rafmacalaba/data-use-mentions-extended huggingface dataset:
costs during the transition towards a greener, carbon-neutral economy. These risks could be related to climate policies, technological change or shifts in investor and consumer sentiment with respect to climate change and the preservation of the environment. As for physical risks, transition risks could be mild if the economy transforms smoothly over time. Yet, if intervention and adaptation are delayed, changes in policy and taxation might be abrupt, leading to significant adjustment costs for companies and households. Financial assets may become stranded, increasing the chance that borrowers are unlikely to repay their debt obligations with negative repercussions for lenders.

Using a large, unbalanced panel dataset consisting of 184 countries over 1980-2019, we contribute to a growing literature by presenting the first systematic cross-country evaluation of the effect of a wide range of climate and environmental related natural disasters on the stability of the banking sector, with a focus on bank asset quality. We find that severe natural disaster episodes can lead to an increase in the level of system-wide non-performing loans(NPLs) to total gross loans (henceforth NPL ratios), and the effects can be persistent. For our global sample, NPL ratios are estimated to increase by 0.37 percentage points one year after disaster episodes associated with an official declaration of emergency. Two and three years after an episode involving multiple types of disasters, the cumulative increases in NPL ratios stand at 0.56 and 0.6 percentage point on average respectively. The relationship between severe climate and environmental related disasters and the level of system-wide NPL ratios is observed across multiple World Bank regions,1 including in East Asia and Pacific (EAP), Latin America and the Caribbean (LAC), South Asia (SA) and SubSaharan Africa (SSA). Moreover, this relationship is also observed across almost all income groups except for lower-middle-income countries. These results should be interpreted with caution due to a limited sample at the country-year level and a lack of a generally consistent cross-country source of major natural disasters.

To address important caveats of the cross-country results regarding data
the previous year). We use imports data for Mexico and the U.S. in order to analyze Chinese competition. Specifically, we calculate the share of China on imports for each NAICS-4-digits code included in the base.

## Where

_Mj, China_ =Imports of country _j_ from China _Mj,k_ =Imports of country _j_ from country k _j_ =Mexico and the U.S.

> 7The correspondence tables fore HS 1996 and NAICS generated through this exercise are available upon request.

7
9 The first term in this decomposition accounts for the impact of changing returns over time, while the second term accounts for the impact of changing household characteristics. There is a reminder (covariance term) which is typically small. The decomposition can be implemented to assess the impact of changes in returns and characteristics for sub-groups of variables by partitioning the vector X.

Finally, the regressions can be used to assess the micro determinants of inequality. When using the unconditional group decomposition of the Gini index, we found that in 1995-96, the between group inequality term accounted for fifteen percent of the national Gini (see Table 3). This does not mean that urban-rural location in itself makes such a difference. The inequality between urban and rural households may be due to difference in household characteristics, and/or in returns to these characteristics (which are sector specific). An example will make this clear.

To estimate the inequality between households differing only in terms of sectoral location, the national sample means XN can be used to compute the urban and rural expected consumption for nationally representative households. These are given by exp(1<sup>3</sup> U'XN) and exp(13R'XN), which were equal to 1.479 and 1.236 in 1995-96. These conditional values are lower than the actual welfare ratios in the two sectors given in Table 1 (2.318 in urban areas and 1.286 in urban areas). This is because urban (rural) households tend to have characteristics such as education (land) which have a higher return in urban (rural) areas. When conditioning on national means, we impose nationally representative household characteristics which are less favorable in each sector than the sample mean characteristics of the sectors themselves. Next, note that if all rural households are assigned the same expected welfare ratio of 1.236, the mean expected rank for rural households, denoted by E(FR), must be equal to half the rural population share
Table 1: Transportation time data validation

|Dep Var:|_log_(<br>(1)|_Xij_)<br>(2)|Days_ij_<br>log(#)<br>(3)|Days_ij_<br>#<br>(4)|Days<sup>_MAR_</sup><br>_ij_<br>log(#)<br>(5)|Days<sup>_MAR_</sup><br>_ij_<br>#<br>(6)|
|---|---|---|---|---|---|---|
|# Days_ij_|-0.143***<br>(0.012)||||||
|# Days<sup>_MAR_</sup><br>_ij_||-0.105***<br>(0.009)|||||
|log(Distance)_ij_|||1.116***<br>(0.062)|7.472***<br>(0.408)|1.203***<br>(0.066)|10.090***<br>(0.535)|
|Contiguity_ij_|2.309***|1.983***|0.403**|-0.950|0.174|-4.492***|
||(0.415)|(0.453)|(0.165)|(1.037)|(0.182)|(1.624)|
|Language Proximity_ij_|0.441***|0.360***|0.016|0.273**|0.001|-0.417**
accounts. If we assume that the number of procurement firms relative to the value of procurement is the same in our sample as in the whole population, we can scale up the share of procurement firms to 3.8% (0.005 / 0.13 = 0.038). However, this is probably a lower bound for the number of firms active in procurement because our procurement dataset is biased towards contracts awarded by the central government, which are bigger than the contracts awarded by other governments layers (e.g., local governments), and hence are probably more concentrated to a few firms.

To have a more credible estimate of the actual number of firms active in procurement, we make use of a recently available dataset provided by Spanish Government, _Plataforma de Contrataci ́on del Sector P ́ublico (PLACSP)_ , where all types of public agencies in Spain are required to upload their procurement activity. Although the platform includes information since 2013, compliance was only strong starting in March 2018, when introducing information on all contracts became compulsory by law.<sup>23</sup> Using information for the year 2019 (when the coverage is already high and before COVID), we measure around 75,000 firms with at least one procurement contract in the platform, which represents around 12% of the total number of active firms in the data. In our baseline calibration, we therefore set _mg_ equal to 12% and use the 3.8% as a robustness exercise.

We calibrate the level parameter _η_ 0 to ensure that the fraction of firms doing procurement equals the fraction of goods bought by the government, _mg_ , which is the equilibrium condition (e) (see section 4.9). Regarding the curvature parameter _η_ 1, we identify it by matching the selection pattern of firms into procurement observed in the data. We proceed as follows. In the data, we select firms with no procurement contracts between 1999 and 2005. Then, we classify
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# 1. Introduction

A stated goal of China’s Belt and Road initiative is to strengthen economic integration and policy coordination in the broad Eurasia region. The initiative includes a series of transportation infrastructure projects, which are proposed along two pillars: the Silk Road Economic Belt and the 21st Century Maritime Silk Road. Specifically, the “Belt” links China to Central and South Asia and onward to Europe, while the “Road” links China to the nations of Southeast Asia, the Gulf countries, East and North Africa, and on to Europe. Six economic corridors have been identified: (1) the China‐Mongolia‐Russia Economic Corridor; (2) the New Eurasian Land Bridge; (3) the China–Central Asia–West Asia Economic Corridor; (4) the China–Indochina Peninsula Economic Corridor; (5) the China‐Pakistan Economic Corridor, and (6) the Bangladesh‐China‐India‐ Myanmar Economic Corridor.

This paper offers a first assessment of the impact of the new and improved BRI transport infrastructure on trade flows of BRI countries.<sup>2</sup> Specifically, we examine the potential trade effects of the BRI using a combination of geographic and econometric analysis. First, we assess the impact of the BRI on trading times using a new database on transport projects linked to the BRI (de Soyres et al., 2018; Reed and Trubetskoy, 2018). Georeferenced data and geographical information systems (GIS) analysis are used to compute the bilateral trade time between capitals, ports and major cities (>100K) in the Belt and Road countries before and after the proposed interventions.<sup>3</sup> Second, we use a gravity model (Head and Mayer, 2014) to estimate the total effect of bilateral trade time on bilateral exports and a comparative advantage model à la Nunn (2007) to estimate the impact of bilateral time to trade on the export pattern in time sensitive products and in products that rely on
, transportation, finance, and retail, as well as for the GBFs, we relied on senior external consultants selected by a large management consulting organization. For health, our team relied on consultants and physicians with practical experience in both developing countries and advanced economies. In total, more than 50 experts participated in the construction of the technology grid. The resulting grid is composed of 7 general and 56 sector-specific business functions and contains a total of 305 technologies (See Section A.1.1 of the appendix for details on the procedures followed to define the grid).

All technologies in the Grid are precisely described so that respondents and enumerators can objectively establish their use. Figure 1 presents the general business functions considered in the survey and the possible technologies that can be used to conduct each of 4The granular information that can be obtained with the FAT survey allows us to explore central questions on technology policy in developing countries. One example, itself a product of this paper, is the World Bank policy report "Bridging the Technological Divide" (Cirera, Comin and Cruz, 2022).

6
Domestic official<br>40 Domestic private 40<br>Domestic official<br>30 30<br>20 20<br>10 10<br>0 0<br>06 07 08 09 10 11 12 13 14 15 16 17 18 06 07 08 09 10 11 12 13 14 15 16 17 18<br>(%) (%)<br>(%) (%)<br><!-- End of picture text --> Notes: Advanced economies excluding the euro area(Australia, Canada, the Czech Republic, Denmark, Japan, Korea, Norway, Sweden, Switzerland , the United Kingdom and United States) and emerging markets(Bulgaria, Columbia, China, Hungary, India, Indonesia, Malaysia, Mexico, Peru, the Philippines, Poland, Thailand, Turkey and South Africa). Lines and shadow areas show the median and interquartile range within each country group, respectively. Calculations of share of governmet debt based on data from Arslanalp and Tsuda (2014a), Arslanalp and Tsuda (2014b) and BIS Debt Securities Statistics (See 2.4 Data for details).

27
43 ‘journals’ (Regs 4.3 and 4a.3) and ‘royalties per capita’ (Regs 4.4 and 4a.4) exhibit positive and statistically significant coefficients.

One reason for the sharp reduction in statistical significance of the estimated coefficients of the knowledge indicators on economic growth on the inclusion of the openness and institution variables could be because of substantial multicollinearity between the openness and institution variables and the other knowledge variables. Table 4b shows the correlation matrix between all of the knowledge variables used in the regressions in Tables 4 and 4a. We see that institutional quality is indeed highly correlated with many of the other knowledge variables. More specifically, note that the correlation between institutions and the human capital stock, patent and the ICT variables are greater than 0.8. On referring back to Tables 4 and 4a, we see that these are precisely the knowledge variables whose independent effects on economic growth were ‘eliminated’. On the other hand, we see that the correlation between institutions and the ‘journals’ and ‘royalties per capita’ are relatively lower, and these are precisely the innovation variables that managed to maintain their statistically significant positive effects on economic growth. Thus, the correlation table suggests that the reason why quite a few of the estimated coefficients of our knowledge indicators ‘lost’ their statistical significance is because they are highly correlated with institutional quality.<sup>49</sup>

# **8. Summary and Conclusion**

Economic theory postulates that knowledge leads to increases in total factor productivity and hence economic growth. However, unlike traditional factors of production, knowledge is not subject to diminishing returns. If true, the increased creation, use, adoption and flow of knowledge in various forms will significantly improve the prospects of sustainable long-term economic development for many countries. In this light, this paper focuses on empirically assessing the effects of knowledge on economic growth.

We argue that knowledge can become the main engine of growth if the economy satisfies certain preconditions. These preconditions include
Sample Age eligible Age and Sisben eligible _Note:_ The table shows the results of a linear probability model on a dependent variable indicating the receipt of Colombia Mayor; fixed effects are omitted from the output. Standard errors are indicated in parentheses.

There is a certain contradiction in the results arising from the last two approaches. If the rollout, that is, the assigned number of beneficiaries, really evolved in a parallel fashion, one would expect to see wide variation in the prioritization score of the last person entering the program because there ought to be differences in the number and characteristics of eligible applicants. In our conversations with officials at the consorcio, we were told that the number of assigned beneficiaries is partially adjusted by the number of applicants a municipality is able to produce. This would explain the similarity in the minimum prioritization scores, but it should also result in more variation in the rollout. In our identification strategy presented in the next section, we partially assume that the official enrollment data do not properly represent actual enrollment levels, and we proceed to derive proxies for actual enrollment directly from the ENCV data.

# **6. Data and Empirical Strategy**

We employ the 2010–13 rounds of the yearly ENCV. The principal reason we use this particular data source is that it provides us with the richest data in terms of variables, sample size, and periodicity. The other frequent large-scale and nationally representative survey is the monthly Gran Encuesta Integral de Hogares (National Integrated Household Survey). While this survey would provide us with a much larger sample size and cover almost all the municipalities in the country, it lacks the breadth of observed variables available in the ENCV. Most importantly, it does not capture whether a household receives the Colombia Mayor benefit. Furthermore, the included variables do not allow for the construction of Sisben scores.

The ENCV sampled a total of 14,801 households in 2010, 25,364 in 2011, 21,383 in 2012, and 21,565 in 2013. The
Finally, we test whether the institutional framework for competition policy amplifies the effect of bank competition on financially dependent firms. There is now a general consensus that a well‐ functioning competition law system, including an independent competition authority, is essential to the protection and promotion of healthy competition, including in the banking sector (see International Competition Network, 2005, and OECD, 2011). Competition law is an important determinant of economic growth yet it crucially depends on the law enforcement efficiency of the government (Ma, 2011). To analyze the impact of the institutional setting for competition policy on economic growth we interact a measure of government effectiveness with our indicator of external finance dependence. We use the World Bank government effectiveness index, which captures perceptions of the quality of public services; the quality of the civil service and the degree of its independence from political pressures; the quality of policy formulation and implementation; and the credibility of the government's commitment to such policies. The indicator ranges from approximately ‐2.5 to 2.5, with higher values signaling stronger government effectiveness. Results are reported in column 7 (labeled “Govt effect”). We find for all specifications a statistically significant and positive coefficient, indicating that more government effectiveness exerts positive effects on growth in sectors that depend on external finance.

This set of regressions conveys one main message: some competition policies might play an effective role in enhancing growth in financially‐dependent sectors. In particular, our findings show that relaxing restrictions on banks’ activities, reducing informational asymmetries by facilitating the availability of credit information, and improving quality and effectiveness of public services all have a positive effect on industry growth performance.

# **7. Conclusions and policy implications**

Using a panel of 23 manufacturing sectors over 2002‐2010, this paper analyzes the effect of bank competition on economic growth of financially dependent industries in the GCC economies. Our results indicate that financially dependent industries grow faster in the presence of higher competition. Therefore, our findings support
12 are their demands for subsidies?). Regional governments respond to these demands based on (i) intensity of these pressures; (ii) the structure of their own preferences; and (iii) their own access to fiscal resources (budget constraints of regional governments). Our supply-side factors reflect various aspects of regional economic wealth, and therefore seems could be used as a measure of budget constraint hardness.

The variables are listed below by type. Unless otherwise noted, all ruble-denominated indicators used in the regressions were per capita in 1991 prices.

# _Supply-side factors_

# _Economic variables_

1. Pre-transfer budget revenue

2. Federal budget transfers

- of which: 3. Mutual settlements

_4._ Subventions 5. Federal Fund for Regional Support (in 1994-95)

# _Social variables_

6. Household gross money income 7. Doctors per 10,000 population 8. Hospital beds per 10,000 population 9. Car ownership (cars per 1,000 population)

# **_Demand-side factors_**

# _Economic variables_

10. Wage-arrears per worker (end of 1993 data used in 1993-95 regressions) 11. Index of real industrial output (as % of 1990 level) 12. Loss-making enterprises (% of registered enterprises in major sectors, 1994 data only) 13. Loss-making enterprises in industry (% of registered enterprises in industry, 1994 data only) 14. Unemployment level (registered unemployed as % of labor force) 15. Percentage of national income obtaining to industry (1992 data only) 16. Index of meat production (proxy for overall decline in the agricultural sector, as % of 1991 level)

# _Social and other non-monetary variables_

17. Life expectancy (years) 18. School children per 1,000 population
land under LDO restrictions, interaction of land restrictions with travel time to urban center, and travel time to the urban center on its own.

To identify the casual e¤ect of LDO restrictions, we need an exogenous source of variations in percentage of land under LDO restrictions in a DSD. The unique role played by malaria infestation starting from 16th century till early twentieth century in the history of land policy of Sri Lanka o¤ers such an exogenous source of variations. The results from a regression of LDO incidence on district level malaria provides a coe¢ cient equal to 0.16 with a ‘t’ statistics of 37.65 after we control for province ...xed e¤ects. Even with only 15 data points on district level malaria prevalence, the results thus show a clear positive correlation between malaria intensity and LDO incidence. As noted before, we cannot use this correlation directly to identify the e¤ects of LDO 18 The potential and actual yield data used in Table 1 and discussed above are not available for the full sample.

16
Among non-contributory transfers, family benefits and social assistance programs are the most progressive, but social assistance has a relatively small redistributive effect given its small size (Table 6). Social pensions have the largest redistributive effect, and together with family benefits have the largest impact on extreme poverty. With the exception of the child birth grant, all of the noncontributory programs are targeted to the bottom 20 percent of the distribution (Figure 6).

In assessing how education and health spending benefit the poor, we have to caution that our analysis does not address the quality of such spending. We use government expenditure data on the various forms of education and health services to estimate unit costs of these programs. The analysis thus assumes that the actual benefit received by individuals is equal to the amount spent per capita. As the quality of school infrastructure, teachers, and health clinics and hospitals vary across the country then this is a clear limitation of the analysis.

The results show that both education and health are progressive and equalizing.<sup>16</sup> For education, 29 percent of total education spending goes to the bottom 20 percent of the distribution, while only 15 percent was concentrated at the top 20 percent. This is mostly on account of primary school spending which has the largest redistributive effect (Table 6). Indeed, primary, gymnasium and vocational school spending benefit families at the bottom of the distribution slightly more than those at the top, in part because lower income households tend to have more children (Figure 7). Interestingly, tertiary education spending is much less concentrated at the top of the distribution than what is typically observed in middle-income countries. It is also interesting to note that 26 percent of spending on kindergarten is benefiting the top 20 percent of the distribution, mostly on account of greater access to these services in urban areas. However, this could also reflect the fact that the subsidy for private kindergarten is as much as 75 percent of the standard subsidy for public services.

Health spending is