rafmacalaba/datause-encoder-data huggingface dataset:
- Islam, M.R., Li, T., Mahata, K., Khanal, N., Werden, B., Giordano, M.R., Praveen Puppala, S., Dhital, N.B., Gurung, A., Saikawa, E., Panday, A.K., Yokelson, R.J., DeCarlo, P.F., Stone, E.A., 2022. Wintertime Air Quality across the Kathmandu Valley, Nepal: Concentration, Composition, and Sources of Fine and Coarse Particulate Matter. ACS Earth and Space Chemistry 6, 2955-2971.

- Jayarathne, T., Stockwell, C.E., Bhave, P.V., Praveen, P.S., Rathnayake, C.M., Islam, M.R., Panday, A.K., Adhikari, S., Maharjan, R., Goetz, J.D., DeCarlo, P.F., Saikawa, E., Yokelson, R.J., Stone, E.A., 2018. Nepal Ambient Monitoring and Source Testing Experiment (NAMaSTE): emissions of particulate matter from wood- and dung-fueled cooking fires, garbage and crop residue burning, brick kilns, and other sources. Atmos. Chem. Phys. 18, 2259-2286.

- Kamyotra, J.S., 2015. Brick Kilns in India. Presentation. Central Pollution Control Board (CPCB), New Delhi.

- Kaushik, R., Khaliq, F., Subramaneyaan, M., Ahmed, R.S., 2012. Pulmonary dysfunctions, oxidative stress and DNA damage in brick kiln workers. Human & Experimental Toxicology 31, 1083-1091.

- Khisroon, M., Khan, A., Imran, M., Zaidi, F., Ahmadullah, Fatima, S.H., 2018. Biomonitoring of DNA damage in individuals exposed to brick kiln pollution from Peshawar, Khyber Pakhtunkhwa, Pakistan. Arch. Environ. Occup. Health 73, 115-120.

- Kumar, S., Maithel, S., 2016. Introduction to Brick Kilns and Specific Energy Consumption Protocol for Brick Kilns Greentech Knowledge Solutions Pvt. Ltd., New Delhi.

- Lakho, N., Zardari, M., 2016. Structural Properties of Baked Clay Bricks Fired with Alternate Fuels. Engineering 8, 676-683.

- Maithel, S., 2013. Evaluating Energy Conservation Potential of Brick Production in India, A Report Prepared for the SAARC Energy Centre, Islamabad. Greentech Knowledge Solutions Pvt Ltd, New Delhi.

- Maithel, S., Kumar, S., Lalchandani, D., 2014. Factsheets about Brick Kilns in South and SouthEast Asia. Greentech Knowledge Solutions Pvt Ltd (GKSPL), New Delhi.

- Maithel, S., Lalchandani, D., Kumar, S., Bhanware, P., Ahuja, S., Uma, R., Athalye, V., Ragavan, S., Joshi, R., Bond, T., Weyant, C., Baum, E., 2013. Zigzag Kiln Performance Assessment – 2012 & 2013. A Shakti Sustainable Energy Foundation Supported Initiative. Greentech Knowledge Solutions, New Delhi, Enzen Global Solutions, Bangalore, University of Illinois at Urbana-Champaign, Illinois, Clean Air Task Force, Boston, New Delhi.

- Maithel, S., Lalchandani, D., Malhotra, G., Bhanware, P., Uma, R., Ragavan, S., Athalye, V., Bindiya, K.R., Reddy, S., Bond, T., Weyant, C., Baum, E., Thoa, V.T.K., Phuong, N.T., Thanh, T.K., 2012. Brick Kilns Performance Assessment: A Roadmap for Cleaner Brick Production in India. Greentech Knowledge Solutions Pvt. Ltd., New Delhi, Enzen Global Solutions Pvt.

38
but stronger for FDA-regulated products for deep RTAs. Similarly, results for MFN and regulatory provisions hold. Finally, the results are also robust to using a continuous measure, counting the number of detailed products (HS10) within HS4-products that require prior notice.<sup>29</sup> Table 8: Alternative measure of regulation-intensity: FDA prior notice

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|||Export pa|rticipation|||Expor|t entry||
|Exp Other...(t-1)|||||||||
|... RTA<br>... RTA x FDA|0.005***<br>(0.000)<br>0.005***<br>(0.001)|0.006***<br>(0.000)<br>-0.004***<br>(0.001)|0.004***<br>(0.000)<br>0.002*<br>(0.001)|0.004***<br>(0.000)<br>0.001<br>(0.001)|0.003***<br>(0.000)<br>0.001***<br>(0.000)|0.003***<br>(0.000)<br>-0.003***<br>(0.000)|0.004***<br>(0.000)<br>-0.001*<br>(0.000)|0.003***<br>(0.000)<br>-0.001**<br>(0.000)|
|... Depth||-0.002**||||-0.000|||
|||(0.001)||||(0.000)|||
|... Depth x FDA||0.021***<br>(0.002)||||0.008***<br>(0.001)|||
|... Depth MFN|||0.012***<br>(0.001)||||0.008***<br>(0.001)||
|... Depth Pref|||-0.008***<br>(0.001)||||-0.007***<br>(0.001)||
|... Depth MFN x FDA|||0.026***<br>(0.003)||||0.008***<br>(0.001)||
|... Depth Pref x FDA|||-0.017***<br>(0.002)||||-0.004***<br>(0.001)||
|... SPSorTBT||||0.002***<br>(0.000)||||0.001***<br>(0.000)|
|... SPSorTBT x FDA||||0.007***<br>(0.001)||||0.003***<br>(0.000)|
|Observations<br>|164,595,130|164,595,130|164,595,130|164,595,130|164,298,452|164,298,452|164,298,452|164,298,452|
|R-squared|0.340|0.340|0.340|0.340|0.156|0.156|0.156|0.156|
|Spatial controls|YES|YES|YES|YES|YES|YES|YES|YES|
|Firm-prod-year FE|YES|YES|YES|YES|YES|YES|YES|YES|
|Dest-prod-year FE|YES|YES|YES|YES|YES|YES|YES|YES|
|Firm-prod-dest FE|YES|YES|YES|YES|YES|YES|YES|YES|

Robust standard errors, clustered at the firm-product level, are in parentheses. The dependent variable _yijp,t_ is a dummy variable equal to 1 if firm _i_ exported (or started exporting) product _p_ to country _j_ in year _t_ . FDA=1 if product _p_ requires submission to FDA for prior notice to be imported into the United States. Coefficients on spatial exporting controls are not reported for brevity. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1

# **5.3 Alternative measure of depth**

We use another measure of depth based on Mario Larch’s Regional Trade Agreements Database from Egger and Larch (2008). Without focusing on individual provisions, this data set classifies RTAs into three broad categories —- partial scope agreements (PS), free trade agreements (FTA), and customs unions (CU) —- that are increasingly more comprehensive.<sup>30</sup> Table 9 provides additional supporting evidence that deep RTAs generate positive spillover effects on third-country exporters. Columns (1) and (4)

> 29This ranges from 0 to 156, with the maximum being for 0406 cheese and curd. Results are reported in Appendix Table A.7.

> 30FTA and CU are as defined in Paragraphs 8(b) and 8(a), respectively, of Article XXIV of GATT 1994, and a partial scope agreement means that the agreement covers only certain products. For more details on the database, see https://www.ewf.uni-bayreuth.de/en/research/RTA-data/index.html.

18
(the 3-year window around the date of the year of the growth event, **_GEit_** ) is regressed on several determinants: where:

- φ is the cumulative normal distribution;

- _FEit_ is a dummy variable that takes the value of 1 at the date of the fiscal event as defined in the benchmark above and during the four years following this date;

- _WWit_ is a proxy for trade (and other) reforms, i.e. a dummy taking the value of 1 during the first five years of a transition towards openness as defined by WW (2003);

- _TOTit_ is a proxy for any external shock, i.e. a dummy taking the value of 1 if the change in the terms of trade for country i and year t is in the upper 90% of the entire sample. Following HPR, this variable is introduced to capture exceptionally favorable external circumstances;<sup>20</sup>

- _HIit_ is a dummy equals to one for High Income countries;

- ∑ _Dt_ is a full set of year effects. _t_ − 1

In equation (1), the year dummies capture the effects of omitted time-related variables like common shocks across countries that could account for a growth event. As to the fiscal dummy event variable, _FEit_ , it is a way to test whether, on average, growth events are preceded by fiscal events. The inclusion of the _WWit_ dummy for trade reform is both to capture the potential growth effects of a trade reform, but also the effects of other ongoing reforms since, very often, trade reforms are part of a broader package of reforms. Finally, as pointed out by Easterly et al. (1993), it is also plausible that many growth accelerations are triggered by favorable external conditions, especially in our context where, due to the short length of time series, we defined growth events over a 5-year window.<sup>21</sup> To control for this, we introduce the _TOTit_ dummy.

> 20 The change in the terms of trade is computed as the first difference of the log of the terms-oftrade index , the latter defined as the ratio of export prices to import prices using the current and constant price values of exports and imports from WDI. We use this index instead of the more traditional net-barter index because of its broader coverage. However, this measure has the disadvantage that it includes the service export sector (see the discussion in Loayza and Raddatz, 2007).

21 Easterly et al. (1993) showed that about 10 percent of the variation in GDP growth and a quarter of the variation in growth volatility can be explained by the observed differences in the volatility of terms-of-trade changes.

21
The World Bank, Human Capital Development and Operations Policy, Working Papers, no. 63, 1995.

- Psacharopoulos, G., A.M. Arriagada and E. Velez. 1992. “Earnings and education among selfemployed males in Colombia.” _Bulletin of Latin American Research_ 11(1): 69-89.

- Psacharopoulos, G., 1995.  “The Profitability of Investment in Education: Concepts and Methods.” World Bank, Human Capital Development and Operations Policy, Working Papers No. 63.

- Psacharopoulos, G. and R. Mattson. 1998. “Estimating the Returns to Education: A Sensitivity Analysis of Methods and Sample Size.” _Journal of Educational Development and Administration_ 12(3): 271-287.

- Psacharopoulos, G. and H.A. Patrinos. 2004a. “Returns to Investment in Education: A Further Update.” _Education Economics_ 12(2): 111-135.

- Psacharopoulos, G. and H.A. Patrinos. 2004b. “Human Capital and Rates of Return,” in _International Handbook on the Economics of Education_ (Chapter 1). Edward Elgar Publishing.

- Psacharopoulos, G. and R. Mattson. 1998. “Estimating the Returns to Education: A Sensitivity Analysis of Concepts and Methods” _Journal of Educational Planning and Administration,_ 12, 3: 271-287.

- Psacharopoulos, G. and H.A. Patrinos. 2002. “Returns to investment in education: a further update.” World Bank Policy Research Working Paper Series 2881.

- Psacharopoulos, G. and H.A. Patrinos. 2008. “Education and Human Capital,” in _International Handbook of Development Economics_ (Volumes 1 & 2, chapter 24). Edward Elgar Publishing.

- Psacharopoulos, G. and E. Velez. 1992. “Schooling, ability, and earnings in Colombia, 1988.” _Economic Development and Cultural Change_ 40(3): 629-643.

- Rosenzweig, M. R. 1995. “Why are there returns to schooling?” _American Economic Review_ 85(2): 153−158.

- Salisbury, T. 2016. “Education and Inequality in South Africa: Returns to Schooling in the PostApartheid Era.” _International Journal of Educational Development_ 46: 43-52.

- Sanchez Torres, F. and J. Nunez Mendez.  2003. “A Dynamic Analysis of Human Capital, Female Work Force Participation, Returns to Education and Changes in Household Structure in Urban Colombia, 1976-1998.” _Colombian Economic Journal_ 1: 110-49.

- Schultz, T.W. (1960), ‘Capital formation by education,’ _Journal of Political Economy_ , 68, 571583.

18
less. The median firm in the sample has 2 paid workers, with only 20 percent of the sample having five or more paid workers. Mean monthly profits are 1628 Bolivianos ($US211).<sup>5</sup> Profits were measured through a direct question, following the recommendations made in de Mel, McKenzie and Woodruff (2007). Half the firm owners are female. Average education levels for owners are quite high, but there is substantial variation. The median owner has 12 years of education, but 20 percent of firm owners have less than 6 years education, and 31 percent have more than 12 years.

# **2.3. Measuring Owner Ability and Background**

A main concern for our empirical work is that there are characteristics of firm owners which affect both profitability and the decision of whether or not to formally register the business. One such factor could be owner ability, which may determine the size of the gain in profits from becoming formal. Other such factors could be family background and wealth, which might affect the ability of the firm to meet the costs of formalizing, and the utility associated with being formal. Our instrumental variables strategy aims to overcome such concerns, but we also attempt to directly measure and control for these variables much more than is possible with variables such as gender, own education, age, marital status and ethnicity found in standard firm surveys.

We use three different measures of owner ability and motivation. The first is mother’s education, consisting of dummy variables for mother having no education (36 percent of firm owners), and for mother having 9 or more years education (16 percent). The second measure is entrepreneurial self-efficacy. Self-efficacy is the first principal component of ten questions intended to measure the self-assessed ability to perform certain tasks. Owners were asked on a four point scale how confident they are that they could do ten business tasks. Examples include their ability to estimate accurately the costs of a new project; to resolve a difficult dispute with a client or supplier in another city; to hire good employees to expand their business; to sell a product to a new client; and to price their business correctly if they wish to sell it. Such a measure is more closely tied to business skills than years of education, and has a correlation of only 0.11 with years of education.

> 5 During March 2007, 1 USD was approximately 7.7 Bolivianos.

- 6 -
**The Social Impact of Social Funds in Jamaica: A Mixed-Methods Analysis of Participation, Targeting and Collective Action in Community Driven Development**

Vijayendra Rao, The World Bank, Washington DC Ana Maria Ibafiez, Fedessarollo, Columbia We are grateful to Ruel Cooke and George Campbell for excellent fieldwork. This paper is the result of a joint OED-DEC data collection effort and we thank the OED team Soniya Carvalho, Anju Gupta Kapoor, Susan Razzaz and Howard White for their participation. We also thank JSIF for their openness and cooperation in the data collection process. We are grateful to Grace Imani Duncan for her input, and to Gershon Feder, Ernmanuella Galasso, Martin Ravallion, Judith Tendler, Michael Woolcock and our colleagues in the Development Research Group for valuable comments and support, both moral and intellectual. The project was partially financed by Dutch and Norwegian Trust Funds. Please direct any correspondence to vrao(aworldbank.or2.
4 gives the density functions for scores 1-10. Again we see signs of bi-modality, with a larger sub-group of staff centered around an answer of 7-8 and a smaller mass centered around 2-3. The mean and median responses to the first question on a 10-point scale were 5.80 and 6 respectively with n=241 (there is a large number of missing values for both these questions). 12.9% of respondents gave the answer _“Depends on the researcher.”_ The mean and median for the second were 5.47 and 6 respectively (n=254).

Tables 10 and 11 give the breakdown of these indicators across VPUs and sectors. The proportion rating DEC researchers as 5+ on their responsiveness varies from 50% in OPC to 100% in WBI. The proportion with 5+ for availability for cross-support varies from 36% in South Asia to 79% in ECA. The differences across sectors in assessments of DEC responsiveness are particularly striking, with only 20% for staff in SDV rating responsiveness as high, compared to 90% of staff in POV, with EP close behind at 86%.

The survey asked about other factors relevant to demand for research, notably its relevance, accessibility and timeliness. (Each category was given an explanation in the survey instrument.<sup>24</sup> ) And again a 10 point scale was used. Figure 5 gives the densities. A marked bimodality is evident for ―relevance‖ and (less so) ―accessibility‖ and the answers to these questions were highly correlated (r=0.74). ―Timeliness‖ is distributed somewhat differently though still correlated with the other measures (r=0.72 for both relevance and access). The mean score on relevance was 5.80 (n=445), 6.12 (n=449) on access and 5.25 (n=383) for timeliness, which appears to be the more important constraint on demand for research. Research takes time, and often requires a longer time horizon than development projects allow.

# **5. The value of research in operational work**

The incentive to learn about the Bank’s research is greatest when it is perceived to be of high value to operational staff in their work. The survey asked: _“please rate the overall value of World Bank research for your work, on a scale of 1-10 where 1 means not valuable at all, 10 means extremely valuable.”_ And again respondents were presented with a 10 point scale where 1=‖not valuable at all‖ and 10=‖extremely valuable.‖ Figure 6 gives the densities of responses to

> 24 ―Relevance‖ was qualified by ―not too theoretical;‖ ―accessible‖ was explained as ―well written and easy to use‖ and ―timeliness‖ was qualified as ―the research was delivered in the necessary time frame to allow you to integrate into AAA and/or lending operation.‖

16
|**Mali**|2006|12.67|92.5|11.72|0.21|0.21|14.75|1.67|8.20|n|
|---|---|---|---|---|---|---|---|---|---|---|
|**Morocco**|2007|127.85|99|126.57|2.28|6.73|23.37|1.81|0.06|y|
|**Mozambique**|2008|18.89|22.8|4.31|0.08|0.12|1.57|0.41|13.62|n|
|**Niger **|2007|9.25|98.6|9.12|0.16|0.08|0.00|1.77|8.31|n|
|**Nigeria**|2004|224.62|50.4|113.21|2.04|2.27|0.00|0.91|8.26|n|
|**Pakistan**|2005|340.26|96.3|327.67|5.24|4.28|15.21|1.55|0.91|y|
|**Senegal**|2005|18.21|96|17.48|0.31|0.79|14.09|1.74|3.05|n|
|**Sierra Leone**|2003|2.72|71.3|1.94|0.03|0.03|0.00|1.28|16.10|n|
|**Suriname**|1999|2.02|15.9|0.32|0.01|0.00|11.25|0.29|0.61|n|
|**Syrian Arab**|2004|70.02|92.2|64.56|0.97|0.86|20.20|1.39|0.02|y|
|**Tajikistan**|2004|8.77|84.1|7.38|0.13|0.25|0.61|1.51|1.70|n|
|**Togo**|2006|4.96|12.2|0.61|0.01|0.23|0.00|0.22|6.42|n|
|**Turkey**|2005|781.24|98|765.62|14.55|0.89|16.49|1.86|0.04|y|
|**Uganda **|2009|39.81|12.1|4.82|0.09|0.75|12.52|0.22|3.10|n|
|**Yemen**|2005|46.13|99.1|45.71|0.82|1.28|0.00|1.78|0.87|y|

Source: World Bank Databank

# **_Qard-al-Hasan_ as a tool for financial inclusion**

Qard-al-Hasan (QH henceforth) is defined in _Shariah_ as an interest free loan. It is usually granted from well off lenders to poor borrowers. It can also be directed from borrowers to intermediaries that can redirect it on their behalf to poor borrowers. QH is therefore a non rewarding loan (with no expected return) and the borrower is under obligation to repay the loan depending on the borrower’s financial capacity to do so. Loan procedures are usually informal and social capital is the basic collateral for this instrument.

The main objectives of Qard al-Hassan could be:<sup>23</sup>

- To help the needy fellow people.

- To establish better relationship among poor and the rich.

- The mobilization of wealth among all people in the society.

- To perform a good deed that is encouraged and appreciated by the _Allah_ (swt) and His messenger.

- To strengthen the national economy.

- To facilitate the poor to create new jobs market and business ventures by using their merits, skills and expertise.

- To establish a caring society.

- To eradicate unemployment problem from the society.

> 23 Please see (Hossain 1955) for a comprehensive exposition of al-qard-al-hasan based on Quran and Sunnah.

22
Cox transformation. As previously discussed, the primary goal of this transformation is to normalize regression residuals and mitigate heteroskedasticity. However, as Savin and White (1978) highlight, neglecting autocorrelation in the context of a Box-Cox transformation could result in misleading conclusions due to the potential presence of functional form misspecification. To ensure robustness, we implement the method proposed by Savin and White (1978), which was later extended by Seaks and Layson (1983).

Another important consideration is the assessment of model goodness-of-fit. When applying the FGLS method to correct for autocorrelation, the statistic—which measures the proportion of variance in the dependent variable explained by the regresRR<sup>2</sup> sors—may not be directly comparable across models, as the dependent variable is transformed based on the autocorrelation coefficient estimated with different sets of regressors (Wooldridge 2016). Similarly, when using the OLS coefficients with Newey-West errors, the traditional maybe biased, since the variance formula for the dependent variable is no longer valid in the presence of a trend. Instead of the , we report stationarity statisRR<sup>2</sup> tics to evaluate whether the residuals exhibit stable statistical properties over time—a RR<sup>2</sup> crucial assumption for ensuring that the error terms are iid. For this purpose, the PhillipsPerron test is employed to evaluate stationarity. Additionally, we report the Akaike Information Criteria, a model selection metric that estimates the relative amount of information loss by a given model, as described in Banks and Joyner (2017) for least-squared analyses.

# **_3.5 Data_**

Area and production data for 144 food commodities—including grains, oilseeds, fruits, fibers, and other crops—were collected FAOSTAT for the period 1961-2021. Together, these commodities represent over 98 percent of the global agricultural land use during the sample period. Calorific data for most commodities are sourced from the FAO’s Food Balance Sheets. Figures 1 and 2 present the calories per kilogram and global production shares of the 15 most significant commodities in 2021, which collectively represented nearly 90% of global food production in terms of calorific output.

While various data sources are available for commodity production and nutritional values, considerable variation exists in data collection methods (e.g., dry vs. raw weight). To ensure consistency, all data used in the analysis are sourced from the FAO. In addition to the crop-level data, we also collect the regional-level data for each crop. Important to note that the FAO Food Balance Sheets provide the calorific content of a specific commodity per 100 grams of edible portion in terms of the retail weight ("as purchased"). As such, it does not consider the calories from the non-edible portion of the crop _— 11 —_