rafmacalaba/data-use-mentions-extended huggingface dataset:
(Bolivia, Mexico, Brazil, Peru, and Chile).<sup>4</sup> The coefficients of temperature (and temperature squared) provide an estimate of the long-run relationship between temperature and welfare (i.e. the output-climate elasticity) inclusive of adaptation. The estimated relationships are then used to simulate the impact of the climate changes that the IPCC projects for the next 50 years. Their poverty analysis is, however, crude. The authors do not attempt to estimate the poverty-output elasticity; they simple assume that a negative relationship exists between per capita income and poverty. As previously explained, income per capita and population growth projections are needed in order to be more precise about the number of poor people fifty years from now. Because of this, the authors are careful to warn that their simulation results should not be interpreted as forecasts but as simply indicative of the direction and magnitude of the effects that might be expected from climate changes. Table 1 below summarizes the estimated impacts of increased temperature on the mean level of welfare along with the _likely direction_ of the effects of anticipated future climate change on poverty and income inequality.

A couple of points are worth highlighting. First, the presented estimates (derived from the country specific elasticities and climate projections) refer to the percentage change in per capita income as a result of climate change relative to a world without it. Second, the direction of the poverty impacts due to climate change is derived by assuming a distribution-neutral change in the mean level of welfare. Third, as in the case of per capita income changes, the increase or decrease in poverty refers to a future situation without and with climate change, and not relative to the current situation. Therefore, a prediction that poverty will increase in Brazil does not imply that poverty will be higher relative to the present, but that it will be higher relative to the no-climate change scenario in 2058. Finally, caution should be applied when looking at the reported effects on poverty and inequality since they are
_34 Why Infrastructure Financing Facilities Often Fall Short of Their Objectives_ the executive body to issue political risk insurance. The GAU was backstopped by a World Bank loan facility to enhance the credibility of the risk coverage. The withdrawal authority for the World Bank loan was delegated to an international bank (ING Bank of the Netherlands). ING was to issue a letter of credit to each individual guarantee holder.<sup>65</sup> The World Bank loan facility that backstopped the GAU amounted to about US$30 million. It was expected that this credit line would backstop guarantees of up to a total of US$150 million.<sup>66</sup> 90. _Activities._ There was little demand for political risk insurance after the PEGF was established in October 1995. To assess future demand, a demand survey was conducted in March 1997. The survey indicated that demand would remain very sluggish in the future and consequently the facility was canceled in May 1997.

91. With hindsight, there are mainly four reasons why the facility generated little demand. First, the PGF faced demand from the Moldovan Government unconditional (broader) and heavily subsidized guarantees in the areas expected to be its principle market, namely foreign supply of agricultural inputs in return for part of the following season's supply; and machinery and equipment sales to the agro processing industry. The situation was compounded by the fact that structural reforms slowed down, especially in the agrosector the sector that the instrument primarily targeted.<sup>6 7</sup> 92. Secondly, there was very little borrowing activity from commercial sources by private Moldovan enterprises as most of the financing was provided by multilateral agencies ineligible for accessing political risk coverage. Thirdly, the pricing of the instruments appears to have been too high to generate demand because the reference price that was used to determine the price of the guarantee was based on risk premia for longterm projects and thus provided an inadequate point of comparison.<sup>6</sup>
disparity, the study concludes that differences in industry accounted for some of the disparity, with female-dominated industries exhibiting negative returns to capital and male-dominated industries having had high returns.

The de Mel et al. (2008) study also challenges the notion that female-owned businesses were poorer and more credit-constrained and shows instead, at least in the context of Sri Lanka, that female entrepreneurs were on average more likely to come from dual earner families and to have had more assets. A somewhat puzzling finding is that the females had on average lower ability, as measured by a digit span recall test that was administered to respondents, which is surprising given that they had on average more years of education. When returns to capital are allowed to vary by ability level of the entrepreneur, it is observed that those with higher measured ability experienced higher returns.

In contrast to PK‟s reported findings, the de Mel et al. (2008) study finds no effect of the randomized grants on school-going for children, nor on health or education expenditures. In general, their findings call into question the efficiency of targeting credit programs to women rather than to entrepreneurs operating the most productive businesses. They suggest that the benefits of targeting credit toward women need to be weighed against the possible costs of not targeting to the highest return activities or to the poorest households.

# **4.1.3  Microcredit Programs in Pakistan**

Hussein and Hussein (2003) survey a large number of microcredit programs that have been implemented in Pakistan and provide an overview of the results of evaluations of these programs along various dimensions. There is mixed evidence on whether microcredit programs have reached the poorest families and whether the programs have substantially alleviated poverty. Some programs were explicitly targeted at the poor and did have a large proportion of poor borrowers, but most programs did not explicitly target the poor and oftentimes did not keep detailed records on the poverty status of borrowers. Programs in Pakistan tended to target households rather than explicitly targeting women, although a few gave loans
methods, particularly the exclusion of children from the LFS data set in 2014, this paper is not able to use the 2014 data set to impute household-level poverty rates.<sup>13</sup> Our analysis is limited to 2013.

Another difference is that this paper’s analysis is conducted at the regional level. Regional aggregation smooths away household differences. Therefore, regressions are run on poverty rates of different subsamples of the host community. Table 13 and Table 14 present results of regressions on sub-samples based on the characteristics of the head of the household. We consider gender, education, and work type. Del Carpio and Wagner (2015) found the impact of SUTPs varies across different groups of the labor force. It is possible that some households are more susceptible or vulnerable than others in managing externalities from large in-flows of SUTPs. However, results from these regressions continue to show that across various groups, the proportion of foreign-born did not have a significant effect on poverty.

A third reason is substitution of labor yielded no net effects at the household level. Not all impacts are negative to the host community. As Del Carpio and Wagner (2015) point out, the influx of refugees also generated better paying jobs that were filled by local Turkish workers. It is possible that at the household level, the net labor market effects from the influx of refugees remained insignificant as some low skilled members lost jobs but then better jobs became available. Summary statistics show that from 2009 to 2013, the share of employed adults in a host community household near the Syrian border actually increased from 24 to 27 percent, and the share of unemployed adults dropped from 5 to 4 percent (Table 10). The share of host community heads of household working in Agriculture, Forestry, Fishing, and Mining also significantly declined near the Syrian border from 2009 to 2013. In 2009, 16 percent of heads of household in this group worked in this sector, and declined to 12 percent in 2013. The share of workers
of the financial and nonfinancial incentives, respectively. Moreover, including the interactions of deviations on covariates with _Fg_ and _NFg_ gives the ATEs that are unbiased and consistent even in the presence of heterogeneity along these covariates including the pre-treatment outcome (Lin, 2013).

# **5.4 Secondary analysis on within-GP inequity**

We use the design of the two incentive schemes to also test the predictions from the theoretical framework in section 4 on electorally-driven policy inequities. While the proposed equity analysis was not the primary goal of the study, it provides useful insights on the mechanisms that might underly the effects of our financial and non-financial rewards on GP presidents, who are also electorally motivated.

The hypotheses derived from the theoretical insights in section 4 are as follows. First, disseminating credible information on president quality to voters across all hamlets as part of our nonfinancial reward, should redress within-GP inequity in resource allocations made by GP presidents during their tenure (in anticipation of receiving such a reward prior to their next election). Second, similar equity effects are not likely to exist, on the other hand, for GP presidents who received financial incentives, insofar as such a reward does not provide credible information on president quality to voters.

For this purpose, we focus on new investments made by the GP president during the evaluation period across four public infrastructure goods and the IAY scheme. We examine the effects on within-GP resource allocation across different types of hamlets that differ in the ability of hamlet residents to accurately observe their GP president’s quality, which—in the context of our study— are likely to be correlated with hamlet residents’ physical proximity to the GP president. We use hamlet-by-program level data to estimate OLS with the following specification: _Ygsp,_ 1 is an indicator variable that takes the value of one if hamlet _s_ in GP _g_ received program _p_ during the evaluation period, and zero otherwise. _Ps_ is a dummy variable that equals one if hamlet _s_ is the GP president’s hamlet of residence, henceforth
improve statistics, including by generating statistics that conform to the EU’s standards (for Excessive Deficit Procedure and ESA2010). But for the moment information on, for example, government assets and non-debt liabilities is usually inadequate, and accrualbased measures of the deficit are unavailable. Good information tends not to be routinely available on accounts payables, arrears (overdue accounts payable), nonfinancial assets, pensions liabilities, and obligations in concessions and other public-private partnerships.

- _Forecasting_ . In at least some countries, economic and fiscal forecasting has had an optimistic bias,<sup>15</sup> though this problem may be less acute than before. Albania, Serbia, and Republika Srpska have taken steps to solve this problem by using external forecasts or review by an independent fiscal council.

- _Medium-term planning._ Similarly, although medium-term spending plans are prepared, they do not always have the influence they are intended to have in the preparation of annual budgets. Possible reasons for this include optimistic revenue forecasts, which encourage spending plans that are themselves unrealistic; investment plans that cannot always be implemented; the major shocks created by the global financial crisis; and a widespread belief that medium-term spending plans will not in the end matter much and are therefore not worth preparing very carefully.

**Quantitative indicators paint a similarly mixed picture** . Scores on the Open Budget Index— which run from 0 to 100, where 100 is ideal—range from 35 in Bosnia and Herzegovina to 50 in Albania, with no scores available for Kosovo or Montenegro (Table 6). Scores for the comprehensiveness of government-finance statistics submitted to the IMF by 2013, which also run from 0 to 100, range from 5 in Montenegro to 21 in Serbia, with no score available for Kosovo. Recent Fiscal Transparency Evaluations for Albania and for North Macedonia do not result in numerical scores, but suggest moderate performance. PEFA scores for expenditure outturns, which indicate the extent to which aggregate on-budget spending is controlled, are good, but scores for medium-term
states. This is consistent with the perception that India suffers from a major problem regarding factor mobility(see for instance Munshi and Rosenzweig, 2016). Importantly, the results appear to be the same for a mostly immobile factor such as land and buildings as for supposedly more mobile factors such as employment or other fixed assets.<sup>8</sup>

# **4. An exploration of the service sector**

In this section we duplicate the analysis of Duranton _et al._ (2015) for the service sector. This is a useful exercise for a number of reasons:

- Can we confirm that factor misallocation affects output misallocation?

- Can we confirm the importance of factor misallocation on output per worker?

- Did policies affect factor misallocation?

- Is land as important for services as it is for manufacturing?

## **_4.1 Data and descriptive statistics_**

Beginning with the 57th round during July 2001 to June 2002, the nsso conducted a survey of the unorganized enterprises belonging to the service sector and this survey was repeated as part of the 63rd round of National Sample Survey (nss) during July 2006 June 2007. The 57th round covered broadly all unorganized service sector enterprises engaged in the activities of hotels and restaurants; transport, storage and communication; real estate, renting and business activities; education; health and social work and other community, social and personal service activities. The 63rd round additionally covered financial intermediation services which was not so in the 57th round while specific sectors such as non-mechanised transport activities related to transport via railways and activities of business, employers and professional organizations were covered in 57th round but excluded from coverage in 63rd round. Neither survey covered the service sector enterprises

> 8While land is immobile, this is not the case for buildings before their location is decided. We must also keep in mind that we are talking about manufacturing land which represents only a small proportion of total land. Its supply should be arguably somewhat flexible unlike the total supply of
due to security concerns, the 10 districts are broadly representative of Afghanistan‘s ethno-linguistic diversity, with five predominantly Tajik districts, four predominantly Pashtun districts, one predominantly Hazara district, and two districts with significant populations of Uzbek and Turkmen minorities. The basic characteristics of households in the sample appear generally comparable with the population of rural Afghanistan, with households in the study sample being slightly poorer, having worse access to medical services, and experiencing slightly better access to electricity (Beath et. al., 2010).

> 5 In each of the ten districts, 50 villages were selected for inclusion in the study, with 25 of the 50 assigned NSP according to a matched-pair cluster randomization. These villages received NSP following the administration of a baseline survey in September 2007, with the remaining 250 control villages assigned to receive NSP in spring 2012.

7
5 intervention indicators. In terms of MDG outcome indicators, however, inequality has been growing in close to half of the countries. They conclude that inequalities remain substantial with poor children more likely to die and be malnourished and less likely to receive necessary health services.

At a more micro-level, Das and Hammer (2007) and Das and Mohpal (2016) attribute a large share of the inequality in quality of care observed in India to the location and lack of information among poor patients. They find little evidence of discrimination in health worker effort in Paraguay, however (Das and Sohnesen, 2007). Lipsky (2010) studies “street-level bureaucrats” (i.e. those public servants that engage directly with the public), and attributes service delivery failure to corruption, misuse of discretion, and lack of resources. Health workers in developing countries lack resources and have discretion. Given this, if poor individuals require more resources, as discussed below, inequity in care is inevitable. The discretion of health care workers is a critical element of effective care and is difficult to restrict (for example, by mandating levels of care per patient). The issue is therefore how to reduce inequity without reducing discretion.

<!-- Start of picture text --> Life expectancy at birth<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>0 20000 40000 60000 80000 100000 120000 140000 160000<br>Per capita income (PPP)<br>Life expectancy at birth<br><!-- End of picture text -->

**Figure 1: Relationship between life expectancy and per capita income in 2014 (source: World Bank Development Indicators)**
Panel A in Table 9 shows that SMEs are more severely affected in their growth from bribery. Note that in this estimation, the requirement of the minimum number of firms in a cluster is reduced to 20 instead of 30. This is done because ES sample design divides the total sample evenly by firm size; small, medium, and large. An average SME is predicted to have sales growth reduced by 2.5 percentage points if they are bribed, which is much lower in magnitude than for the entire sample of firms and is also insignificant. SMEs have a lower dispersion in their sales, and the difference between the bribery impact for firms at the 25<sup>th</sup> and 75<sup>th</sup> percentile of sales is smaller than for the entire sample; bribed SMEs with sales at the 25<sup>th</sup> percentile have sales growth that is 2.6 percentage points less than SMEs with sales at the 75<sup>th</sup> percentile.

When analyzing all firm sizes, the interaction between bribery and age was insignificant in predicting growth. When analyzing only SMEs, we find that SMES older than 10 years are less affected by bribery (columns 3 and 4). This result shows that being in the market for longer periods allows firms to acquire experience that is needed to handle market distortions. Interestingly, older SMEs actually grow 4.4 percent higher than young SMEs when bribed. For SMEs, age is more important than sales levels in terms of characteristics that can help compensate distortions.

The data includes many small countries with a low number of bribery clusters that are used as an instrument. We exclude countries that completed Indicator Surveys, a smaller variation of Enterprise Surveys. Indicator Surveys conducted in mostly Caribbean economies targeted only 150 firms due to small country size. Variation in bribery measure across location-sector cells is low in these countries. There are eighteen countries in this sample compared to 29 countries in the whole region.
# **Dependent variable**

In the Enterprise Surveys, firms were asked whether any fixed assets such as machinery, vehicles, equipment, land, or buildings were purchased (new or used) in fiscal year 2012. The amount spent on purchases of fixed assets was also collected. Thus, there are two dependent variables. The first is a binary variable that takes on a value of 1 if the firm purchased any fixed assets, and 0 if it did not. The second is the amount of expenditure on the fixed assets. For the three economies, 42 percent of the firms purchased fixed assets, with Tanzania having the highest (54 percent) followed by Kenya (46 percent) and finally, Uganda (28 percent). Kenyan firms spent the most on fixed assets with a mean of USD 1,205,834, followed by Tanzania (37,200 USD), and Uganda (22,422 USD). Summary statistics are presented in table 3.

# **Main explanatory variables**

The main explanatory variable is a binary variable that takes on a value of 1 if a firm has used mobile money for any transaction, and 0 if it has not. For the three East African economies in the sample, 54 percent of the firms have used mobile money for transactions. Across countries, the percent of firms using mobile money ranges from 50 to 60 percent, with 50 percent in Kenya, 57 percent in Uganda, and 60 percent in Tanzania.

We do consider additional measures of mobile money use in order to capture whether the purpose for which mobile money is used matters. In the sample considered for analysis, 9 percent of firms use mobile money to pay employees, 24 percent to pay suppliers, 23 percent to pay utility bills, and 36 percent to receive payments from customers. There is heterogeneity across the three countries. In Uganda, 28 percent of firms use mobile money to pay suppliers, which is higher than Kenya (20 percent) and Tanzania (25
# **Table 4: Portfolio of S&E Articles by Field (2001)**

||Japan|US|OECD|World<br>wide|
|---|---|---|---|---|
|Number of articles|57,420|200,870|551,402|649,795|
|Of which (%)|||||
|clinical medicine|28.7|31.7|30.7|28.4|
|biomedical research|14.0|16.9|15.0|14.2|
|biology|6.1|6.2|6.8|6.8|
|chemistry|14.9|7.1|10.3|11.9|
|physics|19.1|8.7|11.9|13.4|
|earth/space sciences|3.0|5.6|5.4|5.4|
|engineering/technology|11.6|6.9|8.2|9.0|
|mathematics|1.4|1.8|2.0|2.2|
|psychology|0.5|4.7|3.3|3.1|
|social sciences|0.5|3.9|2.6|2.3|
|health science|0.1|2.5|1.6|1.4|
|professional fields|0.1|3.9|2.2|2.0|

Source: Science and Engineering Indicators 2004 (National Science Board 2004) 47
facilities from April to mid-October of the 2016 academic year. The study sample includes 1,056 enrolled students aged 10 to 16 years old.<sup>9</sup> This age range is relevant in the Salvadoran context because that is when children and adolescents are likely to be recruited by gangs.

All randomly selected participants attended two sessions per week that lasted 1.5 hours each. The intervention was implemented by volunteers of the Salvadoran branch of Glasswing International, a nongovernmental organization (NGO) working in Central America and Mexico. Every session is a combination of: (1) a discussion oriented toward fostering children’s conflict management, violence awareness, and social skills; and (2) the implementation of curricula including activities such as scientific experiments, artistic performances, and others.

In the first part, instructors discuss concrete methods for regulating participants’ violent behavior using experiential learning or role-playing. This allows participants to take action in their lives, face difficulties, and learn how to solve problems proactively. To apply this therapy in the ASP, a tutor implements an experiential learning activity in which she mentions day-to-day difficulties that children typically face in low-income contexts. She asks them to brainstorm solutions to those problems and evaluate them. The second part of the session includes the implementation of ludic activities related to each club category. This section aims to motivate students to participate in the intervention and increase ASP attendance.

To measure the program’s overall impact and exploit excess demand, we rely on the first stage of Dinarte (2018)’s experimental setting. We randomly granted some of the enrolled students participation in the intervention while the remaining students were assigned to a comparison group. Before the intervention, we collected self-reported data on personal and family characteristics from enrolled students. Follow-up self-reported data included questions to measure the intervention’s impact on attitudes, violence, and crime; exposure to risky spaces; and educational or personal expectations. We combined this self-reported information with administrative records on math, reading, and