rafmacalaba/data-use-mentions-extended huggingface dataset:
household endowments and contexts influence the access to and use of the VUP and other programs and how this in turn shapes asset accumulation. A number of factors come into play here such as geographic factors, i.e. accessibility and transport to the public works site (not shown here) or a number of household characteristics such as the total labor capacity of the household, alternative work opportunities and care requirements of the households.

The household characteristics contextualize welfare trajectories at two stages: First, they affect access to the program(s); second, they shape the utilization of the income transfer and other program support for productive and risk management purposes.

For stage 1, while access to the VUP is formally restricted by the eligibility criteria outlined above, households identified a number of additional characteristics which influenced their ability to access the VUP and also their use of other programs, notably other asset transfer programs and subsidized health insurance. These characteristics include: the sex of the household head or potential VUP beneficiary, their education level, the total labor capacity and care obligations within the household and initial endowments in assets and land. In all these aspects, we noted a strong interplay with the sex of the household head which emerges as another contextual factor related to gender dynamics across all the identified factors. In exploring each of these factors in the quantitative data, we find that typically education levels are low. Very few of the beneficiaries have received any form of formal education which in turn shapes economic opportunities. Low literacy levels are more pertinent among the female respondents; male respondents more frequently reported to be able to read and write. The qualitative data reveal a strong gender divide with respect to the control of resources ( _“As the husband, I control the income”_ (male, PW-M1)) and initial asset base with women reporting being historically traditionally excluded from inheritance and thus subject to lower land ownership compared to their male counterparts ( _“My parents passed away a long time ago. The land they had
**Table A7: Alternative Construction for the Ebola-Related Disruption Index**

**Standard deviations in brackets, standard errors in parentheses**

||**Mean**|**Between district**<br>**variation**<br>**(share of total)**|**Unconditional**<br>**coeff on ELA**|**Conditional**<br>**coeff on ELA**|**Conditional coeff on**<br>**ELA with distance**<br>**interaction**|**Conditional coeff on ELA**<br>**with distance interaction**<br>**and model selection**|
|---|---|---|---|---|---|---|
|**_A. Health Policy Components_**|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|**PHU Ever Closed**|.140|254|.001|-.000|.046|.076|
||[.348]|.|(.049)|(.050)|(.075)|(.075)|
|**PHU Ever Disrupted**|.320|212|.000|-.006|-.077|-.069|
||[.468]|.|(.065)|(.065)|(.098)|(.096)|
|**PHU Functioning Score**|89.6|327|-1.19|-1.02|-1.00|-.342
the exclusion restriction requires that our instrument only affects political opinions through the mechanism of exposure to the bed net campaign, which seems quite plausible given the role of survey date randomization in generating the discontinuity.

# **VI. Results**

In this section I present the main finding: that malaria control efforts in Tanzania have resulted in substantial public opinion gains for local politicians. Graphical evidence and regression estimates are provided for each outcome of interest. The regression tables provide our reduced form and IV estimates.

## **Main Estimates**

The main results are that, in the reduced form specifications, village chairman, the ward councilor, ward executive, and MP all see significant, substantive increases in their approval ratings. The approval increase they all receive is on the order of 7 to 13 percentage points, from base approval rates of 70 to 80 percent. The effects are substantively large and highly significant: for village chairman, for example, I can reject that the coefficient equals zero with 99% confidence. With zone fixed effects, results are of the same magnitude for village chair and councillor, but are smaller and not significant for the village and ward executives, and the MP.

Panel C shows the continuous measure of approval (1-4, with 1 representing strong disapproval and 4 strong approval), for which I create a standardized measure of approval for ease of interpretation. Using this measure, there are large and highly significant increases in approval for all outcomes in the main specification (between 0.24 and 0.39 standard deviation increases). Panel D uses the panel dimension of the data and controls for the respondent’s approval of the same leader in the 2008 survey. In the IV specifications (Panel E), the same patterns of significance are present (for all except the village executive officer there is a significant treatment effect), and the effect 16
Khisroon, M., Khan, A., Imran, M., Zaidi, F., Ahmadullah, Fatima, S.H., 2018. Biomonitoring of DNA damage in individuals exposed to brick kiln pollution from Peshawar, Khyber Pakhtunkhwa, Pakistan. Arch. Environ. Occup. Health 73, 115-120.

- Kumar, S., Maithel, S., 2016. Introduction to Brick Kilns and Specific Energy Consumption Protocol for Brick Kilns Greentech Knowledge Solutions Pvt. Ltd., New Delhi.

- Lakho, N., Zardari, M., 2016. Structural Properties of Baked Clay Bricks Fired with Alternate Fuels. Engineering 8, 676-683.

- Maithel, S., 2013. Evaluating Energy Conservation Potential of Brick Production in India, A Report Prepared for the SAARC Energy Centre, Islamabad. Greentech Knowledge Solutions Pvt Ltd, New Delhi.

- Maithel, S., Kumar, S., Lalchandani, D., 2014. Factsheets about Brick Kilns in South and SouthEast Asia. Greentech Knowledge Solutions Pvt Ltd (GKSPL), New Delhi.

- Maithel, S., Lalchandani, D., Kumar, S., Bhanware, P., Ahuja, S., Uma, R., Athalye, V., Ragavan, S., Joshi, R., Bond, T., Weyant, C., Baum, E., 2013. Zigzag Kiln Performance Assessment – 2012 & 2013. A Shakti Sustainable Energy Foundation Supported Initiative. Greentech Knowledge Solutions, New Delhi, Enzen Global Solutions, Bangalore, University of Illinois at Urbana-Champaign, Illinois, Clean Air Task Force, Boston, New Delhi.

- Maithel, S., Lalchandani, D., Malhotra, G., Bhanware, P., Uma, R., Ragavan, S., Athalye, V., Bindiya, K.R., Reddy, S., Bond, T., Weyant, C., Baum, E., Thoa, V.T.
Using this information, the park VR experiences for Treatment-1 (T1) and Treatment-3 (T3) were digitally enhanced with the following elements: (i) additional entry/exit to the park; (ii) street lights; (iii) tea kiosk; (iv) emergency phone; (v) group tables and benches; (vi) signals promoting respect of others; (vii) picnic area for families; (viii) removal of trash; (ix) garbage bins; (x) shady trees and tall grass; (xi) play fixtures for children; and (xii) greener grass.<sup>6</sup> To add social diversity to the park for Treatment-2 (T2) and Treatment-3 (T3), 22 actors were hired to populate the park in the VR experience. The actors differed in sex (men and women), age (children, young adults and older adults) and ethnicity (Sindhi, Pashto and Muhajir). They were shown walking around and interacting with each other. Although the actors did not speak, their physical characteristics and clothes matched the visual traits of various ethnic groups in Karachi.<sup>7</sup> These characteristics were wellnoticed by those who participated in the VR experience: 93 percent of those who were exposed to the diversity treatments (T2 or T3) reported seeing men; 92 percent reported seeing women; 73 percent reported seeing Sindhis; 72 percent reported seeing Pashtuns; and 84 percent reported seeing Muhajirs.

# **_3.2. Data_**

The data used in this study were collected through a computer-assisted survey conducted right after the participants were exposed to the VR intervention. The survey included questions about the park and the people seen in the VR experience, as well as demographic information about the participants.

In total, 2,010 respondents participated in the study by experiencing one of the four VR interventions and completing the follow-up survey. However, during data analysis, it became clear that some participants may
infrastructures determines the benefits enjoyed by the host community including patronage, loyalties, jobs, contracts, scholarships, social facilities, and compensation (Folami, 2017). The rents from oil and gas companies to communities and the ethnic divide promotes a strong attachment to land in this area. A second contribution of our study is to consider offshore gas flaring. Most evidence on health impacts of flaring is from land-based settings, reflecting a preponderance of studies of fracking. While a large literature considers environmental impacts of offshore oil and gas production (see Wiese et al., 2001 for an early study), little is known about impacts of offshore gas flaring on human health. Yet half of all of Nigeria’s flaring is offshore, with three-quarters of these flaring locations located within 60 km of areas where people live. Evidence has shown that the type of pollutants generated by flaring can affect air quality up to 108 miles (174km) away (Mauzerall, et al., 2005) so it is likely that that offshore flaring has an impact on coastal communities. The examination of the health impact of offshore flaring also has important policy implications in Nigeria where there is a longstanding debate about the distribution of the gains from offshore oil exploration (Egede, 2005). Finally, our study demonstrates the value that can be added to geo-referenced survey data by linking to satellite observations of environmental phenomena, especially for countries that lack the wherewithal for conventional monitoring (Gibson and McKenzie, 2007; Donaldson and Storeygard, 2016).

The rest of the paper proceeds as follows: Section 2 describes some related literature examining gas flaring and human health, Section 3 describes the data and our method of linking satellite-detected gas flaring locations and estimates of gas flared volumes with human health data from DHS. Section 4 contains our results and Section 5 concludes.

# 2. Related literature

A large literature relates air pollution to a host of negative health outcomes (see Manisalidis et al., 2020 for a review). However, identifying causal impacts of
. The third group includes participants with reservation wages of up to 20% above the average starting salary level; this group should be most affected by the incentive if reservation wages are binding. We indeed find a large aggregate employment effect for this group. The share with formal employment increases by 15 p.p. (Panel A) and the aggregate time formally employed goes up by more than 3 months (Panel B). This sharp increase is exclusively driven by an increase in work with permanent contracts, while the share with temporary work and informal work slightly decreases (Panel A). Strikingly, the intensive margin increase in (permanent) formal employment comes almost entirely from a reduction in informal work (Panel B), illustrating the importance of reservation wages for youths’ career choices. By contrast, we do not ob-

> 26Specifically, we find that in the endline survey that the treatment group does not report higher level of job search activities. One limitation of this evidence is that we only collect job search data for the endogenously determined subgroup of people without work in our sample.

29
WDI. We assume<br>that unemployed and informal workers contribute<br>to pensions at a fraction,_d,_set to 2%, of the rate<br>among formal employees, and adjust our estimates<br>accordingly.|
|Informal<br>Rudimentary<br>Labor (LR)|The total number of workers in<br>the rudimentary economy, using<br>primary production technologies<br>with<br>little<br>capital<br>and<br>low<br>productivity|We use ILO estimates of the share of ‘vulnerable<br>employment’, defined as the sum of un‐paid family<br>workers and own‐account workers, i.e. self‐<br>employed workers without employees, combined<br>with labor force and employment data from WDI.|

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expenditure during sickness are also positive, but imprecise, and only significant in one specification. A previous literature estimates that breastfeeding differences explain about 9 percent of the gender gap in post-neonatal child mortality in India (Jayachandran and Kuziemko (2011)) and that sex differences in vaccinations explain between 20 - 30 percent (Oster (2009)). We estimate that the contributions of breastfeeding and vaccination to the ultrasound-led decline in EFM are, respectively, 19 percent and 7 to 10 percent (details in Online Appendix C).

For reasons discussed in Section 3, we also conducted this analysis using NFHS data. The NFHS contains information not only on immunization and breastfeeding, but also reports the number of antenatal checks during pregnancy. The results are broadly similar and are available in Table A.12.

Our strictest specifications identify gender gaps and do not directly deliver an estimate of

> 40 Of course, only a fraction of families with firstborn daughters do not want subsequent girls and only a fraction of those will commit abortion. The contention is that, after ultrasound becomes available, a larger fraction of those families that do have daughters want them.

> 41Breastfeeding results are based on the last two surviving births of a mother and are based on the sample of children above age two to take into account censoring issues.

> 42Medical expenditure is conditional upon illness and includes doctor’s fees, medicines, and costs of special diets during the illness. The specifications are similar to those for mortality except that we drop the urban indicator (since REDS covers only rural households) and the wealth quintiles (not reported in REDS), and, since we have a smaller sample, we drop _ρbt_ , _ξbGj_ , and _νsGj_ and replace _φst_ with state-specific linear time trends.

19
2003), lack of clarity in land rights (Bennet 2002), regional economic disparities (Tadjoeddin _, et al._ 2001b), the mis-management of natural resources (ICG 2001; Ross 2002), and the capture of economic power by an ethnic group (Chua 2002).

Table 1 sets out hypotheses about potential factors associated with local conflict. Our hypotheses have been derived from the available international literature and on-going qualitative work (Barron _, et al._ 2004a). We briefly review these factors and suggest mechanisms by which these could be associated with conflict. Section 4 identifies quantitative proxies for each of these hypothesis measures at the village/neighbourhood, sub-district ( _kecamatan)_ , and district ( _kabupaten/kota)_ level.

**<u>Table 1: Local Conflict Incidence Hypotheses</u>**

|Hypothesis Class|Hypothesis|Proxies|
|---|---|---|
|Economic|_Poverty increases conflict_|- Consumption based poverty measure defined at<br>district level (headcount/”depth”)<br>- Relative Poverty of Village (BKKBN) as compared to<br>district average (defined using family planning lists)<br>- Village level share of unemployed|
||_Inequality among individuals increases_<br>_conflict_|- Consumption based Gini coefficient in district|
||_Sudden loss of local income increases_<br>_conflict_|- Number of poor (according to family planning lists)<br>increased in the past year<br>- Natural disaster in thepast threeyears|
||_Economic development increases conflict_|- Does village have electricity?<br>- Share of families that work outside agriculture<br>- Are there residents who live outside of village for<br>work or school?<br>- Are there people who live in this village for work or<br>school who are not residents?<br>- Share of land that changed use
following 2011 - contradicting the headline estimates of the leaked 2017 NSS survey. These apparently contradictory results, combined with restrictions on the release of the NSS-2017 consumption survey, has given rise to a new Great Indian Poverty Debate, a sequel to the debate from the 1990s (Deaton and Kozel, 2005; Kijima and Lanjouw, 2005).

The private sector has recently stepped in by fielding its own household consumption survey called the Consumer Pyramid Household Survey (CPHS). The CPHS may be preferred to alternative data sources used to date for several reasons (but remains second-best to the NSS household consumption expenditure survey for poverty measurement). First, it collects detailed expenditure information on about 115 items, offering household consumption data for the first time since the NSS-2011. Second, the CPHS contains a panel of approximately 174,000 households that covers 28 states representing over 95% of India’s population. Third, it is conducted continuously at four-month intervals since its launch in January 2014. This opens the possibility of tracking poverty and inequality at a frequency higher than what has been traditionally feasible based on NSO’s quinquennial consumption expenditure surveys. The CPHS is already being used in empirical research. Chanda and Cook (2020) and Chodorow-Reich et al. (2020) use it to estimate the impacts of the demonetization policy, Deshpande (2020) and Gupta et al. (2021a, 2021b) have used the survey to quantify the impact of Covid induced lockdowns on labor market indicators, and Ghatak et al. (2020) employ the CPHS to study rates of consumption and savings in low-income households in India.

Despite these advantages, the CPHS also has its limitations. The CPHS adopts a measure of consumption that is not readily comparable to that of the NSS, stemming from differences in survey instruments. Furthermore, scholars have questioned the representativeness of the survey compared to NSS surveys, due to differences in sample design and geographical coverage (for instance Somanchi and Dreze 2021 and Somanchi, 2021). Both of these differences will have
constructed using the ResearchGate database, distinguishing between the overall _h_ -index and the _h_ -index that excludes selfcitations. Due to better data availability, we use the overall _h_ -index from the ResearchGate database in our baseline meta-regression. Figure 8 (left panel) shows the histogram of the highest _h_ -index from among the co-authors per study. The figure indicates that pass-through coefficients are most often published by authors with _h_ -index between 17.5 and 20.

11
feed of our study participants. Since the respondents took our survey on Messenger, we could use anonymous identifiers to create a “Custom Audience” for the treatment group.<sup>37</sup> Subsequently, we handed over this audience to MNM, which then remarketed ads from their own Facebook page for two weeks, mirroring the nationwide campaign.<sup>38</sup> The remarketing campaign reached 2,119 individuals (about 90% of the total audience) at least once in two weeks, for an average frequency of 4.9 ads per person. These ads were displayed in the users’ feeds, alongside with other competing ads and organic content from various sources. Crucially, participants were unaware that the ads were targeted at them based on their survey participation. Consequently, the remarketing campaign closely resembled the nationwide campaign, lending ecological validity to the individual-level study.

Two weeks after the end of the remarketing campaign, we invited respondents to fill out a followup survey. Those who completed it are included in the individual study sample (N=1,542). Annex Table D22 shows the distribution of individuals’ and households’ characteristics in the treatment and control conditions. The sample is strongly balanced across most covariates such as age, gender, education and bed net ownership, with a few small imbalances on household size and unemployment rate. We include a battery of control variables in the regressions to show that our results are robust to these small imbalances.

We focus on two primary behavioral outcomes where we anticipated effects within our two-week window: bed net usage and seeking medical attention in case of a high fever. To facilitate the comparison, the questions were similar to those used in the longitudinal panel survey (see Annex B). To mitigate priming and survey effects, we asked these questions only once, at follow-up. Our workhorse model is a Linear Probability Model, which is estimated using Ordinary Least Squares. It is represented in the following form: where _i_ is an individual
# Tables

_Table 1: Enterprise Surveys – Countries and Years Surveyed_ _<u>(bold denotes presence of question “Which Bank Provided the Most Recent Line of Credit or Loan?”)</u>_

|**Region**|**Country**|**Survey Years**|**Region**|**Country**|**Survey Years**|
|---|---|---|---|---|---|
||Albania, Bosnia and Herzegovina, Bulgaria,<br>Croatia, Czech Republic, Estonia, Hungary,<br>Kosovo, Latvia, Lithuania, Moldova,<br>Montenegro, North Macedonia, Poland,<br>Romania,Serbia,Slovak Republic,Slovenia|2009,**2013, 2019**|**Asia**|Azerbaijan, Kazakhstan, Kyrgyz<br>Republic, Mongolia|2009,**2013, 2019**|
|||||Georgia,Tajikistan,Uzbekistan|2008, **2013, 2019**|
||Belarus|2008,**2013, 2018**||||
|**Europe**|Türkiye,Ukraine<br>Russian Federation|2008, **2013, 2019**<br>2009, **2012, 2019**||Armenia|2009, **2013, 2020**|
|||||Egypt, Arab Rep.|**2013**, 2016,**2020**|
||Cyprus,Italy,Malta,Portugal|**2019**|**Middle East**|||
||Greece|**2018**|**and North**<br>**Africa**|Tunisia|**2013, 2020**|
|||||Jordan, Lebanon, Morocco,
: 255-275.

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Gali, J. and T. Monacelli. 2005. “Monetary Policy and Exchange Rate Volatility in a Small Open Economy.” The Review of Economic Studies 72(3): 707-34.

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