rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 1: Climate and Environmental related Natural Disasters across Income Groups and Disaster types**

|Disaster group|Disaster type|High<br>income|Lower<br>middle|Upper<br>middle|Low<br>income|World|
|---|---|---|---|---|---|---|
||||income|income|||
||Drought|45|129|123|106|403|
|Climatological|Wildfre|269|32|99|14|414|
||**Total**|**314**|**161**|**222**|**120**|**817**|
||Earthquake|216|208|630|67|1,121|
|Geophysical|Volcanic activity|31|63|107|6|207|
||**Total**|**247**|**271**|**737**|**73**|**1,328**|
||Flood|949|1,563|1,663|733|4,908|
|Hydrological|Landslide|99|202|310|80|691|
||**Total**|**1,048**|**1,765**|**1,973**|**813**|**5,599**|
||Extreme temperature|254|144|139|13|550|
|Meteorological|<br>Storm|1,684|1,063|854|200|3,801|
||**Total**|**1,938**|**1,207**|**993**|**213**|**4,351**|

**Table 2: Non-performing loan ratios across regions and periods**

This table disaggregates the average NPL ratios (non-performing loans net of provisions to capital) over 5-year intervals and regions. The numbers are computed using the IMF’s Financial Soundness Indicators data base.

|Region|1995-<br>2000|2000-<br>2005|2005-<br>2010|2010-<br>2015|2015-<br>2020|Full period|
|---|---|---|---|---|---|---|
|East Asia & Pacifc (EAP)|45.56|26.94|13.36|5.79|4.39|13.33|
|Europe & Central Asia (ECA)|32.47|26.73|26.64|34.21|26.03|27.69|
|Latin America & Caribbean (LAC)|-|-1.72|-1.78|2.97|9.59|1.82|
|Middle East & North Africa (MENA)|-|32.67|21.05|9.33|6.48|13.14|
|North America (NA)|-|3.63|8.43|10.12|5.44|7.80|
|South Asia (SA)|-|17.40|12.23|14.96|15.77|14.13|
|Sub-Saharan Africa (SSA)|-|11.12|15.04|9.86|11.28|12.23|
|World|39.02|16.68|13.57|12.46|11.28|12.88|

Tables 1 and 2 present statistics of key variables in this study. In Table 1 we see the disaster figures broken down by income and disaster groups/subtypes. The most frequent disaster episodes are hydrological, which mostly affect upper and lower middle income countries, and meteorological, which predominantly affect higher and lower middle income countries. Table 2 displays how average NPL ratios4 vary over time and regions. Overall, the NPL ratio has been declining since 1995. This decline is most notable in EAP and MENA. In LAC and SA, the trend has been growing since 2005.

Figure 2 exhibits the relationship between NPL ratios (right axes) with the number of disaster events during five-year intervals (left axes). Generally, we observe a downwards trend in NPL ratios, except for LAC and SA. In these two regions, the NPL ratios have been steadily increasing. Against the backdrop of rising disaster episodes, the increases in LAC and SA suggest a possible relationship between financial stability and natural disasters. The following empirical section examines this hypothesis in more detail.

> 4NPL ratios are calculated as nonperforming loans net of provisions to capital. This indicator is often used as a capital adequacy ratio and is an important indicator of the capacity of bank capital to withstand losses from loan defaults.

7
versus no. 20) (Figure 1, Panel B). Compared to other South Asian countries, the country’s urban stunting rate ranks in the middle (Figure 2).

_Urban versus rural areas within Bangladesh:_ Based on DHS data, we compare urbanrural trends in child stunting rates over the period 1997–2014 for Bangladesh as a whole (Figure 3, Panel A), as well as by division (Figure 3, Panel B). Stunting rates are lower for urban than rural children in Bangladesh, consistent with the pattern documented for developing countries in general (IFPRI 2016; Van de Poel, O’Donnell, and Van Doorslaer 2007). Bangladesh’s urbanrural stunting rate gap is on the lower end among South Asian countries (Figure 2). In addition, compared to a large set of developing countries, Bangladesh’s urban-rural stunting rate gap is smaller than those of most other countries (IFPRI 2016).

Bangladesh’s urban and rural child stunting rates are declining over time. In addition, the gap in rural and urban stunting rates has narrowed from 16 percentage points (pp.) in 1997 to 7 pp. in 2014. The trends in urban and rural stunting rates are not uniform across divisions, and are also more erratic within all divisions. Comparing the first year to the last year in our time series, the gap in rural and urban child stunting rates has narrowed markedly in several divisions.

# D. _Urban primary healthcare_

While nutrition sensitive in nature, primary health services in Bangladesh, in particular reproductive, maternal, newborn, and child health (RMNCH) services serve as a main delivery mode for nutrition-specific interventions. Given this, we perform an extended review of the empirical and qualitative research on the supply and demand for, and quality of, such services in the country’s urban areas.

_Supply_ : Nongovernment organizations have played a pivotal role in Bangladesh’s health development. Chowdhury et al. (2013) state that the contribution of NGOs to national health development was mainly due to strong NGO leadership, international donor support, and government permission and partnership. Urban governments, which are mainly responsible for public primary health service in their areas, have contracted out the operation of a majority of public primary health facilities to NGOs. Nongovernmental organizations also operate their own facilities. Urban governments and NGOs deploy community health workers to engage in health 11
As was pointed out by Gordon (1997), the Phillips curve summarizes the dependence of inflation on three basic factors: inertia, demand, and supply. This Phillips curve framework is often referred to as the traditional Phillips curve to separate it from the new Phillips curve.<sup>17</sup> The new Phillips curve in spirit is similar to the traditional Phillips curve as it also links inflation positively with economic activity. However, the new Phillips curve differs from the traditional Phillips curve as it relates inflation to movements in real marginal cost in lieu of the output gap. Proponents of the new Phillips curve argue that this framework is more adequate since it is obtained from a model of staggered nominal price setting by monopolistically competitive firms, and thus has more solid theoretical foundations. Gali et al. (2001), however, show that by imposing certain restrictions on technology and labor market structure, and within a local neighborhood of the steady state, real marginal cost is proportionally related to the output gap.

The traditional Phillips curve’s popularity arises from, in part, its relative success as a forecasting tool. Stock and Watson (1999) argue that “As a tool for forecasting inflation, it is widely regarded as stable, reliable and accurate, at least compared to the alternative.” In parallel to the widespread use of the traditional Phillips curve for industrial countries, recent studies by Coe and McDermott (1999) and Simone (2000) have considered emerging market economies. The former study estimates Phillips curve based on output gaps for 13 Asian countries. Their findings indicate that the output gap is a significant determinant of inflation in 11 out of 13 countries even after controlling for other variables such as monetary disequlibria. The latter investigation estimates time varying Phillips curves for Chile. His conclusions suggest that although the model,

> 17 See Goodfriend and King (1997) for a comprehensive survey of the new Phillips curve literature.

18
# 2.4 Matching Health Data to Water Quality Data

The primary challenge to evaluate the pollution‐health relationship is the endogeneity of pollution exposure. Pollution is not randomly assigned and is often the byproduct of productive activities. In the case of nitrates in the water, it is largely a byproduct of intensive agriculture and untreated urban waste. Thus, a naïve approach which examines impacts of local pollution on local impacts will likely conflate the positive effects of increased production with the negative externalities of water pollution, and underestimate the latter’s effect. To circumvent this bias, we construct a measure of upstream pollution using the geography of river flow. Similar techniques to identify upstream‐downstream relationships have been applied in recent economics literature (Garg et al., 2018’ Do et al., 2018 and Keiser, 2018). This choice is predicated on the fact that the decision to pollute upstream is orthogonal to downstream health, while geography dictates that pollution flows downstream.

We make use of a digital elevation model from the Shuttle Radar Topography Mission (SRTM) mission to identify the direction of stream flow and to track upstream and downstream through surface waters in India. We link the districts in our sample to all other districts that are upstream from it as connected by the stream network as shown in Figure 1. Since water quality decays over time, we bound the distance between upstream and downstream district‐pairs such that the upstream district is the closest upstream district, and up to 300 km apart. For any given downstream district, we then calculate the average concentrations of nitrate‐nitrogen pollution in the upstream districts.

# 2.5 Data for Vietnam and Sub‐Saharan Africa

To test for external validity of the results from India, we also measure the impact of nitrogen pollution on health in other geographic settings: Vietnam and 33 countries in Africa.

# _Data for Vietnam_

The water quality data come from the Mekong River Commission (MRC), which collects data for four countries (Cambodia, Lao PDR, Thailand, and Vietnam) spanning the years 1985‐2010, and cover the main tributaries of the Mekong River. Our health data come from the latest Vietnam Living Standards Survey (VLSS) of 1997–98 where we focus on the health outcomes of children aged 4 to 12 years. The VLSS 97–98 was a nationally representative survey that sampled almost 6,000 households across the country. For each member of the surveyed household, the survey contains information on gender, year of birth, age, and anthropometric outcomes. At the household level, information is available on the ethnicity of the household head and the province of residence. Our sample is restricted to those who have always resided in the same place. Because nitrate‐nitrogen levels in Vietnam are relatively lower, exposure to nitrate pollution is examined at levels that are above the 75th percentile in the distribution, or roughly 7
# **II. The Growth Identification and Facilitation framework**

26. New Structural Economics notes that modern economic growth is a process of continuous technological innovation, industrial upgrading and diversification, and improvements in the various types of infrastructure and institutional arrangements that constitute the context of business development and wealth creation. At any given point in time, the structure of a country’s endowment, that is, the relative abundance of factors that the country possesses, determines relative factor prices and thus the optimal industrial structure. A low-income country with abundant labor or natural resources and scarce capital will have comparative advantage and be competitive in labor-intensive or resource-intensive industries. Hence, the optimal industrial structure in a country which will make the country most competitive is endogenously determined by its endowment structure. For a developing country to reach the income level of advanced countries, it needs to upgrade its industrial structure to the same relative capital-intensity of the advanced countries.

27. A country’s endowment structure is not static, but will depend on the rate of capital accumulation and technological progress. The change in relative prices associated with these changes will affect the type of industries in which the country has a latent comparative advantage and hence the optimal industrial structure, given that, in order to be competitive, the new industry needs to be consistent with a country’s latent comparative advantage.<sup>5</sup> Of particular importance to the latent comparative advantage is the wage level. By imitating or licensing to obtain technology—a process that is less expensive than inventing the technology on their own—low-income countries will be able to produce the same commodities at a significantly lower cost than developed countries provided the enabling conditions have been created. That way the country can exploit the latecomer advantages by developing matured industries in dynamically growing, more advanced countries with endowment structures similar to theirs. By following carefully selected lead countries, latecomers can emulate the leader-follower, flyinggeese pattern that has served well all successful economies since the 18<sup>th</sup> century.

28. The process of upgrading the industrial structure to a higher level consistent with the factor endowment cannot rely solely on the market mechanism. For example, starting a new industry may be difficult because of the lack of complementary inputs or adequate infrastructure for the new industry even if the targeted industry is consistent with the economy’s comparative advantage. Private firms will not be able to internalize those investments in their decision to upgrade or diversify. Therefore, the government has an important role in providing or coordinating investments in necessary infrastructure and complementary inputs.

> 5Lin and Monga: “ Growth Identification and Facilitation--The Role of  the State in the Dynamics of Structural Change“, World Bank Policy Research Working Paper.

13
When it comes to estimating business outcomes, a key issue is how to handle businesses which are closed. Our approach is to code the outcomes for these firms as zero.<sup>19</sup> That is, a business which is closed is assumed no longer to have a formal license, a business bank account, or other such outcomes. For several savings outcomes for which it is possible that individuals are saving even without operating a business, we use the sample of firms still in business since we lack data on these outcomes for those whose businesses have closed. Appendix Table 2 shows there is no impact of any of the different treatments on business closure rates.

In addition, we follow the methodology of Kling et al. (2007) to test the significance of families of outcomes in a single aggregate. For each family of outcomes, we convert all outcomes so that the signs of all the variables in a family go in the same direction; calculate the z-score of each variable by subtracting the control group mean and dividing by the control group standard deviation; and take an average of the z-scores in the family. When considering the heterogeneity of outcomes, we follow Fink et al. (2014) and use the Benjamini and Hochberg (1995) method for limiting the false discovery rate (FDR).

# **4.2 Impacts on Formalization**

Table 4 reports the impacts of our different treatments on the three key dimensions of formality. These measures are self-reported by business owners from the four follow-up surveys.<sup>20</sup> Although we asked the business owners to show the certificates for each of the dimensions of formality, a significant number of them – including of those that we have delivered business registration certificates – said they had them in a secure place like at home. Hence, reporting only on certificates shown to enumerators would underestimate the impacts on these measures.

We see that obtaining a business registration certificate is rare in the absence of our treatment – only 8 percent of the control group firms have a BRC on average at follow-ups. All three treatments have large and significant impacts on the likelihood a firm has a BRC, varying from a 52 percentage point increase for the BRC alone assistance to 64 percentage point increase for the

> 19 We obtain similar results if we treat the businesses that are closed as attrition. 20 Appendix Table 3 shows the results by survey round for the impacts on having a BRC. Although we can reject equality of impacts over time, the impacts are significant in each round, and vary by at most 7 percentage points. Differences across rounds vary from the time path of control firms registering on their own, and of firms closing down and no longer being formally registered.

17
# **Table III:** Program impacts on HIV and Anemia (beneficiaries)

## **<u>Panel A:  Baseline Dropouts</u>**

||=1|if HIV Pos|T Y<br>itive|=1 if<br>Anemic<br>T Y|
|---|---|---|---|---|
||During<br>Program|End of<br>Program|wo ears<br>After<br>Program|wo ears<br>After<br>Program|
||(1)|(2)|(3)|(4)|
|=1 if Conditional Schoolgirl|0.022|0.020|0.012|0.039|
||(0.024)|(0.023)|(0.026)|(0.035)|
|Mean in Control Group|0.06|0.094|0.135|0.255|
|Sample Size|373|694|715|711|
|**Panel B: Baseline Schoolgirls**|||||
|=1 if Conditional Schoolgirl|-0.020**|-0.003|-0.001|0.012|
||(0.009)|(0.011)|(0.019)|(0.031)|
|=1 if Unconditional Schoolgirl|-0.015|-0.019*|-0.002|-0.065*|
||(0.012)|(0.012)|(0.023)|(0.033)|
|p-value UCT vs. CCT|0.616|0.237|0.980|0.068|
|p-value Treatment|0.112|0.249|0.996|0.122|
|Mean in Control Group|0.026|0.035|0.055|0.243|
|Sample Size|1,192|2,002|1,977|1,979|

_Notes:_ Regressions are OLS models with robust standard errors clustered at the EA level. All regressions are weighted to make them representative of the target population in the study EAs. An individual is considered anemic if her hemoglobin count is less than or equal to 11g/dL if pregnant and less than or equal to 12d/dL if non-pregnant based on WHO guidelines to define mild anemia. Baseline values of the following variables are included as controls in the regression analyses: age indicators, stratum indicators, household asset index, highest grade attended, and an indicator for never had sex. We restrict the sample to respondents who were surveyed during the latest household survey conducted two years after the program (Round 4). Parameter estimates statistically different than zero at 99% (***), 95% (**), and 90% (*) confidence.
by Hanna _et al_ . (2005), they calculated a total loss double that in the previous decade. Comparing this rate of contribution of Greenland’s ice sheet to SLR with the IPCC estimate for the 20<sup>th</sup> century, the new measures are roughly two to five times greater. In another study of mass loss for Greenland using repeat altimetry, Krabill _et al_ . (2004) found that between 1993-1994 and 1998-1999, the ice sheet was losing 54 ± 14 gigatons of ice per year (Gt/yr). In contrast, net mass loss over the 1997-2003 interval averaged 74 ± 11 Gt/yr. At these rates of net mass loss, the contribution of the Greenland ice sheet to SLR is roughly double the rate assumed in the IPCC Third Assessment (2001) report<sup>2</sup> .

In Antarctica, using the Gravity Recovery and Climate Experiment (GRACE) satellites, Velicogna and Wahr (2006) have determined mass variations of the entire Antarctic ice sheet during 2002-2005.<sup>3</sup> Their results indicate that the mass of the ice sheet decreased significantly, at a rate of 152 ± 80 cubic kilometers of ice per year; most of this loss came from the West Antarctic ice sheet (WAIS). This rate is several times greater than that assumed in the IPCC Third Assessment, and the IPCC admitted that its final estimate did not take into account the dynamic changes in the WAIS. Increasing concern also attaches to the stability of the WAIS, which currently rests on bedrock below sea level. Mercer (1978) speculated that human-induced global warming could cause the WAIS to be released into the ocean by a sliding mechanism (also referred to as WAIS collapse). This would cause a rapid rise in sea level, since it would be triggered solely through a displacement of the WAIS without its having to melt. Were the WAIS to collapse, it would raise average sea level by approximately 5 to 6 meters (Tol et al., 2006).

While there remains considerable uncertainty about the above scenarios, and the time horizon over which they may unfold, recent research and expert opinion indicate that significant SLR may occur earlier than previously thought.<sup>4</sup> This has prompted a number of researchers to model the estimated impact of significant increases in SLR (these are sometimes termed ‘extreme climate scenarios’). A number of studies have provided estimates of the potential impacts for specific developed countries (e.g. France, the

> 2 360 gigatons of ice correspond approximately to 1 mm of sea level.

> 3 The GRACE result for total Antarctic ice mass change includes complete contributions from such regions as the EAIS coastline and the circular cap south of 82°S, which has not been completely surveyed with other techniques.

> 4 See Vaughan and Spouge, 2002.

4
Figure 2: Major Importers of Wood Furniture and Wood Manufactures from Major Developing Country Producers a/ <!-- Start of picture text --> US 27.1%<br>24.9%<br>Developing<br>Japa ~~~~~19.5%<br>21.5%<br>Other developed<br>6.6%<br><!-- End of picture text --> a/ Major wood furniture and wood manufactures developing country exporters are: Brazil, Chile, Indonesia, Korea, Malaysia, Philippines, Singapore, and Thailand. Other major expiters not in the group due to data unavailability are: China, and Hong Kong. Source: Calculation from U.N. Trade Statistics.

20
we control for (log of) GDP per capita ( _Income_ ) taken from World Development Indicators (WDI), World Bank.

Countries that are concerned about gender discrimination in hiring practices are also likely to be concerned about other forms of gender discrimination and hence they are likely to have other women friendly laws benefiting women’s employment. To guard against the implied spurious correlation, we control for an overall measure of gender disparity in the laws ( _WBL_ ) constructed using Women, Business and the Law database (see Table 1 for details). _WBL_ ranges between 0 and 18, with higher values implying greater gender disparity favoring men over women.

The literature on human capital suggests that labor market participation of women increases with education (Schultz 1994, Psacharopoulos 1994). Education is regarded as an investment in human capital, and higher levels lead to higher paying and socially more acceptable white collar jobs for women, drawing them into the labor force (Goldin 1994, O’Neil and Polachek 1993, Morrison et al. 2007). Education can have other indirect effects on women’s labor force participation through for example lower fertility rates, greater consciousness among women to improve their standard of living and greater empowerment for women (Morrison et al. 2007, Duryea et al. 2001). It is also plausible that a more educated women population may be more assertive in demanding the non-discrimination law. To account for these factors we control for two education related variables taken from WDI, World Bank – _Education gap_ equal to the average value of the ratio of women’s to men’s gross enrollment rates in primary, secondary and tertiary education; and _Women’s education (level)_ equal to the average value of women’s gross enrollment rates in primary, secondary and tertiary education.

There is some concern in the literature regarding enrollment rates such as the fact that these rates capture only access to education and not educational attainment. Hence, as a 10