rafmacalaba/data-use-mentions-extended huggingface dataset:
Based on gendered indicators included in the 2012 survey, we group empowerment into five factors indicating five types of latent abilities:<sup>5</sup>

- _(1) Decision-making_ is the ability to make decisions about various aspects of one’s own life and household affairs. We combine five indicators to construct this ability: whether a woman can make decisions alone about her own work, about her medical treatment, about her children’s medical treatment, about children’s marriage, and about items to cook on a daily basis.

- _(2) Mobility_ is the ability to move about independently. It is based on the ability to visit various places, including health centers and friends’ or relatives’ homes; to travel a short distance by trains or buses; having traveled outside a rural area to a city or town during the 5 years preceding the survey; and having traveled outside the state during the five years preceding the survey.

- _(3) Financial autonomy_ is the ability to make purchase decisions and access assets or finance. It is based on four attributes: the ability to purchase expensive household durables, such as a refrigerator or television set; the ability to purchase land or other real estate; possession of a bank account; and having her name on the ownership or rental document of her domicile.

- _(4) Reproductive freedom_ is the ability to make reproductive choices. We consider three measures for this ability: whether a woman uses any contraceptives; whether she can decide how many children to have; and whether the actual or desired number of children is less than three. Literature on gender equity and reproductive outcomes mostly suggests that there is an inverse relationship between a woman’s empowerment and the number of children she has (Upadhyay et al.).<sup>6</sup> The third indicator is measured by: i) the number of children for those who would not conceive any more children (either by choice, or because
- Laeven, L. and F. Valencia (2020). Systemic banking crises database ii. _IMF Economic Review 68_ (2), 307–361. 18

- Leduc, S. and Z. Liu (2016). Uncertainty shocks are aggregate demand shocks. _Journal of Monetary Economics 82_ , 20–35. 16

- Lenza, M. and G. E. Primiceri (2022). How to estimate a vector autoregression after march 2020. _Journal of Applied Econometrics 37_ (4), 688–699. 12

- Lindsten, F., M. I. Jordan, and T. B. Sch ̈on (2014). Particle gibbs with ancestor sampling. _Journal of Machine Learning Research 15_ , 2145–2184. 10, A6

- Miescu, M. S. (2019). Uncertainty shocks in emerging economies: A global to local approach for identification. _Available at SSRN 4176889_ . 16

- Mumtaz, H. (2018). A generalised stochastic volatility in mean var. _Economics Letters 173_ , 10–14. 19

- Mumtaz, H. and P. Surico (2018). Policy uncertainty and aggregate fluctuations. _Journal of Applied Econometrics 33_ (3), 319–331. 1, 5, 8, 18

- Ng, S. (2021). Modeling macroeconomic variations after covid-19. Technical report, National Bureau of Economic Research. 12

- Pastor, L. and P. Veronesi (2012). Uncertainty about government policy and stock prices. _The journal of Finance 67_ (4), 1219–1264. 7

- P ́astor, L. and P. Veronesi (2013). Political uncertainty and risk premia. _Journal of financial Economics 110_ (3), 520–545. 7

- Pindyck, R. S. (1990). Irreversibility, uncertainty, and investment. 6

- Ramey, V. A. (2016). Macroeconomic shocks and their propagation. _Handbook of macroeconomics 2_ , 71–162. 18

-
# **1. Introduction**

The 2008 financial crisis, characterized in part by mounting losses for individuals, has generated interest in better understanding how to promote savvier saving and borrowing behavior. The ability of individuals to make informed financial decisions is critical to developing sound personal finance, which can contribute to more efficient allocation of financial resources and to greater financial stability at both the micro and macro level (see, e.g., Lusardi, 2008; Lusardi and Tufano, 2009a,b). Efforts to improve financial literacy can also be an important component of efforts to increase saving rates and lending to the poorest and most vulnerable consumers (Cole, Sampson and Zia, 2010).

Our paper extends the existing literature in a new direction, using a panel survey of financial literacy administered to a nationally representative sample of over 1,000 Russian individuals prior to and during the 2008 financial crisis. Russia is a particularly important country to study given the large increase in consumer credit it has recently experienced: Consumer loans (excluding mortgages) in Russia grew at an astonishing rate: from about US$ 10 billion in 2003 to over US$ 170 billion in 2008—accounting for over 10% of GDP in 2008 versus less than 1% in 2003 (World Development Indicators, 2010). This is one of the few panel data sets on financial literacy, and with it we are able to address some novel questions, such as: What is the level of financial literacy in a country without a legacy of consumer credit and financial education? Are there not only financial but also real consequences of low financial literacy? Are lower levels of financial literacy related to greater financial vulnerability during a crisis, i.e., are less financially literate individuals less able to deal with financial crises?

2
Policy Research Working Paper 10534

# **Abstract**

Transport connectivity is an important determinant of agglomeration economies and urbanization. However, measuring its impacts is a complex task when causality is considered. An important empirical challenge comes from potential endogeneity of infrastructure placement. To deal with the endogeneity problem, first, the paper constructs detailed georeferenced connectivity measurements based on micro shipping data collected over 10 years. Then, the system generalized method of moments regression is applied. Using unique data from the Caucasus and Central Asian countries, the paper estimates the impact of transport connectivity on agglomeration economies. It finds that agglomeration economies are significant and persistent in the region. Thus, the existing firm clusters are likely to continue growing. However, a constraint is also found. Large cities exhibit congestion diseconomies. Finally, the paper shows that the improvement of transport connectivity, especially local market accessibility, has a significant effect on agglomeration. By contrast, no clear evidence to support the impact of improved regional connectivity on agglomeration is observed yet. To take full advantage of agglomeration economies at the regional level, further efforts may be needed, for instance, toward increasing efficiency in transportation and logistics, improving the freight load, and/or reducing the time and costs of border crossing, which add to overall transport costs and times.

This paper is a product of the Transport 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 author may be contacted at aiimi@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.
, she reveals not only the transaction amount but also her account balance. If female customers’ preference for female agents is (at least partially) driven by a desire to conceal financial information from male agents, we would expect female customers to also be more likely to transact with female agents when they have higher account balances. We matched data on account balances at one point in time (end of October 2017) with customer transaction data. For the analysis, we restrict the data to all customer transactions occurring within 10 days after the time point for which the balance data is available and thus posit that the balance information will affect transaction behavior within the 10-day window after the date for which we have knowledge about customers’ balances.

In Column 6 of Table 4, we add the customer balance variable and an interaction term between customer gender and account balance to our main specification. The coefficient on account balance in logs is smaller than 1 (odds ratio= 0.937, p-value = 0.000), indicating that customers (of both genders) with higher balances are less likely to transact with female agents. This is consistent with the results from descriptive statistics showing that customers at male agents had higher balances. For female customers, however, the effect reverses its direction, indicating that female customers are more likely to transact with 9
||(0.095)|
|Basic assets score (0-4)|0.015|
||(0.055)|
|Radio owned (1=yes)|-0.177*|
||(0.105)|
|Food insecurity score (0-12)|-0.071***|
||(0.018)|
|Received support from social programs (1=yes)|0.168|
||(0.117)|
|Total land size (in ha)|0.125***|
||(0.040)|
|No. of tropical livestock unit (TLU) owned|-0.025|
||(0.019)|
|No. of adopted quickwins (0-5)|0.173***|
||(0.046)|
|Grows main crop (1=yes)|0.257|
||(0.315)|
|Constant|-2.103***|
||(0.424)|
|Pseudo R-squared|0.180|
|Observations|1,513|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ Probit regression results for calculation of propensity score for FREG membership. FREG stands for ‘Farmer research and extension group’. For variable definitions see notes of Table 1. Robust standard errors in parentheses. Baseline variables used as covariates. Significance levels: *** p<0.01, ** p<0.05, * p<0.1.

8
:

> 24 Given that the information system of the program does not  reports SISBEN scores for participant families, uniform scores for the whole sample are calculated by using  the proxy means testing  algorithm  and the information from the Census of the Poor 1994-2003. 25 This may happen for at least two reasons. First, take up rates were in the order of 65 percent so participation is not universal among the eligible population. Second, households above the threshold may lobby with local authorities to gain access to the program. Concerns of nonrandom sorting that could arise from this or other gaming behaviors to influence the _Sisben_ poverty index score are discussed in the robustness section.

19
Policy Research Working Paper 6119

# **Abstract**

This paper exploits the staggered rollout of Thailand’s universal health coverage scheme to estimate its impacts on whether individuals report themselves as being too ill to work. The statistical power comes from the fact that there is an average of 62,000 respondents in the labor force survey at each survey date and no less than 68 survey dates, most of which are just one month apart. The analysis finds that universal coverage reduced the likelihood of people reporting themselves to be too sick to work: the authors estimate the effect to be −0.004 one year after universal coverage and −0.007 three years after. The estimated effects are much larger among those age 65 and over. Universal coverage had a much larger effect on health (about four times larger) than the Village Fund scheme, which provided free credit to rural households through a subsidized microcredit scheme and which was rolled out around the same time as universal coverage.

This paper is a product of the Human Development and Public Services 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 awagstaff@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
amount of energy usage in a country from any source as published by the OECD World Energy Balances.</mark> <mark>Our data on sovereign defaults to private external creditors comes from Farah Yacoub, Graf von Luckner and Reinhart (2022) and takes the form of an annually assessed categorical variable – that is, in default or not (1 or 0). It covers 193 sovereigns and 3 non-sovereigns since 1800, but we use it only from 1900. Default spells are generally considered over once the default signal has been cured for two years. Substantively, it captures defaults as perceived by financial market participants and rating agencies and consequently the revealed inability of the sovereign to meet its commitments as contracted. Thus, the definition hews closely to how rating agencies qualify a</mark> 9
the number of individuals in the household. However, this adjustment does not account for differing degrees of need within a household (e.g., a child typically needs less food than an adult to maintain the same welfare level). Additionally, households can be expected to benefit from economies of scale in relation to heating and certain other costs. Two common equivalence scales used are the square root scale and the OECD-modified scale. The square root scale simply takes the square root of household size. The “OECD modified” scale gives a fixed weighting of 1 to the first adult household member, 0.5 to the second and additional household members aged 14 and over, and 0.3 to each child under 14.<sup>11</sup>

# 2.2 Global indicators of effective progressivity<sup>12</sup>

Musgrave and Thin (1948) first proposed a global measure of effective progressivity as the ratio of (one minus) the Gini coefficient of post-tax income to (one minus) the Gini coefficient of pre-tax income. However, more recent literature has distinguished the concepts of progressivity and redistribution more distinctly. Indeed, Kakwani (1977) pointed out that the Musgrave and Thin (1948) metric was actually a 10 Although there are strong arguments that consumption provides a better reflection of the wellbeing of poor households, the fact that income is the base of the tax to be modelled creates a natural preference for income data as this will maximize the accuracy of the tax modelling. 11 A more elaborate option is the parametric scale introduced by Cutler and Katz (1992). The scale is calculated as: � , where is the equivalent size of household , measures the degree of need of children relative to adults; specifies economies of scale in consumption; αα is the number of adults in household and ii aa,ii cc,ii ii is the number of children. This scale explicitly allows for adjustment of need between adults and children, and for mm = �nn
14(1), 147–166

- Kakwani, N. (1977) ‘Applications of lorenz curves in economic analysis.’ _Econometrica_ 45(3), 719–728

- Kakwani, N., A. Wagstaff, and E. van Doorslaer (1997) ‘Socioeconomic inequalities in health: Measurement, computation, and statistical inference.’ _Journal of Econometrics_ 77, 87–103

- Kanbur, R. (1987) ‘Transfers, targeting and poverty.’ _Economic Policy_ 4(1), 112–147

- Kawachi, I., B.P. Kennedy, K. Lochner, and D. Prothrow-Stith (1997) ‘Social capital, income inequality and mortality.’ _American Journal of Public Health_ 87(9), 1491–1498

- Khorshed Alam Mozumder, A.B.M., T.T. Kane, A. Levin, and S. Ahmed (2000) ‘The effect of birth interval on malnutrition in bangladeshi infants and young children.’ _Journal of Biosocial Science_ 32, 289–300

- Kunst, A.E., F. Groenhof, J.P. Mackenbach, and EU Working Group on Socioeconomic Inequalities in Health (1998) ‘Occupational class and cause specific mortality in middle aged men in 11 european countries: comparison of population based studies.’ _British Medical Journal_ 316, 1636–1642

40
# **Earnings and Education**

The issue of returns to education is of particular interest for Latvia, where the labor force is increasingly well-educated, as shown in Table 3. Earnings functions specifications using LFS 2002 are presented in Appendix Tables 24 and 25. These specifications account both for observable characteristics of individuals, and those of the firms in which they are employed. Appendix Table 24, however, does not control for occupation and plant size, while Appendix Table 25 does. Comparison of these two sets of results helps to understand how human capital is rewarded in the labor market, and also gives useful insights related to gender and ethnic issues. See Box 3 for a discussion. Figure 22 summarizes the findings about current returns to education in the Latvian labor market. Appendix Tables 27 provides means of the variables, used in earnings functions by gender and ethnicity.

**Table 3.  Full-Time Employees by Educational Attainment, 1997-2002**

|**Education**|**1997**|**1998**|**1999**|**2000**|**2001**|**2002**|
|---|---|---|---|---|---|---|
|Higher|18.8|20.0|20.9|21.9|22.1|23.2|
|Secondary<sup>a</sup>|65.6|65.9|65.2|64.3|63.3|64.4|
|Vocational<br>Basic|3.9|3.4|3.9|3.6|3.3|1.7|
|Basic|10.4|9.5|9.1|9.2|10.3|9.8|
|Less than<br>Basic|1.4|1
For the treatment effect of student loans, we find that SOFES recipients have a 5.5%-point higher chance of choosing a Humanities and Social Sciences (Area I) subject, a 9.8%-point lower chance to choose an Economics and Administration (Area II) subject, and a 3.1%-point higher chance to choose an Engineering and Natural Sciences (Area III) subject (although the latter effect is statistically insignificant).

# **7. Conclusion and Policy Considerations**

In this paper we studied the impact of one student loan scheme on access to higher education and the behavior of students in terms of performance and decisions regarding study field and jobs on the side. While there is a substantial literature on the relationship between financial support and university enrollment, the question how financial support impacts on student behavior has received little attention. However, as human capital is built during the program, it is important to have insight into the factors that influence the behavior of students. In this paper we tried to gain such insight in the context of recent experiences with student loans for private university programs in Mexico.

To that end, we used the Mexican household survey, and novel data from two sources. First, the SOFES database is a rich source of information on a wide range of variables concerning socio-economic background, educational attainment and information regarding student behavior. To construct a control group, we proposed a regression-discontinuity design. Treatment is measured by the size of the credit for which the student is eligible. The estimated treatment effect is thereby connected to what we have labeled the internal margin, i.e. outcome differences due to variation in credit levels within the group of SOFES students. The second data source is a large-scale survey among students and graduates, both with and without a credit from SOFES. The control group is then formed by the people who did not receive financial support from SOFES. The impact of treatment thus reflects the effects along the external margin, i.
most severe global challenge − from 29 percent in 2021 to 23 percent in 2022 − is related to the beginning of the war in Ukraine.

There is no direct means of testing the competing priority hypothesis, so we draw on indirect evidence. We use responses to the following questions about general attitudes about respondents’ living situation: _To what extent do you agree with the following statements: “The economic situation in our country is better today than around 4 years ago”; “I have done better in life than my parents”; “My household lives better nowadays than around 4 years ago”; “All things considered, I am satisfied with my life now”; “Children who are born now will have a better life than my generation”; “All things considered, I am satisfied with my job as a whole.”_ Responses to these questions take five values ranging from “strongly disagree” to “strongly agree.” They capture a general sense of worry about respondents’ livelihoods. We assume that this general sense of worry would crowd out concerns about climate change, as they are less immediately linked to the respondent’s livelihood.

<u>Table 3</u> displays the aggregate responses. Respondents’ assessment of their life situation improved between 2016 and 2023 across all six dimensions, with the differences in averages being positive and statistically significant. Figure 3 shows qualitatively similar results at the country level. The response averages are higher in 2023 than in 2016 (the markers are above the 45-degree line), indicating that, on average, respondents’ life situation across these six dimensions improved in most countries, and the general sense of worry may have declined. In theory, this result should be associated with an increase in concerns about climate change – but this is not what we observe.

We substantiate these results with Google trend data. The competing priority hypothesis would require the relative number of searchers about climate change to decline between 2016 and 2023. But the opposite is observed
Most officials believe that more capacity is needed, rather than simply more money to improve student learning. This is likely true for several countries in the survey, since above a certain level of minimum spending, additional financing is unlikely to improve learning in an education system that is not aligned for learning (World Bank, 2018). Findings from Education Public Expenditure Reviews in the region show that education budgets are often not allocated based on specific needs and tend to be inequitably distributed across provinces/districts in the country (World Bank, 2020; Afkar et al., 2020; World Bank, 2021). However, if current spending is _below_ a certain level, additional spending could go a long way toward improving learning. At a cross-country level, there is a correlation between education quality and per student spending expressed in per capita GDP (World Bank, 2023), so the data lead us to expect that if Lao PDR or Mongolia spent more on education, learning would improve. The officials surveyed from these countries tend not to agree with this proposition. Over the whole sample, there is surprisingly no association between the extent to which officials believe that more money will not matter, and whether they flagged money as the main barrier to learning (Figure 18). In addition to capacity, there are additional challenges in many EAP education systems that may explicitly or implicitly drive officials’ perceptions about why additional resources will not improve learning (e.g., patronage, corruption, and politics). In addition, officials’ perceptions may be driven by demand-side factors linked to learning unrelated to public spending (children’s ability, household engagement in children’s education, private tutoring availability, etc.).

_Figure 18: Correlation between the officials selecting money as the main barrier to improving learning and officials agreeing with the statement that more money would not make an impact._ <!-- Start of picture text --> 100%<br>90%<br>80%<