rafmacalaba/datause-encoder-data huggingface dataset:
To model estimated damage due to a particular earthquake event the data from the ShakeMaps and GESI are used. Then, one identifies the value of peak ground acceleration that each nightlight cell in Indonesia experiences by matching each earthquake point with its nearest nightlight cell. If the cell is further away than 1.5 kilometers or if it experiences shaking (PGA) of less than 0.05 the value is set to 0. In order to derive at a region _j_ specific earthquake damage index, _ED_ , the following equation is applied: where _DR_ is the damage ratio according to the peak ground acceleration, _pga_ , and the urban-rural qualification of cell _i_ , defined for a set of 8 different building quality _q_ categories. The weight _wi_ is the same as before; the sum of the nightlights of the affected cells over the sum of the total provincial nightlights.

# **5.1.2 Results**

With the above method, we find that 27 of the 34 provinces were damaged by earthquakes at some point in time.<sup>11</sup> Table 7 shows that the big islands Java and Sumatra have the most affected nightlight cells, given how densely populated they are and how much seismic activity is experienced there this is expected.

Table 7: Times a Lit Nightlight Cell is Damaged by Earthquake by Province

|Province<br>Times|Nightlight Cell|Damaged<br>Percentage of Total|
|---|---|---|
|Aceh|1,170|22.28|
|Sumatera Utara|722|13.75|
|Sumatera Barat|511|9.73|
|Sulawesi Tengah|354|6.74|
|Jawa Barat|353|6.72|
|Sumatera Selatan|283|5.39|
|Jawa Tengah|259|4.93|
|Jawa Timur|242|4.61|
|Bengkulu|165|3.14|

Finally, there were 5,251 cases where the instance hit a nightlight cell that was lit. Table 8 shows that the individual nightlight cell weights are small, as expected, but the impact of having buildings of quality 4 is that within a cell that is hit, on average a bit more than 6 percent of the buildings are destroyed.<sup>12</sup> Table 8: Descriptives of Weights and Intensity for Building Quality 4 (excluding weights of zero)

|Statistic|N|Mean|St. Dev.|Min|Max|
|---|---|---|---|---|---|
|Weights|5,251|0.226|0.407|0.009|9.328|
|Damage|5,251|0.062|0.043|0.046|0.547|
|Intensity|5,251|0.016|0.035|0.0004|0.859|

Weights and Intensity multiplied by 1,000

> 11The seven not affected were Bangka Belitung, Kalimantan Barat, Kalimantan Selatan, Kalimantan Tengah, Kalimantan Timur, Kalimantan Utara and Kepualauan Riau.

> 12We did the same for buildings of quality 0 (the best) and 7 (the worst), something which led to maximum damage values of 33% for the best buildings and 84% for the worst versus 55% for our base case, showing how the overall damage is highly dependent on building quality information.

17
economies after the GFC (Mc Cauley et al., 2015). For example, in the period 2009-12 emerging market debt funds received flows of $86 million, more than twice the $39 million received in the period 2003-06. As flows into emerging market funds increased, emerging market corporate bond yields declined, and the number of corporate bond issuances increased. In particular, there was substantial growth in the issuance of large denomination bonds. Investors tended to favor large, indexeligible bonds because they are more liquid and limit the risk of underperforming market benchmarks (Calomiris et al., 2019).

The empirical research shows that as interest rates declined in developed economies (particularly in the United States) since 2009, bond issuance activity by emerging market nonfinancial corporates expanded (Burger et al., 2018). Some calculations conclude that, without the monetary expansion in the United States, nonfinancial corporate bond issuances by emerging economies would have been about half of the actual issuance volume (Lo Duca et al., 2016). Firms’ balance sheet data unveil the same type of pattern. Overall, a 1 percentage point decline in the United States policy rate seems to increase leverage in emerging market firms by 9 basis points (Alter and Elekdag, 2020). Consistent with these patterns, other empirical evidence suggests that economy-specific factors (such as better macroeconomic fundamentals or institutional quality) became less relevant in explaining corporate leverage in the post-GFC period, whereas global financial conditions gained importance (Feyen et al., 2015; Ayala et al., 2017; Herwadkar, 2017).

Although there is some evidence that the low interest rates and abundant liquidity in bond markets might have relaxed corporate borrowing constraints (Alter and Elekdag, 2020), there is also evidence that speculative investment opportunities, such as carry trade, fueled bond issuance activity by nonfinancial corporates in emerging economies. Traditionally associated with the financial sector, carry trade is the activity of profiting from interest rate differentials in different markets by borrowing at low interest rates in one market and on-lending the proceeds in another market at relatively higher 9
also find that firms expand many years after they internationalize suggesting that market timing is not the only force underlying internalization.

However, Doidge, Karolyi, and Stulz (2009) argue that the findings in Gozzi et al. (2008) are the result of the inclusion of other cross-listings besides US exchange listings. They argue that a valuation premium is not predicted by theory for those non-exchange cross listings. Doidge, Karolyi, and Stulz (2009) show that a US cross-listing premium exists every year from 1990 to 2005 for exchange listed firms for which a premium is predicted by the bonding view.

Several other papers use firm-level data to examine the motivations for going abroad and the effect of internationalization at the firm level. Sarkissan and Schill (2004) find evidence of a proximity effect, that is, geographical proximity and other affinity factors such as trade links and common language determine cross-listing. Ayyagari and Doidge (2010) find that controlling shareholders use a U.S. cross-listing to sell their stake and facilitate changes in ownership and control. Others have examined the effect of equity market liberalizations (e.g. Henry, 2003; Patro and Wald, 2005) in reducing the cost of capital.

While most of the theories and empirical evidence are based on equity capital issuances around the world, the theories also hold implications for debt issuances. Recent studies suggest that international debt issuances are equally, if not more, important. Gozzi, Levine, and Scmukler (2010) study the extent of internationalization at the firm level by analyzing 168,513 equity and debt issues of firms from 116 countries over the period 1991-2005. They find that debt markets dwarf equity markets both in their internationalization (35% of debt capital was raised abroad compared to 10% of equity issues) and how corporations raise capital (firms raise four times more money through bond sales relative to equity issues).

# _5.4.3 Private Equity and Venture Capital_

Outside of bank financing and public debt and equity markets, private equity and venture capital are important financing sources in developed countries. The private equity market in the U.S. originated in the 1980s and has gone through several boom and bust phases. However, only recently have U.S. private equity firms have gone global in scope. And even then, Stromberg (2007) reports that private equity transactions outside of North America and Western Europe 47
(8) ln (Mit) = β0 + β1 Pi + β2 Pi ln(Sit) + β3 ln(Li) + β4 ln(Rit) + β5 ln(Dit) + Ɛit Coefficient interpretation from equation (7) β0 log(α0)+(α1+1) log(δ0) β1 (α1+1) γ β2 (α1+1) δ1 β3 (α1+1) δ2 β4 α2(1) β5 α2(2) where, for municipality i in year t:

- Mit = Road maintenance expenditure (taka)

- Pi = _Pucca_ (paved) road share

- Sit = Mean annual groundwater salinity (dS/m) Li = Total municipality road length (km)

- Rit = Municipality-based revenue (taka)

- Dit = Development income (taka) Ɛit = Random error term

Municipality income is determined by local revenues and development funds provided by outside agencies.<sup>10</sup> Although development funds are formally earmarked for investments, they are fungible to some degree because their availability enables municipality administrators to allocate fewer local resources to investment and more to recurrent expenses such as road maintenance. We introduce revenue (R) and development income (D) separately in (8) to allow for less-than-complete fungibility. A priori, we would expect the estimated coefficient of D to be less than or equal to the coefficient of R. Full fungibility implies statisticallyindistinguishable coefficients.

# **5.2  Estimation**

The unbalanced nature of our panel (Table 2) precludes spatial panel estimation in Stata<sup>11</sup> but, in any case, Figure 1 suggests that spatial autocorrelation is not likely to be a

> 10 International donors provide a significant share of Bangladesh’s development funds, but municipalities generally receive the funds from the central government.

> 11 A balanced panel is required for the xsmle estimator developed by Belotti, Hughes and Mortari (2013).

12
the outcome of a CV survey in terms of mean and median WTP per household among households in their own country and among all European households for avoiding a specified future forest loss in the Amazon. Delphi studies tend to differ according to whether they ask for the experts ́ recommended course of action, or to predict an outcome if some action was taken. Our study falls into this latter category, consistent with the “Oracle of Delphi” as we ask our experts for their best guess about average individual (national, and European) preferences for a public good, were a CV survey to be carried out.

Our Delphi expert survey serves at least three purposes. The first is as an independent check of level and plausibility of numbers obtained in the respective population surveys. Secondly, the Delphi surveys will allow us to make valuation assessments for a much larger set of countries than the individual surveys; and thus hopefully provide a basis for calibrating relative population values across countries (including many which will not be surveyed directly). Thirdly, the Delphi surveys include questions the answers to which can be useful for guiding the future random population sample SP surveys.

Even though the wider project, involving SP surveys of random population samples, will use both CV and CE, only the CV format is used in our Delphi surveys, including the current one. This is because Delphi surveys by their nature need to be “direct” (seeking a direct WTP estimate) as is the nature of CV and not of CE. The overall project will then provide two separate types of calibration: between CV estimates for population samples and SP experts; and between CV and CE for the population samples.

We are aware of only one previous similar Delphi CV survey, Carson et al (2012), published recently but conducted in 1997. European SP experts were in that survey asked to provide their best guess for the mean and median WTP for households in their country and in Europe overall, given that a CV survey, asking households about their WTP for restoring the World Cultural Heritage Site of Fes Medina in Morocco, would then have been conducted. This Delphi CV survey was part of a larger effort (including CV surveys of Moroccan households as well as foreign tourists) to estimate the global economic benefits of a World Bank financed restoration program for this World Heritage site. The results from this survey showed that SP experts were willing and comfortable in providing their best guesses of the outcome of a national and a European CV survey of this global public good. The Delphi survey found a positive median and mean WTP for European households. No actual SP survey of the non-use values outside of Morocco was however conducted, to make it possible to validate and calibrate of the Fes Medina Delphi CV survey.

Turning now to this survey, the categories of global welfare gains, accruing to the global population outside of South America, that may result from reduced Amazon rainforest losses, include the following ecosystem services: 5
|**Table 2: Heterogeneity in Baseli**|**ne Size of Firms**|**Participating in**<br>**Mean**|**Business Tr**<br>**Monthly P**|**aining Experi**<br>**rofits (USD)**|**ments**<br>**Monthly R**|**evenues (USD)**|
|---|---|---|---|---|---|---|
|**Study**<br>**% wi**|**th zero employ**|**ees Employees**|**Mean**|**S.D.**|**Mean**|**S.D.**|
|Berge et al. (2012)|n.r.|1.08|480|384|2102|3083|
|Males|n.r.|1.18|528|432|2586|2876|
|Females|n.r.|1.03|455|354|1847|3160|
|Bruhn and Zia (2012)|n.r.|2.08|700|n.r.|n.r.|n.r.|
|Calderon et al. (2012)|60|1.6|121|183|398|610|
|De Mel et al. (2012)|95|0.06|35|17|109|99|
|Drexler et al. (2012)|60|n.r.|n.r.|n.r.|747|1215|
|Giné and Mansuri (2011)|90|2.43|n.r.|n.r.|n.r.|n.r.|
|Males|86|2.51|n.r.|n.r.|380|n.r.|
|Females|95|2.34|n.r.|n.r.|80|n.r.|
|Glaub et al. (2012)|n.r.|1.5|n.r.|n.r.|100|n.r.|
|Karlan and Valdivia (2011)|n.r.|0.22|-165|4118|534|1230|
|Klinger and Schündeln (201|n.r.|8|n.r.|n.r.|6916|17333|
|Mano et al. (2012)|n.r.|n.r.|2200|2700|4717|5658|
|Sonobe et al. (2011)|||||||
|Tanzania|n.r.|5|530|1056|866|1393|
|Ethiopia|n.r.|33|19599|38048|142311|354163|
|Vietnam - Steel|n.r.|17|2627|4181|105787|98526|
|Vietnam - Knitwear|n.r.|20|-888|7234|7055|16509|
|Valdivia(2012)<br>Notes:<br>n.r. denotes not reported|n.r.|0.23|n.r.|n.r.|740|1696|

28
may be close to $40,000 in present-value terms for women and more than $70,000 for men, assuming a 3 percent discount rate.<sup>4</sup> The estimated annual salary losses also allow a comparison with the simulations discussed in the introduction, and they are of the same order of magnitude. Azevedo et al.’s (2021a: 18–20) estimated range for the average annual student loss is between (2017 PPP) $366 and $1,776. Our results range between (2019) $1,589 and $3,722. Our results might tend to be higher for two reasons. First, we use 2019 rather than 2017 US dollars. And second, Azevedo et al.’s results represent the averages for students across the world. In contrast, we estimate the impact for students in a high-income country, where average salaries are significantly higher.

**Table 3: Estimates of annual income loss and the present value of lifetime income loss**

|Exposed to school<br>disruption in:|Wo<br>Annual salary loss<br>(US$)|men<br>Present value of<br>lifetime loss(US$)|M<br>Annual salary loss<br>(US$)|en<br>Present value of<br>lifetime loss(US$)|
|---|---|---|---|---|
|Elementary school|1,589|20,663|2,667|40,185|
|Middle school|2,010|30,141|2,605|44,536|
|Secondary school|2,384|39,964|3,722|70,248|

Note: All values are in 2019 US dollars, using the exchange rate of $3.29 per Kuwaiti dinar. This is the average exchange rate in 2019, the source year of the data.

Assumptions: Working life of women is twenty-five years. Working life of men is thirty years. Discount rate is 3%. Annual salary loss is computed from the average salary of a Kuwaiti national of a given gender working in the civil service. Present value of lifetime loss uses average annual salary of a Kuwaiti national of a given gender working in the civil service with a given number of years of service. Average number of years before joining labor force is 11.8 (elementary school), 7.3 (middle school), and 3.8 (secondary school) for women and 11.6 (elementary school), 7.1 (middle school), and 3.6 (secondary school) for men. Source: Bilo et al. (2021), authors’ calculations.

> 4 There are two reasons for the gender difference. First, male average earnings are higher. Second, we assume a longer working life for men because the earliest retirement age in some cases comes after 25 years of service for women but only after 30 years of service for men.

8
factors that differ and that would deliver different insights about the impact of policy‐based lending. Another aspect of heterogeneity could be explored by splitting up the dependent variables by the six economic questions in the CPIA. So, for instance, the impact of policy‐based lending may differ as between interventions in trade and interventions in private sector regulation. Third, one could study the impact of purely sectoral operations (education, health, infrastructure etc.), if suitable dependent variables could be identified. Finally, given that macroeconomic crises often usher in periods of fundamental structural reform, it may be worth relaxing our assumption of a constant coefficient on the cumulative length of the engagement, so as to explore the possibility of cycles of reform effectiveness.

# **References**

- Besley, T., Persson, T., 2011. Pillars of Prosperity. Princeton University Press, Princeton and Oxford.

- Bogetic, Z. & Smets, L., 2017.  Association of World Bank policy lending with social development policies and institutions.  Policy Research Working Paper Series 8263, The World Bank.

- Devarajan, S., Dollar, D., Holmgren, T., 2001. Aid and reform in Africa: Lessons from ten case studies. World Bank, Washington, D.C.

- Dollar, D., Svensson, J., 1998. _What explains the success or failure of structural adjustment programs?_ Macroeconomic and growth group, The World Bank, April 1998.  Also: _Economic Journal_ 110 (466): 894‐917, October.

- Dreher, A., 2002. The development and implementation of IMF and World Bank conditionality. Hamburg: HWWA Discussion Paper 165.

- Easterly, W., 2005. What did structural adjustment adjust? The association of policies and growth with repeated IMF and World Bank adjustment loans. Journal of Development Economics 76, 1–22.

- Kapur, D., Lewis, J. P., Webb, R., 1997. The World Bank: Its first half century. Volume 1. History. Brookings Institution Press, Washington, D.C.

- Koeberle, S. G., 2003. Should policy‐based lending still involve conditionality? The World Bank Research Observer 18 (2), 249–273.

- Moll, P., Geli, P., Saavedra, P., 2015. _Correlates of success in World Bank development policy lending_ . Policy Research working paper no. WPS 7181. Washington, DC: World Bank Group. January.

- Paris Declaration on Aid Effectiveness, 2005. Issued jointly by 91 countries, most international aid organizations, and 14 civil society organizations. On the web at http://www.oecd.org/dac/effectiveness/34428351.pdf

- Navia, P., Velasco, A., 2003. The politics of second‐generation reforms. In: Kuczynski, P.‐P., Williamson, J. (Eds.), After the Washington Consensus: Restarting Growth and Reform in Latin America. Institute for International Economics.

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# **C.3 Construction of Satisfaction Indices**

As described in Section 4, we followed the procedure in (Kling et al., 2007) to construct three standardized indices of patient satisfaction: a sub-index that focuses on satisfaction with tests, a sub-index focused on satisfaction with medications, and an overall satisfaction index that includes both sub-indices and the question about care. Below, we list the individual questions asked in our home survey, and then we explain how the indices were constructed step by step.

## **Questions to Measure Satisfaction with Testing**

- How strongly do you agree or disagree with the statement “The doctor/nurse clearly explained the medical tests and their results to me?”

- How strongly do you agree or disagree with the statement “The doctor/nurse based the treatment decision strongly on the result of my medical tests?” OR How strongly do you agree or disagree with the statement “The doctor/nurse based the treatment decision strongly on the result of [patient name]’s medical tests.”

- How strongly do you agree or disagree with the statement “The doctor/nurse should have done additional medical testing before prescribing treatment”?

- How strongly do you agree or disagree with the statement “The doctor/nurse should have carried out fewer tests”?

## **Questions to Measure Satisfaction with Medications**

- Think of the treatment you got at the clinic or the drugs the doctor prescribed. How strongly do you agree or disagree with the statement “The doctor should have given me a different or additional treatment or drug”? OR Think of the treatment the patient got at the clinic or the drugs the doctor prescribed. How strongly do you agree or disagree with the statement “The doctor should have given [patient name] a different or additional treatment or drug”?

- Think of the treatment you got at the clinic or the drugs the doctor prescribed. How strongly do you agree or disagree with the statement “The doctor should have prescribed fewer drugs”? OR Think of the treatment [patient name] got at the clinic or the drugs the doctor prescribed. How strongly do you agree or disagree with the statement “The doctor should have prescribed fewer drugs”?

C5
# **Disaster Risk Preparedness of Households in the Caribbean**

Boaz Anglade, Emilia Cucagna, Jacobus de Hoop, and Anna Luisa Paffhausen

**Keywords:** Caribbean; Disasters; Natural hazards; Preparedness; Survey **JEL Codes:** F64, H84, N56, O13, Q54, Q56

The authors are all affiliated with the World Bank and appear in alphabetical order. The corresponding author is Jacobus de Hoop, who can be reached at: jdehoop@worldbank.org. Boaz Anglade can be reached at <u>banglade@worldbank.org, Emilia Cucagna at mcucagna@worldbank.org, and Anna Luisa Paffhausen at apaffhausen@worldbank.org. We thank Carlos Rodriguez Castellan and Eliana Rubiano Matulevic for detailed</u> inputs and suggestions on earlier drafts.