rafmacalaba/data-use-mentions-extended huggingface dataset:
and Sample Balance**

Table 2 presents summary statistics for the main characteristics of clients and their households. These variables have been constructed from survey data collected at endline. We thus concentrate on variables that are unlikely to have been affected by the intervention itself. For each variable we present, in Column 1 of Table 2, the mean for the control group when the experiment started. The table also tests for systematic differences in these characteristics between the control and treatment groups, with the difference in means between the control and treatment group, and the p-value for a t-test of equality of these means presented in Columns 2 and 3, respectively. As the allocation of the sanitation loan program was random across GPs, we expect no systematic differences between the two groups at the time of the intervention roll-out.<sup>23</sup>

> 20This retrospective measure of toilet ownership matches well with baseline data available for a sub-sample of households. Specifically, the two measures are identical in 78% of cases, with a mismatch in 22% of cases, which could be due to misreporting, or from recall error.

> 21It was not possible to obtain data for the remaining 12 percent of clients, since our partner MFI no longer had all the information required by the credit bureau in order to avail of these records.

> 22We do detect some differences in the characteristics of clients for whom we obtained credit bureau data, relative to the full sample of clients. Specifically, those for whom we obtained credit bureau data are more likely to be married, and less likely to live in female-headed households. Household heads also tend to have more years of education. This is shown

12
Sanyal, Paromita and Vijayendra Rao, 2019 _Oral Democracy: Deliberation in Indian Village Assemblies_ ,” Cambridge University Press, Cambridge UK Schiller, Robert, 2019, _Narrative Economics: How Stories go Viral and Drive Major Economic Events_ , Princeton University Press, Princeton.

Sharan, MR, 2021, _Last Among Equals: Power, Caste and Politics in Bihar’s Villages_ , Westland Press, Delhi Sharan MR and Chinmay Kumar, 2021, “Something to Complain About: How Minority Representatives Overcome Ethnic Differences,” mimeo, Department of Agricultural and Resource Economics, University of Maryland Singh, Bhrigupathi, 2015, _Poverty and the Quest for Life: Spiritual and Material Striving in Rural India_ , University of Chicago Press, Chicago and London <mark>Small, Mario Luis. 2009, “`How Many Cases Do I Need?’: On Science and the Logic of Case Selection in Field-Based Research.”</mark> _Ethnography_ <mark>10, no. 1 (March): Pp: 5–38.</mark> <mark>Small, Mario Luis, 2011, “How to Conduct a Mixed Methods Study: Recent Trends in a Rapidly Growing Literature,”</mark> _Annual Review of Sociology_ , 2011, Vol. 37, pp. 57-86 Small, Mario Luis, 2018, “Rhetoric and Evidence in a Polarized Society,” Coming to Terms with a Polarized Society Lecture Series, Institute for Social and Economic Research and Policy, Columbia University, New York, March Srinivasan, TN, “On Studying Socio-Economic Change in Rural India,” Chapter 10 in P. Bardhan (editor) _Conversations Between Economists and Anthropologists_ , Oxford University Press, Oxford and Delhi Stephens-Davidowitz, Seth, 2014, “The cost of racial animus on a black candidate: Evidence using Google search data,” _Journal of Public Economics,_ Volume 118, Pp; 26-40 <mark>Thaler, Richard H.</mark> 2016. "Behavioral Economics: Past, Present, and Future.
war (henceforth, war-cohort). The findings from the analysis of education are then used to motivate the analysis of employment outcomes of the war-cohort.<sup>11</sup> The analysis of the effect of conflict on employment of the war-cohort is based on a sample of 10,583 prime-age men and women (age 22-49 in 2007). The definition of employment in the last 14 days is based on the questions 1, 2, 3, 5 and 6 from Module 5: Labor Market Section. The definition is consistent with the ILO definition of employment (see Appendix B). The analysis of wages is based on monetary and in-kind wages received by an individual from the occupation in which an individual was employed the longest number of hours. The choice which occupation was the main and which was secondary was made by the 2007 TLSS survey personnel based on the answers to questions 5 and 7 of Module 5: Labor, Part B. Question 5: “For how many hours a week in the last 14 days did you do this work?” Question 7 (to be filled by survey personnel: “Check for first and second highest answers to Q5 (hours worked per week) for this individual.” Based on the answers individual’s wages and in-kind payments from the main and secondary occupations were calculated. The average total income earned in the primary (287 somoni per month) and secondary (7 somoni per month) occupations are drastically different, indicating that secondary jobs provide only a minimal supplementary income.

# **4.2 Conflict exposure variable**

The geographical exposure to the conflict differed significantly in Tajikistan. The southern and eastern regions, such as Khatlon and the Raions of Republican Subordination (RRS) and the country capital Dushanbe were severely affected by the conflict over a long period of time. These regions suffered from repeated clashes between the government and the opposition, or were subjected to the occupation by various factions participating in the conflict over several years. To evaluate the
rice alone may not economically justify such improvements; the land may have to be planted to higher-value crops. Investment required for crop 15/ See Barker and Herdt, pp. 26-27.

- 16/ This section draws on field surveys conducted in Indonesia and reported in Stephen D. Mink, Paul A. Dorosh, and Douglas H. Perry, "Corn Production Systems," and in Stephen D. Mink, 'The Economics of Input Use," in C. Peter Timmer, ed., The Corn Economy of Indonesia (Ithaca: Cornell University Press, 1987), pp. 62-104.
the impacts of climate change on agricultural productivity in the absence of adaptation using panel data at the district level (Deschenes and Greenstone 2007). Combining these estimates of the impacts of climate change on the returns to land and labor with the household specific information on endowments of land and labor, one is able to derive household-specific impacts of the climate change on consumption which is a prerequisite for a proper distributional analysis.<sup>21</sup> The main results emerging from the study of Jacoby et al. (2011) are as follows: First, the substantial fall in agricultural productivity (17 percent overall inclusive of adaptation) that is predicted as a result of warming will translate into a much more modest consumption decline (of 6 percent on average) for the majority of households. This is because these households derive the bulk of their income from wage employment and (rural) wages are estimated to fall by only a third as much as agricultural productivity. The same general pattern is observed in the case of no adaptation.<sup>22</sup> Second, climate change will have heterogeneous impacts across geographical areas and across the income distribution (see Figure 1). Ignoring cereal price effects, climate change appears "progressive" insofar as wealthier households suffer proportionally greater consumption losses. A household in the top percentile of the per capita expenditure distribution would experience nearly a two percentage point greater decline in consumption than a household in the bottom percentile. This progressivity is driven by the skewed land distribution and the fact that larger landowners are concentrated in the higher percentiles. By contrast, temperature-induced wage declines are relatively more costly to the poor than to the rich, mainly because the poor tend to engage in climate sensitive agricultural employment. Third, once the welfare effects or rising cereal prices are taken into account; climate change impacts are regressive, falling more heavily on the poor than the rich. This is true in urban areas, where it is assumed that cereal price effects are the only welfare consequence of
16 earlier work when stability was measured by the likelihood of a systemic crisis.

Tables 3 and 4 show the first results for the Survey IV data, and here it is necessary to point out an even smaller sample size, relative to Tables 1 and 2, as a result of the missing values needed for the construction of the indexes. Interestingly, an increase in the capital regulation index now is associated with a significant decrease in the development of the banking sector, though we would note that the contemporaneous nature of the right- and left-hand side variables raises a caution in the interpretation of this or other results in these two tables. Private monitoring remains positively associated with banking development and with efficiency (via a lower interest margin) in Table 3, though the former effect disappears with government-owned banks. Official supervisory powers, entry requirements, and activity restrictions have no statistical significance whatsoever.
of returns unchanged over time), thus giving us an average population growth rate of 2.7 percent over 2019‐2038.

We continue to draw on United Nations (2017) for projections of the working‐age population share, and ILO (2018) for LFPR projections – the latter is available until 2030, and so we keep the values thereafter stable at the 2030 level. The same working‐age population share and LFPR time series are used across all scenarios.

# _4.1.3 Human capital_

We follow the same approach as in Section 3.1.3. Changes in the average years of schooling are estimated based on shifts in the population (including the return of refugees) and improvements in enrollment rates. Where we reduce (voluntary mobility case – moderate and pessimistic scenarios) or increase refugee returns (forced repatriation case) compared to the UN statistics (voluntary mobility case – optimistic scenario), we apportion the adjustment to different age groups based on the UNHCR profile of the age distribution of refugees.

School destruction and/or non‐functioning schools are the key reasons for low enrollment rates (World Bank 2019). Further, displaced families will likely be hindered in their attempts to access education services. Since the prospects for reconstruction are relatively weak under the pessimistic scenario, we assume primary and secondary enrollment rates only return to pre‐conflict levels (93 and 67 percent respectively) by 2038, while the tertiary enrollment rate rises to 50 percent (from 36 percent) by that time. This timeline from the given initial levels is roughly in line with the trajectory of estimations/projections of enrollment ratios for developing countries in Barro and Lee (2015) (see their Chapter 3, Figure 3.5) and is longer than what Syria historically took to reach those rates.<sup>50</sup> We

> 48 In the UN population statistics, the net migration numbers are reported for the five‐year period between the middle of the respective bracketing years (
61 Figure 7: Price Effects Separately and Jointly<sup>(both sectors)</sup> <!-- Start of picture text --> 0.3<br>0.2<br>$ 0.1-<br>a _<br>-0.2<br>-o 0.1 - -<br>Percentiles<br>-Joint price effect -Economic Growth - Returns to Education<br>-Returns to Experience -Retums to being female<br>Source: "Pesquisa Nacional por Amostra de Domicilios" (PNAD), 1976 and 1996.<br><!-- End of picture text -->
performance. Institutions from Eastern Europe and Central Asia and those from South Asia out-performed those from other regions in terms of financial self-sufficiency. An institution’s age and size are significantly positively linked to financial performance across all three indicators.<sup>14</sup> Finally, neither the indicator for being constituted formally as a for-profit bank nor the average loan size variable are strongly linked to the financial performance indicators. The latter result shows that, even after controlling for region and other covariates, institutions that make smaller loans are not less profitable on average.<sup>15</sup> Given the maximum and minimum values for our variables in Table 1, there is some concern that outliers partly drive the results. We therefore use a robust estimation technique that weights observations based on the absolute value of their residuals, in which observations with large residuals are assigned smaller weights (Appendix B Table 1).<sup>16</sup> Those results are similar to the base results, although there are a few minor differences. First, the coefficient on the portfolio yield is no longer significant in the OSS specifications, which could be because outliers were driving that result in Table 7. This also could be an indication of the importance of the adjustments to the raw data made by the MIX. That is, the unadjusted OSS data might have included outliers that drove a spurious positive correlation with yields. In any event, the yield coefficient in the FSS and ROA regressions remains positive and significant. The coefficients and significance levels for the costs variables are also similar to the base specifications, as are the

> 14 In addition to controlling for age in the base regressions, we also ran models on subsets of MFIs of similar vintage (5-20 years old).  Because the performance indicators for young MFIs are widely dispersed, our results are at least as strong when we restrict the sample in this way. See Appendix A for reasons why data from young MFIs might be most in need of adjustment.
small variances particularly among large firms, and there is a positive association between labor productivity and employment sizes. The main purpose of this study is to demonstrate that main difference between stagnant and dynamic clusters can be attributed to the difference in management and innovative capacities of entrepreneurs.

The empirical part of this paper uses enterprise data collected in industrial clusters in Ghana, Ethiopia, Tanzania, Bangladesh, Vietnam, and China, with quite different characteristics and growth performances. The data indicate that firms’ employment sizes are small if production fluctuates wildly, a reflection of bad management. The data provide suggestive evidence for the hypothesis that employment size is not closely associated with labor productivity in the absence of innovations. By contrast, in the clusters that have experienced innovations, employment sizes have grown rapidly and are associated positively with labor productivity.

The rest of this paper is organized as follows. The next section briefly reviews the factors associated with productivity, including localization economies and diseconomies. In Section 3, we develop a model highlighting the impact of managerial capacities on labor employment. In Section 4, we discuss entrepreneurship and innovations in industrial clusters. Toward the end of this section, we will develop hypotheses. Section 5 documents the empirical findings based on the selected case studies. Section 6 discusses policy implications and concludes the paper.
43|38|22|7|
|Open defecation|% pop|75|36|38|28|11|
|Domestic water consumption|liter/capita/day|20|64|51|115|196|
|Revenue collection|% sales|83|80|94|60|99|
|Distribution losses|% production|29|38|35|40|29|
|Cost recovery|% total costs|40|102|89|67|86|
|Operating cost recovery|% operating costs|56|143|125|94|121|
|Labor costs|connections per employee|107|103|176|96|203|
|Total hidden costs|% of revenue||||||
|Average effective tariff|U.S. cents per m3|81|76|**77**|**45**|—|

_Source:_ Demographic and Health Survey (DHS) and AICD water and sanitation utilities database (www.infrastructureafrica.org/aicd/tools/data). _Note:_ Access figures calculated by the AICD based on the 2000 and 2006 DHS figures published by the Joint Monitoring Program (WHO 2010). — = Not available.

While the urban-rural disparities in South Sudan are not as glaring as in other parts of Africa, this is for the wrong reason: an overall limited level of access. Similarly, there are no glaring disparities between various income levels. Estimations from the recent poverty assessments suggest that a staggering 64 percent of the wealthiest quintile of South Sudanese lacks access to any type of toilet facility. In stark contrast, in Sudan, only 12 percent of the wealthiest quintile of the population has no access to toilets compared to 50 percent of the poorest. Around 2 percent of the wealthiest quintile of South Sudanese had flush toilets and a further 22 percent had access to pit latrines. In terms of water supply, the wealthiest and poorest quintiles of the South
and evasion: An empirical

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- **Lobel, Felipe, Thiago Scot, and Pedro Zuniga** , “Corporate Taxation and Evasion Responses: Evidence from a Minimum Tax in Honduras,” Technical Report, Job Market Paper 2021.

- **Mascagni, Giulia and Andualem Mengistu** , “Effective tax rates and firm size in Ethiopia,” _Development Policy Review_
# **Table 3. The Effect of Land Title on Investment**

The sample consists of all nonfinancial listed firms in the CSMAR database from 2008 to 2017.This table presents parameter estimates from OLS regression of investment on Titling Firm dummy variable for various specifications. The dependent variable is the averaged investment (capital expenditure divided by PP&E) between 2013 and 2017 net of various benchmark levels. That is, In column (1) and (2) the benchmark level is calculated using the average investment between 2008 and 2012. In column (3) the benchmark level is calculated using the average investment between 2010 and 2012. In column (4) the investment in 2012 is used as the benchmark level. In Column (5) the investment in 2008-2009 is used as the benchmark level. _Titling Firm_ is a dummy variable that is 1 if the firm has allocated land before the land title scheme starting from 2012 and 0 if otherwise. All variables are winsorised at the upper and lower 1-percentile. Industry FE denotes whether the industry of the listed firms (categorized according to industry categorization from CSMAR database) is considered in the specification. Exchange FE denotes whether the effect of exchange (Shanghai or Shenzhen Exchange) in which the firms are listed is considered in the specification. The _t-statistics_ are computed using standard errors that are White-corrected. *, **, *** indicate significance at the 1%, 5% and 10% level respectively.

|||∆<br>|(Investment/Total F<br>|ixed Asset)<br>||
|---|---|---|---|---|---|
||Long P|eriod|Median Period|Short Period|Using 2009|
||(1)|(2)|(3)|(4)|(5)|
|Titling Firm|0.345***|0.301***
Government equity injections + δ government loans + Δ SOE loans + Δ DTL

1.77 16.96 0 0 303.02

_Source_ : Authors, based on data from the World Bank Infrastructure SOE Database.

_Note_ : SOE scores were calculated using the Altman Z” parameters as follows: Z” = 3.25 + 6.56 * (current assets – current liabilities)/total assets + 3.26 * retained earnings/total assets + 6.72 * EBIT/assets + 1.05 * book value of equity/total liabilities What is unique about the SOE data is that the estimated Z” scores have very fat tails: Many SOEs have extremely high Z” scores, and many have with extremely low scores. Table 4 compares the distribution of the Z” score for private firms from Altman (2018, table 6) with the distribution of the Z” score for SOEs in our sample. It shows that private firms have more of a bell-curve shape and our data follow a _U_ -shape, with very thick tails on both ends.

**Table 4 Distribution of infrastructure SOEs along the Altman Z” score range**

|_Rating_|_Average Z"_<br>_across_<br>_samples_|_Altman's_<br>_estimates_<br>_(percent of 4,147_<br>_privatefirms)_|_Infrastructure_<br>_Number of_<br>_SOEs_|_SOE sample_<br>_Percent of_<br>_121 SOEs_|
|---|---|---|---|---|
|AAA/AA+|8.15|1.2|30|24.8|
|AA/AA-|7.78|2.2|5|4.1|
|A+|7.61|2.3|3|2.5|
|A|7.04|4.8|3|2.5|
|A
However, a follow-up paper McKenzie and Sansone (2019) highlights the difficulties of picking the outright competition winners. They compare the relative performance of three approaches to predicting outcomes from the YouWin! competition: business plan scores from judges with business experience; simple ad hoc models used by researchers who study entrepreneurship; and machine learning approaches. The results are sobering: i) business plan scores from judges are uncorrelated with performance; ii) gender, age, ability and business sector do have some predictive power (education might have an effect also, but applicants were required to apply using the internet which likely screened out individuals with little education); iii) machine learning methods did not enhance predictive power and; iv) the overall predictive power of all approaches is very low, highlighting the fundamental difficulty of picking competition winners. This of course does not mean that the program had no impact; it just means that it could have had a similar or even stronger impact had it been possible to identify promising projects with greater accuracy. Business plan competitions appear to have been successful in several other African countries, although not on the same scale: for a description of results for Ethiopia, Tanzania, and Zambia, see Fafchamps and Quinn (2017). In fact, business plan competitions in other African countries are in the works.

While such competitions seem useful, there is no substitute for business-to-business linkages for raising productivity and employment in small firms. The “putting out system” or sub-contracting has been important since the first industrial revolution and is still common in many parts of the world including China and India; this small enterprise sector is often referred to as the “cottage industry.” A detailed description of the way this worked in Taiwan several decades ago highlights the sophisticated nature of these enterprises in one village in rural Taiwan (Niehoff 1987). In that setting, all but one of the enterprises were self-financed with no formal guidance apart from production and export brokers; these brokers were identified as an important source of ideas