rafmacalaba/datause-encoder-data huggingface dataset:
It is important to stress that a surprisingly large fraction of the expected value of tropical storms is caused by the most harmful storms. Table 6 presents the expected fraction of total damages caused by the top 10 percent and top 1 percent worst storms. Storms are ranked by the damage they cause. Even with the current climate, the top 10 percent worst storms cause 90 percent of the total damages from tropical storms. The top 1 percent worst storms cause 58 percent of all the damages. With the future climate, those percentages would likely climb. The top 10 percent worst storms are predicted to cause 93 percent of the damages and the top 1 percent of storms are predicted to cause 64 percent of all damages from tropical storms.

Table 6: Fraction of Total Damages Caused by Most Harmful Storms

|Model|Climate|Top 10 Percent<br>Worst Storms|Top 1 Percent<br>Worst Storms|
|---|---|---|---|
|CNRM|Present|90.9%|63.7%|
|CNRM|Future|93.6%|70.4%|
|ECHAM|Present|87.2%|53.4%|
|ECHAM|Future|92.7%|62.3%|
|GFDL|Present|88.9%|48.9%|
|GFDL|Future|92.1%|59.3%|
|MIROC|Present|92.6%|63.9%|
|MIROC|Future|94.3%|65.5%|

Note: Assumes baseline in 2100 and minimum pressure damage model.

Many of the most powerful storms would destroy everything in their path. We assume that these storms destroy all the capital within five coastal counties. Table 7 presents the predicted percent of storms that cause total destruction in each simulation. All the simulations assume the future baseline and use the minimum pressure damage model. These powerful storms are present in both the current and future climates. The only climate model where the number of truncations is significantly different between the current climate and the future climate is the MIROC model.

22
# **Figure S2.1: Tariffs and Tariff Changes Across Income Groups**

<!-- Start of picture text --> Tariffs (percent) Annual Tariff Changes (percentage points)<br>50 10<br>8<br>40<br>6<br>30 4<br>2<br>20 0<br>-2<br>10 -4<br>-6<br>0 -8<br>All AM EM LIC All AM EM LIC<br>1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 2014 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013<br><!-- End of picture text --> _Source:_ See Table S1.2.

_Note:_ The income groups are equally weighted averages of the tariffs of countries belonging to the <u>particular income group.</u>

# **Figure S2.2: Tariffs in Selected Countries Across Income Groups (percent)**

<!-- Start of picture text --> EM<br>AM<br>20 100<br>80<br>15<br>60<br>10<br>40<br>5 20<br>0 0<br>USA Japan Germany Canada Australia China Russia Brazil India Mexico<br>LIC<br>30 160<br>25 140<br>120<br>20 100<br>15 80<br>10 60<br>40<br>5 20<br>0 0<br>Nigeria Vietnam Myanmar<br>Uzbekistan Bangladesh (rhs)<br>1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 2014 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 2014<br>1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 2014<br><!-- End of picture text --> _Source:_ See Table S1.2.

_Note:_ AM = advanced economies, EM = emerging economies, LIC = low income developing countries.

6
# **1. Introduction**

COVID-19 triggered one of the most precipitous and dramatic shocks to households and regional economies since the end of World War II. Within weeks of the World Health Organization’s declaration of COVID-19 as a global pandemic on March 11, 2020, the virus had spread across most of the world.<sup>2</sup> As the crisis unfolded, there was an immediate need for fast, accurate data on the economic effects of the pandemic. The World Bank Group worked with national statistical offices to field phone surveys in dozens of member countries, with the goal of expeditiously measuring how the pandemic was impacting households. These surveys asked respondents about directional income changes, both for total and various sources of income. In addition, they asked about access to critical services and resources, food security, employment status, behavioral adjustments, and overall well-being. Insights from these survey data have proven to be invaluable for documenting the impact of the shocks on households in many developing countries and informing policy responses: they are enabling more precise targeting in assistance and recovery programs, whether in the form of direct cash transfers to residents or interventions to assist firms in the hardest-hit sectors. However, fielding phone surveys takes critical time, which is not always an available resource in the midst of a major crisis, and requires substantial resources to sustain over longer periods.

Real-time indicators – like satellite images, pollution data, and Google mobility data – are immediately available at low cost. Hence, it is not surprising to see that, for example, mobility data from Google has become popular for understanding community responses to the pandemic. When lockdowns were implemented, Google mobility data shows corresponding declines in activity in commercial areas and increases in time spent at residential spaces. These data are available in close to real time and can be used to document compliance with mobility restrictions. Similarly, near real-time data on nitrogen dioxide pollution showed less pollution in some areas in the aftermath of the shock (Masaki et al., 2020). The varied speed of the onset of the crisis and the progression of recovery in different countries, and regions within them, raises the natural question of how well these real-time indicators track the economic indicators collected in the phone surveys, and which real-time indicators are most informative.

> 2 Director-General’s opening remarks at the media briefing on COVID-19, 11 March 2020. <u>https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-atthe-media-briefing-on-COVID-19---11-march-2020.</u>

2
Thus, the results presented in the empirical section do not correct for errors-in-variables in the elasticity data.

# 4 Results

Table A0 shows the summary statistics for the variables used in our empirical analysis. The sample mean of the U.S. MFN tariffs is 6.7%. The sample mean of the U.S. preferential tariffs applied to Latin American exports is 3.8%. In the case of the elasticities of export supply, the sample mean is 12.248. The main empirical results of the paper are presented in tables 1-6. The results shown in these tables use data organized at the HS 8-digits level and under the assumption that the errors are spherical. We leave the discussion of the presence of non-spherical errors to the next section. The total number of observations at the HS 8-digit level is 14,372.

Tables 1 and 2 present the results of Tobit regressions for equations (27) and (28), respectively, using the fi rst quartile of the product of elasticities of export supply and tariff preferences to divide the sample. As explained in detail, the observations for which the product of the elasticity of export supply and tariff preference is greater than the value of the fi rst quartile threshold should have tariff preferences predicted by equation (27). Otherwise, they should have tariff preferences predicted by equation (28). Similarly, tables 3 and 4 and tables 5 and 6 show the results of Tobit regressions for equations (27) and (28) using the second and third quartiles of the product of elasticities of export supply and tariff preferences to divide the sample, respectively. The results shown in tables 3-6 are robustness checks to the results presented in tables 1 and 2.

The coefficients’ estimates shown in table 1 con fi rm the basic predictions of the theoretical model since coefficient φ0 is positive and coefficients φ1 and φ2 are negative. All estimates are signi fi cant at the 1% level and they are robust to the choice of thresholds which determines the value of the political organization dummies of exporters and importers. Since coefficients φ1 and φ2 have the predicted signs and are all signi fi cant, we then conclude that domestic 27
||P90|(9)<br>0.005<br>[0.007]|5,239<br>0.192<br>[0.113]*<br>5,239<br>0.114|[0.106]<br>5,239|s per firm in the city,|e as an instrument for|its square, interactions|d interactions with the|opulation in 91, 80 and|
|---|---|---|---|---|---|---|---|---|---|
|Self Employed|P50|(8)<br>0.01<br>[0.005]**|5,239<br>0.308<br>[0.109]***<br>5,239<br>0.269|[0.100]***<br>5,239|he number of inspected firm|pectors per firm in the stat|to the nearest labor office,|ortation costs, its square an|ooling and share of urban p|
||P10|(7)<br>0.033<br>[0.006]***|5,239<br>0.310<br>[0.119]***<br>5,239<br>0.308|[0.118]***<br>5,239|h column on the log of t|number of state level ins|cteristics include distance|utional quality, city transp|capita, population, av. sch|
|ners|P90|(6)<br>0.004<br>[0.005]|5,240<br>0.112<br>[0.092]<br>5,240<br>0.091|[0.087]<br>5,240|reported at the top of eac|ffice interacted with the|he third). City level chara|te level measures of instit|l controls for income per|
|ers<br>Informal Wage Ear|P90<br>P10<br>P50|(3)<br>(4)<br>(5)<br>-0.015<br>0.02<br>0.006<br>[0.006]**<br>[0.006]***<br>[0.003]*<br>Panel A: OLS|5,230<br>5,240<br>5,240<br>-0.326<br>0.041<br>0.061<br>[0.133]**<br>[0.096]<br>[0.066]<br>5,230<br>5,240<br>5,240<br>-0.240<br>0.049<br>0.059<br>Panel B: IV<br>Panel C: IV - Controls for Schooling|[0.122]**<br>[0.096]<br>[0.066]<br>5,230<br>5,240<br>5,240|reports the estimated coefficients of the regression of the city variables|instrumental variable estimates using city distance to the nearest labor o|in each city (10th percentile in column one, 50th in the second, 90th in t|with the number of inspectors per firm in the state, interactions with sta|y, city altitude, city latitude and city longitude. It also includes the city leve|
|ormal Wage Earn|P50|(2)<br>-0.002<br>[0.004]|5,230<br>-0.085<br>[0.070]<br>5,230<br>-0.033|[0.068]<br>5,230|gnificant at 1%. Panel A|ics. Panel B reports the|he schooling distribution|ty, its square, interactions|ures of institutional qualit|
|F|P10|(1)<br>Log Inspected Firms per firm in city<br>0.007<br>[0.004]*|Observations<br>5,230<br>Log Inspected Firms per firm in city<br>0.108<br>[0.077]<br>Observations<br>5,230<br>Log Inspected Firms per firm in city<br>0.131|[0.080]<br>Observations<br>5,230|Standard errors in brackets, * significant at 10%; ** significant at 5%; *** si|controlling for state level dummy variables and several city level characterist|enforcement. Panel C replicates Panel B but adds as control the percentile of t|with state level measures of institutional quality, distance to the state capital ci|number of inspectors per firm in the state and interactions with state level meas|
International Center for Research on Women, Washington, DC.

Mohan, Tarini. 2017. “A Field of Her Own: Advancing Rights of Women Farmers Can Revolutionize the Rural Ecosystem.” August 4, 2017. _India Express_ , Mumbai.

https://indianexpress.com/article/opinion/columns/indian-women-farmers-agriculture-sectorpatriarchy-land-ownership-4781311/.

MoHua, GoI (Ministry of Housing and Urban Affairs, Government of India). 2020. _A Toolkit for Property Tax Reforms._ Vol. 1. Cityfinance.in, Ministry of Housing and Urban Affairs, Government of India, New Delhi. https://cityfinance.in/resource/Property_Tax_Reforms_Toolkit.pdf.

National Investment Promotion and Facilitation Agency. 2021. “Decoding India’s 2021 Gender Responsive Budget.” Strategic Investment Research Unit, Invest in India, National Investment Promotion and Facilitation Agency, New Delhi (blog), March 3, 2021. https://www.investindia.gov.in/team-indiablogs/decoding-indias-2021-gender-responsive-budget.

Oduro, Abena D., Carmen Diana Deere, and Zachary B. Catanzarite. 2015. “Womens' Wealth and Intimate Partner Violence: Insights from Ecuador and Ghana.” _Feminist Economics_ 21 (2): 1–29.

O'Sullivan, Michael. 2017. “Gender and Property Rights in Sub-Saharan Africa: A Review of Constraints and Effective Interventions.” World Bank Policy Research Working Paper No 8250, <mark>World Bank, Washington, DC.</mark> Quisumbing, Agnes R., Neha Kumar, and Julia A. Behrman. 2011. “Do Shocks Affect Men's and Women's Assets Differently? A Review of Literature and New Evidence from Bangladesh and Uganda.” IFPRI Discussion Paper 01113, Poverty, Health and Nutrition Division, International Food Policy Research Institute, <mark>Washington, DC.</mark> RBI (Reserve Bank of India). 2019. “Debt: States’ Medium-Term Fiscal Challenge.” Reserve Bank of India, Mumbai. https://www.rbi.org.in/Scripts/PublicationsView.aspx ?id=19240#CH31.

Stewart, Miranda. 2017. _Tax, Social Policy and Gender: Rethinking Equality and Efficiency_ . Canberra: ANU Press.

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Stotsky, Janet G. 1997. “How Tax Systems Treat Men and Women Differently.” _Finance and Development_ (IMF) 34 (no. 1), March 1997. https://doi.org/10.5089/9781451953206.

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32
can improve the performance of their legal and judicial systems. The remainder of the paper focuses on one area of potential reform.

# <u>Procedural Complexity</u>

Clearly, it would be an impossible feat to discuss all the elements which affect judicial performance in a single paper.<sup>28</sup> As stated earlier in the paper, this paper aims to illustrate some general principles and discuss certain aspects of procedural law in this context. The focus on legal procedure does not imply that it has been found to be universally the most important or pressing area for judicial reform. Just as countries differ in the design of their laws and other institutions, so they differ in terms of where reform needs are the greatest. In this section, I base the discussion on a recent project initiated jointly by the World Bank, Harvard University and Lex Mundi, (the Project) analysed empirically (looking at a sample of over 100 countries) how excessive formalism affects judicial performance.<sup>29</sup> Since this Project is unique in that it covers a large number of countries it allows for comparisons between countries.

The Project focuses on a commercial transaction relevant for the everyday lives of ordinary people: small debt collection and it also studies another non-related transaction, tenant eviction, as a “control” event. The project is summarized in Annexes 2 and 3 which are from the World Bank, 2001 and Djankov et al 2003. It assesses how many procedural steps are required from the time notice is served to a debtor in default until enforcement of the court’s decision. The construction of the index which I will refer to as procedural complexity is described in Djankov et al (2003).

> 28 Botero et al (2002) provide a good summary of the existing literature which addresses country experience with judicial reform.

> 29 This project was originally undertaken for the World Development Report, 2002, World Bank, 2001. See Djankov et al (2003) for a detailed description. See also Annex 3.

26
# **1. Introduction**

Bangladesh has seen robust economic growth during the last decade, with real GDP growing by more than 6 percent on average every year during 2006 – 2015. Additionally, the economy has seen a long‐term acceleration in growth since the late 1970s when the real GDP was growing by just over 4 percent annually. The acceleration in real GDP growth has also translated to an acceleration in real GDP per capita growth as the gap between real GDP growth and population has been widening over time.<sup>2</sup> Beginning from $352 in 1980, real GDP per capita (measured in 2010 US dollars) crossed $700 in 2008 and is on mark to cross $1,000 next year. The sustained economic growth during the last few decades has helped the country graduate from a low‐income country to a lower‐middle‐income country as classified by the World Bank (World Bank, 2015).

<!-- Start of picture text --> 8<br>6<br>4<br>2<br>0<br>‐2<br>‐4<br>‐6<br>GDP GDP per Capita<br>Source: World Development Indicators<br>Average Annual Growth Rate (%)<br>1961 ‐ 1965 1966 ‐ 1970 1971 ‐ 1975 1976 ‐ 1980 1981 ‐ 1985 1986 ‐ 1990 1991 ‐ 1995 1996 ‐ 2000 2001 ‐ 2005 2006 ‐ 2010 2011 ‐ 2015<br><!-- End of picture text --> To better understand the sustained growth achieved by the country, it is useful to explore how some of the key growth drivers have evolved during this period. Capital accumulation has been a key driver of economic growth for many countries and Bangladesh is no exception to this phenomenon. Bangladesh has realized continued rise in the investment share of GDP during the last 35 years. The investment share of GDP has almost doubled from 14.4 percent of GDP in 1980 to 28.9 percent of GDP in 2015. Most of this increase has been driven by gains in private investment. With respect to its Asian neighbors, the country currently has a higher investment rate compared to Pakistan and Nepal but lags behind China and India.

The country has witnessed many changes in the labor market that have been important for growth. The demographic transition has led to a decline in the dependency ratio with the share of working age population rising continuously over time. Starting from 52 percent of the

> 2 Real GDP per capita saw negative growth during 1960 – 1980, but has been rising ever since.

2
16

# Table 2

# Production, Demand Structure, and Elasticities in the Auto Industry

|Premium Rate on Imports|36.4Z|<br>(31.81)|
|---|---|---|
|Rents Accruing to Foreigners-|7.87|<br>(7.87)|
|Rents Accruing to US Citizens<br>Wage Distortion in Autosb/|0.87<br>27.0?|<br>(0.0)|
|Production and Demand-/|||
|Gross Output|124.2||
|Domestic Final Demand Sales|111.0||
|Intermediate Sales|8.3||
|Exports|4.9||
|Imports|||
|Intermediates|2.3||
|Final Demand|30.3||
|Elasticities and Parameters-|||
|Capital-Labor Substitution|0.8||
|Import-Domestic Substitution<br>Elasticity-|1.9||
|Composite Final Demand|0.8||
|Export-Domestic Transformation Elasticity-|2.9||
|Calibrated Domestic Conjecture (D d)|0.72||
|Derived Price Elasticity of Demand (6 )|1.37||
|Cost Disadvantage RatioeI|0.11||

Notes: Second column estimates assume no rent capture by US dealers of Japanese auto imports.

- a/ 1984 USS billion.

- _b/_ Krueger and Summers (1988).

- c/ Sources for demand and supply elasticities are detailed in de Melo and Tarr (1990a).

- _dl_ The selected CES (CET) functions imply that the corresponding substitution (transportation) elasticitieR are compensated import demand (export supply) elasticities.

- _el_ Friedlaender, Winston and Wang (1983); and Winston and associates (1987).
||Modern service include exports in<br>||
|---|---|---|
|Modern Services|telecommunications, computer and information<br>services, other business services, financial<br>services, insurance, royalties and license fees.|Balance of<br>Payments,2008.IMF|
|Openness|Ratio of exports and imports to GDP|World Bank, 2008, WDI|
|Personal, cultural and<br>recreational services|(i)audiovisual and related services and (ii)other<br>personal, cultural and recreational services.|Balance of<br>Payments,2008.IMF|
|Population Growth|Year on year growth in population|World Bank, 2008, WDI|
|Public Expenditure on<br>Education|Public expenditure on education as a share of<br>GDP, Public expenditure on education as a share<br>of total Government expenditure|World Bank, 2008, WDI|
|Public Expenditure on<br>Health|Public expenditure on health as a share of GDP,<br>Public expenditure on health as a share of total<br>Government expenditure|World Bank, 2008, WDI|
|Roads Paved|Roads, paved (% of total roads)|World Bank, 2008, WDI|
|Savings|Gross domestic savings as a ratio of GDP|World Bank, 2008, WDI|
|Service Exports|Services refer to economic output of intangible<br>commodities that may be produced,transferred,<br>and consumed at the same time.|Balance of<br>Payments,2008.IMF|
|||World Bank, 2008,WDI<br>Sivasubramanium,2007 for|
|Service Labor Productivity|Value added by services (at constant 2000 US$)<br>divided by total employment in the tertiary sector|India Balance of Payments,<br>2008. IMF.<br>Bosworth,Collins,2007 for<br>China, Bosworth, Maertens,<br>2009|
|Service Growth|Growth in the service value added|World Bank, 2008, WDI|
|Service Sophistication|EXPY of the country's exports basket, calculated<br>using the formaula given in the text|Balance of<br>Payments,2008.IMF|
|Share of bilateral trade|Ratio of total bilateral trade to total trade|Terms of Trade, 2009, IMF|
|Share of 60+ population|Population over 60 as a ratio of total population|Bloom, Canning and Fink,<br>2008|
|Share of exports|Ratio of bilateral exports to total exports|Economist Intelligence Unit,|
|Traditional Services|Traditional services include travel, transportation,|Balance of|
|Value Added|Value added in Agriculture, Industry and Sercies<br>in constant 2000 US$|World Bank, 2008, WDI|
|Value Added Share|Value added in Agriculture, Industry and Sercies<br>inpercent of GDP|World Bank, 2008, WDI|

40
time to haul water (for households without source on plot), and the percentage of girls aged 5- 15 attending school, for each of the four study periods. Greater details by type of source and region are provided in Appendices B and C.

The analysis of the four DHS rounds indicates that about 18% of surveyed households have access to water in the residence or in the yard, either through a piped access or through a well (see Table 2).<sup>9</sup> For these households the time spent to haul water is less than one minute (per round of fetching water). For households without any water access on premises, the average round-trip time to the source is between 18 and 23 minutes depending on the survey year. The most important sources located outside the residence are public taps, used by 20% to 27% of the surveyed households (varying by year), boreholes or public wells (29-39% of households), and surface water such as rivers and streams (11-26% of households), see Appendix B. The share of households who rely on surface water has decreased over time. In general these households also spend more time hauling water (20 to 30 minutes) than households relying on other types of sources.

Water infrastructure and average time spent hauling water vary significantly across the ten regions (see Appendix C). The share of households with access to water on the premises varies between 48% and 56% in Greater Accra (the capital region), while it can be less than 10% in Volta, Brong-Ahafo, Northern, Upper West and Upper East regions. The density (histograms) of the time variable in each region (for the 2008 DHS round) is shown in Appendix D. This regional pattern is similar in all four study periods.

School attendance for girls aged 5 to 15 years also varies significantly depending on the type of water source the household relies on and across regions. The higher school enrollment observed for girls living in households with water access in the residence cannot be considered as evidence of a causal relationship, since it could in principle be spurious. As noted, households with good water supply access are likely to also be wealthier and more educated, and to have stronger preferences for their children to attend school.

The higher proportion of girls attending school in 2008 (Table 2) might have been the consequence of the Five Year Action Plan for Girls’ Education in Ghana, implemented by the Ghana Education Service from 2003 to 2008. Among other actions, scholarships were offered to girls at Junior and Senior Secondary School levels; and incentives were provided for

> 9 The percentage of households having access to water in residence or in the yard can only be calculated for the first three rounds of the survey. In 2008 the list of sources does not distinguish between wells in residence and well outside the residence. The figure shown in the table ( _21%_ ) corresponds to the proportion of households reporting a time spent to haul water equal to 0 minute.

9