rafmacalaba/data-use-mentions-extended huggingface dataset:
two mutually exclusive categories for females assigned to the _Goal_ arm: set 2 or 3 strategies or set 0 or 1 strategy. For each outcome defined at the start of the rows, the coefficients from a single regression are presented in two sub-rows, with estimates of _β_ 1 in column 1 and the coefficients on the two _Goal_ arm indicators in columns 2 and 3 in the first of the two rows, labeled treatment x post, and the estimate of _γ_ 1 in the second of the two rows, labeled treatment x post x Goal. Column 4 presents the outcome mean at endline among females in ELA only communities who were not assigned to the _Goal_ treatment, and column 5 shows the number of observations in the model. See notes for Table 2 for detail on outcome definitions and control variables. Standard errors, clustered at the club level, are presented in parentheses below coefficient estimates in columns 1–2. .01 ***, .05 **, .10 *. _Source._ Female baseline and endline data, balanced panel.

63
the political elites are ethnic kin with a victimized group. In related work, the authors argue that more liberal hosting policies attract additional refugees (Blair et al. 2021b).

These _de jure_ policies relate to attitudes. Figure 1 plots an index of these data on policies against the Gallup World Poll measure of attitudes towards immigrants. The positive relationship indicates that countries with more positive attitudes also allow refugees more legal access to the labor market. Causal relationships could run 4
# **4. Latin American ninis in global perspective**

How do the dimensions of Latin America’s nini problem compare to those of other regions? Using a collection of household surveys at the global level standardized by the World Bank’s Global Income Distribution Dynamics (GIDD) project,<sup>8</sup> we are able to quantify the total number of youth out of school and not working at the global level. To compute the total number of ninis at the global level, we rely on two main assumptions. First, we assume that the share of ninis in countries for which we did not have a reliable household survey close to 2010 is the same as the average for the region to which they belong. For instance, the share of ninis in Bolivia is assumed to be 19.3 percent, which is the average share for Latin America and the Caribbean. Second, we assume that the share of ninis remained constant between 2010—the last year for which a reliable household survey is available for a large number of countries—and 2015. Our calculations thus take into account the total global youth population of 1.19 billion in 2015 and assume that the share of ninis is the same as that observed circa 2010. In 2015 there were some 260 million youth ages 15 to 24 out of school and not working.<sup>9</sup> More than 35 percent of the world’s ninis live in South Asia, and more than one fifth of them in East Asia and the Pacific.

Figure 7 shows the share of youth aged 15 to 24 who are ninis in all countries in the world where household data is available (circa 2010). The countries with the highest incidence of ninis are Iraq (51 percent), Guyana (44 percent), Nigeria (43 percent), and Bangladesh (41 percent), each with shares of more than 40 percent. At the other end of the spectrum, Sweden (4.2
in heterogenous panels”, _Journal of Econometrics_ 115, 5374.

Javorcik, B. S. 2004. Does foreign direct investment increase the productivity of domestic firms? In search of spillovers through backward linkages. _The American Economic Review_ , 94(3), 605–627.

Javorcik, B., and Spatareanu, M., 2005. “Disentangling FDI spillover effects: what do firm perceptions tell us”. In T.H. Moran, E.M. Graham, M. Blomstrom (Eds.), _Does Foreign Direct Investment Promote Development?_ Institute for International Economics and Center for Global Development, Washington, D.C. (2005), pp. 45–72.

Kemegue, F. & Mohan, R., 2009. "US FDI flows to ASEAN-5: Do Geographic Neighbors Matter?," _Applied Econometrics and International Development_ , Euro-American Association of Economic Development, vol. 9(1).

Kokko, A. and Gustavsson, P., 2004. "Regional integration, foreign direct investment, and regional development," EIB Papers 5/2004, European Investment Bank, Economics Department.

Kugler M. 2006, “Spillovers from foreign direct investment: Within or Between Industries”, _Journal of Development Economics_ , 80 (2006) 444-477.

Levin, A., Lin, C.F. and Chu C.S.J., 2002. “Unit root tests in panel data: asymptotic and finite-sample properties”, _Journal of Econometrics_ 108, 1-24.

Levinsohn, J. and A. Petrin (2003) Estimating Production Functions Using Inputs to Control for Unobservables. The Review of Economic Studies, 70(2), 317-341.

Lucas, R., 1990. "Why doesn't Capital Flow from Rich to Poor Countries?". _American Economic Review_ 80 (2): 92–96.

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mention their comfort level, and instead were asked separately whether they could use an account by themselves). Specifically, about 30 percent of inactive account holders do not use their account because they do not feel comfortable doing so by themselves. More men with an inactive account (34 percent) than women with an inactive account (26 percent) say they do not feel comfortable using it by themselves, perhaps because men typically make larger or more complex transactions. There was no income or educational attainment gap for inactive account holders who gave this reason.

# **_6.2 The case of Sub-Saharan Africa_**

The Global Findex 2021 survey also asks mobile money account owners in Sub-Saharan Africa if they are able to use their mobile money account without help. Across Sub-Saharan Africa, one in three (31 percent) mobile money account holders cannot use their account without help (Figure 7). In countries such as Liberia, Malawi, and South Africa, this share is much higher, with more than 50 percent of mobile money users needing help using their account.

Among mobile money account holders, women, the poor, the less educated, the young and those outside the labor market are more likely to need assistance, making them at potentially higher risk of financial abuse from agents or family members.

Gender differences are relevant: female mobile money account holders are 5 percentage points more likely than men to need assistance. In many countries, like Ghana, Liberia, Malawi, and Senegal, this share is much higher, at more than 10 percentage points. On the other hand, in countries like Benin, Cameroon, Côte d’Ivoire and Nigeria, male and female mobile money account holders are equally likely to need help.

25
one untapped growth source—the sustainable use of groundwater for irrigation. We measure potential agricultural production gains in Sub-Saharan Africa through groundwater irrigation.

To do so, we construct a novel dataset by merging gridded agricultural data with local shallow aquifer data and their corresponding details. We then use our dataset to simulate the gains in production that can be achieved if we use groundwater irrigation to fill in the existing crop water deficits that persist in the region’s rain-fed croplands. While running our simulations, we impose conditions during our simulations to only compute gains in regions with sufficient renewable groundwater. We also limit our analysis to aquifers suitable for small-scale irrigation and accessible at low depths to ensure cost effectiveness.

Our findings show the median production gains range from 27.97% to 129.42%, depending on the crop type and the method used to predict crop yield gains. The most pronounced gains are for maize and rice crops. Our results remain consistent, even with changes in the method and dataset used to measure groundwater availability. Most importantly, our analysis enables policymakers to delineate regions likely to experience agricul18
Kong SAR, China and Singapore (Figure 1, Panel A).<sup>13</sup> By 2014, China’s market capitalization had become the second largest in the world after that of the United States.

The expansion of market capitalization coincided with increases in equity prices and issuances. The price index in China rose rapidly since 2014, significantly diverging from the indices in Hong Kong SAR, China and Singapore, despite sharing similar trends up to 2013 (Figure 1, Panel B). Moreover, the aggregate amount of equity raised in mainland China doubled between 2007-13 and 2014-20 (Figure 1, Panel C). While mainland China and Hong Kong SAR, China shared similar equity issuance trends before 2014, a clear divergence has occurred since then. The pattern of equity issuances suggests a significant impact of internationalization on domestic equity financing that has yet to be explored in the literature. We fill this gap by using a rich dataset on equity issuance activity.

# **3. Data and Methodology**

# **3.1. Data**

We merge transaction-level data on equity issuances with balance sheet data of domestic and foreign listed Chinese firms with residence and major business operations in mainland China. The transaction-level data come from Refinitiv’s Securities Data Corporation (SDC) Platinum, which provides detailed transaction-level information on new equity issuances during 1990-2020. The balance sheet and income statement data come from Worldscope. Lastly, we augment our merged dataset with firm-level data on ownership structure from Wind.<sup>14</sup>

> 13 One of the key internationalization reforms– the Stock Connect program – also affected the capital market in Hong Kong SAR, China. Indeed, part of the growth in the market capitalization in Hong Kong SAR, China, since 2013 could be attributed to Southbound trading activities from the Connect program. Nonetheless, the market capitalization in mainland China rose substantially more after 2013 (Figure 1, Panel A).

> 14 All value variables in our sample are
# **V.   CONCLUSIONS**

The data collected via the 2011–12 Bank Regulation and Supervision Survey provide interesting insights into the state of regulatory and supervisory practices around the world against the background of the global financial crisis. The unique dataset, made publicly available at <u>http://www.worldbank.org/financialdevelopment, illustrates differences in regulation between</u> crisis and non-crisis countries and also highlights the ways in which bank regulation and supervision has changed so far as a result of the global financial crisis.

Comparing responses from crisis and non-crisis countries, we find that crisis countries (those with either full-fledged or borderline systemic banking crisis in 2007–09) tended to allow for less stringent definitions of capital, more discretion in how banks calculated capital requirements, and also exhibited lower actual capital ratios. At the same time, the analysis revealed that banks in crisis countries faced fewer restrictions to engage in non-bank activities such as insurance, investment banking and real estate activities. Crisis countries were also more lax in the treatment of bad loans and loan losses. Similarly, regulators in crisis countries were less able to demand banks to recapitalize banks, to constitute greater provisions or to suspend bonuses or management fees. Finally, even though crisis countries had stronger information disclosure requirements, the incentives for the private sector to monitor banks’ risks were weaker in crisis countries. Overall, while these tests do not imply causality, they nevertheless suggest that crisis countries had weaker regulatory and supervisory frameworks compared to those countries that fared better during the global financial crisis.

The survey results also underscore the evolutionary nature of the regulatory and supervisory changes at the national level. The recent financial crisis did not trigger a major and sudden change in national regulatory and supervisory frameworks around the world and most of 13
11 areas by 20 percent. We also find that, although we find no significant effect for the blanket insurance coverage variable in rural areas in Table 4, we find a borderline significant positive effect for CMS when we separate out cover by scheme in Table 5. Work unit insurance cover also emerges with a positive and borderline significant effect in rural areas. We find some evidence, as reported at the bottom of Table 4, that in rural areas the effect of insurance varies by province.

The evidence from the Gansu panel (Table 6) is also consistent with the hypothesis that health insurance can increase financial risk, and the marginal effect is non-negligible. This is despite the fact the insurance seems to reflect mostly insurance for school children, and the fact that the primary sampling units are all rural areas.

The results for the Health VIII cross-section survey in Table 7 are sensitive to whether or not we treat insurance as endogenous, especially so in the case of GIS/LIS. The relative insensitivity of the CMS effect to the use of IV reflects the fact that CMS operated in only two of the seven provinces sampled (Henan and Guizhou), and in the villages where it existed, most people surveyed were members. The direction of bias in the case of the GIS/LIS effect is consistent with our speculation earlier that this scheme has a high favorable risk profile whose effect we can only partially control for through our observed covariates. The failure to take into account the endogeneity of insurance status in the case of GIS/LIS results in a substantial underestimate of the effect of insurance on the probability of high out-of-pocket health spending, and the IV marginal effects are similar in size to those from the CHNS. Treating insurance as endogenous also makes the difference between the effect being significant and not. The results in Table 7 suggest that in the few villages where it existed, CMS reduced the risk of unduly high
believed that the data set represents base year equilibrium. The resulting empirical model may then be used to conduct _counterfactual simulations_ in order to examine the likely change in equilibrium values of endogenous variables induced by changes in some exogenous variables. We briefly describe the calibration of the general equilibrium model and the estimation of the parameters characterizing the general quadratic Lorenz curve.

# _Calibration of the General Equilibrium Model_

The necessary data for an empirical economy-wide model must be organized in a frame that reflects the _circular flow of economic activity_ for the chosen year. The Social Accounting Matrix (SAM) offers such a framework. It provides an analytically integrated data set which reflects various aspects of the economy such as production, consumption, trade, accumulation and income distribution. A SAM is a square matrix, the dimension of which is determined by the chosen degree of disaggregation which itself is constrained by the issues of concern and data availability. Each account is represented by a combination of one row and one column with the same label. _Each entry represents a payment to a row account by a column account_ . Thus, all receipts into an account are read along the corresponding row while payments by the same account are recorded in the corresponding column. In accordance to the principles of double-entry bookkeeping, the whole construct is subject to a consistency restriction which makes the column sums equal to the corresponding row sums. This restriction also means that the SAM obeys Walras’ Law in the sense that, for an **n** -dimensional matrix, if the ( **n-1** ) accounts balance, so must the last one. Table 2.1 shows the structure of the SAM underlying the basic model described above. The government account is not included because all taxes and transfers are assumed to be zero in the base year<sup>4</sup> .

> 4 This base case was chosen by the authors because they were also interested in finding the optimal configuration of the tax instruments.  Such a configuration would not distort private sector decisions
implementng technologies or methods to improve energy usage efciency. Those who have adopted energy efciency solutons are prompted to detail the specifc technologies or practces in use. These practces encompass questons about LEED certfcaton for buildings, adoptng efcient lightng systems, adherence to ISO 14001 or 50001 standards, or engagement in carbon trading schemes. About 38 percent of the surveyed frms acknowledge that they do not employ energy efciency technologies or practces. Yet, this fgure masks considerable cross-country heterogeneity. For example, in Armenia, 99 percent of the surveyed frms reported using at least one energy-saving approach. By contrast, in Paraguay, a striking 86 percent of frms indicated they do not engage in any such practces. For a more detailed breakdown, see Table A2 in the Annex.</mark>

# **3. Estimation methodology**

<mark>To assess the impact of energy shocks on frm performance, we utlize the BPS sample for 24 developing countries over the 2019-2023 period, covering large fuctuatons in country-level electricity prices. We hypothesize that a frm's susceptbility to the energy price shock is contngent on the shock's magnitude at a specifc tme, the frm's energy requirements for its operatons that we assume are sector-specifc, and any energy efciency measures or lack thereof that can make the frm-level energy efciency deviate from its sector’s average.</mark> <mark>As explained in the data secton, the variatons in the shock intensity are quantfed through the monthly electricity data for each country matched to frm surveys by the month of the frms’ interviews. The BPS data's unique advantage lies in recording the precise survey date for each frm, introducing frm-level variaton in shock exposure across countries. This aspect is critcal as the shock's magnitude is shown to vary from month to month (Figure 2). The energy usage intensity is determined based on CPRS classifcaton; sectors such as manufacturing, constructon, and transportaton are categorized as energyintensive, in contrast to agriculture, retail, and other services,
period (1997-2006/07). The three most frequent shocks reported include expenses and foregone income due to illness, dowry and wedding related expenses, and floods. The data suggests households with lower endowments in terms of education, and land/asset ownership, are more vulnerable to shocks. The study, however, is unable to shed any light on how households cope with these shocks.

This paper uses household survey data collected in September-October 2009 on a nationally representative sample of 2000 households in Bangladesh to examine the nature of 2
. . . . . . . . . . . .|70|
|Table15|Share of IAB Countries,2007-08<br>. . . . . . . . . . . . . . . . .|71|
|Table16|Lists of Countries<br>. . . . . . . . . . . . . . . . . . . . . . . . .|72|
|Table17|Correlation Among Explanatory Variables . . . . . . . . . . .|73|
|Table18|Correlation Among WGI Variables<br>. . . . . . . . . . . . . . .|74|
|Table19|Construction of the FDI Openness Variable<br>. . . . . . . . . .|75|
|Table20|Construction of the FDI Arbitration Variable<br>. . . . . . . . .|76|
|Table21|Construction of the FDI Procedures Variable<br>. . . . . . . . .|77|
|Table22|Composite Country Scores of Selected FDI Regulation . . . .|78|

iv
simulation that encompasses all levels of basic education, since the LAYS is designed to capture the education life of students from 4 to 17 years of age. Two, it has the largest country coverage, with 157 countries and 97% of the world’s population aged 4–17. And three, it combines access (including dropout rates) with quality.

The second simulation exercise focuses exclusively on the expected learning losses at early secondary, as measured by PISA and defined in terms of an average PISA score. The third, and last, simulation translates the impact of a PISA mean score shock into the share of children performing below the minimum proficiency level, as defined by OECD and UIS in the context of the SDG 4.1.1c.<sup>48</sup> One important element in these simulations is the possibility to present results in monetary terms. In order to do that we use expected earnings information from ILO (2020) and World Bank (2020c), and the expected long run return to education. We also compute aggregate results by bringing all expected earnings losses to their present value, assuming a work life of a 45 years and a 3% discount rate. In order to make these results more realistic, we also adjust the aggregate loss by the expected adult survival rate (following the World Bank HCI), and the fact that not all workers will always be in gainful employment (following the measure of Human Capital Utilization described in Pennings, 2019).

We propose three global scenarios for the construction of our lobal simulation (Table 1). In the optimistic scenario, we assume that the length of school closures _(s)_ , as defined above, is an average of 3 months. In the intermediate scenario, we expect schools to be closed for 5 months. In the last, and most pessimistic scenario, we expect schools to be closed for 7 months, or 70% of the school year.

These scenarios are aligned with what we have
31 A lower level of the weighted average tariff rate than that of the simple average tariff rate indicates that tariffs effectively suppress imports of items subject to high tariff rates, as sectors with high tariff will generally have a lower weight as imports are smaller. For higher level of weighted average rates, the opposite is the case. Agricultural goods fall into the latter: high tariffs do not completely prevent their imports, while they appear to do so in the case of industrial products. There are two caveats, however. First, as indicated earlier, most of Bulgaria’s imports are subject to preferential, mostly zero tariff rates. The UNCTAD database does not take into account preferential trade. In consequence, weighted tariff rates are probably much higher for all countries in Table 12.

**Table 12** : _Average Applied Tariffs and Bound Rates: Bulgaria’s preferential partners as available for most recent year (in percent)_

|||||Average|Applied MF|N Tariff Ra|te (%)||||
|---|---|---|---|---|---|---|---|---|---|---|
|||Total Goo|ds|Agricultur|al Goods|Industrial|Goods|Bound Ra<br>goods|te of All|Binding|
|Reporter|Year|Simple<br>Average|Weighted<br>Average|Simple<br>Average|Weighted<br>Average|<br>Simple<br>Average|Weighted<br>Average|<br>Simple<br>Average|Weighted<br>Average|Coverage<br>inpercent|
|Albania|2002|8.5|8.6|9.9|9.5|8.4|8.2|7.0|7.9|100|
|Bosnia and Herzegovina *
inequality rose between 2015 and 2018 but has declined since then. Despite the recent positive trends, income inequality persists at a notably high level, ranking the country as the most unequal country in the EU.** Despite solid income growth among poorer households, growth has not been inclusive enough **.** The per adult equivalent Gini Index increased from 37.7 in the income year 2015 to 40.8 in the income year 2018, in contrast to the trends in inequality in the region, which remain primarily stable (Figure 2, panel a).<sup>3</sup> More recently, income inequality has decreased, but the country continues to have the highest inequality among EU countries (Figure 2, panel b). The at-risk-of-poverty rate (AROP), a measure of income inequality rather than absolute poverty, has shown an upward trend, with 22.9 percent of the population having incomes below the national poverty line in 2021 (income year), the highest in the EU (Figure 2, panel c). Most of the observed inequality increase can be attributed to widening disparities in labor market income, with poorer individuals tending not to work or to be concentrated in lower-wage occupations. By contrast, the tax ratio, share of adults employed, pension income, and social income have only marginally helped slow the increase in the Gini coefficient (World

> 3 Similar trends are observed using the per capita Gini Index.

3