rafmacalaba/data-use-mentions-extended huggingface dataset:
).

We contribute to several strands of the literature studying asset ownership and the link between wealth taxation (including property taxes) and gender. Many countries recently highlighted the need to improve the collection of gender-disaggregated data on taxation in general, and on men’s and women’s property and capital ownership in particular, which is less commonly available (OECD, 2022). We add to a growing literature documenting gender patterns in property ownership and wealth (Holden and Tilahun, 2020; Kotikula and Raza, 2021; Komatsu et al., 2021; Gaddis, Lahoti, and Swaminathan, 2022; Scot et al., 2023), corroborating previous findings that women own fewer properties than men, particularly as property values increase. We document a substantial wealth gap, with women’s ownership shares declining significantly for higher-valued properties.

> 3. For recent work on gender and business cycles, see Gomes (2024).

> 4. However, we acknowledge that our study does not evaluate the effectiveness of gender-specific interventions implemented elsewhere, such as Awasthi, Pyle, Aggarwal, and Rakhimova (2023).

3
whether this econometric specification adequately captures the way heterogeneity impacts on subjective welfare; I will return to this point after considering some examples.

In what appears to have been the first panel data study of subjective welfare, van de Stadt et al., (1985) modeled a money metric of subjective welfare in the Netherlands. They did not allow for latent individual effects correlated with the covariates, but they did allow for dynamics, by including the lagged subjective welfare measure as a regressor, which picks up at least some of the heterogeneity. More recently, the papers by Winkelmann and Winkelmann (1998), Ravallion and Lokshin (2001) and Senik (2004) allowed for a time-invariant latent individual effect when modeling subjective welfare data. I will discuss the example of the Ravallion and Lokshin paper in more detail as this provides insights into the implications of allowing for latent heterogeneity for standard regression results on the determinants of subjective welfare.

Using panel data for Russia on responses to the ELQ, Ravallion and Lokshin confirmed some of the results in past studies using single cross-sections, but not others. Household income was found to be a highly significant predictor of self-rated economic welfare. Controlling for household income, individual income was a far weaker predictor, suggesting that there is some degree of income pooling. Health shocks lowered subjective economic welfare, at given values of other variables in their model, including incomes. The demographic effects found in crosssectional studies (notably of household size, at given income per capita) were not found to be robust. The extent of the scale economy of household size in individual subjective welfare suggested by a number of papers in the literature may well reflect latent personality effects on the demographic characteristics of the respondent’s household.

In keeping with past work, Ravallion and Lokshin found evidence of an incomecompensated welfare cost of unemployment. They argue that most plausible interpretation is that the current welfare loss arises from some combination of lower expected future incomes and more uncertain incomes, the latter arising from greater exposure to uninsured risk outside
2019). For example, evaluators stop collecting data at the end of their study in most impact evaluations. However, participants of policy interventions or projects (the treatment group) remain captured in administrative records, which can provide valuable information to estimate long-term effects.

Evidence proving the value of administrative data is vast. Chetty et al. (2011) found evidence on income effects of having an experienced kindergarten teacher and higher-achieving classmates; and Aizer and Eli et al. (2016) found that cash transfers to poor families can have positive educational, mortality, and income outcomes on the families’ children decades after the transfers. As stated before, administrative data do not need to be supplied by government; there is a long tradition of case studies using administrative records from company human resources departments, for instance. Fernandez and Greenber (2013) found evidence of gender and race inequality in the hiring processes of companies; and Fernandez and Rubineau (2019) analyzed the impact of network recruitment effects on the gender “glass ceiling” in the biopharma industry.

When administrative records are good quality, they can even be linked to households or other individuallevel surveys to add variables to the survey, but also to measure and improve survey accuracy. Davern et al. (2008) used an imputation method to test the validity of self-reported health insurance coverage in survey data; Meyer and Mittag (2019) used administrative data to identify patterns and uncover 22
range of respondent characteristics show that within countries, respondents that were female and less educated

> transfers, but was less likely to include support received in the form of subsidies or tax/payment exemptions. The timing of the survey is also not ideal for capturing the timing of support. Nevertheless, these phone surveys are a rare source of cross-country information on the extent to which some form of fiscal support reached households at different times over the pandemic.

> 22The challenges with targeting are in part due to existing constraints of the social protection system in poorer countries, in particular high levels of informality and the lack of a social registry which could be used to quickly reach those in need. In fact, cash transfers, the main form of income support provided during the pandemic, were directed at informal workers in 79 percent of programs. In contrast, wage subsidies which mainly benefit formal workers were the most common labor market policy directed at individuals in HICs (Kamran et al. 2023). Many governments relied on inefficient subsidies instead of targeted transfers to respond to the crisis (World Bank 2022b).

> 23In some countries, there is evidence that well-established cash transfer programs helped mitigate the impact. In Ethiopia, Abay, Berhane, et al. (2023) show that worsening food insecurity was offset for households participating in the country’s flagship social safety net program. Aggarwal et al. (2022) presents similar findings in Liberia and Malawi in that receipt of cash transfers improved food security of rural households.

> 24This indicator is part of a set of self-reported questions that make up the Food Insecurity Experience Scale (FIES) and is associated with increased difficulties in accessing food due to resource constraints. The indicator was available for a total of 60 countries, including 4 HICs, 16 UMICs, 23 LMICs and 17 LICs.

26
subsidized regime and persons from rural areas who are affiliated with the contributory regime receive the third best care. Persons from rural areas who are affiliated with the subsidized regime or who are not insured at all receive the worst care. One of the greatest difficulties mentioned by the users of subsidized EPSs is the frequent changes in service providers contracted by their EPSs, which leads to frequent problems in treatment continuity.

**Figure 9: Ranking in the Quality of HIV/AIDS Care in Colombia According to the Insurance Modalities of Patients and Area of Residence**

<!-- Start of picture text --> 1<br>RV<br>Big cities<br>2<br>RC – U<br>3<br>3<br>RS – U<br>RC– R<br>4<br>4<br>RS – R RV – U&R<br><!-- End of picture text -->

# **Legend:**

RV Big cities: “Régimen de aseguramiento vinculado” for people living with HIV in big cities; RC-U: “Régimen contributivo” in urbana reas; RS-U: “Régimen subsidiado” in urbana reas; RC-R: “Régimen contributivo” in rural areas; ” RS-R: “Régimen subsidiado” in rural areas; RV-U & RS-U: “Régimen vinculado” in urbana reas (excluding big cities) and rural areas.

# **Source:** Técnicas de Salud 2011.

In the specific case of diagnostics services (HIV tests), there are social and gender inequities in Colombia. The 2010 ENDS survey<sup>47</sup> shows that a substantial percentage of women in certain age groups are lacking in knowledge about where to take the HIV test. Only 19% of women below the age of 19 and 28% of women above the age of 40 have taken the test. Furthermore, within both of these age groups, women living in

> 47 MPS
# FIGURE 4 Drivers of potential growth

## A. Contributions to potential growth

<!-- Start of picture text --> Percent<br>10 TFP Capital Labor Potential growth<br>8<br>6<br>4<br>2<br>0<br>World AEs EMDEs<br>C. Potential growth in EMDE regions<br>Percent Potential growth<br>10<br>Actual growth<br>8<br>2000-2019 potential growth<br>6<br>4<br>2<br>0<br>EAP ECA LAC<br>E. Share of economies with potential growth<br>below 2000-10 average, 2011-21<br>Percent<br>100<br>80<br>60<br>40<br>20<br>0<br>EAP ECA LAC<br>2000-21 2000-10 2011-21 2000-21 2000-10 2011-21 2000-21 2000-10 2011-21<br>2000-10 2011-21 2000-10 2011-21 2000-10 2011-21<br><!-- End of picture text -->

## B. Contributions to potential growth

<!-- Start of picture text --> Percent<br>10 TFP Capital Labor Potential growth<br>8<br>6<br>4<br>2<br>0<br>EMDEs EMDEs excl.China<br>D. Potential growth in EMDE regions<br>Percent Potential growth<br>10 Actual growth<br>2000-2019 potential growth<br>8<br>6<br>4<br>2<br>0<br>MNA SAR SSA<br>2000-21 2000-10 2011-21 2000-21 2000-10 2011-21<br>2000-10 2011-21 2000-10 2011-21 2000-10 2011-21<br><!-- End of picture text --> <!-- Start of picture text --> F. Share of economies with potential growth<br>below 2000-10 average, 2011-21<br>Percent<br>
are considered. In particular in Eastern Europe, increasing efficiency has dominated the redistributive motive and many countries have actively lowered the tax burden on single and dual earner households, i.e. the households with traditionally large tax burdens. In contrast, tax burden increases are most often driven by budget consolidation considerations and mainly observed in countries affected by the financial crisis, such as Greece and Portugal.

Our analysis relates to the literature in the following ways. Kanbur (2018) presents a theoretical treatment of how progressivity in the vertical redistribution, i.e. across income groups, can be negatively affected if this redistribution also implies transfers among socio-politically relevant (horizontal) groups. Paulus and Tasseva (2017) analyze how changes in tax-benefit policies and market incomes have influenced income distributions in Europe. Our paper additionally focuses on population subgroups, job types, and intergenerational analysis. Dolls, Doorley, Paulus, Schneider and Sommer (2018) look at distributional changes from demographic change (upskilling, population aging), relying on demographic projections for analysis. We focus on the current demographic structure but analyze how age groups have been affected differently. Other papers that study inequality and redistributive policies after the financial crises focus on a subset of countries or income years (Navicke 2017, Callan, Doorley, Savage 2018, Bargain, Callan, Doorley, Keane 2017) or focus on specific measures of austerity (Figari, Paulus, Sutherland 2017).

3
to home biases. On the other hand, foreign bank branches and subsidiaries of European banks that are funded in local markets might be able to better absorb loan deleveraging pressures.

- Assets are 2.8 times RWAs. This figure is based on the median in a recent sample of internationally operating banks.

- The relative regional credit distribution of some countries is equal to the distribution average of all other countries, given the lack of information on regional distributions of emerging market credit for some countries.

**35. In the short term, banks need to adjust their balance sheets to comply with EBA**

**requirements. A worst-case scenario implies a €245 billion reduction in EMDE credit** . Banks that were identified as having a capital shortfall in EBA stress tests in 2011 have €1.35 trillion outstanding in EMDE credit (about 8.3 percent of their total loans). In a worst-case scenario (Scenario 1), if banks need to meet the full €114.7 billion capital shortfall fully through deleveraging and if the deleveraging occurs fully through the loan book, the estimated decline in EMDE credit could be as much as €245 billion, or 19 percent of the outstanding EMDE credit (figure 22). If banks are able to raise capital to cover half the shortfall (Scenario 2), the estimated short-term impact on EMDE credit would be lower, at €131 billion (10 percent of their

> 18 These figures are taken from IMF Financial Soundness Indicators.

> 19 Given the simplifying assumptions used in the simulations (box 3), the results should be interpreted as suggestive.
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Fally, T., and R. Hillberry (2018): “A Coasian Model of International Production Chains,” _Journal of International Economics_ , 114(1), 299–315.

- Feenstra, R., and G. Hanson (1999): “The Impact of Outsourcing and High-Technology Capital on Wages: Estimates for the United States, 1979-1990,” _Quarterly Journal of Economics_ , 114(3), 907–940.

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Ford, M. (2015): _The Rise of Robots_ . Basic Books, New York.

- Fort, T. C. (2017): “Technology and Production Fragmentation: Domestic versus Foreign Sourcing,” _Review of Economic Studies_ , 84(2), 650–687.

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percent of the countries, but this did not change the qualitative nature of the results discussed in this section.

> 14 The regions are Sub-Saharan Africa, East Asia and the Pacific, Eastern Europe and Central Asia, Latin America and the Caribbean, South Asia, and Middle East & North Africa. The data source for the regions is WDI, World Bank. 15 Data on religions are taken from Maoz and Henderson (2013). The data are available for every 5 years. We use year 2010 values for all the countries in our sample. The data are available at: <u>http://www.thearda.com/Archive/Files/Descriptions/WRPNATL.asp.</u>

> 16 For the two measures from Freedom House’s Economic Freedom of the World, we use year 2013 values for all the countries. Due to missing data for several years, we are unable to match these variables to the year the ES was conducted.

25
be Giffen goods, with the income effect outweighing the substitution effect. However, these patterns are also consistent with a sharp penalty on undernourishment limiting households’ ability to substitute away from staple grains and forcing households to sacrifice more nutritious food items — which represent a more expensive source of calories — and non-food goods.

# **Section 4. Data**

The data for our analysis are taken from the 2018/19 Nigerian Living Standards Sur12
When the social gap between two individuals becomes too wide, the positive externalities for the poorer individual disappear.

My results show that social interactions matter. While this is not the first paper to find significant peer effects, this paper adds to a small set of studies that can identify causal peer effects that are not contaminated by endogenous sorting of pupils between schools. The size and significance of these estimates in a developing-country school system have important implications for social policy. Segregation exerts an effect on pupils through peer effects that cannot be overcome by equalizing school funding, pupilteacher ratios or syllabi. In that sense my results give concrete meaning to the U.S. Supreme Court’s dictum in 1954 that separate is inherently unequal.

The results also show that segregation is not a zero sum game, but may have aggregate consequences. The functional form used in previous analyses has largely ruled out the possibility of aggregate consequences from segregation by assumption. Relaxing these assumptions, I find somewhat troubling results, pointing to an equity-efficiency trade-off, with segregation yielding higher average scores and much higher inequality in scores between rich and poor. Nevertheless, the fact that inequality in achievement between rich and poor pupils appears in the simulation to be much more sensitive than mean scores to variation socioeconomic segregation may provide a cautionary note when formulating education policies.

# **References**

- Akerlof, A. (1997). Social distance and social decisions, _Econometrica_ **65** (5): 1005–27.

- Ammermueller, A. & Pischke, J.-S. (2009). Peer effects in European primary schools: Evidence from the Progress in International Reading Literacy Study, _Journal of Labor Economics_ **27** (3): 315–347.

- Bedard, K. & Dhuey, E. (2006). The persistence of early childhood maturity: International evidence of long-run age effects, _Quarterly Journal of Economic_ **121** (4): 1437–1472.

- Brock, W
In 1981, the labor force in China totalled 452 million, out of a population of 990 million. Of the 452 million persons in the labor force, 70 percent (316.4 million) were engaged in agriculture; 9 percent (40.7 million) in heavy industry; 6 percent (27.1 million) in light industry; 10 percent (45.2 million) in services (which includes education, health, public administration, dafense, commerce, housing and miscellaneous services); and 5 percent (22.6 million) in infrastructure (which includes electricity, construction, and transport (World Bank, 1985s 40). As of 1981, the Ministry of Education estimated the status of the total labor force with respect to training, as shown in Table 1:

# Table 1s Education Level of the Chinese Labor Force, 1981

|Education Level|Zof total<br>labor force|
|---|---|
|Higher|0.5|
|Technical Secondary|0.9|
|Senior Secondary|6.7|
|Junior Secondary|20.5|
|Primary|37.8|
|Less than primary|33.7|

Source: World Bank, 1983: 137.

This report is concerned with the 0.9 percent reported on the second line, plus some undefinabile fraction of the 6.7 percent on the third line, who were given vocational education in senior secondary schools. A fair estimate is that we are dealing with technical and vocational education arrangements that have involved about 2.0 to 2.5 percent of a labor force of 500 million, or between 10 and 12.5 million workers. Other workers have received some technical or vocational training on-the-job or in association with their work. It was reported to us that China has some 400,000 firms (of which 6,000 are designated as 'large"), employing 70 million workers. Many of these firms, but especially the large
v4 3. Tenure, turnover andpopular vote share ofthe ChiefExecutive and the ChiefExecutive's Party A key hindrance to development is the tendency of many political leaders to make opportunistic decisions that entail long run costs to society that far outweigh short-run benefits. The expropriation, directly or via taxation or regulatory policy, of private assets is the most obvious example of such decision making. Leaders with shorter horizons in office are more likely to find such choices to be worthwhile, arid there is evidence suggesting that this effect is significant, at least in less democratic countries (Clague, et al., 1996; see also Alesina, et al., 1996). All research that has documented this effect has relied on data on the tenure and turnover of governments (cabinets or prime ministers in parliamentary systems) or presidents. This information is available in this data set, as well, for larger number of countries than previously available. However, the DPI also provides opportunities to circumvent several ambiguities that arise from reliance on existing data.

First, executives whose terms are shortened by constitutional limits on tenure might react differently to limits on their time in office compared to other executives, with similar horizons that are limited in other, perhaps non-constitutional ways. This data set therefore includes variables indicating whether there are constitutional restrictions on executive terms and whether sitting executives can be re-elected.

|

Second, executives with a stronger hold on power should, all else equal, have a longer horizon than other executives. In particular, one would expect leaders with more popular support, or weak opposition, to have a longer horizon. The strength of the political position of leaders can be taken into account, at least partially, using variables that record the share of votes that presidents received in their last election, and the share of votes of the prime
the number of individuals who declare to have received standard pensions is actually higher in PNADC than in administrative records. 24F<sup>25</sup> This suggests that some respondents are mistakenly reporting to have received pensions when they are actually BPC beneficiaries. We thus adjust the underdeclaration of BPC beneficiaries in PNAD-C by allocating some of the pensioniers to the BPC program. The reallocation is done from urban pensioners to either BPC or rural pensions by using a linear probability model that identifies the most likely low-asset individuals, which are arguably those who are more likely to be BPC beneficiaries and that reported to be pensioners. 25F<sup>26</sup>

- _Rural pension._ Rural pensions can be contributory but are treated as noncontributory because most of the beneficiaries are informal workers who have never paid social security contributions (in this case, only retired by age). Any man over 60 or woman over 55 with at least 180 months of rural activity is eligible for a rural pension by law. Unfortunately, rural pensions are not directly identifiable in PNAD-C 2019: the survey does not distinguish pensioners who receive their benefits through the rural pension program from those who contributed through their formal employment ( _aposentado urbano_ ). It also does not distinguish pensioners from their survivors ( _pensionista_ ). Given this data limitation, we impute the status of rural pension recipients using a realocation methodology from the broad pensioniers pool. In particular, we identify as rural pensioners men over 60 and women over 55 living in rural areas and whose declared pension value is smaller than 2.5 minimum wages in 2019. Survivor beneficiaries of rural pensioners, in turn, are identified as either: a) individuals living in a rural area who declared to receive a pension, and that did not fit the age criteria; or b) individuals living in a rural area who declared to receive a pension, fit the age criteria and whose pension value was higher than 1.5 minimum wages. 26F<sup>27</sup> All rural