rafmacalaba/data-use-mentions-extended huggingface dataset:
using the 2001 Nepal Census I compute the percentage of international migrants from a Village Development Committee (VDC)<sup>6</sup> as an instrument for migration in 2010-2011 as in (3). Following is the equation for migration decisions.

where _Mi,j_ is an identier that individual _i_ lives in household _j_ with or without a migrant, **_Xijc_** is a vector of controls as dened in equation (1). _Zijc_ , dened in (3), is an exogenous instrumental variable that must satisfy as good as randomly assigned, the rst stage, and the exclusion criteria conditions. The 2001 migration decisions are likely to be random to the 2011 labor supply and migration decisions as migrants from a community in 2001 would not have anticipated the community's labor market conditions in 2011, which satises as good as randomly assigned condition. However, migration networks provide information about the economic opportunities at the destination, potential costs, and might reveal migrations' impact on their family's wellbeings to the community, which might inuence other community members' migration decisions (rst stage). Similarly, as long as a decade lagged unobserved community characteristics do not inuence individual's labor supply in 2011, the 2001 community level migration shares are unlikely to feature in the 2011 labor supply equations. Hence, the instrument is likely to aect the outcome only through the endogenous variable, satisfying the exclusion criteria condition. Following is the instrument.

where _MIG_ 2001 _,c_ is the total number of people living abroad in 2001 from a VDC _c_ and _POP_ 2001 _,c_ is the population of the VDC _c_ in 2001. It is important to note that the IV estimates are likely to be greater than the OLS estimates as the instrumental variable approach identies the causal impact of treatment on outcome only on the compliers; that is, IV can recover only the local treatment eect (LATE) (Angrist, 1991).<sup
aggregate statistics and facility surveys both indicate that many providers in every context have substantial excess capacity. Instead, there are two different workforce crises at play, whose interaction offers a promising new approach to system-wide quality improvement.

The first crisis arises from substantial variation in utilization. When combined with the temporal clumping of patients, the few facilities where utilization is high can quickly develop long queues. If we take patient demand as fixed, such queues could be better managed by applying stochastic queuing theory as a standard tool in facility personnel management. This would require detailed time and motion studies as well as the development of appropriate administrative tools and protocols, but it does not require a fundamental reassessment of the health system’s human resources.

The harder question is why there is such variation in caseloads to begin with. Our results show that the variation in caseloads is not just a question of rural versus urban clinics, large versus small clinics, or clinics with more and less competent providers. It could be that caseloads reflect historical spatial patterns while circumstances have changed – for instance, clinics may represent relatively fixed investments while changes in population distributions or improvements in transportation networks have occurred rapidly. We do not have the scope here to pursue this further, although we note that contemporary research using data such as the geolocations of clinics, population density estimates, and

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poverty of 26.7 million.

In countries like Kenya and Tanzania, an additional 0.7 million poor people would be expected to be lifted from extreme poverty, at PPP$1.90 a day. This is approximately equivalent to an additional 1.0 and 0.9 percentage points reduction in the extreme poverty headcount rate. In South Asia, Pakistan would see some significant additional reductions in extreme poverty with 1.1 million people being lifted compared to the baseline. Bangladesh and India are expected to see a smaller number of people lifted out of poverty i.e. 0.2 million (0.11 percent of headcount) and 0.03 million (0.002 percent of headcount), respectively. In Nepal, the scenario with BRI infrastructure investment alone lifts an additional 60 thousand people out of extreme poverty compared to the baseline. In East Asia and Pacific, Philippines expects to see approximately 90 thousand people lifted out of extreme poverty compared to the baseline.<sup>11</sup>

> 8 In 2015, the World Bank estimated that 53.69 percent of the population in developing countries lived with less than PPP$5.50 a day – or 3,369 million. The baseline scenario, which contemplates a continuation of current demographic and economic trends, projects a reduction of 5.91 percentage points in the poverty headcount at PPP$5.50 a day by 2019 and an additional reduction of 12.64 percentage points by 2030.

> 9 Poverty estimates were obtained by linking results of a CGE model with a simple global microeconomic model. The initial global distribution of per capita consumption/income is based on PovcalNet data. Country-specific growth rates in domestic absorption per capita (real household consumption per capita) from the macro CGE are fully transmitted to household data assuming distribution-neutrality. To calculate the number of poor, the total population in each country is adjusted using United Nations population projections. This is based on the 158 countries in the BRI study that were
within this group may result in a lack of tailored policy responses, as policymakers may not be aware of the extent of their poverty, whether they are persistently poor or move in and out of poverty frequently, or the overlapping deprivations they face.

**Unofficial data on Roma populations collected by the Fundamental Rights Agency (FRA) shows large deprivations in several areas compared to the general population, which tend to persist over time.** Despite a well-defined strategy, the EU Roma Strategic Framework for equality, inclusion, and participation, and the allocation of EU funds for Roma inclusion through the European Structural and Investment Funds<sup>8</sup> , progress in enhancing development outcomes has been sluggish. Although recent data from the 2021 FRA Roma surveys in the EU demonstrate some improvement in recent years, the Roma community continues to grapple with limited access to essential services (such as education, health, and adequate housing conditions), enduring precarious living conditions, including in isolated communities.<sup>9</sup> In health, there is a significant life expectancy gap between Roma populations and the general European population, with estimates ranging from 5 to 20 years less. Adequate housing conditions are scarce. In housing, Roma communities often endure substandard living conditions, face challenges in

> 6 <mark>The persistent at-risk-of-poverty rate shows the percentage of the population living in households where the equivalized disposable income was below the at-risk-of-poverty threshold for the current year and at least two of the preceding three years. Source: Eurostat, 2024</mark>

> 7  Other vulnerable groups such as disabled individuals, sexual minorities, and migrants are also not covered in the EU-SILC surveys.

> 8 Source: EU funding for Roma equality, inclusion and participation

> 9 For example, enrollment rates for early childhood education (children aged 3 up to the primary compulsory school age) are extremely low and dropped from 44 to 42 percent between 2016 and 2021 in the EU.  The data also underscores a significant gap in
FF</sup> CC + ββ CC The International Labour Organization provides data for the share of formal employment in total employment (ILO 2020b). This variable, denoted as , allows calculating the historical values for formal and informal employment, KK,II tt ss , FF KK,II tt tt tt CC = �1 −ss �∗CC, II KK,II where total employment, , is available in MFMod. CCtt = sstt ∗CCtt The budget constraint is given as CCtt , where is aggregate wealth, is the net wage paid for formal labor and is the wage paid for informal labor. The first order condition KK FF FF II II FF tt tt tt tt tt tt tt tt tt for the informal sector can then be written as WW CC = (1 + ττII )WW CC + WW CC WW WW tt WW . σσ �<sup>CCtt</sup> ~~�~~ II II tt II tt WW = ββ WW Based on the estimates in Table 1 of Ahmed (2012), we assume CCtt = 0.29 , = 0.71 and 2. ββ<sup>FF</sup> ββ<sup>II</sup> = 1 −ββ<sup>FF</sup> σσ= Available data for Pakistan, do not separate out direct taxes for labor and capital, so that we need to assume that the same tax rate applies to both labor and capital. The effective direct tax rate is thus 31
, but there are important differences between less and more advanced economies that affect priorities as well as the available and desirable set of policy instruments that can support longer and more productive working lives. These include, _inter alia_ , labor market structure, access to social protection, human capital levels across generations, norms, and the technical capacity and political space for reform. Nevertheless, countries that are now building up their education, labor, and social protection systems can also learn not to repeat errors made by more advanced economies. The experience from HICs and some MICs shows that the cost of putting in place inadequate solutions can be substantial. Unsustainable pension systems or insufficient attention to skills development for older generations have long-term repercussions that can be very difficult (politically and technically) to undo. Poorer countries need to avoid costly mistakes, such as entrenching costly future pension payment contingencies in social protection design, or ignoring human capital investments, the effects of which will manifest themselves in the decades to come.

**The dual labor market structure of most L/MICs presents a particular dilemma for policy makers.** The International Labor Organization (ILO) estimates that, on average, about 70 percent of the employed workforce is informally employed in developing countries.<sup>12</sup> On the one hand, high informality and low-productivity work mean that policies focusing on formal workers and formal firms will be less effective in changing aggregate outcomes, despite promising evidence in some areas that policy can affect old-age productivity and employment decisions. On the other hand, many workers in

> 12 Based on statistics for 61 developing countries, from Elgin et al. 2021.

5
. Third, it has been subject to extensive quality checks, which among other things allow us to identify problematic observations and set them aside if necessary.

The objective of the paper is to use this large data set to establish the stylized facts concerning the effects on the private saving rate of its key policy and non-policy determinants identified in the literature. To do this, the paper estimates a variety of empirical equations for the private saving rate. Private saving regressions are estimated for. a worldwide sample of countries, as well as separately for industrial and developing country subsamples. For completeness, the paper also presents regression results for the national saving rate. In order to encompass a broad range of saving determinants, and hence theoretical views about saving, we use a variety of reduced-form linear specifications rather than one narrow model of saving derived from first principles.2 We believe that this approach provides a useful first step to identify the key empirical regularities in need of structural explanation.

We estimate our empirical equations using various panel data procedures, paying particular attention to the issues of simultaneity and country heterogeneity that are mostly ignored in earlier studies. Specifically, our large panel data set allows the use of “internal”
difficulties (Orphanides and Norden 2002 and Garrat et al. 2008). For a discussion of the determinants of the long-run neutral rate, see Ferreira and Shousha (2023).

8 Countries have adjusted their frameworks and targets meaningfully during the sample, but classifying different regimes is not straightforward, especially as some economies had multiple targets, and others had substantial discretion in how they adjusted policy. Ball (2011), Bernanke and Mishkin (1992) and Federal Reserve Bank of San Francisco (2003) document these changes in frameworks and targets over time. Section 3.2.1 discusses how these differences in targets and frameworks can explain some of the differences in rate cycles over time.

9 We include countries in the ERM in the earlier half of our sample, as well as Switzerland when it had a one-sided band on its exchange rate from 2011-2015.

10 For details of the main policy interest rates and changes over time, see the database “Long Series on Central Bank Policy Rates” compiled by the BIS and available at: <u>https://www.bis.org/statistics/cbpol/cbpol_doc.pdf.</u> 9
the population. It does not matter if the reference group is constituted by the poor, the rich or both groups because individual satisfaction is only defined within the reference group.

Theory would therefore suggest at least two mechanisms through which income inequality may affect individual satisfaction. The first is that a rise in income inequality signals future mobility and increases present satisfaction. This implies a positive relation between income inequality and life satisfaction (the Hirschman/Rothschild mechanism). The second mechanism is that a rise in income inequality leads to an increase in relative deprivation and a decrease in life satisfaction (the Runciman/Yitzhaki mechanism). Moreover, while the Hirschman/Rothschild mechanism does not have clear predictions on which income group benefits the most from increased inequality, the Runciman/Yitzhaki mechanism indicates that the poor are more deprived and should be more inequality averse than the rich.

It is important to clarify at this point what we mean by inequality aversion and how we interpret the sign of the happiness-inequality relation. Economics and statistics offer different definitions of inequality aversion. One is the definition derived from risk theory, which describes inequality aversion as the concavity of the utility curve. A second is the inequality aversion parameter used in statistical indexes of inequality which attributes a different weight to incomes located in different parts of the income distribution. One example is the Atkinson inequality measure. A third is the inequality aversion measured with experimental questionnaires and games specifically designed to capture the taste for inequality, for example the work conducted in recent years by Amiel and Cowell (1992). A fourth approach is to consider a negative relation between life satisfaction and income inequality as a sign of inequality aversion. For example, Clark (2003) argues that workers may not be inequality averse because he finds a positive relation between happiness and income inequality and Schwarze and Harpfer (2003) argue that Germans are only weakly inequality averse because a reduction in inequality does not increase well-being. In this paper we follow this last approach by interpreting a positive sign of the happiness-inequality relation as
# **Figure 10. Importance of China in Foreign Equity Portfolios**

This figure shows the evolution of foreign equity position in China. Panel A shows the evolution of China’s weight in foreign equity positions relative to all emerging economies (CPIS Weight) and China’s weight in the MSCI Emerging Markets Index (MSCI Weight). The panel also plots (in red) two counterfactual scenarios for China's weight in the MSCI in 2018-20: with an inclussion factor (IF) for the A shares equal to 100 percent and 0 percent. Panel B shows the evolution of foreign equity positions in China and other emerging economies (in grey) in billions of 2011 U.S. dollars (USD). The definition of emerging economies follows the MSCI classification of emerging countries in 2020. Sources: Coordinated Portfolio Investment Survey (CPIS) and MSCI.

## **A. CPIS Weight versus MSCI Weight**

<!-- Start of picture text --> 50%<br>IF = 100%<br>45%<br>40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br>CPIS Weight MSCI Weight<br>China's Weight<br><!-- End of picture text --> <!-- Start of picture text --> IF = 100%<br>IF = 0%<br><!-- End of picture text -->

## **B. Foreign Equity Positions in China and other Emerging Markets**

<!-- Start of picture text --> 1,400<br>1,200<br>1,000<br>800<
We argue and offer empirical evidence that migration reshapes village household expenditure patterns in direct and indirect ways that existing models do not adequately address. The modeling approach we employ controls for the endogeneity of household migration decisions while testing for differences in expenditure patterns between migrant and non-migrant households. We estimate this model for both international and internal migration. The data to estimate the model are from the Mexico National Rural Household Survey of 2003. This survey gathered detailed information on incomes, migration, and expenditures from a nationally representative sample of 1,782 households in rural Mexico.

Findings from the econometric analysis reveal that expenditure patterns differ significantly between migrant and non-migrant households, sometimes in surprising ways. This is true for both international and internal migration. Other things (including total expenditures) being equal, compared with otherwise similar households without migrants, households with international migrants have large marginal budget shares for investments, health, and consumer durables and small marginal budget shares for food and housing. Households with internal migrants have relatively large marginal budget shares for health, housing, services and education and small marginal budget shares for supermarkets, consumer durables, and investments.

# **Remittances and Expenditures in Migrant Households**

Past research on remittance use offers a partial and possibly distorted view of how remittances influence demand, due to the fungibility of income. Moreover, it often rests 4
Based on those estimates, migrant households eligible for PEP were 3.9 pp less likely to have children less than one year old, 7 pp less likely to have one-year-olds, and 1.8 pp less likely to have two-year-olds. As expected, PEP eligibility had no significant impact on the likelihood of having three-year-olds given the program’s implementation timing. In addition, the results are robust to the exclusion of control variables.<sup>10</sup> When we restrict our sample to data collected at baseline and in 2020 (panel A), we only observe a policy impact on the probability of having children one year old or less, which aligns with the program’s rollout. For that reason, in panel A, we observe policy impacts that are not statistically different from zero for the likelihood of having children two and three old. As we add the years 2021 data in panel B, we observe a policy impact on the probability of having children less than one year old, one year old, and two years old.

The results in panels A and B suggest that PEP’s fertility impacts were not only immediate but also grew larger one year after the program’s rollout, reflecting the usual delay in benefiting from regularization. For example, access to social services requires having PEP plus a SISBEN vulnerability score, which can take time to obtain from public authorities. Likewise, it can be time consuming to find a formal job, which explains the program’s larger impact one year after implementation.

In sum, our main findings align with the timing of the program’s rollout and robustly support our hypothesis that PEP reduced household fertility.

# **VI. A Robustness Tests**

We conduct a series of sensitivity checks to gauge the extent of attrition in our sample and assess the robustness of our findings to various sample changes.

# _Attrition Concerns_

Since we exploit the panel nature of the survey data for our analysis, a natural concern is the extent to which
most recent edition was completed in 2021 for 123 economies (Demirguc-Kunt et al., 2022). It includes indicators of access to and use of formal and informal financial services that have been collected in previous editions of Findex, as well as indicators of the use of cards, mobile phones, and the internet to make and receive payments, including digital merchant payments and payments from the government. In addition to wider country coverage than other data sources, the key advantage of the Findex dataset is that it provides nationally representative demand-side indicators of DFS usage. In contrast, studies that have used supply-side indicators not only have used data for a smaller sample of countries (in which advanced economies were more heavily represented), but also relied on indicators for a subset of DFS providers. For example, Claessens et al. (2018) rely on data collected from surveys of fintech platforms and other public sources by the Cambridge Center for Alternative Finance (CCAF) that explicitly excludes digital credit provided by commercial banks, focuses on the larger fintech platforms, and excludes other providers of digital credit such as online mortgage lenders, big tech firms, and e-commerce platforms.<sup>9</sup> In the empirical analysis that follows, the dependent variable indicates the share of the adult population in a country that made or received a digital payment in 2020-2021 using any type of provider (see Table 1 for a detailed variable description).

# **Bank Regulation and Supervision Survey Dataset**

The Bank Regulation and Supervision Survey (BRSS), collected by the World Bank Group, provides comparable economy-level data on how banks are regulated and supervised around the world. The most recent edition was completed for 160 jurisdictions in 2019 (Anginer et al., 2022; World Bank, 2019).<sup>10</sup> In the analysis that follows, the BRSS indicator is the index of overall restrictions on banking activities. Following Barth, Caprio, and Levine (2013), it is

> 9 See Claessens