rafmacalaba/data-use-mentions-extended huggingface dataset:
of the factor proportions theory under the assumption that all countries can access the same technologies. The second assumes Hicks-neutral technological differences across countries.

Under the first strand, Harrigan (1995) focuses on the production side of the factor proportions model and uses data on manufacturing output and factor endowments for 20 OECD countries during the period 1970-1985. The findings strongly suggest that capital abundance and unskilled labor are sources of comparative advantage in most sectors, but the evidence on the effects of skilled labor (and land) is rather weak.

Schott (2003), on the other hand, investigates whether developed and developing countries specialize in different subsets of products as a result of their differences in factor endowments. Using value-added, capital stock, and employment data from UNIDO for up to 45 developed and developing countries across 28 manufacturing industries (aggregated into HO aggregates based on input intensity) in 1990 he finds that labor abundant countries produced relatively little of the most capital-intensive goods. Batista and Potin (2014) extend Schott’s work to explain the dynamics of industrial specialization over time by examining a panel of 44 developed and developing countries over 1976-2000. They find substantial Rybczynski effect in that countries that accumulate capital produce fewer labor-intensive goods and more capital-intensive goods. They further find that poor labor-abundant countries that accumulate capital diversify their output by moving away from labor-intensive industries to capital-intensive industries, while rich capital-abundant countries that accumulate capital specialize in the production of highly capital-intensive goods.

Romalis (2004) uses a factor proportions model to examine whether it could explain the structure of commodity trade by integrating a multicountry version of the HeckscherOhlin model with Dornbusch et al (1980) model with a continuum of goods and Krugman (1980) model of monopolistic competition and transport costs. His approach assumes no factor intensity reversals and fixed factor shares within industries and across countries. His results corroborate the following two predictions. First, countries tend to capture larger production and trade shares of products that use their abundant factors more intensively.

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the income loss in regions that were adversely hit by trade. In that case, the uptake of social security disability insurance was substantially larger than the uptake of unemployment and trade adjustment assistance support.

**_Effects on job training_** We assess whether workers of firms suffering more adverse GFC foreign shocks were more likely to receive job training. The individual training records contain information on different training categories. Technical upgrading courses are aimed at workers seeking to update, deepen, or complement the professional skills they have acquired in a specific occupation or field or work. Their duration varies between 40 and 120 hours. Other training categories (Habitation, Qualification, initiation courses and Apprenticeships) are aimed at training a new generation of practitioners of a given occupation or profession.

We estimate Equation (2) using as outcomes the workers’ probability of receiving any type job training and the probability of receiving technical upgrading courses. The estimates presented in columns (6) and (7) of Table 9 do not show significant increases in the likelihood of receiving training for workers in firms suffering a worse GFC foreign shock.

# **VII. Responses in a Different Labor Market**

Our evidence so far shows persistent effects of foreign shocks on workers’ employment and wages in Brazil, firm restructuring through occupational changes and firm scarring in terms of exit as well as a contraction in revenues, employment, and productivity. One may be concerned about the external validity of these results, in particular whether the responses to foreign shocks would be similar in a labor market with (even) less flexibility than that of Brazil. To address this concern, we consider the case of Ecuador, a developing country in Latin America with a more rigid labor market de jure and de facto than Brazil.<sup>44</sup> We compiled a rich administrative data set from Ecuador that merges employer-employee matched data with education census, firm registry data, and firm-level customs data for the 2006-2017 period (details are provided in Appendix E). Similar
.0300)|(0.0518)|(0.0588)|(0.0384)|
|Heath index|-0.0104<sup>_∗∗∗_</sup>|-0.0126<sup>_∗∗∗_</sup>|-0.00490|-0.0308<sup>_∗∗∗_</sup>|-0.0197<sup>_∗∗∗_</sup>|
||(0.00252)|(0.00299)|(0.00506)|(0.00501)|(0.00399)|
|Mental health index (PHQ9)|-0.00601<sup>_∗∗_</sup>|-0.00826<sup>_∗∗∗_</sup>|-0.00961<sup>_∗_</sup>|-0.0150<sup>_∗∗∗_</sup>|-0.00993<sup>_∗∗_</sup>|
||(0.00266)|(0.00310)|(0.00513)|(0.00568)|(0.00410)|
|Has relative(s) in same site|-0.000291|-0.00384|0.00620|-0.0423|-0.0107|
||(0.0150)|(0.0159)|(0.0290)|(0.0259)|(0.0229)|
|Has relative(s) in Western countries|0.00164|-0.0288|-0.0196|0.111|0.0160|
||(0.0406)|(0.0446)|(0.0858)|(0.0695)|(0.0521)|
|Father
delineate the extents of metro areas. Cores are pre-defined from a global settlement point layer<sup>9</sup> based on a population threshold. Rather than rely on sub-national administrative units, the AI instead relies on a globally gridded population data set. In such a data set, the underlying units that undergo aggregation are grid cells that are of a uniform geographic size – in practice, 30-arc seconds, which is approximately 1 km<sup>2</sup> at the equator.

Constructing the AI first requires the specification of three thresholds – a minimum population threshold to identify settlement points that qualify as metro cores, a travel time threshold, and a population density threshold. While Uchida and Nelson (2009) experimented with a range of thresholds, the AI has become synonymous with thresholds of 50,000 for the population of the core, 60 minutes for travel time, and 150 people per km<sup>2</sup> for population density. Hence, the AI defines a group of population grid cells as constituting a metro area if each of those grid cells have a population density of at least 150 people per km<sup>2</sup> and fall within a 60-minute travel time radius of a settlement point that has an associated population of at least 50,000. An important feature of the AI is that it, in contrast to the other two “satellite data based” approaches, _does not include a contiguity requirement_ . This means that, in principle, a metro area may not consist of a single contiguous block of grid cells. Another important feature of the AI is that if there are two or more cores that fall within 60 minutes travel time of each other then they, effectively, merge into a single extended metro area.

# **_Approach #3: The cluster algorithm_**

Rather than attempting to delineate a metro area based on its functional area, using either commuting flow data or estimated travel times, Dijkstra and Poelman’s (
sparsity.<sup>11</sup> In empirical analyses in practice, local-area level remoteness is often characterized through the urban-rural dichotomy, which is rather blunt. Alternatively, the size or density of a particular settlement can potentially provide a more nuanced indication of a resident population’s centrality or remoteness. However, this approach rests critically on the delineation of boundaries for any particular settlement, which is empirically fraught (for example, due to clustering of towns).

At the same time, one open question, which would be a fruitful direction for future work to refine our new indicators, relates to the ways in which the delineation of local areas affects the measures. Intuition suggests that the most precise and informative measures of remoteness and dispersion would be based on data geo-coded at the individual household level.<sup>12</sup> In practice, for the Pacific Island countries, our calculations center around data for local-area communities. These are based on exogenously defined local-area (administrative, or survey catchment area) boundaries. The implications of these boundary definitions on the remoteness and dispersion measures is, we suspect, an empirical question, dependent on interior settlement patterns, and the boundaries themselves. One immediate direction for further refinement would be to re-estimate and compare the indicators using a range of geographical areas— from the finer to the coarser.

As a final note, it is worth highlighting that none of the measures we present in this paper explicitly accounts for the island-nature of PIC’s geography. However, they do reflect uninhabited areas of ocean in an implicit sense. Areas of uninhabited ocean lead to large measures of distance between communities (including between communities on outer islands and the national capital). This uninhabited distance results in high measures of remoteness, and relatedly, dispersion.

> 11 Part of the motivation for this work was a desire, for the purpose of empirical analysis, to apply a consistent approach to identifying ‘remote’, outer islands in different PICs. It became clear that it is
# Casley Gera, 2024).

Finally, although the evidence primarily assesses the impact of class size on test scores, student repetition and promotion rates – which are generally closely linked to learning – may also respond to reductions in class size. Walter (2018), assessing evidence from a panel of 20 high-, middle- and low-income countries, estimates that reducing PTRs by improving the allocation of existing teachers between schools could improve student promotion rates by between 0.1 and 4.2 percentage points.

In this paper, we present evidence of the impact of a five-year package of interconnected interventions intended to reduce class size in eight disadvantaged districts of Malawi. The intervention, implemented over five years, constructed classrooms; provided additional finance to schools to support hiring of contract teachers and construction of learning shelters; and provided results-based finance to reward improvements in staffing in lower grades. The interventions were targeted to eight districts with longstanding disadvantages in staffing, learning environments, and learning outcomes, particularly for girls.

Employing administrative data and data from a nationally representative independent sample of public primary schools, we estimate the impacts of the intervention on class sizes, repetition rates, and test scores. We exploit the targeting of the interventions to particular districts to conduct differencein-difference analysis between schools and students in the targeted districts and those in a comparison group of 17 rural districts (all non-urban, mainland districts which did not receive a similar intervention). We find that the interventions succeeded in reducing PTRs and pupil-classroom ratios (PCRs) in lower primary; reduced repetition rates in lower primary by 0.7 points; and closed the gap in learning outcomes between the targeted districts and the rest of Malawi. We also find suggestive evidence that the treatment reduced the gap in learning between girls and boys. The findings suggest that even in a low-income environment with significant constraints, targeted efforts to reduce class sizes can close district-level gaps in learning.

Our study is one of the first to assess the impact of an interconnected set of interventions to reduce class
brief review of the literature on the optimal level of debt. Section 6 concludes.

# 2. Evolution of EMDE debt since 2000

Prior to the global financial crisis, rapid growth helped narrow fiscal deficits and reduce government debt ratios, especially in EMDEs (Figure 1.A and 1.B; Kose et al. 2017). In addition to robust growth, debt relief under the Multilateral Debt Relief Initiative and Heavily Indebted Poor Countries (HIPC) Initiative contributed to the decline in debt in low-income countries (LICs) and lower middle-income countries. Fiscal deficits that reached 3 percent of GDP in EMDEs, on average, in 2001 turned into fiscal surpluses amounting to 0.7 percent of GDP, on average, by 2007. Over the same period, EMDE government debt fell by 12 percentage points of GDP to 36 percent of GDP. In LICs, government debt declined by 59 percentage points to 41 percent of GDP from 2001 to 2007.

> 2 Adverse consequences of high and rising debt are also discussed by CRFB (2019), using data for the United States. The study argues that high and rising debt weighs on income growth, limits the scope for fiscal policy, raises interest rates, raise the fiscal burden for future generations, and increases the risk of crises. Alcidi and Gros (2019) show that countries with high debt pay a risk premium. Additional debt accumulation increases a risk premium and raise the cost of the entire stock of debt. This could lead to a feedback loop between high debt levels and high risk premia, resulting in explosive debt dynamics. Boskin (2020) presents a detailed critique of Blanchard (2019). He analyzes fiscal projections in the United States and concludes that substantial increases in debt levels could lead to higher taxes and lower future incomes.

3
# The Labor Market Effects of Disability Benefit Loss<sup>∗</sup>

Anikó Bíró Cecília Hornok Judit Krekó Dániel Prinz Ágota Scharle

**Keywords:** disability insurance; benefit reduction; employment **JEL Codes:** H55, J14

> ∗Bíró: Centre for Economic and Regional Studies (biro.aniko@krtk.hu). Hornok: Kiel Institute for the World Economy (cecilia.hornok@ifw-kiel.de). Krekó: Budapest Institute for Policy Analysis and Centre for Economic and Regional Studies (judit.kreko@budapestinstitute.eu). Prinz: World Bank (dprinz@worldbank.org). Scharle: Budapest Institute for Policy Analysis (agota.scharle@budapestinstitute.eu). Bíró and Krekó were supported by the “Lendület” program of the Hungarian Academy of Sciences (grant number: LP2018-2/2018). Krekó and Scharle were supported by a National Research, Development and Innovation Fund (OTKA) research grant (grant number: K-135962). We thank Edina Berlinger, Márta Bisztray, Márton Csillag, Sandra Gain, Rafael de Hoyos, Gábor Kertesi, Rita Pető, Petra Reszkető, Ágnes Szabó-Morvai, Balázs Váradi, Roula Yazigi, and seminar participants at the Centre for Economic and Regional Studies, the Kiel Institute for the World Economy, ESPE, COMPIE and RES Conferences for helpful comments. This paper uses a linked database of data provided by the Hungarian State Treasury, the National Health Insurance Fund, the Educational Authority, the National Tax and Customs Administration, the Ministry of Economic Development and cleaned by the Databank of the Institute of Economics, Centre for Economic and Regional Studies. We thank the Databank of the Centre for Economic and Regional Studies for providing access and guidance in using their database. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent
# **Social Capital, Finance, and Consumption: Evidence from a**

# **Representative Sample of Chinese Households**<sup>**1**</sup>

Robert Cull World Bank Li Gan Texas A&M Univ. and SWUFE Nan Gao Zhong-Nan Univ. of Finance and Economics Lixin Colin Xu World Bank JEL codes: G14, G15, O16, O17.

Keywords: social capital, finance, informal finance, consumption

> 1 We have benefited from useful discussions with Janet Landa, Steve Knack, Claudia Ruiz and Thierry Tressel. The authors gratefully acknowledge funding from the World Bank’s Knowledge for Change Program for this project.
will require a time lag to alter the actual holdings of foreign currency balances.

Alami (1999) tested for a long-run equilibrium relationship (co-integration) between the ratio of foreign currency denominated deposits to total liquidity, the expected rate of change of the exchange rate, and an interest rate differential between domestic and foreign currency deposits. He used quarterly data for Egypt covering the period 1981:IV to 1993:IV and tested for cointegration among the different variables utilizing the Engle-Granger methodology (Engle and Granger 1987). The co-integration results were further assessed by implementing the Johansen methodology (Johansen and Juselius 1990) to confirm the uniqueness of the co-integrating vector.

13
of Occupations.” _Social Science Research_ 25, no. 3 (1996): 201-239.

**Hsieh, Chang-Tai, Erik Hurst, Charles I. Jones, and Peter J. Klenow.** “The allocation of talent and US economic growth.” _Econometrica_ 87, no. 5 (2019): 1439-1474.

**International Labor Organization.** “International standard classification of occupations: Structure, group definitions and correspondence tables.” _International Labor Office, Geneva_ (2012).

**National Institute of Statistics and Censuses.** “Encuesta de Hogares de Propósitos Múltiples.” San Jose, Costa Rica (2009).

**National Institute of Statistics and Censuses.** “Encuesta Nacional de Hogares.” San Jose, Costa Rica (2020).

48
> The adjustment factor accounts for the fact that survey growth is systematically lower than growth in national accounts, e.g. see Ravallion (2003), Deaton (2005), Pinkovskiy and Sala-i-Martin (2016), Lakner et al. (Forthcoming), Prydz et al. (Forthcoming). The pass-through factor is estimated using a machine-learning algorithm to account for systematic variation in pass-through rates between sub-samples of the data. We report

> 1 The government decided to indefinitely withhold the survey citing concerns over data quality. See Jha (2019)  and Press Information Bureau Government of India, Ministry of Statistics & Programme Implementation issued on November 15, 2019.

> 2 We use the revised 2011 PPPs published in May 2020. Following the World Bank’s global poverty measures, we use different PPPs for urban and rural areas to account for spatial price differences (Atamanov, et al. 2020). Throughout the paper urban and rural poverty are estimated separately and aggregated to the national estimate using the population weights in the World Development Indicators (WDI).

> 3 Using the CES surveys collected in 2004/05, 2009/10 and 2011/12, which collect a consumption aggregate as well as covariates that are also present in the 2014/15 survey, Newhouse and Vyas (2019) estimate several models of household consumption per capita. These models are then used to project household consumption into the 2014/15 CES, which did not collect information on aggregate household consumption, and hence estimate poverty. This poverty estimate underpins the World Bank’s global poverty estimate for 2015, see Chen et al. (2018) and World Bank (2018). We use a different set of variables, and different training and target data sets, but a methodology similar to Newhouse and Vyas (2019).

> 4 This is similar to the way surveys are brought to a common reference year in the World Bank’s global poverty measures, see Chen and Ravallion  (2010), Prydz et al. (
is not a flaw in the survey data, which are not designed to accurately measure the very top of the income distribution, but it does have implications for fiscal incidence analysis. Another limitation is the challenge of assigning direct taxes to individuals when the data does not explicitly distinguish between those in formal employment from those in informal employment. As such, we needed to impute formal employment based on proxy characteristics from the data to model the incidence of direct taxes, as these are not paid by individuals working in the informal economy.

As suggested by the macro-validation of the CEQ exercise for Cambodia, discrepancies exist between official statistics and modelled aggregate values of several elements of the tax and benefit system.<sup>20</sup> These

> 20 <mark>Several elements of the tax system are simulated relatively well when compared to administrative statistics. For example, the exercise captures about 96 percent of employment injury insurance, 99.2 percent of property tax, 99.2 percent of education expenditure, and 93.1 percent of health care costs. Simulations of other fiscal elements are less precise. This applies to instruments such as the salary tax (23.1 percent of the administrative aggregate) or means of transportation tax (35.3 percent). The limited precision in modeling social security contributions, health</mark>

28
DHS sample, and by only 2.5 percentage points in the LSMS sample.<sup>9</sup> Similarly, for locations close enough to the road networks, bringing an electric grid 10 km closer marginally increases the probability of working by 8 percentage points in the DHS sample, and 6 percentage points in the LSMS sample. Differences in estimates obtained from the two samples differ as both surveys cover different sets of countries. Our preferred estimates come from the DHS sample, which covers more countries.

The joint marginal effect of these two types of infrastructure is positive, indicating that roads and electricity play complementary roles in job creation in sub-Saharan Africa. In other words, there are significant employment gains that arise from providing both types of infrastructure simultaneously. The estimates of Table 4 in the Appendix imply that being closer to a main road increases the positive effect of being closer to an electricity grid on the probability of being employed. These effects are statistically significant at the 0.1 percent level. To put these estimates in perspective, using the DHS sample, Figure 6 shows that the impact of increasing proximity to the electric grid grows for households that are closer to the road, and vice versa - the impact of increasing proximity to roads grows for households that have greater proximity to the electric grid. Being 10 km closer to the electric grid increases the probability that an individual is working by more than 8.4 percentage points when the individual’s household is located next to a main road, but only by 4 percentage points when located 10 km away from the main road network. Similarly, being 10 km closer to a main road network increases the probability that an individual is working by more than 25 percentage points when that individual’s household is located next to the electric grid, but by only 21 percentage points when the household is located 10 km away from the electric grid.

Figure 6: Marginal impact of proximity to roads (left) and electricity (right) on employment <!