rafmacalaba/datause-encoder-data huggingface dataset:
Table 5: The evolution of the economy and demographic variables under program B.

|t|λ1,t|lt|c1,t|e<sup>p</sup><br>1,t|y1,t|λ2,t|kt|c2,t|st|e<sup>p</sup><br>2,t|e<sup>s</sup><br>2,t|y2,t|y<sup>′</sup><br>t|y<sup>′′</sup><br>t|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|1|1.400|1.020|0.573|0.251|3.211|2.800|1.000|0.786|1.477|0.737|0.500|6.931|0.489|0.513|
|2|1.806|0.693|0.701|0.717|3.564|3.127|1.450|1.111|1.198|0.934|0.634|7.208|0.734|0.804|
|3|2.500|0.423|0.904|1.000|4.211|3.180|1.416|1.469|1.309|1.000|1.000|9.152|0.961|1.037|
|4|3.284|0.338|1.312|1.000|5.933|4.646|1.788|2.111|1.685|1.000|1.000|12.194|1.476|1.551|
|5|4.279|0.328|2.012|1.000|8.838|5.993|2.630|3.106|2.392|1.000|1.000|17.395|2.362|2.436|
|6|5.578|0.408|3.121|1.000|14.683|7.992|4.539|4.551|3.563|1.000|1.000|26.538|3.658|3.741|
|t|q<sup>c</sup><br>i,t|q<sup>a</sup><br>1,t|q<sup>a</sup><br>2,t|N <sup>c</sup><br>1,t|N <sup>c</sup><br>2,t|N <sup>o</sup><br>1,t|N <sup>o</sup><br>2,t|M <sup>a</sup><br>t|ξt|F1,t|F2,t|P1,t|P2,t|Pt|
|0|0.076|0.174|0.174||3.840||||0.423|4.51|0.65||||
|1|0.062|0.193|0.285|4.603|4.110|1.400|0.990|0.415|0.899|5.88|1.19|47.09|8.47|55.56|
|2|0.052|0.150|0.201|3.917|3.486|1.221|0.402|1.012|1.442|8.66|5.02|61.79|29.54|91.33|
|3|0.041|0.126|0.162|3.304|3.012|1.126|1.066|0.192|0.109|14.18|8.42|91.16|51.15|142.31|
|4|0.031|0.104|0.139|2.709|2.586|1.494|1.046|0.490|0.306|17.74|14.73|110.04|82.95|192.98|
|5|0.020|0.100|0.100|2.151|2.151|1.836|1.283|0.427|0.217|18.29|21.30|109.53|115.74|225.28|
|6|0.020|0.100|0.100|2.151|2.151|2.357|1.625|0.390|0.167|14.73|24.87|95.85|143.64|239.49|

> 1 The rural sector: A1,1 = 1.000, gA1,t = 0.222, λ1,0 = 1.150, ω = 0.800, c1,min = 0.500, ep1,min<sup>= 0.001,b1= 0.350,θ1= 0.400.</sup>

> 2 The urban sector: A2,1 = 2.000, gA2,t = 0.000, λ2,0 = 2.300, ω1 = 0.539, ω2 = 0.458, ω3 = 0.001, ω4 = 0.001, c2,min = 0.584, e<sup>p</sup> 2,min<sup>= e</sup> 2<sup>s</sup> ,min<sup>= 0.001,b2= 0.450,θ2= 0.400.</sup>

> † du = 0.500, αi = 0.667, γ = 0.40, δ = 0.20, β = 0.60, ρ = 0.60, φ = 0.75. yt′<sup>growsat1.98%perannum.Fi,t,Pi,tandPtin</sup> millions.
II. Stylized Facts of Brazil’s Fuel Market for Transportation This section presents key characteristics of Brazil’s complex automotive fuel markets pertaining to products, actors, technology, and policy that might have an impact on emission intensity, the target of our analysis, thus informing our estimation strategy.

# II.1. Products: Volumes and Prices

Brazil has a diversified fuel market for transportation where multiple fuels co-exist including notable shares of renewable biofuels. Four major types of retail fuels ( _i.e._ , diesel oil B, gasoline C, hydrous ethanol, and compressed natural gas or CNG) are on the market, some of which are mixtures of different source fuels ( **Table 1** ). While diesel oil and gasoline have been the most consumed fuels, ethanol produced from sugarcane entered the market as early as the 1970s after the first oil crisis and the launch of the Pró- Álcool program.<sup>1</sup> Ethanol was initially used as anhydrous ethanol mixed into gasoline, and later also as hydrous ethanol used independently in neat ethanol-powered vehicles or flex-fuel vehicles. The combined volume of anhydrous and hydrous ethanol consumed (in toe) has now grown to around two-thirds that of gasoline A, the major component of retail gasoline C. Starting from the mid-2000s, biodiesel entered the market and has been mixed with diesel in increasing proportion to form diesel oil B, and a new variant of diesel oil B S10 with much less sulfur content was introduced after 2012. In addition, CNG has existed since the 1990s, though still in relatively small amounts and not available for sale in all FUs.

Table 1. Multiple Types of Retail Automotive Fuels Co-exist in Brazil

|Type of Fuel|Characteristics|Applications|
|---|---|---|
|Diesel oil B|Mix of diesel oil A and biodiesel<br>(around 10-13% in recent years);<br>Sulfur content of 500mg/kg|Heavy and light vehicles produced before<br>2012|
|Diesel oil B S10|Mix of diesel oil A and biodiesel;<br>Sulfur content of 10mg/kg|Heavy and light vehicles produced after<br>2012 with post-treatment technologies to<br>reduce emissions|
|Gasoline C|Mix of gasoline A and anhydrous<br>ethanol (around 25-27% in recent<br>years);Sulfur content of 50mg/kg|Light vehicles and motorcycles including<br>flex-fuel vehicles|
|Hydrous Ethanol|Around 95% ethanol and the rest<br>water|Flex-fuel<sup>2</sup>and neat ethanol vehicles|
|CNG|A blend of light compounds<br>consisting of carbon and hydrogen|Vehicles converted to use CNG (that can run<br>on both CNG and gasoline)|

_Sources: Petrobras, ANP, and CETESB._

> 1 The first use of ethanol fuel in Brazil dates back decades earlier, though mostly experimental and the usage was sporadic after World War II due to cheap oil prices.

> 2 88% of the new licensed vehicles had this technology in 2016 according to the Brazilian National Agency for Petroleum, Natural Gas and Biofuels (ANP).

4
Table 8a: Industry-level competition analysis in manufacturing sector - baseline trade tariffs

||Baseline estimation|Excluding ind<br>weights|ustry<br>Excluding initial level of<br>dependent variable<br>Includ<br>empl.|ing initial female<br>share for industry|Including initial female<br>empl. share for industry<br>and its change|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
||||A. Change in log total employment|||
|Decline in industry tariff|-0.055|0.055|-0.084|-0.025|0.103|
||(0.065)|(0.201)|(0.057)|(0.075)|(0.067)|
||||B. Change in log female employment|||
|Decline in industry tariff|-0.254|-0.010|-0.451**|-0.245|0.209|
||(0.219)|(0.211)|(0.184)|(0.209)|(0.197)|
||||C. Change in female employment share|||
|Decline in industry tariff|0.322|-0.057|0.254|n.a.|n.a.|
||(0.278)|(0.114)|(0.324)|||
||||D. Change in male segmentation|||
|Decline in industry tariff|-0.106|0.139|-0.275**|-0.105|-0.055|
||(0.122)|(0.108)|(0.130)|(0.119)|(0.073)|
||||E. Change in female segmentation|||
|Decline in industry tariff|0.148|0.238*|0.088|0.119|0.016|
||(0.098)|(0.121)|(0.102)|(0.086)|(0.059)|
||||F. Change in combined segmentation|||
|Decline in industry tariff|-0.074|0.146|-0.190|-0.069|-0.083|
||(0.098)|(0.107)|(0.115)|(0.083)|(0.095)|

Notes: Estimations include 50 industry-level observations, are weighted by log industry employment, winsorize changes at 5%/95% level, include initial values of dependent variables, report robust standard errors, and are transformed to unit standard deviations.
McCann, M.W. (1994). _Rights at Work_ . Chicago: University of Chicago Press.

McCann, M.W. (1998). ‘How does Law Matter for Social Movements?’ in B.G. Garth & A. Sarat. (Eds) _How Does Law Matter?_ Evanston, Illinois: Northwestern University Press, pp. 76108.

McLeod, R. (2000) ‘Soeharto’s Indonesia: A Better Class of Corruption’, _Agenda_ , 7 (2), pp.99112.

Mietzner, M. (2009) _Indonesia’s 2009 Elections: Populism, Dynasties and the Consolidation of the Party System_ , Sydney Lowy Institute.

Mietzner, M. (2010) ‘Political Conflict Resolution and Democratic Consolidation in Indonesia: The Role of the Constitutional Court,’ _Journal of East Asian Studies_ , 10, pp.397-424.

Miguel-Stearns, T. (2015) ‘Judicial Power in Latin America: A Short Survey’, _Legal Information Management_ , 15 (2), pp.100-107.

Munger, F. (2014) ‘Revolution Imagined: Cause Advocacy, Consumer Rights, and the Evolving Role of NGOs in Thailand’, _Asian Journal of Comparative Law_ , 9 (1), pp.29-64.

National Commission for Protection of Child Rights. (2017) Annual Report. New Delhi: NCPCR. <u>http://www.ncpcr.gov.in/showfile.php?lang=1&level=2&&sublinkid=1653&lid=1669</u> accessed on 16 August 2018.

Neuborne, B. (2003) ‘The Supreme Court of India,’ _International Journal of Constitutional Law_ , 1 (2) pp. 476-510.

Nugroho, H. (2005) ‘The Political Economy of Higher Education: The University as an Arena for the Struggle for Power’ in Vedi Hadiz and Daniel Dhakidae (eds.) _Social Science and Power in Indonesia_ , Singapore and Jakarta: ISEAS and Equinox, pp.143-165.

O’Connell, P. (2007) ‘On Reconciling Irreconcilables: Neoliberal Globalisation and Human Rights’, _Human Rights Law Review_ , 7 (3), pp.483-509.

Pande, S. (2014) _The Right to Know, the Right to Live: Grassroots Struggle for Information and Work in India,_ unpublished doctoral dissertation, Institute of Development Studies.

Phillipson, B. (2008) _Low Cost Private Education: Impacts on Achieving Universal Primary Education._ London: The Commonwealth Secretariat.

Ravi, A., Goyal, S. and Dey, S. (2015) ‘Redressing Teachers’ Grievances, A Study of Eight States,’ _Economic and Political Weekly_ , 50 (25), pp.160-164.

Right to Education Project (nd) ‘Defining the Right to Education’, available at: <u>http://r2e.gn.apc.org/node/233, accessed 16 March 2017.</u> Rosenberg, G. (1991) _The Hollow Hope: Can Courts Bring About Social Change?_ Chicago: University of Chicago Press.

29
find women to be less altruistic (see references in Croson and Gneezy 2004). The literature seems more settled in showing that men are more likely to be overconfident than women who are less willing to enter tournaments and less eager to compete (Barber and Odean 2001, and Gneezy et al. 2003, and Niederle and Vesterlund 2007). The literature also shows that women are more averse to risk than men; a finding which is probably driven by the differences in overconfidence.<sup>5</sup> Women also seem to hold the higher moral grounds when it comes to making ethical decisions in the workplace (Glover et al. 1997, and Reiss and Mitra 1998).

Engaging in corruption is certainly an ethical decision though gender differences in corruption have received little attention from the literature. Two recent seminal papers Dollar et al. (2001) and Swamy et al. (2001) show that women are less likely to be involved in bribery, more willing to strongly condemn corrupt behaviors, and that a greater representation of women in higher executive or legislative public offices reduces countries’ overall corruption levels. Dollar et al. (2001) find in a large cross-section of countries that greater representation of women in parliament significantly reduces corruption. They control for important country characteristics that may affect both corruption and women’s participation in public life such as the level of economic development, or political and civil freedom. Swamy et al. (2001) use several independent datasets to establish the relationship between gender and corruption. They uncover a strong correlation (they do not claim causality) between gender and corruption using macro-data as Dollar et al. (2001), but more importantly their result is supported by micro-evidence using both firm-level data from a World Bank study in Georgia, and individual-level data from the World Values Surveys.

Few studies so far dispute the findings of Dollar et al. (2001) and Swamy et al. (2001). A notable exception is Sung (2003) who argues that increased women’s participation and reduction in corruption have no causal relationship and both are in fact caused by a fairer

> 5 See Croson and Gneezy (2004) for references. Although the evidence overwhelmingly supports a higher aversion to risk for women there are important categories of individuals, e.g. managers or professional business persons, for whom women display an equal or even lower aversion to risk than men. Selection issues, whereby more risk-loving women choose these specific jobs may, however, be at play. Most studies also show that gender differences decrease with substantial increases in payoffs. A limited number of studies find no gender differences towards risk.

6
Yang, Chenlung, John G. Wacker and Chwen Sheu. 2012. "What makes outsourcing effective? A transaction-cost economics analysis." _International Journal of Production Research_ . 50(16): 4462–4476.

- Zhou, Kevin Z., Laura Poppo, and Zhilin Yang. 2008. "Relational ties or customized contracts? An examination of alternative governance choices in China." _Journal of International Business Studies_ 39, 526–534.

<mark>Zhou, Kevin Zheng, and Laura Poppo. "Exchange hazards, relational reliability, and contracts in China: The contingent role of legal enforceability."</mark> _<mark>Journal of International Business Studies</mark>_ <mark>41, no. 5 (2010): 861-881.</mark> <mark>Zhou, Kevin Zheng, and Dean Xu. "How foreign firms curtail local supplier opportunism in China: Detailed contracts, centralized control, and relational governance."</mark> _<mark>Journal of International Business Studies</mark>_ <mark>43, no. 7 (2012): 677-692.</mark> 60
**Patients Characteristics**

|Male|-0.021|0.022|-0.031|0.016|**0.031**|0.002|-0.001|-0.009|
|---|---|---|---|---|---|---|---|---|
||-0.59|-0.66|-1.11|-0.81|**(1.79)***|-0.22|-0.25|-1.27|
|Age|0|-0.002|0.002|0|0|0|0|**0**|
||-0.04|-1.4|-1.64|-0.12|-0.09|-0.51|-0.29|**(1.76)***|
|Education|**0.035**|**0.065**|0.002|0.002|**0.019**|0.003|0|0.001|
||**(1.80)***|**(2.24)****|-0.2|-0.16|**(3.78)*****|-0.73|-0.53|-0.63|
|Reason visit|-0.085|-0.011|-0.007|0.01|**-0.081**|-0.005|-0.011|0.004|
||-1.13|-0.41|-0.15|-0.71|**(2.86)*****|-0.8|(1.94)*|-0.8|
|Multiple Problems|0.014|0.036|-0.019|0.016|**0.034**|**0.013**|0.002|0.002|
||-0.32|-1.19|-0.51|-1.2|**(1.85)***|**(2.09)****|-0.68|-0.52|
|Number of days sick|-0.001|0|-0.001|**0.001**|0|0|**-0.001**|**0**|
||-0.42|-0.41|-0.79|**(3.41)*****|-1.48|-1.53|**(5.01)*****|**(3.03)*****|
|Time to reach facility|**0**|0|**0**|0|**0**|0|**0**|0|
||**(5.31)*****|-1.24|**(3.22)*****|-0.18|**(3.75)*****|-1.31|**(23.1)*****|-1.19|
|Visited another provider|**0.249**|**0.284**|**0.109**|**0.043**|**0.042**|0.017|0|0.006|
||**(3.13)*****|**(7.51)*****|**(2.66)*****|**(2.42)****|**(3.79)*****|-1.45|-0.02|-1.59|
|Observations|530|805|530|805|530|805|405|379|
|Pseudo R-squared|0.2|0.18|0.17|0.15|0.27|0.2|0.25|0.25|
|LogLikelihood|-293.32|-403.14|-240.58|-174.11|-148.65|-89.21|-62.02|-34.82|

Robust z-statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% Bypassing in rural areas does not seem to react to medical staff size or absenteeism. Indeed, the rural poorest tend to bypass facilities in order to go to lower quality level facilities and lower price providers. This is confirmed by the negative and significant coefficients on basic infrastructure and on the price differential between the facility they attend and the average facility in the district.

Table 14 which shows the bypassing patterns of specific types of providers mirrors table 13. Bypassing a private urban provider implies seeking a provider with better infrastructure, and more and better medical personnel. Expectation of a higher quality of service leads to an increase of 6% and 1.9% in the likelihood that the patient has skipped a private provider. The results obtained for rural areas probably reflect the fact that many rural facilities are monopolies and therefore rural patients do not have a choice.

Finally, comparing table 13 and 14 to table 12 of the entire sample makes clear that bypassing is an urban phenomenon in Chad. Indeed, table 12 shows that among people who seek care in a private facility, overall, there is a significant increase of 5.8% that they have bypassed a public facility. This is mostly driven by bypassing behavior in urban areas. Indeed, in table 14 the same coefficient is not 32
in the 2010 Census consisting of parents and children between 19 and 24 years old, where all members of the household are Native or non-Native. This excludes families whose children do not reside with them. This selection is smaller than in other countries, as children in Argentina tend to remain in the household of their parents for years after reaching adulthood: in 2010, 60 percent of all people between 19 and 24 years old still resided with their parents.<sup>9</sup> We focus on households in which all members are Native or non-Native for simplicity.<sup>10</sup> For each household, we calculate the average years of education of the parents, in case more than one is present, and the average years of education of the children.

Figure 3 shows the distribution of years of education of the parents disaggregated by ancestry (Natives and non-Natives). While there is great overlap in the distribution, nonnative parents have on average 1.20 more years of education than native parents. For the next generation, the difference in education between Natives and non-Natives is smaller (0.85 years) but still large.

Figure 3: Education by Generation and Ancestry <!-- Start of picture text --> Parents Children<br>Average Education Average Education<br>Natives: 8.05 Natives: 10.50<br>Non-natives: 9.24 Non-natives: 11.35<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>Years of Education Years of Education<br>Natives Non-Natives Natives Non-Natives<br>30 30<br>20 20<br>Percent Percent<br>10 10<br>0 0<br><!-- End of picture text --> _Notes:_ Households with children between 19 and 24 years old and all of same ancestry.

- 9 This number is higher for native families; with more educated native youth living with their parents than for non-natives. This “positive selection” in education for natives living at home relative to non-natives suggests that the differences in education transmission that we describe in this section may underestimate the actual differences.

- 10 This consists of 98.8% of the households. We have also studied households with a combination of natives and non-natives; they exhibit a transmission of education similar to non-natives.

13
# **_Passive strategies: Reducing expenditure_**

**Figure 7: Expenditure adjustments in the previous two years**

<!-- Start of picture text --> Reduced consumption of staple<br>foods<br>Reduced consumption of luxury<br>goods<br>Delayed utilities payments<br>Reduced vacations<br>Reduced alcohol or tobacco<br>consumption<br>Reduced health expenditures<br>Reduced use of own car<br>Stopped/reduced help to friends<br>or relatives<br>0 20 40 60<br>percent of respondents<br>Affected a great deal or a fair amount Not affected by crisis<br><!-- End of picture text --> Source: LiTS II (2010).

The most frequent coping mechanism used by households affected by the crisis was reducing the consumption of staple foods such as milk, fruit, vegetables or bread **,**<sup>8</sup> closely followed by cutting the consumption of luxury goods. More than half of households affected “a great deal” or “a fair amount” reduced their staple food consumption, compared with 20 percent among those unaffected by the crisis. Other frequent expenditure adjustments included delaying payments on utilities (29 percent of affected households), cutting back on vacations (26 percent), reducing spending on alcohol and tobacco (25 percent) and cutting health expenditures (24 percent). Seven percent of households had their utilities disconnected because of delayed payment.

> 8 This could be in response to a combination of lower income (from labor market shocks) and food price inflation.

15
|Gini|Gini index (%)|128|423|34.406|6.980|22.968|59.777|
|---|---|---|---|---|---|---|---|
|Theil|Theil index (%)|128|423|23.417|10.882|8.824|70.786|
|_WDI (Source: The Wor_|_ld Bank -_https://datacatalog.worldbank.org/home_)_|||||||
|Gini|Gini index (%)|128|423|38.295|9.062|20.700|65.800|
|_SWIID (Source: Standa_|<br>_rdized World Income Inequality Database - _https://fsolt.org/swi|id/_)_||||||
|Gini|Gini index (%)|128|423|38.445|8.637|17.900|65.400|
|**Subnational inequalit**|<br>**y (GSAP)**|||||||
|_Source: The World Ban_|_k_ (https://datacatalog.worldbank.org/home)|||||||
|Gini|Gini index|128|4,129|35.627|8.045|13.371|66.448|
|Theil|Theil index|128|4,129|25.421|13.966|3.359|192.672|
|90/10 ratio|Ratio of the income of the 10% richest to that of the<br>10% poorest.|128|4,100|2.940|9.837|0.000|131.202|
|80/20 ratio|Ratio of the income of the 20% richest to that of the<br>20% poorest.|128|4,099|2.627|7.712|0.000|106.748|
|Palma ratio|Ratio of the income of the 10% richest to that of the<br>|128|4,150|0.434|1.933|0.000|64.359|
|**_Satellite weather data (_**|40% poorest.<br>**_1979_–****_2019)_**|||||||
|_Source: European Unio_|_n’s Copernicus programme_ (https://sentinels.copernicus.eu/web<br>|/sentinel/mis|sions/sentinel-5|p)||||
|Temperature|Average temperature (C)|134|5,090|18.185|7.996|-9.417|30.790|
|Rainfall|Average rainfall (mm)|134|5,090|3.880|3.178|0.006|34.882|
|_Source: Climatic Resea_|<br>_rch Unit_ (https://crudata.uea.ac.uk/cru/data/hrg/)|||||||
|Temperature|Average temperature (C)|134|5,090|18.328|8.030|-11.082|30.426|
|**_Crop yield data_**||||||||
|_Source: Iizumi and Sak_|_ai (2020)_|||||||
|Rice|Average crop yield (1981–2016)|45|10,257|3.215|3.041|0.000|22.314|
|Maize|Average crop yield (1981–2016)|76|14,870|2.412|2.480|0.000|27.743|
|Soybean|Average crop yield (1981–2016)|19|2,953|1.719|1.494|0.000|9.518|
|Wheat|Average crop yield (1981–2016)|66|10,178|3.350|3.142|0.000|15.636|
|**_Variables used in hete_**|**_rogeneity analysis_**|||||||

71
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