rafmacalaba/datause-encoder-data huggingface dataset:
- Golub, Stephen. 1997. "International Labor Standards and International Trade." _IMF Working Paper_ WP/97/37. International Monetary Fund, Washington, D.C.

- Green, Francis, Andy Dickerson, and Jorge Saba Arbache. 2000. "A Picture of Wage Inequality and the Allocation of Labour Through a Period of Trade Liberalization: the Case of Brazil." Canterbury: University of Kent. Processed.

- Grootaert, Christiaan, and Harry Anthony Patrinos. 1999. _Policy Analysis of Child Labor: A Comparative Study._ New York: St. Martin's Press.

- Haltiwanger, John, and Manisha Singh. 1999. "Cross-Country Evidence on Public Sector Retrenchment." _World Bank Economic Review_ 13(1): 23-66.

- Hanson, Gordon H., and Ann Harrison. 1999. "Trade Liberalization and Wage Inequality in Mexico." _Industrial and Labor Relations Review_ 52(2): 271-88.

- Heckman, James, and Carmen Pages. 2000, "The Cost of Job Security Regulation: Evidence from Latin American Labor Markets." NBER Working Paper 7773. National Bureau of Economic Research, Cambridge, Mass.

- Heywood, Colin. 1988. _Childhood in Nineteenth-Century France. Work, Health and Education Among the Classes Populaires._ Cambridge: Cambridge University Press.

- Ilahi, Nadeem. 1999. "Children's Work and Schooling: Does Gender Matter? Evidence from the Peru LSMS Panel Data." World Bank, Washington, D.C. Processed.

- Jacoby, Hanan G., and Emmanuel Skoufias. 1997. "Risk, Financial Markets, and Human Capital m a Developing Country." _Review of Economic Studies_ 64(3): 311-35.

- Jalan, Jyotsna, and Martin Ravallion. 1999. "Income Gains to the Poor from Workfare: Estimates for Argentina's Trabajar Program." Policy Research Working Paper 2149. World Bank, Washington, D.C.

- Kikeri, Sunita. 1997. "Privatization and Labor: What Happens to Workers when Governments Divest?" World Bank Technical Paper 396. Washington, DC: The World Bank.

- Krueger, Alan B. 1997. "International Labor Standards and Trade." In Michael Bruno and Boris Pleskovic, eds., _Annual World Bank Conference on Development Economics 1996._ Washington, DC: The World Bank.

- Lanjouw, Jean O., and Peter Lanjouw. 2000. "Rural Nonfarm Employment: An Update." _Agricultural Economics_ 26(1): 1-23.

- Levinsohn, James. 1999. "Employment Responses to Intemational Liberalization in Chile." _Journal of International Economics_ 47(2): 321-44.

- Lipsey, Robert E., and Fredrik Sjoholm. 2001. "Foreign Direct Investment and Wages in Indonesian Manufacturing." NBER Working Paper 8299. National Bureau of Economic Research, Cambridge, Mass.

- MacIsaac, Donna, and Martin Rama. 2001. "Mandatory Severance Pay: Its Coverage and Effects in Peru."Policy Research Working Paper 2626. World Bank, Washington, D.C.

- Madani, Dorsati. 1999. "A Review of the Role and Impact of Export Processing Zones." Policy Research Working Paper 2238. World Bank, Washington, D.C.

- Maloney, William F., and Pablo Fajnzylber. 2000."Labor Demand and Trade Reform in Latin America." World Bank, Washington, D.C.

- Maloney, William F., and Jairo Nuniez. 2001. "Measuring the Impact of Minimum Wages: Evidence from Latin America." Policy Research Worldng Paper 2597. World Bank, Washington, D.C.

36
Normal times in periods of five years to show the years that pull the beta convergence coefficient down to a negative value.

Figure A.4. The Golden Decade was strong but short-lived: the beta convergence was statistically significant and negative between the financial crisis and the oil price crash.

Source: based on Penn World Table version 10.0 data and Patel et al (2021) methodology. Note: The horizontal axis shows the natural log of real per capita GDP, and the vertical axis shows average annual real growth over the period listed. Shaded area represents a 95% confidence interval around the regression line. The sample excludes oil exporters and countries with populations under 1 million.

Table A.1 BB countries and FCV classification

|**COUNTRY NAME**<br>|**REGION NAME**<br>|**INCOME LEVEL**<br>|**POPULATION**<br>**(MILLIONS)**<br>|**SHARE IN**<br>**WORLD'S**<br>**POP**<br>|**BB?**<br>|**FCV?**<br>|
|---|---|---|---|---|---|---|
|**MYANMAR**|East Asia and Pacific|Lower middle income|54.2|0.683|BB|FCV|
|**KOREA, DEM**<br>**PEOPLE'S REP**|East Asia and Pacific|Low income|26.1|0.329|BB|Non FCV|
|**CAMBODIA**|East Asia and Pacific|Lower middle income|16.8|0.212|BB|Non FCV|
|**LAO PDR**|East Asia and Pacific|Lower middle income|7.5|0.095|BB|FCV|
|**MONGOLIA**|East Asia and Pacific|Lower middle income|3.4|0.043|BB|Non FCV|
|**UZBEKISTAN**|Europe and Central Asia|Lower middle income|35.6|0.450|BB|Non FCV|
|**KAZAKHSTAN**|Europe and Central Asia|Upper middle income|19.6|0.248|BB|Non FCV|
|**AZERBAIJAN**|Europe and Central Asia|Upper middle income|10.1|0.128|BB|Non FCV|

37
The Puerto Rican import data are the source of the dependent variable in our pooled product-level gravity regressions. For independent variables, we calculate great circle distances from each country or U.S. customs district to the port of San Juan. For reasons that we describe later in the paper, we parameterize export supply in the shipments’ origin rather than relying on fixed effects to sweep out heterogeneity in supplying regions’ product-level export supplies. To do this we calculate each origin’s total export supply of a given HS6 product in a given year, and include it as a control in the gravity regression estimated at the HS6 level of aggregation. The export supply measures are calculated using the CEPII (2022) data for non-U.S. origins and U.S. export data for U.S. origins.

Our analysis also exploits variation across products in characteristics that relate to demand for particular kinds of shipping and/or the freight rates that might be charged for transporting a given dollar value of that product. We use four product characteristics to predict reliance on particular kinds of shipping: a) the value share of a product’s annual imports that moves by sea, b) the log weight-to-value ratio of imports in the commodity, c) the squared log weight-to-value ratio, and d) the value share of imports that are shipped in containers. We calculate all these measures with U.S. import data so that they are exogenous to the flows we observe involving PR.<sup>16</sup> We estimate the model separately over subdivisions of final and upstream products. Our primary tool for separating final and upstream goods is the upstreamness measure from Antràs et al. (2012), though we also use the United Nations’ BEC classification to identify a sample of consumption goods for use in a robustness check.

be imperfect, but quite reasonable.

> 16We exclude imports from Canada and Mexico in these calculations so the U.S. data we use reflect the air-vs-sea choice that is available to shippers on PR routes. In this we follow Hummels and Schaur (2013) who use air and sea shipments to measure the value of time in the movement of U.S. import shipments. All U.S. export and import data files were taken from (U.S. Census Bureau, 2018). All dollar values are deflated by the U.S. consumer price index and expressed in 2019 dollars.

10
intensity affects the performance of downstream sectors. The latter, on the other hand, uses some proxies of inter-sectoral linkage or the direct inputs in gross output to examine the effects of services reforms on downstream manufacturing sectors without quantifying precisely services input intensity. The current paper connects the two literatures: we measure precisely services input intensity as the ratio of embodied services to manufacturing value-added, considering both direct and indirect input usages; then, we directly quantify the effect of services development on the export performance of manufacturing sectors. In addition, we also consider the interaction between embodied domestic services and embodied foreign services and how they affect manufacturing export performance, depending on countries' domestic services development and services input intensity.

Finally, the second hypothesis in this paper considers how access to foreign services markets may help developing countries to bypass their possibly inefficient domestic services provision. By distinguishing domestic from foreign services input, we implicitly assume that they are incomplete substitutes.<sup>7</sup> Such a bypass effect is also discussed in a theoretical model by Ju and Wei (2010), which derives the conditions under which financial globalization can serve as a substitute for reforms of the domestic financial system. This is also broadly consistent with the theory of comparative advantage – countries with underdeveloped services sectors benefit from imported services, but our paper shows that these benefits may go beyond services sectors through intersectoral linkages.

# **III. Empirical analysis**

In this section, we test empirically the following two hypotheses.

Hypothesis 1: _the effect of domestic services development on manufacturing export competitiveness is larger_ ( _more positive_ ) _for manufacturing sectors that use services as inputs more intensively._

> 7 The magnitude of the Armington elasticity of substitution between domestic and foreign varieties depends on several factors such as the time windows (long run vs. short run) and the level of product disaggregation. In general, estimates of the elasticity are usually quite small at the macroeconomic level. This is why, for example, Obstfeld and Rogoff (2007) found that rebalancing the U.S. current account would require a 30 percent depreciation of the U.S. dollar. Even at the sector level, the suggested Armington elasticity in Global Trade Analysis Project (GTAP Commodity Model) is less than two for most of the services categories, generally lower than those of manufacturing sectors (Hertel, 1997). The U.S. International Trade Commission (e.g., USITC-128 Sector Model) uses similar estimates for financial and business services sectors (Donnelly et al., 2004).

8
# **Figure 9**

|45|Forecasted share of nuclear energy in 2010 by energy project and according to year of publication<br>Note: US scenarios are depicted as large squares|
|---|---|
|35<br>40||
|30||
|20<br>25<br>**Nuclear share (%x100)**||
|15||
|10||
|0<br>5||
|1|970<br>1975<br>1980<br>1985<br>1990<br>1995<br>2000<br>2005<br>2010|
||Department of the Interior<br>EUOGEP<br>Energy Research and Development Agency<br>European Union / DGTREN|
||Ford Foundation<br>Greenpeace<br>Harvard Business School<br>IEA|
||IIASA<br>IPCC<br>OECD<br>PEW|
||Shell International BV<br>Union of Concerned Scientists<br>WEC<br>WEC and IIASA|
Policy ReseaRch WoRking PaPeR 4721

# **Abstract**

The authors use firm-level survey data from the manufacturing sector in 20 Sub-Saharan African countries to explore the links between labor market regulations and net job creation. A first look at firm characteristics, perceptions, and the dynamics of employment at the firm level suggests that labor regulations are not the main “binding constraint” on job creation. Other issues seem more important at this level of development. The analysis estimates the determinants of net job creation incorporating the legal origin of the country as a proxy for regulation. The findings show that, after controlling for other firm-level characteristics, legal origin is uncorrelated with net job creation in the short run.

This paper—a product of the Poverty Reduction and Economic Management Department, Africa Region—is part of a larger effort in the department to analyze the relationships between labor market policy, economic growth, and poverty reduction in Sub-Saharan Africa. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The authors may be contacted at lfox@worldbank.org, and aoviedo@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. 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._ Produced by the Research Support Team
33

**Figure 8**

**Kernel densities of (lagged) international reserves over GDP by occurrence of a current account reversal**

_Panel A: raw data_ <!-- Start of picture text --> 1988 1996<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Reserves/GDP Reserves/GDP<br>.08 .08<br>.06 .06<br>.04 .04<br>Density Density<br>.02 .02<br>0 0<br><!-- End of picture text --> _Panel B: net of current account balance, country dummies and year dummies_ <!-- Start of picture text --> 1988 1996<br>-.5 0 .5 -.5 0 .5<br>Reserves/GDP Reserves/GDP<br>15<br>10<br>10<br>Density Density5<br>5<br>0 0<br><!-- End of picture text --> solid line: reversals short-dashed line: no reversals
# **<u>Table A.7. Nigeria: ADF Tests on the residuals of the three equations, 1970 - 2005</u>**

||L<br>with intercept|evel<br>With intercept and<br>trend|
|---|---|---|
|Residual from eq. 1 (total GG expenditure)|-5.31***|-5.22***|
|Residual from eq. 2 (current GG expenditure)|-6.60***|-6.50***|
|Residual from eq.3 (capitalGGexpenditure)|-5.20**|-5.14***|

_*: reject unit root at 10% level_ _**: reject unit root at 5% level_ _***: reject unit root at 1% level_

## **_Data Sources and Variable Definitions_**

### **<u>Table A.8 Data Sources and Variable Definitions</u>**

|**Variable**|**Definition**|
|---|---|
|Total government expenditure|General government, total expenditure and net lending, current LCU|
|Current expenditure|Public consumption expenditure, current LCU|
|Capital expenditure|Gross public capital formation, current LCU|
|Oil revenue|Oil fiscal revenue, current LCU|
|Oil exports|Value of oil exports, current US Dollars|
|Deflator|Gross domestic product deflator|
|Exchangerate|Exchangerate, NationalCurrency perUSDollar|

### **Source**

_IMF World Economic Outlook, Article IV consultations and staff estimates_

|**Variables used in the regressions**||
|---|---|
|Real total government expenditure|= total government expenditure/exchange rate / US deflator|
|Real Current expenditure|= current expenditure /exchange rate / US deflator|
|Real Capital expenditure|= capital expenditure /exchange rate / US deflator|
|Real Oil revenue|= fiscal oil revenue /exchange rate / US deflator|
|Real Oil exports|= oil exports / US deflator|
|Dum|= 1 if real oil revenue in year t is smaller than in t-1, zero otherwise|
|Overhang variable|= dum*real oil revenue|
|Dum84|= 1 if year >=1984, zero otherwise|
|Dum84*Oil revenue|= Dum84*realoil revenue|
|Note: the oil fiscal revenue is used as t<br>proxy oil revenuesforoil-richcountrie|he independent variable for Nigeria regressions, and oil exports are used to<br>s due to data shortage.|

Nina Budina

> C:\USER_PRM\2006\ORCpaper\Debtanddevelopmentconference\New Folder\Nigeria_DO_Feb15.doc

> 02/15/2007 1:07:00 PM

32
benefits on non-farm income do not vary much by landholding. Overall, only rich households accrue significant positive impacts in total income (21.2 percent).

The trend is somewhat similar when it comes to educational outcomes. Schooling years for both boys and girls improve as a result of electrification, but such benefits for landrich households are slightly higher than that for land-poor households. For example, while boys’ schooling years for rich households go up by 0.16 grade due to electrification, that for poor households go up by 0.13 grade. As for children’s study hours, it is only land-rich households that post significant gains from electrification – 16.3 minutes and 20.5 minutes per day for boys and girls respectively, with no significant impacts for land-poor households.

What we can infer from this discussion is that physical capital makes a difference in the distribution of electrification benefits, and it matters more for rich households than for poor households. We have seen before that households with more physical assets are the ones to access electricity first when it is available in the community and they also consume more electricity than poor households. So it is not surprising that rich households would reap more electrification benefits than the poor households.

# **8.  Discussion and Conclusion**

The benefits of rural electrification have long been debated in the development literature. Although a large number of studies have found positive associations between rural electrification and development outcomes, there have been few studies that have tackled the issue of causality after taking care of endogeneity biases. This study is one of the few that have addressed the issue of correlation versus causation head on. This has been made possible by employing robust econometric techniques that tackle counterfactual and endogeneity issues which often limit the quality of impact assessment exercises. However, in the end it must be admitted that all cross-sectional analysis have their shortcomings, and moreover, assessed impacts may be short-term. The patterns observed today may not hold in the future. Panel analysis gives a better opportunity for evaluation of longer term impacts of development projects. REB in Bangladesh is now conducting a follow-up survey of the same households. Once that data is available for analysis, the findings of this study may be put to test.

According to the findings in this study, the rural electrification program has a strong and robust impact for both economic and educational outcomes. For example, the gain in 22
above combinations, although the gradual increase in both areas between 2006 and 2012 is statistically significant at conventional levels (95 percent) for math but not for science (90 percent only).

_Table 1: Changes in Learning Outcomes, by Year and Subject Area_

|**Reading**|**2006**|**2009**|**2012**|
|---|---|---|---|
|2000|-44.53 (-3.41)|-19.99 (-1.68)|-22.27 (-1.85)|
|2006|-|24.54 (2.63)|22.26 (2.30)|
|2009|-|-|-2.28 (-0.36)|
|**Mathematics**||||
|2006|-|6.81 (0.89)|7.18 (2.30)|
|2009|-|-|4.79 (-0.36)|
|**Science**||||
|2006|-|9.59 (1.20)|14.39 (1.81)|
|2009|-.|-|4.79(0.76)|

_Source_ : Authors’ calculations based on PISA-OECD. T-statistics included in parenthesis.

Although the evolution in average test scores is an important indicator of the quality of education services, equally important is the variation or dispersion of test scores. Given its linkages with future labor market outcomes, the inequalities that we observe today in test scores is a good predictor of the inequalities in incomes that will prevail in the future (Checchi and van de Werfhorst, 2014). Table 2 shows the evolution in dispersion in test scores for all three subjects, over the period 2000 to 2012. The dispersion measure presented in Table 2 is the coefficient of variation. Between 2000 and 2006, when learning outcomes in reading—the only subject allowing a comparison between those two years—registered a large decrease, the dispersion also increased substantially. The increase in dispersion in reading test scores was reversed during the period 2006 to 2012. A reduction in inequality of learning outcomes during the period 2006 to 2012 was also observed in math and science test scores. As shown below, the reduction in dispersion of test scores is largely accounted for by improvements in performance by the bottom end of the distribution.

_Table 2: Evolution of Dispersion in Learning Outcomes, by Year and Subject Area_

||**2000**|**2006**|**2009**|**2012**|
|---|---|---|---|---|
|**Reading**|23.6|28.7|25.0|23.8|
|**Math**|28.1|23.2|21.9|19.5|
|**Science**|24.7|23.2|23.3|20.8|

_Source_ : Authors’ calculations based on PISA/OECD. Dispersion is measured as the coefficient of variation defined as the standard deviation over the mean multiplied by 100.

9
- When historical losses are not available, the rating methodology should rely on simulated losses derived from statistical crop yield models.

- Crop revenue insurance rating requires innovative procedures that are available through recent experience on the U.S. crop insurance market.

The guiding principles outlined in this paper aim at devising viable crop insurance products. They are based on a sound actuarial approach. Experience shows that such a rigorous approach may be missing in government-sponsored crop insurance programs, because the high social welfare content imposed by the government may not be compatible with these insurance guidelines. For example, premiums rates derived under these actuarial principles may be considered as too high to meet social objectives. As a consequence, a prerequisite in the implementation of these guiding principles is to make a clear distinction between social insurance, which cannot be achieved through marketbased products, and market-based insurance, which should rely on the above principles.

3
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