rafmacalaba/datause-encoder-data huggingface dataset:
Do the findings of low group differences in Sugao apply also when groups are defined in terms of criteria other than social group or occupation? Unlike Palanpur, we do not have the data at hand for Sugao that would allow us to assess whether groups defined in terms education (of household head, or of other family members) tend to “stand apart” to a greater extent than was observed for groups defined in terms of caste. However, we are able to redefine groups in terms of landownership status (distinguishing also between irrigated and unirrigated land). Experimenting with a variety of different ways in which to categorize households in terms of landownership status, we find that neither the classic between-group contribution, nor the ELMO statistic, ever take on a significant value. The highest value for the ELMO statistic occurs when the population in 1977 is divided into two groups: those with less than 5 bighas of land (just under one acre) versus the rest. In this case the ELMO statistic takes on a value of 0.04 – on par with, but certainly not higher than, the low values obtained for the ELMO statistic when the Sugao population is broken down into a variety of different caste and occupational groups. Nor is there any evidence of a change over time in terms of the salience of this type of group definition. As was found for Palanpur, dividing households into groups based on landownership does not appear to yield any new insights into our understanding of the “structure” of inequality in Sugao.

# **6.  Conclusion**

This paper has demonstrated that standard inequality decomposition analysis does not always appear to capture well the particular story of group differences and the evolution of inequality at the village level in India. In particular, in the small north Indian village of Palanpur, such a decomposition analysis provides a misleading picture of the importance of caste differences. The paper has suggested that one difficulty with the standard approach is 26
The rest of the paper is structured as follows. Section 2 describes the data and methodology. Section 3 presents the analysis of how the business environment affects African firms, Section 4 does the same for pollution, and Section 5 briefly focuses on the effects of improving these key variables. Section 6 concludes and hints to some possible course of action for African cities.

# 2. Data and Methodology

Because this paper builds on Reyes, Roberts, and Xu (2017), the main data source we use is also the World Bank Enterprise Survey Dataset. This survey, conducted by the World Bank, takes place in 709 cities, in 128 countries, and collects information on the business climate to understand how this affects firms’ performance.<sup>8</sup> This data set includes information on firm characteristics (such as size, age, number of employees), firm performance (such as employment growth rate, labor productivity, total factor productivity), and business environment variables (such as obstacles to grow, overdraft facility). A list of key variables and sources is presented in Table 1. Summary statistics are shown in Table 2.

It is worth noting that WBES data are a representative sample of the non-agricultural economic activity at the country level. Although the data are geographically stratified, this is done on a country-by-country basis, which results in no common stratification for all firms. Moreover, because the population of interest is limited to registered firms with more than 5 employees, most of these firms are clustered around the main urban centers. This could introduce some bias because more productive or fast-growing firms may be attracted to big cities. However, when estimating the model for different subsamples depending on city size, results do not show significant differences.<sup>9</sup> Furthermore, even though firms’ geographic distribution is representative for the country, representativeness at the city level is not automatically assured. Because this paper focuses on the correlation between pollution and firm performance -at the firm level, representativeness at

> 8 For more details on WBES, see http:\\www.enterprisesurveys.org.

> 9 Results appear to be slightly stronger for a subsample of firms located in cities with a population between 250,000 and 1 million people.

6
Research Components of World Bank Education Projects, since 1982.

|Area||Country<br>and Loan/Credi<br>Ntaumber|Approved<br>|Research<br>Coumponents<br>[|Current<br>Status<br>of Studies,<br>inctuding<br>Completed Docuits/Data|
|---|---|---|---|---|
||||textbook<br>loan scheme, evaluation<br>system;<br>(vii)<br>andto conduct tracer<br>studies<br>on<br>polytechnic<br>and technical<br>school graduates<br>(para<br>3.12 p.22).||
|ASIA|Malaysia<br>Li<br>2887-MA|1988|University<br>Development<br>Proiect.<br>Component to<br>improve<br>University<br>mnagement,<br>to design<br>and<br>introduce<br>performance indicators<br>suchasunit<br>costs,<br>resource utilization<br>(para.<br>2.13-17 p.15<br>Amex6).|Activities<br>as detailed<br>in originat<br>project<br>documents<br>not conducted<br>in manner originally<br>envisaged.<br>However, studies<br>are being used to develop the<br>Sixth Malaysia Plan, and for strategic<br>management<br>plans for the 1990s.|
|ASIA<br>ASIA|Maldives CR 1981-MAL<br>|<br>hepal CR 1198-lEP<br>l|<br>1989<br>1982<br>|Education and Training<br>Project.<br>(i)<br>a labor strategy<br>drawing on previous<br>research materiat;<br>and<br>(ii)<br>a study of teacher recruitment,<br>training<br>and deployment. (para 2.8, p. 17 - Annex 3).<br>Second Educational<br>(Technical<br>and<br>Vocational).<br>Feasibility<br>studies<br>in:<br>(i)<br>secondary education;<br>(ii)<br>nonformal and adult education<br>(para.<br>2.11 p.16).<br>tiii)<br>Evaluation<br>studies<br>on outputs of project<br>centers and relevance to industry's<br>needs.<br>(iv)<br>Tracer study of graduates<br>(2.12 16).|Studies not initiated.<br>(i)<br>and (ii)<br>not initiated;<br>(iii)<br>bi-a.maalprogress report<br>of Pokhancmipsq<br>only;<br>(iv)<br>available<br>in Nepali.|
|ASIA|Nepal CR 1534-NEP|1985|Agricultural<br>ManpowerDevelotment.<br>Noresearch<br>component s.|Confirmed.|
|ASIA|Nepal CR 2044-NEP|1989|Engineering Education.<br>Noresearch component.|Confirmed|
|ASIA|Nepal<br>CR 2047-NEP|1989|Thiswasan emergency rel ief<br>loan<br>for<br>school<br>rehabilitation.<br>No research<br>coffponent.||

45
13

|**Table**|**1. Descri**|**ptive statisti**|**cs for sele**|**cted variabl**|**es**|||
|---|---|---|---|---|---|---|---|
|Variable|Mean|Standard<br>Deviation|Median|Minimum|Maximum|Coeff. of<br>variation, %|N<br>obs|
|Average degree of expenditure<br>decentralization,1995-2001|0.65|0.13|0.68|0.09|0.83|20|85|
|Per capita GRP in 2001, 1,000 current<br>RUR|52,922|65,068|34,620|10,799|400,405|123|86|
|Monthly subsistence minimum in<br>2001,current RUR|1,714|596|1,496|1,212|4,167|35|83|
|Per capita GRP in 2001, multiples of<br>annual subsistence minimum|2.36|2.12|1.90|0.51|13.6|90|83|
|Index of ethnolinguistic<br>fractionalization,1989|0.33|0.2|0.29|0.05|0.85|61|86|
|Average spread between the votes for<br>the winner and the runner up in the 1st<br>round of elections, percentage points<br>(1994-2001)|44.3|21.7|42|1.8|100|49|85|
|Corruptionperception index,2001|0.59|0.21|0.63|0|1|36|37|
|Area,1,000 km<sup>2</sup>|239|464|77|8|3,103|194|86|
|Population in 2001,1,000people|1,552|1,273|1,252|18|6,436|82|86|
|Excess budget revenue (net federal<br>transfers as share of GRP)in 2001|-0.02|0.13|-0.04|-0.71|0.51|650|83|
|Average share of fuel industry in<br>industrialproduction, %|13.4|21.3|5.6|0|94.6|159|85|
|Share of privatized companies in<br>retail trade and services as of July 1,<br>1996,%|77.5|18.7|83.7|20.3|100|24|75|
|Gini coefficient for personal incomes<br>in 2001|0.4|0.04|0.4|0.34|0.51|10|83|
|Social transfers (excluding pensions)<br>as%of incomes in 1995|3.82|1.37|3.56|2.41|11.48|36|76|
|Gini coefficient for municipal<br>government per capita own revenue in<br>2001|0.34|0.16|0.29|0.17|0.9|47|71|
|Infant mortality in 1995, per 1,000<br>children born|19|3.9|18.3|12|34|21|83|
|Urbanization in 1995|66.3|15.6|67.8|0|92.4|24|83|
|Number of students per 10,000<br>population in 1995|133|66|136|0|343|50|83|
|Labor force with university degree in<br>2001, %|20.3|3.9|20.1|12.2|32.5|19|83|
|Spending on infrastructure and<br>development as % of regional budget<br>in 1998|10.3|6.7|7.7|3.1|40.0|65|81|
|Spending on infrastructure and<br>development as % of regional budget<br>in 2001|20.0|8.7|17.7|4.6|45.2|44|83|

_Source_ **:** See Appendix 1.
**Table 1: 2007 UHSS sample for seven cities and 2007 summary data**

|City|Sample<br>size|Of which DB<br>households|% of DB<br>households|DB spending<br>per capita of<br>participants<br>(Yuan per<br>month)|DB line<br>(Yuan per<br>month)|Mean<br>income<br>(Yuan per<br>person per<br>month)|
|---|---|---|---|---|---|---|
|Beijing|33286|352|1.06|269.29|327.78|1835.00|
|Shenyang|12080|207|1.71|144.24|244.69|1129.73|
|Jinan|8000|195|2.44|132.81|249.09|1022.53|
|Wuhan|4689|205|4.37|116.81|219.38|1054.30|
|Chongqing|14324|1128|7.87|104.26|178.33|1021.73|
|Tianshui|912|134|14.69|102.79|148.00|555.50|
|Pingliang|820|221|26.95|103.89|138.00|531.44|
|Urban China|493975|16365|3.31|101.74|182.40|974.67|

<u>Note: DB spending and poverty line data from MOCA. The last row gives aggregates for all of urban China, not just</u> the seven cities listed.

**Table 2: Exit and entry from the** **_Dibao_ program 2007-2010** <u>(a) 2007 as the base year</u>

||DB in 2<br>No|009?<br>Yes|Total|DB in<br>No|2010?<br>Yes|Total|
|---|---|---|---|---|---|---|
|DB in<br>No|931|98|1,029|820|99|919|
|2007?<br>Yes|216|824|1,040|272|675|947|
|Total|1,147|922|2,069|1,092|774|1,866|

<u>(b) 2009 as the base year</u>

||D<br>No|B in 2010?<br>Yes|Total|
|---|---|---|---|
|DB in<br><br>No|959|59|1,018|
|2009?<br>Yes|133|715|848|
|Total|1,092|774|1,866|

22
estimates are biased towards zero, providing a conservative assessment of the true association. It is for example unlikely that areas where records were incomplete are also areas with lower prevalence of cancer; soil movements, while affecting the measurement of exposure, are otherwise not believed to have an effect on the prevalence of cancer. Such “classical” measurement errors in the right‐hand side variable are therefore expected to an attenuation bias; the results are biased towards _not_ finding an association between exposure and the prevalence of cancer.

Second, individual‐level data used to conduct the analysis are taken from a cross‐sectional survey done 26 years after the end of the Vietnam War, with no retrospective information. Thus, two additional issues might arise: one relates to a change of location of individuals between spray applications and present time (e.g. soldiers in sprayed areas returning home at the end of the war or households recently settled in the survey commune), and the other to the fact that the household survey captures information on cancer only for people alive at the time of the enumeration. Population movements or “natural” attrition due to death unrelated to dioxin contamination lead to an increased measurement error in the outcome variable, which adversely affects the precision of the estimation without biasing it.<sup>11</sup> Finally, if higher exposure is believed to increase mortality rates, then areas more heavily sprayed will exhibit a _lower_ prevalence of cancer; the negative coefficients on the binary exposure measure in table 4, columns 1 to 6, are consistent with such potential attrition bias.

_4.2.3. Omitted variables and causal interpretation of the findings_ Other potential cancer risk factors can confound the positive association between exposure and cancer observed in table 4.

Herbicide spray application campaigns are not a random process, but responded to local conditions that may persist until present times and be factors of cancer. Alternatively, bombing and spraying campaigns can in turn create local conditions that are themselves risk factors for cancer. Variables measuring past bombing intensity are added to all the regression specifications in order to capture some of the common geographical determinants of both bombing and herbicide spraying campaigns (the correlation between bombing and spraying is

> 11 The effect of soldiers returning home from heavily sprayed areas would on the other hand tend to bias results towards not finding any association between exposure and cancer prevalence. Furthermore, administrative restrictions severely reduced the scope for migration. VNHS however does not contain migration information to explicitly investigate the issue.

15
**Table 2 Random effects matching regressions for performance and size variables of SOEs versus private firms**

||_(1)_|_(2)_|_(3)_|_(4)_|_(5)_|_(6)_|_(7)_|
|---|---|---|---|---|---|---|---|
|_Variable_|_Assets to_<br>_GDP_|_Liabilities to_<br>_GDP_|_ROAA_|_Adjusted_<br>_ROAA_|_EBITDA to_<br>_revenues_|_EBITDA to_<br>_average_<br>_assets_|_Employee_<br>_costs_<br>_(share)_|
|SOE|1.991***|0.881***|–2.386***|–5.215***|–16.39***|–4.66***|20.45***|
||(0.262)|(0.112)|(0.874)|(0.955)|(4.446)|(1.102)|(2.075)|
|Leverage|–0.00582***||–0.0491***|–0.0666***|0.0603*|–0.0145*|–0.00102|
||(0.00154)||(0.00790)|(0.00843)|(0.0324)|(0.0084)|(0.0151)|
|Size (ln(assets)||0.0791***|–0.0176|0.120|3.586***|0.673***|–0.733***|
|||(0.00777)|(0.109)|(0.118)|(0.521)|(0.115)|(0.237)|
|World Bank region|Y|Y|Y|Y|Y|Y|Y|
|dummies||||||||
|Year dummies|Y|Y|Y|Y|Y|Y|Y|
|Sector dummies|Y|Y|Y|Y|Y|Y|Y|
|World Bank income|Y|Y|Y|Y|Y|Y|Y|
|level dummies||||||||
|Constant|Y|Y|Y|Y|Y|Y|Y|
|Observations|3,798|3,800|3,383|3,381|3,728|3,371|2,505|
|Number of firms|596|597|570|570|590|567|386|
|Mean of dependent|0.619|0.329|2.098|1.469|18.84|6.526|14.63|
|variable<br>_Note_: EBITDA:Earnin|gs before inter|est, taxes, dep|reciation, and|amortization|. ROAA: Retur|n on average|assets.|

_Note_ : EBITDA: <mark>Earnings before interest, taxes, depreciation, and amortization. ROAA: Return on average assets.</mark> Standard errors in parentheses. Errors are clustered by strata derived from the coarsened exact matching procedure.

* _p_ < 0.1, ** p < 0.05, *** _p_ < 0.01.

In line with this finding, EBITDA to revenue is 16 percentage points lower and EBITDA to average assets 4.5 percentage points lower at SOEs than at the average private firm. These magnitudes are large considering that the average EBITDA to revenues is 18.8 percent and the average EBITDA to assets 6.5 percent, meaning that being an SOE almost implies a zero margin.

Ownership plays a big role in explaining the weak results of SOEs, as similar private companies perform much better. These abnormal losses may also explain SOEs’ propensity to borrow more and their fiscal dependence.

Column 7 reveals that the share of employee costs to total costs is much larger in SOEs (35 percent) than in private firms (20 percentage points higher), suggesting that governments hire inefficiently or that one of the QFOs these SOEs perform is related to creating employment. Their inability to shed labor in hard times suggests that their financial performance is likely to deteriorate over time and to be highly affected by negative shocks.

Do subsidies distort SOE behavior, by encouraging them to remain or become unprofitable? To investigate the question, we split the SOE firm-year observations into those that receive subsidies (the sum of operation and capital subsidies) and those that do not (table 3).

18
nal of child deprivation — that may usefully, when combined with remote-sensed data, inform the design of social protection and other government assistance. We do not, however, take a position regarding the precise nature of the support required to alleviate the underlying deprivation(s). Further, a weather disturbance in an agrarian context may be thought of as an additional deprivation (in the sense used by Sen (1999)), because it constrains choices and livelihood opportunities. Support triggered via an adaptive safety net should therefore not be limited to maternal and child health, but instead involve sectors most relevant to that location and country.

# **2.2 Measuring Shocks to Crop Production**

There is no consensus on the best approach to monitoring crop production in Sub-Saharan Africa. At the same time, there is broad agreement on both the challenges and the relevant sources of information. There are two related challenges to measuring crop production in these data-constrained contexts (e.g. Burke and Lobell (2017), Jin et al. (2017)). First, there is a dearth of information on actual crop yields, particularly for small-holder farmers — farmers that constitute the majority of poor households. Second, without adequate ground-truthing, analyses of high-resolution satellite data provide noisy predictions. This is exacerbated by the fact that areas under crop production are constantly changing and the mix of crops produced together is hard to classify across contexts. Consequently, it is also challenging to transfer knowledge gained in a particular context to other locations.

Therefore, organizations charged with monitoring crop production in Sub-Saharan Africa rely on remote-sensed information averaged at broader spatial scales (e.g. Funk et al. (2019)) — traditionally focusing on vegetation (e.g. Becker-Reshef et al. (2010), Brown (2014)) and rainfall (Funk et al. (2015)), both of which have longer time series than the newer measures. Unlike rainfall, the Normalized Difference Vegetation Index (Tucker (1979)) is a cumulative measure. With available ground data, as is the case in the United States, remote-sensed vegetation anomalies can be calibrated to historic crop yields to build models that provide accurate out-of-sample estimates (Johnson (2014)). However, when ground data are scarce, vegetation indices cannot be properly calibrated and less is known about the relationship with crop-productivity in particular contexts (Burke and Lobell (2017)). Rainfall, during the growing season, is unambiguously correlated with crop productivity. However, the influence of rainfall on yields depends on its timing and distribution. Therefore, aggregating rainfall estimates over a growing season can be misleading (Sivakumar (1992), Barron et al. (2003)).

Given these challenges, we employ the following strategy. First, we focus on shocks at a particular spatial scale: a country’s first administrative level (Hijmans et al. (2019)) — 7
the program. A related alternative explanation is that the implementation of the program may have been more efficient when the number of evaluation treatment group villages in the neighborhood (rather than the total number of beneficiaries) was higher. While both notions of implementation scale gains seem a priori reasonable, we argue that they are unlikely to explain our results.

To examine the presence of differences in implementation and supply, two preliminary points should be noted. First, any variation in program delivery in a given geographic area should evenly benefit those program recipients who reside within it. This is at odds with the evidence reported in column 1 of table 5 that program spillovers appear spatially concentrated within relatively small areas surrounding the evaluation villages. According to this line of thought, school supply-side changes would likely affect the enrollment decisions of both recipients and non-recipients, an idea difficult to reconcile with the evidence of heterogeneous externalities reported in column 2 of table 3.

Second, we identify spatial spillovers using only the variation in program frequency generated by the randomized experiment. This variation is small compared to the overall scale of the program (as seen in table 1, in a given five-kilometer radius there are on average ten beneficiary villages but only 0.6 evaluation villages, among which 0.4 are assigned to the treatment group and 0.2 to the control group). Program frequency is thus not much different in neighborhoods that have more treatment-group villages compared to those that have more control-group villages, and the resulting infra-marginal changes in the scale of the program are unlikely to trigger any supply-side efficiency gain.

We next run a battery of complementary tests aimed at detecting the presence of supply-side responses associated with experimental variations in the frequency of the treatment in the areas surrounding evaluation villages. We begin with two measures of implementation efficiency at the village-level. According to qualitative interviews with beneficiaries, local program staff, school teachers, and health staff (see <u>Adato et al. 2000),</u> one major source of inefficiency in program delivery was the delays in the delivery of the form for school attendance monitoring (E1 form), and the associated delays in the payment of scholarships. First, we use program administrative data on the monetary transfers (for scholarships and school supplies) delivered to eligible households to compute the number of months from incorporation until the first disbursements were made to the beneficiary 27
data cover 3,350 “expired” loans past their original due date by 2,147 SHGs in 299 VOs. They started in 2003 after the majority of VOs were formed and coincide with a major drive for SHG formation up to 2006.

Table 1 illustrates that of the 40 million rupees (about 1 million USD) transferred through these loans, about 60% emanated from CIF resources. With only 63% fully repaid (and 23% of the amount still outstanding), repayment is lower for these compared to bank (87%) and internal (89%) loans. The variables at SHG and loan levels that might affect repayment rates as summarized in table 2 include size, source, length, and interest rate of the loan and a dummy for high (monthly or less) repayment frequency and the SHG’s size, age, and membership composition.

The rules governing monitoring and loan recovery, the main subject of this study, are VO-specific and were determined by SHGs in existence at the time of VO formation. SHGs formed thereafter adopted the rules of the VO in their village by default, providing us with a source of exogenous variation. Rules differ in four key dimensions, namely (i) the modalities for SHGs to obtain and repay loans; (ii) VOs’ monitoring of financial affairs by member SHGs; (iii) the extent to which the VO provides public goods; and (iv) SHGs being required to make regular thrift payments that are deposited with the VO.

Repayment modalities include existence of a “loan recovery” committee to monitor SHGs’ credit worthiness (e.g., through rating) and whether fees need to be paid by SHGs who miss an installment. We would expect both to increase repayment probability. VOs’ monitoring of SHGs’ financial affairs is proxied by three variables, namely whether the VO (i) regularly inspects member SHGs’ books at monthly meetings; (ii) employs a trained book keeper; and (iii) regularly audits members' books. Again, all of these should help to reduce defaults. VOs’ provision of non-financial services to raise members’ economic potential is proxied by two dummies indicating whether in-kind consumption credit and marketing services are provided. As such benefits can be cut off in case of default they should enhance repayment incentives, especially insofar as alternative sources for the associated benefits are unavailable. VOs’ collection of thrift from member SHGs provides a cash collateral that can be withheld in case of default and thus should increase repayment incentives as well.

Summary statistics for these variables reported in table 3 suggest that 36% of the VOs in the sample applied a sanction for SHGs who miss an installment, 41% had a loan recovery committee, 35% provided in-kind consumption credit, 25% provided marketing services, 47% collected thrift from their member SHGs, 82% employed trained book keepers, 37% of the SHGs in the sample were regularly audited, and 23% presented books at VO meetings.

3