rafmacalaba/data-use-mentions-extended huggingface dataset:
Table 6: Ancestry and the Transmission of Education

||Yea<br>|rs of Educa<br>|tion Child<br>|ren<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Years of Education Parents|0.338***|0.328***|0.309***|0.310***|
||(0.005)|(0.004)|(0.005)|(0.005)|
|Edu. Parents x Native||||-0.008<br>(0.014)|
|Native|-0.441***|-0.390***|-0.346***|-0.276*|
||(0.072)|(0.067)|(0.062)|(0.165)|
|Urban||0.717***|0.593***|0.593***|
|||(0.051)|(0.039)|(0.039)|
|Constant|8.221***|7.646***|8.162***|8.160***|
||(0.055)|(0.064)|(0.061)|(0.060)|
|Observations|733,020|733,020|733,020|733,020|
|R-squared|0.221|0.225|0.237|0.237|
|Department FE|N|N|Y|Y|

_Source_ : REDATAM INDEC Census Argentina 2010. _Notes_ : Standard errors clustered at the department
official confirmation that the complaint has been addressed. We see that the distribution peaks at eight days, i.e., the time the pothole has to be repaired by Moscow city ordinance. And 80 percent of all complaints are addressed in the two weeks’ time, indicating a highly efficient response rate.

We employ three popular measures of electoral competitiveness as our dependent variables.<sup>11</sup> The _total votes for the incumbent_ measure shows the degree of overall support for city authority among the voters. The _margin of victory_ measure (i.e., the difference between the votes for the election winner versus the runner-up candidate) indicates the city authority's electoral strength vis-à-vis opposition candidates. The _voter turnout_ measure (i.e., the percentage of eligible voters who cast the ballot) captures the degree of city residents’ overall electoral engagement.

Appendix Table A.1 shows the summary statistics for the variables employed in the empirical analysis.

We model the effect of digital public engagement by estimating the following empirical specification: + ∑ . (1) MM−KK The specification described by equation (1) is the spatial autoregressive model (SAR) for panel data ii,tt ii≠jj ii,jj jj,tt mm=MM−KK−3 ii,tt,mm ii tt ii,tt YY = ρρ∑ ww YY XX ββ + αα + γγ + εε (Elhorst 2014; LeSage and Pace 2009), where indices _{i,j}_ , _t_ and _m_ describe municipalities, years, and months before elections date, respectively; indicates measures of electoral competitiveness; indicates the degree of digital public engagement measured by cumulative pothole complaints ii,tt YY over the 3-months rolling window ii,tt,mm _M-K_ , where _K_ are months before the election month, _M_ ; is XX the element of the spatial weight matrix; and are parameters to be estimated with ii,jj ww indicating the spatial lag (i.e.,
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.<sup>_∗_</sup> _p < ._ 1 _∗∗ p < ._ 05 _∗∗∗ p < ._ 01 24
Another stream of the literature has exploited micro-data to investigate the effect of various shocks, such as conflict and weather conditions, on multiple socio-economic characteristics. Several studies document how adverse weather conditions reduce agricultural incomes and can push households into poverty. Hill and Mejia-Mantilla (2017) describe the negative effects from droughts in Uganda. Porter (2012) finds that weather shocks reduce consumption in the long run among rural households in Ethiopia. Similarly, Hill and Porter (2016) conclude that in Ethiopia consumption declined by 9% in rural areas due to a moderate drought. For the case of Somalia, Pape and Wollburg (2019) estimate an increase in poverty of 9 percentage points among rural households attributed to a drought shock.

In terms of the drivers of conflict, Dube and Vargas (2006) use municipality-level data in a difference-in-difference framework to understand the dynamics of Colombia’s civil war. They find a negative relationship between coffee prices and the incidence and intensity of conflict, while a positive relationship between oil prices and violence. Besides, large evidence supports the adverse impact of conflict-related violence on welfare conditions. Mercier, Ngenzebuke, and Philip (2016) use household-level panel data for Burundi and conclude that exposure to violence condemns vulnerable households into chronic poverty. Hill and Mejia-Mantilla (2017) find a negative effect from conflict and prices on poverty in Uganda. In South Sudan, Müller, Pape, and Ralston (2019a) investigate the effects of conflict-induced cancellation of programs on their designated beneficiaries and describe the welfare status of households displaced by violence. In the same country, Pape and Phipps (2018) analyze the impact of conflict on the socio-economic and psychosocial well-being of teenage girls, including on income opportunities, aspirations and marriage. Other authors provide evidence on how conflict increases the likelihood of chronic poverty due to disruption of income-generating activities and depletion of infrastructure and basic services (Bratti, Mariapia, and Alfonso 2009; Bozzoli and Brück 2009; Bozzoli, Brueck, and Muhumuza 2016).

# 3. Empirical analysis

The World Bank
*_85_**|**_28_**|**_7_**|**_12_**|**_9_**|**_1_**|**_4_**|**_24_**|
|Market functioning|65|23|7|10|8|1|4|12|
|Non-bank financial institutions|50|26|4|6|7|0|2|5|
|Public debt management|28|6|3|4|4|0|2|9|
|**Liquidity and funding**|**_143_**|**_36_**|**_13_**|**_18_**|**_22_**|**_7_**|**_8_**|**_39_**|
|Liquidity support|125|33|12|13|20|5|8|34|
|Easing of policy rates|106|25|12|15|18|5|5|26|
|Assetpurchases|30|18|1|1|3|0|2|5|
|**Payments Systems**|**_90_**|**_21_**|**_6_**|**_10_**|**_12_**|**_7_**|**_6_**|**_28_**|
|Digital payments|82|17|6|9|11|7|6|26|
|Relaxation of compliance requirements|17|3|0|2|1|0|2|9|
|Cash / Cheque usage restrictions|14|2|0|2|1|2|2|5|
|Cash acceptance|15|4|2|3|2|2|1|1|

Source: World Bank COVID-19 Financial Sector Policy Response Database.

Note: World Bank regional
reported at baseline) drive the response to illness shocks.<sup>16</sup> A male worker who is the highest earner in the household works 0.76 days and 7.6 more hours in response to the illness of another worker compared to another male. The results with usual hours of work lose statistical significance at conventional levels, but are still large quantitatively and point in the same direction as the earnings results.

Again, there is no differential effect for women who are the highest wage earners or have the highest usual hours of work (as reported in the baseline survey) in their households. This is not because too few women report the highest earnings or usual hours of work in their households to give us the power to detect these effects. Indeed, table A9 shows the joint distribution of gender and status as the highest earner and member with the longest usual hours of work in the household among the individuals employed at baseline. While it is more common for men to be the highest earners or have the most usual hours of work in the household – mirroring the result in table 1 that men on average have somewhat larger average hours of work and greater earnings than women – 42 percent of women are the highest earners in their household and 48 percent of women have the highest usual hours of work.<sup>17</sup> Instead, it appears that even (relatively) high-earning women have caregiving and other duties around the home that prevent them from increasing their labor supply when another worker is ill.

We now turn to another dimension of heterogeneity: risk aversion. The more risk averse an individual, the more he or she will dislike fluctuations in consumption, and thus, the greater the incentive to increase labor supply in order to smooth consumption. Survey respondents

> 16 Table O1 in the online appendix (http://faculty.washington.edu/rmheath/onlineappendix_HMR.pdf) shows that these results are robust to alternate measures of hours and earnings, namely
use of third-party information, behavioral design) can generate a larger impact overall.

# 4.3 Other Compliance Outcomes

As the experiment had a strong impact on the targeted compliance outcomes, notably income tax ling and payment, it is appropriate to consider also spillovers on other compliance outcomes. Indeed, taxpayers might perceive the intervention as a general increase in enforcement, and improve compliance also with other tax ling and payment obligations. Alternatively, they might perceive the intervention as pertaining to income tax compliance only, and increase compliance with the targeted tax but compensate for lost income by reducing compliance with another obligation towards the government. To shed light at this, we use rich administrative data on rm's ling of informative declarations, sales tax declarations, sales tax payment, deregistration, and switches to the simplied regime<sup>29</sup> , all of these representing compliance outcomes which were not directly targeted by the intervention. We also consider income tax compliance in 2013, which may be positively or negatively aected by the intervention (targeting non-lers for 2014), as a non-negligible share of rms le (or pay) their taxes with substantial delay (see Brockmeyer and Hernandez (2016) for details), and 80% of non-lers for 2014 were also non-lers in 2013. This is to our knowledge the rst study to examine the impact of enforcement on such a large variety of compliance measures.

Table 6 shows the treatment impact on the above-mentioned outcomes, for experiment 1 in Panel A and experiment 2 in Panel B. Columns 1-3 in Panel A show that treated rms were marginally more likely to le an information report (D151) about their supplier, and signicantly more likely to le an information report about their client.<sup>30</sup> With a 4.8 p.p. increase over a control group average of 12.8%, the eect is twice as large for the information treatment as for the baseline
> ) and LandScan Settlement Layer (LandScan SL) <mark>(Cheriyadat et al. 2007)</mark> . These data sets either are not available for Somalia or are incomplete for the country or they might not specifically be trained for Somalia since the country is facing a constant regional instability and severe drought that forced substantial migration within the region. In addition, during this work, several new data sets on settlements were collected from a variety of sources (table 1), each of which georeferencing settlements absent from the other existing settlement layers. This has motivated the current work to generate an improved high-resolution settlement map by combining the datasets listed in table 1. Appendix 1 gives more detail on the processing done on each data set. The resulting settlement map was used to improve population density predictions and EA delineation.

# **2.4.3 High-resolution population density estimates for Somalia**

A high spatial resolution map (100m x 100m) is necessary to estimate the population of each sub-region created during the automated EA delineation process. Multiple high to moderate resolution global modeled population datasets are freely accessible to download including WorldPop 7
compatible with fixed effects, which we include to tackle potential confounders. Thus, as a robustness check, we use a Poisson Pseudo-Maximum likelihood model (PPML), as proposed by Correia (2016) and Correia et al. (2019). In the results section, we present our baseline linear probability models. Figures in Appendix A.2 show that our estimates are robust to using linear probability models fixed-effects or PPML estimators.

We build on specification 1 to assess the effectiveness of individual adaptation actions in mitigating diarrheal diseases in section 4.2. Potential actions influencing the incidence of diarrhea for which we have data in the DHS are toilet and fridge ownership, urban/rural indicators, and education level. For all of them, we create a dummy variable equal to 1 when the household owns the asset, lives in an urban area, or has at least some education, respectively. Then, we interact the bins of significant weather variables in equation 1 with indicators of adaptation actions. The coefficient on the interaction term assesses the effectiveness of the considered adaptation action in decreasing the impact of weather on diarrhea incidence in children. It is important to keep in mind that we do not have exogenous variation in these characteristics when interpreting the results. As a result, households owning a specific asset or characteristic may disproportionately adopt other adaptation strategies that increase their resilience to climate-related risks.

In Appendix A.4, we examine the long-term consequences of in-utero environmental haz– ard exposure on child health. We achieve this by imputing environmental variables maximum temperature, average pollution levels, and average precipitation – for each trimester of pregnancy. Our findings reveal no substantial impacts on reported birthweight or measured indicators of stunting or underweight among our dataset’s children. However, these findings are subject to limitations from the relatively small number of observations available for these specific variables and the challenges in precisely assigning exposure levels. Indeed, the pollution exposure assignment during pregnancy is only accurate if households have not relocated too far since birth. Otherwise, the
|**208,900**|**203,700**|<br>**2.5**|

We should stress that this estimated rate of return, while clearly attractive, is extremely conservative as a guide to the lifesaving value of air pollution abatement in Beijing. For a large, non-SOE plant, our econometric results imply a MAC of $332 for 100 tons of S02 abated when the abatement rate is 10%. The equivalent figure for 100 tons of particulates abated is around $120. If (as seems likely, given overall abatement rate of 2.5%) there are large non-SOE plants in Beijing which are abating at less than 10%, **then failure to enforce greater abatement is equivalent to valuing a Beijing worker's life at less than $500. Continued inaction means ignoring a public investment with whose social rate of return is likely to be in excess of 3,000%! We believe that the opportunities for such high returns may be quite rare in China's urban health sector.** We conclude that it would make extremely good economic sense to tighten air pollution control in Beijing. Our abatement cost estimates show that it would also be sensible to focus on particulate emissions from large facilities.

# **6.2 Case 2: Zhengzhou**

Our results for Beijing do not necessarily yield appropriate conclusions for other urban areas in China. In the first place, our cost analysis reflects only microeconometric evidence on marginal abatement costs (MAC) for end-of-pipe treatment. These almost certainly overstate overall MAC, because process changes often provide the least-cost abatement opportunities for industrial facilities. Secondly, Beijing is not a representative Chinese city for environmental analysis. It is extremely large, and considerably more polluted than the average large urban area in China.

Our second case is designed to address both of these problems. For the abatement cost analysis, we use our cross-provincial econometric results on pollution intensities because they incorporate all abatement options available to Chinese
|
|---|---|---|---|---|---|---|---|
|E-commerce/transactions|1|||||||
|Public intent data|0.2979*|1||||||
|Private intent data|0.3496*|0.3998*|1|||||
|Personal data|0.0875|0.1736|0.4589*|1||||
|Nonpersonal data|-0.1046|-0.2214*|-0.0853|0.1249|1|||
|Cybersecurity and<br>cybercrime|0.2924*|0.4031*|0.6232*|0.5423*|-0.1744|1||
|Cross-border data<br>transactions/flows|0.1329|0.2458*|0.5573*|0.8216*|0.0348|0.6087*|1|

_Source:_ Author’s calculation based on results from the diagnostic. _Note:_ * indicates that correlation coefficients are significant at the 5 percent level or better.

13
The HFPS data analyzed in this paper<sup>4</sup> comes from seven of these countries and spans one year of the pandemic, from May 2020 to May 2021. Countries included in the analysis are Indonesia, Lao PDR, Mongolia, Myanmar, Philippines, Papua New Guinea (PNG), and Vietnam.<sup>5</sup> While the design of the HFPS differs slightly across countries, all surveys share a core set of indicators that provide insight into the socioeconomic impacts of the pandemic, including information on employment, income losses, coping mechanisms, food insecurity, and access to services.

The paper contributes to the burgeoning literature on the distributional impacts of the COVID-19 pandemic around the world. Numerous studies have documented the labor market and income impacts of the pandemic in high-income countries such as the United States, Japan, and the European Union, among many others. In the United States, Chetty et al. (2020) find that the pandemic led to widespread layoffs, which were greater and more long-lasting for low-wage workers. Other studies such as Gould and Wilson (2020) have documented the disproportionate impact the pandemic has had on black, Hispanic, and female workers in the United States. Several recent studies have used the World Bank’s global harmonized data

> 2 While most countries administered the HFPS in consultation with their local NSO, Mongolia was the only country in which the NSO was the implementing agency of the HFPS. Country-specific briefs and reports, produced by the country teams, can be found in the website links available here: <u>https://www.worldbank.org/en/topic/poverty/brief/high-frequency-monitoring-surveys</u>

> 3 All HFPS used here are nationally representative of households that have phones; hence their national representativeness depends on the country’s mobile phone penetration. In addition, by design, the questionnaires are much shorter (ideally, they should take less than 15 minutes to complete, with relatively simple questions
adults live in urban areas, where, in addition, access to antiretroviral treatment is easier. We can thus assume that their food consumption and nutritional status will be affected negatively by the food crisis. Although there is no way, with our data, to measure the direct impact of the food crisis on the overall welfare of households, we can use the change in income, consumption, food consumption and nutritional status of children between 2007 and 2008 as proxies for that impact. The food crisis indeed reached its peak between January and June 2008 and can be considered a systemic shock.

Income, consumption and food consumption are measured on a monthly per adult equivalent basis, in meticais<sup>3</sup> . Income is the sum, over all members of the household, of labor income (wage, household business or farm), non labor income (insurance, pension, etc.), private and institutional transfers, and home produced food. Consumption is the sum of food consumption (purchased and home produced), non food items purchased, education, housing (water, electricity, etc.), health, ceremony and special events expenditures. Real income and consumption are obtained using the Mozambique Consumer Price Index (INE, 2009). As the data in 2007 and 2008 were collected across several months, the monthly values of the CPI were used to match the date of interview of each household, to ensure the inflation index used is as close as possible to what the household actually experienced. Unfortunately we were not able to use a regional CPI which, to our knowledge, does not exist in Mozambique, and thus the national index was used. The food-and-drink CPI was used to deflate the food consumption variable.

The anthropometric measures used to proxy for households’ nutritional status are the weightfor-age z-scores for children aged 1 to 10 years old, the body-mass-index and the height-for-age z- scores for children aged 5 to 19 years. These were computed using the WHO growth standard (De Onis et
To explore these results further, Figure 6 presents the comparison between country-specific extreme and moderate poverty rankings based on the $2.5 and $5.3 per person per day poverty lines and the 2011 PPPs with the corresponding estimates based on the $2.5 and $4 per person per day poverty lines and the 2005 PPPs. Any deviation from the 45 degree line denotes differences between the poverty rankings. The figure shows that, with few exceptions, most notably the Dominican Republican, country-specific extreme and moderate poverty rankings are generally stable.

- _4.4 A decomposition approach to understand changes introduced by new poverty lines and PPPs_

Poverty headcounts calculated using 2011 PPP values differ from those calculated using 2005 PPP values, not only because the value of the poverty lines are different, but also because the base year (i.e, 2005 or 2011) and the relative costs of living between countries (i.e., 2011 PPP round or 2005 PPP round) are different. For instance, an extreme poverty line of $2.5 per person per day in 2005 PPP values is drastically different from an extreme poverty line of $2.5 per person per day in 2011 PPP values. As explained above, the $2.5 per person per day poverty line at 2011 PPP values is lower, in real terms, than the $2.5 per person per day poverty line at 2005 PPP values. The difference between the headcounts calculated with both poverty lines is due not only to the general inflation in prices that all the countries experienced, but also to the fact that (i) the relative prices between countries – reflected in the PPP values – changed from 2005 and 2011, and (ii) the set of underlying official poverty lines used to calculated the regional lines also changed.

Thus, the difference between the poverty headcounts using both the poverty lines in 2005 PPP values and the ones in 2011 PPP values can be decomposed into three components. The first component is the change
**_Table 5. Fiscal and gender earner category comparison (percentage of total households)_**

|||**GE**<br>|**NDER EARN**<br>|**ER CATEGORIE**|**S**<br>|
|---|---|---|---|---|---|
|||Female<br>>= 60%|Male<br>>= 60%|No income|Neither<br>earns 60%|
|**RIES**|No workers, no dependents|2|4|7|0|
|**EGO**|No workers, children in school|1|5|6|0|
|**CAT**|Pensioners|2|5|4|1|
|**CAL**|Worker(s), no dependents|1|22|1|2|
|**FIS**|Worker(s), children in school|3|30|1|3|

Source: 2017-18 Household Expenditure and Income Survey (HEIS) and World Bank calculations.

Overall, this section presented three household typologies for the Jordanian population. Care categories are perhaps the most relevant when the interest is on how the fiscal system affects women as the main caretakers in Jordan, and how can it potentially affect women and household decisions such as whether women are economically active. Fiscal categories show how more complex demographic household compositions interact with fiscal policy. As shown in Table 5, the ‘no workers’ and ‘pensioners’ fiscal categories are more likely to be female-majority earner households, whereas male majority earner households have disproportionately high representation among worker households. These categories allow further understanding of the interaction between income, gender, and fiscal policy, isolating important components of fiscal policy related to household demographics such as public education and pensions in addition to income
D. Financial regulatory reforms include those business reforms categorized under Access to Credit, Minority Investor Protection, Enforcing Contracts and Resolving Insolvencies. E. Unweighted regional averages of the Chinn-Ito index (KAOPEN) measuring a country’s degree of capital account openness. 1 represents fully open capital account. AE and EMDE averages are for 2016. F. Indicator variable taking on value 1 when a country experiences a year-on-year decrease in the openness index and -1 when a country experiences a year-on-year increase. Based on 145 EMDEs.