rafmacalaba/datause-encoder-data huggingface dataset:
Promoting Democracy in Fragile States unique equilibrium. Voters who prefer the patron candidate and are pivotal will vote for the patron candidate so as to secure the policy and expressive benefits. Non-pivotal voters who prefer the patron candidate will vote for the patron so as to secure the expressive benefit (and avoid and costs of betraying the patron candidate). The outsider candidate only wins in equilibrium if there is a majority preferring the outsider _and such voters can coordinate_ . Even in such cases, the outsider does not command unanimity: even if those who prefer the patron candidate are not pivotal, they can still vote for the patron candidate to obtain an expressive benefit, knowing that the outsider candidate is not positioned to impose costs for doing so.

This analysis has implications for the types of interventions that could change election behavior. First, at the most basic level, votes against the patron candidate requires that voters view outsider candidates as preferable (note that the same is not true for votes _for_ the patron candidate). Preferences will be a function of the information to which voters have access. Voters may have access only to biased and highly limited information about the choices available to them at the ballot box (Snyder, 2000). Patrons would presumably prevent the inflow of information that may increase accountability (Besley and Burgess, 2002; Bratton et al., 1999; Djankov et al., 2010). In fragile states, poor access to information via the media is due not only to poverty, but also such strategic incentives.<sup>9</sup> Biased and restricted electoral information limits voters’ ability to reflect on their preferences in light of diverse points of view and credible alternatives to a patron’s parochial appeals (Fujiwara nerability to coercion causes electoral outcomes to depart from the policy preferences of the electoral majority.

9For example, BBC Media Action (2012) finds that citizen-oriented media innovations in fragile states are typically initiated by externally supported NGOs or international organizations, despite their wide popularity.

7
categorizing experiments across education fields was straightforward for those experiments related to health and safety, there were some topics that overlapped between experiments in the fields of engineering, science, and technical education with those pertaining to general education. The determining factor to sort these studies in one of these two field, was the use or not of a virtual laboratory. If the training imparted aimed to emulate and/or substitute for a real laboratory, the experiment was included in the field of engineering, science, and technical education – independently of the subject (see Table A1).

**Figure 1: Descriptive statistics of the primary experiments in the meta-analysis.**

<!-- Start of picture text --> Total number of experiments (N=92)<br><!-- End of picture text --> <!-- Start of picture text --> Impacts assessed using RCTs 78<br>Immersive VR 79<br>Basic education (K to 12) 13<br>Grade Level<br>TVET / OJT 29<br>Higher education 50<br>Efficiency 12<br>Socio-emotional 14 Type of<br>skill<br>Technical 29<br>assessed<br>Cognitive 37<br>General education 22<br>Field of<br>Health and safety 35<br>Engineering, science, and TVET 35 study<br>0 10 20 30 40 50 60 70 80 90<br><!-- End of picture text --> _Source_ : Author’s elaboration Developing comparable and fair metrics constitutes a critical aspect to accurately assess the impacts of VR training on learning and skills development. As mentioned above, in order to provide a fair assessment of the available literature, the meta-analysis weights results based on two aspects, notably, intensity of the treatment (i.e. exposure time to VR training) and experiment sample size (Figure 2). These two variables display important variations in the primary studies included in the analysis. While half of the experiments expose students to more than one hour of VR training, 20 experiments report very short VR exposure (less than 15 minutes), while 22 report an exposure that surpasses 5 hours. Moreover, while most experiments included in the analysis are 13
return to capital (π in the weighted average EOCK discussed above) of an average of 11.6 percent over the 2003–2011 period.<sup>9</sup>

**Table 4 Net capital income and gross rate of return to private capital (reproducible** **<u>capital, land, and inventories), 2003–2011</u>**

|Year|Average labor<br>share adjusted<br>for mixed<br>income<br>[million pesos]|Net capital<br>income (NDP<br>− adjusted<br>labor income)<br>[million pesos]|Net capital<br>income over<br>GDP|Private capital<br>+ land +<br>inventories<br>over GDP|Return to<br>private<br>capital|Indirect taxes<br>over private<br>capital + land<br>+ inventories|Gross return<br>to private<br>capital (π)|
|---|---|---|---|---|---|---|---|
|2003|3,375,856|3,103,564|41.1%|4.20|9.8%|0.7%|10.5%|
|2004|3,599,175|3,812,738|44.5%|4.16|10.7%|0.6%|11.3%|
|2005|3,903,296|4,117,474|44.5%|4.15|10.7%|0.6%|11.3%|
|2006|4,169,429|4,889,967|47.1%|4.07|11.6%|0.6%|12.2%|
|2007|4,478,671|5,402,976|47.7%|4.06|11.8%|0.6%|12.4%|
|2008|4,810,099|5,932,086|48.7%|4.12|11.8%|0.7%|12.5%|
|2009|4,909,416|5,220,203|43.7%|4.46|9.8%|0.6%|10.4%|
|2010|5,067,639|6,111,796|46.8%|4.30|10.9%|0.7%|11.5%|
|2011|5,586,190|6,875,168|47.8%|4.20|11.4%|0.7%|12.1%|
|**Average **|||||**10.9%**||**11.6%**|

_Source:_ SHCP; for mixed income: UN and SHCP.

# **_3.2.4 Estimation of the net rates of return on domestic and foreign savings: Analysis of returns in stock and security markets, pension funds, and banking sector reports_**

**To assess the rates of return on savings, we look at real rates of return on government securities, private sector instruments, and pension funds (see Table 5).** There are an increasing number of instruments and institutions in the capital markets in Mexico. To construct a representative series of returns for domestic savers, we estimate the real returns obtained by pension funds in Mexico, as well as the equity returns from the stock market index (MXXD) reported by the stock exchange. We also look at returns on interest rates on long-term government bonds and debt in real terms per year. We use yearly average values for equity returns. We then assess the information from the stock exchange using the Erb, Harvey, and Viskanta (1995, 1996) methodology (see Annex II). Using different reports from Banamex and the SHCP, we used equity to debt ratios of 40/60 to weigh<sup>10</sup> the returns, reflecting the market

> 9 The estimate of the gross return to private capital could be further refined by: (1) adding in the imputed rent on private owner–occupied housing and (2) adjusting the income and capital stocks for the investments in intangible assets. These two adjustments would increase the return on capital, but would be offset by including the full value of private working capital, namely, the cash balances held for transaction purposes and net receivables. 10 The share allocated to debt is taken to include savers’ investments in housing.

18
households using the CPI. Exploiting the variation in the timing of the survey we use the average rate of the monthly CPI increase in the three months prior to the survey interacted with a dummy for net-food consumers (defined as households in which nobody works in agriculture) to calculate the price shock experienced.<sup>6</sup> Previous analysis has shown that there is a five month delay in the response of nominal wages to sudden food price increases (Headey et al., 2012).

In Ethiopia crop yields are very dependent on rainfall and vary considerably by year, and across regions. We take advantage of the fact that in Ethiopia, much analysis has been done by policymakers on variability of rainfall, and crop production. The Livelihoods, Early Assessment and Protection project (LEAP) system, developed in 2006 by the Government of Ethiopia in collaboration with the World Food Programme (Hoefsloot and Calmanti, 2012), uses crop-modeling approaches to estimate the likely rainfall-induced crop loss in woredas throughout Ethiopia based on a water balance model and crop types.<sup>7</sup> The LEAP database contains estimates of the proportion of crop lost as a result of insufficient or poorly timed rainfall for each woreda from 1996 to 2012. These data are merged with the household survey data based on the woreda in which each household resides.<sup>8</sup> Table 1 shows how frequently different shocks are reported in the data, with the most prevalent shocks being adverse food prices for urban areas, and drought and crop damage for rural areas. Food price shocks are reported far more in 2011. The proportion in 2005 reporting the shock was only 2%, but almost 20% in 2011, reflecting much higher monthly increases in the CPI present during the survey period in 2010/11. The crop loss figures from LEAP show the proportion of crops lost in each community, weighted by crop type (where zero means no crop losses suffered). In 2010, few if any households experienced crop losses of more than 30%, whereas in 2005 there were some zones that experienced very high crop losses. In sum, 2011 was a better year for crops than 2005 (and better than a 15 year average) but a worse year for food price inflation (the worst in the last 15 years).

Table 2 shows the mean and standard deviations of the main variables used in the analysis.

# **3 Estimating vulnerability to poverty**

## **3.1 Measuring vulnerability**

Vulnerability to poverty is conceptually distinct from poverty, due to the uncertainty that is incorporated into the analysis (Moser, 1998; Alwang et al., 2001). The focus

> 6Note that this does correlate strongly with the self-reported price shock.

> 7See `http://www.wfp.org/disaster-risk-reduction/leap` , last accessed 28th October 2016. The LEAP model also provides corresponding estimates of the number of households in need of assistance based on a household economy model which can be seen as an alternative to our consumption based approach.

> 8Merging the two datasets together was a complex exercise, and approximately 8,000 observations were lost along the way mainly in pastoral areas.

7
et al. (2013) argue that social transfers may generate counter-cyclical demand for food that may exacerbate the price hikes if those receiving the transfers have a greater propensity to spend on food than those being taxed to fund the transfers.

An important consideration when designing targeted transfers is to ensure such programs provide adequate assistance for negatively affected workers to search for alternative employment opportunities without preventing economy-wide restructuring following permanent price shocks. This implies that the support provided to displaced individuals should ideally not be provided indefinitely and should be tied to the search for alternative employment best suited to their skill set, or to acquiring new skills that increase the probability of finding alternative employment.

Other policies to protect household welfare from adverse price changes tend to be less effective. One example is temporary trade insulation, which may in fact exacerbate international price increases if all countries insulate at the same time. Martin and Anderson (2012) find that for the 2006–08 price surge, insulating policies affecting the rice market explain 45 percent of the increase in the international rice price. In addition, trade insulation may be difficult to reverse, thus preventing the insurance role played by trade in addressing idiosyncratic shocks. Finally, trade insulation appears to work only in the short run to reduce the pass-through from international prices; Ivanic and Martin (2014) show that domestic food prices in developing countries ultimately converged to international prices by 2013 (Figure 6).

**Figure 6: International and domestic food price indices**

<!-- Start of picture text --> 2.1<br>1.9<br>1.7<br>1.5<br>1.3 World Bank Food Price Index<br>1.1 Population‐weighted Food CPI, developed<br>countries<br>0.9<br>Population‐weighted Food CPI, developing<br>countries<br>0.7<br>Jan‐06 Jul‐06 Jan‐07 Jul‐07 Jan‐08 Jul‐08 Jan‐09 Jul‐09 Jan‐10 Jul‐10 Jan‐11 Jul‐11 Jan‐12 Jul‐12<br><!-- End of picture text --> _Source: Ivanic and Martin (2014) based on FAO and OANDA_ Price subsidies represent another policy response to offset the adverse impact of agricultural trade shocks, but these tend to be costly, ineffective and highly distortionary. The above-mentioned evidence from Haiti suggests that price subsidies can have adverse consequences if not properly designed. Attanasio et al. (2013) argue that price subsidies are less effective than targeted transfers 11
total of 43,565 doctors, nurses, and midwives would need to be hired. Although this target is not plausible in the immediate run due to fiscal constraints in each country, it provides a sense of how far these health care systems are from being robustly prepared to face the next health challenges.

Ebola has weakened already fragile systems, and it should be the catalyst to strengthen the systems far beyond their pre-Ebola levels. Our estimates suggest that substantial investment in health systems – and specifically in the health care workforce – is urgently required not only to improve future epidemic preparedness and meet basic needs, but also to limit the secondary health effects of the current epidemic owing to the depletion of the health workforce.

# **References**

1. WHO. _Ebola Situation Report 20 May 2015_ : World Health Organization; 2015.

2. Kieny MP, Evans DB, Schmets G, Kadandale S. Health-system resilience: reflections on the Ebola crisis in western Africa. _Bull World Health Organ_ 2014; **92** :849-924. doi:10.2471/BLT.14.149278 3. Chothia F. Ebola drains already weak West African health systems. _BBC News_ . September 24, 2014. http://www.bbc.com/news/world-africa-29324595. Accessed March 16, 2015.

4. Takahashi S, Metcalf CJE, Ferrari MJ, et al. Reduced vaccination and the risk of measles and other childhood infections post-Ebola. _Science_ 2015; **347** :1240-1242. doi: 10.1126/science.aaa3438 5. UNDP. Ebola Crisis in West Africa. _United Nations Development Programme_ . 2015. http://www.undp.org/content/undp/en/home/ourwork/our-projects-and-initiatives/ebolaresponse-in-west-africa.html. Accessed March 16, 2015.

6. Hayden EC. Ebola's Lasting Legacy. _Nature_ 2015; **519** :24-26. doi:10.1038/519024a 7. World Bank. _The Socio-Economic Impacts of Ebola in Sierra Leone: Results from a High Frequency Cell Phone Survey_ . Washington DC: World Bank; 2015.

8. WHO. _Health worker Ebola infections in Guinea, Liberia and Sierra Leone: A preliminary report_ : World Health Organization; 2015.

-

- 9. World Bank. World Development Indicators. 2015. <u>http://data.worldbank.org/data catalog/world-development-indicators. Accessed March 1, 2015.</u>

10. WHO. Global Health Observatory Data Repository. <u>http://apps.who.int/gho/data/node.main.A1444. Accessed February 25, 2015.</u> 11. Castillo-Laborde C. Human resources for health and burden of disease: an econometric approach. _Hum Resour Health_ 2011; **9** (4). doi:10.1186/1478-4491-9-4 12
Policy Research Working Paper 6996

# **Abstract**

The paper examines the levels and trends in access to education and educational outcomes across generations of Egyptian youth. Examination of three cohorts of individuals aged 21 to 24 (born between 1964 and 1967, 1974 and 1977, and 1982 and 1985) shows that access to education has substantially improved during the last three decades. Completion rates increased by more than 60 percent at the preparatory level and 70 percent at the secondary level and the college completion rate more than doubled. However, significant inequities remain in access to education and educational outcomes. The fraction of never enrolled among the cohorts is still large, affecting more girls than boys, more rural than urban areas, and more children of parents with lower level of education and in elementary occupations, such as subsistence agriculture. The analysis of test-scores from the Trends in International Mathematics and Science Study (TIMSS) and national examinations shows that more than a quarter of learning outcome inequality is attributable to circumstances beyond the control of a student, such as parental education, socioeconomic background and place of birth. In Egypt, inequality of opportunity in learning outcomes emerges early and builds up progressively throughout the education levels. Access to higher education continues to remain significantly lower for children from rural areas and for those whose parents have a low level of education or are engaged in elementary occupations. Tracking into vocational and general secondary schools, which depends on a high-stakes national examination, and high and unequal levels of household expenditures in private tutoring substantially contribute to unequal learning outcomes.

This paper is a product of the Social Protection and Labor Global Practice Group. 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 lersado@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
mechanism here.

Air pollution affects labor supply decisions in the short-run primarily by increased absenteeism at work due to sickness.<sup>5</sup> The health effects of PM2 _._ 5 exposure are well documented (WHO, 2013; McDuffie et al., 2021; Pandey et al., 2021). They are due to exposure over both the short term (hours, days) and long term (months, years) and include respiratory and cardiovascular morbidity, such as aggravation of asthma, respiratory symptoms and an increase in hospital admissions. Many studies find evidence of reduction in working hours due to increased pollution levels (e.g. Hanna and Oliva (2015); Hansen and Selte (2000)). In Lima, the capital city of Peru, Arag ́on et al. (2017) find that the impact is significantly larger at higher levels of pollution but is concentrated among households with susceptible dependents (i.e. small children and elderly adults) at moderate levels of pollution. This is consistent with evidence from the United States that finds exposure to air pollution increases student absenteeism by increasing cases of acute asthma (Komisarow and Pakhtigian, 2022). To the extent that workers have care-giving responsibilities for school children or the elderly, increases in acute incidents among these populations may translate into reductions in labor supply among the working age population.

Pollution may also impact medium-run labor supply decisions by inducing migration. Chen et al. (2017) suggest that air pollution is responsible for large changes in inflows and outflows of migration in China, and the reduction of the overall population through net outmigration by 5 percent in a given county. Khanna et al. (2021) find that pollution in China has larger effects on highly skilled workers, who are more likely to emigrate, generating large productivity impacts. Gao et al. (2023) find that migration decisions are highly responsive to an unexpected disclosure of pollution information. Pan (2023) finds that families relocate to cleaner counties after adults receive asthma diagnoses. If air pollution induces workers to migrate away from high productivity areas to lower productivity areas even internal migration may have impacts on GDP.

Air pollution not only impacts labor supply but can also impact workers’ performance at the workplace. A substantial literature provides evidence of impaired cognitive and physical functions due to increased PM2 _._ 5 levels (Aguilar-Gomez et al., 2022). Chang et al. (2016) study the effect of outdoor air pollution on the productivity of indoor workers at a pearpacking factory in Northern California. They find that a 10-unit change in PM2 _._ 5 decreases worker productivity by roughly 6 percent at pollution levels well below current US air quality standards. Adhvaryu et al. (2019) study the effects of PM2 _._ 5, measured at the hourly level

> 5In the long-run air pollution can reduce labor supply by changing the size of the workforce either through increased deaths (Pandey et al., 2021), reduced live births (McDuffie et al., 2021), or increased net out-migration (Chen et al., 2022; Khanna et al., 2021)

5
# **4. Conclusions**

Using the world-wide World Bank Enterprise Survey, we have examined how femaleheaded firms compare with male-headed firms in productivity level and growth, and how the cultural, institutional and business environments shape the female leadership effect. We find that conditional on a rich set of controls, female-headed firms robustly exhibit a lower productivity level by around 15 percentage points, and labor productivity growth by around 2 percentage points. The productivity level and growth disadvantages are significantly larger in manufacturing than in services—in fact, there is no productivity growth disadvantage at all in service sectors. In addition, the female leadership disadvantage is much more pronounced in SMEs, but non-existent in non-SMEs. We also find that female leadership disadvantage is larger where (i) personal networks are more important, as in the case of higher local usage of trade credit, higher local use of bank finance, a worse rule of law, and lower local internet penetration; and (ii) where competition is more severe, as in the case of higher local informal competition, and stronger national individualistic culture. Contrary to the literature, we do not find that female business leaders suffer more disadvantage where corruption is more severe. The findings provide confirmation of some of the lab and field experiment results on gender—such as women’s difficulty with or indifference to competition—and other evidence on personal network access, and they also show a lack of support for the notion that corruption hurts women more than men. The lack of female business leadership interaction with corruption could show the selection effects of female business leaders from women in general.

We also pay particular attention to African countries. In contrast to the world average, African countries overall have little female disadvantage on productivity performance. But in manufacturing sectors, the female disadvantage in productivity level and growth is more pronounced than the world average, while in service sectors there is _advantage_ in productivity growth. Female leadership disadvantage in productivity performance is more amplified where local access to bank finance is greater, and where local electricity outage is more frequent.

23