rafmacalaba/datause-encoder-data huggingface dataset:
# Appendix B. Definitions and Summary statistics

|**Variable Name**|**Level**|**Definition**|**Obs#**|**Mean**|**Median**|**Min**|**Max**|**St. Dev.**|
|---|---|---|---|---|---|---|---|---|
|Number of borrowers as % of<br>population|Country|The share of MFI borrowers as a percentage of population. Data for the number of microfinance<br>borrowers comes from MIX market. All borrower and microenterprise data proxies are for 2010 or<br>the latest available year.|41|3.14|2.14|0.01|11.96|3.35|
|Average loan size, per capita<br>GNI|MFI|Average loan size relative to the average per capita Gross National Income, taking the average from<br>2007 to 2009 of MIX dataset.|770|0.57|0.25|0.01|16.22|1.17|
|% of Women borrowers|MFI|Number of Active Borrowers who are women / Number of Active Borrowers, taking the average<br>from 2007 to 2009 of MIX dataset.|735|0.65|0.64|0.01|1.00|0.27|
|Portfolio at Risk(>30days)|MFI|The value of all loans outstanding that have one or more installments of principal past due more<br>than 30 days. This includes the entire unpaid principal balance, including both the past due and<br>future installments, but not accrued interest. It also includes loans that have been restructured or<br>rescheduled, taking the average from 2007 to 2009 of MIX dataset.|748|0.07|0.05|0.00|0.96|0.09|
|Financial Self-Sufficiency<br>(FSS)|MFI|Ratio greater than one if institution generates sufficient revenue to cover its cost adjusted for<br>inflation and the subsidies that MFIs receives such as donated equity and concessional loans.<br>FSS=Adjusted Financial Revenue / Adjusted (Financial Expense + Impairment Losses<br>on Loans +Operating Expense) , taking the average from 2007 to 2009 of MIX dataset.|759|1.02|1.04|0.07|3.17|0.27|
|Overall Score Nominal (0-100)|Country|Overall score for each country is calculated as the weighted sum of the "Regulatory Framework and<br>Practices" and "Supporting Institutional Framework" category scores, reduced by the adjustment<br>factor.<br>Using the Microscope scores in 2010.|41|42.85|43.90|19.70|67.80|12.87|

57
coverage and improved reporting (as noted by EM-DAT), we focus our analysis on data from 2000 onwards.

We define an intensity shock, measuring the number of people affected by natural disasters using data from EM-DAT following Fomby, Ikeda, and Loayza (2013), as: where _I_ is the country and _t_ is years.<sup>2</sup> Unlike in Fomby, Ikeda, and Loayza (2013), and more recently Bayoumi, Quayyum, and Das (2021), we use the actual intensity measure as the climate shock rather than a dummy variable based on some threshold value. Table 1 provides the name, description, and source of the other variables used in this section. Loayza et al. (2012) show that the type of disaster may matter for its impact on the economy, however, we study all disasters.

We assume that any unreported event is considered to be a non-occurrence, and hence assigned an intensity value of zero. This decision is supported by several factors. First, the improved data coverage by EM-DAT from 2000 onwards significantly reduces the likelihood of major events going unreported. Also, within our sample we notice an association between missing damage values and lower climate shock intensities, suggesting that unreported events are likely to be smaller in scale. Second, we underscore the importance of establishing a counterfactual scenario without any shocks. Solely analyzing climate event datasets might not provide a complete picture of counterfactual fiscal resilience impacts. Therefore, treating all periods outside of reported events as zero allows us to approximate a counterfactual scenario devoid of shocks.

# 3.3. Results

Natural disaster shocks are associated with a statistically significant decline in output, with no empirical evidence supporting threshold effects related to the size of the shock and some evidence of compounding effects. Figure 2.A shows the impact of all available climate shocks and large climate shocks that affect 1 percent of the population. It is based on close to 3,000 natural disasters from 2000 to 2021 in 156 economies. These shocks are associated with a loss of output that is statistically significant up to eight years after the disaster. There is no evidence of threshold effects as the impacts are similar when these shocks are large, that is, they are in the 95<sup>th</sup> percentile of the

> 2 It is also possible to define a damages shock which looks at the economic cost of a natural disaster. We prefer an intensity shock, however, for several reasons. First, there are less than half as many damages observations as observations on people affected by natural disasters. Second, damages are likely based on insured damages. Third, estimates of economic cost are harder to determine and more subjective.

9
also interesting to note that for the 1971-93 period, the average firms raised about Rs. 0.321 crores from public issues. Within here, the Rs. 0.574 crores for the 1981-93 period is significantly higher than the Rs. 0.068 crores for the 1971-80 period.<sup>29</sup>

# Conclusions and discussion

There are many ways to characterize the decisions that firms make regarding the sources and uses of funds. The results in this paper suggest that on the sources of funds side, the firm's fundamental choice is between internal and external finance. Likewise, on the uses of funds side, the choice is between physical and financial investments. Overall, the evidence in this paper suggest that the stock market plays a very limited role as a source of finance for Indian as well as U.S. firms. The interesting puzzle this finding then poses is that given this limited financing role of the role of the stock market, why do managers worry so much about share prices?

On a comparative basis, the fundamental difference between firm financing choices in India and the U.S. relate to the smaller role of internal finance and the bigger role of extemal debt in Indian firms compared to U.S. firms. This result is consistent with theoretical predictions, given that information and agency problems are less severe for Indian firms compared to U.S. firms, since the Indian financial system is predominantly a bank-oriented one and is closer to Japanese and German financial systems than the US and UK financial systems.

The arguments presented in this paper also suggest that there is a fundamental methodological difference between a study of sources and use of funds and a study of the

**29** These figures are based on the raw data used for Table 11, taken from the various issues of RBI's Report on Currency and Finance.

26
<!-- Start of picture text --> =<br>eo<br>[=]<br>wo<br>LL)<br>oO<br>w<br>DB ern<br>c<br>fab]<br>=<br>—<br>2<br>oa<br>ate<br>WS<br>Ol<br>oo<br>=<br>2010 2011 2012 2013 2014 2015 2016 2017 20186 2019 2020<br><!-- End of picture text -->
increases by 360%. This suggests that our OLS/FE estimators are in fact inconsistent and biased.

Table 4: Second-stage regressions for IV models with intercept dummies

|Variables|1|2|3|4|5|
|---|---|---|---|---|---|
|I_GDPpc|-0.00009<sup>∗∗</sup><br>(0.00004)|-0.0001<sup>∗∗∗</sup><br>(0.00004)|-0.00009<sup>∗∗</sup><br>(0.00004)|-0.00009<sup>∗∗</sup><br>(0.00004)|-0.0001<sup>∗∗</sup><br>(0.00004)|
|I/GDP|0.096<sup>∗∗∗</sup><br>(0.030)|0.093<sup>∗∗∗</sup><br>(0.031)|0.094<sup>∗∗∗</sup><br>(0.030)|0.097<sup>∗∗∗</sup><br>(0.029)|0.098<sup>∗∗∗</sup><br>(0.029)|
|Tax Rate|-0.096<sup>∗∗∗</sup><br>(0.025)|-0.099<sup>∗∗∗</sup><br>(0.027)|-0.093<sup>∗∗∗</sup><br>(0.025)|-0.096<sup>∗∗∗</sup><br>(0.026)|-0.087<sup>∗∗∗</sup><br>(0.024)|
|Tot. ODA|0.001<sup>∗∗</sup><br>(0.0004)|0.001<sup>∗∗</sup><br>(0.0005)|0.001<sup>∗∗</sup><br>(0.0004)|0.001<sup>∗∗</sup><br>(0.0004)|0.001<sup>∗∗</sup><br>(0.0005)|
|Polit. Stab.|1.585<sup>∗∗</sup><br>(0.705)|2.161<sup>∗∗</sup><br>(0.969)|1.579<sup>∗∗</sup><br>(0.708)|1.682<sup>∗∗</sup><br>(0.834)|1.702<sup>∗</sup><br>(0.912)|
|Latitude|0.019<sup>∗</sup><br>(0.010)|0.020<sup>∗</sup><br>(0.011)|0.018<sup>∗</sup><br>(0.010)|0.019<sup>∗</sup><br>(0.010)|0.017<sup>∗</sup><br>(0.010)|
|Latitude_Sq.|0.0008<sup>∗∗</sup><br>(0.0004)|0.0009<sup>∗∗</sup><br>(0.0004)|0.0008<sup>∗∗</sup><br>(0.0004)|0.0009<sup>∗∗</sup><br>(0.0004)|0.0009<sup>∗∗</sup><br>(0.0004)|
|D_Fragile||0.851<br>(0.785)||||
|D_Africa|||-0.214<br>(0.356)|||
|D_AF_Frag.||||0.285<br>(0.662)|0.160<br>(0.623)|
|D_AF_Non.Fr.|||||-0.478<br>(0.588)|
|constant|1.988<sup>∗</sup><br>(1.118)|2.008<sup>∗</sup><br>(1.049)|2.109<sup>∗</sup><br>(1.189)|1.898<sup>∗</sup><br>(1.024)|1.937<sup>∗</sup><br>(1.095)|
||N = 245<br>R<sup>2</sup><br>adj<sup>= 0.10</sup>|N = 245<br>R<sup>2</sup><br>adj<sup>= ...</sup>|N = 245<br>R<sup>2</sup><br>adj<sup>= 0.10</sup>|N = 245<br>R<sup>2</sup><br>adj<sup>= 0.07</sup>|N = 245<br>R<sup>2</sup><br>adj<sup>= 0.07</sup>|
|Sargan (χ<sup>2</sup>)|4.26 (0.37)|2.78 (0.59)|4.37 (0.36)|4.12 (0.39)|4.38 (0.36)|
|Basmann (χ<sup>2</sup>)|4.12 (0.39)|2.66 (0.62)|4.21 (0.38)|3.98 (0.41)|4.21 (0.38)|
|W-H (χ<sup>2</sup>)|4.47 (0.035)|6.03 (0.015)|4.36 (0.038)|4.10 (0.04)|3.62 (0.06)|
|DWH (F )|4.56 (0.033)|6.12 (0.013)|4.47 (0.035)|4.20 (0.04)|3.73 (0.05)|

Note: Over-identifying restrictions are conducted through Sargan(χ<sup>2</sup> ) and Basmann (χ<sup>2</sup> ) tests, and Endogeneity tests are conducted through W-H (F ) test and DWH (χ<sup>2</sup> ). DWH refers to Durbin—Wu —Hausman χ<sup>2</sup> −test, and WH refers to Wu—Hausman F-test. Entries in parentheses are p-values. List of Instrucments: Latitude, Latitude Sq, Legal_French, Legal_Geram, Tropical, Logitude, Landlock.

All ODA coeffi cients remain statistically significant at 5% with the predicted signs and support the intuition that external aid helps growth. However, unlike Table 2, Polit. Stab. is now statistically significant at 5% in all specifications with a strong positive effect on economic growth. Accounting for various intercept dummies—including dummy for Africa, dummy for fragile states, 24
# **5. Results**

# _First-Stage Results_

OLS Regression results from the first-stage models are given in Appendix 2 Tables A1-A10. Across the ten pseudo-surveys used here, the R<sup>2</sup> ranges from 0.415-0.53 (see Table 1). The explanatory power of the models in this analysis is in the general range of models from past applications. The R<sup>2</sup> for models for particular strata ranged from 0.45 to 0.77 in Ecuador (Hentschel et al, 2000), 0.29 to 0.63 in Madagascar (Mistiaen et al, 2002), and 0.47 to 0.72 in South Africa (Alderman et al, 2002). The explanatory power achieved with the PROGRESA models is rather good given that the households in the PROGRESA communities are more homogenous than those within a stratum in a typical application. All the communities in the PROGRESA sample were selected for the program because they were poor and rural, based on indicators in the 1990 and 1995 censuses. Consequently, the households are more similar to one than another than the households in an entire stratum of a country.

Household size was used in all models, and some variables were selected in models for several pseudo-surveys, but there was generally little consistency in models chosen across pseudo-surveys. The estimated location effects were generally small, with variances ranging from 0.9% to 3.1% of the overall variance of the disturbance term after the addition of cluster-level means. This can also be seen in that the models achieved levels of explanatory power very close to what would be achievable with models that employed, instead, a cluster-level fixed-effects specification (see Table 1).

# _Second-Stage Results_

# 5.1 <u>Point Estimates and Precision</u>

Tables 2 and 3 present illustrative results for the headcount rate based on two pseudo surveys: 2 and 3.<sup>15</sup> These tables present for each of the 20 target populations a measure of the true headcount rate as well as the estimated headcount rate based on a variety of procedures. Column 1 presents estimates and standard errors based on the

> 15 These two pseudo-surveys have been chosen arbitrarily in order to avoid unnecessary repetition. Qualitative conclusions are unchanged if other, or all, pseudo-surveys are examined.

11
# **5.2.1 Mechanisms**

Did the reform increase the speed of justice by improving the efficiency of the procedure, or by increasing its intensity? We use our rich case and hearing-level court data to document the channels through which the decree affected procedural efficiency versus formalism at pretrial stage. The results show that judges respond to the decree by increasing the decisiveness of the pre-trial proceedings. We make three main observations. First, cases are more likely to be desk-rejected or fast-tracked to deliberations (Panels A and B, Figure 4; cols 4 and 5, Table 3).<sup>21</sup> Second, judges schedule fewer hearings with no change in cases’ likelihood of being heard (Panels C and D, Figure 4; cols 6 and 7, Table 3). Instead, we find judges are more likely to impose strict deadlines on parties requesting an adjournment (Panel E, Figure 4; col 8, Table 3). Overall, these results corroborate the notion that the decree led to actual efficiency gains at each step of the pre-trial procedure.

# **5.2.2 Heterogeneity**

Did the reform similarly affect all case and judge types, or did it generate heterogeneous responses across case and judge types? We explore two dimensions of case and judge heterogeneity in the impact of the decree on procedural delays: case complexity, proxied by claim amount, and judge baseline speed.<sup>22</sup> Do judges differentially intensify the procedure for more complex (larger) versus simpler (smaller) cases? We first confirm that claim size indeed proxies for case complexity as it is associated with a longer, more complex pre-trial procedure before the reform (first row, Table 4). We then make three central observations. First, we find the decree equally increased the

> 21Again, assuming common trends across chambers does not change our point estimates (cols 4 and 5, Table A2).

> 22Table 2 shows substantial variation in case complexity. Among cases that include a payment claim, the average claim amount is CFA franc 71.5 million (approximately USD 135,000) and the median is CFA franc 8 million (approximately USD 15,000). We also observe substantial variation in judge-level speed by claim amounts (Figure A9).

16
Anderson (2008).<sup>13</sup> Following the existing literature, six health measures are used to construct the health index from the data available in IFLS 5: the general health status (GHS) as perceived by the visiting health worker, self-reported health status, dummy for not being underweight (using the information on body mass index (BMI)), natural logarithm of lung capacity score, dummy for no hypertension, and the adult height (in centimeters).<sup>14</sup> The math test index uses scores from three separate tests which measure different aspects of mathematical skills. A separate cognition index, uses scores from the Raven test and two memory tests, available in IFLS 5.<sup>15</sup> The final index is the economic index which combines income and years of education completed for males. Because of low labor force participation among females, a dummy indicating if a woman is in the labor force (or in school) is used, instead of the income measure. All the indices and the constituent variables are constructed in such a way that a greater value implies a better outcome. Data Appendix section S5 of the Appendix discusses the details (source, construction, motivation, and so on) of all constituent variables. Table 1 reports the summary statistics of outcomes of interest for male and female samples separately.

District-level information on health facilities is added to the IFLS data for the year 1983 from PODES to deal with possible confounding factors. PODES is a village-level survey data. To arrive at the figures for district level, the available village level data is aggregated at the district level.<sup>16</sup> 13The Appendix, section S4 titled “Construction of the Summary Index”, explains the construction of the index . 14The midwife program can also affect other dimensions of health like the likelihood of diabetes and inflammation level in the body. The IFLS collects the information on HbA1C (a measure of blood sugar level) and c-reactive protein (a measure of inflammation) for a limited number of individuals—the sample decreases by about three-fourths if this information is included. To avoid this decrease in sample size, this information is not used for the construction of the health index.

> 15Cognitive (non-numerical tests) and math skill tests (numerical tests) are intentionally separated. Please note that math tests require numerical literacy, and a health intervention may not necessarily increase educational attainment while may increase other dimensions of cognition nonetheless (Costa, 2015). Thus, combining math with other tests on cognition may or may not improve the overall index which may lead to erroneous conclusions.

> 16More information about the data set can be found in https://www.rand.org/labor/bps/podes/ 1983ag.html

8
shacks and tents to traditional, more durable buildings.9

# **2.2 Labor Income Uncertainty**

To measure uncertainty linked with the household income, we follow previous literature (Skinner (1988), Dardanoni (1991) etc.) by postulating that occupations are associated with different levels of income uncertainty. The ALCS contains information on households’ three principal sources of income. Most of these sources can be mapped to an occupation, like farming, teaching etc.10 Household income is derived from multiple sources not only because more than one member contributes to the aggregate but also because sometimes a single household member derives income from multiple sources. We use all three sources of income to classify households into various groups.

We put households that report the same first, second, and third sources in the same order into a single occupational group. We use a more restrictive classification to separate households that may have had the same sources of income but may have differed in terms of how important they are to the household income. To ensure we have enough households in a single group so that the corresponding variance is well measured, we drop those occupational groups that contain fewer than 40 observations. We end up with 83 such occupational groups. The quantitative analysis of precautionary behavior hinges on the extent of variation of income uncertainty that is observed in the data. Our classification of households into many occupational groups instead of a few is thus essential to tease out the relationship between income uncertainty and wealth accumulation. In regards to the sample selection, we exclude all households that do not report their income and those with heads aged less than 18 or over 64 years old. We also drop those households with retired or unemployed heads as well as households for which any of the three primary sources is a non-labor source such as pension earnings, rental income, and _Zakat_ (borrowing). This leaves us with 13,156 households which constitute our benchmark sample.

Not surprisingly, agriculture is the dominant activity from which household income is derived. The production and sale of field crops (non-opium) is the most common source of household income and is the primary source of income for 21 percent of the households in the sample. At the other extreme, road and building construction is reported to be the primary income source by only 0.01 percent of the households. The average annual income in our sample is AFN 124,000 while the median household earned income is AFN 93,000. There exists a considerable variation in aggregate household income across provinces. Households in Kabul and Khost have the highest

> 9We list the distribution of house types in the table A.3 of the appendix.

> 10Table A.2 in the appendix lists the 28 different sources of household income.

7
<!-- Start of picture text --> Figure 2: Percentage of slums by sanitary conditions,<br>India 2008-9 and 2012<br>100<br>78<br>80 71<br>60<br>41<br>40 31 31 27 32 2008-09<br>15 16 16 2012<br>20<br>0<br>Tap major No latrine No drainage No garbage Approach<br>source of facility facility disposal road<br>drinking arrangement waterlogged<br>water in rain<br><!-- End of picture text -->

**_Source_** : NSSO (2013)

<!-- Start of picture text --> Figure 3: Maternal & Child Health Service Coverage<br>in Slums, Chennai and Delhi, 2005-06<br>99 99<br>100<br>89<br>80<br>58<br>60<br>52<br>42<br>40<br>Chennai<br>Delhi<br>20<br>0<br>% of children aged % of women who % of live births born<br>12-23 mths, fully received 3+ <5 years before the<br>vaccinated before Antenatal Care visits survey, delivered<br>the survey with assistance from<br>health personnel<br><!-- End of picture text -->

**_Source:_** Gupta et al (2009: Figures 3.5, 3.8 and 3.9), derived from India’s NFHS-3 survey.

16
# 1 Introduction

Health and human capital are important drivers of economic growth, and research from multiple disciplines has shown that health during infancy is critical in determining how children develop throughout their lives. Malnutrition is a key obstacle to achieving appropriate levels of early childhood development. While substantial progress has been made in combating malnutrition at a global level, chronic maternal and child malnutrition remains a serious problem in parts of the developing world. Even within developing countries, the rural poor disproportionately bear the burden of child malnutrition (World Development Report 2008). A key task for researchers and policy makers alike is to examine the reasons behind malnutrition in rural areas.

Two important causes of malnutrition are a lack of information and a lack of income. Unlike other childhood diseases, malnutrition is a “silent killer” - symptoms are not obvious until it is often too late to reverse stunting or its other deleterious consequences (UNICEF 2011). Therefore, providing parents and/or pregnant mothers information on best practices regarding nutrition and maternal health issues can lead to significant reductions in malnutrition. The second relevant cause is a lack of income. Even if parents know the right ways to care for a child, they might lack the resources to engage in these best practices. For example, young children need diets that are higher in nutritional content than adult diets (Dewey, 2013). However, income constraints might prevent the parents from engaging in these activities. In this paper, we evaluate a program that provided information on best practices regarding child care and cash to families in extremely poor areas with pregnant mothers and/or children below the age of 2.

The context of our study is Nepal, where maternal and child malnutrition remains a serious problem. Nepal has one of the highest malnutrition, stunting and wasting rates for children under the age of 5. According to the Nepal Living Standard Survey 2010/11, 46.7 percent of children under the age of 5 are stunted, 15.1 percent are wasted, and 36.3 percent are underweight. Moreover, pregnant mothers have sub-optimal weight gain during pregnancy. The consequences are significant and long-term, ranging from increased neonatal mortality and morbidity to irreversible adverse physical and cognitive outcomes that harm health, productivity and economic growth (Pelletier, Frongillo Jr, Schroeder, and Habicht, 1995; Strupp and Levitsky, 1995; Alderman, Hoddinott, and Kinsey, 2006). The economic costs of – malnutrition are very high an estimated 2-3 percent of GDP (US$250 million 1