rafmacalaba/datause-encoder-data huggingface dataset:
_Handbook_ , and to write essays about how they can protect themselves from HIV/AIDS; and 3) reducing the cost of education to keep children in school longer. To shed light on the role of risk reduction, we also compare our results to those of Dupas (2005) who conducted, in the same schools, a randomized evaluation of the impact of informing teenagers about variation in HIV rates by age and sex.

The study involved 70,000 students from 328 primary schools. Our primary measure of the effectiveness of these interventions is teenage childbearing, which is associated with unprotected sex, the main driver of HIV/AIDS in this population. We also collected measures of knowledge, attitude, and behavior regarding HIV/AIDS. We find that after two years the teacher training program had little impact on students’ knowledge and self-reported sexual activity and condom use, or on teen childbearing. However, it increased students’ tolerance toward people with HIV/AIDS and girls exposed to the program were more likely to be married to the fathers of their children. Its overall impact on exposure to the HIV risk is unclear. Debates among peers on the role of condoms and an associated essay competition on ways students could protect themselves against AIDS increased self-reported use of condoms, without increasing self-reported sexual activity. (The condom debate and essay competition took place too recently for us to be able to detect any possible reduction in childbearing.) Dupas (2005) reports that informing girls about variation in HIV rates by age and sex led girls to avoid crossgenerational partnerships, which are particularly risky. Reducing the cost of education by paying for school uniforms reduced dropout rates and the incidence of teen childbearing.

The remainder of the paper proceeds as follows. Section 2 provides background information on the Kenyan context. Section 3 describes the programs. Section 4 4
WPS4143

# **Do remittances have a flip side? A general equilibrium analysis of remittances, labor supply responses, and policy options for Jamaica**

## **Maurizio Bussolo and Denis Medvedev**<sup>*****</sup>

Development Prospects Group, The World Bank, 1818 H St NW, Washington, DC 20433

### **Abstract:**

Econometric analysis has established a negative relationship between labor supply and remittances in Jamaica. We incorporate this ex-post evidence in a general equilibrium model to investigate economy-wide effects of increased remittance inflows. In this model, remittances reduce labor force participation by increasing the reservation wages of recipients. This exacerbates the real exchange rate appreciation, hurting Jamaica’s export base and small manufacturing import-competing sector. Within the narrow margins of maneuver of a highly indebted government, we show that a revenue-neutral policy response of a simultaneous reduction in payroll taxes and increase in sales taxes can effectively counteract these potentially negative effects of remittances.

**Keywords:** Jamaica, Remittances, Labor supply, Tax policy, General equilibrium

**JEL classification:** D58, F24, J22, H24, H31

### World Bank Policy Research Working Paper 4143, March 2007

_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._

> * Corresponding author. Tel: 202-210-3520, Fax: 202-522-2578, Email: dmedvedev@worldbank.org

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different for these two countries. In the US the poor and vulnerable categories expand in each period (except for the period 2004-2006 where the vulnerable category remains unchanged) while the middle class shrinks. In contrast, the opposite happens in Vietnam.

Could the larger changes with the vulnerability index in Vietnam be driven by the fact that the vulnerability line is fixed in one period and simply adjusted for other periods? We address this question by assuming that the rate of change of vulnerability is equal for both periods 20042006 and 2004-2008 in this country, and then re-estimate the consumption dynamics using the new vulnerability lines estimated above. Estimation results (shown in Table 2.3, Appendix 2) are qualitatively similar and indicate that the poor and vulnerable categories shrink in both periods while the middle class expands. However, the changes in the vulnerability lines unsurprisingly result in different income categories accounting for different proportions of the population.

# **3.2. Synthetic Panel Data**

As noted above, we do not have panel data to develop estimates of the vulnerability line in India. In order to implement the procedure described in the preceding sections, it is necessary to first convert the series of cross-section datasets available for India into synthetic panel data. We do so based on an approach outlined in Dang, Lanjouw, Luoto and McKenzie (2014) (DLLM) and Dang and Lanjouw (2013).

# **3.2.1. Overview of Framework**

We start with a brief summary of the framework that can be used to construct synthetic panel data from two rounds of cross sectional data (See the above cited studies for more details).

Let xij be a vector of household characteristics observed in survey round _j_ (j= 1 or 2) that are also observed in the other survey round for household i, i= 1,..., N. These household characteristics can include such time-invariant variables as ethnicity, religion, language, place of 23
Educational Profiles by Gender **and** Residence Table 2 presents the educational profiles of the sample by gender and type of residence. There are clearly large differences in levels of educational attainment between males and females, and between those residing in Lima, other urban and rural areas. Females, on average, have completed only 5.5 years of schooling compared to 7 years for males. While only 8 percent of the males never attended school, 25 percent of the females never enrolled; and, only 28 and 12 percent of the females had some secondary and higher education, respectively, compared to 35 and 18 percent of the males. These results are very similar to the 1981 census results (Appendix Table A.1). In comparison to the census data, average years of schooling for males and females have risen slightly since 1981; a smaller proportion report primary schooling as their terminal level since a slightly larger proportion have continued on to higher education.

Table 2 Educational Attainumet of Males and Females, Residents in Lima, Other Urban, and Rural Areas

|Educational attainment|Females|<br>Males|Lima|Other<br>Urban|Rural|
|---|---|---|---|---|---|
|Average years of schooling|5.5|7.1|8.8|7.5|3.2|
|(standard deviation)|(4.6)|(4.4)|(3.9)|<br>(4.3)|(3.5)|
|Highest level of schooling<br>attended (percent of sample)||||||
|Never attended|24.3|7.7|3.2|7.3|33.2|
|Primary|35.9|39.4|24.0|34.9|50.2|
|Secondary|28.2|35.3|48.9|37.4|13.8|
|Post-secondary|11.7|17.7|23.9|20.4|2.9|

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# **<u>Introduction</u>**

This paper provides new evidence on the evolution of global interpersonal income inequality between 1988 and 2008. This inequality concept measures inequality amongst all individuals in the world irrespective of their country of residency thus implicitly assuming a “cosmopolitan” social welfare function and translating the concern for within-country inequality to the global level. Over the period 1988-2008, the face of globalisation changed dramatically with the integration of many developing countries into the world economy. Global interpersonal inequality captures the effects of these shifts on both within- and between-country inequality.

We find that the inequality in the global income distribution, as measured for example by a Gini index, does not change very much over this period. However, this hides substantial re-ranking of country-deciles and changes in the regional composition of different parts of the global distribution. We first present new evidence on the evolution of global interpersonal inequality. We then dig deeper and analyse changes in the composition of the global distribution of income.

Measuring global inequality empirically is substantially more difficult compared with withincountry inequality. In the absence of a global household survey, we need to resort to combining national surveys. Our database includes 565 household surveys across five benchmark years and each country-year observation is represented by the average income of ten income decile groups.<sup>2</sup> National surveys collect information in terms of local currencies, so we need to convert these into a common currency preferably adjusting for differences in the price level (across countries _and_ over time).<sup>3</sup> We should point out that in constructing our global distribution we mix income and consumption surveys. We refer to them interchangeably, as is customary in this literature, although we are obviously fully aware of the important differences between the two concepts.<sup>4</sup>

> 2 Each decile is weighted by its population, so we measure interpersonal global income inequality where each individual is assigned the income of his or her income decile.

> 3 Analyses of national income inequality are often done on nominal incomes, thus assuming a common national price level. We follow these national studies by ignoring differences in the price level within countries (except for the case of China, India and Indonesia where we allow for rural-urban price differentials).

> 4 However, we improve on earlier approaches by keeping the type of survey (income or consumption) constant over time for a particular country.

2
Panel B, which focuses on STIs, shows a similar pattern with even larger coe¢ cients, though the coe¢ cients do not reach statistical signi...cance due to the rarity of STIs. In a larger sample, we expect it would be possible to ...nd statistically signi...cant e¤ects. In the context of this paper, we consider the magnitude of these ...gures generally supportive of the ...gures for health shocks.

Taken together, Tables 6 and 7 suggest that the supply of sex (including unprotected sex) is very sensitive to even small, short-term shocks, likely due to the fact that women do not have access to more e¤ective consumption smoothing mechanisms.

# 5.3 Robustness Checks

The results of the previous section suggest that sex workers accept a signi...cant amount of health risk to deal with short-term illness shocks, which implies that other consumption smoothing techniques have failed. In this section, we provide several robustness checks to test more closely whether our results appear consistent with a consumption smoothing explanation for the labor supply response. Ideally we would be able to check whether responses are smaller for women that have access to formal consumption smoothing mechanisms such as individual savings accounts in a bank, but the vast majority of women in this sample are unbanked and only have access to group savings accounts that heavily restrict their withdrawals. For this reason, we are unable to compare women with varying levels of access to smoothing mechanisms. Instead, we examine how the results vary by income level and by perceived HIV status.

## 5.3.1 Results by Income Level

If women supply more risky sex as a consumption smoothing mechanism, it should be the poorest women (those that are least able to a¤ord medicine) who are most likely to use unprotected sex as a risk-coping technique. There exists signi...cant heterogeneity in sex worker earnings: over the sample period, a woman at the 25th percentile of the sex worker income distribution earned 520 Ksh (US $7.43) per day, while a woman at the 75th percentile earned 1,089 Ksh (US $15.56) per day.

To explore whether it is poorer women that are most sensitive to the shocks, we construct 21
# **The Performance of Decentralized School Systems: Evidence from Fe y Alegría in Venezuela**<sup>**1**</sup>

Hunt Allcott Daniel E. Ortega Harvard University, Cambridge, MA Instituto de Estudios Superiores de allcott@fas.harvard.edu Administración (IESA) and Corporación Andina de Fomento, Caracas, Venezuela. daniel.ortega@iesa.edu.ve JEL Classification: I2, H4 Keywords: Schooling Quality, Private Education, Fe y Alegría.

> 1 The authors thank Rosa Amelia Gonzalez of IESA and administrators at Fe y Alegría, especially Noelbis Aguilar, for their invaluable support during this project. We also thank Pablo Acosta, Guido Imbens, Elizabeth King, Harry Patrinos, Miguel Urquiola, participants in the 2006 meeting of LACEA in Mexico City and participants in the PPP in Education Conference at the World Bank for valuable comments and suggestions. The paper also benefited from comments from Quentin Wodon at the World Bank’s Development Dialogue on Values and Ethics. Hunt Allcott acknowledges financial support from the Ochoa Brillembourg Fellowship and from the Andean Development Corporation (CAF). Any remaining errors are because neither of the authors attended a Fe y Alegría school, and remain our own.
16 each individual element _Cj_ of the vector of circumstances, are computed using the regression coefficients from (10) and are defined as:<sup>20</sup> Inspection of (12) immediately reveals that, for any given partition, these partial shares sum up to the overall parametric estimate of between-group inequality, given by (11). Besides this attractive additive decomposability property, this definition of circumstancespecific shares also satisfies a path-independence property. Although we have already noted that the overall _non-parametric_ decomposition (9) is path-independent by construction, _parametric_ estimation of the _partial shares_ – based respectively on the smoothed and standardized distributions – are not the same.<sup>21</sup> However, as we show in the Appendix, equation (12) is the simple average between the direct and residual estimates of the partial shares, which correspond to the smoothed and standardized distributions, respectively. It is therefore a simple example of a Shapley decomposition, where averaging across alternative paths eliminates path-dependence. See Shorrocks (1999), and Foster and Shneyerov (2000).

Our second proposed advantage variable, namely imputed consumption, does not share the distributional peculiarities of the asset index. Imputed consumption _ci_ takes only positive values, so that equation (5) can be applied directly. The main advantage of using the mean log deviation (rather than the variance) in this case, is that the distributions of imputed consumption do not have mean zero by construction, so that mean- or scaleindependence becomes, once again, a desirable property for _I()._ Moreover, unlike the variance, the mean log deviation also satisfies the principle of decreasing transfers, a

> 20 Note that the estimates of the partial shares rely on the validity of the specific reduced-form coefficients _ψ_ . They are not, therefore, lower-bound estimates like the measures in (9) or (11). They are meaningful estimates of the contribution of a particular circumstance to inequality of opportunities only under the much stronger assumption that those coefficients are unbiased, i.e. that any circumstance variables omitted from

> the reduced-form regression _y_ = _C_ ψ + ε are orthogonal to **_C_** . While we report some of the partial shares given by (12) in Section 5, we do not insist much on them, given this strong caveat.

> 21 Just as a smoothed distribution is obtained from a vector _y_ and a partition _Π_ by replacing every<sup>_y_</sup> _ik_<sup>with</sup> the type-specific mean, μ _k_ ( _y_ ) , a standardized distribution is obtained by multiplying every<sup>_y_</sup> _ik_<sup>by</sup> μ μ _k_ ( _y_ ) .
<!-- Start of picture text --> .12<br>.1<br>.08<br>.06<br>MFI1<br>.04 PRIME<br>MFI2<br>.02<br>0 0-5 5-10 10-15 15-20 20-25 25-50 50+<br>Landholding (decimals)<br>MFI1: MFI in PRIME area MFI2: MFI in non-PRIME area<br><!-- End of picture text -->

**Figure 5: Distribution of total participants by landholdings**

Is PRIME also a well-designed program to reach the seasonally poor who suffer from occasional starvation or experience meal rationing during the lean season? Since the baseline information on seasonal food deprivation was collected before PRIME was introduced, we can examine how households varied in program participation by their priorprogram participation status of food deprivation. Observation of household participation in PRIME and MFIs by meal consumption patterns during _monga_ period shows that, regardless of its meal-consumption pattern, a household is twice as likely to join PRIME than a regular microfinance program, which may be attributable to PRIME’s focus on addressing immediate household needs during the lean season (Figure 6).<sup>20</sup>

> 20 Households that were MFI members before the PRIME intervention (about 15 percent of panel households) were excluded from this figure in order to capture the behavior of new participants.

25
estimates obtained for consumption or income. For example, while one–week recall for food consumption is common in surveys, there are some countries that use a longer period, which is likely to give a lower estimate of consumption and hence higher measured poverty.

We use standard additively separable poverty measures for which the aggregate measure is the (population-weighted) sum of individual measures. In this paper we report two such poverty measures. The first measure is the headcount index given by the percentage of population living in households with consumption or income per person below the poverty line. We also give estimates of the number of poor, as obtained by applying the estimated headcount index to the population of each region (under the assumption that the countries without surveys are a random sub-sample of the region). Results are also available from the authors for the poverty gap index,<sup>7</sup> although the basic patterns reported here are similar.

We take it as axiomatic that simply moving individuals between urban and rural areas (or countries), with no absolute loss in their real consumption, cannot increase the aggregate measure of poverty. Relocation on its own cannot change aggregate poverty. This assumption, in combination with the additivity assumption, implies that we are justified in confining our attention to absolute poverty measures, by which we mean that the poverty line is intended to have a constant real value both between countries and between urban and rural areas within countries.

This does not allow for any welfare effects of relative deprivation in better off countries or urban areas. One cannot rule out welfare costs of relative deprivation, although it is unclear just how important an issue this is likely to be in poor countries. The fact that the poverty lines found across countries tend to have little or no income gradient among poor countries (though a much steeper gradient among developed countries) suggests that relative deprivation is not such an issue for measuring poverty in this setting (Ravallion, 1998). Ravallion and Lokshin (2005) present evidence for one of the poorest countries, Malawi, suggesting that the poorest people

> 7 The poverty gap index is the mean distance below the poverty line as a proportion of the line where the mean is taken over the whole population, counting the non-poor as having zero poverty gaps.

5
Second, by “enjoying” more services, the uninsured at the same time have to pay more out-ofpocket, perhaps not at their initiative. Further inquiry into this issue is warranted to assess the welfare impact of capitation in reducing services among the insured while increasing services among the uninsured.

One might argue that our study is limited by the shortcomings of the hospital data set (the multiple missing observations for several outcomes, if systematic, could bias our results one way or another) and by the lack of information on several important outcomes. However, we see no compelling reason for the missing cases to be systematic. We do not have direct data on the number of uninsured and insured patients seeking care in hospitals and have to estimate these from the Living Standards Surveys; nevertheless, our results are very robust to various analytical samples and methods used.

Our study contributes to the scant literature on the impact of capitation provider payment in developing countries. Despite its non-standard design and the fact that providers do not bear the full financial risk, the Vietnam case again exemplifies the text book effects of capitation. This suggests that providers are sensitive even to a small level of incentives. Moreover, our study clearly points to the need to anticipate intended and unintended effects of any payment policy and the trade-offs among policy objectives. For all the optimism over capitation as a new payment method in many countries, perhaps a more sophisticated, blended capitation could be used, which employs performance based incentives and disincentives for quality as well as important health indicators (for example, see the Turkish model in World Bank 2013 ). In any case, the importance of collecting relevant data and closely monitoring the policy impact cannot be more strongly emphasized.

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# **7.1. Results: Girls’ Enrollment**

In Table 3 I recreate the estimates presented in Chaudhury and Parajuli (2006; 2008): a first difference approach that uses only two years of data – 2003 and 2005. This serves two purposes: (1) to ensure that the data released by the government to both sets of authors is the same and that comparable decisions were taken in cleaning and setting up the data for analysis and (2) it highlights the fact that using boys’ enrollment as a counterfactual is an unwise choice in the context of this gender-targeted program.

In particular, note that the estimates in Table 3 suggest that the average school in the treated district experiences an increase of 11 girl students in classes 6 to 8 when compared to the average school in the control district. Similarly the average school in the treated district experiences an increase of 10 boy students in classes 6 to 8 when compared to the average school in the control district. From Table 2, in 2003, a typical school in the treated district has roughly 42 girl students in classes 6 to 8 and roughly 77 boy students. Thus the point estimates from first differencing (without controls) suggest a 26 percent increase in girls’ enrollment and a 13 percent increase in boys’ enrollment.<sup>8</sup> Given this evidence that is suggestive of possible spillover effects, I choose not to perform a so-called triple difference analysis with boys as a counterfactual.<sup>9</sup> Also the choice of an aggregate dependent variable masks potential variation in outcomes that may exist by class. Consequently in Table 4, I examine the enrollment of girls in class 5.

This is not an eligible grade under the program. However, if households anticipate that the

> 8 The first percentage is calculated as ����� ~~�~~ �100 and the second is calculated as ����� ~~�~~ �100

> 9 Strictly speaking I choose not to use the difference in the evolution of boys’ enrollment in the treatment district compared to that in the control districts.

12