rafmacalaba/datause-encoder-data huggingface dataset:
# **B Data Appendix**

## **B.1 The Correlates of War Diplomatic Exchange Data Set**

The Diplomatic Exchange data set captures diplomatic representation at the level of charg ́e d’affaires, minister, and ambassador between states from 1817-2005. The data set covers all members of the Correlates of War (COW) interstate system. The data is hosted on the COW website. For the post-World War II era, the data is collected from various data sources including Europa World Year Book series, ministries of foreign affairs of various countries, Statesman’s Year Book, and the _Code Diplomatique_ .

In this paper, we rely on the most recent version of the data—the 2006 version (Bayer, 2006). The 2006 version includes information for the following years: 1817, 1824, 1827, 1832, 1836, 1840, every five years between 1844 and 1914, every five years between 1920 and 1940, and every five years between 1950 and 2005. The data is therefore available every five years for the period we cover in our analysis (from 1960 onward). The dyadic data describes the level of diplomatic representation and diplomatic exchange between members in the COW system.

For each pair of countries (country 1, country 2) in the COW interstate system, the diplomatic exchange data set provides information on the following 3 variables: the diplomatic representation of country 1 in country 2, the diplomatic representation of country 2 in country 1, and if any diplomatic representation between country 1 and country 2 exists.

The dyadic data captures the level of diplomatic representation between countries. The diplomatic exchange variables that describe the level of diplomatic representation for a given pair of countries is coded as 0 if there is no evidence of diplomatic exchange, 1 if diplomatic representation is at the level of charg ́e d’affaires, 2 if diplomatic representation is at the level of minister, 3 if diplomatic representation is at the level of ambassador, and 9 for all other levels of diplomatic representation.

In our analysis, we use dummy variable indicators to capture any diplomatic representation (whether it is at the level of charg ́e d’affaires, minister or ambassador) in the Arab Republic of Egypt for each country of destination of Egyptian migrants. For robustness checks, we also use two alternative definitions. The first one captures instead the diplomatic representation of the Arab Republic of Egypt in each country of destination of Egyptian migrants, while the second alternative definition captures the existence of any diplomatic exchange between the two countries. It is a dummy variable coded as 0 if neither side was represented in the other side and coded as 1 if at least one side was represented in the other side.

66
# **_Biofuels_**

The increasing interaction between the price movements of energy and non‐ energy commodities during the boom focused attention on the impact of grow‐ ing demand for biofuels, including for maize‐based ethanol (mainly in the US) and oilseed‐based biodiesel production (mainly in Europe). During the boom, maize and crude oil prices moved in tandem, pointing to an emerging new and fixed relationship between them. Obviously, maize and its use for ethanol moved into the picture as significant factors affecting price developments. But how much impact was there, and was there a similar one in oilseeds, resulting from their use for biodiesel?

The contribution of biofuels to the recent price boom, and especially the price spike of 2007/08, has been hotly debated. Mitchell (2009) argued that bio‐ fuel production from grains and oilseeds in the US and the EU was the most im‐ portant factor behind the food price increase between 2002 and 2008, accounting, perhaps, for as much as two thirds of the price increase. Gilbert (2010), on the other hand, found little direct evidence that demand for grains and oilseeds as biofuel feedstocks was a cause of the price spike.

FAO (2008) compared a baseline scenario, which assumes that biofuel production will double by 2018, to an assumption that biofuel production will remain at its 2007 levels; it concluded that in the latter case grain prices would be 12 percent lower, wheat prices 7 percent lower, and vegetable oil prices 15 per‐ cent lower than in the baseline scenario. OECD (2008) arrived at similar conclu‐ sions for vegetable oils, finding that their prices would be 16 percent lower than the baseline if biofuel support policies were abolished; eliminating biofuel subsi‐ dies would have smaller impacts on the prices of coarse grains (‐7 percent) and wheat (‐5 percent). Rosegrant (2008), who simulated market developments be‐ tween 2000 and 2007 (excluding the surge in biofuel production), concluded that biofuel growth accounted for 30 percent of the food price increases seen in that period, with the contribution varying from 39 percent for maize to 21 percent for rice. Looking ahead, Rosegrant found that if biofuel production were to remain at its 2007 levels, rather than reaching its mandated level, maize prices would be lower by 14 percent in 2015 and by 6 percent in 2020.<sup>**10**</sup> Banse and others (2008) compared the impact of the EU’s current mandate to (i) a no‐mandate scenario and (ii) a mandate whereby the US, Japan, Brazil al‐ so adopt targets for biofuel consumption. They estimate that by 2020, in the base‐ line scenario (no mandate), cereal and oilseed prices will have decreased by 12 and 7 percent, respectively. In the EU‐only scenario, the comparable changes are ‐7 percent for cereal and +2 percent for oilseeds. By contrast, under the “global” scenario (adding biofuel targets in US, Japan, and Brazil) oilseed prices will have

**_Page 11_**
**Table 5: Possible explanators of effect sizes differences from meta-regressions**

||(1)<br>Combination of<br>supply and<br>demand side<br>financial<br>incentives|(2)<br>Control group<br>with budget<br>equalization|(3)<br>Baseline outcome<br>value|(4)<br>High bias risk<br>study|_N_<sup></sup>|
|---|---|---|---|---|---|
|Modern family planning|4.27 (0.068)|-2.29 (0.505)|-0.01 (0.900)|-1.21 (0.663)|16|
|4+ antenatal care checks|1.15 (0.592)|-2.35 (0.033)|0.07 (0.090)|0.98 (0.590)|22|
|Maternal tetanus vaccination|-6.55 (0.593)|0.88 (0.797)|0.01 (0.907)|2.00 (0.583)|14|
|Facility delivery|-0.30 (0.853)|-0.48 (0.810)|-0.03 (0.344)|1.79 (0.352)|35|
|1+ postnatal care checks|-1.42 (0.333)|-5.87 (0.242)|0.04 (0.395)|-1.11 (0.574)|21|
|Full child vaccination|0.09 (0.976)|-3.33 (0.275)|-0.03 (0.565)|0.33 (0.885)|22|

Notes: Table shows results from bivariate random effects meta-regressions. Coefficients in columns 1, 2 and 4 are the percentage point differences in mean effect sizes between the group described in the column title and the respective reference group, _p-_ values of the differences shown in parentheses. For column 1 reference group are interventions with either supply or demand side financial incentives, for column 2 it is interventions where the control group receives no treatment, and for column (4) it is interventions where the bias risk is classified as low or medium. Column 3 gives the percentage point change associated with a one percentage point increase in the baseline outcome value.<sup></sup> Because no baseline outcome values are available for two facility delivery and two postnatal care check effect sizes, the number of observations underlying the results in column 3 are 33 and 19, respectively, instead of 35 and 21 for the meta-regressions.

27
Table 3: Impact of civil liberties on project rates of return

||A|Speci<br>B<br>________<br>_________|ficationa:<br>C|D<br>_______|Effect of<br>one std.<br>dev.<br>increase on<br>**~~~ERR**<br>**d:**|
|---|---|---|---|---|---|
|Freedom House<br>Civil (1978-<br>19 87)b (N=649)|1.81<br>(.0005)c|1.16<br>(.079)|1.71<br>(.002)|1.07<br>(.114)|1.57|
|Humana (1982-<br>1985)<br>(N=236)_|.290<br>(.003)|.299<br>(.007)|.296<br>(.002)|.289<br>(.013)|5.19|
|Media pluralism<br>(1983-1987)<br>(N=448)|4.61<br>(.0001)|4.45<br>(.002)|3.66<br>(.001)|3.43<br>(.026)|3.12|
|Freedom to<br>organize (1983-<br>1987) (N =448)|3.17<br>(.0001)|1.81<br>(.184)<br>I___|2.41<br>(.006)|-0.26<br>(.854)|2.70c|

Notes: a) for description of the specifications see table 2; b) Annual values from 197887. The other three indices are single values extrapolated to cover the listed time period; c) p-levels in parenthesis; d) for the calculation in column V, the standard deviations--for the entire sample for which each variable is available--are 1.47, 17.97, 0.91 and 1.12 respectively (see appendix table Al.1); e) uses estimate from column C. Source: Authors' estimates.

This finding of a positive relationship between civil liberties and ERRs is the central positive finding of our paper. We complete this section by showing the statistical robustness of this finding; we argue in the following section that it is plausible that better civil liberties actually are a factor in producing better projects.

One possible concern with the econometric results above is that they are driven by a few outlying observations, as some projects have very high estimated rates of return. We have dealt with that problem in two ways. First, in addition to a Tobit specification accounting for the lower

- 16 -
Policy Research Working Paper 6522

# **Abstract**

The importance of gender equality is reflected not only in the Millennium Development Goals, but also in the World Bank’s Gender Action Plan launched in 2007 as well as in other treaties and actions undertaken at regional and international levels. Unlike other work on gender and poverty, which is mostly based on monetary measurement, the present study makes use of a counting approach to examine gender issues in Burkina Faso and Togo using household surveys. Focusing on six dimensions _(housing, basic utilities, assets, education, employment, and access to credit)_ largely recognized as Millennium Development Goal targets, the main findings of the study indicate that overall individuals are the most deprived in education in Burkina Faso, while the reverse situation is true in Togo. Gender inequality is observed in all dimensions since women always seem to be more deprived than men. The situation is also marked by regional disparities. Moreover, the assessment of dimensional contributions shows different patterns for each country. While _employment_ proves to be the main contributor of gender inequality in Burkina Faso, three dimensions _(assets, access to credit, and employment)_ account together for most of the total contribution to gender inequality in Togo. There is also a positive correlation between multidimensional deprivation and women’s age in Burkina Faso, whereas both measures seem to be uncorrelated in Togo.

This paper is a product of the Poverty Reduction and Economic Management Unit, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at ybatana@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
# Figure 12 USA MCY, M1 Y, QMY and DCPY over time

<!-- Start of picture text --> 0.9-<br>0.8<br>0.7-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~I<br>0.6-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'.<br>0.7-<br>o0.2 > a i 4: - . . . : !~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~....<br>04 .... ..... . .... .. ...<br>o<br>0.1 . ...............................<br>1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983<br>1952 1954 1956 1958 1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984<br>year<br><!-- End of picture text --> <!-- Start of picture text --> MCY -M1y QMY ---- DCPY<br><!-- End of picture text -->
||Bwc 0-1:<br>**U.ICY**COICER16I**N iUA**<br>**SEC1OR**<br>**LEtIOIt**||
|---|---|---|
|**Country Concasns**|**Theoretical Caicans**|**Otil C**|
|<br>(Related<br>to Fiscal<br>Politics)|<br>(Related<br>to Economic/Fiscal<br>Theory)|**peraoaa ommnes**<br>(Related<br>to Lending)|
|Territorial<br>Distribution<br>of Population<br>and<br>Economic Activities|Factor Mobilty<br>(Labor, Capital.<br>Technology)<br>(Voting<br>with the Feet, Mobility<br>ofEnterprlses)|Impact of Bank Lending on Overall<br>Urban System|
|Size ofVery Large Citles|Economies/Diseconomies of Agglomeration|LargeCitiesAbsorb Most Credit|
|Ibglected<br>Rural Areas and_Tovns_|Internal<br>Linkages of Settlement<br>Systems|Credittcrthiness<br>Independent of City Size|
|Horizontal<br>Equity<br>(Weds)|Fiscal<br>Equity<br>(Effort/Capacity)|Selecting<br>Borrowing Communitles|
|Deterioration<br>of Levels of Service|Cost-Effectiveness<br>of Expenditures|Replicability<br>of Lending Operations|
|Spatially<br>"Unif.orm" Development?|Area Comparative Advantage|Strategic<br>Use of Credit|
|Sectoral<br>Coordination|Al locative<br>Efficiency<br>(Intersectoral)|Managing Multi-Sectoral<br>Loans|
|FiscalAssignment (Vertical<br>Balance)|Externalitles/Economies<br>of Scale<br>Development & Poverty<br>Alleviation<br>Functions|Shortage of Counterpart<br>Funds|
|Growth of Subnational<br>Fiscal<br>Gap|Budgetary Predictability<br>of<br>Revenues/ExpendItures/Subsidle<br>s|Linking<br>Lending with RevenueSharing|
|Short-term<br>Liquidity<br>Constraints|Growth of Domestic Deficit|Credit<br>InLieu of Incremental<br>Grants|
|Solvency of institutlons|Impact onNational<br>Flnanclal<br>Markets|Suitable<br>Flnanclal<br>intermediarles|
|Wlillingness/Capacity<br>to Pay|Resource Mobilization/Efficient<br>Pricing|CostRecovery (Direct/Indirect)|
|Limited<br>Local Capabilities<br>of<br>Management/PI anning/Imp lemertation|Responsiveness to LocalNgeds/Preferences|Curbing Growth of Parastatals|
|Local Autonomy (Participatory<br>Planning)|Accountability<br>for Use of Resources|Decentralizing<br>Project<br>Development|
|Growth of the Informal<br>Sector|Promotion of Private<br>Sector<br>Initiative|Revising the Regulatory<br>Framewrk|
|Fragmentation<br>of the Urban Sector|Urban Investment Policy/Tax<br>Reform|Nature of Conditionality|
|Limited<br>Factual<br>Knowledge|Theoretical/Operational<br>Questions|Sector Research Naeds|
- E"Brown" sectors indices should be positively correlated with:

- 1) Particular focus on public health, indexed by life expectancy;

- 2) Urban share of total population;

- 3) Urban populaFion density;

- 4) Manufacturing share of national output.

Our analysis of overall regulatory performance reveals strong cross-country associations with income per capita, security of property rights, and general development of the legal and regulatory system. Surprisingly, however, we find only insignificant or perverse associations with degree of popular representation and freedom of information.

For both the Green and Brown indices, performance is again strongly associated with income per capita, freedom of property and (in small samples) measures of regulatory efficiency. The two specifically rural-sector variables (population density; proportion of GDP in agriculture and forestry) are only weakly associated with the Green index. T'e fit is much better for the Brown index: degree of urbanization, population density and manufacturing share in GDP all have the expected signs and relatively high significance. Life expectancy as a proxy for public health priority has no independent effect.

In summary, our findings suggest that a detailed, quantified analysis of the UNCED reports can yield comparable and plausible indices of environmental policy performance across countries. Cross-country variations in our environmental index are wellexplained by variations in income per capita, degree of urbanization and industrialization, security of property rights, and general administrative efficiency.
To our knowledge, this is the first paper to document and utilize data on the rapid growth of secondary schools across Brazil over the two decades starting in the 1990s.

This study contributes to a literature examining the relationship between education and fertility in young women. Lowering the explicit cost associated with education, by providing free school uniforms (Duflo, Dupas and Kremer 2015) or the removal of school fees (Chicoine 2020), leads to a greater number of girls in primary education and significantly reduces childbearing for young women in African countries. Increasing the time spent in education also affects teenage childbearing. In Chile, a greater number of full day, as opposed to half day, schools decreases the probability of adolescent motherhood (Berthelon and Kruger 2011). Papers using variation from changes to mandatory schooling laws in high-income countries find that increases in the mandatory schooling age lead to a large and significant decrease in motherhood for young women in Norway, the US and the UK (Black, Devereux and Salvanes 2008; Monstad, Propper and Salvanes 2008; Geruso and Royer 2018). However, a UK policy allowing young women to leave school sixmonths earlier does not lead to an increase in teenage motherhood (James and Vujic 2019).

The findings in the literature indicate that there are multiple structural channels at work in the education-fertility relationship. School attendance restricts the time outside school available to young men and women (an _incarceration effect_ ), but education may also a change preferences or opportunity costs of young men and women (a _human capital effect_ ) (Black, Devereux and Salvanes 2008).

One may also expect expanding access to schools to lead to changes in fertility through indirect channels. Secondary school education may increase a young adult’s value in the marriage market (Fort, Schneeweis and Winter-Ebmer 2016), or promote greater assortative matching, both 4
Cameron, A.C., and Trivedi, P.K. (2005) “Microeconometrics Methods and Applications” Cambridge University Press Currie, J. and Madrian B.C. (1999) “Health, health insurance and the labor market”, in Handbook of Labor Economics, Volume 3, Edited by O. Ashenfelter and D. Card, cap.50, pp.3309-3415 Cutler, D.M. and Lleras-Muney, A. (2006) “Education and health: evaluating theories and evidence”, NBER working paper n.12352 Ermish, J.F. and Francesconi, M. (2001) “Family structure and children’s achievements”, Journal of Population Economics, 14:249-270 Ezzati, M. et al (2003). Estimates of global and regional potential health gains from reducing multiple major risk factors. The Lancet. 26;362(9380):271-80.

Fersterer, J. and Winter-Ebmer, R. (2003) “Smoking, discount rates, and returns to education” Economics of Education Review, 22:561–566 Fumagalli, E; Suhrcke, M. and Rocco, L. (2010). Obesity in Egypt: trends, socioeconomic inequalities and determinants. Mimeo, University of East Anglia: Norwich.

Gannon, B. (2005) “A dynamic analysis of disability and labor force participation in Ireland.” Health Economics. 14: 925–938.

Greene, W.H. (2002) “Econometric Analysis - 5<sup>th</sup> edition”, Prentice Hall Griliches, Z. (1979) “Sibling Models and Data in Economics: Beginnings of a Survey”, Journal of Political Economy, 87(5):S37-S64 Institute of Medicine (2010). Promoting Cardiovascular Health in the Developing World: A Critical Challenge to Achieve Global Health. Committee on Preventing the Global Epidemic of Cardiovascular Disease: Meeting the Challenges in Developing Countries; Institute of Medicine.

Jones, M.K. (2007), “How accurate are estimates of the impact of disability on labor market participation? Evidence from the Health Survey for England,” Swansea University, School of Business, Working Paper.

Kan, K. (2007) “Cigarette smoking and self-control”, Journal of Health Economics 26:61–81 Kapteyn A., Smith J.P., van Soest A. (2009), Work Disability, Work, and Justification Bias in Europe and the U.S., NBER Working paper 15245 22
24

# **Table 2**

|Dependent variable:|**Growth of Ma**|**nufacturing E**|**xports from C**|**OMTRADE**|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|||**Annual **|**data**||
|Real Exchange Rate Change|**‐1.099 *****<br>0.246|**‐1.429 *****<br>0.411|**‐1.056 *****<br>0.216|**‐1.718 *****<br>0.609|
|Real Exchange Rate Change interacted with Participation in GVCs||**0.010 ***<br>0.006|||
|Real Exchange Rate Change interacted with Position in GVCs|||**0.177**<br>0.370||
|Real Exchange Rate Change interacted with Backward Participation in GVCs||||**0.007**<br>0.006|
|Real Exchange Rate Change interacted with Forward Participation in GVCs||||**0.041**<br>0.032|
|||**5 year av**|**erage**||
||**‐1.097 *****|**‐1.750 *****|**‐1.395 *****|**‐1.432 *****|
|Real Exchange Rate Change|0.238|0.342|0.251|0.222|
|Real Exchange Rate Change interacted with Participation in GVCs||**0.019 *****<br>0.006|||
|Real Exchange Rate Change interacted with Position in GVCs|||**‐1.119 *****<br>0.087||
|Real Exchange Rate Change interacted with Backward Participation in GVCs||||**0.026 *****<br>0.006|
|Real Exchange Rate Change interacted with Forward Participation in GVCs||||**‐0.024**<br>0.018|
|All Regressions:|||||
|Lag real GDP and Foreign real GDP|Y|Y|Y|Y|
|Time Fixed Effects|Y|Y|Y|Y|
|Country Fixed Effects|Y|Y|Y|Y|

Note: *** indicates statistically significant 1%, ** indicates statistically significant at 5%, and * indicates statistically significant at 10%. Point estimates in bold letters and robust standard errors below. The grey shaded areas are jointly significant using Wald tests.

Countries as in the Appendix.