rafmacalaba/datause-encoder-data huggingface dataset:
Capital flows to developing countries are positive, small, and mostly take the form of lending instead of foreign investment. The bold line in Figure 1 shows the combined net foreign asset position of 47 non-OECD countries as a share of their combined wealth from 1970 to 1997. Throughout this period these countries have been net recipients of capital. Their net capital imports average about 10 percent of their wealth, fluctuating moderately between 8 and 12 percent. Figure 1 also shows that loans are the preferred asset to finance capital flows to poor countries. Roughly three-quarters of net capital flows take the form of net lending, with the remainder consisting primarily of foreign investment in poor countries. Foreign investment by poor countries in rich countries is negligible. Appendix 1 describes the data and provides a number of robustness checks regarding these basic facts.<sup>1</sup> It seems safe to argue that a theory of North-South capital flows requires at least two ingredients to be able to explain these empirical observations. The first one would be one or more incentives for capital to flow from rich to poor countries. Natural candidates for this role are diminishing returns at the country level and/or countryspecific production risk. If either of these two forces are present, the risk-adjusted rate of return to capital declines as more capital is invested in a country, creating an incentive to invest in countries that have little capital. In the absence of a countervailing force, this incentive would only be eliminated if capital stocks per person were equalized across countries. Since the set of developing countries considered in Figure 1 has about four fifths of the world population and owns about one fifth of the world’s wealth, equalization of capital stocks per person would require a net foreign asset position of –300 percent of their wealth! The theory clearly needs a second ingredient to explain why capital flows are so small.

A popular view is that the theory just needs to recognize that rich countries have better technologies and human capital, and this is why investors keep most of their capital in rich countries even in the presence of diminishing returns and

> 1 Throughout the paper we use the data of Kraay, Loayza, Servén and Ventura [2000]. See Sinn [1990], Rider [1994] and Lane and Milesi-Ferretti [2001] for alternative sources of data.

1
# **Dynamics of Off-Farm Employment in Sub-Saharan Africa: A Gender Perspective**

## **Goedele Van den Broeck**<sup>***†**</sup> **and Talip Kilic**<sup>**‡1**</sup>

**JEL Codes:** J16; J21; O12.

**Keywords:** Off-farm employment, wage employment, non-farm self-employment, labor mobility, gender, youth, Ethiopia, Malawi, Nigeria, Tanzania, Uganda, Sub-Saharan Africa.

> 1*Corresponding author. †Post-Doctoral Researcher, Division of Bioeconomics, Department of Earth and Environmental Sciences, KU Leuven, Leuven, Belgium, goedele.vandenbroeck@kuleuven.be. ‡Senior Economist, Living Standards Measurement Study (LSMS), Survey Unit, Development Data Group, The World Bank, Rome, Italy, <u>tkilic@worldbank.org.</u> The authors thank (in alphabetical order) Aslihan Arslan, Calogero Carletto, Eva Egger, Kashi Kafle, Neha Paliwal, Athur Mabiso, Amparo Palacios Lopez, and Alberto Zezza for their comments during the presentations at the International Fund for Agricultural Development and the World Bank.
9. If there is no reconciliation and negotiations become stuck in stalemate, this is what may very well happen in South Africa.

10. For a good example, see the discussion of the Indonesian transmigration program in Kebschull (1986) and Fasbender and Erbe.

11. See Wakjira (1977) for the extreme example of an over-zealous pace of implementation in Ethiopia.

12. The prohibition may also have been relaxed beca. it was difficult or impossible to enforce. Adams (1991), for example, reports that on one resettlement scheme 48 percent of women engaged in off-plot employment at a time when the prohibition was still intended to be in effect.

13. See, for example, the later studies by Clough (1968) and von Haugwitz (1972) which are largely based on the farm management survey work done in the mid-1960s.

-33-
Countries vary systematically on political, economic, and cultural characteristics, all of which could impact individual levels of support.

To begin, at the national level, a country’s existing level of foreign aid could impact citizen opinion on foreign aid. On the one hand, citizens in countries with high existing foreign aid allocations may express more support for foreign aid in general. On the other, when considering marginal aid (the question of whether to increase foreign aid, as asked by Gallup International) citizens in countries with high levels of existing aid may express _less_ support than citizens from other countries. Relatedly, military spending may affect attitudes toward aid in donor countries. Definitions of foreign aid often couple military and economic spending.<sup>4</sup> Where military spending is high citizens may feel that their country is fulfilling its global citizenship duties through enhancing security. While this logic is very likely to apply in the United States, it is also likely to matter in France, the U.K. and even Canada, which specializes in international peacekeeping.

Country population may matter as a country-level variant of the global awareness variables. In smaller nations, a larger share of those one can interact with are foreigners, and the greater one’s awareness of foreign affairs. In contrast, in countries with larger populations, citizens are able to assist “far-off” disadvantaged people without resources ever crossing national boundaries. As an example, even if Americans know as much about the way people live 500 miles away as an Austrian or Spaniard does, the people 500 miles away are still Americans, not Hungarians or Moroccans or Portuguese. The size of a country – measured by population and area – almost inevitably makes larger countries more provincial.

Similarly, citizens in donor countries with experience as a colonial power may have greater awareness about development issues in aid recipients. A history as a colonial power indicates sustained contact with a developing country. Citizens of colonial powers are likely to be better informed about events in the developing world, understand development issues facing other countries, and be aware of inequality between colonizers and the colonized. These citizens may also feel a greater sense of responsibility for the welfare of people in ex-colonies and the rest of the developing world. Citizens of colonial powers are therefore hypothesized to be more likely to

> 4 The standard data source on aid (the OECD DAC) excludes military aid.

7
# 7. References

- Alderman, Harold, Ugo Gentilini, and Ruslan Yemtsov. 2018. _The 1.5 Billion People Question._ Washington DC: The World Bank.

Amemiya, Takeshi. 1977. "The Maximum Likelihood and the Nonlinear Three-Stage Least Squares Estimator in the General Nonlinear Simultaneous Equation Model." _Econometrica_ 45 (4): 955– 968.

- Augurzky, Boris, and Christoph M. Schmidt. 2001. "The Propensity Score: A Means to An End." IZA Discussion Paper No. 271.

- Balasubramanian, Sujata. 2015. "Is the PDS already a cash transfer? Rethinking India’s food subsidy policies." _The Journal of Development Studies_ 51 (6): 642--659.

Ballard, Terri, Jennifer Coates, Anne Swindale, and Megan Deitchler. 2011. _Household Hunger Scale: Indicator Definition and Measurement Guide._ Washington, DC: Food and Nutrition Technical Assistance III Project (FANTA).

Barrett, Christopher B., and Mark A. Constas. 2014. "Toward a theory of resilience for international development applications." _Proceedings of the National Academy of Sciences of the United States of America_ 111 (40): 14625-14630. Accessed 9 4, 2019.

https://pnas.org/content/pnas/111/40/14625.full.pdf.

Black, Dan A, and Jeffrey A Smith. 2004. "How robust is the evidence on the effects of college quality? Evidence from matching." _Journal of Econometrics_ 121 (1-2): 99-124.

Blattman, Chris, Michael Faye, Dean Karlan, Paul Niehaus, and Chris Udry. 2017. "Cash as Capital." _Stanford Social Innovation Review._ (Summer).

https://ssir.org/articles/entry/cash_as_capital#bio-footer.

Bryson, Alex, Richard Dorsett, and Susan Purdon. 2002. "The use of propensity score matching in the evaluation of active labour market policies." _Department for Work and Pensions_ (Department for Work and Pensions) Working Paper No. 4.

Caliendo, Marco, and Sabine Kopeinig. 2005. "Some Practical Guidance for the Implementation of Propensity Score Matching." _IZA Discussion Papers_ No. 1588.

- Chaudhuri, Shubham. 2003. "Assessing vulnerability to poverty: concepts, empirical methods and illustrative examples." New York City: Department of Economics, Columbia University.

Cissé, Jennifer Denno, and Christopher B. Barrett. 2018. "Estimating development resilience: A conditional moments-based approach." _Journal of Development Economics_ 135: 272-284. Accessed 9 4, 2019. https://sciencedirect.com/science/article/pii/s0304387818303511.

Currie, Janet, and Firouz Gahvari. 2007. "Transfers in Cash and In-Kind: Theory Meets the Data." _Journal of Economic Literature_ 46 (2): 333-383. Accessed 10 16, 2019. https://aeaweb.org/articles?id=10.1257/jel.46.2.333.

19
monetized to represent a percentage change in household consumption. The methodology is presented in Fuchs and Meneses (2017, 2018). For medical costs, the exercise distributes the total cost directly attributed to tobacco illnesses across deciles based on the share of tobacco consumers in each consumption decile. To estimate the increase in working years, the years of life lost because of smoking are distributed across deciles proportionately to the number of households that smoke tobacco. Subsequently, the income gain is approximated by increasing consumption proportionally to the number of working years gained and averaged across deciles.

**Table 6. Simulations Adding Savings in Medical Costs and Increases in Income Because of More Working Years**

_Percentage change in consumption, increase of 25% in the price of cigarettes_

|||_Medium elas_<br>_S_|_ticity_<br>_Gains due to_<br>|||_High elast_<br>_S_|_icity_<br>_G d_||
|---|---|---|---|---|---|---|---|---|
|||_avings in_|_more_|||_avings in_|_ains ue to_||
|_Decile_|_Consumption_<br>_effect_|<br>_medical_<br>_costs_|_working_<br>_days _|_Total_<br>_effect_|_Consumption_<br>_effect_|<br>_medical_<br>_costs_|_more working_<br>_days _|_Total_<br>_effect_|
|1|−0.2|0.5|2.7|3.0|0.2|0.8|5.0|5.9|
|2|−0.2|0.4|3.8|3.9|0.2|0.6|6.7|7.6|
|3|−0.2|0.3|4.2|4.3|0.2|0.6|7.4|8.2|
|4|−0.2|0.2|3.1|3.2|0.2|0.5|7.8|8.5|
|5|−0.2|0.1|2.8|2.8|0.2|0.5|9.2|9.9|
|6|−0.1|0.1|2.7|2.6|0.2|0.4|8.4|9.0|
|7|−0.1|0.1|2.7|2.6|0.2|0.3|8.6|9.1|
|8|−0.1|0.1|2.8|2.7|0.2|0.3|9.7|10.1|
|9|−0.1|0.1|2.7|2.6|0.2|0.2|9.3|9.7|
|10|−0.1|0.0|2.5|2.4|0.1|0.1|8.5|8.8|

The results show that, compared with the direct consumption effects, the gains in health associated with reduced cigarette consumption in response to a price increase are substantially larger in magnitude. The main gains arise from the increased years of life, which translate into more years working. In a scenario of a 25 percent price increase and medium elasticity, these gains are about 3 percent of consumption. The highest direct consumption losses occur in a scenario with high response and a 75 percent price increase. The increased number of productive years because of a 25 percent rise in the price of cigarettes is on the order of 20 times larger than the direct consumption impacts in a medium-response scenario and 45 times larger in a higher-response scenario. These results are useful in highlighting that the short-term losses because of tobacco taxes are much smaller in order of magnitude than the economic benefits because of better health.

Yet, these estimates must be interpreted with care because they are subject to several caveats. First, the estimate of medical costs was calculated 14 years ago and therefore may not reflect the current situation. Second, the estimates are based on a series of assumptions about access to medical care by the population (for example, that 25 percent of people experiencing a disease caused by tobacco would 16
response rate was quite high with only 87 of the health workers or 6.8 percent refusing to respond. The health workers agreed to disclose their monthly informal payments intake probably partly because it is a widespread common knowledge phenomenon and also because of the sheer lack of accountability in the sector.<sup>11</sup> It is unlikely that the omission of the handful of workers who refused to respond introduce a selection bias. However, we prefer to check whether they differ in some systematic way from the rest of the sample using the large set of variables collected during the survey. We regress each observable on a dummy variable which equals one if the informal payment data is missing. The results are given in table 7 for the full sample, and the sample without the Gorno-Badakhshan Autonomous Oblast (GBAO) region. It is mostly the age-related variables that stand out significant in the regressions. The non-responders are younger, less experienced, and less likely to be married and have children. They are also more likely to work in GBAO. However, as we will see later the results are robust to the exclusion of GBAO. These minor differences do not question the representativeness of our sample.

The paper uses two measures of corruption. First we consider the average monthly intake of informal payments an individual health worker reports. Informal payments are very hard to measure from household surveys because the general public has a hard time distinguishing the legitimate fees from illegal ones. An advantage of the procedure used by the survey is that health workers’ knowledge in that regard is perfect. They clearly know what is formal from what is not and therefore we can be confident that we are only capturing informal payments intended for the personal use of the staff that collects them. Second, we ask the health workers whether they supply labor outside the facility and the average number of hours they provide on a weekly basis. Moonlighting is clearly a strong determinant of absenteeism and one can legitimately think that health workers are in fact “stealing” public money if it displaces facility work.

> circumstances people respond to such direct questions but this is out of the scope of this paper and is left for future research.

> 11 We found out after the data collection effort that Miller et al. (2000) carried out a qualitative survey with a similar (in spirit) question in four countries (Ukraine, Bulgaria, Slovakia, and Czech Republic). They use data from 1,307 interviews with various public officials such as health workers (292 in the sample), teachers, police officers, customs officials, passport officials, court officials, etc. They found that 89 percent of health workers were willing to confess accepting “money or expensive gifts” from patients.

10
containment measures over time and across countries. While most countries experienced an initial shock that restricted mobility between March and April of 2020, some countries, like the Philippines and Indonesia, experienced more prolonged reductions in mobility with little recovery over time (Figure 1a). Other countries such as Myanmar and Mongolia showed signs of recovery in mobility in May 2020, soon after the initial lockdown, but later in the year faced sharp declines in mobility due to a renewed surge in COVID-19 cases. Finally, countries like Lao PDR, PNG, and Vietnam experienced relative normalcy in mobility during 2020 after the initial shock, reflecting comparatively low local transmission of COVID-19 until 2021.

Multiple rounds of the HFPS have helped capture the socioeconomic and distributional impacts of the pandemic at varying periods of each country’s experience during the pandemic. For example, in Indonesia and the Philippines, the first survey round occurred at a time when mobility was on average more than 40 percent lower than baseline levels, while the subsequent round with employment and income data occurred at a time of relative improvement (Figure 1b). On the other hand, the first round of the HFPS in Mongolia and Myanmar was implemented at a period of comparative recovery, while later rounds (rounds 3 and 4 for Mongolia, round 4 for Myanmar) took place during a time of lockdown in each country.<sup>13</sup> While mobility varied relatively less across rounds in Lao PDR, PNG, and Vietnam, it was still below pre-pandemic levels in Lao PDR and Vietnam during the included survey rounds.

Figure 1a. Some countries experienced more severe containment measures that restricted mobility Percent change in mobility compared to pre-pandemic baseline Figure 1b. Survey timing and frequency differed across countries HFPS field dates and mobility index

> project - but found that in some countries, there was a significant disconnect between restrictions imposed and actual movement of people as indicated by the mobility data (in some countries, measures were stringent but mobility during the same period varied significantly), particularly as the pandemic progressed. The variation in work stoppages and earnings was more closely related to mobility than stringency.

> 13 In Mongolia’s round 4 survey, the reference period for employment was not the week preceding the survey but April 5-11, 2021, before the lockdown occurred.

9
environmental and health risks of nuclear energy. This tendency was reinforced by fading memories of the Three Mile Island and Chernobyl accidents. Moreover, dramatic increases in fossil fuel prices prior to the current global economic contraction and the imperative to cut greenhouse gas emissions came to the fore of public concerns and debates, likely contributed to more positive attitudes toward nuclear power . Between 2005 and 2008, the percentage of Europeans favoring nuclear power increased from 37 to 44 percent, while the share of those opposed to it declined from 55 to 45 percent (EU, 2008). Recent international polls sponsored by the nuclear industry showed substantial support for nuclear power. A survey of more than 10,000 people in 20 countries found that more than two-thirds of the respondents believed that their countries should begin using or increase the use of nuclear energy (NEI, 2009). In the United States, those in favor moved from 49 percent in 1983, when the question was first asked, to 74 percent in 2010 (figure 2). Moreover, by 2010, those who ―strongly favor‖ nuclear energy outnumbered those who are ―strongly opposed‖ by more than three to one (NEI, 2010).

**Figure 2 Changing public attitudes toward nuclear power in the United States, 1983–2010**

<!-- Start of picture text --> 80<br>74%<br>Favor<br>60<br>40<br>Oppose<br>20<br>23%<br>0<br>1983 1985 1987 1989 1991 1993 1995 1998 2000 2002 2004 2006 2008 Mar-10<br><!-- End of picture text --> _Source_ (NEI, 2010).

The extraordinary events in Fukushima quickly and fundamentally altered the nature of the nuclear debate.<sup>2</sup> Once again, the future of nuclear power is clouded in uncertainty. These

> 2 The severity of the nuclear event at Fukushima Daiichi has been rated 7 on the International Nuclear and Radiological Event Scale (INES), the highest level and the same as the 1986 Chernobyl nuclear power plant

3
Policy Research Working Paper 10197

# **Abstract**

Using data from 41,873 individuals across 17 African countries and 13 studies, this paper maps data from various self-reported scales to 10 socio-emotional skills and examine gender differences in these skills and their relationship with education and earnings. Apart from self-control, the findings show a significant male advantage in self-reported skills—men have an aggregate socio-emotional skill level 0.151 standard deviations higher than women, equivalent to the socio-emotional skill gained over 5.6 years of education. This is robust to controlling for positive self-concept. Closing the gender gap in education would close 17percent of this gap. While overall socio-emotional skill and education are positively correlated for both men and women, women do not have a positive correlation with education for some individual socio-emotional skills. The male advantage in socio-emotional skills increases at higher education levels. Socio-emotional skills are associated with higher earnings, especially for women. However, the specific skills associated with higher earnings differ by gender. Interpersonal skills are more strongly correlated with earnings for women than for men, and measures of these skills are often underrepresented, which indicates a key direction for future research. The paper further examines differences in the relationship between socio-emotional skills and earnings by levels of education and occupation. It discusses the implications of these results for interventions seeking to hone women’s socio-emotional skills for labor market success and to address the gender norms that may perpetuate gaps in socio-emotional skills.

This paper is a product of the Gender Innovation Lab, 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://www.worldbank.org/prwp. The authors may be contacted at cdelavallade@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