rafmacalaba/datause-encoder-data huggingface dataset:
8 The panel data for Vietnam are from the two waves of the Vietnam Living Standards Survey (VLSS) conducted in 1992-93 and 1997-98. The panel consists of 4302 households (20493 individuals) from 59 of Vietnam’s 60 provinces. The results below are for household consumption rather than income, defined and computed in the same way in both years.<sup>10</sup> The 1998 data have been deflated using the national CPI. The panel data imply a real annual rate of growth of per capita household consumption of 6.6%, and a rise in the Gini from 0.3223 in 1993 to 0.3346 in 1998, giving a value of _∆ G_ equal to 0.0123 (Table 1). This change in the Gini is somewhat smaller than the VLSS-based figure reported by Glewwe (2003), namely 0.023. This seems likely to be due to the fact that the figure reported here is based on the households interviewed in both years, while Glewwe’s is based on the full 1998 sample which includes 1,200 households interviewed in 1998 but not in 1993.

The data from the CHNS and VLSS panels are thus very consistent with a story of rapid income and consumption growth, accompanied by very modest increases in inequality. The next section uses the decomposition of section II to see how far these very slight increases in the Gini coefficient mask horizontal and vertical redistribution in the move from period 0 to period 1.

# IV. **DECOMPOSITION RESULTS**

Tables 2 and 3 show the results for China (1989-97) and Vietnam (1993-98) for different bandwidths.<sup>11</sup> Consistent with the results of van de Ven et al. (2001), _V_ initially increases in absolute size as _w_ is increased, but subsequently declines as _w_ is increased further. This reflects two opposing tendencies: an ‘averaging effect’, which tends to increase _V_ as _w_ is raised (the

> 10  The VLSS measure of consumption reflects food and nonfood consumption during the previous 12 months.  The procedure used for both years is described in detail in the VLSS 1998 basic information document, which is downloadable from <u>http://www.worldbank.org/lsms/country/vn98/vn98bif.pdf.</u>

> 11  These are defined in local currency units in the base year—RMB or Yuan and 1989 in the case of China, and Dong and 1993 in the case of Vietnam.  In both cases, the values are per person, and are annual figures.
# **Table A2: Impacts of mental health on labor outcomes – Coefficient estimate of IVLogit-FE model**

|Dependent variable:|Employment<br>status|Labor force<br>participation|Permanent<br>job|Skilled<br>occupation|Work in<br>agriculture|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Standardized mental health scores|-0.765***|-0.542***|-0.185|-0.680***|0.746***|
||(0.108)|(0.079)|(0.120)|(0.143)|(0.112)|
|_First stage of 2SLS (dependent variable_|_is standardize_|_d mental health_|_scores)_|||
|Trauma experience*Year after arrival|-0.039***|-0.023***|-0.039***|-0.039***|-0.039***|
||(0.009)|(0.006)|(0.009)|(0.009)|(0.009)|
|Kleibergen-Paap F stat.|17.692|35.644|17.692|17.692|17.692|
|Dep. Mean|0.455|0.418|0.163|0.135|0.746|
|Other controls|Yes|Yes|Yes|Yes|Yes|
|Individual FE|Yes|Yes|Yes|Yes|Yes|
|Survey wave FE|Yes|Yes|Yes|Yes|Yes|
|Observations|3,687|7,768|3,687|3,687|3,687|
|Number of individuals|1,609|2,182|1,609|1,609|1,609|

_Notes:_ *** p<0.01, ** p<0.05, * p<0.1. Results of IV-Logit-FE model. Standard errors in parentheses are clustered at the individual level. Control variables include age group dummy variables, marital status, household size, language skills (listening, speaking, reading, and writing), residence area, and home ownership. Mental health scores are the sum of the 6-item Kessler Psychological Distress Scale (K6), with a higher score indicating worse mental health. The scores are standardized to have a mean of zero and standard deviation of one. The critical value of the F-test from Stock and Yogo (2005) is 16.38. Permanent job is defined as working fulltime/parttime with permanent contract (the reference group is fixed-term contract and casual employment). Skilled occupation is defined as jobs with occupational scales above medium.

34
# 5. Conclusions

Most children in C6te d'Ivoire perform some form of work: work for wages, work on the farm or the household enterprise, or home care tasks. In urban areas, two out of every three children in the age group 7-17 years work and about half of them combine this with school attendance. In rural areas more than four out of every five children work, but only about a third of them manage to combine this with schooling. Full-time child work, which can be expected to have a major negative impact on the child's personal development, is less prevalent but not negligible. In urban areas, 7% of children work full-time, for an average of 46 hours per week. In rural areas, more than one-third of children work full-time for an average of 35 hours per week, with the highest incidence in the Savannah region. While the incidence of full-time child work rises with age, it is by no means limited to older children: the average age of the full-time child worker in C6te d'Ivoire is 12.7 years. The damage to the development of these children is made clear by the fact that they have received on average only 1.2 years of education, have a higher incidence of illness and injury, and are less likely to receive medical attention.

The figures cited pertain to 1988 and reflect a gradual increase of child labor over the decade of the 1980s which was characterized by a severe economic crisis in Cote d'Ivoire. Our results suggest that during this crisis, reduced labor 63
evolution of living standards in that village between the early 1940s and the late 1970s (Dandekar, 1986). This village, unlike Palanpur, was already closely integrated with the broader Indian economy even in the 1940s, and there is little evidence that a particular set of households in the village, defined in terms of caste or other social characteristics, has come to “stand apart” from the rest of the village over time. In this setting, the ELMO decomposition points in the same direction as the conventional inequality decomposition in suggesting that income inequality in Sugao is largely driven by individual-specific characteristics that are only weakly associated with caste or social-group characteristics.

Thus, this paper makes both a methodological and a substantive contribution to the literature on ethnic diversity. It illustrates the relevance of re-visiting standard methods of measuring group-based inequality and demonstrates that the ELMO statistic is better able to capture persistent inequalities where they are salient than standard measures, but is no different from standard measures when group-based inequality is not salient. The paper further illustrates this point in the important case of caste inequality in India by comparing repeated censuses of village-wide survey data, measured over several decades, in two Indian villages located in very different parts of the country.

# **2.  The Mathematics of Group-Based Inequality Decompositions**<sup>**iii**</sup>

In the standard approach to decomposing inequality by population subgroup, decomposable inequality measures can be written as follows:<sup>iv</sup> _I_  _I w_ (  )  _I B_ (  ) where _I w_ (  ) is a weighted average of inequality within population sub-groups, while<sup>_I_</sup> _B_ (  ) stands for between-group inequality and can be interpreted as the amount of inequality that would be found in the population if everyone were given the average income of their group.

7
and 54 percent of Vanuatu RSE households had only one seasonal worker spell during the two years of our study.

We then begin with panel data regressions of the impact of the RSE in each country, using the full sample of households from each country. Letting _Yi,t_ be an outcome of interest for household _i_ in survey round _t_ , we begin with the following difference-in-differences specification: Where _EverRSEi_ indicates whether household _i_ ever participates in the RSE over the four waves of our sample, and δt are survey round dummies. The coefficient of interest is then γ, which gives the average treatment effect of participating in the RSE. We do not include additional timevarying controls in this regression, since we have few time-varying variables that are not potentially themselves affected by the RSE. Standard errors are clustered at the household level to account for autocorrelation in the error term εi,t across survey waves. The same equation is also estimated using _RSEDurationi,t_ in place of _RSEi,t,_ , in which case γ gives the average household-level impact of one month’s duration in the RSE.

Difference-in-differences controls for any baseline level differences in the outcome _Yi,t_ at the group-level. An alternative approach is to control for baseline differences at the household-level through the addition of household-level fixed effects. We estimate this via the following specification: Where μi is the fixed effect for household _i_ . Again we estimate this using both our measures of household-level RSE participation, and cluster the standard errors at the household level.

The underlying assumption in both the difference-in-differences and the fixed effects specifications is that after controlling for level differences among households, they would have exhibited the same trends in the outcome variables in the absence of the RSE. However, this assumption is less credible when the households we are comparing have very different characteristics. We therefore follow the recommendations of Crump et al. (2009), who recommend estimating a propensity score, and then dropping observations with estimated propensity scores outside the range [0.1,0.9]. This ensures the regression is estimated only for the sample where the covariate distribution overlaps for the RSE and non-RSE households.

Our study includes many of the features identified as desirable for propensity-score matching (Dehejia, 2005). Our surveys of RSE and non-RSE households were conducted at the same time in the same villages (and hence local labor markets) using the same questionnaire. We have good knowledge of the characteristics villages and employers were looking for in selecting workers and can include these in the matching specification. In addition we have more than one period of 10
# **Methodology**

Due to resource limitations, this study is necessarily a rapid exploratory assessment. Six countries form a sample representative of different stages of housing sector and policy development (Armenia, Lithuania, Poland, Romania, Russia, and Serbia).<sup>5</sup> For this group the study is the first in-depth tenure structure analysis with disaggregate household data using the Living Standards Measurement Surveys (LSMS) and annual Household Budget Surveys (HBS). These data are enhanced by other evidence from national statistics, analysts’ reports and findings,<sup>6</sup> and interviews with experts and market participants in all six countries. The review of policies and best practice relies on interviews and selective analysis of legislative and regulatory texts. More in-depth analysis is both desirable and possible, but the form of analysis used in the study provides sufficient insight to support, or reject, the key hypothesis.

# **Organization of the Report**

Chapter 2 discusses conceptual considerations about what determines rental sector size in a market economy and the basic drivers of rental demand and supply found in the literature. It also briefly addresses the implications of the existence of rental sectors for economic efficiency, and rationales for policy interventions designed to support or create efficiency.<sup>7</sup> Chapter 3 provides a basis for supporting or rejecting the key hypothesis. It identifies supply and demand structures that correspond with the conceptual framework and main issues identified in Chapter 2.

Chapter 4 deals with actual tenure choice policies in the studied countries in three ways:

- _First_ , by analyzing existing investor types and the extent to which they pursue formal or informal market practices, and how their basic business models are structured.

- _Second_ , by identifying gaps in the legal and regulatory frameworks governing rental contracts and tenant-landlord relations, then analyzing the tax environment.

- _Third_ , by discussing the broader housing policy context of tenure choice policies in the studied countries, giving specific attention to the privatization, “communalization,” and restitution processes, as well as to the current strategies of retaining and redeveloping a social housing sector.

> 5 Countries represent various groups: Armenia, small post-Soviet countries; Lithuania, small EU accession countries; Poland, large EU accession countries; Romania, medium EU pre-accession countries; Russia, its own category; Serbia, postconflict southeastern Europe.

- 6 The LSMS and HBS are administered by the statistical agencies within each country with technical assistance from donor organizations. In Russia a one-time survey, the NOBUS, was used. This survey, administered by the statistical authorities, was intended to improve on the questionnaire design of the annual HBS.

- 7 For more detailed discussions of available theories and evidence see Annex 1.

3
# **Proof of Proposition (2)**

Given the assumptions that the poorest do not have any bargaining power and the richest can punish the teacher with certainty, it follows that there exists a threshold _y_<sup>_M_</sup> _< y,_ ̄ such that the following equality holds (assuming that the teacher maximizes expected income): It is easy to check that the expected income from bribery 1 _− δ_<sup>ˆ</sup> ( _y_ ) [ _B_<sup>_∗_</sup> ( _y_ ) + _w_ ] is a de{ } creasing function of income if the bargaining power effect of income is strong enough in following sense: Thus equation (5) and inequality (6) imply together that when the bargaining effect of income is strong enough to satisfy inequality (6), _∀ yi > y_<sup>_M_</sup> , the following inequality holds: When inequality (7) is satisfied, it is optimal for the teacher not to ask for bribes facing a household with income _yi > y_<sup>_M_</sup> .

# **Data Description: HIES 2010**

The HIES is considered to be a high quality household survey implemented by Bangladesh Bureau of Statistics (BBS) with assistance from the World Bank. The survey utilizes the same three stage stratified sampling strategy to select a nationally representative sample as the NHSC. The survey selected 612 PSUs randomly. From each PSU, 20 households were selected. The total sample size is 12,240 households. We follow the same strategy to select our sample as we did in the case of NHSC 2010. We dropped households living in metropolitan areas, and households who did not have school age children or who did not have children enrolled in school. Our final sample size is 7,031 households. The total household income in NHSC 2010 is Taka 12821 which is comparable to the household income in HIES 2010: Taka 13712.
WPS3945

# Migration, School Attainment and Child Labor: Evidence from Rural Pakistan

## Ghazala Mansuri*

### Abstract

Inequalities in access to education pose a significant barrier to development. It has been argued that this reflects, in part, borrowing constraints that inhibit private investment in human capital by the poor. One promise of the recent proposals to open international labor markets to allow for the temporary economic migration of low skilled workers from developing to developed countries is its potential impact on human capital accumulation by the poor. The large remittance flows from migrants to their communities of origin underscores this aspect of migration. However, migration can also transform expectations of future employment and induce changes in household structure that can exert an independent effect on the private returns to investment in human capital. The paper explores the relationship between temporary economic migration and investment in child schooling. A key challenge for the paper is to deal appropriately with selection into migration. We find that the potential positive effects of temporary economic migration on human capital accumulation are large. Moreover, the gains are much greater for girls, yielding a very substantial reduction in gender inequalities in access to education. Significantly, though, the gains appear to arise almost entirely from the greater resource flows to migrant households. We cannot detect any effect of future migration prospects on schooling decisions. More significantly, we do not find any protective effect of migration induced female headship on schooling outcomes for girls. Rather, female headship appears to protect boys at the cost of girls.

Keywords: Migration, Child Labor, Education, Gender Inequality

## World Bank Policy Research Working Paper 3945, June 2006

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> * Development Research Group, World Bank, 1818 H St. NW, Washington DC 20433. Email: gmansuri@worldbank.org.
25 Table 5. Distribution of Remittance-Receiving Households by Decile Group, Ranked by Predicted Per Capita Household Expenditure, Excluding Remittances

|**Rank**|**Households**<br>**receiving**<br>**internal**<br>**remittances**<br>**(from**<br>**Guatemala)**|**Internal**<br>**remittances as**<br>**percent of total**<br>**per capita**<br>**household**<br>**expenditure**<br>**(including**<br>**remittances)**|**Households**<br>**receiving**<br>**international**<br>**remittances**<br>**(from USA)**|**International**<br>**remittances as**<br>**percent of total**<br>**per capita**<br>**household**<br>**expenditure**<br>**(including**<br>**remittances)**|
|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|
|(decile)|(percent)|(percent)|(percent)|(percent)|
|Lowest 10|8.7|50.1|9.0|60.1|
|Second 10|2.1|8.9|2.7|34.5|
|Third 10|3.1|14.8|3.5|39.3|
|Fourth 10|4.7|12.3|6.0|24.6|
|Fifth 10|5.8|11.2|8.1|23.0|
|Sixth 10|10.1|8.1|10.0|24.1|
|Seventh 10|13.4|9.6|15.4|22.8|
|Eighth 10|18.2|13.0|15.7|21.3|
|Ninth 10|22.5|10.2|18.7|14.8|
|Top10|11.4|19.6|10.9|19.1|
||100.0||100.0||

Notes: Households ranked into decile groups on the basis of predicted per capita household expenditure (excluding remittances). For those households receiving internal remittances (from Guatemala), column (2) shows the percent of total per capita household expenditure (including remittances) coming from internal remittances. For those households receiving international remittances (from USA), column (4) shows the percent of total per capita household expenditure (including remittances) coming from international remittances. See equation (2) and text for predicted income equation.

Source: Calculated from Guatemala 2000 ENCOVI Survey.
27 central planning, the overall economic environment is rather uncertain and in permanent flux, and the external evaluation of enterprise restructuring plans is difficult and costly. Therefore many potential bank loans face a relatively high risk-return ratio<sup>31.If banks fully</sup> included the transaction costs, which result from extensive information screening, interest rates and therefore financial cost would become too high.<sup>In this environment many</sup> externally financed investment projects might become unprofitable and<sup>restructuring would</sup> not take place to a sufficient degree.

Another concern has been the considerable cross-ownership<sup>among banks and their</sup> affiliated investment funds<sup>32</sup> in the Czech and Slovak Republics. Many of the banks were partly privatized in the first wave of privatization with remaining shares still<sup>held by the</sup> state. Domestic bank sponsored funds often hold minority stakes<sup>in other banks and</sup> sometimes even stakes in its management company's mother bank (see<sup>Table 18). There is</sup> a high degree of concentration in banking in transitional economies leading<sup>to limited</sup> competition among banks. Thus. cross ownership among banks limits<sup>banking competition</sup> even more and should therefore be of concern for policy makers33

**Table 18: Cross ownership in the banking sector in the Czech**<sup>**and Slovak**</sup> **Republics**

||CS|IB|KB|CP|VUB|SIB 1/|SP 2/|
|---|---|---|---|---|---|---|---|
|Share privatized through vouchers|37|52|53|65|52|52|48|
|Share held by domestic bank sponsored funds|16|32|26|7|35|23|20|
|Share held by the affiliated fund|0|17|3|1|11|19|5|

1/ Slovak lnvesticni Banka (Slovak Investment Bank).

2/ Slovak Pojistovna (Slovak Insurance Company).

Note: For other bank names see Table 3. Source: Mejstrik (1994).

The role of financial intermediaries as corporate monitors is also influenced<sup>at a macro</sup> level. In other Central and Eastern European countries with high budget deficits<sup>banks</sup>

> 31 For a more detailed discussion of this issue see Dittus (1994).

- 32 On cross ownership in Czech privatization see also Coffee (1994, 22).

- 33 See footnote 15 and adjoining text.