rafmacalaba/data-use-mentions-extended huggingface dataset:
# **A. Prioritizing quality and efficiency of spending**

The WBG is a strong advocate of quality and efficiency of spending related to the SDGs. Specifically, this has entailed advising countries to spend on the right sectors, and to use the right spending metrics—rather than to simply spend more (Fay et al. 2019). These aspects have guided the WBG’s work with its client countries on developing plans to finance investment gaps and the SDGs, including through regular Public Expenditure Reviews and the country engagement component of the _Human Capital Project_ , which identifies policy priorities to achieve the education and health-related SDGs (i.e., SDG 3 and SDG 4). The WBG has also engaged in data collection related to spending efficiency, such as the ThinkHazard and CityScan platforms.

Several sectoral examples illustrate how important public spending choices are for development outcomes. For example, access to education is not the same as improved learning outcomes, and education systems in many developing economies do not deliver the learning that builds human capital (SDG4; World Bank 2011b, 2018e). Regarding climate change, the WBG has shown that cost-effective investments are available to advance SDG 13: each dollar invested in early warning systems has been estimated to avoid more than $4 in losses (Hallegatte 2012).<sup>18</sup> The WBG has underscored that better public spending in agriculture in Sub-Saharan Africa is crucial for improving agricultural productivity (Goyal and Nash 2017).

# **B. Creating a supportive policy environment**

The WBG recognizes that policy reforms, including legal and regulatory reforms, are essential for achieving the SDGs, and works with client countries to implement reforms.<sup>19</sup> As a key part of forming its country programs, the WBG conducts Systemic Country Diagnostics (SCDs) to identify the development challenges and opportunities, including policy challenges. The countrylevel engagements that follow SCDs, including Development Policy Operations and the Programfor-Results financing, are the chief means by which
’ permanent income (see below). We exclude from the analysis Argentina because we were not able to match assets with other surveys and build an income measure. The analysis is therefore based on a pooled dataset combining six countries that was created from the national datasets, with a total sample size of 12,297 observations.

The outcomes of interest, our dependent variables, are the values and attitudes regarding political, economic, and social issues. Because of the large number of questions asked in the surveys, and likely similarity of answers within themes, we group questions using principal component analysis (PCA), and create thematic “values indexes.” Principal component analysis reduces the dimensionality of a data set with a large number of interrelated variables, while retaining as much 6
OECD.<sup>9</sup> Mexico does not stand out particularly in the preceding comparison. As in most countries, the public sector directly or indirectly finances the lion's share of education expenditures. Mexico does stand out in contrast to its OECD partners, however, when these expenditures are disaggregated by level of instruction. As shown in OECD (1998, pg. 97) the ratio of spending per student at the tertiary level compared to spending at the primary level exceeded 500 percent in 1994 and was the highest among OECD countries. (The ratio in Korea, for example, is close to the OECD average of 264 percent, or less than half the Mexican ratio.)

> 9These figures comprise total expenditures on education, including privately purchased materials, whereas the earlier figures shown in Table 3 are identical to the what the OECD expenditure review refers to as direct spending on educational institutions.
in the accumulation of skills (Figure VII.l). A growing literature attempts to quantify the extent of the misallocation of talent and associated productivity losses in various contexts.

**Several studies emphasize the misallocation resulting from discrimination and social norms against specific demographic groups.** Hsieh et al. (2019) documents that the occupational distribution of women and black men has changed dramatically in the United States since the 1960s, and concludes that the relaxation of the labor market barriers faced by these groups can explain between 20% and 40% of the observed economic growth. Ashraf et al. (2022) exploit personnel data from a multinational firm active in many countries to show that women’s wages are higher for countries with lower female labor force participation, consistently with the idea that these labor markets are characterized by larger barriers faced by women (and hence more positively selected female workers). Hurst et al. (2021) provides more evidence on the barriers faced by black workers, showing that in the United States the discrimination against this group is stronger for occupations involving interaction with others, suggesting that the growing demand for these tasks might underlie the stagnation of the wage race gap since the 1980s. It is plausible that the relaxation of these barriers might itself encourage human capital

19
Table 7: Effect of savings default on planned vs. unplanned expenditures

||(1)<br>F|(2)<br>ood|(3)<br>n|(4)<br>on-durables|
|---|---|---|---|---|
||Planned|Unplanned|Planned|Unplanned|
||Panel A:|One week after|transfer||
|Savings Default|652.018<br>(432.842)|54.034<br>(52.608)|-275.008<br>(213.848)|31.277<br>(31.092)|
|Observations|472|472|472|472|
|Mean of dependent variable in Cash group|4711.85|278.37|2315.56|64.89|
|SD of dependent variable in Cash group|5085.93|620.60|2764.69|303.39|
|R-squared|0.21|0.28|0.23|0.05|
||Panel B:|Two weeks afte|r transfer||
|Savings Default|158.904<br>(425.306)|-10.275<br>(40.799)|297.821<br>(235.307)|23.619<br>(33.923)|
|Observations|312|312|312|312|
|Mean of dependent variable in Cash group|3748.37|185.56|1334.38|39.54|
|SD of dependent variable in Cash group|4540.24|454.46|1998.36|228.99|
|R-squared|0.32|0.19|0.26|0.04|

Categorization into planned and unplanned purchases is based on respondents’ answers during
# **3. Method**

# _3.1 Constructing Household Food Expenditure and Diet Quality_

We first constructed household-level food expenditure. Using data from the FIES 2015 household expenditure module, we grouped individual food items surveyed in FIES 2015 into the same 19 food groups in NNS 2013.<sup>2</sup> Then, at the household level, we aggregated total consumption and total expenditure for each food group. Units were standardized to grams and pesos for consumption and expenditure, respectively. To determine household-level values, total expenditures were divided by total consumption to find the unit value of each food group. The unit value of a food group calculated by this approach was essentially the average prices of all food items within that food group weighted by their quantities consumed. This method of deriving unit values of food groups follows Deaton (1987).

_<u>Table 1. FBDG Recommended Food Intake</u>_

||_Children_<br>_1-6_|_Children_<br>_7-12_|_Teens_<br>_13- 19_|_Adults_|_Elderly_|_Pregnant_<br>_&_<br>_Lactating_<br>_Women_|
|---|---|---|---|---|---|---|
|**Rice, Rice Products,**|||||||
|**Corn, Root Crops,**<br>**Bread, Noodles**|162.5|250|350|325|262.5|306.25|
|**Vegetables**|42|42|300|300|300|375|
|**Fruits**|150|100|300|250|200|200|
|**Eggs**|25|N/A|50|50|50|50|
|**Fish, Shellfish, Meat &**|||||||
|
from other economic factors.

We employ a new worldwide establishment dataset, WorldBase, that reports the performance of over 12 million establishments in 2005-2008. This dataset provides several distinct advantages over alternative data sources. First, unlike many other sources that lag considerably in assembling data, WorldBase reports updated 2008 operational information for establishments around the world.<sup>7</sup> Although the data coverage still does not re‡ect world population, it enables us to provide one of the ...rst systematic micro-level analysis on the recent crisis.

Second, the dataset, covering establishment activities both before and during the Global Financial Crisis, enables us to compare the role of foreign ownership during the crisis with the non-crisis years. Using the non-crisis period as a counterfactual helps us identify the e¤ect of foreign ownership, including production and ...nancial linkages, on establishment responses to adverse economic shocks.

Third, central to our analysis, the unit of observation in WorldBase is establishment (a single physical location at which production operations or services are performed) instead of ...rm. Establishments have their own addresses, business names, and managers, but might be partly or wholly owned by other ...rms. The database supports the linking of establishments to ...rms using information on domestic and global parents and the DUNS identi...cation.<sup>8</sup> Fourth, the dataset covers a larger number of countries than alternative data sources, enabling a comprehensive analysis of the crisis for both developed and developing nations and for countries at the epicenter as well as on the periphery of the crisis. It also allows us to investigate how the role of foreign ownership varies across countries depending on the incidences of the crisis.

WorldBase is compiled by Dun & Bradstreet (D&B) and covers public and private enterprises in over 100 countries and territories. Dun & Bradstreet has been the leading source of commercial credit and marketing information since 1845, and presently operates in
reasons and channels, including those who migrated through irregular channels (Özden et al., 2011). However, it is possible that these data sources may misreport irregular migrants for various reasons. To the extent such misreporting exists, measurement errors can mute the estimated relationship.

**Income** : Per capita GDP serves as the key measure of income in this paper. The GDP data are taken from the Penn World Tables (PWT) 10.0 release dated June 18, 2021 (Feenstra, Inklaar, and Timmer 2015).<sup>7</sup> This release contains the most comprehensive and up-to-date data on GDP, in purchasing power parity (PPP) terms, covering 183 countries between 1950 and 2019. Per capita GDP is calculated by dividing the expenditure-side real GDP at chained PPPs (in 2017 US$) by the country’s population.<sup>8</sup> GDP for 2020 is computed by applying the annual real per capita GDP growth rate between 2019 and 2020 available from the World Development Indicators (World Bank 2022). Similarly, per capita GDP is back-casted for earlier years based on annual real per capita GDP growth rates for countries for which the GDP series begins only after 1960. Countries that are missing GDP in this data set are omitted from the analysis.

**Education** . Data on the educational attainment of the populations of origin countries are from the Barro-Lee Educational Attainment Dataset (Barro and Lee, 2013).<sup>9</sup> This data set provides harmonized information on the percent of adult population that have no education, some primary education, some secondary education, and some tertiary education for all countries in five-year intervals from 1950 to 2015. This analysis uses the 2015 data to proxy for educational attainment in 2020.

**Education of migrants** : While comprehensive data on the educational attainment of migrants is absent, the analysis turns to data from the United States as a case of migration to high-income destinations
Clark (2003) using the British Household Panel Survey finds a positive correlation between happiness and inequality for the employed population. A study by Alesina et al.(2004) found that individuals tend to be less happy if inequality is high but that this effect is stronger in the EU than in the US. Also, the poor and left-wing people in the EU are less happy if inequality is high while this phenomenon is not visible in the US. Graham and Felton (2006) looked at Latin American countries and found that inequality (measured in terms of relative wealth) made people in upper quintiles happier and those in the poorest quintile less happy but they also find that the Gini coefficient is non significant in a happiness equation. Senik (2004) does not find a significant correlation between happiness and inequality for Russia using the Russian Longitudinal Monitoring Survey. A study by Helliwell (2003) finds no evidence that income inequality is correlated with happiness and, according to Veenhoven (1996) “ _Income inequality in nations appears almost unrelated to final quality of life as measured by average happiness_ (...)” (p. 34).

Table A3 in the annex provides more detailed information on the cited literature in chronological order. Leaving aside the first study by Morawetz et al. (1977), we can observe some similarities and dissimilarities. The data sets used in these studies are all different with the exception of two papers which both use the US-GSS study. Three studies use longitudinal panels of individual observations, four studies use cross-country studies with multiple years and one study uses a cross-country study with one year. The estimation models used can be ordered logit, ordered probit or OLS and this is a normative choice rather than a choice dictated by the data. The measure of inequality is the Gini for all studies except for part of the Hagerty (2000) study. Some papers estimate the Gini from the data set used while others extract the gini from other data sets. The Gini can also be estimated for countries
banks’ risk profile by the banks themselves and by their supervisors (Pillar 2). But Basel III also brings new duties into the authorities’ agenda, which include identifying systemically important financial institutions (SIFIs) – which among other criteria are large and highly interconnected – and deciding on the countercyclical capital buffer – based on the ratio of credit (bank and non-bank) to GDP. As a result of this, it will be highly demanding in information gathering.<sup>11</sup> Despite the fact that only internationally active banks of Basel Committee member countries<sup>12</sup> will have to implement Basel III, in many other non-BCBS countries supervisors will also adopt it, as has been the case with Basel II. For those that do not, international banking groups operating in their jurisdictions will likely be implementing the most sophisticated approaches on a global basis; hence detailed data will be needed to support the home supervisor.

The paper is organized as follows. Section II explores how PCR data can be used to design the prudential regulation for credit risk. Section III describes the role of PCRs in bank supervision. Making use of the supervisory responses to the World Bank’s survey on credit reporting systems, it exemplifies how supervisors around the globe are taking advantage of PCR data to enhance their supervisory process. In Section IV, the paper describes how PCRs can support the implementation and supervision of Basel III, while Section V explains their importance in steering a broader macro prudential surveillance. Having determined the usefulness of PCR data for central banks and bank supervisors, Section VI characterizes the desirable features that public credit registries should have. Finally, Section VII contains some concluding remarks.

# **II. The role of PCRs in bank regulation**

Until Basel III becomes globally widespread and authorities admit the use of banks’ internal rating systems, regulatory frameworks for credit risk may not be completely aligned to the underlying risk of banks portfolios. However, loan loss provisions should be commensurate with expected
32

# <u>Figure 6: Contribution to the poverty gap of out-of-pocket expenditures</u>

Addition to Per Capita Poverty Gap Due to Out−of−Pocket Health Expenditures, PL=$1.90−a−day Addition to Per Capita Poverty Gap Due to Out−of−Pocket Health Expenditures, $3.20−a−day <!-- Start of picture text --> $0.18+ $0.18+<br>$0.12–$0.18 $0.12–$0.18<br>$0.08–$0.12 $0.08–$0.12<br>$0.05–$0.08 $0.05–$0.08<br>$0.02–$0.05 $0.02–$0.05<br>$0.00–$0.02 $0.00–$0.02<br>no data no data<br><!-- End of picture text --> Addition to Per Capita Poverty Gap Due to Out−of−Pocket Health Expenditures, $5.50−a−day <!-- Start of picture text --> $0.18+<br>$0.12–$0.18<br>$0.08–$0.12<br>$0.05–$0.08<br>$0.02–$0.05<br>$0.00–$0.02<br>no data<br>IBRD 44311 | APRIL 2019<br><!-- End of picture text -->

<!-- Start of picture text --> Addition to Per Capita Poverty Gap Due to Out−of−Pocket Health Expenditures, $10.00−a−day<br><!
women were either less empowered or already more empowered, and hence more likely to respond to the treatment.

> 10Blocks are small groups of villages and are the administrative unit directly above the village council. Within each district, the placement decision of _Mahila Samakhya_ is made by block, rather than by village, hence these blocks are the appropriate unit of analysis when examining whether the program is targeted in any way. For blocks without the program, we use data from the 2001 census.

13
also highlight that the initial period of resettlement and return after prolonged displacement is likely a fruitful period for the international community to engage IDPs by capitalizing on their improved perceptions of empowerment.

In the Philippines and Iraq, we find that the settings of displacement have different effects on engagement and participation among IDPs. Based on the matching models, we show that campbased displacement is in general negatively related to personal empowerment when compared to observationally similar urban displaced populations. But we should caution that city-based displacement is not a panacea: in the Philippines, we find mixed relationships between urban status and perceptions of empowerment when comparing urban IDPs to rural IDPs using matched samples.

Again, inconsistencies of relationships across displacement settings highlight the importance of understanding and responding to nuances of the specific case. Survey data suggests that varying 25
those who are already relatively rich, in which case the policy may end up widening gaps. Similarly, certain types of taxes that require the rich to pay more (relative to their income) can reduce inequality while, conversely, taxes that fall heavier on the poorer parts of the distribution, can contribute to widening gaps. Thus, how governments spend and raise their revenue directly influences the amount of redistribution the fiscal system achieves, which in turn influences inequality.

There has been relatively little enquiry on the role the _overall_ fiscal system can play in reducing inequality in China. One notable exception is Lustig and Yang (2020) which used the Commitment to Equity approach (Lustig, 2018 and 2022) to describe the fiscal redistribution in China using data from the 2014 China Family Panel Studies survey.<sup>2 3</sup> The main finding of that paper was that while the fiscal system reduced inequality overall, it also widened rural-urban gaps. The study attributed the latter to the differences in contributory pensions between urban and rural areas. These results were consistent with findings from several other studies (Li, Zhu and Zhan 2017 and Xie 2018), and somewhat contrary to Wang and Lou (2017) who found that the VAT had inequality-increasing impacts that were sizeable enough to make the overall fiscal system inequality-increasing.

Against this backdrop, this paper revisits this question using more recent representative data of Chinese households, the 2018 round of the China Family Panel Survey (CFPS). The motivation for the paper is two-fold. First, inequality has continued to remain high even as the emphasis on common prosperity has intensified. As such, continuing to highlight the role fiscal policy has been playing and could play in addressing this challenge using fresh data and evidence is important. Second, since 2014 there have been several policy changes whose impacts can be picked up in the updated analysis, though in some cases this will be confounded by the fact that this paper also introduces some methodological upgrades. For example, Lustig and Yang (
Kenworthy, Jeff, and Felix, Laube. 2001. _The Millennium Cities Database for Sustainable Transport_ . International Union of Public Transport, Brussels.

Miller, Ted R., 2000. ―Variations Between Countries in Values of Statistical Life.‖ _Journal of Transport Economics and Policy_ 34: 169-188.

Newbold, Stephen C., Charles Griffiths, Chris Moore and Ann Wolverton, 2009. ―The ―Social Cost of Carbon‖ Made Simple.‖ Discussion paper, National Center for Environmental Economics, US Environmental Protection Agency, Washington, DC.

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Parry, Ian W.H. and Jon Strand, 2010. ―International Fuel Tax Assessment: An Application to Chile.‖ _Environment and Development Economics_ , forthcoming.

Parry, Ian W.H. and Govinda R. Timilsina, 2010. ―How Should Passenger Travel in Mexico City be Priced?‖ _Journal of Urban Economics_ 68: 167-182.

Parry, Ian W.H. and Kenneth A. Small, 2009. ―Should Urban Transit Subsidies Be Reduced?‖ _American Economic Review_ 99: 700–724.

Parry, Ian W.H., David Evans, and Wallace E. Oates, 2010. ―Are Energy Efficiency Standards Justified?‖ Discussion paper, Resources for the Future, Washington, DC.

Pope, C. A., 3rd, R. T. Burnett, et al., 2004. ―Cardiovascular Mortality and Long-Term Exposure to Particulate Air Pollution: Epidemiological Evidence of General Pathophysiological Pathways of Disease.‖ Circulation 109: 71-77.

Pope, C. A., 3rd and D. W. Dockery (2006). ―Health Effects of Fine Particulate Air Pollution: Lines that Connect.‖ _Journal of the Air & Waste Management Association_ 56
indirect taxes (value added tax or VAT and excises) and adding subsidies, we arrive at _post-fiscal_ or _consumable income_ which reflects the actual amount of market goods and services consumed by households. Our _final income_ includes the cash equivalent of the cost of public health and education services consumed by households.

This paper analyzes the redistributive and poverty-reducing effect of the tax-benefit system under the assumption that pensions are treated as direct transfers, while contributions to the pension system are subtracted from gross income. This is based on the nature of the pension system in many of the countries considered for the analysis. In particular, the PAYG system formed in the Soviet Union has undergone reforms in the 2000s aimed at adding contributory pillars. This resulted in a divergent mix of pension systems in the region, but with one common feature – the weak link between contributions and actual pensions payments (Grishchenko 2016). Thus, it is more natural to consider pensions as transfers similarly to other contributory programs (such as unemployment benefits).

The analysis used here is point-in-time and does not incorporate behavioral or general equilibrium effects. The analysis is based on economic rather than statutory tax incidence. For example, it is assumed that personal income taxes and contributions by employees and employers are borne by labor in the formal sector. Individuals who are not contributing to social security are assumed to pay neither direct taxes nor contributions. Consumption taxes are fully shifted forward to consumers. According to the common practice adopted in the region, the welfare indicator used is income per capita. Finally, it is worth noticing that CEQ analysis relies as much as possible on information about social transfers and taxes reported in the survey. Therefore, corporation taxes cannot be included in the analysis. If the survey does not include questions on certain items, the values are either simulated or imputed. Because the survey data is being used for the assessment of taxes and benefits, the annual amounts of tax revenues and social spending do not necessarily coincide with those found in other sources, in particular National Accounts