rafmacalaba/datause-encoder-data huggingface dataset:
past CEQ for Colombia, direct taxes and direct transfers slightly reduce inequality, while indirect taxes and subsidies appear to be neutral, and education and health spending has the most significant effect in terms of reducing inequality. In the current exercise, the effect of direct transfers and direct taxes seems to be higher, although not significant. The same goes for spending on education and health.

<!-- Start of picture text --> FIGURE IV-4 : GINI COEFFICIENTS FOR COLOMBIA<br>0.6<br>0.574<br>0.567 0.567<br>0.559<br>0.55 0.531 ENPH (Pensions<br>0.551 0.517 0.515 as deferred<br>income)<br>0.5 ENPH (Pensions<br>considered<br>0.454<br>direct transfers)<br>0.45<br>ECV 2010*<br>0.4<br>Market Income Disposable Income Consumable Income Final Income<br>(*) Lustig, Melendez, and Rodriguez-Castelán (2014)<br>Source: World Bank Group staff estimates based on ENPH<br><!-- End of picture text --> The redistributive effect of the tax system and social spending in Colombia is above the regional average (Figure IV-5). Argentina, Brazil, and Costa Rica are the only countries for which information is available that have a tax system and social spending that help reduce inequality to a greater extent than the Colombian system.

<!-- Start of picture text --> FIGURE IV - 5 : REDUCTION IN INEQUALITY WHEN MOVING FROM MARKET INCOME TO FINAL INCOME<br>0<br>-0.05<br>-0.1<br>-0.15<br>-0.2<br>-0.25<br>Contributory pensions as deferred income Contributory pensions as direct transfers<br>Source: The Impact of Taxes and Social Spending on Income Distribution and Poverty in Latin America. An Application of the<br>Commitment to Equity Methodology (CEQ), 2017. Colombia World Bank group staff estimates using the 2017 ENPH<br>Note: The results for all countries other than Colombia were obtained by applying the old methodology of the CEQ, which<br>among other things did not include indirect effects of indirect taxes, accounted for income in kind differently, while<br>the results for Colombia were obtained by applying the new methodology of the CEQ.<br>Argentina, 2012 Brazil, 2009 Costa Rica, 2010 Colombia, 2017 Uruguay, 2009 Mexico, 2010 Chile, 2013 Ecuador, 2011 LAC Venezuela, 2013 Dominican Rep., 2016 Bolivia, 2009 Nicaragua, 2009 El Salvador, 2011 Peru, 2009 Honduras, 2011 Guatemala, 2011<br><!-- End of picture text --> methodological differences and the second of them to the differences in public policy. Given the unavailability of all information it is impossible to isolate these two effects.

30
Following the literature, we are using a threshold of 0.50, but we also perform the analysis for other thresholds obtaining vulnerability curves (Figure C.1 in Appendix C shows results with other thresholds). Although the levels are different, the pattern and conclusions are replicated in a similar manner.

In the counterfactual exercise, the vulnerability in Mexico decreases from 20% to 12%, suggesting the cyclones contribute to vulnerability by 8 p.p. This difference represents around 11 million individuals who become vulnerable when the hazard from cyclones is considered. In Honduras, the simulation of earthquake hazard suggest that this hazard raises vulnerability from 33% to 41%; this difference of 8 p.p. represents around 600,000 (0.6 M) new vulnerable people. In the rest of the countries, the vulnerability increases 1 p.p. In Brazil, droughts increase vulnerability from 15% to 16% (nearly 2 million individuals becoming vulnerable). For landslides, the vulnerability rate in the Dominican Republic ranges from 6% to 7% and in Ecuador from 13% to 14% (0.1 M and 0.28 M individuals become vulnerable, respectively). In El Salvador, the vulnerability rate from droughts specification changes from 16% to 17% (almost 100,000 individuals becoming vulnerable).

According to the World Bank (2022) report based on Jafino et al. (2020), climatic events could push between 2.4 million and 5.8 million people in the region into extreme poverty by 2030. In this study, from the simulated scenario, we estimate that natural hazards push around 14.2 million people to vulnerability in moderate poverty. Although this is a lower bound because we are including only one hazard per country, the number is in line with other simulations in macro frameworks.

**Figure 6.** Vulnerability rates comparison between counterfactual scenario and actual scenario.

<!-- Start of picture text --> Low harm Actual harm<br>45% 41% 41% 41%<br>39%<br>40% 37%<br>33%<br>35%<br>30%<br>25%<br>20%<br>17%<br>20% 14% 16% 16%<br>15% 12%14% 13% 11%14% 12% 15%<br>10% 6%7%<br>5%<br>0%<br>Floods Landslides Earthquakes Cyclones Droughts<br>Ecuador Mexico Dom. Rep. Ecuador Brazil Honduras Dom. Rep. Ecuador Brazil Honduras Mexico Honduras Brazil El Salvador Honduras<br><!-- End of picture text --> Note: The results correspond to the specifications, including one type of natural hazard controlled by exposure, coping mechanisms, and region. Source: authors' calculations based on ThinkHazard!, EMDAT, and SEDLAC database.

**Vulnerability profiles**

18
<!-- Start of picture text --> 2<br>oS Oo @ EE T T T T 1<br>= so @ si @ cz HUp<br>Lo @ SK<br>om N -2<br>— 1<br>———<br>5S - @ RO<br>i fo)<br>Ow<br>Qa. v -6 y = -62.536x - 1.3954<br>_— = eu. [0.17] [0.50]<br>GD ac -8 R* = 0.202<br>£&<br>O -10 @ lv<br>@ BG<br>-12<br>fe) 0.03 0.06 0.09 0.12 0.15<br>Change in minimum to avareage wage ratio,<br>2010-2016<br><!-- End of picture text -->
-26-

References:

- Anand, R., A. Chhibber and S. van Wijnbergen (1988), "External Balance and Output Growth in Turkey: Can They Be Reconciled?", forthcoming in D. Rodrik and T.Aricanli (eds.), Turkey in the Decade Ahead.

- Anand, R., A. Chhibber, R. Rocha and S. Van Wijnbergen (1988): "Turkey External Debt, Fiscal Policy and Sustainable Growth" Mimeo, The World Bank.

- Bischoff, C.W (1969): "Hypothesis Testing and the demand for capital goods," Review of Economic and Statistics, 51 (August), 354-368

- Bischoff, C.W (1969):" Business Investment in the 1970's: A Compassion of Models," Brookings Papers on Economic Activity, 1, 13-63.

- Blejer, M. and M.S. Khan (1984): "Government Policy and Private Investment in developing countries," Staff Papers, International Monetary Fund.

- Chirinko, R.S. (1986): "Will the Neo-Classical Theory of Investment Please Rise?: The General Structure of Investment Models and Implications for Tax Policy", Mimeo, March 1986, NBER.

- Chhibber, A. and J. Khalilzadeh-Shrirazi (1988), "Public Finance in Adjustment Programs", mimeo, World Bank.

- Coen, R.M., (1971), "The Effect of Cash Flow on the Speed of Adjustment", in G. Fromm (ed.), Tax InceRtives and Capital Spending, Brookings Institution.

- Conway, P. (1987), Economic Shocks and Structural Adjustment: Turkey after 1973, North Holland Publ. Co.

- Ersel, H. and G. Sak (1987): "Public Borrowing and the Development of Financial Markets in Turkey," Mimeo, Capital Markets Board of Turkey.

- Eisner, R. and Strotz, R. (1963): "Determinants of Business Investment," In Commission on Money and Credit Impacts of Monetary Policy, Englewood Cliffs, NJ: Prentice -Hall, pp 60-138.

- Eisner, R. (1972): "Components of Capital Expenditures: Replacement and Modernization versus Expansion," Review of Economics and Statistics, pp 54,297-305.

- Eisner, R. (1970): "Tax Policy and Investment Behaviour; further comment", American Economic Review, 60 (September) 746-752.

- Elliot, J. W. (1973): "Theories of Corporate Investment Behaviour Revisited," American Economic Review, 63 (March, 195-207.

- Feldstein, M. S. and Foot, D. (1971): n The other Half of Gross Investment: Replacement and Modernisation Expenditures," Review of Economics and Statistics," 53 (February); 49-58.
- 2 -

# **I. INTRODUCTION**

Transport infrastructure is among the most important factors in increasing firm productivity and supporting local businesses. In particular, transport connectivity plays an important role in influencing firms’ decisions on where to locate. Proximity to markets is essential to firms, and transport infrastructure is its main determinant. In Hungary, road availability is considered to be a significant determinant of firm investment (Boudier-Bensebaa 2005). Access to good port facilities is also essential for private investors (Belderbos and Carree 2002; Deichmann et al. 2005). Foreign direct investment tends to be concentrated in countries with good transport infrastructure (Cieślik and Ryan 2004; Milner et al. 2006; Procher 2011; Lee et al. 2012; Mare and Graham 2013).

The traditional inventory model (e.g., Arrow et al. 1951) also suggests that firms can reduce inventory costs by lowering transportation costs. In general, it is financially costly to firms to carry inventory, restraining their operational flexibility. In the United States, the level of firm inventory declined by 7 cents for every additional dollar of investments in the highway capital (Shirley and Winston 2004). In China, each dollar of road spending has been estimated to reduce firms’ held inventories by 2 percent (Li and Li 2013). Using the Golden Quadrilateral project linking India’s four largest cities as a quasi-natural experiment, Datta (2012) found that firms in cities located along the improved highway reduced their inventory by 7 operating days’ supply on average.

When firms are located near one another, transport and transaction costs can be minimized. The new economic geography literature indicates that agglomeration economies can make an industrial cluster or a city attractive (e.g., Krugman 1991; Fujita et al. 1999). This creates a dynamic effect that continues to influence further investment decisions of firms. Therefore, although the cost of distance has been declining in recent years because of new technologies such as information and communications technology, firms still prefer to be located near one another to share the common input markets of labor and intermediate inputs and thus minimize trade and transaction costs.
(ii) whether exposure to individuals from diverse backgrounds in the park changes perceptions against outgroups, i.e. groups that had different sex and ethnicity than themselves.

Early work in behavioral economics focused on developing decision tasks in laboratory settings to verify the validity of the economic rationality hypothesis (Kahneman and Tversky, 1975). However, more recently the debate has shifted to whether findings from laboratory settings can be generalized to the real world, since laboratory settings can sometimes fail to incorporate context (Harrison and List 2004, Levitt and List 2007). People make decisions and choices based on the environments that they find themselves in, which is defined as “ecological rationality” (Gigerenzer and Todd, 1999). This is even more important when studying work related to social cohesion, emotion and empathy because people might only reveal their true preferences when exposed to the right context. The VR technology provides an opportunity to embed ecological context while maintaining a high degree of experimental control (Seinfield et al., 2018). The effectiveness of VR as an empathy tool has been shown in various studies, such as simulating forest fires to investigate how subjects change their propensity to pay for fire prevention policies (Fiore et al., 2009), inducing a full body ownership illusion that allows offenders to be in the body of a victim of domestic abuse and thereby modifying social-perceptual processes (Seinfield et al., 2018), and simulating disasters closer to home and thereby increasing empathy for victims of far-away disasters (Deep Empathy, MIT Media Lab). Additionally, compared to studies that use control conditions (e.g. watching a video, reading or imagining the life of another), VR can produce more attitudinal and behavioral change since it includes movements comparable to the real world.

Karachi’s socially diverse setting offered an interesting context for the VR experiment discussed in this paper. Serving as the commercial hub of Pakistan, Karachi is a megacity of 15 million people who belong to different ethnicities. According to the 1998 Census, the latest year for which ethnicity information was available at the city level, 48.5 percent of the city’s residents were Urdu speakers (Muhajirs), 13.9 percent were Punjabi speakers (Punjabis), 11.4 percent were Pashto speakers (Pashtuns), 7.2 percent were Sindhi speakers (Sindhis), 4.3 percent were Balochi speakers (Balochs), and the remaining 14.5 percent were categorized as Others (Pakistan Bureau of Statistics, 1998). While many neighborhoods are segregated based on income, the city also has clusters with ethnically homogenous neighborhoods being affected by urban violence emanating from inter-ethnic and political tensions in the recent decades. The relationship between social groups remains difficult and many residents struggle to find adequate mechanisms for their voice and inclusion (World Bank 2017, Breman 2012). Not only does the city have a shortage of public spaces that could facilitate interaction between the city’s diverse 3
Table 3: Impact of Heat Exposure on Standardized Scores

||Standardiz<br>|ed Score<br>|
|---|---|---|
||(1)|(2)|
|T_>_33C: Not Growing|_−_0_._012<sup>_~~∗∗∗~~_</sup><br>(0_._003)||
|T 30-33C: Not growing|_−_0_._007<sup>_∗∗∗_</sup><br>(0_._002)||
|T 27-30C: Not growing|_−_0_._002<sup>_∗_</sup><br>(0_._001)||
|T_>_33C: Any growing|_−_0_._008<sup>_∗∗∗_</sup><br>(0_._003)||
|T 30-33C: Any growing|_−_0_._002<br>(0_._002)||
|T 27-30C: Any growing|_−_0_._0001<br>(0_._001)||
|T_>_33C: School Year||_−_0_._010<sup>_∗∗∗_</sup><br>(0_._003)|
|T 30-33C: School Year||_−_0_._005<sup>_∗∗∗_</sup><br>(0_._002)|
|T 27-30C: School Year||_−_0_._001<br>(0_._001)|
|T_>_33C: Not School Year||_−_0_._008<sup>_∗_</sup><br>(0_._005)|
|T 30-33C: Not School Year||_−_0_._006<br>(0_._004)|
|T 27-30C: Not School Year||_−_0_._001<br>(0_._003)|
|Female Control|X|X|
|Other Temperature Bins:|X|X|
|<br>**Fixed Effects**|||
|Year|X|X|
|School|X|X|
|Stream|X|X|
|Observations<br>R<sup>2</sup>|2,132,635<br>0.248|2,132,635<br>0.248|

Notes. Estimates are from linear regressions using panel fixed effects. The dependent variable for all regressions is the standardized test scores of students taking the 12th grade exams and is constructed to have a mean of (or close to) zero. All regressors include the number of days in the temperature bin by season-school year. All regressions control for the number of days in the corresponding season-school year precipitation bins. Errors are clustered at the school level. *p _<_ 0.1 **p _<_ 0.05 ***p _<_ 0.01.

22
# **Annex 1: Administering the River Basin Survey (Report prepared by INBO)**<sup>**12**</sup>

About 200 questionnaires were sent by e-mail, fax or surface mail to RBOs, national administrations in charge of water, and international commissions.<sup>13</sup> Some answers were clear:

- positive in some cases, as some administrations forwarded the questionnaire to the relevant bodies,

- or negative from the international commissions, those usually being a small coordination unit not able to answer the questions that were actually relevant to the different countries concerned, as members of the International Commission. In the case of the Danube River for instance, 13 countries are involved and compiling data from all these countries is a difficult task.

The only positive answers from International Commissions came from the Zambezi River Basin Authority and the Niger Basin Authority.<sup>14</sup> As far as the national administrations are concerned, if we except some countries like Burkina Faso, the Czech Republic and Romania for instance, the answers were mainly negative (Ivory Coast, South Africa, Bulgaria, etc.) as the creation of RBOs was planned but not effective at that time.

# **Difficulties encountered**

The first difficulty is that people usually are not keen about answering any kind of questionnaire or do not give it first priority. Therefore several reminders (three) were necessary to have any kind of reply.

Another difficulty encountered is that there are several kinds of RBOs with different responsibilities, therefore one single questionnaire could not cover them all, or some questions were outside RBOs responsibilities.

There are: _RBOs that have existed for a long time_

> 12 RBOs with no entries in the table were either not approached nor been followed.

> 13 INBO Members have commented about this questionnaire and found it non adapted to practical RBOs in the field and, therefore, faced numerous difficulties in answering it.

> 14 Questionnaires from RBOs of transboundary rivers were eliminated from the sample.

46
demographic prevalence patterns and indications of trends among childbearing women." AIDS 11: 339—45.

Garnett, Geoff P., Nicholas C. Grassly, and Simon Gregson. 2001. "AIDS: The Makings of A Development Disaster?" Journal of International Development 13: 391—409.

Gertler, Paul, Manisha Shah, and Stefano Bertozzi. 2003. "Sex Sells, But Risky Sex Sells for More." Processed.

Gregson, Simon, Heather Waddell, and Stephen Chamdiwana. 2001. "School Education and HIV Control in Sub-Saharan Africa: From Discord to Harmony?" Journal of International Development 13: 467—85.

Gregson, Simon, Nicola Terceira, Phyllis Mushati, Constance Nyamukapa, and Catherine Campbell. 2001. "School Education & Avoidance of Early HIV Infection: The Mediating Roles of Social Capital & Psychosocial Factors among Young Women in Rural Zimbabwe." Processed.

Grossman, Michael. 1972. "On the Concept of Health Capital and the Demand for Health." The Journal of Political Economy 80 (2): 223—55.

Hargreaves, James R., and Judith R. Glynn. 2002. "Educational attainment and HIV-1 infection in developing countries: a systematic review." Tropical Medicine and International Health 7 (6): 489—98.

Kamali, A., M. Quigley, J. Nakiyingi, J. Kinsman, J. Kengeya-Kayondo, R. Gopal, A. Ojwiya, P. Hughes, L. M. Carpenter, and J. Whitworth. 2003. "Syndromic management of sexually transmitted infections and behaviour change interventions on transmission of HIV-1 in rural Uganda: a community randomised trial." The Lancet 361: 645—52.

Kenkel, Donald S. 1991. "Health Behavior, Health Knowledge, and Schooling." The Journal of Political Economy 99 (2): 287—305.

Kiefer, Nicholas M. 1988. "Economic Duration Data and Hazard Functions." Journal of Economic Literature 26(2): 646—79.

Kilian, Albert H. D., Simon Gregson, Bannet Ndyanabangi, Kenneth Walusaga, Walter Kipp, Gudrun Sahlmuller, Geoffrey P. Garnett, Godwill Asiime-Okiror, Geoffrey Kabagambe, Peter Weis, 35
an international setting. Papers such as Claessens and Laeven (2003), and La Porta, Lopez-de-Silanes, Shleifer, and Vishny (1997) point out the importance of the investment environment as a determinant of financial development.

The variable of interest in this study, FPI, is included in its net form (inflows minus outflows) for the countries in the sample. This data was collected from World Bank’s World Development Indicators and includes debt and equity investments made by investors foreign to the issuer’s domicile nation. Figures are given as net levels and are scaled by a proxy for the size of the nation, the country’s GDP.

Instruments of the variable of interest are included due to the endogenous nature of FPI. Relative interest rates are included to control for investor demand for interest-bearing securities in the firm’s domicile nation versus those of other countries (Samak and Helmy, 2000). Share is included to address the attractiveness of a given economy based on market capitalization (generally in more developed capital markets), or the decrease in the cost of equity and the resulting increase in the price of existing shares that occurs with market integration (Patro and Wald, 2005; Henry, 2000). To provide an additional determinant of international trade/investment, I include the change in real foreign exchange rates to provide a meaningful value indicator of capital investments (Agarwal, 1997). FPI volatility is calculated as the variance of FPI levels for the trailing three year term (time t-3 through t-1). These scaled values are used to elicit predicted values of scaled net foreign portfolio capital flows based on the work of Agarwal (1997). Bekaert, Harvey, and Lundblad (2003), Henry (2000), Patro and Wald (2005), and a host of other papers address this multi-faceted environment.

The basis of nation type for the analysis, property rights, is used to examine the ultimate impact of the importance of the development of the same. This variable is chosen following La Porta et al. (1997) and La Porta, Lopez-de-Silanes, and Shleifer (2006), who find the importance of security law and investor protection significant. This variable is an appraisal by the Heritage Foundation of the level of freedom an individual has to accumulate personal property. It is based on the extent and enforcement of laws designed to protect property accumulation. Specifically, it takes into consideration the independence of a country’s judiciary system as well as the ability of an individual to enforce his right to hold property 14
borrowers in 119 countries by lenders with ultimate parents in 10 countries.<sup>6</sup> This sample is used in the regressions explaining lead bank selection. For about 70% of the individual loan contributions, Dealscan does not report the loan volume, which limits the number of borrower countries in our sample to 82 and the number of lender countries to 10 in the loan volume regressions.<sup>7</sup> We consider loan volume regressions where loan volumes are aggregated alternatively at the borrower country-year level and the borrower country-lender country-year level. Panel A of Table 1 provides summary statistics for variables at the borrower-country level. In particular, Volume is the sum of the US dollar value of all loans with a mean of $2.6 billion. To be able to use observations with a zero loan volume, we consider two alternative transformations of the loan volume. First, Log(Volume + 1) is the natural logarithm of 1 plus the sum of the US dollar value of loans with a mean of 11.99. Second, Arsinh(Volume) is the transformed loan volume using the inverse hyperbolic sine function, with a mean of 12.41.<sup>8</sup> We construct three credit ratios that inform on potential regulatory arbitrage through the usage of a foreign subsidiary located in the borrower country or in a third country. First, Foreign subsidiary/total volume is computed as the share of loans provided by any foreign subsidiary with a mean of 0.095. Figure 2 plots the development of the share of cross-border loan volume provided by a foreign subsidiary over the period 1995-2015. Foreign subsidiary/total volume is apparently procyclical, increasing from about 7% in 2001 to about 16% in 2008. Furthermore, Borrower-country subsidiary/total volume is the share of loans

> 6 <mark>By a loan we refer to the individual contribution of a lender in a facility. Facilities are credit agreements between a borrower and one or more lenders.</mark>

> 7 <mark>For the top 5 lender countries, Table A2 in the Appendix provides information on the top 3 borrower countries and the top 3 subsidiary countries. For lenders in Germany, Japan, and the United Kingdom, the United States is the top borrower as well as subsidiary country. For lenders in the United States, the United Kingdom is the top borrower country as well as the top subsidiary country. For lenders in France the top borrower country is the United States and the Netherlands is the top subsidiary country.</mark>

> 8 <mark>The inverse hyperbolic sine transformation transforms loan volumes according to the function arsinh�x�� ln�x�</mark> <u><mark>√x</mark></u><sup>�</sup> <mark>�1�.</mark>

8