rafmacalaba/data-use-mentions-extended huggingface dataset:
**Finally, the study's beneficiaries represent a sample of low-income households eligible for these benefits, with a take-up rate estimated in the baseline year (2020).** However, given the lack of detailed information about the characteristics of households that are more likely to receive benefits, selecting beneficiaries was conducted randomly. While this approach provides a reasonable approximation, further research on the specific attributes of benefit recipients could enhance the precision of the findings and inform more targeted policy adjustments.

# IV.Results

**This section presents the main results.** First, it examines the effectiveness and poverty-reduction impact of the Monthly Social Assistance (MSA) and Heating schemes before the policy reform of the social support system. Then, it assesses the potential impacts of the reform by assessing how restrictive the income thresholds are compared to the consumption basket and using microsimulation techniques to evaluate the potential welfare impacts of the reform. By delving into different policy scenarios, we aim to gain a deeper understanding of the potential implications and ramifications of these policy adjustments, particularly in their impact on poverty incidence, poverty gap, and income inequality.

Assessment of the MSA and Heating Scheme effectiveness and poverty reduction impact under Baseline Scenario (before the Policy Reform)

**Our analysis indicates that the take-up of the MSA is significantly lower than the heating allowance.**

Our estimated take-up rates are 56% for MSA and 99% for the heating allowance. These findings bear significant policy implications, suggesting potential areas for intervention to tackle barriers to enrollment in safety net programs.

**Our results show that while the MSA scheme exhibits strong targeting, its coverage of the poor and**

**generosity are relatively low** . In this baseline scenario, we simulate the benefits in line with the 2020 income data, which is part of the CEQ fiscal incidence analysis. We consider the existing MSA rules and replicate the total number of beneficiaries based on administrative data. This scenario illustrates that the MSA benefit reaches only 11.5
<mark>Rawlings, S. and Z. Siddique. (2019) “Domestic Abuse and Child Mortality.” Forthcoming in</mark> _<mark>Oxford Bulletin of Economics and Statistics</mark>_ <mark>Rudman, L. A., and J. E. Phelan 2008. “Backlash effects for disconfirming gender stereotypes in organizations.” Research in organizational behavior 28: 61–79</mark> Schaller, J. (2013) “For richer, if not for poorer? Marriage and divorce over the business cycle.” Journal of Population Economics 26, no. 3: 1007-1033.

Schuler, S. R., S. M. Hashemi, A. P. Riley, and S. Akhter. (1996). “Credit programs, patriarchy and men's violence against women in rural Bangladesh”. _Social Science & Medicine_ , 43(12), 1729-1742.

SIGI. 2019. “Information about Variables and Data Sources for the SIGI 2019.” Social Institutions and Gender Index. <u>https://www.genderindex.org/data/</u> Stevenson, B., & Wolfers, J. (2006). “Bargaining in the shadow of the law: Divorce laws and family distress.” _The Quarterly Journal of Economics_ , 121(1), 267-288.

<mark>Tauchen, H. V., A. D. Witte, and S. K. Long (1991). “Domestic violence: A nonrandom affair.”</mark> _<mark>International Economic Review</mark>_ <mark>, 491-511.</mark> <mark>Tertilt, M., and G. J. Berg. (2015). “The association between own unemployment and violence victimization among female youths.”</mark> _<mark>Journal of Economics and Statistics</mark>_ <mark>, 235(4-5
# **Introduction**

Oil wealth has served Azerbaijan well, helping the country achieve high growth rates, significant poverty reduction, accumulation of large foreign exchange reserves and reduction in debt levels. Economic growth averaged 13 percent a year during 2002-12 and Azerbaijan transitioned into a middle-income country. Azerbaijan is the 77th largest exporting economy in the world (MIT 2018). The oil industry is the primary sector in the economy, which covers 93 percent of the country’s exports. Azerbaijan’s economy is not diversified, which means that the economy is fragile due to the dependence on oil prices. The country’s further advances in economic development, given anticipated declining oil production, will critically depend on the country's underlying asset base of human capital.

The country’s basic (primary and secondary) education system has experienced significant expansion in the last few decades. There is almost full universal enrollment in primary and secondary education (UNESCO 2018). This is despite relatively low spending on education, at 2.5 percent of GDP, and only 7 percent of public expenditures. But enrollment in tertiary education is only 27 percent, slightly higher for women at 30 percent, compared to men at 26 percent.

Azerbaijan’s productivity of human capital is below potential. According to the Human Capital Index, a child born in Azerbaijan today will be 60 percent as productive when she grows up as she could be if she enjoyed complete education and full health (World Bank 2019). This is because although a child who starts school at age 4 can expect to complete 11.6 years of school by her 18th birthday, given the quality of education in Azerbaijan, expected years of school is only 8.8 years when adjusted for learning. Azeri employers claim that it is difficult to find workers with the 2
2007-2019. We define formality for employees with some written contract and employers and self-employed with a Tax Identification Number (TIN). The 2020 and 2021 values are not included as the National Household Survey for those years did not capture information on the Tax Identification Number.

# **5.2 Drivers of job quality**

As Figure 3 illustrates, job quality has consistently been higher for workers in larger firms and employers compared to those in micro and small firms and the self-employed. However, as above-mentioned, the latter group represents approximately 70 percent of the employed population in Bolivia. The differences in job quality across these groups can be attributed to the varying shares of good jobs within each component. Table 4 (presented in section 3) provides the distribution of good jobs for each component in Bolivia from 2007 to 2021.

Examining gender disparities, Table 13 highlights the differences between men and women in the percentage of workers who satisfy each component of job quality. Unsurprisingly, men exhibit higher satisfaction rates than women in almost every component except overqualification. It is worth noting, however, that the gap in the share of good jobs is more pronounced in components related to income rather than access to the pension system (as depicted in Figure 4). These findings, in line with Brummund et al. (2018), suggest that the income gap between genders poses a greater challenge for equity in the labor market than access to formal jobs, which is approximated by access to social security.

27
fxed efects|YES|YES|YES|
|Observations|8,004|8,076|3,924|
|R-squared|0.053|0.067|0.235|
|Number of country|186|192|187|

Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. The dependent variable in columns (1) and (2) is the GDP per capita growth computed using Penn World Table data and World Bank data respectively. Column (3) uses growth of night light per capita form the National Oceanic and Atmospheric Administration (NOAA). “Conflict incidence” is a dummy that takes a value of 1 if the number of battle related deaths is above a threshold of 0.008 per 1000 population. Dummies 1<sup>_st_</sup> year after conflict, 2<sup>_nd_</sup> year after conflict, etc. take a value of 1 in the _x_<sup>_th_</sup> year after the war ends.

literature on within-country comparisons fairly well. Results are in columns (1) and (2) of Table 4. We find that groups in conflict receive about 30 percent less light per capita than other groups in the same country and year.<sup>13</sup> Interpreted with the loglinear-relationship between light and GDP this is equivalent to a difference of about 8 percent in GDP per capita. During the time after conflict this difference to other groups narrows. To show this we introduce two sets of dummies. First we introduce a dummy for the first five years after conflict and a second dummy for the remaining years. We see that light per capita is 20 percent less in groups that come out of conflict and remains significantly lower after five years. Second, we look at the first ten years and find, again, that groups coming out
- **Sorkin, Isaac** , “Ranking Firms Using Revealed Preference,” _The Quarterly Journal of Economics_ , 2018, _133_ (3), 1331–1393.

- **Startz, Meredith** , “The value of face-to-face: Search and contracting problems in Nigerian trade,” _Working Paper_ , 2018.

- **Sullivan, Daniel M and Alan Krupnick** , “Using Satellite Data to Fill the Gaps in the US Air Pollution Monitoring Network,” _Working Paper_ , 2018, p. 33.

- **Sun, Cong, Matthew E. Kahn, and Siqi Zheng** , “Self-protection investment exacerbates air pollution exposure inequality in urban China,” _Ecological Economics_ , 2017, _131_ (C), 468–474.

- **UBOS** , “The National Population and Housing Census 2014 - Main Report, Kampala, Uganda,” Technical Report 2016.

- **United Nations, Population Division, Department of Economic and Social Affairs** , “World Urbanization Prospects: The 2018 Revision,” Technical Report (ST/ESA/SER.A/420). New York: United Nations. 2019.

- **WHO, (World Health Organization)** , “Ambient Air Pollution: A Global Assessment of Exposure and Burden of Disease,” Technical Report 2016.

36
**A1.9 Heritage Foundation/Wall Street Journal (HFWSJ)**

The Heritage Foundation is a research and educational institute whose mission is to formulate and promote conservative public policies. The Heritage Foundation was established in 1973 and is headquartered in Washington, DC. As of May 1999, the Heritage Foundation can be reached on the web at hbto:_/www.heritaoe.ora.

In 1995 the Heritage Foundation, in partnership with the Wall street Journal, launched its annual Index of Economic Freedom, This index covers 161 countries and measures economic freedoms and prospects for growth in the global economy. The index is designed for cross country research and to assist international investors and aid donors to allocate their resources. We use data from the 1998 edition of this Index.

This index is based on a detailed assessment of 10 different factors, including foreign investment codes, taxes, tariffs, banking regulations, monetary policy, and the black market. For some of these, assessments are mechanically based on objective data, while others are generated as subjective ratings based on a pre-specified checklist. These checklists are completed drawing on a large number of public and private sources. The 10 factors underlying the 1998 Index of Economic Freedom are listed in Table A1.9 47
# **Figure C6 Skilled-unskilled wage skill premium and terms of trade**

<!-- Start of picture text --> ARG BOL BRA CHL<br>50 100 150 100 120 140 60 80 100 120 100 150 200<br>COL CRI ECU HND<br>90 100 110 120 80 100 120 90 100 110 120 80 100 120<br>MEX NIC PAN PER<br>90 95 100 105 80 100 120 80 100 120 100 120 140 160<br>PRY SLV URY VEN<br>90 100 110 120 80 100 120 90 100 110 120 50 100 150 200<br>Terms of trade (2000=100)<br>1.1 1.3 1.6 1.2<br>1 1.2 1.15<br>.9 1.4 1.1<br>1.1<br>.8<br>1 1.05<br>.7 1.2 1<br>1<br>1.35 1 1.5<br>1.31.25 .95.9 .9.8 1.41.3<br>1.2<br>1.15 .85 .7 1.2<br>1.4 1.4 1.2 .9<br>1.3 1.3 1.1 .8<br>1.21.1 1.2 1 .7<br>1 1.1 .9 .6<br>Wage Premium, Skilled-Unskilled<br>1.3 1.1 1 .9<br>1.2 .8<br>1.1 1 .8<br>1 .7<br>.9 .9 .6 .6<br><!-- End of picture text --> Source: own computations based on SEDLAC (CEDLAS and World Bank).

51
---|---|---|---|
||Zoom|Zoom|Zoom|Skype|Skype|Skype|delivery|delivery|delivery|
|First case|0.25|||0.21|||-0.13<sup>***</sup>|||
||(0.22)|||(0.13)|||(0.04)|||
|First 10 cases||0.57<sup>**</sup>|||0.38<sup>***</sup>|||-0.06||
|||(0.24)|||(0.13)|||(0.06)||
|First 100 cases|||0.61<sup>***</sup>|||0.44<sup>***</sup>|||-0.01|
||||(0.22)|||(0.06)|||(0.10)|
|R-squared|0.86|0.87|0.87|0.74|0.75|0.75|0.88|0.88|0.88|
|Observations|8,208|8,208|8,208|8,208|8,208|8,208|7,872|7,872|7,872|

Note: The data used covers January-April of 2018-2020. For every country where google trends data is available, the search index reflects searches in English and the most dominant local language. Each regression includes weekly dummies to capture aggregate temporal trends. Standard errors, clustered at country level, are given in parentheses.<sup>*</sup> _p_ < 0.10,
|Secondary|2.1|Yes|Direct|
|---|---|---|---|
|Tertiary|0.7|Yes|No|
|Other n.p.c.|0.4|No|No|
|Health /4|5.8|Yes|Indirect|
|Housing|0.0|No|Indirect|
|**Other expenses**|15.7|No|No|

Source: authors Note:

*Grouped includes benefits that could not be individually identified or simulated using SILC information.

/1 Includes Cash benefit in case of adoption of a child up to the age of 5 and other family and children's contributory benefits /2 Includes lump sum pregnancy allowance, financial support for preventing abandonment, bringing up of a child by relatives or foster family and other family, and children's non-contributory benefits.

/3 Includes monthly allowance for a child without a right to a survivor's pension; targeted allowance for schoolchildren enrolled in 8th grade at a state or municipal school; and lump sum allowance in case of adoption, granted to adoptive parents of Bulgarian children. /4 Poverty effects were not estimated.

# III. Methodology: Expanding the Equity Commitment Assessment to understand fiscal policy with a child lens

## The CEQ approach

**The fiscal incidence approach is a comprehensive assessment method for analyzing the tax/benefit system** . It employs a diagnostic tool called the Commitment to Equity Assessment (CEQ) to evaluate the fiscal system's welfare impacts. The World Bank Poverty and Equity group has collaborated with the Economics Department at Tulane University to implement the CEQ in various countries across different regions. This approach represents one of the pioneering efforts to assess developing countries' tax/benefit systems thoroughly. It takes into account indirect subsidies, taxes, and in-kind benefits like free education and healthcare. The assessment aims to enable comparisons across countries and, over time, provide insights
which we refer to as the “geographic” control group; and (3) the non-fishery workers living in the _Formosa_ -affected provinces that work in other unaffected industries, referred to as the “industry” control group. The means difference test (last column) indicates drastic and statistically significant declines in average fishery incomes of the _Formosa_ -affected individuals. In contrast, we do not observe any statistically meaningful changes in incomes of the “control” fishers.<sup>10</sup> A crucial aspect of the DiD method employed in this paper is the validity of the parallel pre-trend assumption. Figure 3 visually addresses this element; the figure plots raw average monthly income profiles for the treatment (thick line) and control groups (thin-dash line), using both LFS 2015 and LFS 2016. While the line plots exhibit strong parallel pre-trend between the two groups, they suggest an abnormal downward deviation from trend by the treatment group, right after _Formosa_ took place (in April 2016). It is also evident that

> 9 An average non-fishery worker living in Ha Tinh, Quang Binh, Quang Tri, and Hue works 41 hours per week and earns 3.9 million VND per month.

> 10 In fact, there is an expected (insignificant) increase in the average fishery earnings of the unaffected southern fishers after the first quarter of 2016. This is because the fishing season usually takes place between May and November each year, as visually seen in Figure 4.

7
**Table 12 Share of Tradable and Non-Tradable in the Consum 3tion Basket** _(percent)_

|||CPI|||1993 Survey||
|---|---|---|---|---|---|---|
|_Expenditure Categories_|_Expenditure_<br>_share_|_Tradable_<br>_share_|_Non-_<br>_tradable_<br>_share_|_Expenditure_<br>_share_|<br>_Tradable_<br>_share_|_Non-_<br>_tradable_<br>_share_|
|Food|48.0|47.8|52.8|47.4|35.6|64.4|
|Lodging|7.8||100.0|12.6||100.0|
|Utilities|8.5||100.0|5.8||100.0|
|Household furnishing|3.4|100.0||1.4|100.0||
|Clothinga)|10.1|100.0||6.7|84.6|15.4|
|Transport|6.8|100.0|100.0|7.0|23.2|76.8|
|Vehicles|5.4|81.8|18.2||||
|Household maintenance|0.6|100.0||0.2||100.0|
|Hygiene|1.0|100.0||2.1|100.0||
|Health|0.7|96.6|3.4|4.6|72.8|27.2|
|Education|1.0|29.5|80.5|3.3|41.5|58.5|
|Miscellaneous|6.7|45.7|54.3
contributed to the foreign born in each census. In most circumstances they did not, and so they were dropped. In cases when these totals did refer to migrants, they were treated as an appropriate aggregate category to be assigned later, as detailed below. Finally, all categories referring to the ―stateless‖<sup>16</sup> were dropped because despite their importance as a minority group in global migrant patterns, there is no way to meaningfully assign them to an origin.

# _Disaggregation of Aggregate Categories_

The disaggregation of the 236 origin regions identified in the censuses is one of the key steps in creating the bilateral migration matrix. Three propensity equations are used to allocate migrants to one of the 226 countries in the master list. Each measure varies in quality depending on the availability of underlying data. The preferred option is to use migration data from the destination country for the relevant year. If this option is not available, information from the destination country for other years is used. Should 15 For example, the Vatican is assigned to Italy, Wake Island to the United States, and Labuan to Malaysia.

16 Some estimates put the number of stateless people (those lacking any citizenship) as high as 11 million, although many of these people will not be captured in censuses. The stateless represent an important category of migrants; for more information, see www.unhcr.org/pages/49c3646c155.html.

11
is made. Secondly, the decision to send a child to school is assumed to be made _after_ all occupational decisions by adults within the household have been made, so that the schooling decision does not affect those earlier decisions. Thirdly, the model does not allow for decisions about the occupational choice of multiple siblings in a household to be made simultaneously or jointly. Fourthly, the composition of the household is taken as exogenous. While each of these assumptions is an

> 9 A toolkit for running the BFL model was prepared by the World Bank. The toolkit provides a how-to guide in the application of the BFL micro-simulation model that enables users to analyze and compare several alternative scenarios on the basis on representative household survey data. It can thus be used in selecting the most cost-effective design or it can be used for sensitivity and cost-benefit analysis. The toolkit can be downloaded at www.worldbank.org/safetynets under _toolkit_ or by clicking on the following hyperlink: BFL model toolkit

9
Proxy Respondents High Income or Wealth Individuals do not respond to the survey and other household members answering on their behalf under-state their incomes or wealth Ex ante solutions for the challenges when using household survey data Following a distinction made by Ravallion (2022), in this section we discuss ex-ante solutions to sparseness, unit non-response and item non-response – solutions that can be implemented in survey design. In the next section we discuss ex-post solutions that use survey and administrative data to address the general challenge of missing top incomes.

A summary of all the solutions to the challenges of measuring the top tail of the income and wealth distributions reviewed in this paper, as well as a description of these solutions and key references, can be found in Table 2 below.

Table 2: Solutions to Measurement Challenges for the Top tail of the Income and wealth distributions

|**Ex Ante**|**Solutions**<br>Oversample rich<br>households|**Challenge**<br>Sparsity|**Data Type**<br>Requires tax data<br>with address<br>information or<br>population<br>census or tax<br>data with low<br>geographic level<br>identifiers|**Description**<br>Use tax data or population<br>census data to oversample<br>rich households|**Key**<br>**references**<br>Kennickel<br>(2019)|
|---|---|---|---|---|---|
||Improve<br>fieldworker<br>protocols|Unit non-<br>response|None|Require multiple visits,<br>improve fieldworker training|Ravallion<br>(2022)|
||Questionnaire<br>design<br>improvement|Item non-<br>response|None|Design questionnaire to<br>obtain
br>(0.07)|
|Ethnic dummies|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean dependent variable|0.50|0.50|0.50|0.50|0.51|0.51|
|Mean _M_||0.76|0.54|0.20||0.52|
|_R_-squared|0.43|0.46|0.46|0.46|0.45|0.45|
|Observations|2,264|2,264|2,264|2,264|2,083|2,083|

_Source_ : Authors’ analysis based on mission locations from Beach (1903); aid projects from AidData, World Bank geocoded research release, version 1.3; measures of human capital and religion from the Demographic and Health Survey (DHS); and control variables from multiple sources, detailed in appendix A. _Note_ : Estimation is by ordinary least squares. Conley (1999) standard errors are given in parentheses, with the cutoff at 2 degrees (220 km). The dependent variable is a dummy for at least one project commitment in the sample period. Control variables include: log distances to coast, explorer route, colonial railway, and Arab trade route; a third-order polynomial in the 18th-century population; presence of a city as of 1800; country dummies; precolonial ethnic dummies; average altitude; Terrain Ruggedness Index; percentage area within 10 km from water source; Caloric Suitability Index; Malaria Ecology Index; and a tropics dummy (which is also interacted with mean altitude). Cells that are more than 90 percent covered by _∗ ∗∗_ barren land or more than 90 percent covered by forest are excluded from the sample. _p < ._ 1 _p < ∗∗∗ ._ 05 _p < ._ 01 39
locations in the midrange between villages and state capitals.

The analysis also has implications beyond India. We find that the accuracy of the assessment increases substantially when combining data from the ground and from outer space. In the process, it appears that some of the most frequently used indicators of urban status can yield misleading results. Among the indicators from the ground, population density is a poor predictor of urban status. Among those from outer space, nighttime light intensity leads to a drastic overestimation of the speed of urbanization.

# **2. A representative sample of units**

The official boundaries of cities, towns and villages are built on historical data and capture the best knowledge governments have of the spatial distribution of economic activities and people. Admittedly, these boundaries may be too small to account for large metropolitan areas, which typically include nearby suburban and exurban areas. But they define the constituency to which each local government provides services and is accountable. And they are the basic sample unit used by national statistical agencies to conduct censuses and surveys. Therefore, important information about cities, towns and villages can be captured at this level of spatial disaggregation.

## _The universe of administrative units_

In this study, we take advantage of newly digitized boundaries of administrative units in India, now available all the way down to the town or village level. Although not official, these boundaries are based on India’s _Administrative Atlas_  _2011_ (ORGI 2011b). They were generated as part of a broader research project, the Spatial Database for South Asia (Li and others 2015). Construction of the boundaries required 4