rafmacalaba/datause-encoder-data huggingface dataset:
We investigate the impact of prudential regulation on the profitability and financial self-sustainability of microfinance institutions, with an eye on the channels through which impacts work. For example, if profit-oriented MFIs find ways to absorb the costs of prudential regulation that leave their profits unchanged, it would be of interest to see whether these costs are absorbed by changing the business orientation and curtailing outreach to smaller borrowers and women because reaching those market segments can be more costly per dollar lent. We also examine whether prudential regulation reduces the share of employees who work in the field. Finally, if prudential regulation imposes costs, we also need to investigate what those costs buy. In particular, we look at whether regulation is associated with improved loan quality.

These issues have been under-studied largely due to lack of data. The latest round of the MixMarket dataset, which provides unusually high-quality financial information for 346 institutions in 67 countries, allows us to address these issues. The MFIs included in the MixMarket are among the largest in the world, and their willingness to submit their financial information to the Microfinance Information eXchange, Inc. (MIX) indicates a commitment to achieving financial self-sufficiency. We expect that these would be the MFIs best positioned to absorb the costs of prudential regulation. If we find evidence of trade-offs associated with such regulation in this group of MFIs, we expect that the effects would be even more pronounced for smaller institutions not included in our database.

Most MFIs face some form of non-prudential regulation. These regulations can include rules governing MFI formation and operations, consumer protection, fraud prevention, establishing credit information services, secured transactions, interest rate limits, foreign ownership limitations, and tax and accounting issues (CLR, 2003). Prudential regulations are less common and are imposed when system-wide concerns are justified or protecting small depositors is an issue.

Previous research on microfinance regulation and prudential supervision focuses on the relationship between financial performance and regulation, treating outreach as a secondary concern. Hartarska (2005) finds that regulated MFIs in Central and Eastern 4
volume from any financial institution for applicants with thin credit bureau files and these increases seems to be primarily driven by institutions other than our partner bank. For applicants with thick credit bureau files, the EFL tool increases the probability of having a loan and loan volume from our partner bank only.

Table 4 examines the effect of the EFL tool on repayment behavior as measured by the Equifax credit score in April 2016 (about four years after the loan application).<sup>16</sup> We find that offering a loan based on the EFL tool leads to a statistically significant decrease in applicants’ April 2016 credit score, ranging from 113 to 241 points, relative to 537 points below the EFL score threshold. There was no statistical difference around the threshold in Equifax credit scores at the time of application (table A3, panel B). And even though the credit score may decrease as loan use increases since current debt amounts are one factor that goes into calculating the credit score, by April 2016 the applicants in our sample should have repaid the loans they obtained as part of the pilot exercise (we do not find an effect on medium-run loan use, as discussed below).

Figure 2 illustrates the effect of the EFL tool on the Equifax credit score visually. At the time of the loan application, there was no discontinuity in the credit score of SMEs in our sample at the EFL score threshold (figure 2A). In April 2016, however, the credit score showed a downward jump at the threshold (figure 2B).

Table 4 also shows that the effect of the EFL tool on the Equifax credit score is only statistically significant for applicants with thick credit bureau files and the coefficients are larger for these applicants than for those with thin credit bureau files. This reduction in the Equifax credit score among thick file applicants, which is potentially due to worse repayment behavior, could be caused by two factors (i) our partner bank offered loans to applicants with bad credit histories if their EFL score was above the threshold and (ii) the portfolio of loans from our partner bank that were part of the pilot exercise had a credit guarantee which may have lowered monitoring and collection incentives, even though the guarantee would be triggered after exhausting the first loss amount to be assumed by our partner bank. As shown in table 3, loan use for thick file applicants increased only through our partner bank (unlike loan use for thin file applicants which increased through other financial institutions), which makes the two factors above particularly relevant for thick file clients.

> 16 As noted previously, the Equifax score is available for all SME loan applicants in our sample.

16
of spatial correlations. We estimate an AR(1) process for real output growth for 20 U.S. states'<sup>0</sup> and for 24 O.E.C.D. economies, and use the observed variance-covariance matrix of the residuals as 2 to generate spatial correlations in our artificial data.

Table 1 reports the coverage rates for this specification for the cases T=25 and T=50 for each of the three estimators (OLS with time dummies, SUR and HAC). From Table 1, it is clear that both OLS and SUR do quite poorly even when T=50, having coverage rates which never rise above .694 and .679 respectively. Even when SUR is correctly specified (in the first column where p=O), its performance is quite poor. The reason for this is that even when T=50, the estimates of the contemporaneous spatial correlations matrix which it uses in a FGLS procedure are very imprecise, as it attempts to estimate a very large number of free parameters. In contrast, our non-parametric spatial correlation consistent HAC estimator performs much better than both OLS and SUR, with coverage rates which range from .818 to .903.

'° Of course, a drawback of this approach is that the estimates of elements of E will not be very precise, as the crosssectional dimension in these regressions is large relative to the time dimension. For this reason, we use only 20 states because annual data for gross state product is only available for 23 years, from 1963-1986. To give some idea of the magnitude of the spatial correlations, note that the average cross-sectional correlation for the U.S. state data is .193, with a maximal value of .629, while for the O.E.C.D., the corresponding figures are .312 and .761.

14
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# **Table of Contents**

# **Page No.**

|Preface -----------------------------------------------------------------------------------------------------|1|
|---|---|
|Case Study 1: The U.S. TreasuryDirect System-----------------------------------------------------|5|
|Introduction-------------------------------------------------------------------------------------------|6|
|Crucial Issues for e-security------------------------------------------------------------------------|12|
|Lessons for developing electronic distribution systems for government securities--------<br>|13<br>|
|Case Study 2: The Philippine Expanded Small Investors Program-------------------------------|16|
|Executive Summary---------------------------------------------------------------------------------|16|
|Retail Distribution of Government Securities—Aiding the Development of Public<br>Debt Markets------------------------------------ --------------------------------------------------|21|
|Economic Environment in the Philippines-------------------------------------------------------|22|
|The Debt Market, Debt Management, and the Expanded Small Investors Program-------|23|
|Legal and Technical Preconditions for Effective Electronic Distribution of Securities--|30|
|How Does the Expanded Program Work?-------------------------------------------------------|31|
|What Incentives Does the Expanded Program Offer Stakeholders?--------------------------|34|
|How Are Business Risks Being Mitigated?------------------------------------------------------|40|
|Conclusions-------------------------------------------------------------------------------------------|45|
|**Tables (Case Study 1)**||
|Table 1: Three Online Systems-------------------------------------------------------------------------|8|
|Table 2: Average returns on various U.S. savings products, 2002--------------------------------|13|
|**Tables (Case Study 2)**||
|Table 1: Outstanding government securities in the Philippines, August 2002-February 2003|25|
|Table 2: A Comparison of savings products for retail investors----------------------------------|28|
|Table 3: Distribution framework for government securities before and after the Expanded<br>Small Investors Program------------------------------------------------------------------------------|35|
|**Figures (Case Study 2)**||
|Figure 1: GDP growth and inflation in the Philippines, 1997-2002------------------------------|23|
|Figure 2: Budget balance in the Philippines, 1995-2001-------------------------------------------|23|
|Figure 3: National government debt in the Philippines, 1997-2002------------------------------|24|
|Figure 4: Process for retail purchases of securities before the Expanded Small Investors||
|Program – Present Environment---------------------------------------------------------------------|34|
|Figure 5: Process for retail purchases of securities under the Expanded Small Investors||
|Program – E-SIP Environment-----------------------------------------------------------------------<br>**Reference**s-------------------------------------------------------------------------------------------------|34<br>45|
total banking credit to the private sector. In terms of the number of employees, schemes in developing countries are somewhat larger than in developed economies; the outlier again is Asia, with a median size of 179 employees.

**<u>Table 1:  Summary statistics, medians</u>**

||**No. of**<br>**Obs.**|**Median**<br>**Age**|**Total**<br>**Outstanding**<br>**Guarantees**<br>**(US$ million)**|**Total**<br>**Outstanding**<br>**Guarantees/**<br>**GDP**|**No. of**<br>**Employees**|
|---|---|---|---|---|---|
|**_All schemes_**|76|15|3,700|0.61|18|
|**_By Income_**||||||
|High|34|27|909|0.21|15|
|Middle/Low|42|13|360|0.30|21|
|**_By Region_**||||||
|Asia|6|23|41,143|4.7|179|
|Latin America|24|11|682|0.06|11|
|Transition|11|14|149|0.35|25|

There is important variation in the degree to which schemes are profit-oriented and subject to taxation. 40% of the schemes are for-profit, while the remaining 60% are non-profit; 52% are subject to corporate income tax, while 48% have tax-exempt status. The likelihood that a scheme is taxable does not vary across income levels of countries. In the East Asian and Pacific region, schemes are more likely to be tax-exempt than in other regions. Non-profit oriented PCG funds are typically tax-exempt.

**<u>Figure 1: The distribution of PCG creation over time</u>**

<!-- Start of picture text --> 15<br>10<br>5<br>0<br><1970 1970‐1979 1980‐1989 1990‐2000 2000‐2006<br><!-- End of picture text --> The large majority of PCG funds in our sample were created with specific goals and thus have restrictions in terms of the sector, type of business or geographic area 9
# **3.3 Main Outcome Data and Measurement**

Our main data source is the _Enquête Agricole de Conjoncture Intégrée aux Conditions de Vie des Ménages_ (EAC-I). We use the 2014 and 2017 editions of the EAC-I (EAC-I 2014 and EAC-I 2017). EAC-I 2014 and EAC-I 2017 are nationally representative<sup>10</sup> multi-topic household with a focus on These were surveys agriculture. surveys implemented by Mali’s Ministry of Agriculture under the Living Standards Measurement Study – Integrated Household Surveys on Agriculture (LSMS-ISA) program.<sup>11</sup> It contains detailed information on the socioeconomic characteristics of farm households, as well as about the crop production activities (type and quantity of fertilizers, pesticides, seeds, credit, etc.). Information is collected at the level of the cultivated plots (area, soil quality, etc.), as well as on the seasonal crops produced (quantity harvested, area on which the crop was harvested, etc.). The EAC-I also contains crucial GPS information that allows us to identify enumeration areas that are exposed to local rainfall shocks.

For each edition of the households were visited twice: the same households survey, interviewed in the visit are re-interviewed in the second visit and the visits were first planned to match the timing of the post-planting and post-harvest periods of the 2014/15 and 2017/18 agricultural rainy seasons. Households interviewed in the EAC-I 2014 differ from those interviewed in the EAC-I 2017. However, the same Enumeration Areas (EA) were visited during these two surveys. Combining the two editions of the EAC-I and excluding the urban areas to focus on rural production, we obtain a final sample of 7,400 households (1,740 in 2014 and 5,660 in 2017).<sup>12</sup> Our main variable of interest is which is measured in crop yields, kilograms per hectare. We define yield as the ratio of crop output reported by the household over the area occupied by the crop on the plot.

> 10The sample covers all regions and areas (urban and rural) with the exception of the region of Kidal, excluded for security reasons from the sample.

> 11The Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) provides financial and technical support to governments in Sub-Saharan Africa in the design and implementation of multi-topic, national, panel household surveys with a strong focus on agriculture. The EAC-I data and documentation are publicly available on https://www.worldbank.org/en/programs/lsms.

> 12Few observations have been omitted due to lack of information on production and/or outliers on production, yields, acreage, etc.

15
The first implementation of the World Bank poverty mapping software was actually done in SAS (Demombynes, 2002). Poverty indicators and their corresponding standard errors were obtained by a bootstrap procedure relying on simulation. This simulation approach is very reminiscent of multiple imputation methods, where every single relevant parameter necessary for simulating vectors of welfare is drawn from its corresponding estimated asymptotic distribution (or an approximation to it). This approach is the one currently used in the World Bank’s tools for implementing the ELL (2002) approach: Stata’s sae package and PovMap. Specifically, the steps of the implemented bootstrap procedure designed to obtain the traditional ELL point estimates and their estimated noise are:

1. Fit the nested error model (1) to the survey data. This yields the vector of initial parameter estimates

- _θ_ ˆ0 = (ˆ _β_ 0 _,_ ˆ _ση_<sup>2</sup> 0<sup>_,_ˆ</sup><sup>_σ_</sup> _e_<sup>2</sup> 0<sup>)</sup><sup>_,_</sup>

where the 0 subscript is used hereafter to indicate that the estimates come from the original household survey. In the implemented version, the model is fit via FGLS as specified in Nguyen et al. (2018).

2. Draw new model parameters as follows. First, regression coefficients are drawn from The variance of the household-level errors is drawn, according to Gelman et al. (2004, pp. 364-365), from where _χ_<sup>2</sup> _n_<sup>_∗_</sup> _−K_<sup>denotesarandomnumberfromachi-squareddistributionwith</sup><sup>_n−K_degreesof</sup> freedom. Here, _n_ is the number of observations in the survey data used to fit the model and _K_ is the number of correlates used in the model. The variance of the cluster effects _ση_<sup>2</sup><sup>_∗_</sup> is drawn, according to Demombynes (2008) and Demombynes et al. (2002), from 3. Using the simulated model parameters in step 2, calculate the welfare for every household in the census _ych_<sup>_∗_fromthemodelas</sup> where the household-specific errors are generated as and the location effects are generated as Then construct the vector containing the _Nc_ simulated welfares for the households in location _c_ , denoted _yc_<sup>_∗_= (</sup><sup>_y_</sup> _c_<sup>_∗_</sup> 1<sup>_, . . . , y_</sup> _cN_<sup>_∗_</sup> _c_<sup>)</sup><sup>_T_,where</sup><sup>_Nc_isthenumberofcensushouseholdsinlocation</sup><sup>_c_.</sup> 4. With the vectors _yc_<sup>_∗_,</sup><sup>_c_= 1</sup><sup>_, . . . , C_,ofsimulatedcensuswelfares,indicatorscanbeproducedforall</sup> the locations. Let _τc_<sup>_∗_=</sup><sup>_f_(</sup><sup>_y_</sup> _c_<sup>_∗_)betheindicatorofinterestcalculatedbasedonthesimulatedvector</sup>

> method from ELL and the parametric drawing of the parameters provide similar results. In tests with pseudo surveys, the delta method seems to provide wider standard errors than the parametric approach (Demombynes et al. 2008), suggesting that perhaps the parametric estimates are too optimistic when compared to the delta method

4
# **Chart 7A: Uphill Flows of Sophisticated Exports and Per Capita GDP, 1986, 1996, and 2005**

<!-- Start of picture text --> 6 7 8 9 10 11<br>Log per capita GDP (PPP in constant $)<br>1986 1996 2005<br>10.5<br>10<br>9.5<br>Uphill flows of sophisticated exports<br>9<br><!-- End of picture text -->

# _Source: UN COMTRADE_

Uphill flows are measured as the average income level of all the destination countries which receive a country’s sophisticated exports (defined here as above-median PRODY exports), where the weights are each destination country’s share in total exports of the sending country. The sample is kept constant for all three periods.

24
to result in greater pressure to cut costs, leading to less discrimination against females and therefore greater female employment (see for example, Bhagwati 2004, Chen et al. 2013, Ederington et al. 2010, Juhn et al. 2014, and World Bank 2011).

Third, recent studies have examined the comparative advantage of men vs. women within industries and across occupations. One argument here is that females enjoy a comparative advantage in cognitive vs. physical skills (Galor and Weil 1996, Juhn et al. 2014, Do et al. 2011). One possibility suggested here by Juhn et al. (2014) is that by lowering the cost of entering foreign markets, trade liberalization causes some firms to start exporting and adopt modern technologies. The use of modern technology, such as use of computers, reduces the need for routine physical tasks, improving females’ labor market outcomes in the blue-collar tasks, while leaving them unchanged in the white-collar jobs. However, the issue is far from settled. Depending on the type of technology adopted, greater export orientation can lead to worsening of female employment. T echnological upgrading and improvements on product quality following trade liberalization may lead to higher capital and skill intensity of production processes. This is likely to adversely affect female employment (see for example, Berik 2000, Joekes 1995, Pearson 1995).

Fourth, according to the Hecksher-Ohlin theory, a country specializes and exports the product that uses the relatively more abundant factor of production in the country more intensively. Since, developing countries are abundant in semi and unskilled labor relative to skilled labor, demand for semi and unskilled labor should increase with exporting activity. In as much as women gravitate toward low-skilled jobs and men cluster toward high-skilled jobs, trade expands job opportunities for females, in both absolute and relative (to males) terms (see for example, Wood 1991, Cagatay and Ozler 1995, Joekes 1995, Cagatay and Berik 1990, Ozler 2000, United Nations 2011).

6
have a large rate of return. We also estimate that providing legal work permits to Venezuelan migrants would lower their rate of informal employment by 17 percentage points, generating important wage increases. Due to the ongoing pandemic, economic conditions are deteriorating rapidly in Ecuador and households that rely on informal employment are particularly vulnerable. As we have documented, Venezuelan workers are highly reliant on informal employment.<sup>24</sup> Thus, policies that help improve their labor market outcomes, while simultaneously shifting the burden away from the most vulnerable Ecuadoran workers, are urgently required.

> 24Preliminary data suggests that Venezuelan migrants are suffering the effects of the pandemic even more intensely than Ecuadorans. Between February and June 2020, it is estimated that 27% of Venezuelan households experienced loss of employment (for at least one member of the household). In comparison, the corresponding figure is 50% for Venezuelan households in Ecuador (Olivieri (2020)).

21
km in the US and €1 in EU (for long distance services. The table below provides a comparison of operating costs for organized trucking.<sup>20</sup>

**Table 4. Comparison of Truck Operating Costs in Western Europe, Eastern Europe and in Africa**

||Fixed Costs<br>(perday)|Variable Cost/<br>km|Distance/Month|
|---|---|---|---|
|Europe (France)|€ 340|€ 0.25<br>€0.35 (toll<br>roads)|10,000 or more|
|Transit South-East Europe|€ 250|€0.3|>10,000|
|Organized Companies in African<br>LLDCs (2005)|$ 90-150|$0.5-$0.8|4,000-8,000|

_Source: CNTR June 2006 statistics (France), Authors’interviews (Africa) and TTFSE project._ In one of the World’s least friendly corridors, Douala-Bangui in Central Africa, where the road is not paved on long sections, the observed traction cost (€1.25) is only 25-50% higher than in EU or the US. The fivefold difference in rate between Douala-Bangui and LA-Chicago (for a distance twice shorter) is mainly due to the load factor differences and the high level of overheads on the corridor.

**Table 5. Comparison of the Estimated Structure of Logistics Costs**

||Douala/Bangui|L.A./Chicago|
|---|---|---|
|Operating Cost per Kilometer|1.25|0.85|
|Distance|1,450|3,000|
|Load Factor|0.5|0.87|
|Transportation Cost (net)|3,625|2,931|
|Overheads|**66%**|**5%**|
|**Total Logistics Costs**|6,017|3,077|

_Source: Authors’ Calculations._ Fixed costs are lower in developing countries because:

> 20 Cost structure in developing countries deserves a more systematic research, especially to extend the knowledge beyond the case of medium-large companies, easy to assess. No comprehensive review has been carried out since the Western Africa/South Asia comparison by Rizet et al (1992).

20