rafmacalaba/data-use-mentions-extended huggingface dataset:
- Lau, C. Q., Johnson, E., Amaya, A., LeBaron, P., and Sanders, H. (2018). High stakes, low resources: what mode (s) should youth employment training programs use to track alumni? evidence from south africa. _Journal of International Development_ , 30(7):1166–1185.

- LeFevre, A. E., Shah, N., Bashingwa, J. J. H., George, A. S., and Mohan, D. (2020). Does women’s mobile phone ownership matter for health? evidence from 15 countries. _BMJ global health_ , 5(5):e002524.

- Leo, B., Morello, R., Mellon, J., Peixoto, T., and Davenport, S. T. (2015). Do mobile phone surveys work in poor countries? _Center for Global Development Working Paper_ , (398).

- Lindhjem, H. and Navrud, S. (2011). Are internet surveys an alternative to face-to-face interviews in contingent valuation? _Ecological economics_ , 70(9):1628–1637.

- Lovibond, S. H. and Lovibond, P. F. (1996). _Manual for the depression anxiety stress scales_ . Psychology Foundation of Australia.

- L’Engle, K., Sefa, E., Adimazoya, E. A., Yartey, E., Lenzi, R., Tarpo, C., Heward-Mills, N. L., Lew, K., and Ampeh, Y. (2018). Survey research with a random digit dial national mobile phone sample in ghana: methods and sample quality. _PloS one_ , 13(1):e0190902.

- Maffioli, E. M. (2019). Relying solely on mobile phone technology: Sampling and gathering survey data in challenging settings.

- Mahfoud, Z., Ghandour, L., Ghandour, B
20|12|17|

Source: Authors calculations based on P-LITS 2 data Special EU Barometer 300/wave 69.2 20. Three main findings are noteworthy (Table 2): (i) “poverty, lack of food and drinking water” and “international terrorism” emerged among the top 3 serious problems (ranked by votes received) in all three countries; (ii) however, a majority of respondents in Kazakhstan and Tajikistan noted “spread of infectious diseases” to be a serious problem (70 and 84 percent respectively); (iii) climate change was selected by a sizeable share of respondents in Kazakhstan (36 percent) and Tajikistan (41 percent), though not as high as in EU27 countries (64 percent).

21. However, when asked to select the single-most serious problem confronting the world, only 5-7 percent of respondents in Tajikistan and Kazakhstan chose climate change (ref. 31 percent in EU27 countries); instead, respondents in these two countries ranked “spread of infectious diseases” and “poverty, lack of food and drinking water” as the two most important problems (Figure 6).

**Figure 6: Tajikistan: Most Serious Problem Currently Facing the World**

<!-- Start of picture text --> 100%<br>Other<br>80%<br>A major global economic downturn<br>60% Climate change<br>International terrorism<br>40%<br>The spread of an infectious disease<br>20%<br>Poverty, lack of food & drinking water<br>0%<br>EU27 countries Kazakhstan Tajikistan<br><!-- End of picture text -->

**Source:** Authors calculations based on P-LITS 2 data; Special EU Barometer 300/wave 69.2
dividing the sum across sectors and countries by the change in domestic emissions, using the following equation: _IC_ denotes import-origin countries, and _EC_ denotes export-destination countries. ∆ _EmissionD_ is the domestic emissions abatement. This formula assumes that any decrease in China’s bilateral exports will be perfectly offset by an equivalent increase in foreign production by the trade partner. This assumption tends to over estimates the leakage rate: In a more complicated model with trade response, the leakage rate would be even smaller. The data on emission intensities of each sector by country is from the Global Trade Analysis Project database (version 10) (2019).

# **5. Equilibrium**

The general equilibrium requires supply-demand balance in each period for each factor and produced goods. Under policies with emissions allowance trading, the allowance supply and demand must match as well. In each period, these requirements determine (a) the prices for the 31 sectors’ produced goods; (b) the wage rate; (c) the rental prices of capital, which differ across sectors (as well as subsectors in the electricity, cement, aluminum, and iron &steel sectors); (d) the four different rental prices of the natural resources, for these resources employed in the solar, wind, hydro, and nuclear electricity production subsectors, respectively; and (e) the CO2 allowance price.

# **6. Dynamics**

The model is solved as a mixed complementarity problem (MCP) with a Newton-based solver, and solves at one-year intervals from 2020 through 2035. Changes in equilibria from one period to the next depend on the increments to the stocks of labor and capital. There is one aggregate capital stock. As discussed earlier, domestic real investment in each period is set as a fixed share of GDP. The stock in the next period is aggregate real domestic investment in the current period net of depreciation over that period. The stocks of land and the four kinds of natural resources (wind, solar, hydro,
of oil based on the U.S. refiner acquisition cost of imported crude oil, and U.S. per capita oil consumption. The oil market data is obtained from the U.S. Energy Information Administration (EIA). The U.S. oil consumption data is based on EIA’s data on petroleum consumption measured in BTUs.<sup>9</sup> The oil production and consumption data is transformed by taking first differences of logs. The monthly data are converted to a quarterly frequency using simple averages.

## **IV.2 Methodology**

This section lays out the empirical framework. First, I present the dynamic factor model, which allows for the extraction of an underlying common factor (henceforth, global GDP factor) using GDP growth rates for 33 countries. Furthermore, I do a variance decomposition to compute the fraction of variation in country GDP growth rates accounted by the global GDP factor to evaluate its performance as a metric of fluctuations in global economic activity. Second, I specify and estimate a structural FAVAR model to assess the effects of U.S. energy market shocks on global economic activity. I discuss an identification strategy based on sign and elasticity restrictions to disentangle U.S. oil supply and energy efficiency shocks.

> 9A robustness check in Section IV.4 uses fossil fuel consumption data (aggregate of coal, natural gas, and petroleum consumption), which is also obtained from EIA.

10
The issue of scale may also give rise to a possible market failure. Fertilizer is commonly sold in 50 kg bags, which are not only heavy and require significant labor to transport but also exclude a significant swath of small farms and, as a result of credit constraints and issues of fertilizer storage, another significant group, women farmers. Thus, the solution would seem to be to sell fertilizer in smaller bags. However, this solution produces another set of problems. First, the labor required to bag fertilizer is multiplied; added to the costs of the bags, this raises the unit cost of the fertilizer. Second, Uttaro (2002) documents that in Malawi, traders have a tendency to adulterate this locally repackaged fertilizer (as opposed to the 50 kg imported bags) with sand, thus creating a quality or monitoring problem. The bottom line is that women with smaller landholdings suffer disproportionately from these divisibility issues and are likely to face higher unit costs than men. These types of issues may explain why we see persistent gender gaps even when the fraction of men using fertilizer is increasing (indicating an expansion of the fertilizer market), as shown in figure 8 above. Ultimately, the market should resolve these economy of scale issues, but it is likely that in many countries, markets are still thin enough to allow them to persist.

Additional research is needed to understand which of these constraints on women farmers’ use of fertilizer are the most binding. It seems clear that these constraints are important, but more work is needed to identify precisely which constraints are relevant in which contexts. For example, drawing upon data from both Malawi and Benin, Minot, Kherallah, and Berry (2000) show that female-headed households are less likely to use fertilizer in Malawi but are about equally as likely to use fertilizer in Benin. However, conditional on the use of fertilizer, women use less per hectare in both countries. These authors’ regression analysis of the determinants of fertilizer use controls for many of the 18
outcome, a 0 value was assigned. Because IPV frequently occurs in the home, cases were assigned to the municipalities of residence of the victims, which are not necessarily the same as the municipalities in which the health care facilities recording the outcomes are located. For the regression analysis, the study used the natural log of the count (adding 0.01 to each value as an adjustment for 0s). The observations were Winsorized in the 99th percentile by replacing the observation with the value of the cutoff for the 99th percentile.

_Homicides._ Vital statistics data on homicides come from Brazil’s national mortality database, the Mortality Information System (Sistema de Informações de Mortalidade. SIM), a registry of every death in the nation.<sup>5</sup> The study defines a death as a homicide if it is classified as one in the SIM or if the cause of death is coded as an assault (codes ICD-10 X85-Y09 of the International Statistical Classification of Related Diseases and Health Problems). The analysis complements the homicide variable with clinical cause of death information because the variable is associated with missing values. (The discrepancy between these definitions of homicide is 6 percent if both data fields are available.) _Hospitalizations for assault._ The Hospital Information System (Sistema de Informação de Hospitalares, SIH) includes information from public and private hospitals.<sup>6</sup> Hospitalizations are recorded when the physician fills out an authorization for hospitalization (Autorização de Internação Hospitalar, AIH), which is used to reimburse the hospital for the procedures performed on patients. These data only capture the most severe incidents because, according to the Ministry of Health, the form is only filled out if patients are admitted overnight. Hospitalizations that involve deaths are excluded because, otherwise, this would duplicate entries in the SIM death registry. Information on the reason for hospital admittance is provided in the ICD-10, which the study used to identify the hospitalizations for assault based on the same codes as in the homicide
<br>covering a global<br>sample.|Annual. Used in<br>WGI since 1996.<br>CIRI Human<br>Rights Data was<br>discontnued in<br>2014 and last<br>used in WGI in<br>2013.|Expert/NGO|<br>Both data sources are expert assessments scored by coding<br>informaton inUS Department of State Country Reports on<br>Human Rights PractcesandAmnesty Internatonal Annual<br>Reports.Coding is done by researchers afliated with the<br>project. A complete descripton of the methodology is<br>availableherefor the Politcal Terror Scale andhere for the<br>CIRI Human Rights Data. Because both products draw on the<br>same primary sources they are treated as a single source in the<br>WGI.|
|IFD|Internatonal<br>Fund for|Rural Sector<br>Performance<br>Assessment|66 IFAD client countries.|Every three<br>years. Used in<br>WGI since 2004|Expert/GOV|Expert assessment of quality of insttutons and policies<br>relevant for rural development scored by IFAD staf subject to<br>centralized review.Questonnaire andprocess are similar to|

40
attacks against the government and allied forces and no significant relationship between unemployment and attacks that result in civilian fatalities. On the other hand, Iyengar, Monten, and Hanson (2011) provide

> 3 A number of studies that draw on data from the Israeli-Palestinian conflict also provide relevant evidence though admittedly more linked to terrorism than insurgency. Berrebi and Klor (2008) and Gould and Klor (2010) find that terrorist attacks have a significant and long-lasting effect on the preferences of the Israeli electorate, whereas Jaeger et. al (2012) show that the effect of local Israeli violence has only a temporary effect on Palestinian civilian political preferences. Benmelech, Berrebi, and Klor (2012) show that high levels of unemployment enable terror organizations to recruit terrorists with higher human capital, leading to more important Israeli targets getting attacked.

6
elevation (mountains) comes from the UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC, 2002). Rainfall data were obtained from the GPCC Global Precipitation Climatology Centre (Schneider et al., 2015). We also collected data on Tunisia’s socio-political landscape, using legislative election data for 2014 from the national election authority (ISIE, 2015). Finally, alternative measures of violent extremism comprise information on political protests, riots, and political violence from the Armed Conflict Location & Event Data (ACLED) Project database (Raleigh et al., 2010) and Zelin (2020)’s data on the locations where extremist group Ansal al-Sharia Tunisia (AST) delivered jihadist religious lectures - a vehicle for the group’s proselytization and recruitment activities. We also compiled data from the National List of Persons, Organizations and Entities Associated with Terrorist Crime curated by the anti-terrorism commission, which tracks all individuals indicted for terrorism as listed in the Official Gazette (CNLCT, 2021). It also lists their addresses, which allows us to georeference them.<sup>6</sup>

# **4 Results**

Our main finding that internet access predicts recruitment is shown in Table 2. Columns 1-6 show progressively more stringent OLS estimation results. Column 1 documents a positive and statistically significant correlation between (the inverse hyperbolic sine of- henceforth ihs) the number of Daesh recruits and internet access rates, while normalizing by the population of each cell by controlling for the logarithm of the number of men in a particular delegation-educationage cell as enumerated in the census. Standard errors are clustered at the delegation level. Recognizing that the nature of internet access might lead error terms in (1) to be spatially correlated, the specification in column 1 is replicated in column 2 but following Conley (1999) to account for spatial correlation of the error term with a 50-km buffer area. The results are very similar. Given the scope for spatial correlation in internet access, we henceforth adopt Conley (1999) standard error correction
drawn from a total of 60 villages across 30 municipalities within the same province.

_Political access to a pro-poor policy instrument_ The Local Government Code of 1991 made municipal governments critical agents in the delivery of health programs such as immunization, reproductive and child health, and nutrition. Municipalities hire and manage nurses, midwives, and community or village health workers, and provide the facilities and equipment for the frontline delivery of basic public health programs. Management of these low-level health personnel is a domain of service delivery that is the exclusive responsibility of municipal governments. Program inputs, such as vaccines, medicines, and nutritional supplements, are typically procured by national health agencies and distributed to municipalities. Higher tier politicians can, therefore, play a role in providing greater health inputs to residents in a municipality; however, the distribution of these inputs is administered through the lower-level health workers that are exclusively managed by municipal governments. Furthermore, the basic nutrition supplements for mothers and young children, that are a typical part of the primary health services provided by municipalities, are probably affordable within

> 16 However, two municipalities are treated slightly differently-- Divilacan and Benito Soliven. In both, following this procedure of selecting the poorest half resulted in the selection of very small barangays, with population below 300 for Divilacan and population much below the average of Benito Soliven. For these municipalities, the largest 2 barangays were selected from among the poorest 75 percent in the municipality.

> 17 The source was a list that national health policy requires village health workers to maintain: a population list of all households in the village that have children under the age of 6 years. In the results section further below, we discuss any potential bias arising from such sampling, concluding that such sampling is likely to underestimate the extent of correlation between reported vote buying and health services.

7
2016/17 aims to produce poverty and welfare statistics with similar accuracy across all districts. In addition, we expect that achieving low RSEs and SEs in the estimation of districtlevel poverty and welfare will be more difficult than at the division level, as well as at the quarterly level. Therefore, allocating an equal number of PSUs across districts ensures that the final sample is evenly distributed across all districts.

In the second stage, we randomly select 20 households from each PSU, resulting in 720 households allocated to each of the districts (36 PSUs*20 households). This brings the total targeted sample size to 46,080 households (2,304 PSUs*20 households). Assigning 36 PSUs to each district was determined as follows. First, this is the minimum sample size needed to achieve an RSE of less than 10 percent for the mean nominal per capita expenditure for each district and division (urban/rural), and for each quarter at the national level with 95 percent confidence (our selected accuracy statistics to determine sample size). To arrive at this number (36), we use a formula (derived in Annex 3) that is a simple extension of the original formula proposed by Cochran (1977) to determine sample size for continuous target variables under simple random sampling when controlling for a desired RSE.<sup>13</sup> This simple extension, which introduces survey design effects to the original formula, results in the following expression: where _n_ is the minimum sample size required for allocation to each district in order to achieve a certain level in the accuracy statistic ���<sup>�</sup> � associated with the targeted variable ��; ��������� is the coefficient of variation of the targeted variable estimated under the assumption of simple random sampling; _DEFF_ is the design effect of the target variable; and ��/� is the critical value of a standard normal distribution with α% confidence level.<sup
Land, 2006).<sup>11</sup> We apply all these five models in the data from Bangladesh and Nepal, for which we have an adequate number of cross-sectional surveys to conduct such analysis.

Up to this point, the discussion focused on outcome variables. But this paper examines also social norms or, rather, attitudes variables towards gender roles and women’s position in society. In the case of norms variables, it may be more difficult to establish whether they have or not a life cycle. The sociology literature and some economic literature view that what happens in the formative years tends to leave permanent marks (Giuliano and Spilimbergo, 2014). Others found that life experiences, which are different across cohorts, are stronger predictors of gender roles attitudes than the age of individuals (Lynott and McCandless 2000). If these are valid conclusions, it means that attitudes will not change as the cohorts age.

# _Heterogeneity analysis_

While the described methods so far relate to the ‘average’ woman in the countries of the region, there is potentially quite a bit of dispersion around this average, and to capture this heterogeneity we analyze separately different groups of women. The groups are formed according to the level of education, wealth quintiles, and rural versus urban location. This heterogeneity analysis is based on the following regression specification: The focus is on the marginal effects of education, location, and wealth. More precisely, for example in the case of wealth ‘W’ the marginal effect for the outcome variable ‘Age at first marriage’ is defined as: Likewise, one can estimate the marginal effects of the other two independent variables. This analysis allows to identify whether the trends across cohorts are different for women who are more educated, live in urban areas, or are richer.

> 11 See Annex 7 for a detailed discussion on each of these models.

10
||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Cismen|Ciswomen|Transgender|Non-binary|All|
|Observations|6,876|106|83|362|7,427|

Note:<sup>a.</sup> The data is collected from World Development Indicators. To compute GDP per capita income for the country ‘United Kingdom and Northern Ireland,' we use the average GDP per capita income of Ireland and the United Kingdom. Besides, for the region Taiwan, we use the GDP per capita of China;<sup>b</sup> We define the regions following the World Bank’s definition as East Asia and Pacific; Europe and Central Asia; South Asia; Latin America and the Caribbean; the Middle East and North Africa; North America; and Sub-Saharan Africa. However, due to South Asia's small sample of responses, the region is combined with East Asia and the Pacific.

6
be worse constraints that are the binding ones, or the structure of the economy in the specific country of study is simply not sensitive to that potential constraint.

**Another popular source of information is enterprise survey data, but this type of data must also be interpreted with caution.** Besides general information about the firm (including size, ownership, location, sector of economic activity, etc.) these surveys record perceptions, indicators of performance and objective business indicators, in areas such as infrastructure and services, access to land, crime, finance, business government relations, labor regulations and environment, etc. So called Investment Climate Assessment (ICA) surveys are extracted mostly from private business enterprises and aim to understand, precisely, what are the main constraints to economic activity. However, there are a number of caveats such as:

> 11 Note that we do not assume we will find one binding constraint but rather a bundle of constraints that all need to be addressed to trigger growth. However, the goal is not to present a laundry list but a few prioritized areas of concern. 12 See Hausmann, et al. (2005), Hausmann, Klinger and Wagner (2008), Rodrik (2004a and 2004b).

> 13 For example, when one would like to assess whether the cost of capital is high in a country, the group of comparators must include countries with similar credit worthiness ratings. When addressing questions about logistics and transport costs, the group of comparators must include land‐locked countries with similar types of traded goods.

8
contribution and a declining trend in their marginal contribution. This can be interpreted as evidence that the interaction between these variables and education has become more intense. That is, the workers’ skills are becoming increasingly more relevant to the determination of their type of participation in the labor market as well as to their position across different economic segments of the economy. The same pattern holds when number of hours worked instead of sector is considered.

The results of the decomposition of the variations in the Theil T index for different intervals of time are shown in Table 4.2. First, when the variables are considered alone, education made the highest gross contribution to the changes in earnings distribution. Second, both the allocation and the income effect were positive in all periods. This means that changes in the distribution of education and in the relative earnings among educational groups were always in phase with alterations in the earnings distribution. Namely, when the income profile related to education became steeper and the inequality of education grew, the earnings distribution worsened as in all periods below.

22