rafmacalaba/data-use-mentions-extended huggingface dataset:
- McKenzie, D. and Yang, D. (2015). Evidence on policies to increase the development impacts of international migration. The World Bank Research Observer, pages 155192.

- Mendola, M. and Carletto, C. (2012). Migration and gender dierences in the home labour market: Evidence from Albania. Labour Economics, 19(6):870880.

- Ministry of Finance (2014). Economic survey: Fiscal year 2013/14. Technical report, Government of Nepal Ministry of Finance.

- Mitchell, J. C. (1969). The concept and use of social networks. Bobbs-Merrill.

- Mu, R. and van de Walle, D. (2011). Left behind to farm? women's labor re-allocation in rural china. Labour Economics, 18:S83S97.

- Murard, E. (2016). Consumption and leisure: The welfare impact of migration on family left behind.

- Nandini, A. (1999). Engendered mobilization-the key to livelihood security: Ifad's experience in south asia. Rome: IFAD.

- Seddon, D., Adhikari, J., and Gurunga, G. (2001). The New Lahures: Foreign Employment and Remittance Economy of Nepal. Nepal Institute of Development Studies.

- Stark, O. and Lucas, R. E. (1988). Migration, remittances, and the family. Economic development and cultural change, 36(3):465481.

- Stock, J. H. and Yogo, M. (2005). Testing for weak instruments in linear iv regression. In Andrews, D. and Stock, J. H., editors, Identication and inference for econometric models: Essays in honor of Thomas Rothenberg, pages 80108. New York: Cambridge University Press.

- Taylor, J. E., Rozelle, S., and De Brauw, A. (
# **1 Introduction**

While the population of Argentina descended mainly from the combination of Native, African and European ancestors (see Wang et al., 2008; Corach et al., 2010; Avena et al., 2012; Parolin et al., 2019), it is a common stereotype to see this population as mainly of European origin.<sup>1</sup> This incorrect view of the Argentine population had a correlate on the scarcity of national statistics on the numbers and characteristics of the native population for most of the national history.<sup>2</sup> We use the data from the 2010 National Census (Instituto Nacional de Estad ́ıstica y Censos, 2010), the first census to ask about native identity or ancestry at the individual level, to study the socioeconomic outcomes of the Native people of Argentina.

We compare Native and non-Native Argentines in terms of outcomes related to housing, education, labor market and health. Consistently with evidence of Native disadvantage in other Latin American countries (see Gandelman et al., 2011; Freire et al., 2015), we find that Natives, on average, fare significantly worse on all of these dimensions in Argentina. The magnitudes of the differences are moderate, corresponding on average to 12 percent of the standard deviation of the outcomes. These differences persist even after controlling for location, showing that differences are not only related to geographical differences.

We also find differences in the transmission of education from parent to children. For each level of education of the parents, the children of Natives have, on average, fewer years of education than the children of non-Natives. This is consistent with what is found for other Latin American countries (see Cruces et al., 2012; Berniell et al., 2021). Given the importance of education for economic outcomes and human development (see, for example, Angrist and Krueger, 1991; Lleras-Muney, 2005), the differences in the transmission of education between
.0055)||
|Provident Fund|0.98***|18.1|-0.038|19.2|-0.026|16.0|-0.15|17.4|
||(0.086)||(0.23)||(0.18)||(0.12)||
|Perc. Change Income|0.054***<br>(0.0028)|1.0<br>|-0.0050<br>(0.0082)|1.0|0.0058<br>(0.0057)|1.0|-0.0062<br>(0.0040)|1.0|
|Observations|23,568||||||||

_Source_ : Authors’ analysis based on survey data.

47
Initiative.*** p < 0.01, ** p < 0.05, * p < 0.1 _School covariates_ variables include: presence of electricity in school, good desks in school, school has a library, a computer lab, running water in the school, student enrollment, student-teacher ratio, percentage of qualified teachers, percentage of female teachers, experience of the school principal, age of school principal, administration experience of school principal, level of education of school principal, school principal is female, share of students with high performance in mathematics, English, and science on the WASSCE. _Student-level covariates_ variables include the following: percentage of students not repeating any grade, percentage of students earned credit and above on GABECE mathematics, percentage of students earned credit and above on GABECE English, percent of students earned credit and above on general science, all at baseline. And socioeconomic characteristics of students—percent of students in households that has a quality roof, a wall, television, car, access to grid electricity, parents have some education.

We later estimated equation (1) using the official WASSCE scores from 2018. The results are presented in Table 5. The result from the preferred specification is strikingly similar to the pre-analysis using the survey and researcher-administered test (0.59 versus 0.54 as reported in Table 4). The estimates from the between-school control are positive and similar in magnitude, although they are not statistically significant. The lack of statistical significance, in this case, is likely due to the small sample size offering limited statistical power. The estimated effects from the preferred specification correspond to a real significant gain in learning improvement that is equivalent to an increase of about 9 percentage points from a low base of 20.18 in the control group.

In Table 5, columns 5 to 8, we use a different outcome measure, which is a dummy variable indicating whether a student received a satisfactory score on both English and 20
our work contributes to making progress in the initial settingup stage. During this exercise, it is important to ensure data privacy of individual remitters. Bearing this in mind, we used MSP system-generated personal unique identification numbers as a variable for remitters.

**Our estimates suggest that the number of foreign workers in Malaysia was between 2.99 million and 3.16 million in 2017–18.** State and nationality distributions of foreign workers in our estimates are consistent with the MOHA data, lending support to the validity of our estimates. Nevertheless, we note that the BNM remittance data could potentially underestimate the number of workers in states with low access to MSPs, as well as nationalities that have access to alternative money transfer mechanisms such as commercial banking and informal transfer channels. This risk could be mitigated over time by BNM’s greater efforts to enhance the financial literacy of residents, including foreign workers, and increase the availability of online remittance services to ease accessibility and further reduce remittance costs. As a complementary measure, BNM could conduct annual foreign worker surveys to better understand their financial management behavior, leveraging the existing network of MSPs.

12
- **Boudreau, Laura** , “Multinational Enforcement of Labor Law: Experimental Evidence from Bangladeshs Apparel Sector,” _Working Paper_ , 2020.

- **Breza, Emily and Arun G. Chandrasekhar** , “Social Networks, Reputation, and Commitment: Evidence From a Savings Monitors Experiment,” _Econometrica_ , 2019, _87_ (1), 175–216.

- **, Martin Kanz, and Leora Klapper** , “Workplace Signaling and Financial Commitment: Evidence from a Field Experiment,” _American Economic Association Papers and Proceedings_ , 2018, _108_ , 438–443.

- **Brown, James R., J Anthony Cookson, and Rawley Heimer** , “Growing Up without Finance,” _Journal of Financial Economics_ , 2019, _134_ (3), 591–616.

- **Bruhn, Miriam and Inessa Love** , “The Real Impact of Improved Access to Finance: Evidence from Mexico,” _The Journal of Finance_ , 2014, _69_ , 1347–1376.

- **Burgess, Robin and Rohini Pande** , “Do Rural Banks Matter? Evidence from the Indian Social Banking Experiment,” _American Economic Review_ , June 2005, _95_ (3), 780–795.

- **Callen, Michael, Suresh De Mel, Craig McIntosh, and Christopher Woodruff** , “What Are the Headwaters of Formal Savings? Experimental Evidence from Sri Lanka,” _Review of Economic Studies_ , 2019.

- **Campbell, John Y., E. Jackson Howell, Brigitte Madrian, and Peter Tufano** , “Consumer Financial Protection,” _Journal of Economic Perspectives_ , 2011, _25_ (1), 91–114.

- **Carlin, Bruce, Arna Olafsson, and Michaela Pagel** , “Fintech Adoption Across Generations: Financial Fitness in the Information Age,” _National Bureau of Economic Research Working Paper_ , 2017.

- **Chernozhukov, Victor, Mert Demirer, Esther Duflo, and Ivan Fernandez-Val
some market power, but we believe the shares are small enough that this is not a first-order concern.

# II DATA AND DESCRIPTIVE STATISTICS

The empirical analysis uses daily logbook data from the three fishing cooperatives noted above, Pichilingue, Sargento, and Abreojos. Daily data on catches from fishing teams in each cooperative were recorded from January 1, 2007, to December 31, 2009. Catch records include a team identifier (for Abreojos and Pichilingue only), the common name of the species caught, the weight of the catch (kilograms), and price per kilogram offered by the cooperative (pesos). The composition of the species fished by the cooperatives partially reflects the biogeography of the Pacific vs. the Gulf coast of B.C.S.; however, there is still substantial overlap, thereby allowing a comparison of the behavior of the different types of cooperatives for a given species.

The logbook data have information on the prices cooperatives paid to their fishermen but, unfortunately, do not have information on the price the cooperative sold the catch at in the market. Using the _Sistema Nacional de Informacion e Integracion de Mercados_ (SNIIM), available from the Mexican government, we have collected data on daily market prices in La Paz for as many species and dates as possible.<sup>5</sup> Using the dates in the cooperative logbooks, these market prices are matched to the cooperative purchases. In cases where a market price is not available for a particular date, the average price for the corresponding week or month is substituted instead (depending on availability).

To examine whether market prices in La Paz are driven by external forces that are exogenous to supply factors in the vicinity of La Paz, we use the SNIIM to obtain market prices from La Nueva Viga, a large national fish market in Mexico City connecting sources to distributors. The La Nueva Viga data contain information on marine fish, crustaceans, freshwater fish, and mollusks/others. B.C.S. is listed as
4 4. The HEFPI 2019 database also includes mean annual household per capita out-of-pocket expenditure in 2011 PPP $ terms.

5. In addition, the 2019 database includes the out-of-pocket expenditure “budget share” – the fraction of household consumption or income absorbed by out-of-pocket expenditures. This turns out to be highly correlated with the incidence of catastrophic expenditures using the 10% threshold (Wagstaff et al. 2019).

Figure 1 shows the new structure of the financial protection side of the 2019 HEFPI database, updating Figure 2 in Wagstaff et al. (2018). The yellow-edged boxes are the new indicators in the 2019 database.

<u>Figure 1: New structure of the financial protection side of the 2019 HEFPI database</u> <!-- Start of picture text --> Financial<br>Protection<br>Catastrophic Impoverishing Out‐of‐pocket<br>expenditures (CATA) expenditures (IMPOV) expenditures (OOP)<br>IMPOV poverty gap<br>CATA 10% IMPOV absolute IMPOV relative Household p.c. OOP<br>(in $ due to OOP)<br>For all abs. poverty lines<br>CATA25% $1.90 and $3.20 per day 60% of median p.c. consumption ($1.90, $3.20, $5.50, $21.70 per day) Household OOP budget share<br>Societal Poverty Line (SPL):<br>$5.50 and $21.70 per day $1.90 per day or 50% of<br>median p.c. consumption<br><!-- End of picture text -->

# 2.2. <u>Refinements to out-of-pocket expenditure estimates</u>

In preparing the 2019 HEFPI database, out-of-pocket expenditure estimates at the household level have been revised for many of the datapoints included the 2018 database.
countries, estimates the “common” production frontier. For consistency, the labor and capital regressions are performed using the same sample. However, results are robust to enlargement of the sample (see Section 4).

Table 1 lists the countries in the sample, classified by EU membership/Schengen area.

**Table 1. Countries in the Study Sample**

|EU Members/Schengen Area|Other ECA Countries|
|---|---|
|Austria, Belgium, Bulgaria, Croatia, Denmark,|Albania, Armenia, Azerbaijan, Belarus, Bosnia|
|Estonia, Finland, France, Germany, Greece,|and Herzegovina, Georgia, Kazakhstan,|
|Hungary, Ireland, Italy, Latvia, Lithuania,|Kosovo, Kyrgyz Republic, Moldova,|
|Luxemburg,<br>Malta,<br>Netherlands,<br>Norway,|Montenegro, North Macedonia, Russia|
|Poland, Portugal, Romania, Slovak Republic,|Federation, Serbia, Turkey, Tajikistan,|
|Slovenia, Spain, Sweden, Switzerland, United<br>Kingdom|Turkmenistan, Ukraine, Uzbekistan|

Since the number of structural indicators could be very large, variables are selected and classified according to their interpretation and in line with the WEF competitiveness approach. Since 2005, the WEF has published the annual Global Competitiveness Index (GCI), which identifies and measures the microeconomic and macroeconomic foundations of national competitiveness. The GCI has identified 12 areas (“pillars”) as sources of national competitiveness.<sup>3</sup> Table 2 lists the indicators used in the analysis, classified according to the WEF-GCI 2018 pillars.

**Table 2. Structural Reform Indicators Classified Following WEF-GCI 2018**

|**Pillar**|**Indicator**|**Description**|**Source**|
|---|---|---|---|
|Pillar 1: Institutions|Government<
fuel price elasticity of carbon emissions can be much higher in cities with robust public transport options. An extension of the current econometric research would incorporate relevant price variables (e.g., fuel prices), as well as their interactions with transit investment variables to test the effects on price elasticities.

# **8.  Summary and Conclusions**

In this paper, we have estimated an urban CO2 emissions model using satellite-measured CO2 concentrations from 2014 to 2020, for 1,236 cities in 138 countries. The model incorporates the global trend in CO2 concentration, seasonal fluctuations by hemisphere, and a large set of georeferenced variables that incorporate CO2-intensive industry structure, emissions from agricultural and forest fires in neighboring areas, demography, the component of income that is uncorrelated with industry structure, and relevant geographic conditions. We resample all model variables to a 10 km global grid, and capture CO2 diffusion from discrete emissions sources via inverse-distance weighting to a grid cell centroid distance of 100 km.

In four econometric estimation exercises, we assign grid cells to cities if they lie within 20, 40 and 60 km of city centroids, or within the boundaries of UN-defined Functional Urban Areas. The results are very similar and robust in all four cases, with the expected signs and generally high levels of significance. We find that economic development has a significant effect on the direction of the 34
/20, the identity of the original respondents was done directly as the name of the household head was available in the information the NSIA shared. In the case of the IE-LFS 2021, no information on the names of previous respondents was available. Instead, we used information on the household head— including age, gender, location, and number of members—to assess if the respondent household was the same that participated in the IE-LFS 2021.

12
To empirically estimate this theoretical model, we exploit information on maternal mental health during the first round and data on cognitive outcomes for our sample of 1,095 children for whom we have PPVT Z-scores from the second and third rounds of data collection. A naïve estimation of the effects of exposure to lagged maternal stress on cognitive development will regress a measure of maternal stress in 2002 on the PPVT Z-scores in 2006/7 and 2009/10, using the following specification: P 0 1M<sup>+ αα</sup> 2<sup>CC</sup> 3<sup>MM</sup> 4<sup>MM</sup> (2) ii,tt ii,tt−1 ii,tt<sup>+ αα</sup> ii,tt<sup>+ αα</sup> ii,tt<sup>+ εε</sup> iitt PPTT = αα + αα where P represents the PPVT Z-scores for child _i_ in period _t_ (i.e., 2006/7 or 2009/10). M<sup>captures the value of any of the three maternal mental health indexes we estimated using</sup> ii,tt PPTT data from 2002. ii,tt−1 , , and are vectors of child, mother, and household/community observable and time-varying characteristics that can lead to differences in cognitive ability across ii,tt ii,tt ii,tt CC MM MM children and influence their parents’ investments in them. These vectors include all the variables presented in Table 1, all of which have been documented to affect child cognition (for a review, see Shonkoff and Phillips, 2000). represents a random, idiosyncratic error term. Under the assumption of complete exogeneity of iitt M 1<sup>,</sup> εε measures performance in the PPVT at each period _t_ for children whose mothers were depressed ii,tt−1<sup>, the parameter of interest, αα
the global level, a figure 20 percentage points lower for LAC (Data obtained from Climate Watch).

> 38 See Grippa and Mann (2020) for an exercise that uses firm-level information to conduct transition risk stress testing.

> 39 Compounding our findings, Ramírez, Thomä, and Cebreros (2020) find a sizable exposure of banks in LAC to transition risk driven by companies increasing fossil fuel production. For instance, the authors show that Brazilian companies could increase their oil and coal production by 40 percent and 11 percent in the next 5 years.

19
are independent/self-employed workers with females (2.28%) suffering more than males (1.31%), particularly those with high school education or less (2.0%).

Morales (2017) use survey, census and registry data covering the 1993-2005 period to study the same caseload of IDPs in Colombia. Short-run effects indicate a negative impact on wages. A one percent increase in population due to IDPs results in a 1.4 % reduction in local wages with this effect being larger for women, particularly low skilled women (2.2%). The effect is also negative and significant for high skilled men but smaller (0.7%). In this case, OLS and IV estimations concord with IV estimations showing larger effects. The long-run effects show instead a positive effect with OLS estimations and a negative effect for low-skilled women and no effects for other groups with IV estimations. There is therefore some evidence that the negative effects tend to disappear in the long-run but not for all groups. This study is also one of the few studies that considers the potential impact on outmigration. It finds that an increase of 1% in population due to IDPs generates an outmigration of 0.2-0.3 people per 100 residents.

The two studies reviewed on labor market impacts in Colombia are therefore very consistent in finding a negative effect on wages with the effect being larger for women and low skilled workers, notably in the informal sector. This effect also seems to be attenuated in the long-run, possibly due to outmigration and other labor market adjustments.

Using administrative panel data on quarterly IDP flows and rental prices by income level between 19992014 for 13 cities in Colombia (which received 66% of all IDPs), Depetris-Chauvin’s and Santos’ (2018) OLS estimates show a significant positive impact of IDP inflows on average rental prices for low- and middle-income housing, which may last up to 10 quarters. Results for high-income housing are nonsignificant. Their IV approach does not show statistically
Policy Research Working Paper 9790

# **Abstract**

This paper uses merged customs and administrative data from Serbian firms to quantify the impact of neighboring firms’ importing experience on the decision to start sourcing inputs from new markets. The analysis finds that firms are more likely to start importing from a new market if neighboring firms in the same industry and location have experience importing from that market and if those firms are increasing their imports over time. Further, the results support a distinction between imports and exports for the decision to enter foreign markets; unlike exports, import sourcing choices are not independent across countries. The analysis finds that imports across origins are substitutes, not complements. The paper also investigates origin-country and firm heterogeneity. The results indicate that the impact of neighboring firms’ importing experience is greater for source countries in the European Union market and for firms that are high productivity, foreign owned, and previous importers. Together, these findings suggest that a firm’s spatial connections are an important contributor to its access to global markets as sources for inputs.

This paper is a product of the Finance, Competitiveness and Innovation 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 swtan@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
**Table 4 – Years of Life Lost, by Gender, 2016**

||**All**|**Women**|**Men**|
|---|---|---|---|
|30-34|120,406|21,587|98,819|
|35-39|185,642|36,704|148,938|
|40-44|300,647|52,518|248,129|
|45-49|415,610|65,885|349,726|
|50-54|773,506|104,615|668,891|
|55-59|1,109,687|141,986|967,701|
|60-64|1,221,584|169,004|1,052,579|
|65-69|917,030|125,746|791,284|
|70-74|347,145|47,219|299,926|
|75-79|416,913|51,640|365,273|
|80 plus|171,339|29,945|141,395|
|**Total**|**5,979,509**|**846,848**|**5,132,661**|

_Source:_ GBD Results Tool (database), Global Burden of Disease Study 2016, Global Health Data Exchange, Institute for Health Metrics and Evaluation, Seattle, http://ghdx.healthdata.org/gbd-results-tool.

# **Tobacco-related medical costs**

The total tobacco-related medical expenses in the case of Russia used in this analysis have been calculated using national data. The direct health care costs related to the treatment of tobacco-related diseases include the costs of medical care provided in outpatient facilities, hospitals, and day-care hospitals. It is calculated by disease using the information on the number of cases and the weighted average costs of the treatment for each