rafmacalaba/datause-encoder-data huggingface dataset:
Jhanjharpur (NP) had built a few public toilets but these were non-functional at the time of the study. Both markets were prone to flooding during the monsoons due to overflow from the Kamala river; but since the construction of a barrage two years ago, this has substantially reduced. The NP authorities say that septic tank cleaning trucks are hired from Madhubani from time to time. North of the railway station, the government housing for the railway and for the Kosi project are the only places where the sanitation is in a better condition. In Satghara CT there is no functioning drainage network, even though it is always flooded during monsoon. In a few areas, especially in Satghara old bazaar, drains were built several years ago but are now choked by the garbage. According to the mukhiya, it is not a priority, because there is a need for better roads and better planning first.

# **_Solid Waste Management_**

None of the settlements studied, except one, provide solid waste collection and treatment services. In these settlements, household garbage is usually dispersed or burnt. In some market places, the local merchants make arrangements to collect and dispose waste (typically through burning). Special one-off arrangements are also put in place by these merchants during festivals in order to handle the spike in solid waste volumes during these times. However, there is no arrangement, indeed, no conception, of safe disposal practices in these settlements.

The exception is the only urban local body in the study, i.e., Jhanjharpur (NP), where there are 16 employees (one for every ward) retained for the purpose of sweeping and collecting solid waste. However, even here, there is no arrangement for disposal. It is worth noting that Gopalpur panchayat is able to organize itself to provide regular water supply, which is a much more complex service to provide but does not provide solid waste management, while Jhanjharpur, which has relatively low governance effectiveness, still provides this service, presumably because it has been allocated this responsibility under the 74th amendment.

# **_Electricity_**

Nationally, for services like electricity, there is no difference between census and statutory towns. Figure 4 below shows the distribution of CTs and STs by the coverage of household electrification. As can be seen, distributionally, there is little to choose between the two. This is unusual since it can be surmised that statutory towns would get preference in electricity networks, and since CTs are administratively villages, their electrification should be similar to rural areas. However, this does not appear to be true. Since, unlike other services discussed here, many urban areas appear to be well served by electricity, the question arises whether the causality may run the other way. More work would be needed to understand if electrified villages are more likely to become CTs and how electrification relates to the formation of CTs.

26
- 29 -

The mortality tables are the RV-85 and the RV-98 in period form, and the RV-98 in a cohort form using rates of mortality improvement from the Canadian Institute of Actuaries. The two discount rates used are the risk-free rate and the corporate bond rate. The MWRs are estimated for premiums of UF 1,000 and UF 4,000. Table 18 reproduces their MWRs for 1999 and 2003 while Table 19 reproduces our estimates for the same years to facilitate comparisons.

As shown in Tables 18 and 19, our MWRs computed with the risk-free rate for 1999 are roughly equal to those presented by James et al for joint annuities, only slightly higher in the case of single males age 65, and higher in the case of single females age 60. The results are somewhat surprising, especially for single male and joint annuities, because the differences between the period RV-98 and the cohort RV-04 should lead to larger differences between MWRs. Moreover, our MWRs are estimated from the total universe of actual sales, while James, Iglesias and Martinez use a sample of quoted annuities from 4 companies. Since annuity sales tend to reflect the best quotes, MWRs based on sales should be higher than those estimated from quotes.

**Table 18**

**<u>Money’s Worth Ratios for Chile Estimated by James et al for 1999 and 2003</u>**

||March 1999||Mar|ch 2003||
|---|---|---|---|---|---|
||RV-98 Period<br>Risk-free rate<br>UF1,000<br>Premium|RV-98 Period<br>Risk-free rate<br>UF1,000<br>Premium|RV-98 Cohort<br>Risk-free rate<br>UF1,000<br>Premium|RV-98 cohort<br>Risk-free rate<br>UF4,000<br>Premium|RV-98 Cohort<br>Corp. bond rate<br>UF1,000<br>Premium|
|Male, 65|0.979|0.981|1.012|1.013|0.905|
|Male, 55|-|0.941|0.976|0.999|0.879|
|Female, 60|0.963|0.925|0.958|0.992|0.845|
|Female, 55|-|0.899|0.929|0.977|0.810|
|Joint|1.000|0.977|1.008|1.025|0.883|

Source: James, Iglesias and Martinez (2005)

**Table 19** **<u>Money’s Worth Ratios for Chile Estimated by this Report, 1999 and 2003</u>**

||March 1999|Mar|ch|2003|
|---|---|---|---|---|
||RV-04 Cohort<br>Risk-free rate<br>Average|RV-04 Cohort<br>Risk-free rate<br>Average||RV-04 Cohort<br>Corporate bond rate<br>Average|
|Male, 65|0.996|1.069||0.955|
|Male, 55|0.981|1.049||0.897|
|Female, 60|1.021|1.077||0.971|
|Female, 55|0.994|1.049||0.905|
|Joint|0.998|1.050||0.892|

Source: Table 4 Our MWRs computed with the risk-free rate for 2003 are higher than those presented by James, Iglesias and Martinez for the same year, with the differences ranging from 3 to 10 percent. Again, the differences are larger in the case of females. These differences are more consistent with the differences between the mortality tables, especially regarding
respondent who reports information on behalf of his spouse and/or young adult child living within the household.

In this paper, we show that alternative survey methods can address the underreporting of women’s and young adults’ labor market outcomes, and thereby measure more accurately underlying gender- and age-based employment gaps. To this end, we designed and implemented a survey experiment within the context of a household survey in El Salvador in order to estimate the impact of two alternative survey methods for the respondents’ reporting of employment and work, relative to the standard household survey approach. Our findings confirm that women report higher employment when they are given examples that enable them to recognize how they contribute to the working population. We also show that young men reported higher values of labor indicators compared to older men when self-reporting was enforced.

The analysis of different survey methods for labor measurement is particularly relevant in a context such as El Salvador where gender and age-based gaps in labor market outcomes are more pronounced compared to economically similar countries, and where informal work is prevalent (World Bank, 2023). For example, according to the 2022 Household and Multipurpose Survey (EHPM), in El Salvador the gender gap in employment was 30.6 percentage points, which has remained stable for the past 24 years, and youth experience 2.4 times more unemployment than adults (UN, 2023). Moreover, 66.8% of the employment in El Salvador occurs in the informal sector, which highlights why it is important for respondents to understand which activities can be classified as work when collecting and providing information. Furthermore, the share of proxy respondents who participated in the 2022 labor module of the household survey is high but even higher for young men (59.4% of 15 to 64-year-old individuals and 85.5% of young men were represented by proxy respondents).

To estimate each survey method’s causal effect on the respondents’ reporting of employment and work, we randomly distributed the 1,008 households and their respective 2,480 working-age household members with equal probability across three groups. The first group (LOA) completed a survey interview that included the LOA module before responding to the standard labor module, and proxy responses were allowed. In the second group (ESR), selfreporting was enforced for all eligible respondents in the standard labor module, but the LOA module was not included. The third group (C) was made up of respondents who completed the 3
are broadly similar to results found using IMR, we interpret this concordance as indicative that the mortality results are indeed effects of the disease and health environment, rather than some other spurious correlate of IMR.

As the table shows, we indeed find effects of important but plausible magnitude of sanitation on adult wages. The dependent variable is the percent of households using a toilet or latrine, rather than defecating in the open. A 10 percentage point decrease in open defecation translates into an approximately 2-3 percent increase in wages. In the context of our identification strategy, we find that in districts where sanitation has improved more quickly over time, the within-district wage profile is less steeply increasing in age. As before, our result is quantitatively stable as a long vector of controls is added, including for state-specific time trends, caste and religious groups, and state-specific urban residence. Also as before, – adding job categories and education indicators although these are, themselves, predictive – of wages does not change the coefficient on early-life sanitation.

# **3.3 Assessing Plausibility of Effect Sizes**

This section considers whether our coefficient estimates are of plausible size. Although no comparable estimates exist in the literature, we can make an approximate guess of a plausible magnitude of the effect we estimate by multiplying quantities that do exist in the literature: where _height (cm)_ is adult height in centimeters and _height (sd)_ is child height-for-age in standard deviations. We assume that a one standard deviation increase in child height becomes a ∆height <u>(cm)</u> one standard deviation increase in adult height, and substitute 6.9 cm for ∆height (sd)<sup>, the</sup> standard deviation of Indian adult male height in the most recent Demographic and Health Survey. We combine three estimates of <u>%∆</u> _<u>y</u>_<sup>Vogl’s (2014) from</sup> ∆height (cm)<sup>from the literature:</sup> Mexico, and high and low estimates for men in the U.S. from Case and Paxson (2008). We ∆height <u>(sd)</u> use five estimates of<sup>Lin</sup><sup>_etal_(2013)fromBangladesh,Kov</sup><sup>_etal_(2013)</sup> ∆open defecation<sup>:</sup> from Cambodia, and one estimate from Spears (2012a) and two from Spears (2013) from India.<sup>10</sup>

> 10The full set of quantities used is available upon request.

10
design. The authors find that the discontinuity design gives good approximations for almost all outcome indicators.

The method is not without its drawbacks. It is assumed that the evaluator knows _M i_ and (hence) eligibility for the program. That will not always be the case. Consider (again) a meanstested transfer whereby the income of the participants is supposed to be below some predetermined cut-off point. In a single cross-section survey, we observe post-program incomes for participants and incomes for non-participants, but typically we do not know income at the time the means test was actually applied. And if we were to estimate eligibility by subtracting the transfer payment from the observed income then we would be assuming (implicitly) exactly what we want to test: whether there was a behavioral response to the program. Retrospective questions on income at the time of the means test will help (though recognizing the possible biases), as would a baseline survey at or near the time of the test. A baseline survey can also help clean out any pre-intervention differences in outcomes either side of the discontinuity, in which case one is combining the discontinuity design with the double difference method discussed further in section 7.

Note also that a discontinuity design gives mean impact for a selected sample of the participants, while most other methods (such as social experiments and PSM) aim to give mean impact for the treatment group as a whole. However, the aforementioned common-support problem that is sometimes generated by eligibility criteria can mean that other evaluations are also confined to a highly selected sub-sample; the question is then whether that is an interesting sub-sample. The truncation of treatment group samples to assure common support will most likely tend to exclude those with the highest probability of participating (for which nonparticipating comparators are hardest to find), while discontinuity designs will tend to include only those with the lowest probability. The latter sub-sample can, nonetheless, be relevant for deciding about program expansion; section 9 returns to this point.

Although impacts in a neighborhood of the cut-off point are non-parametrically identified for discontinuity designs, the applied literature has more often used an alternative parametric method in which the discontinuity in the eligibility criterion is used as an instrumental variable for program placement; we will return to give examples in section 8.

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correction are not very different from each other, which is also signalled by a statistically insignificant coefficient on the Mills ratio.

In terms of region heterogeneity, EEA, LAC, and SSA are the regions where the export promotion budgets seem to have the strongest impact on exports, which are also statistically different from zero. For the OECD and MENA the coefficients are not statistically different from zero. Note also that although EEA has the largest point estimate its coefficient is not statistically different from the one obtained for SSA. Thus, although there is some interesting heterogeneity across regions, we do not seem to be able to estimate it very precisely. All the other coefficients show a similar pattern to the one observed in Tables 1 and 2.

One could use the Heckman estimates to compare the “return” for each $ invested on export promotion across regions. A quick back-of-the-envelope calculation suggests that for each $1 invested on export promotion there are $100 of additional exports in EEA, $70 in LAC, $38 in SSA, $5 in the OECD, and $53 less exports in MENA. But it is important to recall that the estimates for the OECD and MENA are not statistically different from zero, and the estimates for the other three regions are not statistically different from each other.

Columns three and four of Table 3 presents the results regarding the heterogeneity of the impact of EPA’s budget across levels of development, with and without sample bias correction. Again, there is no much difference between the estimates of columns three and four, suggesting that this is not an issue in the full sample. The estimates suggest that at low levels of development the effect of EPA expenditures on exports may be negative, but this rapidly increases with the level of income.

However, the effect of the level of development on the estimated efficacy of EPA expenditures might be capturing underlying correlations among the level of development, the size of EPA budgets, and the marginal returns to EPA expenditures. As can be seen in the top quadrant of Figure 11 –where we plotted the predicted effect of EPA’s budget on log of exports per capita from the Heckman estimations against the size of the EPA budget– there is an inverted U-shape relationship between the effect of EPA budgets on the log of exports per capita and the size of EPA budgets per capita. This suggests –everything else equal– that at 20
second round (402 households). By treating each subsample as a cross-section, I avoid any bias that might arise from the use of panel datasets to validate the methodology.

I apply the following three specifications:

- Specification 1: includes household head age, age squared, gender, and years of education.

- Specification 2: adds regional fixed effects to the first specification.

- Specification 3: adds the interaction between variables in specification 1 and 2.

The paper also validates the methodology in other contexts by using the same panel datasets as the one presented by Cruces et al. (2015). These are three panel datasets for Chile, Nicaragua, and Peru: (i) the 1996 and 2006 National Socio-Economic Characterization Survey (CASEN in Spanish) panel survey from Chile; (ii) the 1998 and 2005 National Household Survey on Living Standards Measurement (EMNV in Spanish) panel survey from Nicaragua; and (iii) the 2008-2009 Peruvian National Household (ENAHO in Spanish) panel survey in Peru. These are nationally representative surveys that have information on education; employment; income in Chile and expenditure in Nicaragua and Peru; and housing, among others. The main objective of these surveys is to measure poverty and living conditions. For a detailed description of these datasets see Cruces et al. (2015). In these validations, the poor are defined as individuals with an official per capita expenditure (in Nicaragua and Peru) and income (in Chile) lower than the official poverty lines.

Unlike Cruces et al. (2015); Dang et al. (2014); and Dang and Lanjouw (2013), the harmonized SEDLAC data in Peru allow us to validate poverty dynamics using the same underlying income models and variables as the one frequently used in regional studies in LAC (for instance, Ferreira et al. 2012 and Vakis et al. 2016). As such, I end the paper by presenting mobility results for LAC using the approach described above and the same underlying income models and variables as in the validation analysis. This allows us to implement the approach in most LAC countries for which data are available using the same underlying period and consistent concepts of income measures, datasets, and mobility measures across countries, thus considerably enlarging the universe of estimates and understanding of mobility trends in the region. I focus on intragenerational mobility in 17 LAC countries for which data are available between 2004 and 2015. In this analysis, the poor are individuals with a harmonized per capita income lower than $4 per person and per day poverty line in 2005 $PPP/day.

8
21. High economic volatility hurts the poor. The 1990s have witnessed several financial crises, including the Mexican and the East Asian crises. During the East Asian crisis, poverty rose markedly (Table 2) as real wages fell and unemployment rose. In addition, there was considerable churning in employment across sectors. To some extent, families can smooth their incomes by increasing labor force participation and through the use of private transfers, though wealthier families are best able to smooth consumption. In crises, households in rural areas where poverty is usually the greatest can sometimes suffer deeper losses than do urban households. School enrollment declines, especially among poorer families, as does the use of health facilities. Crises have typically proved short-lived, though the ability of households plunged into poverty during crises to recover in line with the economy as a whole remains an open question. In view of the risk of economic volatility and its implications for the poor, strengthening social safety nets is an important agenda item in many MICs.

**Table 2. Poverty Incidence and Gini Coefficients**

|Country|Country<br>Year<br>pYear|<br>P<br>Overall|overty Inciden<br>Urban|ce<br>Rural|Gini<br>Coefficient|
|---|---|---|---|---|---|
|Indonesia|1997|11.0|9.2|12.4|38.0|
||1998|13.8|12.0|_15.2_|37.0|
||19981/2|1<br>19.9|15.8|23.0||
|Korea|1997|2.6|7.5|..|27.9|
||1998|7.3|10.0||28.5|
||1998|..|22.9|..|..|
|Malaysia|1997|8.2|..|..|49.6|
||1998|10.4|..|..|49.8|
|Thailand|1997|9.8|1.2|11.8|47.7|
||1998|12.9|1.5|17.2|48.1|

Notes: ".." denotes data not available.

I/ refers to deflation according to revised price data.

2/ refers to fourth quarter (other urban data refer to first quarter).

Source: Fallon, P. and R. Lucas. "The Impact of Financial Crises on Labor Markets, 'Household Incomes and Poverty: A Review Of Evidence." _World Bank Research Observer_ (forthcoming).

# **High Poverty in Some Sub-National Regions**

22. The national rate of poverty incidence for a given country often masks the fact that, particularly in large countries, the level of poverty is much more serious in some geographical regions than others. In China, for example, across the neighboring provinces spanning the regions between Hong Kong and Myanmar, the rural poverty rate in the mountainous province of Guizhou was 7-10 times higher than in the coastal province of Guangdong just a few hundred kilometers away. In Brazil, although the 8
# **A Note on the Simple Algebra of the Shared Prosperity Indicator**<sup>**1**</sup>

David Rosenblatt and Tamara J. McGavock<sup>2</sup> Keywords: Economic growth, income distribution, inequality, development JEL Codes: D6, D63, E01, I3, O1, O4 Economic Policy Sector Board

> 1 The authors would like to thank Kaushik Basu for helpful discussions.

> 2 World Bank and Cornell University, respectively.   Questions and comments can be sent to: drosenblatt@worldbank.org
**Table 1. Summary Statistics**

This table reports the summary statistics of our sample for January 2013 to June 2016. All variable definitions <u>are provided in Table A1.</u>

||# Obs|Average|p10|Median|p90|Std dev|
|---|---|---|---|---|---|---|
|_Panel A. Bank-month-level variables_|||||||
|ExposureEnergyb,m(%)|897|9.9|0|5.9|25.4|12.1|
|Tier 1 Capital Ratiob,m(%)|897|15.4|12.5|15.3|18.5|2.2|
|Total Lendingb,m(logs)|897|21.8|19.6|21.3|24.2|1.8|
|Delinquencyb,m(%)|897|2.4|0.4|2|4.7|2|
|CDSb,m(basis points)|367|410|321|425|492|71|
|Stock Priceb,m(index)|470|3.4|0.7|3.9|5.2|1.6|
|ExposureEnergyb,Aug14(%)|897|8.0|0|5.0|19.6|6.5|
|_Panel B. Loan-level variables of firms i_|_n energy-relate_|_d sectors_|||||
|Total Lendingf,b,m(‘000)|34,741|89,790|96|1,560|126,930|307,605|
|Loans to working capitalf,b,m(‘000)|34,741|78,848|25|1,218|96,521|285,116|
|Loans to investment,b,m(‘000)|34,741|2,980|0|0|131|13,470|
|Interest Ratef,b,m(%)|31,257|11.4|4.4|11.8|18.0|10.7|
|Maturityf,b,m(months)|31,257|25|2.4|20.4|52.0|25.7|
|Collateralf,b,m(%)|31,257|14.6|0.0|0.0|54.7|29.1|
|_Panel C. Loan-level variables of firms i_|_n non-energy s_|_ectors_|||||
|Total Lendingf,b,m(‘000)|1,684,329|6,225|48|511|5,179|58,224|
|-<br>Working capitalf,b,m(‘000)|1,600,896|5,509|46|500|4,511|54,745|
|-<br>Investmentf,b,m(‘000)|145,324|11,782|53|1,159|20,000|71,286|
|Interest Ratef,b,m(%)|1,684,329|13.4|7.8|13.0|19.0|4.2|
|Maturityf,b,m(years)|1,668,951|33|3.0|19.3|43.1|111.8|
|Collateralf,b,m(%)|1,684,329|13.2|0.0|0.0|50.0|27.2|
|_Panel D. Firm-year-level variables_|||||||
|Total Lendingf,y(‘000)|66,592|10,337|69|732|7,962|117,140|
|-<br>Working Capitalf,y(‘000)|64,561|8,902|67|696|6,894|105,158|
|-<br>Investmentf,y(‘000)|7,009|15,757|57|1,511|26,872|93,249|
|AvgExposureEnergyf,Aug14(%)|66,592|13.4|3.2|14.5|19.6|4.8|
|Liabilitiesf,y(millions)|2,132|6,466|42|344|20,947|15,655|
|Assetsf,y (millions)|2,350|19,607|101|1,146|50,739|47,695|
|Revenuesf,y(millions)|2,350|7,056|179|818|23,431|15,245|
|AvgExposureEnergym,Aug14(%)|2,350|9.8|7.7|10.3|11.5|2.1|
|_Panel E. State-quarter-level variables_|||||||
|Total GDPs,q(index)|512|4.6|4.6|4.6|4.7|0.1|
|AvgExposureEnergys,Aug14(%)|512|12.1|9.6|12.1|14.3|1.6|

27