rafmacalaba/data-use-mentions-extended huggingface dataset:
local CO2 emissions, reflecting the time lag between local emissions of CO2 molecules and their full dispersion into the global mix. We follow convention by terming this the “concentration anomaly” since it measures the local deviation from the global background CO2 concentration. In our global emissions model, we classify the determinants of local concentration anomalies in three categories. The first comprises activities in the most significant CO2-emitting industry sectors. The Intergovernmental Panel on Climate Change (IPCC) (Gale et al. 2005) has identified four dominant industrial sources of CO2 emissions: (i) power plants, (ii) steel mills, (iii) cement plants, and (iv) oil refineries. The second category includes CO2 emissions from agricultural and forest fires. The third category comprises population-related emissions other than those directly associated with CO2-intensive industrial activity. These include motor vehicle emissions, which are not measured reliably at the spatial resolution required for our analysis.<sup>1</sup> We would expect traffic emissions per capita to increase with income per capita, all else being equal. Population-related factors also include CO2 emissions from household heating and cooling, which are not captured by data for central power plants.<sup>2</sup> The subway component of the model affects population-related emissions by

> 1 Recent research has used Google Traffic to infer vehicular emissions from high-resolution traffic congestion data for some cities (Heger et al. 2018; Dasgupta, Lall, and Wheeler 2021). However, no currently available technology enables direct estimation of global vehicular emissions at 25 km resolution.

> 2 Household air conditioning is powered by fossil-fired home generators in many hot low-income areas where utility-scale power is either nonexistent or unreliable. For a detailed assessment, see Lam et al. (2019).

3
related to the youth skill development scheme, which is funded by a rule-bound formula, not unlike most other private and public programs that we examined earlier. Moreover, the results also show that the fund allocation-rules were strictly adhered to, and that our incentives did not negatively affect the funding in the control GPs.

The results are also robust to adjusting for multiple hypotheses testing. At the bottom of Table 3, we report the adjusted p-values calculated using the Westfall and Young (1993) step-down procedure for the family-wise error rate. The effect of the financial incentive on the evaluation score as well as on the private transfer and public investment indices is statistically significant at conventional level. The multiple-hypothesis-corrected _p_ -values are 0.062, 0.062, and 0.072, respectively. Similarly, the multiple-hypothesis-corrected _p_ -values for the estimated effect of the non-financial incentive on the two indices are 0.058 and 0.091.

# **6.2 Within-GP resource allocation**

Table 4 uses the hamlet-program level data to examine the impact on within-GP inequity across hamlets based on their physical proximity to the GP president,<sup>14</sup> and reports the estimated coefficients _π_ , _β_ , and _ψ_ from equation (2).

We find that among control GPs, pres-hamlets were 5.9 percentage points more likely to receive program-specific resources from the GP president compared to nonpres-hamlets in the GP. Nonfinancial incentives reduced this within-GP inequity in resource allocation between pres- and nonpres-hamlets by 8.7 percentage points. Both estimates are statistically significant at the 5 percent level.

In comparison, financial incentives reduced within-GP inequity in access to resources by only 4.4 percentage points, and the estimate is not statistically significant at conventional levels. Additionally, the equity effect of nonfinancial incentives on within-GP resource allocation is also statistically significantly larger from that of financial incentives: the _p_ -values of the _F_ -test of this difference is 0.097.

Overall, these results—in line with the theoretical predictions
(as per the central projection of our conflict �� �� simulation), and ���� of around ‐30 percent at the end of the conflict, this would give us �� of �� �� approximately ‐25 percent.<sup>46</sup> This is about 50 percentage points below Syria’s pre‐conflict long‐term average: 23 percent (20‐year average over 1991‐2010 based on WEO data). For the post‐conflict pessimistic scenario, we calibrate the transition for Syria’s �� by 50 percentage points to 25 percent, �� in eight years, based on the experience of Lebanon. Lebanon was subject to persistent political instability during its recovery, and external assistance specific to its reconstruction program was limited, though it did receive large capital inflows attracted by high interest rates that enabled it to run current account deficits. For the optimistic scenario, we assume that Syria’s saving ratio increases by 60 percentage points to 35 percent in five years, following the timeline and change experienced by Kuwait as it recovered to its pre‐conflict saving‐to‐GDP level. Resource‐rich, high‐income Kuwait made a strong recovery after the sharp decline as its oil production capacity was quickly restored amid a comprehensive economic recovery and reconstruction program.<sup>47</sup> For the moderate scenario, we take an average of the projections for �� under the other two scenarios. See Appendix 5 for further details. �� Once we have the projection for �� for each scenario, we then split it between public and private �� investment by continuing to assume that 40 percent of total investment is public, following pre‐ conflict trends. This is consistent with the estimated relative shares of destruction between public and private capital during the conflict. In terms of the efficiency of new public investment, we keep efficiency unchanged at 0.570 under the pessimistic scenario and assume a rise from 0.570
significantly correlated with connectivity to global knowledge (as reflected by use of internet, and also export participation and use of consulting services for the SLLSE data) and access to skills (as reflected by use of training, namely whether the firm trains its workers in the case of the WBES, and by the highest level of education that the entrepreneur has completed and whether the entrepreneur has taken part in training to improve business skills in the case of the SLLSE). In the 2011 WBES, use of internet and whether the firm trains workers are both statistically significant (at the 1% level) and positively associated with all four available measures of innovation; the average education level of employees is statistically significant (at the 1% level) and positively associated with process innovation (the introduction of new or significantly improved methods of manufacturing products or offering services). In the 2009 SLLSE, whether the firm used a consulting service is statistically significant (at the 1% level) and positively associated with four of the five measures of innovation, while internet use is statistically significant and positively associated with introducing new products or services, and with organizational innovation; the highest level of education completed is significantly associated with three measures of innovation, while training to improve business skills is associated with two measures. Access to finance, on the other hand, is not significantly associated with any of the four measures of innovation in the 2011 WBES data, and only significantly (at the 1% level) associated with two of the five measures of innovation (organizational and marketing innovation) in the SLLSE data. What is most striking in these findings is the similar patterns of association across two very different datasets sampled in two different years (in 2010 and 2008, respectively).

We also explore how different types of training are associated with innovation. In addition to whether or not managers received business training, the SLLSE collected information on the type of organization providing the training. In Table 12, we replace our measure of whether the manager has
set of demographics using retrospective information for the pre-migration period, while refugees still lived in Afghanistan prior to the Taliban’s take over. They include individual characteristics—such as age, gender, employment status, student status, and number of friends in Tajikistan before migrating; and household demographics, encompassing 34 variables characterizing household demographics, dwelling characteristics, income, and asset ownership.

Our primary analysis explores the effects of exposure to extremism on refugee integration, including both social and economic integration, using the rich set of outcome variables collected in the census. We also complement this analysis examining the effects of extremism on secondary outcomes including educational attainment, mental health, income and consumption, and labor market-related variables. For each outcome, we estimate effects on individual related variables and also combine them in an index outcome variable to gain precision.

Our data on Taliban’s territorial control between 2017 and 2021 comes from the Foundation for Defense of Democracies’ Long War Journal. It encompasses information on the districts controlled by the Taliban, U.S. allies, and contested territories between the two factions. The data set combines information from NATO, press reports, government agencies, and the Taliban itself. From these, we create district-yearly data that reflect the territorial control in Afghanistan from 2017 to 2021, classifying each district into three categories: Taliban-controlled, U.S. allies-controlled, and contested territories between both groups. Using this categorization we estimate the share of the territory controlled by each of the factions by province and year in Afghanistan between 2017 and 2021. We then utilize the census data to determine the province of birth for each Afghan refugee living in Tajikistan in 2021 and merge this information to calculate our three treatment variables, which include: the average share of a province that was Taliban-controlled, U.S. alliescontrolled, or contested between the two factions for each individual’s province of birth 4
# Building Public Support for Reducing Fossil Fuel Subsidies: Evidence across 12 Middle-Income Countries<sup>*</sup>

Christopher Hoy (World Bank), Yeon Soo Kim (World Bank), Minh Nguyen (World Bank), Mariano Sosa (World Bank), and Sailesh Tiwari (World Bank)

**JEL Classification:** D04, D80, D90, H20, H30, H50

**Keywords:** Political Economy, Public Finance, Subsidies, Climate Change, Fossil Fuels, Energy Policy, Randomized Experiment

> * This study has been pre-registered on the American Economic Association RCT Registry (ID number AEARCTR-0011580). This work was financed by the Distributional Impacts of Fiscal and Social Policies Global Solutions Group of the Poverty and Equity Global Practice of the World Bank. Ethics approval for this work was processed through the Australian National University Human Research Ethics Committee (Protocol 2023/187). Saad Imtiaz provided brilliant analysis and interpretation of the qualitative answers in the survey. We are incredibly grateful for the support, input, and guidance provided by many colleagues, including Zeina Afif, Elcin Akcura, Benu Bidani, Mohamad Chatila, Michelle Dugas, Jungkyu Rhys Lim, Luis-Felipe Lopez-Calva, and Ana Maria Munoz Boudet. We also greatly appreciate the comments provided by Kevin Carey, Defne Gencer, Gabriela Inchauste, Mustafa Kaba, and Victor Hugo Orozco Olvera on an earlier version of this paper. Finally, we wish to thank our Poverty and Equity Global Practice colleagues who provided fiscal incidence analysis results for the countries included in this study.
policy adopted by the Swedish government over 1985–1991 which placed refugees to initial hosting municipalities independent of individual characteristics. Using the Sweden Longitudinal Individual Data, the authors find substantive evidence of sorting across locations. Conditional on sorting, they find that less skilled migrants benefit from living in enclaves. An increase of one standard deviation in ethnic concentration results in a 13 percent increase in earnings. A second important result is related to the quality of the enclave. According to their findings, high-income ethnic groups gain more when living in enclaves compared to low-income ethnic groups.

Using a similar methodology, Damm (2009) analyzes the effects of the ethnic enclave size on labor market outcomes of migrants in Denmark. The identification strategy relies on the spatial dispersal policy adopted by the Danish government over 1986–1998, which consisted of a random allocation of refugees across locations. The dataset is extracted from the administrative records for migrants in Denmark between 1984 and 2000. The author documents strong evidence of self-selection into ethnic enclaves of refugees with unfavorable unobserved characteristics. Compared to Edin et al. (2003), Damm (2009) finds that one standard deviation increase in the size of the enclave results in a 18 percent increase in earnings, but regardless of the skills level. Furthermore, the author documents that ethnic networks disseminate job information, which translates into higher hourly earnings due to better matching.

Murard (2021) studies the long-term effects of the mass refugee inflow into Greece after the GreekTurkish war of 1919-1922. The author combines different census and survey data from various source with information at different levels of geographic aggregation. The results show higher inter-generational mobility among refugees than natives, as shown by the “catching-up” of second-generation refugees, who currently have levels of educational attainment, occupations and income similar to natives. The empirical findings related to the effects on the social cohesion of the host communities, with the analysis of indicators such as refugees’ political values, trust and civic engagement, suggest that a larger concentration of refugees in co-ethnic enclaves can
, had savings accounts. While Bumala is just a few kilometers from the region where the present study took place, their data were collected over two years before our household survey. The daily wage earners (primarily market vendors and bicycle taxi drivers) included in their study may also be somewhat worse off than our subjects.

> 18Gugerty (2007) surveys ROSCA participants in Busia and Teso Districts in western Kenya; she argues that the social component of ROSCA participation helps individuals overcome savings constraints. Anderson and Baland (2002) show income-earning women living in Nairobi slums use ROSCAs to protect their savings from their husbands. Dupas and Robinson (forthcoming) show that female daily income earners make more productive investments when given access to even a costly savings account.

> 19There are several reasons for the asymmetry between the probability of making a transfer and the probability or receiving a transfer. First, households comprising only children and/or the ill or handicapped could not participate (because only those adults who could be physically present for the session and were able to hear the oral instructions could take part), but are likely to be net recipients of transfers. Another reason is that most households in any village will make a small transfer toward the funeral expenses of their neighbors; since funerals are relatively rare events, we would expect to see an asymmetry between the likelihood of giving and receiving in the short-term. Finally, transfer data are quite noisy, and households are more likely to report transfers made than transfers received (Comola and Fafchamps 2011).

> 20A _harambee_ is a self-help effort in which community members contribute money or resources to assist a particular person in need. They may be for sending a child to school, paying for a wedding, or any number of other purposes. The concept existed within a number of different tribal groups in Kenya, but was made into a national rallying cry by Kenya’s first president, Jomo Kenyatta (Ngau 1987).

> 21We show that the randomization of the price of avoiding making an
/gas stove, refrigerator, washing machine, bicycle, motorcycle, car, minibus, lorry, beer-brewing drum, sofa, coffee table, cupboard/drawers, lantern, desk, clock, iron, computer, satellite dish, solar panel and generator, and (ii) a series of dwelling covariates, including number of dwelling rooms per capita and categorical variables that identify construction material (permanent; semi-permanent; traditional); roof type (grass; iron sheets; clay tiles/concrete/plastic sheeting/other); floor type (sand; smoothed mud; smooth cement/wood/tile/other); water source (piped/well; borehole; other), and toilet facility (flush/VIP toilet; traditional latrine; other/none).

12 These differences are due to the survey setting, as the IHS4 aims to represent the national population of Malawi in 2016, while the IHPS is based on the national population in 2010.

7
# **8. ANNEX**

# **Table A1 Estimates Based on Equation (1)**

|**a. PRESTIGE INDEX**||**DECADE: 1970-19**|**79**|**DECADE**|**: 1980-1989**|
|---|---|---|---|---|---|
||**CENSUS 1980**|**CENSUS 1990**|**CENSUS 2000**|**CENSUS 1990**|**CENSUS 2000**|
|**Age**|0.0170***|0.00514|-0.0109**|0.0127***|0.00411***|
||[0.00230]|[0.00320]|[0.00436]|[0.00113]|[0.00142]|
|**Age squared**|-0.000218***|-0.0000787*|7.94e-05*|-0.000172***|-7.58e-05***|
||[0.0000386]|[0.0000405]|[4.43e-05]|[1.64e-05]|[1.64e-05]|
|**Years since arrival (dummy with value 1**<br>|-0.0415***<br>|-0.0260***<br>|-0.0254***<br>|-0.0331***<br>|-0.0204***<br>|
|**for second half of the decade)**|[0.00299]|[0.00328]|[0.00357]|[0.00231]|[0.00229]|
|
.011_|_0.634_|_0.393_|
|Lottery x Beneficiary|-0.270|0.527|-0.286|
||_0.171_|_0.517_|_0.594_|
|R-squared|||0.008|
|Mean of dep. var. for NB in NL vil.|0.069|13.1|-0.641|
|P-val of B = NB in L villages|_0.310_|_0.813_|_0.979_|
|Model|Tobit|Tobit|OLS|
|Percentage of censured observations|0.622|0.033||
|Numberof households|299|212|330|

Note: This table reports the estimation of the following specification: _y ij = α ij + β 1 Beneficiary ij + β 2 Lottery j + β 3 Lottery j *Beneficiary ij + ε ij_ , where _i_ indexes the individual (household) and _j_ indexes the village. _Beneficiary ij_ is a dummy that takes value of 1 if the individual _i_ in village _j_ is a beneficiary of the NAIVS voucher program in 2010/11. _Lottery j_ is a dummy that takes value of 1 if beneficiaries in village _j_ were chosen using a lottery. The models presented here are not adjusted for any baseline covariates. Dependent variables are constructed base on questions asked on follow-up household survey. Data are restricted to baseline respondents in both panels. Panel A reports results using full baseline sample of households. In Panel B the sample is restricted to households in non-lottery who are similar in observable characteristics based on a nearest neighbor propensity score matching algorithm. The sample in Panel B also include households in lottery village who share the propensity score distribution support of households in no lottery villages. Column 1 reports a percentage of maize production that is sold. Column 2 reports on log of total household income, including non-agricultural activities. Dependent variable in Column 3 is constructed using the households reported worry for scarcity of food, number of meals
|433|290|143|
|N|12937|9337|3600|13522|9734|3788|

_Notes:_ This table provides estimates of the impact of logarithm of refugee intensity on status of household consumption poverty (first three columns) and wealth poverty (last three columns). Household sample weights from ESS2 applied. Columns labeled “All,” “Rural,” and “Urban” report results for all, rural, and urban households, respectively. All columns control for household (HH) FEs, round FEs, and 12 time-varying characteristics. The coefficient estimates for these 12 control variables are not reported here, but can be available on request. Robust standard errors clustered at EA level are given in parenthesis. Asterisks: *, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively.

45
# **1 Introduction**

The economic environment in the Middle East and North Africa (MENA) regions is characterized by a long-lasting stagnation in job creation. Over the period 2016-2019, employment growth was about 1.4%, which is well below the performance of lower-middle and upper middle-income countries (respectively 4,8% and 3,3%). One crucial determinant of employment growth is finance. Recent evidence documents the importance of such relationships for MENA countries, especially in regard to the positive effect of access to credit on employment and investment (Ayyagari et al., 2021). Yet, an obstacle to the virtuous role of the financial system is represented by the _disconnectedness_ of private firms from the banking sector. This has been shown to be a distinguishing feature of MENA countries and a possible element contributing to the poor job creation of the private sector in the region (De Lima et al., 2016; Amin, 2021). Another important feature of MENA countries is informality. Data from the World Bank Enterprise Survey (hereinafter, WBES) shows that 29% of MENA firms report to be exposed to competition from informal firms, with this share reaching more than 40% of firms in Lebanon and Tunisia. A large informal sector is a possible threat to the proper functioning of the economy and to the operation of formal firms which are negatively affected by the competition from informal ones (Distinguin et al., 2016; Rozo and Winkler, 2021; Avenyo et al., 2021).

This paper explores the link between jobs, finance, and informality. We begin our analysis by looking at the link between jobs and finance. Using longitudinal firm-level data for MENA countries, we document that jobs creation is positively associated with access to finance. At the same time, we show that access to finance is lower for formal firms more exposure to informal competition. As a possible mechanism underlying this result, we provide evidence that formal firms that are more exposed to informal competition have worse _expectations_
127 NGOs appear to flourish vhere the government has had difficulty building strong health or family planning infrastructures, as in Africa, but their role is mixed in other regions. NGOs can also play key roles where governments cannot--by taking the lead and supporting contraceptive services--like sterilization that are perceived as too controversial to be a part of the official government program. How much NGOs compete with for-profits is not known. The for-profits are more efficient than government while NGOs are in some undefined place in between. Lack of information prevents any generalizations about NGO performance and efficienLy.

Although options for promoting private activity have been discussed (see Table V.1 for a summary of the suggested options), none has been tried and documented, so these have emerged from theory, discussions with the affected parties, and a reviev of the literature on somewhat similar actions. The planned and ongoing efforts in this area deserve greater attention and evaluation and the next subsection addresses these gaps.

# Unansvered Questions and Areas for Future Activity

Despite the volume of activity in the area, the many surveys that have addressed some aspects of the private sector, and the modest attempts to understand the mechanism, efficiency and effectiveness of family pianning programs in the private sector, a great deal of pertinent data and analysis are lacking. Indeed, fev firm conclusions can be made regarding the experience with private sector approaches, partly because so many are in the formative stages, but also because the level of evalua on has been so poor. Similarly, conclusions regarding potential users of commercial products are inadequate both in terms of appropriate data and analysis. Data on costs and efficiency are exceedingly poor. Cost data, where they exist, are incomplete, and comparative

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