rafmacalaba/datause-encoder-data huggingface dataset:
The substitution possibilities among fuels is the key driver to a successful implementation of market-based policy instruments in reducing GHG emissions from the transportation sector. This is because the higher are substitution possibilities among the transportation fuels, the larger would be the efficacy of market-based instruments. The substitution possibilities between fuels or between modes of transportation can be measured in terms of elasticity of substitutions. In addition, by comparing estimates of partial elasticities of substitution (a measure of fuel switching) with that of total elasticities of substitution (a measure of both fuel switching as well as changes in total fuel use), the importance of alternative market mediated policies can be evaluated. The benefits of a policy that affects the relative prices of fuels, and thus drives fuel-switching, can be compared with those of a policy that promotes fuel-efficiency.

Several existing studies have focused on estimating price elasticity of transportation fuels, particularly gasoline. For example, studies such as Dahl and Sterner (1991), Dahl (2012), Espey (1998), Kayser (2000) and Holmgren (2007) estimated that the short-run own-price elasticity of gasoline spans a range between -0.08 and -0.54. Other studies, such as Burke and Nishitateno (2013), estimated the long-run own-price elasticity of gasoline to vary between −0.2 and −0.5. Existing studies have also compared the long-run elasticity of car travel with long-run own-price elasticity and reported the former is lower than the latter because of increase in fuel efficiency.<sup>3</sup> For example, Dargay (2007) finds that the elasticity of car travel to fuel prices in the long-run was

> 3 The average miles per gallon of the light-duty vehicle fleet in the United States increased from 14.9 miles in 1980 to 20 miles in 2020 and further to 21.4 miles in 2014.  Similarly, the average miles per gallon of the new light-duty vehicle fleet increased from 24.3 miles in 1980 to 28.5 miles in 2000 and further to 36.4 miles in 2014 (US Bureau of Transportation Statics,

> https://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_statistics/html/table_04_ 23.html).

3
XXO INTRODUCTION Figure 15 Assumed Total Fertility Trend, Less Developed Countries, and U.N. and Previous World Bank Trends <!-- Start of picture text --> 3.9<br>3.8-<br>3.6 -2.02<br>3 4<br>3.3<br>3.2-<br>3.1 e<br>3.0 2.9 _<br>Flgur 2.8 17 Assme LieEpcac rn,<br>2.s<br>2.5<br>155.9 1900 9520020-525.001.5 050<br><!-- End of picture text --> <!-- Start of picture text --> Figure 17 Assumed Life Expectancy Trend,<br>Less Developed Countries, and U.N.<br>and Previous World Bank Trends<br>71.7<br>70-<br>69<br>67<br>66<br>65<br>64 - Cu<br>63.<br>62<br>61<br>60<br>sq m 2*5M520 20.5lm Mo8.. 0-<br><!-- End of picture text --> Flgure 16 Assumed Total Fertility Trend, More Developed Countries, and U.N. and Previous World Bank Trends <!-- Start of picture text --> t2.5<br>20<br>1<br>U.N.<br>4<br>10<br>F - 8AsmdLf xetnyTed<br>S<br>1.2<br>590 19095 142 15 25145 MO9~ 2010-IS 2015-20 2M20:<br><!-- End of picture text --> Figure 18 Assumed Ufe Expectancy Trend, More Developed Countries, and U.N. and Previous World Bank Trends <!-- Start of picture text --> 79M<br>77n<br>76-<br>75-<br>7<br>VA 990 .15 1995.251 25D.05 202510 2002.15 2015-20 20202.2'<br><!-- End of picture text -->
Using Probabilistic Models to Appraise and Decide. Ley-Borrás and Fox

# **Building coherent SDRFI strategies**

The decision node _SDRFI strategy_ in the influence diagram means deciding among a set of sovereign disaster risk financing strategies, each set consisting of a number of financial instruments to be used by the country. Each strategy may include instruments for _risk retention for high frequency events_ (reserves, calamity funds), _risk retention for medium frequency events_ (emergency loans, contingency credits (CAT‐ DDO), budget reallocations), _risk retention for low frequency events_ (tax increases, domestic or external credit) and _risk transfer for low frequency events_ (catastrophe bonds, parametric insurance, traditional insurance) (Ghesquiere and Mahul 2010, p.20). Even if not all the instruments are available for a particular country, the number of possible combinations of instruments can be enormous. It may be impractical to enumerate all combinations of instruments, and much more to evaluate them.

To design a small number of coherent strategies that are both feasible and promising, we can use a _strategy generation table_ (Howard 1988). This tool consists of a table with columns labeled with the name of a financial instrument that is feasible to use for a specific country. The body of the column includes possible levels of use of the instrument, say from making it a key part of the strategy to not using it at all, or by different ways of using the instrument. A strategy is formed by selecting a level or form of use from each column.

An illustration of a partial strategy generation table is shown in Figure 3; it includes only financial instruments for responding to low frequency events but, nevertheless, the six types of instruments considered, with only three or four alternatives each, can generate 3,072 combinations. Because not every combination is a reasonable strategy, it is valuable to use the table for designing coherent strategies that are significantly different from each other.

**Figure 3. An illustration of a partial** **_strategy generation table_ . Two strategies are shown.**

|**Strategy**<br>**theme**|**_Risk Retenti_**<br>Tax increase|**_onfor lowfrequency events_**<br>Domestic<br>credit<br>External<br>credit|**_Risk Transfer_**<br>Traditional<br>insurance|**_for lowfreque_**<br>Parametric<br>insurance|**_ncy events_**<br>Catastrophe<br>bonds|
|---|---|---|---|---|---|
|**Risk**<br>**Retention**<br>**Emphasis**<br>**Risk Transfer**<br>**Emphasis**|Consumer<br>tax<br>Special<br>purpose tax<br>Mix of<br>several taxes<br>No tax<br>increase|Up to Z<br>amount<br>Up to Y<br>amount<br>Up to X<br>amount<br>No domestic<br>credit<br>Contingent<br>credit<br>Line of credit<br>Conventional<br>credit<br>No external<br>credit|Flood<br>insurance<br>Earthquake<br>insurance<br>Flood and<br>Earthquake<br>insurance<br>No traditional<br>insurance|Flood<br>insurance<br>Earthquake<br>insurance<br>Flood and<br>Earthquake<br>insurance<br>No parametric<br>insurance|Wide<br>earthquake<br>bond<br>Pilot<br>earthquake<br>bond<br>No<br>catastrophe<br>bond|

Source: R. Ley‐Borrás

**13**
16

# Table 2: Incentive and Revenue effects of a MAT Under Uncertain

|Rate<br>of re-<br>turn|No<br>MAT<br>MERT|2% MAT<br>MERT|Tax<br>Revenue<br>Without|Absolute<br>Revenue<br>Gain (*)|Relative<br>Revenue<br>Gai.n|Increased<br>MERT per<br>unit of|
|---|---|---|---|---|---|---|
|uncer-|||MAT|due|due|Revenue|
|tainty||||to MAT|to MAT|to MAT|
||(1)|(2)|(3)|(4)|(5)|(6)|
|0.0|32.3%|32.27%|1.2065|0|0|0|
|0.1|32.3%|32.45%|1.2078|0.0065|0.5%|26.6%|
|0.5|37.10%|39.05%|1.3931|0.0731|5.3%|26.7%|
|0.8|44.59%|46.86%|1.6787|0.0855|5.1%|26.5%|
|1|50.13%|52.50%|1.8909|0.0893|4.7%|26.5%|
|3|108.93%|111.53%|4.1815|0.0995|2.4%|26.1%|
|5|167.01%|169.61%|6.5224|0.1015|1.6%|25.6%|
|8|250.83%|253.38%|10.0468|0.1025|1.0%|24.9%|

***** The revenue gain from **the** introduction of **a** MAT **is the** difference between the net present value **of** after tax cash flows from a unitary investment after tax with and without **MAT** for each level of uncertainty.

Without uncertainty--and with positive profits--, the MAT option is never exercised, so with and without MAT MERTs are the same. With low uncertainty--say a standard deviation of 0.1--, a 2% minimum tax does not matter much either, because the spread around the 20% rate of return still leads to enough taxable profits for the MAT not to come into play. Of course, the lower the average rate of return, the more likely it is that a given MAT provisions will become binding. Hence, the lower the average rate of return, the more valuable the MAT option. For instance, assuming a before tax rate of return of 10% instead of 20% would allow the MAT to kick in much earlier. In that case, for a standard deviation of 0.1, the MERT is 26.75% without MAT and 28.28% with MAT. Of course this claim assumes that the MAT rate remains the same as expected rate of returns are lowered.
The impact is robust to controls for student absenteeism. The estimated impact of student absenteeism is of the same magnitude as the effect of shocks to teachers. Since every teacher teaches many students, this raises the possibility that excess teaching capacity, which allows for the greater use of substitute teachers, could signi fi cantly increase learning achievement. The protective effect of such insurance could have larger impacts on learning achievement than insuring and supporting students and their families. Moreover, in countries with a high HIV/AIDS burden, substantial welfare gains could accrue through a reduction in the frequency or impact of shocks associated with absenteeism. For example, Bell, Devarajan and Gersbach (2003) posit huge declines in human capital due to the effects of the HIV/AIDS epidemic on affected economies. The results presented in this paper provide strong micro-foundations for this assumption and challenge conclusions that fi nd a small (or no) impact of the HIV epidemic on the education sector (Bennell 2005).

To contextualize our empirical results, we present a model where households determine the optimal path of educational attainment given teacher and other school inputs (Das and others 2004). In this framework, teachers possess three attributes—observable characteristics (age, gender and experience), unobserved non-time varying attributes (such as ability or motivation) and unobserved time varying attributes (such as the health status). As households try to optimize over future time periods, they face uncertainty related to two school-based inputs: uncertainty in the quality of the teacher (non-time varying) and timing and severity of shocks to the teacher (time-varying).

The model shows how variation in the non-time varying and time-varying attributes affect learning achievement. It thus allows us to advance the prominent role of households in determining their children’s cognitive achievement. In addition, it clari fi es the identi fi cation assumptions required to estimate the impact of teacher-level shocks on student learning. In particular, one contribution is to show that the impact of teacher-level shocks on learning is identi fi ed only if non-time varying heterogeneity for both students and teachers is adequately dealt with through the estimation procedure.

The empirical results are based on a rich teacher-student matched data set from Zambia that we collected in 2002. In addition to school, teacher, and student characteristics, the data include test scores for a sample of pupils over two years. This panel of test scores allows us to deal with omitted variable bias associated with student tracking, but does not account for unobserved changes in teacher characteristics. To do so, we exploit a tradition in which some teachers stay with the same student cohort throughout primary school. By restricting attention to the sample of pupils with the same teacher in both years, and this is the basis of our identi fi cation strategy, we address concerns that arise from unobserved child and teacher heterogeneity.

3
25 outstanding when covered workers receive their lump-sum benefits are deducted from these benefits. Nearly half the participants in provident funds have used them to finance purchases of homes. People's behavioral response to their prospective pension benefits, or "social security wealth," is a matter of substantial dispute. The basic issue is this: to what extent do people reduce private saving because their expected pensions replace that which they need to save? If the reduction is of a substantial magnitude, as argued by Feldstein (1974, 1982), then, unless reserves are used to replace these savings in the generation of capital, a social security system will reduce a nation's total savings and capital stock. Barro (1974) has argued, to the contrary, that private inter-generational transfers offset the effects of public transfers even in a pay-as-you-go system, so that saving is unaffected. For & more extensive survey of the theoreti-al issues and empirical evidence see Wallich (1983), Shome and Squire (1983), or Munnell (1986).

A further argument is sometimes made about savings and capital accumulation in developing countries in particular. Many people in these countries have no prior experience with financial institutions. Social security programs give them such experience and therefore, it is said, encourage a "habit of saving." Thus they stimulate financial savings in addition to those generated through the programs' reserves.

Social security systems affect the labor market both by inducing earlier retirement and by introducing distortionary
other countries controlling for the stage of development, non-linearities in development, and country size. Only India had a positive coefficient, but it was not significantly different from zero. In 2006, India was a significant and large positive outlier. Its share of service export in total exports was 18.38 percentage points greater than the norm. China also had a positive coefficient but it was not significantly different from zero.

<!-- Start of picture text --> Figure 6 Modern & Traditional Service Export Growth, 2000-2006<br>40%<br>35% Modern<br>Traditional<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Malaysia Thailand Korea Singapore China India South Asia East Asia World<br>‐5%<br><!-- End of picture text --> _Source:_ Balance of Payments, IMF. 2008.

_Note_ : Modern service include exports in telecommunications, computer and information services, other business services, financial services, insurance, royalties and license fees. Traditional services include travel, transportation, construction and personal, cultural and recreational service exports. * Data for China is from 1995-2007.

Modern impersonal service exports have grown much faster than the traditional service exports (Figure 6)<sup>1</sup> . South Asia seems to be an exception in this regard too. Growth of modern services in South Asia has been more than double the global growth rate. It is much higher than any of the East Asian countries with India leading the pack. A good measure of modern impersonal service export is IT and IT-enabled services. Crosscountry data for IT exports is available only from 2000 onwards. In 2000, India stands out as a significant and positive outlier when we compare its share of IT and IT-enabled exports in total exports of goods and services with the rest of the world controlling for the

> 1 The modern impersonal service includes what Baumol called in 1984 “progressive impersonal services.” They can be delivered impersonally and electronically.  These include communication, banking, insurance, and business related services.  These services take advantage of ICT, globalization, scale economies, and benefit from higher productivity growth rates. Traditional personal service includes what Baumol has called “stagnant personal services” such as trade, hotel, restaurant, beauty shops, barbers, education, and health. It requires face to face transaction.  Traditional services also include “stagnant impersonal services” such as transport, government, and public administration service.

15
<!-- Start of picture text --> a,<br>Qa<br>oO<br>is) 10% In percent of British GDP until 1914,<br>a ° in percent of US GDP thereafter<br>5<br>&<br>fav]<br>&<br>Cel<br>fo)<br>x<br>£ 5%<br>2<br>3<br>fe)<br>&<br>fav]<br>a<br>g<br>oS 6<br>1790 1815 1840 1865 1890 1915 1940 1965 1990 2015<br>li Military HE Financialrescue Development<br>EN Relief = Unknown<br><!-- End of picture text -->
_L. Fox and A.M. Oviedo_ _Are Skills Rewarded in Sub-Saharan Africa?_ 17,000 workers in 3,800 firms.<sup>15</sup> We exclude foreign workers (a very small fraction of the total sample) as their wage patterns are markedly different from national workers.

# **III. Institutional differences and manufacturing characteristics across Sub-Saharan Africa**

# _Institutions_

The fast recent growth in manufacturing employment observed in our sample is remarkable achievement, especially given the constraints faced by firms.<sup>16</sup> For instance, when firms are asked to express the degree of obstacle to their business posed by the lack of infrastructure (electricity, water, telecommunications), between 80 and 95 percent of firms qualify it as a major or severe obstacle.<sup>17</sup> Comparing more “objective” data on business regulations, such as the Doing Business indicators, also places SSA at the bottom the ranking (see Table 3). These rankings alone have led many observers to predict a dim future for the sub-continent.

# INSERT TABLE 3

But as Figure 2 above shows, within SSA there are important differences, particularly between Anglophone (common law) and Francophone (civil law) countries. Notably, in virtually all categories of the Doing Business indicators (entry, licensing, registration, labor, contracts, trade, taxation, access to finance, investor protection, bankruptcy), the Anglophone group’s ranking stands ahead of the Francophone group’s. For instance, in the labor regulation category, the Anglophone group has better scores in hiring, rigidity of hours, and has lower non-wage labor costs. Furthermore, even though the actual firing costs (in terms of weeks of wages) are higher in Anglophone countries, the overall firing index is lower (that is, firing is less difficult legally).

# _Firms_

Tables 4 and 5 provide information about the firms in our sample. Despite the similar industry composition of our sample across both legal groups, we do find a few differences in other firm characteristics: 21 percent of firms in the Francophone area are partly (or entirely) foreign owned, which is the case only for 13 percent in the Anglophone area. Thus, despite an apparently weaker investment climate, Francophone countries attract more foreign investment.

# INSERT TABLE 4

> 15 The number of observations on the wage regressions fall to approximately 10,000 due to missing values. See Table A15.

> 16 For a detailed discussion of employment growth in manufacturing and the role of regulations, see Fox and Oviedo (2008).

> 17 See Fox and Oviedo (2008).

7
Alvaredo, Facundo, Anthony B. Atkinson, Thomas, Piketty, and Emmanuel Saez (2013), “The Top 1 Percent in International and Historical Perspective” _Journal of Economic Perspectives—_ vol. 27, no 3, pp. 3-20.

Atkinson, Anthony B. (2002), “Top Incomes in the United Kingdom over the Twentieth Century”. University of Oxford, Discussion papers in social and economic history, No. 43. Available at <u>http://www.nuffield.ox.ac.uk/users/atkinson/TopIncomes20033.pdf.</u> Atkinson, Anthony B. (2009), “Factor shares: the principal problem of political economy?”, _Oxford Review of Economic Policy_ , vol. 25, no. 1, pp.3–16.

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Brandolini, Andrea and Timothy M. Smeeding (2006), “Patterns of Economic Inequality in Western Democracies: Some Facts on Levels and Trends’, _Political Science and Politics_ , vol. 39, no 1, January, pp. 21-26 Burkhauser, Richard V., Shuaizhang Feng, Stephen P. Jenkins and Jeff Larrimore (2012), “Recent Trends in Top Income Shares in the USA: Reconciling Estimates from March CPS and IRS Tax Return Data”, _Review of Economics and Statistics_ , May, vol. 94, no. 2, pp. 371-388.

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Fisher, Irving (1919), “Economists in public service”, Annual address of the President, _American Economic Review_ , vol. 9, no. 1, Supplement, Papers and proceedings of the 35<sup>th</sup> annual meeting of the AEA, March 1919, pp. 5-21.

19
To more confidently establish the importance of innovative capacity as a determinant of income, we then develop a rich data set for the United States at the _county_ level. This vastly expands the degrees of freedom and permits controlling for an extensive set of geographical, growth related, human capital variables and instrumenting for possible endogeneity of engineering using the Land Grant Colleges program. Further, we incorporate patenting density as collected by Acemoglu et al. (2013). Patenting appears only weakly explained by county engineering density and hence, we treat it as driven by human capital more dedicated to invention (Griliches (1990); Sokoloff (1988)) while engineering may arguably be capturing more adoptive activities. Both variables show an important and robust effect: a one standard deviation in engineering density in 1900 accounts for a 16% rise in income today, and patenting capacity another 10%. For a broader sample of countries in the hemisphere, we then demonstrate the continued relevance of engineering at the “state” level. Applying the well-estimated US coefficient to the national level data, a 1 standard deviation in engineering density in 1900 accounts for a 36% difference in income today. We then use historical evidence to validate our ranking of innovative capacity and through case studies document the divergent development outcomes arising within industries and even identical goods in countries with the differing innovative capacities we quantify. Finally, we present some suggestive findings on the determinants of innovative capacity.

# **2 Data**

## **2.1 Measuring engineering/innovative Capacity**

Our primary measure of innovative capacity at the national level across all countries is the number of engineers with domestically emitted university degrees per 100,000 male workers. Unlike literacy data, which countries sometimes collect as part of the census, countries do not tabulate such information in a uniform fashion. Hence, we construct these series using three sources of data.

5