rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 6035

# **Abstract**

Do trade shocks affect workers differently because of their age? This paper examines the issue by estimating the lifetime mobility of workers based on the sectors in which they work. Using U.S. data, the paper shows that mobility costs rise with a worker’s age and years of experience, but stay the same regardless of his or her education level. In addition, using a general-equilibrium simulation of counterfactual trade-liberalization policies in the metal manufacturing sector, the paper shows that trade shocks affect workers with higher mobility costs more, for both winners and losers of the policy shocks. But the effects taper off over a worker’s lifetime, especially when they are close to retirement.

This paper is a product of the Trade and Integration 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 author may be contacted at eartuc@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
SMCs as part of ELECTIONS-CAPACITY, in comparison with the main topics of discussion in SMS messages received by the CDP.

# **3.4 Descriptive Statistics**

**<u>Table 5</u>** shows the observed values at endline in the control group villages and the various treatment groups, for a range of indicators.

The findings suggest that, absent the intervention, the problems of the school system in Sindh have not considerably improved. In terms of school functioning, only 80 percent of schools in the control group were found to be open at the unannounced visit at endline. In terms of enrollment, the average school had 71 students in grades from _katchi_ (pre-primary) to Grade 5, suggesting the continuation of Sindh’s general pattern of multiple small schools. Only 46 percent of students at control schools at endline were female, suggesting continuing gender disparities in enrollment.

Infrastructure remained an area of significant shortage in control schools. The average control school had 2.2 classrooms available for use at endline. Sixteen percent employed at least one openair classroom. Toilets appear to be an area of significant shortage: only 75 percent of control schools had toilet facilities available, with an average student-toilet ratio of 59:1. Only 51 percent of control schools had electricity, 60 percent drinking water, and 70 percent a boundary wall.

In terms of staffing and teacher attendance, the control endline data suggest no significant improvement absent the intervention. The average control school had 2.25 teachers<sup>35</sup> ; an average

> 35 The number of teachers in control schools has not decreased as a result of the interventions. We believe that any impact on new teachers in treatment schools is from assignment of teachers to these schools, and not resulting from

32
ratio of non-performing loans to total loans, the ratio of equity to capital, and the return over assets. _S_ stands for systemic risk; it is a matrix that includes measures of country and exchange rate risks. The former is captured by the spread on Argentine and, separately, Uruguayan sovereign bonds over comparable U.S. bonds, as expressed in Argentina’s Emerging Market Bond Index Plus or EMBI+ and the Uruguay Bond Index or UBI, respectively. Exchange rate risk (or more precisely the currency premium) is measured by the 12-month forward (NDF) exchange rate relative to the spot exchange rate for Argentina. For Uruguay, we use the spread of the average interest rate on peso time deposits (with maturity of more than one month and less than six months, in the top private banks) relative to the rate on similar dollar deposits. _E_ stands for exposure and is a matrix that includes indicators of individual banks’ exposure to systemic risks. More precisely, we use the share of government debt (bonds and loans) over total bank assets as a proxy for exposure to “country” (sovereign default) risk. For exposure to exchange rate risk, we use the ratio of dollar loans over bank capital for Argentina and the ratio of dollars loans over assets for Uruguay.<sup>12</sup> All regressions control for bank specific effects, α _i_ ..

Bank fundamentals and the indicators of bank exposure to systemic risks are lagged for two reasons. First, in both countries, balance sheet data are released to the public by bank regulators with a delay of three to four months; hence, the choice of our lag structure, which captures more precisely the information set available to depositors at any point in time. Second, this lag structure also helps to reduce potential endogeneity problems.

Results for Argentina and Uruguay are reported separately in Tables 1 to 6. Simple inspection of the tables reveals a similar pattern. The first column presents a regression of the

> 12
intermediate inputs and aggregate productivity. _The Review of Economic Studies 88_ (5), 2275–2307.

- Duflo, E., M. Kremer, and J. Robinson (2008). How high are rates of return to fertilizer? Evidence from field experiments in Kenya. _American Economic Review 98_ (2), 482–488.

- Duflo, E., M. Kremer, and J. Robinson (2011). Nudging farmers to use fertilizer: Theory and experimental evidence from Kenya. _American Economic Review 101_ (6), 2350–2390.

- Eckert, F. and M. Peters (2023). Spatial structural change. Working paper.

- Ederington, J. and M. Ruta (2016). Nontariff measures and the world trading system. In K. Bagwell and R. W. Staiger (Eds.), _Handbook of Commercial Policy_ , Chapter 5, pp. 211–277. Elsevier.

- Evenett, S. and J. Fritz (2020). The global trade alert database handbook.

- Fajgelbaum, P. and S. J. Redding (2022). Trade, structural transformation, and development: Evidence from argentina 1869–1914. _Journal of Political Economy 130_ (5), 1249–1318.

- Fan, T., M. Peters, and F. Zilibotti (2023). Growing Like India– The Unequal Effects of Service-led Growth. _Econometrica 91_ (4), 1457–1494.

FAO (2025). Employment indicators 2000–2023: July 2025 update.

- FAO and WFP (2022). _Special report - FAO/WFP crop and food security assessment mission (CFSAM) to the Democratic Socialist Republic of Sri Lanka_ . Rome: United Nations.

- FAO and WFP (2023). _Special report - FAO/WFP crop and food security assessment mission (CFSAM) to the Democratic Socialist Republic of Sri Lanka_ . Rome: United Nations.

- Farrokhi, F. and H. S. Pellegrina (
<mark>goods that proxy standards of living (cooking fuel, drinking water, and electricity, among others). While these indices are a step-up from poverty headcounts in that they measure the “intensity” of the poverty and allow for granularity in exploring “how the poor are poor,” these data are also ill-suited for our task. The most widely used index, Oxford’s MPI, only starts in 2010 and observations are not available every year; this is the same issue we face with poverty headcount data if we try to rely only on survey data as opposed to imputations.</mark> <mark>We therefore turn to alternative indicators that proxy some of the components of these indicators, but for which more complete time series exist to try to complement the current picture. Taken in concert, these indicators suggest that defaults have persistent negative effects on benchmark indicators and therefore the general welfare of the population. Food availability is curtailed, electricity consumption drops, and even child mortality and life expectancy seem to have worse-than-control outcomes more than a decade after default. The picture of welfare that emerges from an exploration of these variables is one of stagnation with respect to the counterfactuals, and thus with respect to the world.</mark> <mark>The recent case of the República Bolivariana de Venezuela after its 2017 default puts in stark relief the insidious association between a major macroeconomic crisis and the welfare of the population. One study by the country’s leading universities and NGOs found that 64 percent of Venezuelans had lost an average of 11 kilograms in 2017 (Encovi 2017).</mark><sup>26</sup> <mark>Nearly 90 percent of respondents also reported that their households’ income was not sufficient to cover the purchase of food and infant mortality had grown to about 70 percent, among other deprivations.</mark> <mark>Additionally, in 2019, the country suffered nation-wide blackouts (Morales-Arilla 2021). If only to state the obvious
held constant over the period. This suggests the model's utility as a versatile tool for identifying potential crisis escalation before periodic assessment data reflect it.

# 6. Discussion

## 6.1 Joint Effects of Climatic Shocks and Economic Variables

This study analyzed the impact of climatic and economic shocks on food security in Afghanistan. The findings highlight that the impacts of climatic shocks, economic factors, and structural constraints on food insecurity must often be interpreted together, emphasizing the need to monitor interconnected indicators. For instance, monitoring drought risks alone is insufficient for early warning applications or policy interventions and must be combined with additional indicators for context. In our model, droughts measured by rainfall and negative NDVI anomalies show higher food insecurity risks when occurring together. Drought alone increases food stress by 4.5 percentage points (1 z-score deviation), while its joint occurrence with negative NDVI anomalies results in a 13.3 percentage point increase, illustrating compounded effects. Seasonal factors further heighten these risks, especially during the pre-harvest lean season.

Economic variables add complexity. In our model, rising food prices reduce food stress by 1 percentage point for every 10%, reflecting mixed impacts for producers and consumers. However, food prices often rise concurrently with fuel prices and exchange rates, collectively worsening food insecurity conditions. A 10% increase in both food and fuel prices leads to a 9 percentage point increase in food stress probability. We also found that global food price hikes amplify food insecurity, making domestic inflation resulting from global pass-through net negative for food security. Finally, 28
keyword search of abstracts and titles in the Web of Knowledge database.  Here, the source country comes from author affiliations. For multiple authored papers, affiliations are counted for each country, so that the total number of affiliations may exceed the total number of articles.  Appendix A lists the search terms used for each stove type.  Note that the use of keyword searches may bias downward counts from countries whose patent abstracts may appear in other languages, such as France and Germany.  However, that Chinese patent counts are larger than even the U.S. and Japan, who do the bulk of global R&D, is still notable.

14
**Figure 2: NEET Rates in MENA Countries**

<!-- Start of picture text --> 50.<br>37.5<br>25.<br>12.5<br>0.<br>Yemen Iraq West Morocco Egypt Tunisia Lebanon Algeria Saudi Arabia U.A. Emirates<br>Bank/Gaza<br><!-- End of picture text --> _Source_ : ILOSTAT, <u>https://ilostat.ilo.org/.</u>

# 3. Research Findings on the Status of NEETs Worldwide

As mentioned in the introduction, a quantitative investigation of the condition and dynamics of NEETs in Morocco is rather new, compared to other parts of developing world where new studies have already become available. Employing longitudinal data, Ranzani and Rosati (2013) present evidence concerning the extent, characteristics, and evolution of the NEET phenomenon in Mexico over a 10-year period. In addition, they investigate the existence and extent of state dependence by disentangling unobserved heterogeneity from genuine state dependence. For example, they find that, compared to other NEETs, female and lower-educated youth are more likely to remain in this status than be employed.

Looking at Turkey, Bilgen Susanli (2016) examines the determinants of its NEET picture, drawing on data from the Household Labor Force Surveys over the 2004-2013 period. A logit analysis indicates that gender and educational attainment are key factors in explaining who is or is not in NEET. A greater number of household members who are in employment is associated with a lower likelihood of NEET. Transition matrixes analysis reveals that NEET status remains highly persistent despite the substantial fall over the sample period. In South Africa, Akinyemi and Mushunje (2017), investigating the determinants of rural youth participation in agricultural activities, show that 21 percent of youth are NEET, 77 percent of them in the 20–24 age bracket. Variables such as age, government funding and parent participation in farming increase the likelihood
incentives and their limitations. Some comments on the provision of incentives when contracts cannot be written or enforced conclude the paper.

As a point of departure, let us assume that the government can actually see and contract for all relevant dimensions of health care. In this case, there is no incentive problem per se as it is possible to ensure that the wage at which the universality objective is met will yield the expected services. The problem is that this level of wages may be very high - much higher than is likely to be forthcoming in any practical sense. One reason is the existence of greater earnings opportunities in richer, more urban areas of the country. On top of this, of course, is the fact that medical personnel are generally much better educated and potentially have higher incomes than the country average and tend to want the amenities that go along with urban life. If they have families they will tend to want to give their children educational opportunities usually found in cities. A recent survey of medical personnel in Indonesia discovered that the anount of money necessary to get them to relocate to the more remote areas of the country (generally away from Java) was several multiples of actual wages.<sup>3</sup> At first glance, there does not appear to be a problem of ineffective incentives, but one of insufficient budget. There could well be good, economic, reasons for insufficient funds. If taxes to pay for government expenditures are distortionary, and most tax systems in poor countries are very distortionary indeed, then there is an additional social cost of meeting the universality objective. This gives a good reason to see if there is a way to reduce the overall cost of provision by exploiting the design of contracts to doctors (see section 2).

At the other extreme, we can assume that the government can observe nothing about physician performance - that is, it can't tell if there is a doctor in attendance in a village at all much less if she is charging (
# WPS3638

## **Workers’ Remittances to Developing Countries: A Survey with Central Banks on Selected Public Policy Issues**

### **José de Luna Martínez**<sup>*****</sup>

#### **Abstract**

This paper presents the findings of a survey of central banks in 40 developing countries across different regions in the world. The survey focused on the following four topics: (i) coverage of national statistics on remittances, (ii) cost of transferring and delivering remittances, (iii) regulatory regime for remittance transactions, and (iv) efforts of developing countries to channel remittance flows through formal financial institutions. The study found that in most countries existing data do not reflect the full amount of remittance inflows that they receive every year. Coverage of instruments and financial institutions through which remittances take place is limited. Moreover, only a few countries measure remittances that take place through informal channels. It was also found that the scope of financial authorities in developing countries to reduce remittance fees is limited, because a large part of the fees charged to customers are set by financial institutions located in the countries where transactions originate. Cooperation between sending and recipient countries is needed to reduce remittance costs. The survey found that in several countries money transfer companies are not properly supervised. Given the increasing international concerns on money laundering and terrorism financing issues, it is important that basic registration and reporting requirements are introduced for this type of company. Registration and reporting requirements should be designed in a way that they do not deter the further development of this type of financial institution. Finally, the survey found that most countries need to establish better mechanisms that would allow them to maximize the developmental effect of remittance inflows. By establishing new savings and investment instruments for remittance recipient households, a larger part of remittance flows might be channeled to finance productive investments, thus fostering economic growth.

#### World Bank Policy Research Working Paper 3638, June 2005
The coefficient of years of schooling reflects the average returns to education, representing the change in wages due to a change in years of schooling  1 ln _w_  _S_  .<sup>8</sup> Psacharapolous and Patrinos (2004) provide an overview of returns to education estimates worldwide and over time using the Mincerian approach, and conclude that they are in the neighbourhood of 10%, with large variations around the mean. Their country specific estimates for men vary from zero returns for Italy [in 1989] to 28% for Jamaica [1989], and for women from negative returns of ‐ 0.8% for Suriname [1993] to 41% for Puerto Rico [1959].

Current and past work shows a general awareness that these comparisons suffer from a number of limitations. A first constraint is the difference in data sample coverage. While estimates for a country’s average returns to education should be based on representative samples, this is not always the case. The estimation sample may contain only formal wage workers, excluding casual and informal wage workers. The data may be obtained from firm surveys, focusing on representativeness for a subset of firms rather than workers.<sup>9</sup> Other causes of concern relate to the method of analysis used. Coefficients obtained from the ‘dummy variable’ (second) approach need to be adapted before they can be compared to those obtained from the continuous approach presented above. There is also substantial variation in what is included as right hand side variables, as some models include occupational variables, leading to weaker returns, while others do not.<sup>10</sup> A key concern that has received much attention relates to the estimation method, as coefficients obtained from OLS are biased since they do not take endogeneity into account. IV or control function estimation addresses this concern provided they make use of valid instrumental variables (Blundell, Daerden and Sianesi 2005). Standard estimates also typically neglect heterogeneity in
. The contribution of diet to the growing double and triple burden of malnutrition in LMIC populations is not well-tested in the research available to date, with most SRM looking primarily at the role of diet in contributing to either undernutrition or overweight, obesity and diet-related NCDs.

The widespread use of diet diversity metrics in young children and women of reproductive age along with emerging metrics of diet quality has potential to better capture the link between diet and overweight/obesity and NCDs. Complementary studies are needed to expand the scope of these efforts to include older children, adolescents, men and older adults. This remains challenging given the limited availability, quality and representativeness of dietary data for such groups in LMICs (Demmler et al. 2024).

While no SRM was found that summarized the effect size for the association of ASF intake with nutritional outcomes, there are several observational studies that have used multi-country datasets to explore this (Zaharia et al. 2021; Headey, Hirvonen, and Hoddinott 2018; Krasevec et al. 2017). Four SRM have estimated the risk of child overweight/obesity associated with consumption of dairy products (Babio et al. 2022; Lu et al. 2016; Vanderhout et al. 2020; W. Wang, Wu, and Zhang 2016).

Where SRM looked at the associations between diet diversity and nutrition and health outcomes, it appears that low diet diversity impacts GDM and poor neonatal birth outcomes in a similar way as unhealthy diets. Similarly, low diet diversity was associated with overweight and obesity among older children in one meta-analysis of only two studies (Gezaw et al. 2023b). We could not find a SRM that looked at the relationship between diet diversity and overweight among children <5 years of age. One cross sectional study in India has looked at this, reporting that a dietary diversity score of 7-9 food items for children 0-59 months of age was associated with increased risk of overweight and obesity (OR 1.22; 95% CI 1.12, 1.34) (Saha
Walloon in the Brussels region of Belgium). Our approach was to include those but flag them in the dataset so they can be dropped in robustness tests since the logic of competing sovereignty movements in the same region might be different from the model we propose here. We treat in analogous fashion cases where the same group is active in adjoining regions since in these cases, our region-based ethnic concentration variable might not capture the effect of ethnic difference on sovereignty demand. These could be cases where the group is too broad (e.g. Aborigines) and we have no data on smaller subgroups; or the group is large and has a historical homeland that does not map onto current administrative boundaries.

# **_Income & Inequality_**

We use household surveys to obtain regional per capita _disposable income_ . Thus, we have a fully comparable statistic across regions and countries. Previous country-level studies of regional separatism have used rough measures of differences in regional GDP per capita within countries. However, GDP data include income from production of natural resources, which does not necessarily remain within the region, limiting the usefulness of that measure for a test of our hypothesis that wealthier regions will have higher demand for sovereignty. A region may appear rich because it produces a valuable commodity (say, oil) while its people are in reality poor. Our household survey data show the actual income level of people living in each region.<sup>38</sup> Relative regional income is the ratio between regional mean per capita income and nation-wide mean per capita income. Income matters only in relative terms –i.e. regional income compared to the rest of the country matters, not income in absolute terms. Obviously, the betteroff regions will have a value greater than 1. We have data for 684 regions with values ranging from 0.08 (Magadan Oblast in Russia) to 2.8 (Moscow). We also use income share of each

> 38 A small problem with household survey data may be in advanced industrial