rafmacalaba/datause-encoder-data huggingface dataset:
# **I INTRODUCTION**

As of 2023, there were an estimated 45 million refugees globally, with the majority seeking refuge in middle- and low-income countries (UNHCR 2023b). Despite considerable efforts by these nations to host and support displaced populations, their capacity and will to do so are often limited by insufficient resources and concerns about potential impacts on their own vulnerable communities. Therefore, high-quality knowledge about the comprehensive effects of hosting displaced populations would benefit these processes.

This enhances the debate the broad socioeconomic conse- study policy by examining quences of hosting the displaced Rohingya population in the Cox’s Bazar area of Bangladesh. Prior research on this topic has investigated the effects of refugee camps on specific outcomes.<sup>1</sup> Nonetheless, this study stands out for its in-depth assessment of the comprehensive socioeconomic effects of on communities in a context where refugee camps nearby forcibly displaced populations are confined to camps and thus entirely reliant on humanitarian aid. This focus provides a unique perspective on the dynamics between refugee camps, humanitarian aid, and host communities.

The displaced Rohingya population in Cox’s Bazar can be broadly categorized into two those who arrived around or before the 1990s and those who arrived after 2017. groups: By the end of December 2023, nearly one million Rohingya were registered in 33 camps in Cox’s Bazar, comprising 35,519 who arrived in the 1990s and 716,915 who fled to Bangladesh (UNHCR 2018) following severe military attacks against the Rohingya community in Myanmar in August 2017. This event marked the largest and fastest forced migration episode in Asia since the Vietnam War. Despite the Bangladesh government’s provision of land for the 33 camps in Cox’s Bazar, concerns regarding security and the on local communities have restricted the and em- potential impact Rohingya’s mobility ployment outside the camps. Consequently, they rely entirely on humanitarian aid.

This study employs a difference-in-differences methodology to examine the development effects of proximity to refugee camps, comparing locations near and far from the camps before and after the 2017 Rohingya influx. As shown in Figure 1, the grids were com-

> 1See Montalvo and Reynal-Querol (2007) and Alix-Garcia et al. (2018) for notable examples.

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7 explores the relative expenditures of households with children in our core sample. The point estimates for education and health expenditures are relatively large (suggesting increases of 18 and 22 percent) although these are not precisely estimated. Approximately 20 percent of these households had any savings, and the average amount saved was $11, suggesting that this is a savings-constrained population that would find it difficult to cover additional education expenditures from savings. All told, these findings indicate that households did not use other compensatory behavior to cover the shortfall in child schooling costs.

# _Alternative explanations for the rise in child work_

This section examines a range of potential alternative explanations for the increase in child work. A first possibility is that the inflow of cash led to changes in the local economy (Angelucci and DeGiorgi (2009) document positive spillovers on non-beneficiaries living in _Prospera_ villages) that increase the returns to work or labor demand in treatment communities. To understand whether the program resulted in such general equilibrium effects, we examined whether wages and economic activity of ineligible households were affected by the program, but find no evidence for such an effect (Appendix Table 7). A second possibility is that household composition changes in response to the cash transfer. For instance, the additional income available to the household may induce increased fertility, in turn decreasing adult female labor supply and increasing demand for child work, but we do find any evidence that _Pantawid_ affected family composition (Appendix Table 8). A third possibility is that school attendance opens up new opportunities for children to work. This may occur if there are few employment opportunities close the home of the child, but commuting to a school near a market (or other economic hub) allows the child to work. However, Appendix Table 9 shows that, if anything, longer distance to the nearest market is associated with a higher probability of being in work. A fourth option would be that children learn about work opportunities from their peers in school. However, given that the villages in our sample are small (215 households on average in the baseline PMT data), this mechanism appears unlikely.

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of computers, and increased decentralization of decision-making. One consequence of such decentralization is the spread of better paid employment opportunities and less inequality of compensation through a production hierarchy, that is, more inclusion in the gains from productive expansion.

In related work on the implications of innovation in management technologies, Bloom et al. (2009) highlight a subtle but important difference between advances in information and communications components. Better information technologies that empower and spur learning by workers such as ERP (Enterprise Resource Planning) for plant managers and CAD/CAM (Computer Aided Design and Manufacturing) for production workers are associated with more autonomy and wider span of control. One key implication is more inclusive growth opportunities by the elevation of local labor productivity and reduction in wage inequality. In contrast, communication technologies like data networks are apt to decrease autonomy for plant managers and workers, substituting away from local knowledge in favor of directives from centralized headquarters, and leading to less inclusive growth by stifling learning and accentuating wage inequality. Despite these differences in types, innovation in management is highly associated with gains in productivity and output-growth opportunities. As such, we hypothesize that these forms of innovation are also, like product and process innovation, likely on average to be inclusive in their overall impacts on enterprise employment.

# **3. Data and empirical specification**

We use establishment-level, cross-section data that are based on the World Bank Enterprise Surveys (ES) collected between 2002 and 2006. We have information on 26,108 manufacturing establishments from 71 countries (15 OECD countries, 6 ̳enhanced engagement‘ developing countries and 50 other developing countries). Most of the establishments represented in the data are registered in the formal/organized sector, and are urban. Sampling is typically stratified by size, sector and location. Any accounts collected in local currency units are converted to constant 2005 US dollars at purchasing-power-parity. Rates of growth are scaled to an annual basis. Table 1a reports sample counts by country and table 1b reports the means and standard deviations of our main variables of interest for two separate country samples based on OECD membership. Table 1c contains a detailed listing of the Enterprise Survey questions underlying the establishment-level business environment indicators used in this study.

Country-level data on the competitiveness of the business environment are taken from the IFC/World Bank Doing Business (DB) reports. Strongly positive correlations among the major DB variables and among their categorized aggregated indicators suggest that national regulation policies come in ̳packages.‘ In line with recent work by Loayza, Oviedo and Serven (2010) and Djankov, McLiesh and Ramalho (2006), we examine the effects of business regulations on 13
There is significant evidence that a more open trade policy and more efficient, internationally diversified trade are associated with higher economic growth in the long run.<sup>3</sup> Globalizers exhibit accelerating GDP growth, catching up with rich countries, while non-integrated developing countries tend to fall further behind. As illustrated in Figure 1, the ranking of country groupings by trade openness (as a share of GDP) mirrors that of country income per capita. Whereas trade has become an increasingly important contributor to economic output in all groupings, the levels of trade openness are significantly higher for high- and middle-income countries, compared to low-income countries.

**Figure 1: Trade openness is positively correlated with growth**

<!-- Start of picture text --> 1991 1996 2001 2006 2011<br>Year<br>High income Middle income Low income<br>100<br>80<br>60<br>40<br>Trade openness (% of GDP)<br>20<br>0<br><!-- End of picture text --> _Source: World Development Indicators_ Note: Trade openness is measured as exports plus imports of goods and services as a share of GDP.

Low-income countries appear to be losing market share to the rest of the world, particularly to emerging economies that have pursued aggressive integration into global markets. Figure 2 illustrates that whereas the share of global exports produced by low-income countries has stagnated at less than 1 percent of total exports, the BRICS (Brazil, Russia, India, China and South Africa) have tripled their share of global exports from 5 percent in 1991 to over 15 percent by 2011.

> 3 See Rodriguez and Rodrik (2000) for a critique of early studies establishing the positive link between trade openness and growth; see Feyrer (2009) and Brückner and Lederman (2012) for more recent evidence in support of the positive trade-growth link using new methods.

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Although the assignation rules used in Malawi allocate teachers both to schools without one teacher per grade (T1) and schools with PTR above 90 or 80 (T2), in this analysis we focus primarily on T2 and the reduction of overall school PTRs. This is done in response to evidence from Malawi which suggests that schools with PTRs above 90 have lower overall learning outcomes (Asim and Casley Gera, 2024). In addition, in this analysis, we exclude non-qualified and student teachers and focus on school pupil-qualified teacher ratios (PqTRs).

In recognition of the fact that the dynamics of school staffing vary significantly regardless of the allocation of new teachers (see Section 4), we focus our analysis on schools which an increase in overall teacher numbers and experienced reduction in PqTR, regardless of whether they were allocated a new teacher. In other words, a school which gained an additional teacher through transfer is treated the same as one which received a newly deployed teacher.

We define a school has having been ‘treated’ with additional teachers in a given year if it meets the following conditions:

1. The number of teachers employed in the school is higher than in the previous year;

2. The school’s overall PqTR was above 90 in the previous year and is now below 90.

We define a control group of schools which had PqTR above 90 in the previous year – meaning they needed a new teacher – but did not experience a net gain in the number of teachers.

As a result of the dynamic nature of teacher allocations, the treated and control schools therefore vary across the various year comparisons. **Table 5.1** summarizes the sample.

**Table 5.1. Impact analysis sample**

||**2017-18**|**2018-19**|**2019-20**|
|---|---|---|---|
|Treatment|902|710|468|
|Control|412|296|330|

Both control and treatment schools experienced changes in PqTR, repetition rate, and dropout rate year-on-year. To capture the differential dynamics between treated and control schools, we employ difference-in-difference (DiD) analysis.

**Table 5.2** (in Section 8, Tables) presents DiD estimates for the impact of treatment on PqTRs. As expected given the definitions of treatment and control, treated schools experienced decline in PqTR compared to control schools in each year – an substantial decline of 41 pupils per qualified teacher.

**Lagged effects for dropout and repetition.** Because dropout and repetition rates are determined by schools at the end of a school year, there is an expected ‘lag’ in impacts for an improvement in school staffing. Recall that new teachers are typically deployed to schools at the start of the new school year in September, with the EMIS data collection taking place around two months later in OctoberNovember. In order to allow time for the impacts of new teachers to be felt and measured, we report lagged effects for these indicators from the following year’s EMIS data. For example, to evaluate the impact of teachers allocated in August/September 2017, we compare:

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preferences between the engineer and the mechanic.

To recap, we have shown that we can recover individual preference parameters by inverting the function between primitives and labor supply decisions using just two observable labor supply elasticities. The key economic insight is that labor supply elasticities allow us to compare preferences for consumption relative to leisure across individuals with different hourly wages by subtracting out the component of hours worked due to wage effects. The component of hours worked _not_ due to wage effects (i.e., the component of hours worked attributable to preferences) then allows us to compare individual preferences.

# **3.4 Labor Supply Frictions**

Our results so far have relied on the assumption that individuals can perfectly optimize their labor supply by changing hours worked on the intensive margin. There is a non-negligible subset of people for whom this is probably a reasonable assumption (e.g., Uber drivers or the self-employed or commission workers); we can immediately apply our method to use labor supply elasticities to learn about the drivers of income inequality _within_ this subset of the population. On the other hand, many individuals do face labor demand inelasticity or market frictions; we provide an imperfect, yet easily implementable, modification to recover productivities and preferences using labor supply elasticities if individuals face frictions. Labor market frictions lead to two issues in applying our method: (1) individuals’ observed hours are no longer equivalent to their optimal hours and (2) elasticities of observed hours with respect to the tax rate, which reflect both labor market frictions as well as how optimal choices change with tax rates, are no longer equivalent to elasticities of optimal hours with respect to primitives (as in Lemmas 3.1 and 3.2). Towards broadening the applicability of our method, consider a world in which all individuals ( _n, α_ ) choose an optimal hours worked _h_<sup>_∗_</sup> ( _n, α_ ) yet face labor supply frictions. Thus, each individual ( _n, α_ ) ends up working _h_<sup> ̃</sup> ( _n, α_ ) = _h_<sup>_∗_</sup> ( _n, α_ ) + _εn,α_ , where _εn,α_ is some deviation from optimal hours (it does not matter what causes this deviation from optimal hours).

Even with frictions that prevent individuals from working their optimal number of hours, we can still learn about the role of productivities vs. preferences in driving income inequality from labor supply elasticities. To solve the first issue that observed hours are not equal to optimal hours, suppose that we were able to elicit (via survey, for example) individuals’ true optimal hours worked _h_<sup>_∗_</sup> .<sup>13</sup> Productivity is equal to observed income ̃ divided by observed hours, _n_ = _z/h_<sup> ̃</sup> , and optimal income is given by optimal hours _h_<sup>_∗_</sup> multiplied by _n_ : _z_<sup>_∗_</sup> = _nh_<sup>_∗_</sup> = ( ̃ _z/h_<sup> ̃</sup> ) _h_<sup>_∗_</sup> .

> 13For example, the National Study of the Changing Workforce asks people how many hours they would prefer to work.

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# **Figure 2: Employment's Link to Education and Household Composition in Uzbekistan**

<!-- Start of picture text --> Employment rate by educational Employment rate by number of children<br>attainment in household<br>100% 100%<br>90% 90%<br>80% 74% 80%<br>70%<br>70%<br>60%<br>60%<br>50% 43%<br>50%<br>40% 33%<br>40%<br>30%<br>30%<br>20% 11%<br>10% 0% 20%<br>0% 10%<br>primary general high vocational tertiary 0%<br>or less secondary schoo; 0 1 2 3 4 5 6 7+<br>Male Female Male Female<br><!-- End of picture text --> _Source: Household Budget Survey of Uzbekistan, 2022_ In all three societies, intrahousehold power tends to be imbalanced, and gender norms tend to be more traditional, with women expected to prioritize caregiving responsibilities and men expected to be the primary breadwinners (figure 3). These expectations can limit women's participation in the labor market and their opportunities for career advancement. Additionally, women in all three countries are underrepresented in leadership positions and are more likely to work in lower-paying occupations (Muradova and Seitz (2021) show this is due in part to discriminatory hiring practices).

**Figure 3: Survey-based perceptions of women's role in work and family life**

<!-- Start of picture text --> Agree: Women should earn less than men to Agree: Women should spend less time<br>sustain peace in the family working than men to dedicate time at home<br>Urban Urban<br>Rural Rural<br>National 34% National 77%<br>Urban Urban<br>Rural Rural<br>National 42% National 73%<br>Urban Urban<br>Rural Rural<br>National 20% National 74%<br>0% 10% 20% 30% 40% 50% 50% 60% 70% 80% 90%<br>UZB UZB<br>KGZ KGZ<br>KAZ KAZ<br><!-- End of picture text --> _Source: Listening to Central Asia surveys, 2022_ 5
i = L, H. With two types of landlords (high/low supervision costs) and two types of tenants (high/low wealth), social surplus is ψ(w, w<sup>�</sup> ) = r<sup>L</sup> (w) + r<sup>H</sup> (w<sup>�</sup> ) for any pair of tenants (w, w<sup>�</sup> ). One can verify using Figure 1 that ψ(w, w<sup>�</sup> ) ≥ ψ(w<sup>�</sup> , w) for any tenant pair such that w < w<sup>�</sup> . In the case where w<sup>�</sup> < <u>wL</u><sup>,forinstance,landlordL’srelatively</sup> high payoff from the MS contract is invariant to tenant wealth, whereas landlord H’s payoff is increasing in w. Higher surplus is thus achieved when the richer tenant works H’s land on an S contract instead of L’s land on an MS contract.

We can now sketch out a simple model of landlord-tenant matching with search frictions. Prior to contracting, potential tenants randomly drawn from the wealth distribution arrive sequentially before landlords who can either accept or reject them. If rejected, the tenant reenters the candidate pool.<sup>17</sup> Given that searching for tenants is costly, landlords will adopt a "reservation wealth" strategy; that is, they will make a hire once they fi nd a tenant whose wealth exceeds a given threshold. Since, by the above argument, landlords with higher supervision costs must have higher reservation wealth, we obtain Proposition 3 In equilibrium, tenant wealth and landlord supervision costs are positively, but not perfectly, correlated.

# 4 Are Supervised Sharecroppers More Productive?

## 4.1 Quantifying landlord supervision

PRHS-01 asks each share-tenant "during [kharif/rabi season] how many times did the landlord meet with you to discuss or supervise your activities on this plot." In case the landlord employed labor overseers, or kamdars, the same question was asked about meetings between the tenant and these individuals as well. Very few share-tenants (less than 4%) report never having had supervisory meetings with their landlord or with a kamdar during the year (meetings with kamdars occurred in about a quarter of sharetenancies). On one-half of all sharecropped plots, tenants report having had more than 30 meetings per year with their landlord/kamdar, and, on half of these plots, tenants claim to have had at least 90 meetings. While many of these conversations may have occurred during non-crucial periods or were not otherwise intended to elicit or enforce effort on the part of the tenant, it is clear that share-tenants in Pakistan are not being left to their own

> 17 It is reasonable to assume in our setting that tenants also face search costs, thus allowing landlords to make "take-it-or-leave-it" offers at the time of contracting.

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with women’s participation in the labor market (Fernandez et al. 2004; Fernandez, 2007; Farre’ and Vella, 2013; Fernandez and Fogli, 2009).

Regarding firm specific characteristics, age of the firm and access to finance may matter. Older firms may be resistant to change and thus reluctant to hire women workers or top women managers (Blum et al., 1994). In some firms, informal networks tend to be dominated by males, and thus the presence of formal training may be crucial for women to move up the career ladder (Rowley 2013). Crime has also been found to be correlated with women’s employment in management positions (Islam, 2013). In our empirical estimations, we try our best to account for all of these factors given the constraints of data limitations.

# **3. Data and Empirical Strategy**

Our main data sources are the World Bank Group’s Enterprise Surveys (ES) and the World Bank Group’s Women, Business and Law (WBL) databases. The ES offer an expansive array of crosscountry comparable firm-level data on firm’s experience of the business environment and on firm’s performance and characteristics, including gender composition of the workforce, gender composition of the ownership, and gender of the top manager. The sample of ES firms used in the paper consists over 60,000 firms across 104 mostly developing countries. ES were conducted in various countries between 2006 and 2016 using a common questionnaire and sampling methodology (stratified random sample), and are representative of the non-agricultural and nonmining formal (registered) private sector of the economies<sup>1</sup> . The WBL database measures legal institutions that discriminate on the basis of gender across the world. WBL data are collected through several rounds of interactions with practitioners with expertise in the different areas

> 1 More information about the ES methodology and country coverage is available on the Enterprise Surveys website <u>http://www.enterprisesurveys.org. Note that each firm has only one observation in the sample.</u>

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