rafmacalaba/datause-encoder-data huggingface dataset:
**challenges?** In particular, we highlight key areas where, in the context of an increasing preponderance of female migration, policy recommendations might differ from situations with lower levels of migration.

After describing the data and methods used, this study proceeds in two parts. Part I offers an in‐depth exploration of the experience of female migrants in the formal sector (using textile workers as an example) and informal (through domestic workers). In Part II, we examine the challenges faced by female migrants in five specific areas, with an eye to understanding how policy can adapt to better serve the needs of migrants and their families: quality of employment, living conditions of migrants, financial inclusion, protection from HIV, and support for migrants’ children.

# Data and Methodology

This study includes qualitative and quantitative analysis of both primary and secondary data sources. We used existing nationally representative data sources to investigate a number of relevant aspects of female migration, including the determinants of migration, individual and household characteristics, and HIV prevalence among migrants. It included both internal and external migrants and disaggregated among the different occupations of migrants to the extent possible.

Among the nationally‐representative data from Lesotho, the Demographic and Health Survey (DHS) provided the most comprehensive information on socio‐economic and health indicators at both individual and household levels. Using the most recent wave of the DHS available (DHS 2014), migrants were identified in two ways: (i) the residence status of listed household members, and (ii) whether or not surveyed individuals have undergone at least one trip of three months or longer during the previous 5 years (see Annex 1 for more detailed information). To focus our analysis on labor migration, only a subsample of individuals who traveled for work<sup>4</sup> was retained as our base specification. The DHS was also particularly appropriate for the analysis of migrants’ vulnerability to HIV infection, because it contains HIV test results that can be merged with socio‐demographic and health data collected in the individual questionnaires. Based on the methodology employed by a previous study,<sup>5</sup> we conducted multivariate regression analysis to estimate the effect of working in predominantly migrant occupations on the probability of being HIV positive.

> 4 The reason for their most recent trip of three months or more was work.

> 5 Corno and de Walque (2012).

10
of imports, b) weight-to-value ratio, and c) containerized-share of imported shipments. In order to avoid a potential bias generated by endogenous choices of transport mode, we calculate these characteristics in U.S. import data (net of flows from Canada and Mexico). Among final products, we find that PR’s home bias towards imports coming from the U.S. mainland is smaller for products that are vesselshipped, physically heavy, and not typically shipped in containers. Among upstream products, evidence of substitution away from U.S. sources is less apparent; instead the composition of PR’s imports exhibits a strong bias against sea-shipped products in general. These effects are consistent with a hypothesis that the JA has shifted PR’s industrial structure away from sectors that use sea-shipped imports as inputs, though other policies may also have contributed to this outcome.

Since we estimate trade distortions on a single trade route, our empirical methods are somewhat different than conventional approaches to estimating the gravity model of trade. As a robustness check we apply precisely the same methods to the imports of three comparison countries: the Dominican Republic (DOM), Jamaica (JAM) and the Bahamas (BHS). The patterns we uncover in PR do not appear in these other countries; giving credence to the view that the effects we observe for PR are due to the JA, rather than to other anomalies in U.S.-Caribbean trade.

In order to provide quantitative context for our estimates, we incorporate external estimates of product-level elasticities of substitution to infer tariff-equivalent JA trade costs among final products, and conduct a compensating variation (CV) calculation that removes JA tariff-equivalent costs in final demand. Our preferred specification produces a mean JA-tariff-equivalent of 30.6 percent among final products. The CV calculation suggests an annual welfare burden of the policy of $1.4 billion the direction of PR. The container ships that work the route typically return to the mainland with less than full loads and, anecdotally, charge much lower rates. The dearth of self-propelled vessels available to carry non-containerized freight may also affect PR’s exports to the U.S., but given PR’s industrial mix it does not seem likely that this is quantitatively important (in a static sense, at least).

4
# **4. Results**

The results of the three models described above are shown in Table 4, while the results of the heterogeneous effects are shown in Tables 5 and 6 for the education and informality quartiles, respectively. Column A in Table 4 shows the first specification in which only one-year sold projects are included; column B shows the results of the unbalanced panel data with random effects in which only multiple-year sold projects are included; and finally, column C shows the unbalanced panel data results with all projects, in which some of them enter only once in the database (if they were sold in one year) or multiple times, depending on how long they took to sell all their properties. All three specifications cluster the standard errors at the neighborhood level to capture unobservable neighborhood characteristics that influence housing prices.

**Table 4: Single-year developments and panel data specifications. Dependent variable: log of housing** **<u>prices (2000 constant prices).</u>**

||**Column A**|**Column B**|**Column C**|
|---|---|---|---|
|**VARIABLES**|**One-year**<br>**observations**|**Repeated**<br>**observations**|**All observations**|
|Private accessibility 2002|0.0144***<br>(0.00442)|-0.00138<br>(0.00431)|-0.00384<br>(0.00328)|
|Private accessibility 2003|0.0241***<br>(0.00879)|0.00913***<br>(0.00337)|0.00777**<br>(0.00337)|
|Private accessibility 2004|-0.00748<br>(0.0152)|0.00867***<br>(0.00315)|0.00709**<br>(0.00310)|
|Private accessibility 2005|0.0248<br>(0.0321)|0.0124***<br>(0.00302)|0.0108***<br>(0.00305)|
|Private accessibility 2006|0.0224***<br>(0.00717)|0.0120***<br>(0.00290)|0.0116***<br>(0.00293)|
|Private accessibility 2007|0.0253***<br>(0.00617)|0.0143***<br>(0.00289)|0.0141***<br>(0.00285)|
|Private accessibility 2008|0.0102<br>(0.00891)|0.0124***<br>(0.00263)|0.0120***<br>(0.00261)|
|Private accessibility 2009|0.0169**<br>(0.00680)|0.0148***<br>(0.00304)|0.0151***<br>(0.00297)|
|Private accessibility 2010|0.0132**<br>(0.00532)|0.0160***<br>(0.00311)|0.0164***<br>(0.00303)|
|Private accessibility 2011|0.0295***<br>(0.00582)|0.0188***<br>(0.00332)|0.0197***<br>(0.00328)|
|Private accessibility 2012|0.0247***<br>(0.00678)|0.0206***<br>(0.00349)|0.0218***<br>(0.00345)|
|Private accessibility 2013|0.0288***<br>(0.00559)|0.0221***<br>(0.00378)|0.0242***<br>(0.00366)|
|Distance to CBD (in logs)|-0.00367<br>(0.0303)|0.114***<br>(0.0343)|0.0649*<br>(0.0337)|
|Distance to the closest Metro|0.0269|0.225***|0.125**|
|station (in logs)|(0.0648)|(0.0711)|(0.0581)|

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Table 7.5: % Differences Between Actual and Optimal AAA (AAA Thematic Shares = Lending Thematic Shares)

|**Region **|**Climate**|**Pollution **|**Land**|**Biodiversity**|**Water **|**Policies**|**Other **|**Total**|
|---|---|---|---|---|---|---|---|---|
|**AFR**|42|258|-20|-93|-77|37|-92|-52|
|**EAP**|490|1,007|955|-14|76|-49|-67|56|
|**ECA**|-20|-48|-28|-97|20|-81|-95|-66|
|**LCR**|1,150|388|-70|-49|33|-30|-87|-18|
|**MNA**|108|24|684|-76|-63|-34|-91|-37|
|**SAR**|-17|224|4,804|-47|448|-11|146|166|
|**Total**|118|198|107|-73|18|-44|-83|0|

Our results also suggest possible differences in the relative magnitudes of thematic changes for lending and AAA. Appendix Table 2 presents results for actual and optimal lending and AAA across sectors and regions. We estimate the relative magnitudes of thematic gaps by calculating the absolute values of regional thematic gaps as percentages of actual thematic allocations. For each theme, the sum of absolute-value percentage gaps provides an indicator of relative “misallocation”. Table 7.6 summarizes the results for three cases: lending, AAA (A -- with thematic AAA shares), and AAA (L -- with thematic lending shares). Both nominal and rank correlations suggest that misallocations are most closely matched for lending and AAA(A) shares; poorly matched for lending and AAA(L); and weakly matched for AAA(A) and AAA(L). We conclude that statements about thematic misallocation are highly sensitive to the comparison standard, and general conclusions do not seem appropriate.

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Table 3.1: Number of CCCN items notified under each GATT article by developing - <u>countries 1974 87</u>

|Article cited as justification|Number|
|---|---|
|VIII: Fees and formalities connected with importation and<br>exportation|316|
|XI.2: Restrictions to apply standards or classifications, manage<br>short supplies or complement agricultural or fisheries support<br>programs|108|
|XVIII: State trading enterprises|15|
|XVIIIB: Balance of payments measures for developing countries|3,437|
|XVIIIC: Industrial development|91|
|XX: General exceptions|131|
|XXI: National security|4|
|Source: OECD 1992, p. 100.||

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most-educated-female-adult FHHs (Appendix A, Tables A.12 and A.13).<sup>14</sup> We further consider the overlaps of three main FHH types (self-reported, potential, and most-educated-female-adult FHHs) and all four main FHH types and show the estimation results in Appendix A, Table A.14, which remain qualitatively similar.

# **4.2. FHH poverty dynamics based on synthetic panels**

We turn next to discussing the results on poverty dynamics based on synthetic panels. For each country, Figure 5 reports the conditional upward mobility rates in the second survey year for the four main FHH types, considered separately with and without any children (Equation (3)). Figure 5 shows considerable (conditional) upward mobility at the national average level (dashed line) for some countries. In particular, the upward mobility rate is 45 percent in Iraq during 2007-2012 (i.e., 45 percent of the initial poor in the first year escape poverty in the second year), 54 percent in Jordan during 2010-2013, and 41 percent in Mauritania during 2014-2019. Still, a significant degree of immobility exists in Egypt, the West Bank and Gaza, and Tunisia, where most of the population remained poor in both years and only about one-third (or less) of the poor escaped poverty in the most recent year: 29 percent for Egypt during 2017–2020, 31 percent in the West Bank and Gaza during 2011–2017, and 21 percent in Tunisia during 2015–2021.<sup>15</sup> Unsurprisingly, non-FHHs have upward mobility rates that are almost the same as the national averages, given their large shares in the population (Table 1). But interestingly, FHHs without

> 14 The estimated marginal effects for the interaction terms with children are qualitatively similar (using the Stata command “ginteff” (Radean, 2023)).

> 15 The survey period lengths generally differ for the six countries so the estimated mobility rates are not exactly comparable across countries or to those in other studies. For a rough reference, Dang and Ianchovichina (2018) obtain a regional upward mobility rate around 52 percent in the early 2000s and 2010s. However, if we assume a similar rate of change for mobility across the years for all countries, we can obtain the average mobility per year for each country (Appendix A, Table A.15).

23
eventually recover to the same pre-disaster levels. Once this happens the asset composition of the economy is likely to be more favorable than prior to the disaster in the sense that it may then be * more amenable to allow the human capital sector to grow while keeping the wage rate pegged at _w_ . Over the intermediate run both _h_ and _k A_ have been increasing. If _h_ has grown at a sufficiently faster pace than _k A_ it is possible that the condition _h_  _hc_ be maintained (see (40)). In this case what was a stagnant economy prior to the disaster becomes a growing one after the disaster<sup>23</sup> .

**Summary** _:  A disaster that affects more intensively the stock of physical capital than human capital may propel a middle-income economy from chronic economic stagnation to permanent economic growth._

This result is consistent with empirical studies that have shown that some countries tend to grow faster over the long run after disasters than before them (Albala-Bertrand 1993). Moreover, certain empirical studies have also shown that growth expansion after disasters tends to happen more often in middle-income countries than in poor countries (Cuaresma et al. 2008). The previous analysis predicts precisely this; we showed that this phenomenon may occur in countries where the private sector plays an important role in providing human capital and where capital markets are well developed which are often middle-income countries.

Some authors including Popp (2006) and Skoufias (2003) have recognized that the destruction of physical assets may promote greater investment in human capital and Duryea et.al. (2003) has even provided rigorous empirical documentation of it for Brazil. What has not been recognized in the literature is that this effect could cause _permanent_ growth effects and can make the difference between stagnation and continuous economic growth over the long run. Under certain conditions facilitated by the existence of well developed capital markets and full participation of the private sector in the

> 23 This phenomenon is consistent with and illustrated by the following observation: Anyone familiar with academics knows that the number of applications for entering universities both to undergraduate and graduate programs shoot up during recessions. Most of the increased university applicants come from the labor force or would have entered the labor force instead of pursuing further studies if the labor market were in better shape.  Also, in times of recession it is much easier to retain faculty members and to attract new ones which otherwise would be employed in the private commercial sector. Effectively, the “real” sector of the economy competes with the human capital sector for both potential students and teachers which otherwise could be employed by the commercial sector of the economy.  This competition becomes much less intense in times of recession, which may have the effect of promoting a more rapid expansion of the human capital of the economy.

49
monthly labor income assuming respondents work 8 hours per day, 5 days per week, 4 weeks per month, and 12 months per year. Business sales are reported in question 24 of Section 6. We assume all business income goes to the owner of the enterprise.

- Education: from Section 2 (Education), question 7 and 8.

## First follow-u : <u>p</u>

- Migration: we collect information on individuals leaving their household from the Post Harvest Wave 1 (February - April 2011), Post Planting Wave 2 (September - November 2012), and Post Harvest Wave 2 (February - April 2013). For each individual, we identify destination state and local government area, and assume that this remains unless a new move is recorded in a more unchanged

- recent wave. For respondents that are not indicated as leaving their household, we take household state and local government area from the most recent wave.

## Second follow-u : <u>p</u>

- Migration: we use information from the Post Planting Wave 3 (August - October 2015) and Post Harvest Wave 3 (February - April 2016). The procedure to infer movers’ location is the same as the one used for the first follow-up. In addition, individuals who migrated between baseline and the first follow-up are assumed to reside in the same location they were living in at the end of the first follow-up, unless a new move is registered in Wave 3.

# **B.7 Kagera Health and Development Survey (KHDS)**

## Baseline (1991/94):

- Self-employment: from section 7, whether the respondent says she is selfemployed during the past 7 days (part A, question 6 “During the past 7 days, have you worked for yourself or your household? For example, as an independent merchant or fisherman, lawyer, doctor, or other self-employed activity?”) in an activity not connected with farming, livestock or fishing (part D, question 1); or self-employed in the past 12 months (part A, question 7). Additionally, require that her main job in the past 12 months is not in farming, livestock or fishing (part F, question 1).

- Wage worker: from section 7, whether the respondent says she is employed during the past 7 days (part A, question 2 “During the past 7 days, have you worked

55
status comes from the bazaar and the castes who are in the majority in the CT, while the surrounding rural areas have a different caste composition, and are not as much in favor of urban status.

# **_Initial Policy Implications_**

The study results open up a number of questions for policy implications in terms of governance, service provision and support for economic growth.

A major policy implication in terms of governance, not just for the four states, but more generally, is the need to plan at a more aggregated level, like the tehsil or the district, and not at the level of the settlement. The existing separation between rural and urban that structures all public interventions and governance structure is not in sync with the spatial and economic reality of these settlements that are perceived as a territorial unit by most of the residents. Various options can be envisaged that would require more or less interventions ranging from evolving solutions to activate efficient district planning committees as exists in today’s administrative organization to more radical solutions such as ending the binary rural/urban status created by the 73rd and 74th constitutional amendments on decentralization.

In terms of service provision, the study shows that the level of basic amenities remains very low. Since these CTs are sites where increasing numbers of people live and work, even in states like Bihar with high levels of outmigration, public investment to ensure higher levels of service provision such as water and sanitation needs to be called for. In addition to increasing population densities, and even in the absence of radical economic transformations, Census Towns appear as emerging centralities in their respective territories. This is illustrated in these towns by the expansion of markets and increased regional commute and brings demands for improvement in the provision of services such as solid waste management and street lighting, which are mostly nonexistent in Census Towns.

In terms of economic activities, another important implication relates to the need for spatially blind policies aimed at improving access to services such as formal finance and electricity, with the objective of enhancing the level of access of basic amenities but also the economic potential of CTs.

31
point is the selection of case studies reported in this essay, which highlight ecologies that are considered vulnerable and important by conservationists.

Another aspect of knowledge building has to do with the construction of basic measures that can be used to quantify existing conditions, changes over time and the relationships between policy and outcomes. To conduct their analysis, the authors of the case studies looked to find measures of human welfare, of environmental impact and of the relationship between the two. Their stocktaking suggests that the state of these measures is uneven and sometimes weak, especially with regard to environmental measures over time. For this reason, many of the research teams found it difficult to fully quantify changes in core natural resources over time or in the services that they provide. For the same reason, the research teams found that broader measures of human welfare that take into account the consequences of air or water pollution were hard to determine. Very broadly, the researchers were generally able to document changes in income and poverty over time by drawing on national and regional surveys. Moreover, economic data on local production and income sources were sometimes available; for example, time-series data in China Less available was information that would allow the researchers to quantify the types of provisioning services provided by the studied ecologies and to measure changes in those services over time and their impact on human welfare. For example, while environmental services such as the provision of drinking water or biodiversity from the Da Wei Shan Nature Reserve in Pingbian are noted, baseline measures from which the authors could draw comparisons are lacking. In India and Madagascar, for example, focus groups and surveys were used to provide broader measures of human welfare, but comparisons over time were based on recall and subject to error. For this reason, many of the conclusions about environmental degradation over time are based on deforestation maps. In their efforts to evaluate the environmental impact of increasing sugar production in Mpumalanga, the research team from South Africa concluded that “little is known about ecological thresholds and reversibility of environmental damage in Incomati.”

# <u>Managing vulnerable ecologies</u>

The case studies describe several mechanisms currently in place to manage natural resources. At a fundamental level, these systems are intended to properly value the broad range of services that ecologies provide and to preserve the core resources that provide those 29
# **THE POVERTY IMPLICATIONS OF ALTERNATIVE TAX REFORMS: RESULTS FROM A NUMERICAL APPLICATION TO PAKISTAN**

By Andrew Feltenstein (Georgia State University, <u>afeltenstein@gsu.edu)</u><sup>1</sup> Carolina Mejia-Mantilla (World Bank) David Newhouse (World Bank) Gohar Sedrakyan (Georgia State University) JEL Classification: C68, H22 Keywords: Tax incidence, Distributional Effects, Computable General Equilibrium Models

> 1 This paper was partially funded by a Poverty and Social Impact Analysis Trust Fund provided to the World Bank by the Department of International Development. The views expressed here are our own and do not reflect those of the World Bank or its Executive Directors.