rafmacalaba/datause-encoder-data huggingface dataset:
evidence that the EFL tool can reduce the risk of the portfolio for unbanked entrepreneurs who have been approved through the traditional screening process.

We also found that the EFL tool can be used to extend credit to some unbanked entrepreneurs who were rejected based on their traditional credit scores, without increasing the risk of the portfolio. However, for banked entrepreneurs, the EFL tool does not perform well as a skimming mechanism in the context examined in this paper.

To study the impact of the EFL tool on unbanked entrepreneurs’ access to credit, we compared the credit use of loan applicants with an EFL score just below and above the threshold set by the implementing institution. We found evidence that those above the threshold are more likely to receive loans (most often from the implementing institution) than those below the threshold.

Our paper contributes to studies of the relationship between individual characteristics (i.e., personality traits) and repayment behavior. Klinger, Khwaja, and del Carpio (2013) analyzed data from 1,580 small business owners with loans from banks and microfinance institutions in Peru, Kenya, Colombia, and South Africa, and found that entrepreneurs’ business profits and repayment behavior patterns are strongly correlated with their individual personality traits. Similarly, Klinger, Khwaja, and LaMonte (2013) and Klinger et al. (2013) studied the repayment behavior of entrepreneurs in Peru and Argentina, respectively, and compared these with patterns in other countries in which they applied the same psychometric tool. Their results show that despite differences in the distribution of personality traits, the dimensions that are related to business performance and credit risk are common across countries (Klinger et al., 2013).

In this paper, we go one step further and examine the potential of the psychometric credit application as a tool to manage portfolio risk and to increase access to credit compared with a traditional credit-scoring method. Our paper is the first external study examining the predictive power of psychometric credit scoring; that is, it uses independently collected data on repayment behavior patterns and is not coauthored by a person affiliated with EFL.

The rest of the paper is organized as follows. Section 2 discusses the EFL tool, the implementation of the tool by the participating institution, and the hypotheses to be tested. Section 3 describes the data and methodology. Section 4 presents the empirical results. Section 5 examines an extension of the results: the use of updated EFL scores generated using a new model. Finally, Section 6 concludes.

4
for child development: Experimental evidence from india. Technical report, National Bureau of Economic Research.

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41
tence of bribery distorts the allocation of resources and the entrepreneurial choices, without financial frictions in a closed, one-sector, neoclassical growth model.

My calibration strategy is similar to the strategy in Leal (2014), which investigates the relationship between the informal sector and misallocation. In his model, firms choose to be small for tax evasion purposes, because big firms cannot escape tax enforcement. Leal (2014) calibrates the model for the Mexican economy with distortions. However, the distortion level is defined as the total tax revenue over the value added associated with the formal sector, instead of calibrating the distortion level itself. My calibration strategy differs from Leal (2014) in terms of estimating the distortion levels. I calibrate the distortion level by using firm-level bribery data and plant-size distribution in Türkiye. Since bribery is illegal and secret, I cannot use the total tax revenue-value added ratio as the distortion rate. In addition, I focus on ‘solving problems’ or ‘getting things done’, instead of only focusing on tax evasion.

# **3 Bribery and Plant Size Data**

## **3.1 Bribery**

There are several corruption and governance indices created by the World Bank and independent institutions, such as the World Governance Indicators and Corruption Perception Index of Transparency International. These indices primarily depend on people’s perceptions rather than direct measures of bribery. Those indices can generally be regarded as indicative of underlying corruption, however, they do not provide information on the general rate of bribery, relative to size, including the level of sales. I use WBES data to analyze bribery, because it consists of data on whether a firm was asked to pay a bribe by government officials as well as the amount of bribes paid by a typical firm. The WBES has been conducted by the World Bank throughout the world, and it provides detailed enterprise-level data from interviews with top managers/owners. Questions vary from government-firm relationships to employee-employer relationships. The WBES has interviewed more than 177,000 firms in 153 countries since 2005. There are numerous questions about bribery (i.e., ‘informal gift or payment’) in the WBES.

I use eight questions in the WBES related to bribery. Six of them are about whether managers have been asked to pay bribes during public transactions over the last two years. These questions ask whether any informal gift or payment was requested during a tax inspection or meeting with tax officials, during the application process for electricity or water connections, for construction-related permits, for import and operating licenses. The percentage 5
in recovery rates suggests that in the medium term ( _t+5_ and _t+6_ greater access to resources, institutional capacity, and potentially further financial support, facilitating their recovery process compared to conflict-affected regions.

Table 7. Regression results for conflicts within 60km buffer, preferred specification at different periods post TCs

||(1)<br>Period -1 to<br>1|(2)<br>Period -1 to<br>2|(3)<br>Period -1 to<br>3|(4)<br>Period -1 to<br>4|(5)<br>Period -1 to<br>5|(6)<br>Period -1 to<br>6|
|---|---|---|---|---|---|---|
|VARIABLES|avg_radBuff<br>05|avg_radBuff<br>05|avg_radBuff<br>05|avg_radBuff<br>05|avg_radBuff<br>05|avg_radBuff<br>05|
|tot_treated|0.0291**|0.0250**|0.0228**|0.0220**|0.0276***|0.0337***|
||(0.0113)|(0.00998)|(0.0111)|(0.0109)|(0.0106)|(0.0105)|
|temp_shock|-0.0366***|-0.0683***|-0.0860***|-0.0653***|0.00238|0.0618***|
||(0.00538)|(0.00422)|(0.00373)|(0.00535)|(0.00460)|(0.00503)|
|did_var|-0.0134*|0.000594|0.00802|0.00634|-0.00966*|-0.0142*|
||(0.00795)|(0.00560)|(0.00705)|(0.00699)|(0.00499)|(0.00758)|
|pop_HRSL_dens_k<br>m2|0.00635***|0.00643***|0.00652***|0.00640***|0.00628***|0.00601***|
||(0.000848)|(0.000863)|(0.000896)|(0.000895)|(0.000876)|(0.000830)|
|Constant|0.199***|0.200***|0.200***|0.202***|0.200***|0.201***|
||(0.0131)|(0.0128)|(0.0134)|(0.0134)|(0.0131)|(0.0126)|
|Observations|16,880|16,880|16,880|16,880|16,880|16,880|
|R-squared|0.773|0.773|0.774|0.772|0.773|0.770|
|Treatment Obs|9684|9684|9684|9684|9684|9684|
|Control Obs|7196|7196|7196|7196|7196|7196|

Notes: Robust standard errors in parentheses;<sup>***</sup> p<0.01,<sup>**</sup> p<0.05,<sup>*</sup> p<0.1; avg_radBuff05 ranges from 1<sup>st</sup> to 99<sup>th</sup> percentile (0.02 to 2.7 nanoWatts/sr/cm<sup>2</sup> )

# Robustness checks

We also ran the regression using only time periods prior to the disaster, from 6 to 1 months before the cyclones hit, to ensure that there were no significant pre-disaster nightlight variation discrepancies between the two groups that could have explained our results. The results presented in Table 8 and confirm that the _did_var_ ( _Treated_<sup>_*_</sup> _PostPeriod)_ variable is not significant. This test implies that our findings are robust, and that usual nightlights variations do not drive the results of our difference-indifference specification, confirming causality.

Table 8. Regression results for conflicts within 60km buffer testing common trends using log(nightlights), 1 to 6 months before the Tropical Cyclones

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Period -2 to -1|Period -3 to -2|Period -4 to -3|Period -5 to -4|Period -6 to -5|
||Mozambique|Mozambique|Mozambique|Mozambique|Mozambique|
|VARIABLES|avg_radBuff05|avg_radBuff05|avg_radBuff05|avg_radBuff05|avg_radBuff05|

17
|Urban<br>Husband’s ideal number of sons:<br>_>_ actual number<br>_≤_actual number<br>(5)<br>(6)<br>0.223<br>-0.242<br>(0.425)<br>(0.254)<br>3,740<br>9,330<br>-0.069<br>-0.070<br>Urban<br>Husband’s ideal number of sons:<br>_>_ wife’s ideal<br>_≤_wife’s ideal<br>(5)<br>(6)<br>-0.459<br>-0.044<br>(0.456)<br>(0.267)<br>3,023<br>9,992<br>-0.048<br>-0.075<br>x<br>x<br>x<br>x<br>x<br>x<br>s specifed by equations 1 and 2. We defne<br>violence index. Panel A shows heterogeneity<br>nce in husband’s and wife’s desired number<br>etween women and men. Individual controls<br>ude the share of female population, the share<br>dard errors are clustered at the district level.|
|---|
|**Panel A: Heterogeneity by husband’s desire to have more sons**<br>Full Sample<br>Rural<br>Husband’s ideal number of sons:<br>Husband’s ideal number of sons:<br>_>_ actual number<br>_≤_actual number<br>_>_ actual number<br>_≤_actual number<br>(1)<br>(2)<br>(3)<br>(4)<br>_Physical violence index_<br>Fraction of female leaders<br>0.537**<br>0.063<br>0.627**<br>0.213<br>(0.267)<br>(0.189)<br>(0.301)<br>(0.231)<br>Observations<br>13,111<br>31,011<br>9,371<br>21,681<br>Outcome mean<br>-0.019<br>-0.001<br>0.002<br>0.028<br>**Panel B: Heterogeneity by diference in husband’s and wife’s ideal number of sons**<br>Full Sample<br>Rural<br>Husband’s ideal number of sons:<br>Husband’s ideal number of sons:<br>_>_ wife’s ideal<br>_≤_wife’s ideal<br>_>_ wife’s ideal<br>_≤_wife’s ideal<br>(1)<br>(2)<br>(3)<br>(4)<br>_Physical violence index_<br>Fraction of female leaders<br>0.470<br>0.136<br>0.810*<br>0.204<br>(0.342)<br>(0.198)<br>(0.434)<br>(0.235)<br>Observations<br>9,887<br>34,005<br>6,864<br>24,013<br>Outcome mean<br>0.018<br>-0.013<br>0.046<br>0.013<br>State fxed efects<br>x<br>x<br>x<br>x<br>District characteristics<br>x<br>x<br>x<br>x<br>Individual characteristics<br>x<br>x<br>x<br>x<br>_Notes_: Data are from the 2015-16 National Family Health Survey (NFHS) of India. All columns present 2SLS estimates a<br>close elections as those in which the victory margin is less that 3 percent of votes. The dependent variable is the physical <br>by husband’s desire to have more sons than what the couple currently has. Panel B examines heterogeneity by the discorda<br>of sons. All regressions include state fxed efects and control for the share of seats in the district that had close elections b<br>include age, years of schooling, and indicator variables for living in a rural area, religion, and caste. District-level controls incl<br>of urban population, and the share of SC-ST population in the district, and the female and male literacy rates. Robust stan<br>***, **, and * denote signifcance at the 1, 5, and 10 percent levels.|

23
# Table C.3. Impacts of Extremism on Integration

Excluding Provinces with more than 50% of Taliban Control in 2017

|Variables in SD.|Integration<br>Index<br>(1)|Feel Comfortable if<br>your Child Socialize<br>with Host Children<br>(2)|Feel Welcomed<br>in this<br>City<br>(3)|Feel Part<br>of this<br>Village<br>(4)|Feel that<br>Tajikistan is<br>your Home<br>(5)|Number<br>of Refugee<br>Friends<br>(6)|Number of<br>Tajikistan<br>Friends<br>(7)|
|---|---|---|---|---|---|---|---|
|_Panel A. Taliban Exposure_||||||||
|_ETi_|-0.557***|-0.508**|-0.098|-0.281**|-0.357**|-0.353**|-0.457***|
||(0.169)|(0.184)|(0.168)|(0.169)|(0.172)|(0.173)|(0.145)|
|FDR q-values|[0.008]|[0.011]|[0.127]|[0.061]|[0.034]|[0.034]|[0.008]|
|R-squared|0.028|0.027|0.031|0.026|0.046|0.059|0.038|
|_Panel B. Contested Territories Exposure_||||||||
|_ECi_|-0.076|-0.089|-0.084|-0.108|0.054|-0.023|-0.042|
||(0.066)|(0.072)|(0.066)|(0.067)|(0.068)|(0.068)|(0.057)|
|FDR q-values<br>6|[0.801]|[0.801]|[0.801]|[0.801]|[0.801]|[0.801]|[0.801]|
|R-squared<br>3|0.026|0.026|0.032|0.026|0.046|0.058|0.036|
|_Panel C. Allies Exposure_||||||||
|_EAi_|0.098|0.100|0.059|0.092|0.000|0.047|0.068|
||(0.051)|(0.055)|(0.051)|(0.051)|(0.052)|(0.053)|(0.044)|
|FDR q-values|[0.213]|[0.213]|[0.239]|[0.213]|[0.529]|[0.239]|[0.213]|
|R-squared|0.026|0.026|0.031|0.027|0.046|0.058|0.036|
|Mean Dep. Variable|0.004|-0.026|0.030|0.038|0.075|-0.006|-0.097|
|Observations|5,383|5,087|5,327|5,262|5,383|5,383|5,380|
|_Controls in All Panels_||||||||
|Individual Covariates|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Household Size in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Enterprise Property in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Dwelling Contract in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Dwelling Type in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Income Type in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of Assets in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

_Notes:_ The Integration Index is constructed using the outcome variables of columns (ii) to (vii) using the methodology of Kling, Liebman and Katz (2007). It implies i) standardizing the variables, ii) averaging, and iii) standardizing the final average again. Individual controls include: age, gender, employed, student status in Afghanistan, and number of friends before moving to Tajikistan. Household controls include the variables listed in Table 2. Standard errors are reported in parentheses and False Discovery Rate (FDR) q-values are reported in brackets.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.
<!-- Start of picture text --> 250<br>200<br>150<br>100<br>50<br>2010 2012 2014 2016 2018 2020 2022 2024<br>—— Oil.. production.. (1000's bbl/day)<br><!-- End of picture text --> <!-- Start of picture text --> USD/bbI 250<br>120 11<br>ioreca:ioreca:ii<br>110 10 200<br>100 9<br>90 8 150<br>80 7<br>100<br>70 6<br>60 5 50<br>50 4<br>30 2 2010 2012 2014 2016 2018 2020 2022 2024<br>2010 2012 2014ll2016Discount;ll2016Discount;2016Discount;Discount;; 2018 2020(RHS)2022(RHS)2022 Brent = 2024 2026 2028 2030 —— Oil.. production.. (1000's bbl/day)<br>120 140<br>120<br>100<br>100<br>60 60<br>40<br>40<br>20<br>20 0<br>2000 2004 2008 2012 2016 2000 2004 2008 2012 2016<br><!-- End of picture text --> <!-- Start of picture text --> USD/bbI<br>120 11<br>ioreca:ioreca:ii<br>110 10<br>100 9<br>90 8<br>80 7<br>70 6<br>60 5<br>50 4<br>30 2<br>2010 2012 2018 2022 2024 2026 2028 2030<br>2014ll2016Discount;ll2016Discount;2016Discount;Discount;; 2020(RHS)2022(RHS)2022 Brent =<br><!-- End of picture text -->
etc...). In particular, they can capture both the direct effects of higher costs of commodity prices and the indirect effects coming from the fact that these commodities are inputs in the production of other goods. Moreover, VAR models have three main advantages over bivariate models. First, they solve the endogeneity problem inherent in the single-equation based methods. Second, they take into account the influence of other macroeconomic variables (supply and demand shocks). Third, in addition to estimating pass-through effect, they assess its dynamics over time.

Two different VAR models are estimated at monthly frequency for Egypt.<sup>4</sup> The first includes the following variables: world food price inflation, output gap, as a measure of economic activity, headline inflation, the change in M2d, and changes in the real exchange rate. This model will estimate the passthrough from world food price inflation to headline inflation. In the second model, headline inflation is replaced by domestic food inflation, and core inflation, providing thus further insights on the transmission of international commodity price shocks to domestic food inflation, as well as the spill-over of the latter to core inflation.

The shocks are identified using the Cholesky decomposition of the variance-covariance matrix of the reduced form residuals. The use of a recursive identification scheme implies that the identified shocks contemporaneously affect their corresponding variables and those variables that are ordered at a later stage, but have no impact on those that are ordered before. Hence, the most exogenous variable, international food prices, comes first. Food price shocks may thus affect all other variables in the system contemporaneously but they are not themselves affected contemporaneously by any of the other shocks. Estimates of the pass-through coefficients are derived from cumulative impulse response function to assess the impact over time of temporary external and domestic shocks on relevant price measures. Variance decomposition analysis is used to determine the relative importance of these shocks for fluctuations in the different price measures.

# 4.2 Data

The models are estimated for the period 2000M7 and 2011M7. All variables are included as the annualized log differences, except for the output gap in the VAR and the growth gap in the 2-step regression. While all variables, except for the exchange rate, were seasonally adjusted before getting their log differences in the VAR, dummy variables are used in the 2-step regression to find out the seasonal effects of various months. Annex 1 provides details on data description and sources.

> 4 Energy prices in Egypt are fixed and therefore are left out of the empirical analysis

12
# FIGURE 5 Global inflation and output growth factors

- A. Global factors for output growth and inflation

# B. EMDE factors for output growth and inflation

<!-- Start of picture text --> Percent<br>Percent<br>8 EMDE GDP factor EMDE inflation factor<br>Global GDP factor Global inflation factor<br>6 6<br>4 4<br>2<br>2<br>0<br>0<br>-2<br>-4 -2<br>-6 -4<br>1972 1977 1982 1987 1992 1997 2002 2007 2012 2017 1972 1977 1982 1987 1992 1997 2002 2007 2012 2017<br><!-- End of picture text --> Notes: The global and group inflation and output growth factors are estimated with the baseline dynamic factor model (twofactor model with a global and a group-specific factor) for 1970-2017. The sample includes 99 countries (25 advanced economies and 74 EMDEs, including 16 low-income countries). Grey shades indicate the periods of global recessions and slowdowns (Kose and Terrones 2015). EMDEs = emerging market and developing economies; GDP = gross domestic product.

21
37 Subject to the intertemporal budget constraint PNt Where Pt ! - is the relative price of nontraded goods or the inverse of the PTt real exchange rate. Subscripts T and N are used to distinguish tradables from nontradablea and Q refers to output. Note that investment, I, is all in tradable&. In this formulation, ~ is the pure rate of time preference, and the utility function is assumed to be intertemporally separable with a constant intertemporal elasticity of substitution 1/b, as in the simulation exercise below. As b + •greater smoothing of consumption is desired. Let ~ ! ~T2/ Ck2 + P2 ~N2 / Ck2 represent the return on capital in period 2. Then the first order conditions to the maximization problem are: <!-- Start of picture text --> from<br><!-- End of picture text --> Thi• faailiar condition from intertemporal models states ·that the real social discount rate in equilibrium is set equal to the real cost of foreign borrowing and the real rate of return on capital, all rates and costs being expressed in terms of the aggregate consumption bundle. The real cost of foreign borrowing and the real return on capital depend on two factors: (a) the coat (return) in terms of traded goods; (b) changes in the aggregate eonauaer price index over time. Thus changes in relative prices over time are an iaportant determinant of intertemporal substitution.
# **Figure 1: Nonparametric regressions of satisfaction with life against log consumption per person in Malawi, 2004**

<!-- Start of picture text --> Stratified by friends' economic welfare<br>4 4<br>Rank 4+5<br>Rank 3<br>Rank 2<br>Rank 1<br>3 3<br>2 2<br>1 1<br>8 9 10 11 12<br>Stratified by neighbors' economic welfare<br>7 4<br>6<br>3<br>4<br>2<br>2<br>0 1<br>8 9 10 11 12<br>Log per capita expenditure<br> life<br>h<br>Satisfaction wit<br>th life<br>i w<br>Satisfaction<br><!-- End of picture text --> <u>Source: Ravallion and Lokshin (2010).</u> 13