rafmacalaba/datause-encoder-data huggingface dataset:
Cai, Fang, Albert Park and Yaohui Zhao. 2008. “The Chinese Labor Market in the Reform Era,” in Loren Brandt and Thomas Rawski, eds., _China's Great Economic Transformation_ . Cambridge: Cambridge University Press.

Cai, Fang. 2011. “Hukou System Reform and Unification of Rural-Urban Social Welfare,” _China and World Economy_ 19(3): 33-48.

Chi, Wei and Bo Li. 2008. “Glass ceiling or sticky floor? Examining the gender earnings differential across the earnings distribution in urban China, 1987–2004,” _Journal of Comparative Economics_ , 36(2): 243-263.

DRC (Development Research Center) and World Bank. 2012. _China 2030: Building a Modern, Harmonious and creative High-Income Society, Conference Edition_ . Washington, DC (February 2012): The World Bank.

Gallagher, Mary, John Giles, Albert Park and Meiyan Wang. 2012. “China’s 2008 Labor Contract Law: Implementation and Implications for Chinese Workers,” Hong Kong University of Science and Technology, draft.

Gao J, Tang S, Tolhurst R, Rao K. 2001. Changing Access to Health Services in Urban China: Implications for Equity. _Health Policy and Planning_ 16(3): 302–312.

Giles, John, Albert Park and Fang Cai. 2006a. “Reemployment of Dislocated Workers in Urban China: The Roles of Information and Incentives,” _Journal of Comparative Economics_ 34(3) (September 2006): 582-607.

Giles, John, Albert Park and Fang Cai. 2006b. “How has Economic Restructuring Affected China’s Urban Workers,” _China Quarterly_ 185 (March 2006): 61-95.

Giles, John, Albert Park, Fang Cai and Yang Du. 2012. “Weathering a Storm: Survey-Based Perspectives on Employment in China in the Aftermath of the Global Financial Crisis,” Policy Research Working Paper 5984. Washington: The World Bank.

Giles, John, Dewen Wang and Albert Park. 2013. “Expanding Social Insurance Coverage in Urban China,” Policy Research Working Paper Series (in press). Washington, DC: 2013; and IZA Discussion Paper (in press). Bonn: Institute for Labor (IZA).

Holzmann, Robert Truman Packard, Jose Cuesta. 2001. Extending Coverage in Multi-Pillar Pension Systems: Constraints and Hypotheses, Preliminary Evidence and Future Research Agenda, in New Ideas about Old Age Security: Toward Sustainable Pension Systems in the 21st Century Edited by Joseph E. Stiglitz, Robert Holzmann, World Bank, Washington, D.C.

Holzmann, Robert, David A. Robalino, and Noriyuki Takayama. 2009. Closing the Coverage Gap: the Role of Social Pensions and Other Retirement Income Transfers, World Bank, Washington D.C.

Hsiao WC. 1984. Transformation of Health in China. New England Journal of Medicine 310: 932–936.

Hsiao WC. 1995. The Chinese Health Care System: Lessons for Other Nations. _Social Science & Medicine_ 41(8):1047–1055.

26
# Electricity Access and Structural Transformation: Evidence from Brazil’s Electrification<sup>_∗_</sup>

Fidel Perez-Sebastian,<sup>_†_</sup> Jevgenijs Steinbuks,<sup>_‡_</sup> José Féres,<sup>§</sup> and Ian Trotter<sup>_¶_</sup> Keywords: Brazil, electricity access, structural transformation, firm size JEL: H54, O18, O41

> _∗_ The authors thank Renata Ayd and Eric Jardim for helping with the data for the model calibration, and Molly Lipscomb, Mushfiq Mobarak, and Dimitri Szerman for the instrumental variables used in the econometric analysis. They also thank German Cubas, Roberto Fattal Jaef, Berthold Herrendorf, Marti Mestieri, Diego Restuccia, Mike Toman, and the seminar participants of the World Bank half-baked Macro seminar, the European Economic Association Annual Congress, the North American summer meeting of the Econometric Society, the SETI annual workshop, and the Spanish Economics Association annual meetings for helpful comments and suggestions. They acknowledge the research funding from the World Bank research support budget.

> _†_ Department of Economics, University of Alicante and Hull University Business School.

> _‡_ Corresponding author. Development Research Group, The World Bank. Address: 1818 H St. NW Washington, DC, 20433. Tel.: +1 202 473 9345. E-mail: jsteinbuks@worldbank.org.

> §Instituto de Pesquisa Econômica Aplicada, Brazil.

> _¶_ Universidade Federal de Viçosa, Brazil.
Table 4: Trade, Legal Origins and Bureaucratic Quality, Baseline Results Dep. Var.: log of average trade flow

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||OLS|IV|OLS|IV|
|Different Legal Origin|-0.10|-0.14|-0.24|-0.21|
||(-2.19)|(-2.89)|(-5.42)|(-4.44)|
|Bureaucratic Quality|0.38|0.47|0.32|0.25|
||(26.2)|(12.64)|(21.81)|(6.8)|
|average weighted tariff|||-0.02|-0.02|
||||(-12.91)|(-12.7)|
|log distance|-1.09|-1.07|-0.97|-0.98|
||(-36.97)|(-35.44)|(-33.55)|(-32.82)|
|log product of GDP|1.07|1.03|1.01|1.03|
||(95.8)|(63.24)|(96.78)|(68.34)|
|log product of land area|-0.20|-0.18|-0.17|-0.17|
||(-21.75)|(-18.06)|(-19.12)|(-17.91)|
|no. of landlocked|-0.33|-0.35|-0.41|-0.40|
||(-7.39)|(-7.81)|(-9.59)|(-9.44)|
|common border|0.98|1.02|0.97|0.94|
||(6.77)|(6.93)|(6.49)|(6.26)|
|common border|0.98|1.02|0.97|0.94|
||(6.77)|(6.93)|(6.49)|(6.26)|
|common language|0.51|0.49|0.54|0.55|
||(9.07)|(8.62)|(9.97)|(10.06)|
|currency union|1.90|1.86|1.18|1.19|
||(7.85)|(7.66)|(4.9)|(4.98)|
|Observations|77594|77594|32752|32752|
|R^2|0.67|0.67|0.74|0.74|
|First stage R^2||0.43||0.44|
|% increase in trade due<br>to 1s.d. institution|88%|119%|70%|53%|

Notes: t-values in parentheses. Standard errors are calculated taking into account country-pair clusters. A constant and year dummies are not reported. IV estimators use the sum of absolute latitude as an instrument for the sum of bureaucratic quality for both countries in a trading pair. The sum of bureaucratic quality ranges from 0 to 8 with mean 4.672 and standard deviation 1.667. The percent increase in trade due to 1s.d. institution (bureaucratic quality) is calculated as follows: exp(coef*sd)-1.

38
means,’ Loscocco and Robinson (1991) find sales lower for women among small business owners in same industry comparisons (equivalent to the 4-digit ISIC) in the US. Their conclusions are drawn from comparing mean sales in industrial sub categories dominated by women compared to the sales in categories dominated by men. There findings are reminiscent of the occupational segregation or occupational crowding literature (e.g., Meng and Miller, 2001, Preston, 1999).

Some studies also indicate that firms owned by women are less profitable than those owned by men. Robb and Wolken (2002) study a sample of white-owned small businesses in the U.S. and show that women owned businesses generate only 78% of the profits generated by male-owned businesses. Bosma et al. (2004) find male Dutch business owners outperformed women in terms of profit. Neither of these studies conditions on the industrial location of the firm.

Other studies, however, do not find females perform less well when other measures of performance (such as return on equity, total factor productivity, growth of sales or employment) are used. In a study from Australia, Watson (2002) shows that women business owners earn similar rates of return on equity and assets as male business owners. Using World Bank Enterprise Surveys (2002-2006), Bardasi et al. (2007) find that in Africa, female-owned businesses are at least as productive as male entrepreneurs when measured by value added per worker and total factor productivity, holding constant the industry in which they work. Similarly, Kepler and Shane (2007) show that there are no significant gender differences in terms of performance outcomes of nascent entrepreneurs.

The research findings with respect to gender differences in the growth of the business are mixed. Some studies show that female-owned enterprises do not underperform in terms of employment growth (Fischer et al., 1993; Chagnati and Parsuraman, 1996) whereas others do (Bosma et al., 2004). Some find that women-owned firms have significantly higher annual sales growth than men (Coleman, 2007).

# **_2.2 Explanations for Differences in Performance_**

In studies where gender differences in firm performance emerge, various explanations for these differences are proposed ranging from psychological and social differences between male and female entrepreneurs to institutional constraints. We focus 4
|**Dependent variable = 1 if the house**|**hold receive**<br>[1]|**d a private t**<br>[2]|**ransfer in th**<br>[3]|**e last 12 mo**<br>[4]|**nths**<br>[5]|[6]|
|---|---|---|---|---|---|---|
|**treated**|**-0.245 ****|*** -0.246 ****|*** -0.247 *****|**-0.251 *** **|**-0.238 *****|**-0.242 *****|
||[0.033]|[0.034]|[0.034]|[0.034]|[0.035]|[0.036]|
|**Village level shocks**|||||||
|Lower yields due to drought or floods||**0.023**|**0.023**|**0.022**|**0.019**|**0.006**|
|||[0.037]|[0.037]|[0.038]|[0.038]|[0.039]|
|Rise in food prices||**-0.032**|**-0.025**|**-0.017**|**-0.001**|**0.000**|
|||[0.036]|[0.037]|[0.037]|[0.038]|[0.038]|
|**HH level shocks**|||||||
|Crop disease|||**0.031**|**0.041**|**0.058**|**0.060**|
||||[0.040]|[0.041]|[0.041]|[0.042]|
|Livestock died or stolen|||**-0.079 ***|**-0.066**|**-0.063**|**-0.058**|
||||[0.043]|[0.044]|[0.045]|[0.045]|
|Theft|||**0.100**|**0.104 ***|**0.097 ***|**0.101 ***|
||||[0.065]|[0.065]|[0.066]|[0.067]|
|House damage|||**0.012**|**0.009**|**0.006**|**0.004**|
||||[0.037]|[0.037]|[0.037]|[0.037]|
|Safety net (food)|||**0.033**|**0.029**|**0.013**|**0.006**|
||||[0.038]|[0.038]|[0.039]|[0.039]|
|Safety net (agr. inputs)|||**-0.022**|**-0.004**|**0.013**|**0.001**|
||||[0.036]|[0.036]|[0.038]|[0.039]|
|**HH assets**|||||||
|Furnitures (mattress, table, chairs)||||**-0.001**|**-0.010**|**-0.026**|
|||||[0.063]|[0.063]|[0.064]|
|Animal (chickens, pigs, etc)||||**-0.100 ***|**-0.091 ***|**-0.103 ***|
|||||[0.052]|[0.053]|[0.053]|
|Agr tools (axe, sickle, hoe)||||**-0.144 *** **|**-0.135 ****|**-0.138 ****|
|||||[0.060]|[0.061]|[0.063]|
|**HH characteristics**|||||||
|HH size|||||**-0.012**|**-0.008**|
||||||[0.009]|[0.011]|
|Chronically ill adults|||||**-0.008**|**-0.026**|
||||||[0.038]|[0.039]|
|Presence of orphans|||||**-0.040**|**-0.040**|
||||||[0.040]|[0.042]|
|Deaths in the past 5 years|||||**0.026**|**0.027**|
||||||[0.040]|[0.041]|
|Disable persons|||||**0.080 ***|**0.082 ***|
||||||[0.046]|[0.046]|
|**Head of HH's charact.**|||||||
|Female headed||||||**-0.018**|
|||||||[0.042]|
|Over 65 yrs old||||||**0.037**|
|||||||[0.042]|
|No education||||||**-0.022**|
|||||||[0.038]|
|Poor health status in the past year||||||**0.058**|
|||||||[0.045]|
|Employment in past year||||||**0.040**|
|||||||[0.051]|
|**Nr. of obs.**|749|749|749|749|748|744|
|**R-sq**|0.05|0.06|0.06|0.07|0.08|0.09|

**Notes:** Table reports estimated marginal effects after probit.Significant at the *** 1% level, ** 5% level, * 10% level. Robust standard errors in squared brackets.

21
Arai et al. (2020) : The Hidden Potential of Call Detail Records in The Gambia.

CDR data to track short-term mobility in the hours and days after Cyclone Mahasen hit Bangladesh in May 2013 (Lu et al., 2016).

# **B. Methodological innovations and CDR data**

The most commonly used methods for processing CDR data are traditional data mining techniques. These include frequency-based analysis, data clustering using unsupervised machine learning, and geovisualization techniques by mapping geolocation (Calabrese, Ferrari, & Blondel, 2014). In recent years, researchers have used de-identified CDR data to compute Origin-Destination matrices in order to better map patterns in travel behavior (Calabrese, Di Lorenzo, Liu, & Ratti, 2011). In combination with additional administrative or survey data, supervised machine learning methods can inform the prediction of outcomes of interest-based solely on patterns in the cell network (Sundsøy, Johannes, Reme, Iqbal, & Jahani, 2016).

The pre-processing of the raw CDR data is essential to accommodate positioning errors in data collection and the first step for processing. The oscillation problem of the user’s location is the leading cause of noise in position data collected from the cellular network as they transfer calls to the nearest base station for traffic management, creating imprecise and overlapping Voronoi polygons (Chen, Ma, Susilo, Liu, & Wang, 2016). The time-based filter is used to ignore oscillation and agglomerative (hierarchical clustering) in methods to extract truthful location data from raw CDR.

However, handling such sensitive data requires appropriate protocols to address concerns around data privacy. While anonymizing data is necessary, Kondor et al. (2015) show that it is theoretically possible to identify users based on their mobility patterns alone (Dániel Kondor et al., 2015). It is, therefore, best practice to restrict access to individual observations and use aggregated indicators for the purpose of analysis.

# **C. Country context: The Gambia**

The Gambia is a West African country of 2.3 million people surrounded by Senegal. The country experienced prolonged spells of violence and instability and is currently undergoing a transition process to restore its democratic institutions. With a Gross National Income (GNI) of 740 USD (current, Atlas method), The Gambia is classified as a low-income country, with more than 10 percent of the population living in extreme poverty. The capital city region at the mouth of the Gambia River encompasses Banjul City and the Kanifing region, with tourist resorts strung southwards along the coast (see Figure 1). Tourism and the civil service are the largest drivers of formal employment. Inland is largely rural, its economy is driven by agriculture, and largely dependent on the flow of domestic and international remittances from migrants. These disparities in access to services and opportunities have led to high levels of internal migration, especially among the young who left rural areas to look for better jobs in the capital city region (The World Bank, 2020).

<mark>Figure 1.</mark> Administrative boundaries of The Gambia

**4** | P a g e
_Figure 4_ - Income gains from reduction in years of life lost related to dietary risks <!-- Start of picture text --> 4.50%<br>3.85%<br>4.00%<br>3.50%<br>3.00%<br>2.45%<br>2.50%<br>1.90%<br>2.00% 1.62%<br>1.30%<br>1.50% 1.10%<br>0.91%<br>1.00% 0.74% 0.68%<br>0.44%<br>0.50%<br>0.00%<br>1 2 3 4 5 6 7 8 9 10<br>Consumption decile<br>Processed and ultra-processed foods<br>Income gains from YLL<br><!-- End of picture text --> Source: Estimates based on POF 2017/2018; Global Burden of Disease 2019. Estimates assume a price shock of 20%.

# 5.4. Medical expenditure

The third component of the model is related to the reduction in medical expenditures due to a smaller incidence of diseases related to the consumption of processed and ultra-processed foods. As was described in section 4.3, these changes are estimated using public expenditure data, which imposes several limitations on the interpretation of the results in this section. Brazil has a public and free national health care system (known by its Portuguese acronym SUS) and, while many richer households opt for private health care, most of the households at the bottom of the distribution, which as we saw are the most affected by changes in consumption following product taxation, are heavily SUS dependent.

Therefore, changes in medical expenditure cannot be understood as a direct household expenditure change, but rather as an estimate that would reflect approximate changes in medical expenses overall as a result of the taxation, including both public and private sector patients, and using public sector cost data will provide a number that underestimates total health expenditure as it does not consider private expenditures. Once again, the most important focus in our analysis is the distribution of these medical expenditures, rather than the value. Our assumption is that the distributional effect of the underestimated medical expenditure would not have a different pattern if we included private expenditure. Finally, as diseases are linked to long-term consumption of these products, the expected medical expenditure reduction is also estimated in the long-term.

Table 8 presents the results for the expected changes in medical expenditure by consumption decile. The medical expenditure analysis shows the same pattern as the previous ones, with a progressive pattern in which lower consumption deciles benefit proportionally more from the reduction of medical expenditure.

There are two possible reasons for this result. The first is the higher elasticity for these lower deciles, which leads to a higher reduction in consumption after the estimated price increase due to taxation. The 19
-19-

# Procedures Used to Estimate the National Accounts Concept of Gross Value Added (contribution to GDP at factor cost) for Establishments Covered by the Mexican Industrial Census

# Mexico

- 1) Gross Value of Output = "Produccion Bruta Total";

- 2) US Value Added Concept = "Produccion Bruta Total" minus the following six items:

- a) "Materias Primas y Auxiliares Consumidas" (raw and intermediate materials used);

- b) "Envases y Empaques" (packaging);

- c) "Combustibles y Lubricantes" (fuels consumed);

- d) "Energia Electrica" (electric energy used);

- e) "Refacciones Accessorios y herriamientas" (repairs, accessories and tools);

- f) "Pagos por Maquila" (payment for contract work);

- Together these six items correspond to the US Cost of Materials Concept (see table 19 of Resumeii General);

- 3) In order to arrive at the present national accounts concept of value added (i.e. the contribution to GDP at factor cost before deduction for imputed financial services), we must further deduct three items which the census includes under the heading "Otros Insumos". These are:

- a) "Pagos por comisiones sobre rentas" (sales commissions);

- b) "Pagos por Servicios de Propaganda" (advertising costs);

- c) "Otros bienes y servicios" (other goods and service inputs);

- When these three items are deducted, we arrive at the Mexican census

- concept of value added ("Valor Aggregado censal bruto"), but this concept is grosser than what we want for national accounts purposes, so we must further deduct three items:

- a) "Gastos por Uso de Patentes y Marcas, Asistencia Tecnica y Transferencia de Technologia" (cost of patents, licences, technical

- assistance and transfer of technology);

- b) "Gastos por alquiler de maquineria y equipo" (costs of renting machinery and equipment);

- c) "CGastos por otros alquileres" (other rental costs);

- 4) In order to arrive at the former national accounts concept of value added, we must further deduct the item "Gastos por intereses sobre creditos y prestamos" (interest costs of credits and loans); In the Mexican case all this detailed information is available for individual industries (see tables 19 and 20 of Resumen General).
group firms (86.1% of control semifinalists) **.** Appendix Table 5.1 compares the overall satisfaction of the treated and control group semifinalists over few dimensions on a scale from 1 to 6. The treated group values more the communication, the structure and design and the training materials provided, the difference is statistically significant. However, the mean grade given by the control group to those dimensions is well above 4. Recall that firms were blind to treatment assignment. Where there is no significant satisfaction difference between the treated and control group is in the feedback received from the jury at the semifinals and the organization of the semifinals. These features were common to both groups. As such, the satisfaction survey indicates the value added of the treatment also in the subjective assessment of the program by participants.

**Appendix Table 5.1:  Treated vs. Control Satisfaction survey – How satisfied are you with each of the following?**

|||Treatm<br>|ent<br>||Contr<br>|ol<br>||
|---|---|---|---|---|---|---|---|
|Dimension|Obs.|Mean|Std. dev.|Obs.|Mean|Std. dev.|p‐value|
|Communication overall|**102**|5.17|.95|**87**|4.55|1.44|0.014|
|Structure and Design of PotB|**101**|5.00|1.14|**86**|4.43|1.26|0.005|
|Training Resources|**102**|5.31|1.02|**84**|4.36|1.25|0.000|
|Jury Feedback|**101**|4.45|1.43|**86**|4.11|1.68|0.486|
|Semi‐Finals (Belgrade Venture|**101**|4.45|1.42|**83**|4.34|1.36|0.861|
|Forum)||||||||

Note: PotB denotes Pioneers of the Balkans program

# **Appendix 6: Additional Details on the Semi‐Finals and Finals**

Appendix Table 6.1 summarizes the characteristics of judges used for the scoring.

|**Appendix Table 6.1: Semi‐Final Judge Chara**|**cteristics**<br>**Mean**|**Std. Dev.**|
|---|---|---|
|Lives in the Western Balkans|0.37|0.49|
|Lives in European Union (except Croatia)|0.48|0.50|
|Male|0.88|0.33|
|Age|39.1|10.4|
|Has Founded a Company|0.75|0.43|
|Years of Experience in their industry|11.5|8.5|
|Company makes venture investments|0.64|0.48|
|Is an Angel Investor|0.37|0.49|
|Regularly Mentors Start‐ups|0.80|0.40|
|**Sample Size**|**65**||
|Note: data unavailable for one judge.|||

Appendix Figure 6.1 shows that the baseline distribution of investment readiness scores is similar for those that participated in the semi‐finals (and therefore received judges’ scores) and those that did not.

62
**Estimates of the potential annual demand for international offsets by 2020.**

|**Source**|**Potential annual demand**|
|---|---|
|**New Carbon Finance**<sup>**~~52~~**</sup>|twice or thrice today’s levels (1,000 to 1,500 MtCO2e)|
|**IDEACarbon**<sup>**~~53~~**</sup>|500 to 1,200 MtCO2e|
|**Point Carbon**<sup>**~~54~~**</sup>|1,700 MtCO2e|
|**Barclays Capital**<sup>**~~55~~**</sup>|600-1,100 MtCO2e|

The same paper assesses the abatement potential of the developing world at approximately 5 Gtons by 2030 in sectors currently eligible under the CDM, plus an additional potential of at least 1.6 Gtons in reductions of emissions from deforestation and degradation (REDD) (UNFCCC 2008a) which are not included in the current CDM. Obviously the full technical abatement potential will not be realized, but the mere order of magnitude evidences the striking imbalance between low demand from industrialized countries and much greater possible supply on the part of developing countries for the post 2012 period.

Leaving the environmental integrity of offsets aside for the moment, the above imbalance dictates a necessary restriction to the supply of carbon credits. Above we proposed one way which would aim at integrating market and non-market elements into the tool box available for funding emission reductions from a particular sector. Another way to reduce supply would be to extract certain sectors from the market and provide the needed financial support through specialized abatement fund(s).

If the decision is to exclude particular sectors from a market mechanism, the following ones would be possible candidates:

- **Industrial gases.** Having completely dominated the early market, industrial gases (HFCs, PFCs and N2O) in June 2009 still represent 27% of the CER supply until 2012.<sup>56</sup> The role of the market

- in continuing support for industrial gas elimination post 2012 needs careful thought. As the cost of their elimination is quite small, the substantial capital flows to purchase these assets would

> 52 New Carbon Finance (2008). “With an international agreement on climate change, the carbon market could be two to three times as large as today”, Press release 28<sup>th</sup> January 2008.

> 53 IDEACarbon (2008). “The long-term potential of the carbon market”, Press release 29th February 2008.

> 54 Point Carbon (2008). Carbon Market Transactions: Dominated by Financials?”, Carbon Market Analyst (21<sup>st</sup> May 2008).

> 55 Barclays Capital (2008). “So long to the longs”, Monthly Carbon Standard (June 2008). This is the (annualized) estimate of the maximum potential demand.

> 56 www.cdmpipeline.org

30