rafmacalaba/datause-encoder-data huggingface dataset:
Urdal & Hoelscher: Urban Youth Bulges and Social Disorder

# **6. References**

Agesa, Jacqueline & Richard U. Agesa, 1999. ‘Gender Differences in the Incidence of Rural to Urban Migration: Evidence from Kenya’, _Journal of Development Studies_ 35(6): 36-58.

Barakat, Bilal & Henrik Urdal, 2008. ‘Breaking the Waves? Does Education Mediate the Relationship Between Youth Bulges and Political Violence?’ Mimeo. Oslo: Centre for the Study of Civil War, PRIO.

Barker, Gary & Christine Ricardo, 2005. _‘_ Young Men and the Construction of Masculinity in SubSaharan Africa: Implications for HIV/AIDS, Conflict, and Violence’, _World Bank Social Development Papers: Conflict, Prevention & Reconstruction Paper_ No. 26. Washington, DC: World Bank.

Braungart, Richard G., 1984. ‘Historical and Generational Patterns of Youth Movements: A Global Perspective’, _Comparative Social Research_ 7(1): 3–62.

Brewer, Laura, 2004. ‘Youth at Risk: The Role of Skills Development in Facilitating the Transition to Work’, _InFocus Programme on Skills, Knowledge and Employability, Working Paper_ No. 19. Geneva: ILO. Available at http://www.ilo.org/public/english/employment/skills/youth/download/wp19.pdf.

Brokerhoff, Martin & Hongsook Eu, 1993. ‘Demographic and Socioeconomic Determinants of Female Rural to Urban Migration in Sub-Saharan Africa’, _International Migration Review_ 27(3): 557-577.

Buhaug, Halvard; Lars-Erik Cederman & Jan Ketil Rød, 2008. ‘Disaggregating Ethno-Nationalist Civil War: A Dyadic Test of Exclusion Theory’, _International Organization_ 62 (3): 531-551 _._ Buhaug, Halvard & Jan Ketil Rød, 2006. ‘Local Determinants of African Civil Wars, 1970–2001’, _Political Geography_ 25(3): 315–335.

Castles, Stephen & Mark J. Miller, 1998. _The Age of Migration_ . Macmillan, Basingstoke.

Choucri, Nazli, 1974. _Population Dynamics and International Violence: Propositions, Insights and Evidence_ . Lexington, MA: Lexington.

Cincotta, Richard P.; Robert Engelman & Daniele Anastasion, 2003. The Security Demographic: Population and Civil Conflict After the Cold War. Washington, DC: Population Action International.

Collier, Paul, 2000. ‘Doing Well Out of War: An Economic Perspective’, in Mats Berdal & David M. Malone, eds, _Greed & Grievance: Economic Agendas in Civil Wars_ (91–111). Boulder, CO and London: Lynne Rienner.

Collier, Paul & Anke Hoeffler, 2004. ‘Greed and Grievance in Civil War’, _Oxford Economic Papers_ 56: 563–595.

Esty, Daniel C.; Jack A. Goldstone, Ted Robert Gurr, Barbara Harff, Marc Levy, Geoffrey D. Dabelko, Pamela Surko & Alan N. Unger, 1998. _State Failure Task Force Report: Phase II Findings._ McLean, VA: Science Applications International.

Fearon, James D. & David D. Laitin, 2003. ‘Ethnicity, Insurgency, and Civil War’, _American Political Science Review_ 97(1): 75–90.

19
income countries. The semi-elasticity is 0.10 and 0.03 for lower- and upper-middle-income countries, respectively. We assume that under-5 mortality is not impacted by income shocks in high-income countries. Unlike the results from low- and middle-income countries, studies analyzing data from the United Stated find mortality to be pro-cyclical (Ruhm, 2000; Dehejia and Lleras-Muney, 2004).

To map the estimated semi-elasticities into our calibration, we define _s_ 0 _−_ 5 to be the share of children under five years old in the total number of children of ages 0 to 15. The semi-elasticity of child mortality with respect to consumption is given by where _βg_ represents the regression coefficients for low-, lower-middle- and upper-middleincome countries. _ν_ (1) equals zero in high income countries.

It is important to note that we convert the annual semi-elasticity estimate into weekly frequency in the quantification, to match the period definition in the model. Unfortunately, we are unable to estimate the mortality semi-elasticity at a shorter frequency due to data – limitations the national accounts data for many developing countries are only available annually. However, we believe the underlying relationship operates at a shorter frequency than annual for several reasons. Baird et al. (2011) find that only contemporaneous GDP deviations are correlated with mortality likelihood even though a large share of the infants in the estimating dataset experienced the majority of the in-utero period in the lagged year and an equal share of infants experienced the majority of their first year of life in the leading year. Moreover, the authors find that the coefficients on economic conditions in utero and after the first month of life are both small and insignificant. By contrast, the coefficient on per capita GDP in the first month is large, significant, and very close in magnitude to the main effect reported in the paper. These results underscore that it is the economic conditions around birth (say the last months of pregnancy and the first months of life) that matter most for infant survival during economic contractions. As 47 percent of under-5 mortality in 2019 occurred in the neo-natal period, it appears clear that the actual frequency through which 27
# **V. Using the State Street Measure of Investor Confidence**

As a check to see whether our indicators capture investor sentiment, it is useful to try to relate them to a variable that purports to capture institutional investors’ risk appetite. In particular, we regress the State Street Advisors measure of their holdings of risky versus other assets—on the various confidence measures we have just described, namely market volatility, industrial production, and stock market performance. As shown in the table below, regressions show a consistently significant and negative effect of market volatility on the State Street Measure of Investor Confidence. The same is true (with a positive sign) of industrial production, and stock market performance.

**Table 4. Risk appetite and selected indicators of confidence**

|Dependent va|riable: Risk Appet|ite|
|---|---|---|
||(Model 1)|(Model 2)|
|Volatility Factor|-2.003|-2.156**|
||(1.245)|(0.968)|
|Industrial Growth Factor|0.545||
||(2.341)||
|MSCI Developed World|0.325***|0.339***|
||(0.077)|(0.077)|
|Constant|85.332***|85.339***|
||(1.292)|(1.276)|
|Observations|60|60|
|R-squared|0.620|0.626|

_R_ = α + β 1 × _Volatility_ + β 2 × _Macro_ + β 3 × _Equities_ In this regression, the dependent variable is represented by ‘Risk Appetite’ (proxied by the State Street Investor Confidence Index).Volatility is the monthly average of the predicted daily common factor across eight variables: VIX, usd/euro volatility, usd/yen volatility, usd/sterling volatility, agriculture commodities price index volatility, energy price index volatility, industrial metals price index volatility, and the TED spread. Industrial growth is the predicted common factor across industrial production growth rates(y-o-y) in the USA, UK, Germany and Japan. Equity market growth is represented by the change (yo-y) in the MSCI for developed economies. Table 4 shows that risk appetite seems to be most closely related to developed world market performance and to market volatility, while industrial growth is positive, but not significant. Figure 5 shows that the regression captures a substantial part of the variation of risk appetite, including the large full during the crisis.

11
- International Finance Corporation (2019b) IFC’s Strategic Alignment with the Sustainable Development Goals. available online. Washington, DC.

- International Finance Corporation (2019c) Annual Report 2019 - Investing for Impact. Report. Washington, DC.

- Jeffers, Jessica, Tianshu Lyu, Kelly Posenau (2024) The Risk and Return of Impact Investing Funds. _Journal of Financial Economics_ . Forthcoming

- Jenkinson, Tim, Miguel Sousa, Rüdiger Stucke (2013) How fair are the valuations of private equity funds?. SSRN Working Paper #2229547

- Johan, Sofia, Minjie Zhang (2016) Private equity exits in emerging markets. _Emerging Markets Review_ 29:133-153.

- Kaplan, Steven, Antoinette Schoar (2005) Private Equity Performance: Returns, Persistence and Capital Flows. _The Journal of Finance_ 60(4):1791-1823.

- Kaplan, Steven, Berk Sensoy (2015) Private Equity Performance: A Survey. _Annual Review of Financial Economics_ 7:597-614.

- Kapur, Devesh, John P. Lewis, Richard Webb (1997) The World Bank - Its First Half Century. Brookings Institution Press, Washington D.C.

- Kenny, Charles, Jared Kalow, Vijaya Ramachandran (2018) Inside the Portfolio of the International Finance Corporation: Does IFC Do Enough in Low-Income Countries? Center for Global Development Policy Paper 115.

- Kerr, William R., Josh Lerner, Antoinette Schoar (2014) The consequences of entrepreneurial finance: Evidence from angel financings. _The Review of Financial Studies_ 27(1):20-55.

- Korteweg, Arthur, Stefan Nagel (2016) Risk‐adjusting the returns to venture capital. _The Journal of Finance_ 71(3):1437-1470.

- Kremer, Michael, Jack Willis, and Yang You (2021) Converging to Convergence. _NBER Macroeconomics Annual._ 36.

- La Porta, Rafael, Florencio Lopez-de-Silanes, Andrei Shleifer, and Robert Vishny (1999) The quality of government. _The Journal of Law, Economics, and Organization_ 15(1):222-279.

- Leeds, Roger S., Nadiya Satyamurthy (2015) Private equity investing in emerging markets: Opportunities for value creation. Palgrave Macmillan.

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- Lerner, Josh, Antoinette Schoar (2005) Does legal enforcement affect financial transactions? The contractual channel in private equity. _The Quarterly Journal of Economics_ 120(1):223-246.

28
BibUography Aspe P. (1991): 'Thoughts on the Structural Transformation in Mexico: The Case of Privatization of Public Sector Enterprises". Mimeo. Secretaria de Hacienda y Credito Publico. June.

Barnes, 0. (1991): "Modernizacion del Sistema Financiero Mexicano", en La Modernizacion Bancana y Financiera, Revista de Administracion Publica no. 81, INAP. Mexico.

Forsyth, P.J. (1984): "Airlines and Airports: Privatization, Regulation and Competition', **Fiscal** Studies _5_ (1).

Jones, L. P. (1982): PubUc Enterprise In Less DeveloPed Countries. Cambridge University Press.

Ortiz, 0. (1991): "Lineamientos Generales de Desincorporacion Bancaria". Septima Reunion Anual de **la** Banca, El Mercado **de** Valores. Mexico, noviembre.

S H C P (1992): **EI** Proceso de Enajenacion de Entidades Paraestatales", Unidad **de** Desincorporacdon **de** Entidades Paraestatales, Secretaria de Hacienda y Credito Publico. Mexico, febrero.

Van de Wall (1989): 'Privatization in Less Developed Countries: A Review of the Issues", World Development (17) 5.

Vickers and Yarrow (1988): Privatizatlon. MIT Press. Cambridge, Mass.

22
Table 1 shows that the real incomes of developing-country migrants rose 47.3 percent over the 1990–2010 period. Combining this with the growth in migrant stock, and using equation (5), this gives a predicted growth in remittances of 120.7 percent over the 1990–2010 period. As a result we estimate that only 21.7 percent of recorded remittance growth is due to changes in the fundamental drivers of remittances, with almost 80 percent therefore potentially due to changes in measurement.<sup>5</sup> The remainder of Table 1 does this also on a country-by-country basis for the 15 developing countries with the highest absolute increases in remittances. We see that for the three countries contributing the most to developing country remittance growth over this period—India, China, and Nigeria—that growth in migrant stocks and migrant incomes can explain less than 4 percent of the recorded growth in remittances.<sup>6</sup> How reasonable is per capita income growth at destination as a proxy for the income growth of migrants? There are several potential concerns. The first is that in some countries the growth in aggregate income has mostly gone to earners at the very top of the income distribution, with the growth of the remainder of the distribution (in which most migrants will likely fall) being much less. The United States is a stark example of this, with the income of the top 1 percent growing 200.5% between 1979 and 2007, while that of the remaining 99% grew only 18.9% (Sommellier and Price 2014). To the extent this occurs, real GDP growth overstates the income growth of migrants. However, this uneven growth is not a global phenomenon. Using data from 118

> 5 The estimates in Table 1 use the definition of remittances in the World Bank’s World Development Indicators, which includes both the ‘workers’ remittances’ and the ‘employee compensation’ lines of the Balance of Payments. As noted before, there is disagreement in the literature as to which aggregate to use. The main alternative approach is that of Chami et al. (2008, p. 4), who recommend that macroeconomic research use solely the workers’ remittances’ line. Appendix C re-creates Table 1 with this alternative definition. The results barely change: 21.5 percent of remittance growth, by that definition, is accounted for by growth in fundamentals.

> 6 There are two countries in Table 1 for which we estimate higher remittance growth than officially recorded. The first is Colombia, whose main migrant destination Venezuela had rapid GDP growth of 247% over 1990 to 2010 as a result of oil revenues. It seems plausible that Colombian migrants did not share equally in this resource-driven growth, and may have had lower income growth. The second is Egypt, whose main migrant destination was Saudi Arabia. Again it seems plausible that migrants to Saudi Arabia had income growth that was considerably less than GDP growth in Saudi Arabia over this period.

14
# **Inequalities in Job Loss and Income Loss in Sub-Saharan Africa during the COVID-19 Crisis**<sup>**1**</sup>

Ivette Contreras-Gonzalez (World Bank) Gbemisola Oseni (World Bank) Amparo Palacios-Lopez (World Bank) Janneke Pieters (Wageningen University, IZA) Michael Weber (World Bank, IZA) JEL Codes: J21, J38, J42, J63 Keywords: Coronavirus (COVID-19), Labor Markets, Jobs, Shocks and Vulnerability to Poverty, Gender and Employment.

> 1 _The authors would like to thank Alain Ntumba Kabundi and Gero Carletto for comments on an earlier draft of the paper. This work has been supported by the World Bank Multi-Donor Trust Fund for Integrated Household and Agricultural Surveys in Low and Middle-Income Countries (TF072496). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript._
# TABLE OF CONTENTS

# Page No.

|A. Introduction . . . . . . . . . . . .<br>. . ..<br>1|
|---|
|B. Overview of Recent Performance .....<br>. . . . . . . . . .<br>.<br>2|
|C. External Factors . . . . . . .<br>.5.... . .<br>.<br>S|
|D. Domestic Factors . . . . . . .<br>.....<br>. . . . . .. . . .<br>.<br>12|
|E. Contributions to Sources of Growth . . . . . . . . . . . . . .<br>16|
|F. Statistical Tests .... . . .<br>. . . . . . . . . . . . .<br>. .<br>.<br>17|
|G. Prospects and Risks .... . . . . .<br>. . . . . . . .<br>. . . .<br>.<br>21|
|H. Co. Lusion . . . . . . . . . . . .. . . . . . . . . . . . . . .<br>23|

# _ANNPvAES_

- la - Country Classification for Developing Member Countries by Income Groups and Bank Regions

- lb - Country Classification for Development Member Countries by WDR Analytical Groups

# STATISTICAL APPENDIX

- Table 1 - Real GDP Growth Rates for Developing Countries, 1965-i988

- Table 2 - Means of Key Macroeconomic Variables, 1980-87

- Table 3 - Commodity Prices

- Table 4 - Terms of Trade, Export and Import Volume Growth for Developing Countries, 1965-87

- Table 5 - Gross Domestic Investment (GDI) for Developing Countries, 1965-87
- 25 -

|**T**|**able 8. 3SLS Estimatio**|**n result with central g**|**overnment subsidies**||
|---|---|---|---|---|
|Dep. Var.|ln_INV_|ln_SAVE_|ln_DUR_|ln_PAY_|
||Coef. Std.Err.|Coef. Std.Err.|Coef. Std.Err.|Coef. Std.Err.|
|ln_SUB_|0.058 (0.009) ***|0.039 (0.009) ***|<br>0.018 (0.004) ***|0.047 (0.011) ***|
|_dTAGT_|0.416 (0.255) *|-0.014 (0.250)|0.127 (0.096)|0.491 (0.306) *|
|_dRISKMT_|1.538 (0.378) ***|0.787 (0.371) **|-0.221 (0.142)|2.587 (0.454) ***|
|_dRESV_|-0.975 (0.343) ***|-0.486 (0.336)|-0.159 (0.128)|-0.625 (0.411)|
|_dWALK_|1.027 (0.746)|2.347 (0.731) ***|<br>-1.454 (0.279) ***|2.310 (0.895) ***|
|_RISKTYPO_|1.121 (0.597) *|0.988 (0.585) *|-0.728 (0.223) ***|0.553 (0.715)|
|_RISKREG_|-0.062 (0.295)|0.293 (0.289)|-0.140 (0.110)|-0.047 (0.354)|
|_RISKINF_|0.571 (0.350) *|0.243 (0.343)|-0.482 (0.131) ***|0.929 (0.419) **|
|_wINV_|0.046 (0.026) *|-0.062 (0.025) **|-0.011 (0.010)|0.042 (0.031)|
|_wSAVE_|0.022 (0.018)|-0.010 (0.018)|0.018 (0.007) ***|0.012 (0.022)|
|_wDUR_|0.084 (0.031) ***|-0.015 (0.031)|-0.031 (0.012) ***|0.056 (0.038)|
|_wPAY_|0.046 (0.057)|-0.119 (0.056) **|0.096 (0.021) ***|-0.285 (0.069) ***|
|ln_N_|-0.143 (0.185)|0.269 (0.181)|0.168 (0.069) **|-0.013 (0.222)|
|_dSSC_|-0.891 (0.276) ***|-0.721 (0.271) ***|<br>0.610 (0.103) ***|-0.220 (0.331)|
|_dPCKG_|0.177 (0.244)|-0.166 (0.239)|-0.079 (0.091)|0.095 (0.293)|
|ln_KWH_|0.208 (0.168)|0.328 (0.164) **|0.218 (0.063) ***|-0.112 (0.201)|
|ln_AGE_|0.943 (0.347) ***|-0.454 (0.340)|-0.445 (0.130) ***|1.634 (0.416) ***|
|ln_AREA_|0.362 (0.193) *|0.202 (0.189)|-0.347 (0.072) ***|0.834 (0.231) ***|
|_dOFFC_|-0.435 (0.243) *|0.320 (0.239)|0.049 (0.091)|-0.470 (0.292) *|
|_dHOSP_|0.272 (0.243)|0.862 (0.238) ***|<br>0.097 (0.091)|0.174 (0.291)|
|_dSCHL_|0.309 (0.155) **|0.639 (0.152) ***|<br>-0.018 (0.058)|0.079 (0.186)|
|_dLIT_|0.436 (0.244) *|0.093 (0.239)|-0.188 (0.091) **|0.362 (0.293)|
|_dAC_|-0.061 (0.668)|-0.920 (0.655)|0.292 (0.250)|-1.761 (0.801) **|
|_dHOTW_|-0.041 (0.191)|-0.031 (0.187)|-0.147 (0.071) **|-0.182 (0.229)|
|_dCGS_|0.578 (0.190) ***|0.931 (0.186) ***|<br>-0.071 (0.071)|0.723 (0.228) ***|
|ln_AWAD_|0.177 (0.036) ***|0.135 (0.035) ***|<br>-0.061 (0.013) ***|0.221 (0.043) ***|
|_dLEAS_|0.054 (0.259)|0.313 (0.254)|-0.242 (0.097) **|-0.357 (0.310)|
|_dMANU_|0.603 (0.179) ***|0.313 (0.175) *|-0.222 (0.067) ***|0.613 (0.215) ***|
|_dENEG_|0.681 (0.213) ***|1.054 (0.209) ***|<br>0.005 (0.080)|0.561 (0.255) **|
|constant|-3.666(3.364)|8.658(3.297)***|<br>7.086(1.259)***|-0.816(4.033)|
|Obs|61||||
|R-square|0.944|0.956|0.951|0.937|
|No. of dummy variables|||||
|Bidder origin prefecture|<br>11|11|11|11|
|Contractyear|7|7|7|7|

Note: A system of four equations is estimated by the three stage least squares. The number of bidders is treated as endogenous. Standard errors are shown in parentheses. *, **, *** indicate the statistical significance at the 10%, 5% and 1%, respectively.
higher education of a father lowers the conditional variance of schooling for both sons and daughters, but the magnitude of the impact is substantially larger for sons (-0.48 for sons, and -0.36 for daughters). The evidence is dierent in Indonesia: there is no signicant dierence across gender.<sup>16</sup> In the online appendix, we discuss the estimates for four subsamples dened by gender and rural/urban location of a child (see Table A.1 in the online appendix section OA.1). The evidence on India suggests that the rural daughters face very dierent educational prospects: the impact of father's education is positive and numerically large for this subgroup, while the eect is negative in the other three subgroups. The nding that the rural daughters are qualitatively dierent from the other three groups also holds in China: there is no signicant impact on conditional variance of rural daughter's schooling, while the eect is negative and signicant in the other three subgroups.

# (4.2) The Evolution of Conditional Variance: Evidence from Decade-wise Birth Cohorts

Table 4 reports the estimates for equations (1) and (2) for decade-wise birth cohorts: 1950-1959, 1960-1969, 1970-1979, 1980-1989. The evidence shows interesting pattern in the evolution of the inuence of family background on conditional variance of children's schooling. If we focus only on the mean eect as is done in the existing literature, the evidence suggests that relative mobility has improved in India and Indonesia over time, while it has worsened in China. However, the impacts on the conditional variance shows a much stronger role of the family background in the recent decades which counteracts the improvements in the mean eects. In all three countries, the inuence of father's education on the conditional variance is negative and substantial in magnitude in the 1980s, suggesting that the children born to educated parents gain in terms of a much lower conditional variance, in additional to a higher conditional mean. There are dramatic dierences in the earlier cohorts across countries: the estimate is negative in China, positive in Indonesia, and a zero eect in India for the 1950s

> 16The standard mean eects (see the IGRC estimates in the odd numbered columns of Table 3) show that the inuence of father's education is much higher for daughters in terms of the rst moment (expected years of schooling) in China. The gender advantages thus are opposite in terms of the mean vs. conditional variance eects. We discuss a simple summary measure of relative mobility that combines these two aspects in section 5 below.

12
energy, rural households lose more than 80 percent of their total consumed energy (i.e., 76.65 kgOE out of 94.3 kgOE), while urban households lose about 55 percent (19.96 kgOE out of 36.2 kgOE). Three-quarters of the bulk of end-use energy consumed by rural households is derived from biomass, used mainly for cooking (and, in some cases, heating). By contrast, the 83 percent of urban households that use mainly modern energy sources benefit from a much wider array of end-use services.

<!-- Start of picture text --> 25 10<br>20<br>8<br>15<br>6<br>10<br>4<br>5<br>2<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Income decile Income decile<br>Total energy End-use energy Total energy End-use energy<br>Energy consumption (kgOE/capita/month)<br>Energy consumption (kgOE/capita/month)<br><!-- End of picture text --> Source: IHDS, 2005. **Figure 2a: Rural household energy-use pattern in India by income decile (per capita measures)** Source: IHDS, 2005. **Figure 2b: Urban household energy-use pattern in India by income decile (per capita measures)** In rural and urban India, the relationship between energy use and income is positive for both total energy and end-use energy consumption (Figures 2a and 2b respectively). In rural areas, energy consumption rises almost monotonically with income. The gap between the two consumption measures remains roughly the same because energy use in rural areas is dominated by biomass. Although the shares of electricity and LPG increase in income deciles 9 and 10, biomass remains the predominant form of household energy use, even for the highest income groups (Figures 3a and 3b).

17