rafmacalaba/datause-encoder-data huggingface dataset:
years of healthy life got lost in 2015 due to this exposure. Another ambient air pollutant ozone was estimated to cause an additional 254,000 deaths in the same year (Cohen, 2015). Although global rates of mortality and morbidity due to PM2·5 exposure have decreased during the 1990-2015 period, the absolute numbers of deaths and disability-adjusted life-years have increased.

Based on data provided by Cohen et al. (2017), Figure 4 presents distribution of global mortality and morbidity due to ambient air pollution to the type of diseases enhanced by the ambient air pollution. Of the total ambient air pollution caused deaths of 4.2 million in 2015, 36% can be attributed to ischemic heart disease and about 20% each to cerebrovascular diseases and chronic obstructive pulmonary disease. Seven percent of the total deaths were caused by lung cancer (Figure 4a). Ambient air pollution poses a major risk to elderly people. Of the total people died due ambient air pollution in 2015, more than 50% were those with age greater than 70 years. About 42% of the total ambient air quality related death in 2015 were women. Of the total morbidity, measured in terms of disability-adjusted life-years (DALYs) in 2015, that was caused by ambient air pollution, 31% was caused by ischemic heart disease followed by lower respiratory infection with 28% of the total DALYs (Figure 4b).

pollution. Exposure is quantified based on population-weighted annual mean concentrations of PM2·5 and ozone. Global annual mean exposure to PM2·5 was estimated at 0·1 × 0·1° (~11 km × 11 km at the equator) resolution using estimates from satellites combined with a chemical transport model, surface measurements, and geographical data. Uniformly distributed TMREL values were 2·4 to 5·9 μg/m3 for PM2·5 and 33·3 to 41·9 parts per billion for ozone. Risks across exposure distribution were determined based on integrated exposure–response functions (IERs). IERs assign concentrations of PM2·5 to each type of exposure on an equivalent μg/m3 basis assuming that risk is determined by the 24-h PM2·5 inhaled dose regardless of the exposure source. The relative risk of COPD mortality from ozone exposure using a linear exposure–response function for respiratory mortality. DALYs and deaths attributable to ambient air pollution were calculated by applying the year-specific, location specific, age-specific, and sex-specific PAF to the numbers of DALYs and deaths. PAF (population-attributable fraction) is the proportion of deaths and DALYs attributable to exposure above the TMREL.

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<mark>Second, to s</mark> tudy the impact of global factors on individual bond yield and maturities, we <mark>construct a data set which captures the universe of 6,307 individual bond deals for 71 emerging and developing economies in the 2000-14 period. These bonds are issued by 210 country-industries. The other 289 country-industries never issued externally during the sample period. Appendix 1 provides details of issuance activity on the country level. Bonds often consist of multiple tranches with different characteristics. Therefore the number of observations in this dataset is 6,925 bond tranches.</mark> Our two bond variables of interest are _yield to maturity_ (defined as the rate of return on a bond assuming the bond is held until maturity at the time of issuance) and _maturity_ (defined as the number of years for which the bond remains outstanding at the time of issuance). We lose tranche observations due to missing data. As a result, we have yield data for 5,962 bond tranches and maturity data for 6,804 (non-perpetual) bond tranches, respectively.

This bond tranche level data set allows us to control for bond-specific characteristics that could influence the two variables of interest. We can therefore account for changes in issuance composition over time. These bond tranche level variables include:

- _Size of bond tranche issued_ refers to the total U.S. Dollar volume of the individual tranche of the deal;

- _Currency_ is an indicator variable that captures the currency in which the tranche is issued;

- _Investment grade type_ is a set of indicator variables that indicates whether the bond tranches are investment grade or not – i.e. a credit rating of BBB- or higher according to S&P or Baa3 or higher according to Moody's. This variable allows us to control for adverse selection issues;

- _Borrower industry_ is a set of indicator variables that captures the industrial sector of the issuing entity (Consumer, Finance, Metals, Professional Services, Transportation, Utilities, and Other);

- _Borrower type_ distinguishes between three different types of borrowing entities, publiclocal (local and state/provincial authorities), public-other (central government) and nonpublic; and

- _Deal type_ is a set of indicator variables which reflects the type of bond tranche such as Asset Backed Securities, Corporate Bond-High Yield, or Sovereign (see Table 2 for more details). The grouping is defined by Dealogic.

# **B.** **<mark>Global Push Factors</mark>**

We study the impact on external bond issuance of four global push factors that proxy for global financial conditions:

1. The VIX index ( _VIX_ ) (Figure 16) captures the options-implied 30-day ahead volatility of the S&P 500 equity index and is the most frequently used indicator as a proxy for global risk appetite, risk, and uncertainty. Higher values of _VIX_ are associated with higher bond yields and lower maturities. Research suggests EMPs have contributed to extremely low volatility.

10
# 2   Financial intermediation and bank lending

A minimum degree of financial intermediation is necessary to establish a market for term finance capable of funding infrastructure projects. This section will examine the degree to which domestic savings are being intermediated in the local financial sectors of the 24 focus countries.

Except in South Africa, the region’s financial sectors tend to be characterized as having a limited range of investment instruments (particularly for longer tenors), with commercial banks predominating, and a shortage of medium- and long-term bank credit and other forms of financing. Institutional and regulatory frameworks are relatively weak, and institutional investors are underdeveloped or nonexistent in some cases. In some countries, such as Chad, Democratic Republic of the Congo, and other countries of the Central African Economic and Monetary Community (CEMAC), the effectiveness of financial intermediation is undermined by factors including weak payment systems and floors on lending rates and ceilings on deposit rates that do not reflect market fundamentals, and regulatory impediments that make local sources of longer-term financing costly and scarce.<sup>5</sup> Among the selected indicators for assessing the level of financial depth and financial intermediation are (i) the total assets of financial intermediaries (and the ratio of those assets to GDP) and (ii) bank credit to the private sector as a share of GDP (table 2.1).

Other traditional indicators of financial development are the ratio of broad money to GDP and the level of real interest rates. However, recent studies have found evidence that these latter indicators may produce misleading signals about the extent of financial development because they do not account for certain factors, such as the economy’s openness to capital flows, banking sector competitiveness, and government borrowing from the financial system (Pill and Pradhan 1995). Bank credit to the private sector as a ratio to GDP is a favored indicator of financial intermediation and financial depth in developing economies, but it too has flaws. It does not adequately take into account nonperforming loans and credit granted by nonbank financial institutions and other financial innovations. Nor does it take into account the impact of commercial bank lending to other financial intermediaries (Pill and Pradhan 1995) None of the indicators we have mentioned captures the effects of the institutional environment on financial depth and development (McDonald and Schumacher 2007; Gelbard and Leite 1999), which can be considerable.

> 5 IMF 2006a; BEAC 2007. Commercial credit is very scarce in the Democratic Republic of the Congo; banks serve chiefly as financial agents for the government (EIU 2006b).

7
Hubbard, R.G. (1998). Capital market imperfections and investment. Journal of Economic Literature, 36(1): 193-225.

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Jiménez, G. and J. Saurina. (2004). Collateral, type of lender and relationship banking as determinants of credit risk. Journal of Banking and Finance, 28(9): 2191-2212.

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48
economic and political systems, to fight it using all the means under its control. The direct means for these challenges is through government mobility restriction policies, which could deter the spread of the pandemic. A country can also try to contain the pandemic by drawing on its formal institutions such as voluntary compliance with social distancing based on the legitimacy of the government under democracy, or direct control under authoritarian systems to address these challenges. A country can further draw on its cultural norms, such as collective cultural traits and general trust among the citizens.

This ongoing pandemic allows us to examine the roles of the government, democratic institutions, and cultural norms in containing the pandemic. If we classify the tasks confronting a society into normal activities—such as growing an economy and provision of basic public goods— and handling unusual crises, dealing with the pandemic is clearly the latter. In dealing with these distinct tasks, the roles of the government, institutions and culture differ greatly. For one thing, in normal times the government could use its formidable bureaucratic machinery to manage many routine tasks. But in crisis, the bureaucratic machinery matters less because of the work stoppage, and the role of leadership would matter much more (Fukuyama 2020), as would institutions and culture. Indeed, recent research suggests that a large portion of social distancing was explained by private individual choices rather than government lockdown policies (for example, Goolsbee and Syverson, 2020). It is thus intriguing how the roles of the government, democratic institutions and culture matter for the pandemic outcomes.

In this paper, we combine the COVID-19 data by the Center for Systems Science and Engineering (CSSE) at JHU, downloaded on November 19, 2020, with cross-country data from the World Bank and other sources to examine the key factors that are correlated with the two indicators of pandemic severity: the pandemic spread (i.e., the number of confirmed COVID-19 cases per 1,000 residents), and the pandemic mortality rate (among the confirmed cases). The explanatory factors are multidimensional, including preexisting demographical and environmental vulnerabilities (i.e., the share of elderly population, obesity prevalence, urbanization, and air pollution), government policy responses (i.e., the number of days that the government initiated some form of closedown, both domestically and internationally), democracy, and culture (i.e., individualistic culture, and general trust). We investigate not only their separate associations with the pandemic outcomes but also their interactions. Allowing for their interactions is important. The drastically different pandemic outcomes are often not due to a specific factor; rather, they are likely 3
illuminated when the power is working but appear dark when there is no power. Across a time series of imagery, areas prone to power outages should have higher variability in light output than areas with no outages.

Most prior research on nighttime light has relied on analysis of annual composite images. While annual composites provide a useful estimate of average brightness across the globe, they depict overly prolonged timespans, smooth away substantial variation in light output over the calendar year, and do not enable precise evaluation of discrete interventions like new village electrification projects, extensions of the power grid into new areas, nor short-term disruptions to the supply of electricity as a result of load shedding or maintenance failures. In this analysis, we return to the original raw nightly data to better study changes in the level of light output, as well as information on the variability of light output, which is a useful indicator of power supply irregularity.

The primary assumption of the approach, building upon a growing body of research (Elvidge et al. 1997b; De Souza Filho 2004; Doll et al. 2006; Sutton et al. 2007; Kiran Chand et al. 2009; Ghosh et al. 2010; Henderson et al. 2012; Min 2015), is that villages that benefit from electrical power appear brighter at night than unelectrified villages because of the use of outdoor lighting and leakage from indoor lighting sources. This relationship has been documented via ground-truthing studies comparing survey-based measures of electricity use to nighttime output in villages in Senegal, Mali, and Vietnam (Min et al. 2013; Min and Gaba 2014).

In partnership with NOAA’s National Centers for Environmental Information (NCEI), we acquired the complete archive of raw DMSP-OLS visual band imagery captured over India from 1993 to 2013. The archive comprises more than 30,000 highresolution image strips captured over nearly 8,000 nights. Each image strip represents a single pass on DMSP’s sun-synchronous, near-polar orbit with a swath width of about 3,000 km. Because India has an east-west width of about 3,000 km, image strips from two or three overpasses were often required to capture all locations on the Indian subcontinent.

The primary data of interest is the visible (VIS) band data, which records light output in a bandpass covering wavelengths from about 0.4 to 1.0 μm, which overlaps the wavelengths most commonly detectable to the human eye. Brightness on the visible band is recorded on a 6-bit scale, with relative brightness (digital number) values from 0 to 63.

Once the data were acquired and organized, the next step was to extract relevant brightness values from the image products. Since energy access and quality 7
# **<u>Appendix Table 5: Kitagawa-Oaxaca-Blinder Decomposition of Monthly Wages by Urban/Rural</u>**

||(1)|(2)<br>Urban|(3)|(4)|(5)<br>Rural|(6)|
|---|---|---|---|---|---|---|
||Overall|Endow.|Coeff.|Overall|Endow.|Coeff.|
|Male|88,236.180***|||10,414.785***|||
||(3,193.346)|||(928.905)|||
|Female|44,692.701***|||350.511***|||
||(2,089.437)|||(62.999)|||
|Raw Difference|43,543.479***|||10,064.274***|||
||(3,816.176)|||(931.039)|||
|Difference due to|17,187.349***|||4,441.201***|||
|Endowments|(1,527.404)|||(941.417)|||
|Difference due to|26,356.130***|||5,623.073***|||
|Coefficients|(3,999.809)|||(871.652)|||
|Individual Age||963.665|75,679.626||-662.908|11,162.860|
|||(588.082)|(70,318.410)||(1,356.438)|(11,173.964)|
|Individual Age Squared||-537.293|-28,527.808||346.444|-4,485.751|
|||(579.477)|(37,623.201)||(1,036.316)|(4,230.380)|
|Number of Adults||205.309|78.882||-275.362***|-4,720.178***|
|in HH||(174.758)|(5,013.285)||(83.966)|(1,579.305)|
|Number of Children Aged||-311.249|4,286.668*||42.065|-222.173|
|0-5 in HH||(286.585)|(2,238.269)||(86.906)|(793.724)|
|Individual owns a Financial||1,012.753|113.283||1,681.028***|405.325|
|Account||(665.705)|(3,840.605)||(436.804)|(318.736)|
|Individual is||-1,100.427**|10,633.930**||-2,386.262**|4,621.336**|
|Married||(539.423)|(5,074.315)||(823.402)|(1,851.284)|
|Individual is Divorced/||-451.522|-705.460||-103.181*|70.545|
|Widowed/Separated||(508.104)|(1,029.446)||(54.110)|(86.881)|
|Individual Completed||2,298.444***|-670.720||309.421**|-173.724|
|Secondary Schooling||(775.995)|(3,566.892)||(127.560)|(143.203)|
|Individual owns a Vehicle||13.361|550.209||6.169|299.741|
|(Bicycle, Motorcycle, Car)||(612.445)|(4,578.642)||(12.716)|(779.649)|
|Individual Works as||-480.883***|-955.571||-62.550**|-224.075***|
|Apprentice||(184.701)|(1,025.306)||(28.634)|(76.100)|
|Individual Works as||7,813.785***|7,187.670**||1,286.452***|-3.543|
|Manager||(656.712)|(3,484.212)||(236.033)|(182.858)|
|Individual Works as||3,280.616***|-3,452.283**||173.668**|-160.536*|
|Skilled Worker||<br>(535.828)|<br>(1,536.530)||(83.856)|(91.850)|
|Individual Works in the||4,485.839***|-6,475.619***||4,987.933***|-52,861.344**|
|Agriculture Sector||(743.844)|(1,591.811)||(1,090.634)|(11,054.282)|
|<br>Individual Works in the||<br>-5.047|<br>-16,744.753**||<br>-901.715|<br>-208.370|
|Service Sector||(105.677)|(5,065.529)||(690.681)|(607.989)|
|Constant|||-14,641.925|||52,122.962***|
||||<br>(31,856.671)|||<br>(16,174.822)|
|Observations|3,790|3,790|3,790|6,801|6,801|6,801|

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

47
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_Table 1: Estimating the gains from trade in health services for the US (Source: Mattoo and Rathindran, 2008)_

|_Procedure_|_US inpatient_<br>_price ($)_|_US inpatient_<br>_volume_|_US outpatient_<br>_price ($)_|_Estimated US_<br>_outpatient volume_|_Foreign price_<br>_including travel cost_<br>_($)_|_Savings if 10% of US_<br>_patients undergo_<br>_surgery abroad instead_<br>_of in the US ($)_|
|---|---|---|---|---|---|---|
|Knee surgery|10,335|399,139|4,142|60,000|1321|376,698,470|
|Shoulder Arthroplasty|5,940|23,300|7,931|N/a|2217|8,674,829|
|TURP|4,127|111,936|3,303|88,064|2413|27,029,437|
|Tubal Ligation|5,663|78,771|3,442|621,229|1280|168,834,441|
|Hernia Repair|4,753|40,553|3,450|759,447|1651|149,254,906|
|Skin lesion excision|6,240|21,257|1,696|1,588,884|805|153,078,349|
|Adult Tonsillectomy|3,398|17,251|1,931|102,749|1006|13,641,759|
|Hysterectomy|5,783|640,565|5,420|N/a|1987|243,163,366|
|Haemorrhoidectomy|4,945|12,787|2,081|137,213|865|21,893,438|
|Rhinoplasty|5,050|7,265|3,417|42,735|1936|8,590,926|
|Bunionectomy|6,046|3,139|2,392|41,507|1502|5,120,817|
|Cataract extraction|3,595|2,215|2,325|1,430,785|1247|154,681,706|
|Varicose vein surgery|7,065|1,957|2,373|148,043|1411|15,350,137|
|Glaucoma procedures|3,882|-|2,292|75,838|1086|9,143,374|
|Tympanoplasty|4,993|754|3,347|149,246|1404|29,258,785|
|Total savings||||||$ 1,384,414,741|

6