rafmacalaba/data-use-mentions-extended huggingface dataset:
- <u>Bell, D., and D. Blanchflower (2010). “Youth unemployment: déjà vu?”</u> _IZA Discussion Paper_ 4705.

- <u>Bergmann, A. and A. Mertens (2011).</u> “Job Stability Trends, Lay-offs, and Transitions to Unemployment in West Germany.” _Labour_ 25 (4): 421-446.

- <u>Bertrand-Cloodt, D., Corvers, F., Kriechel, B., and J. van Thor (2012). “Why Do Recent Graduates</u> Enter into Flexible Jobs?” _International Labour Review_ 157(3): 157-175.

- <u>Blundell, R., Duncan, A., and C. Meghir (1998). “Estimating Labor Supply Responses Using Tax</u> reforms,” _Econometrica_ , 66(4): 827-861.

- <u>Boeri, T. (1999). “Enforcement of employment security regulations, on-the-job search and</u> unemployment duration.” _European Economic Review_ , _43_ (1): 65-89.

- <u>Bollinger C., Hirsch, B., Hokayem, C., and J. Ziliak (2019). “Trouble in the Tails? What We Know</u> about Earnings Nonresponse 30 Years after Lillard, Smith, and Welch.” _Journal of Political Economy_ , 127(5): 2143-2184.

- <u>Bratberg, E., Salvanes, K. and K. Vaage (2010). “Has Job Stability Decreased? Population Data</u> from a Small Open Economy.” _Scandinavian Journal of Economics_ , 112: 163-183.

- <u>Brekelmans, S., and G. Petropoulos (2020). “Occupational Change, Artificial Intelligence and the</u> Geography of EU Labour Markets.” Bruegel Working Paper 2020/03 :33.

- <u>Browning, M.
Not all studies, however, find an increase in bank risk taking. For instance, Wheelock and Wilson (1994) and Alston et al. (1994) find that deposit insurance has not led to an increase in bank failures in the United States. Karels and McClatchey (1999) also fail to find evidence that the adoption of deposit insurance increased the risk taking of U.S. credit unions. Peria and Schmukler (2001) using data from Argentina, Chile and Mexico during the 1980s and 1990s, find that depositors withdraw funds and require high interest rates during times of distress thus providing market discipline. They attribute their finding to deposit insurance schemes not being fully credible. Wagster (2007) finds an increase in idiosyncratic risk of Canadian banks and trust companies after adoption of deposit insurance, but finds that the overall risk declines.

The reason for the mixed findings could be due to other important factors, such as banks’ charter values, credibility of the insurance scheme or the effectiveness of monitoring by nondeposit creditors, affecting the impact of deposit insurance on risk taking. Failure to take into account some of these factors may explain the mixed findings in the empirical literature. It is also difficult to disentangle the positive stabilizing effects deposit insurance from negative effects that result from moral hazard. While deposit insurance can increase moral hazard and make financial systems more vulnerable to crises during good times, it can also enhance depositor confidence and reduce the likelihood of contagious bank runs during crisis periods. The net effect of deposit insurance on bank risk and stability depends on whether the benefits of deposit insurance outweigh its costs. Some recent papers have tried to determine whether the “moral hazard” effect dominates the “stability effect” of deposit insurance.

Ngalawa, Tchana and Viegi (2016) argue that if the adoption of deposit insurance is associated more with bank insolvency than it is with bank runs, then one can conclude that the moral hazard effect dominates. Using an empirical approach that distinguishes liquidity problems arising from panic withdrawals of deposits from
welfare services (health care, food) and long-term infrastructure services is more acute for the scare public finance resource. They further extended the data until 2005. It also finds that the increased access to infrastructure services (i.e., improved quantity and quality of infrastructure) would decrease income inequality as their econometric models show a strong negative correlation between the income inequality measure (Gini coefficients) and the synthetic indices used to measure infrastructure quantity and quality. The correlation coefficients vary between -0.47 and - 0.56. The study also shows that improved infrastructure not only reduced income inequality and poverty in Sub-Saharan Africa but also all over the world, more prominently in East and South Asia.

Chatterjee and Turnovsky (2012) developed a general equilibrium growth model to analyze the impacts of investment on income inequality (and also on infrastructure and economic growth). Considering four alternative sources to finance the public investment: lump-sum tax, capital income tax, labor income tax and consumption tax, their model shows that public spending on infrastructure (public capital) would have differing impacts between the short-run and long-run. In the short-run, public investment causes income inequality to decline; however, the declining trend decreases over time, and income inequality gets worse (increase) in the long-run.

Using state-level panel data on infrastructure investment and per capita income for the 1950 -2010 period, Hooper et al. (2018) investigates the relationship between infrastructure and income inequality. The study finds that investment in physical infrastructure (highways) and human capital (higher education) reduces income inequality (Gini indices) in the United States. The relationship is found stronger at the bottom 40 percent of the income distribution. The study also finds that investments in highways are more effective at reducing inequality. US states with a higher Gini coefficient at the bottom 40 percent had lower infrastructure investment during the previous decade. The finding suggests that infrastructure investment is an important factor in reducing poverty and income inequality not only in developing countries but also in developed
# Table 7.  Inequality measures for household per capita consumption expenditure, household per capita wealth, and individual wealth, by specification of individual wealth (all differences significant at p<0.001)<sup>(1)</sup>

||Among full<br>Gini in|sample:<br>dex||Gini index||A|mong only<br>75/25|those own|ing any ass<br>GE|ets (value><br>(0): Theil|0):<br>L|G|E(1): Theil|T|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||HH per<br>capita<br>wealth|Indiv.<br>wealth|HH per<br>capita<br>exp.|HH per<br>capita<br>wealth|Indiv.<br>wealth|HH per<br>capita<br>exp.|HH per<br>capita<br>wealth|Indiv.<br>wealth|HH per<br>capita<br>exp.|HH per<br>capita<br>wealth|Indiv.<br>wealth|HH per<br>capita<br>exp.|HH per<br>capita<br>wealth|Indiv.<br>wealth|
|Cambodia|||||||||||||||
|Indiv. wealth|||||||||||||||
|specification:<sup>(2)</sup>||||||||||
income inequality in the U.S. Using several labor supply elasticity estimates and using data on incomes and hours worked from the American Time Use Survey, we apply our method to recover individual productivities and preferences.<sup>5</sup> We begin with a baseline set of elasticity parameters taken as averages from a number of labor supply studies discussed in Chetty (2012). Under the baseline elasticities, we infer high income people actually have lower average preferences for consumption compared to middle income people. Essentially, the baseline labor supply elasticities imply that high income people should work substantially more than low income individuals; because we observe a relatively flat hours gradient over the income distribution, we infer that high income people have, on average, lower preferences for consumption relative to leisure. However, we then vary the elasticity parameters and show that we will infer preferences are increasingly important in driving income inequality if we use (1) a larger difference between the income and hours elasticities and/or (2) larger income effects to recover individual productivities and preferences. Thus, a larger difference between the income and hours elasticities and larger income effects imply that more of the difference in incomes between rich and poor is due to preferences.

Finally, to highlight how our findings on the determinants of income inequality change our understanding of the welfare benefits of redistribution, we simulate optimal tax schedules that account for both productivity and preference heterogeneity driving inequality. While the general methodology to recover determinants of income inequality devised in this paper is free of normative assumptions, for the purpose of welfare calculations, we adopt the normative stance developed in Fleurbaey and Maniquet (2006) in which differences in productivities merit redistribution whereas differences in preferences do not merit redistribution. We simulate optimal tax schedules, accounting for both productivity and preference heterogeneity, under different values of the relevant labor supply elasticities and compare these schedules to the optimal tax schedules in which all income inequality is due to productivity heterogeneity as in Mirrlees (1971) or Saez (2001).
research that considered the impacts of paternity leave policies in developing economies is by Amin, Islam and Sakhonchik (2016). Using data from 53 developing economies, the authors find a strong positive association between the provision of paternity leave and female employment at the firm level.

We contribute to this body of literature in a number of ways. Firstly, our data set in this analysis covers 190 countries over 52 years, and captures leave granted to both parents; this allows us to provide what is, to the best of our knowledge, a novel overview of the global trends in family leave policies over five decades. Secondly, the global nature of our data allows us to consider the relationship between leave policies on women’s labor market outcomes across regions. Finally, while earlier studies have tended to focus on either leave granted to mothers or to fathers, we assess both types of leave by focusing on the gap between the leave allocated to mothers and fathers.
<!-- Start of picture text --> — C r ss a . e<br>_ TRAFFIC PER USER: ' WA, .<br>2.66 X : 5 wi Q N<br>1.2 ]<br>08 ) g<br>. 0 qLjy 7"w &<br>IBRD 48263 |<br>JULY 2024<br><!-- End of picture text -->
# Figure 3. Contributions to potential growth in EMDE regions

<!-- Start of picture text --> A. Contributions to regional potential B. Contributions to regional potential<br>growth growth<br>Percent TFP Capital Labor Potential growth Percent TFP Capital Labor Potential growth<br>10 8<br>8<br>6<br>6<br>4<br>4<br>2 2<br>0 0<br>EAP ECA LAC MNA SAR SSA<br>2000-21 2000-10 2011-21 2022-30 2000-21 2000-10 2011-21 2022-30 2000-21 2000-10 2011-21 2022-30 2000-21 2000-10 2011-21 2022-30 2000-21 2000-10 2011-21 2022-30 2000-21 2000-10 2011-21 2022-30<br><!-- End of picture text --> Sources: Haver Analytics, Penn World Tables, UN Population Prospects, World Bank. Note: EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, SSA = Sub-Saharan Africa. Period averages of annual GDP-weighted averages. Estimates based on the production function approach. Sample includes 6 countries in EAP, 9 in ECA, 16 in LAC, 5 in MNA, 3 in SAR, and 14 in SSA. Note that quantitative estimates may differ from those presented in Figure 2 because of sample differences. Figure 2 ensures sample consistency across measures; Figure 3 ensures sample consistency across time.
Table 3 presents a set of regression results for Argentina, Brazil, and Mexico. The model using only negative information performed as expected – the explanatory variables for past defaults and missed payments were highly significant and positively impact default for all three countries. The specification of the model with both positive and negative information also provides results which are broadly consistent across countries. More importantly, in each country we found that <u>positive information adds to the explanatory power of the model. If lenders can better identify</u> risky borrowers, then they can reduce portfolio risk or extend more credit. We discuss this result in greater depth below.

The positive information variables also performed consistently between countries in the sample. Longer credit histories were highly significant and negatively associated with default. In Mexico and Argentina where data on collateral were available, the relevant coefficient was statsitically significant and greater collateral anmounts were associated with lower default probabilities. Ratings data were also shown to be valuable predictors of default. For example, in the case of Brazil a loan with a rating of “D” was twice as likely to enter default as one rated “C” and almost five times more likely than one with a “B” rating. Borrowers with more open credit lines (Argentina) or those with accounts in several banks (Brazil) were found to be more risky. In the only country where data on interest rates were used - Mexico - we found a positive and significant relationship between the probability of default and the interest rate.

In some cases, the behavior of explanatory variables was uneven across countries. For example, evidence on the effect of loan size on default probability was mixed. Loan size exhibits a strong negative impact on default in Brazil, as well as in Argentina where the coefficient is also negative but much weaker. In Mexico, however, loan size is shown to be positively linked to default, but only weakly significant.

The simple credit scoring model similar to the one reported above was then used to define
# **Comparability**

Cross-country comparable data sets that track certain indicators over time—often created by think tanks, research institutions, and international organizations— allow countries to benchmark their performance against peers, which they can use to evaluate national policies and assess national priorities. Countries often respond with reforms in areas where they are lagging.

As one example, the Democratic Republic of Congo (DRC) made gender equity reforms upon seeing data from the Women, Business and the Law Index – an index created by the World Bank to compare laws and regulations affecting women’s economic opportunity across economies. The reform effort, supported by DRC’s ministries of Gender and Justice, led to changes the DRC’s Family Code, which for decades contained legal provisions that prevented married women from carrying out economic activities. The adoption of a new Family Code in July 2016 allowed married female entrepreneurs in DRC to start formal businesses, open bank accounts, register a company, and perform a host of other economic activities without interference from their husbands. The law also allows them to have a greater voice in management of the marital property and raised the legal marriage age for girls from 15 to 18.

In Sri Lanka, policy proposals were made upon seeing a provincial breakdown of the Human Capital Index (HCI) – an index created by the World Bank to measure the amount of human capital a child born today can expect to attain by age 18. Though Sri Lanka performs better than its neighbors in terms of human capital, the provincial breakdown confirmed high levels of stunting in the less developed provinces. This contributed to the Manifesto of the President proposing a special child nutrition program to address child under-nutrition, a nutrition allowance for children from poor families, and a nutrition aid program for pregnant women in the Estate Sector.

# **Accessibility**

When governments make data widely available, they empower individuals to make better choices through more information and knowledge. The digital revolution has increased the potential and ease with which information can be
policy, external facilitators were paid Rs. 1,000 per day, and worked for 30 - 45 days at a stretch, earning more than Rs. 30,000 - Rs. 45,000 in a single rotation with the RRLP. They completed 4 rotations in a year, thus earned up to Rs. 180,000 annually (approximately $3,000, using market exchange rates at the time). Internal CRPs on the other hand, were paid Rs. 750 per day and worked for about 90 days in a year, earning approximately Rs. 68,000 (approximately $1,133, using market exchange rates at the time). In other words, internal CRPs earned about a third of the earnings of the external CRPs. In our conversations, SERP management and government officials justified the higher salaries of the external facilitators on the grounds that they had more experience and were leaving their homes and communities to come and work in another state.

To test this assumption, we perform a Mincerian regression of monthly earnings, (i.e., a regression of earnings on education, experience and observable attributes [Table 4]). This specification, widely used by labor economists, allows us to examine whether the income “premium” observed among SERP staff in the descriptive statistics may be driven by their observable professional attributes. In the results in Table 4, we find no evidence that the monthly

> 23 In these instances, they were paired with individuals from within the SERP team who had a stronger grasp of Hindi.

11
analysis and findings. Section 6 discusses the school-level QAT score regression analysis and findings. Section 7 discusses the regression-discontinuity analysis and findings on the threat effect on mean QAT scores arising from QAT failure on marginal first-time failers and the carrot effect on mean QAT scores and test participation arising from missing teacher bonus qualification on marginal bonus nonqualifiers. Section 8 summarizes the main findings and provides some concluding remarks.

# **2. Context and program features**

The Foundation Assisted Schools (FAS) program was introduced into an education landscape characterized by three defining features. One: equitable access to schooling and attainment and achievement are acute, persistent challenges. Estimates using household sample survey data for Punjab from 2004/05, the year prior to the launch of the FAS program, indicate that the school participation rate (grade 1+) of children ages 6–15 years was 65.7%—this share drops to 61.2%, 60.9%, and 48.7% when the sample is restricted to girls, children from rural households, and children from the poorest (bottom expenditure quintile) households, respectively. Conditional on any schooling (grade 1+), while 91.8% of individuals ages 17–21 years completed primary school (grade 5), only 40.7% of them completed secondary school (grade 10), with significantly lower shares for rural children and, in particular, poor children. The National Education Assessment System (NEAS) for Pakistan finds that scaled mean scores in mathematics and language assessments for a sample of grade-4 students in public schools in Punjab in 2005 were significantly less than 500, suggesting that, on average, students, obtained less than 50% of the points in the assessments (Government of Pakistan 2006). Das et al. (2006) and Andrabi et al. (2007) also find that the learning levels of grade-3 students in a selected sample of villages in Punjab measured in 2004 were far below curriculum standards in English, Urdu
in fundamental level and 11% are in high school. Given that about half of the Brazilian population is white, the relatively low presence of Brazilians white in the regular schooling needs to be explored further. Although race data on all Venezuelan refugees and migrants are not available, data for 2019 suggests that about 25% of the Venezuelans refugees and migrants are white, suggesting that white-nonwhite schooling disparity exists for both Venezuelan refugees and migrants and Brazilians.

Figure 4b shows that average Venezuelans tend to be older than Brazilians in lower grades, from grade 1 to grade 5, suggesting that Venezuelans are more likely to be mismatched to class. Difficulty in evaluating Venezuelan students’ prior knowledge and language barrier may lead to Venezuelans being enrolled into the lowest grade possible, which may not only demotivate Venezuelan students from learning but also add to additional cost to the government. Some cities in Brazil are already responding to this mismanagement. For example, in Manaus, some teachers and school sta↵have received Spanish-language training, and in the city of Pacaraima, schools have developed Portuguese language classes focused on the needs of Venezuelan students (Selee & Bolter, 2020).

> 26Since there is no data on those, who are not dropping out, this paper cannot investigate the reasons behind the high dropout rate but better outside opportunities, lower returns to higher education, lacking the knowledge of the official language, along with the tradition of boys being the breadwinner, are most likely to contribute to the phenomenon.

18
of indigenous origin, and although 79% are literate, 57% has not completed primary school. Finally, 94% of mothers attended antenatal care while they were pregnant with the index child. In terms of children’s characteristics (Panel B), half of index children are boys and 16% of them are the eldest. Cognitive outcomes, as measured by PPVT Z- scores are practically unchanged between the two rounds, even if, as expected, the mean score increases as the children age, reflecting a larger vocabulary. The average child in the sample

> 12 We also created a group called “Others” for all shocks that were not classified by enumerators into the 14 categories. We do not use them in our analysis because this group includes a diverse and potentially unrelated set of events.

8
ran exploratory analysis with polyglot, which categorizes each word as positive or negative, but averaging the results of such word counts can lead to in-cohesive results since the sentiment of a sentence may not be captured by the positive or negative nature of its individual words as seen in Appendix 9.4 (Chen and Skiena (2014)). Future research may train such a tool on conflict related social media messages using expert annotators to verify tone. This could be used to study potential conflicts or differences in opinions between returnees and the population already living in an area in order to understand the assimilation of returnees or IDPs in a location.

Finally, researchers may wish to explore the sentiment towards refugees in neighboring countries such as Turkey and Lebanon and how social media discussion responds to increased migration. Such work could be augmented with satellite data and other mobility related data from google maps to identify differences in locations where returnees settle and where settlements already exist.

24
# **Abstract**

Empirical evidence suggests that global trade reforms are unlikely to produce analogous results across countries, especially when analyzing their effect on poverty. This implies that the analysis of trade reform on social welfare cannot be generalized and needs to be conducted on a country by country basis. Moreover, even within the same country, geographic areas, households, and individuals are likely to be differentially affected, some of them benefiting more than others, while others might lose. With this in mind, this paper provides a quantitative estimate of the effect on Mexican households from the implementation of the Doha development agenda. The analysis utilizes a twostep approach for which changes in prices and factors are estimated through a CGE model (GTAP) and then mapped into the welfare function of the household using household survey data. The empirical approach used in this study aims to measure the impact of Doha implementation by tracing changes in the household prices of goods and factors and their impact on household welfare, taking particular account the role of domestic price transmission.

The findings suggest that multilateral trade liberalization alone would have a negative effect on Mexican households, even though very small. However, when the implementation of the Doha development agenda is complemented by domestic policies aimed to increase productivity and improve domestic price transmission, the overall effects become positive. The results point to the importance of domestic price transmission in determining the variance of the effects across households.

ii