rafmacalaba/datause-encoder-data huggingface dataset:
2012). Examples of countries where competition has helped reduce interest rates on loans are Bolivia and Cambodia. In Bolivia, BancoSol began operating in 1992, and, thanks to a competitive environment, it was able to lower its costs and pass along the savings to its clients in the form of lower interest rates. In Cambodia, interest rates in the microfinance market have dropped, leading to a decline in the interest rates on loans provided by informal moneylenders (Helms and Reille 2004). So, we can conclude that competition can help to reduce interest rates on loans acting as an alternative policy to the use of interest caps.

However, since more players do not automatically lead to greater competition, some conditions are necessary to create the environment for lower interest rates: a stable macroeconomic situation, an appropriate legal and regulatory framework, price transparency, consumer financial literacy, availability of credit information, and strong financial architecture, such as credit bureaus and credit agencies.

# **_5.2. Financial Consumer Protection_**

Adequate financial consumer protection has become an important pillar for the stability of the financial sector. It also helps protect high-risk groups from abuses like predatory lending and unscrupulous business practices, without the need of imposing a ceiling on interest rates (Helms and Reille 2004). Such protection also improves the governance of financial institutions and helps them consider all the risks that arise when dealing with retail customers.<sup>29</sup> An effective framework for consumer protection should provide five elements: (i) transparency on disclosure of interest rates and all other loan costs, conditions, and terms; (ii) choice by guaranteeing fair and non-abusive business practices in the selling of financial products and services (e.g. high interest rates charged by non-banking financial institutions in some countries) and the collection of payments; (iii) redress through a mechanism for reporting complaints and resolving disputes; (iv) privacy of personal financial information; and (v) trust in the professionalism of financial institutions and in their ability to deliver what they promise. In addition, the framework should also provide programs for financial education (World Bank 2012).

# **_5.3. Financial Consumer Literacy_**

Because financial education and consumer protection are complementary, financial education must be part of consumer protection programs. A well-educated consumer should be able to understand the variety of financial products and services available in the marketplace and make appropriate use of them. This is particularly important for low-income people for whom good money management is a daily challenge.

> 29 The World Bank, for its part, has been analyzing good practices for consumer protection since 2009.

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# **4. Results**

# **4.1 Gender differences in levels of socio-emotional skills**

Table 3 and Figure 2 report estimates of the average gender difference in socio-emotional skills. Following equation (1), we begin by estimating the gender gap with project fixed effects in Model A. Our preferred specification is reported in Model D and follows equation (2), with additional controls for age, marital status, education.<sup>9</sup> We find that women have significantly lower levels of socio-emotional skills, even after controlling for education and demographic characteristics. The gender gap in socio-emotional skills decreases from 0.18 (model A) to 0.151 when adding the full set of controls (model D). Finally, in Model E, we add controls for employment status and the results do not substantially change, indicating that the gender differences in socio-emotional skills we find are not driven by women’s lower likelihood of engaging in paid work. Whereas controlling for education does change some results meaningfully, controlling for employment does not. This suggests that employment does not substantially develop socio-emotional skills in this context. We explore the relationship between socio-emotional skills and employment in more detail in section 4.3 of the paper.

To provide some context on the magnitude of the gender gap in our preferred specification in Model D, the conditional gender gap is equivalent to 5.6 years of education. As a comparison, the gender gap in education in our sample, after controlling for age, marital status and project fixed effects, is 0.96 years. Therefore, closing the gender gap in education would close about 17% of the socio-emotional skills gender gap. Additionally, the gender gap is equivalent to a 5 percentile increase in socio-emotional skills levels, based on the women’s distribution. This gap is similar without project fixed effects, indicating that the gap holds across the whole sample and is not only due to within-country variations. One factor to note is that sample sizes differ across the specific individual skills, we therefore take the aggregate measures as our preferred estimates of general differences in skills and conduct robustness checks in section 5 to examine how sensitive gender gaps in individual skills are to skills measures. We confirm that these main results with our aggregate measures are unlikely to be driven by sample selection, using data from the Ghana and Kenya STEP surveys, which include a random sample of respondents drawn from a general urban population. Consistent with our pooled results, we find in the STEP surveys that men have significantly

> 9 The findings below are not sensitive to additionally controlling for number of children. Because of the reduced sample size, these results are not presented here but available upon request.

18
<u>Table 5. Impact on enrollment: treatment versus control</u>

|||OLS|||IV||
|---|---|---|---|---|---|---|
||Check 1|Check 2|Check 3|Check 1|Check 2|Check 3|
|Log (Total number of students)|0.285*|0.320**|0.288*|0.903*|0.978**|0.737*|
||(0.16)|(0.14)|(0.15)|(0.47)|(0.42)|(0.39)|
|Log (Students, grade 1)|0.451***|0.410***|0.295*|1.436***|1.251***|0.750*|
||(0.17)|(0.15)|(0.17)|(0.52)|(0.44)|(0.42)|
|Log (Students, grade 2)|0.084|0.134|0.459**|0.276|0.408|1.225***|
||(0.18)|(0.16)|(0.17)|(0.51)|(0.45)|(0.47)|
|Log (Students, grade 3)|0.282|0.259*|0.071|0.723|0.791*|0.19|
||(0.18)|(0.15)|(0.18)|(0.51)|(0.47)|(0.45)|
|Log (Students, grade 4)|0.371|0.410*|0.143|1.028|1.253*|0.306|
||(0.24)|(0.22)|(0.16)|(0.69)|(0.65)|(0.41)|
|Log (Students, grade 5)|0.177|0.353|0.395|0.681|1.076|0.965|
||(0.32)|(0.31)|(0.32)|(0.85)|(0.86)|(0.77)|
|Log (Students, grade 6)|0.12|0.319|0.273|0.489|0.974|0.673|
||(0.32)|(0.31)|(0.31)|(0.86)|(0.86)|(0.72)|
|Log (Female students, grade 1)|0.504***|0.439***|0.27|1.612***|1.339***|0.672|
||(0.17)|(0.15)|(0.18)|(0.57)|(0.49)|(0.45)|
|Log (Female students, grade 2)|0.089|0.224|0.485***|0.285|0.684|1.285***|
||(0.18)|(0.17)|(0.16)|(0.51)|(0.50)|(0.46)|
|Log (Female students, grade 3)|0.307*|0.310**|0.121|0.848|0.945*|0.33|
||(0.18)|(0.16)|(0.18)|(0.53)|(0.49)|(0.47)|
|Log (Female students, grade 4)|0.358|0.398*|0.097|0.985*|1.216**|0.195|
||(0.22)|(0.21)|(0.17)|(0.60)|(0.60)|(0.44)|
|Log (Female students, grade 5)|0.214|0.247|0.379|0.757|0.755|0.934|
||(0.25)|(0.23)|(0.24)|(0.67)|(0.64)|(0.58)|
|Log (Female students, grade 6)|0.119|0.24|0.272|0.444|0.732|0.669|
||(0.23)|(0.22)|(0.24)|(0.63)|(0.63)|(0.56)|
|Regions FE|Y|Y|Y|Y|Y|Y|
|Baseline Controls|Y|Y|Y|Y|Y|Y|
|Number of schools|94|93|94|92|93|89|

Note. Each number represent the estimated coefficient of regressing each enrollment outcome (in natural logarithm) against treatment status. All regressions controls for region fixed effects and baseline characteristics. IV Estimation: reports coefficients of the second stage (2SLS) regression, where the first state regress actual transfer (as reported in Check 2) as a function of treatment status.

22
on the occurrence of financial crises are from Laeven and Valencia (2013). We also identify recessionary periods based on Braun and Larrain (2005) as in Abiad et al. (2011). Specifically, we identify recessions using real annual GDP data from the World Bank World Development Indicators (WDI) for the 1995-2015 period. First, we extract the trend in the logarithm of real GDP using a Hodrick-Prescott filter with parameter 6.25. Second, we identify recessions whenever the detrended series exceeds one country-specific standard deviation below zero. Third, we identify the start of a recession as the year following the peak until the trough.

# **3.4 Descriptive statistics**

Table 1 reports descriptive statistics for the main variables used in the empirical analysis, at the country-year level for financial variables and at the country-industry-year level for export variables (see Supplementary Table S1 for summary statistics for the individual countries); in Figure 1, we display the relationship among key variables. There is substantial variation in financial structures across countries. Looking at the relative importance of banks versus stock markets, the ratio of bank credit over the sum of stock market capitalization and bank credit equals 43% at the 25th percentile and 75% at the 75th percentile. To grasp the magnitude of these figures it is useful to observe that for Germany, an advanced country in our sample with a developed stock market but with a traditionally bank-oriented financial system, the ratio equals about 71%. Croatia, a Balkan country that features a relatively well-developed stock market, has a ratio of about 56%, and, further down in the scale, Mexico has a ratio of approximately 38%. Outside our sample, in 2000, the United States, a country often taken as an example of highly market-oriented financial system, the ratio approximately equaled 24%. Corporate bond markets tend to be smaller than stock markets, and in fact in our sample at the 25th percentile the ratio of bank credit to the sum of bond market capitalization and bank credit equals 76%. In our sample, there is also substantial variation in foreign bank participation. The share of bank assets held by foreign banks ranges from as low as 0% for the Islamic Republic of Iran or 7% for Thailand to as high as 82% for El Salvador or 90% for Croatia.

On the export side, the average number of exporters in an ISIC 2-digit industry ranges from a few dozen for countries such as Cambodia and Paraguay to about 3,000 for Spain and Turkey and 8,000 for Germany. The data for the mean value of export sales also exhibit pronounced cross-country variation. While not reported in the table, our data also reveal that the mean values tend to be substantially higher than the median values of export per exporter, suggesting that exporters tend to be concentrated in the segments of mid-sized and large businesses, while the incidence of export is significantly lower for small 10
# 4. Results

We find that, in rural areas, more drought‐exposed households experienced a significant reduction in consumption and increase in poverty. An increase of one standard deviation in drought exposure during the 2016/17 drought led to a decline in household consumption of 26 percent, based on the preferred regression specification with the full set of controls. One standard deviation increase in drought exposure corresponds to a seven percentage‐point negative anomaly in NDVI, relative to the pre‐drought average. This reduction in consumption corresponds to an increase of 15 percent in the probability of being poor (Table 2, column III). The drought had no significant effect on poverty in consumption in urban areas, nor in the combined sample of urban and rural households (Table 2, column I and II; Table A.2).

_Table 2: Drought impact on poverty and consumption._

||(I)|(II)|(III)|
|---|---|---|---|
|**_Sample_**|Full urban + rural sample|Full urban sample|Full rural sample|
|**_Outcome variable(Yit)_**||PovertyStatus||
|**Drought Impact**|‐0.00535|‐0.0201|0.264***|
|**S.E.**|(0.0508)|(0.0568)|(0.0812)|
|**Outcome variable**||ln(Core Consumption)||
|**Drought Impact**|1.59e‐05|0.0286|‐0.146**|
|**S.E.**|(0.0386)|(0.0345)|(0.0665)|
|**Controls(Xit)**|Yes|Yes|Yes|
|**Observations**|7,214|5,678|1,536|
|**R‐squared**|0.352|0.345|0.522|

Source: Authors’ own calculations.

Note: ***p<0.01, **p<0.05, *p<0.1. Poverty status results estimated using Probit, Consumption results estimated using OLS. Drought effect expressed in standard deviations of NDVI loss.

Implementing equation (2) with controls through quantile regressions allows assessing the drought’s impact on consumption at different points along its distribution. In rural areas, the drought’s impact on consumption was smaller for the poorest households. Higher drought exposure had no significant impact on consumption for the poorest 10 percent of rural households, reduced consumption by 17 percent for rural households at the twentieth percentile, and between 20 and 25 percent for the top 80 percent of rural households (Figure 4). In urban areas, the impact is around zero across the income distribution (Figure A.3).

6
of producing policy-relevant agricultural data, the LSMS-ISA emphasizes the design and validation of innovative survey methods, the use of technology for improving survey data quality, and the development of analytical tools to facilitate the use and analysis of the data collected.

The surveys supported by the LSMS-ISA are implemented by the National Statistics Office of each participating country. Each survey interviews between 3,000 and 5,000 households at baseline. The panel dimension of the surveys necessitates tracking of households as well as individuals who no longer reside at baseline dwelling locations. Each interviewed household receives a multi-topic household questionnaire, coupled with agricultural questionnaires on crop, livestock and fishery production, conditional on household participation in these domains. While the content of the questionnaire modules can exhibit cross-country variation driven by country specificities, there are also marked similarities in the type and the wording of key questions, and the level at which information is solicited.

On asset ownership, currently, the surveys uniquely identify reported owners with the household (i.e. up to 2 owners identified from the household roster) at the asset level, for agricultural land, livestock, and household non-farm businesses. Specific to agricultural land and non-farm businesses, up to 2 managers are identified from the household roster for each cultivated plot/non-farm business, and in the case of livestock, up to 2 caretakers are again uniquely identified for each livestock type reported to be owned. The surveys require the manager/caretaker of each plot/non-farm business/livestock type to report on the entity in question. If a proxy respondents is used, the proxy respondent is often identified, linked to the household roster, in the questionnaire instrument – though this aspect of survey design does exhibit inter-temporal and cross-country variation. The LSMS-ISA is working towards enhancing the scope of individual-disaggregated information collection on ownership of and rights to physical and financial assets, in line with the recommendations of the methodological research that is reviewed in section 4.<sup>16</sup>

# **3.8**

# **Global Findex**

<mark>Launched in 2011 by the World Bank Development Research Group, with funding from the Bill and Melinda Gates Foundation, the Global Financial Inclusion (Global Findex) database collects survey data on how individuals save, borrow, make payments, and manage risk. Thus, it includes detailed information on the financial assets owned by individuals.</mark> completion of fieldwork in each country. For more information about the LSMS and the LSMS-ISA, please visit www.worldbank.org/lsms.

> 16 Expanding the scope and cross-comparability of the information collected on financial assets could be an easy win for the LSMS-ISA, perhaps exploring synergies with the questionnaire design promoted as part of the Global Findex.

16
**Table 3. Effect of preschool availability on maternal employment with various preschool data**

|||||**Wo**<br>|**rk participa**<br>|**tion**<br>||||
|---|---|---|---|---|---|---|---|---|---|
|Preschool data:|PO|DES years o|nly|I|nfer in-betwe|en|L|inear project|ion|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|**Panel A: Public**||||||||||
|Preschool *||0.048**|0.080***||0.057***|0.074***||0.062***|0.082***|
|Eligible||(0.021)|(0.022)||(0.017)|(0.016)||(0.020)|(0.019)|
|Preschool|-0.014|-0.011|-0.017|0.002|-0.016|-0.015|-0.007|-0.021|-0.019|
|density<br>Eligible child|(0.026)|(0.015)<br>-0.051***|(0.015)<br>-0.034***|(0.023)|(0.013)<br>-0.051***|(0.012)<br>-0.034***|(0.027)|(0.016)<br>-0.052***|(0.014)<br>-0.035***|
|||(0.008)|(0.007)||(0.007)|(0.005)||(0.007)|(0.005)|
|**Panel B: Private**||||||||||
|Preschool *||-0.000|0.001||0.001|0.002||0.001|0.003*|
|Eligible||(0.002)|(0.002)||(0.002)|(0.001)||(0.002)|(0.001)|
|Preschool|-0.010***|-0.003|-0.001|-0.007**|-0.003*|-0.001|-0.008**|-0.002|0.000|
|density|(0.003)|(0.002)|(0.002)|(0.003)|(0.002)|(0.002)|(0.004)|(0.002)|(0.002)|
|Eligible child||-0.042***|-0.025**||-0.046***|-0.032***||-0.047***|-0.034***|
|||(0.012)|(0.010)||(0.011)|(0.008)||(0.011)|(0.008)|
|Method|DD|DDD|DDD-FE|DD|DDD|DDD-FE|DD|DDD|DDD-FE|
|Observations|22,737|76,951|76,951|67,431|226,400|226,400|67,788|227,559|227,559|
|Mean|0.538|0.534|0.534|0.529|0.520|0.520|0.530|0.521|0.521|

Note: Column 1-3 are restricted to PODES years only. Column 4-6 infer preschool data in-between PODES years using the closest upper year available, e.g. year 1992 sandwiched between PODES 1990 and 1993 will use 1993 round. Column 7-9 predict preschool density using linear projection with the closest two data points available, e.g. year 1992 fits a linear projection using preschool density data in PODES 1990 and 1993. Column 1, 4, 7 regress work participation on preschool density in DD method. Column 2, 5, 8 regress work participation on the interaction of preschool density and preschool eligibility dummy in DDD method. Column 3, 6, 9 add individual fixed effect to the DDD method. All regressions include district, year, mother’s age fixed effects and an urban residence dummy; and do not include district-specific trends. Panel A and B look at the effect of public and private preschool densities separately. Standard errors clustered at the district-level is shown in parentheses; *, **, and *** indicate statistical significance at 10, 5, and 1 percent, respectively. Observations and dependent variable means are indicated in the last two rows.

48
market value of equity (equity issuances), and then using the residuals as our instrument.<sup>29</sup> The first-stage regressions indicate equally powerful instruments than the non-orthogonalized instruments. The second-stage regressions in Columns (4) are similar in value and not statistically significantly different from the non-orthogonalized specifications reported in Columns (2).

The IV coefficients in Tables 5-7 are slightly smaller or equal in magnitude, but not statistically different, than the comparable OLS coefficients reported in Table 3. In the absence of measurement error of capital inflows, the OLS coefficient should be greater than or equal to the IV coefficient because OLS captures supply and demand effects and those effects are additive. Our results are consistent with that expectation. The equality of the magnitude of the OLS and IV coefficient suggest that supply-side influences account for most of the variation in equity capital inflows. However, there is reason to believe that equity inflows are measured with error, which biases the OLS coefficient downwards, implying that the true OLS coefficient is probably larger than the estimated value.<sup>30</sup> In the absence of measurement error, a larger OLS coefficient would suggest that demand-side influences also are present.

Overall, we find that whether one measures the attractiveness of other countries’ equity markets to foreign investors using the MSCI weights, other countries’ market value or other countries’ equity issuance volume, the results are similar: supply-side effects of

> 29 The orthogonalized market value ε_mvct  for country _c_ in year _t_ is constructed as the residual of the following regression: log(MVem)t = βlog⁡(MVown)ct + εct, where MVemt is the sum of all 25 emerging markets’ value of market capitalization in year _t_ , MVOwnct is the value of own market capitalization for country _c_ in year _t_ , and ε_mvct is the residual to be predicted. The orthogonalized issuance volume is calculated in the same way. For each instrument, 25 time-series regressions are estimated separately for each country. Country values of market capitalization are downloaded from the World Bank’s WDIs.

> 30 As Lane and Milesi-Ferretti (2017, p. 21) note in their discussion of the capital inflow data: “One concern ... is the increasing difficulty in properly assessing external exposures ... particularly in light of the size of crossborder asset trade intermediated by financial centers [which complicates the measurement of inflows into a particular country] ... This difficulty affects virtually all categories of cross-border holdings ...” As a result, under the assumption that our identification is correct, the OLS coefficient in our setting could be larger or smaller than the IV estimate.

25
<u>Assumption 4: Access to electricity depends in part on physical proximity to power</u> generating plants, which we assume do not influence outcomes independently of electrification or the other controls, including other exogenous geographic variables.

As noted in section 2, the exclusion restriction is never beyond question and whether or not it is accepted must depend crucially on what other controls are available since one is making a conditional independence assumption. By exploiting the relatively rich REDS data on village attributes and the (long) panel structure we believe that the exclusion restriction in Assumption 4 is defensible.

Armed with the extra IV, we test the null hypothesis that electrification has the same effect at each date, conditional on other factors in the controls. It is this homogeneity restriction that allows us to use _Eij_ 82 as an IV for _Eij_ 99 . (In other words, this is the test of the exclusion restriction for this IV, which is only possible given that we have the second IV.) If the test fails then we will not employ our estimator.<sup>25</sup> In implementing Assumption 4, we use the distance in kilometers from the village to the nearest power generating plant within the state. As noted in the last section, this is measured at two points in time, namely 1965 and 1975. (We expect these to have opposite signs in the firststage when explaining the change in household electrification.) As compared to distance to the nearest power substation (which supplies feeder lines to villages and is hence located closer to villages), distance to the nearest power generating plant is more likely to be orthogonal to unobserved factors affecting household outcomes and electricity access. Villages do not shift location in rural India, and even by 1999, power generating plants were often several hundred kilometers away from villages (see Table A1). Furthermore factors affecting plant location (such as access to natural resources) can be controlled for in the regression framework. Distance to the nearest power generating plant for the different years we examine also has a strong significant impact on household electrification.

To address concerns about the validity of the exclusion restriction stemming from the possibility of omitted geographic factors we allow a complete set of district fixed effects, as well as control variables for village characteristics, in the vector _X_ . We include initial conditions (1982 values) of the household and community characteristics detailed in Section 3 as well as

> 25 Strictly, our model is still identified if the test fails. However, with only the second, geographic, IV left (namely the distance to power stations) we naturally do not feel that this would adequately capture the exogenous variation in household electrification.

22