rafmacalaba/data-use-mentions-extended huggingface dataset:
# **INTRODUCTION**

While it is commonly believed that labor market returns to education are highest for the primary level of education and lower for subsequent levels, in Colclough, Kingdon and Patrinos (2010), we found evidence to suggest that the pattern is changing. The causes of such changes, and their implications for both education and labor market policy, are significant. The possible reasons put forward for this include: (1) technological change favoring higher order skills; (2) increasing coverage at lower levels of schooling; and (3) the quality of schooling. Of course, it could be a combination of all three factors. The issue is revisited here.

The pattern of economic rates of return to investments in education can help us to understand the benefits of schooling. The pattern of the returns to education are important for students and their families making important and costly schooling decisions. They are also important signals for policy makers. Since the 1980s, the returns to schooling overall have increased (Psacharopoulos and Patrinos 2018). More recently, the returns to higher education have increased the most (Montenegro and Patrinos 2014).

A review of current patterns of the returns to schooling by level and region is presented, along with a discussion of the possible causes of changes and the subsequent policy implications. Among other aspects, the education investment patterns over time are assessed. The analysis is based on estimates of the returns to schooling from more than 140 countries and more than 1,000 estimates from 1980 to 2014. The data come from countries all over the world, representing more than 90 percent of the world’s population, and are disaggregated by gender.

The fact that the more educated have improved their position despite an increase in their numbers must mean that the demand for more educated workers has increased more than supply over time, causing an increase in the overall returns to schooling.

# **LINK BETWEEN EDUCATION AND EARNINGS**

The origins of scientific interest in the contribution of schooling to
Policy Research Working Paper 10243

# **Abstract**

The Sudanese economy has faced several shocks over the years—sometimes resulting in devastating impacts on the economy and the welfare of Sudanese households. Poor households are often particularly vulnerable to shocks. The extent of the impacts of shocks on household welfare depends on the nature and severity of the shocks as well as households’ capacity to manage its risk of exposure to shocks ex ante and/or mitigate the impact of shocks ex post. This paper applies this framework to examine the impact of shocks on the welfare of Sudanese households and explore coping strategies typically utilized by households to mitigate the negative effects of shocks. The paper uses the 2009 National Baseline Household Survey (NBHS) and the 2014/15 National Household Budget and Poverty Survey (NHBPS) to document the main types of shocks that Sudanese households are exposed to and describe the profile of Sudanese households likely to be vulnerable and/ or resilient to shocks. To complement this analysis, the paper uses the most recent round of the data collected in 2014/15 (containing information on idiosyncratic shocks) together with data on covariate shocks such as rainfall and conflict obtained from other sources to estimate the impact of shocks on household welfare. Since the impact of shocks on household welfare is likely to be multidimensional, various indicators of household welfare such as consumption, poverty status, assets, dietary quality, and diversity are considered in the paper. Results from the analysis are used to highlight the state of social protection in Sudan and discuss the need for an expansion of the existing system. The prevalence of shocks in Sudan is most common among poor, agricultural, and rural households. Floods/droughts have the largest negative effect on the welfare of Sudanese households. The large negative effects of shocks on the welfare of Sudanese households (particularly those with low capacity to cope with shocks) highlight significant limitations in households’ ability to fully mitigate the impact of shocks.

This paper is a product of the Poverty and Equity Global Practice.
. For example, Zheng et al. (2020) and Adams-Prassl et al. (2020) estimated the degree to which people can work from home during the pandemic to mitigate workplace closures or capacity limits. Other studies relied on occupation-level data from O*NET (Dingel & Neiman, 2020; Avdiu & Nayyar, 2020) or workers’ task content information from pre-COVID labor force and skills surveys (Hatayama et al., 2020; Gottlieb et al., 2020; 5
with an average income per capita, life expectancy, and infant mortality similar to those of the continent as a whole (Appendix Table S2).

# **_Sampling_**

All country data sets were representative at the national level except for Nigeria, which, mainly owing to security concerns, covered 12 of 37 states and was representative at the state level. The surveys used a multistage cluster-sampling design to ensure data were representative of rural and urban areas, private and public facilities, and of each facility type (e.g., primary care clinics versus secondary care hospitals).

The surveys collected a broad set of facility and provider specific information, using standardized instruments, with an approach that relied on clinical vignettes, direct physical verification of provider presence through unannounced visits, and visual inspections of the equipment and facilities. Every interview respondent was asked for their consent to participate in the survey. Sample instruments are publicly available.<sup>18–22</sup> In total, the sample includes information from 89,826 vignettes from 13,754 providers and absenteeism observations from 22,747 providers across 8,061 facilities in 10 countries. In each country, the sample size was selected in order to provide representative data for the country and for sub-groups (e.g., rural versus urban areas).

# **_Empirical methods_**

There are three essential conditions for health workers to provide quality care. First, providers have sufficient knowledge to diagnose and treat patients suffering from conditions that are amenable to care. Second, they are available to provide care when patients seek service. Third, providers have the means (e.g., equipment and drugs) to diagnose and treat patients. We operationalize the assessment of these necessary, but not sufficient, conditions for quality primary care using three methods. Diagnostic and treatment accuracy (knowledge) were 5
# **IV.2.  Results**

The Tobit estimation on panel data is defined as follows.

_VRSTEit_<sup>=</sup> _f_ ( _WAGEit_ , _GOVEXPit_ , _PUBTOTit_ , _GDPPCit_ , _URBAN it_ , _AIDSit_ , _GINI it_ , _EXTAIDit_ , _INSTit_ , _CONS_ ) where _VRSTEit_ = Variable returns to scale DEA efficiency score for single output and multiple output cases.

- _WAGEit_ = Wages and salaries (% of total public expenditure)

- _GOVEXPit_ = Total government expenditure (% of GDP)

- _PUBTOTit_ = Share of expenditures publicly financed (public/total)

- _GDPPCit_ = GDP per capita in constant 1995 US dollars

- _URBAN it_ = Urban population (% of total)

- _AIDSit_ = Dummy variable for HIV/AIDS

- _GINI it_ = Gini Coefficient

- _EXTAID  = it_ External aid (% of fiscal revenue)

- _INSTit_ = Institutional indicators including ICRG country risk, World

- Governance Research Indicators (Corruption Control), or a dummy for state failures from the State Failure Task Force database.

- _CONS_ = Constant

Tables 13 and 14 report the results for the single-input single-output case and the multiple-input multiple-output case, respectively. The more interesting findings are:

- a. We find that countries with larger expenditure levels also register the more inefficient scores.  This result is robust to changes in the output indicator selected, to considering health or education, and to adopting either the single –output or multiple output frameworks. The trade-off between size of expenditure and efficiency is quite robust.

- b. Countries in which the wage bill represents a higher fraction of total expenditure tend to be more inefficient.  This result does not hold for health in the multiple output framework. This difference could be due partly to the relatively decreasing number of health care professionals in the world, especially in the poorer countries (Liese, et.al., 2003).  Further investigation would be required to examine why this is not the case in education.

- c. Countries in which public financing is a larger share of total expenditure
payments, we summed the total payments made to get a comparable out‐of‐pocket expenditure measure. The median price paid was CDF 1,500, or approximately US$1.60, while the average price was CDF 2,373, or US$2.58.<sup>10</sup> Nine percent of the women received antenatal care without any out‐of‐pocket expenditure.

# 4. Wealth‐Quality Gradient

We start our investigation of the inequality in antenatal care in the DRC by comparing inequality in coverage with inequality in effective coverage, using concentration indexes (Wagstaff, Paci, and Van Doorslaer 1991). Using the household survey data, we define antenatal care to be “effective” if essential services were provided. Specifically, we consider a visit to be complete if the following services were provided: (1) blood pressure was measured; (2) blood and urine samples were taken; (3) the woman was informed about pregnancy complications; and (4) the woman was given iron supplementation.<sup>11</sup> If any of these services was not recalled, the antenatal care is defined to be ineffective. Although 82 percent of the women reported receiving antenatal care during their most recent pregnancy, only 11 percent reported receiving effective care. We calculate the concentration indexes with the recently pregnant women ranked by household wealth.

As shown in table 1, the indexes for coverage and effective coverage are significant at the 99 percent level. However, the concentration index of effective coverage, 0.179, is more than three times the concentration index in coverage, at 0.050, and significantly larger than the concentration index of 0.051 calculated for effective coverage of antenatal care in Kenya by Nguhiu, Barasa, and Chuma (2017).<sup>12</sup> Restricting our analysis to the sample of women who received any antenatal care, we proceed by comparing in more detail the quality of care received by women
<mark>16.1. All cleaning, processing, and analysis was conducted by the research team and the code is available</mark><sup>_29_</sup> <mark>.</mark>

**<mark>Food Insecurity Experience Scale:</mark>** <mark>The estimates of prevalence of (i) moderate or severe and (ii) severe food insecurity among adult individuals are based on the eight-question Food Insecurity Experience Scale (FIES), which was included as a module in the high-frequency phone survey. The FIES is an experience-based metric of food insecurity severity, which relies on people’s direct responses to questions about their experiences with access to adequate food. This metric makes it possible to compare prevalence rates of food security across national and sub-national populations</mark><sup>_30_</sup> <mark>. Widely used around the world, FIES is an appropriate measure for assessing food security as it is a direct measure of food insecurity which produces comparable estimates of food insecurity experienced by people in different contexts.</mark>

<mark>In the high-frequency phone surveys that inform our analyses, the FIES questions had a reference period of the last 30 days. Following the FIES standard survey model, eight questions were asked, aimed to capture whether the respondent or other adult households members: (i) were worried they would not have enough to eat, (ii) were unable to eat healthy and nutritious food, (iii) ate only a few kinds of foods, (iv) had to skip a meal, (v) ate less than they thought they should, (vi) ran out of food, (vii) were hungry but did not eat, and (viii) went without eating for a whole day.</mark> <mark>The approach to process and analyze FIES data comes from Item Response Theory</mark><sup>_31_</sup> <
Tanzania. _Unpublished manuscript:_ https://editorialexpress.com/cgibin/conference/download.cgi?db_name=CSAE2016&paper_id=1018.

- Canning, David, Sangeeta Raja, and Abdo S Yazbeck. 2015. Africa's Demographic Transition: Dividend Or Disaster?

- Chay, Kenneth Y and Michael Greenstone. 2003. The Impact of Air Pollution on Infant Mortality: Evidence from Geographic Variation in Pollution Shocks Induced by a Recession. _Quarterly Journal of Economics_ 118 (3): 1121-1167.

- Chetty, Raj, Nathaniel Hendren, and Lawrence F Katz. 2016. The effects of exposure to better neighborhoods on children: New evidence from the Moving to Opportunity experiment. _Am Econ Rev_ 106 (4): 855-902.

- Currie, Janet and Douglas Almond. 2011. Human capital development before age five. _Handbook of Labor Economics_ 4:1315-1486.

- Dahl, Gordon B and Lance Lochner. 2012. The impact of family income on child achievement: Evidence from the earned income tax credit. _Am Econ Rev_ 102 (5): 1927-1956.

- Duflo, Esther. 2012. Women Empowerment and Economic Development. _Journal of Economic Literature_ 50 (4): 1051-1079.

- Duflo, Esther, Pascaline Dupas, and Michael Kremer. 2015. Education, HIV, and Early Fertility: Experimental Evidence from Kenya. _Am Econ Rev_ 105 (9): 2757-97.

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- Filmer, Deon and Norbert Schady. 2014. The Medium-Term Effects of Scholarships in a LowIncome Country. _Journal of Human Resources_ 49 (3): 663-694.

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http
Table 16 uses FI index and FI effective index as dependent variables in Tobit regressions. Countries with lower income per capita levels, after controlling for deposit and loan penetration, have more topics under the mandate of the regulators. This result also holds for countries with a positive economic outlook. The existence of explicit deposit insurance is negatively related to both indices, and loan penetration is positively related to the FI effective index. Hence, poorer countries seem to have a more loaded financial inclusion agenda.

The extent of reforms, measured by a similar index constructed using the ten areas of financial inclusion reforms surveyed, is also negatively related to GDP per capita, and not related to the degree of deposit or loan penetration. However, once the items in the financial inclusion agenda are controlled for using the two indices described above, the effect of GDP per capita is no longer observed. Countries that have a more loaded financial inclusion agenda seem to have a higher number of reforms in 2009 (Table 17), although it is not possible to comment on the effectiveness of the reforms.

When examining specific areas of financial inclusion topics, we find that countries with lower GDP per capita and lower deposit penetration are more likely to have promotion of basic accounts as a mandate of the regulator with a designated team (Table 18), whereas regulators in countries with lower GDP per capita and a positive economic outlook tend to have promoting access to finance in rural areas as a mandate (Table 19). Financial consumer protection and financial literacy are the two topics that are likely to receive more attention in countries where loan penetration is higher (Table 20), but countries with explicit deposit insurance tend to be less likely to have financial literacy as a mandate for the regulator with a designated team. Lastly, countries with lower GDP per capita levels are more likely to have their regulators work effectively on promoting access to finance by SMEs (Table 21).

# **5 Conclusion**

Using the _Financial Access_ database by CGAP and the World
The program also offers two cash bonus benefits. The first is a teacher bonus for a high level of school test performance: once every academic year, a maximum of five teachers in each program school where at least 90% of students in tested classes obtain a score of 40% or higher in the QAT receive an award of 10,000 rupees (US$118) each. The second is a competitive school bonus for top school test performance: once every academic year, the program school in each of the seven main program districts which has the highest share of students with a score of 40% or higher in the QAT is awarded 50,000 rupees (US$588).

> 12 The exact subsidy amount was heavily guided by a survey conducted by PEF in 2005 in selected districts which showed that the vast majority of private schools that operate in rural areas and disadvantaged urban neighborhoods charge between 50–400 rupees per month (US$0.6–4.7) in fees. Based on this information, the subsidy amount was set at the upper-segment of this price range (Malik 2007).

11
00|1.00|
|Urban cluster|454,728|0.33|0.00|0.47|0.00|1.00|
|Rural|454,728|0.40|0.00|0.49|0.00|1.00|
|**DB urban classifcation:**|||||||
|Core|454,728|0.28|0.00|0.45|0.00|1.00|
|Suburb|454,728|0.21|0.00|0.41|0.00|1.00|
|Town|454,728|0.13|0.00|0.34|0.00|1.00|
|Other rural|454,728|0.50|1.00|0.50|0.00|1.00|

Source: International Urban Poverty Database. Note: The statistics above are based on the household-level data pooled for 19 countries. Real per capita consumption expenditures are deflated using a spatial deflator calculated using the WorldPop 250m dataset and the DOU method.

84
## [ _Insert Table 6 around here_ ]

Overall, the double-hurdle estimation suggests that certain debt characteristics – in particular, a bond backed by future-flow receivables, SPVs, and collateralized bond – raise the probability of piercing the sovereign ceiling and increase the distance above the ceiling. The magnitude of the effect of future-flow securitization structures is the largest compared to SPVs and other collateralized bonds. Other debt characteristics, country-level variables, and global variables serve the role of useful controls but are in most cases themselves not robustly related to the sovereign ceiling.

# **5. Robustness check**

In the analysis above, a large sample of sub-sovereign bonds was used to estimate the influence of sovereign ratings. However, as we only observe ratings when sub-sovereign bonds were issued, the sample may differ from the population of all bonds. In this section, we address the issue of possible sample selection bias using a two-step Heckman selection model to check the robustness of our baseline results.

Our identification strategy relates to Francis, Aykut, and Tereanu (2014) which reshaped loan and bond-level data into country-quarter panel to correct the selection bias by estimating the probability of corporate bond/loan issuance in the first stage using the average bond-level characteristics as an exclusion restriction. For running the selection regression, we similarly need to collapse the bond-level dataset at higher unit, at industry level for instance, to have observations for each month and country where a borrower did not issue any bonds. We decide to categorize bond issuers into eight industry types (based on their ownership type (public vs. private), industry (financial vs. non-financial), and the governing law they used for issuance, and take average sovereign and sub-sovereign ratings for each country-industry-month pairs to build a balanced panel. Should at least one firm in the same industry for each country issue bonds in a particular month using the same governing law, we observe the distance between the sovereign and sub-sovereign ratings for each country-industry-month pairs.

In several of the emerging market countries covered in our sample
, in an extensive panel-data study between 1974 and 2006, find that international patent grants have a positive impact on bilateral exports to the patent-destination country, though these effects varied across sectors. De Rassenfosse, et al. (2020) studied transactions data among French exporting firms between 2002 and 2011, concording patent categories with traded products. They found that owning patents generated a patent premium in exports to countries where patents were sought, which emerged primarily from greater export volumes rather than higher prices.

Again, neither strand of literature addresses the basic question of how IP-related PTAs influence bilateral patent application flows, which is our concern in this study. Rather than a 2
Table E.3: Program impacts on therapy only versus therapy plus cash

|||||De|pendent v|ariable (N=9|47)||||
|---|---|---|---|---|---|---|---|---|---|---|
||Ant<br>beh|isocial<br>aviors|Inc|ome|Self co|ntrol skills|Time p|references|Antivi<br>anticrim|olent and<br>inal values|
||Mean|p-value|Mean|p-value|Mean|p-value|Mean|p-value|Mean|p-value|
||dif.||dif.||dif.||dif.||dif.||
|Independent variable|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|_Both vs just therapy_|||||||||||
|Short term survey|-0.05|0.53|0.17|0.10|0.03|0.78|0.20|0.06|0.03|0.76|
|Long term survey|-0.22|0.02|-0.16|0.13|0.05|0.60|0.10|0.34|-0.12|0.17|
|_Both vs just cash_|||||||||||
|Short term survey|-0.22|0.01|-0.08|0.44|0.21|0.04|0.24|0.03|-0.03|0.
or the World Bank JoIn database. The data is country-specific where available; otherwise we calculate the average for a country's income class. The ILO (2020) data are from the ILOSTAT database <u>(https://ilostat.ilo.org/, retrieved May 2020) and from the World Bank (2020b) the data is from the</u> Global Jobs Indicators Database <u>(https://datacatalog.worldbank.org/dataset/global-jobsindicators-database, retrieved May 2020). We also adjust the aggregate loss by assuming that not</u> all future workers will always be employed by giving a range of estimates based on labor force participation (Azevedo et al. 2020).

To estimate the present value (PV) of lifetime earnings, we assume a working life of forty-five years and a three percent discount rate for all groups. We use data for the total number of students, _S_ , from UNESCO.

We assume that some of the learning loss will be mitigated by distance learning measures. Therefore, we include _β_ , an adjustment factor to allow for some distance learning during the period of school closure. We assume an optimistic level _β_ , at 0.9, indicating that only 10 percent of students suffer learning loss due to the distance learning opportunities, a conservative estimate that assumes that having distance learning opportunities equates with learning. The actual learning losses might be considerably higher.<sup>45</sup> Our estimate of distance learning coverage comes from <u>UNESCO. Finally, we use World Bank data on GDP by country. We estimate total losses within</u> a range by adjusting labor force participation between 0.7 and 1.0.

7
>1<br>0<br>F. 2020 inflation forecasts for NIRP<br>economies<br>Percent<br>3 April 2016 April 2010<br>2<br>1<br>0<br>Jan-15 Apr-15 Jul-15 Oct-15 Jan-16 Apr-16 Jul-16<br>Switzerland Japan Euro Area Sweden Hungary<br><!-- End of picture text -->

# **D. 2016 growth forecasts for NIRP economies**

Sources: Haver Analytics, Bloomberg, Consensus Economics, World Bank. A. Last observation is 2016Q1.

B. Inflation measured as year-on-year changes in the price index for personal consumption expenditures for the United States and headline consumer price indexes for others. Last observation is June 2016.

C. Peak is 2013Q1 for euro area, 2009Q2 for Sweden, 2012Q1 for Hungary, 2010Q1for Denmark, 2009Q4 for Switzerland, and 2009Q3 for Japan. Last observation is June 2016.

- D. Consensus Economics growth forecasts for 2016 over time. Last observation is June 2016.

- E. Consensus Economics inflation forecasts for 2016 over time. Last observation is June 2016.

- F. Consensus Economics inflation forecasts for 2020.

6