## c2

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---

### Context and objectives
- Population growth in advanced economies is slowing, life expectancy is rising, and the number of elderly people is soaring; the United Nations projects that by the middle of this century, total population will be shrinking in almost half of advanced economies.
- The burden of aging will fall on those currently considered to be of working age, who in a few decades will support close to double the number of elderly people they do now.
- Focus: labor force participation because the participation rate, together with population growth, is the key determinant of labor supply; participation data have good geographic and temporal coverage by gender and age; economic theory guides expected life-cycle patterns of participation.
- Questions addressed:
  - Evolution of labor force participation rates across advanced economies, by worker characteristics, and changes after the global financial crisis.
  - Drivers of changes in aggregate participation and attachment of various groups: demographic shifts, cyclical effects (including global financial and European debt crises), policies/institutions, and demand-side shifts (automation, structural transformation).
  - Prospects for participation using cohort-based projections.

### Empirical approach and analysis structure
- Stock-taking of changes in labor force participation across groups over the past three decades.
- Complementary analytical approaches:
  - Quantify country-level participation change consistent with demographic shifts since the mid-2000s.
  - Assess drivers of participation for specific groups via cross-country and over-time analysis.
  - Analyze individual participation predictors: education, family composition, exposure to technological advances (routinization).
  - Evaluate long-term prospects using a cohort-based model.

### Aggregate and group-level key findings
- Aggregate patterns:
  - Over the past 30 years, the aggregate average labor force participation rate in advanced economies as a group has barely changed.
  - Several countries saw aggregate participation gain more than 5 percentage points (examples in source), while many others remained within a couple of percentage points of their 1985 rates.
  - Narrowing of the distribution of participation rates across advanced economies.
- Age and gender:
  - Female participation increased dramatically since the mid-1980s; for the median advanced economy, the female rate increased by close to 10 percentage points since the mid-1980s.
  - Participation picked up considerably among older workers (notably ages 55–64, and even those older than 65 in the past decade).
  - Young (ages 15–24) participation fell; greater school enrollment and investment in human capital are contributors.
  - Prime-age men (ages 25–54), particularly those with lower educational attainment, have become increasingly detached over the past 35 years; for the median advanced economy, male participation was more than 4 percentage points lower in 2016 than in 1985.
- Life expectancy:
  - Life expectancy at birth increased by about seven years, and at age 50 by more than five years, since 1985.

### Role of demographics and cyclical effects
- Aging and the drag from the global financial crisis explain a significant share of the decline in aggregate participation of men during the past decade.
- For women, participation increased despite aging and unfavorable cyclical developments, pointing to policies and other factors shaping labor supply.
- United States: participation declined significantly more than aging alone would predict.
- Cyclical drag from high unemployment after the crisis depressed participation, especially in Europe and the United States; this drag diminished as recovery took hold.

### Role of policies, institutions, structural change, and technology
- Policies and institutions (tax-benefit systems, public spending on active labor market programs, family-friendly measures), structural changes, and gains in educational attainment account for the bulk of the increase in labor force attachment of prime-age women and older workers in the past three decades.
- Technological advances (routinization) have weighed on participation rates of most groups; countries more exposed to routinization and declines in the relative price of investment saw larger declines in participation for certain occupations.
- Migration: net migration accounts for roughly half of the population growth in advanced economies since the mid-1980s; migration plays a significant role in alleviating aging pressures.

### Microdata evidence and mitigating factors
- Microdata (logit regressions on EU-LFS samples) confirm:
  - Exposure to routinization is significantly negatively associated with participation, larger for men and pronounced for workers 55 and older.
  - Higher spending on active labor market programs and education attenuates the negative association between routinization and detachment.
  - The negative effect of routinization is about one-third as large in countries at the 75th percentile of ALMP spending compared with the 25th percentile.
  - Urban areas show smaller negative effects of routinization, highlighting the importance of diverse local job pools.
- Household effects:
  - Being part of a couple and having children raises male participation but lowers female participation.
  - Tertiary education substantially raises odds of being active (e.g., tertiary education roughly doubles odds of being active relative to up-to-lower-secondary baseline, with larger effects for women).

### Decomposition and cross-country regression framework (groups: 15–24; 25–54 men; 25–54 women; 55+; 15+)
- Empirical specification: country and year fixed effects; controls for output gap (GAP), policies X, demand factors D, education Z; Driscoll-Kraay standard errors.
- Key group-specific associations (coefficient estimates; standard errors in parentheses; significance noted in source):
  - Lag of Output Gap:
    - Ages 15–24: 0.360*** (0.112)
    - Ages 25–54, men: 0.072*** (0.020)
    - Ages 25–54, women: 0.170* (0.092)
    - Ages 55+: –0.0060 (0.068)
    - Ages 15+: 0.183*** (0.044)
  - Routinization × Relative Price of Investment:
    - Ages 25–54, men: 0.302*** (0.048)
    - Ages 25–54, women: 1.793*** (0.206)
    - Ages 15+: 0.536*** (0.175)
  - Urbanization:
    - Ages 15–24: 0.668*** (0.142)
    - Ages 25–54, men: 0.101*** (0.019)
    - Ages 25–54, women: 0.355*** (0.071)
    - Ages 15+: 0.249*** (0.047)
  - Education (percent tertiary):
    - Ages 15–24: –0.275*** (0.057)
    - Ages 25–54, women: 0.332*** (0.030)
    - Ages 55+: 0.389*** (0.050)
    - Ages 15+: 0.135*** (0.031)
  - Tax Wedge:
    - Ages 25–54, women: –0.129*** (0.029)
    - Ages 55+: –0.263*** (0.037)
    - Ages 15+: –0.240*** (0.026)
  - Public Spending on ALMP:
    - Ages 15–24: 0.041*** (0.014)
    - Ages 25–54, women: 0.039*** (0.006)
    - Ages 15+: 0.031*** (0.007)
  - Public Spending on Early Childhood Education and Care (Ages 25–54, women): 3.708*** (1.210)
  - Share of Part-Time Employment (Ages 25–54, women): 0.946*** (0.118)
  - Job-Protected Maternity Leave (Ages 25–54, women): 0.025*** (0.006)
  - Statutory Retirement Age (Ages 55+): 0.661*** (0.174)
  - Public Spending on Old-Age Pensions (Ages 55+): –0.750*** (0.154)
- Model fit and sample sizes:
  - Number of Observations: 571 (columns 1, 2, 5), 489 (column 3), 568 (column 4).
  - Countries: 23.
  - R^2 by regression: Ages 15–24: 0.515; Ages 25–54, men: 0.606; Ages 25–54, women: 0.887; Ages 55+: 0.686; Ages 15+: 0.578.

### Regional and subnational evidence (United States and Europe)
- United States (metropolitan-area regressions; dependent variable: change in participation rate, 2000–16):
  - Average Real GDP Growth coefficients (selected): 0.442*** (0.145); 0.444*** (0.144); 0.368*** (0.140).
  - Change in Old-Age-Dependency Ratio: –0.144*** (0.040); –0.130*** (0.041); –0.152*** (0.038).
  - Initial Exposure to Routinization: –2.811** (1.153); –2.492** (1.222).
  - Initial Exposure to Offshoring: –4.212*** (0.935); –4.929*** (0.962).
  - Observations: up to 370; R^2 up to 0.414.
  - Interpretation: metropolitan areas with higher initial exposure to automation and offshoring saw larger declines in participation rates.
- Europe (regional cross-section):
  - Average Real GDP Growth coefficients (selected): 0.457 (0.325); 1.061*** (0.383); 1.176*** (0.387).
  - Change in Old-Age-Dependency Ratio: –0.282*** (0.056); –0.211*** (0.072); –0.218*** (0.072).
  - Initial Exposure to Routinization: 4.258** (1.995); 5.435*** (1.815).
  - Initial Exposure to Offshoring: 4.157** (1.968); 5.518*** (1.846).
  - Observations range across specifications; R^2 up to 0.730.
  - Interpretation: European regions more exposed to routinization and offshoring in 2000 experienced, if anything, larger participation gains during 2000–16—possible roles for added-worker effects, institutional frameworks, and smaller occupational mix changes.

### Migration and participation (Box highlights)
- Net migration accounted for about half of population growth in advanced economies since the mid-1980s.
- Box 2.4 projected participation scenarios:
  - Baseline aggregate participation rate would decline by 7.4 percentage points by 2050.
  - High migration: drop would be 0.8 percentage point less.
  - Low migration: drop would be 0.8 percentage point more.
  - If no new migration allowed, decline would be 2.7 percentage points larger.
- Migrant vs native participation (24 advanced European economies):
  - Young migrants participation: 42 percent; young natives: 36 percent.
  - Migrants 55 and older participation: 5 percent; natives: 6 percent.
  - Prime-age migrant women: 75 percent; prime-age native women: 81 percent.
  - Convergence: an additional year in host country increases odds of participation by 5–6 percent.
  - Counterfactual convergence: allowing migrants’ participation to increase to natives’ levels would raise overall participation by 1.4 percentage points (holding shares constant).

### Cohort-based projections and scenarios (trend to 2050)
- Cohort model: age-gender-group trend rates combined with UN World Population Prospects demographic projections.
- Baseline projection to 2050:
  - Median trend participation rate will fall by 5½ percentage points over the next 30 years (absent policies to boost participation).
  - A 5½ percentage point decline would translate into a 3 percentage point reduction in potential output by 2050 for the typical advanced economy (labor share of income assumed to be 56 percent).
  - Participation projected to hover around 50 percent or lower in Belgium, France, Italy, Portugal, and Spain.
- Illustration: Closing Gender Gaps scenario
  - Converge prime-age women’s participation to prime-age men over next 20 years.
  - Median aggregate participation would be 2½ percentage points higher by end of projection horizon relative to baseline.
- Illustration: Extending Working Lives scenario
  - Ages 55–59 converge to 50–54 rate over 20 years; ages 60–64 converge to 50–54 rate over 40 years; gender gaps unchanged.
  - In 2050, median aggregate participation is projected to be 2¾ percentage points higher than baseline.
  - Large increases among workers older than 65 could entirely offset or reverse the drag from aging.
- Illustration: Implementation of Policies scenario
  - Policies converge to 90th (or 10th) percentile of observed levels over next 20 years.
  - Forecast using cross-country coefficients suggests bringing policies to best-practice levels would raise aggregate participation by about 1¼ percentage points relative to baseline by 2050.

### Limits of policy, technological adjustment, and fiscal considerations
- Many countries have counteracted aging by strengthening attachment of specific groups, but dramatic demographic shifts could overwhelm policy ability to offset aging.
- If technology does not deliver offsetting productivity gains, reconsidering immigration policies and encouraging older workers to postpone retirement may be necessary.
- Policy measures can be costly and politically challenging because of cross-generational distributional consequences.
- Active labor market programs can mitigate displacement but may be expensive; effectiveness depends on design.

### Policy implications and recommendations (concise)
- Reduce disincentives to work: reform tax-benefit systems (for example, reduce labor tax wedge).
- Strengthen job-matching and activation policies: higher spending on ALMPs, training, and education.
- Family-friendly policies to raise female participation: public spending on early childhood education and care, flexible work arrangements, parental leave.
- Encourage longer working lives for older workers: raise statutory retirement ages, improve actuarial fairness of pension systems, protect safety nets for vulnerable individuals.
- Facilitate migrant integration: recognition of qualifications, language training, policies to accelerate labor market integration.
- Support geographically and sectorally targeted assistance for workers displaced by routinization and offshoring, including retraining and mobility support.

### Data, methods, and robustness notes
- Aggregate cross-country panel regressions cover 23 advanced economies, annual data 1980–2011; Driscoll-Kraay standard errors; extensive robustness checks (logistic transform, SUR, Beck and Katz, HAC/Newey-West, five-year averages, excluding GFC years, drop-one-country).
- Microdata: European Union Labour Force Survey (2000–16); logit regressions on random sample of 10,000 respondents per country per year (24 countries in some analyses; 18-country subsample for some regressions); standard errors clustered at country-year level; fixed effects for country, region, and year.
- Cohort model: system of 11 seemingly unrelated regressions by age group, cohorts born 1925–1994 across 17 advanced economies; trend participation predicted assuming zero output gap.

*International Monetary Fund | April 2018*

### Introduction

### c2 - Introduction

### Context and objectives
- Population growth in advanced economies is slowing, life expectancy is rising, and the number of elderly people is soaring. The United Nations projects that by the middle of this century, total population will be shrinking in almost half of advanced economies.
- The burden of aging will fall on those currently considered to be of working age, who in a few decades will support close to double the number of elderly people they do now.
- Unless more people participate in labor markets, aging could slow advanced economies’ growth and, in many cases, undermine the sustainability of their social security systems (Clements and others 2015).
- The chapter focuses on labor force participation because:
  - The participation rate, together with population growth, is the key determinant of labor supply.
  - Labor force participation data have good geographic and temporal coverage, and by gender and age group, and capture pent-up supply (part-time wanting full-time, unemployed willing to work).
  - Economic theory guides expected life-cycle patterns of participation.

- The chapter addresses the following questions:
  - How have labor force participation rates evolved across advanced economies? Do dynamics differ systematically by worker characteristics? Have trends changed after the global financial crisis?
  - What are the key drivers of changes in aggregate participation rates and attachment of various groups? Specifically:
    - How much of recent changes can be attributed to accelerated demographic shifts and cyclical effects, including recessions associated with the global financial and European debt crises?
    - Historically, what has been the role of policies and institutions that shape decisions to work, compared with forces that may have shifted demand for certain types of workers, such as automation and structural transformation?
  - What are the prospects for labor force participation?

### Empirical approach and analysis structure
- The chapter:
  - Takes stock of changes in labor force participation of different groups of workers in advanced economies over the past three decades.
  - Uses complementary analytical approaches:
    - Quantifies the change in country-level participation rates consistent with demographic shifts since the mid-2000s.
    - Assesses drivers of participation among specific groups by examining differences across countries and over time.
    - Analyzes predictors of individual participation decisions, including education, family composition, and exposure to technological advances.
    - Evaluates long-term prospects using a cohort-based model.

### Key findings (as presented in the chapter)
- Aggregate and group-level patterns:
  - Although aggregate labor force participation rates in advanced economies show divergent trajectories, surprisingly similar trends emerge across countries for specific groups of workers.
  - Participation by women has increased dramatically since the mid-1980s.
  - More recently, participation has picked up considerably among older workers and has fallen among the young.
  - In almost all advanced economies, prime-age men (ages 25–54), particularly those with lower educational attainment, have become increasingly detached from the labor force over the past 35 years, although participation rates are still high and vary little across countries.

- Role of demographics and cyclical effects:
  - Aging and the drag from the global financial crisis can explain a significant share of the decline in the aggregate participation rate of men during the past decade.
  - The rise in the participation rate of women, even as women’s average ages increased and despite unfavorable cyclical developments, underscores the important role of policies and other factors in shaping labor supply decisions and mitigating the effect of aging.

- Role of policies, institutions, and structural change:
  - Policies and institutions—such as the tax-benefit system, public spending on active labor market programs, and policies targeted to encourage specific groups to participate—together with structural changes and gains in educational attainment, account for the bulk of the dramatic increase in the labor force attachment of prime-age women and older workers in the past three decades.
  - Technological advances, such as routinization—the automation of tasks for which labor can be easily substituted by capital—have weighed on participation rates of most groups of workers.
  - The decrease in the relative price of investment is associated with larger declines in participation in countries more exposed to routinization because of the mix of their workers’ occupations, which may partially explain lower prime-age male participation.

- Microdata and mitigating factors:
  - Microdata confirm the significant impact of exposure to routinization on detachment from the labor force.
  - Policy efforts aimed at enhancing connective networks in labor markets can partially offset this effect.
  - Higher spending on active labor market programs and education is associated with a lower likelihood that a person previously employed in a routinizable occupation will drop out of the labor force.
  - This likelihood is also significantly lower in urban areas, pointing to the importance of access to diverse pools of jobs.

- Limits of policy and the role of migration:
  - Many countries have so far successfully counteracted the negative forces of aging on aggregate participation by strengthening attachment of specific groups.
  - Policies that reduce disincentives for joining or remaining in the labor force and policies that help workers combine family and work life can broaden gains.
  - Further investment in education, training, and activation policies can encourage labor market activity and make the workforce more resilient to technological progress and globalization.
  - Dramatic demographic shifts projected in advanced economies could overwhelm the ability of policies to offset aging.
  - Illustrative simulations suggest aggregate participation will eventually decline—even if gender gaps are fully closed—and participation of older workers must rise significantly to stem the decline.
  - Unless technology delivers offsetting productivity gains, many advanced economies will need to rethink immigration policies to boost labor supply and encourage older workers to postpone retirement.
  - Net migration accounts for roughly half of the population growth in advanced economies over the past three decades.

### Patterns of labor force participation documented
- Aggregate participation:
  - Over the past 30 years, the aggregate average labor force participation rate in advanced economies as a group has barely changed.
  - The group aggregate masks significant country differences: several countries saw aggregate participation gain more than 5 percentage points (for example, Germany, Korea, the Netherlands, and Spain), while many others remained within a couple of percentage points of their 1985 rates.
  - There has been a remarkable narrowing of the distribution of participation rates across advanced economies.

- Gender and age group trends:
  - Female participation: For the median advanced economy, the female labor force participation rate increased by close to 10 percentage points since the mid-1980s, with larger gains in countries where women were historically less likely to work, leading to convergence across advanced economies.
  - Male participation: For the median advanced economy, the participation rate among men was more than 4 percentage points lower in 2016 than in 1985.
  - Young (ages 15–24): Significantly less likely to be part of the labor force in 2016 than in 1985; declining attachment reflects greater investment in human capital and higher school enrollment rates.
  - Older workers (ages 55 and older): Participation increased significantly since the mid-1990s, particularly for ages 55–64, and in the past decade even those older than 65 have been remaining in the labor force longer.
  - Life expectancy: Life expectancy at birth increased by about seven years, and at age 50 by more than five years, since 1985, prompting many countries to adopt policies encouraging longer working lives.

### Caveats on interpretation
- The chapter emphasizes patterns and correlations rather than establishing causality between policies, structural, and individual characteristics and labor force participation.
- Many variables at the individual level—education, marriage, fertility—coincide with participation choices, and changes in labor market policies may reflect evolving societal and cultural attitudes.
- Sorting out causality is beyond the chapter’s scope; the aim is to present descriptive patterns and associations to guide potential policy action.

*International Monetary Fund | April 2018*

### 1. Changes in School Enrollment and Labor Force

### 1. Changes in School Enrollment and Labor Force

### Trends in Youth Participation and Returns to Education
- Labor force participation of the young (ages 15–24) in advanced economies is falling, while their school enrollment is rising.
- Reported regression/statistics shown in source:
  - y = –0.58**x – 0.81
  - y = –2.15*x – 0.40**
- Distribution and measures presented cover periods including 2000–12, 1987–2013, and 2000–16 with changes expressed in percentage points and percent.

### Prime-Age Participation: Men and Women (25–54)
- Women’s participation has increased almost across the board in advanced economies; men’s participation has stagnated or declined, especially for the less educated.
- Data highlights:
  - Prime age is defined as 25–54.
  - Young children are those below the age of 6; older children are those ages 6–14.
  - Educational attainment defined by ISCED 2011: primary = levels 0–2; secondary = levels 3–4; tertiary = levels 5–8.
- Cross-country patterns:
  - Rise in female labor force participation is widespread: single and married women, those with young children (below age 6) or older children (below age 15), natives and immigrants show higher participation in 2016 than in 2000 across Europe.
  - Decline in participation for prime-age men is deepest for those with the lowest educational attainment.
  - The United States shows particularly deep declines in participation for both women and men in the prime-age category across all levels of educational attainment.
- Why the decline in prime-age men’s participation is concerning:
  - Decline is broad-based across almost all advanced economies.
  - Prime-age men remain the largest segment of the labor force and traditional main income earners; even small declines can have sizable macroeconomic consequences.
  - Detachment during peak productive years is associated with lower happiness and life satisfaction, poorer health and higher mortality, and depressed employment prospects (citations in source).

- Labor force and population composition in 2015 for the average advanced economy:
  - Labor force composition: 37 percent prime-age men; 31 percent prime-age women; 11 percent ages 15–24; 21 percent older than 55.
  - Population composition: 20 percent prime-age men; 20 percent prime-age women; 12 percent ages 15–24; 31 percent older than 55.

### Nonparticipants: Composition and Reasons for Inactivity
- Nonparticipants include students, retired, those who have never worked, and those previously employed but no longer employed.
- Gender differences in reasons for inactivity:
  - Women more likely to report leaving the labor force to look after children.
  - Men more likely to report illness and disability as reasons for not being employed.
- A nontrivial share of the inactive are potentially “involuntarily inactive” (used to work but stopped due to economic/demand-side factors); dismissed-from-previous-job responses form a lower bound for this group.
- Time-series shifts (2000 to 2016) observed:
  - Share of students increased among the young and prime-age groups.
  - Share of those in (early) retirement among prime-agers fell.
  - Share of those who never worked fell among prime-age women and those 55 and older.
  - Illness and disability became relatively more important over time as a reason for nonparticipation.

### Sectoral and Occupational Patterns: Role of Routinization
- Involuntary nonparticipants drop out disproportionately from certain sectors:
  - Wholesale and retail trade, manufacturing, mining and quarrying, and utilities account for more than half of the involuntarily inactive, even though fewer than one-third of active workers are attached to these sectors.
- Excess involuntary inactivity tends to be concentrated in sectors with greater share of routine jobs that are vulnerable to automation.
- Displacement disproportionately affects lower- and middle-skill occupations.
- Vulnerability to routinization is especially pronounced in the middle and lower parts of the income distribution.
- Indexes and sector/occupation codes used in analyses (as listed in source): ACC, ADM, AGR, AGRIC, CLER, CON, CRAFT, EDU, ELC, ELEM, FIN, HEA, INF, MACH, MAN, MNF, MNG, OTH, PROF, PUB, REA, SERV, TECH, TRA, TRD, WAT.

### Participation Trends Before and After the Global Financial Crisis
- For young and older workers, little difference in participation trends for the median economy pre- and post-crisis.
- For prime-age workers:
  - Decline in participation accelerated for prime-age men after the global financial crisis.
  - The rate at which prime-age women joined the labor force slowed after 2008.
  - Employment rates increased in most advanced economies before the crisis, but have since declined in over half of them.
  - Before the crisis, employment gains were matched by unemployment declines and participation increases in most countries; postcrisis, employment declines translated into both rising unemployment and falling participation in about half the sample.
- Flows into inactivity suggest the share of discouraged workers (inactive now, but unemployed the previous year) has been increasing since the crisis and is approaching the precrisis peak.

### Conceptual Framework and Policy-Relevant Drivers of Participation
- Two key factors underpin aggregate participation changes:
  - Shifts in the age structure of the population.
  - Changes in labor force attachment of individuals of different ages.
- Individual determinants of participation decisions include gender, educational attainment, previous occupation, household structure.
- Institutional, policy, and cultural factors also matter:
  - Tax-benefit systems can directly affect incentives to supply labor (via labor tax wedge).
  - Wage-setting institutions can affect participation indirectly through labor demand.
  - Active labor market programs can induce participation by supporting jobseekers and preventing permanent detachment.
  - Cultural attitudes influence the disutility of market work.
- Policies tailored to specific groups can affect labor supply decisions:
  - Provision of childcare and family-friendly policies increase opportunity cost of staying home and can raise participation for parents.
  - For older workers, financial incentives and policies that affect retirement decisions are relevant.

*Source: c2 - 1. Changes in School Enrollment and Labor Force (PDF chapter).*

### 3. All, Ages 15–244. All, Ages 55 and Older

### 3. All, Ages 15–244. All, Ages 55 and Older

### Decomposition of labor market shifts (2000–16; 2000–08; 2008–16)
- Employment declines became more pronounced after the global financial crisis and increasingly translated into lower participation alongside rising unemployment (Figure 2.9).
- Change in employment rate, change in unemployment rate, and change in inactivity rate are shown in percentage points; country labels use ISO codes.

### Role of aging and cyclical conditions
- Analysis uses a shift-share decomposition separating:
  - “Within changes”: changes in participation rates within each age group (holding population shares fixed).
  - “Between changes”: shift in the relative sizes of age groups (holding participation rates fixed) — used to approximate the role of aging.
  - Interaction term.
- The cyclical component of participation changes is estimated from the historical relationship between detrended aggregate participation rates and output (or unemployment) gaps, allowing for differential response in severe recessions.
- Key aggregate findings (2008–16; Figure 2.10):
  - For men (average advanced economies): observed changes in participation are broadly consistent with shifts in the population age profile since 2008 and the drag from the global financial crisis.
  - For women (average advanced economies): participation increased significantly despite aging (although not in the United States), implying policies and other factors matter.
  - United States: participation declined significantly more than aging alone would predict.
  - Europe and other advanced economies: gains in participation within demographic groups partially offset or exceeded the drag from aging.
  - Cyclical drag from high unemployment after the crisis depressed participation, especially in Europe and the United States; this drag diminished as recovery took hold.

### Analytical approaches and scope
- Empirical strategy:
  - Cross-country panel regressions across 23 advanced economies (1980–2011, annual data) estimate reduced-form models of labor force participation for groups: ages 15–24; ages 25–54 men; ages 25–54 women; ages 55+; all ages 15+.
  - Specification includes country and year fixed effects, controls for output gap (GAP), policies X, demand factors D, education Z, and lags; Driscoll-Kraay standard errors used.
- Focused drivers:
  - Tax-benefit system: labor tax wedge; unemployment benefit generosity.
  - Active labor market programs (ALMP) spending.
  - Migrant integration policy restrictiveness.
  - Wage-setting institutions: union density; coordination of wage setting.
  - Women-specific: public spending on early childhood education and care; length of job-protected maternity leave; share of part-time employment.
  - Older-worker-specific: statutory retirement age; public spending on old-age pensions and incapacity.
  - Structural transformation and routinization: relative service employment; urbanization; trade openness; routinizability × relative price of investment.
  - Education: shares with secondary and tertiary education.

### Main empirical findings (associations; Table 2.1)
- General:
  - Education, cyclical conditions, long-lasting shifts in labor demand, and labor market policies are strongly associated with participation rates.
  - Responsiveness varies substantially across demographic groups.

- Selected coefficient estimates from Table 2.1 (coefficient estimate; standard error in parentheses; significance):
  - Lag of Output Gap:
    - Ages 15–24: 0.360*** (0.112)
    - Ages 25–54, men: 0.072*** (0.020)
    - Ages 25–54, women: 0.170* (0.092)
    - Ages 55+: –0.0060 (0.068)
    - Ages 15+: 0.183*** (0.044)
  - Routinization × Relative Price of Investment:
    - Ages 15–24: 0.3030 (0.299)
    - Ages 25–54, men: 0.302*** (0.048)
    - Ages 25–54, women: 1.793*** (0.206)
    - Ages 55+: 0.505* (0.288)
    - Ages 15+: 0.536*** (0.175)
  - Lag of Trade Openness:
    - Ages 15–24: 0.059*** (0.022)
    - Ages 25–54, men: –0.0050 (0.005)
    - Ages 25–54, women: 0.010 (0.014)
    - Ages 55+: –0.059*** (0.009)
    - Ages 15+: 0.012* (0.007)
  - Relative Service Employment:
    - Ages 15–24: –0.002 (0.010)
    - Ages 25–54, men: –0.002 (0.002)
    - Ages 25–54, women: 0.015*** (0.005)
    - Ages 55+: 0.0090 (0.006)
    - Ages 15+: 0.010** (0.004)
  - Urbanization:
    - Ages 15–24: 0.668*** (0.142)
    - Ages 25–54, men: 0.101*** (0.019)
    - Ages 25–54, women: 0.355*** (0.071)
    - Ages 55+: 0.1940 (0.115)
    - Ages 15+: 0.249*** (0.047)
  - Education (percent secondary):
    - Ages 15–24: –0.0500 (0.042)
    - Ages 25–54, men: 0.019*** (0.007)
    - Ages 25–54, women: 0.211*** (0.017)
    - Ages 55+: 0.038* (0.021)
    - Ages 15+: 0.063*** (0.017)
  - Education (percent tertiary):
    - Ages 15–24: –0.275*** (0.057)
    - Ages 25–54, men: 0.0190 (0.015)
    - Ages 25–54, women: 0.332*** (0.030)
    - Ages 55+: 0.389*** (0.050)
    - Ages 15+: 0.135*** (0.031)
  - Tax Wedge:
    - Ages 15–24: –0.103 (0.064)
    - Ages 25–54, men: –0.002 (0.015)
    - Ages 25–54, women: –0.129*** (0.029)
    - Ages 55+: –0.263*** (0.037)
    - Ages 15+: –0.240*** (0.026)
  - Unemployment Replacement Ratio:
    - Ages 15–24: –0.002 (0.068)
    - Ages 25–54, men: –0.041*** (0.007)
    - Ages 25–54, women: –0.035 (0.033)
    - Ages 55+: –0.081 (0.050)
    - Ages 15+: –0.078*** (0.025)
  - Public Spending on ALMP:
    - Ages 15–24: 0.041*** (0.014)
    - Ages 25–54, men: 0.0050 (0.005)
    - Ages 25–54, women: 0.039*** (0.006)
    - Ages 55+: –0.025** (0.009)
    - Ages 15+: 0.031*** (0.007)
  - Restrictiveness of Migrant Integration Policies:
    - Ages 15–24: 0.491*** (0.098)
    - Ages 25–54, men: –0.047** (0.020)
    - Ages 25–54, women: –0.462*** (0.049)
    - Ages 55+: 0.056 (0.088)
    - Ages 15+: –0.207*** (0.049)
  - Union Density:
    - Ages 15–24: –0.009 (0.068)
    - Ages 25–54, men: –0.001 (0.011)
    - Ages 25–54, women: 0.153*** (0.044)
    - Ages 55+: –0.115*** (0.032)
    - Ages 15+: –0.015 (0.025)
  - Coordination of Wage Setting:
    - Ages 15–24: 1.104*** (0.245)
    - Ages 25–54, men: 0.131** (0.063)
    - Ages 25–54, women: 0.701*** (0.219)
    - Ages 55+: 0.0400 (0.222)
    - Ages 15+: 0.256** (0.120)
- Additional group-specific policy variables (reported in table for relevant regressions):
  - Public Spending on Early Childhood Education and Care (Ages 25–54, women): 3.708*** (1.210)
  - Share of Part-Time Employment (Ages 25–54, women): 0.946*** (0.118)
  - Job-Protected Maternity Leave (Ages 25–54, women): 0.025*** (0.006)
  - Statutory Retirement Age (Ages 55+): 0.661*** (0.174)
  - Public Spending on Old-Age Pensions (Ages 55+): –0.750*** (0.154)
  - Public Spending on Incapacity (Ages 55+): –0.421 (0.562)

- Model fit and sample:
  - Number of Observations: 571 (columns 1, 2, 5), 489 (column 3), 568 (column 4).
  - Countries: 23.
  - R^2 by regression:
    - Ages 15–24: 0.515
    - Ages 25–54, men: 0.606
    - Ages 25–54, women: 0.887
    - Ages 55+: 0.686
    - Ages 15+: 0.578

### Interpretations and mechanisms
- Education:
  - Higher shares with secondary and especially tertiary education are associated with higher participation, particularly for prime-age women and older workers.
- Cyclical sensitivity:
  - Participation rates are procyclical, with larger associations for groups more marginally attached to the workforce (young and women).
- Structural transformation and job composition:
  - Relative gains in service employment are associated with increased entry of prime-age women into the labor force.
  - Urbanization is associated with higher participation across groups, likely via larger job opportunity sets.
- Technology and routinization:
  - In countries where the initial occupation mix is tilted toward routine tasks, a decline in the relative price of investment (automation) is associated with lower participation, indicating adjustment difficulties for displaced workers.
- Labor market policies:
  - ALMP spending is positively associated with participation for the young and prime-age women.
  - Generous unemployment benefits and higher tax wedges are negatively associated with participation for some groups (notably prime-age women and older workers).
  - Migrant integration policy restrictiveness shows heterogeneous associations across groups.

*Source: IMF staff calculations, World Economic Outlook: Cyclical Upswing, Structural Change (April 2018).*

### Box 2.2 and Box 2.3 for subnational evidence from the

### Box 2.2 and Box 2.3 for subnational evidence from the United States and Europe

### Policies and Participation: broad findings
- Higher labor tax wedges and more generous unemployment benefits are associated with lower labor force attachment for most groups of workers. Tax wedge is measured in percent of labor costs. The unemployment benefits gross replacement rate is measured in percent of work income.
- Higher public spending on active labor market programs tends to raise the share of young and prime-age women working or seeking employment. Activation policies are proxied by spending on active labor market programs per unemployed person as a share of GDP per capita.
- Policies that encourage the integration of migrants are associated with higher participation of prime-age workers, with more pronounced effects on women. A migration policy index is constructed by cumulating major changes in policies and regulations guiding postentry rights and other aspects of migrants’ integration, with a higher value denoting more restrictive policies.
- Higher coordination of wage setting is associated with greater labor force participation for most groups of workers; the correlation between unionization and participation is less robust.
- Empirical comparison of two predictive models for participation (one excluding labor market policies and one including them) shows that including policies raises the correlation between actual and predicted participation:
  - Correlation baseline: 0.21; Correlation baseline plus policies: 0.28
  - Correlation baseline: 0.34; Correlation baseline plus policies: 0.73
  - Correlation baseline: 0.66; Correlation baseline plus policies: 0.85
  - Correlation baseline: 0.29; Correlation baseline plus policies: 0.54

### Policies primarily affecting women and older workers
- Family-friendly policies are associated with higher participation among women. Specific measures noted:
  - Public spending on childcare and education is measured as percent of GDP.
  - Job-protected maternity leave is measured in weeks.
  - In the figures, * indicates an increase in the variable by 0.1 unit; ** indicates an increase in the variable by 10 units.
- Retirement incentives strongly affect participation of older workers:
  - Raising statutory retirement age is associated with delayed exit from the labor market. Statutory retirement age is measured in years.
  - Greater pension plan generosity encourages early retirement. The implicit tax on continued work is defined 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. The pension replacement ratio is the ratio of mean disposable income of those ages 65–74 to the mean disposable income of those ages 50–59.
  - Spending on old-age pensions and incapacity are measured as percent of GDP and are purged of fluctuations due to cyclical and demographic factors.

### Technology, structural change, and regional differences
- Technological change (routinization / automation) weighs on labor force participation for most groups, with the exception of the young.
- Combining policies, education, structural shifts, and technology for the period 1995–2011:
  - Supportive policies and educational gains were key factors behind the increase in participation of prime-age women and older workers; structural transformation contributed positively as well.
  - For the young, a large share of the decline in participation remains unexplained by the model and is captured by common time effects—possible drivers include global forces such as technological progress, globalization, changing returns to education, rising life expectancy, or effects of the global financial crisis.
- Geographical contrasts:
  - The rise in participation of US women relative to the average European trend can be attributed to more supportive policy changes in Europe and larger gains in educational attainment among prime-age European women.
  - The notable decline in participation of US prime-age men and youth relative to Europe contains a sizable unexplained residual; hypotheses specific to the United States include rising disability, opioid use, higher incarceration, and improved leisure technology.

### Micro-evidence on individual participation decisions
- Microdata analysis (logit regressions on a subsample of 18 countries) models individual participation as a function of personal characteristics, family composition, location, and exposure to routinization.
- Key micro findings:
  - Higher education strongly increases participation: tertiary education roughly doubles the odds of being active relative to attainment up to lower secondary education, with somewhat larger effects for women.
  - Living in an urban area raises participation, likely due to greater access to a diverse labor market.
  - Natives are more likely to participate than immigrants.
  - Family composition matters with strong gender asymmetries:
    - Being part of a couple and having children is associated with higher participation of men but lower participation of women (relative to being the only adult in a household without children).
    - More children are associated with lower participation of women, but higher participation of men.
  - The presence of other employed adults in the household is associated with a higher likelihood of being active, pointing to common labor market dynamics within households.
- Vulnerability to automation is measured by assigning a routinizability score to an individual’s (most recent) occupation following Chapter 3 of the April 2017 WEO and Das and Hilgenstock (forthcoming).

*Source: IMF staff calculations; Annex 2.4 and figures referenced in the text (1995–2011 period and microdata subsample of 18 countries).*

### Annex 2.5 provides a detailed description of the empirical

### c2 - Annex 2.5 provides a detailed description of the empirical

### Empirical findings on routinization and participation
- Exposure to routine tasks is significantly negatively associated with labor force participation; working or having worked in an occupation more vulnerable to routinization is associated with lower odds of participation.
- The negative effect of routinization is larger for men and especially pronounced for workers 55 and older.
- Magnitude example: a unit change in routinization scores roughly corresponds to the difference in the routinization score of technicians and the routinization score of managers. About 87 percent of prime-age male managers are active, about 84 percent of prime-age male technicians are in the labor force— the difference in their routinization scores alone can explain about one-third of this 3 percentage point difference in participation rates.
- Country-by-country estimates confirm that the effects of vulnerability to routinization are significant and negative in most countries and are typically more pronounced for men than for women.
- The negative effect of routinization is smaller in urban than in rural areas.

### Policy interactions and mitigating factors
- The analysis augments a logit model with an interaction between routinization score and policy measures (for example, spending on active labor market programs or employment protection).
- Policies can offset at least some of the negative association between routinization and participation.
- Higher spending on active labor market programs attenuates the link between participation and routinizability:
  - The negative association between routinizability and participation is about one-third as large in countries at the 75th percentile of active labor market spending as in countries at the 25th percentile.
  - Disaggregated data suggest the finding is driven by spending on training, which mitigates some of the negative effect for prime-age women.
- Stricter employment protection (making hiring and firing more difficult) offsets some of the adverse individual participation effect of being in a routinizable occupation, though possibly at the cost of reduced labor market flexibility and fewer job prospects for groups such as youth.
- For prime-age men, a higher level of wage-setting coordination is associated with a smaller negative effect of routinization.
- Findings underscore the importance of easing geographical mobility to help workers adjust to local labor demand shocks.
- Caveats on active labor market programs: they can be expensive; success hinges on design features; evidence on effectiveness is mixed.

### Micro and data notes
- Logit regressions are based on a random sample of 10,000 respondents per country per year from the European Union Labour Force Survey over the period 2000–16 and for 18 countries. Only effects significant at the 10 percent level are shown. The base category for education is “up to lower secondary education.” For family composition, the base category is “one adult without children.” Changes in odds ratios are shown. See Annex 2.5 for specification details.
- In Figure 2.16 notes, logit regressions are on a random sample of 10,000 respondents per country per year from the European Union Labour Force Survey over the period 2000–16 for 24 countries. Lines show 95 percent confidence interval. Lighter colors denote effects not statistically significantly different from each other at the 10 percent level. ALMP = active labor market programs; RUR = rural; URB = urban.

### Cohort-based analysis of participation
- A cohort-based model estimates trend labor force participation for finely disaggregated age groups of men and women across 17 advanced economies, accounting for age-gender-specific and birth-year-gender-specific determinants of labor supply.
- The model decomposes participation into age effects (age participation profile) and cohort effects (shifts from these profiles due to birth cohort characteristics).
- Age profiles: labor force attachment exhibits a hump shape over the life cycle; men are more likely to be part of the labor force than women across all ages, with the gender gap particularly pronounced during prime age.
- Cohort effects:
  - Trend male participation rates have not changed significantly across cohorts, except for a slight dip in participation of recent cohorts, notably deeper in the United States.
  - For women, there has been a large increase in participation across cohorts. Example: women born in the 1970s are 4 percentage points more likely to work or seek employment than women born in the early 1930s.
  - The dispersion of cohort effects for women is significantly smaller for later cohorts, signaling convergence across countries.
  - Cohort effects have plateaued recently and even edged down, especially in the United States, implying historical cohort-driven gains in female participation may no longer deliver further increases without policy action.

### Projection scenarios and quantitative impacts
- Baseline projection scenario to 2050:
  - Combines estimated age-gender-group trend rates with demographic projections from the United Nations World Population Prospects.
  - Absent policies to boost participation, the median trend participation rate will fall by 5½ percentage points over the next 30 years.
  - All else equal, a decline in aggregate participation of this magnitude would translate into a 3 percentage point reduction in potential output by 2050 for the typical advanced economy (labor share of income assumed to be 56 percent for the calculation).
  - Declines are broad-based; participation rates projected to hover around 50 percent or lower in Belgium, France, Italy, Portugal, and Spain.
- Illustration: Closing Gender Gaps scenario
  - Assumes prime-age women’s participation rates gradually converge to those of prime-age men over the next 20 years.
  - Under this scenario, the median aggregate participation rate would be 2½ percentage points higher by the end of the projection horizon relative to the baseline.
- Illustration: Extending Working Lives scenario
  - Assumes participation rate of ages 55–59 converges to that of ages 50–54 over the next 20 years, and participation rate of ages 60–64 converges to that of ages 50–54 over the next 40 years, keeping gender gaps unchanged.
  - In 2050, the median aggregate participation is projected to be 2¾ percentage points higher than in the baseline.
  - Large increases in participation among older workers, especially those older than 65, could entirely offset or reverse the drag from aging.
- Illustration: Implementation of Policies scenario
  - Assumes policy settings converge gradually over the next 20 years to the 90th (or 10th) percentile of levels observed among advanced economies (interpreted as “best possible” levels for participation).
  - Using estimated cross-country coefficients to forecast impacts by age-gender group and aggregating with projected demographic weights, this simulation suggests bringing policies to best-practice levels would raise aggregate participation by about 1¼ percentage points relative to the baseline by 2050.

*Sources: Das and Hilgenstock (forthcoming); Eurostat, European Union Labour Force Survey; and IMF staff calculations.*

### Conclusions and Policy Implications

### Conclusions and Policy Implications

### Key findings on population aging and participation
- The increase in longevity is one of the most remarkable successes in human history (Bloom and others 2015), but coupled with the decline in population growth it could have serious macroeconomic consequences.
- Older workers participate in the labor force at much lower rates; population aging raises concerns about the supply of labor in advanced economies, with implications for potential growth and the sustainability of social insurance systems.
- Despite the acceleration in population aging over the past decade, many advanced economies have been able to counteract its downward pressure on labor force participation; in about half of advanced economies, the aggregate labor force participation rate increased after the global financial crisis.
- Aggregate trends mask differing gender patterns:
  - Aggregate participation rates of men have declined since the crisis, broadly in line with changes in the age structure of populations and the drag from the global financial crisis.
  - Women’s participation increased in most countries, despite aging and adverse cyclical developments, underscoring the importance of policies and other factors in shaping participation rates.

### Long-term age-group developments
- Participation trends by age group over the past 35 years:
  - Participation of young men and women and prime-age men has been declining for the past 35 years.
  - Participation of prime-age women has increased dramatically since the mid-1980s.
  - Participation of older workers has picked up considerably since the mid-1990s.
- Data constraints on participation by age groups of workers older than 65 prevent simulations of alternative scenarios such as raising effective retirement ages to maintain the proportion of life spent in retirement or indexation of effective retirement ages to healthy life expectancy.

### Drivers of participation changes
- Changes in labor market policies and institutions, structural changes, and gains in educational attainment account for the bulk of the increase in labor force attachment of prime-age women and older workers in the past three decades.
- Technological advances, namely automation:
  - While beneficial for the economy as a whole, have weighed on the labor supply of most groups of workers and can partially explain declining prime-age male participation.
  - Individual-level evidence: detachment from the labor force is significantly more likely among individuals whose current or past occupations are more vulnerable to automation (vulnerability to routinization).
  - Higher spending on education and active labor market programs, and access to more diverse labor markets, tend to attenuate the negative effect of automation vulnerability.

### Projections and simulations
- In the absence of policy efforts, expected demographic developments could lead to large declines in aggregate participation rates.
- The chapter’s simulations imply that by 2050, overall participation rates could fall by 5½ percentage points in the median advanced economy.
- Simple illustrative simulations suggest that even if countries converge to the best (observed) policy settings for encouraging labor supply, expected demographic shifts may still depress participation rates in advanced economies, taking a toll on economic activity.

### Role of migration and technological progress
- Net migration accounts for roughly half of the population growth in advanced economies over the past three decades—any efforts to curb international migration would further exacerbate demographic pressure.
- Technological advances that transform production processes and reduce the need for labor could help alleviate the challenges to aggregate growth from aging, but:
  - Policymakers should be mindful of the difficult adjustment such transformations may entail for some sectors, occupations, and geographic areas.
  - Policies should deal with concerns of workers displaced by technology, including effective support for retraining, skill building, and occupational and geographic mobility.
  - Increasing investment in education and training can make the workforce more resilient to changing labor needs and encourage labor force participation.
  - Investing more in the education of the young is critical to prepare them for the jobs of the future.

### Policy implications and recommendations
- There is scope for policies to counteract aging by enabling those willing to work:
  - Reform the tax-benefit system—for example, by reducing the labor tax wedge—to encourage work.
  - Strengthen policies that improve the job-matching process.
  - Family-friendly policies are effective in attracting women to the labor force:
    - Public spending on early childhood education and care.
    - Flexible work arrangements.
    - Parental leave.
  - For older workers, reduce incentives to retire early by:
    - Raising statutory retirement ages.
    - Making pension systems more actuarially fair.
    - Ensure reforms do not jeopardize other goals, such as a basic social safety net for vulnerable individuals.
- If technological progress does not deliver offsetting productivity gains, many countries may need to reconsider immigration policies to boost domestic labor supply, alongside policies to encourage older workers to postpone retirement.
- Recognize that some policies may entail significant fiscal costs and may be politically challenging because of cross-generational distributional consequences.

### Youth labor force participation (Box highlights)
- Median labor force participation rates for the overall working-age population in advanced and emerging market and developing economies have fluctuated around 60 percent over the past 25 years.
- Youth labor force participation has fallen in both groups of economies; whether this is concerning depends on whether declines reflect growth in school enrollment or an increasing share of idle youth.
- Youth share of the population:
  - In emerging market and developing economies, young people comprise about 18 percent of the population on average, about 6 percentage points higher than their share in advanced economies.
- Secondary school enrollment:
  - For the median advanced economy, secondary school enrollment rose more than 10 percentage points since 1990, to about 97 percent in 2010.
  - In emerging market and developing economies, median secondary enrollment rose almost 40 percentage points, to about 70 percent.
- Gender gaps in youth labor force participation:
  - Median youth labor force participation has trended down for both females and males in advanced economies; the initial female participation gap of about 10 percentage points has shrunk to just a couple of percentage points in recent years.
  - The gender gap remains very large in emerging market and developing economies, at about 20 percentage points.
  - Individual-level analysis shows a wide range of youth gender gaps across countries, from about 5 percentage points to almost 70 percentage points in the latest year for which data are available, but a broad-based improvement over time (most countries show a shrinking gap).
- Policy responses for youth include a mix of labor market, social policy, and other reforms.

### US regional patterns (Box highlights)
- The decline in US labor force participation over the past two decades is broad based and deviates from many advanced European economies.
- Possible drivers for the US decline include cyclical effects, the severity of the Great Recession, structurally lower labor demand from trade and technology (especially for low-skill workers), lower labor supply (incarceration, disability, pain), waning cohort effects for women’s participation, and policy factors.
- Regional and metropolitan patterns:
  - Between 2000 and 2016, participation declined in almost all US states; declines were most pronounced in the Southeast and parts of the Midwest and West, and much smaller in the Mid-Atlantic and New England.
  - Pre-2000, participation increased almost across the board by an average of more than 5 percentage points between 1976 and 2000.
  - Labor force participation rates declined between 2000 and 2016 in three-quarters of metropolitan areas; among the 50 most populated areas declines were pronounced.
- Lower participation in metropolitan areas is strongly associated with exposure to routinization and offshoring, supporting the role of deteriorating job opportunities for some workers as a result of technology and globalization in their detachment from the workforce.

*Source: Conclusions and Policy Implications, Chapter 2, c2 - Conclusions and Policy Implications*

### 1. Change in Participation Rate, 2000–16

### 1. Change in Participation Rate, 2000–16

### Changes in US States and Metropolitan Areas
- Broad-based declines in labor force participation across US states and metropolitan areas between 2000 and 2016; only 16 states displayed increases (typically with already high participation), most of which were comparably small.
- Declines were typically larger for states as a whole than for their metropolitan areas, exacerbating urban-rural differences.
- Metropolitan areas are assigned to states based on the US Office of Management and Budget definition; for metropolitan areas assigned to multiple states, blue bars show population-weighted averages of surrounding states (Figure 2.2.2, panel 3).
- Decomposition of metropolitan-area labor market changes (Figure 2.2.3):
  - Period comparisons shown: 1990–2000, 2000–16, 2000–08, 08–16 (numbers represent simple averages across metropolitan areas).
  - Employment, unemployment, and inactivity rates defined as total employment, total unemployment, and total inactive population as a percentage of total population.
- Exposure maps show variation in 2000 initial routine and offshoring exposure by state (Figure 2.2.4).

### The Role of the Crisis and Changing Margins of Adjustment (US and metropolitan areas)
- Before 2000: employment increased on average, matched by declines in unemployment and increases in participation.
- After 2000: employment declined, matched by increasing unemployment and falling participation.
- After the global financial crisis, lower employment increasingly translated into lower participation; although most employment declines translated into rising unemployment before the crisis, after the crisis participation fell sharply.

### Drivers of Labor Force Participation (US metropolitan-area regressions)
- Cross-sectional regressions at the metropolitan-area level examine associations between 2000–16 changes in labor force participation rates and:
  - Cyclical conditions (average real GDP growth)
  - Aging (change in old-age-dependency ratio)
  - Education (change in postsecondary share)
  - Initial exposure to routinization and offshoring (proxies for jobs at risk of automation or offshoring)
- Key regression results (Table 2.2.1; dependent variable: change in labor force participation rate):
  - Column (1) Average Real GDP Growth: 0.442*** (0.145)
  - Column (2) Average Real GDP Growth: 0.444*** (0.144)
  - Column (3) Average Real GDP Growth: 0.368*** (0.140)
  - Change in Old-Age-Dependency Ratio: –0.144*** (0.040); –0.130*** (0.041); –0.152*** (0.038)
  - Change in Postsecondary Share: 0.037 (0.023); 0.040* (0.023); 0.053** (0.022)
  - Initial Exposure to Routinization: –2.811** (1.153); –2.492** (1.222)
  - Initial Exposure to Offshoring: –4.212*** (0.935); –4.929*** (0.962)
  - Observations: 370, 370, 335, 335, 335 (columns 1–5)
  - R^2: 0.289, 0.319, 0.360, 0.369, 0.414
- Interpretation:
  - Significant effects of cyclical conditions, aging, and education.
  - Metropolitan areas with higher initial exposures to automation and offshoring saw larger subsequent declines in participation rates, suggesting possible permanent displacement of some workers.

### Policy implications and recommendations (US context)
- In the short and medium term, support should be provided to workers displaced as a result of automation and globalization to dampen negative effects concentrated in particular sectors, occupations, or geographic areas.
- Support for displaced workers is important even if overall economy benefits from productivity gains or job creation in other sectors.

### Evidence for Europe (regional analysis)
- Heterogeneity across regions:
  - Labor force participation declined in about one-third of European regions between 2000 and 2016.
  - Participation declined in only about 27 percent of European regions between 2000 and 2008 and in about 45 percent of regions between 2008 and 2016.
  - Some countries exhibited uniform patterns across regions (e.g., declines in all regions in Norway and Romania; increases in all regions in Spain and Sweden); others showed significant within-country differences (e.g., France, Germany, Portugal, the United Kingdom).
- Urban-rural divide:
  - Rural areas saw larger drops or smaller increases in participation rates than urban areas (Figure 2.3.2, panel 3).
- Role of the crisis and margins of adjustment:
  - In Europe, employment increased on average until the crisis (with falling unemployment and rising participation). After the crisis, employment decline translated into rising unemployment and, on average, still small increases in participation—i.e., margins of adjustment changed later than in the United States.
- Drivers of participation in European regions (Table 2.3.1; dependent variable: change in labor force participation rate):
  - Average Real GDP Growth: 0.457 (0.325); 1.061*** (0.383); 1.176*** (0.387)
  - Change in Old-Age-Dependency Ratio: –0.282*** (0.056); –0.211*** (0.072); –0.218*** (0.072)
  - Change in Postsecondary Share: 0.187*** (0.053); 0.145** (0.069); 0.117* (0.070)
  - Initial Exposure to Routinization: 4.258** (1.995); 5.435*** (1.815)
  - Initial Exposure to Offshoring: 4.157** (1.968); 5.518*** (1.846)
  - Observations: 148, 148, 223, 140, 139 (columns 1–5)
  - R^2: 0.645, 0.644, 0.646, 0.730, 0.729
- Interpretation:
  - Cross-sectional evidence confirms significant effects of aging, cyclical conditions, and education.
  - Unlike the US findings, European regions more exposed to routinization and offshoring in 2000 experienced, if anything, larger participation gains during 2000–16.
  - Possible explanations for the positive correlation in Europe:
    - Long-horizon specification may capture added-worker effects (secondary earners entering labor market as household income falls), consistent with sharp rise in female participation and rise in two-earner households.
    - Institutional frameworks and policies in Europe may have allowed potentially affected workers to remain attached to the workforce and/or encouraged new entrants.
    - Smaller changes in occupational mix in Europe suggest fewer jobs automated or offshored than in the United States.
  - Policy implication: striking within-country differences call for explicit recognition of the spatial dimension of economic vulnerability.

### Migration and the Labor Supply in Advanced Economies
- Demographic context:
  - Slowing population growth and rising life expectancy put downward pressure on labor supply; aging may ultimately outweigh participation gains among marginally attached groups (women, older workers).
  - Net migration has accounted for about half of the population growth in advanced economies since the mid-1980s, while natural population growth (difference between fertility and mortality) has been falling (Figure 2.4.1).
- Migration effects via age composition:
  - Migrants are more likely to be of prime working age than natives (they typically arrive after completing education and often leave when they retire), affecting aggregate participation through age-composition effects (Figure 2.4.2, panel 1).
  - Eurostat scenarios illustrate expected evolution of aggregate labor force participation in advanced European economies under alternative migration scenarios; differences stem solely from changes in age composition due to net migration.
  - Example (Eurostat baseline scenario): it would imply an increase in Germany’s migrant stock from the current 14 percent to 29 percent.
- Policy implication:
  - Migration assumptions embedded in population projections play a very significant role in alleviating aging pressures.
  - In the absence of migration, decline in participation would be significantly deeper.
  - Support for migrants’ rapid labor market integration will yield significant further gains.

*Source: IMF staff, Box 2.2 and Box 2.3 (World Economic Outlook: Cyclical Upswing, Structural Change, April 2018).*

### Box 2.4. Storm Clouds Ahead? Migration and Labor Force Participation Rates

### Box 2.4. Storm Clouds Ahead? Migration and Labor Force Participation Rates

### Projected Participation Scenarios and Key Statistics
- Baseline aggregate participation rate would decline by 7.4 percentage points by 2050.
- Allowing for an increase in net migration:
  - Drop would be 0.8 percentage point less under the assumption of high migration.
  - Drop would be 0.8 percentage point more under low migration.
- If no new migration is allowed, the decline in participation would be 2.7 percentage points larger.
- These migration effects would be especially large in high-migration countries.

### Participation Effects of Migration (age, gender, and compositional effects)
- Disaggregated data from 24 advanced European economies:
  - Young migrants are more likely to be in the labor force than young natives: 42 percent versus 36 percent.
  - Participation among migrants 55 and older is slightly lower than for natives: 5 percent versus 6 percent.
- Prime-age workers:
  - Participation of prime-age men is very similar for natives and migrants.
  - Participation of prime-age women differs: migrant women 75 percent versus native women 81 percent.
- Convergence over time:
  - Migrant participation rates converge toward those of natives with years in the host country, especially for prime-age women.
  - An additional year in the host country is estimated to increase the odds of participation by 5–6 percent (holding individual and household characteristics constant).
- Counterfactual convergence effect:
  - Allowing migrants’ participation rates to increase to natives’ participation rates would result in an additional 1.4 percentage point increase in overall participation (relative to a no convergence scenario), even holding the relative shares of the age groups in the population constant.

### Migrants’ Participation Decisions (drivers and differences relative to natives)
- Factors that increase the odds of being active for both migrants and natives:
  - Higher education.
  - Household composition matters.
  - Lower routinizability (threat of automation) is linked to higher likelihood of being active.
- Differences for migrants versus natives:
  - The positive effect of higher education on the odds of being active is significantly smaller for migrants (suggesting recognition of foreign qualifications or language barriers).
  - Household composition effects are much larger for migrants: being married and having children has larger negative effects on the participation of migrant women than on that of native women.
  - Local labor market effects are weaker for migrant women.

### Policy Implications and Recommendations
- Policies that support migrant integration could increase the positive effect of migration on participation beyond age-composition effects. Examples highlighted:
  - Recognition of educational qualifications.
  - Language training.
- Broader economic effects of higher migration flows:
  - Could contribute to labor supply and the host economy by increasing output per capita through boosting demand and investment.
  - Could contribute to technological progress and increase labor productivity via skill complementarity.
- Such integration-enhancing policies could help mitigate future negative effects of aging and help make social safety nets more sustainable in advanced economies.

### Data, Methods, and Coverage Notes
- Micro-level analysis based on 2000–16 European Union Labour Force Surveys (Eurostat); disaggregated statistics described use a random sample of 10,000 respondents per country per year.
- Countries included in the micro-level figures: AUT, BEL, CYP, CZE, DEU, DNK, ESP, EST, FIN, FRA, GBR, GRC, IRL, ITA, LTU, LUX, LVA, MLT, NLD, NOR, PRT, SVK, SVN, and SWE (labels use ISO country codes).
- Logit regressions are based on a 10,000 respondents per country per year random sample of 18 countries; regressions control for age, gender, urban/rural location, year, country and region fixed effects, and the output gap. Only effects significant at the 10 percent level are shown.
- The additional 1.4 percentage point counterfactual assumes population shares of the eight groups (young, prime-age men, prime-age women, and people 55 and over for natives and migrants separately) are constant at their 2000–16 average shares, and assumes prime-age and 55-plus migrants’ participation increase to natives’ levels while young migrants’ higher participation remains constant.

*Source: Box 2.4, "Storm Clouds Ahead? Migration and Labor Force Participation Rates," World Economic Outlook, April 2018.*

### Annex   Figure 2.2 .4 .  Average Annual Changes   in Labor Force

### Annex   Figure 2.2 .4 .  Average Annual Changes   in Labor Force Participation Rates

### Shift-Share Analysis: decomposition of aging effect
- Gender-specific aggregate labor force participation rate LFP_i,t^a is expressed as the sum across age groups g (15–24, 25–54, 55–64, 65 and over) of group-specific participation rates LFP_i,t^{a,g} weighted by population shares pop_i,t^{a,g} / pop_i,t^a (equation (2.1)).
- The aging effect is computed as the difference between the actual participation rate and a counterfactual rate obtained by holding gender- and age-group-specific participation rates at their 2008 levels LFP_i,2008^{a,g} while allowing observed population shares to vary.

### Estimating the role of cyclical conditions
- Detrended aggregate labor force participation LFP*_i,t is obtained with the Hodrick-Prescott (HP) filter (alternative detrending via the Corbae-Ouliaris (CO) filter or deviations from a three-year moving average produce qualitatively similar results).
- Regression specification (equation (2.2)) for LFP*_i,t includes:
  - UG_{i,t−k}: unemployment gap (current unemployment minus NAIRU),
  - Crisis_{i,t−k}: dummy = 1 for currency crisis, sudden stop, debt crisis, or banking crisis (Gourinchas-Obstfeld database),
  - interaction terms UG × Crisis,
  - country fixed effects π_i and time fixed effects τ_t,
  - k = 0,1 lags (baseline uses k = 1; richer lag structures give qualitatively similar results).
- The cyclical effect at time t is the regression’s predicted value; the change since 2008 is measured as the difference in the predicted cyclical component relative to its 2008 value.
- Results are robust to allowing sensitivity to the cycle to vary across economies and to alternative detrending methods (CO filter, three-year moving average), which limit HP endpoint distortions.

### Aggregate cross-country analysis: policies and other factors (equation (2.3) framework)
- Dependent variable: participation rate of worker group g (groups: 15–24; prime-age men 25–54; prime-age women 25–54; older workers 55+; all 15+).
- Regressors:
  - X: policies and institutions (some group-specific),
  - D: demand-side shifts for group g,
  - GAP_{i,t−1}: cyclical position (output gap; results similar using unemployment rate),
  - Z: other labor-supply determinants (education),
  - country and time fixed effects π_i^g and τ_t^g (equation (2.3)).
- Inference: Driscoll and Kraay (1998) correction to standard errors used to address cross-country dependence, autocorrelation, and heteroscedasticity; results robust to alternative standard-error corrections.

### Key explanatory variables and measurements (as used in the aggregate analysis)
- Cyclical position: output gap (alternative: unemployment rate).
- Exposure to technological progress: interaction of relative price of investment and country exposure to routinization (occupation-level routinization scores following Autor and Dorn (2013)); average relative price of investment across advanced economies used to limit endogeneity.
- Structural transformation: relative service employment (service/industrial employment) and share of urban population.
- Education: share with highest level reported as primary, secondary, or tertiary (Barro-Lee database).
- Labor tax wedge: ratio of average tax for a single-earner family to total labor cost (OECD 2000–16, extended back to 1979; odd-year series interpolated for even years).
- Unemployment benefit generosity: gross replacement rate (OECD; averaged across earnings levels, family situations, and durations; odd-year series interpolated).
- Active labor market program (ALMP) spending: ALMP spending per unemployed person as percent of GDP per capita (OECD).
- Restrictiveness of migration policy: cumulative index from DEMIG POLICY database (higher = more restrictive).
- Union density: net union membership as proportion of wage earners (OECD).
- Coordination of wage setting: 1–5 index from Amsterdam Institute for Advanced Labour Studies (higher = more centralized).
- Work–household reconciliation proxies: public spending on early childhood education and care (% of GDP); proportion of employees with part-time contracts; job-protected maternity/parental/extended leave (weeks).
- Retirement incentives: statutory retirement age (Social Security Programs sources); generosity proxied by old-age and incapacity spending (% of GDP) purged of cyclical and demographic influences; robustness checks use implicit tax on continued work and aggregate replacement ratio (ages 65–74 vs 50–59, Luxembourg Income Study).

### Decomposition of contributions
- Contributions from regressors S ∈ {X, D, GAP, Z} to changes in participation of group g between years t and t′ are computed as Ĉ_{i,t,t′}^{S,g} = β̂_{S,g} (S_{i,t′}^g − S_{i,t}^g) (equation (2.4)).

### Robustness and estimation checks (summary of Annex Tables 2.4.1–2.4.5 approach)
- Results presented separately for youth (15–24), prime-age men (25–54), prime-age women (25–54), older workers (55+), and aggregate 15+ participation (Annex Tables 2.4.1–2.4.5).
- Robustness variants include:
  - Logistic transformation of dependent variable,
  - Seemingly unrelated regressions (SUR) across groups,
  - Beck and Katz (1995) estimator,
  - HAC and Newey-West standard-error corrections,
  - Five-year averages to control cyclical distortions,
  - Dropping 2008–2009 (GFC) years,
  - Expanding sample to include economies reclassified as advanced after 2006,
  - Replacing output gap with unemployment rate,
  - Dropping one country at a time to test influence of individual economies.
- Baseline sample for youth regressions: 23 advanced economies, annual data 1980–2011; Driscoll-Kraay standard errors reported in baseline columns.
- Example baseline coefficient magnitudes and significance for youth (Annex Table 2.4.1, column (1) baseline):
  - Lag of Output Gap: 0.360*** (Driscoll-Kraay standard error (0.112))
  - Routinization × Relative Price of Investment: 0.303 (0.299)
  - Lag of Trade Openness: 0.059*** (0.022)
  - Relative Service Employment: −0.002 (0.010)
  - Lag of Urbanization: 0.668*** (0.142)
  - Education (percent tertiary): −0.275*** (0.057)
  - Tax Wedge: −0.103 (0.064)
  - Public Spending on ALMP: 0.041*** (0.014)
  - Restrictiveness of Migrant Integration Policies: 0.491*** (0.098)
  - Coordination of Wage Setting: 1.104*** (0.245)
  - Number of observations: 571; Countries: 23; R^2 = 0.515
- Significance notation: * p < .10; ** p < .05; *** p < .01.

*Source: IMF staff calculations (Annex Figure 2.2.4 and accompanying Annex 2.3–2.4 text).*

### Annex Table

### Annex Table (Robustness of Drivers of Labor Force Participation)

### Prime-Age Male (Ages 25–54): Robustness (Annex Table 2.4.2)
- Sample and model:
  - Sample of 23 advanced economies (AEs) during 1980–2011 using annual data; all specifications include country and year fixed effects.
  - Number of Observations: 571 (columns with full sample), 489 (SUR), 132 (five-year averages), 525 (excluding GFC), 593 (including all AEs).
  - Countries: 23 (most columns), 25 (including all AEs).
  - R 2: 0.606 (baseline), 0.622 (logistic transformation), 0.997 (Beck and Katz specification), 0.695 (five-year averages), 0.611 (including all AEs).
- Robust positive and statistically significant drivers (baseline column (1) unless otherwise noted):
  - Lag of Output Gap: 0.072*** (standard error (0.020)).
  - Routinization × Relative Price of Investment: 0.302*** (0.048).
  - Lag of Urbanization: 0.101*** (0.019).
  - Education (percent secondary): 0.019*** (0.007).
  - Unemployment Replacement Ratio: –0.041*** (0.007).
  - Coordination of Wage Setting: 0.131** (0.063).
- Robust negative and statistically significant drivers:
  - Lag of Trade Openness: –0.005 (not significant in baseline), but significant in some specifications (e.g., column (3) –0.012*** (0.004)).
  - Restrictiveness of Migrant Integration Policies: –0.047** (0.020) in baseline.
- Other notable coefficients (baseline):
  - Relative Service Employment: –0.002 (0.002).
  - Education (percent tertiary): 0.019 (0.015).
  - Tax Wedge: –0.002 (0.015).
  - Public Spending on ALMP: 0.005 (0.005).
  - Union Density: –0.001 (0.011).
- Robustness checks reported:
  - Column (2): logistic transformation; column (3): SUR; column (4): Beck and Katz (1995); column (5): HAC standard errors; column (6): Newey-West; column (7): five-year averages; column (8): excluding GFC years 2008–2009; column (9): including Czech Republic and Slovak Republic; column (10): replacing lag of output gap with lag of unemployment rate; column (11): median coefficient from drop-one-country distribution (10th and 90th percentiles reported).

### Prime-Age Female (Ages 25–54): Robustness (Annex Table 2.4.3)
- Sample and model:
  - Sample of 23 AEs during 1980–2011 using annual data; all specifications include country and year fixed effects.
  - Number of Observations: 489 (most columns), 117 (five-year averages), 443 (excluding GFC), 511 (including all AEs).
  - Countries: 23 (most columns), 25 (including all AEs).
  - R 2: 0.887 (baseline), 0.870 (logistic), 0.971 (Beck and Katz), 0.891 (Newey-West), 0.881 (five-year averages).
- Robust positive and statistically significant drivers (baseline column (1)):
  - Routinization × Relative Price of Investment: 1.793*** (0.206).
  - Lag of Output Gap: 0.170* (0.092).
  - Relative Service Employment: 0.015*** (0.005).
  - Lag of Urbanization: 0.355*** (0.071).
  - Education (percent secondary): 0.211*** (0.017).
  - Education (percent tertiary): 0.332*** (0.030).
  - Public Spending on ALMP: 0.039*** (0.006).
  - Public Spending on Early Childhood Education and Care: 3.708*** (1.210).
  - Share of Part-Time Employment: 0.946*** (0.118).
  - Job-Protected Maternity Leave: 0.025*** (0.006).
  - Union Density: 0.153*** (0.044).
  - Coordination of Wage Setting: 0.701*** (0.219).
- Robust negative and statistically significant drivers (baseline):
  - Tax Wedge: –0.129*** (0.029).
  - Restrictiveness of Migrant Integration Policies: –0.462*** (0.049).
- Other notable coefficients (baseline):
  - Unemployment Replacement Ratio: –0.035 (0.033) (not significant in baseline but significant in some specs).
- Robustness checks as in male table; column (11) reports 10th and 90th percentiles (e.g., Routinization × Relative Price of Investment: (1.672; 1.914)).

### Older Workers (Ages 55 and over): Robustness (Annex Table 2.4.4)
- Sample and model:
  - Sample of 23 AEs during 1980–2011 using annual data; all specifications include country and year fixed effects.
  - Number of Observations: 568 (most columns), 489 (SUR), 132 (five-year averages), 522 (excluding GFC), 589 (including all AEs).
  - Countries: 23 (most columns), 25 (including all AEs).
  - R 2: 0.686 (baseline), 0.681 (logistic), 0.925 (Beck and Katz), 0.737 (Newey-West), 0.666 (including all AEs).
- Robust positive and statistically significant drivers (baseline column (1)):
  - Education (percent tertiary): 0.389*** (0.050).
  - Statutory Retirement Age: 0.661*** (0.174).
- Robust negative and statistically significant drivers (baseline):
  - Lag of Trade Openness: –0.059*** (0.009).
  - Tax Wedge: –0.263*** (0.037).
  - Public Spending on Old-Age Pension: –0.750*** (0.154).
  - Public Spending on ALMP: –0.025** (0.009).
  - Union Density: –0.115*** (0.032).
- Other notable coefficients (baseline):
  - Routinization × Relative Price of Investment: 0.505* (0.288).
  - Lag of Urbanization: 0.194 (0.115).
  - Education (percent secondary): 0.038* (0.021).
  - Public Spending on Incapacity: –0.421 (0.562) (not significant in baseline).
  - Coordination of Wage Setting: 0.040 (0.222) (not significant).
- Robustness: column (10) (replacing output gap with unemployment rate) shows Lag of Output Gap coefficient median across drop-one-country distribution (–0.045; 0.014) in column (11).

### Aggregate Labor Force Participation (Ages 15 and older): Robustness (Annex Table 2.4.5)
- Sample and model:
  - Sample of 23 AEs during 1980–2011 using annual data; all specifications include country and year fixed effects.
  - Number of Observations: 570 (most columns), 132 (five-year averages), 524 (excluding GFC), 592 (including all AEs).
  - Countries: 23 (most columns), 25 (including all AEs).
  - R 2: 0.578 (baseline), 0.569 (logistic), 0.983 (Beck and Katz), 0.596 (five-year averages), 0.602 (including all AEs).
- Robust positive and statistically significant drivers (baseline column (1)):
  - Lag of Output Gap: 0.183*** (0.044).
  - Routinization × Relative Price of Investment: 0.536*** (0.175).
  - Lag of Urbanization: 0.249*** (0.047).
  - Relative Service Employment: 0.010** (0.004).
  - Education (percent secondary): 0.063*** (0.017).
  - Education (percent tertiary): 0.135*** (0.031).
  - Public Spending on ALMP: 0.031*** (0.007).
  - Coordination of Wage Setting: 0.256** (0.120).
- Robust negative and statistically significant drivers (baseline):
  - Tax Wedge: –0.240*** (0.026).
  - Unemployment Replacement Ratio: –0.078*** (0.025).
  - Restrictiveness of Migrant Integration Policies: –0.207*** (0.049).
- Other notable coefficients (baseline):
  - Lag of Trade Openness: 0.012* (0.007).
  - Union Density: –0.015 (0.025) (not significant in baseline).
- Robustness: column (9) replacing output gap with unemployment rate reports a median (10th; 90th) for Lag of Output Gap of (0.143; 0.2) in column (10) (drop-one-country).

### Estimation and Standard-Error Notes (applies across tables)
- Columns and standard-error treatments:
  - Column (1): Baseline; Driscoll-Kraay standard errors reported in parentheses in columns (1), (2), (7)–(10).
  - Column (2): Logistic transformation of dependent variable.
  - Column (3): SUR estimation (bootstrapped standard errors reported in column (3) for several tables).
  - Column (4): Beck and Katz (1995) estimator (HAC standard errors assuming a panel-dependent correlation structure reported in column (4) where indicated).
  - Column (5): HAC standard errors (without correction for cross-sectional dependence).
  - Column (6): Newey-West corrected standard errors.
  - Column (7): Five-year averages.
  - Column (8): Excluding global financial crisis (GFC) years 2008 and 2009.
  - Column (9): Including Czech Republic and Slovak Republic (recently joined AEs).
  - Column (10): Replacing lag of output gap with lag of unemployment rate.
  - Column (11): Median coefficient from distribution of estimates obtained by dropping one country at a time; column (11) reports the 10th and 90th percentile of estimated coefficients in parentheses.
- Significance notation:
  - * p < .10; ** p < .05; *** p < .01.

*Source: IMF staff calculations.*

### Annex 2.5. The Role of Individual and

### Annex 2.5. The Role of Individual and Household Characteristics: Micro-Level Analysis

### Data and empirical approach
- Sample: European Union Labour Force Survey for 24 advanced economies during 2000–16.
- Estimation: Logit models on a random sample of 10,000 people per country per year.
- Dependent variable: Dummy indicating whether someone is in or out of the labor force.
- Fixed effects and clustering:
  - Country, region, and year fixed effects included.
  - Results robust if interacted country-year fixed effects are included instead.
  - Standard errors clustered at the country-year level.
- Main labor force status coding: employed, unemployed, or out of the labor force (students, retired, permanently disabled, compulsory military service, domestic tasks, otherwise inactive), assigned based on activity during the reference week.

### Explanatory variables and controls
- Demographics and background:
  - Age; Age Squared.
  - Gender (for the 55 and older group).
  - Born in country or abroad.
  - Urban or rural residence.
  - Highest level of education: lower secondary (base), upper secondary, tertiary.
- Family composition:
  - Number of children.
  - Other employed adults in the household.
  - Household type categories (baseline = one adult without children): one adult with children; couple without children; couple with children; other household structure.
- Occupation:
  - Routinization score of current occupation (if employed) or last occupation (if unemployed/inactive).
- Macro control:
  - Lagged output gap.
- Income: baseline specification does not control for income due to data limitations. Robustness checks include:
  - Actual income decile for employed individuals.
  - Predicted income decile for unemployed/inactive (predicted using age, gender, education, migration status, location, sector, occupation; and country, region, and year fixed effects).

### Robustness and interpretation regarding income
- When (predicted) income decile is included:
  - The effect on women’s participation of being part of a couple and having children turns positive.
  - The effect of other employed adults in the household turns negative.
  - Income itself has a negative effect, suggesting individuals in upper deciles may be able to afford to drop out of the labor force.
- Results on vulnerability to routinization and education are very similar to those in the baseline.

### Key regression coefficients (exponentiated coefficients from logit regressions)
Note: All specifications include country, region, and year fixed effects. Base category for education is “up to lower secondary education.” Base category for family composition is “one adult without children.” Standard errors clustered at the country-year level. *p < .10; **p < .05; ***p < .01.

- Age
  - Men, Ages 25–54: Age 1.158*** (0.011); Age Squared 0.998*** (0.000)
  - Women, Ages 25–54: Age 1.320*** (0.014); Age Squared 0.997*** (0.000)
  - All, Ages 55+: Age 1.396*** (0.113); Age Squared 0.998*** (0.001)
  - Men, Ages 25–54 (with predicted income decile): Age 1.261*** (0.018); Age Squared 0.997*** (0.000)
  - Women, Ages 25–54 (with predicted income decile): Age 1.347*** (0.021); Age Squared 0.997*** (0.000)
  - All, Ages 55+ (with predicted income decile): Age 1.356*** (0.151); Age Squared 0.998*** (0.001)

- Male (indicator)
  - All, Ages 55+: Male 1.196*** (0.031) (column 3)
  - All, Ages 55+ (with predicted income decile): Male 1.539*** (0.046) (column 6)

- Education (base = up to lower secondary)
  - Upper Secondary
    - Men, Ages 25–54: 1.719*** (0.032)
    - Women, Ages 25–54: 1.709*** (0.033)
    - All, Ages 55+: 1.209*** (0.036)
    - Men, Ages 25–54 (with income): 1.737*** (0.056)
    - Women, Ages 25–54 (with income): 1.855*** (0.060)
    - All, Ages 55+ (with income): 1.102** (0.046)
  - Tertiary
    - Men, Ages 25–54: 2.759*** (0.082)
    - Women, Ages 25–54: 2.961*** (0.077)
    - All, Ages 55+: 1.594*** (0.059)
    - Men, Ages 25–54 (with income): 2.217*** (0.097)
    - Women, Ages 25–54 (with income): 2.763*** (0.115)
    - All, Ages 55+ (with income): 1.240*** (0.063)

- Born in Country
  - Men, Ages 25–54: 1.489*** (0.035)
  - Women, Ages 25–54: 1.333*** (0.024)
  - All, Ages 55+: 1.091** (0.046)
  - Men, Ages 25–54 (with income): 1.761*** (0.051)
  - Women, Ages 25–54 (with income): 1.520*** (0.050)
  - All, Ages 55+ (with income): 1.167** (0.075)

- Urban
  - Men, Ages 25–54: 1.008 (0.019)
  - Women, Ages 25–54: 1.024* (0.013)
  - All, Ages 55+: 1.019 (0.027)
  - Men, Ages 25–54 (with income): 0.896*** (0.027)
  - Women, Ages 25–54 (with income): 0.864*** (0.022)
  - All, Ages 55+ (with income): 0.866*** (0.037)

- Number of Children in Household
  - Men, Ages 25–54: 1.049*** (0.009)
  - Women, Ages 25–54: 0.816*** (0.007)
  - All, Ages 55+: 0.960* (0.020)
  - Men, Ages 25–54 (with income): 1.094*** (0.012)
  - Women, Ages 25–54 (with income): 0.869*** (0.012)
  - All, Ages 55+ (with income): 1.039 (0.035)

- One Adult with Children (household type)
  - Men, Ages 25–54: 1.042 (0.059)
  - Women, Ages 25–54: 0.846*** (0.026)
  - All, Ages 55+: 1.785*** (0.394)
  - Men, Ages 25–54 (with income): 1.045 (0.087)
  - Women, Ages 25–54 (with income): 0.846*** (0.039)
  - All, Ages 55+ (with income): 1.217 (0.330)

- Couple without Children (household type)
  - Men, Ages 25–54: 1.356*** (0.035)
  - Women, Ages 25–54: 0.906*** (0.034)
  - All, Ages 55+: 0.842*** (0.025)
  - Men, Ages 25–54 (with income): 1.757*** (0.083)
  - Women, Ages 25–54 (with income): 1.741*** (0.128)
  - All, Ages 55+ (with income): 1.161*** (0.051)

- Couple with Children (household type)
  - Men, Ages 25–54: 1.726*** (0.052)
  - Women, Ages 25–54: 0.757*** (0.028)
  - All, Ages 55+: 1.446*** (0.128)
  - Men, Ages 25–54 (with income): 2.141*** (0.114)
  - Women, Ages 25–54 (with income): 1.248*** (0.088)
  - All, Ages 55+ (with income): 2.429*** (0.350)

- Other Household Structure
  - Men, Ages 25–54: 0.937** (0.027)
  - Women, Ages 25–54: 0.868*** (0.030)
  - All, Ages 55+: 0.812*** (0.038)
  - Men, Ages 25–54 (with income): 1.212*** (0.063)
  - Women, Ages 25–54 (with income): 1.334*** (0.092)
  - All, Ages 55+ (with income): 1.726*** (0.138)

- Other Employed Adult(s) in Household
  - Men, Ages 25–54: 1.497*** (0.035)
  - Women, Ages 25–54: 1.152*** (0.038)
  - All, Ages 55+: 1.703*** (0.091)
  - Men, Ages 25–54 (with income): 0.992 (0.043)
  - Women, Ages 25–54 (with income): 0.601*** (0.046)
  - All, Ages 55+ (with income): 0.636*** (0.079)

- Routinization Score of Occupation
  - Men, Ages 25–54: 0.825*** (0.011)
  - Women, Ages 25–54: 0.900*** (0.010)
  - All, Ages 55+: 0.716*** (0.013)
  - Men, Ages 25–54 (with income): 0.467*** (0.012)
  - Women, Ages 25–54 (with income): 0.490*** (0.012)
  - All, Ages 55+ (with income): 0.488*** (0.016)

- Lagged Output Gap
  - Men, Ages 25–54: 1.037*** (0.006)
  - Women, Ages 25–54: 1.023*** (0.004)
  - All, Ages 55+: 1.031*** (0.007)
  - Men, Ages 25–54 (with income): 1.042*** (0.008)
  - Women, Ages 25–54 (with income): 1.030*** (0.008)
  - All, Ages 55+ (with income): 1.037*** (0.012)

- Predicted Income Decile (included in columns 4–6)
  - Predicted Income Decile 0.952*** (0.001) — reported for Men, Women, All (columns 4–6)

- Number of Observations
  - Column 1 (Men, Ages 25–54): 491,820
  - Column 2 (Women, Ages 25–54): 474,240
  - Column 3 (All, Ages 55+): 86,441
  - Column 4 (Men, Ages 25–54 with income): 474,434
  - Column 5 (Women, Ages 25–54 with income): 443,687
  - Column 6 (All, Ages 55+ with income): 63,982

### Implications highlighted by the micro analysis
- Education and lower routinization are strongly associated with higher probability of being in the labor force.
- Household composition and presence of other employed adults affect participation differently by gender and age.
- Macroeconomic conditions (lagged output gap) are positively associated with being in the labor force.
- Higher (predicted) income decile is negatively associated with participation, consistent with higher-income individuals being able to opt out.

### Link to cohort-based projection approach (Annex 2.6) — methodological note
- Cohort-based analysis (Annex 2.6) estimates a system of 11 seemingly unrelated regressions (one for each age group) for each country, separately for men and women, covering cohorts born between 1925 and 1994.
- Age-group-specific trend participation rates are predicted assuming a zero output gap; aggregate trend is a three-year moving average of age-group trend rates weighted by population shares.
- Projected scenarios for trend participation use United Nations World Population Prospects data (medium fertility and migration), with three illustrative scenarios:
  - Scenario 1: For ages 25–54, women’s participation rates gradually converge to those of men over the next 20 years.
  - Scenario 2: For ages 55–59, participation converges to the 50–54 rate over 20 years; for ages 60–64, to the 50–54 rate over 40 years.
  - Scenario 3: Policies converge to the 90th (or 10th) percentile of levels observed among advanced economies over the next 20 years; impacts simulated using coefficients from the cross-country empirical model.

*Source: IMF staff calculations.*

### Annex to Eurostat Metadata. Luxembourg.

### Annex to Eurostat Metadata. Luxembourg.

### References
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### Key topics and themes represented in the cited literature
- Female labour force participation and its determinants, trends, and future prospects
- Immigration, migration, and effects on native workers, wages, and innovation
- Gender gaps, family policies, and gender equality implications for labour markets
- Labour market entry during recessions, scarring effects, and long-term career impacts
- Employment protection, collective wage agreements, and institutional determinants of labour market flows
- Job polarization, routine-biased technological change, offshoring, and STEM labour dynamics
- Unemployment effects on mortality, life satisfaction, and psychological outcomes
- Social security, retirement, and pension effects on labour supply
- Measurement and econometric methods for panel data, cross-section dependence, and dynamic models
- Trade openness, economic diversification, and macroeconomic policy impacts on labour outcomes
- Healthy life expectancy and population projections

*Source: Annex to Eurostat Metadata. Luxembourg.*

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_Source: https://www.imf.org/-/media/files/publications/weo/2018/april/c2.pdf_