rafmacalaba/datause-encoder-data huggingface dataset:
of imperfect substitution and imperfect transformability grant a certain degree of autonomy of domestic prices with respect to foreign prices and prevent the model from generating corner solutions. Furthermore, they permit the model from cross-hauling a feature normally observed in real economies. The balance of payments equilibrium is determined by the equality of foreign savings (which are exogenous) to the value of the current account. With fixed world prices and capital inflows, all adjustments are accommodated by changes in the real exchange rates: increased import demand, due to trade liberalization, must be financed by increased exports, and these can expand due to improved resource allocation. Price decreases in importables drive resources toward export sectors and contribute to falling domestic resource costs (or real exchange rate depreciation).

_<u>Factor Markets.</u>_ Labor is divided into two categories: skilled and unskilled. These categories are considered imperfectly substitutable inputs in the production process. Moreover, some degree of market segmentation is assumed: composite capital is sector specific, and labor markets are segmented between agriculture and non-agriculture, with labor fully mobile within each of the two broad sectors, but fully immobile across them. These restrictive conditions are imposed on the modeling framework so that it mimics in the best possible and least contentious way the short-term impact of trade reforms on the Nicaraguan economy. Dynamic features, market imperfections, and other complications could be introduced. However, questions about the links between trade policy and growth would emerge and, although important, is a much more difficult issue.<sup>6</sup> Finally, the segmented version of the model also facilitates linking the macro results of the CGE model to the household survey micro-model, where households are not allowed to respond to price changes by migrating, increasing their human capital endowments, or even changing their consumption choices.

The labor market specification is a key element of the model and an important driver of poverty and distributional results. Therefore, its specification calls for some clarification and justification. The labor market skill segmentation<sup>7</sup> has become a

> 6 No systematic analysis of the ex-ante predictions of CGE studies exists; however, a few papers have evaluated these models’ performance in predicting the NAFTA outcomes and have found that they did not score too well, especially in terms of sectoral reallocations (i.e., relative growth rates of exports and production). Generally, these studies highlight that introducing dynamic effects in these models is very difficult. For an example of these studies on NAFTA see Kehoe (2003).

> 7 See Taubman and Wachter (1986) for a general discussion of labor market segmentation.

5
# **6. References**

- 1  Doll R, Hill AB. Lung cancer and other causes of death in relation to smoking. _British medical journal_ 1956; **2** :1071.

- 2  Wynder EL, Graham EA. Tobacco smoking as a possible etiologic factor in bronchiogenic carcinoma: a study of six hundred and eighty-four proved cases. _Journal of the American medical association_ 1950; **143** :329– 336.

- 3  General S. The health consequences of smoking—50 years of progress: a report of the surgeon general. In: _US Department of Health and Human Services_ . Citeseer 2014.

- 4  World Health Organization. Tobacco and its environmental impact: an overview. 2017.

- 5  World Health Organization. _WHO report on the global tobacco epidemic 2015: raising taxes on tobacco_ . World Health Organization 2015.

- 6  Eriksen M, Mackay J, Ross H, _et al. The tobacco atlas._ American Cancer Society 2013.

- 7  World Health Organization. WHO report on the global tobacco epidemic 2017.

- 8  The World Bank. Curbing the epidemic: governments and the economics of tobacco control. _Development in Practice Series_ 1999; **8** :196.

- 9  Chaloupka FJ, Cummings KM, Morley CP, _et al._ Tax, price and cigarette smoking: evidence from the tobacco documents and implications for tobacco company marketing strategies. _Tobacco Control_ 2002; **11** :i62–i72.

- 10  Debrott Sánchez D, others. _Economía del Control del Tabaco en los países del MERCOSUR y Estados Asociados: Chile_ . Organización Panamericana de la Salud 2006.

- 11  Denisova I, Kuznetsova P. The effects of tobacco taxes on health: An analysis of the effects by income quintile and gender in Kazakhstan, the Russian Federation, and Ukraine. Published Online First: 2014

- 12  Verguet S, Gauvreau CL, Mishra S, _et al._ The consequences of tobacco tax on household health and finances in rich and poor smokers in China: an extended cost-effectiveness analysis. _The Lancet Global Health_ 2015; **3** :e206–e216.

- 13  Peto R, Lopez AD. The future worldwide health effects of current smoking patterns. _Tobacco and public health: Science and policy_ 2004;:281–286.

- 14  Jha P, Peto R. Global effects of smoking, of quitting, and of taxing tobacco. _New England Journal of Medicine_ 2014; **370** :60–68.

- 15  Eriksen M, Mackay J, Ross H, _et al. The tobacco atlas._ American Cancer Society 2013.

- 16  Health UD of, Services H, others. _The health consequences of smoking: a report of the Surgeon General_ . 2004.

- 17  Cromwell J, Bartosch WJ, Fiore MC, _et al._ Cost-effectiveness of the clinical practice recommendations in the AHCPR guideline for smoking cessation. _Jama_ 1997; **278** :1759–1766.

- 18  Borland R. Tobacco health warnings and smoking-related cognitions and behaviours. _Addiction_ 1997; **92** :1427–1435.

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26 Figure 4: Population in Small Towns and Big Cities: Africa <!-- Start of picture text --> 0.3<br>1990<br>2000<br>2010<br>0.2<br>0.1<br>0.0<br>1,000 10,000 100,000 1,000,000 10,000,000<br>Density<br><!-- End of picture text --> Size of urban center (people, log scale) Source: Dorosh and Thurlow (2013).
the developing world. However, expected yield gains, costs of further developing these technologies, and the political acceptance of genetically modified foods are all highly uncertain.

To quantify the extent to which the advances in crop technology can further boost agricultural yields over the next century, we first need to assess the magnitude of existing yield gaps at the global scale. In a comprehensive study, Lobell _et al._ (2009) report a significant variation in the ratios of actual to potential yields for major food crops across the world, ranging from 0.16 for tropical lowland maize in Sub-Saharan Africa to 0.95 for wheat in Haryana, India. For the purposes of this study, we employ the results of Licker _et al._ (2010), who conduct comprehensive yield gap analysis using global crop dataset of harvested areas and yields for 175 crops on a 0.5<sup>_◦_</sup> geographic grid of the planet for the year 2000. Using these estimates, we calculate the global yield gap as the grid-level output-weighted yield gap of the four most important food crops (wheat, maize, soybeans, and rice). The resulting estimate suggests that average yields are 53% of potential yields, which is close to the median estimates by Lobell _et al._ (2009). As a further robustness check we employ the Decision Support System for Agrotechnology Transfer (DSSAT) crop simulation model (Jones _et al._ , 2003), run globally on a 0.5 degree grid in the parallel System for Integrating Impacts Models and Sectors (pSIMS; Elliott _et al._ 2014b) to simulate yields of the same four major food crops under best agricultural management conditions and compare simulated yields to their observed yields. The resulting yield gap estimates were not substantially di↵erent.

In the optimistic (i.e., “good”) state of advances in crop technology, we assume that yields continue to grow linearly throughout the coming century, eliminating the yield gap by 2100. This high yield scenario rests on the assumption of continued strong growth in investment in agricultural research and development, widespread acceptance of genetically modified crops, continuing institutional reforms in developing countries, and public and private investments in the dissemination of new technologies. The erosion of any one of these component assumptions will likely result in a slowing of crop technology improvements. And there are some grounds for pessimism. In a comprehensive statistical analysis of historical crop production trends, Grassini _et al._ (2013) note that “despite the increase in investment in agricultural R&D and education [...] the relative rate of yield gain for the major food crops has 9
as follows. During normal times, the short-run elasticities _βk_ , _∀k_ = 1, 2, 3 capture the mapping between NPLs and the macroeconomic variables. However in crisis times, when decision horizons are shorter due to panic effects, herd behavior and increasing uncertainty about future developments, the economy as a whole and the repayment capacity of its agents in particular become more sensitive to changing macroeconomic conditions. We approximate this increased sensitivity simply by using the long-term multipliers pertaining to the estimated NPL regression ((5)), that is, by using _λk_ = _βk_ /(1 _− ρ_ ), _∀k_ = 1, 2, 3 as the elasticity in crisis times, where _ρ_ is the coefficient on the one period lagged value of NPLs.

# **4.4. Linking Macroeconomic Scenarios to Changes in NPLs**

The changes in NPLs in the future period due to changes in the macroeconomic variables under the different scenarios, are calculated as follows. Let future scenario S = _{PIT_ , _Stress}_ , so that S corresponds to either the PIT or the Stress scenario. The change in NPLs under future scenario S denoted by ∆ _NPL_<sup>S</sup> _t_ +1<sup>isthenconstructedbytakingthe</sup> difference between the future value of the macroeconomic variable of interest under scenario S and its TTC value, multiplied by the corresponding impact elasticity from the NPL regression in (5).

# For example, suppose that we are interested in the PIT scenario so that S = _PIT_ .

the crisis periods are to be determined exogenously. One could do this again by taking the crisis dating of Laeven and Valencia (2008). However, experimentation with a threshold type non-linear model where the identifiers were taken from Laeven and Valencia (2008) resulted in a number of unsatisfactory outcomes which lead us to discard these results. The issues that were faced were similar to the ones encountered in the construction of the Stress International scenario, i.e., there were instances when the response to a crisis in fact reduced the size of the coefficients on the macroeconomic variables, suggesting a reduced sensitivity of NPLs to worsening macroeconomic conditions. The difficulties are related to the relatively small sample of data that is available to carry out the estimation of the elasticities, the large variation of the data around the conditional means, and the low (annual) frequency of the available time series. See also Buncic (2011) on this issue in the context of non-linear exchange rate models.

30
|_Panel B: Controls, Bas_<br>Age|_eline_<br>12.490|12.490|12.490|-0.001|-0.001|18,718|
|---|---|---|---|---|---|---|
||_(0.557)_|_(0.555)_|_(0.553)_||||
|Female|0.507|0.517|0.523|-0.010|-0.016*|18,718|
||_(0.500)_|_(0.500)_|_(0.499)_||||
|Sixth Grader|0.478|0.479|0.477|-0.001|0.001|18,718|
||_(0.500)_|_(0.500)_|_(0.499)_||||
|Macedonian|0.668|0.723|0.705|-0.055|-0.037|18,718|
||_(0.471)_|_(0.448)_|_(0.456)_||||
|Albanian|0.277|0.206|0.226|0.072|0.052|18,718|
||_(0.448)_|_(0.404)_|_(0.418)_||||
|Roma|0.017|0.020|0.021|-0.003|-0.003|18,718|
||_(0.131)_|_(0.140)_|_(0.142)_||||
|Other|0.037|0.052|0.049|-0.014|-0.012|18,718|
||_(0.189)_|_(0.221)_|_(0.215)_||||
|TV at Home|0.957||0.945||0.012|10,355|
||_(0.204)_||_(0.228)_||||
|PC at Home|0.959||0.955||0.005|10,355|
||_(0.197)_||_(0.208)_||||
|Car at Home|0.868||0.870||-0.002|10,355|
||_(0.339)_||_(0.337)_||||
|Family Goes on|||||||
|Vacation|0.707||0.684||0.023|10,355|
||_(0.455)_||_(0.465)_||||
|Mother Lives at|||||||
|Home|0.975||0.969||0.007*|10,355|
||_(0.155)_||_(0.174)_||||
|Father Lives at|||||||
|Home|0.952||0.943||0.009*|10,355|
||_(0.214)_||_(0.233)_||||
|Mother College|||||||
|<br>Educated|0.303||0.304||-0.001|10,355|
||_(0.460)_||_(0.460)_||||
|Father College|||||||
|Educated|0.288||0.284||0.005|10,355|
||_(0.453)_||_(0.451)_||||

* p < 0.05, ** p < 0.01, *** p < 0.001

_Notes:_ Standard deviations in parentheses. T-tests with robust standard errors clustered at the school level. Sample of students with non-missing information in all the socio-emotional variables analyzed (i.e. the sample of students used in the regressions analysis). All figures rounded to three decimal places.

_Source:_ Own survey data, administrative data, school year 2015/2016, own calculations.

Table 2 Panel A focuses on students' socio-emotional skills taken from surveys and GPAs taken from official administrative records, whereas Panel B focuses on student’s socio-economic characteristics. The sample includes all students with non-missing information on all outcomes and controls in the regression analysis of Table 3. As can be seen, in contrast to GPAs taken from official records, we encountered a significant amount of missing information on socio-emotional skills and characteristics, mainly due to some surveys 10
# **Fiscal Adjustment and Growth in Sub-Saharan Africa: Overview and Lessons from the Current Downturn**

Hippolyte Fofack<sup>1</sup> _The World Bank, Washington DC, 20433_

**Keywords** : Fiscal adjustment, low deficit targets, pro-cyclical fiscal policy, transfer function

JEL Classification Numbers: E17, E62, N17, O55

> 1 This paper has benefited greatly from suggestions and comments by Melvin Ayogu, Shanta Devarajan, Ibrahim Elbadawi, Mwangi Githinji, Blanca Moreno-Dodson, Leonce Ndikumana, Raj Nallari, Yaw Nyarko, Xiao Ye and Etienne Yéhoué; I would like to thank them all for their contribution which has helped improved earlier drafts. I would also like to thank Nihal Bayraktar for excellent research assistance.
manufacturing census. Our 1993 manufacturing employment is 2,836,277 and the 1993 Census manufacturing employment is 3,246,039, suggesting that our data cover about 87.4% of total manufacturing employment. Based on these comparisons, we believe that our data are reliable.

# **3. Methodology**

We now turn to our methodology for studying job and worker flows. To facilitate comparison with the developed countries that dominate the existing literature, we use established definitions of both job flows and worker flows (e.g. Davis and Haltiwanger 1992). We begin with the methodology for our worker-flows statistics. When an establishment hires a new employee, we refer to this event as an accession. For a given year, we define the accession percentage according to the following formula where _acc j_ , _t_ is the number of employees in establishment _j_ in year _t_ who were not working in

> establishment _j_ in year _t_ − 1, _empl j_ , _t_ is the number of employees of establishment _j_ in year , _t_

> and _empl j_ , _t_ − 1 is the number of employees of establishment _j_ in year _t_ − 1. Similarly we define

the separation percentage as where _sep j_ , _t_ is the number of employees in establishment _j_ in year _t_ − 1 who were not working

> in establishment _j_ in year _t_ . It is now natural to define the net-growth percentage in

6
From equations (6) and (7), one can confirm the intuitive notion that the application of Bayes' rule leads to an upward revision of the expected fraction of reform beneficiaries ( _γ_ ) when Δ _α_ t+1 additional winners are revealed (and vice versa after the revelation of losers). Because this implies that beliefs about _γ_ can change over time, the critical value for _α_ ( _α_<sup>*</sup> ) also becomes time varying. In particular, after plugging (6) into (5), we obtain Now, the key question is whether we can obtain _α_ t ≥ 1/2 before _α_ t ≥ αt∗. If this is the case, the government is able to reveal that the median voter is a reform winner (which happens when _α_ t crosses 1/2) before this pivotal voter begins opposing the reform package (which happens if and only if _α_ t exceeds αt∗ while _α_ t < 1/2). Subsequently, the government can complete the reform with no risk of losing majority support.

Because empirical studies such as those of Carlin and Mayer (1992), Frydman, Rapaczynski, and Earle (1993), Marcincin and Van Wijnbergen (1997), and Gupta, Ham, and Svejnar (2008) all present evidence that reforms start by revealing ex post winners, it is interesting to see what our model predicts would happen if the reform follows such a selective path.<sup>14</sup> To investigate this, we make the following assumption on the sequencing within the reform:

# **Assumption 1.** _Sequencing is such that the reform starts by revealing ex post winners._

The reason for the presence of this selection bias can be twofold. First, it can result from a situation of asymmetric information in which the government knows ex ante who will benefit and who will lose from reform (but, as in Perotti (1995), the government is unable to transmit this information credibly to the public).<sup>15</sup> Especially in our privatization example, this assumption seems realistic because the government (as the incumbent owner of the firms that are to be privatized) has inside information on firm profitability and future policies that may benefit or harm each firm. If this government then follows the conventional wisdom and begins by reforming the ex post

> 14 Roland (2008, 4) nicely summarizes this literature by noting, "The few studies on the determinants of privatizations suggest that the more profitable firms were privatized first, which is consistent with political economic theories of privatization where the sequencing of privatization is used to gather support for further privatization".

> 15 In this sense, the government in our model is a bit like Monty Hall in the "Monty Hall problem": he knows ex ante behind which doors the gains and losses are located. Note that the counterintuitive solution to the Monty Hall problem follows from the fact that sampling takes place without replacement.

14
described in Pukthuanthong and Roll (2009) and Bekaert and Wang (2009). Higher R-squared for a given bank suggests that the bank is exposed to similar sources of credit risk as other banks in a given country. Higher R-squared also suggests that there are channels of inter-dependency between the bank and others in a given country. Both interconnectedness and common exposure to risk makes the banking sector more vulnerable to economic, liquidity and information shocks.

# **2.3. Competition Measure**

In this paper, we use the Lerner index as our main measure of lack of competition. The Lerner index is a proxy for profits that accrue to a bank as a result of its pricing power in the market. It is measured at the bank level and has been utilized in a number of banking studies.<sup>7</sup> We follow the methodology used in Demirguc-Kunt and Martinez-Peria (2010) and first estimate the following log cost function for each country: _log(Cit) = α + β1×log(Qit) + β2×(log(Qit))_<sup>_2_</sup> _+ β3×log(W1,it) + β4×log(W2,it) + β5×log(W3,it) +β6×log(Qit)×log(W1,it) + β7×log(Qit)×log(W2,it) + β8×log(Qit)×log(W3,it) + β9×(log(W1,it))_<sup>_2_</sup> _+ β10×(log(W2,it))_<sup>_2_</sup> _+ β11×(log(W3,it))_<sup>_2+ β_</sup> _12_<sup>_×log(W_</sup> _1,it_<sup>_)×log(W_</sup> _2,it_<sup>_)_</sup> (5)<sup>_+ β_</sup> _13_<sup>_×log(W_</sup> _1,it_<sup>_)×log(W_</sup> _3,it_<sup>_)+ β_</sup> _14_<sup>_×log(W_</sup> _2,it_<sup>_)×log(W_</sup> _3,it_<sup>_)+ Θ×Year Dummies_</sup> _+ Ω×Bank Specialization Dummies + εit_ In equation (5) above, _Ci_ t is total costs and is equal to the sum of interest expenses, commission and fee expenses, trading expenses, personnel expenses, other admin expenses, and other operating expenses, measured in millions of US dollars. _Qit_ is the quantity of output and is measured as total assets in millions of US dollars. _W1,it_ is the ratio of interest expenses to the sum of total deposits and money market funding. _W2,it_ is measured as personnel expenses divided by total assets. _W3,it_ is the ratio of administrative and other operating expenses to total assets. The subscript _i_ denotes bank i and the subscript _t_ denotes year t. We take natural logarithm of all variables and estimate the regression for each country in our dataset using pooled ordinary least squares (OLS). We include calendar year and bank specialization dummies in the regression. All variables are winsorized at the 1<sup>st</sup> and 99<sup>th</sup> percentile levels to reduce the

> 7 See for instance, Demirguc-Kunt and Martinez-Peria (2010) and Beck, De Jonghe, and Schepens (2011).

8
BURKINA FASO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE The international market is among the most concentrated (that is, least competitive) in the region, with a Herfindahl-Hirschmann Index (HHI) of 23 percent (table 7). Not surprisingly, only Air France provides regular links to Europe and the bulk of Burkina’s air transport market is international, though intra-African flights are double the number of intercontinental flights.

**Figure 11. Evolution of seats and city pairs in Burkina Faso**

<!-- Start of picture text --> a. Seats b. City pairs<br>450,000 16<br>400,000 14<br>350,000 12<br>300,000<br>10<br>250,000<br>8<br>200,000<br>6<br>150,000<br>100,000 4<br>50,000 2<br>- 0<br>Total International Intercontinental Domestic Total International Intercontinental Domestic<br>2001 2004 2007 2001 2004 2007<br>City Pairs<br>Number of seats<br><!-- End of picture text --> _Source:_ Bofinger 2009. Derived from AICD national database (www.infrastructureafrica.org/aicd/tools/data). _Note:_ As reported to international reservation systems. Intercontinental flights excludes flights between Nigeria and South Africa.

In common with many other African countries, Burkina continues to face significant safety and security issues in air transport. Burkina Faso has not undertaken the U.S. Federal Aviation Administration/International Aviation Safety Assessment (FAA/IASA), audit and none of its carriers have passed the International Air Transport Association/IATA International Safety Audit (IATA/IOSA).

Burkina Faso’s existing airport is unsuitable, and the country needs to address capacity constraints and security issues. The immediate challenge for the government is to keep the existing airport in an operational condition, upgrading installations to follow international standards. These short- and mid-term measures could set the ground for stable and increased traffic that might economically justify—in the longer term—the construction of the proposed new airport at Donsin (World Bank/Mott Mac Donald 2009).

Relying on the performance of the surface multimodal route, while an option for international trade in the current situation, is undoubtedly a limited and imperfect option in the longer term, particularly given the government’s hopes of diversifying Burkina’s economy toward nontraditional products—such as horticulture—and mining. There is much room for improving and expanding air transportation given the demands of the region, and certainly modal diversification beyond surface transport is critical to improving trade and diversifying the economy.

21