rafmacalaba/datause-encoder-data huggingface dataset:
_Relative Firm Size._ To test Prediction 3, that family-managed firms are relatively smaller as contractual institutions improve, we run the following regression The key parameter of interest is _γ_ , which our model predicts will be negative. Columns (4) and (5) of Table II show that family-managed firms are about 37% smaller than their professionally-managed counterparts. This is also reflected in the first three columns, where a 10% increase in firm size is associated with around a 1% reduction in the probability that firm is family-managed. Column (6) estimates the interaction term in the regression above, and confirms that family-managed firms tend to be relatively smaller in contexts where contractual institutions are stronger.

# **_V.B. Within-Country_**

We now exploit variation in our regional proxy for contractual institutions - trust, as measured in the WVS - to more carefully test the predictions of the model in our primary dataset spanning 134 regions in 11 countries. As discussed in Section III.C., trust correlates with measures of rule of law and contracting enforcement at the country-level, motivating our use of it as a proxy for contractual institutions.

While our results should be interpreted as associations consistent with the model rather than causal relationships, our data allow us to control for country-level unobservables and region characteristics (such as GDP per capita and the share of college-educated workers) when examining patterns of trust and firm behavior. We show that failing to account for these differences would lead us to substantially overestimate the effect of trust on management modes. Nevertheless, these relationships we previously documented at the country-level still exist once we purge the data of these sources of endogeneity.

_Management Mode Choice_ . We begin by testing Prediction 1, that an improvement in contractual institutions is correlated with a decrease the probability of being family-managed, conditional on firm size, by running the following regression Here _i_ indexes firms, _r_ indexes regions, _c_ indexes countries and _k_ indexes 3-digit industries. _β_ is identified off variation in trust and management modes across regions within countries for firms of the same size. The vector of controls _Xirc_ contains a region’s GDP per capita and share of workers with a college education, as well as firm age. To strengthen confidence in our results and confirm the mechanisms at play we extend our baseline regression to test for differential mode choices in complex industries (Prediction 5.i) as well as differential response of large firms and firms in more complex industries to changes in trust (Predictions 2 and 5.ii).

Columns (1) to (4) in Table III show that, consistent with Proposition 1, an increase in trust is associated with a reduction in the probability of a firm being family-managed. Columns (1) and (2) highlight 14
# **6 Main Results**

The empirical analysis proceeds in two steps. I first examine the impact of debt relief on the financial position of beneficiary households and the real effect of the bailout on households’ investment decisions and productivity. In the second part of the analysis, I explore potential mechanisms that can explain the observed economic behavior of beneficiary households after the program, and focus specifically on the impact of debt relief on borrower expectations.

## **6.1 The Level of Household Debt**

Table 5 presents RD estimates of the change in total household indebtedness. Panel A reports baseline estimates using the sample of all surveyed households and employing four different RD specifications: a basic discontinuity specification without controls and bank*district fixed effects, a linear control function specification with a complete set of bank*district fixed effects _φbd_ , with and without additional loan and household controls, and a quadratic control function specification with bank*district fixed effects. To validate these results, Table 5, Panel B, replicates the estimates for two restricted samples: (i) an audited robustness sample consisting of all households with audited and matching electronic land records, and (ii) a reduced bandwidth sample that omits the top and bottom quartile of the land distribution to either side of the program threshold. As in subsequent tables, the reported coefficients are local average treatment effect (LATE) estimates, measuring the effect of debt relief on households benefiting from 100% unconditional debt relief, relative to borrowers qualifying for only 25% conditional debt relief.

The point estimates in Table 5 indicate that debt relief leads to a significant and persistent reduction in the overall indebtedness of beneficiary households. The economic magnitude of this effect is substantial: on average, the total indebtedness of households that had their outstanding balance cancelled entirely declined by between Rs 24,000 (US$ 470) and Rs 26,000 (US$ 508), or approximately 30% of the pre-treatment mean of overall household debt.<sup>18</sup> These estimates remain stable and statistically significant in alternative samples and are robust to the inclusion of additional controls and bank and district fixed effects. Given that households were surveyed approximately one and a-half years, or two

> 18Note that if there is measurement error in recall data, this would likely lead to downward bias in estimates of the change of overall indebtedness, so that the true effect of debt relief may be larger than the reported point estimates.

18
Table 1 shows that the Microcredit Summit numbers are driven by large lenders in Asia and the Pacific, and most of those are in India and Bangladesh. Of total world outreach at the end of 2013, 79 percent of customers lived in Asia and the Pacific, including 86 percent of all women served and 89 percent of all counted as being the poorest. Given the overlap in the two groups, it is unsurprising that 91 percent of “poorest women” are also from Asia and the Pacific. In contrast, 16 percent of all borrowers are from Sub-Saharan Africa or Latin America and the Caribbean, and just 8 percent of the poorest women.<sup>7</sup>

||**Total**|**Women**|**Poorest**|**Poorest**<br>**Women**|
|---|---|---|---|---|
|Sub-Saharan Africa|7.6%|5.4%|7.6%|6.2%|
|Asia and Pacific|79.1%|85.8%|88.7%|90.6%|
|Latin America and Caribbean|8.2%|6.8%|2.4%|2.1%|
|Middle East and N. Africa|2.5%|1.8%|1.1%|1.0%|
|"DevelopingWorld"|97.4%|99.8%|99.9%|99.9%|

# **Table 1. Regional client numbers as percentage of global totals**

Customers by region, gender, and poverty level Data as of December 31, 2013. Adapted from Table 7, Microcredit Summit Campaign, _State of the Summit Report 2015_ https://stateofthecampaign.org/2015/12/15/2015-report-regional-data/

> 7 The published data in the _State of the Summit Report 2015_ include an incorrect entry for the number of women served in Asia and the Pacific (166,908,164 was reported as the number of total clients, and the same figure was mistakenly reported as the number of women; Table 7 of the report). In Table 1 and Table 2 here, we substitute a number based on data from the _State of the Summit Report 2014_ . The 2014 report lists 161,022,985 total clients in Asia and the Pacific and 130,880,298 female clients in the region (81 percent of the 2014 total). Here, we assumed a stable gender ratio and replaced the Summit’s number of female clients with 131,195,613, which is also 81 percent of the Asia and Pacific total in 2013. Data from the 2014 report are available at https://stateofthecampaign.org/2014/03/21/2014-report-regional-data/

8
However, this study has a similar problem as the other difference-in-difference studies discussed previously. The study effectively uses workers outside regulated industries as a control group. To the extent that those workers benefit from climate policy, the difference-in-difference estimator will misinterpret that gain for the control group as a loss for workers in regulated industries.

# **B. Macro-econometric Models**

Several studies use macro-econometric models to assess the macroeconomic effects of climate change policies, as well as other economic and environmental policies. Macroeconometric models use advanced statistical techniques to estimate the historical correlation between economic variables such as GDP, employment, and inflation. They then use those estimates to forecast the implications of climate policy scenarios. Unfortunately, these models rely on the assumption that past correlations can predict future outcomes, and most of these models rely on data that are not available in most developing countries.

Lehr et. al. (2012) analyze the labor market implications of a large-scale investment in the renewable energy sector in Germany. They use a sophisticated econometric simulation and forecasting model, PANTA-RHEI (which is an extension of the INFORGE forecasting model), for a set of scenarios and compare the resulting economic outcomes. All the parameters are estimated econometrically using time-series data from 1991 to 2008.

Three different types of economic scenarios are considered: (i) two price paths for international energy prices (low-price scenario and high-price scenario) following projections from the International Energy Agency, (ii) three forecasts for domestic investment in renewable energy (the lead scenario from the German government’s annual updates, high investment in the photovoltaic sector, and low investment in the photovoltaic sector), and (iii) four export forecasts for renewable energy technology (minimum, slow, optimistic, and maximum). The PANTA-RHEI model calculates the 20
14

**Figure 6  Percentage of people living in households that cited LPG or electricity as the primary source of energy for cooking and annual expenditures per capita in urban areas**

<!-- Start of picture text --> 100 $6,000<br>90<br>$5,000<br>80<br>70<br>$4,000<br>60<br>50 $3,000<br>40<br>$2,000<br>30<br>20<br>$1,000<br>10<br>0 $0<br>LPG Electricity Expenditures per capita in 2022 dollars<br>Source : World Bank staff calculations using household survey data.<br>: The South African survey did not ask enough questions to enable estimation of total household expenditures.<br>Figure 7 Percentage of people living in households that cited LPG or electricity as the primary<br>source of energy for cooking and annual expenditures per capita in rural areas<br>70 $2,800<br>60 $2,400<br>50 $2,000<br>40 $1,600<br>30 $1,200<br>20 $800<br>10 $400<br>0 $0<br>LPG Electricity Expenditures per capita in 2022 dollars<br>% of population<br>Annual expenditure per capita<br>% of population<br>Annual expenditure per capita<br><!-- End of picture text --> _Source_ : World Bank staff calculations using household survey data.

_Note_ : The South African survey did not ask enough questions to enable estimation of total household expenditures.

**Figure 7  Percentage of people living in households that cited LPG or electricity as the primary source of energy for cooking and annual expenditures per capita in rural areas**

_Source_ : World Bank staff calculations using household survey data. _Note_ : The South African survey did not ask enough questions to enable estimation of total household expenditures.

Comparison of households with evidence of LPG use and those who named LPG as the primary cooking fuel is shown in Figure 8. In Burkina Faso, Liberia, Niger, South Africa, Tanzania, and Zambia, less than
- Partridge, Mark, and Dan Rickman, “Distance from Urban Agglomeration Economies and Rural Poverty”, Journal of Regional Science 48:2 (2008), 285-310.

Rodrik, Dani, “Unconditional Convergence”, NBER Working Paper 17546 (2011).

- Rosenthal, Stuart, and William Strange, “Evidence on the Nature and Sources of Agglomeration Economies”, in Vernon Henderson and Jacques François Thisse (eds.) Handbook of Regional and Urban Economics, Volume 4 (Amsterdam: North-Holland, 2004), 2119-2171.

- Rosenthal, Stuart, and William Strange, “Female Entrepreneurship, Agglomeration, and a New Spatial Mismatch”, Review of Economics and Statistics, forthcoming (2011).

- Schneider, Friedrich, “Size and Measurement of the Informal Economy in 110 Countries Around the World”, World Bank Working Paper (2002).

- Schneider, Friedrich, and Dominik Enste, “Shadow Economies: Size, Causes, and Consequences,” Journal of Economic Literature 38:1 (2000), 77-114.

- Schoar, Antoinette, “The Divide Between Subsistence and Transformational Entrepreneurship”, in Lerner, Josh, and Scott Stern (eds.) Innovation Policy and the Economy, Number 10 (Chicago, IL: University of Chicago Press, 2009), 57-71.

- Siggel, Eckhard, “The Indian Informal Sector: The Impact of Globalization and Reform”, International Labour Review 149:1 (2010), 93-105.

- Trivedi, Pushpa, L. Lakshmanan, Rajeev Jain, and Yogesh Gupta, “Productivity, Efficiency, and Competitiveness of the Indian Manufacturing Sector”, Reserve Bank of India Paper (2011).

- Unni, Jeemol, “Wage and Incomes in Formal and Informal Sectors in India”, Indian Journal of Labor Economics 48:2 (2005), 311-317.

- Virmani, Arvind, and Danish Hashim, “J-Curve of Productivity and Growth: India Manufacturing Post Liberalization”, IMF Working Paper 11/163 (2011).

- Volpe Martincus, Christian, “Spatial Effects of Trade Policy: Evidence from Brazil”, Journal of Regional Science 50:2 (2010), 541-569.

- Zenou, Yves, “Rural-Urban Migration and Unemployment: Theory and Policy Implications”, Journal of Regional Science 51:1 (2011), 65-82.

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# **Table 1. Inequality and Poverty in the 2010s Compared to the 1980s, Selected Countries and Country Groups**

|**Country**|**Initial**<br>**Gini**<br>**(1980s)**|**Final Gini**<br>**(2010s)**|**Change**<br>**in Gini**|**Initial**<br>**Poverty**<br>**(1980s)**|**Final**<br>**Poverty**<br>**(2010s)**|**Change**<br>**in**<br>**Poverty**|
|---|---|---|---|---|---|---|
|Brazil|60.9|<br>55.2|-5.8|37.5|8.6|-28.9|
|Canada|40.7|<br>45.5|4.7|0.4|0.4|0.0|
|China|30.2|<br>41.4|11.2|...|15.2|...|
|France|48.2|<br>49.0|0.8|1.6|0.1|-1.5|
|Germany|42.5|<br>51.9|9.4|...|0.1|...|
|India|42.1|<br>49.0|6.9|84.9|61.7|-23.2|
|Indonesia|39.6|<br>42.6|3.1|91.1|33.9|-57.2|
|Italy|43.9|<br>49.3|5.4|0.8|1.9|1.1|
|Japan|37.8|<br>45.6|7.8|...|0.6|...|
|Mexico|46.8|<br>47.2|0.4|19.0|10.2|-8.8|
|Russian Federation|35.3|<br>45.6|10.4|...|0.5|...|
|South Africa|65.7|68.5|2.8|...|36.4|...|
|Turkey|44.4|<br>43.1|-1.3|13.2|2.6|-10.6|
|United Kingdom|46.4|<br>52.9|6.5|1.2|0.3|-0.9|
|United States|44.7|<br>50.8|6.1|0.7|1.2|0.5|
|**Country classification**|||||||
|Advanced economies|42.6|<br>46.9|4.3|0.8|0.5|-0.3|
|Emerging markets|44.9|<br>45.1|0.2|34.7|9.0|-25.7|
|Low-income developing<br>countries|46.2|<br>44.9|-1.2|62.3|46.4|-16.0|
|**World average**<br>_Sources_: Authors’ calculatio|44.3<br>ns based on data|<br>45.5<br>from Standardi|1.2<br>zed World Incom|29.1<br>e Inequality Database (|12.1<br>SWIID) and Wo|-16.9<br>rld Bank.|

_Sources_ : Authors’ calculations based on data fro <mark>m Standardized World Income Inequality Database (</mark> SWIID) and World Bank. _Note_ : A negative/positive change in the Gini market coefficient indicates less/more inequality. Initial Gini market (1980s): average index for the 1980s. Final Gini market (2010s): average index for the 2010s up to 2019. Initial poverty ratio (1980s) at $3.20 a day: average index for the 1980s. Final poverty ratio (2010s) at $3.20 a day: average index for the 2010s up to 2019. The data points given for advanced economies, emerging market economies, and low-income developing countries use the IMF classifications and <u>data for all countries in those categories.</u> 5
water and sanitation infrastructure has focused on child diarrhea as an outcome variable for a variety of reasons: first, lower diarrhea prevalence is the most direct presumed effect of improved water and sanitation infrastructure. Second, epidemiological intervention studies in the field are expensive, which limits feasible sample sizes and, as a result, also the statistical power to detect changes in lower frequency events such as short-term mortality. This fact is unfortunate from a policy perspective since reducing diarrhea, unlike combating HIV, malaria and tuberculosis, has not been made an explicit target of the MDGs, and is therefore generally not as high on policy priority lists. The international community is instead highly committed to reducing child mortality (Millennium Development Goal 4). While diarrheal studies provide important information about the immediate health effects of water and sanitation, the link from water and sanitation to child mortality is indirect and cannot directly be derived from estimates on child diarrhea.

Moreover, few studies have explicitly analyzed the health benefits of sanitation infrastructure. The Independent Evaluation Group of the World Bank (2008) postulates in a review of impact evaluations on water supply and sanitation: _“All types of intervention could benefit from further studies, but the most obvious gap is the lack of evidence regarding sanitation...there is a great need to engage in more such studies to support the case – which appears to exist on the basis of limited evidence – for more investment in sanitation.”_ In this paper, we aim to fill this evidence gap by combining all available Demography and Health Surveys (DHS) with complete household information into a large international data set that allows us to investigate the relation between water and sanitation and health in a large range of developing country settings over the last 25 years. The Demography and Health Surveys contain an extensive list of household characteristics including access to water and sanitation, and also measures of child morbidity and mortality at the household level. To deal with the large degree of heterogeneity in water and sanitation facilities across countries, we construct three levels of water and sanitation technology, respectively. Following the existing literature, we first analyze diarrhea as the dependent variable, followed by child mortality. Moreover, we analyze both the differences in child health between households with and without access to water and sanitation technologies, as well as differences in child health between rural villages (urban 4/36
The World Bank _Doing Business_ report measures to a certain degree the quality of secured transactions systems through the Legal Rights Index<sup>19</sup> which is part of the Getting Credit indicator. While not every aspect of a comprehensive secured transactions system is covered by the criteria of the Legal Rights Index, it covers a number of aspects related to: (i) the scope of the law (possessory and non-possessory security rights, general description of assets, debts and obligations, security interests in future assets, extension of the security interests to the products and proceeds); (ii) the priority scheme for creditors (outside and in bankruptcy proceedings); (iii) the publicity mechanism through the registration of notices in a collateral registry, and; (iv) the enforcement mechanisms, including out of court enforcement. The Legal Rights Index has some important gaps in how security interests are created and perfected, in measuring which countries have a good registry system and also in determining the types of transactions and the types of parties that should be covered by the secured transactions law. In addition to this, there is not complete consensus among international experts in a couple of the criteria, including the exempt from an automatic stay on enforcement and the absolute priority of secured creditors. Other minor aspects are also missing given the limitations of the methodology. However, despite these gaps, the Legal Rights Index proves to be a good benchmarking tool to assess creditors’ rights and secured transactions in a given jurisdiction and the ranking of the MENA countries reflect relatively well the regional overview that has been validated by some stand alone diagnostics in specific countries.

An overview of the Doing Business’ Legal Rights Index indicator for the region in 2009 illustrates that secured transactions systems are still hugely underdeveloped. Individually, there is not a single country in the region that has introduced reforms in its secured transactions system, which is illustrated by the position of individual countries in the ranking (see Table 2). The first group of countries in the ranking (Jordan, UEA, Saudi Arabia, Oman, Bahrain, Kuwait) are 106 in the overall ranking, while West Bank & Gaza is the last country with a score of 0 in Legal Rights. The infrastructure for modern secured transactions regimes is largely missing in the MENA countries.

> 19 The strength of legal rights index measures the degree to which collateral and bankruptcy laws protect the rights of borrowers and lenders and thus facilitate lending.

10