rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 1: 1998-2002<br>- Gis ‘P, i “ a ar. - .<br>Dime. Ww | As<br>| ro \ Ging,<br><!-- End of picture text -->

<!-- Start of picture text --> 2: 2003-2009<br>. are 7 ia < wt ey — .<br>Poe, , one<br><!-- End of picture text --> <!-- Start of picture text --> 3: 2010-2019<br>7 are ~ < te ay _— ;<br>y ere > ~<br>om f Ae<br>Cluster: [1 (2 INA | . f<br>iene|<br><!-- End of picture text -->
# **Table 9. Well-Being**

|||**M**|**idline Surve**|**y**||**End**|**line Survey**||
|---|---|---|---|---|---|---|---|---|
|||Control<br>Group<br>|Treatment<br>|- Control<br>||Control<br>Group<br>|Treatment<br>|- Control<br>|
||Obs|Mean|Coeff.|_p-value_|Obs|Mean|Coeff.|_p-value_|
|**Beneficiary woman**|||||||||
|Self-esteem indicator (Rosenberg Scale)|n.a|n.a|n.a|n.a|812|21.29|0.01|_0.971_|
|Mental Health indicator|428|14.21|0.15|_0.644_|793|14.65|-0.26|_0.230_|
|**Husband**|||||||||
|Self-esteem indicator (Rosenberg Scale)|n.a|n.a|n.a|n.a|602|21.55|-0.03|_0.901_|
|Mental Health indicator|306|13.64|0.70|_0.137_|612|14.77|-0.18|_0.476_|

Notes _:_ Sample of mental health outcomes at midline: households surveyed in rounds 3 and 4. Coefficients from an OLS regression of the lefthand side variable on a treatment dummy, controlling for strata dummies, survey-time dummies, and baseline traits (see Table 3 note). ***, **, * indicate significance at 1, 5 and 10 percent.

48
Figure 5 illustrates the improved trend in well‐being observed among the treatment group participants relative to the control group participants. At the 5‐week mark, positive trend is observed in the WHO‐5 scores across both groups. At 3 months, the positive and increasing trend in personal well‐being is maintained in the treatment group, whereas the control group experiences a decline. Entrepreneurs in the treatment group, therefore, experience improved well‐being (that is, they are in good spirits, relaxed, engaged, and so on more often) as a result of going through the training program‐ months after the secession of the intervention.

Figure 5. Psychological Indicators Across the Treatment and Control Groups <!-- Start of picture text --> Average WHO‐5 scores<br>100<br>90<br>80<br>70<br>60<br>50<br>Baseline 5 weeks 3 months<br>Treatment Control<br>Source: Saraf, 2019; World Bank Group, 2018<br><!-- End of picture text -->

# 6.3 Discussion

Our results suggest that although cash transfers (with an average size of US$14,300) may improve mental health by perhaps addressing financial constraints and related stress, combining it with a low‐cost PM+E intervention (at a cost of US$300/trainee) can further improve mental health (both prevalence and intensity of depression and anxiety) as well as well‐being among SME entrepreneurs in FCV contexts. The odds of experiencing depression and anxiety for entrepreneurs in the treatment arm is substantially lower (0.46x) compared to the control group.

Across all outcome indicators, impact is larger 3 months after the intervention, compared to immediately after at 5 weeks. This suggests that benefits of a CBT‐based training curriculum, 21
the population of interest in a two-stage stratified sample with random sampling of schools and within each school. Survey weights reflect the different probabilities of schools and students to be sampled. The domains of reading, mathematical and scientific literacy are covered not merely in terms of mastery of the school curriculum, but in terms of important knowledge and skills needed in adult life, particularly in the labor market (OECD 2007).

Table 2 presents descriptive statistics. Most schools in the Netherlands PISA 2006 sample are private. Most schools can fire a teacher, which is not unexpected since most schools are private, but in the Netherlands almost all schools can fire teachers. Achievement data is used to evaluate teachers in most schools, with almost three-quarters saying that it is so. Also evident is the high degree of school-based management in the Netherlands, as more than 80 percent of schools report that they can set teacher salary increases. Few schools directly involve parents in school budget decisions, at only 9 percent overall. Teachers in the Netherlands are certified, and few schools are located in rural areas. In terms of differences between public and private schools, there is a slightly higher probability that public schools can fire teachers in the Netherlands, and parental involvement in school budget decisions is higher in public schools. Religion is more important for choosing private schools. The student-teacher ratio is slightly lower in private schools. Private school students have mothers who are relatively less educated. Only 26 percent of the mothers of private school students have a university education, while more than 33 percent of the mothers of public school students have a university education mother.

12
<!-- Start of picture text --> 0 100 200 300 400 500<br>HH cumulative loans from all MFIs (Tk. '000)<br>Pr(moderately indebted) Pr(over-indebted)<br>Pr(seriously indebted)<br>1<br>.8<br>.6<br>.4<br>.2<br>Predicted probability of being indebted<br>0<br><!-- End of picture text -->

**Figure 2: Predicted probabilities of various types of indebtedness with the change in**

**microcredit borrowing amount (all 3 years)**

all households that borrow from microcredit organizations would benefit in the same way. An in-depth analysis is thus perhaps necessary to determine, which households, among the longterm borrowers or members of multiple programs, are indeed over-indebted and what could be done to help them manage the extent of over-indebtedness.

# <u>8. Conclusions</u>

The available literature describes the potential effects of over-indebtedness on both microfinance borrowers and lenders. Over-indebtedness affects a lender’s financial or institutional sustainability, and the effect on borrowers can be a debt trap that pushes them further down into poverty.

When borrowers are nearly indebted, they struggle to make repayments, cut back on basic consumption as well as other important household expenditures, such as education or 27
|Global Partnershipfor Education - Balochistan Education Project|Juan Baron|
|---|---|
|Global Partnershipfor Education - Balochistan Education Project|Juan Baron|
|Global Partnershipfor Education - Balochistan Education Project|Juan Baron|
|Global Partnershipfor Education - Balochistan Education Project|Juan Baron|
|Global Partnershipfor Education - Balochistan Education Project|Juan Baron|
|Global Partnershipfor Education - Balochistan Education Project|Juan Baron|
|Global Partnershipfor Education - Balochistan Education Project|Juan Baron|
|Global Partnershipfor Education - Balochistan Education Project|Juan Baron|
|Global Partnershipfor Education - Balochistan Education Project|Juan Baron|
|Reducingthe Vulnerabilityof Small Farmers to Price Risk|Katie KennedyFreeman|
|Reducingthe Vulnerabilityof Small Farmers to Price Risk|Katie KennedyFreeman|
|Reducingthe Vulnerabilityof Small Farmers to Price Risk|Katie KennedyFreeman|
|Reducingthe Vulnerabilityof Small Farmers to Price Risk|Katie KennedyFreeman|
|Reducingthe Vulnerabilityof Small Farmers to Price Risk|Katie KennedyFreeman|
|StrengtheningLabor Market Monitoringand Performance in the C|a Gonzalo Javier Reyes Hartley|
|StrengtheningLabor Market Monitoringand Performance in the C|a Gonzalo Javier Reyes Hartley|
|StrengtheningLabor Market Monitoringand Performance in the C|a Gonzalo Javier Reyes Hartley|
|Results Based Financingfor Sanitation and Hygiene|Emmanuel Nkrumah|
|Results Based Financingfor Sanitation and Hygiene|Emmanuel Nkrumah|
|Results Based Financingfor Sanitation and Hygiene|Emmanuel Nkrumah|
|Results Based Financingfor Sanitation and Hygiene|Emmanuel Nkrumah|
|Cash Transfer Program for Vulnerable Children in Northern Togo|John Van Dyck|
|Cash Transfer Program for Vulnerable Children in Northern Togo|John Van Dyck|
|Cash Transfer Program for Vulnerable Children in Northern Togo|John Van Dyck|
|Cash Transfer Program for Vulnerable Children in Northern Togo|John Van Dyck|
|Cash Transfer Program for Vulnerable Children in Northern Togo|John Van Dyck|
|Quito Metro Line One|Alejandro Hoyos Guerrero|
|Quito Metro Line One|Alejandro Hoyos Guerrero|
|Quito Metro Line One|Alejandro Hoyos Guerrero|
|Quito Metro Line One|Alejandro Hoyos Guerrero|
|Quito Metro Line One|Alejandro Hoyos Guerrero|
|Quito Metro Line One|Alejandro Hoyos Guerrero|
|Quito Metro Line One|Alejandro Hoyos Guerrero|
|Quito Metro Line One|Alejandro Hoyos Guerrero|
|Quito Metro Line One|Alejandro Hoyos Guerrero|
|Quito Metro Line One|Alejandro Hoyos Guerrero|
|Quito Metro Line One|Alejandro Hoyos Guerrero|
|Quito Metro Line One|Alejandro Hoyos Guerrero|
|Quito Metro Line One|Alejandro Hoyos Guerrero|
|Tanzania: ExpandingRice Production|Emma Isinika Modamba|
|Tanzania: ExpandingRice Production|Emma Isinika Modamba|
|Tanzania: ExpandingRice Production|Emma Isinika Modamba|
|Tanzania: ExpandingRice Production|Emma Isinika Modamba|
|Tanzania: ExpandingRice Production|Emma Isinika Modamba|
|Tanzania: ExpandingRice Production|Emma Isinika Modamba|
|Tanzania: ExpandingRice Production|Emma Isinika Modamba|
|Tanzania: ExpandingRice Production|Emma Isinika Modamba|
**Table 6b: Per Capita Annual Aggregate Consumption (PCAgC) in ’000** **<mark>local currencies converted to Indian Rupees unde</mark>** <mark>r</mark> **<mark>2</mark> 011 ICP Consumption PPPs and Counterfactual PPPs of Table 5 (Base Country: India)**

|||**PCAgC**|**evaluated**<br>|||**PCAgC**<br>|**evaluated**<br>|||**PCAgC**|**evaluated**<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Region**|**Country**||**at**|**Region**|**Country**||**at**|**Region**|**Country**||**at**|
|||**2011**<br>**ICP**<br>**PPP**|**Counter**<br>**factual**<br>**PPP**|||**2011 ICP**<br>**PPP**|**Counter**<br>**factual**<br>**PPP**|||**2011**<br>**ICP**<br>**PPP**|**Counter**<br>**factual**<br>**PPP**|
||Benin|20.63|25.83||South Africa|115.97|69.10||Albania|113.67|336.47|
||Burkina Faso|13.34|23.76||Swaziland|81.54|131.56||Bosnia and Herzegovina|118.60|16.37|
||Cameroon|32.17|39.52||Tanzania|14.42|25.43||Bulgaria|153.65|25.01|
||Chad|20.68|20.32||Togo|16.71|24.55||Latvia|192.37|38.22|
||Congo, Rep.|11.89|13.19|**AFRICA**|Uganda|19.47|24.79||Lithuania|231.62|52.94|
||Congo,Dem. Rep.|11.15|16.49||Zambia|24.91|43.45||Macedonia(FYR)|132.81|27.17|
||Cote d'Ivoire|27.72|21.81||Bangladesh|29.95|27.31|**EUROSTAT-**||165.88|80.91|
||Djibouti|24.08|14.25||Bhutan|56.00|32.89|**OECD**|Mexico|||
||Egypt|119.46|54.28||Cambodia|31.89|25.54||Montenegro|172.48|27.33|
||Ethiopia|13.71|12.17||China|60.66|36.45||Romania|156.64|41.14|
||Gabon|83.71|33.80||Fiji|75.60|43.22||Russia|212.55|40.95|
||Gambia(The)|17.10|20.00||**India**|42.34|42.34||Serbia|141.57|28.40|
||Ghana|31.40|30.99||Indonesia|67.31|57.50||Turkey|192.33|236.08|
||Guinea|11.04|12.29||Lao PDR|32.79|26.73||Bolivia|51.27|25.08|
|**AFRICA**|Kenya|27.13|29.65|**ASIA AND**|Maldives|0.61|0.33||Brazil|138.75|63.88|
||Lesotho|35.36|44.04|**THE**|Mongolia|77.05|47.47||Colombia|109.75|51.76|
||Liberia|8.48|0.16|**PACIFIC**|Nepal|25.88|20.79|**LATIN**<br>|El Salvador|102.04|100.54|
||Madagascar|18.65|45.82||Pakistan|54.99|42.85|**AMERICA**|Guatemala|87.15|38.70|
||Mali|14.66|20.01||Philippines|62.89|51.09||Honduras|52.50|48.29|
||Mauritania|29.26|19.93||Sri Lanka|89.55|35.24||Nicaragua|50.23|99.24|
||Mauritius|165.44|69.10||Thailand|118.73|30.92||Peru|100.11|39.38|
||Morocco|60.35|27.48||Vietnam|41.89|35.24|**WESTERN**|Iraq|75.36|64.49|
||Mozambique|12.46|23.73||Armenia|102.30|50.15|**ASIA**|Jordan|124.21|28.03|
||Namibia|81.61|98.12|**COMMON**|Azerbaijan|117.17|35.54||Yemen|38.69|14.35|
||Nigeria|29.06|33.60|**WEALTH**|Belarus|162.14|42.89|**CARIBBEAN**|Jamaica|101.42|46.28|
||Republic of Cabo|66.49|23.54|**OF**|Kazakhstan|159.83|45.43|||||
||<br>Verde|||**INDEPEN**<br>|<br>Kyrgyz Rep.|<br>49.10|<br>27.48||**MEAN 73.30**||**43.21**|
||Senegal|26.94|29.34|**DENT**<br>|<br>Moldova|79.18|30.95|||||
||Sierra Leone|16.72|13.04|**STATES**|Tajikistan|42.37|20.84||**MEDIAN 58.17**||**31.94**|
||||||Ukraine|110.74|27.79|||||

43
WPS3443

**Improving Credit Information, Bank Regulation and Supervision:**

**On the Role and Design of Public Credit Registries**

Andrew Powell, Nataliya Mylenko, Margaret Miller, and Giovanni Majnoni

# **Abstract**

This paper analyzes how data in public credit registries can be used both to strengthen bank supervision and to improve the quality of credit analysis by financial institutions. Empirical tests using public credit registry data were performed in collaboration with the central banks in Argentina, Brazil and Mexico. The results of the empirical tests confirm the value of the data for credit risk evaluation and provide insights regarding its use in supervision, including in calculations of credit risk for capital and provisioning requirements or as a check on a bank’s internal ratings for the Basel II’s internal rating-based approach. We also define a set of critical design parameters and use the results to comment on appropriate public registry design. Finally, the paper includes a discussion of the relationship between the different objectives of a public credit registry and how they influence the registry’s design.

# World Bank Policy Research Working Paper 3443, November 2004

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

**_______________**

> * The authors are from the World Bank (Giovanni Majnoni, Margaret Miller and Nataliya Mylenko) and from the Universidad Torcuato Di Tella (Andrew Powell). This paper has been completed as part of a project on “Credit Information, Credit Risk Measurement, and Solvency Ratios in Emerging Countries” supported by a grant of the World Bank Research Committee. Andrew Powell gratefully acknowledges support from the World Bank, where he visited between August and December 2003. Email: <u>gmajnoni@worldbank.org</u> , mmiller5@worldbank.org, <u>nmylenko@worldbank.org, apowell@utdt.edu. We are grateful to Matias Gutierrez from the Banco Central de la</u> Republica Argentina, Jose Luis Negrin , Javier Marquez and Alberto Romero from Banco de Mexico and Ricardo Schectman from Banco Central do Brazil for invaluable help and support in the quantitative analysis.
and also whether they will commute by a non-motorized mode (walking and bicycling), public bus or subway, or private automobile. Road travel by buses and cars causes congestion given existing road capacities, thus travel times as well as fuel use are endogenous, because cars consume more (less) fuel per kilometer as congestion reduces (increases) average speed. The core area is completely built up and we assume that the housing supply (measured as aggregate floor space) in this area is equal to the existing stock and unalterable. Hence, in the core the supply of housing is treated as perfectly inelastic. In the periphery, there is plenty of vacant land much of it available for housing development. We assume that the supply of housing in the periphery is perfectly elastic. This choice of assumptions is guided by our limited data as we lack an empirical basis for modeling construction costs and developer behavior in Beijing. Commuters are grouped into quintiles and we use data on wages and rough assumptions of nonwage earnings to construct income levels for each quintile (see Anas, Timilsina, Zheng (2009)). Our treatment of consumer preferences recognizes that the sensitivity to the monetary cost of commuting falls with income, while the sensitivity to travel time rises with income.

How can the model described above generate a lock-in effect with respect to aggregate CO2 emissions or aggregate gasoline use? The scenario we follow to detect the effect is as follows. We first simulate the base 2005 situation. Next, we assume that a significant highway investment takes place that affects suburb-to-suburb commuting in the periphery. Thus, for example, we increase road capacity in the peripheral area by 20% which increases speed for those residing and working in the periphery, reducing travel time, gasoline consumption and emissions per kilometer, the intensive margin. In the extensive margin, the reduced travel time attracts more residences and jobs to the periphery which, in turn, lowers rents and congestion in the core and increases those relying on the auto mode in both the core and the periphery. The third step is to contrast the above road capacity expansion in the periphery by the alternative of expanding transit service in the core (reducing the transit travel time for core to core commuting by, say, 20%). Doing so works in a way that is much the opposite of the peripheral road expansion. Jobs and residences in the core increase at the expense of the periphery, core housing rents rise and car users in the core become more congested as some bicyclers, walkers and peripheral commuters relocating to the core switch to bus commuting, causing more road congestion for cars due to more buses on the road. Now in the final step, the question we ask is whether the same, larger or lower changes will accrue from the same transit improvement in the core, when 5
Table 7: Ex ante comparison: performance of exploration sampling and standard RCT in simulated samples based on parameter vector ( _β_<sup>ˆ</sup><sup>_E_</sup> _,_ ˆ _κ_<sup>_E_</sup> ).

_Panel A: Averages of Posterior Estimates._

||Exploration|Sampling|Standar|d RCT|
|---|---|---|---|---|
|Treatment|Avg. posterior<br>expected policy|Avg. posterior<br>probability|Avg. posterior<br>expected policy|Avg. posterior<br>probability|
||regret|optimal|regret|optimal|
|T1A|1.18%|4.39%|1.08%|12.61%|
|T1B|0.02%|92.2%|0.08%|81.29%|
|T1C|2.10%|0.45%|2.01%|0.48%|
|T2A|2.67%|0.07%|2.66%|0.03%|
|T2B|1.52%|2.89%|1.38%|5.60%|
|T2C|4.51%|0.00%|4.61%|0.00%|
|_Panel B: _|_Average Realized Val_<br>Exploration|_ues._<br> Sampling|Standar|d RCT|
||Average|Percentage|Average|Percentage|
||policy|best arm|policy|best arm|
||regret|identifed|regret|identifed|
||0.01%|99.00%|0.07%|93.00%|

Notes: The table shows averages from 100 simulated samples drawn using the parameterˆ vector given by the means of the estimated posteriors from wave 2 of the IVR experiment, _β_<sup>_E_</sup> = ( _−_ 2 _._ 63 _, −_ 2 _._ 49 _, −_ 2 _._ 78 _, −_ 2 _._ 89 _, −_ 2 _._ 67 _, −_ 3 _._ 32) and _κ_ ˆ<sup>_E_</sup> = 0 _._ 5. For each sample draw, the same first wave was used, the second wave was drawn either using the exploration sampling shares based on the estimates from the first wave, or using equal assignment shares.

Panel B of Table 7 uses the fact that we know the parameter vector that generated the simulated samples, and therefore know the policy regret from choosing a different arm from T1B. This means we can calculate the average policy regret and share of optimal decisions from making the final choice after each simulated experiment (which is based on posterior policy regret). According to panel B, in 99% of the time (93% in the RCT) the experimenter correctly chooses T1B based on this decision metric. The average posterior regret from T1B is only slightly higher than the realized average policy regret;<sup>28</sup> both show an 0.06% reduction in regret from adaptive over non-adaptive sampling. Panel B shows the decision metric that should be used to choose between the adaptive and the non-adaptive design (Panel A shows the expected value of the posterior estimates after the experiment). The posterior estimates show some remaining uncertainty. This is partly due to the school random effects: some of the measurement effort is spent on estimating the school averages, which adds uncertainty to the final estimates.

Prior to an experiment, the researcher of course does not know what the true parameters are, and they

> 28Note that regret in Panel B only occurs when the experimenter does _not_ choose T1B.

40
|**Dif**<br>**(std. err.)**|(-0.0021)<br>-0.15_∗∗∗_<br>(-0.0037)<br>-0.011_∗∗∗_<br>(-0.0014)|-0.00044<br>(-0.0016)<br>-0.0084_∗∗∗_<br>(-0.0012)<br>2.09_∗∗∗_<br>(-0.15)<br>0.12_∗∗∗_<br>(-0.005)|0.11_∗∗∗_<br>(-0.0042)|0.066_∗∗∗_<br>(-0.0032)<br>0.11_∗∗∗_<br>(-0.0043)<br>have had at least<br> calculated as an|
|---|---|---|---|---|
|**Dalits**|0.46<br>0.036|0.037<br>0.026<br>114.9<br>0.5|0.72|0.85<br>0.69<br>arried and <br> 1000 and is|
|**Non-Dalit Hindus**|0.31<br>0.024|0.037<br>0.018<br>117<br>0.62|0.83|0.92<br>0.81<br> Muslim women who are m<br>ales)/(number of males) _×_|
|**Dif**<br>**(std. err.)**|(-0.0026)<br>0.15_∗∗∗_<br>(-0.0047)<br>-0.029_∗∗∗_<br>(-0.0019)|0.0024<br>(-0.0021)<br>-0.022_∗∗∗_<br>(-0.0016)<br>0.89_∗∗∗_<br>(-0.19)<br>0.16_∗∗∗_<br>(-0.0063)|0.16_∗∗∗_<br>(-0.0051)|0.059_∗∗∗_<br>(-0.0038)<br>0.17_∗∗∗_<br>(-0.0054)<br>men and 9269 <br>umber of fem<br>_∗∗∗p <_0_._001|
|**Muslims**|0.16<br>0.055|0.035<br>0.04<br>116.2<br>0.46|0.68|0.86<br>0.63<br>1217 Hindu wo<br>cluster as the (n<br>5, _∗∗p <_0_._01,|
|**Dalit Hindus**|0.31<br>0.026|0.037<br>0.019<br>117<br>0.62|0.83|0.92<br>0.81<br> total sample of 5<br>io is defned per <br>theses: _∗p <_0_._0|
|**Non **||||based on a <br>The sex-rat<br>rs in paren|
||rking away from home<br> **and Health Inputs**|rotein<br>emoglobin level<br> against measles|against BCG|against polio<br> against DPT<br> (i) Individual data are <br>prior to the survey; (ii) <br>sters; (iii) Standard erro|
||en wo<br>**ition **<br>Meat|Plant p<br>Dairy<br>en’s h<br>nated|nated|nated <br>nated <br>Notes: <br> birth <br>ver clu|
||Wom<br>**Nutr**<br>Eat|Eat <br>Eat <br>Wom<br>Vacci|Vacci|Vacci<br>Vacci<br>Table 2: <br>one male <br>average o|

24