rafmacalaba/data-use-mentions-extended huggingface dataset:
difference between the control and treated districts. Standard errors are clustered at the district level. Our main coefficient of interest is which estimates the effect of living in medium and strong conflict intensity districts on the female decision-making index after the conflict breakout compared to low intensity areas. ββ We cannot argue that treatment and control groups are randomly assigned, meaning that there are no structural differences between strong and low exposed districts. In fact, conflict and violence is hardly ever exogenous and is usually correlated with baseline socio-economic conditions (Blattman and Miguel 2010). To address this potential bias, the difference-indifference approach controls for cluster fixed effects which captures the time-invariant preexisting differences between both groups. Moreover, we augment our specification with the set of covariates in i which controls for specific individual and household characteristics. ii However, for a valid difference-in-difference estimation, the common trend assumption must be XX However, for a valid difference-in-difference estimation, the common trend assumption must be satisfied. This requires that the level of female participation in decision making in the treatment and control groups should follow the same trend in the absence of the conflict event, and that the deviation that occurs in the treatment group is only due to conflict breakout. This implies checking the underlying trend in intrahousehold female decision-making for both groups in at least one point in time prior to 2014. Unfortunately, we do not have household survey data prior to 2014 to test this assumption, therefore we resort to the Yemen Demographic and Household survey (DHS) in 2013 to test this assumption. We focus on similar module from the _women in the household module_ , particularly on decisions related to household purchases and health. Because the DHS data is only available at the governorate level, we aggregate all our indicators to this level. Figure IV.6 provides visual evidence of an underlying common trend in the treatment (medium and strong intensity) and control (low intensity) groups, and a deviation in the trend induced by the treatment effect following the conflict breakout in 2015.

12
respondent’s education level (Altaf and Deshazo, 1996; Aadland and Caplan, 1999; Huang and Ho, 2005), conscience about the seriousness of solidwaste-related pollution problems (Huang and Ho, 2005, Ichoku et al., 2009), past positive experience in receiving the SWM services and trust in the proposed project (Afroz et al. 2009). This suggests that SWM service is a normal economic good. Female respondents have a general tendency to be willing to pay more than the male respondents (Aadland and Caplan, 1999; Fonta et al., 2007; Ichoku et al., 2009) Family size seems to affect negatively the WTP (Huang and Ho, 2005) but the families having kids may be ready to pay more (Lake et al. 1997). This finding in fact echoes to the conclusion of Johnstone and labonne (2004). Based on an OECD-country macroeconomic database, they found the family size does not affect significantly the demand for SWM, since the children and adults apparently do not play the same role in the determination.

Several studies, especially those conducted in the developing countries, also reveal an important phenomenon that although people rank improper solid waste disposal as the top environmental problem, the user fee that they are willing to pay can only partially cover the cost of the service. Bluffstone and Deshazo (2003) concluded that the WTP for upgraded landfills covers only about 80-90% of the cost for a project in Lithuania to upgrade their SWM system to European level. Naz and Naz (2005) found the ratio of WTP over the total cost to be only 22-35% in the Philippines. Palatnik et al. (2006) also mentioned the necessity of subsidy to achieve an efficient level of recycling for the case of Israel.

Table 1 lists the WTP values reported in various CV studies. Although the components of the hypothetically proposed projects are different from each other, a general impression is that the WTP for SWM service does not occupy an important share in household income
results can be obtained using the prediction equations from (St. Jeor and Stumbo 1999). There have been laboratory measurements of REE in India but none for a large or representative sample of individuals and none generating specific predictive equations. (Ferro-Luzzi et al. 1997) attempt a validation of the FAO equations on Indian data and find a reasonable fit.

The method used here for imputing REE to individuals in other data sets is to regress the REE predicted by the (Henry 2005) equations using the 19
reducing their exposure are based on a few simple, robust findings: Hourly pollution levels in cooking and living areas are quite similar because cooking smoke diffuses rapidly and nearly-completely into living areas. However, outdoor pollution is far lower. At present, young children are only outside for an average of 3 hours per day. For children in a typical household, pollution exposure can be halved by adopting two simple measures: increasing their outdoor time from 3 to 5 or 6 hours per day, and concentrating outdoor time during peak cooking periods. We recognize that weather and other factors may intervene occasionally, and that child supervision outdoors may be difficult for some households. However, the potential benefits are so great that neighbors might well agree to pool outdoor supervision once they became aware of the implications for their children’s health.

2
125*|0.108|0.143*|0.067|0.182*|
|Mth d b SD|(S.E.)|(0.058)|(0.083)|(0.067)|(0.068)|(0.073)|
|a - orer numers ()|Comp. mean|-0.0828|-0.214|0.0143|-0.0905|-0.0745|
||Obs.|2,862|1,274|1,588|1,814|1,048|
||Coeff.|-0.022|-0.012|-0.032|-0.020|-0.023|
|Abtt i SD|(S.E.)|(0.044)|(0.064)|(0.056)|(0.058)|(0.059)|
|srac reasonng ()|Comp. mean|0.0310|-0.102|0.130|-0.0317|0.0977|
||Obs.|2,862|1,274|1,588|1,814|1,048|

p<0.001***; p<0.01**; p<0.05* Note: Each cell-block is the result of a separate regression. Standard errors clustered at the village level are in parentheses. “Comp. mean” refers to the comparison group mean for the outcome variable. Column (1) regressions control for child characteristics (age, household size, household wealth asset index, parenting practices, mother's education and gender) and baseline child development measures (BMI, cognitive, fine motor, gross motor, and language). Columns (2) and (3) regressions use the same controls as column (1)
12 degree days were practically the same in 2017 and in 2018, the two years that we examine in detail below.

In addition to documenting expenditures, income and government transfers, the HBS does contain information about housing tenure, the structural characteristics of dwelling, and some of the appliances therein. For example, we know if the home is served by piped natural gas (gascentr=1), district heating (heating=1), is equipped with a gas water heater (gaskol=1), or has an electric stove (elektrpl=1). The exact nature of the heating system however must be inferred. We assume that if the household has district heating (heating=1) but is also served by piped natural gas, then the latter is used for either cooking or for heating water (if gaskol=1), or both.

If a home is connected to the natural gas network and zero district heating expenditure is reported, then we assume natural gas is used for space heating, for heating water (unless gaskol=0), and for cooking (unless elektrpl=1). The data sets also specify whether the dwelling is on a centralized, or on a separate and independent, heating system.

# _3.B. Homes and Heating_

Homeownership (92%) was stable over 2014-19, as were the shares of households living in single-family homes (52%), or in units in multi-family buildings (41%). The exact size of the dwelling is only available in the 2014 and 2016 HBS; Ukrainian families live in and must heat on average about 40 square meters. The shares of the samples served by the different types of fuels or heating are likewise very stable over the 6 years. About three-quarters of the sample are served by piped natural gas. About 31% of the households are served by district heating. About 45% of the sample uses piped natural gas for heating, 15% uses piped natural gas to feed its water heater, and
10 To correct for differences in how countries report earnings, Freeman and Oostendorp (2000) use a standardization procedure to make the data comparable across countries and time. In 2005, they provided an update for their earnings measures for the 1983-2003 ILO October Inquiry data using an improved version of the standardization procedure and the application of country-specific data type correction factors. A detailed technical documentation of the standardization procedure for the 1983-2003 ILO October Inquiry data is available online at http://www.nber.org/oww/.

11 We also included data for Turkey for the year 1994.

12 Another way to capture the effect of mobility cost-reducing network effects is to use the past total number of migrants instead of the past emigration rate as the instrument for contemporaneous migration. We confirm that the results do not change.

13 Countries with negative net emigration are coded to have an emigration rate equal to zero.

14 The measure is inspired by Beine, Docquier and Ozden (2011). They use a similarly constructed proxy as an instrument for the total diaspora of migrants in 1990 (rather than the high-skilled emigration rate).

15 Calculating partial correlations confirms that the past total emigration rate is well correlated with the high-skilled emigration rate in 2000.

16 We use the Huber-White method to adjust the variance-covariance matrix from our least squares results.

30
sub-counties was randomly selected from a list of villages that were not included in the quantitative survey. The final villages were selected based on the availability and support of the community elders.

The team worked to ensure the representation of various groups of individuals within the communities when recruiting research participants. Two key informant interviews were conducted to create the community profiles (1 man and 1 woman) in each village. The team also held in-depth life history interviews with 16 individuals (8 men and 8 women), and eight focus groups were convened (2 with young women, 2 with young men, 2 with older women, and 2 with older men) across the two villages. While the key informant interviews focused on aspects of the community as a whole, the in-depth interviews included questions on decision-making dynamics in the household generally as well as those pertaining to household water management. The focus groups include discussions of decision-making and power dynamics between husbands and wives, but also between sons and mothers as well as daughters-in-law and mothers-in-law.

# **4. Who is and is not involved in decision-making?**

In the quantitative data, only 13 percent of men and 33 percent of women are directly involved in making decisions for all seven activities (Table 1). These low numbers are driven, in part, by the range of activities chosen and by the fact that we have interviewed all household members, not just the primary couple. Young men and women in their late teens and twenties, who had not yet started their own household, were not likely to be involved in any household decisions.

The decisions that women are least likely to participate in are those on household expenditures (specifically regular water expenditures and large household purchases). In the qualitative interviews, when asked about decisions regarding finances, some married women indicated they would pool money they earned with their spouse and decide jointly on the priority of expenditures, such as school fees, food, water, health care and household purchases. But in other households, women
to find those who are most in need of a subsidy. One example is the Georgian Winter Heating Assistance Program (GWHAP), put in place in 1998 as electricity prices were set to increase in Georgia. This scheme provides subsidy recipients with free electricity for a fixed volume of kilowatt-hours per year (Lampietti et al 2003) The Chilean water subsidy is another—and perhaps the most widely cited example — of a means-tested consumer utility subsidy. The program subsidizes between 40 and 70 percent of up to 15 cubic meters of water for poor households.

Our survey of utility tariffs and subsidies revealed little systematic information on the use of administrative targeting in water and electricity utilities (Komives et al 2005). It is clear, however, that many utilities in Latin America at least offer separate social tariffs or special discounts for disadvantaged customers. Use of categorical targeting to reach pensioners in particular is very common on Eastern Europe and Central Asia.

5
to trace the role of informal slum leaders (pradhans) as intermediaries between the formal government and the urban poor. The qualitative evidence draws on interviews and observations carried out in four different slums on intra-community dispute resolution, risk management procedures, and the processes by which leaders gain legitimacy and exert authority. We find that length of tenure, political status, environmental hazards, and migrants’ distance from their home districts all loom large as the drivers of the problems that communities and their leaders invoke. Links to political parties, a proven track record, and claims to a democratic mandate are the primary sources of legitimacy, even when leaders have acquired power through traditional means.

Our broader quantitative survey, informed by themes that emerged from the qualitative research, was administered to a representative sample of households and community leaders from 30 randomly-selected recognized slums. It reveals that slum leaders play a central role as intermediaries with the formal authorities. Pradhans are more likely to speak on the slums’ behalf in the newest slums, while facilitating direct access to politicians among residents of ethnic enclaves. Education and political affiliation are more important than seniority in determining the pradhan’s influence, which is strongest in the newest slums and in ethnic enclaves. In contrast, direct access to formal government is the preserve of the wealthy and wellconnected.

Section II discusses the scholarly and social context of the study and the methodology. Section III presents qualitative evidence on the governance mechanisms at work within four different slums, while Section IV presents the quantitative analysis of these issues. Section V concludes.

3
we use the log minimum capital recruitment in the analysis. Summary statistics for this variable are in panel B of table 1.

Our measure of bank concentration is the asset share of the three largest banks in a country in the year 2000 from the GFDR 2013 database. We chose this variable as opposed to other measures of bank concentration that include a larger number of players in the banking market (asset share of the five largest banks or Herfindahl index) since our hypothesis 2 in section 2 relates specifically to the existence of a few very large banks. That is, the disincentives to sharing credit information are particularly strong for banks that capture a very large share of the market. Panel B of table 1 shows that the average level of bank concentration in our sample is 0.73, with a minimum of 0.21 and a maximum of 1.

Data on the minimum capital requirement and on bank concentration is only available for about 135 countries, thus reducing the number of countries included in our empirical analysis.

# **3.3 Control variables**

The last panel of table 1 shows the descriptive statistics for the control variables used in this paper. The first two variables are (i) a measure of economic development (log of GDP per capita in the year 2000 from the World Development Indicators) and (ii) a proxy for the depth of the financial sector (ratio of private credit to GDP in 2000 from the Financial Structure database).

We include two measures of a country’s institutional environment. The first one is an indicator of the quality of contract enforcement: the log number of days it takes to enforce a contract from the Doing Business database. We use the first available year for this variable (2003), covering 144 countries. Our second institutional variable is a proxy of creditor rights quality: the credit rights index from Djankov, McLiesh, and Shleifer (2007) for the year 2000. This index measures the strength of legal rights of creditors against defaulting debtors and
, we describe the institutional context. Section 3 details the sources and composition of our tax data. Section 4 characterizes residential property ownership in _Tres de Febrero_ by gender. Section 5 brings three stylized facts on property tax compliance and also leverages experimental evidence on differences in compliance responses across genders. Finally, Section 6 discusses some policy implications behind our results and then concludes.

# **2 Institutional Context**

Tres de Febrero is an urban municipality (county) in the Greater Buenos Aires metro area, Argentina. As of 2022, it had approximately 365,000 residents and 115,000 households, making up 1.2% of the total population of Argentina (INDEC, 2022). It has the third highest population density in the province of Buenos Aires, with great connectivity and accessibility to neighboring areas, and contributes 2.4% to the total urban tax valuation of the province, ranking 13th among the 135 municipalities in fiscal valuation.

4
**Table 2. Available global healthy diet metrics over the lifecycle**

|**Level of diet**<br>**quality**|**Children 6-23 months**|**Adolescents (10-19 years)**|**Women of reproductive age**<br>**(15-49 years)**|**Adults (>15 years)**|
|---|---|---|---|---|
|Energy<br>(in)sufficiency<br>|Per capita calorie supply (compared t<br>|o age/sex specific requirements)<br>|||
|Micronutrient<br>(in)adequacy|**MDD**(minimum diet diversity,<br>previous day)<br>**EFF**(egg and/or flesh food<br>consumption, previous day)|NO VALIDATED INDICATOR<br>**MDD-W/Food Group**<br>**Diversity Score for girls/boys**<br>**15-19 years**<sup>**4**</sup><br>Prevalence of less than daily...<br>- fruit consumption<br>-vegetable consumption|**MDD-W**(minimum diet<br>diversity – women, previous<br>day)|**Food Group Diversity Score**<br>(previous day)|
|(Un)Healthy<br>diets|**ZVF**(zero vegetable or fruit<br>consumption, previous day)<br>**SwB**(sweet beverage<br>consumption, previous day)<br>**Unhealthy food consumption**<br>(sentinel unhealthy foods, previous<br>day)|NO VALIDATED METRIC<br>Average number of times per<br
details, see Appendix A.4) and by the expansion of academic programs<sup>16</sup> . Academic programs refer to the number of concentrations across universities that offer a particular academic degree<sup>17</sup> , such as economics, engineering, and other programs. I account for these changes in the empirical specifications by including various fixed effects. Finally, in principle, there may be discrepancies between matriculated applicants and those eventually attending public IHEs. Nevertheless, the gap is likely negligible considering the institutional features (e.g., limited transfer possibility, free tuition, and dormitories) and future return on education. It should not pose a significant threat to the consistency of the findings.

# **4 Data**

I compiled three datasets to assess the gender quota impact on outcomes of interest. First, I attained the Kankor matriculation dataset for 2013–2018, from publicly available sources to compute variables of interest. Second, I obtained district-level nightlights data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Stray Light Corrected Nighttime Day/Night Band Composites Version 1 via Google Earth at the second administrative unit level. Finally, I used the school database in Afghanistan available on the Ministry of Education (MoE) website. It contains information on school names and district locations, allowing me to match test-takers to their school districts.

First, in the Kankor dataset, I observed applicants’ gender, final test scores, high school name, high school graduation year, the province where the applicants took the exam, the institution’s name for those who matriculated into 2-year associate degrees, and university-unique-concentration identifier for applicants enrolled into 4-year public IHEs on 1,151,042 Kankor test-takers for 2013–2018. I limited the data to those applicants enrolled into the 4-year public universities (29% of all test-takers). The

> 16See Table A2.1 for the conceptual difference between concentrations, academic programs, and universities.

> 17See Table A3.2 for the
a bank. The dependent variable corresponds to the provision rates of working capital loans. _Earmarkfbt_ is an indicator variable that equals one for all consecutive periods after a firm receives its first earmarked loan from a bank, and zero otherwise. _Riskft_ is an indicator variable that equals one for firms with credit risk at the start of the earmarked relationship above the median and zero otherwise. Credit risk is measured using the rating of the first earmarked loan issued to firm _f_ . _MPft_ is an indicator variable that equals one if the market power in period _t_ of banks operating in firm _f_ 's zip code (measured by the Herfindahl Index) is above the median and zero otherwise. Other controls include the length of the relationship duration of each firm-bank pair and loan characteristics at the time of issuance (maturity, volume and a dummy variable indicating if the loan was collateralized). All regressions include fixed effects for the ten loan rating categories established by regulators, as well as fixed effects for the type of loan (whether the loan has a fixed rate or a rate indexed by Brazil's CDI). Standard errors are doubled clustered at the bank and time level.

23
|Controls|||||||
|Age|No|Yes|Yes|Yes|Yes|Yes|
|Firm size|No|No|Yes|Yes|Yes|Yes|
|Share of white-collar workers|No|No|No|Yes|Yes|Yes|
|Capital/worker|No|No|No|Yes|Yes|Yes|
|Exports/sales|No|No|No|No|Yes|Yes|
|FDI share|No|No|No|No|Yes|Yes|
|Time efects|No|No|No|No|No|Yes|
|Observations|1402|1402|1402|1402|1402|1402|

Standard errors in parentheses

* _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01

All specifications include firm’s fixed effects. Source: Authors’ calculations using data from ENTIC, INEGI IV1: IT intensity dummy based on name from (Bloom et al., 2012b) and Share of urban households with computers (2000 and 2010 Population and Housing Censuses) IV2: IT intensity based on investment from (O’Mahony and Van Ark, 2003) and Share of households with internet (MODUTIH) IV3: IT intensity dummy based on name from (Bloom et al., 2012b) and Share of households with internet (MODUTIH) IV4: IT intensity based on investment from (O’Mahony and Van Ark, 2003) and Share of urban households with computers (2000 and 2010 Population and Housing Censuses) IV5: IT intensity dummy based on name from (Bloom et al., 2012b) and Share of urban households with Internet (2000 and 2010 Population and Housing Censuses) 20
*|||||||||||||
|LIC|51|29|20|58|25|17|51|28|21|50|27|24|
|LMIC|50|33|17|46|34|19|42|38|20|42|42|17|
|UMIC|43|36|21|43|38|18|36|42|22|35|41|24|
|HIC|31|45|23|30|46|24|31|43|26|31|41|28|
|**Regions**|||||||||||||
|AFR|48|32|19|50|31|19|47|32|21|45|31|24|
|ECA|26|49|25|29|47|24|27|47|26|28|46|26|
|LCR|47|34|19|45|37|18|42|35|22|40|36|24|
|SAR|48|36|17|42|45|13|42|38|20|45|39|15|
|EAP|44|33|23|43|34|23|40|36|24|38|40|23|
|MNA|36|44|19|34|43|22|32|45|23|37|38|25|
|**All countries**|41|38|21|41|39|21|39|39|23|37|39|24|

_Source:_ World Bank calculations using UIS and IMF online databases. _Note:_ The figures in the tables show the percentage of the public budget spent on each education level. Pri. = Primary, Sec. = Secondary, and Tert. = Tertiary