rafmacalaba/datause-encoder-data huggingface dataset:
in Column 5. In Column 6, the dependent variable takes value 1 if the FA is offered a credit bonus using the sample of FAs offered a bonus.

FAs in our study are on average 28 years old, roughly one-fourth are female, and slightly more than half of them have at least a high school degree (equivalent to 12 years of education). The average duration of employment with NRSP is 26 months, and NRSP was the first job for roughly two-fifths of FAs.

FAs manage on average 14 COs every month. Their average monthly portfolio consists of 91.4 active loans (new and ongoing), with roughly PKR 100,000 (USD 1,685) disbursed each month. The mean recovery rate on installments due at the end of each month is around 98 percent, while only 70 percent of such installments is recovered fully by the 20th of that month.

Slightly more than half of the FAs prefer a hypothetical bonus to be paid on credit outcomes as opposed to social outcomes. During the baseline interview, each FA was asked to rank what they liked most about working in NRSP. Roughly half of the FAs reported that the ability to help people is what they liked most. One-fifth of them had also done volunteer work before joining NRSP.

Out of 162 FAs in the study, 132 FAs were successfully interviewed in the follow up survey. This attrition rate is almost identical and not statistically different across the three groups (see bottom of Table 1), suggesting that attrition bias is not a concern when examining impacts of bonus on outcomes measured in the follow up survey. In addition, CO meetings held by 31 FAs were never visited by a supervisor. These 31 FAs do not appear in the verified MPRS data, restricting our sample to 131 FAs when examining social outcomes. The selection into this restricted sample is again not statistically different across control FAs, Cr-FAs, and Soc-FAs (see bottom of Table 1). Similarly, 73 FAs for whom we have subjective assessments from supervisors are not differentially selected across the two bonus and control groups.

Appendix Tables D.1, D.2, and D.3 present means of baseline variables in the control and the two bonus groups, and their differences for the verified MPRS, follow up, and supervisor evaluation restricted samples, respectively. None of the differences in means (out of 63 differences) in Appendix Table D.1 are statistically significant at the 10 percent conventional level. In Appendix Tables D.2 and D.3, four and three differences are statistically significant at the 10 percent level respectively. In all samples, the F-tests at the bottom of Columns 4-6 cannot reject the hypothesis that all variables are jointly insignificant in explaining assignment to an experimental arm.

# 4.2 Partnership

Roughly two-fifths of FAs co-manage their entire CO portfolio with other FAs, while slightly less than one-fourth manage all their COs on their own. Appendix Figure C.1 plots the distribution 16
Policy Research Working Paper 5246

# **Abstract**

The returns to schooling or the skill premium is a key parameter in various literatures, including globalization and inequality and international migration. This paper explores the skill premium and its link to exports in Latin America, thus linking the skill premium to the emerging literature on the structure of trade and development. Using data on employment and wages for over five million workers in sixteen Latin American economies, the authors estimate national and industry-specific skill premiums and study some of their determinants. The evidence suggests that both country and industry characteristics are important in explaining skill premiums. The analysis also suggests that the incidence of exports within industries, the average income per capita within countries, and the relative abundance of skilled workers are related to the underlying industry and country characteristics that explain skill premiums. In particular, higher sectoral exports are positively linked with the skill premium at the industry level, a result that supports recent trade models linking exports with wages and the demand for skills.

This paper—a product of the Office of the Chief Economist, Latin America and the Caribbean Region and the Trade and Integration Team, Development Research Group, and—is part of a larger effort in the both departments to assess the role of the structure of international trade in development. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at dlederman@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
One of the main findings is that the prevalence of non‐functional water and sanitation facilities is a significant issue. More information is necessary to establish what the secondary sources are, and if they are adequate for effective health service delivery. There is also a need for more comprehensive data to be collected not just on where arsenic testing has been carried out, but what the results were and whether proper treatment was administered to mitigate the issue before usage.

In relation to our health indicator for human development, the degree of overlap between upazilas with high levels of stunting and low levels of WASH is remarkably high and calls for a closer look at the determinants of stunting in those regions.

Despite low income levels, the west of Bangladesh had relatively higher levels of WASH in its community clinics. As noted in the above sections, there are two main caveats to keep in mind. One, that the index is a relative measure and a perfect score of 1 is by no means indicative of adequate WASH. Two, it is worth entertaining the possibility that there could have been measurement errors in the collection of data for the rapid assessment. If that is the case, we can expect to see over‐ or under‐estimated rates of WASH at community clinics.

While the lowest income areas are not necessarily the ones where WASH is the poorest, it is worth noting that the Union Parishad’s limited revenues, particularly own resource mobilization, will most probably be a constraint to achieving adequate level of WASH by the SDG standards. Therefore, the local governments who are accountable for the improvement of community clinics need support at the national level through dedicated budget accounts. This pattern where WASH levels are higher in poorer regions could also reflect that more Bangladeshis in poorer regions use community clinics whereas those who live in richer regions bypass community clinics and may prefer to use private health services or hospitals even for the first line of care, causing greater neglect of community clinics in richer regions. If this happens to be the case, then the poorest of the poor in relatively higher income regions are likely to be left out to a greater degree than the poorest of the poor in relatively low‐income regions.

However, this assessment only gives us a partial picture. To understand the depth of the problem, a more extensive analysis would be necessary. The simplicity of the rapid assessment survey affords representativeness at the cost of depth of information. Therefore, while this analysis provides a preliminary account of the state of WASH in community clinics, it does not sufficiently establish a baseline against which progress towards SDGs can be measured. The DGHS an MoHFW will benefit from adopting the core survey components designed to assess SDG indicators detailed in the WHO/UNICEF (2018) guidelines, which can then be adjusted to fit the Bangladeshi context.

10
we assemble a comprehensive dataset on firms’ security issuances in capital markets around the world and match this information with balance sheet and income statement data.

Our data on firms’ capital raising activity come from Security Data Corporation’s (SDC) New Issues Database, which provides transaction-level information on new issues of common and preferred equity and bonds with an original maturity of more than one year, starting in the 1970s. Given that SDC does not collect data on debt issues with a maturity of less than one year, our dataset does not include commercial paper issues with such short-term maturities. Moreover, since we focus on security issues, we do not consider bank lending.

The SDC database is divided into twelve regional sub-databases covering different markets: Asian Pacific Domestic (Hong Kong, Indonesia, Malaysia, Philippines, Singapore, Taiwan, and Thailand,); Australian/New Zealand Domestic (Australia, New Zealand, and Papua New Guinea); Canadian Domestic (Canada); Continental European Domestic (Austria, Belgium, Bulgaria, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Luxembourg, Netherlands, Norway, Poland, Portugal, Slovakia, Spain, Sweden, and Switzerland); Indian and Subcontinent (Bangladesh, India, Pakistan, and Sri Lanka); International (Eurobonds and other cross-border issues); Japanese Domestic (Japan); Korean Domestic (South Korea); Latin American Domestic (Argentina, Bolivia, Brazil, Colombia, Costa Rica, Ecuador, Guatemala, Mexico, Panama, Peru, Uruguay, and Venezuela); United States (United States); United Kingdom Domestic (United Kingdom); and Rest of the World (countries not included in other SDC regional sub-databases, such as China). The academic version of SDC to which we have access does not include the Canadian and Korean Domestic sub-databases. Therefore, we exclude all Canadian and South Korean firms from our analysis. While data for 8
The WTO separates support policies into three groups, with separate negotiating modalities for each of them. Translating from the OECD producer support measure to the WTO’s aggregate measure of support framework is not straightforward. The market price support component captures both the market access and export subsidy pillars of the WTO agricultural negotiations. The remaining portion of the OECD measure poses a significant challenge for quantifications in the context of the WTO domestic support negotiations, as these are differentiated according to “traffic light” designations (amber, blue, and green boxes) that intend to characterize the level of distortion created by a particular policy implementation. This complexity of moving from the OECD’s comprehensive domestic support data base to the WTO domestic support framework is the reason we draw on the published study by Jensen and Zobbe (2006) for our Doha agricultural scenarios. These authors consider in detail not only the WTO designations of support, but also the associated binding overhang versus actual support levels that we can not evaluate by looking at the OECD producer support estimates in isolation.

The Doha scenario considered in this paper derives from the so-called July 2004 Framework Agreement (WTO, 2004) as embodied in the core scenario from the Hertel and Winters volume (2006) and is summarized, along with the other policy scenarios considered in this paper, in Table 6. The first column of this table highlights the implications for cuts in support in the rich countries’ agricultural sectors – the main focus of this paper. This Doha scenario assumes that industrial countries with domestic support in excess of 20 percent of production cut their bound commitments by 75 percent, while others cut by 60 percent. However, even with these ambitious reductions, the gap between bindings and applied policies, as well as the inclusion of market price support 23
Table 6: Spatial Lags

||World <br>(1)|Bank aid 1995–2014<br>(2)<br>(3)|
|---|---|---|
|Mission|0.11<sup>_∗∗∗_</sup>|0.11<sup>_∗∗∗_</sup><br>0.14<sup>_∗∗∗_</sup>|
||(0.03)|(0.03)<br>(0.05)|
|Mission lag||0.01<br>0.01<br>(0.02)<br>(0.02)|
|Mission _×_ Mission lag||_−_0.04<br>(0.06)|
|Ethnic dummies|Yes|Yes<br>Yes|
|Mean dependent variable|0.26|0.26<br>0.26|
|_R_-squared|0.43|0.43<br>0.44|
|Observations|5,840|5,840<br>5,840|

_Source_ : Authors’ analysis based on mission locations from Beach (1903); aid projects from AidData, World Bank geocoded research release, version 1.3; and control variables from multiple sources, detailed in appendix A.

_Note_ : Mission lag refers to the (up to) eight neighbors adjacent to each cell. Estimation is by ordinary least squares. Conley (1999) standard errors are given in parentheses, with the cutoff at 2 degrees (220 km). The dependent variable is a dummy for at least one project commitment in the sample period. Control variables include: log distances to coast, explorer route, colonial railway, and Arab trade route; a third-order polynomial in the 18th-century population; presence of a city as of 1800; country dummies; precolonial ethnic dummies; average altitude; Terrain Ruggedness Index; percentage area within 10 km from water source; Caloric Suitability Index; Malaria Ecology Index; and a tropics dummy (which is also interacted with mean altitude). Cells that are more than 90 percent covered by barren land or more than 90 percent covered by forest are excluded from the sample.<sup>_∗_</sup> _p < ._ 1 _∗∗ p < ._ 05 _∗∗∗ p < ._ 01 24
To assess the acceptability of a water price and better estimate households’ willingness to pay (WTP) for water services, this study introduces a multiple bounded discrete choice (MBDC) household survey model and discusses how the model can be used for making better pricing and subsidy policies. With the MBDC survey approach, households are presented a list of potential prices of water supply and are asked to select a level of acceptance for each of the prices offered. With this approach, a mean WTP for a specific level of water service can be estimated for each household and the acceptability of different groups of households can be analyzed for each of the potential prices.

In this study, an empirical MBDC was conducted in five districts of the Chongqing municipality in China. In this city, domestic water service quality is seriously inadequate and financial resources are insufficient to improve service quality. About 1,500 heads of households were interviewed. We assessed household attitudes toward various prices and willingness to pay for an improved water service. We also tested the ordering effect of the price list with the MBDC model.

The following section of this paper presents and discusses the methodology of the WTP and acceptability assessment. Section III presents the household survey in Chongqing. Section IV presents the results of the analyses of acceptability, affordability, and willingness to pay for water service improvement in Chongqing. Section V concludes the paper.
- 28 -

|**Table 5**|**. Summary**|**statistic**|**s**||||
|---|---|---|---|---|---|---|
||Abb.|Obs.|Mean|Std.Dev.|Min|Max|
|Monthly power consumption (kWh)|_q_|4762|355.57|207.89|0.24|4,216.11|
|Dummy for meter installed|_I_|4762|0.88|0.33|0|1|
|Marginal price 1/|_MP_|4762|7.96|1.94|4.21|9.21|
|Nordin’s difference variable 1/|_D_|4762|774|655|-5,673|3,684|
|Monthly average high temperature|_TEMP_|4762|26.10|3.41|14.60|35.30|
|Monthly precipitation|_PREC_|4762|61.42|44.50|2.00|237.70|
|Monthly income 1/|_Y_|4762|38,204|52,950|100|1,627,629|
|Number of household members|_HH_|4762|4.55|1.60|1|16|
|Dummy for power use for lighting||4762|1.00|0.06|0|1|
|Dummy for power use for heating||4762|0.31|0.46|0|1|
|Dummy for power use for cooking||4762|0.49|0.50|0|1|
|Dummy for power use for water heating||4762|0.72|0.45|0|1|
|Dummy for power use for others||4762|0.91|0.28|0|1|
|<br>Dummy for gas use for lighting||4762|0.01|0.11|0|1|
|Dummy for gas use for heating||4762|0.44|0.50|0|1|
|Dummy for gas use for cooking||4762|0.70|0.46|0|1|
|<br>Dummy for gas use for others||4762|0.01|0.08|0|1|
|Number of home appliances owned:|||||||
|<br>Color TV||4762|1.14|0.51|0|5|
|Video||4762|0.36|0.50|0|4|
|Refrigerator||4762|0.96|0.28|0|4|
|<br>Freezer||4762|0.01|0.12|0|3|
|Washer||4762|0.72|0.46|0|2|
|Dishwasher||4762|0.02|0.14|0|2|
|Electric or gas stove||4762|0.80|0.44|0|6|
|Air conditioner||4762|0.08|0.33|0|5|
|Water Boiler||4762|0.63|0.51|0|3|
|Computer||4762|0.08|0.28|0|3|
|Duration of power outages per day (hours)||4762|3.59|3.29|0|22|
|<br>Dummy for urban areas||4762|0.65|0.48|0|1|
|<br>Memorandum items:|||||||
|Monthly electricity payment 1/||4762|2,252|1,825|1|37,000|
|Block rate below threshold 1/|_BR_1|4762|4.44|0.15|4.21|4.61|
|Block rate above threshold 1/|_BR_2|4762|9.01|0.25|8.42|9.21|
|Block tariff threshold (kWh)|_q_0|4762|247.23|41.73|210|300|
|Normative amount of consumption(kWh)|_~~q~~_|4678|341.69|73.69|150|400|

1/ In constant 2005 Albania lek.

# **VI. MAIN ESTIMATION RESULTS AND POLICY IMPLICATIONS**

First of all, the OLS estimation is performed (Table 6). The price elasticity is found positive. This is no doubt biased for many uncontrolled bias factors. Particularly, the positive elasticity is attributable to the typical failure to control the price endogeneity under the increasing
production for most crops.<sup>7</sup> The exceptions would be (according to these authors) rice straw and sugarcane bagasse where 100% of the residues can be removed. Note that the numbers estimated here are the maximum potential, not necessarily the realistic potential.

Tables 2 (a) and (b) show the regional amount of feedstock produced, and the associated levels potential for second-generation ethanol production in the medium term (2015/16). The crops included in this study are corn, sorghum, barley, rice, wheat, and sugarcane. Given the environmental constraints, the potential global availability of crop residues is about 1529 million tons, and could contribute 436 billion liters of bioethanol in the medium term.

# **Table 2: Potential availability of selected agricultural residues and biofuel production in 2015/16**

Unit: million tons for feedstock; billion liters for biofuels

# (a) **Corn stover, sorghum straw and barley straw**

||Corn|Stover|Sorghu|m Straw|Barley|Straw|
|---|---|---|---|---|---|---|
||Feedstock|Biofuel|Feedstock|Biofuel|Feedstock|Biofuel|
|Asia|80.18|23.25|3.68|0.99|2.47|0.77|
|Africa/Middle East|8.79|2.55|0|0|6.80|2.11|
|CIS|6.18|1.79|0|0|14.87|4.61|
|European Union|20.94|6.07|0|0|27.03|8.38|
|Other Eastern European|||||||
|Countries|1.28|0.37|0|0|0|0|
|Latin America|34.17|9.91|1.46|0.39|1.09|0.34|
|North America|135.37|39.26|7.90|2.13|8.02|2.49|
|Oceania|0|0|0.99|0.27|3.18|0.99|
|ROW|2.87|0.83|10.51|2.84|0.63|0.19|
|**Total**|**289.78**|**84.04**|**24.55**|**6.63**|**64.08**|**19.86**|

Source: ROW=rest of the world.

> 7 The same approach was used by Smeets et al. (2007), but a lower removal rate was used in that work.

6
# **References**

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- Best, M. C., Hjort, J., and Szakonyi, D. (2023). Individuals and Organizations as Sources of State Effectiveness. _American Economic Review_ , 113(8):2121–2167. 17

- Bosio, E., Djankov, S., Glaeser, E., and Shleifer, A. (2022). Public Procurement in Law and Practice. _American Economic Review_ , 112(4):1091–1117. 14

- Breza, E. and Liberman, A. (2017). Financial Contracting and Organizational Form: Evidence from the Regulation of Trade Credit. _The Journal of Finance_ , 72(1):291–324. 4, 13

- Carreri, M. and Martinez, L. R. (2022). Fiscal Rules, Austerity in Public Administration, and Political Accountability: Evidence from a Natural Experiment in Colombia. 17

- Cattaneo, M. D., Crump, R. K., Farrell, M. H., and Feng, Y. (2022). On binscatter. 31

- Checherita-Westphal, C., Klemm, A., and Viefers, P. (2016). Governments’ payment discipline: The macroeconomic impact of public payment delays and arrears. _Journal of Macroeconomics_ , 47:147–165. 13

- Conti, M., Elia, L., Ferrara, A. R., and Ferraresi, M. (2021). Governments’ late payments and firms’ survival: Evidence from the European Union. _The Journal of Law and Economics_ , 64(3):603–627. 4

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- Dahis, R., Carabetta, J., Scovino, F., Israel, F., and Oliveira, D. (2022). Data Basis: Universalizing Access to High-Quality Data. preprint, SocArXiv. 3

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18
# **REMOVING FOSSIL FUEL SUBSIDIES VS CARBON TAX in a MENA OPEN ECONOMY**

## **Figures 11a and 11b. CO2 Emissions: Morocco**

<!-- Start of picture text --> Fig. 11a. Carbon tax =$5 per ton CO2<br>100<br>80<br>60<br>40<br>20<br>0<br>2015 2020 2025 2030 2035 2040<br>Carbon tax = 5 & no fossil fuel subsidies<br>Carbon tax = 5 & fossil fuel subsidies<br>No carbon tax & fossil fuel subsidies<br><!-- End of picture text --> <!-- Start of picture text --> Fig. 11b. Carbon tax =$50 per ton CO2<br>100<br>80<br>60<br>40<br>20<br>0<br>2015 2020 2025 2030 2035 2040<br>Carbon tax = 50 & no fossil fuel subsidies<br>Carbon tax = 50 & fossil fuel subsidies<br>No carbon tax & fossil fuel subsidies<br>No carbon tax & No fossil fuel subsidies<br><!-- End of picture text --> 32
**Table 1: Calibration parameters for the MAC and INV models for each temperature target using the high cost, linear calibration.**

|_T_ = 80 yr|_T _<sup>_∗_</sup>= 1_._7 <sup>_◦_</sup>C<br>_r_ = 2 % yr<sup>_−_1</sup><br> ̄_B_ = 770 GtCO2|_σB_ = 220 GtCO2|
|---|---|---|
|Sector| ̄_γ_<br>�<br>$ tCO<sup>_−_1</sup><br>2<br><br>�<br> ̄_a_[GtCO2yr<sup>_−_1</sup>]|_δ_[%yr<sup>_−_1</sup>]<br> ̄_c_<br>�<br>$ tCO<sup>_−_1</sup><br>2<br><br>�|
||GtCO2 yr<sup>_−_1</sup><br><br>|<br><br><br>GtCO2 yr<sup>_−_3</sup><br>|
|Waste|243.9<br>0.82|3.3<br>88683|
|Industry|36.6<br>5.47|4<br>13924|
|Forestry|24.24<br>8.25|0.8<br>8815|
|Agriculture|24.57<br>4.07|5<br>8934|
|Transport|26.7<br>3.74|6.7<br>9722|
|Energy|16.68<br>11.99|2.5<br>6065|
|Buildings|62.5<br>3.2|1.7<br>22725|
||_T _<sup>_∗_</sup>= 2 <sup>_◦_</sup>C||
|_T_ = 80 yr|_r_ = 2 % yr<sup>_−_1</sup><br> ̄_B_ = 1340 GtCO2|_σB_ = 380 GtCO2|
|Sector| ̄_γ_<br>�<br>$ tCO<sup>_−_1</sup><br>2<br><br>�<br> ̄_a_[GtCO2yr<sup>_−_1</sup>]|_δ_[%yr<sup>_−_1</sup>]<br> ̄_c_<br>�<br>$ tCO<sup>_−_1</sup><br>2<br><br>�|
||GtCO2 yr<sup>_−_1</sup><br><br>|<br><br><br>GtCO2 yr<sup>_−_3</sup><br>|
|Waste|243.9<br>0.82|3.3<br>95912|
|Industry|36.6<br>5.47|4<br>14378|
|Forestry|24.24<br>8.25|0.8<br>9533|
|Agriculture|24.57<br>4.07|5<br>9661|
|Transport|26.7<br>3.74|6.7<br>10514|
|Energy|16.68<br>11.99|2.5<br>6559|
|Buildings|62.5<br>3.2|1.7<br>24577|

# **3 Alternative calibrations**

## **3.1 High cost, linear calibration**

In this subsection, we use the high cost, linear calibration of the marginal abatement cost curves (blue lines in Figure 1) in our optimization scheme. We find that while the quantitative implications of our analysis are altered, the qualitative results are robust to this alternative calibration.

### **3.1.1 Calibration**

We use the same scheme as outlined in § 2 to calibrate the INV and MAC model. We use (2.12) to ensure the relative costs between each sector are equal in both models, and that the total cost of each policy without uncertainty is equal. The numerical values for the marginal abatement cost and marginal investment cost coefficients are shown in Table 1.

8