rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 12: Potentially important background variables and their partial effects on mean WTP in the expert ́s own country for implementing plan A in rounds 1 and 2.**

# **(* = significant at level 15%, ** at level 10%, *** at level 5%)**

|Variable|Rou|nd 1|Rou|nd 2|
|---|---|---|---|---|
||Log-linear|Linear|Log-linear|Linear|
|How close is Delphi estimate<br>to true value (1-10)|0.040|-2.24|0.063|-0.76|
|How difficult to implement<br>CV study on Amazon (1-10)|-0.111 (*)|-1.42|-0.088|-0.63|
|How difficult to implement<br>CV study on domestic<br>biodiversity (1-10)|-0.030|-4.29 (*)|-0.038|-4.13 (*)|
|Familiarity with CV<br>literature. (1-10)|-0.137|-1.96|-0.197 (**)|-1.76|
|Familiarity with CE literature<br>(1-10)|<br>0.064|-1.68|0.066|-2.99|
|Number of CV and CE<br>surveys on biodiversity done|-0.037|-0.78|-0.017|-0.53|
|Number of papers on CV and<br>CE published, last 5 years|0.007|1.26|-0.015|1.40|
|Number of papers on CV and<br>CE reviewed, last 5 years|-0.006|-0.26|-0.020|-0.27|
|Fraction of research time<br>spent on CV and CE, %|0.013(**)|0.20|0.016 (***)|0.15|
|Read JEEM regularly<br>(dummy)|1.00 (***)|34.5 (***)|0.903 (***)|30.8 (***)|
|Read AJAE regularly<br>(dummy)|-1.14 (***)|-31.2 (***)|-1.03 (***)|-24.0 (***)|

21
According to the GHFPH, the general pattern holds, but with higher overall job losses: for all private sector workers, the reported rate of job loss was 50.9 percent for those with less than secondary education, and this falls to, 32.5 percent for those with university and 22.1 percent for those with post-graduate degree lost jobs (Table 5-24). The rate at which lower secondary- educated workers reported reduced income due to reduced

|_Table 5-23 Georgia: Distri_|_bution of PFPS Job Los_<br>**Wave 1**|_ses (Percentage of Jobs L_<br>**Wave 2**|_ost) by Educational Attainment_|
|---|---|---|---|
|||CFUWBES2|CFUWBES2|
||CFUWBES1<br>Modeled|Model-predicted|Survey Measured|
|**Primary or Lower**|25.6|13.4|13.2|
|**Lower Secondary**|22.2|14.0|12.4|
|**Secondary**|21.2|11.6|10.8|
|**University**|14.9|-3.3|2.4|
|**Post-Graduate**|16.2|-1.9|3.7|
|**Total**|19.0|5.9|7.8|
|Sources: CFUWBES and L|FS 2019.|||

working hours was nearly 50 percent, which was more than twice that of those with higher levels of education.

5.7 Projected Job Losses by Wage Level Many observers have predicted that the loss of jobs would be unequally shared, with the poor most greatly impacted by this crisis. Our method projects such unequal job losses in Jordan, but not in Georgia.

# **5.7.1 Jordan**

Our method projects that workers whose wages pre- pandemic were in the bottom quintile of the wage distribution in Jordan will have lost jobs at a rate of 19 percent by the summer of 2020, relative to 13.5 percent of those in the top wage quintile (Table 5-25). This unequal result is projected to have persisted into the winter, when 30.4 percent of jobs in the bottom quintile are predicted lost and 21.4 at the top.

|_Table 5-24 Georgia: Labo_<br>_Sector Employees Experie_<br>**Highest Educational**<br>**Attainment**|_r Market Impacts of_<br>_ncing since March 2_<br>**Lost jobs**<br>**because of job**<br>**or business**<br>**losses due to**<br>**COVID-19**|_COVID-19 by Educatio_<br>_020_<br>**Stopped working**<br>**to avoid exposure**<br>**to the virus**|_nal Attainment, Perce_<br>**Had reduced**<br>**income because of**<br>**job or business**<br>**losses from**<br>**COVID-19**|_nt of All Private_<br>**Reduced income**<br>**due to reduced**<br>**working hours**|
|---|---|---|---|---|
|Lower Secondary|50.9|0.0|50.9|49.1|
|Secondary|30.5|3.2|69.5|19.9|
|University|32.5|0.0|55.0|15.0|
|Post-Graduate|22.1|2.1|54.2|22.0|
|Source: GHFPS.|||||

41
# 'Table 4

||1|989|1|990|1|991|
|---|---|---|---|---|---|---|
||Estimate|t stat|-Estimate|t staL|Estimate|t stat|
|a<br>:|<br>8.346|3.76|0.936|2.297|LOO1|7.28|
|/3 ::<br>0|:p: -10.29|-1.21-|2.422|1.278|1.590|1.92|

The regressions sketch an interesting pattern: while investment (scaled by depreciation) is a negative function of profitability in the last year of the old regime (1989), the coefficient on profitability turns positive in 1990 and 199 1. And in the last year the relevant coefficient is almost signific-1n at the standard 5%9D test size (1.92 instead of 1.96). This provides an indication ti justment did take place in sensible directions: expansion took place in the more profitable firms.

# _Wage Setting Behavior_

Did wages consume all the surplus, or are there signs of restraint? Figure 2 shows .the share of wage costs (basic wages plus payroll taxes and social insurance contributions) in a measure of gross value-added (GVA, equal to profits before tax plus wage costs plus depreciation).

-Figure

Wages/Gross **Value** Added AAA Wages/Gross **Vldue** Added AA _--_ **_-_** _-i ! -- I_ 0 _;--<----_ **0.1** " l* :r 2: Wages/Gross 2 Value Added 2 **-O So1001'IN li5** <!-- Start of picture text --> Wages/Gros Value Added A<br>-I<br>0.1-<br>5;II~~<br><!-- End of picture text -->

- ~~In interpreting the graphs, it should be remembered that GVA was artificiEally high to start with aj.~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ owing to implicit CMEA subsidies in 1990. Further, there was a huge compression in wages after the big bang to offset higher input costs and interest rates. After the fear of bankruptcy had subsided around: mid-1990 and macro policy was loosened,:wages grew rapidly, partly tO comypensate for the initial
# **(1) Introduction**

Increasing inequality over the last few decades in many developing countries has led to a renewed interest in understanding intergenerational economic mobility. However, progress has been slow, constrained by two types of data limitations: a lack of data required for measurement of permanent income (socioeconomic status) across multiple generations, and exclusion of non-resident children from household surveys.<sup>2</sup> This paper provides an analysis of intergenerational educational mobility in rural China with a focus on the role of nonfarm occupations that addresses both of these issues. We take advantage of a rich data set that covers three generations and does not suffer from coresidency bias. A research design is developed that relaxes the single factor model of intergenerational educational mobility, and constructs a broader measure of socio-economic status by combining parents’ education and occupation in the absence of long-term panel data on income. To the best of our knowledge, this is the first paper in the literature that deals with heterogeneity in intergenerational educational persistence in villages due to parental occupations (farm versus nonfarm).

The economics literature on intergenerational educational mobility has focused on parental education as the relevant indicator of family background for understanding intergenerational linkages (see Bjorklund and Salvanes (2011) for a recent survey, and on China, see, among others, Knight et al. (2013), Sato and Li (2007), Emran and Sun (2011)). This emphasis on parental education is eminently appropriate when the goal is to estimate the causal effects of parental education on children’s schooling (for a recent survey, see Holmlund et al. (2011)). However, this approach may be less than satisfactory in understanding

> 2The potential importance of coresidency bias in the estimating intergenerational persistence of economics status has been underscored by many (Behrman (1999), Bardhan (2013)). A recent analysis shows that the estimates can be biased downward by almost 50 percent in coresident sample (Emran and Shilpi (2015)). Most of the available household surveys in developing countries have information only on the coresident children and parents.

1
# **References:**

Adamowicz, W., P. Boxall, M. Williams, and J. Louviere (1998), “Stated Preference Approaches for Measuring Passive Use Values: Choice Experiments and Contingent Valuation.” _American Journal of Agricultural Economics_ 80:64-75.

Bateman, I. et al. (2002) _Economic Valuation with Stated Preference Techniques: A Manual_ . Edward Elgar, Cheltenham, U.K..

Bennett, J. and E. Birol (eds.) (2010), _Choice Experiments in Developing Countries: Implementation, Challenges and Policy Implications_ (Edward Elgar, 2011).

Bennett, J. and R. Blamey (2001), _The Choice Modelling Approach to Environmental Valuation_ . Cheltenham: Edward Elgar.

Carson, R. T. (1998), Valuation of Tropical Rainforests: Philosophical and Practical Issues in the Use of Contingent Valuation. _Ecological Economics_ , 24, 15-29.

Carson, R. T. (2011), _Contingent Valuation. A Comprehensive Bibliography and History_ . Northampton, MA: Edward Elgar Publishers.

Carson, R. T., Flores, N. E. and Meade, N. F. (2001), Contingent Valuation: Controversies and Evidence. _Environmental and Resource Economics_ , 19, 173-210.

Carson, R. T. et al (2011), Valuing the Protection of Belum-Temengor to the Malaysian Public. Mimeo, December 2011.

Carson, R. T., M. B. Conaway, and S. Navrud (2013), Preliminary Valuation of a Cultural Heritage Site of Global Significance: A Delphi Contingent Valuation Study. Forthcoming in Rizzo, I and A. Mignosa (eds.) 2013: _Handbook on the Economics of Cultural Heritage_ . Edward Elgar Publishing, Cheltenham, UK and Northampton MA, USA.

Hanley, N., R. E. Wright, and V. Adamovicz (1998), Using Choice Experiments to Value the Environment. _Environmental and Resource Economics_ , 11, 413-428.

Holmes, T. P., and W. L. Adamowicz (2003), Attributed-based methods. Ch. 6 in P. A. Champ, K. J. Boyle, and T. C. Brown, eds., _A Primer on Nonmarket Valuation_ . Boston: Kluwer Academic Publishers.

Horton, B., G. Colarullo, I. Bateman, and C. Peres (2002), Evaluating Non-Users’ Willingness to Pay for the Implementation of a Proposed National Parks Program in Amazonia: A UK/Italian Contingent Valuation Study. CSERGE Working Paper ECM 02-01.

26
19 vaccine.<sup>7</sup> Some evidence suggests that social isolation, lockdowns, and COVID-19 vaccination could save lives. Despite the significant societal costs associated with social distancing due to decreased economic activity, Thunström et al. (2020) discovered that social distancing could save approximately 1.24 million lives. According to Greenstone and Nigam (2020), social distancing policies would prevent 1.7 million deaths compared to the unmitigated scenario, and the benefits would be worth about U$8 trillion. Acemoglu et al. (2021) found that lockdowns that target different age group are more cost beneficial than uniform lockdowns. Bermingham et al.’s (2021) studies suggested that the first vaccine dose reduced the risk of COVID-19 death by 52.6% among individuals aged 80 years. In Another study (Rahmani et al., 2022), it is indicated that that immunizing against COVID-19 with BNT162b2 mRNA, mRNA-1273, and ChAdOx1, as well as using these three components in combination, was associated with favorable effectiveness against SARS-CoV2 incidence rate, hospitalization rate, and mortality rate in the first and second doses in various populations.

The calculation of COVID-19 mortality costs over time provides us with a number of valuable lessons. With these estimates, policy makers could take stock in order to make an informed decision on preventing, preparing, and responding to future pandemics. Côte d'Ivoire has a weak public health system that is unprepared to handle potential pandemic threats, as evidenced by its low overall global health security index score of 31.2<sup>8</sup> (out of a possible score of 100). Consequently, there is an urgent need to provide funds specifically for health security. Thus, there is a dire need to make a dedicated financial investment in health security. Policy makers in Côte d'Ivoire could, therefore, allocate pandemic funds in national budgets and explore opportunities to collaborate with philanthropic and multilateral organizations to increase domestic resource matching. As a result of their collaboration, for every dollar spent by policy makers in Côte d'Ivoire on pandemic preparedness and response, the matching donor contributes U$2 (a 2:1 matching ratio). Policy makers could use these funds to invest more in the surveillance system, allowing outbreaks to be contained before they spread. Furthermore, modern, and strong laboratory

> 7 <u>https://data.who.int/dashboards/covid19/vaccines?m49=384&n=c</u>

> 8 The data are from the 2021 Global Health Security index (https://www.ghsindex.org/country/cote-divoire/). The data suggest that Côte d’Ivoire has low score in preventing (12.4), detecting (29.6), responding (35.3), health (17.1), norms (47.7), and risk (45.2).

15
Policy Research Working Paper 8390

# **Abstract**

This paper presents new empirical evidence on how fiscal space in Sub-Saharan Africa has evolved over the past 15 years. Fiscal space is a multi-dimensional concept that is proxied by indicators capturing aspects of fiscal sustainability, balance sheet vulnerabilities, external debt positions, and market perception. The analysis relies on a new comprehensive database developed on a wide array of indicators (28) for a large set of countries in the world—of which 48 are in Sub-Saharan Africa. The analysis finds that, breaking with history, Sub-Saharan African countries were able to conduct countercyclical policies amid the 2008–09 global financial crisis, thanks to built-up liquidity and policy buffers. The evidence shows that fiscal adjustment efforts in the region were reversed amid the 2014–16 plunge in commodity prices, and oil and minerals and metals exporters saw a sharp deterioration in their primary balance sustainability gap. The paper finds a great deal of heterogeneity in the post–global financial crisis evolution of the fiscal space in the region. In countries with reduced fiscal space, the increase in the number of tax years to repay the debt fully was 1.1 years for the representative country, and in over one-third of the countries, this increase was more than one standard deviation above the median.

This paper is a product of the Office of the Chief Economist, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at ccalderon@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
Because of the importance of this age group, the NEET phenomenon has increasingly gained attention in both developed and developing countries. In Morocco, by contrast, labor market analysis (see, for example, Verme et al., 2016a, 2016b) has tended to focus on understanding what determines unemployment or inactivity within the entire population, with no specific focus on those between 15 and 24 years old. The present paper aims at filling this informational gap by providing a first-of-itstype comprehensive analysis of the characteristics and dynamics of NEETs in the last decade.

Examining NEET profiles is a relatively straightforward exercise using repeated cross-sections on the labor force, but without access to longitudinal data, it is rather more complicated to construct transition matrices that trace their movement in and out of the NEET condition. The Moroccan Labor Force Surveys do have a panel component (50 percent of the sample) but it rotates every two years, making it all but impossible to construct a transition matrix over a longer period than that.

Yet because of the extreme importance of gaining an understanding of the duration and persistence of the NEET condition, our team worked diligently to overcome the data limitations of existing methods by constructing a Synthetic Panel (SP) of individuals between 15 and 24 over a nine-year period (2010-2018). To our knowledge, this represents a primer in the synthetic panel literature, which has so far focused on welfare dynamics and only occasionally on labor market outcomes.<sup>1</sup> The adaptation of synthetic panel methodology to the labor market also opens up the possibility of conducting panel-type analyses in this field without relying on panel data sets, which are often scarce in developing countries. An important advantage we intend to exploit in constructing the SP is the possibility of comparing year-on-year estimates (for example, from 2010 to 2011) to the corresponding panel component within the data.

The paper is organized in six sections. The first is the current Introduction. The second section profiles NEETs, drawing comparisons in different countries around the world. Section 3 summarizes research and findings related to the NEET phenomenon. Section 4 presents data and the methodology we applied, while section 5 displays results. Section 6 draws conclusions.

> 1 Using a synthetic panel approach to study the German labor market during the 1993-2014 period, Burda and Seele (2013) document the vital role played by part-time employment in reallocating a modest increase in total hours worked over a large number of new workers, leading to net employment growth. Beblavy et al. (2013) undertake a synthetic panel analysis of adult learning for cohorts aged 25 to 64 in 27 European countries, using the European Labour Force Survey and studying the factors affecting educational attainment, participation in education and training.

3
impacts similar in magnitude to those found in rural areas. Today, the Mexican program provides payments to about one-quarter of all families in Mexico that constitute on average 10-20% of those families‟ household income.

# **6.1.1  Impact of PROGRESA on Time Use**

Parker and Skoufias (2000) analyze the impacts of the PROGRESA program on children and women‟s time use using the experimental data that was gathered in rural areas of Mexico. We focus here on the results they report pertaining to adults. The time use questions asked about time use in the previous day.

Theoretically, the expected impact of the program on time use is ambiguous. The additional income that families receive under the program would be expected to reduce the labor supply of all family members, assuming that leisure is a normal good. On the other hand, most of the program benefits are tied to children attending school and the highest level of benefits are given to the older children. If children are no longer able to work as much outside school, either at home or at work, then the program could lead to a substitution of mothers‟ time. The CCT program also required that children attend health clinics and that mothers attend group meetings, which directly impacted mothers‟ time allocation.

Parker and Skoufias (2000) find that the labor force participation of beneficiary mothers in these rural villages was low, about 18% prior to the program. Working mothers typically work as unpaid workers in family businesses, in self-employment or in non-agricultural work. In comparison, men‟s labor force participation rate is about 90%, with men often participating in day laborer types of activities and being heavily involved in agricultural work.

Parker and Skoufias (2000) evaluate the impact of the program using double difference regression models that compare the change in the labor force participation rate before and after the program of treatments and controls.<sup>23</sup> They do not find any evidence that the program significantly affected the labor force participation of either women or men. One plausible reason for this finding is that the program eligibility criteria did not create disincentives for working. The criteria did not depend on whether the household members were working, but rather on other factors designed to capture the poverty of the household (such as whether the house had a dirt

> 23 Because the data are generated from a random experiment, it is not really necessary to adjust for preprogram differences, but Parker and Skoufias‟s (2000) approach differences out any preprogram differences in the labor force participation of treatments and controls that may have randomly arisen.

48
# **Table A17:  Heritage Foundation Index of Economic Freedom (HER)**

|**Data Provider**|Heritage Foundation|
|---|---|
|**Description**|The Heritage Foundation is a nongovernmental research and educational institute<br>headquartered in Washington, United States, advocating conservative public policies.|
|**Website**|www.heritage.org|
|**Data Source**|Index of Economic Freedom|
|**Type**|Expert Assessment|
|**Respondents**|Staff of Heritage Foundation|
|**Frequency**|Annual since 1995|
|**Coverage**|Global sample of countries|
|**Public Access**|Yes|
|**Description**|Heritage constructs an Index of Economic Freedom consisting of 10 components. There<br>were major revisions to the methodology in 2006 and 2007. We use data from the three of<br>these components that are based on subjective assessments of Heritage staff and are<br>comparable over time: Investment Freedom, Financial Freedom, and Property Rights.<br>These indicators are scored on a 100-point scale. Note that the indicators refer to data from<br>the previous year: we therefore lag the data from this source by one year.|

||2006|2005|2004|2003|2002|2000|1998|1996|
|---|---|---|---|---|---|---|---|---|
|**Voice and Accountability**|||||||||
|NA|..|..|..|..|..|..|..|..|
|**Political Stability**|||||||||
|NA|..|..|..|..|..|..|..|..|
|**Government Effectiveness**|||||||||
|NA|..|..|..|..|..|..|..|..|
|**Regulatory Quality**|||||||||
|Foreign investment|X|X|X|X|X|X|X|X|
|Banking / Finance|X|X|X|X|X|X|X|X|
|**Rule of Law**|||||||||
|Property Rights|X|X|X|X|X|X|X|X|
|**Control of Corruption**|||||||||
|NA|..|..|..|..|..|..|..|..|
|**Country coverage**|**157**|**157**|**155**|**155**|**156**|**155**|**161**|**150**|
|**Year of Publication**|**2007**|**2006**|**2005**|**2004**|**2003**|**2001**|**1999**|**1997**|

55