rafmacalaba/data-use-mentions-extended huggingface dataset:
GNI per capita, smoothed for exchange rate changes using the Atlas method, rather than GDP per capita, which is used more commonly in the aid allocation literature. The World Bank also uses GNI per capita in its lending classifications (including eligibility for IDA) and its income classifications. Unsurprisingly, poverty selectivity of World Bank‐administered aid (IDA or trust funds) tends to be somewhat weaker if we use GDP per capita in our regressions instead of the measure the World Bank uses in its own allocation policies, GNI per capita. The choice of GNI or GDP makes little difference, however, for most other results, including the policy selectivity of aid.<sup>17</sup> Data for both income measures and for population come from the World Development Indicators (WDI 2015).

Aggregated and partially‐disaggregated CPIA scores for IDA‐eligible countries are publicly available from the WDI (2015) since 2005, and range from a minimum score of 1 (lowest quality policies) to a maximum of 6 (highest quality). For non‐IDA countries and prior years, scores are not publicly available, but were obtained with the necessary permissions by one of the authors from internal World Bank databases. In contrast to Wagner (2016), we are therefore able to include observations dating to 2002, and for non‐IDA as well as IDA countries. In a few cases, CPIA scores are not assigned after countries fall into arrears with the World Bank and are no longer classified as active borrowers. This typically is the case when the quality of policies and governance are very low, as indicated by their CPIA scores for years when they were active borrowers. In these cases, we replace the missing score with the lowest score received by any other country in this year, but results are not sensitive to dropping these observations instead.

Our main tests do not include other control variables, because our interest is mostly in comparing selectivity across aid sources. We control in some tests for a fragile
A major strength of disaster inventories is their ability to record shocks at any time they occur so long as they get reported. Over the long run, this means that coverage of shocks improves as shock recording in underlying sources gets more exact and comprehensive.<sup>37</sup> In contrast, temporal coverage in microdata is intermittent: limited to the years in which the survey is implemented and the recall period of the survey.

# **Detail of available information**

The breadth of coverage in disaster inventories across time and space often comes at the cost of limited detail and missing information.<sup>30,37,48</sup> This particularly concerns the economic and welfare impact of shocks which are often difficult to quantify, especially so for uninsured damages.<sup>37,40</sup> Detail and completeness of the available information in disaster repositories is thus related to the size and salience of a shock. Further, the data provided in disaster inventories typically offers little opportunity for disaggregation, be it by affected population groups, geographies, or other key variables. As a consequence, disaster inventories often lack sufficient information to quantify disaster impacts on (asset) poor but highly vulnerable population groups.<sup>21</sup> As microdata sources elicit shock reports directly from those affected by them, they offer a high level of detail and completeness of information even for small or idiosyncratic events, for shocks that affect poor or marginalized population groups, and for their impact on uninsured damages and other, less salient domains of disaster impact. Depending on the design of the survey, shock and impact recording is often highly granular and allows for disaggregation and analysis along a broad range of ancillary information collected in the survey.

# **Accuracy of data**

Data recorded in disaster inventories typically relies on reports from one or few sources. Especially where events are small and therefore only reported in a single data source, this exposes disaster inventories to possible inaccuracies in the
||0.2|
|Median|0.5||0.9|
|3rdQuartile|1.3||2.2|
|_Circumstance_|_Mean of {IO_|_Cs} by circ_|_umstance*_|
|Parents Eduaction|1.9||2.5|
|Income per capita|2.2||2.3|
|Urban-rural location|1.6||2.2|
|Number of children|1.9||2.1|
|Regional location|1.4||2.0|
|_Circumstance_|_Frequency of high_|_value {IOC_<br>_**_|_s} by circumstance_|
|Income per capita|7||8|
|Number of children|7||5|
|Regional location|4||6|
|Parents Eduaction|7||5|
|Gender|1||2|
|Presence of elderly|0||2|
|Presence of parents|0||2|
|Urban-rural location|5||2|

(*) list only the five most unequalizing circumstances based on the mean of the column corresponding to each circumstance in the IOC matrix (see Tables A.7a and A.7b in the appendix). ( **) reports the count of cells in the column corresponding to each circumstance of the IOC matrix with values in the top quartile of the distribution, that is the number of cells indicating inequality of opportunities by circumstance. Source: HIECS 2000 and 2009 and DHS 2000 and 2008. Author's calculations.

Comparing means indicates broad trends but carries the risk of missing significant effects of certain circumstances on specific opportunities or the spread of the influence of each circumstance across the set of opportunities. To detect those cases and complement the previous analysis, the third panel

> 33 See Tables A.5-1-a, A.5-1-b, and A.5-1-c in the Appendix.

> 34 This result differs
Figure 5: Proportion of parents in extended families living with their married son by their son’s birth order <!-- Start of picture text --> 61.03<br>17.66<br>14.18<br>4.726<br>2.405<br>youngest middle oldest unknown more<br>Birth order<br>Source: 2002 Albania LSMS<br>60<br>40<br>Percent<br>20<br>0<br><!-- End of picture text --> _Source: 2002, 2005 and 2012 Albanian LSMS; 2008-09 Albanian DHS_ 52
Mirhosseini, S., Mahmoudi, N., & Valokolaie, S. (2017). Investigating the Relationship between Green Tax Reforms and Shadow Economy Using a CGE Model - A Case Study in Iran. _Iranian Economic Review_ , 21(1), 153–167.

New Climate Economy (2016). _The Sustainable Infrastructure Imperative: Financing for Better Growth and Development_ . The Global Commission on the Economy and Climate.

<mark>NPC (2016).</mark> _<mark>National Economic Accounts Statistics of Ethiopia: Estimates of the 2015/16(2008 EFY), National Planning Commission. Addis Ababa, Ethiopia.</mark>_ Rashid , S., Tefera, N., Minot, N., and Ayele, G. 2013. _Fertilizer in Ethiopia: An Assessment of Policies, Value Chain, and Profitability_ . IFPRI Discussion Paper 01304.

Ruggeri Laderchi, C. (2014). _Transitional Policies to Assist the Poor While Phasing out Inefficient Fossil Fuel Subsidies That Encourage Wasteful Consumption_ . Contribution by the World Bank to G20 Finance Ministers and Central Bank Governors.

Thurlow, J. (2008). _A Recursive Dynamic CGE Model and Microsimulation Poverty Module for South Africa_ . International Food Policy Research Institute, Washington, DC.

Winkler, H (2017). Reducing energy poverty through carbon tax revenues in South Africa. _Journal of Energy in Southern_ Africa 28 (3): 12-26. http://dx.doi.org/10.17159/2413-3051/2017/v28i3a2332 World Bank, Ecofys and Vivid Economics (2017). State and Trends of Carbon Pricing 2017. World Bank, Washington, DC.

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from WDI and for Argentina we do not line up the population since the surveys used are not nationally representative.

2) Line up welfare per capita to reflect growth between the year of the survey and 2013. Depending on the country we look at growth estimates of household final consumption expenditure or GDP per capita. We assume that welfare per capita of every household moves at a same growth rate.

3) In order to keep consistency with PovcalNet poverty estimates, we adjust the poverty line in each country to replicate PovcalNet’s poverty rates.

# **Child Population**

Developing countries in this study include all countries except Australia, Belgium, Cyprus, Finland, France, Germany, Greece, Iceland, Ireland, Israel, Italy, Luxembourg, Japan, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom and United States. The world’s number of countries, 214, reflects the number of countries according to WDI.

In Tables 1 and 2 the number of children in the World and in developing countries include three fifths of UNDESA’s population in the 15 to 19 age bracket (we assume that the population in the age bracket is uniformly distributed by age). No such assumption is maintained for the child population in the GMD sample. For 24 countries, UNDESA’s population is unavailable and we use WDI’s population instead. For these countries, UNDESA population totals for children are estimated using averages across all other countries in the same region and income group.

# **China**

China is a special case because the World Bank does not have access to individual level records from the Chinese Household Budget Survey (HBS), which is the source of official Chinese poverty statistics. The World Bank’s international poverty estimates for China are instead based on an approximate distribution derived from grouped data, which cannot be used for poverty profiling. This study therefore utilizes household level data from the 2013 Chinese Household Income Project Survey (CHIPS), made available to the
, the most recent year for which location data are available. Region 1 schools and Upper Basic (middle) schools are excluded from the estimation sample but shown on the map to illustrate the locations of secondary schools throughout the system. Not all schools in the sample appear on the map due to missing location data.

10 The strategy resembles that of Lucas and Mbiti (2012a, 2012b), who study the effect of free primary schooling in Kenya using variation in pretreatment local enrollment rates.

> 11 An alternative possibility mentioned by a referee is that the policy led to greater scrutiny of “ghost” students, reducing reported enrollment. The direction of misreporting in response to the policy is therefore unclear.

> 12 Increased enrollment in private schools in later periods partially explains the fall in male enrollment. We explore private school enrollment later in the paper.

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|**Geography**||**2019**–**20**|||**2015**–**16**||
|---|---|---|---|---|---|---|
||**Urban**|**Rural**|**Total**|**Urban**|**Rural**|**Total**|
|Delhi(NCT)|22.6|27.4|22.7|34.7|*|34.9|
|INDIA|38.3|45.7|43.3|35.2|40.1|38.4|

_*_ Percentage not shown; based on fewer than 25 unweighted cases.

_Source:_ National Family Health Survey 2015–16 and 2019–20, Ministry of Health and Family Welfare (MoHFW).

# 1.3 Gender and property rights in India

Throughout history, numerous cultural, legal, political, and social factors have systematically impeded or barred females from accessing property rights, including both land and physical property. The economic literature offers more thematic studies investigating issues related to gender bias in land ownership than studies focusing on gender issues relating to physical property ownership (houses and commercial buildings). On the other hand, multiple studies refer to both land and property ownership interchangeably. This study considers both subjects in parallel, and review of the findings highlights core issues associated with gender bias. The central focus of the study is ownership of immovable property in urban areas, however, and not (rural) land.

Gender inequality in access to property existed in India to a large extent due to discriminatory inheritance and marital laws and practices. This was the case before India enacted the 2005 Hindu Succession (Amendment) Act (HSAA), which removed gender discriminatory provisions from the Hindu Succession
1.69 billion children. This represents around 82 percent of the developing world’s child population in 2013, which was 2.05 billion. 85.4 percent of the 767 million extreme poor reported in PovcalNet are contained in the GMD sample.

**Table 1: Coverage of PovcalNet and GMD. Population and Child Population in 2013**

||World|Developing countries|GMD sample|
|---|---|---|---|
|Number of countries|214|190|89|
|Population (millions)|7157.3|6233.7|5249.1|
|Children (millions)|2239.6|2052.5|1686.1|
|Extreme poor (millions)|766.7|766.7|655.0|
|Percent of children that are poor|||19.5%|
|Percent of extreme poor that are children|||50.2%|
|Extremely poor children (millions)|384.9|384.9|328.7|

Notes: See Appendix 1. The estimated number of extremely poor children is obtained by multiplying the total number of extreme poor by the percent of extreme poor in the sample that are children. As in PovcalNet, developed countries are assumed to contain no extremely poor persons.

> 7 The term welfare refers to either income or consumption per capita, depending on which measure is used by the country for their national estimates.  For more information on how these welfare aggregates are constructed, see Ferreira et al (2015)

4
employed or working compared to other women living in communities with similar average time spent on housework and who were not exposed to the LOA module . Moreover, although among women assigned to the LOA the estimated difference in the reporting of work by being exposed to an average of one hour spent on housework by other women in the community is not statistically significant at the conventional level, it is worth noting that the magnitude of the difference is large (8.4 pp). This, therefore, indicates that time spent on housework is a potentially relevant mechanism that requires further exploration with a larger sample size.<sup>20</sup> Lastly, women’s average time spent on housework in the community does not affect the effectiveness of the ESR in any of the samples under analysis. These findings are consistent with the hypothesis that norms around domestic obligations affect mostly women’s own assessment of what constitutes work or employment.

# **7 Robustness Checks and Additional Results**

## **7.1 Sample Characteristics**

To address how our study sample compares to the average household in the regions surveyed we look at the average characteristics of our respondents and the average Salvadoran respondents of the 2022 Household and Multipurpose Survey (EHPM) living in San Salvador and Usulután. We present this comparison in Table A9. Overall, we find that the two samples are similar in terms of age, sex composition, human capital, and marital status, all of which confirm that the average household members who participated in our experiment are similar to the average adult respondent of the EHPM sample.

## **7.2 Assessing Sensitivity from Selection of Control Variables**

As we discussed in Section 4, we use a double LASSO approach to determine the control variables for our estimations. For each main outcome, we test for the stability of our estimated coefficients after excluding the control variables that LASSO selected. As we show in Tables A10 to A12, the estimated coefficients and their statistical significance are stable
To take forward a framework that also appropriately prioritizes the use of finite resources, the costs and benefits of policy options must be properly weighed within the context of the “dynamic interaction of actors, institutions, and structural factors over time” (United Nations and World Bank, 2018). Tools necessary to achieve this outcome must drive optimal justice sector decisions in a systematic, data-informed, and resource-efficient way, including drawing on World Bank analytical products. These products include legal needs surveys, Functional Reviews, Institutional and Expenditure Reviews, Justice for the Poor dispute resolution analytics, the Doing Business survey (which includes a Quality of Judicial Process Index), The Women, Business and the Law index, and the Data and Evidence for Justice Reform platform. By drawing together data from all analytical processes, scope for comparison of various justice-related datasets is enabled.<sup>4</sup> Most importantly, the analytical approaches assist in identifying the various elements of justice processes whose costs and benefits may then be traced.

Early CBA tools, such as the Washington State Institute of Public Policy's (WSIPP) Benefit-Cost Tool (Aos and Drake, 2010) and the Manning Cost Benefit Tool (MCBT) (College of Policing, 2022), were developed to support an evidence-based policing approach. More recent developments have been undertaken by Manning and Wong, which represents an extension of the above-mentioned MCBT (now adopted by the College of Policing UK), are beginning to incopporate machine learning and artificial intelligence, including the development of CBT APP (Manning, 2018) and ‘Smart’ CBTs (Manning et al., 2018), take important steps towards robust and time-sensitive analytical methods. These tools (currently in various stages of development), which have been validated using a range of crime data,<sup>5</sup> provide a framework with systematic data management capacity that enables user input decisions as sufficiently fair, credible, and acceptable. A second is ‘social inclusion’ which is the extent
**Figure 3:** Gains to GDPPC from closing the Gender Employment Gap (%): GEGI vs. 2050 LTGM

<!-- Start of picture text --> YEM<br>IRQ<br>SYR<br>JOR<br>PSE<br>IRN DZA<br>EGY<br>LBN<br>MARTUN<br>SAU DJI<br>LBY<br>OMN<br>KWT BHR<br>ARE<br>QAT 45 degree line<br>Fitted values: y=1.56x+0<br>0 10 20 30 40 50 60 70 80<br>2050 LTGM<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>Aggregate capital fully adjusts (GEGI)<br>10<br>0<br><!-- End of picture text --> Note: x axis values are the 2050 gains in output per capita from closing the gender gap by 2050 obtained calibrating the LTGM/LTGM-NR, green lines from figure 2. y axis values are the Gender Employment Gap Index (GEGI) from Pennings (2022) (ARE, BHR, KWT, OMN, QAT and SAU are adjusted for unequal male-female populations), blue lines from figure 2. The regression run to obtained the fitted values equation does not assume no constant (the 0 constant is obtained as a result of the regression).

# GEGI than the LTGM by 2050 (Figure 3).

The time path of the boost to growth in the LTGM is shown in Figure 4 and Figure 5. The adjustment path is only slightly non-linear, so a good approximation of the boost to growth at 2035 is around half of the boost to growth by 2050. These paths also suggest that medium term growth gains of boosting female employment are substantial, even if the implementation takes some time. For example, a steady
This analysis also helps clarify the importance of focusing on gross capital flows as opposed to net capital flows. We document these patterns by systematically analyzing the cyclical behavior of gross capital flows: the capital inflows by foreign agents ( _CIF_ ) and the capital outflows by domestic agents ( _COD_ ). Positive _CIF_ and _COD_ both associate with increases in gross international investment positions. To construct _CIF_ and _COD_ , we use balance of payments data from the International Financial Statistics of the International Monetary Fund, from 1970 to 2009 for 103 countries. _CIF_ equals net purchases of domestic assets by non-residents and is thus equal to the sum of all liability inflows. _COD_ equals net purchases of foreign assets by domestic agents and is thus equal to the negative of the sum of all asset inflows, including international reserves. Hence, net capital flows are equal to the difference _CIF-COD_ .

Our main findings are the following. (i) Over the last four decades, the volatility of gross capital flows ( _CIF_ and _COD_ ) has been large and increasing, especially relative to the much lower volatility of net capital flows. This reflects the increasingly positive correlation between _CIF_ and _COD_ . (ii) Gross capital flows are pro-cyclical. In other words, during expansions foreign agents increase their purchases of domestic assets and domestic agents increase their purchases of foreign assets. During crises, especially during severe ones, there is a reduction in gross capital flows, with reductions in both _CIF_ and _COD_ . However, _CIF_ tends to fall more during crises as these tend to be associated with lower net capital flows. The 2008 financial crisis is a clear example of such _retrenchment_ , i.e. a simultaneous decline of both _CIF_ and _COD_ , but we show that retrenchment was a feature of previous episodes as well. (iii) A decomposition of gross capital flows reveals interesting heterogeneity in the behavior of their components around crises. In the case of _CIF_ , its reduction is due to declines in all its components for all country groups. In the case of _COD_ for
effect of fixed broadband internet adoption and productivity using data from a sample of German manufacturing and services firms between 2001 and 2003. The identification strategy relies on an instrumental variable approach considering that, by that time, only 60 percent of the firms had adopted broadband internet. However, the authors find that broadband internet adoption has a causal effect on innovation activities. Aboal and Tacsir (2015) complement the previous findings by exploring the role of ICT adoption (especially, the internet) in the performance of manufacturing and services firms in Uruguay. The authors find that investment in ICT has a significant and positive correlation with the productivity of both manufacturing and services firms. DeStefano et al. (2018) study the heterogenous effects of ICT adoption (especially, broadband internet) on firm performance in the UK from 1999 to 2005. The authors use a comprehensive dataset on the physical units of ICT used within a firm, known as the Ci Technology Database (CiTDB) and conclude that ICT adoption has a causal effect<sup>7</sup> on firm size (measured by sales or employees) but not on productivity. In contrast, more recent studies, such as Cusolito et al. (2020), estimate the effects of ICT adoption (like the creation of a website and email address) on the productivity of manufacturing firms in 82 developing countries from 2002 to 2019 and find significant positive effects on firms’ revenue-based total factor

> 7 To correct for endogeneity concerns, the authors use an instrumental variable two-stage least squares (2SLS) approach, in which they exploit the variation in the determinants of ICT.

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, and affects a larger number of workers (Maloney and Núñez Méndez, 2004).

As with the developed countries, the findings in the less developed countries in terms of the effects on unemployment and demand are inconclusive. One of the first studies on Latin America was conducted by Bell (1997) based on panel data from manufacturing firms in Mexico and Colombia. The results of this study indicated that during the period 1981‐1987 the firms responded to increases in wage costs by reducing the number of their employees. In Colombia, increases in the minimum wage triggered significant industrial job losses in the country,

> 3 In interpreting most of the results cited in this review, it is important to keep the definition of elasticity in mind. In this document, employment elasticity as it refers to the minimum wage is the percentage change in the level of employment following a 10 percent increase in the minimum wage.

> 4 _<mark>Vinculante</mark>_ <mark>in Spanish [literally ‘binding’], as it is used here, means that the distribution of earned wages tends to be close to the minimum wage.</mark>

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