Abstract
This Selected Issues paper assesses the youth unemployment problem in advanced European economies, especially the euro area. Youth unemployment rates increased sharply in the euro area after the crisis. Much of these increases can be explained by output dynamics and the greater sensitivity of youth unemployment to economic activity compared with adult unemployment. Labor market institutions also play an important role, especially the tax wedge, minimum wages, and spending on active labor market policies. The paper highlights that policies to address youth unemployment should be comprehensive and country specific, focusing on reviving growth and implementing structural reforms.
Youth Unemployment in Europe: Okun’s Law and Beyond1
Youth unemployment rates increased sharply in the euro area after the crisis. Much of these increases can be explained by output dynamics and the greater sensitivity of youth unemployment to economic activity compared to adult unemployment. Labor market institutions also play an important role, especially the tax wedge, minimum wages, and spending on active labor market policies. Policies to address youth unemployment should be comprehensive and country specific, focused on reviving growth and implementing structural reforms.
A. Context
1. Youth unemployment has increased sharply in Europe in the aftermath of the global crisis in 2008 and remains at historic highs in the current weak recovery. Youth unemployment has moved up the policy agenda in Europe, and policies to deal with this issue have been formulated at both the EU (European Commission, 2012 and 2013a) and national levels.
2. This paper assesses the youth unemployment problem in advanced European countries, especially the euro area. It documents the main trends in youth and adult unemployment before and after the crisis (Section B), identifies the cyclical and structural drivers of youth and adult unemployment (Section C, D and E) and outlines elements of a comprehensive strategy to address the problem (Section F).
B. Stylized Facts
3. Historically high rates. Youth unemployment rates are currently at unprecedented levels in the euro area. 2 The global crisis has reversed a decade-long trend of modest declines in youth unemployment; the youth unemployment rate in the euro area in 2013 (some 28 percent) is almost double the pre-crisis level (15 percent in 2007).
4. Larger than adult unemployment. Adult unemployment has also ticked up after the crisis, but less so than youth unemployment. While unemployment rates typically tend to be higher for the youth than for adults because of a smaller youth labor force,3 these differences have increased sharply after the global crisis.
Euro Area:Youth Unemployment Rate
(Percent)
Citation: IMF Staff Country Reports 2014, 199; 10.5089/9781498308007.002.A001
Source: EurostatEuro Area: Adult Unemployment Rate
(Percent)
Citation: IMF Staff Country Reports 2014, 199; 10.5089/9781498308007.002.A001
Source: Eurostat5. Wide divergence across countries. Youth unemployment rates vary widely across the euro area in magnitude as well as in trajectory. These cross-country differences have been exacerbated during the crisis. The hardest hit euro area countries have had unprecedented increases in youth unemployment rates, ranging from 25 percent in Ireland to 43 percent in Spain on average during 2007–13. In countries that fared better, youth unemployment rates increased only marginally (Austria, Netherlands) or even fell (Germany). Pre-crisis youth unemployment rates, however, have little bearing on the youth unemployment dynamics after the crisis.
Levels and Changes in Youth Unemployment
Citation: IMF Staff Country Reports 2014, 199; 10.5089/9781498308007.002.A001
Source: Eurostat, Staff estimates6. Significant consequences. Large and persistent youth and adult unemployment rates lower potential output due to hysteresis effects (skill attrition and depreciated human capital) or the outward migration of skilled labor. Youth unemployment erodes social cohesion and institutions. For individuals it may lead to scarring—a lower probability of future employment and lower wages.
C. Determinants
7. Framework. This paper analyzes the relative significance of two main drivers of youth unemployment—business cycle fluctuations and the institutional setup and features of the labor market. It contrasts the impact of these factors on youth versus the adult labor market.
8. Coverage. The analysis covers 22 advanced European countries—18 in the euro area, as well as Denmark, Sweden, Norway and the United Kingdom. It is based on annual data from 1980 to 2012, although the actual size of the sample varies depending on data availability, especially for labor market characteristics.
9. Labor market features. Labor market features are grouped into several interrelated categories: the opportunity cost of working; hiring costs; the role of collective bargaining; measures of labor market duality; education and training; and spending on active labor market policies. A number of different measures are used in each category. Labor market features vary widely across countries but change slowly over time (see Annex 1 for definitions).
10. Addressing data limitations. Data on labor market characteristics is usually not available for the full sample period, and is especially limited for new entrants to the euro area. Data gaps make it infeasible to produce country-by-country estimations to determine the country-specific effects of institutional variables and the business cycle. As a workaround for the data limitations, a two-pronged approach was adopted. One approach was to restrict the impact of labor market features to be the same, while allowing the impact of the business cycle to vary across countries. This approach was implemented using one labor market factor at a time as well as a combination of factors. The second approach also assumes common effects of labor market factors across countries, but it allows the impact of labor market institutions to vary across countries via its interaction with the business cycle. However, to ensure robustness, only one institutional variable was considered at a time (see Annex 2 for more details).
D. The Business Cycle
11. Okun’s Law. The Okun’s Law, proposed by Arthur Okun in 1962, is the empirical regularity that changes in unemployment rates and output growth are negatively related.4 Many studies confirm this for overall unemployment but research on youth is less common. Some authors highlight the sensitivity of youth unemployment to the business cycle (OECD (2006), Scarpetta (1996), Scarpetta et.al. (2010), European Commission (2013b)).
12. Okun’s law holds… Empirical analysis unambiguously confirms that the Okun’s law holds. This is true regardless of how the business cycle is measured—by real GDP growth or the output gap (i.e., the difference between the actual output and its potential). Cyclical factors explain around 50 percent of the changes in youth unemployment rates (and 70 percent of the increase in unemployment rates in stressed euro area countries) and around 60 percent of changes in adult unemployment rates across all advanced European countries.
13. …with wide variation across countries. The sensitivity of unemployment rates to the business cycle (Okun’s coefficient) varies across countries. Estimates range from not significantly different from zero (e.g., Austria5) to -1.9 in Spain, i.e., a one percent decline in growth increases youth unemployment rates by almost 2 percentage points. Countries with the biggest increases in youth unemployment rates since the crisis tend to be those that are most affected by the business cycle (i.e., they have the largest Okun’s coefficients).
14. Much more sensitive to the cycle. In every country the estimated Okun’s coefficient is higher for the youth than for adults, on average almost three times as large. This may be due to both the nature of youth labor force (described above) and special features of youth employment: concentration in cyclically sensitive industries and in small and medium-sized enterprises (SMEs).
Euro Area: Okun’s Law Coefficients/1
Citation: IMF Staff Country Reports 2014, 199; 10.5089/9781498308007.002.A001
/1 Shaded bars and dots indicate insignificant resultsSource: Eurostat, Staff estimatesYouth Employment By Sector, 2000–07, percent
Citation: IMF Staff Country Reports 2014, 199; 10.5089/9781498308007.002.A001
Source: Eurostat, Staff estimates15. Concentration in cyclically sensitive sectors. Youth employment is concentrated in sectors which tend to be more sensitive to the business cycle: manufacturing, construction and real estate, wholesale and retail trade, and hotels and restaurants. Together these sectors comprise between 65–75 percent of youth employment in countries where youth unemployment increased the most after the global crisis.
16. Concentration in SMEs. SMEs6 employ the majority of the labor force, with the average employment share especially high in some Southern European countries—more than 75 percent for Spain, Italy and Portugal, for example. SMEs face unique financial constraints in the current environment of financial fragmentation and private sector deleveraging. This appears to increase the extent to which youth unemployment rates respond to growth (Box 1).
SMEs. The Role of Financing Constraints and Youth Unemployment
Methodology. The Okun’s law framework is augmented with the interaction of GDP growth and the average share of SMEs, or with financial constraints. Financing constraints are measured as the percentage of firms in industry and services sectors which report in surveys financial constraints as a factor limiting production. The surveys do not separate SMEs as a separate category, but it is reasonable to assume that they are more affected by financial constraints than larger firms.1
Results.
– Greater financial constraints are associated with higher youth unemployment. Controlling for country-specific fixed effects and output gap, an additional percentage point of firms reporting financial constraints raises youth unemployment rates by 0.3–0.4 percentage points (industrial and services sector respectively). The effect on adult unemployment rates is smaller (0.2 percentage points).
– A percentage point increase in the average employment share in SMEs (or the SME share of value added) lowers the Okun’s coefficient by 0.01, making unemployment more pro-cyclical.
SME Share in Employment, percent
(Average, 2008–2013)
Citation: IMF Staff Country Reports 2014, 199; 10.5089/9781498308007.002.A001
Source: European Commission staff estimatesE. Labor Market Features
17. Not only the business cycle. Output changes on average explain much of the increase in youth unemployment, but not in every country in Europe. Excluding countries worst affected by the crisis—Greece, Portugal, Spain, Latvia, and Ireland—cyclical factors explain on average about 35 percent of the changes in youth unemployment rates across advanced European countries (e.g., 33 and 27 percent in France and Italy, both of whom have high youth unemployment rates). What are the other explanatory factors? In particular, to what extent do labor market institutions and skills play a role in explaining labor market outcomes across advanced European economies as well as within individual countries?
How much does growth explain changes in youth unemployment?
Citation: IMF Staff Country Reports 2014, 199; 10.5089/9781498308007.002.A001
Source: Eurostat, Staff estimates18. Role of labor markets. There is a large body of literature on the role of labor market characteristics in determining aggregate unemployment in Europe (most prominently, the much cited 1994 OECD Jobs Strategy). Some of the labor market characteristics considered in the literature to have an impact on youth unemployment include unionization (Bertola, 2007), hiring and firing regulations, minimum wages and hiring costs (Bernal-Verdugo, 2012), and labor market flexibility (OECD, 2006, Choudhury et.al. (2012)).
19. Labor markets explain size of unemployment, not changes. Labor market features have significant effects on the levels of youth and adult employment and unemployment, but not on changes.7 Put simply, the rapid divergence of youth unemployment rates in the aftermath of the crisis has not been accompanied by dramatic changes in labor market features.
20. Several features matter. A number of labor market features have an impact on youth unemployment rates, in particular, lower hiring costs (tax wedge, minimum wages relative to the median wage), and higher spending on active labor market policies per unemployed person (ALMP), especially on training, reduce unemployment. The opportunity costs of working (e.g., benefit replacement rates) and low skill levels tend to raise unemployment, whereas collective bargaining (e.g., union density), labor market duality (e.g., protection of workers), have mixed effects. These effects remain significant after controlling for several labor market features at a time.
21. Variable effects across countries. Allowing for interactions between labor market features and the business cycle reveals significant cross-country differences in the impact of labor market features on labor market outcomes. For instance, a 1 percentage point increase in the tax wedge increases youth unemployment rates by between 0.6–1.4 percentage points in univariate models with the interaction term.8
High Hiring Costs, High Unemployment
22. Theory. Taxes on employers and employees, in combination with statutory minimum wage rates, affect both the supply and demand for labor. High tax rates on labor income depress the supply of labor and drive a wedge between marginal productivity and the reward for work. If higher taxes translate into higher wages, then the increase in labor costs can reduce labor demand and increase unemployment. High payroll taxes and employers’ social security contributions are even more likely to raise labor costs in the presence of wage floors generated by statutory minimum wages. If employers succeed in shifting the tax burden to the employees in the form of lower wages, this could reduce labor supply, especially for low-wage earners (which would conceivably include the youth). OECD (2012) shows that since 2007 young people have on average been at a big disadvantage in countries in which the minimum wage is relatively high as a percentage of median pay.
23. Recent developments. Hiring costs remain above average for several countries. The tax wedge declined in the Scandinavian countries and Germany, while either remaining unchanged or even increasing in other European countries (in particular, Greece and Spain). The minimum wage to median wage ratio has remained unchanged for the vast majority of advanced European countries while marginally increasing in France, Greece, Portugal and Spain.9
Tax Wedge, percent
Citation: IMF Staff Country Reports 2014, 199; 10.5089/9781498308007.002.A001
Source: European Commission.Note: latest data for Cyprus is for 2007.Ratio of Minimum to Median Wage
Citation: IMF Staff Country Reports 2014, 199; 10.5089/9781498308007.002.A001
Source: OECD24. Empirical results. The empirical analysis indicates that greater hiring costs—larger tax wedge and/or minimum wages relative to the median wage—are associated with higher youth and adult unemployment rates and lower employment rates for both youth and adults. A 1 percentage point increase in the tax wedge raises youth unemployment rates by between 0.3-1.3 percentage points. The effect on adult unemployment is smaller (around 0.4-0.5 percentage points). Higher minimum wages (relative to median wages) raise youth unemployment by 0.4–1.2 percentage points. Because many young people are hired at minimum wage jobs, they may be particularly vulnerable to increases in the cost of hiring.
High Opportunity Cost of Working, High Unemployment
25. Theory. High unemployment benefits raise unemployment by reducing the willingness to search intensively for jobs and/or to accept job offers (lower labor supply), and by increasing the reservation wage, i.e., the salary at which the unemployed would be willing to work instead of receiving unemployment benefits (lower labor demand). Moreover, tax and benefit systems can interact to create an unemployment or inactivity trap which arises when individuals who qualify for social protection benefits have little financial incentive to work because the combined effects of increased tax payments and withdrawn income-tested benefits offset the potential gain in disposable incomes from increased earnings.
26. Recent developments. Overall, the picture remains largely unchanged over 2001–12, regardless of which indicators are used to capture the incentives of the unemployed to seek work (the net benefit replacement rate or the “inactivity trap”). There are a few exceptions, e.g., Germany and some Scandinavian countries which have reduced the benefit replacement rates and measures of the “inactivity trap.” In contrast, these indicators have remained unchanged for the vast majority of advanced European countries while marginally increasing in the euro area countries under stress.
Net Replacement Rate, percent
Citation: IMF Staff Country Reports 2014, 199; 10.5089/9781498308007.002.A001
Source: European CommissionInactivity Trap, percent
Citation: IMF Staff Country Reports 2014, 199; 10.5089/9781498308007.002.A001
Source: European Commission27. Empirical results. Higher opportunity costs of working are associated with higher youth and adult unemployment rates. In univariate models with the interaction term, a one percentage point increase in net replacement rates raises youth unemployment rate by 0.1–0.5 percentage points depending on the country, more than it does for adults (0.1–0.2 percentage points). Country-specific estimates find significant and positive effects of higher marginal tax rates on income (inactivity trap) on youth unemployment for most countries, with a stronger effect for youth unemployment than for adults. The young may be more sensitive to net replacement rates because unemployment benefits allow them time to find a more desirable job. However, an aggregate indicator may mask country-specific differences in eligibility for unemployment benefits, e.g. in some countries, people who never had a job may not be eligible for unemployment benefits.
Dual Labor Markets, Shifting Composition of Unemployment
28. Theory. Dual labor markets feature a high share of temporary employment contracts with lower employment protection. Studies show that the impact of employment protection legislation (EPLs)—legislation governing the hiring and firing of employees—on labor market outcomes is small and ambiguous.10 It can lower job separation rates by increasing the cost of firing, but also the job finding rate by increasing the reluctance to hire workers in the first place. Labor market duality has been associated with lower youth employment rate in a sample of 17 OECD countries over 1960–1996 (Bertola et al., 2007).
29. Recent developments. Young workers tend to be employed on temporary contracts more so than adult workers. The disparity between the adults and youth in this regard is particularly large in Spain, Italy and Portugal, which have had some of the largest increases in youth unemployment. In Spain, labor market adjustments have focused on shedding workers on temporary contracts, i.e. mainly the youth.
Protection of Temporary Workers, rating
Citation: IMF Staff Country Reports 2014, 199; 10.5089/9781498308007.002.A001
Source: OECDShare of Temporary Workers,
(percent, 15–24 years)
Citation: IMF Staff Country Reports 2014, 199; 10.5089/9781498308007.002.A001
Source: Eurostat30. Empirical results. Higher protection for temporary contracts lowers unemployment rates for youth and adults, but the effects for youth are stronger since a higher share is employed on temporary contracts. A unit increase in the EPL rating11 lowers youth unemployment rates by 2.5-5 percentage points and adult unemployment by 1.5–2 percentage points. A higher share of youth on temporary contracts increases youth unemployment and lowers employment by 0.3–0.4 percentage points, but it has no significant effects on adult employment or unemployment rates. Hence stronger labor market duality can shift the composition of employment toward adults.
Stronger Collective Bargaining, Limited Effect on Youth Unemployment
31. Theory. Higher incidence of collective bargaining has the potential to lower employment, but the impact of collective bargaining depends on the level at which the bargaining occurs. Firm level bargaining tends to limit wage increases beyond productivity levels, thereby having less of an impact on employment and unemployment rates. Very centralized or coordinated bargaining systems may also be less detrimental to employment as the overall impact on the labor force can be explicitly incorporated in the bargaining process, and thereby minimize the effect on unemployment. Thus the relationship between the strength of collective bargaining and unemployment tends to be hump-shaped, having the worst effects on unemployment when collective bargaining systems are neither fully centralized nor decentralized.
32. Empirical results. Overall, higher union density has a limited effect on youth unemployment (i.e., not significant). Some specifications indicate that a percentage point increase in union density could lower youth unemployment rates by 0.2–0.6 percentage points.12 However, this finding is not robust to alternative specifications, including variations in control variables or allowing country specific interactions. The results from some specifications suggest that higher union density may be associated with an altered employment composition as well, perhaps because employers prefer to hire the non-unionized youth instead of the unionized adults in order to preserve the flexibility to adjust the work force as needed, leading to higher employment for the youth and lower employment for adults.
Union Density, percent
Citation: IMF Staff Country Reports 2014, 199; 10.5089/9781498308007.002.A001
Source: OECDMore Vocational Training, Less Unemployment
33. Theory. Educational attainment may have a large impact on employability (OECD, 2013). Vocational training and expanded access to training could play a significant role in reducing temporary work and contribute to making temporary jobs a stepping stone toward open-ended contracts (OECD, 2004).
34. Recent developments. The share of workers in the population with low-education has been declining steadily across all countries. But the level of formal education may not provide a complete picture of the skills of the young unemployed. Vocational training and apprenticeships are also important forms of skilling, but are difficult to measure. Survey data on the reason for temporary contracts suggests that temporary contracts for the youth are associated with education, training, or probation in countries with low youth unemployment rates like Germany, Netherlands, and Austria. The share of those in education, training and probation is relatively smaller in Greece, Spain, Portugal, and Cyprus.
Reason for Temporary Work Contract Among Youth, 2012, percent
Citation: IMF Staff Country Reports 2014, 199; 10.5089/9781498308007.002.A001
Source: Eurostat35. Empirical results. Access to vocational training—measured by the share of temporary workers under probation or vocational training—significantly reduces youth unemployment by around 0.3 percentage points, but has no significant effect for adults. A higher share of individuals with low education has generally no significant effects on youth unemployment or employment rates, but has a strong negative effect on adult unemployment and employment rates. Low education may be less of an obstacle for youth employment, perhaps because young workers can be more easily trained than adults.
More Spending on Active Labor Market Policies, Lower Unemployment
36. Theory. According to OECD (2006), most macro-econometric studies have found significant positive effects of spending on ALMP, especially on training, on aggregate unemployment. However, microeconomic evaluation studies of ALMPs find that the effectiveness of programs vary, and that apparently similar programs can yield very different outcomes (e.g., Card, et. Al., 2010, and Kluve, 2010). Studies also show that ALMP programs that specifically target young people are not very effective regardless of the type of the program (i.e., they have a lower probability of yielding positive results).
37. Recent developments. Spending on ALMP varies widely across countries, and several countries have increased spending in this area after the crisis. Given dramatic increases in unemployment during the crisis, ALMP funds have had to be distributed across greater numbers of the unemployed.
38. Empirical results. Higher spending on active labor market policies, especially training, is associated with significant reductions in youth unemployment rates. An additional 1000 euro per unemployed increase in ALMP spending reduces youth unemployment by around 0.3 and adult unemployment by around 0.1 percentage points. It also raises employment rates by 0.2 percentage points for youth and 0.1 percentage points for adults.
F. Conclusions and Policy Recommendations
39. No magic bullet. The youth unemployment problem in the euro area is multi-faceted and varies across countries. Substantial cross-country differences in the composition and dynamics of youth unemployment suggest that no single policy at the EU or national level is likely to solve the problem. The solution would need to target the country-specific factors affecting youth unemployment. Experience from other countries indicates that there is no one-size-fits all approach to tackling youth unemployment (ILO, 2013).
40. Strong sustainable growth is key…. A comprehensive strategy to tackle youth unemployment in the euro area should focus on creating conditions for sustainable growth, given the higher sensitivity of youth unemployment to the business cycle. In the short-term, policies to restore the housing sector, improve competitiveness in trade and tourism activities would be particularly beneficial as these are also sectors where youth employment is concentrated. Historically, euro area countries have reduced youth unemployment rates by growing much more strongly than they are currently expected (Box 2).
A Historical Perspective on Growth and Youth Unemployment
Not unprecedented. Youth unemployment rates are peaking in the euro area, but such unemployment levels are not unprecedented. In Spain, for example, youth unemployment rates are close to (around 90 percent of) the previous peak (1986 and 1994). Youth unemployment rates remains below their historical peaks for France and Italy.
Helped by growth. Euro area countries succeeded in reducing high youth unemployment rates in the past, but they have done so in the context of stronger growth than currently envisaged. In some cases, growth rates would have to be double or even triple the current forecasts.
41. Labor market reforms will help. The empirical results show that growth explains about half the increase in youth unemployment overall, and about a third in some high youth unemployment countries (such as France and Italy). Therefore, growth alone cannot solve the youth unemployment problem. Empirical analysis also shows that labor market reforms would pay dividends. As the economic recovery solidifies and unemployment rates return closer to their historical averages, labor market institutions may play an increasingly large role in labor market dynamics. Reforms could include: lowering hiring costs by reducing the tax wedge and reconsidering minimum wage policies (which largely affect the youth) to increase labor demand; reform of unemployment benefits to better incentivize the transition from inactivity to work; improvements in skill levels and work-related training; and, ALMPs.
42. …and may complement the effect of the business cycle. Labor market institutions affect the sensitivity of youth unemployment rates to the business cycle in Greece, Ireland, Italy and Spain. For example, an increase in the tax wedge increases the cyclical responsiveness of youth unemployment in Greece and also for Spain. Similarly ALMP spending seems to significantly reduce the effect of cyclical changes on youth unemployment in Ireland, Italy and Spain. Thus, a reduction in ALMP spending per unemployed in Ireland and Spain in the aftermath of the crisis may have aggravated the effect of the sharp drop in growth on youth unemployment rates.
Interaction between labor market features and output gap, coefficient
Citation: IMF Staff Country Reports 2014, 199; 10.5089/9781498308007.002.A001
Notes: GRR – gross replacement rate, NRR – net replacement rate, ITRAP – inactivity trap, MIN2MED – ratio of minimum to median wage, TWEDGE – tax wedge, UDENS – union density, EPT – OECD temporary employment protection index, TSHARE – share of temporary employees for a given age group, TPROB – share of temporary employees on probation in total temporary employees for a given age group, LOWEDUC – share of population with lower secondary education, ALMPTOT – total spending on ALMP (thousands euro per unemployed), ALMPTRAIN – spending on ALMP training policies.Source: Staff Estimates43. Reforms better as a package. Given the estimated effects on youth unemployment, the amounts of ALMP spending required to make a sizeable dent in historically high youth unemployment rates would be too large to be feasible. Thus ALMP spending will need to be complemented with growth and other labor market reforms to yield the maximum effect. Empirical analysis shows that ALMPs are likely to be more effective if they are part of the broader, comprehensive strategy to address structural impediments to greater youth employment, e.g., higher tax wedges reduce the effectiveness of ALMP spending in Austria and Germany.
44. However, ALMP is not a panacea by itself. ALMP programs need to be designed and monitored properly as meta analysis of such programs show that the impact and cost-effectiveness of ALMPs vary significantly based on their design.
Annex 1. Data Definitions
| Variable | Definition | Source |
|---|---|---|
| Output gap | (Real GDP - Real potential GDP) as a percent of real potential GDP | WEO |
| GDP growth | Year-on-year growth of GDP, constant price | WEO |
| Unemployment rate | Unemployed population as a percent of labor force in corresponding age cohort. | Eurostat |
| Net replacement rate | Net benefits replacement rate is defined as the ratio of net income while out of work (mainly unemployment benefits if unemployed, or means-tested benefits, if on social assistance) divided by net income while in work. A lower net replacement rate is associated with greater incentive to search for and take up a job when unemployed. | European Comission Tax and Benefits Indicators Database |
| Gross replacement rate | Average of the gross unemployment benefit replacement rates for two earnings levels, three family situations and three durations of unemployment. | OECD |
| Inactivity trap | The inactivity trap - or the implicit tax on retuning to work for inactive persons - measures the part of additional gross wage that is taxed away when an inactive person (not entitled to receive unemployment benefits but eligible for income-tested social assistance) takes up a job. In other words, this indicator measures the financial incentives to move from inactivity and social assistance to employment. | European Comission Tax and Benefits Indicators Database |
| Minimum wage/Median wage | Minimum wage relative to median wage for full-time workers. This ratio is set to be zero for countries without a national minimum wage. | OECD |
| Protection of temporary workers | Strictness of employment protection for tempororary contracts. | OECD |
| Share of temporary workers | Temporary employees as percentage of the total number of employees. | Eurostat |
| Tax wedge (EC) | The tax wedge is defined as the proportional difference between the costs of a worker to their employer (wage and social security contributions, i.e. the total labour cost) and the amount of net earnings that the worker receives (wages minus personal income tax and social security contributions, plus any available family benefits). Tax wedge measures both incentives to work (labour supply side) and to hire persons (labour demand side). | European Comission Tax and Benefits Indicators Database |
| Union density | Trade union density corresponds to the ratio of wage and salary earners that are trade union members, divided by the total number of wage and salary earners (OECD Labour Force Statistics). Density is calculated using survey data, | OECD |
| Adjusted bargaining power | Employees covered by wage bargaining agreements as a percentage of all wage and salary earners in employment with the right to bargaining, adjusted for the possibility that some sectors or occupations are excluded from the right to bargain (removing such groups from the employment count before dividing the number of covered employees over the total number of dependent workers in employment). | The QOG (Quality of Government) Institute, University of Gothenburg |
| Share of temporary workers on probation | Proportion of total temporary workers on probation (other reasons for being on temporary contracts include “could not find a permanent job”, “did not find a permanent job”, “in education or training”). | Eurostat |
| Share of low-educated workers | Persons with lower secondary education attainment. | Eurostat |
| ALMP total spending per unemployed | Active labor market policy: total spending per unemployed. | Eurostat |
| ALMP spending per unemployed on training | Active labor market policy: spending on training per unemployed. | Eurostat |
| Variable | Definition | Source |
|---|---|---|
| Output gap | (Real GDP - Real potential GDP) as a percent of real potential GDP | WEO |
| GDP growth | Year-on-year growth of GDP, constant price | WEO |
| Unemployment rate | Unemployed population as a percent of labor force in corresponding age cohort. | Eurostat |
| Net replacement rate | Net benefits replacement rate is defined as the ratio of net income while out of work (mainly unemployment benefits if unemployed, or means-tested benefits, if on social assistance) divided by net income while in work. A lower net replacement rate is associated with greater incentive to search for and take up a job when unemployed. | European Comission Tax and Benefits Indicators Database |
| Gross replacement rate | Average of the gross unemployment benefit replacement rates for two earnings levels, three family situations and three durations of unemployment. | OECD |
| Inactivity trap | The inactivity trap - or the implicit tax on retuning to work for inactive persons - measures the part of additional gross wage that is taxed away when an inactive person (not entitled to receive unemployment benefits but eligible for income-tested social assistance) takes up a job. In other words, this indicator measures the financial incentives to move from inactivity and social assistance to employment. | European Comission Tax and Benefits Indicators Database |
| Minimum wage/Median wage | Minimum wage relative to median wage for full-time workers. This ratio is set to be zero for countries without a national minimum wage. | OECD |
| Protection of temporary workers | Strictness of employment protection for tempororary contracts. | OECD |
| Share of temporary workers | Temporary employees as percentage of the total number of employees. | Eurostat |
| Tax wedge (EC) | The tax wedge is defined as the proportional difference between the costs of a worker to their employer (wage and social security contributions, i.e. the total labour cost) and the amount of net earnings that the worker receives (wages minus personal income tax and social security contributions, plus any available family benefits). Tax wedge measures both incentives to work (labour supply side) and to hire persons (labour demand side). | European Comission Tax and Benefits Indicators Database |
| Union density | Trade union density corresponds to the ratio of wage and salary earners that are trade union members, divided by the total number of wage and salary earners (OECD Labour Force Statistics). Density is calculated using survey data, | OECD |
| Adjusted bargaining power | Employees covered by wage bargaining agreements as a percentage of all wage and salary earners in employment with the right to bargaining, adjusted for the possibility that some sectors or occupations are excluded from the right to bargain (removing such groups from the employment count before dividing the number of covered employees over the total number of dependent workers in employment). | The QOG (Quality of Government) Institute, University of Gothenburg |
| Share of temporary workers on probation | Proportion of total temporary workers on probation (other reasons for being on temporary contracts include “could not find a permanent job”, “did not find a permanent job”, “in education or training”). | Eurostat |
| Share of low-educated workers | Persons with lower secondary education attainment. | Eurostat |
| ALMP total spending per unemployed | Active labor market policy: total spending per unemployed. | Eurostat |
| ALMP spending per unemployed on training | Active labor market policy: spending on training per unemployed. | Eurostat |
Annex 2. Methodology
A. Estimating the Okun’s Coefficient
1. The estimation of Okun’s coefficient for individual countries was conducted using the following specification:
Change in unemployment rate over time
Where:
Urateit: Unemployment rate in a certain age group in country i, year t
Growthit: GDP growth rate in country i, year t
Output gapit: Output gap in country i, year t (Source: WEO)
Countryi: Country fixed effect
The estimated bi would be the Okun’s coefficient.
B. Impact of Labor Market Institutions
2. Specification in levels. Economists have advanced a number of models for unemployment rate, which are consistent with using unemployment rate in levels as dependent variable in reduced form equations. For example, Nickell and Layard (1999) develop a wage bargaining model with numerous identical firms, showing that equilibrium level of unemployment rate will be decreasing in any exogenous factor that increases job separation rate (represented in our case by the output gap), increases the search effectiveness of the unemployed (represented by ALMP policies), lowers the benefit replacement ratio, lowers the strength of the workers in the wage bargain (union density) or raises the elasticity of product demand facing the firm. The latter argument even suggests scope for including variables associated with product market regulation into unemployment equations. Other examples of similar models include Scarpetta (1996) and Bassanini and Duval (2006), who estimate a specification very similar to ours.13
3. Univariate model with interaction terms. This model assumes that: (i) the effects of the business cycle may depend on labor market features, (ii) this dependence may be different across countries and (iii) that the effect of the structural variable itself does not depend on the country, except indirectly via the business cycle. These assumptions, together with limitations of data availability mean that structural variables can only be considered one at a time, otherwise the high number parameters to estimate relative to the size of the sample will prevent efficient estimation of the coefficients.
We consider the following specification:
where ui,t is one out of six dependent variables (youth and adult unemployment and long-term unemployment rates, as well as employment rates), ci is the dummy variable equal to 1, if dependent variable is from country I,
5. Multivariate approach. This specification considers several labor market features at a time and assumes that the impact of labor market features, if any, is common across all countries. It allows the impact of the business cycle (output gap) to vary across countries.
Where:
Urateit: Unemployment rate in a certain age group in country i, year t
Xijt: Labor market institution j, in country i, year t
A variety of robustness checks are performed e.g. including time effects, using different measures of the output gap, and youth unemployment, etc. These results are available upon request.
References
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Prepared by Angana Banerji, Huidan Lin, Sergejs Saksonovs (all EUR) and Rodolphe Blavy (EUO). We thank Xiaobo Shao for excellent research assistance and Katherine Cincotta for general assistance. We also thank Ana Lamo (ECB) and Alessandro Turrini (European Commission), the participants of seminars at the ECB and European Commission, as well as EUR country teams for helpful feedback and comments.
Henceforth, youth refers to individuals aged 15–24 years, and adults refers to individuals aged 25–64 years. Unemployment refers to the unemployment rate.
The youth labor force tends to be smaller than the labor force for other age cohorts because young individuals may choose to pursue full-time education, although participation in education does not necessarily exclude participation in the labor force (e.g., part-time work or apprenticeships). The youth labor market is also characterized by frequent search and matching as individuals look for better jobs, using intermediate stages for accumulating experience (and perhaps, occasionally, dropping out of the labor force).
See Ball, Leigh, Loungani, 2013 for an extensive discussion on this topic.
Even in those cases, some studies have found that the Okun’s law holds for measures of hours worked.
SMEs are defined as firms with less than 250 employees, turnover of less than 50 million euro or a balance sheet less than 43 million euro.
These results are robust to changes in specification. Results for changes in unemployment rates are available upon request. Results discussed in the text refer to the multivariate model unless otherwise specified.
Several studies find that higher labor tax wedge raise unemployment and the impact of the tax wedge is strengthened when combined with the impact of the minimum wages and the strength of collective bargaining.
The minimum wage was frozen in Portugal in the second half of 2011 and cut in Greece in the second half of 2012 under the financial assistance programs.
Labor market duality is measured by the OECD’s employment protection indicator (EPL) for temporary and permanent workers, and the share of temporary workers as a percent of total employees. In-sample variation in the data is larger for temporary employment protection indicators than permanent employment protection indicators.
The rating is on a scale from 1 to 6.
This result is based on the OECD’s indicator on union density which measures the incidence of unionization among the employed, but does not measure the degree of centralization.
Bassanini and Duval (1996) estimate a reduced form equation consistent with a variety of theoretical models of labor market
