rafmacalaba/data-use-mentions-extended huggingface dataset:
beneficiary villages as well as those of equation (4) with fully discretized controls for the numbers of experimental treatment, evaluation, and non-evaluation beneficiary localities.<sup>15</sup> While the former provides an average spillover effect, the later specification allows us to check for the presence of non-linearities in the marginal effects of neighboring evaluation localities assigned to treatment.

Finally, several other features of the empirical specifications depicted above should be noted. First, the parameter _α_ 2 in equation (1) is estimated out of the subset of eligible households of the controlled experiment that have other evaluation villages in the neighborhood of radius _d_ . For a radius of five kilometers, we have such identifying variation for 42 percent of the evaluation villages.

_X_ Second, the inclusion of the vector of sociodemographic variables _i_ , _j_ , _d_ in equations (1), (3), and (4) is meant to increase the precision of the estimates. The control variables are all measured at baseline using the 1997 data in order to avoid any endogeneity concern, and, taking advantage of the panel dimension of the data, include, in particular, baseline school enrollment. The remaining control variables are as follows: child’s gender and age (both in levels and squares), parental education, distance to the nearest city, the share of eligible households, the presence of a secondary school in the locality, total population in the locality, the number of localities, total population, and the mean degree of marginalization in the neighborhood. We also include state- and year-fixed effects.

Lastly, in order to account for the fact that evaluation villages may belong to multiple neighborhoods, we cluster standard errors for groups of partially overlapping neighborhoods. These groups are defined as sets of evaluation villages such that each village lies within the radius-based neighborhood of another village of the set. Intuitively, as soon as an evaluation village belongs to two radius-based neighborhoods, those two neighborhoods will belong to the same cluster. This allows for correlations
in the near future (Hazan et al. 2024). Hatzius et al. (2023) conclude that generative AI could substitute up to one-quarter of current work, thereby raising US labor productivity growth by approximately 1.5 percentage points over the next ten years.

To date, only a few papers have considered the potential impacts of AI on low- and middle-income countries. The most recent and prominent examples are Pizzinelli et al. (2023) and Cazzaniga et al. (2024). Pizzinelli et al. (2023) expanded the AIOE index with a measure of complementarity and applied it to two high-income countries (the United Kingdom and the United States) and four middle-income countries (Brazil, Colombia, India, and South Africa). The authors identified differences between countries when examining AI occupation exposure due to disparate employment structures. However, when accounting for complementarity, these differences diminished. In all countries, the authors detect a higher occupational exposure to AI for women and highly educated workers, as well as for workers with higher earnings. Cazzaniga et al. (2024) further develop this approach and find that, using ILO employment indicators, nearly 40 percent of workers worldwide are exposed to AI. Additionally, they conclude that AI may exacerbate inequality. Overall, they conclude that the degree of AI exposure varies across economies, with advanced economies reporting a higher prevalence of AI-impacted jobs, reaching up to 60 percent compared to 40 percent in emerging markets and 26 percent in low-income countries.

Gmyrek et al. (2023) employ a distinct methodology, prompting ChatGPT-4 to gauge occupational and task-level exposure to AI. The resulting estimates are then applied to country-level data from 59 countries, comprising eight low-income countries, 24 lower-middle-income countries, 19 upper-middle-income countries, and eight high-income countries. The authors conclude that the effect of AI on labor markets varies across country income groups. Focusing only on workers in Latin America, Gmyrek et al. (2024) further estimate that it is mostly younger, urban-based and better educated workers
Policy Research Working Paper 8970

# **Abstract**

The development of pertinent and effective in-service teacher training remains a policy challenge for many countries in Latin America and the Caribbean. Ecuador stands out as a country in the region that has made significant investments in teacher training in the past decade. However, most in-service training provision has been designed without enough elements to properly address teachers’ skills gaps. This paper proposes a roadmap for improving the design of in-service teacher training in Ecuador using available data from student and teacher assessments. Although countries in the region have made important efforts to carry out periodic evaluations of student and teacher performance, the data resulting from these evaluations are rarely used to guide teacher development programs. The analysis presented in this paper suggests that doing so has the potential to raise program pertinence while allowing the prioritization of investments in teachers and students with the greatest needs.

This paper is a product of the Education Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at dangelurdinola@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
or not PRP caused or was the effect of these other changes in pay policy.

# **Box 1: The elements of pay flexibility**

OECD countries have move towards flexible pay arrangements in the public sector which is in essence some combination of three key features, or design elements:

- **Performance-related pay:** Enabling pay to differ for civil servants doing the same job by linking a portion of the civil servants‘ pay to individual or group effort or performance with performance targets set either at the individual level and/or the group level.

- **Differentiation:** Pay differences within and across government ministries, departments, and agencies for civil servants doing the same job - based on the need to attract and retain qualified staff for those jobs, or to persuade staff to accept new working arrangements. The need for special incentives for attraction or retention can be based on labor market conditions and/or cost of living in the localities where agencies operate or as a function of the specific skills that the agency competes for in the labor market, based for example on job evaluations and labor market surveys.  Differentiation can affect: (i) all staff paid by the entity, with the result that there are agency-specific pay scales; or (ii) particular occupational groups or cadres **;** (iii) specific individuals, with scarce but vital skills; or (iv) specific locations entailing particular hardship.

- **Delegation:** Transferring authority over pay setting and human resource management from a central civil service agency to ministries, agencies, or departments. The transfer may entail transferring from the central to the entity level (or a mixture): pay negotiations (if any); setting the overall wage bill; and design of pay scales.

While the three dimensions of pay individualization, differentiation and delegation describe analytically distinct elements of pay flexibility, reform examples in the real world often combine all elements. There are three reasons why components of pay flexibility tend to
below the national poverty line of IQD 110,000 and eligible for support. Despite having the same qualification cutoffs (IQD 110,000), the difference in the estimated eligibility rates between the SEVAT and MoLSA PMT can be attributed to: (i) difference in targeting model - while the MoLSA PMT is devised to assess households’ monetary poverty, the SEVAT model evaluates vulnerability, (ii) difference in the survey methodologies between the GOI’s SWIFT and the CLCI’s MPCA surveys – the SWIFT collected detailed household food and non-food expenditures from a nationally representative sample to estimate monetary poverty in Iraq, whereas the MPCA asked only about the total expenditure in a single question; research shows that these measures can be significantly different (Beegle et al., 2012).

_Table 2: Share of households eligible in each targeting (n = 10818)._

|Eligible criteria|Cutoff|IDQ (Monthly per capita<br>consumption)|Number<br>eligible|Share of the total<br>sample(%)|
|---|---|---|---|---|
|SEVAT|Log10||||
|Extreme vulnerable|<4.84|<70,000|2,921|27%|
|Extreme - Mid vulnerable|<4.96|<92,000|5,029|46%|
|All vulnerable group|<5.04|<110000|6,815|63%|
|Non<br>vulnerable<br>/<br>not<br>qualified|>5.04|> 110,000|4,003|37%|
|Legacy-VA|||||
|Extreme vulnerable|from 17 -<br>26|Host or displaced > 3<br>months|4,868|45%|
|Extreme - Mid vulnerable|from 17 -<br>26|Host or displaced|6
- Paying bills (note: included as both a physical and mental/managerial task) ( _bills_ )

- Helping children with schoolwork ( _homework_ )

- Caring for sick family members ( _caring_ )

## _Mental/managerial tasks_

- Planning what food to buy ( _food planning_ )

- Planning the family budget ( _budget_ )

- Paying bills ( _bills_ )

- Planning clothing purchases ( _clothing_ )

- Researching options for children’s education ( _education_ )

- Deciding when someone should see a doctor ( _doctor_ )

Respondents chose whether they, their spouse, they and their spouse equally, a maid or servant, their mother, their father, or someone else usually did each of the above tasks. “Paying bills” is included as both a physical and a mental/managerial tasks, as in Morocco, paying bills still frequently involves physically reporting to a business or postal office in order to make a payment. Only currently married respondents were asked who is primarily responsible for making decisions about children’s education and helping them with homework. Appendix Figures 1 to 3 display the distribution of responses for each item.

# **3.3 Attitudes and Perceived Norms Survey Items**

The survey also asked respondents to indicate whether they strongly agree, agree, disagree, or strongly disagree with the following items (in parentheses are the shorthand names used to refer to these questions in subsequent analyses and visualizations):

- Taking care of the home and children is a woman’s primary responsibility. ( _primary_ )

- It is important for fathers to be involved in daily housework and childcare tasks (for example, tasks such as cleaning, feeding, bathing, and dressing). ( _daily_ )

- When mothers work outside the home, children suffer. ( _suffer_ )

- It can be good for the family when mothers work outside the home. ( _work good_ )

8
participants the payroll accounts group are able to mobilize new savings by reducing consumption. When we disaggregate the results by type of consumption, we find that this reduction in expenditures is concentrated entirely in discretionary spending, while food consumption remains constant. In column (9), we additionally consider larger purchases of more than BDT 1,000 (approximately 20% of the median monthly wage). The results indicate a statistically significant decrease in such larger purchases for our treatment group relative to the cash wage payments control group. However, we cannot statistically distinguish the treatment effects between the payroll account and account groups (p-values 0.561 and 0.966).

**Shock mitigation.** Summary statistics from our baseline survey highlight the fact that many workers face savings constraints and find it difficult to smooth consumption across the month.<sup>44</sup> Given that direct deposits increase savings and appear to decrease spending on discretionary goods, it is natural to ask whether they also help workers smooth consumption and mitigate large financial shocks. In Table 6, column (10) we consider the impact of payroll accounts on the ability to mitigate unanticipated income shocks. The results indicate that participants in the payroll accounts treatment group experience significantly fewer economic shocks that they are unable to mitigate with existing precautionary savings. However, we cannot distinguish the effect in the payroll account group from that of the account only group. We report additional results on mechanisms for smoothing consumption in Appendix Table D.12, which reports treatment effects on earnings and work satisfaction. In line with the results on shock-mitigation we find that, at endline, workers in the payroll account group are much less reliant on salary advances to at the end of the month. This is consistent with the increase in precautionary savings and reduction in discretionary spending in the payroll account group that we have documented above.

Taken together, our results on the real effects of payroll accounts are consistent with the prior literature and suggest that through more intensive engagement with the financial
moving from non-employment to employment would pay a higher cost of 4.22 times the annual wage. These estimated costs are high, revealing large labor friction in Argentine labor markets. Our estimates are lower than those reported in Artuc, Chaudhuri, and McLaren (2010) for the U.S., where the average moving cost is around 6.565, and _ν_ is 1.884. Allowing for job quality terms _η_ , Artuç and McLaren (2015) estimate more modest _C_ , ranging from as low as 0.99 to as high as 1.54 (with _ν_ =0.257), also for the U.S. Finally, estimating a comprehensive model that allows for worker heterogeneity with Brazilian data, DixCarneiro (2014) nds median mobility costs ranging from 1.4 to 2.7, which are smaller than ours (though Dix-Carneiro's estimates show large dispersion across the population).<sup>12</sup>

> 11Our estimates of capital adjustment cost parameters for Argentina can be directly compared with those in Cooper and Haltiwanger (2006) for the U.S. as we use the same specications. Cooper and Haltiwanger (2006) estimate comparable xed costs ( _γ_ 1<sup>_US_</sup> = 0 _._ 039 ), but lower quadratic adjustment costs ( _γ_ 2<sup>_US_</sup> =0.049) and lower irreversibilities ( _γ_ 3<sup>_US_</sup> = 0 _._ 975 ). This implies that capital is more exible in the U.S. than in Argentina, especially in terms of the irreversibility cost of investment. These dierences, as well as the magnitudes of the estimates, are, however, sensible and plausible. Bloom (2009) reports larger values for the partial irreversibility cost, with capital reselling losses of 42.7 percent, and for the quadratic adjustment cost parameter (0.996). The xed cost parameter _γ_
directly by its importer ( **DAVAX** _sr_ ): Therefore, the GVC share in bilateral exports is which can be computed for the exporting country _s_ as a whole: or even at world level: The GVC-related trade indicator proposed above is not the first measure based on ICIO tables that has been developed to gauge the relevance of GVCs in international shipments. Following the seminal article of Hummels et al. (2001), various measures of a country or a region’s integrations in international production networks have been proposed (Johnson and Noguera, 2012; Rahman and Zhao, 2013; Los et al., 2018). The ‘vertical specialization’ index ( **VS** ) of Hummels et al. (2001) is probably one of the first and most popular of these measures. However, as pointed out by the authors themselves, this is a partial measure of participation in global value chains, since it only considers the backward linkages (i.e. it measures the import content of a country’s exports, see equation (1)). In order to take forward linkages into account, Hummels et al. (2001) also suggest considering the exports of intermediate products that later are further processed and re-exported (they label it **VS1** ). However, they do not propose a precise formulation of this measure, since it can be implemented only in a fully-fledged ICIO framework that was not available at the time of writing.

By exploiting the bilateral source-based decomposition presented here above, we can provide a precise measure of the share of exports related to ‘forward’ supply linkages ( **GVC** _forward_ or **VS1** in Hummels et al., 2001 nomenclature), which can be computed as the difference between **DVA** _source_ and **DAVAX** . Then, the overall **GVC** indicator of equation (22) can be decomposed into a ‘backward’ component, corresponding to
, Koelle, Michael, and Simon Quinn. 2022. “Do Capital Grants Improve Microenterprise Productivity?” Working Paper.

- Jayachandran, Seema. 2021. “Microentrepreneurship in Developing Countries.” In _Handbook of Labor, Human Resources and Population Economics_ : 1-31, Elsevier.

- Jayachandran, Seema. 2021b. “Social Norms as a Barrier to Women’s Employment in Developing Countries.” _IMF Economic Review_ 69 (3): 576–95.

- Jin, Yizhou and Zhengyun Sun. 2020. “Lifting Growth Barriers for New Firms: Evidence from an Entrepreneur Training Experiment with Two Million Online Businesses.” Working Paper.

- Kayumbi, Valentine. 2021. “Impact of the IFC Training: KCB Bank MSME Customers Story.” Blog available

- https://www.growlearnconnect.org/blogs/impact-ifc-training-kcb-bank-msme-customers-story

- Kevane, Michael, Aishwarya Lakshmi Ratan, and Diva Dhar. 2021. “Evidence Review of Women-Led Small

- and Medium-Sized Enterprises (SMEs) before, during, and after COVID-19: Examining Barriers and Opportunities.” Working Paper Bill & Melinda Gates Foundation.

- Khandelwal, Vatsal and Juni Singh. 2023. “Can Network Ties Help Facilitate Entrepreneurship?” Working Paper.

- Klinger, Bailey, Khwaja, Asim, and Carlos del Carpio. 2013. _Enterprising Psychometrics and Poverty Reduction_ . _Springer Briefs in Psychology_ , Springer, New York.

- Koshy, Elizabeth and Lucia Sanchez. 2021. “Supporting Women-led Businesses in Low- and Middle-Income Countries Through the COVID-19 Crisis and Beyond.” Innovations for Poverty Action.

- Kugler, Maurice, Viollaz, Mariana, Duque, Daniel, Gaddis, Isis, Newhouse, David, Palacios-Lopez, Amparo, and Michael Weber. 2021. “How Did the COVID-19 Crisis Affect Different Types of Workers in the Developing World?” Jobs Watch COVID-19, World Bank.

- La Porta, Rafael and Andrei Schleifer. 2014. “Informality and Development.” _Journal of Economic Perspectives_ 28(3): 109-26.

27
2

# 1. Introduction

The current era is marked by the highest number of forcibly displaced people (FDP) in history. As a result of the war in Ukraine coupled with ongoing conflict and disaster in countries such as Afghanistan, Ethiopia, Somalia, the Syrian Arab Republic, the República Bolivariana de Venezuela, and the Republic of Yemen, the number of people forced to flee due to fears of persecution, conflict, violence, human rights violations, and other events seriously disturbing public order had reached more than 110 million.<sup>2</sup> Understanding the extent and nature of the challenges they face is crucial for developing effective policy responses to address their needs and support their successful integration into their host communities or return to their places of origin or previous residence. However, existing data gaps on FDP make it challenging to design and implement such responses.

The aim of this paper lies in understanding existing gaps in microdata on FDP – refugees and internally displaced persons (IDPs). This paper does this by undertaking a review of all existing micro-level datasets in the UNHCR Microdata Library (MDL)<sup>3</sup> and World Bank (WB)<sup>4</sup> MDL from 53 low-income and middleincome countries with a significant presence of FDP.<sup>5</sup> The microdata in these databases are documented in compliance with international standards and practices. They contain a rich source of information on various attributes of datasets, including the country and dates of data collection, sampling strategy, survey modules, and other related aspects. While submission of datasets to the libraries is voluntary and thus does not guarantee an exhaustive list of all publicly available existing datasets on FDP, they are considered to be among the largest databases of microdata concerning development and forced displacement (Thompson 2010; EGRISS 2023).

For the purpose of this study, microdata are defined as primary data collected from household surveys. Our focus lies in identifying micro-level datasets that are publicly available and designed
level, and employment information is often difficult to obtain and potentially manipulatable by households. The impact of the decision to exclude this information is explored further in Section (5).

The final Sudan national PMT presented in Table 3 has an adjusted R-Squared just above 0.57, which is in the range of 0.40 to 0.65 commonly reported in other PMT studies (Brown, Ravallion, and Van de Walle 2018; Sebastian et al. 2018). The number of household members is the largest contributor to the PMT score, with larger households having a lower score. This is typical of PMT models because welfare is defined in per capita terms and, thus, household size tends to be an important predictor of welfare (Sebastian et al. 2018). Additional socioeconomic and demographic characteristics of household members contribute to the PMT score. For example, the dependency ratio, measured as the ratio of the sum of household members younger than 15 and 65-years-old and older to total household members, contributes negatively to the PMT score. Lower schooling levels among household members are also associated with a lower household PMT score and households that rely on external aid or farming have a lower PMT score. Households headed by a female also have a lower PMT score.

Housing and asset ownership contribute to the household’s PMT score in expected ways. Houses with more rooms per household member have higher PMT scores, while houses made of mud, wood, and straw, as well as with dirt floors, have lower PMT scores. Lack of access to household services, such as lighting, drinking water, and improved cooking fuels, was also associated with lower PMT scores. Additionally, not having assets, such as a motorcycle, motor vehicle, radio, computer, refrigerator, television, air conditioner (AC), or mobile phone, was also associated with a lower PMT score.

To evaluate the performance of the national PMT in identifying the poor for inclusion in welfare programs, we explore the PMT’s performance