rafmacalaba/datause-encoder-data huggingface dataset:
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reform may be related to what is known in the psychology literature as negativity bias or positive-negative asymmetry. While the concept refers to a broad range of psychological phenomena it has also been found to apply to information processing. In a survey of the extensive literature on the issue, Baumeister et al. [2] conclude that ’bad information is processed more thoroughly than good’. This may help to explain why some survey respondents could come up with examples of negative impact more readily than for positive impacts. It is worth stressing that the survey results suggest that information about the reform is linked with a lower negativity bias. Satisfaction with the reform also differed depending on the respondents’ trust in the government’s intention and ability to deliver on the proposed reform. In particular, satisfaction with the reform was higher among those respondents who thought that the government would be able to deliver on its promise (42 percent) than among those who had no trust (22 percent). Satisfaction with the reform was also higher among those leaning politically with the government (44.1 percent) than among those who favored the political opposition (18.9 percent). This may reflect the well-established fact that people assimilate information in a way that is skewed in the direction of support for their antecedent beliefs (Glaeser and Sunstein [7]). In our context, such biased assimilation of information may simply take the form of supporters of the political party in government paying more attention to or believing the positive aspects of the reform proposed by their party.

While our dataset provides unique information about individual’s perception and knowledge about the subsidy reform, it has a few caveats worth mentioning. First, the surveys are not a panel, but separate cross sections. We are no able to track changes in satisfaction for the same individual in different periods, and have to rely on change in representative samples of the population. Some variables, such as the level of information of the respondent about the reform, are not included in the intermediary surveys, which prevents us from following its evolution in time. Finally, different surveys were collected in different times of the year, and some control variables such as income or occupation could have been affected by seasonality. It is unlikely that our main variables of interest (satisfaction, information, access to the benefit, and political partisanship) are significantly affected by seasonal effects.

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The expression for the bilateral trade volume, Equation (2), suggests a gravity-like estimation. Noting that _Bd_ equals _Ed p_ 1 _d_ −σ , we can use the GDP of the importing _E_ nation to proxy for _d_ , and the GDP of the exporting nation to proxy for<sup>_n_</sup> _o_ ;<sup>_n_</sup> _o_ is related to the endowment of the exporting nation17. The remaining terms, including bilateral trade costs, τ _od_ 1<sup>−</sup> σ , and additional nation-o specific factors affecting<sup>_n_</sup> _o_ can be controlled for using time-invariant pair dummies which in our case, based on the facts observed previously, will pick up on the distance between markets and the directionality in trade (North/South).

This framework is linked to diversification to the extent that it is clear from the model that the range of exported goods is not only affected by the factors we have analyzed before, but also, it is somehow linked to the export threshold. In particular, as the fixed market entry costs have been falling over time for exporting firms, the number of zeros in export vectors should also be falling - fact supported also by the evidence presented in the previous section-. This drop in bilateral trade costs, or the fixed market entry costs, may not only be stimulating bilateral exports, but it can also induce firms to start exporting new categories of goods that were previously not exported or to start selling to new destinations.

One concrete story would be that developing countries have been signing FTAs and as a consequence the associated costs with exporting have been reduced.<sup>18</sup> If the fixed cost of entering the FTA markets falls, then a wider range of firms will find selling to the FTA to be worthwhile. As a consequence, the number of goods exported to the FTA will increase. More goods, in turn, mean a higher trade volume. The fundamental difference between this result and the standard gravity model is that censoring here is an issue. When trading is not profitable, we find a censored data -zero export valuesin that category.

Our empirical strategy is to focus on the bilateral trade flows that switch from zero to a positive value for each possible destination, although we also look at the change in existing trade flows. With this aim, in what follows, we undertake the Tobit estimation, which views the non-existent trade flows as censored data.

> 17 See Helpman (2004) for details.

> 18 For instance, higher transparency in the rules for exporting to FTA members lower information costs for exporters which translates to a fall in the fixed cost of entering the FTA. We are going to assume that the FTA lowers the fixed market entry costs for its members.

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rations - because this is income realized within corporations and not distributed to individuals, it is only observable when connecting corporations to their shareholders. In the next section, we describe how our inequality measures change when we cannot observe direct ownership and instead infer it from distribution of dividends.

In Figure 3b, we present a simplified version of the decomposition, splitting income between labor and capital using the convention of assigning 70% of mixed income as labor and 30% as capital. That provides a symmetric view of income composition within the top 10% – labor income represents 90% of of total income for those at the top 10%; at the top 0.1% labor and capital are equally split; and at the very top labor income tends towards zero.<sup>22</sup>

# **6.2 Top income shares**

Our key results relate to the level and evolution of top income shares in Honduras since the early 2000s. We summarize the share of income flowing to different groups . of top earners in Figure 4 We highlight two features of those findings.

First, the remarkable stability of those shares over the years. The share of income flowing to the top 1% highest earners, for example, starts at just below 29% in 2003, falls to approximately 25% in the period 2008 - 2013 and returns to approximately 29-30% at the most recent period. Similarly, the share of the top 0.1% stays at the range of 18% between 2003 and 2015, before climbing slightly to just above 20% in 2017-2019. These are particularly striking given all the economic and political changes faced in the country in the last two decades, including a substantial move of labor force from agriculture to the service sector; a 40% increase in the minimum wages in 2008; and the recession and political crisis that followed the 2009 military coup that deposed the president. In the following section, we document that not only the top income shares in Honduras remained stable, but the identity of individuals at the top of the distribution is highly persistent – suggesting the existence of deep roots of the inequality scenario in the country.

Second, we note these numbers place Honduras among the countries with highest inequality in the world. In Table 3, we provide top shares for a series of countries for which the same distributional national accounts approach was undertaken, so results are broadly comparable with our measurements. With almost 30% of income flowing to the top 1%, Honduras ranks among the highest inequalities in the world - slightly above Mexico (28%) and the Dominican Republic (29%). Countries with higher top

> 22In Figure A.2a, we show how the labor share of total income in National Accounts fell by approximately 6 p.p. between 2011 and 2019, from 70% to 64%. This change in aggregate labor share is fully explained by developments within the top 1%, as we show in Figure A.2b. The labor share for those at the bottom 99% was stable at approximately 80% throughout the period, while it fell by over 10 p.p. for those at the top 1%, as the participation of capital income increases from about 50% to over 60%.

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**Table 5. Decomposition of Nonfarm Income Inequality in Egypt and Jordan**

|Income source|Proportion of<br>households<br>receiving<br>income source<br>(PK)|Share in<br>nonfarm<br>income<br>**(SK)**|Gini<br>coefficient for<br>income source(3)<br>(GK)|Gini<br>correlation with<br>total income<br> <br>rankings<br>(RK)|Contribution of<br>income source<br>to overall<br>income<br>inequality<br>(SKGKRK)|Relative<br>concentration<br>coefficient of<br>income source<br>**_(g= R9,_**|Percentage<br>contribution<br>to nonfarm<br>income<br>inequality|
|---|---|---|---|---|---|---|---|
|(a) Rural Egypt||||||||
|Government<br>employment|0.550|0.488|0.641|0.435|0.136|0.704|41.6|
|Private sector|0.147|0.123|0.908|0.369|0.041|0.846|12.6|
|Unskilled labor|0.324|0.235|0.839|0.367|0.072|0.777|22.0|
|Self-employment<br>|0.209|0.155|0.897|0.562|0.078|1.273|23.8|
|Other|_|_|_|||||
|Total|-|1.000|-|-||-|100.0|
|(b) Rural Jordan||||||||
|Government<br>employment|0.526|0.735|0.658|0.625|0.302|0.718|69.7|
|Private sector|0.145|0.183|0.919|0.528|0.089|0.848|20.6|
|Unskilled labor|0.079|0.073|0.965|0.477|0.034|0.804|7.8|
|Self-employment|-|-|_|_|-|_|_|
|Other|0.006|0.009|0.999|0.898|0.008|1.568|1.9|
|Total|-|1.000|-|-||-|100.0|

# Notes: All estimates are based on annual per capita household income.

- (a) Source ginis (GK) are high because they include households with zero and negative incomes from different income sources. Source ginis can exceed unity if many of y1 are negative.
have used information on the purchase of car seats and bicycle helmets to infer WTP for reduced death and injury.<sup>33</sup> Other studies are based on parents’ WTP for chelation therapy for children with body lead burdens. Some of the literature relies on stated preference studies. As stated in a recent OECD volume on children’s health (OECD 2006) only 15 studies directly compare parents’ willingness to pay for improvements in their own health with WTP for improvements in their children’s health. Many of these studies value reductions in acute illness, and only one study was conducted in a developing country (Liu et al., 2000).

The consensus from these studies is that parents are willing to pay more to reduce health risks to young children than to themselves—generally about twice as much—but that this effect decreases with child age. The result is also not universal: Jenkins et al. (2001) and Mount et al. (2001) find that parents are willing to pay more to reduce mortality risks to themselves than to their children. Only one of the studies cited by OECD (2006) was conducted in a developing country. Liu et al. (2000), in a stated preference study in Taiwan, find that mothers are willing to pay about twice as much to avoid a day of acute illness in their children as they are to avoid the day of illness themselves.

# _The Adult-as Child Approach_

The adult-as-child approach would attribute a VSL to children by extending the life cycle model backwards in time. This raises the question of budget constraints: out of whose income does a child consume when young? Nevertheless, one could use equation (5) [or (6)] above, contingent on assumptions about the child’s consumption over his lifetime, to calculate a VSL for children. How this would compare to the VSL for an adult would depend on the level consumption over the child’s lifetime—is it expected to be higher than for an adult due to income growth?—as well as on the discount rate and survival probabilities used. If the analyst is willing to make the assumptions necessary to simplify the VSL to equation (7), it is almost surely the case that the VSL used for children would be higher than for adults, given that children have a high discounted remaining life expectancy than adults.

# V.  Valuing Injuries

# A. Principles of Valuation

In principle the value of avoided injury should be measured by what an individual is willing to pay to avoid it.<sup>34</sup> Willingness to pay (WTP) should capture the value of the pain and suffering avoided, as well as the value of time lost due to injury (both leisure

> 33 The assumption in these studies is that if a safety device is purchased, the marginal benefit of the device—the VSL multiplied by the size of the risk reduction—must exceed the marginal cost.  For the parent who just finds it worthwhile to purchase the device, the cost of the device divided by the risk reduction achieved provides an estimate of the VSL.

> 34For minor injuries, this is the amount a person is willing to pay to avoid illness with certainty; for serious injuries by the WTP to reduce the _risk_ of injury.

22
**Figure 3.** Establishment of private schools over time

<!-- Start of picture text --> Over-time distribution of school establishment<br><!-- End of picture text --> <!-- Start of picture text --> Bangladesh India<br>100 100<br>80 80<br> 60 60<br>40 40<br>20 20<br>0 0<br>1890 1910 1930 1950 1970 1990 2010 1890 1910 1930 1950 1970 1990 2010<br>Establishment year Establishment year<br>Rural Private Urban Private Rural Government Urban Government<br>Over-time distribution of school establishment<br>Nepal Pakistan<br>100 100<br>80 80<br> 60 60<br>40 40<br>20 20<br>0 0<br>1910 1930 1950 1970 1990 2010 1910 1930 1950 1970 1990 2010<br>Establishment year Establishment year<br>Rural Private Urban Private Rural Government Urban Government<br>Cumulative share (percent) Cumulative share (percent)<br>Cumulative share (percent) Cumulative share (percent)<br><!-- End of picture text --> _Source:_ Authors, based on data from the Primary School Census 2006 for Bangladesh, District Information System for Education 2009/10 for India (not including Uttar Pradesh and Madhya Pradesh), Flash Data 2009/10 for Nepal, and Primary and Secondary School Census 2005 for Pakistan.

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Table 1. Main features of global recessions and expansions (with quarterly series)

||Duration (quarters)|Amplitude (percent)|Average (percent)|
|---|---|---|---|
|**Recessions**||||
|1974:1-1975:1|5|-9.3|-1.9|
|1981:4-1982:4|5|-4.5|-0.9|
|1990:4-1991:1|2|-0.9|-0.5|
|2008:3-2009:1|3|-15.3|-5.4|
|Average|4|-7.5|-2.2|
|**Quarters with n**|**egative growth**|||
|1970:4|1|-0.7|-0.7|
|1980:2|1|-4.8|-4.8|
|1981:2|1|-0.3|-0.3|
|1998:1|1|-0.2|-0.2|
|2001:3|1|-0.5|-0.5|
|**Expansions**||||
|1975:2-1981:3|26||2.5|
|1983:1-1990:3|31||2.8|
|1991:2-2008:2|69||2.2|
|2009:2-2019:3|42||2.1|
|Average|42||2.4|

Note: The table shows the periods identified as global recessions and expansions, using the algorithm in Harding and Pagan (2002), or those with negative per capita growth. Amplitude and average are based on per capita global GDP growth. “Amplitude” is measured as a percent change in per capita output during each recession (i.e., a cumulative change over the denoted period). “Average” refers to average annualized growth during each period.

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<!-- Start of picture text --> Feenstra, Robert C., 1994, "New Product Varieties and the Measurement of International Prices",<br>The American Economic Review, 84(1), 157-177.<br>Feenstra, Robert C., and Hiau Looi Kee, 2006, "Export Variety and Country Productivity: Esti-<br>mating the Monopolistic Competition Model with Endogenous Productivity", Mimeo.<br>Feenstra, Robert C., and Hiau Looi Kee, 2007, "Trade Liberalization and Export Variety: A<br>Comparison of Mexico and China", The World Economy, 30(1), 5-21.<br>Fischer, Ronald, and Pablo Serra, 2000, "Standards and Protection", Journal of International<br>Economics, 52, 377-400.<br>Fontagné, Lionel, Friedrich von Kirchbach, and Mondher Mimouni, 2005, "An Assessment of<br>Environmentally-Related Non-Tariff Measures", The World Economy, 28(10), 1417-1439.<br>Gandal, Neil, and Oz Shy, 2001, "Standardization Policy and International Trade", Journal of<br>International Economics, 53, 363-383.<br>Ganslandt, Mattias, and James R. Markusen, 2001, "Standards and Related Regulations in In-<br>ternational Trade: A Modeling Approach", Working Paper No. 8346, NBER.<br>Grossman, Gene M., and Elhanan Helpman, 1994, "Protection for Sale", American Economic<br>Review, 84(4), 833-850.<br>Helpman, Elhanan, Marc Melitz, and Yona Rubinstein, 2007, "Estimating Trade Flows: Trad-<br>ing Partners and Trading Volumes", Working Paper, www.economics.harvard.edu/faculty/ help-<br>man/papers/TradingPartners.pdf.<br>Helpman, Elhanan, Marc J. Melitz, and Stephen R. Yeaple, 2004, "Export Versus FDI With<br>Heterogeneous Firms", The American Economic Review, 94(1), 300-316.<br>Hoekman, Bernard, 2007, "Doha, Development, and Discrimination", Pacific Economic Re-<br>view, 12(3), 267-292.<br>Hummels, David, and Peter J. Klenow, 2005, "The Variety and Quality of a Nation’s Exports",<br>The American Economic Review, 95(3), 704-723.<br>Kee, Hiau-Looi, Alessandro Nicita, and Marcelo Olarreaga, 2006, "Estimating Trade Restric-<br>tiveness Indices", Policy Research Working Paper No. 3840, The World Bank.<br>27<br><!-- End of picture text -->
In the case of soil carbon, emissions flow in a year also counts emissions released over the last 20 years since soil carbon emissions from land-use change occur over time for 20 years (IPCC, 2006). Alternatively, particularly to calculate carbon debt or payback period, soil carbon related emissions from land-use change activities in a year also count the emissions for the next 20 years. Although either approach gives the same amount of cumulative emissions over the 20-year period, we used the first approach to calculate emissions in a given year.

# **3. Definitions of Baseline and Scenarios**

The baseline<sup>7</sup> represents continuation of business as usual for economic development, population growth and biofuel development. Existing biofuel policies (e.g. already implemented mandates, subsidies and import duties) are part of the baseline<sup>8</sup> . Future mandates for increased biofuel use compared to existing mandates are included in the scenarios. Like in most dynamic CGE models, three variables -- population growth, savings and investment and productivity -- are the key drivers to generate a baseline. Population growth, depreciation rate of capital stock and productivity growth are exogenous to the model. Growth in aggregate labor supply tracks the working age population (defined as those between 15 and 65 years). Savings and investment determines the overall level of the capital stock along with the rate of depreciation. Sectoral productivity growths are consistent with recent trends (World Bank, 2009). Another exogenous variable in the model baseline is the growth of energy prices, which are calibrated with projections made by the Energy Information Administration of the U.S. Department of Energy<sup>9</sup> .

For the purpose of the study we define two scenarios. The first scenario considers the implementation of biofuel use targets consistent with what countries already have announced (hereafter “AT” scenario). The second scenario generally considers a doubling of the

> 7 The base year for calibrating the model is 2004, the year for which the social accounting matrices, the main database for the model, were constructed. Although our base year is 2004, the model is calibrated to replicate major historical statistics until year 2009.

> 8 Impacts of removal of subsidies and import duties have been analyzed in detailed in a separate paper. 9 A module that can represent both conventional and un-conventional oil and gas reserves and production would be ideal; however, the model used here does not have that capacity. Hence, we used energy price forecasts from other sources instead of generating them endogenously in the baseline.

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