rafmacalaba/datause-encoder-data huggingface dataset:
<u>Table 4. Skills of young adults in Pakistan (Part 2)</u>

|**Skill**|**Sample**|**Mean**|**SD**|**Min**|**Max**|**N**|**Source**|
|---|---|---|---|---|---|---|---|
|**Panel A. LEAPS sample**||||||||
|Grit|LEAPS|3.45|0.71|1|5|4395|LEAPS Data|
|Openness to experience|LEAPS|3.42|1.06|1|5|4475|LEAPS Data|
|Conscientiousness|LEAPS|4.17|0.72|1|5|4475|LEAPS Data|
|Extraversion|LEAPS|3.31|1.09|1|5|4475|LEAPS Data|
|Agreeableness|LEAPS|4.33|0.69|1|5|4475|LEAPS Data|
|Emotional stability|LEAPS|2.28|1.08|1|5|4475|LEAPS Data|
|**Panel B. Other samples**||||||||
|Grit|Adults aged 25 and older (US)|3.65|0.73|1|5|1545|Duckworth et al. (2007)|
|Grit|West point cadets 2010 (US)|3.75|0.54|1|5|1308|Duckworth et al. (2007)|
|Grit|Adults between 15-64 years old|2.72|0.6|1|4|3843|STEP Data Kenya|
|Grit|Adults between 15-64 years old|2.98|0.61|1|4|3978|STEP Data Macedonia|
|Openness to experience|Adults between 15-64 years old|3|0.56|1|4|3844|STEP Data Kenya|
|Openness to experience|Adults between 15-64 years old|3.28|0.55|1|4|3979|STEP Data Macedonia|
|Conscientiousness|Adults between 15-64 years old|3.22|0.52|1.33|4|3844|STEP Data Kenya|
|Conscientiousness|Adults between 15-64 years old|3.05|0.5|1|4|3979|STEP Data Macedonia|
|Extraversion|Adults between 15-64 years old|2.85|0.59|1|4|3845|STEP Data Kenya|
|Extraversion|Adults between 15-64 years old|3.02|0.61|1|4|3979|STEP Data Macedonia|
|Agreeableness|Adults between 15-64 years old|2.86|0.57|1|4|3843|STEP Data Kenya|
|Agreeableness|Adults between 15-64 years old|3.28|0.59|1|4|3978|STEP Data Macedonia|
|Emotional stability|Adults between 15-64 years old|2.69|0.5|1|4|3843|STEP Data Kenya|
|Emotional stability|Adults between 15-64years old|2.09|0.66|1|4|3979|STEP Data Macedonia|

_Notes_ . This table shows descriptive statistics for the grit and big-five measures. Panel A reports on the LEAPS sample. Panel B shows results from other samples reported on in the literature. For the STEP data, we computed the results using the STEP surveys conducted by the World Bank, in 2013. Sample sizes differ due to item non-response.

45
indicators. The microsimulation model we employ predicts that the poverty rate would continue increasing from 9.2 percent in 2010 to 11.1 percent in 2011, on account of a further 4 percentage points increase in the unemployment rate and a 2 percentage points reduction in the employment rate during 2010-2011. The profile of the “new poor” suggests that they are more similar to the “non-poor” population than to the “old poor” population that is below the poverty line according to 2010 data. In particular, the “new poor” younger, more urban, and have higher skills levels. Among those “new poor” who were employed in 2010, the primary employment sector is services.

The validation exercises reveal that microsimulation-based projections of the 2010 poverty rate based on actual 2009 micro data and actual macroeconomic dynamics, are in line with the 2010 poverty headcount. However, the poverty increase in 2009 is overestimated, a result that also echoes recent findings from the ECA region in Tiongson and Sulla (2013). This can be due to a number of factors, including the divergence between income dynamics (employed by the model) and consumption dynamics that poverty estimates are based on, and the fact that the microsimulation model does not allow for consumption smoothing via savings or borrowing. It could also be due to the fact that the baseline micro data does not adequately capture the crisis response of automatic stabilizers such as unemployment benefits or social assistance. In fact, when we account for increases in all social protection benefits (including pensions) between 2008 and 2009, as recorded in the HBS data, the poverty projection for 2009 becomes smaller, in fact, statistically indistinguishable from the actual 2009 poverty headcount estimate.

In light of this, the estimated further increase in poverty in 2011 seems plausible, particularly since the structural parameters of the underlying model are unlikely to have changed dramatically over time. At the same time, modeling assumptions vis-à-vis the dynamics of consumption and income, as well as the effects of the social protection payments, are important, and the 2011 prediction should be regarded with the appropriate caveats in mind.

Notably, the microsimulation-based forecast for 2011 diverges from the prediction based on the elasticity approach, which suggests a reduction in poverty in 2011, based on positive growth in GDP during 20102011. Furthermore, estimates for 2010 suggest that the elasticity approach forecasts can fall rather far from the true poverty estimates, and can even imply poverty dynamics of opposite sign from the true poverty changes between two points in time. Using HFCE data instead of GDP data appear to offer an improvement, especially when the poverty measure is based on expenditure data, which need not follow GDP dynamics.

12
2001, WHO and many other countries including the United States have adapted a more rigorous standard of greater than 10 ppb. The government and development actors in Bangladesh should adhere to more rigorous standards when testing and installing new water sources, as even small doses have a significant effect on child development.

**Table 1. 10-item module used to construct the ECDI (Source: MICS 2012-13 Report, Bangladesh)**

|**Domain**|**Component**|**Criteria for being on track**|
|---|---|---|
|Literacy-numeracy|Identifyat least ten letters of the alphabet<br>Read at least four simple andpopular words<br>Name and recognize symbols of numbers 1 to 10|2/3 components must be true|
|Learning|Follow simple instructions<br>Carryout tasks independently|1/2 components must be true|
|Physical|Pick upa small object with two fingers<br>Healthyenough toplay|1/2 components must be true|
|Social|Gets alongwell with other children<br>Does not kick,bite,or hit other children|2/3 components must be true|
||Does notget distracted easily||
|Overall EDCI|Literacy-numeracy<br>Learning|3/4 domains must be true|
||Physical||
||Social||

16
**Table 1A: Descriptive statistics of the overall sample**

||**Number of studies**|**Number of interventions**|
|---|---|---|
|General studies|157|239|
|_General studies – Access_|_69_|_95_|
|_General studies – Learning_|_106_|_178_|
|Girl‐targeted studies|20|31|
|_Girl‐targeted studies – Access_|_18_|_26_|
|_Girl‐targeted studies – Learning_|_11_|_14_|
|Total|177|270|

Note: Access and learning do not sum to the total because multiple studies report both learning and access outcomes.

**Table 1B: Descriptive statistics of evaluated programs**

|||All interventions|General interventions|Girl‐targeted interventions|
|---|---|---|---|---|
|||A|ccess||
|Education level|||||
||_Primary_|56(47%)|50(53%)|6(23%)|
|_Secondary (lower_|_and/or upper)_|18(15%)|9(9%)|9(35%)|
|_Bothprimary _|_and secondary_|27(23%)|20(21%)|9(35%)|
||_Other_|18(15%)|16(17%)|2(8%)|
|Location|||||
||_Urban_|13(11%)|11(12%)|2(8%)|
||_Rural_|39(32%)|31(33%)|8(31%)|
||_Both_|69(57%)|53(56%)|16(62%)|
|Components|||||
||_Single_|66(55%)|51(54%)|15(58%)|
||_Multiple_|55(45%)|44(46%)|11(42%)|
|||Le|arning||
|Education level|||||
||_Primary_|124(65%)|118(66%)|6(43%)|

26
# Appendix Table 13

Probability of Firms of Different Sizes Getting Approval for the Credit Program: Heckman Correction

|||||Credit P|rogram||||
|---|---|---|---|---|---|---|---|---|
||All F|irms|Small|Firms|Mediu|m Firms|Large|Firms|
||(1)<br>1st Stage|(2)<br>2nd Stage|(3)<br>1st Stage|(4)<br>2nd Stage|(5)<br>1st Stage|(6)<br>2nd Stage|(7)<br>1st Stage|(8)<br>2nd Stage|
|Risk|0_._547|_−_0_._123|0_._563|_−_0_._082|1_._916|_−_0_._352|2_._422|_−_0_._507|
||(0.035)|(0.066)|(0.038)|(0.077)|(0.161)|(0.206)|(0.324)|(0.690)|
|Increase in Sales Dummy|0_._187|0_._072|0_._196|0_._087|0_._124|0_._008|0_._093|0_._022|
||(0.008)|(0.027)|(0.008)|(0.031)|(0.020)|(0.026)|(0.034)|(0.043)|
|Decrease in Sales Dummy|0_._190|0_._073|0_._197|0_._085|0_._131|0_._010|0_._102|0_._034|
||(0.007)|(0.027)|(0.008)|(0.032)|(0.019)|(0.026)|(0.033)|(0.043)|
|Use Employment Program|0_._117|0_._021|0_._114|0_._028|0_._081|_−_0_._016|0_._178|_−_0_._004|
||(0.005)|(0.014)|(0.006)|(0.016)|(0.011)|(0.013)|(0.019)|(0.051)|
|Inverse Mills Ratio||0_._186||0_._211||0_._031||0_._097|
|||(0.083)||(0.093)||(0.085)||(0.198)|
|Dependent Variable Mean|0.656|0.913|0.646|0.908|0.728|0.918|0.576|0.900|
|Dependent Variable Std. Dev.|0.475|0.281|0.478|0.289|0.445|0.275|0.494|0.300|
|Number of Firms|62,859|36,593|47,784|27,271|10,037|5,944|4,504|1,359|
|R<sup>2</sup>|0.063|0.030|0.063|0.032|0.110|0.070|0.127|0.162|
|Industry FE and Municipality FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|_Predicted Default Probability_|||||||||
|Firms With Previous Loans|0.084|0.090|0.095|0.102|0.055|0.061|0.031|0.036|

This table reports probit estimations of the probability of a firm with previous loan getting approved for the credit program on a set of firm-level characteristics for the credit program eligible firms sample, and implementing a two-step Heckman selection correction in the approval estimation. Firms with previous loans are those that either have bank credit outstanding in December 2019 or receive a bank loan over the period 2012–2019. Columns 1–2, 3–4, 5–6, and 7–8 correspond to all (small, medium, and large) firms, small firms, medium firms, and large firms, respectively. In the first-stage columns, the dependent variable is equal to one if the firm applies to the program and zero otherwise. In the second-stage columns, the dependent variable is equal to one if the firm’s loan application is approved and is zero otherwise. Small, medium, and large firms correspond to firms that have annual sales less than US$0.8 million, between US$0.8 and US$3.5 million, and between US$3.5 and US$35 million, respectively. Risk corresponds to the fitted values of the regression specification reported in Table 1, Column 8. Increase (decrease) in sales dummy is equal to one if sales growth is greater (lower) than or equal to 2% (-2%), and zero if sales growth is between -2% and 2%. Use employment program is a dummy variable equal to one for employment program participation and is zero otherwise. Standard errors, shown in parentheses, are clustered at the industry and municipality level.

31
We offer a simple model to motivate our empirical strategy. The model demonstrates how some reforms could be classified as small or large depending on the relative magnitude of the costs and benefits of registration. Our results imply that in countries with high initial registration costs, the benefits of registration are significantly below the costs of registration, likely because of limited access to finance or rigid labor markets. This is consistent with Djankov et al. (2002) who show that high registration costs do not serve public interest, but only benefit politicians and bureaucrats.<sup>3</sup> The results in our paper suggest non-trivial economic magnitudes on the number of newly registered businesses. For example, we find that across OECD countries in our sample, on average, a reduction of 50 percent or more in the number of procedures, days or costs leads to an average increase in the number of new registrations of 14, 19 and 30 percent, respectively. These results are remarkably consistent with previous studies of registration reforms in two different OECD countries. A reform in Mexico that decreased the number of procedures by about 60 percent resulted in a 5 percent increase in the total number of firms, which translates to a 24 percent increase in new registrations in the municipality of Guadalajara (Bruhn, 2008). A reform in Portugal that reduced costs and days by 50 percent or more resulted in an increase in firm startups of about 17 percent, mostly among “marginal” firms that would have been most deterred by burdensome regulations, such as small firms in low-tech sectors (Branstetter, et al., 2010).

Our analysis is motivated by earlier studies that find that new firms are the ones most likely to grow (Lingelbach, et al., 2005; Johnson, et al., 2000) and to create new jobs (Audretsch, et al., 2006; McMillan and Woodruff. 2002). For example, studies using longitudinal data sets on

> 3 Our results are also consistent with de Mel, et al. (2012) who find that firms in Sri Lanka rationally refrain from formalizing, since they see few benefits from doing so – while a few firms seem to be suboptimally informal. Furthermore, their results suggest that a relatively modest increase in the net benefits to firms of formalizing could dramatically increase the rate of formalization.

4
Financial Regulation and Development Table 4: Largest Disasters in Ethiopia

|Type|(1)<br>Date|(2)<br>Total Deaths|
|---|---|---|
|Flood|13 August 2006|364|
|Flood|5 August 2006|498|
|Epidemic|September 1988|7,385|
|Drought|June 1987|367|
|Epidemic|January 1985|1,101|
|Drought|May 1983|300,000|
|Epidemic|January–December 1981|990|
|Drought|December 1973|100,000|
|Epidemic|January 1970|500|
|Drought|July 1965|2,000|

_Note:_ This table reports the most important disasters in Ethiopia between 1960 and 2015, from the most recent to the oldest. In recent years, Ethiopia has not experienced any disaster that could be related to the policy introduction. The data source is EM-DAT (http://emdat.be).

by one of the most severe droughts Eastern Africa has experienced in the past 60 years,<sup>12</sup> and this may be a reason for concern. As clarified by Figure 12, this disaster affected mostly Somalia, Kenya, and Ethiopia. However, this might be a limited concern for this study because while Somalia was hit in the most densely populated region of the country (around the capital city Modagishu), Ethiopia was hit in a low-density and predominantly rural area, as clarified by the lower panel of Figure 12. In particular, according to some controversial relief statistics, the number of Ethiopians affected by this disaster was between a few hundred and 700,000,<sup>13</sup> which is a sizable number, but limited relative to the 2011 population of 89.39 million. In Table 4, we also report a list of the major disasters that have occurred in Ethiopia since 1960, and verify that this drought does not qualify as a disaster in the Emergency Events Database (EM-DAT) definition.<sup>14</sup>

# **3 Evidence from Antebellum USA**

In this section we present an analysis using data and policies from American history, in the period between late 1700 and mid 1800, known as Antebellum era. We exploit the state-level introduction of bank taxes, which gradually took place over this period. In Section 3.1, we present a slightly different version of the theoretical model in Section 2, showing that lending/profit taxes can generate an increase in safe assets equivalent to the mandatory policy. Section 3.2 gives some historical

> 12Refer to the BBC article “Horn of Africa sees ‘worst drought in 60 years’ ”, 28 June 2011, available at http: //www.bbc.co.uk/news/world-africa-13944550.

> 13Refer to the Huffington Post article “Ethiopia: Hunger During Worst Drought In 60 Years”, 17 August 2011, available at http://www.huffingtonpost.com/2011/08/17/ethiopia-hunger-drought_n_928989.html.

> 14The EM-DAT is maintained by the Centre for Research on the Epidemiology of Disasters (CRED) and defines a disaster as an event satisfying at least one of these characteristics: “ _•_ Ten (10) or more people reported killed. _•_ Hundred (100) or more people reported affected. _•_ Declaration of a state of emergency. _•_ Call for international assistance.”

34
While this perverse outcome from tightening pollution standards is not always the rule, it is a fairly general result that the optimal pollution standards are less stringent in the presence of imperfect monitoring and costly enforcement than in the idealized world of perfect monitoring and costless enforcement.

An important implication is that the level of environmental quality depends on the level of compliance with environmental regulations in practice, and not on how "tough" the environmental standards are in statutes. Unfortunately, as reported by Eskeland and Jimenez (1991b), monitoring, enforcement, and regulatory capacities have been weak in developing countries (p.16): In Mexico, for instance, the influence of regulations has been limited by the resources of the enforcement agency and the low level of fines. In Columbia, laws have included formulas for calculating a tax on discharges to water, but no apparatus has been in place to monitor and bill polluters. In India, inefficient legal processes have reduced the disincentive effects of lawsuits against polluters.

Indeed, enforcement efforts have been weak in some developed countries as well. Magat, Krupnick, and Harrington (1986) conductimd a study of regulatory effort to enforce safety and environmental regulations in the United States during the 1970's and early 1980's and found that the resources devoted to enforcement were inadequate.

Several economists have emphasized recently the importance of targeting current monitoring and erforcement efforts and resources on particular firms chosen on the basis of 'heir past compliance 52
database is enormous), we have developed a routine in R that aggregates pixel counts by year for grid cells that are .0243 decimal degrees on a side. This translates to side dimensions of 2.7 kilometers at the equator, so each grid cell contains a maximum of 8,100 Hansen pixels. We code pixels by year: 1 if most of the clearing occurred in that year and 0 otherwise. Figure 1 illustrates the resolution of the grid-scale data in Est Province, Cameroon, with an overlay for protected areas. Substantial variation in forest clearing is apparent across protected areas, both inside and outside of park boundaries.

Using ArcGIS 10, we convert Hansen grid-scale rasters to point files (counts register at grid centroids) and intersect the point files with tropical forest area shapefiles and level-one administrative boundary files from GADM.org. We export the results to text files for operations in Stata that will be explained in the next section. We also export the intersections of Hansen point files with boundary files for protected areas and level-one administrative areas.

# **2.2 Protected areas**

The core exercise for this paper is construction of zones that straddle the borders of individual protected areas. The supporting database is constructed in three steps. (1) We extract tropical forest protected areas from the WDPA to create shapefiles for the Asia/Pacific region (including parts of Australia), Africa and the Latin America/Caribbean region (including southern Florida in the US). (2) We convert the polygons for individual parks to boundary point files, labeling each boundary point with the park’s WDPA ID number. (3) We intersect the point files with level-one administrative boundary files and export the results to text files for further operations in Stata.

3
34 synchronously and the regression results for the two are also rather similar, except that residual serial correlation is a more serious problem when the dependent variable is NPBAHR.

As an estimate of newly installed capacity relative to total capacity, two measures have been tried. The first Is simply the ratio of area planted after 1966 to the total area planted, denoted KNPLR, a slight variant of this being KNPLR2 where the divisor was OLDARBA. The second measure Is NEWCAPR2, the estimated proportlonal contribution to total cocoa production due to the area planted since 1966. This is based on the potentlal production calculations alluded to earlier. The regression results using the different variants are broadly similar but those based on NRWCAPR2 are marginally superior in terms of statistical fit.

The estimated equations reported here incorporate a small variation on equation (27) above. The latter **may** be properly interpreted as an equation for Planned rate of new planting. Allow the actual rate to differ from the planned rate by an amount that dep.erd2 upon the percentage error of price expectation, denoted by CHPRI, which in the present case is simply the percentage change in PRICOCO between current and last period. That is, price
Cox, D., Jimenez, E, and Okrasa, W. (1997). Family Safety Nets and Economic Transition: A Study of Worker Households in Poland. _Review of Income and Wealth,_ 43(2), 191-209.

Cox, D and Fafchamps, M. (2008). Extended Family, and Kinship Networks: Economic Insights and Evolutionary Directions, _Handbook of Development Economics, Volume 4_ , T.P. Schultz and J. Strauss (eds.), Amsterdam: North Holland Press (2008).

Cox, D., and Eser, Z., and Jimenez, E. (1998). Motives for Private Transfers Over the Life Cycle: An Analytical Framework and Evidence for Peru. _Journal of Development Economics_ . 55(1), 57-80.

Cox, D., and Soldo., B. J. 2004. Motivation for Money and Care that Adult Children Provide for Parents: Evidence from “Point-Blank” Survey Questions. _Center for Retirement Research Working Paper_ .

Frankenberg, E., Lillard, L., and Willis, R. J. (2002). Patterns of Intergenerational Transfers in Southeast Asia. _Journal of Marriage and Family,_ 64(2002), 627-641.

Giles, J., Zhao, C. and Wang, D. (2010). “Can China’s Rural Elderly Count on Support from Adult Children? Implications of Rural-to-Urban Migration,” _Journal of Population Ageing_ 3(3) (December 2010): 183-204.

Goh, E. C. L. (2009). Grandparents as Childcare Providers: An In-depth Analysis of the Case of Xiamen, China. _Journal of Aging Studies,_ 23(1), 60-68.

Hurd. M. D., Smith. J. P., and Zissimopoulos, J. M. (2007). Inter-Vivos Giving Over the Life Cycle. _RAND Working Paper Series No. WR-524._ Kim, H. (2010). Intergenerational Transfers and Old-Age Security in Korea, 227-278, 2010. _The Economic Consequences of Demographic Change in East Asia_ , NBER-EASE Volume 19 (2008), Takatoshi Ito and Andrew Rose, editors, 227 - 278.

Kotlikoff, L. J. (1988). Intergenerational Transfers and Savings. _The Journal of Economic Perspectives,_ 2(2), 41-58.

Kung, J. K., and Lin, J. Y. (2003). The Causes of China's Great Leap Famine. _Economic Development and Culture Change_ , 52(1), 51-73.

22
<!-- Start of picture text --> (67,227] (35,67] (23,35] (11,23] [0,11]<br>(b) 2012<br>Number of Terrorist Attacks across Provinces<br>Figure 4: (380,862] (206,380] (109,206] (67,109] [3,67]<br>(a) 1973 – 2014<br><!-- End of picture text --> 37