We are grateful to the General Statistics Office and the Ministry of Labor, Invalids, and Social Affairs of Vietnam for their collaboration. The team benefited significantly from inputs, comments, and assistance from Josefina Posadas, Aylin Isik-Dikmelik, Abla Safir, JJ Naddeo, Cristian Camilo Sanin Camargo, Van Cam Nguyen, and Corinne Bernaldez. The team is also grateful to Quang Hong Doan, Jacques Morisset, Lieu Ngoxuan, Shawn W. Tan, Trang Thu Tran, and Judy Yang for their assistance.

> 2 Authors are available at <u>m.julia.granata@gmail.com, hmoroz@gmail.com, and nnguyen26@worldbank.org.</u>
**| olyPlanning, nd Research |**

**Educaionand Employment**

From the mid-1950s to the 1960s, the govemment of Peru undertook a major expansion of public education, increasing the number of schools, requiring primary schools that offered an incomplete cycle to add grades, and increasing school inputs (principally teachers and textbooks).

The authors examine the effects of Peru's educational policies, and the effects of family background and community characteristics on the schooling levels of a sample of adults. Data on males and females were analyzed separately by birth cohort, using a sample of 5,644 females and 5,241 males aged 20 to 59.

# The authors found that:

inedicator that the link between socioeconomic ba,-kground and access to schools had weakencd.

* The relative effect of parents' education differed for sons' and daughters' schooling. Fathers' education had tv -ce as great an effect on sons' schooling as mothers' education; for daughters, both parents' education had equivalent, strong positive effects.

* Primary schools expanded rapidly even in rural areas, so rural residence did not adversely affect the primary schooling of males. Urban or rural residence did matter greatly for females, however, suggesting less desire by rural parents to invest in the schooling of their daughters.

* The govemment's policy to expand the schools played a major role in raising education levels and narrowing the gap between rural and urban residents, and males and females.

* The impact of parents' years of schooling and occupations on the educational levels of their children lessened over time as the supply of schools expanded throughout the country - an

- Urban residence at the age of 13 remained an important deterninant of educational attainment because of the great disparity between urban and rural areas in availability of secondary schools.

* The availability of material school inputs, such as textbooks and desks at the primary level, had a large positive effect on final school levels.

This paper is a vroduct of the Education and Employment Division