rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 5966

# **Abstract**

Swaziland and Lesotho have the highest HIV prevalence in the world. They also share another distinct feature: during the last century, they sent a large numbers of migrant workers to South African mines. This paper examines whether participation in mining in a bordering country affects HIV infection rate. A job in the mines means leaving for long periods away from their families and living in an area with an active sex industry. This creates potential incentives for multiple, concurrent partnerships. Using Demographic and Health Surveys, the analysis shows that migrant miners ages 30-44 are 15 percentage points more likely to be HIV positive, and women whose partner is a migrant miner are 8 percentage points more likely to become infected. The study also shows that miners are less likely to abstain or use condoms, and female partners of miners are more likely to engage in extramarital sex. The authors interpret these results as suggesting that miners’ migration into South Africa has increased the spread of HIV/ AIDS in their countries of origin. Consistent with this interpretation, the association between HIV infection and being a miner or a miner’s wife are not statistically significant in Zimbabwe, a country where the mining industry is local and does not involve migrating to South Africa.

This paper is a product of the Human Development and Public Services Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at l.corno@ucl.ac.uk and ddewalque@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
18 As to structural reforms, changes in relative prices that remove the biases against labor should help reduce poverty in the long run. Devaluation of the exchange rate will help the poor if the) -roduce tradable goods and will hurt them if they consume tradable goods, such as imported necessities. Removal of the ceilings on agricultural prices will benefit the rural poort who are net producers of food, but hurt the urban poor, who are net consumers of food.

It is difficult to assess the effects of adjustment programs on the poor for three reasons. First, it is inherently difficult to establish causality -- to isolate the effects of adjustment programs from other factors -- and particularly to determine whether alternative policies would have done better or worse. Second, socioeconomic data on the living conditions of the poor are scarce and often of dubious quality. Although many of the poor work in the informal sector, data on the output of that sector and on other variables are usually not included in official statistics. Third, adjustment programs are relatively new, and their long-run positive effects probably take longer than the experience with adjustment so far.

At the same time, while a complete analysis of adjustment programs must await the conclusion of the entire adjustment period, interim evaluations such as those here are still necessary.' In general, the limited cross-sectional data on changes in poverty do not suggest that adjustment lending has on average increased poverty. Furthermore, aggregate data support similar conclusions: I The central topic of World Development Report 1990, World Bank (1990) is poverty. This report includes additional analyses of the impact of adjustment programs on poverty.
initiated by the United Nations led to the preparation of guidelines for refugee and IDP statistics including poverty statistics. The United Nations Statistical Commission (UNSC) established in 2016 an international Expert Group on Refugee and Internally Displaced Persons Statistics (EGRIS) made of national and international organizations and several expert statisticians. This process has produced two main documents: The International Recommendations on Refugee Statistics (EGRIS, 2018), and the International Recommendations on IDP Statistics (EGRIS, 2020).<sup>13</sup> The first document recommends measuring the proportion of population below the international poverty line among the social inclusion indicators (indicator 1.1.1), and the second document recommends measuring the proportion of population living below the national poverty line among the livelihoods and economic self-reliance indicators (indicator 1.2.1). While not specifically focused on poverty, these documents recognize the importance of measuring poverty among FDPs and provide general indications on the indexes to measure and the data necessary to measure such indexes. In 2022, monetary poverty was identified as one of the 12 priority SDG indicators that should be measured and disaggregated for both refugees and IDPs (UNHCR, 2020), with the recommendation being picked up by the IAEG-SDG (2019). As already mentioned, the UNHCR has started to introduce standards for the measurement of well-being of FDPs while the new World Bank-UNHCR Joint Data Center established in Copenhagen is expected to develop and fund further methodological advancements as well as playing a pivotal role in the production of poverty data for displaced populations especially through National Statistics Offices. Also other organizations such as WFP and IOM are developing their own standards for surveying FDPs and measuring various well-being indicators.

# Conclusion

The paper provided a first insight into the state of the literature on the measurement of poverty among FDPs. We argued that the economics profession and poverty specialists across the social sciences largely neglected these populations for a combination of factors including lack of interest and microdata. This changed with the beginning of the Syrian conflict in 2011 and the peak of the European migration crisis in 2015. These events have generated partnerships between development and humanitarian organizations that contributed to boost microdata collection among FDPs, poverty and welfare studies worldwide, and is also sparking a virtuous cycle of poverty measurement innovations. As FDPs require special solutions to questions such as sampling, consumption measurement, and targeting, this search is also producing innovative approaches that can serve the poverty measurement community at large.

> 13 Since 2022, the group is renamed the Expert Group on Refugee, IDP and Statelessness Statistics (EGRISS).

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Angeles et. al. (2011) evaluated the government of Mexico’s Program of Childcare Centers ( _Programa de Estancias Infantilies_ - PEI) by contrasting employment outcomes of women whose children got a spot to women whose children remained on the waiting list. As the treatment was not randomized, they did extensive comparisons of characteristics of women on the waitlist to mothers who were in the program. They found a few statistically significant differences, but not ones they considered to be economically significant, with the exception that mothers on the waitlist lived in households with a male head at a higher percentage (78%) than mothers with childcare (70%). This could imply that women without as much male income in the household had a greater need to work, somewhat impacting their results. However, the magnitudes of the results are larger than this difference between households, suggesting that this bias is not driving the findings. Results indicate that the childcare program increases employment by 18%. Angeles et. al. (2011) also found improvements in work stability and an increase in working hours. Benefits were greatest to women who had not worked prior to the childcare program.

An alternative strategy is to compare mothers to themselves at multiple time periods: when they were on the waitlist and when their children were in care. The individual fixed effect approach reduces selection bias because the mothers’ unobservable characteristics are the same in both cases, as long as those characteristics are time invariant. Ranganathan and Pedulla (2021) used this approach to study how mothers of young children who work in an Indian garment factory respond to availability of factory-provided childcare.<sup>13</sup> Ranganathan and Pedulla (2021) take advantage of one factory’s limited capacity to contrast women’s work attendance before and after their children were accommodated in the childcare facility. When women receive access to employer-sponsored childcare, their odds of being present at work are 1.71 times higher than when they do not have access to such childcare provisions. However, this identification strategy relies on the assumption that workers cannot anticipate when the childcare slot becomes available. Ranganathan and Pedulla (2021) explain that it is unlikely that mothers would know when vacancies would arise due the large size of the factory and because they cannot observe the satisfaction of other workers with the program.

Although Ranganathan and Pedulla’s (2021) outcome in their Indian study is not directly comparable since they measure days missed at work rather than focusing on a binary employment outcome, the qualitative conclusion remains that childcare provision supports more regular work. Similar to the Brazilian and Chilean RCT studies (Barros et al. 2011; Martinez A. and Perticara 2017), the estimates of these quasi-experimental studies are only for women who have actively sought out child care services.

> 13 The Indian government mandates employer-provided childcare through the Factories Act of 1948.

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For better estimation results, it may also be useful to transform (some) variables in both surveys to normality before standardizing them. We come back to discuss this more in the next section.

# **IV.2. Updating Poverty Estimates with Different Survey Sources IV.2.1. Data Description for the LFS**

The Employment Unemployment Survey (LFS) is the official source of employment and unemployment data in Jordan. While it shares certain similarities with the HEIS such as a twostage cluster stratified sampling design and a common sample frame based on the Population and Housing Census of 2004, its design is different. In particular, between 660 and 680 PSUs (depending on the year) were selected in the first stage out of a total of 1,336 PSUs for the whole country, and within each selected PSU, 10 households were randomly selected at the second stage. Twelve governorates are divided into 24 rural and urban strata and the six major cities across the country with more than 100,000 people are strata on their own, which together form 30 strata in total. The LFS collects data on employment status, occupation, and economic activities for between 11,000 and 12,500 households on a quarterly basis, and these data are representative of the population for each quarter. The LFS questionnaire practically remains the same during the period under study. We analyze all 24 quarterly rounds of the LFSs from 2008 to 2013 in this paper.<sup>25</sup> The LFS does not collect data on assets but collects demographic and employment variables, as does the HEIS (those variables are used in Model 3, Table 2). The LFS also collects data on wage income in the past month for each worker, which is categorized in five income groups: less than 100 JD, 100 to 199 JD, 200 to 299 JD, 300 to 499 JD, and 500 JD or more. Since a considerable number (around 38 percent) of household heads did not work and thus had no

> 25 Half the sample households in the LFS are designed to be renewed across two consecutive years and for two straight quarters within a year. However, DOS does not maintain any identifying information that allows the construction of panel households or individuals over time, and the data provided to us have no non-Jordanians in three quarters in 2011 and 2012.  For these reasons, we analyze each quarter of the LFS separately, and average four quarters within each year to obtain the yearly estimates later.

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dening the circumstances is not a clean measure of eort as it partly reects the eects of family background.<sup>14</sup> As measure of eort, they use the sterilized residual from the regression of the residual squared (the residual from the earlier stage) on circumstances.

There is a small literature on intergenerational mobility that exploits the information in conditional variance using quantile regressions. See, for example, Grawe (2004) on the United States and Kishan (2018) on India. The focus in this approach on estimating dierent conditional mean functions corresponding to the quantiles of children's education. Grawe (2004) provides an interesting analysis of the pitfalls in relying on functional form of the CEF to learn about credit constraints in the context of income mobility. His analysis suggests that a quantile regression approach can be useful in understanding the existence of credit constraints.

# (3) Data and Variables

We use the following household surveys for our empirical analysis: China Family Panel Studies (CFPS) for China, India Human Development Survey (IHDS) for India, and Indonesia Family Life Survey (IFLS) for Indonesia. These data sets are suitable for our analysis because they do not suer from any signicant sample truncation arising from coresidency restrictions commonly used to dene household membership in a survey. A truncated sample is likely to underestimate the conditional variance, for example when the data miss observations on highly educated children who left the natal house for college.

The data for China come from the China Family Panel Study (CFPS) 2010 wave, which has a unique T-Table design that presents a complete family network, in which household members' education information is also available. For more detailed discussions about the unique advantage of CFPS in analyzing intergenerational mobility related questions, please see Fan, Yi ang Zhang (2021) and Emran, Jiang, Shilpi (2020). The data for India come from the India Human Development Survey (IHDS) 2012 wave. We follow Emran, Jiang, Shilpi (2021) closely, which updates and expands the sample of father-child pairs for years of schooling in India in two major ways compared to the earlier studies such as in Azam

> 14The circumstances usually include parent's education, occupation, race, ethnicity, geographic location, and gender.

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predictions are then generated by taking the population-weighted mean of the AGEB-level predictions.<sup>1</sup> This approach leverages AGEB-level data, which leads to more accurate and efficient estimates than area-level models specified at the municipal level and requires minor modifications of publicly available software to generate point estimates and confidence intervals.<sup>2</sup> Six main findings emerge:

1. Combining survey and geospatial data substantially improves the precision and accuracy of state-level estimates of both labor force participation (LFP) and unemployment rates. Estimated mean absolute deviation falls by approximately 62 and 50 percent for female and male participation rates, respectively, and by approximately 52 and 25 percent for female and male unemployment rates.

2. When considering municipal estimates of labor force participation rates, incorporating geospatial data significantly improves accuracy and greatly improves precision. In estimates from repeated simulations, estimated mean absolute deviation falls by about 43 percent for women and 53 percent for men, and rank correlation increases by 0.13 for women and 0.11 for men. After adjusting direct survey estimates to equalize coverage rates, estimated mean squared error for male and female LFP falls by a factor of more than four.

3. Because unemployment rates are very low, municipal estimates of unemployment rates based on a linear model are not reliable. Although estimated mean absolute deviation falls for both man and women, relative bias is high, rank correlation falls significantly for women and the estimates exhibit little variation.

4. Accuracy is substantially lower in out-of-sample municipalities than sampled municipalities.

5. When using a sample that simulates fully enumerating sampled AGEBs, small area estimates offer very minor improvements on direct survey estimates.

6. A model specified at the AGEB level generates more accurate estimates than one specified at the municipal level. The improvement due to using AGEB rather than municipal variation is much larger for male LFP, which is harder to predict, than for female LFP.

In short, the results support the use of augmenting survey data with geospatial data when estimating state-level statistics and when estimating municipality-level female and male labor force participation rates. When estimating municipal unemployment rates, however, the estimates are not reliable because the sample contains insufficient information to train a linear model. In particular, the sample contains no unemployed men in 44 percent of municipalities and no employed women in 68 percent of municipalities. Modeling unemployment rates using a linear model is problematic in this setting, highlighting the usefulness of software that can conveniently estimate and apply a wider range of non-linear empirical best predictor models.

> 1 While this is in spirit similar to the sub-area model proposed in Torabi and Rao (2018), the approach outlined in that paper is difficult to implement using publicly available software. 2 Code is available from the authors upon request.

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**Table 3: Principal components analysis (PCA)**

_1_<sup>_st_</sup> _<u>factor loadings of household assets</u>_

|**Asset**|**Nigeria**|**Mali**|**India**|**Pakistan**|**Tajikistan**|**Indonesia**|**Yemen**|**Colombia**|
|---|---|---|---|---|---|---|---|---|
||2018|2009|2011|2018|2015|2017|2014|2017|
|Has access to<br>electricity|0.77||0.76||0.19|0.36|||
|Has good floor<br>material|0.53||||0.31|0.47|0.60||
|Owns air conditional|0.42||0.50||0.50|0.62|||
|Owns bicycle|-0.02||||||0.23||
|Owns car|0.51||||0.49|0.65|0.44||
|Owns computer|0.45|0.77|0.38|0.80|0.59|0.68|0.56|0.84|
|Owns cell phone|0.42|0.41|0.54|0.21|0.22||0.40|0.40|
|Owns radio|0.20||0.01||0.04||0.09||
|Owns sewing machine|0.20||||0.46||||
|Owns stove|0.65||0.71||0.46||0.47||
|Owns television|0.82||||0.27||0.63||
|Owns washing<br>machine|0.38||||||0.79||
|Owns fan|0.81||0.82||||0.47||
|Owns refrigerator|0.66||||0.67|0.66|0.80||
|Owns boat||||||-0.16|0.03||
|Owns landline||0.78||0.76|0.24||0.61|0.81|
|Owns motorcycle|||||0.02|0.47|0.10||
|Has flushed toilet|||||||0.48||
|Owns land||||||0.18|||
|Has good roofing<br>material||||||0.31|0.49||
|Owns electric water<br>pump<br>Source:Authors’ calculatio|ns from the|Global M|onitorin|g Database (|GMD)||0.54||

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD)</u> <u>Notes: This table shows the first factor loadings from principal components analysis (PCA) done with different assets</u> and household infrastructure and amenities. These factor loadings are the weights used in creating an _asset index_ variable for each country. Empty cells indicate missing data. Mali, Pakistan, and Colombia have data on only three assets. In these cases, _asset ownership_ variable is instead created as a binary variable of owning at least a computer or landline phone. The survey period spans two consecutive calendar years in Nigeria (2018.75), Mali (2009.89), India (2011.5), and Pakistan (2018.5). By convention, the decimal indicates the share of the survey conducted in the second year. For example, 75% of the Nigeria survey was conducted in 2019. The floor of the survey period is indicated in the table.

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this problem we estimate the model in two stages. In the first stage we employ dynamic panel data estimation techniques (Arellano and Bover 1995, Blundell and Bond 1998) to instrument for the capital stock using lagged input prices and output as instruments. In the second stage we estimate the model using instrumented capital stock in lieu of actual capital stock.

Third, the system of equations to be estimated is a non-linear problem, where the parameters _φ_<sup>_j_</sup> and _ζ_ should be evaluated at each data point across the timeseries dimension. This makes traditional estimation approaches, such as e.g., iterated feasible generalized non-linear least squares (IFGNLS), non-applicable as they exhaust the degrees of freedom in panels with short individual dimension _N_ . Instead, following Popp (2001) and Sue Wing (2008), we treat the system of equations as a _conditionally linear_ problem. Conditional on realization of unknown parameters _φ_<sup>_j_</sup> and _ζ,_ the system of equations becomes a seemingly unrelated regression, which is efficiently estimated by full information maximum likelihood (FIML).

The values of parameters _ζ_ and _φ_<sup>_j_</sup> are chosen to maximize the value of the model’s goodness-of-fit criterion, and are obtained by a multidimensional stochastic grid search (Judd 1998, p. 296). To minimize the computational burden of a multidimensional grid search, based on earlier empirical findings (e.g., Jorgenson and Fraumeni 1981, Baltagi and Griffin 1988, Newell et al. 1999, Sue Wing 2008, Webster et al. 2008) we set the estimation bounds for the exogenous technological change parameter, _ζ,_ to vary between -0.03 and 0.05, and the elasticity of input efficiency of capital stock with respect to input price changes, _φ_<sup>_j_</sup> _,_ between 0 and 1.5.<sup>14</sup> Because the cost share equations in the system of equations (14) add to one, only two share equations are estimated.

While the system of equations (3), (7), (14), and (15) forms our basic empirical model we also estimate a restricted model, assuming that the input efficiency of capital stock does not change, so _γi,t_<sup>_j_issetto1(orboth</sup><sup>_ζ_and</sup><sup>_φj_areset</sup> to zero). Under this restriction the model becomes the dynamic factor demand model of Pindyck and Rotemberg (1983). We then use the likelihood-ratio test to evaluate the significance of the input efficiencies of capital stock in the models of energy demand.

To quantify the operational response to current price changes holding all previous prices constant, we compute own-price and cross-price elasticities of

> 14We have examined the model sensitivity to relaxing the range of estimation bounds, and the results turned out to be robust (no corner solutions).

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# **Table 10.  Gross savings rate and uninsurable idiosyncratic risk**

In this table are reported the results of a fixed effects, panel estimation, of gross savings rate over risk sharing coefficients (under alternative definitions). Fixed <u>effects are all significant, but not reported in the table.</u>

|||Gross savi|ngs rate (% GD|P)|
|---|---|---|---|---|
|_REC_<br>−<br>1<br>β|0.610<br>[0.01]||||
|_ROL_<br>−<br>1<br>β||0.200<br>[0.12]|||
|_REC_<br>_h_<br>−<br>1<br>β|||0.188<br>[0.07]||
|_ROL_<br>_h_<br>−<br>1<br>β||||0.138<br>[0.07]|
|Obs.|245|252|230|226|
|2<br>_R_|0.56|0.53|0.62|0.61|

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