rafmacalaba/datause-encoder-data huggingface dataset:
Europe and the Newly Independent States have lower return on assets relative to others, and weak evidence that the breadth of outreach may be related to regulation. After controlling for the endogeneity of regulation, Hartarska and Nadolnyak (2007) find that regulation has no impact on financial performance and weak evidence that regulated MFIs serve less poor borrowers. Mersland and Strøm (2009) use an endogenous equations approach to find that regulation does not have a significant impact on financial or social performance, where regulation is measured by a regulation dummy variable.

These previous efforts to study the effect of regulation are based on a binary regulation indicator variable. Based on information from the MIX database collected for the _Microbanking Bulletin_ publication, which we cross-referenced with information from other sites, we construct a measure of whether an MFI faces prudential regulation of the sort described in CLR (2003). We construct two variables that are the focus of the analysis, a dummy variable equal to one if an MFI faces onsite supervision, and another equal to one if that supervision occurs at regular intervals. Within the same country, we find that some MFIs face onsite supervision while others do not, depending on their ownership structure, funding sources, activities, and organizational charter. To our knowledge, this is the first dataset that allows for within-country variation regarding MFI regulation and supervision.

Our results suggest that microfinance institutions subjected to more rigorous and regular supervision are not less profitable compared to others despite the higher costs of supervision. We also observe that this type of supervision is associated with larger average loan sizes and less lending to women, hence indicating a reduced outreach to segments of the population that are more costly to serve. We also find that controlling for the non-random assignment of supervision is important in obtaining these results.

Lacking time series data on MFIs’ performance before and after supervision, our empirical strategy is to start with a rough classification of the extent to which MFIs in our sample are profit-oriented based on their sources of funding. We hypothesize that greater reliance on commercial sources (like deposits) than on non-commercial sources (like donations) would lead an MFI to be more profit-oriented. While it is true that 5
**Table A.14:** Effects on Public Goods Provision: Index Components (2021 Administrative Data)

||**Index**|Water|Sewage|Garbage|Street Lighting|Asphalt road|Public transit|Kindergarten|Prim. Sch.|Polindes|Puskesmas|
|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
|New village head|0.503<sup>_∗_</sup>|0.212|0.064|0.825<sup>_∗_</sup>|0.472<sup>_∗∗_</sup>|0.059|0.328|0.194|-0.159|-0.025|0.044|
||(0.263)|(0.357)|(0.392)|(0.404)|(0.256)|(0.127)|(0.331)|(0.332)|(0.304)|(0.354)|(0.289)|
|Observations|378|378|378|378|378|375|378|378|378|378|378|
|Control mean|0.23|0.15|0.14|-0.083|0.17|0.42|0.26|0.062|0.11|-0.099|-0.17|
|Robust p-value|0.053|0.453|0.997|0.079|0.046|0.587|0.235|0.407|0.534|0.696|0.957|
|<br>MSE-opt. bandwidth|18.7|22.5|21.9|16.6|20.8|23.1|18.6|20.2|18.5|15.3|25.0|
|Effective obs.|161|181|177|141|173|182|160|172|160|133|196|

_Notes_ : This table reports RD estimates of _γ_ in equation (1) obtained via the non-parametric method from Calonico et al. (2014). In column 1, the dependent variable is a standardized index of local service provision constructed using the 2021 _Podes_ survey. Remaining columns report RD estimates on the individual index components. The index has the following 10 components: drinking water, sewage, garbage collection, street lighting, paved roads, public transit, kindergartens, primary schools, village maternities ( _polindes_ ), and community health centers ( _puskesmas_ ). We first standardize each individual component before taking the village-level average of all components. The sample includes all villages in our sample that conducted their last election before 2021. * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Robust bias-corrected standard errors in parentheses.
Table A.1: Contribution to the Unemployment Rate and Share of Formal Employment Volatility using Fijuta and Ramey Decomposition.

||Inflows|U<br>Outflows-F|nemployment R<br>Outflows-I|ate<br>Outflows-S|Error|Inflows|Fo<br>Outflows-I|rmal Employm<br>Outflows-S|ent<br>Outflows-U|Error|
|---|---|---|---|---|---|---|---|---|---|---|
|**Whole Sampl**|**e**||||||||||
|Mexico|0.82|0.20|0.02|-0.03|-0.01|0.69|0.08|0.01|0.19|0.04|
|Brazil|0.69|0.22|0.11|-0.03|0.01|1.22|-0.21|-0.16|0.18|-0.02|
|**Recession**|||||||||||
|Mexico|0.76|0.24|0.04|-0.01|-0.02|0.84|-0.05|-0.01|0.17|0.05|
|Brazil|0.65|0.26|0.09|0.00|0.00|1.31|-0.15|-0.22|0.08|-0.02|

Notes: The table presents the contribution of the cyclical component of each flow to cyclical volatility of the unemployment rate an the share of formal employment for Mexico and Brazil following Fujita and Ramey, 2007. We define recession as output below trend. O=Out of the Labour Force, U=Unemployment, E=Employment, S=Informal Self-Employed, I=Informal Salaried, and F=Formal Sector, all as proportions of working age population. Data for Mexico (left panels) is drawn from the quarterly National Urban Labor Survey (ENEU) from 1987:Q1 to 2004:Q4. Data for Brazil (right panels) is drawn from the Monthly Employment Survey (PME), quarterly averaged from 1983:Q1 to 2001:Q2.

47
47

# An2endix table Al. Chile: annual expenditures on nutrition intervention Droarams. umillion 1987 Us S

|YEAR|PNAC|PAZ|JUNJ1I(CADELincluded<br>after<br>1987)|TOAL|
|---|---|---|---|---|
|1974|35.4|-|6.4|-|
|1975|36.2|-|5.1||
|1976|36.4|-|6.5||
|1977|43.1||10.8||
|1978|36.9|35.8|19.2|91.9|
|1979|40.1|37.3|19.7|97.1|
|1980|29.3|38.7|17.1|85.1|
|1981|36.3|44.7|16.8|97.3|
|1982|37.9|43.0|17.9|98.8|
|1983|29.7|43.8|16.9|90.4|
|1984|47.9|46.7|16.0|110.6|
|1985|42.1|47.6|17.0|106.7|
|1986|35.9|43.1|18.2|97.2|
|1987|44.0|34.4|17.2|95.6|
|1988|42.7|31.5|19.8|94.0|

Source: Muchnik and Vial (1990)
_5_

# Rural Credit Reaches Few Rural Households

Despite the imnportance of credit in increasing agncultual productivity, few rural households are able to obtain credit. A stiking finding of the rural credit survey is that institutional sources (the Agicultural Development Bank, commercial banks, and cooperatives) provide credit to only a small proportion of rural households. The 1985 survey reports that there were 9.24 million rural households in Pakistan, of which 5.18 million were &rming households and 4.05 million were nonfarm households. Only 32 percent of the housel.olds (2.95 million) reported taking loans, and 27 percent reported outstanding debt. Of households that borrow, a mere 10 percent (240,000 households) borrow from institutional sources (ADBP, 76 percent; commercial banks, 17 percent; and cooperative societies; 6 percent). The remaining borrow from noninstitutional informal sources (friends and relatives, 67 percent; landlords, 11 percent; factories, 2 percent; and money-lenders, 2percent). The total outstanding debt of all farn households at the time of the survey was Rs. 25,076 million. Thirty-two percent of this amount was owed to formal institutions and the rest to the informal sector. The average debt per household owed to institutional sources was Rs. 32, 955 -- nearly 4.5 times more than the average debt of Rs. 7,390 from noninstitutional sources. Comparison with selected Asian countries by Mellor in Some Issues in Istitutional Finance for Agricultural Development: International Evidence and Implications for Pakistan, PIDE, 1995 shows that the percentage of borrowings of farm household from institutional sources were higher for Philippines, Thailand and India than for Pakistan.

Noninstitutional Credit is Expensive vet Remains the Predominant Mode of Rural Finance While the share of noninstitutional credit declined in recent years from 89 percent of total rural credit in 1973 to 62 percent in 1990, it remains the predominant mode of rural finance (table 1.2 ). This pattern is similar to that of many other Asian countries (see table 1.3 ).

The IFPRI survey shows that the cost of credit varies significantly depending on the credit source (Table 1.4). The average interest rate charged by formal sources in 1990 was 12.8 percent. The average interest rate charged by friends and relatives, at 14.3 percent, was only slightly higher, but the rate charged by informal sources was as high as 33 percent. Although the rate varies across different sources, there is little variation in the rate paid by rich and poor borrowers. Thus, it appears that the neither the formal nor the informal sources vary their rate according to the assets owned by borrowers; instead, the assessment of risk is on other criteria.
# Appendix 4: Logit models:

## Table 24

|Dependent variable: N|eed of fisc|al adjustme|nt based o|n Escolano|et al.(2014|)|||
|---|---|---|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Rigid expenditure|0.003**|-0.000|0.007***|0.004***|0.004**|-0.002|0.005*|0.008**|
|**Economic factors**<br>(% of GDP)|(0.001)|(0.001)|(0.002)|(0.001)|(0.002)|(0.004)|(0.003)|(0.004)|
|GDP growth (t-1)||-1.953***||-1.329***|-1.294***|-1.318***|-1.329***|-1.286***|
|||(0.346)||(0.288)|(0.286)|(0.280)|(0.288)|(0.285)|
|Inflation (t-1)||-0.326||-0.384**|-0.474***|-0.425***|-0.411**|-0.364**|
|||(0.248)||(0.158)|(0.144)|(0.160)|(0.163)|(0.154)|
|U.S. Interest rate||-0.021**||-0.013*|-0.011|-0.012|-0.013*|-0.013*|
|||(0.010)||(0.007)|(0.007)|(0.007)|(0.007)|(0.007)|
|**Political factors**|||||||||
|Gini|||0.003**<br>(0.002)|0.003**<br>(0.001)|0.002**<br>(0.001)|-0.001<br>(0.003)|0.003**<br>(0.001)|0.003***<br>(0.001)|
|Rule of law|||-0.026*** <br>(0.009)|-0.019**<br>(0.008)|-0.011<br>(0.008)|-0.019**<br>(0.008)|-0.010<br>(0.016)|-0.019**<br>(0.008)|
|Margin of Majority<br>Election's year<br>**Interactions**|||0.189**<br>(0.081)<br>0.037*** <br>(0.014)|0.162*** <br>(0.055)<br> 0.037*** <br>(0.012)|0.149*** <br>(0.050)<br> 0.035*** <br>(0.012)|0.150*** <br>(0.056)<br> 0.036*** <br>(0.012)|0.162*** <br>(0.056)<br> 0.036*** <br>(0.011)|0.337***<br>(0.130)<br> 0.036***<br>(0.012)|
|EMEs|||||0.040<br>(0.075)||||
|EMEs * Rigidity|||||0.001<br>(0.002)||||
|Gini * Rigidity||||||0.000<br>(0.000)|||
|Rule of law * Rigidity|||||||-0.000<br>(0.001)||
|Majority * Rigidity||||||||-0.008<br>(0.005)|
|Observations|1,521|1,500|1,360|1,342|1,342|1,342|1,342|1,342|
|Country FE|No|No|No|No|No|No|No|No|
|r2_p|0.0121|0.121|0.0738|0.182|0.194|0.187|0.183|0.187|
|Robust standard errors<br>*** p<0.01, ** p<0.05,|clustered<br>* p<0.1|by country|in parenth|eses|||||

- 53 -
A total of 504 households were randomly selected from the 36 village clusters (14 households in each site), with 10 households receiving the Mirt stove in each village (total of 360 households). The remaining 144 households (4 from each site) did not receive the stove and serve as controls to measure fuelwood usage. As we only analyze Mirt stove use in this paper, however, we do not use these controls.

Participants of the survey were informed about the nature of the survey and experiment and formal oral consent was received from all respondents. Full information was provided to respondents on stove features, the use of the SUMs, details about the treatments applied to them and that stove use is not required to receive a stove. Respondents were told that the stove is expected to save wood and were encouraged to use them. While the strategy to address refusals was to replace refusing households by randomly choosing from remaining households in each site, there were no refusals and no attrition until the final data collection period.

The baseline survey conducted in 2013 included household characteristics and socioeconomic variables. In 2016, when the final monitoring period occurred, a second survey was also conducted. This survey updated baseline household characteristics, but particularly focused on kitchen characteristics and use/disuse and abandonment of the Mirt stove. Field workers were experienced and were trained directly by investigators, with approximately four enumerators working under each of the five field supervisors. All of the supervisors and many of the enumerators who conducted the follow-up field work in 2016 also participated in the baseline research conducted in 2013.

In addition to installing the stoves in the injera baking area in the first period of the study, the fieldworkers installed the SUMs on the stove using heat-resistant tape and initiated them in each of the five periods the study was conducted. The five monitoring periods were: June-August 2013 12
development groups and randomly selected from within this group for survey of rural households. From each district, 7–9 villages were selected, depending on the district’s size and listing of India’s rural population.<sup>4</sup>

# **4.2** **<u>Survey Features</u>**

The IHDS is ideal for estimating the impact of electricity access and consumption on welfare indicators owing to its exhaustive coverage of survey topics and sample representativeness. It is India’s first survey for measuring detailed income, as well as consumption and ownership of consumer goods. Income-related questions cover a variety of sources (e.g., wages and salaries, net farm, net family business, property, and pension). The survey also contains education-related questions, including educational outcomes, study hours, school enrollment, and school completion rate, along with household characteristics. Compared to the Demographic and Health Surveys and Living Standards Measurement Surveys, the IHDS covers energy more extensively. It includes elaborate questions on fuel use, cash expenditures for fuels, time spent collecting biomass fuels, and types of stoves and electric appliances used in the household. It also asks detailed questions related to electricity use, including reliability of power supply and source of household electricity (i.e., whether serviced by the local state electricity board or a neighbor). Such detailed energy questions allow us to investigate the drivers of household demand for electricity and analyze the welfare impacts of household access more comprehensively.

In addition to household-level data, the survey covers key features of the villages where the surveyed households are located (e.g., availability of social development programs in the area or distance to village facilities). Such area characteristics can directly affect outcomes of interest, such as employment, income and poverty indicators, and the probability of electricity presence in the village. Because this survey covers villages with and without electricity, it allows for identifying an

> 4 The 2005 IHDS findings are comparable to those of the 2004–05 National Sample Survey, 2005–06 National Family Health Survey (NFHS-3), and the 2001 Census.

9
1 1 _IV. The Size and Composition of Poverty, 1978-87_ In the eighties, the extent of poverty significantli increased ln Poland and Yugoslavia, and remained about the same In Hungary. Table 4 shows the overall percentage of people estimated to live below the official poverty line In the three countries.10 In Poland, the share of the poor increased from less than 10 percent of the population (before the crisis) to about 22 percent In 1987. In Yugoslavia, poverty rate was some 17 percent In 1978; by mid-1980's It stabilized at 25 percent. In Hungary, poverty rate oscillated throughout the period between 14 and 16 percent, without any apparent tendency to go up or down. For comparative purposes poverty Incidence based on official poverty lines for several OECD countries Is shown In Table 5. These estimates (except for Japan) are based on the same poverty lines discussed In the previous section.

_Table 4. Estimated Poverty Rates for Total Population_

|Year|Poland|Yugoslavia|Hunzarv|
|---|---|---|---|
|1978|9.2|17.5|15.4|
|1979|9.7|||
|1980|11.1||13.8|
|1981|13.9|||
|1982|19.8||14.8|
|1983|23.7|12.8|16.7|
|1984|21.9|21.5||
|1985|19.1|25.7|15.7|
|1986|17.3|25.1||
|1987|22.7|24.8|13.8|

Source: See Appendices 1 and 2.

_Table 5. Poverty Incidence in Several OECD Countries_

|Greece (1982)|22.7|
|---|---|
|Ireland (1987)|21.2|
|United States (1985)|14.0|
|Sweden (1985)|8.6|
|West Germany (1983)|6.1|
|United Kingdom (1985)|4.5|
|Finland (1985)|2.9|
|Japan (1984)|1.0|

Sources: Atkinson (1991) for all countries except Japan (Larin (1989)). Data for West Germany based on the higher poverty line (D2 in Figure 1).
|Fertilizer x IPM|β_F x IPM_|||||-0.078|-0.913|
|---|---|---|---|---|---|---|---|
|Pesticide x IPM|β_P x IPM_|||||0.016|0.514|
|_Inefficiency effects_||||||||
|Constant|δo|-0.780|-2.295 **|-0.175|-1.076|-0.545|-1.644|
|Age|δ_A_|0.002|0.456|0.001|0.202|0.001|0.225|
|Education|δ_E_|0.012|0.411|0.022|1.062|0.007|0.302|
|Owner|δ_O_|0.012|0.142|0.041|0.607|0.034|0.279|
|Training|δ_T_|-0.044|-0.307|-0.047|-0.432|-0.105|-0.415|
|Experience|δ_Exper_|0.000|0.048|0.001|0.253|0.000|-0.306|
|Health|δ_Health_|0.010|0.183|0.043|1.080|0.018|0.577|
|Farm scale|δ_Farm scale_|0.006|0.322|-0.028|-1.712 *|0.006|0.346|
|Bogra|δ_Bogra_|0.934|2.087 **|0.387|2.854 ***|0.624|4.428 ***|
|Chapinawabganj|δ_Chapi_|-0.247|-0.244|-0.741|-0.700|-0.157|-0.220|
|Chittagong|δ_Chitta_|-0.080|-0.161|-0.102|-0.459|-0.149|-0.464|
|Comilla|δ_Comilla_|0.812|1.713 *|0.304|2.835 ***|0.553|4.424 ***|
|Jessore|δ_Jessore_|0.831|1.693 *|0.330|3.137 ***|0.549|4.877 ***|
|Kishoreganj|δ_Kishor_|0.658|0.998|0.228|1.828 *|0.446|2.298 **|
|Munshiganj|δ_Munsh_|-0.312|-0.538|-0.224|-1.174|-0.165|-0.842|
|Narshingdi|δ_Narsh_|0.798|1.604|0.342|3.084 ***|0.537|4.484 ***|
|Mymensingh|δ_Mymen_|0.794|1.600|0.332|3.019 ***|0.479|4.110 ***|
|Rajshahi|δ_Rajshahi_|0.948|2.415 **|0.452|4.600 ***|0.651|6.396 ***|
|_Variance parameters_||||||||
|Sigma-squared|σ<sup>2</sup>= σu<br>2+σv<br>2|0.078|4.396 ***|0.086|9.130 ***|0.078|13.354 ***|
|Gamma|γ = σu<br>2/(σu<br>2+σv<br>2)|0.190|0.721|0.171|1.144|0.066|0.972|
|Log-likelihood||-54.790||-94.274||-74.755||
|Number of observations||569||569||569||

_Note_ : *, **, *** - significant at the 10%, 5%, and 1% level, respectively.

In no case do we find significance for IPM, whether it enters as a single control (dummy variable) or interacted with each input (with the single exception of hired labor). We also find no significance for other hypothesized inefficiency factors. The overall significance of the regional dummies suggests that local environmental and geographic factors may have important effects on farmer inefficiency.<sup>9</sup> Table 7 presents statistics for farm-level technical efficiency scores estimated by the model. Mean technical efficiency across farms is 83%, with variation from a minimum of 63% to over 98%. The distribution of scores is fairly central, with 65% of the farmers around 75-90% efficient and a fairly uniform distribution (20%) above and below this range of technical efficiency. Other studies of Bangladesh rice farming have yielded similar average efficiency scores (Coelli, Rahman and Thirtle, 2002; Rahman, 2003; Wadud and White, 2000). These results imply that, on average, farmers can increase rice

> 9 For example, soil quality attributes would be one area for further investigation.

17
**Table 8. Estimated results of intensive margin**

|Dependent variable<br>ln(Employment)|(1)<br>OLS|(2)<br>FE-Heckit|
|---|---|---|
|ln(Export Sales)|0.075***|0.207**|
||(5.99)|(2.18)|
|ln(value of machinery)|0.048***<br>(6.03)|0.006<br>(0.36)|
|ln(cost per worker)|-0.181***|-0.478***|
||(-2.95)|(-3.68)|
|ln(age of firm)|0.477***|0.181|
||(6.80)|(0.30)|
|Indirect exporter (yes=1)|-0.481|0.213|
||(-1.44)|(0.72)|
|Innovation (yes = 1)|-0.215||
||(-1.12)||
|Foreign owners (yes=1)|0.229<br>(0.97)||
|Female owners (yes=1)|0.216<br>(1.02)||
|Registered (yes=1)|0.370**<br>(2.14)||
|Training (yes=1)|0.777***<br>(4.57)||
|Foreign tech (yes=1)|0.378*<br>(1.76)||
|Imported inputs (yes=1)|0.590***<br>(4.70)||
|2016 dummy (yes=1)|-0.397***|-0.183|
||(-3.22)|(-0.64)|
|2020 dummy (yes=1)|-0.259*|-0.018|
||(-1.78)|(-0.04)|
|Lambda||-0.545*<br>(-1.70)|
|Constant|2.854***|6.288**|
||(4.68)|(2.52)|
|Year dummies|Yes|Yes|
|Observations|594|594|
|R-squared|0.363|0.579|

Notes: Column (1) reports ordinary least squares estimates, column (2) reports fixed effects estimates with lambda included as an additional control. Lambda is the inverse mills ratio from the Heckman Correction (Heckman 1974) calculated from a first stage probit from Table 7, column (1) above. In the second stage, the sample is restricted to exporters. Unweighted. Robust t-statistics in parentheses. *** p<0.01, ** p<0.05, * p<0.1 32
Table summarizes the estimated marginal effects from probit estimations based on our model. Standard errors are in parentheses.

⁎⁎⁎ Denotes significance at the 1% level.

⁎⁎ Denotes significance at the 5% level.

⁎ Denotes significance at the 10% level.

The results indicated that gender played a significant role. The impact of gender was less on savings than on borrowing. Also, we saw that women were more likely to access informal savings (5.7 percent) and informal credit (11.7 percent) than their male counterparts. To explain further, women have 44 percent and men 38 percent probability of saving using an informal source. In the case of formal savings, the comparable probabilities were 90 percent for women and 93 percent for men. Turning to credit, women have 78 percent probability compared to 66 percent for men of borrowing from an informal source, while the probability gap was higher for formal credit at 43 percent for women against 59 percent for men. This analysis reinforces the previous finding above (Table 2), where women fared worse than men in accessing formal finance. Why this occurs is again an intriguing question given the gender parity in education which Sri Lanka enjoys. This calls for further analysis, as studies by Lusardi & Mitchell (2009) and Christelis et al. 2010 have shown that sometimes what matters here is ‘what’ subjects were studied by women including their level of ‘financial literacy’, and the ‘teachable moments’<sup>27</sup> they experienced. At the same time, supply side effects such as product design or delivery issues with respect to women customers need to be considered.

While the relationship between age and use of informal finance is not significant, we observe that those in the oldest age group are less likely to save overall. Given the country’s challenging demographic transition towards a more aging population with more women, as well as women’s greater longevity compared to men – low access to old age savings could become a problem<sup>28</sup> specific to Sri Lankan women.

The results validate that when it comes to education, the trend remains the same. Table 3 shows how the progressively more educated are increasingly more likely to refrain from informal finance. Those in formal employment are increasingly more likely not to access informal savings and credit.

> 27 Kaiser and Menkoff (2017 ). 28

> Sri Lanka Development Update World Bank (February 2019).

18