rafmacalaba/data-use-mentions-extended huggingface dataset:
# _Social Development_

Two aspects of social development are considered: human development and income inequality. For ranking countries in terms of their achievements on human development, we solely rely on the United Nations' index on human development. This index incorporates life expectancy, adult literacy, educational enrollments and per capita GDP in purchasing power parity terms. Egalitarian nature of the society is captured by an inverse rank on the Gini coefficients estimated by Deininger and Squire (1996). Table 4 shows that fiscal decentralization is positively correlated and statistically significant with both the indices (see also Figure 3).

# _Macroeconomic Management_

It is frequently argued that a decentralized public policy environment of the type found in developing countries contributes "to the aggravation of macroeconomic problems" (Tanzi, 1996, p.<sup>3</sup> 05). In the following, we reflect upon the available empirical evidence on aspects of monetary and fiscal policies to form a perspective on this issue.

# _Monetary Policy_

Monetary policy is clearly a central function and best entrusted to an independent central bank (Shah, 1994, p.<sup>1</sup> 1). The critical question then is whether or not independence of central bank is compromised under a decentralized fiscal system. One would expect, a priori, that the central bank would have greater independence under a decentralized system since such a system would require clarification of rules under which a central bank operates, its functions and its relationships with various governments. For example, when Brazil in 1988 introduced a decentralized federal constitution, it significantly enhanced the independence of the central bank (Bomfim and Shah, 1994). On the other hand, in centralized countries the role of the central bank is typically shaped and influenced by the Ministry of Finance. In an extreme case, the functions of the central 14
**Table 2: Descriptive Statistics and Treatment Balance**

||Control<br>Infor|Information<br>mation Experi|p-value<br>ment|Info only|Ratings only|Negative Comments<br>Ratings Experiment|All Comments|P-value|
|---|---|---|---|---|---|---|---|---|
|_Owner Characteristics_|||||||||
|Female|0.23|0.26|0.582|0.28|0.27|0.20|0.28|0.153|
|Age|38.0|39.2|0.224|38.7|39.1|39.2|39.7|0.732|
|At least 2 years college|0.62|0.65|0.446|0.65|0.66|0.62|0.65|0.885|
|_Firm Characteristics_|||||||||
|Construction sector|0.20|0.18|0.426|0.17|0.19|0.20|0.17|0.796|
|Entertainment sector|0.09|0.10|0.675|0.12|0.13|0.08|0.09|0.213|
|Hospitality sector|0.19|0.20|0.652|0.19|0.20|0.23|0.19|0.832|
|ICT sector|0.14|0.13|0.454|0.13|0.16|0.11|0.12|0.579|
|Light Manufacturing sector|0.38|0.39|0.812|0
a 1-5 scale % of respondents rating these items 4 or 5 on a 1-5 scale<br>0% 20% 40% 60% 0% 20% 40% 60%<br>Natural run-off Loan portfolios<br>Securities and trading<br>Divestments portfolios<br>Bank subsidiaries and<br>Constraining asset growth branches<br>Non-bank activities (e.g.<br>Write-offs/provisions insurance, leasing, asset<br>mngt)<br>Source: Deloitte Bank Survey 2012 Source: Deloitte Bank Survey 2012<br><!-- End of picture text -->

**In terms of magnitude, the most recent deleveraging projections are significant and point to a reduction between 5 and 10 percent of total European bank assets, although they should be seen as illustrative only since they are shrouded by market and policy uncertainties and data gaps.**<sup>**22**</sup> In October 2012, the International Monetary Fund (IMF)

> 22 For example, market conditions could improve enabling banks to raise more equity (as they have been doing so far) and avoid asset shedding and loan reductions. It is also difficult to assess whether other banks and financial institutions are able to compensate for the effects of deleveraging banks. Also, regulatory changes such as the recent easing of Basel III’s Liquidity Coverage Ratio can also affect the deleveraging outlook.

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Moving to the central question, namely, the difference between the cost of transporting one quintal of each good one kilometre on a _kutcha_ track and an all-weather village road, respectively, the corresponding pairs of estimates and the associated standard errors are reported in Table 5. The difference between these pairs of estimates ranges from about Rs.7-8 for horse gram and millets (as for paddy) to about Rs.18-20 for cotton and green gram. All estimates of the coefficient on d0 are positive and statistically significant; the hypothesis that the coefficients on d0 and d1 are the same is rejected for all these crops. These results suggest that a household that markets a one-tonne ‘representative’ basket of paddy and these crops and whose village benefits from the conversion of a 5-km. _kutcha_ track to a PMGSY road would enjoy a reduction of about Rs.500 in transport costs on this score alone. A corresponding improvement in effective net prices might also induce substitution among crops; but the data needed to establish such an effect, which involve before and after comparisons, were not canvassed in the household survey for want of time and resources. The effect on cropping patterns was, however, taken up in the focus group interviews.

Table 5: Transport costs for 1 quintal of selected commercial crops, _kharif_ 2009

|Crop|_n_|uncensored|d0 (km.)|d1 (km.)|pseudo _R_<sup>2</sup>|
|---|---|---|---|---|---|
|cotton|53|22|22.26<br>(3.49)|3.87<br>(2.29)|0.3748|
|horse gram|75|70|14.52<br>(5.61)|7.53<br>(3.36)|0.0705|
|green gram|48|41|33.00<
the loss of all naturalized inventors who have acquired their host country citizenship. Appendix S3 compares nationality data with ethnic name recognition algorithms (see Breschi et al. 2015), which has the advantage of taking into account all the naturalized immigrants who have acquired their host country nationality. Although some differences emerge—large coefficients when name recognition algorithms are used— the main conclusions of this paper remain unaltered.

To anticipate the results to come, I find a robust effect of highly-skilled diasporas on the internationalization of inventive activity between developed, receiving countries and developing, sending economies: a 10% increase in the inventor diaspora abroad is associated with a 2.0–2.2% increase in international patent collaborations. The evidence found survives the inclusion of a large number of controls, fixed-effects (FE), robustness checks, and identification issues. Moreover, the effect is stronger for inventor-to-inventor collaborations—co-inventorship—than for applicant-to-inventor co-patents—R&D offshoring—suggesting that diaspora effects specifically mediate interpersonal relations between co-workers.<sup>3</sup> The outline of the paper is as follows: section 2 reviews previous theoretical and empirical contributions on the relationship between migration and other international economic interactions. Section 3 presents the novel dataset on inventor migration flows and develops the methodological setting, including all the econometric concerns. Section 4 presents the results and section 5 concludes. The reader may also find the appendix useful, giving more details on the novel data used and robustness results.

> 3. This paper uses the term “applicant” to describe the owner of the patent, unless otherwise stated, which is normally a firm or research institution for which the inventors usually work. I am aware that in some patent jurisdictions the owner is termed “assignee,” although this term is not used here.

4
, Robotics and the Future of Work_ . New York: Oxford University Press.

Banerjee, Abhijit, Shawn Cole, Esther Duflo, and Leigh Linden. 2007. “Remedying Education: Evidence from Two Randomized Experiments in India.” NBER Working Paper 11904, National Bureau of Economic Research, Cambridge, MA.

Banerjee, Abhijit, Esther Duflo, Clément Imbert, Santhosh Mathew, and Rohini Pande. 2020. “EGovernance, Accountability, and Leakage in Public Programs: Experimental Evidence from a Financial Management Reform in India.” _American Economic Journal: Applied Economics_ 12 (4): 39–72.

Baye, Michael R., John Morgan, and Patrick Scholten. 2004. “Price Dispersion in the Small and in the Large: Evidence from an Internet Price Comparison Site.” _Journal of Industrial Economics_ 52 (4): 463–96.

Beaudry, Paul, David A. Green, and Benjamin M. Sand. 2016. “The Great Reversal in the Demand for Skill and Cognitive Tasks.” _Journal of Labor Economics_ 34 (1).

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course this also implies that excess external imbalances have to be managed as their fruits are less supportive of growth.

<!-- Start of picture text --> Figure 1. Absolute Convergence and Excess Growth in Europe Prior to the Crisis<br>Convergence among European Countries Actual vs. Predicted Growth<br>(1997-2008) (2005-2008)<br>GEO<br>ARM<br>10 10 BLR<br>BIH<br>8 8<br>ARM BIH<br>MNE<br>SVKLTULVA BGRROM<br>6 LVAPOLEST SVK BLRSVN IRL 6 SVNCZE ESTPOLHRV UKRSRB ALB<br>4 4<br>GRCESPFIN LUX FINGRC<br>AUTESP<br>2 MDA TURMNEMKDSRB UKR PRT FRAITADEU 2 FRA PRT<br>ITA<br>0 0<br>7 8 9 10 11 1 2 3 4 5 6<br>Initial Level of Income, in logs Predicted Growth, %<br><!-- End of picture text --> Source: Authors’ calculations based on the World Bank and IMF data (2011). Note: Labels refer to countries placed outside the confidence bands. The dots are countries in between the bands.

# **2. Unprecedented Flows of Capital in Emerging Europe**

From a pure financing perspective, the foreign capital inflow (i.e., the current account deficit) serves to augment the economy’s scarce resources and to close the saving-investment mismatch. The reliance on external finance can be an outcome of domestic imbalances (e.g., low and stagnant saving rates) and of the behavior of non-residents (e.g., foreigners perceive a country’s productive or financial assets as attractive). This implies that the net amount of foreign capital in the recipient economy is simultaneously determined along with the saving and investment decisions of domestic economic agents.<sup>3</sup> Yet, before we undertake an empirical exploration of simultaneity, some stylized facts
which cash transfers could impact exposure to GBV. We estimate the impact of CCTs on four indices – one corresponding to each mechanism. These indices are constructed using average z- scores based on Katz, Kling and Liebman (2007). Please refer to Table 2 below for more information on construction of each index. Considering that the CCT could have a distinct effect on women (primary grantees) and men (grantees’ partners), we estimate these effects separately on female and male samples, respectively.

First, to examine the mechanism on well-being, we create an index using average z scores of the following outcomes: (i) patient health questionnaire (PHQ) index, which measures extent or severity of depression (construction closely follows Kroenke, Spitzer, Williams (2001)), and (ii) respondent’s perception of their 11 Physical violence includes the act of (i) push, shake or throw something at the victim, (ii) slap, (iii) punch, (iv) kick, (v) strangle or burn, (vi) twist arm or pull hair or (vii) threaten with knife/gun or other weapon.

12 Sexual violence includes (i) physically forcing the victim for sexual intercourse or to perform unwanted sexual acts, (ii) force to do something sexual that is humiliating/denigrating, (iii) threatening the victim to engage in sexual intercourse.

13 Emotional violence includes (i) insult or made you feel back about yourself, (ii) humiliate you in front of others and (iii) intimidate or threaten or directly or through a threat to someone you care about.

14 Economic violence includes (i) not allowed to engage in legitimate work, (ii) deprive or threaten to deprive of financial resources and right to use conjugal or commonly owned property, (iii) control your money or properties or force you to work and (iv) destroy your personal property, pets or belongings, household property, or threaten to do so
# **6. Conclusions**

This paper provides a novel analysis based on the combination of high‐resolution, geo‐spatial data and household data from Vietnam. Despite several limitations related to the available data sets, this study reveals the first empirical insights into the potential role of environmental risks in the livelihoods of poor people, using data sets that have become recently available and measuring a comprehensive set of environmental variables.

The findings show the following: (i) at district level there are large hotspots of high poverty and environmental risks; (ii) ethnic minorities and poor households are much more exposed to multiple environmental risks than other groups, and also within rural and urban zones poorer households live in communes exposed to higher environmental risks; and (iii) environmental risks relate to lower consumption levels, but less so to consumption changes over time. In particular, households in communes with steeper slopes and higher rainfall and temperature variability and greater flood and drought hazards have lower consumption levels, while those living in communes with higher air pollution have lower consumption growth. Although causal relationship between risks and consumption cannot be clearly established with the data, these findings suggest that poor people are disproportionally exposed to environmental risks.

More work is needed to confirm that environmental risks pose a threat to poverty eradication and shared prosperity. First, longer time‐series data is needed to evaluate whether environmental conditions have long‐ lasting impacts on household living standards. Second, this study mostly relies on global data sets from remote sensing or modeling work, which needs to be verified and refined with monitored data at the subnational level – optimally from ground stations. Moreover, this study could only define historic risk profiles and not the actual environmental conditions and risk materialization within the survey years. The expansion of these analyses with such data will be an important area for deepening the findings of this work.

Even in the absence of such analyses, some evidence already exists that shows the detrimental impacts of environmental risks on poverty and household welfare. An accompanying
agriculture, but the switch is not quite as large as for women: 13% more men combined agriculture and self-employment in 1996.

Ugandan heads of households, both men and women, were much more likely to diversify into other activities than were men and even women as a whole. Over time, female heads show a large drop in farming from 73 to 54%, while male heads' farming participation remained virtually stable at 49%. The participation of both male and female heads in self-employment and farming increases, but it does so much more for female heads: males and females start out at almost the same rate of participation of 16 and 14% respectively, but females' participation increases to 38% and males' increases to 28%. Male heads also show a switch from participation in wage work toward self-employment. Overall the trends in female participation in Uganda closely mirror those in Ghana and show a movement toward more non-farm work.

# _5.2 Income and Income Shares_

In this section we investigate patterns in reported income by gender and sector _(see Tables 10-13)._ For both countries, there are apparent problems with the comparability of the income figures across the two periods even after adjusting for prices. Therefore, we report only the relative differences for male and female groupings. For Ghana, we use individual level incomes for both periods, but for Uganda, we use household level income data.

For both countries, our data indicate that women earned substantially less than men, though for Ghana the gap may be closing if the relative changes over time for men and women can be compared. For Ghanaian women, the highest average incomes came from 16
livia, Paraguay, and Peru, the distance between the median and the 10th percentile of their respective distributions declined considerably during the period (Messina and Silva 2018).

Although the precise contribution of the minimum wage to the decline of wage inequality remains to be established, there is increasing evidence suggesting that it played an important role. Maurizio and Velazquez (2015) build counterfactual wage distributions using semiparametric methods to analyze the impact of the minimum wage on several inequality measures in Argentina, Brazil, Chile, and Uruguay. They find that, except for Chile where the growth of the minimum wage was more moderate, the minimum wage was a significant driver of wage compression in the bottom half of the distribution. Ferreira, Firpo, and Messina (2017) find that in Brazil increases in the minimum wage significantly helped reduce inequality during the boom years (2002-2012), but not during the preceding period of slow growth (1995-2002). Engbom and Moser (2017) focus on the formal sector in Brazil and find that, once spillover effects are accounted for, the minimum wage had large compressing effects on the distribution of wages.

# **9. Conclusions**

After wage inequality increased in the majority of the countries in Latin America between 1995 and 2002, it fell by 26 percent between 2002 and 2015, measured by the 90/10 log hourly earnings ratio. This paper characterized the evolution of inequality in the region between 1995 and 2015. It used harmonized household survey data for 16 Latin American countries and linked employer-employee data for Brazil, Costa Rica, and Ecuador. It characterized the main stylized facts associated with changes in inequality, discussed the potential driving forces and highlighted promising avenues for future research.

Using harmonized household data representing about 90 percent of the Latin American population, the study uncovered six empirical regularities behind the wage inequality reduction of the 2000s. First, the reduction in wage inequality was a regionwide phenomenon (Costa Rica was the only country where inequality increased). Second, wage inequality fell more in
.

To estimate the proportion of imports used for exports in the firm-level export revenue function regressions, this paper builds on the classic approach to estimate production function introduced by Olley and Pakes (1996), who use firm investment as a proxy for firm productivity. In this paper, to control for unobserved firm productivity that influences both imports and exports in the export revenue regressions, firm’s capital goods imports —part of a firm’s investment— is used as a proxy. This data is readily available from customs transaction records, allowing for an effective way to account for the unobserved productivity. The results from the firm-level export revenue function regressions show that, as expected, while the estimated proportion of imports used in exports is significantly less than one for the non-processing exporters that also produce for the domestic market, it is not significantly different from one for the processing exporters. This outcome reflects both economic reality and Chinese government regulations, adding credibility to the estimates. Firm-level DVAR is constructed using these estimated import proportions, and then aggregated to the industry and country levels. The resulting aggregate DVAR for China shows an upward trend from 2000 to 2016, driven primarily by processing exporters. This pattern is consistent with earlier findings from Koopman et al. (2012) and Kee and Tang (2016), even with extended time coverage into the more recent years. Robustness checks using bootstrapping confirm that these DVAR estimates are statistically similar to those in

> 3See United Nations Statistical Division (1999).

2
As we observed before, firms that have savings accounts have higher cash flows. The causality is likely to run the opposite direction – i.e. firms with higher cash flows are more likely to have savings accounts. Firms with savings accounts are not more likely to make investment than firms without savings account.

Firms that claim higher financing obstacles actually make more investments and invest larger amounts. This is most easily explained by reverse causality – firms with good investment projects are more likely need external finance and hence claim finance is an obstacle. When actual usage of credit products and finance obstacles are included together, we find that usage of credit products continues to be strongly significant, while financing obstacles measure loses some of its significance. The results suggest a stronger relationship of actual usage of credit products with investment.

Finally, we investigate whether two proxies for uncertainty – macroeconomic and policy uncertainty are related to cash flows or investment behavior. We find that macroeconomic uncertainty decreases the likelihood of investment, but has no effect on the amount of investment. Policy uncertainty does not have a significant relationship to our variables of interest.

# **8. Regression Analysis of Listed Firms Data 8.1 Financial Savings and Cash Stocks**

We report results of regression of our three key dependent variables – financial savings, cash stocks and investment to capital ratios - on the set of explanatory variables discussed above. To mitigate simultaneity in firm decisions we lag all explanatory variables one year. Previous year values may be considered predetermined when the firm chooses its savings policy in the current year. We include time dummies to control for aggregate macroeconomic patterns. We estimate the model by OLS with firm clustered standard errors.<sup>19</sup>

> 19 Because one of our key explanatory variables is not time-varying – volatility of sales – we do not estimate the model with fixed effects.

27
<sup>21</sup> .

The case of Vietnam illustrates the scale and dynamics of evolving flood exposure. Almost one-third of Vietnam’s coastline is occupied by towns and built-up settlements, and its 28 coastal provinces are home to 46.6 million people<sup>22</sup> . With the safest and most productive locations already occupied, new developments are increasingly forced onto hazardous land and previously avoided areas that are further from regional hubs or more exposed to natural hazards. Risks are exacerbated when urban planning fails to prioritize densification of safe areas to avoid expanding into risky zones. Evidence from satellite-based nightlight imagery has confirmed that areas in Vietnam with high urban and economic growth face significantly higher flood risk than low-growth areas<sup>22</sup> . About 27 percent of areas with low urban and economic growth are estimated to be exposed to a 100-year flood, compared to some 50 percent of highgrowth areas.

Despite the rising interplay between rapid urbanization trends and flood disasters, these risks are mostly documented in anecdotal evidence and case studies. Previous global flood risk assessments provide only limited evidence of the long-term evolution of global settlement expansion and flood exposure.

The lack of high-resolution flood hazard maps and annual settlement footprint data has been a key factor preventing systematic analysis of flood exposure trends over time. Although satellite imagery is now widely used to understand the spatial patterns of urban development across the world, most data sets only cover the most recent years. The Global Human Settlements Layers were seminal as they produced global urban footprints for four discrete time steps—1975, 1990, 2000, and 2014<sup>23</sup> ; yet inaccuracies and large time gaps between observations limit the insights into the rapid dynamics of urbanization patterns.<sup>24</sup> Global settlement footprint data sets with high spatiotemporal resolution have become available recently, but have thus far not been used in global flood hazard assessments<sup>25,26</sup> .

By using global historical