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References

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Appendix A. Sample

The samples include the following countries:

Aggregate analysis: Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Italy, Ireland, Japan, Korea, Luxembourg, Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom, United States.

Individual-level analysis: Austria, Belgium, Cyprus, Czech Republic, Denmark, Germany, Estonia, Finland, France, Greece, Iceland, Ireland, Italy, Latvia, Lithuania, Netherlands, Norway, Portugal, Slovakia, Slovenia, Spain, Sweden, Switzerland, United Kingdom.

Appendix B. Variable Construction and Sources

In the following, we report how we construct the variables entering the regression analysis and the original data sources:

  • Aggregate and group-specific labor force participation rates are expressed as a share of the relevant population group. Sources: OECD, Employment database; Eurostat; National authorities, Barro-Lee Educational Attainment dataset.

  • The cyclical position is captured with the output gap, while robustness tests use the unemployment rate. Source: IMF, World Economic Outlook database.

  • Exposure to technological progress is measured as the interaction between the cross-country average relative price of investment and the country’s exposure to routinization through its initial occupational mix. The latter consists of scores that rely on occupation-level measures, which order occupations by their share of routine tasks, and then use the employment shares of these occupations to construct country-level measures of routinizability.33 Sources: IMF, World Economic Outlook database, Autor and Dorn (2013), Eurostat, and Population censuses.

  • Trade openness is measured as the sum of exports and imports in percent of GDP. Source: IMF, World Economic Outlook database.

  • Potential shifts in the demand for different types of labor due to structural transformation are measured as the ratio of employment in the services sector relative to employment in the industrial sector. Sources: World Bank, World Development Indicators; European Union, Level analysis of Capital, Labour, Energy, Materials, and Service inputs. The share of urban population is also used. Sources: World Bank, World Development Indicators.

  • Educational attainment is measured as the share of the population within a specific age-gender group with highest level of education reported as primary, secondary, or tertiary. Source: Barro-Lee Educational Attainment dataset.

  • The labor tax wedge is defined as the ratio between the average tax paid by a single-earner family (one parent at 100 percent of average earnings with two children) and the corresponding total labor cost for the employer. The labor tax wedge is available from the OECD for 2000 to 2016, and was extended back to 1980 using Bassanini and Duval (2006) and IMF (2016b). The latter series is available only in uneven years; the value of the labor tax wedge in even years is obtained by linear interpolation. Sources: OECD, Tax database; Bassanini and Duval (2006); IMF (2016b).

  • The generosity of the unemployment benefits system is measured as the gross replacement rate, gross unemployment benefit levels as a percentage of previous gross earnings. The summary measure with the best coverage is the average of the gross unemployment benefit replacement rates for two earnings levels, three family situations, and three durations of unemployment. Such measures are available in uneven years, and are interpolated to obtain their values for even years. The reported values are for the average worker from 2001 to 2011, and average production worker from 1961 to 2005. The two series are spliced. Source: OECD, Benefits and Wages: Statistics.

  • Public expenditure on active labor market programs is calculated as active labor market programs spending per unemployed person in percent of GDP per capita, following Gal and Theising (2015). Source: OECD, Employment database.

  • Restrictiveness of migration policy is an index with information about all changes to the existing legal framework relevant for migration (see also De Resende, 2014). We focus on major changes in policies guiding the post-entry rights or other aspects of migrants’ integration. These changes are cumulated starting 1980 to construct an index for each country, with a higher value denoting more restrictive policies. Source: International Migration Institute, DEMIG POLICY database.

  • Union density is measured as net union membership as a proportion of wage earners in employment. Source: OECD, Employment database.

  • Coordination of wage setting is an index of the centralization of bargaining. The index runs from 1 to 5 with values defined as (1) Fragmented wage bargaining, confined largely to individual firms or plants, (2) mixed industry and firm-level bargaining, weak government coordination through minimum wage setting or wage indexation, (3) negotiation guidelines based on centralized bargaining, (4) wage norms based on centralized bargaining by peak association with or without government involvement, and (5) maximum or minimum wage rates/increases based on centralized bargaining. Source: Amsterdam Institute for Advanced Labour Studies, Database on Institutional Characteristics of Trade Unions, Wage Setting, State Intervention, and Social Pacts.

  • Policies that help reconcile work inside and outside the household are proxied by public spending on childcare and education as a percent of GDP; the proportion of employees with a part-time contract to total employees; and job-protected maternity leave, defined as the total number of weeks of job-protected maternity, parental, and extended leave available to mothers, regardless of income support. Sources: OECD, Social protection database; OECD, Employment database; OECD, Family database.

  • Retirement incentives are proxied by the statutory retirement age and by the generosity of pension schemes. Several alternatives are used to capture the generosity of pension schemes. The measure with the best country and time coverage is old-age and incapacity spending as a percent of GDP. This measure is first purged from fluctuations due to cyclical and demographic factors (namely, share of the population in different age groups and health status, proxied by life expectancy) that may mechanically generate a negative correlation with the labor force attachment of older workers. As a robustness check, the analysis considers the (conceptually more appropriate but less widely available) implicit tax on continued work, calculated as the change in the present value of the stream of future pension payments net of contributions to the system from working five more years for typical workers at different ages. An alternative measure also considered is the aggregate replacement ratio, calculated as the ratio of the mean disposable income of ages 65–74 to the mean disposable income of ages 50–59. This variable is computed for select years based on the availability of household survey data and is interpolated for the missing years. Sources: Social Security Programs throughout the World; OECD, Social protection database; Duval (2003); IMF (2016b); Luxembourg Income Study database.

*

The views expressed in this Working Paper are those of the authors and do not necessarily represent those of the IMF or IMF policy. Working Papers describe research in progress by the authors and are published to elicit comments and to encourage debate. We thank, without implicating, Stephanie Aaronson, Oya Celasun, Romain Duval, Gian Maria Milesi-Ferretti, Davide Furceri, and Maury Obstfeld for their comments and suggestions. We are also grateful to Mitali Das, Romain Duval, and Davide Furceri for sharing their data on routinization and labor market polices. Benjamin Hilgenstock, Christopher Johns, and Jungjin Lee provided excellent research assistance.

International Monetary Fund, Research Department, fgrigoli@imf.org.

International Monetary Fund, Research Department, zkoczan@imf.org.

§

International Monetary Fund, Research Department, ptopalova@imf.org.

1

The labor force participation rate is the fraction of the adult population (ages 15 and over) either working or looking for work. In the rest of the paper, labor force participation and workforce attachment are used interchangeably.

2

For a discussion of the impact of automation and artificial intelligence on labor markets and inequality see for instance UN (2017), which emphasizes that this would likely involve job creation as well as job destruction, and that economic, legal, regulatory and socio-political factors will also affect whether the benefits of automating will outweigh the costs.

3

The discussion of the long-term trends is based on the analysis of participation rates in 21 AEs for which data are available over 1985{2016, to ensure sample consistency. The patterns described are qualitatively identical if all AEs are included in the analysis.

4

While some in this age group are in school and in the labor force, there is a significant association between increasing enrollment rates and declining participation rates across countries.

5

The concept of idle youth is distinct from the NEETs (defined as those not in employment, education, or training) since the latter includes unemployed individuals. Youth unemployment is high and increased significantly after the global financial crisis in many AEs (Banerji et al., 2015).

6

For a discussion of earlier trends in retirement, see Blondal and Scarpetta (1999) and Gruber and Wise (2000).

7

While it is possible that higher female participation allowed some men to drop out of the labor force, there is little evidence to that effect at the country level. Correlations between changes in prime-age female and male participation rates are, if anything, positive; and participation of married men declined by less than participation of single men (Figure 4, panel 2).

8

The decline in labor force participation of prime-age men attracted considerable attention in the United States. See, for example, CEA (2016), Eberstadt (2016), Krause and Sawhill (2017), Krueger (2017), and Abraham and Kearney (2018).

9

Due to data availability constraints, the analysis on participation by various demographic characteristics can be performed for a significantly shorter time span and a smaller sample of countries. It relies on individual-level data from the European Union Labour Force Survey to construct country-level participation rates for the subgroup of workers by marriage status, number of children, and immigration status, and on Eurostat data, complemented with data from national authorities, to build a picture of participation by educational attainment.

10

In 2015, the labor force of the average AE had the following composition: 37 percent of workers were prime-age men, 31 percent of workers were prime-age women, 11 percent of workers were aged 15{24 and 21 percent of workers were older than 55. The population of the average AE had the following composition: 20 percent were prime-age men, 20 percent were prime-age women, 12 percent were aged 15{24, 31 percent were older than 55.

11

In line with the stylized facts already discussed, comparing the years 2000 and 2016 suggests that over time the share of students increased, both among the young and the prime-age, while the share of those in (early) retirement among the prime-age fell, as did the share of those who never worked before among prime-age women and those over 55. Illness and disability became relatively more important over time as reason for non-participation.

12

In a simple static labor supply model, a parent could choose to remain home and take care of an infant or a young child at the cost of their hourly wage (the foregone earnings) less the price of childcare services purchased outside the home. A more generous childcare subsidy would increase the parent’s wage net of childcare cost, thus raising the opportunity cost of staying home and increasing labor supply on the extensive margin.

13

See, for example, Ngai and Petrongolo (2017) for a model of structural transformation where relative gains in women’s labor market outcomes are driven by changes towards the service-producing sector, as well as Olivetti and Petrongolo (2017) for empirical evidence on the role of the industrial structure in accounting for cross-country differences in gender outcomes. For a discussion of gender-based comparative advantage, see Feingold (1994), Galor and Weil (1996), Baron-Cohen et al. (2005), Christiansen et al. (2016), Rendall (2010), and Cortes et al. (2018), among others.

14

Increasing evidence suggests that adverse initial labor market conditions can have substantial long-term effects on the earnings of college graduates. See, for example, Genda et al. (2010), Kahn (2010), and Oreopoulos et al. (2012).

15

Appendix A reports the list of countries.

16

The empirical approach in the paper is widely used in the cross-country literature on labor market. Blanchard and Wolfers (2000), Bertola et al. (2007), IMF (2003), Genre et al. (2005), Bassanini and Duval (2006), Bassanini and Duval (2009), De Serres et al. (2012), Murtin et al. (2014), and Gal and Theising (2015) examine determinants of employment and unemployment, among others. Jaumotte (2003), Genre et al. (2010), Blau and Kahn (2013), Cipollone et al. (2013), Thévenon (2013), Dao et al. (2014), Christiansen et al. (2016) look into the determinants of female labor force participation and employment, and Blöndal and Scarpetta (1999) and Duval (2004) investigate the effects of retirement decisions.

17

The vast theoretical literature on labor supply offers a large number of models with different assumptions, including about (i) the ability of consumers to transfer capital across periods and to consider more generally a lifecycle framework, (ii) the extent to which labor supply decisions are taken by the household rather than the individual worker, (iii) the role of uncertainty about future income, household composition, and/or health status, and (iv) the manner in which government programs affect the incentives to work (see Blundell and MaCurdy, 1999, for a review). Developing a macroeconomic theory of labor supply encompassing all these features for different groups of workers is beyond the scope of this paper.

18

Appendix B discusses the construction of the variables in detail

19

Lagrange Multiplier tests point to the existence of serial correlation and the modified Wald test for group-wise heteroskedasticity indicates the presence of heteroscedasticity in the specifications for all population groups. Also, the Pesaran test and Frees test reject the null hypothesis of cross-sectional independence.

20

Some of the variables explored when looking at individuals (including choices about educational attainment, marriage, and fertility) are still simultaneous with decisions about participating in the labor force.

21

Appendix A reports the list of countries

22

The negative association between labor force participation and the share of population with partial or completed tertiary education in ages 15–24 likely reflects that they are still in school.

24

See Olivetti and Petrongolo (2017) and the references therein for a recent review of the evidence on the economic consequences of family policies, as well as, Jaumotte (2003), Genre et al. (2010), Blau and Kahn (2013), Cipollone et al. (2013), Thévenon (2013), Dao et al. (2014), Christiansen et al. (2016), and IMF (2016b).

25

See Blundell et al. (2016) and for a review of the literature on retirement incentives and labor supply, as well as IMF (2016b).

26

See, among others, Blau and Goodstein (2010) and Hurd and Rohwedder (2011) for evidence from the United States, and Borsch-Supan and Ferrari (2017) for evidence from Germany.

27

See CEA (2016), Eberstadt (2016), Krause and Sawhill (2017), and Abraham and Kearney (2018) for a review of the literature. Krueger (2017) discusses the poor health status of men not in the labor force, and the rising use of pain medication. Case and Deaton (2017) document an increase in mortality rates due to addiction, depression, and suicide (“deaths of despair”) among white, prime-age adults, and hypothesize this may be rooted in the steady deterioration of their job opportunities. Holzer et al. (2005), Pager et al. (2009), and Schmitt and Warner (2010) provide evidence of the drastic increase in incarceration rates, and ex-prisoner population in the United States, who face significant barriers to employment. Aguiar et al. (2017) argue that the decline in labor supply of young men could be linked to improvements in video gaming and other recreational computer activities. It should be noted, however, that the extent and direction of causality of these hypotheses is difficult to establish empirically. Abraham and Kearney (2018) provide a rough quantification of the role of various factors in employment rate trends in the United States since 1999 based on existing studies.

28

Evidence from sub-national data presented in Koczan and Hilgenstock (2018a), and Koczan and Hilgenstock (2018b) also suggests that the adverse effects of technological progress on participation may have been significantly more pronounced and longer-lasting in the United States than in Europe.

29

While baseline specifications do not control for household income due to data limitations and endogeneity concerns, we test the robustness of the results to the inclusion of the predicted income decile.

30

While the baseline specification relies on a cross-country panel, country-by-country estimates confirm these findings: the effects of vulnerability to routinization are significant and negative in most countries.

31

It should, however, be added that active labor market programs can be expensive, their success hinges crucially on specific design features, and evidence on their effectiveness more broadly is mixed (see IMF/WB/WTO, 2017 for a recent literature review). Surveying the evidence from North American and European studies, Heckman et al. (1999) conclude that public employment and training programs had at best a modest positive impact on earnings by raising employment probability. Card et al. (2010) find substantial variation in estimated program effectiveness across studies.

32

In the case of the regression of the aggregate participation rate, the SUR is not estimated.

33

The cross-country average relative price of investment across all AEs is used to minimize endogeneity concerns and capture changes that are due to global technological progress (rather than, for example, country-specific capital taxation policies).

Drivers of Labor Force Participation in Advanced Economies: Macro and Micro Evidence
Author: Mr. Francesco Grigoli, Zsoka Koczan, and Petia Topalova