rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Percent of Users Using Full Name<br>a<br>5<br>3-<br>A><br>© Female Mill Male<br>N=369. Vertical bars represent confidence intervals at the 95 percent confidence level<br><!-- End of picture text --> <!-- Start of picture text --> Percent of Users Using Business Name<br>3-<br>2-<br>.o- —<br>| Female Mil Male<br>N=369. Vertical bars represent confidence intervals at the 95 percent confidence level<br><!-- End of picture text -->

<!-- Start of picture text --> Percent of Users Using Own Photo<br>a<br>57<br>37<br>A><br>| Female Mill Male<br>N=369. Vertical bars represent confidence intervals at the 95 percent confidence level<br><!-- End of picture text -->

<!-- Start of picture text --> Percent of Users Using Business Photo<br>3<br>2-<br>0-<br>5 Female Mill Male<br>N=369. Vertical bars represent confidence intervals at the 95 percent confidence level<br><!-- End of picture text -->
survey provides more general—and generalizable—insights on the demographic and political characteristics of Delhi slum dwellers, the qualitative evidence—by virtue of the large differences between the slums selected—enabled us to explore in greater depth the strategies chosen by slum dwellers to survive and advance.<sup>vi</sup> The scale and intensity of poverty in slums, and the fragility of the public services available to respond to it, bring forth an extraordinary range of coping strategies on the part of residents. Community leaders, front-line service staff, elected politicians, and slum residents themselves are the key players in this drama. The qualitative evidence from the four slums— Lakshmi, Rajiv, Madanpuri and Pooriyaganj<sup>vii</sup> —reveals that the institutions of governance that have emerged in Delhi’s slums span a spectrum between traditional village institutions transplanted and adapted to the urban environment, and novel leadership structures. In the newest (and thus most precarious) slums, such as Madanpuri, the leaders themselves are being forced to adapt, assuming new identities and finding common ground among disparate and often disorganized sub-groups.

# _The Adaptation of Traditional Structures_

The governance structures within the Rajiv colony<sup>viii</sup> , near the walled city of Delhi, owe much to the traditional caste and gram panchayats that have their roots in rural India. Despite being a well-established slum that houses between 15,000 and 20,000 households, the Rajiv colony has little slum-wide “government” per se. Instead, each of its constituent communities—the Rajasthanis, Dholakwalas (traditional drummakers) and Bengalis—govern themselves, bound together by communal ties and a shared sense of solidarity in the face of pervasive risks, distinctive (usually low) social status, and (in the case of the Bengalis) illegal political status.

10
**Table 4: Poisson, negative binomial, and gamma estimation results for final goods and parts and components separately.**

||(1)<br>Poisson<br>Final|(2)<br>Poisson<br>Parts|(3)<br>Neg. Bin.<br>Final|(4)<br>Neg. Bin.<br>Parts|(5)<br>Gamma<br>Final|(6)<br>Gamma<br>Parts|
|---|---|---|---|---|---|---|
|**LPI (exp.)**|**0.695*****|**0.558*****|**1.120*****|**1.400*****|**1.105*****|**1.443*****|
||**(0.000)**|**(0.000)**|**(0.000)**|**(0.000)**|**(0.000)**|**(0.000)**|
|**LPI (imp.)**|**0.327*****|**0.479*****|**0.674*****|**1.227*****|**0.664*****|**1.241*****|
||**(0.001)**|**(0.000)**|**(0.000)**|**(0.000)**|**(0.000)**|**(0.000)**|
|Log(distance)|-0.345***|-0.372***|-0.811***|-0.682***|-0.804***|-0.729***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Contiguous|0.667***|0.562**|0.788***|0.646***|0.796***|0.585**|
||(0.002)|(0.041)|(0.003)|(0.002)|(0.003)|(0.013)|
|Language|0.286*|0.419**|0.659***|0.518***|0.683***|0.535***|
||(0.076)|(0.027)|(0.002)|(0.008)|(0.002)|(0.009)|
|Colony|-0.464**|-0.273|0.777**|0.527**|0.763**|0.481*|
||(0.027)|(0.302)|(0.017)|(0.029)|(0.018)|(0.056)|
|Colonizer|1.207**|1.345**|0.008|0.003|-0.002|-0.026|
||(0.016)|(0.011)|(0.975)|(0.991)|(0.993)|(0.932)|
|Log(GDP exp.)|0.866***|0.787***|0.972***|0.729***|0.980***|0.752***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Log(GDP imp.)|0.748***|0.682***|0.733***|0.638***|0.746***|0.669***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Constant|-33.028***|-31.756***|-34.422***|-31.487***|-34.902***|-32.606***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|R2|0.296|0.283|0.160|0.143|0.159|0.144|
|Observations|20880|20880|20880|20880|20880|20880|

_Note: The dependent variable in each case is exports. All trade costs variables are transformed as per Baier and Bergstrand (2009), as discussed in the main text. Estimation methods are indicated at the top of each column. Prob. values based on robust standard errors adjusted for clustering by country pair are in parentheses beneath the parameter estimates. Statistical significance is indicated by * (10%), ** (5%), and *** (1%)._ 27
ment under the presence of bribery opportunities. For instance, the policy that increases tax rates for big enterprises will not have a large impact on the aggregate output nor the tax revenues collected, since enterprises can decrease the effective tax rates by increasing bribery payments.

Although this study uses the most detailed data about plant size distribution and bribery payments in Türkiye, there is still room for increasing the quality of the dataset by focusing on the size distribution of small enterprises and by having a larger sample by including small firms in bribery surveys. Hence, future studies can conduct more precise calibrations with more detailed datasets for both plant size and bribery payments. Future research can also focus on three different extensions of this model. The first extension is about the relationship between bribery and aggregate variables such as GDP per capita, mean size and managerial quality at the cross-country level. We know that bribery incidence is negatively correlated with these aggregate variables. Therefore, having a model which incorporates these cross-country facts can provide a better understanding of the relation between development and bribery. Second, the relationship between managerial skill accumulation and bribery is worth investigating. Managers exposed to bribery requests tend to spend more time and resources to ‘get things done’. Instead, they could use these resources to accumulate their managerial skills and increase their profits. For example, they could spend their time and income getting an MBA degree, instead of spending time on making connections with officers and bribing them. Thus, if the environment of Bhattacharya _et al._ (2013)-where managers can accumulate skills- and the setup of this paper were integrated, research could focus on the effects of bribery on managerial skill accumulation. Third, the level of distortions can be endogenized by allowing agents to choose whether or not to be (corrupt) government officials. With this extension, some of the high ability agents can choose to be a government official, depending on bribery returns. In addition, they can increase the distortion levels to enjoy more bribery returns. As a result, a bribery opportunity could have the opposite effect from the conclusion of this study.

19
||**Table 1. Present Value Analys**|**is: Lease Finan**|**ce vs. Financed Purch**|**ase**|
|---|---|---|---|---|
|**Period**|**Lease Finance**|**Cash Flow**|Discount Factor|Present Value|
|||**B**|_(18%)_||
|O|10% Security deposit|20,000|1.0000|20,000|
|0|<br>1% Front-end fee|2,000|1.0000|<br>2,000|
|Year**I**|Tax shield on front-end fee<br>Monthly lease rentals|(700)<br>5,364.72|0.8475|(593)|
||<br>Total lease rentals|252,137.14||165,851|
||Tax shield on lease rentals|(88,247)||(60,235)|
|Month 48|Refund of security deposit|<br>(20,000)|0.4894|(9,787)|
|Month 48|<br>Payment of residual value|<br>20,000|0.4894|9,787|
||<br>Total, with tax shields|||127,023|
||<br>Total, without tax shields|||187,851|
|Period|F<br>Financed Purchase|Cash Flow<br>BF"<br>1|Discount Factor<br>(18%)|Present Value<br>B-<br>E|
|0|Acquisitbon<br>cost|200,000|<br>1.0000|<br>200,000|
|0|<br>Equity down-payment|80,000|1.0000|<br>80,000|
||<br>Monthly amortizations|3,047.21|||
||<br>Total amortization payments|182,832||111,703|
||<br>Annual depreciation (5 years)|40,000|||
|Years 1-4|<br>Tax shield (Depreciation)|(56,000)||(37,660)|
||<br>Total, with tax shields|||154,043|
||<br>Total, without tax shields|||191,703|
|With tax shiel|ds: Leasing advantage: in&|||27,020|
||<br>as %|||17.5%|
|No tax shield|s: Leasing advantage: inB|||3,952|
||<br>as %|||2.1 %|

Source: Appendix Table 1.

16. The probability is high that the tax shield factor might be irrelevant for informnally organized MSBs which can easily escape the jurisdiction of tax regulators and collectors. The tax shield factor is irrelevant to the borrower when that borrower is able to avoid taxation of income. Leasing still would present an advantage because the up-front down payment or security deposit in a lease contract is 10% or L20,000 versus F.80,000 or 40% equity stake under bank financing. With leasing the borrower has R.60,000 more available for operating capital. Moreover, the discounted present value of lease rental payments is F187,851 compared to the discounted present value of loan amortization payments totaling 1.191,703 when the impact of tax shields is ignored or removed in both leasing and bank financing techniques.

# **Advantages to Lessors**

17. Leasing provides to lessors profitable new opportunities to reach borrowers and expand into existing markets. The rapid growth of leasing in a broad cross-section of countries indicates that leasing as a financial product has addressed an important unsatisfied demand for financing and, at the same time, attracted borrowers away from the more traditional financial products such as bank loans. The advantages that leasing offers to lessors includes the following: 9
# **Appendix 2: Table 4 full regression results**

# **Table A2.1: First Stage Regression Results (Table 4)**

||Depend<br>|ent Variable<br>|
|---|---|---|
||(1)|(2)|
||Ln Height<br>at baseline|Years of schooling<br>at baseline|
|Share of farm harvest lost prior to baseline|-0.025**<br>(0.010)||
|Deviation of long rains when child was 2-3<br>years old||0.001**|
|||(0.000)|
|7 years old|0.024*|-0.063|
||(0.013)|(0.240)|
|8 years old|0.082***|0.115|
||(0.013)|(0.240)|
|9 years old|0.123***|0.138|
||(0.014)|(0.256)|
|10 years old|0.143***|0.492**|
||(0.014)|(0.245)|
|11 years old|0.176***|1.139***|
||(0.014)|(0.244)|
|12 years old|0.224***|2.141***|
||(0.013)|(0.232)|
|13 years old|0.253***|2.793***|
||(0.014)|(0.252)|
|14 years old|0.277***|3.820***|
||(0.014)|(0.242)|
|15 years old|0.310***|4.503***|
||(0.014)|(0.255)|
|Male|-0.007|-0.106|
||(0.005)|(0.094)|
|Household head years education|0.001|0.044**|
||(0.001)|(0.021)|
|Household head male|0.007|-0.297|
||(0.011)|(0.195)|
|Household head age|0.000|0.008*|
||(0.000)|(0.004)|
|Ln per capita consumption (TZ shillings)|0.006|0.367***|
||(0.007)|(0.116)|
|Ln per capita consumption missing|0.072|4.409***|

24
employment information at higher disaggregation levels, the common practice is to equally weight all occupations at 8-digits merged to the same 4-digit occupation. Once the crosswalk is done, there are two options to categorize employment into green and non-green: a) assume that the 4-digit occupation is green if it contains at least one green task, or b) assume that only the share of green tasks within the 4-digit occupation represents green employment. If we compute estimates using the O*NET GEP methodology (green enhanced occupations and green new and emerging occupations), then 104 4-digit occupations are green or partially green,<sup>61</sup> which represent between 28 and 15 percent of employment in Indonesia, depending on whether a) or b) is assumed (Figure 4.2). Similarly, when carrying out the text analysis method and applying the narrow green dictionary to the O*NET occupational database, 164 4-digit occupations are green, which represent between 36 and 20 percent of employment, respectively. The employment level is higher using this latter method since the text analysis captured more green tasks and occupations in the O*NET taxonomy than the O*NET GEP.

These results imply that, given the level of greening of the economy in Indonesia, the text analysis applied to the ISCO-08 taxonomy may better reflect Indonesia’s level of greening of the economy.

**Figure 4.2 Green employment using O*NET taxonomy, O*NET GEP and text analysis applied to O*NET taxonomy**

<!-- Start of picture text --> 15 20<br>28<br>36<br>85 80<br>72<br>64<br>GEP GTI with O*NET taxonomy GEP GTI with O*NET taxonomy<br>Occupation at 4-digit level is green if contains at least Only the share of green tasks is considered green<br>one green task employment<br>Non-green Green<br><!-- End of picture text --> _Source_ : authors calculations based on O*NET database

# Application #2: Skills demanded by online jobs add to high-skilled green occupations

Understanding the skills that are in demand for green jobs is essential for supporting the transition to a greener economy. This knowledge can then be used to shape skills policies, particularly in terms of adjusting education and training programs. It can also inform civil society about the specific skills that employers are seeking when hiring for green occupations. The BG online job vacancy data can be a valuable resource for exploring the skills that are demanded for green occupations. Green occupations

> 61 Elliot et. Al (2021) find that in the Netherlands 106 out of 436 ISCO occupations have some level of greenness when using a crosswalk from O*NET-SOC to ISCO.

29
_Development_ , _38_ (3), 244–259. https://doi.org/10.1016/j.worlddev.2009.06.004 Motta, V. (2020). Lack of access to external finance and SME labor productivity: does project quality matter? _Small Business Economics_ , _54_ (1), 119–134. https://doi.org/10.1007/s11187-018-0082-9 Paunov, C. (2016). Corruption’s asymmetric impacts on firm innovation. _Journal of Development Economics_ , _118_ , 216–231.

https://doi.org/10.1016/j.jdeveco.2015.07.006 Petersen, M., & Rajan, R. (1995). The Effect of Credit Market Competition on Lending Relationships. _Quarterly Journal of Economics_ , _110_ (2), 407–443. https://doi.org/Article Qi, S., & Ongena, S. (2019). Will Money Talk? Firm Bribery and Credit Access.

_Financial Management_ , _48_ (1), 117–157. https://doi.org/10.1111/fima.12218 Qian, J., & Strahan, P. E. (2007). How Law and Institutions Shape Financial Contracts: The cas of Bank Loans. _The Journal of Finance_ , _62_ (6), 2803–2834. https://doi.org/10.3386/w11052 Rajan, R. G., & Zingales, L. (1998). Financial Dependence and Growth. _American Economic Review_ , _88_ (3), 559–586.

Şeker, M., & Yang, J. S. (2014). Bribery solicitations and firm performance in the Latin America and Caribbean region. _Journal of Comparative Economics_ , _42_ (1), 246– 264. https://doi.org/10.1016/j.jce.2013.05.004 Ullah, B. (2020). Financial constraints, corruption, and SME growth in transition economies. _Quarterly Review of Economics and Finance_ , _75_ , 120–132. https://doi.org/10.1016/j.qref.2019.05.009 Vial, V., & Hanoteau, J. (2010). Corruption , Manufacturing Plant Growth , and the Asian Paradox : Indonesian Evidence. _World Development_ , _38_ (5), 693–705.

34
# **3.3 Science**

**Table 12:** The estimated impact of ‘Vietnam’ on Science PISA test scores

||||||Sci|ence|||||
|---|---|---|---|---|---|---|---|---|---|---|
|Variables|(1|)|(2|)|(3|)|(|4)|(|5)|
|VIETNAM|134.56|(4.91)|115.45|(4.37)|103.85|(4.56)|101.83|(4.63)|88.54|(5.99)|
|FEMALE|-|-|-2.21|(1.61)|-3.08|(1.58)|-1.95|(1.78)|-4.93|(1.46)|
|PRESCHOOL|-|-|43.06|(3.42)|37.94|(3.60)|36.09|(3.65)|24.36|(3.57)|
|REPEAT|-|-|-53.8|(2.63)|-50.74|(2.86)|-48.77|(3.26)|-40.6|(3.24)|
|ST08Q01|-|-|-9.28|(1.11)|-9.02|(1.16)|-8.76|(1.28)|-8.45|(1.26)|
|ST115Q01|-|-|-5.95|(1.68)|-5.55|(1.72)|-6.88|(1.81)|-6.18|(1.89)|
|BOOK<br>~~N~~|-|-|-|-|0.08|(0.01)|0.06|(0.01)|0.06|(0.01)|
|PARPRESSURE|-|-|-|-|5.87|(3.94)|6.42|(3.96)|-0.27|(3.76)|
|FUNDMOM|-|-|-|-|0.25|(0.05)|0.22|(0.05)|0.17|(0.06)|
|COUNCILMOM|-|-|-|-|-0.18|(0.05)|-0.2|(0.05)|-0.12|(0.06)|
|DUTYMOM|-|-|-|-|-0.02|(0.05)|-0.04|(0.06)|-0.06|(0.07)|
|PROPCERT|-|-|-|-|-|-|11.67|(4.91)|5.35|(5.47)|
|TCSHORT|-|-|-|-|-|-|-0.68|(1.82)|2.7|(1.83)|
|TCM<br>~~S~~TUASS|-|-|-|-|-|-|16.04|(6.86)|9.43|(7.39)|
|TCM<br>~~P~~EER|-|-|-|-|-|-|-2.65|(6.05)|-5.68|(5.79)|
|ASS<br>~~P~~ROG|-|-|-|-|-|-|-5.42|(16.65)|-23.01|(9.17)|
|ASS<br>~~P~~ROM|-|-|-|-|-|-|4.23|(5.79)|12.41|(6.68)|
|ASS<br>~~N~~AT|-|-|-|-|-|-|7.33|(4.71)|1.49|(4.93)|
|ASS<br>~~S~~CH|-|-|-|-|-|-|2.63|(6.01)|0.66|(6.72)|
|ASS<br>~~C~~UR|-|-|-|-|-|-|-6.33|(7.54)|-4.87|(8.73)|
|STU<br>~~F~~EEDB|-|-|-|-|-|-|4.59|(4.48)|5.90|(4.68)|
|PCGIRLS|-|-|-|-|-|-|-|-|18.92|(11.11)|
|PRIVATESCL|-|-|-|-|-|-|-|-|-1.03|(5.34)|
|TOWN|-|-|-|-|-|-|-|-|-8.25|(3.16)|
|CLSIZE|-|-|-|-|-|-|-|-|0.83|(0.20)|
|COMPWEB|-|-|-|-|-|-|-|-|17.22|(5.61)|
|SCMATEDU|-|-|-|-|-|-|-|-|5.38|(1.75)|
|EXC2<br>~~P~~LAY|-|-|-|-|-|-|-|-|8.52|(3.34)|
|EXC6<br>~~M~~ATHCOMP|-|-|-|-|-|-|-|-|1.5|(4.53)|
|EXC10<br>~~S~~PORT|-|-|-|-|-|-|-|-|-1.73|(9.13)|
|EXC11<br>~~U~~NICORN|-|-|-|-|-|-|-|-|8.56|(5.04)|
|SCORE<br>~~P~~UBLIC|-|-|-|-|-|-|-|-|11.28|(3.75)|
|LEADINST|-|-|-|-|-|-|-|-|1.57|(1.95)|
|QUAL<br>~~R~~ECORD|-|-|-|-|-|-|-|-|-0.92|(5.55)|
|SCHSEL|-|-|-|-|-|-|-|-|1.52|(3.11)|
|TEACCLIM|-|-|-|-|-|-|-|-|-1.82|(2.46)|
|R2|30.|75|41.|14|43.|35|46|.11|47|.35|
|N|484|83|462|67|440|46|35|302|27|224|

Notes: Figures in parentheses are t-values (95% significance level). For a more detailed description of variables, please see Tables A2, A3, A4 in the Appendix.

Teacher related variables for Reading only account for a further decline of 2 points, and school variables reduced the dummy by 14 points to a level of 60.31 reported in column (5) of Table 11. Appendix Table A6, available on the online github repository, reports the regressions with rotated variables included which brings the dummy coefficient down to a lowest level of 52.04. Finally we look at the Science results.

The gap between Vietnam and Dev7 is largest for science scores, with column (1) in Table 12 indicating the dummy coefficient at 134.56. The pattern of reduction in the dummy is 25
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Water supply and sanitation

Banerjee, Sudeshna, Vivien Foster, Yvonne Ying, Heather Skilling, and Quentin Wodon. 2008a. ―Cost Recovery, Equity, and Efficiency in Water Tariffs: Evidence from African Utilities.‖ AICD Working Paper 7, Africa Region, World Bank, Washington, DC.

- Banerjee, Sudeshna, Heather Skilling, Vivien Foster, Cecilia Briceño-Garmendia, Elvira Morella, and Tarik Chfadi. 2008b. ―Ebbing Water, Surging Deficits: Urban Water Supply in Sub-Saharan Africa.‖ AICD Background Paper 12, Africa Region, World Bank, Washington, DC.

- IBNET (The International Benchmarking Network for Water and Sanitation Utilities). 2009. www.ibnet.org/.

Gulyani, Sumila, Debabrata Talukdar, and Darby Jack. 2009. ―Poverty, Living Conditions, and Infrastructure Access: A Comparison of Slums in Dakar, Johannesburg, and Nairobi.‖ AICD Working Paper 10, Africa Region, World Bank, Washington, DC.

Keener, Sarah, Manuel Luengo, and Sudeshna Banerjee. 2009. ―Provision of Water to the Poor in Africa: Experience with Water Standposts and the Informal Water Sector.‖ AICD Working Paper 13, Africa Region, World Bank, Washington, DC.

Morella, Elvira, Vivien Foster, and Sudeshna Ghosh Banerjee. 2008. ―Climbing the Ladder: The State of Sanitation in Sub-Saharan Africa.‖ AICD Background Paper 13, Africa Region, World Bank, Washington, DC.

- WHO (World Health Organization) Joint Monitoring Programme (JMP). 2010a. ―Senegal Estimates for the Use of Improved Drinking-Water Sources.‖ www.wssinfo.org/resources/documents.html.

49
KENYA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 5. Benchmarking water and sanitation indicators**

||Unit|Low-income<br>countries|**Kenya**|Middle-income<br>countries|
|---|---|---|---|---|
|Access to piped water|% pop|10.1|**17.9**|56.4|
|Access to stand posts|% pop|16.1|**9.4**|20.4|
|Access to wells/boreholes|% pop|38.3|**21.6**|6.3|
|Access to surface water|% pop|33.8|**46.4**|13.9|
|Access to septic tanks|% pop|5.3|**9.0**|44.0|
|Access to improved latrines|% pop|9.3|**8.0**|0.9|
|Access to traditional latrines|% pop|47.9|**64.3**|33.0|
|Open defecation|% pop|37.1|**18.3**|15.8|
|Domestic water consumption|liter/capita/day|72.4|**63.0**|Na|
|Urban water assets in need of rehabilitation|%|35.5|**42.0**|25.0|
|Revenue collection|% sales|96.0|**95.0**|99.2|
|Distribution losses|% production|33.0|**40.0**|23.1|
|Cost recovery|% total costs|56.0|**58.0**|80.6|
|Total hidden costs as % of revenue|%|130.0|**173.9**|84.9|
||||Ot|her developing|
|U.S. cents per m3|**Kenya**|Scarce water|resources<br>|<br>regions|
|Residential tariff||**38.9**|60.26||
|||||30 – 600|
|Non-residential tariff||**45.7**|120.74|. .|

Source: Banerjee and others 2009; Morella and others 2009, derived from AICD water and sanitation utilities database downloadable from http://www.infrastructureafrica.org/aicd/tools/data **.**

**Figure 5. Growing reliance on surface water and static trend in improved sources**

<!-- Start of picture text -->
1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>access method<br>Kenya Sub-Saharan Africa<br>% of population<br><!-- End of picture text -->

Source: Gulyani, Sumila, Talukdar, and Jack 2009.

What is particularly worrisome is that whereas only 0.3 percent of the population is gaining access to some improved form of water service each year, as much as 1.05 percent of the population is moving into reliance on surface water. What this means is that between the mid-1990s and the mid-2000s, Kenya 13