rafmacalaba/datause-encoder-data huggingface dataset:
a coordination game in which each player simultaneously and independently chooses either to “hunt hare” or “hunt stag.” If a player hunts hare, his payoff is positive but low and is independent of what the other player chooses. If a player hunts stag, his payoff is high if the other player also hunts stag, but negative if the other player hunts hare. The experiment tests individuals’ willingness to conduct a mutually beneficial transaction with an anonymous partner at the expense of taking a risk on the partner’s cooperation. This game has two Nash equilibria: a Pareto efficient equilibrium with both players hunting stag, and a Pareto inferior equilibrium with both players hunting hare. The philosopher Brian Skyrms writes that “the whole problem of adopting or modifying the social contract for mutual benefit can be seen as a [many-person] stag hunt....the state of nature [is] an equilibrium...[and there is] the problem of transcending it” (Skyrms 2004, p. 9), Based on stag hunt experiments with US subjects (e.g., Van Huyck, Battalio, and Beil 1990), the ability of fixed pairs of individuals to form the social contract was believed to be universal. But an experiment with men in north India showed that it is not. In the experiment, as expected, most lowcaste pairs were able to form the social contract; but most high-caste pairs were not (Brooks, Hoff, and Pandey 2015). The high-caste men appeared to perceive a coordination failure as an insult or transgression. After obtaining a loss from a coordination failure, only 42% of high-caste players continued to hunt stag in the next period, compared to 71% of low-caste players. The high-caste men who suffered a loss in the coordination failure may have been motivated to retaliate, but doing so hurt their ability to signal that they would try to achieve the efficient social contract. The highcaste behavior in response to coordination failure tended to block coordination on the social contract. In contrast, low-caste men appeared to perceive coordination failures that caused them losses as a mistake, rather than a wrong that triggered retaliation. A follow-up experiment with other subjects provided supporting evidence: high-caste men were much more likely than low-caste men to think it appropriate to beat an individual who because of adverse or ambiguous circumstances caused a loss to another person.

Because how we perceive the world affects what we do, it affects in a deep sense who we are. However, standard economics is built on the assumption that social worlds do not shape cognition or preferences. Standard economics moreover assumes that individuals have unlimited powers of objective reasoning. All individuals exposed to the same data and experiences would come to the same conclusions. The rational expectations model goes even further. It makes sufficiently stringent assumptions that, with enough dialogue, _all_ individuals would come to hold the same 32
Small, Kenneth A. and Kurt Van Dender, 2006. “Fuel Efficiency and Motor Vehicle Travel: The Declining Rebound Effect.” _Energy Journal_ 28: 25-52.

Small, Kenneth A., and Camilla Kazimi, 1995. “On the Costs of Air Pollution from Motor Vehicles.” _Journal of Transport Economics and Policy_ 29: 7-32.

> Stern, Nicholas, 2007. _The Economics of Climate Change_ . Cambridge University Press, Cambridge, U.K.

Tol, Richard S.J., 2007. “The Social Cost of Carbon: Trends, Outliers and Catastrophes.” Economics Discussion Papers, No. 2007-44, Kiel Institute for the World Economy.

US DOE, 1996. _Policies and Measures for Reducing Energy Related Greenhouse Gas Emissions: Lessons from Recent Literature_ . Report No. DOE/PO-0047, US Department of Energy, Office of Policy and International Affairs, Washington, D.C., July.

Vergara, W. and H. Haeussling, 2007. “Transport and Climate: Lessons from the Partnership between Mexico City and the World Bank” Latin America and Caribbean Region Sustainable Development Working Paper 29, The World Bank, Washington, DC.

Viscusi, W. Kip and Joseph E. Aldy, 2003. “The Value of a Statistical Life: A Critical Review of Market Estimates throughout the World.” _Journal of Risk and Uncertainty_ 27: 5-76.

Weitzman, Martin L., 2008. “On Modeling and Interpreting the Economics of Catastrophic Climate Change.” Discussion paper, Department of Economics, Harvard University.

West, J.J., P. Osnaya, I. Laguna, J. Martinez, and A. Fernández, 2003. “Co-Control of Urban Air Pollutants and Greenhouse Gases in Mexico City.” Final report to US National Renewable Energy Laboratory, subcontract ADC-2-32409-01.

West, Sarah and Roberton C. Williams, 2007. “Optimal Taxation and Cross-Price Effects on Labor Supply: Estimates of the Optimal Gas Tax.” _Journal of Public Economics_ 91: 593-617.

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> Willig, Robert D., 1976. “Consumer’s Surplus without Apology.” _American Economic Review_ 66: 589-97.

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> World Bank, 2002a. _Cities on the Move: A World Bank Urban Transport Strategy Review_ . World Bank, Washington, DC.

44
from a specific home region are in perfect competition with each other.<sup>13</sup> That is, it is analogous to the Armington structure, except that production also takes place in the host country.

For domestic firms, costs are defined by the costs of local primary factors and intermediate inputs. When multinationals service providers decide to establish a local presence, they will import some of their technology or management expertise. That is, foreign direct investment generally entails importing specialized foreign inputs. Thus, the cost structure of multinationals differs from national-only service providers. Multinationals incur costs related to both imported primary inputs and local primary factors, in addition to intermediate factor inputs. Foreign provision of services differs from foreign provision of goods, since the service providers use local primary inputs. For multinational firms, the barriers to foreign direct investment raise their costs of production. The reduction of the barriers lowers these costs, freeing the capital and labor that was used to overcome the barriers for use elsewhere in the economy. Thus, the reduction in the constraints on foreign direct investment allows the domestic economy to capture rent rectangles. In addition, reducing barriers induces foreign entry until profits are driven to zero, so there are also triangles of efficiency gains.

# **4.  Data of the Model and Evidence for Key Elasticities**

# **4.1 Ad Valorem Equivalents (AVEs) of the Barriers Against Foreign Suppliers of Business Services**

A new database of the ad valorem equivalents of barriers against foreign providers of services in eleven business services sectors in 103 countries was developed for this project. We also commissioned new surveys of the regulatory regimes in services of Kenya and Tanzania as a basis of estimating both the discriminatory barriers against foreign service providers as well as the non-discriminatory regulatory barriers that impact both domestic and foreign suppliers of services. This work is documented in Jafari (2014a; 2014b; 2014c; 2014d and 2014e).

> 13 Development of a multi-region model version that includes the full monopolistic competition structure for services found in the small-economy model of Balistreri, Jensen and Tarr (2011) is a challenging goal for future research.  The formulation would require significant first-order data development, however, that reconciles the multi-region GTAP accounts on cross-border service provision with country-level data.  Faced with the data challenges we are more comfortable in this report to make the conservative assumption that there are no varietyinduced productivity impacts in the business services sectors.

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In this study, we use three main sources of data. First, we use a commercial database of housing prices and dwelling characteristics developed by SOFTEC (a housing market consultant firm) which covers the period from 2002 and 2013. Second, we calculate the distance of each household to the closest public transit stations of each transportation investment using maps with detailed information on road networks and public transport infrastructure investments. Finally, we use Mexico’s Housing and Population Censuses at the lowest level of geographical disaggregation available, called AGEB,<sup>7</sup> to calculate the average level of education and the proportion of informal workers in each neighborhood. In the following paragraphs, additional details are provided on both the housing price data and the Censuses, including a brief discussion of the specific variables extracted for our analysis.

# _2.1.Housing data_

We use housing information gathered by SOFTEC<sup>8</sup> on all new construction in Mexico City between 2002 and 2013. Information on sales prices and characteristics of projects are provided by private developers to SOFTEC at the beginning of the construction of the project, and every three months until the last property in the construction project is sold. Projects include all types of residential construction, including gated-communities of individual dwellings or buildings, or just a single building. We use housing information gathered for the last trimester each year from 2002 to 2013 (information regarding sales from October, November, and December of the same year).

The data are aggregated at the project or _fraccionamiento_ (housing divisions or housing projects) level and include the date when the construction project enters into the SOFTEC inventory, the date in which all units in the same project were sold, and the date when the construction was completed (all the housing units built). Besides providing several characteristics of the project as well as the housing units included in it, the data set also

> 7 The AGEB (or geographic and statistical area) is defined as a territorial unit inside a municipality. There are urban and rural AGEBs depending on their housing density. An urban AGEB is a geographic area of a group of blocks delimited by streets, avenues, sidewalks or other construction easily identified, in which its land is used mainly for occupational purposes, industries, provision of services or commercial purposes. All urban AGEBs must be located inside urban localities.

> 8 SOFTEC is a private company created in 1988 with the objective of collecting data to monitor the housing supply in Mexico. According to conversations with their sales representative, their database represents about 85% of the total housing supply in Mexico, gathering approximately 350,000 observations (projects) per year at the national level.

8
# **Corporate Debt and Stock Returns: Evidence from U.S. Firms during the 2020 Oil Crash**

Rabah Arezki, Caleb Cho, Ha Nguyen, Kate Nguyen, Anh Pham<sup>1</sup> JEL Classification: E44; G12; Q43 Keywords: Oil Prices, Stock Returns, Debt

> 1 Rabah Arezki is a Senior Fellow at Harvard’s Kennedy School of Government. Caleb Cho is an Economist at the United States Bureau of Labor Statistics (BLS). Ha Nguyen is a Senior Economist at the World Bank. Kate Nguyen is a MBA Candidate at INSEAD. Anh Pham is an Assistant Professor at George Mason University. We thank Olivier Blanchard, Bertrand Candelon, Jean-Pierre Landau, Jordan Neyland, and Lemma Senbet for helpful comments. The findings, interpretations, and conclusions expressed in this paper do not necessarily reflect the views of the World Bank and BLS or the governments they represent. The World Bank and the BLS do not guarantee the accuracy of the data included in this work.
Policy Research Working Paper 6904

# **Abstract**

This paper uses a unique micro panel dataset of Brazilian students to investigate the impact of working while in school on learning outcomes. The potential endogeneity is addressed through the use of difference-in-difference and instrumental variable estimators. A negative effect of working on learning outcomes in math and Portuguese is found. The effects of child work range from 3 to 8 percent of a standard deviation decline in test score, which represents a loss of about a quarter to a half of a year of learning on average.

This paper is a product of the Office of the Chief Economist, Development Economics Vice Presidency. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at patrick.emerson@oregonstate.edu, vladimir.ponczek@fgv.br, and andre.portela.souza@fgv.br.

_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
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Hossain, Khandker Zakir and Ahmed, Faruque. 2000. “Growth and Dimension of the Microfinance Sector in Bangladesh” Paper 23 presented at the Asian Regional Conference. Johnson, Simon, John McMillan, and Christopher Woodruff. 2002. “Property rights and finance.” _American Economic Review_ 92(5), 1335–1356 Kremer, Michael; Jean N. Lee; and Jonathan M. Robinson. 2011. “The Return to Capital for Small Retailers in Kenya: Evidence from Inventories,” Working Paper.

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# **Uncertainty of emission reduction of starch & sugar energy crops-based ethanol**

Although biofuels are treated as “carbon neutral” under the Kyoto Protocol, their actual Greenhouse Gas (GHG) emissions are the subject of much debate. Recent life cycle analysis (LCA) studies in China indicated that fuel ethanol derived from sugar and starch energy crops have possibility to less sustainable than fossil fuels(Fig.4) due to large indirect consumption of coal during planting process. Sustainable regulations are required.

<!-- Start of picture text --> 1200<br>Cassava<br>1000<br>800 Corn Wheat<br>600<br>Sweet sorghum<br>400 Gasoline FFV:E85<br>SI:E10 Cellulose<br>SI<br>SI:E100<br>SI:E85<br>200<br>DISI SI:E85 FFV:E85 FFV: E85<br>FFV: E85 FFV: E85 FFV: E85<br>0 DISI HEV: E85<br>g/km<br><!-- End of picture text --> Fig.4. Fuel ethanol Well-to-Wheels (WTW) GHG emissions(CO2 eq. g/km) Notes: SI: Spart Ignition; DI: Direct Injection; DISI: Direct injection spark ignition; PISI: , FFV: Flexible Fuel Vehicle; DISI HEV: Hybrid Electric Vehicle Source: based on analysis of three published Well-to-Wheels (WTW) studies in China; data from Zhang, Shen & Han, et al. (2008), Ou et al. (2009), and CATARC/GM(2007).

# **3.2 Biodiesel from waste oil**

Waste oil from food oil is major feedstock of current biodiesel production in China.

# **3.2.1 Resource potential**

The consumption of food oil was about 26.8 million tonnes in China in 2010 (Chang, et al.2012), and it is annually increasing due to population growth and income rise. It is estimated that 20%-30% of waste oil generated every year. And if 50% of them can be collected, the technical availability for biodiesel production is about 2.68 ~ 4.02 million tonnes (Guo, et al, 2010).

# **3.2.2 Supply cost**

Although termed as ‘waste oil’, it has commercial value and find competitive applications in 10
|**Table 10B: Quantile Regress**<br>Dependent variable: Logof m|**ions with Firm**<br>onthlyrevenue<br>p10|**Controls**<br>p25|p50|p75|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Days per month (log)|0.107|-0.078|-0.107|-0.023|
||(0.16)|(0.29)|(0.23)|(0.17)|
|CasualDummie|-0.031|-0.214|-0.236|-0.124|
||(0.09)|(0.32)|(0.26)|(0.19)|
|Age|0.019|0.015|0.007|-0.045**|
||(0.02)|(0.03)|(0.03)|(0.02)|
|AgeSquared|-0.006|-0.011|-0.001|0.077***|
||(0.02)|(0.04)|(0.03)|(0.03)|
|Sex|0.325***|0.334**|0.346***|0.278***|
||(0.08)|(0.14)|(0.11)|(0.10)|
|Schooling|0.124***|0.038|-0.019|-0.014|
||(0.02)|(0.05)|(0.04)|(0.03)|
|Schooling2|-0.004***|0.0001|0.003|0.003|
||(0.00)|(0.00)|(0.00)|(0.00)|
|Prior-Casualwork|0.038|0.057|0.112|0.201*|
||(0.08)|(0.16)|(0.12)|(0.11)|
|Prior-employee firm|0.081|0.077|0.012|0.233**|
||(0.07)|(0.12)|(0.10)|(0.09)|
|Apprenticeship Completed|0.001|0.022|0.063|0.165**|
||(0.07)|(0.12)|(0.09)|(0.08)|
|Contractor|0.686***|0.310|0.302|0.554***|
||(0.10)|(0.24)|(0.22)|(0.21)|
|Program*Contractor|-0.231*|-0.201|-0.186|-0.113|
||(0.14)|(0.28)|(0.24)|(0.23)|
|Program-Constructor|0.172**|-0.030|-0.029|0.009|
||(0.08)|(0.13)|(0.11)|(0.10)|
|Workers|0.067*|0.166***|0.141**|0.097*|
||(0.04)|(0.06)|(0.06)|(0.06)|
|Capital-Intensity|0.029**|0.036|0.019|0.006|
||(0.01)|(0.03)|(0.02)|(0.02)|
|Input-Intensity|0.027|0.077**|0.035|0.038|
||(0.02)|(0.04)|(0.03)|(0.03)|
|Constant|2.087***|3.455***|4.838***|5.579***|
||(0.69)|(1.12)|(0.87)|(0.68)|
|Pseudo R2|0.22|0.14|0.15|0.21|
|N|345|345|345|345|

Note:

- *, **, *** indicate significance at the 10%, 5% and 1% level respectively

- Subcity dummies included but not reported

-21-
Several empirical studies (e.g., Barrett et al. 2001, Lanjouw and Lanjouw 1995, 2001) draw similar conclusions. Asset endowments of households determine their ability to diversify incomes by participating in non-farm activities. One of the most important determinants is the level of education among household members. Many high-return, non-farm jobs require a certain amount of human capital, creating barriers for less educated households to take advantage of such diversifying opportunities. For example, Estudillo and Otsuka (2010) show that secondary and tertiary education is positively related to non-farm income in the Philippines.

Social capital is another factor explaining the ability of individuals to work off-farm. Workers who lack strong social networks, such as women or immigrants, have less access to well-paid non-farm jobs. And limited access to infrastructure can leave households in remote rural areas unable to participate in non-farm income diversification (Barrett et al. 2001). In Africa, if access to urban areas is given, migration income becomes more important than nonfarm rural income in the total income of rural households (Reardon 1997). Access to proper infrastructure is not only important for households to access the non-farm employment sector, but also for this sector to function properly and be profitable, thereby guaranteeing job opportunities for rural households. For example, electricity shortages that make the use of generators necessary can raise costs and reduce profits for rural enterprises (Lanjouw and Lanjouw 1995, 2001).

Although there are barriers to high remunerative rural jobs, households that are poor in assets still engage into non-farm work. Lanjouw (2001) shows that there are two types of offfarm employment in El Salvador, one in which labor productivity and wages are high and another one that plays the role as a ―last resort‖ employment for poor households. Whereas the former can lead to upward income mobility of workers, this is not necessarily the case for the latter. Lanjouw argues that even low-paying, non-farm jobs support the poor‘s income if, for example, unemployment was the alternative. Himanshu, Lanjouw, Mukhopadhyay, and Murgai (2010) find that employment growth in the rural non-farm economy is strongly correlated with growth in neighboring urban centers, and that poverty reduction has occurred partly by generating employment opportunities for historically disadvantaged rural groups.

Although the literature suggests that richer rural households engage more in higherpaying non-farm activities, there is still a consensus that diversification improves the incomes of 29
**Figure 2: Monthly income and consumption by season (2005)**

<!-- Start of picture text --> Greater Rangpur<br><!-- End of picture text --> <!-- Start of picture text --> 50<br>40<br>Total consumption<br>30<br>20 Total income<br>Food consumption<br>10<br><!-- End of picture text --> <!-- Start of picture text --> Rest of the country<br><!-- End of picture text --> <!-- Start of picture text --> Total income<br>Total consumption<br>Food consumption<br><!-- End of picture text --> Sources: HIES survey 2005.

This requires an examination of household poverty against seasonal income and consumption data to find out the extent of poverty in a particular season.

We propose that seasonality affects household’s ability to maintain a minimum livelihood. A decrease in seasonal income lowers household’s consumption. It lowers consumption enough to force many households down below poverty level. The question is how many rural households experience seasonal hunger during the lean season.

We consider three poverty indicators and their seasonal variations: moderate poverty, food poverty and extreme poverty. A household is considered moderate poor if its per capita expenditure (food and non-food) is less than the total poverty line established for the region as defined in Table 1.<sup>14</sup> Similarly, a household is considered food-poor if its per

> 14 Table 1 shows regional food and total poverty lines for 2000 and 2005.  It illustrates that the living expenditure in the Rangpur region is less than that in rest of the country, after adjusting for the consumer price index.  It also shows that there has been very little increase in the poverty lines between 2000 and 2005.

10