rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2: Growth Improvement Determinants'**

|Dependent Variable|Real p|er capita GD|P Growth|
|---|---|---|---|
||(1)||(2)|
|Volume oftrade/GDP'|2.50||1.24|
||(7.13)||(3.35)|
|Gov. casunnptip/GDP`|-0.46||-1.33|
||(-1.32)||(-3.68)|
|Mation'|-3.35||-5.33|
||(-9.23)||(-16.43)|
|M2/GDP|1.71||0.50|
||(4.44)||(1.33)|
|InvesnetIGDP <sup>2</sup>|4.07|||
||(10.55)|||
|Initial GDP|-4.75||-5.61|
||(-9.03)||(-13.76)|
|Average Years of Secmdary Schooling|0.12||0.26|
||(1.24)||(2.17)|
|Black Markd Premum'|-1.12||-1.92|
||(-3.71)||(-6.49)|
|Tenns of Trade Growth|7.76||7.39|
||(9.39)||(12.04)|
|Population Growth|-0.99||-0.64|
||(-11.93)||(-11.04)|
|Canstant Tem|-0.07||0.72|
||(-0.33)||(3.76)|
|Period Dummy, 1991-93'|-1.66||-1.97|
||(4-13)||(-12.83)|
|Tests of GMM casistency (p-values):||||
|Sargan test|0.59|0.41||
|Scial-correlaticn test'|0.67|0.64||
|R'|0.46|0.35||
|Waldtestofjointsigificance<br>(p-value)|<br>0.00|0.00||
|# of observatios|352|355||
|Mean of dependent variable|1.84%|1.85%||

't-satistics givea in parenthesis 2In the regessicn, this variable is included as log (viable) In the regressien, this variable is included as log ( I +variable) 4 Dummies are included in the regressims for all time paiods

> 5 The null hypthesis is that the errors in the first-diffacence regressicn exhibit no secm d-order serial correlatioi. that is, E((K.,-EJ,) (Ev,-E 1 ,. 3 )) 0

21
Using Pooled Information and Bootstrap Methods to Assess Debt Sustainability in Low Income Countries Constantino Hevia<sup>_∗_</sup> JEL codes: F34, F47, H63, H68 Keywords: Low income countries, debt sustainability, panel vector autoregression, bootstrap

# Sector board: EPOL

> _∗_ The author is with the World Bank, DECMG. I am grateful to Aart Kray, Norman Loayza, Claudio Raddatz, and Luis Serven for valuable comments and suggestions. I owe special thanks to Luca Bandiera, Leonardo Hernandez, and Juan Pradelli for advice and endless discussions about the current IMF-World Bank Debt Sustainability Framework, and to the World Bank's PREMED for support. I am also thankful to Bledi Celiku for very able research assistance. The ndings, interpretations, and conclusions expressed in this paper are entirely those of the author. They do not necessarily reect the view of the World Bank, those of the Executive Directors, or the governments they represent.
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Thus, the TPR represents the average (across simulations) share of “poor” areas in the census that are correctly classified as poor by the predictions, while the FPR represents the average share of “non-poor” areas that were incorrectly predicted to be poor. After plotting 50 points, one for each quantile, we calculate the area under the curve (AUC), which is a summary measure of targeting accuracy for each prediction. AUC values typically range from a low of 0.5, in which case the prediction is no more accurate than a random guess, to a perfect score of 1.

- Poverty Targeting Simulations. Finally, to check the implications of using different estimates for targeting purposes, we perform targeting simulations based on predicted poverty rates in Malawi and Tanzania. The simulations are conducted for ten different population coverage thresholds ranging from 5 to 50 percent. At each threshold, predictions from each method are used to identify beneficiary areas that will be given simulated transfers – Traditional authorities in Malawi and Districts in Tanzania. Beneficiary areas are selected based on the predicted poverty rates obtained from each method, starting with the area predicted to be poorest, until the covered population equals the population threshold. Per capita expenditure is then increased by a constant amount, equal to ten percent of the poverty line, for all households in beneficiary areas. This simulates each household obtaining a transfer proportional to the size of their household.

We then report the poverty gap (P1) for each method _m_ and population coverage rate _q_ , defined as: where _H_ is the number of households in the census, _nh_ is the size of household h, Z is the poverty line, and _yqmsh_<sup>_′_isthepost-transferpercapitaconsumptionforhouseholdh(inthecensus)forsimulation</sup><sup>_s_,</sup> method _m_ , and population coverage rate _q_ . Therefore, _Tmsq_ is the eligibility threshold for method _m_ associated with population coverage rate _q_ in simulation ˆ _s_ . This is equal to the _q_<sup>_th_</sup> percentile of the population-weighted distribution of _ymsi_ , the area-level predictions of the asset index generated by method _m_ in simulation _s_ . This ensure that _q_ percent of the population is covered by the hypothetical transfer program, regardless of the prediction method used to identify beneficiary areas.

17
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The tables contain numerous controls that relate to destruction, aid, and social capital. Here we examine effects conditional on all other controls. We focus the discussion on our preferred specification, column 3 of table 4. Table 7 shows the village controls, base coefficients, and 2009 differentials for this specification. Some controls significantly affect volunteerism in 2007, but no net effects are significant in 2009, a key result. We treat the controls as at least pre-determined relative to late 2007 and some as completely exogenous, as we will note in each case.

Two standard controls in the literature are village size, or the number of households post-tsunami, and village diversity. Size could reduce volunteerism by aggravating free-rider problems and increased diversity could reduce cohesiveness and raise the psychic costs of volunteering (Alesina and LaFerrara 2000, Costa and Kahn, 2003). Village size post-tsunami is recorded in 2005 from the PODES, before households may later split to strategically garner more housing aid. Diversity is measured from our survey in 2007. Villagers are not diverse in ethnicity or race, but there is occupational 9 diversity of households. We form the index 1   _sij_ 2 , where<sup>_s_</sup> _ij_ 2<sup>is the squared share of</sup> _j_  1 employment in occupation _j_ across 9 occupations in village _i._<sup>11</sup> In 2007 in table 7, increased size does reduce volunteerism consistent with free rider problems with an elasticity of -0.258 in 2007 in column 1, although the net effect is smaller and insignificant in 2009 (from combining columns 1 and 2). Diversity has the anticipated negative sign in both tables but is statistically weak.

We then turn to a set of variables that could be interpreted as measures of social capital, although they may have other channels of influence. Greater social capital should

> 11. Occupations with their average shares are: fishing (28%), aquaculture (5%), agriculture (29%), trade (8%), transport (4%), construction (6%), public service (9%), other (6%), and unemployed (6%).

26
more likely to be found in the IMSS data in the control group compared to the treatment group. For this reason, average employment in the IMSS data before the intervention, i.e., both in 2005 and 2006, is higher in the control group (about 8 full-time employees) than in the treatment group (6.2 full-time employees).

# **4. Results and Discussion**

Table 3 reports the main specification, using OLS to compare treatment to control in the crosssection. All regressions include controls for the variables used for stratification (both the strata dummies as well as the re-randomization variables<sup>14</sup> as suggested in Bruhn and McKenzie (2009), and a control for the timing of the survey<sup>15</sup> ). In Column 1 we estimate the average intent to treat effect without controlling for the baseline value of the outcome variable, and in Column 2 we report the average intent to treat effect with controlling for the baseline value of the outcome variable. For observations where the baseline value of the outcome is missing, we replace this value with zero and include a dummy variable indicating that the value is missing, in order to keep the observation in the sample.

# **4.1 Business Performance, Short Run and Long Run**

Table 3 Columns 1 and 2 both show no short-term significant treatment effect on employment, sales, or profits. However, the results suggest that the consulting improved enterprise productivity as measured by the residual from a productivity regression and also as measured by return on assets (ROA), by about one fifth of a standard deviation for both measures (s.e. = 0.11 standard deviations and 0.13 standard deviations, respectively). We performed three robustness checks of the results, which are reported in Appendix Tables 4, 5, and 6. First, we winsorized the outcome variables at the 1% and 5% levels to check whether the results are driven by outliers (Appendix Table 4). Second, we run all regressions only on the sample of 221 enterprises that report all outcomes variables at follow-up (Appendix Table 5). Third, we estimate a difference-in-difference specification, rather than a crosssectional specification (Appendix Table 6). The robustness checks show very similar results to the ones

> 14 Due to baseline data entry typos that were discovered and corrected after the randomization took place, a few values of the variables included in the randomization procedure do not correspond to the true baseline values. The strata dummies and re-randomization controls included in the regressions contain the values originally used in the randomization procedure. All other baseline data used in the summary statistics and regressions contains the correct baseline values.

> 15 Appendix Table 3 shows that 70.4% of treatment group enterprises and 62.6% of control group enterprises were interviewed in March (p=0.12). Almost all of remaining enterprises were interviewed in April 2009 or May 2009, with only four enterprises being interviewed in June 2009. Treatment and control enterprises are equally likely to have been interviewed either in March or April (percentage point difference of 1.3 and p-value of 0.66).

12
# Table 2: Correlation Between Road Size and Pollution

||(1)<br>Avg log(Pollution) Resid.|(2)<br>Avg log(Pollution) Resid.|(3)<br>log(Pollution) Resid.|(4)<br>log(Pollution) Resid.|
|---|---|---|---|---|
|Median Road Size|0.0774<sup>_∗∗∗_</sup>|0.0708<sup>_∗∗∗_</sup>|||
||(0.0118)|(0.0161)|||
|Closest Road Size|||0.0988<sup>_∗∗∗_</sup>|0.0597<sup>_∗_</sup>|
||||(0.0156)|(0.0334)|
|N|972|972|52965|52965|
|R2|.3516|.1636|.1591|.0334|
|Fixed Efects|Subcounty||Subcounty||
|Level of Observation|Grid Cell|Grid Cell|Poll. measure|Poll. measure|
|SE clustering|SHAC|SHAC|Grid Cell|Grid Cell|

Notes: Standard errors are displayed in parentheses (* p _<_ 0.10, ** p _<_ 0.5, *** p _<_ 0.01). SHAC standard errors are Bartlett (spatial weighting kernel decaying linearly in distance) and the distance cutoff for spatial correlation is 5km. We control for log distance to the main city in the region. In regressions at the grid cell level, we control for whether the grid cell contains any road (dummy), whether the grid cell is incomplete (i.e., _<_ 500m x 500m), its area, as well as a dummy for whether it is in our main surveyed area. The top and bottom one percent of pollution residuals are trimmed. Regressions at the pollution measure level have the same geographical coverage than regressions at the grid cell level and include a dummy for whether observations are in our main surveyed area. Road size goes from 1 (Trail/Track) to 5 (Highway). The procedure to construct pollution residuals is detailed in section 5.1.

42
consequences for small firms requiring longer-term capital investment from tier 1 and tier 2 banks.

On the deposit side, the analysis confirms a significant shift towards offering interest only on longer term deposits and eliminating any interest offerings on current accounts. This shift is most evident among tier 3 banks where interest bearing accounts dropped precipitously to nearly zero percent of portfolio in response to the interest rate caps, down from a previous average of 36%. These changes in deposit composition are indicative of the banking sector’s response and their preference for maintaining their interest margins.

These changes in loan and deposit books have important implications for financial inclusion. First, our findings suggest that the interest rate caps certainly did not attenuate the upward trend in NPLs, and therefore did not aid financial inclusion. Instead, we find a continued rise in NPLs after the caps.

Second, a vibrant and growing SME and consumer sector is an important cornerstone of a typical developing economy. Yet, the data from the CBK-regulated commercial banking sector and commercial bank surveys show no loan growth in the SME sector and an expectation of further loan rationing in the future.

Third, the documented flight towards corporate clients, tightening of risk mitigation measures, and offering of loans with shorter maturities in tier 3 banks all have potential consequences for financial inclusion of smaller firms and consumers and could reverse the recent gains in financial access achieved in the last decade. Unable to access credit from traditional financial institutions, riskier borrowers are forced to turn to the unregulated market, such as informal money lenders, that charge much higher rates and lack consumer protection.

Overall, these findings suggest that the interest rate caps were largely unsuccessful in mitigating the downward trends in financial inclusion due to prevailing market and economic conditions, at least in the short run.

The rest of this paper proceeds as following. Section II presents a short background and context for interest rate caps in Kenya and poses hypotheses for the empirical analysis. Section III discusses the assessment challenges associated with identifying the impact of interest rate caps. Section IV presents the qualitative evidence, and Section V the quantitative evidence for this study. Finally, Section VI concludes with policy recommendations.

# **II. Background and Hypotheses**

## **_Background_**

<mark>In August 2016, the President of Kenya signed the Banking (Amendment) Bill 2015, which came into full effect in mid-September 2016.</mark><sup>3</sup> <mark>The law caps the maximum interest rate charged</mark>

> 3 See Appendix A for the complete text of the signed bill.

4
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# _3) Subsidy design could be improved but only up to a certain limit_

Another important result from studies such as Komives, et al (2005) is that there is a limit to the targeting improvement that can be achieved by improving the design of quantity based subsidies. Their simulations show that the improvements in the distributional impacts of some of the subsidies shown in Table 2 would increase only marginally if the design was improved. For our purposes, it is interesting to analyze the reasons for this last result. The main problem seems to be the access problem. If a significant proportion of poor users are not connected to the service (i.e. do not use public transport services or are not within the catchment area of the transport service), then any subsidy distributed through the price of the service is not going to be progressive.

**Table 3: Distributive impact of different subsidies where some administrative** **<u>selection of beneficiaries is made</u>**

|**Country, city**|**Type of subsidy**|**Ω**|**Error of**<br>**exclusion**|
|---|---|---|---|
|_Geographic targeting_||||
|_Electricity cases:_||||
|Colombia,Bogotá|Geographically defined tariffs with IBT|1.10|3.7|
|Mexico|Geographically defined tariffs with IBT|0.60|n/r|
|_Water cases:_||||
|Nicaragua,Managua|IBTplus slumdiscount|1.18|5.0|
|Venezuela, Merida|BT plus slum discount|1.09|0.0|
|Colombia, Bogotá|Geographically defined tariffs with IBT|1.09|1.9|
|_Means testing:_||||
|Electricity cases:||||
|Argentina|Average<br>of<br>provincial<br>jeans-tested<br>subsidy|1.50|94.0|
|Georgia, Tbilisi|Limited free allowance of electricity for<br>targetedhouseholds|1.20|75.0|
|_Water cases:_||||
|Argentina|Average<br>of<br>provincial<br>jeans-tested<br>subsidy|1.23|76.0|
|Chile|Discounts of 40-70% on 15m3 for<br>targetedhouseholds|1.63|78.0|
|Paraguay, urban|Discount on 15 m3 for targeted<br>households<br>(means<br>test<br>based<br>on<br>housing characteristics)|1.64|93.1|

_Source_ : Table 6.3, Komives, et al (2005). _Note:_ Real subsidies reported. Simulated cases not shown.

# **7.2 Evidence from the transport sector**

The distributive performance of transport subsidies has been scarcely researched, marking a stark contrast with the electricity and water sectors. This paper summarizes the 34
growing Mobile-Money services across sub-Saharan Africa (The Economist 2013; Jack and Suri 2014; Suri and Jack 2016).

Business enterprises and household welfare Earlier, we emphasized that business enterprises and non-agricultural startups in rural economies shape poverty inclinations. This section descriptively analyze this belief by exploring whether households’ involvement in the various business enterprises play any role in observed household welfare/poverty.

**<u>Table 10: Exploring linkages between household poverty and business enterprises</u>**

|<br>DV|<br>(1)<br>Absolute<br>poor|<br>(2)<br>Absolute<br>poor|<br>(3)<br>Absolute<br>poor|<br>(4)<br>Absolute<br>poor|<br>(5)<br>Absolute<br>poor|<br>(6)<br>Absolute<br>poor|
|---|---|---|---|---|---|---|
|Business Enterprise|-0.0234*|-0.0231*|-0.0717**|-0.0722**|-0.0771***|-0.0767***|
||(0.0139)|(0.0140)|(0.0290)|(0.0291)|(0.0287)|(0.0287)|
|MOBILE use activity|||-0.0466|-0.0453|-0.0569*|-0.0560*|
||||(0.0334)|(0.0334)|(0.0336)|(0.0336)|
|Bus. Enterprise X MOBILE|||0.00856|0.00605|0.00622|0.00443|
|use|||(0.0502)|(0.0503)|(0.0502)|(0.0501)|
|Female|||||-0.0522*<br>(0.0273)|-0.0450<br>(0.0276)|
|Married|||||0.0240<br>(0.0379)|0.0253<br>(0.0380)|
|Constant|0.00351*|0.00145|1.3EXP-13|1.3EXP-16|0.0347|0.0554|
||(0.00208)|(0.00551)|(<0.001)|(<0.001)|(0.0645)|(0.0691)|
|# of Observations|6,859|6,859|2,471|2,471|2,367|2,367|
|R-squared (%)|27.00|27.00|25.20|25.30|27.10|27.20|
|Cluster FEs|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FEs|No|Yes|No|Yes|Yes|Yes|

_Notes_ : Table reports estimates from regressions of household poverty status on their engagement in non-agricultural business enterprises and in some cases mobile use and activity. Columns differ based on the combination model type and fixed effects included. All standard errors are clustered at the region level and shown in parentheses. Significance levels are indicated by *** 1%, ** 5%, * 10%. _Unit of Analysis_ : **cluster-level.** MOBILE use activity is hardly significant once households’ engagement in business <u>enterprises is controlled for.</u> Table 10 reports results based on OLS regressions of household poverty on business enterprises. Results from 6 models are presented; in some versions of the poverty–business relationship, we directly control for mobile use activity. But as expected, mobile use is hardly significant once households’ engagement in business enterprises are controlled for, highlighting the dominant role of business enterprises. The coefficient on “Business Enterprise is negative and significant across the wide range of models. These range between a -0.023 to -0.077; implying that households who engage in non-agricultural business enterprises are about 2.3% to 7.7% points ‐ 19 ‐
tuition costs aimed to tackle this dimension.<sup>7</sup> Third, bootcamps are male dominated environments. Historic enrollment data provided by the coding schools indicates that, on average, 80 percent of students are male.<sup>8</sup> In order to address this issue, the coding schools in both locations worked closely with to define to make more inclusive. In or- gender specialists strategies bootcamps gender der to increase female participation, this aspect was explicitly highlighted and publicized in the attraction campaigns (explained in detail in section 2.2). Fourth, even conditional on participation, previous pilots of bootcamps identified a prevalent problem of retention for female participants (Hammond et al., 2018). For this, the eligibility criteria (explained in section 2.2 as well) was designed to attract a sample of women with high motivation, willingness to complete the course, and high potential to benefit from it.

Last, logistical issues aimed at increasing program participation were also considered in the design. Participants could choose location, shift, and between two learning modes. Each city counted with two locations of the learning center and three different class shifts (morning, afternoon and evening). One learning mode included mostly face to face classes that lasted at most 3 and a half hours a three times a week. The other one was a day, "blended mode", a mix of face to face and online classes.<sup>9</sup> The part-time design of the bootcamp was key in order to allow participants to continue working or studying another career, if needed.

In Buenos Aires, the bootcamp lasted 20 weeks (with a total of 210 hours of instruction). Most classes started in early April and finished in late July 2019. The last class started in mid-May 2019 and finished in mid-September 2019. In Bogotá, the bootcamp lasted 16 weeks (with a total of 170 hours of instruction). Most classes started in early August and finished in late November 2019. The last class started in mid-September and finished midDecember 2019.<sup>10</sup>

> 7Maffioli et al. (2020) show that even though most entrepreneurs have a positive willingness to pay for training, demand falls sharply as price increases: 76 percent of the entrepreneurs attend training when it is free, but only 43 percent attend when they are charged one-quarter of the cost, and only 11 percent when charged the full cost. The paper also shows that charging a higher price increases attendance among those who pay, suggesting a psychological effect where paying for training makes entrepreneurs value it more.

> 8The previous pilots counted with a share of female students of 30 percent in Lebanon, 25 percent in Kenya, and 28 percent in Colombia (World Bank, 2018).

> 9Considering these different locations of the learning centers, shifts and modes, there were a total of 12 classes available for enrollment in Buenos Aires and 10 in Bogotá.

> 10For a detailed timeline of implementation and research activities in each country, please see Figure A.3 in the Appendix.

6