rafmacalaba/data-use-mentions-extended huggingface dataset:
and science<sup>25</sup> . U.S. census data indicate that only nine percent of U.S.-born graduates work in scientific fields whereas 17 percent of foreign-born graduates work in scientific fields.

The results also indicate indirectly that the United States gains from trade in graduate education services. Relatively open access to international students has allowed U.S. universities to accept the brightest graduate students in science and engineering from all over the world. In turn, international graduate students contribute to innovation and patenting. Presumably, this is because international graduate students are relatively concentrated in such fields as science and engineering. Indeed, in a number of highly ranked engineering schools, international students account for nearly 80 percent of doctoral students, while in fields such as law they rank as low as one percent.<sup>26</sup> Further, because of work restrictions for international students, domestic students have greater opportunities to be employed in non-research activities in both university and nonuniversity institutions. Hence, it is not surprising that the presence of international students along with skilled immigrants, including international faculty, exchange visitors, research fellows and post-doctoral research associates, is a significant factor behind sharp increases in innovation and patenting at universities.

# **5. Concluding Remarks**

This study provides the first systematic econometric results about the contributions of foreign graduate students and skilled immigrants to U.S. innovation and technological change. While it may have become conventional wisdom in some circles

> 25 <u>http://thestar.com.my/news/story.asp?file=/2004/12/7/latest/20038USstudent&sec=latest</u>

> 26 _Open Doors_ , Institute for International Education

24
<!-- Start of picture text --> Fig 2 Orissa: distribution of household<br>operational and holdings, 1953-54 to 1982<br>100%<br>0 0)2 860%% ! e :<br>m<br>40% ~<br>Ml 1-2 ha<br>o 20% l]0.4 -I ha<br>Cu<br>1953-54 1961-62 1971-72 1982<br><!-- End of picture text --> Source: NSS data, reported in Sharma (1994) Agricultural land accounts for 59 percent of the total land area of the state. The share of total land area under various forms of agricultural land use is shown in Table 1. The changing proportions of total operated area accounted for by each size class of land holding is shown in Figure 3, for the period 1953-54 to 1982. Land reforms, demographic and socio-economic change together appear to have brought about little net change in the share of the total operated area accounted for by sub-marginal, marginal and small land holdings (i.e. those of 2 ha or less). Within this broad group, marginal land holdings (0.4 - 1 ha) increased their share of total operated area at the expense of small holdings (1-2 ha). Medium sized holdings (2-6 ha) accounted for the greatest proportion of total operated area, having gained at the expense of large (>6 ha) holdings owing to ceilings restrictions and estates abolition.

<!-- Start of picture text --> Fig 3 Orissa: share of total operated area<br>by holding size, 1963-54 to 1982<br>2! 100%<br>~ 80%<br>a 60% 0 ~ ~ , ~ ~ ~~ > 6 ha<br>.9 40% ~ 2 -6 ha<br>0<br>20 ~1- 2 ha<br>20%
part of the city (Figure 1B-D). The positive correlation between wealth and air pollution is similar to many other cities in low- and middle-income countries (Behrer and Heft-Neal, 2024).

The Georgian Ministry of Environmental Protection and Agriculture (MEPA) maintains four pollution monitors in Tbilisi, concentrated in the central part of the city. The location of these monitors is shown in Figure 1E. The data from these monitors is publicly available on MEPA’s website but does not seem to be regularly accessed by residents of Tbilisi.<sup>4</sup> Despite not frequently checking outdoor pollution levels, residents of Tbilisi regularly report that pollution is a major issue for them. For instance, a 2017 survey found that 42% of the residents of Tbilisi named pollution as the most important infrastructure issue facing the city (CRRC, 2017). A 2021 follow-up survey found that air pollution followed only unemployment and traffic as the most important issue in Tbilisi (CRRC, 2021).

Tbilisi’s built environment was established over three major modern phases of development: the pre-Soviet period in the late 19<sup>_th_</sup> and early 20<sup>_th_</sup> century, the Soviet period from the 1920s to the early 1990s, and the post-1990 period (Figure A1) (Salukvadze and Golubchikov, 2016). Each period was associated with construction of unique housing types with quality, in particular insulation from the outside environment, tending to increase over time.<sup>5</sup> Housing built prior to 1920 is low rise, 2-3 story structures with large windows and 2<sup>_nd_</sup> story balconies. Housing built during the Soviet era largely resembles the large apartment blocks common in most Post-Soviet Republics. Post-1990s housing is similar to much of the modern high rise development throughout Europe with mid-to-large towers that may or may not have small balconies for each unit. In general, housing quality is lower than in the United States or Western Europe
# **7 Conclusion**

We merge granular satellite weather data with unique global firm-level data to examine the impact of rising temperatures on firms’ performance, adaptation capacity, and the policy constraints limiting their ability to adapt. Using standardized microdata from the World Bank’s Enterprise Survey covering 134 countries from 2008 to 2023 and over 160,000 firms— more than half of which are located in low and lower-middle income countries—we evaluate the marginal effects of weather deviations from historical trends on firm revenues. We explore transmission channels such as firm investments, labor productivity, wages, energy intensity, and innovation, and assess how firms’ characteristics and the policy constraints they face affect their ability to adapt to rising temperatures.

We find significant negative impacts of higher-than-average temperatures on corporate revenues among firms in low- and lower-middle-income countries. Small and medium-sized firms are especially vulnerable to rising temperatures, with revenues dropping by 12% when annual average temperatures exceed the historical average by 0.5C. The effect is driven by lower labor productivity, also reducing the compensation of labor. Younger firms and firms in heat-sensitive sectors are more severely affected, supporting our causal interpretation. In low and lower-middle income countries, firms with limited access to finance, burdensome regulations, and lower security are more susceptible to temperature increases, further undermining the economy’s resilience to climate change.

Taken together, our results highlight two novel facts about economic resilience to rising heat. First, the performances of most manufacturing and services firms in low- and low-middle income countries has been affected by rising temperatures, with substantial differences in the degree of vulnerabilities depending on firms’ specific characteristics. Second, local policy constraints including finance, business regulation, and the quality of public goods are associated with firms’ ability to adapt to climate change. This suggests that poor business environments may be undermining developing countries’ economic resilience. Our results underline the importance of designing local context-specific policies to support climate adaptation in 24
On average, Sub-Saharan African oil and gas rich countries enjoyed a surplus of their fiscal balances (excluding grants) compared to the rest of Sub-Saharan African countries during the boom. However, with the boom years coming to an end, the average overall fiscal balance of these oil and gas rich countries reached the deficit levels of their mineral-rich and non-resource-rich counterparts (see Figure 32).

Figure 32. Average overall fiscal balance (% of GDP) during and after the boom period.

_Notes: Oil and gas rich countries considered as oil and gas rich are Chad, Nigeria, Cameroon, Equatorial Guinea, Gabon, Republic of Congo and Angola; while mineral-rich countries are the remaining resource-rich countries, as defined by Lundgren, Thomas, and York (2013). Source: authors’ calculations using World Bank and IMF Government Finance Statistics Yearbook data._

# **Governance and corruption**

Improvements in governance can reduce collusion of some stakeholders and prevent capture by interest groups leading to the leakage of resource rents that should otherwise accrue to governments and citizens. Restuccia and Rogerson (2008) and Hsieh and Klenow (2009) describe how transparency can improve resource allocation and Stiglitz (1999), Bellver and Kaufmann (2005) argue that transparency makes governments more accountable, weakening the power of special interests leading to better policies and institutions (Kubota and Zeufack 2020). Sub-Saharan Africa has much to do in this area, since the region has the lowest scores of transparency in the world according to the World Bank Statistical Capacity Index and the IMF’s Special Data Dissemination Standards 43
are classified as an urban resident, while the rest are considered rural. In wave 1, 66 out of 146 (45 percent) of the regions in the sample are considered urban according to this definition. Across all waves, 223 out of 599 (37 percent) are.<sup>14</sup> The LASSO model selection and estimation in each case are carried out only for the relevant subsample of regions, drawing on multiple imputations conducted in the full sample.

Tables 3a-3d generally show much stronger predictive performance of the model in urban regions. In wave 1, the model explains 48 percent of the variation in reported income decreases on average across the imputations in urban regions, as opposed to only 14 percent in rural regions. However, predictions in rural areas are more accurate for the reported declines in farm income in wave 1, explaining 35 percent of the decline in variation, with google search data for assistance, school, and student being statistically significant predictors. When looking at variation within urban regions over time, the model explains 55 percent of the variation in reported declines in total income, with changes in google searches for employment and online school having significant predictive power. The models for predicting variation over time within rural regions are much less accurate, with R2 values ranging from 0.1 for total income to 0.25 for wage income loss. These results suggest that google search data in particular is better suited to predicting changes in welfare in urban areas than rural areas.

# _4.3 Decomposing the Explanatory Power of Predictors_

Turning back to the models estimated on the full sample of areas, we conduct Shapley R<sup>2</sup> decompositions to better understand the role of different types of variables in explaining variation in the model. Table 8 reports the results, which show the share of the R<sup>2</sup> that is accounted for by ten different sets of variables. The sets are the Google mobility data, Google searches related to food
at conventional significance levels, they suggest that brokers who serve only one importer exhibit lower excess interaction. Brokers who have been active for a longer period of time tend to have more declarations subject to excess interaction.

Finally, for 20 inspectors that participated in the survey of inspectors we implemented in 2017 we are able to correlate their views with the share of declarations they handled that were subject to excess interaction. Appendix Table A26 shows that inspectors with a higher share of declarations with excess interaction report on average significantly higher overall job (but not pay) satisfaction, higher esprit de corps, and are much more likely to claim that they know the most fraudulent firms.<sup>50</sup> Taken together, the fact that excess interaction increases with inspector tenure and that brokers based in Toamasina have a higher propensity to handle declarations with excess interaction points to the importance of establishing personal relationships and private information acquisition.

# **8.2 How Are Tax Revenue Losses Distributed?**

Unfortunately, our data do not allow us to identify how participants in the corruption scheme divided the surplus generated by tax savings associated with the scheme; it is thus not possible to precisely pinpoint how much each participant gained, which is furthermore complicated by the fact that officials in the IT department, the port manager, and importers likely also have taken a cut. Instead, we present estimates of the distribution of tax revenue losses across inspectors and brokers by semester, ranking inspectors and brokers in terms of their contribution to overall revenue losses (with rank 1 assigned to the inspector or broker with the highest revenue losses in a given semester), using our preferred measure of counterfactual additional tax yield (calculated using external reference prices and correcting for potential underreporting of quantities) if there had not been significant excess interaction.

Table 5 reports that on average an inspector collects 4.8 million USD worth of tax revenue per semester (6 percent of total taxes collected in the port per semester). Average tax revenue losses associated with
to corporations and foreigners, and the “popular” market for domestic tenants. We are mainly interested in the popular market in addressing this question.

Both private and public sector data are hard to come by for this market.<sup>60</sup> Evaluations of new contract rents start with surveying rental ads and broker listings or holding interviews. Anecdotal evidence from such sources obtained during the study indicates new contract rents for one-room (studio) apartments starting from US$50 per month in Yerevan (Armenia), through US$100 in Vilnius and Bucharest, to US$250 in Warsaw and Moscow. More representative analysis in this direction was beyond the scope of this study.

**Table 9. Rents, Rent-to-Income and Cost-to-Income Ratios in Studied Countries**

||**Armenia**|**Poland**|**Romania**|**Russia***|**Serbia**|
|---|---|---|---|---|---|
|Rentsin LCU||||||
|- public|11000|154|200188|381|nil|
|- private|15044|124|799487|646|4645|
|Private/public|136.8%|80.5%|399.4%|169.6%|n.m.|
|Rent-to-income**||||||
|- public|23.1%|8.3%|4.7%|5.7%|nil|
|- private|27.1%|7.3%|8.9%|9.7%|18.8%|
|Private/public|117.5%|87.7%|188.1%|172.4%|n.m.|
|Housing costs-to-income***||||||
|- owners|
The “grievance” approach (Posen, 1993; Gurr 1994; Petersen, 2002) asserts that civil conflict is fueled primarily by a failure to peacefully resolve political grievances, ordinarily caused by ethnic or social cleavages that are held by a sub-section of the population. Economic factors can still have an important effect on insurgency by fueling these grievances, but only indirectly. Grievance theories predict that development projects should not affect violence in so far as they do not affect underlying social or ethnic tensions or contribute to the resolution of the resulting grievances.

Finally, the “hearts and minds” theory (Berman, Shapiro, and Felter, 2011) asserts that the level of violence is, in part, determined by general attitudes towards the government. Increased support for the government makes it more difficult for insurgents to recruit additional members, thereby tightening insurgents’ labor constraints (Condra et. al.., 2010), while also making it easier for the government to gather intelligence, locate insurgents, and disrupt insurgent movements. These two effects reduce violence, but differ in timing, with the information-sharing effect almost immediately apparent, with the recruitment effect taking longer. Thus, according to the hearts and minds theory, development projects which increase support for the government can reduce violence.

# **II.2. Empirical Evidence**

Recent research has attempted to empirically test the aforementioned theories of conflict using data on ongoing insurgencies in Afghanistan, Iraq, and the Philippines.<sup>3</sup> Berman, Felter, and Shapiro (2009) test the “opportunity cost” theory, examining the correlation between unemployment rates and insurgent attacks in Iraq and the Philippine. Contrary to the predictions of the theory, they observe a negative relationship between unemployment and attacks against the government and allied forces and no significant relationship between unemployment and attacks that result in civilian fatalities. On the other hand, Iyengar, Monten, and Hanson (2011) provide

> 3 A number of studies that draw on data from the Israeli-Palestinian conflict
In international context, Jordan’s fiscal policy performs poorly in terms of reducing poverty and inequality. In 2018, Jordan’s fiscal policy reduced inequality by 2.6 points in monetary terms (25<sup>th</sup> out of 47) and 5.8 points including health and education (28<sup>th</sup> ). In 2010, the reductions were 1.6 points (35<sup>th</sup> ) and 2.3 points (3<sup>rd</sup> worst), although as we mentioned the 2010 and 2018 results are not comparable for a variety of technical reasons. Jordan’s poverty reduction impact (1.5 points) is modest in comparison with countries that achieve poverty reduction through their fiscal systems, although this discounts that in many countries the fiscal system actually increases poverty.

Over the last few years Jordan has experienced sluggish economic growth and weak fiscal performance, by the end of December 2019, owing primarily to weak domestic resource mobilisation, the debt-to-GDP ratio including arrears rose to reach 99 percent of GDP (WB, 2020). Jordan’s revenue and spending as a percentage of GDP is relatively high at the aggregate level compared to other countries in the CEQ database. However, the composition of both has historically been less progressive than elsewhere. Fiscal reforms of the last decade have been positive – a move away from food and fuel subsidies and an increase in poverty-targeted direct transfers – but remain far from the international best practice. A key driver of these results is Jordan’s reliance on less progressive sources of revenue and forms of expenditure. Personal income tax (PIT) and targeted direct cash transfers such as NAF or the new Takaful programme perform much better in terms of progressivity than indirect taxes and subsidies, respectively. However, because direct taxes and transfers represent relatively small contributions to total revenues and expenditures, they have a limited impact on poverty and inequality reduction. For example, revenue
with non-homothetic preference consider “Engel’s law” which refers to low income elasticity for food produced by the agriculture sector. They show that the productivity improvement in the agriculture sector combined with Engel’s law explains most of the declines in agricultural employment share.

Given the ongoing structural change in Africa and low-income countries (as shown by Diao et al. (2017) and Rodrik (2018)), understanding the role of structural change in aggregate convergence is the focus of this paper. In assessing the contribution of structural change to convergence, it is important to recognize that industry and service sectors are made up of a highly heterogeneous set of activities that vary widely in their skill- and capital-intensity as well as their productivity. Understanding these differences is essential to help the policies that can foster sustained productivity growth. This paper investigates how the sector-specific source or the changing sectoral composition (i.e., structural change) has contributed to the aggregate beta convergence. This paper extends the literature in two dimensions:

1. It constructs a new sectoral dataset for 8 sectors and 91 countries over 1995-2018 (and for 60 countries over 1975-2018). This is the first comprehensive database covering a broad range of both advanced economies and emerging and developing economies (EMDEs) over a long time period. This more detailed dataset and a more recent sectoral decomposition improves the scope to assess the contribution of structural change in productivity convergence, particularly as the estimates are sensitive to the level of aggregation (Üngör (2017)).

2. This paper is the first to decompose aggregate beta convergence into contributions from within-sector productivity growth and from between-sector productivity growth, for a large number of countries ranging from advanced economies to low-income countries, whereas Wong (2006) have only focused on advanced economies.

The paper starts by describing the new dataset. Then this data is used to decompose aggregate productivity growth into within- and between-sector contributions. The main section examines convergence across countries and examines the extent within and between sectoral reallocation are contributing to convergence
an expert review panel) also receive lower outcome ratings. The most common results framework shortcomings identified in our sample of 120 operations were inadequate baseline information or indicator targets and an absence of indicators relating to the project’s expected outcome.

Further evidence on the stickiness of design flaws comes from our data on project restructuring. Almost two-thirds of the 120 projects in our sample data set were restructured, some more than once. There is no penalty attached to restructuring per se. Indeed, failure to restructure promptly or even cancel is often mentioned as a contributor to poor performance in ICRRs. But restructuring to compensate for poor design, as measured by changes to project components, is also associated with lower outcome ratings.

To contextualize the qualitative data set findings, we construct an index of design features where the weight for each individual element (listed in the top panel of Table 8) is determined through principal components analysis. This index is significantly related to the project rating – a one standard deviation increase in this index is associated with a 0.22 point increase in the rating. By contrast, an index derived from standard project characteristics related to project success, such as the duration of the project development period, is also significantly related to the IEG rating but at only half the magnitude as the design index. Another benchmark is the CPIA at year of project approval. This measure is not significantly related to the project rating in the qualitative data (likely due to the small sample size), although the magnitude of the point estimate, 0.30, is only slightly larger than the coefficient on the design index.

An additional measure linked to project design quality is whether the project has been informed by prior analytic work tailored to the sector and country of operation. The World Bank devotes significant resources on the production of analytic work and therefore we would hope to see the impact of such 16