rafmacalaba/datause-encoder-data huggingface dataset:
39 developing country. Attention to transport-related air pollution in developing countries, therefore, should not only concentrate on automobiles but also should consider buses and their travel characteristics. As indicated before the contribution of motorcycles and three-wheelers to total VKT also deserves careful assessment.

The modal share of motorized passenger trips in selected developing cities is given in Table 18. The total number of trips is closely related to city population. The relationship obtained by regressing city population with number of trips (sample size = 19) is given below:

- where, Y is number of trips per day in million; and X is the population of urbanized area in million

Non-motorized trips, however, constitute a good portion of total trips made in major cities of developing countries. For example, in Bombay and Calcutta more than a fourth of the trips are by non-motorized modes, mostly walking [Pendakur 1988]. Of motorized trips, buses account for a significant portion as indicated in Table 18.

**Table 18. Modal share of motorized passenger trips in selected cities, 1980**

|City|Auto|Modal<br>Taxi|Share (%)<br>Bus|Para-<br>transit|Rail or<br>Subway|Other|
|---|---|---|---|---|---|---|
|Bangkok|25|10|55|10|**-**||
|Bombay|8|10|34|13|34|**-**|
|Calcutta|-|2|67|14|10|4|
|HongKong|8|13|60|-|19|-|
|Jakarta|27|-|51|-|1|21|
|Karachi|3|7|52|18|6|13|
|Manila|16|2|16|59|-|8|
|Seoul|9|15|68|0|7|0|
|Bogota|14|1|80|0|0|5|
|Mexico City|19|-|51|13|15|2|
|Rio de Janeiro|24|2|62|2|11|-|
|Sao Paulo|32|3|54|-|10|1|
|Abidjan|33|12|50|-|-|5|
|Nairobi|**45**|-|**31**|**15**|**0**|**9**|
|Cairo|15|15|70|-|-|-|
|**Tunis**|24|4|61|-|10||
|Amman|44|11|19|26|**-**|**-**|
|Ankara|23|10|53|9|2|2|

Source: [World Bank 1986]
Note: The coefficients measure the time fixed effects relative to 2007:Q1. The figures in italics are p-values for a test for whether there is a difference between the fixed effects between that year and the previous year. Values are shown in the prices of 2007. Boldfaced numbers are statistically different from the previous year at the 5% level; yellow cells indicate lower real expenditure per capita in 2009 than in 2008.

# **Robustness**

Our focus is on a monetary measure of wellbeing in the form of real consumption per capita. Could Thai households be maintaining their consumption levels by reducing investment – for instance, by pulling children out of school so they can work, or by delaying purchases of assets such as cars?

Figure 8 shows age-specific school enrollment rates for 2007-2010, based on data from the socio-economic surveys. The pre-school rate refers to children 5 and younger, and subsequent age groups are associated with the relevant school level: 6-11 for primary education, 12-14 for lower secondary school, 15-17 for upper secondary school, and 18-22 for higher education. There was no perceptible drop in enrollment rates during the 2008-2009 recession – the period shown by the shaded areas in Figure 8.

<!-- Start of picture text --> 120<br>Primary school<br>100<br>Lower secondary school<br>80<br>Upper secondary<br>school<br>60<br>Higher education<br>40<br>Pre-school<br>20<br>0<br>2007q1 2007q3 2008q1 2008q3 2009q1 2009q3 2010q1 2010q3<br>Age specific enrollment rate, %<br><!-- End of picture text -->

**Figure 8.  Age-Specific School Enrollment Rates, Thailand, 2007:Q1 – 2010:Q4** Source:  Thailand Socio-Economic Surveys of 2007, 2008, 2009, and 2010.  Shaded area marks period of recession.

The results of a more formal test are shown in Table 6: we regress the age-specific enrollment rates on binary variables representing each quarter, and test whether there is a difference from one year to the next. We are able to separate private from public schools. The only measurable Losers and Winners in Recession: Thailand 2008-09 21
- **Miner, Luke** , “The Unintended Consequences of Internet Diffusion: Evidence from Malaysia,” _Journal of Public Economics_ , 2015, _132_ , 66–78.

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- **Rudolf, and Markus Ziese** , “GPCC Full Data Reanalysis Version 7.0 at 0.5 : Monthly Land-Surface Precipitation from Rain-Gauges built on GTS-based and Historic Data: Gridded Monthly Totals,” `https://bit.ly/3BTrP3O` 2015. Accessed: 2021-04-28.

- **Soufan Center** , “Foreign Fighters: An Updated Assessment of the Flow of Foreign Fighters into Syria and Iraq,” _New York_ , 2015.

22
30 Tmble 4: Labor Abeorpdn Potentl from Sie h the USSR and IS Repubac: Counteractuul at btee for 1990

|A. USS<br>.- _(_ __ _ _ _ __<br>____________<br>) _-_-__-_<br>|_-__-_-_-_ (2) _ ___ _ _<br>_Additional_<br>_* _|_Additiosal &qqwpl)mw_<br> <br>|.....<br>(..<br>_AdditInald_<br> <br>|
|---|---|---|---|
|_Residual_|_Mqlownt InServew_|_aqf_<br>_PrseInt_|_Rnplwt_<br>_iServce_|
|_Sector_<br>_forServicen_|<br>_for USSR_|<br>_&lo)Wd_<br>_in Serve_|<br>_for Rmuia_|
|(%|JDUbOil-aA|N||
|_(1)_World Eomie. Eadi||||
|TotaiS*Vlc<br>-10.6|6.0|10.6<br>|**2.8**<br>|
|Tauportation A CowAnuicatiom<br>1.8|.0.2|-1.3|-1.2|
|Trade<br>_6.8_|0.7|6.3|0.4|
|_AUlOdurSwvke_<br>_9.e_<br> <br>|3.5<br>|9.3|2.0<br>|
|Diacpay a/<br>4.2|2.0|..|1.7|
|12) OECD Emaation||||
|Total So8v<br>-4.5|2.5|4.5||
|Tranqatation a Cmumicatii<br>2.3|4.2|.2.3||
|Trad<br>-5.3|0.6|5.3||
|Banking & JIuance<br>-9.0|0.1|9.0||
|<br>Houi<br>krCosanunal<br>Swvie<br>-3.5|0.7|3.5||
|Public Servicw<br>9.0|-1.4|_-9.0_||
|<br> <br>aMl'ediscrpncyiscrated by m<br>iriAecir in th scope of ind<br>B. 1lRob<br>(ordercd by income per capita in 1990)<br>WoeSdEconomier samiq<br>_Total_|<br>ividual sectar between the USS|R andlLO.<br>_Additiona_|_Addft_<br>_ao Eom.t_|
|_Em,plopmnt_|_Reiual_|_ny,lo)et_|_su X f Service_|
|_Reu"blic_<br>_In Servkc_|_for Servki_|_InSevies_<br>_&loeswt_||
|_._<br>I<br>------<br>51990:.ellio<br>-|-<br>**-**<br>**--**-u|L)--|-<br>%-|
|USSR<br>56.4|-10.5|5.92|10.5|
|_Estonia_<br>0.3|-11.0|0.04|11.0|
|_LaMA_<br>0.6|-11.3|0.07|11.3|
|Rusia<br>31.5|-9.1|2.86|9.1|
|Belare<br>1.9|-16.7|0.32|16.7|
|Lithuania<br>0.7|_16.5_|0.12|16.5|
|Ukraine<br>9.3|.13.5|1.32|13.5|
|Moldova<br>0.7|-19.0|0.14|19.0|
|Anmenia<br>0.6|-8.9|0.06|3.9|
|Georia<br>1.1|-10.0|0.11|10.0|
|KAzkhzan<br>3.2|-5.2<br>|0.17|5.2|
|Azerbaijan<br>1.1|_-6.6_|0.07|6.6|
|<br>T*nids<br>0.5|-7.7|0.04|7.7|
|KPgWn<br>0.7|4.6|0.06|S.6|
|Uzbeldan<br>2.3|-10.7|0.30|10.7|
|Tadzhaist<br>0.7|-11.5|0.07|11.5|

So*-: Appcdix Table A3 _and_ authon' calculations bauod on rerion anlyu^.i
local wind speeds at any particular locality where a tropical typhoon directly passes over or nearby. The estimated wind speeds from past typhoons were merged to contemporaneous household surveys (FIES) at the barangay level and consumption expenditures were then regressed against windspeed (or a related damage index) and socioeconomic household characteristics.

The estimates revealed that exposure of a barangay to a typhoon in the 6 months before the month of interview is associated with a statistically significant decline in total per capita expenditures and in specifically food and protein per capita expenditures. Expenditures on fruits and vegetables, cereals, education, medical services and nonfood items appear to be unaffected. These results are found to be robust to changes in the measure of the shock or to controlling for potential lagged effects, and unobserved household heterogeneity. The estimated coefficients from the regression model were also used to estimate ex-ante household vulnerability to poverty (i.e. the likelihood of household consumption falling below the poverty line) in the event of future natural disasters of different intensities.

It is important to bear in mind that the methodological framework employed in this paper for typhoons can also be employed with fairly minor modifications to identify the households vulnerable to poverty from a variety of other natural hazards. Models analogous to the wind field model for typhoons based on freely accessible remotely sensed data can be used for the estimation of the intensity of other natural hazards such as floods, earthquakes and even tsunamis (e.g., Skoufias, Strobl and Tveit, 2017). Thus, ex-ante estimates of the households vulnerable to poverty from floods and earthquakes can also be derived. Machine learning methods can also be used to improve the out-of-sample predictive ability of the regression models applied to retrospective data to estimate the relation between disaster intensity, welfare impacts, and household socio-economic characteristics. Finally, if the interest is on identifying the vulnerable to all kinds of covariate and idiosyncratic shocks, and not just to a specific shock as in this paper, it would be useful to explore and contrast the estimates of vulnerability to poverty obtained from alternative methods (e.g. the method proposed by Gunther and Harttgen, 2009).

28
Table 7: Statistics

||(1)|(2)|(3)|(4)<br>Price off|(5)<br>ered|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
||0|1,000|3,000|5,000|10,000|15,000|20,000|Obs.|
|**Panel A. BDM sample**|||||||||
|WTP_≥_Price|1.00|1.00|0.98|0.94|0.78|0.48|0.32|457|
|Paid something|.|0.70|0.65|0.43|0.29|0.16|0.11|383|
|Paid full|.|0.68|0.63|0.40|0.24|0.12|0.07|383|
|Paid full (if credit)|.|0.63|0.65|0.33|0.19|0.27|0.14|185|
|At least 1 class|0.76|0.65|0.63|0.43|0.29|0.16|0.11|457|
|At least 3 classes|0.65|0.59|0.60|0.41|0.29|0.16|0.07|457|
|At least 5 classes|0.59|0.53|0.58|0.38|0.27|0.16|0.07|457|
|At least 5 classes (if paid full)|.|0.78|0.89|0.93|0.93|1.00|1.00|146|
|<br>All 10 classes|0.18|0.15|0.19|0.13|0.06|0.04|0.00|457|
|N. of classes|5.43|4.89|5.00|3.34|2.29|1.28|0.68|457|
|N. of classes (if at least 1)|7.18|7.51|7.96|7.82|7.94|8.00|6.33|211|
|<br>N. of classes (if paid full)|.|7.09|7.74|8.17|8.07|9.00|8.50|146|
|Observations|74|66|43|103|118|25|28|457|
|**Panel B. TIOLI sample**|||||||||
|Paid something|.|||0.79|0.44|0.33||311|
|Paid full|.|||0.71|0.33|0.19||311|
|Paid full (if credit)|.|||0.65|0.35|0.20||158|
|At least 1 class|0.90|||0.79|0.48|0.37||374|
|At least 3 classes|0.86|||0.76|0.43|0.32||374|
|At least 5 classes|0.78|||0.68|0.40|0.27||374|
|At least 5 classes (if paid full)|.|||0.89|0.90|0.87||109|
|<br>All 10 classes|0.32|||0.24|0.11|0.05||374|
|N. of classes|6.95|||6.18|3.54|2.43||374|
|N. of classes (if at least 1)|7.68|||7.82|7.43|6.64||211|
|<br>N. of classes (if paid full)|.|||7.98|8.02|7.78||109|
|Observations|63|||62|126|123||374|

36
|_T_ = 0_, X_)<br>(5)<br>IV-BP<br>0.059***<br>(0.021)<br>0.013<br>(0.042)<br>0.065***<br>(0.022)<br>-0.019<br>(0.037)<br>-0.002<br>(0.043)<br>0.018<br>(0.042)<br>0.037<br>(0.048)<br>Yes<br>and (2) report<br>ence between<br> who report to<br>the treatment<br>n parentheses.<br>n about access<br>e). To control<br>p-values of an|
|---|
|_, X_)_−E_(_Y |_<br>(4)<br>Probit<br>0.042***<br>(0.016)<br>0.010<br>(0.038)<br>0.054***<br>(0.018)<br>-0.004<br>(0.031)<br>-0.017<br>(0.037)<br>0.012<br>(0.035)<br>0.017<br>(0.038)<br>Yes<br> Columns (1)<br>eport the differ<br>n households <br>icipation with<br>are reported i<br>n, informatio<br>g in the villag<br>and we report|
|_E_(_Y |T_ = 1<br>(3)<br>Probit<br>0.054**<br>(0.021)<br>0.056<br>(0.044)<br>0.074***<br>(0.025)<br>0.002<br>(0.040)<br>0.015<br>(0.042)<br>0.037<br>(0.042)<br>0.064<br>(0.043)<br>No<br>pondents only. <br>ns (3) and (4) r<br>fference betwee<br>ng program part<br>t village level <br>tribe and religio<br>of women livin<br>ps of variables|
|(2)<br>Treatment<br>0.908<br>[0.289]<br>0.726<br>[0.446]<br>0.863<br>[0.344]<br>0.367<br>[0.482]<br>0.484<br>[0.500]<br>0.537<br>[0.499]<br>0.450<br>[0.498]<br>available for res<br>rackets. Colum<br>stimates the di<br>by instrumenti<br>rors clustered a<br>ousehold size,<br>(size and share<br>e selected grou|
|(1)<br>Control<br>0.854<br>[0.353]<br>0.670<br>[0.471]<br>0.788<br>[0.409]<br>0.365<br>[0.482]<br>0.469<br>[0.499]<br>0.501<br>[0.500]<br>0.387<br>[0.487]<br>sehold, data<br>viations in b<br>Column (5) e<br>e who didn’t<br> Standard er<br>respondent, h<br>aracteristics<br>dicator on th<br>riables.|
|1. Mosquitoes mentioned among malaria vectors<br>2. Malaria is a problem in community<br>3. Children mentioned among most affected by malaria<br>4. Pregnant women mentioned among most affected<br>5. In the previous 6 months, heard or saw messages about:<br>5a. ITNs<br>5b. Early seeking behavior<br>5c. Environmental management<br>Controls<br>Note: *** p_<_0.01, ** p_<_0.05, * p_<_0.1. One observation per hou<br>sample means restricted to control and treatment group, standard de<br>treatment and control groups using probit regression (model 17). <br>have received the spraying campaign in the last 5 months and thos<br>group indicator and using a 2-equation probit model (model 18). <br>Controls include gender, age, education and marriage status of the<br>to water, dwelling characteristics, sub-zone dummies and village ch<br>for joint signifcance, we run a probit regression of the treatment in<br>F-test for the joint signifcance of the coeffcients on the selected va|

23
rural India. From 1999 until its replacement with a new program in 2012, the Indian central government operated a “flagship” rural sanitation program called the Total Sanitation Campaign (TSC). Averaging over implementation heterogeneity throughout rural India, Spears (2012a) finds that the TSC reduced infant mortality and increased children’s height, on average. In a follow-up study, Spears and Lamba (2012) find that early life exposure to improved rural sanitation due to the TSC additionally caused an increase in cognitive achievement at age six. Similarly, Hammer and Spears (2012) report a randomized field experiment in Maharashtra, in which children living in villages randomly assigned to a treatment group that received sanitation motivation and subsidized latrine construction grew taller than children in control villages. Section 5.1 considers the estimates of these causally well-identified studies in the context of this paper’s results.

# **1.2 Open defecation is common in India**

Of the 1.1 billion people who defecate openly, nearly 60 percent live in India, which means they make up more than half of the population of India. These large numbers are roughly corroborated by the Indian government’s 2011 census, which found that 53.1 percent of all – – Indian households and 69.3 percent of rural households “usually” do not use any kind of toilet or latrine. In the 2005-6 National Family Health Survey, India’s version of the DHS, 55.3 percent of all Indian households reported defecating openly, a number which rose to 74 percent among rural households.

These statistics are striking for several reasons. First, open defecation is much more common in India than it is in many countries in Africa where, on average, poorer people live. UNICEF and the WHO estimate that in 2010, 25 percent of people in sub-Saharan Africa openly defecated. In the largest three sub-Saharan countries – Nigeria, Ethiopia, and – the Democratic Republic of the Congo in their most recent DHS surveys, 31.1, 38.3, and 12.1 percent of households report defecating openly.

Second, despite accelerated GDP growth in India, open defecation has not rapidly de5
# **Table 7a: Ease of Doing a Business**

||**The Ease**|**of Doing Bu**|**siness, ran**|**k(1-185)**|||||||
|---|---|---|---|---|---|---|---|---|---|---|
|Accessibility of incorporation fees (0-1)<br>Accessibility of electricity connection fees (0-1)<br>Accessibility of construction permit fees (0-1)<br>Accessibility of property registration fees (0-1)|-22.817**<br>[6.215]|*<br>-27.124***<br>[5.279]|-19.732***<br>[5.259]|-24.530**|*||||||
|Regulatory Transparency Index (0-1)||||[5.617]|-69.655***<br>[9.636]||||||
|Voice and Accountability (0-100)||||||-11.213***<br>[3.868]|||||
|OBI Rank (0-100)|||||||-0.738***<br>[0.211]||||
|Transparent Policymaking Rank (1-7)||||||||0.552***<br>[0.095]|||
|E-Government Development Index (0-1)|||||||||-200.204*<br>[24.395]|**|
|Open Government (WJP) (0-1)<br>||||||||||-170.930**<br>[45.986]|
|Log GNI pc|-25.555**|*-24.539***|-25.819***|-25.288**|*-22.038***|-22.931***|-17.622**|*-18.578**|*<br>-2.004|-10.675**|
||[1.465]|[1.441]|[1.428]|[1.421]|[1.522]|[2.164]|[3.239]|[2.366]|[3.425]|[4.957]|
|Constant|327.243**<br>[11.556]|315.617**<br>[11.995]|321.692**<br>[11.700]|322.635**<br>[11.173]|323.235**<br>[10.360]|286.073**<br>[18.033]|271.764**<br>[20.235]|203.193**<br>[25.891]|209.362**<br>[18.166]|258.375***<br>[24.215]|
|Observations<br>|185|185|185|185|185|183|93|118|178|66|
|R-squared|~~0.596~~|~~0.627~~|~~0.601~~|~~0.614~~|~~0.689~~|~~0.599~~|~~0.533~~|~~0.693~~|~~0.684~~|~~0.662~~|
|~~Robust standard errors in brackets~~<br>*** p<0.01, ** p<0.05, * p<0.1|||||||||||

# **Table 8a: Regulatory Quality**

|**Regulator**<br> <br>|**y quality, r**|**ank(0-100**|**)**|||||||
|---|---|---|---|---|---|---|---|---|---|
|Accessibility of incorporation fees (0-1)<br>0.212*<br>[0.118]<br>||||||||||
|Accessibility of electricity connection fees (0-1)|0.380***<br>[0.093]|||||||||
|Accessibility of construction permit fees (0-1)||0.404***<br>[0.082]||||||||
|Accessibility of property registration fees (0-1)|||0.436***|||||||
||||[0.093]|||||||
|Regulatory Transparency Index (0-1)||||1.096***<br>[0.157]||||||
|Voice and Accountability (0-100)|||||0.382***<br>[0.062]|||||
|OBI Rank (0-100)||||||0.014***<br>[0.003]||||
|Transparent Policymaking Rank (1-7)|||||||-0.008***<br>[0.002]|||
|E-Government Development Index (0-1)||||||||2.553***<br>[0.471]||
|Open Government (WJP) (0-1)|||||||||3.197***<br>[0.812]|
|Log GNI pc<br>0.476***|0.454***|0.466***|0.458***|0.411***|0.342***|0.272***|0.391***|0.167**|0.196**|
|[0.028]|[0.028]|[0.025]|[0.025]|[0.027]|[0.036]|[0.045]|[0.039]|[0.066]|[0.086]|
|Constant<br>-4.199***|-4.058***|-4.157***|-4.160***|-4.172***|-2.877***|-2.891***|-2.620***|-2.696***|-3.047***|
|[0.231]|[0.228]|[0.212]|[0.212]|[0.199]|[0.304]|[0.301]|[0.427]|[0.355]|[0.375]|
|Observations<br>183|183|183|183|183|183|93|117|178|66|
|R-squared<br>0.622|0.652|0.66|0.662|0.713|0.709|0.63|0.765|0.673|0.719|
|Robust standard errors in brackets||||||||||
|*** p<0.01, ** p<0.05, * p<0.1||||||||||

28
average one year of CPI data to align them with 2011. This is in contrast to the RCS lines, which required on average 12 years of inflation data to create comparable 2011 currency values. Figure 2 plots the national poverty lines used in our analysis on national median consumption or income level (as used for poverty analysis). The lighter, hollow dots in both panels reflect the full sample of 699 poverty lines dating from 1984 through 2013. The darker, filled dots are the 104 lines that are the closest to 2011 for each country. Panel A plots the lines and median wellbeing in level-level space, while Panel B is the same plot except the axes have been log-transformed. The plots reveal a few noteworthy points. First, there is a strong positive relationship between a country’s assessments of basic needs and their level of wellbeing, similar to that noted by Ravallion and Chen (2011). Also, the sub-sample of 104 from circa 2011, overlaps well with the complete scatter of lines, suggesting some stability in this positive relationship over time.<sup>19</sup> Panel B also includes predicted lines at the 90<sup>th</sup> and 10<sup>th</sup> quantiles from the bi-variate (quantile) regression of the poverty line on median consumption. These predicted lines have similar slopes and the ratio of these lines (in levels) is approximately 2 over the entire range. This suggests that at any given level of national wellbeing, the range in values of national poverty lines is large, with the most generous line consistently being about twice as large as the least generous line. It is this latter result that we view as an argument against a global count of the poor that treats each country’s own line as the relevant threshold, such as proposed by Gentilini and Sumner (2012). Allowing for such significant differences in the definition of basic

> 19 The circa-2011 lines have shifted up and to the right, simply reflecting that over time, median consumption (or income) and poverty lines have been increasing across the globe.

12
# II. **Banking on stability: what do we know?**

..no degree of regulatory wisdom could; or should- have made the 1920s a profitable time for banks in agricultural regions affected by drastic declines in prices **_arLdJ_ laUdU V** VuTes...'V **LIL 1UrGLUL1.AJL.Vn** co .hve **V** dor,e, **bULtL did LLVo d,** mk **ILNh LV UJe** system as a whole less susceptible to shocks and more resilient in its response to failures. Calomiris, 1989 There has been little disagreement that one of the important goals of banking and, more generally, finance is to help individuals and society cope with changing economic

**,rpuilmat~ant'** **_(Tp. u;n_ 19** ?Q7) On. of the mn.t hbasic **firlnotinne** of ,fin§nn, **.nnmplu** the mobilization of za-vmgs, itslfi represenits a way **for** MUUIVIUuLLS **Lo** PtULMc UiIiCisCVUS frUm economic downturns. And from the (small business) man on the street, looking for a loan, to the sophisticated consumer of derivative products, the function of transforming risk (reducing it through aggregation and enabling it to be carried by those better able to bear it) also is a key way to deal with economic volatility (World Bank, 2001).

But is banking a source of stability? Although they note that the distinction can be overdrawn, Allen and Gale (2000) suggest that markets tend to be destabilizing, whereas banks and other intermediaries, by virtue of being able to re-contract more seamlessly, help **_toe+al7_** co.me **_AA_** d _at_ lpeast theoreic*a!!y, bar bs sholo be f ard=!ot-mg in their decisions. Tney snould nold a well diversifiea portfolio, taKing provisions and holding capital in order to ensure their survival. Banking and the building of special relationships does not fit with the perfectly competitive model, and bank charters have a value, which bankers can capture by making sure that they survive. Failure in banking, as in other industries, can send valuable signals and should be permitted, but there is a reduction in information capital when banks shut down, so that society suffers some loss.