rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 10126

# **Abstract**

This paper examines the Arab Republic of Egypt’s labor market transition dynamics post–Arab Spring based on the two most recent rounds of the Egypt Labor Market Panel Survey conducted in 2012 and 2018. In addition to providing disaggregated-level analysis by examining labor market transitions by gender, education, and age groups, the paper provides a cross-country, cross-regional perspective by comparing Egypt’s labor market transitions with Mexico’s, relying on data from the Encuesta Nacional de Ocupación y Empleo. To match the span of Mexico’s transitions (which are measured over a one-year period) and Egypt’s (which are measured over six years), the analysis uses Monte Carlo simulations of repeated discrete-time Markov chains. Based on these results, the Egyptian labor market appears to be highly rigid compared to the Mexican labor market, which instead shows a large degree of dynamism regardless of individual initial labor market states at baseline. Auxiliary regression analyses focusing on transitions to and from the dominant absorbing labor market states in Egypt —public sector employment for both genders, nonparticipation for women, and the informal sector for men—show that having a post-secondary education is associated with a lower probability of remaining out of the labor force for women who were already out of the labor force at baseline, while being married at baseline is found to be a significant predictor for women to stay out of the labor force if they were already so. Among men, the better educated are found to be more likely to secure formal employment, be it in the public or private sector, and are more likely to keep their public formal jobs once they secure them.

This paper is a product of the Office of the Chief Economist, Middle East and North 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 jdeng1@worldbank.org, nelmallakh@worldbank.org, lflabbi@email.unc.edu, rgatti@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
These findings indicate that the risk factors can be put into three categories – analogous to findings from psychology<sup>11</sup> – which are: ‘hygiene factors’ that investors require to consider a market place – such as no legal restrictions on private sector involvement in the power sector – but once in place, the attention of investors shift to other risk factors such as certainty of cash flow. The latter category is ‘motivating factors’, which propel investors to actually enter a marketplace. There are other risk factors for the surveyed investors – that might be considered ‘non-factors’ in the current market set-up – such as domestic debt and equity markets, which only 7% of investors consider to be a deal breaker (the second lowest scores, only after segment is unbundled, which 4% of investors consider a deal breaker) and only 2% ranked as the most important factor (likely those that do not have international sources of financing).

> 11 Herzberg’s motivation-hygiene theory proposes that there are certain factors in the workplace, which cause satisfaction (so-called ‘motivators’) and those that cause dissatisfaction (so-called ‘hygiene’ factors). While hygiene factors need to be in place, they are not sufficient by itself.

20
# **I. Introduction**

Whether we support income redistribution depends on not only how well off our parents were or how egalitarian the society we grew up in (predetermined factors of income), but also the bad luck and the successes and the hardships that have shaped our lives (mobility experiences).<sup>1</sup> As Piketty (1995) suggests, and the empirical literature confirms, our past mobility experiences refine our beliefs in the relative importance of hard work and luck, which affects our expectations of future income and, in turn, determines our support for redistribution.<sup>2</sup> The literature is scant, however, on how we learn from these mobility experiences— how the particulars of fortunes and hardships affect our support for redistribution. The timing of hardships, for example, is likely to matter: Being unemployed ten years ago possibly nudges us to support redistribution, but not as much as being unemployed last year does. The intensity of hardships matters too (going through hardships in which we had to cut down on food consumption to survive matters more than being unemployed with benefits), so does its persistence (ten-year unemployment in the past ten years increases support for redistribution more than one-year unemployment does).

In this paper, we examine how these particulars of mobility experiences affect support for redistribution in Eastern Europe and Central Asia. We use the Life in Transition Surveys (LITS): The first wave (LITS I) interviews more than 29,000 individuals in 29 countries and records their labor-market and life histories over a period of 18 years from 1989 to 2006; the second wave (LITS II) records the life history of 39,000 individuals during the recent global crises in 2007-08. Using these labor-market and life histories, we construct measures of

> 1 Upward- or downward mobility as part of society-wide (Piketty, 1995) or own (Benabou and Ok, 2001) future income prospects has been discussed with support from empirical studies. Factors that explain variation in support for redistribution include cultural differences (Alesina and Glaeser, 2004), parents’ influence (Benabou and Tirole, 2005), perception of fairness (Alesina and Angeletos, 2005), and status of social standing (Corneo and Gruner, 2002).

> 2 In general, theories of fairness judgments (Adams, 1965; Piketty, 1995) suggest that gainers from reforms who believe in hard work and effort are likely to oppose redistribution.

2
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specifically those with low income and high costs. To be more precise, it includes households with energy costs exceeding a 'reasonable level' (the median) and whose residual equivalized income would fall below the official poverty line if they were to spend that amount. Notice that two types of households meet these conditions: 1) Households that are income poor and have relatively high energy expenditures; and 2) Households that are just above the poverty line and would fall into income poverty after considering energy expenses (households vulnerable to poverty), with relatively high energy expenditures. This definition excludes income-poor households with relatively low energy expenditures and households above the poverty line that do not fall into poverty when energy expenses are taken into account. Similarly to the M/2 and the 2M metrics, the “LIHC” also uses relative thresholds, making comparisons over time difficult unless the thresholds are anchored.

**A similar definition is the after-fuel-costs poverty (AFCP) indicator, developed by Hills (2011).** Still, it considers only one condition: household equalized income (possibly subtracting housing costs and domestic energy costs) is below the standard threshold of 60% of equivalized national income (Figure 4).<sup>29</sup> In addition to the incidence of energy poverty, the energy poverty gap can also be estimated. The depth of energy poverty is defined as the amount by which the assessed energy requirements of fuelpoor households exceed the threshold for reasonable costs.

**The energy expenditure can be the observed energy expenditure, for example, from the HBS, or it can be modeled. In the UK, the energy cost is modeled rather than based on observed spending.** This ensures that one does not overlook households with low energy bills simply because they actively limit their energy use at home, for example, by not heating their home. It is calculated by combining the fuel requirements of the household with corresponding fuel prices. This approach is data intensive, as these required costs capture four areas of energy requirements: a. Space heating; b. Water heating; c. Lights and appliances; and d. Cooking. The modeling ensures that the household achieves adequate warmth, subject to various characteristics concerning the dwelling and its occupants.

_Figure 3: Low Income-High Costs (LIHC) Measure_

> energy expenditures. Several EU countries have adopted different versions of this definition, including France. Modifications include different treatment of housing costs (included or excluded), expressing the energy spending in absolute terms or as a share, etc.

> 29 This measure is close to the LIHC definition, but it does not include an energy expenditure threshold. A limitation of the AFCP is that it categorizes a significant number of households with extremely limited incomes as being in energy poverty, regardless of their actual fuel needs. Under this definition all income poor households are considered energy poor, regardless of their energy use. This is a group that it is normally excluded under the LIHC definition, but is considered energy poor under this definition.

16
7 Testing of these hypotheses Is difficult owing to data lnitatlons. There are no recent studies of the relative productivity of ejidos and private farms: the avalable sources are based either on agricultural census data up to 1970 or pre1970 case studies. In the period before 1970, ejidos appear to have had poorer and on average than small private farms and to have had lesser access to credit and public investment; also, there Is evidence of scale-blas In the government's dolvery of subsidized capital Inputs. These factors should be borne In mind In interpreting pre-1970 studies.

- Sources based on the census record reach varying conchjsions about the

- relative productivity of the two tenure sectors- summarizing, It wou appear that - ejidos had lower land yields before 1970 but higher output per unit of purchased capital kiputs; ejldos also show lower labour productivity than small private farms, al the more so If a cost Is Imputed to unpaid family labour. However, given the clndestine renting out of ejido land to private farmers, the census data probably fall to reflect the real distribution of Inputs and outputs between the two tenure sectors; this places In doubt any concluslons about relative productivity that are derived from the census data.

Of the case studies, only one (dating to 1967) controls effectively for both land tenure and enterprise scale: the study concludes that, In Irrigated reglons, ejidos outperform private farms in each size stratum (the gap between sectors being largest In the case of capital productivity, confirming the results of census-based studies).

The Incompleteness of the 1981 census data does not permit calculatlon of relative productivity In the recent period. However, land use data from this census suggest that ejidos are likely to be at least as productive as small private farms: ejldo parcels up to five hectares are better Irrigated than comparablysized private farms and show a higher proportion of land in agricultural use; also, the ratio of fallow land to cropland Is the same for both tenure categorles In this size class. Thus, controlling for enterprise size, there Is no conclusive evidence that Individual ejidos are less productive than private farms.

# Policy recommendations

There are major political constraints to tampering with the Mexican land reform; moreover, the evidence does not suggest that radical change In land tenure would greatly enhance agricultural growth. However, while the Institutional differences between individual ejidos and small private farms appear not to generate major ditfferences In Indicators related to productivity, at the margin, ejldatarios face more constraints on productivity growth than private farmers. Rather than seeking to undo the land reform, policy Initiatives should focus on making piecemeal knprovements to the existing structure. Specifically,

1. Granting of titles to ejidatarlos should not be contingent on parcel size.

2. The existing drive to provide ejidatarlos with usufruct titles should be accelerated.
Policy Research Working Paper 7827

# **Abstract**

Globalization is creating many new trade and growth opportunities, with services trade increasingly becoming an issue for export-oriented economies. Services are important to country trade strategies, because they represent activities in which countries may have a comparative advantage, and they are drivers of competitiveness for the whole economy. This paper uses data from the World Development Indicators, two new databases (the Export in Value-Added database from the Global Trade Analysis Project, and Trade in Services data), and firm-level data. The paper employs a wide range of indicators to analyze the trade competitiveness of the services sector in the Russian Federation. Since service exports are less than would be expected considering Russia’s level of development, the study finds that the contribution of services to export diversification could be heightened significantly. The scale of Russian business services exports is relatively low, although exports of traditional services, like transport and travel, are performing well. Despite the relatively minor importance of exports of modern services, the category of other business services has in recent years been growing fast, and business services have strengthened their revealed comparative advantages. Yet Russia still has much potential for expanding trade in modern services. There is also potential to diversify services exports to other markets, such as France, Germany, Japan, and elsewhere in Asia, which today seems underexploited. Finally, although exports of direct services are low, services such as transport, distribution, finance, and other business services are making major contributions to other exports, in particular energy.

This paper is a product of the Trade and Competitiveness 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 ssaez@worldbank.org and erik.vandermarel@ecipe.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
Dwan, K., D. G. Altman, J. A. Arnaiz, J. Bloom, A.-W. Chan, E. Cronin, E. Decullier, P. J. Easterbrook, E. Von Elm, and C. Gamble. 2008. “Systematic Review of the Empirical Evidence of Study Publication Bias and Outcome Reporting Bias.” _PLoS One_ 3 (8): e3081.

Ernst, E., and A. R. White. 1998. “Acupuncture for Back Pain: A Meta-Analysis of Randomized Controlled Trials.” _Archives of Internal Medicine_ 158 (20): 2235.

Franco, A., N. Malhotra, and G. Simonovits. 2014. “Publication Bias in the Social Sciences: Unlocking the File Drawer.” _Science_ 345 (6203): 1502–5.

Frijters, P., T. S. Kong, and E. M. Liu. 2015. “Who Is Coming to the Artefactual Field Experiment? Participation Bias among Chinese Rural Migrants.” Working Paper 20953. National Bureau of Economic Research.

Glennerster, R., and K. Takavarasha. 2013. _Running Randomized Evaluations: A Practical Guide._ Princeton, NJ: Princeton University Press.

Gluud, L. L. 2006. “Bias in Clinical Intervention Research.” _American Journal of Epidemiology_ 163 (6): 493–501.

Goldacre, B. 2014. B _ad Pharma: How Drug Companies Mislead Doctors and Harm Patients._ New York: Macmillan.

Guyatt, G. H., S. O. Pugsley, M. J. Sullivan, P. J. Thompson, L. Berman, N. L. Jones, E. L. Fallen, and D. Wayne Taylor. 1984. “Effect of Encouragement on Walking Test Performance” _Thorax_ 39 (11): 818–22.

Guyatt, G. H., E. J. Mills, and D. Elbourne. 2008. “In the Era of Systematic Reviews, Does the Size of an Individual Trial Still Matter?” _PLoS Medicine_ 5 (1).

38
# **BETWEEN GROUP INEQUALITY AND TARGETED TRANSFERS**

Quentin T. Wodon World Bank (PREM, LAC) JEL numbers: D31, 015, H23 E-mail: qwodongworldbank.org Acknowledgments: I am grateful to Robert Lerman for many discussions on this topic. The data was provided by the Bangladesh Bureau of Statistics as part of a long term collaborative effort with the World Bank. The help of Dileni Gunewardena is also gratefully acknowledged.
The remainder of this paper is devoted to empirical tests of the model’s predictions. The _R_ next section describes how we operationalize _i_ , γ , α and<sup>_A_</sup> _i_<sup>using data from the Survey on</sup> _A i_ Monitoring the Paris Declaration and other sources.

# **3. Data and Hypotheses**

_Measuring trust in country systems_ _R_ We operationalize _i_ , the share of recipient-managed aid, using the OECD DAC’s 2008 _A i_ Survey on Monitoring the Paris Declaration (SMPD). This survey provides indicators of aid delivery practices not included in the DAC’s standard aid reporting systems for donors. Survey indicators cover the number of missions and country analytic studies donors undertake jointly, the share of technical assistance that is coordinated with recipient governments’ capacity building programs, and other aid management practices. Most of these indicators are beyond the scope of the present study, and we focus on the few that address most directly donors’ use of country financial management systems for implementing aid projects and programs.

The SMPD is designed to measure progress toward a set of specific targets for 2010 agreed by donors and recipients on delivering aid in ways believed to enhance its development effectiveness. A baseline survey was conducted in 2006, and a report by the DAC (OECD, 2008a) summarizes progress toward the targets comparing the 2006 and 2008 survey results. The report’s conclusions regarding progress are highly tentative, however, because of data quality issues concerning the 2006 survey.

Based on lessons learned, guidance on definitions was substantially strengthened for the second round survey, and the comparability of data reported by donors and by recipients was improved significantly relative to the 2006 survey (OECD, 2008a). Moreover, the number of recipient countries participating increased from 34 in 2006 to 54 in 2008. For purposes of this study, therefore, we treat 2006 as a pilot exercise, and use data only from the 2008 survey.<sup>9</sup> We measure trust in country systems using three variables constructed from the SMPD: 1) use of recipients’ public financial systems for the management of aid funds ( _PFM_ ), 2) direct budget support ( _DBS_ ), including sector budget support, and

- 3) aid disbursed through program-based approaches ( _PBA_ ), inclusive of budget support.

> 9 A third and final survey is scheduled for 2010, and comparisons over time with the 2008 survey should be reasonably valid.

11
Table 1: Relationship between general management and post-COVID outcomes; manufacturing.

||(1)<br>∆Sales|(2)<br>Temp.|(3)<br>Perm.|(4)<br>Furloughed|(5)<br>Laid Of|(6)<br>Pivot<br>Product|(7)<br>Remote<br>Work|(8)<br>Arrears|
|---|---|---|---|---|---|---|---|---|
|Management|0.036<sup>_∗∗∗_</sup>|-0.330<sup>_∗∗∗_</sup>|-0.353<sup>_∗∗∗_</sup>|-0.028|0.052|0.141<sup>_∗∗_</sup>|0.231<sup>_∗∗∗_</sup>|-0.207<sup>_∗∗∗_</sup>|
||(0.013)|(0.109)|(0.124)|(0.070)|(0.079)|(0.061)|(0.061)|(0.066)|
|Size (log)|0.004|-0.127<sup>_∗_</sup>|0.043|0.177<sup>_∗∗∗_</sup>|0.097<sup>_∗∗_</sup>|0.040|0.107<sup>_∗∗∗_</sup>|0.005|
||(0.007)|(0.070)|(0.068)|(0.046)|(0.049)|(0.039)|(0.039)|(0.042)|
|Sales (log)|0.023<sup>_∗∗∗_</sup>|-0.056|-0.092<sup>_∗∗_</sup>|-0.118<sup>_∗∗∗_</sup>|-0.057<sup>_∗_</sup>|-0.031|0.088<sup>_∗∗∗_</sup>|-0.066<sup>_∗∗_</sup>|
||(0.005)|(0.040)|(0.042)|(0.031)|(0.032)|(0.026)|(0.026)|(0.027)|
|Observations|2332|2229|1863|1767|2386|2391|2391|2208|
|Adjusted _R_<sup>2</sup>|0.181||||||||
|Pseudo _R_<sup>2</sup>||0.178|0.153|0.149|0.098|0.103|0.111|0.103|
|df<br>~~m~~|24|21|19|22|24|24|24|24|

Notes: Standard errors in parentheses (1) is change in sales (share). (2) is temporary firm closure. (3) is permanent firm closure. (4) and (5) are whether workers were furloughed or laid off. (6) refers to pivot of product or service offered. (7) is starting or increasing remote work arrangement. (8) is whether the firm is likely to fall into arrears. Lockdown is # days from lockdown to interview. All specifications include controls for country, sector, number of days from lockdown, firm age, and export dummy. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01

**13**
Table 27

|Dependent variable: Nee|d of fiscal|adjustment|based on de|bt/revenu|e||||
|---|---|---|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Rigid expenditure minus|0.006***|0.003|0.006*|0.001|0.014*|0.019|-0.018*|0.010|
|structural as % GDP|(0.002)|(0.002)|(0.004)|(0.004)|(0.008)|(0.015)|(0.010)|(0.013)|
|**Economic factors**|||||||||
|GDP growth (t-1)||-1.670***||-2.131***|-1.948***|-2.084***|-2.042***|-2.120***|
|||(0.382)||(0.376)|(0.388)|(0.383)|(0.390)|(0.376)|
|Inflation (t-1)||-0.066||0.099|0.127|0.096|0.112|0.100|
|||(0.108)||(0.109)|(0.107)|(0.109)|(0.105)|(0.108)|
|U.S. Interest rate||-0.009||-0.011|-0.015|-0.013|-0.015|-0.011|
|||(0.011)||(0.012)|(0.012)|(0.013)|(0.013)|(0.012)|
|**Political factors**|||||||||
|Gini|||0.002|0.002|0.002|0.005*|0.002|0.002|
||||(0.001)|(0.001)|(0.001)|(0.003)|(0.001)|(0.001)|
|Rule of law|||0.006|0.017|0.018|0.015|-0.022|0.016|
||||(0.010)|(0.012)|(0.014)|(0.012)|(0.022)|(0.012)|
|Margin of Majority|||-0.089|-0.075|-0.067|-0.061|-0.064|0.022|
||||(0.080)|(0.084)|(0.091)|(0.084)|(0.087)|(0.170)|
|Election's year|||-0.011|-0.010|-0.006|-0.010|-0.007|-0.011|
||||(0.015)|(0.015)|(0.016)|(0.015)|(0.016)|(0.015)|
|**Interactions**|||||||||
|EMEs|||||0.200*<br>(0.106)||||
|EMEs * Deviation|||||-0.022**<br>(0.009)||||
|Gini * Deviation||||||-0.000<br>(0.000)|||
|Rule of law * Deviation|||||||0.005**<br>(0.002)||
|Majority * Deviation||||||||-0.015<br>(0.019)|
|Observations|2,799|2,738|1,949|1,918|1,918|1,918|1,918|1,918|
|Country FE|No|No|No|No|No|No|No|No|
|r2_p|0.00308|0.0185|0.00510|0.0252|0.0313|0.0264|0.0281|0.0256|
|Robust standard errors c<br>*** p<0.01, ** p<0.05, *|lustered by<br>p<0.1|country in p|arentheses||||||

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