rafmacalaba/data-use-mentions-extended huggingface dataset:
# **_Measurement of attitudes_**

Our main outcome is the response to the question “is the city or area where you live a good place to live for immigrants from other countries?”. We use this question because it is the only question on immigrants in most GWP (and therefore global) country-years. However, it is not a direct question about perceptions towards immigrants, and the interpretation is potentially ambiguous. We therefore assess our measure with extensive supplementary data.

First, we examine the individual-level correlations between our main outcome and other questions that are asked in a subset of GWP country-years which capture views about immigrants more directly. Table 1 follows the measurement methodology in Asher et al. (2021). It reports coefficients from regressions of the outcome listed in the left column as the dependent variable, our main outcome as the independent variable, and additional fixed effects in some specifications. The results show that respondents who say their area is a good place to live for immigrants are significantly less likely to agree with statements expressing negative views of immigrants. This is consistent across all nine measure and within region-years.

Second, we compare the region-year averages of our main outcome with questions on immigrant perceptions asked in other surveys. We compile 12 additional cross-country surveys repeated over time by reputable organizations.<sup>19</sup> We merge these with GWP and with each other at the lowest possible sub-national level. We then synthesize common questions across different surveys to further populate country-year and region-year measures on attitudes toward immigrants.<sup>20</sup> The synthesized questions and their sources are listed in Table A2.

Table A3 follows the same methodology as Table 1 but uses region-year level averages instead of individual level correlations. The results tell the same story; our main outcome, whether the respondent believes the current area is a good place for

> 19The datasets are the Afrobarometer, Arab Barometer, Asian Barometer, European Election Study, European Social Survey, Eurobarometer, International Social Survey, Latinobarometer, Pew
994|0.978|0.988|1.281|1.572|1.201|1.723|
|Average|0.746|0.872|0.880|0.897|1.119|1.166|1.126|1.619|
|**Panel B: Poverty**|||||||||
|Malawi|0.744|0.763|0.872|0.831|0.464|0.452|0.495|0.575|
|Tanzania|0.871|0.865|0.884|0.943|0.297|0.297|0.295|0.515|
|Average|0.808|0.814|0.878|0.887|0.380|0.374|0.395|0.545|

Note: The first four columns present the coverage rate across simulations for each country. The coverage rate is defined as the proportion of confidence intervals that contain the true value, derived from the census. The last four columns present the average width of the confidence interval across simulations for each country. The results are based on 100 simulations for each country and method. Candidate features include all variables used in the main specifications, along with mosaiks and interactions between features and urbanity.
# Table 1: **2014 Summary Statistics by 2015 Firm Type**

Panel A variables were collected during the February 2014 census. Panel B variables were collected during the summer 2014 baseline survey. Panel C variables were collected retrospectively, during a June 2015 follow-up survey. The sample is split into “one-person” and “has workers” firms using information from the weekly panel data collected in March and April 2015, consistent with the remainder of the analysis. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

||Full<br>Sample<br>_mean_|One-<br>Person<br>_mean_|Has<br>Workers<br>_mean_|_diff_<br>D|_p-val_<br>iff|
|---|---|---|---|---|---|
|**Panel A: Source- Census**||||||
|Male|0.26|0.26|0.26|0.00|0.98|
|Years of schooling|8.90|8.91|8.89|0.02|0.95|
|Owner age|35.67|35.95|35.31|0.64|0.52|
|Firm age|9.66|9.21|10.24|-1.03|0.26|
|Has any worker(s) besides owner|0.49|0.25|0.79|-0.54|0.00***|
|Firm size (including owner)|2.12|1.40|3.04|-1.64|0.00***|
|Revenues Last Month (GHC)|215|149.77|290.
M. Regulatory substance- quality of supply index for the Philippines and comparators, 2015

||**Colombia**|**Peru**|**Philippines**|**Vietnam **|<sup>**International**</sup><br>**benchmark**|
|---|---|---|---|---|---|
|**Quality Regulation**|100%|100%|92%|71%|75%|
|**Quality of Service Standards**|100%|100%|100%|75%|82%|
|Requirement to meet quality of service standards|||||100%|
|Specific quality of service standards are formally<br>written and publicly available for- quality of the<br>product, quality of the service and customer<br>service|||||97%|
|Performance on quality of service standards is<br>public|||||71%|
|Fines for failing to meet quality of service<br>standards|||||59%|
|**Quality of Service Enforcement**|100%|100%|83%|67%|68%|
|Requirement to report technical data on a<br>periodic basis|||||100%|
|Regulator specifies how to collect technical<br>performance data|||||71%|
|Regulator reviews or validates technical<br>performance data|||||47%|
|Automated information management systems<br>are required to measure the quality or reliability<br>of the power supply|||||71%|
|Measurements of the quality or reliability of<br>power supply are made public|||||65%|
|Financial incentives to meet customer service<
# **Geographic targeting**

The poverty impacts of targeted vs. categorical programs may also vary geographically, for instance between urban and rural areas. Figures 11 and 12 present the differential poverty impact of a national program in urban and rural areas that transfers equal amounts to beneficiaries in both regions. Overall, despite the poverty incidence being on average three times higher in rural areas, there appears to be surprisingly little differences in program’s impact between rural and urban areas. In fact, a few results may appear counter intuitive at first sight, and we discuss them next.

In many countries, both categorical and targeted transfers achieve greater poverty reduction in urban than in rural areas. This is because, independently of poverty incidence levels, the poverty gap is smaller in urban areas (poverty gaps in rural areas are on average 3.5 times higher), hence, equal amounts transferred to beneficiaries achieve greater poverty reduction in urban areas. However, aside from Bolivia where the same targeted program achieves twice as much poverty reduction in urban than in rural areas, differences in poverty impact remain fairly modest, from 1.6 times in Nicaragua to 0.9 times in Colombia. This suggests that, on average, benefits ought not to differ much between urban and rural areas, though they could be slightly higher in rural areas.

**Figure 11: Impact on urban areas (children only)**

<!-- Start of picture text --> 60%<br>Categorical<br>50%<br>Imperfectly Targeted<br>40%<br>Targeted<br>30%<br>20%<br>10%<br>0%<br>Change in poverty<br><!-- End of picture text --> Source: Own calculations based on SEDLAC databases 19
Beck, Demirguc-Kunt, and Levine (2006) database, which contains various indicators of ...nancial development across countries and over time. We use the private credit to GDP as a proxy for country’s ...nancial depth. The variable ranges from 0:21 for Senegal in 2008 to 0:052 for Malawi in 2004. The annual data for GDP per capita is taken from the World Development Indicator report 2006, and is reported in constant 2000 US dollars. Financial development and GDP per capita are correlated at 78% in our sample.

Additionally, we use the level of outstanding short-term credit (TCc) and trade credit insurance (ICc) as reported in the Global Development Finance (GDF) as a share of GDP, as proxies for trade ...nancing. Finally, as an alternative control for the country’s quality of ...nancial systems, we use the Getting Credit Index (EGCc) from the World Bank Doing Business Survey (WBDBS) data for the year 2004<sup>19</sup> . The index ranks countries according to the strength of legal rights and the depth of credit information.

> 16Since HS 8-digit product classi...cations are country speci...c, we ...rst had to harmonise the classi...cations among all countries. Then we match it with the CN 8-digit Eurostat classi...cation, for which the risk index is computed.

> 17Senegal reports data from 2000 till 2008; Mali from 2005 to 2008, Malawi and Ghana from 2004 till 2008; and Tanzania from 2003 till 2009.

> 18Including these exporters, results hold in a similar way. Results are available upon request.

> 19This is the only available year.

13
household impact of these, differ widely by sector of economic activity, with some sectors being subsidised and some paying cross-subsidies.

Public education and health<sup>14</sup> expenditures included here account for about 6 percent of GDP in 2018. Early childhood education (KG1 and KG2) starts at age 4. Basic schooling (primary and middle school) is free and education is compulsory for all children in this cycle (between the ages of 6 and 15) (Abu-Ghaida, 2016). Enrolment rates in this basic cycle are close to universal, except for children in the bottom decile, 8 percent of whom are not enrolled in school. Most children at this level attend public schools, over 70 percent in deciles 1 to 7 and still a sizeable share, 53 and 35 percent respectively, in the top two deciles. Secondary schooling lasts 2 years and has a vocational and an academic track, the latter being followed by tertiary education (Abu-Ghaida 2016).

According to figures from the 2015 census, 68 percent of Jordanians are covered by health insurance (High Health Council 2016). Jordan provides public insurance through the Ministry of Health (MOH), as well as through the Royal Military Service and the University Hospitals (Halasa-Rappel _et al._ 2019). The Civil Insurance Program (CIP), managed by MOH and which has a contributory and a subsidised component. CIP covers mostly civil servants and their dependents, who contribute 3 percent of their monthly salary up to a maximum contribution of JOD 30 per month, as well as children under six years old, older adults above 60 years old and those affiliated to NAF and other poor households being referred to by the MoSD, whose premiums are fully subsidised by the government. Those insured under CIP can receive mostly free

> 11 For water, these estimates represent the full cost-recovery reflecting the production costs after taking into account all other losses. Estimates are from the World Bank Water team based on Water Authority of Jordan audited financials.

> 12 The
|1.1|
|ROW|2,762|2,124|2,698|2,702|2,801|3,693|-6.3|8.2|54|39|41|37|<br>35|34|
|**TOTAL IMPORTS**|5,074|5,410|6,505|7,278|7,987|10,901|1.6|13.8|100|100|100|100|<br>100|100|

Note: Pan-European partners include EU-25, Romania, Turkey, and EFTA. Other FTA partners include SEE-5 and Israel. Source: Derived from UN COMTRADE database as reported by Bulgaria.

**Annex Table 2:** _Pan-European, other FTA partners and ROW in Bulgaria’s industrial imports in 1996-2003 (in million of US dollars and percent)_

|||Industri|al impo|rts(milli|on of U|S dollar|s)||Sh|are in|total in|dustri|al imp|orts(i|nperce|nt)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||1996|1997|1998|1999|2000|2001|2002|2003|1996|1997|1998|1999|2000|2001|2002|2003|
|**A. Pan-European**|1,992|2,029|2,493|2,995|3,441|4,183|4,648|6,486|46|48|58|62|58|76|78|77|
|
the studies had high methodological quality; 60% had intermediate quality; and 20% had limited quality (Figure 11).

**Figure 11. Methodological quality of the studies in MSM populations, by country. Andean Region; 2009**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>Limited<br>50%<br>Intermediate<br>40%<br>High<br>30%<br>20%<br>10%<br>0%<br>Peru n= 15 Ecuador n= 9 Bolivia n= 5<br><!-- End of picture text -->

**Source:** Authors’ calculation using information from _Database and References for an Evidence-based Strategy_ in Peru, Ecuador and Bolivia.

**Findings:** In Peru and Ecuador, the proportion of studies in MSM populations with sound methodological content was similar to the proportion observed in the general analysis for all studies (80% vs. 82% in Peru; and 56% vs. 52% in Ecuador).  In Bolivia, the percentage of studies in MSM populations with sound methodological quality was far lower than the proportion for all studies (20% vs. 68%). This finding reveals that in Bolivia, there have been proportionally fewer studies in key populations and their methodological quality is lower than the general median.

# **Female sex workers**

In the Peru’s database, there were 11 studies done on female sex workers. Seventy-three percent of them had high methodological quality; 18% had intermediate quality; and 9% had limited quality. In Ecuador, there were eleven studies done on female sex workers. Forty-five percent of them had high methodological quality; 45.5% had intermediate quality; and 9% had limited quality. In Bolivia, there were three studies done
*|-0.267***|
|fuel exporter dummy|(1.46)|(-4.05)|(-5.21)|
|Time trend|0.0155|0.324***|0.291***|
|(=0 in 2000)|(0.62)|(7.32)|(6.24)|
|n|1,378|1,392|1,378|
|R-squared|0.136|0.309|0.244|
|RMSE|4.74|7.98|7.91|

_Note:_ Figures in parentheses are robust t-statistics. *, **, ***: significant at 10, 5, and 1 percent respectively. Annual data from 1996–2013 for low- and middle-income countries. RMSE = root mean square error, or sample standard deviation.

14
# _Modification 3: Accounting for Methane_

Methane is a major greenhouse gas. Following the same logic as with CO2, the deviation of the country (per capita) emissions and stocks from the global average can be calculated for each country. Excess emissions or deviations were multiplied by the country’s population and the GCI. The results are reported in Annex Table C **.** To the extent that methane is more problematic than carbon for climate change, the GCI could be set higher. Per capita methane emissions are over four times the global average in Australia, Canada, New Zealand, and Russia. Countries such as France, Germany, and the UK have per capita emissions lower than the global average. In addition, the price of the excess stock emitted since 1990 can be calculated. Annex Table D reports the results.

In terms of methane emissions, Russia takes the lead as the main emitter. With a stock-based approach, emissions of Brazil and Australia become more significant. China is the second largest recipient following India if only methane emissions are considered.

# _Modification 4: Forestry_

One large carbon sink is a country’s forests. Bastin et al. (2019) use spatially granular data to estimate forest restoration potential across the globe and find room for an additional 0.9 billion hectares of forest cover, which could store about 750 Gt of CO2 at maturity.

To the extent that a country has protected its forests, given its stage of development and its population density, it should get some credit for it.<sup>6</sup> To objectively compute a measure for ‘excess’ forest cover, the proposed framework predicts a baseline forest cover in a country given its natural environment and initial forest endowment (in 1990) as well as hard-to-control factors like its stage of development (e.g., more agriculture means less forest cover), and its population (larger population for a given terrain means less forest cover). The actual forest cover less the predicted
networking (World Bank’s Global Antidumping Database, 2012). Indonesia applies safeguards to a larger set of products, for example, lighters, glass, aluminum foil food containers, wire of iron, cotton yarn, woven fabrics of cotton, tarpaulins, articles of iron or steel wire, conveyor belts, articles of finished casing and tubing, polypropylene in granule form, and mackerel. The rest of Eurasia’s trading partners considered in this note do not apply any safeguards against Eurasia. Countervailing measures against the region are absent from China, Japan, and the EU.

Table 14. Antidumping Investigations Source: World Bank’s Global Antidumping Database (2012). Data available up to 2011.

# **Preferential Trade Agreements**

Following the dissolution of the Soviet Union in 1991, the economies of Eurasia concluded a plethora of trade agreements with countries throughout the world but primarily among themselves in an effort to maintain the economic integration of the region. The result was a ‘spaghetti bowl’ of trade agreements. In fact, 31 out of the 39 bilateral free trade agreements (FTAs) that Eurasian economies have concluded are between two countries in the region, and 26 of these entered into force in the 1990s. This is in addition to four plurilateral regional trade agreements (RTAs) concluded solely between Eurasian countries with another in early announcement. (See Appendix for the list of RTAs and Figure 3 for a pictorial representation of the ‘spaghetti bowl’ effect.) In addition, other regional organizations have been used as platforms to build regional integration and trade facilitation initiatives.<sup>9</sup> Bilateral and plurilateral RTAs also exist between Eurasian countries and parties outside the region, although Moldova and Ukraine have been the most successful in negotiating these. Many are in early announcement, suggesting Eurasian countries are just beginning to shift their focus for preferential trade initiatives to outside the region.

Trade agreements can potentially result in trade creation through tariff reductions, trade diversion from third parties through increased preference margins
46

# Diego (October).

- Dewatripont, Mathias and Jean Tirole (1994). The Prudential Regulation ofBanks. Cambridge: MIT Press.

- Felli, Leonardo and Antonio Merlo (2000). Endogenous Lobbying. Mimeo, New York University Department of Economics (March).

- Grossman, Gene M. and Elhanan Helpman (1996). “Electoral Competition and Special Interest Politics.” Review ofEconomic Studies 63: 265 - 286

- Hellman, Thomas F., Kevin C. Murdock and Joseph E. Stiglitz (2000). “Liberalization, Moral Hazard in Banking, and Prudential Regulation: Are Capital Requirements Enough?” American Economic Review 90:1, 147 - 165.

- Honohan, Patrick and Daniela Klingebiel (2000). “Controlling Fiscal Costs of Banking Crises.” Mimeo, The World Bank (May).

- Humana, Charles (1992). World Human Rights Guide. New York: Oxford University Press. (1986). World Human Rights Guide. London: Hodder and Stoughton.

- Hutchcroft, Paul D. (1998). Booty capitalism: the politics ofbanking in the Philippines. \thaca, NY : Cornell University Press, 1998.

- Jaggers, K. and T.R. Gurr (1995). “Tracking Democracy’s Third Wave with Polity IH Data.” Journal of Peace Research 32: 469-482.

- Johnson, Ronald and Gary Libecap (1999). “Information Distortion and Competitive Remedies in Government Transfer Programs: The Case of Ethanol”. Working paper.

- Keefer, Philip (1999). “Political institutions and crisis: The effects of political checks and balances on the dynamics of financial sector distress.” Paper prepared for the meetings of the Western Economics Association, July 1999.

- and David Stasavage (2000). “Bureaucratic Delegation and Political Institutions: When Are Independent Central Banks Irrelevant?” The World Bank, Policy Research Working Paper No. 2356 (June).

- Keefer, Philip and Stephen Knack
important drivers of infiltration. Much of the literature examining infiltration rates has focused on the United States, where infiltration rates estimates are concentrated between 0.2 and 0.3 (Bi et al., 2021; Liang et al., 2021; Lunderberg et al., 2023).<sup>2</sup> Understanding how infiltration rates may differ in developing countries is critical as these are the parts of the world facing the greatest threats from high levels of ambient air pollution (Shaddick et al., 2020; Fuller et al., 2022). This also contributes to our understanding of how vulnerability to air pollution varies across the income distribution (Behrer and Heft-Neal, 2024). There also remains disagreement about the extent to which infiltration rates vary across SES status. Some studies show notable inequality in infiltration rates during high pollution events (Krebs and Neidell, 2024) and others find no meaningful relationship between infiltration rates and SES (Burke et al., 2021). However, these studies often use crowd-sourced information from publicly available PurpleAir monitor data, a non-random sample of households having bought PurpleAir monitors. In our experiment, we randomly installed monitors in the city and provided them to households that are representative of the broader population.

# **2 Context and Design**

## **2.1 Air pollution in Tbilisi**

Located between Armenia, the Russian Federation, and Azerbaijan, Tbilisi is the capital of the Republic of Georgia and a city of roughly 1.1 million people. Like many cities in the former Soviet Republics, Tbilisi suffers from elevated average levels of ambient air pollution. From 1998 to 2018, average monthly PM2 _._ 5 levels were approximately 20 _μg/m_ 3 (Van Donkelaar et al., 2019) in Tbilisi, 4x the WHO recommended annual average of 5 _μg/m_ 3.<sup>3</sup> While the level of ambient pollution in Tbilisi is low relative the levels seen in the worst cities in
Combining the temporal and geographic discrepancies noted in figures 1‐4, we estimate the correlation between the share of respondents in a governorate reporting to rely on a food coping strategy and measures of violence in the month before the survey. Specifically, estimates of the following specification are presented: Share_Copinggt = γg + φt + β ln(Violenceg,t‐1)+ εgt where _g_ denotes governorate and _t_ denotes the month‐year time period; _Share_Coping_ denotes the share of the respondents that report relying on the food coping strategy being analyzed, where specifications are estimated using each of the five coping strategies collected by the WFP survey; _Violence_ denotes the measure of violence being analyzed, where there are different specifications for the number of incidents and the number of fatalities; γ denotes a governorate fixed effect that captures all time‐invariant factors that affect coping strategies in governorate _g_ ; and φ denotes time‐specific factors that affect all governorates at the same time period (such as a sudden tightening of the ports).

The coefficient of interest is β, which denotes the correlation between violence in the month before the WFP survey and the share of respondents in a governorate who report relying on the food coping strategy for at least one day in the previous week, once controlling for governorate and time‐specific factors. If coping strategies were higher (corresponding to worse food access) in regions where there was more violence in the month before the survey, one would expect β to be greater than zero. The actual coefficient can be interpreted as the change in the share of the mobile phone‐using population relying on food coping strategies in response to a doubling of the violence, which represents an increase of 70 fatalities in a governorate in a month.<sup>17</sup> Table 1 reports estimates from the empirical specification. Columns (1)‐(4) report specifications using the number of people killed as the measure of violence; and columns (5)‐(8) report specifications using the
and local ecosystems.

Using new survey data, this paper attempts such an accounting for farmers in Bangladesh. We compare outcomes for farming with IPM and conventional techniques, using simple input-use accounting and estimation of conventional and frontier production functions, along with farmers’ assessments of their own health status and local ecological conditions.

The remainder of the paper is organized as follows. Section 2 describes recent trends in pesticide use, the associated problems, and the current status of IPM programs 2