rafmacalaba/data-use-mentions-extended huggingface dataset:
be difficult also to distinguish between closely related indicators and which convey better information. Also, by failing to account for the sector specific responses existing tools lack focus. Arguably, the identification of constraints at the country level will reflect the constraints that affect sectors that are core to an economy because of averaging effect, but by the same token average effects might hide constraints that are very important for a core sector but not so much for the rest of the economy.

Ideally, one would want to achieve a level of analysis that cuts across both dimensions i.e., those which affect the economy as whole (cross-cutting enabling conditions), and those aspects which affect only particular economic sectors or firms. Moreover, the analysis should be detailed enough to offer concrete guidance for policy makers in view of implementation and focus on results.

# Constraints benchmarking

In this section, we briefly describe the investment constraints benchmarking tool (CBT) from which this empirical work derives, and which was developed to inform the World Bank Group’s Country Private Sector Diagnostics. The idea behind the tool is two-fold. In a spirit similar to the WEF, the CBT brings together and organizes in one place a substantial set of indicators. The tool builds on international surveys which contain information regarding factor prices and quantities (such as the World Development Indicators), and metrics of policies (such as Doing Business and Investment Climate surveys). It also builds on previous attempts to create composite measures of these dimensions such as the World Economic Forum’s competitiveness index. Second, and in a development from existing tools, the CBT proposes a way to measure each country’s performance at the sector level (instead of just at the economy-level) by observing the revealed preference of individual foreign direct investments.

7
arguably also yields a public good; and in that of health, any reductions in pain and suffering or in infant and child mortality. The second step involves an assertion of belief, namely, that the sum of these benefits for the community in question is at least Rs 0.59 million a year. If doubts persist on this score, then recourse must be made to the existence of net external benefits that arise in the economic system as a whole (see Section 6 below).

The sceptical reader will stick with that which has been measured with some confidence, and on that basis establish the discount rate at which the project would just pass muster. Setting the annual stream of benefits at Rs 2.69 million, he or she will obtain _ρ_ = 7 _._ 0 percent, which also establishes how great the leap of faith must be when the bar is set at 10 percent. Does _gb_ = 0 _._ 02 supply the required boost? Eq. (2) yields _B_ 1<sup>+(</sup><sup>_ρ_= 0</sup><sup>_._10</sup><sup>_,_0</sup><sup>_._02) = Rs 2.77 million.This is still not quite enough,but now only very</sup> modest help from elsewhere is needed.

The poor villages in Orissa’s uplands are much less commercialized, but labor there is also arguably under-employed. Recall from Section 4.1 that in a sample of 30 such villages, the estimated aggregate annual commercial benefits average at about Rs 2.70 million. Proceeding as before, the Orissa Survey yields estimated savings in travel20
, respectively.<sup>7</sup> The development level of trade facilitation in the EU15 ranks well on average; with customs efficiency rated the highest.

The new EU8 member countries, however, are less developed in these four areas with an average value of 0.60, 0.73, 0.65, and 0.64, (development level of 72%, 83%, 81%, 81%

> 5 See annex 1 for a summary of the statistics and data sources of the four trade facilitation indicators.

> 6 Due to the data constraint, we could not measure the efficiency of the port facilities and inland waterways separately.

> 7 The data for Luxembourg is not available. We use the average of the other 14 member countries as that of EU 15.

6
2001). We use Epanechnikov kernel and Gaussian kernel estimators (Fan 1992; Heckman _et al._ 1998).

# **<<A>>** _IV. POLITICAL REFORM INDICATORS AND CONTROL VARIABLES_

To study how a regime transition toward democracy affects agricultural and food policies, we need data on democratization episodes. Unfortunately, although various democracy data sets exist, none of these data sets provides a specific coding of regime transitions. Therefore, we follow the same strategy as recent studies that have investigated similar questions at the aggregate level by relying on the Polity2 index from the Polity IV data (Marshall and Jaggers 2007).<sup>12</sup> The composite Polity2 index assigns a value ranging from − 10 to +10 to each country and year, with higher values associated with better democracies on the basis of several institutional characteristics, such as the openness of elections or constraints on the executive branch. Following Persson (2005) and Giavazzi and Tabellini (2005), we code a country as “democratic” in each year that the Polity2 index is strictly positive, setting a binary indicator called _democracy_ to one (zero otherwise). A reform into (or out of) democracy occurs in a country-year when this democracy indicator switches from zero to one (from one to zero).

A potential shortcoming of this definition of _political reform_ is that being near any particular divide may differ from being far from the divide.<sup>13</sup> Indeed, the threshold of zero for Polity2 corresponds to a generous definition of democracy. However, as emphasized by Persson and Tabellini (2008) and others, this definition has the important advantage that many large changes in the Polity2 score are clustered around zero, an important property given our identification strategy based on the within-country variation in the data. Consequently, using a higher threshold for the definition of democracy has the 14
# **1. Introduction**

While universal social programs—whereby everyone is covered—are excellent at reaching the poorest, the beneficiaries can include many people who do not need this form of public help. Governments have tried many ways of assuring better “targeting,” with the explicit aim of concentrating the benefits of a social policy on poor people. The means used vary in their data requirements, methodological sophistication and costs (both administrative and broader social costs).

Readily measurable proxies for consumption or income are often used in efforts to reduce poverty in settings in which the means-testing of benefits is not an administratively feasible option, as in most low-income countries (and many middle-income countries). Efficiency considerations point to the need for indicators that are not easily manipulated by actual or potential beneficiaries. Proxy variables, such as gender and education, family size and housing conditions, have been common.<sup>2</sup> A score based on these variables is used in validating other targeting methods, such as those based on community-level subjective assessments of who is “poor.” The scores are also entering many social-protection registries—national data bases that are used in various ways including to flag ineligible households in future schemes.

The main challenge has been in setting the score’s weights. Various “poverty scorecards” or “basic needs indicators” have been used. Some versions use _ad hoc_ weights, such as taking a simple average of the scores across components.<sup>3</sup> Practitioners have turned to more sophisticated statistical methods in an effort to further improve targeting accuracy. These methods have come to be known as _proxy means testing_ (PMT).<sup>4</sup> This paper assesses an increasingly popular solution in which the weights in the PMT are identified from regression coefficients for household consumption or income as a function of readily observed covariates. The regression is calibrated to survey data and then used to make the out-of-sample predictions for the relevant population. This has
<!-- Start of picture text --> Figure 4.6: How the LAYS measure is affected by using the top‐5 performers as the<br>numeraire<br>TIMSS<br>Years of schooling LAYS (Numeraire = Top 5)<br>LAYS (Numeraire = Singapore)<br>PISA<br>Years of schooling LAYS (Numeraire = Top 5)<br>LAYS (Numeraire = Singapore)<br>Source: Authors’ analysis of TIMSS 2015, PISA 2015, and Barro and Lee (2013) data.<br>Notes: Illustration for case where learning starts at Grade 0 for TIMSS and age 6 for PISA.<br>15<br>10<br>Years<br>5<br>0<br>Australia Bahrain Botswana Canada Chile Egypt, Arab Rep. England Hong Kong SAR, China Hungary Iran, Islamic Rep. Ireland Israel Italy Japan Jordan Kazakhstan Korea, Rep. Kuwait Lithuania Malaysia Malta Morocco New Zealand Norway Qatar Russian Federation Saudi Arabia Singapore Slovenia South Africa Sweden Thailand Turkey United Arab Emirates United States<br>15<br>10<br>Years<br>5<br>0<br>Albania Algeria Argentina Australia Austria Belgium Brazil Bulgaria Canada Chile Colombia Costa Rica Croatia Cyprus Czech Republic Denmark Dominican Republic Estonia Finland France Germany Greece Hong Kong SAR, China Hungary Iceland Indonesia Ireland Israel Italy Japan Jordan Kazakhstan Korea, Rep. Latvia Lithuania Luxembourg Macao SAR, China Malaysia Malta Mexico Moldova Netherlands New Zealand Norway Peru Poland Portugal Qatar Romania Russian Federation Singapore Slovak Republic Slovenia Spain Sweden Switzerland Thailand Trinidad and Tobago Tunisia Turkey United Arab Emirates United Kingdom United States Uruguay Vietnam<br><!-- End of picture text --> 24
Weber, “Politics as a Vocation,” in _From Max Weber_ , ed. H. H. Gerth and C. W. Mills, Oxford University Press (1946) p. 95.

> 78 Frederick Winslow Taylor, _The Principles of Scientific Management_ , New York and London: Harper & Brothers (1911).

> 79 T. Davies, “Open Data in Developing Countries: Emerging Insights from Phase 1,” World Wide Web Foundation (2014). 80 Ibid.

> 81 Jesuit Hakimani Centre, “Open Government Data for Effective Public Participation: Findings of a Case Study Research Investigating Kenya’s Open Data Initiative in Urban Slums and Rural Settlements,” Nairobi Kenya (2014).

> 82 ODAC, “The Right to Know, the Right to Live: Turning the Right to Information into a Living Reality,” Cape Town (2010). 83 R. Hazell, B.Worthy, and M. Glover, "The Impact of the Freedom of Information Act on Central Government in the UK: Does FOI Work?" Palgrave Macmillan (2010).

> 84 L. Peisakhin and P. Pinto, "Is transparency an effective anti-corruption strategy? Evidence from a field experiment in India," _Regulation & Governance_ 4(3) (2010) p. 261–280.

13
|Normally from the time of<br>arrival of goods at the<br>port/airport/land border until<br>their release to the importer or a<br>third party on his behalf|World Customs<br>Organization : Time<br>Release Methodology,<br>and (simplified) TTFSE<br>methodology|
|1.5|Revenue<br>Collected/Customs<br>Employee|USD/employee|Total revenue collected/ Total<br>number of customs employees|TTFSE methodology|
|1.5.1|Revenue Collected|USD|Revenue collected by customs|TTFSE methodology|
|1.5.2|Customs Employees|Number of<br>people|Total number of customs<br>employees|TTFSE methodology|
|1.6|Customs<br>Environment /<br>Efficiency|number 0-1<br>(The value of 1<br>stands for the<br>best<br>performer)|<br>Average of two indexed inputs:<br>hidden import barriers other than<br>published tariffs and quotas,<br>irregular extra payment or bribes<br>connected with export and<br>import payments|<br> <br>GEP 2004 database<br>Chapter Five, (2003)|

57 ____________________________________________________________________________________________________________
timeline).

First, we installed 41 outdoor air pollution monitoring devices throughout Tbilisi to improve the monitoring network of the city. These PurpleAir monitors provided a reliable and low-cost way to monitor PM2 _._ 5.<sup>6</sup> Another advantage is that they allow to have comparable results to other academic studies of air pollution (e.g. Burke et al. (2021); Krebs et al. (2021); Liang et al. (2021)). Figure 1E shows the location of the installed monitoring devices. They were installed by the Caucasus Research Resource Center (CRRC) in collaboration with MEPA and the Tbilisi City Hall. In most cases, outdoor monitors were installed in public schools to ensure reliable access to power and an internet connection. Installation took place during a two-week period at the beginning of January 2023.

The second phase of the intervention was a household survey that took place at the end of March 2023 and beginning of April following the installation of the outdoor monitors. CRRC surveyed 862 randomly selected households within 230 randomly selected census blocks in Tbilisi. Surveyed census blocks are shown in navy in Figure 1 in the bottom portion of Panel E. We describe the details of the selection of census blocks and households in Appendix 1.

Our information intervention was embedded in the survey design. The experiment contained a control and three treatment arms: _Control -_ Households were surveyed, but no information about air pollution was provided by the survey team.

_Treatment 1 (Leaflet) -_ At the end of the baseline survey, households were given a leaflet with information about air pollution: what it is, how it impacts individuals’ health, actions that people can take to reduce exposure to air pollution - like using ventilation while cooking or avoiding high traffic areas - and a scale explaining how health effects increase as the level of pollution rises. The information and scale in the leaflet were based on the U.S. EPA brochures on the dangers of air pollution.
01|2.7|
|Syria|0.92|0.01|0.0|
|Tunisia|1.38|0.25|0.6|
|UAE|1.34|0.28|3.1|
|Yemen|0.24|0.02|-|
|MENA|0.97|0.28|5.3|
|GCC .|0.87|0.23|4.4|
|Non-GCC|1.03|0.31|6.1|
|Oil|0.80|0.16|3.8|
|Non-Oil|1.22|0.45|7.1|

Sources: AXCO Reports, Swiss Re. Sigma, IMF/WB databases, national sources

**Table 2: Average penetration rates and assets (% of GDP), MENA and other regions**

||**MENA**|**East Asia**<br>**& Pacific**|**New**<br>**EU-10**<br>**Countries**|**Other**<br>**Europe &**<br>**Central**<br>**Asia**|**High**<br>**income**<br>**OECD**|**Latin**<br>**America**|**South**<br>**Asia**|
|---|---|---|---|---|---|---|---|
|**Average non life premium **|0.97|0.8|1.7|0.9|2.3|1.3|0.5|
|**Average life premium *
the interaction between road and internet access, and validates the hypothesis according to which, gaining access to the internet reinforces the positive effect of roads on employment.

The complementary impact on hours worked is strong. Proximity to roads reverses the negative impact of the internet. As indicated by column (3), individuals connected to fast internet work approximately 23 fewer minutes a week than those not connected. This negative sign could be explained by either fewer work opportunities or the increase in the productivity generated by the internet (Rollo and Paunov, 2015; Hjort and Poulsen, 2019). However, when the distance to the closest road decreases by a kilometer, the number of hours of work per week for individuals connected to submarine internet increases to four hours. As for the isolated effect of the road, the number of hours worked per week increases by about 32 minutes as main roads become one kilometer closer to the location of the household/firm. Indeed, with better proximity to a main road, the travel time to work certainly decreases, which could explain the positive effect of improved road access on the number of hours worked per week. The addition of the accessibility to high-speed internet to a location that has better accessibility to main roads boosts the number of hours worked per week by four hours. So, roads and internet complement each other, leading to at least 3.5 extra hours of work.

Both investments benefit firms that tend to have longer hours of operation and more full-time employees, but the complementary impacts are small. The impact of internet access on hours of operation is relatively small. From the WBES, we find that, in the absence of internet access, an increase in the proximity to the closest main road increases a firm’s operation hours by 3 hours and 46 minutes. By contrast, bringing a main road one kilometer closer leads connected firms to operate approximately only 6 minutes more than non-connected ones. Similarly, firms connected to the internet operate approximately half an hour more than non-connected ones
Eckel, C., C. Johnson, C. Montmarquette, and C. Rojas (2007). Debt aversion and the demand for loans for postsecondary education. _Public Finance Review 35_ (2), 233– 262.

- Field, E. (2009). Educational debt burden and career choice: Evidence from a financial aid experiment at NYU Law School. _American Economic Journal: Applied Economics 1_ (1), 1–21.

- Friedman, M. (1955). The role of government in public education. _Economics and the public interest_ , 123–153.

- Guiso, L., T. Jappelli, and D. Terlizzese (1996). Income risk, borrowing constraints, and portfolio choice. _The American Economic Review 86_ (1), 158–172.

- Mincer, J. (1974). _Schooling, Experience, and Earnings. Human Behavior & Social Institutions No. 2._ National Bureau of Economic Research, Inc., 261 Madison Ave., New York, New York 010016.

- Palacios-Huerta, I. (2003). An empirical analysis of the risk properties of human capital returns. _American Economic Review 93_ (3), 948–964.

14
—governments, humanitarian organizations, and UN agencies—can directly affect access, but linking access to outcomes (such as infant mortality) is more complex, materializes with a long lag, and depends on several factors that are outside policy makers’ control. We use ACLED data to explore possible conflict dynamics.

We report two sets of findings. First, we find no evidence that a larger refugee presence increases support for restrictive migration policies (though in some years it is associated with a somewhat heightened sense of personal insecurity). Second, using a difference-in-differences (DiD) research design, we find robust evidence that access to education, health care, and roads significantly improved for those living near refugee settlements, and that these residents recognized 4
.pdf and http://www.worldbank.org/en/news/press‐ release/2017/05/19/new‐world‐bank‐support‐to‐address‐food‐insecurity‐in‐yemen‐aims‐to‐reach‐9‐million‐ yemenis).</u> 15 Authors’ calculations from the unit‐level WFP mobile phone survey conducted in November 2017 demonstrate that the vast majority of the population relies on either solar power or generators for their primary energy source. 16 Approximately 8 percent of phone numbers do not respond to two straight surveys, which results in their removal from the sample. However, the WFP is unable to observe whether a phone number exists or not in the random digit dialing procedure, and thus we are unable to identify the share of active phone numbers that do not respond to the survey.

5
term analysis (Petkova et al., 2013).<sup>6</sup> However, some recent examples that look at air pollution in Africa exist. Roy (2016) focuses on the health effects of air pollution and some economic costs, emphasizing the simultaneous challenges the continent faces: economic development and pollution control. Kirchberger (2016) looks at the spatial links between pollution (and other disamenities) and living standards in Sub-Saharan Africa, while Bishop (2017) develops a broader approach by concentrating not only on pollution but also on the health and economic effects of climate change in general. However, to the best of our knowledge, there is no study that focuses on the links between pollution and productivity in Africa.

The contribution of our paper is twofold. First, it extends the literature, particularly the work of Reyes et al. (2017), to focus on Africa, looking at how BE variables affect firm productivity. We follow the same approach by allowing the BE factors to affect firm growth differently for African firms than for those in the rest of the world, and then offer quantitative assessment of key factors for improving African firm growth. We offer diagnoses of hindering business environment (BE) factors for firm performance in Africa with a focus on issues related to agglomeration (or urban factors). Second, it contributes to the pollution literature by focusing on air pollution as another possible determinant of firm productivity and exploring whether its relationship with African firm performance is different, compared to the rest of the world. Although the link between pollution and productivity is well established (see Kahn et al. (2017) for a survey on this topic), this paper focuses on Africa and how air pollution is correlated to worker and firm productivity in that region.

Following Reyes et. al (2017), we use the World Bank Enterprise Survey data set. We distinguish three different classifications for business environment (BE): Basic BE, Refined BE, and Agglomeration Environment. Basic BE
(11)|
|Are banks limited in their lending to a single borrower or a group of inter-related<br>borrowers?|99<br>(97)|97<br>(92)|100<br>(99)|
|**C. Reserve Requirements**|**% Yes**|**% Yes**|**% Yes**|
|Is Central Bank reserve and/or deposit requirements in place in your jurisdiction?|86<br>(90)|69<br>(76)|93<br>(95)|
|Are banks required to hold reserves in foreign currencies or other foreign-<br>denominated instruments?|35<br>(25)|26<br>(8)|39<br>(31)|

Source: Calculations based on the 2011–12 Bank Regulation and Supervision Survey, which reflects values as of 2010. Values in parentheses refer to the 2007 Bank Regulation and Supervision Survey, which captures data as of 2005. ADV: advanced economies, EMDEs: emerging and developing economies (Table 1 provides the complete list of countries).

# **Section 8: Deposit (Saving) Protection Schemes**

Section 8, including 29 questions, covers a wide variety of issues related to depositor protection schemes, such as the authority of the deposit insurance agency, the membership and coverage requirements for the deposit insurance scheme, and the depositor insurance reimbursement practices among other topics. More than three quarters of respondents reported having an explicit deposit insurance protection system for commercial banks in their jurisdictions (Table A8). Of the countries with such an explicit system, almost 80 percent have a legally separate deposit insurance agency (Figure A5). Furthermore, in 95 percent of jurisdictions, participation of domestic banks in the deposit insurance system is compulsory. However, in 51 and 71 percent of the countries the deposits at foreign branches and subsidiaries of domestic banks