Back Matter
  • 1 https://isni.org/isni/0000000404811396, International Monetary Fund
  • | 2 https://isni.org/isni/0000000404811396, International Monetary Fund
  • | 3 https://isni.org/isni/0000000404811396, International Monetary Fund
  • | 4 https://isni.org/isni/0000000404811396, International Monetary Fund

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1

Team led by John Bluedorn, under the guidance of Romain Duval. The authors would like to thank Jorge Alvarez, Benedicte Baduel, Angana Banerji, Davide Furceri, Faten Saliba, Petia Topalova, and other IMF colleagues and seminar participants for their insightful comments. We would also like to thank Rita Almeida at the World Bank and International Labour Organization colleagues for helpful discussions and advice. We are also grateful to IMF colleagues Mitali Das and Benjamin Hilgenstock for sharing their dataset on countries’ exposure to routinization.

2

See ILO (2017). Age ranges defining the working-age population and youth sometimes differ across data sets and publications. Unless indicated otherwise, the International Labour Organization (ILO) definitions are used: ages 15–64 (working age) and 15–24 (youth). Note that the rate of those not in education, employment, or training (NEET), or youth inactivity rate, comprises young people who are not in school and who are either unemployed or out of the labor force. Throughout this discussion note, the average emerging market and developing economy is defined as the notional emerging market and developing economy at the median values of the variables of interest for the available country data for emerging market and developing economies.

3

Country group median population figures are calculated from the data published by the UN DESA Population Division (2017).

4

Assuming a standard production function and constant capital-to-output ratio and productivity, output growth in the steady state is equal to the growth of the employed workforce.

5

See the Technical Appendix for the structural policy indicators considered in the empirical analysis. Data availability limits the set of structural policies and characteristics that may be investigated.

6

Unless noted otherwise, informal employment is as defined by the ILO. Roughly, it is employment without regular legal protections. See Hussmans (2005) for further details regarding the definitions of informal employment and the informal sector.

7

Although not focused on youth as such, see Fabrizio and others (2017) for recent work on income inequality and structural policies in low-income countries. This work finds that sector-targeted structural policies have distributional consequences when workers’ mobility is limited.

8

For a comprehensive overview of schooling and learning more generally in fostering economic development, see WBG (2017). See also Quintini and Martin (2014) and O’Higgins (2017) for research on the transition from school to work in emerging market and developing economies. OECD (2017) discusses the transition through the lens of youth perceptions.

9

See WBG (2017) for an in-depth analysis of trends and drivers of schooling and learning outcomes around the world.

10

For more in-depth description and discussion particular to the Middle East and North Africa, see the recent contribution of Purfield and others (2018).

11

See the Technical Appendix for details on how the microdata inform estimates of youth gender gaps in labor market outcomes, taking into account individuals’ characteristics.

12

See the Technical Appendix for details on the individual-level probability model used to calculate the decomposition.

13

UNCTAD (2017) paints a similar picture. It examines the flow and stock of operational robots across advanced and emerging market and developing economies. Dao and others (2017) note how differences in sectoral structure between advanced and emerging market and developing economies contribute to differences in vulnerability.

14

See Figure 13 for results and the Technical Appendix for further details on the analysis.

15

The findings on the cyclical sensitivity of youth versus adult unemployment in advanced economies are similar to those in Banerji and others (2014) and Banerji, Lin, and Saksonovs (2015).

16

For the microdata analyses, youth are defined as 15- to 29-year-olds, expanding slightly on the ILO definition. Adults are defined here as 30- to 64-year-olds. Microdata are from the International Integrated Public Use Microdata Series (Minnesota Population Center 2017; IPUMS International), the ILO School-to-Work Transition Survey (2016a), and the European Bank for Reconstruction and Development and the World Bank Life in Transition Survey (2016). See the Technical Appendix for further details regarding the microdata and sample coverage.

17

Sparsity of data coverage limits the ability to jointly estimate the impacts of all potentially relevant structural policies and characteristics. See the Technical Appendix for further details.

18

The individual-level results described here are based on the harmonized national census data from IPUMS International. The probability model for labor market status by age group (youth/adult) and gender (women/men) includes age (captured by five-year age groups), having children or not, marital status, nativity (native or foreign-born), educational attainment (captured by level of schooling completed, including primary, secondary, and tertiary), and dwelling ownership by the household. See the Technical Appendix for further details on the probability model specification and estimation.

19

To conserve space, the estimated effects for adults are shown only in the Technical Appendix.

20

WBG (2012) came to a similar conclusion regarding youth and women and employment costs.

21

See the Technical Appendix for the full results on youth differentiated by skill level.

22

See also Gonzales and others (2015) and WBG (2018b) for related work on the impact of equal protection under the law on women’s labor market outcomes.

23

See the Technical Appendix for further details.

24

Ribe, Robalino, and Walker (2010) estimate the average emerging market and developing economy labor tax wedge at about 35 percent, which is fairly close to the average tax wedge across Organisation for Economic Co-operation and Development (OECD) member countries as computed by the OECD. However, unlike the World Bank’s calculations, the OECD average does not include the implicit tax generated by severance pay. The OECD’s own estimates as published in its Going for Growth database for selected emerging market and developing economies show wide cross-country variation. In some cases the tax wedge is very high—for example, above 30 percent for Brazil and China—and in others it is quite low, at less than 10 percent for India and Indonesia (for India, the average wedge cited is based on a one-earner couple with two children).

25

For example, see Card, Kluve, and Weber (2018) and Fox and Kaul (2017). See the Technical Appendix for further description of the literature on ALMPs for youth.

Work In Progress: Improving Youth Labor Market Outcomes in Emerging Market and Developing Economies
Author: Mr. JaeBin Ahn, Zidong An, Mr. John C Bluedorn, Gabriele Ciminelli, Zsoka Koczan, Mr. Davide Malacrino, Daniela Muhaj, and Patricia Neidlinger