rafmacalaba/datause-encoder-data huggingface dataset:
This analysis contains four key steps: the specification of a plausible DDA agreement including the translation of these into cuts in actual agricultural support; calculating the impacts of such reforms on global trade, prices and production; tracing these global impacts back to different classes of farm households within the US; and tracing them back to households in our focus developing countries. The combination of these steps into a holistic framework represents a significant contribution of this work, which brings together data and modeling components to conduct global scale analysis.<sup>1</sup> _DDA Specification:_ There have been many studies of WTO trade reforms in the context of the DDA, but few of these bear close relationship to the actual negotiations undertaken in Geneva or to actual trade barriers in the world at the time the DDA will be implemented. In contrast, recent studies based mostly on the GTAP 6 database, recognize the significance of trade preferences for developing countries’ exports and also that the DDA will be implemented in a world in where China has acceded to the WTO and the EU has been enlarged (reference CEPII work here).<sup>2</sup> This is the approach taken here. We build on two recent World Bank projects which begin with tariff line data and specify agricultural market access scenarios based on detailed analysis of tiered formula cuts in current levels of tariff bindings (Anderson and Martin, 2006; Hertel and Winters, 2006). In cases where post-reform bindings fall below currently applied tariff levels, liberalization is predicted to occur. If this is not the case, no actual liberalization occurs despite the reduction in tariff bindings. This detailed analysis is particularly critical for analyzing developing countries, where bound tariffs are high and reductions in these

> 1 We also offer modest methodological advances on the previous literature in two of the four steps.

3
54 Moreover, the witlldrawal of food f ton, tl,e clild at home may have a net detrimental effec.: on his/her diet.

When the household finds it worthwhile to participate in the program, the question usually is "how does this compare with an income transfer of the same value?".26 Selowsky (1978) identifies two parameters that would determine the efficiency of the program which aims at increasing caloric consumption of children: "....(a) the food distributed (by the program) to each child as a fraction of the food previously consumed at home and (b) the marginal propensity to spend on children's food ....... " (p. 56). The product of these two (which are almost by definition less than unity) will yield the percentage increase in the caloric intake of the child. On the basis of this model, Knudsen (1981) shows with data from Tamil Nadu, India, that a food transfer of the value of 10% of family income, for example, will induce in a family with per capita caloiic intake of 2,110 calories (80% of FAO requirements) to increase the caloric intake of a child with a ration of 290 calories by only 40 calories.27 26 If the program is located in a school or day-care center, participation depends on the proportion who attend school and the regularity of their attendance. In many cases, the existence of the feeding program at the school may increase attendance by making school a more attractive option for the household. On-site programs also rely on regular attendance, which may be particularly difficult for children of working mothers.

27However, as the child is likely to be fed by the program a more expensive diet than it would receive at home, the family's value of this diet might be lower, and conseqtuently the child's net benefit from the ration may be even less than the benefit calculated above. As the household cannot vithdraw the ration from the child, especially if it is food specifically consumed by children, the household will withdraw other foods from the child. If those foods are relatively intensive in calories, compared with the ration, actual caloric consumption of the child may even drop.
horizons lengthen. Applied to Kenya, this suggests that the initial binding constraint to overcome would be political risk; once this is achieved and the investment climate improves, attention might have to turn to other constraints such as infrastructure.

Turning to the macro, few economists would quarrel with the proposition that macroeconomic stability is necessary for sustained economic growth and a good investment climate. However, the focus has shifted (especially after the most recent emerging market public debt crises which began in 1997-98 and ended with the Argentine default of 2001) from short-run fiscal deficits and inflation to balance in the government’s intertemporal budget constraint.<sup>11</sup> Satisfying this constraint rests on the capacity to eventually generate adequate primary fiscal surpluses, which in turn depends upon future growth and tax collection.<sup>12</sup> Two key points emerging from recent empirical work are that balancing the government’s intertemporal budget calls for (i) a comprehensive approach to managing the public finances. Paying attention to primary deficits, real interest rates and growth rates is not enough—equally important are bailouts and contingent liabilities, the efficiency and predictability of taxation, expenditure composition and the rate of return on public investments; and (ii) sound macro-micro linkages.<sup>13</sup> We briefly sketch the latter and make the connection with political risk.

Define government net worth as the present value of future primary surpluses (expressed as a ratio of GDP) minus the initial government debt-to-GDP ratio.<sup>14</sup> Since the discount rate is the real interest rate minus the real growth rate (assumed to be positive for convergence), a reduction in interest rates as a result of reduced political risk will unambiguously increase net worth and strengthen government solvency. What about an accompanying reduction in tax rates? This will have two opposing effects: it will tend to lower revenues and hence the present value of primary

> 11 The framework which dominated IFI policy thinking on macroeconomics and growth until 1997 might well have been Fischer (1993).

> 12 Solvency requires that the present value of future primary surpluses (discounted at a rate equal to the real interest rate minus the real growth rate) equal or exceed the initial debt-to-GDP ratio.

> 13 See IMF (2003), Budina and Fiess (2005), Gill and Pinto (2005). 14 See Serven (2007).

8
<u>Table 4. Skills of young adults in Pakistan (Part 1)</u>

|**Subject**|**Difficulty level**|**Question**|**Percent correct**|**N**|
|---|---|---|---|---|
|**Panel A. Cognit**|**ive Test**||||
|Mathematics|70% or more get|Tick box next number that matches the number of objects|79 %|4139|
|Mathematics|around 50% get|678+923|56 %|4139|
|Mathematics|30% or less get|7/3=__|5 %|4139|
|Urdu|70% or more get|Match picture: Book|78 %|4139|
|Urdu|around 50% get|Join letters and write word: m-a-l-k|48 %|4139|
|Urdu|30% or less get|Fill blank in the story by selecting the correct word|29 %|4139|
|English|70% or more get|Match picture: Banana|77 %|4139|
|English|around 50% get|Missing letter to match picture: Flag|53 %|4139|
|English|30% or less get|Use word in sentence: deep|16 %|4139|
|**Panel B. Functi**|**onal Literacy and Nu**|**meracy Assessment**|||
|Numeracy|70% or more get|Read elec bill. How much money do you need to pay for the month of November?|80 %|4402|
|Numeracy|around 50% get|Multiplication per bracket - Elec bill. How much money do you have to pay?|49 %|4402|
|Numeracy|30% or less get|Multiplication: Kv* cost per Kv - Elec bill. How much money do you have to pay|16 %|4402|
|Literacy|70% or more get|Respondent was able to understand the greeting message in Urdu|73 %|4402|
|Literacy|around 50% get|Respondent was able to understand the greeting message in Roman Urdu|55 %|4402|
|Literacy|70% or moreget|Respondent was able to save contact on mobilephone|69 %|4402|

_Notes._ This table shows select questions for the three subjects tested on paper (Mathematics, Urdu, and English; Panel A), and for the functional numeracy and literacy skills assessment (Panel B). For each category, we show a question that at least 70% of respondents solved correctly, a question that approximately 50% solved correctly, and a question that less than 30% of respondents solved correctly. All functional literacy questions were correctly solved by at least 30% of the respondents. For each question, we indicate the proportion of respondents who answered the question correctly and the number of respondents. Sample sizes differ due to item non-response.

44
w

# Table 10: Brazil - Absolute and Relative Incidence<sup>of Household Type by Per Capita Income Class,</sup> Three Metropolitan Areas

|||Recife|||SboPaulo|||**Pr tOAleg**|**re**|
|---|---|---|---|---|---|---|---|---|---|
|Income<br>Class|All<br>FHH|FHH<br>wlminors|FHH w/minors<br>No Adults|All<br>FHH|FHH<br>w/ninors|FHH w/minors<br>No Adults|All<br>FHH|FHH<br>w/minors|FHH w/minors<br>No Adults|
|||||Absol|ute Incidence|||||
|Lowest 5%|40.9|35.5|21.0|27.0|19.3|11.2|26.7<br>|19.9<br>|13.2<br>|
|5-10%|32.2|27.7|9.9|19.7|16.7|9.7|21.6<br>|15.9<br>|8.4<br>|
|10-25%|24.1|15.8|5.9|19.5|11.2|4.1|21.8<br>|11.3<br>|4.5<br>|
|25-50%|21.8|12.1|2.7|14.0|5.3|1.6<br>|16.3<br>|6.0<br>|1.7<br>|
|above 50%|18.1|60.|1.2|15.6|2.8|1.1<br>|17.5<br>|3.5<br>|1.9<br>|
|All|21.8|11.6|3.7|16.5|6.2|2.6|18.5|6.7|3.1|
|||||Relati|ve Incidence|||||
|Lowest 5%|1.9|3.1|5.7|1.6|3.1|4.3|1.4<br>|3.0<br>|4.3<br>|
|5-10%|1.5|2.4|2.7|1.2|2.7|3.7|1.2<br>|2.4<br>|2.7<br>|
|10-25%|1.1|1.4|1.6|1.2|1.8|1.6|1.2<br>|1.7<br>|1.5<br>|
|25-50%|1.0|1.0|0.7|0.8|0.9|0.6|0.9<br>|0.9<br>|0.5<br>|
|above 50%|0.8|0.5<br>|0.3<br>|0.9<br>|0.5<br>|0.4<br>|0.9|0.5|0.6|
|All|1.0|1.0|1.0|1.0|1.0|1.0||||

Soume: auzows tabulations Note: Income refers to the sum of the income from all sources<sup>of all adult members (age 18 or more) of the household.</sup> Relative incidence is the share of the house type in the income<sup>group weighed by its share in the population.</sup> A number higher than one indicates over-representation.
# Job Fairs:

Matching Firms and Workers in a Field Experiment in Ethiopia<sup>*</sup> Girum Abebe<sup></sup> , Stefano Caria<sup></sup> , Marcel Fafchamps,<sup>§</sup> Paolo Falco<sup>¶</sup> , Simon Franklin,<sup></sup> Simon Quinn<sup>**</sup> and Forhad Shilpi<sup></sup>

> * We thank audiences at the CSAE Conference 2017, SERC Work in Progress Seminar, SEEDEC Conference 2017, RES Annual Conference 2017 and NEUDC 2016; in particular Prithwiraj Choudhury and Pieter Serneels who provided helpful comments and feedback. We thank Jali Bekele, Biruk Tekle, Marc Witte, Alemayehu Woldu and Ibrahim Worku for outstanding research assistance. Data collection and experimental implementation were funded by The World Bank Research Support Budget (RSB), GLM — LIC, and by the International Growth Centre. 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. The project would not have been possible without the constant support of Rose Page and the Centre for the Study of African Economies (University of Oxford), nor without the support of the Ethiopian Development Research Institute in Addis Ababa. This RCT was registered in the American Economic Association Registry for randomized control trials under Trial number AEARCTR-0000911.

-  Ethiopian Development Research Institute: `girumabe@gmail.com`

- Oxford Department of International Development, University of Oxford: `stefano.caria@qeh.ox.ac.uk`

- § Freeman Spogli Institute, Stanford University: `fafchamp@stanford.edu`

- OECD: `paolo.falco@oecd.org`

-  Centre for Economic Performance, London School of Economics: `s.franklin1@lse.ox.ac.uk`

- ** Department of Economics, University of Oxford: `simon.quinn@economics.ox.ac.uk`

-  World Bank: `fshilpi@worldbank.org`
# Reference List

"60% of World Will Live in Cities by 2030" 2004. _Financial Times._ September 10. Acemoglu, Daron and Simon Johnson. 2006. "Disease and Development: The Effect of Life Expectancy on Economic Growth." NBER Working Paper 12269. Cambridge, MA: Adams, Peter, Michael D. Hurd, Daniel McFadden, Angela Merrill, and Tiago Ribeiro. 2003. "Healthy, Wealthy, and Wise? Tests for Direct Causal Paths Between Health and Socioeconomic Status." _Journal of Econometrics_ 112(1): 3-56.

Alderman, Harold, John Hoddinott, and Bill Kinsey. 2003. "Long-Term Consequences of Early Childhood Malnutrition." FCND Discussion Paper 168. Washington, DC: International Food Policy Research Institute.

Alsan, Marcella, David E. Bloom, and David Canning. April 2006. "The Effect of Population Health on Foreign Direct Investment Inflows to Low- and MiddleIncome Countries." _World Development_ 34(4): 613-630.

Baeza, Cristian C. and Truman G. Packard 2006. _Beyond Survival_ . Palo Alto, CA: Stanford University Press.

Basu, Alaka Malwade and Rob Stephenson. May 2005. "Low Levels of Maternal Education and the Proximate Determinants of Childhood Mortality: a Little Learning Is Not a Dangerous Thing." _Social Science & Medicine_ 60(9): 20112023.

Behrman, Jere R., Paulita Duazo, Sharon Ghuman, Socorro Gultiano, Elizabeth M. King, and Nanette Lee. 2005. "Evaluating the Early Childhood Development Program in the Philippines." processed.

Behrman, Jere R., John Hoddinott, John A. Maluccio, Reynaldo Martorell, Agnes Quisumbing, and Aryeh D. Stein. 2003. "The Impact of Experimental Nutritional Interventions on Education into Adulthood in Rural Guatemala: Preliminary Longitudinal Analysis." processed.

Beran, David and John S. Yudkin. November 2006. "Diabetes Care in Sub-Saharan Africa." _The Lancet_ 368.

Birt, Michael P. 2006. "Chronic Neglect." _Foreign Policy_ September/October 96-95.

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# **4.3.1. Selecting the subset regressors**

We follow the empirical BMA literature and stay within the natural conjugate prior framework for computational simplicity, thereby avoiding the need to use simulation methods to compute marginal likelihoods. We use a Normal (Gaussian) prior for the regres- _−_ sion coefficients with a prior mean of 0 and Zellner’s g prior for the variance, so that closed form marginal likelihoods can be computed. That is, for a given model (ie., set of included regressors), we have the prior on the regression parameters being _φ_<sup>_gdp_</sup> _|σ_<sup>2</sup> _ε_<sup>_∼_</sup> _N_ (0, _σ_<sup>2</sup> _ε_<sup>g(</sup><sup>_Z′Z_)</sup><sup>_−_1), where</sup><sup>_Z_is the (</sup><sup>_N × L_) design matrix representation of the regressors</sup> _zli_ in (14) and g is a prior hyperparameter.<sup>33</sup>

A well known advantage of using the g _−_ prior setup is that only the hyperparameter g needs to be specified by the user. We follow Fernandez _et al._ (2001) and set g = max( _N_ , _L_<sup>2</sup> ) which in our set-up yields g = _L_<sup>2</sup> . We further use uniform priors on the model probabilities. This choice results in an expected model size of _L_ /2 = 21 variables. It is evident that having an expected number of 21 regressors in a cross-sectional regression with 49 observations is still rather unsatisfactory. Nevertheless, the uniform prior was used with the intention to reduce the number of relevant variables to a subset, and not to the final set of relevant development indicators. Our choice of the model prior is thus a conservative one, in the sense that we prefer a medium sized expected model size to one that shrinks the number of variables more aggressively.

As there are 2<sup>42</sup> _>_ 4.3 _×_ 10<sup>12</sup> possible (linear) regression models that can be created with 42 potential regressors, we use the Model Composition MCMC (MC<sup>3</sup> ) algorithm of Madigan and York (1995) to generate draws from model space.<sup>34</sup> We run a chain of 75 million MCMC iterations, where the first 25 million are discarded as burn-in draws. We check the convergence of the (model space) Markov chain by computing the correlation between the model iteration counts and analytic posterior model probabilities for the best 5000 models. This correlation is well over 99%, indicating that the Markov chain on the model has The PIPs of the included variables in the BMA space converged. procedure, together with a brief description of the 42 variables included, are reported in Table 3. The results are sorted by largest to smallest PIP value, with the dashed horizontal line marking the 25% PIP cut-off value.

The posterior inclusion probabilities reported in Table 3 show that the `prudential1` and `cba` ~~`e`~~ `conomic` variables have the highest inclusion probabilities with values close to 100%, indicating that these two variables are included in almost every regression model

> 33 See Koop, 2003 pages 269 _−_ 273 for more details regarding this set up and a general overview of BMA. 34 See also Koop, 2003 pages 269 _−_ 273 for more details regarding this algorithm.

25
# **Figure 3.  Catastrophe Reinsurance Cycles**

<!-- Start of picture text --> Figure 4<br>400 200%<br>U.S. CATASTROPHE REINSURANCE PRICE INDICES<br>(Source 1984-2001: PARAGON, a former subsidiary of the Benfield Group<br>350 Source 2001-2007 Q1 : LFC averages based on Secondary Cat Bond prices and ILW Prices) 175%<br>300 150%<br>250 125%<br>200 100%<br>150 Post 75%<br>Andrew Post<br>Katrina<br>100 50%<br>Post<br>9/11<br>50 25%<br>0 0%<br>-50 -25%<br>1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008<br>Q1<br>500<br>Reinsurance Prices Indices for Different Zones<br>450<br>Based on Guy Carpenter Rate on Line Indices (2006 Cat Report) and<br>Lane Financial LLC Index<br>400<br>Australia New Zealand Japan Proportional EQ<br>Japan EQ Price Index US Cat Property RoL Index<br>350<br>World RoL (Ex US) Index World RoL index<br>France RoL LFC Index<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br>de-trended Changes in blue<br>% Year to Year Changes in Index plus<br>Paragon and LFC Index of Catastrophe Prices<br><!-- End of picture text --> Source: Lane Financial LLC (2008).

8
numbers in the last two columns indicate the ratios of the respective business- to economy-class footprints.

These “relative footprint” values are somewhat lower than their respective s values (as result of the weighting of the space and weight factors related to individual travelers, with relatively more weight to the former factor), but not dramatically lower. Another finding is that the footprint of economy-class passengers needs to be lower, and for business-class passengers higher, than the overall average footprint per traveler. This is also seen from the table; albeit the reduction (below unity) is small for economy-class (since this class constitutes a very large fraction of overall air travel in terms of traveled distance).

Our argument that weight-related arguments should make average footprint numbers less variable across travel classes, depends on passenger weight (including all weight factors that depend on individual passengers such as their luggage, and their seat) being independent of travel class on average. This is likely not the case. First, seats are heavier in premium classes, more so in first class than in business class. Secondly, passengers in premium classes are allowed to carry more luggage and may thus do so (although we do not have data for the average weight of passenger luggage by class). Thus, most likely, the impact of this factor will in practice be smaller than the numbers reflected in our calculations in this chapter. Put otherwise, the correct relative footprint numbers are closer to being proportional to space allocation, than those shown in Table 3.1.

# **4. Calculations of Relative Space Requirements by Travel Class**

# **4.1 Step 1: Relative Seat Configurations**

We will now go to the more practical footprint calculations, in part with basis in data for WBG travel in Calendar Year 2009 (CY-09). In calculating the carbon footprint from a given amount of air travel one first needs to find the class-specific per-passenger footprint. As noted, seat configurations in airplanes differ between travel classes, with premium-class seats occupying a larger space in a given plane than seats in coach class. Fewer passengers can then fit into a given space in first and business classes than in economy class. It is also crucial to recognize that these seat configurations are endogenous: they are chosen by airlines to maximize profits, and such a procedure implies that more space is generally is given in any aircraft to premium-class passengers. Seats could be spaced differently; as increased premium class passengers are accommodated, coach-class passengers are crowded out more than one-to-one. In addition, load factors are generally lower on first and business classes than on economy class.

The model in section 3 showed that when space and not weight considerations for passengers is the main constraining variable for airlines (so that passenger weight plays a very small role), the correct class-specific carbon footprint is (close to) proportional to the average space taken up by passengers traveling on different classes. When by contrast weight considerations play a larger 10
- Yamauchi, Futoshi (2016), “Rising real wages, mechanization and growing advantage of large farms: Evidence from indonesia.” _Food Policy_ , 58, 62–69.

- Yang, Shijian, Hongping He, Shangling Lu, Dong Chen, and Jianxi Zhu (2008), “Quantification of crop residue burning in the field and its influence on ambient air quality in suqian, china.” _Atmospheric Environment_ , 42, 1961–1969.

- Zhang, Tianran and Martin Wooster (2016), “Small fire detection algorithm development using viirs 375m imagery: application to agricultural fires in eastern china.” In _EGU General Assembly Conference Abstracts_ , volume 18.

- Zheng, Siqi and Matthew E Kahn (2017), “A new era of pollution progress in urban china?” _Journal of Economic Perspectives_ , 31, 71–92.

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