rafmacalaba/datause-encoder-data huggingface dataset:
# **References**

Aguayo-Tellez (2011) “The impact of trade liberalization policies and FDI on gender inequalities: A literature review”, Background Paper prepared for the World Development Report 2012: Gender Equality and Development, World Bank.

Aigner, D. J. and Cain, G. G. (1977) “Statistical theories of discrimination in labor markets”, _Industrial and Labor Relations Review_ , vol. 30, pp. 175-187.

Arndt, C., and Tarp F. (2000) “Agricultural Technology, Risk, and Gender: A CGE Analysis of Mozambique”, _World Development,_ vol. 28, pp. 1307-1326.

Arndt, C., Robinson, S. and Tarp, F. (2006), “Trade Reform and Gender in Mozambique”, _Nordic Journal of Political Economy_ , vol. 32, pp. 73-89.

Arpaia, A., Pérez, E. and Pichelmann, K. (2009) “Understanding Labour Income Share Dynamics in Europe”, Economic Papers 379, May, European Commission, DirectorateGeneral for Economic and Financial Affairs Publications, B-1049 Brussels.

Arrow, J. K. (1971) “Some models or racial discrimination in the labor market”, _Rand Journal of Economics_ , pp. 1-54.

Bchir, H., Decreux, Y., Guérin, J.-L. and Jean, S. (2002), “MIRAGE, a computable general equilibrium model for trade policy analysis”, Working Paper No. 17, Centre d’Études Prospectives et d’Informations Internationales.

Becker, G. (1957) _The Economics of Discrimination_ , University of Chicago Press, Chicago.

Bergmann, B. R. (2005) _The economic emergence of women_ , 2<sup>nd</sup> Ed., Palgrave Macmillan, New York.

Black, S. E. and Brainerd, E. (2004) “Importing equality? The impact of globalization on gender discrimination”, _Industrial and labor relations review_ , vol. 57, pp. 540-559.

Black, S. E. and Strahan, P. E. (2001) “The division of spoils: Rent-sharing and discrimination in a regulated industry”, _American Economic Review_ , vol. 91, pp. 814-831.

Blau, F. D. and Kahn, L. (2000) “Gender differences in pay”, _Journal of Economic Perspectives_ , vol. 4, pp. 75-99.

Blau, F. D. and Kahn, L. M. (2006) “The US Gender pay gap in the 1990s: Slowing convergence”, _Industrial and labor relations review_ , vol. 60, pp. 45-66.

Boeters. S. and Savard, L. (2012) “Labor market modeling in a CGE context” in Dixon, P. And Jorgenson, D. (Eds.) _Handbook of Computable General equilibrium modeling_ , Elsevier, Northholland, available at: discussion-paper-201-labour-market-cge-models%20.pdf 21
at a particular aspect of institutions- enforcement of contracts-and its impact on the volume of international trade. The authors construct a theoretical model to show how imperfect enforcement of contracts can reduce the volume of trade in goods for which quality issues are important. Using a gravity equation the paper incorporates proxies for the enforcement of contracts and finds that the measures of contract enforcement affect the volume of trade in both differentiated and homogeneous goods, but the impact is larger for differentiated goods. Also employing a gravity equation, Depken and Sonora (2005) estimate the effects of economic freedom on U.S. consumer exports and imports for the years 1999 and 2000. They find that better institutional quality of the partner country has a positive effect on the amount of exports from the U.S. to that country.

In this paper we examine the influence of infrastructure, institutional quality, colonial and geographic context, and trade preferences on the pattern of bilateral trade. We are interested in threshold effects, and so emphasize those cases where bilateral country pairs do not actually trade. We depart from the institutions and infrastructure literature in this respect, using selection-based gravity modeling of trade flows. We also depart from this literature by mixing principal components (to condense our institutional and infrastructure measures) with a focus on deviations in the resulting indexes from expected values for given income cohorts to control for multicollinearity.<sup>1</sup> Here, we work with a panel of 284,049 bilateral trade flows from 1988 to 2002. Matching bilateral trade and tariff data and controlling for tariff preferences, level of development, and standard distance measures, we find that infrastructure, and institutional quality, are significant determinants not only of export levels, but also of the 1Recent related work involving thresholds, zeros in bilateral trade, and trade growth along extensive and intensive margins in a gravity context, includes Hummels and Klenow (2005), Evenett and Venables (2003), and Felbermayr and Kohler (2004).

5
Hill.

- Meadows, D.H., D.L. Meadows, J. Randers, and W.W. Behrens III. 1972. _The Limits to Growth: A Report for the Club of Rome’s Project on the Predicament of Mankind_ . New York: Universe Books.

- Nappi, C. 1990. “The Food and Beverage Container Industries: Change and Diversity.” In _World Metal Demand: Trends and Prospects_ , ed. J. E. Tilton. Resources for the Future Press, New York.

- Newell, R.G., A.B. Jaffe, and R.N Stavins. 1999. “The Induced Innovation Hypothesis and Energy‐Saving Technical Change.” _Quarterly Journal of Economics_ 114(3): 941‐974.

- Olmstead, A.L., and P. Rhode. 1993. “Induced Innovation in American Agriculture.” _Jour‐ nal of Political Economy_ 101(1): 100‐118.

- Pei, F., and J. E. Tilton. 1999. “Consumer Preferences, Technological Change, and the Short‐Run Income Elasticity of Metal Demand.” _Resources Policy_ 25: 87‐109.

- Pesaran, M.H. and R. Smith. 1995. “Estimating Long‐Run Relationships from Dynamic Heterogeneous panels.” _Journal of Econometrics_ 68: 79–113.

- Pesaran, M.H. 1997. “The Role of Economic Theory in Modelling the Long Run.” _Economic Journal_ 107(440): 178–191.

- Pesaran, M.H., Y. Shin.1999. “An autoregressive distributed‐lag modelling approach to cointegration analysis.” In _Econometrics and Economic Theory in the 20th Century: The Ragnar Frisch Centennial Symposium (Econometric Society Monographs, pp. 371‐413)_ , ed. S. Strøm. Cambridge University Press, Cambridge.

- Pesaran, M.H., Y. Shin, and R. Smith. 1999. “Pooled Mean Group Estimation of Dynamic Heterogeneous Panels.” _Journal of the American Statistical Association_ 294(446): 621‐634.

- Pindyck, R. S. 1979. “Interfuel Substitution and the Industrial Demand for Energy: An International Comparison.” _The Review of Economics and Statistics_ , 61(2): 169‐179.

- Prais, S.J., and H. S. Houthakker. 1955. _The Analysis of Family Budgets._ Cambridge Univer‐ sity Press, Cambridge.

- Prebisch R. 1950. _The Economic Development of Latin America and its Principal Problems_ , United Nations, New York.

- Radetzki, M. 2006. “The Anatomy of three Commodity Booms.” _Resources Policy_ 31(1): 56‐ 64.

- Radetzki, M., R. Eggert, G. Lagos, M. Lima, and J.E. Tilton. 2008. “The Boom in Mineral Markets: How Long Might It Last?” _Resources Policy_ 33(3): 125‐128.

- Radetzki, M., and J.E. Tilton. 1990. “Conceptual and Methodological Issues.” In _World Metal Demand: Trends and Prospects_ , ed. J.E. Tilton. Resources for the Future Press, Washington, DC.

- Renner, S., and F. W. Wellmer. 2019. “Volatility Drivers on the Metal Market and Expo‐ sure of Producing Countries.” _Mineral Economics_ , <u><mark>https://doi.org/10.1007/s13563‐019‐</mark></u>

_— 29 —_
# V. **Bibliography**

- Anand, Ritu, and Sweder van Wijnbergen. 1989. "Inflation and the Financing of Government Expenditure: An Introductory Analysis with an Application to Turkey." _World Bank Economic Review_ 3(1): 17-38.

- Anand, Ritu, and Sweder van Wijnbergen, Ajay Chhibber. 1990. "External Balance and Output Growth in Turkey: Can They Be Reconciled?" In Tosun Aricanli and Dani Rodrik, eds., _The Political Economy of Turkey: Debt, Adjustment, and Sustainability._ Houndmills, United Kingdom: Macmillan Press.

- Anand, Ritu, Sweder van Wijnbergen, and Roberto Rocha. 1988. "Inflation, External Debt, and Financial Sector Reform: A Quantitative Approach to Consistent Fiscal Policy with an Application to Turkey." NBER Working Paper 2731. National Bureau of Economic Research, Cambridge, Mass.

- Campos, Ed and Sanjay Pradhan. 1996. _Budgetary Institutions and Expenditure Outcomes._ Policy Research Working Paper # 1646. The World Bank, Washington, DC.

- Dillinger, William and Steven Webb. 1999. "Fiscal management in federal democracies: Argentina and Brazil", World Bank Policy Research Working Paper #2121, Washington DC.

- Easterly, William. 1998. "Fiscal Illusion, " World Bank Policy Research Working Paper #2109. Washington, D.C.

- Euromoney, 1997. _Excellence in debt management._ Kari Nars, Editor. London, England.

- Ghanem, Hafez. 1997."Ukraine: Public Expenditure Review," the World Bank, Washington, D.C.

- Hemnming, Richard, 1999. _A Framework for Assessing Fiscal Vulnerability._ IMF. Work in progress.

- Honohan, Patrick, 1999. "Fiscal Contingency Planning For Banking Crises," the World Bank Policy Research Working Paper, Washington, D.C. (forthcoming).

- Hull, John. 1997. _Options, Futures and Other Derivatives._ Prentice Hall, Upper Saddle River, New Jersey.

- Kharas, Homi, and Deepak Mishra. 1999. "Hidden Deficits and Currency Crises." EASPR, World Bank, Washington, D.C.

- Polackova, Hana. 1998. "Government Contingent Liabilities-a Hidden Risk to Fiscal Stability." Policy Research Paper #1989. The World Bank, Washington, D.C.

Schick, Allen, 1999. Budgeting for Fiscal Risk. Unpublished.

- Selowsky, Marcelo, 1998. "Fiscal Deficits and the Quality of Fiscal Adjustment" in _European Union Accesion: The Challenges for Public Liability Management,_ World Bank.

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varying factors at the household level, such as temperature and precipitation, factors that could directly affect our outcome variables (see the variables in table A.1). Finally, we also consider that there might be regional trends that correlate with conflict intensity, which is why we also control for region-time trends τr,w (region-wave with the wave-based panel and region-round in the round-based panel). We cluster our standard errors at the LGA level to account for spatial correlation.

Our analysis might be further confounded by the fact that a household might receive assistance from informal safety nets after a conflict event. To understand how receiving assistance might affect our findings, we run alternative specifications by removing households that reported any such assistance after the most recent event (from the telephone survey) as well as households that reported having received either cash or in-kind transfers in the GHS panel. A small subset (just 6 percent) of the households reported any assistance.

Even though our fixed effect estimators remove the time-invariant household characteristics, we are still interested in examining whether the targeting of households was systematically driven by household characteristics. We do so by investigating the differences across households that experienced any conflict during the survey period and those that did not by running a t-test with our outcome variables of interest and with a number of household and geographical characteristics related to the household’s location. We find that in 2010–11, households that were exposed to conflict at any time during the survey were indeed similar to those not exposed to conflict.

The results are reported in appendix C, table C.1. Panel a compares household characteristics, and panel b compares household location-specific geographical characteristics.<sup>20</sup> We can see from panel a that in the first round of the GHS, there are no statistically significant differences in the poverty status, level of consumption, and the CSI between households that had experienced conflict events between 2010 and 2017 and households that had not. We find only a couple of statistically significant differences in household characteristics across these samples. Larger households and polygamous households were more likely to have experienced conflict events. This is unsurprising because our conflict indicators include variables such as a household member being robbed or a household member being injured due to an attack. Larger households (a variable that correlated strongly with polygamy)

> 20 The summary statistics and the description of the geographical variables are presented in appendix A, tables A.1 and A.2, respectively.

15
# **_Appendix Figure D1. Apparel export growth (year-to-year) decomposition for Ethiopia_**

<!-- Start of picture text --> Ethiopia<br>100<br>78.6<br>72.0<br>67.9<br>62.4<br>57.154.8 54.852.2 55.5<br>50 42.942.1<br>34.5<br>30.3<br>19.9 22.222.4 21.9<br>17.0 16.5 14.8<br>10.9 10.6 10.6<br>3.4 2.6 7.3 1.3 6.6 4.7<br>0<br>-0.2 -2.2 -1.8 -1.5 -4.4 -1.3 -2.1 -0.9<br>-7.3 -7.7 -7.1 -9.4<br>-16.0 -18.4 -16.2 -14.2<br>-24.4<br>-41.8<br>-50 -46.1<br>Agg. Export Growth Continuing Exporters New Exporters Exiting Exporters<br>Appendix Figure D2. Apparel export growth (year-to-year) decomposition for Mauritius<br>Mauritius<br>20.920.3<br>20 18.5 17.5<br>14.5 15.215.3<br>12.7 13.112.6<br>6.35.3<br>2.0 1.0 0.7 0.7 2.7 0.0 0.3 0.3 0.83.10.5 1.6 1.2 0.6 0.9 0.7 0.0 0.5<br>0<br>-1.0 -2.8 -1.7-1.1 -1.4 -2.2-2.1 -0.3 -1.3 -2.8 -1.0 -0.2 -1.9-2.4 -0.1 -0.9 -0.2 -0.3 -1.1<br>-5.5 -5.2-4.9<br>-11.4 -11.8 -12.0 -12.9-11.8 -10.6-10.0<br>-20 -16.1 -17.3<br>-29.3<br>-33.8<br>-40<br>-42.8<br>Agg. Export Growth Continuing Exporters New Exporters Exiting Exporters<br>2005--2006 2006--2007 2007--2008 2008--2009 2009--2010 2010--2011 2011--2012 2012--2013 2013--2014 2014--2015 2015--2016 2016--2017<br>2000--20012001--20022002--20032003--20042004--20052005--20062006--20072007--20082008--20092009--20102010--20112011--20122012--20132013--20142014--20152015--2016<br>Export Growth<br>Export Growth<br><!-- End of picture text -->

# **_Appendix Figure D2. Apparel export growth (year-to-year) decomposition for Mauritius_**

12
Figure 8. Value of one standard deviation increase in the ventile share at different points of national income distribution (measured in terms of mean country income) <!-- Start of picture text --> 60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>Ventile of national income distribution<br>Increase in mean country income (in %)<br><!-- End of picture text --> Note: Calculated from Tables 5 and 6.

31
- 91 -

is available. It was estimated that at least 50 traffic wardens were required to monitor the nonstop traffic and a degree of compliance of only 75% was expected due to enforcement problems. The cost per transaction of _SEK1_ .97 _[US36.4_ cents] in 1990 figures for the manual proposal is considerably higher than that of the electronic proposal a couple of months earlier.

101. In Autumn 1990, a bill was placed before the Swedish Parliament to introduce road pricing in Stockholm on an experimental basis from January 1991 to June 1993. As a result of higher petrol prices around the time of the Gulf war, and reservations about the manual zone fee proposal, the road pricing experiment was deferred until an acceptable electronic system that could protect privacy becomes available.

# B. The Demis Paka. 1221-92

102. In January 1991, the three political parties in the City and County of Stockholm, who form a two-thirds majority in Parliament, signed an agreement on traffc and the environment known as the Dennis agreement.70 They agreed that major investments totalling SEIQ8 billion [US$4.73 billion] will need to be spent - half of them are for public tranport and the other half are for ring roads around Stockholm. What they had left undecided was the issue of whether or not to implement road pricing in Stockholm in 1997 and, in the Gothenburg area in 1994 (Ministry of Transport and Communications, 1991). The Swedish Parliament has adopted the pnnciple of marginal social cost pricing - including congestion, environment, road maintenance and accident costs. For the Stockholm region, the marginal social costs for car taffic alone are estmated to be _SEK_ 10.0 billion [US$1.69 billion] in 1990, of which SEK2.5 billion [US$0.42 - billion] only a quarter -- is collected (regnr, 1991). Fully three quarrs of the total marginal costs are composed of congestion costs (42%) and accident costs (35%). The annual Th nualized capital and opatg coda nd revmu figur ae ten from Abbott (1990) whs the not of th data is from City of Stockholm, 1989b.

- ° ITh three myor parties ae the Socid Demoats (which Consevatives (25%) amd ie Liberals (10%).

about 40% of Padriat), tho
<!-- Start of picture text --> Financial Intermediation Pension & Insurance Funding & Act. Auxiliary to Financial Intermediation<br>100 4.5 140 4.0<br>9080 4.03.5 120 3.5<br>70 3.0 100 3.0<br>6050403020 2.52.01.51.0 806040 2.52.01.51.0<br>10 0.5 20 0.5<br>0 0.0 0 0.0<br>Labor Prod. 1997-2004 EBRD Index 1997-2004 Labor Prod. 1997-2004 EBRD Index 1997-2004<br>Slovenia Poland Czech Rep. Estonia Hungary Latvia Slovak Rep. Lithuania Hungary Poland Czech Rep. Estonia Latvia Slovenia Slovak Rep. Lithuania<br>Index Employee Index<br>Thousands of PPP USD per<br>Thousands of PPP Euros per Employee<br><!-- End of picture text --> Source: WIIW database 2005, KLEMS database 2005 and EBRD Transition Report. Note: In Panel B financial intermediation is NACE 65 category.

Heterogeneity in demand conditions across countries as well as other unaccounted for factors can influence the performance of different services sub-sectors. Therefore, it is not surprising that Panels A and B of Figure 14 uncover only a weak relationship between the EBRD index and labor productivity across services sub-sectors. Regression analysis can control for this heterogeneity. Table 1 shows the results from regressions of growth in WIIW labor productivity (Panel A) or growth in KLEMS labor productivity (Panel B) on one year lagged values of the EBRD index. The regressions are estimated using (i) OLS including country dummies, sub-sector dummies, year dummies, and an interaction between sub-sector dummies and year dummies, or (ii) sub-sector fixed effects including year dummies. Year dummies account for business cycle or other macroeconomic factors affecting equally all sub-sectors, while services sub-sector dummies account for fixed differences in productivity growth across sub-sectors. The interaction dummies control for problems in the measurement of output in services subsectors and for problems related to the use of imperfect deflators for real value added.

The results in Panels A and B of Table 1 suggest a positive effect of liberalization on services labor productivity growth. However, the effects are relatively weak except in column (1) of Panel B. The weakness of results may be due to data problems: (i) small sample size as the regressions include for each country and year only the services subsectors for which EBRD indexes are available which are 3 in Panel A (those shown in Panel A of Figure 14) or 6 in Panel B (those shown in Panel B of Figure 14), and (ii) imperfect measurement of progress in liberalization by the EBRD indexes. The weak results may also indicate that liberalization has not yet translated into an improvement in performance because the reforms have not been deep enough or because they require 32
residing within 50 km of combustion power plants. Specifically, we use modeled population at 100-meter spatial resolution available at five-year intervals from the Global Human Settlement Layer (GHSL) database<sup>8</sup> (European Commission, 2023). This dataset represents a significant advancement compared to prior attempts at modeling population distribution in space. It achieves this by refining raw census data into grid cells through the integration of remote-sensed imagery, specifically by utilizing the volume of residential areas. To avoid double counting the population in cases where the 50 km radius for different power plants overlaps, we partition these areas using a Thiessen method. We then compute the sum of the population residing in these areas in 2000, 2005, 2010, 2015, and 2020. We linearly interpolate the data obtained in the previous step for each power plant to create an annual frequency times series of the exposed population.

Additionally, to better characterize the population residing in proximity of a power plant, we use the Thiessen polygons derived previously and 2019 Human Development Index (HDI) data down-scaled to 0.1<sup>_◦_</sup> x0.1<sup>_◦_</sup> resolution from Sherman et al. (2023) to compute mean plant level HDI. We then compare the plant-level HDI with country-level HDI data from Smits and Permanyer (2019) to determine whether the population residing near power plants is systematically different.

Last, to study how excess PM2 _._ 5 concentrations and exposure may change during the operational lifespan of power plants already in operation, we use runoff projections from 22 climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6). All these models provide runoff projections for the key Shared Socioeconomic Pathways (SSPs): SSP1-2.6, SSP2-4.5, and SSP3-7.0. Using this information on 66 future paths of runoff (22 models _×_ 3 SSPs), we compute for each path and hydropower watershed the average runoff anomaly at monthly frequency between 2020 and 2060. The resulting dataset is then used to compute the evolution of the fraction of hydropower generation capacity affected by drought under the different models and SSPs. We also use the KC and Lutz (2017) population growth rate projections corresponding to each SSP to model the evolution of the population over the same time period.

8https://data.jrc.ec.europa.eu/dataset/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea 11
Winners Never Quit, Quitters Never Grow: Using Text Mining to Measure Policy Volatility and Its Link with Long-Term Growth in Latin America<sup>*</sup> Oscar Calvo-González<sup>†</sup> , Axel Eizmendi<sup>‡</sup> , Germán Reyes<sup>§</sup> JEL Classification: E60, H11, O11, O54 Keywords: Policy stability, Growth, Text Mining, Presidential Speeches, Latin America

> * The authors would like to thank Laura Chioda, Alexandru Cojocaru, Peter Hakim, Luis Felipe Lopez-Calva, Bill Maloney, Ambar Narayan, Mariano Tommasi, Roy Van der Weide, and participants at an internal seminar for fruitful discussions and suggestions. We also thank Sandra Pastrán, Evangelina Cabrera, Yalile Uarac, and Cesar Polit and Martha Narvaez, for compiling and forwarding to us multiple speeches from Argentina, Paraguay, Chile and Ecuador, respectively. Natalia García-Peña Bersh helped compile data from Colombia. We are grateful to the Research Support Budget from the World Bank for financial support.

> † Poverty and Equity Global Practice, World Bank. Mail: ocalvogonzalez@worldbank.org

> ‡ University of Oxford, Oxford, UK. Mail: axel.eizmendi.larrinaga@gmail.com

> § Cornell University, Ithaca, NY, USA. Mail: gjr66@cornell.edu