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Employment by age, education, and economic growth: effects of fiscal policy composition in general equilibrium

  • Freddy Heylen EMAIL logo and Renaat Van de Kerckhove
Published/Copyright: October 12, 2013
The B.E. Journal of Macroeconomics
From the journal The B.E. Journal of Macroeconomics Volume 13 Issue 1

Abstract

We build and parameterize a general equilibrium OLG model that explains hours worked by three active generations, education by the young, the retirement decision of older workers, and aggregate per capita growth as functions of the level and structure of taxes and government expenditures. We find that our model’s predictions match the facts remarkably well for all key variables in many OECD countries. We then use the model to investigate the effects of various fiscal policy shocks. To promote employment, especially among older workers, and economic growth, our results strongly prefer labor tax cuts targeted at older workers and higher productive government expenditures financed by a reduction of non-employment benefits and/or higher consumption taxes. We also evaluate the welfare effects for current and future generations of alternative policy changes.

Keywords: employment by age; endogenous growth; taxes; fiscal policy; human capital production; overlapping generations.; JEL Classification Codes: E62, J22, O41
Corresponding author: Freddy Heylen, SHERPPA, Ghent University, Sint-Pietersplein 6, B-9000 Ghent, Belgium, Phone +32 9 264.34.85, e-mail:
  1. 1

    Not all studies investigating the relationship between education and growth come up with significant positive results, however (e.g., Pritchett 2001). de la Fuente and Doménech (2006) point at the low quality of schooling and human capital data as an important factor that may explain the mitigated results in many studies.

  2. 2

    We impose this assumption of 2080 hours on all countries. Although it may be open to discussion, note that imposing a different number has no influence on our analysis and results.

  3. 3

    We think of the former as time to relax and time to spend on personal activities of short duration. We think of the latter as time to enjoy activities that last longer and that ask for longer term commitment (e.g., long journeys, non-market activity as a volunteer).

  4. 4

    The main results in this paper are not in any way influenced by the magnitude of π, Ω or ρ.

  5. 5

    Our approach to model early retirement benefits as a function of a worker’s last labor income, similar to standard non-employment benefits, reflects regulation and/or common practice in many countries. For further details on this, see Buyse, Heylen, and Van de Kerckhove (2013, their footnote 5) and Duval (2003).

  6. 6

    While we allow for more flexibility than most of the literature when we introduce time-varying taste for leisure parameters in Equation (1), we do the opposite here by imposing a constant efficiency profile. Our assumptions imply a constant hourly wage profile in the third period. Although the consensus view in labor economics is that the wage profile is hump-shaped, and declines around the age 50–55, our assumptions are in line with recent micro evidence from the US Panel Study of Income Dynamics which challenges this consensus view (Rupert and Zanella 2010).

  7. 7

    Standard non-employment benefits and early retirement benefits are a function of a worker’s last labor income in Belgium (see also footnote 5). Public pensions are proportional to average annual labor income earned over a period of 45 years, with equal weights to all years (OECD 2005).

  8. 8

    And with the values of two parameters in the human capital production function (v, κ) that we discuss below (see also footnote 9).

  9. 9

    From our model’s predictions and the true data for 13 countries we computed for each variable (n1, n2, n3,e, R, growth) the root mean squared error normalized to the mean. We minimized the average normalized RMSE over all six variables. More precisely, we adopted the following iterative procedure. We chose values for v and κ and then calibrated the efficiency parameter ϕ and the scale parameter σ. The values for v and κ had no influence on the calibration results for γj and ρ. Given the obtained values for ϕ and σ, we computed the average normalized RMSE over all six variables. We then checked whether changes in v and κ, and a recalibration of σ and ϕ , could further reduce this statistic. We did this until no further reduction was possible.

  10. 10

    In the period that we study, this is the case in Austria, Belgium, France, Germany, Finland, Ireland, and the UK. Workers cannot be structurally non-employed and still receive unemployment benefits in the Netherlands, Italy, Denmark, Norway, Sweden, Spain, Portugal, Switzerland and the US (OECD 2004, www.oecd.org/els/social/workincentives, Benefits and Wages, country specific files).

  11. 11

    For example, even if we compute the true data in Table 1 as averages over a longer period, these averages need not be equal to the steady state. Countries may still be moving towards their steady state. Also, this exercise only concerns the last 15 years. Due to lack of data – especially with respect to marginal labor tax rates and non-employment benefits before the mid 1990s – it is impossible for us to relate changes in growth and employment to changes in policy within countries over longer time periods.

  12. 12

    The same approach to test the empirical validity of a model by comparing model simulations with cross-country data has been adopted before by Dhont and Heylen (2008, 2009), Alonso-Ortiz (2011) and Erosa, Fuster and Kambourov (2012).

  13. 13

    A major element behind the deviation for this country seems to be underestimation of the fallback income position for structurally non-employed young workers. OECD data show very low replacement rates in Italy. However, as shown by Reyneri (1994), the gap between Italy and other European countries is much smaller than it seems. Reyneri (1994) points to the importance of family support as an alternative to unemployment benefits. Fernández Cordón (2001) shows that in Italy young people live much longer with their parents than in other countries, especially those without job.

  14. 14

    The (minimized) average root mean squared error normalized to the mean over our six endogenous variables of interest is always higher. See also footnote 9. More details are available upon request.

  15. 15

    We do not study the effects of changes in the public pension system in this paper. For an analysis of pension reform in a very similar model as ours, see Buyse, Heylen and Van de Kerckhove (2013).

  16. 16

    The choice of 2% is arbitrary. Imposing smaller or larger shocks would not generate different results as far as the sign and the relative size of effects is concerned.

  17. 17

    More precisely, the benchmark is the model’s prediction for the average of the six core euro area countries in our sample (see Table 1) using the preference and technology parameters reported in Table 2 and these countries’ initial policy parameters as shown in Tables 3 and 4.

  18. 18

    Employment effects after the policy changes reported in Table 5 are available upon request. In general, employment rates adjust to the new steady state reported in Table 5 very rapidly.

  19. 19

    Note that the strong welfare gains (for future generations) from substituting productive government spending for public consumption (Δgy>0, Δgc<0) partly reflect our assumption that public consumption is not useful to the individuals. Turnovsky (2000) and Dhont and Heylen (2009) do include public consumption in the individuals’ utility function. In that case, welfare effects of substituting gy for gc are still positive, but much smaller than in the case where productive spending is financed by overall benefit cuts.

We thank Raouf Boucekkine, Tim Buyse, David de la Croix, Koen Hendrickx, Glenn Rayp, Alessandro Sommacal, Dirk Van de gaer, and two anonymous referees for constructive comments and discussions during the development of this paper. We also benefited from comments received at the 25th Annual Congress of the European Economic Association (Glasgow, August 2010), at the 16th World Congress of the International Economics Association (Beijing, July 2011), and during seminars in Brussels, Lille and Louvain. We are grateful to Tatiana Gordine (OECD) for her help in the construction of non-employment benefit data. We acknowledge support from the Flemish government (Steunpunt Fiscaliteit en Begroting – Vlaanderen) and the Belgian Program on Interuniversity Poles of Attraction, initiated by the Belgian State, Federal Office for scientific, technical and cultural affairs, contract UAP No. P 6/07. Any remaining errors are ours.

Appendix 1: Construction of data and data sources

In this appendix we provide more detail on the construction of some of our performance variables and policy variables.

Employment rate in hours (in one of three age groups, 1995–2007)

Definition: total actual hours worked by individuals in the age group as a percentage of potential hours worked.

Actual hours worked=total employment in persons × average hours worked per week × average number of weeks worked per year

Potential hours=total population in the age group×2080 (where 2080=52 weeks per year×40 hours per week)

Data sources:

  1. Total employment and total population by age group: OECD Stat, Labour Force Statistics by Sex and Age. Data are available for many age groups, among which 20–24, 25–34, 35–44, 45–49, 50–54, 55–64. We constructed the data for our three age groups as weighted averages.

  2. Average hours worked per week: OECD Stat, Labour Force Statistics, Average usual weekly hours worked on the main job. These data are available only for age groups 15–24, 25–54, 55–64. We use the OECD data for the age group 15–24 as a proxy for our age subgroup 20–24, the OECD data for the age group 25–54 as a proxy for our age (sub)groups 25–34, 35–49 and 50–54.

  3. Average number of weeks worked per year: Due to lack of further detail, we use the same data for each age group. The average number of weeks worked per year has been approximated by dividing average annual hours actually worked per worker (total employment) by average usual weekly hours worked on the main job by all workers (total employment). Data source: OECD Stat, Labour Force Statistics, hours worked.

Education rate of the young (age group 20–34, 1995–2007)

Definition: total hours studied by individuals of age 20–34/potential hours studied

As a proxy we have computed the ratio: (fts20–34+0.5 pts20–24+0.25 pts25–34)/pop20–34

with: fts the number of full-time students in the age group 20–34

  pts the number of part-time students in the age groups 20–24 and 25–34.

  pop total population of age 20–34

Full-time students are assumed to spend all their time studying. For part-time students of age 20–24 we make the assumption (for all countries) that they spend 50% of their time studying, part-time students of age 25–34 are assumed to spend 25% of their time studying. Due to the limited number of part-time students, these specific weights matter very little.

Data sources:

  1. Full-time students in age groups 20–24, 25–29, 30–34: OECD Stat, Education and Training, Students enrolled by age (all levels of education, all educational programmes, full-time)

  2. Part-time students in age groups 20–24, 25–29, 30–34: OECD Stat, Education and Training, Students enrolled by age (all levels of education, all educational programmes). We subtracted the data for full-time students from those for “full-time and part-time students.”

Data are available in 1995–2007. However, for several countries (quite) some years are missing. Period averages are computed on the basis of all available annual data.

Average effective retirement age (1995–2006)

Definition: Average age of all persons (being 40 or older) withdrawing from the labor force in a given period.

Data source: OECD, Ageing and Employment Policies – Statistics on effective age of retirement.

Annual real potential per capita GDP growth rate (aggregate, 1995–2007)

Definition: Annual growth rate of real potential GDP per person of working age

Data sources:

  1. real potential GDP: OECD Statistical Compendium, Economic Outlook, supply block, series GDPVTR.

  2. population at working age: OECD Statistical Compendium, Economic Outlook, labour markets, series POPT.

Tax rate on labor income (τj for j=1,2,3)

Definition: Total tax wedge, marginal tax rate in % of gross wage earnings. The data cover personal income taxes and social security contributions paid by employees on their wage earnings as well as social security contributions and payroll taxes paid by employers.

Data source: OECD, Statistical Compendium, Financial and Fiscal Affairs, Taxing Wages, Comparative tax rates and benefits (new definition).

The OECD publishes marginal labor tax rates for several family and income situations: single persons at 67%, 100% and 167% of average earnings (no children), single persons at 67% of average earnings (two children), one-earner married couples at 100% of average earnings (two children), two-earner married couples, one at 100% of average earnings and the other at 33% (no children, 2 children), two-earner married couples, one at 100% of average earnings and the other at 67% (2 children). Our data in Table 3 are the averages of these eight cases. Data for 2000–2004.

Government debt (Dt)

Definition: General government gross financial liabilities.

Data source: OECD Statistical Compendium, Economic Outlook, N° 89, Government Accounts.

Net benefit replacement rates (bj for j=1, 2, 3a)

Definition: The data concern net transfers received by long-term unemployed people and include social assistance, family benefits and housing benefits in the 60th month of benefit receipt. They also include unemployment insurance or unemployment assistance benefits if these benefits are still paid, i.e., if workers can be structurally unemployed for more than 5 years without losing benefit eligibility. The data are expressed in % of after-tax wages. The OECD provides net replacement rates for six family situations and three earnings levels. Our data in Table 4 are the averages of these 18 cases. Data for 2001–2004.

Data source: OECD, Tax-Benefit Models, www.oecd.org/els/social/workincentives

Data adjustment: Original OECD data for Norway include the so-called “waiting benefit” (ventestønad), which a person could get after running out of unemployment benefits. Given the conditional nature of these “waiting benefits,” they do not match our definition of benefits paid to structurally non-employed individuals. We have therefore deducted them from the OECD data, which led to a reduction of net replacement rates by about 19% points. For example, recipients should demonstrate high regional mobility and willingness to take a job anywhere in Norway. The “waiting benefit” was terminated in 2008. We thank Tatiana Gordine at the OECD for clarifying this issue with us.

Early retirement replacement rates (b3b)

To calculate our proxy for b3b we have focused on the possibility for older workers in some countries to leave the labor market along fairly generous early retirement routes. Duval (2003) and Brandt, Burniaux, and Duval (2005) provide data for the so-called implicit tax rate on continued work for 5 more years in the early retirement route at age 55 and age 60. The idea is as follows. If an individual stops working (instead of continuing for 5 more years), he receives a benefit (early retirement, disability…) and no longer pays contributions for his future pension. A potential disadvantage is that he may receive a lower pension later, since he contributed less during active life. Duval (2003) calculated the difference between the present value of the gains and the costs of early retirement, in percent of gross earnings before retirement. We use his data as a proxy for the gross benefit replacement rate for older workers in the early retirement route. To compute the net benefit replacement rate, we assume the same tax rate on early retirement benefits as on unemployment benefits. We call this net benefit replacement rate rer. However, these implicit tax rates are only very rough estimates of the real incentive to retire embedded in early retirement schemes and are subject to important caveats (Duval, 2003, p. 15). The available implicit tax rates take into account neither the strictness of eligibility criteria nor the presence of alternative social transfer programs that may de facto be used as early retirement devices. Our assumption will be that a realistic replacement rate for the early retirement route (b3b) will be a weighted average of rer and b3a, where we take the latter as a proxy for the replacement rate in alternative social transfer programs. If rer>b3a, older workers will aim for the official early retirement route, but they may not all meet eligibility criteria and have to fall back on alternative programs. If rer<b3a, workers will aim for the alternative, but again they may not be eligible. We propose that b3b=ξb3a+(1-ξ)rer. Underlying the data in Table 4 is the assumption that ξ=0.5. Correlation between b3b and rer lies around 0.92. Cross-country differences roughly remain intact. Our results in the main text do not depend in any serious way on this assumption for ξ.

Data Source: OECD, Tax-Benefit Models, www.oecd.org/els/social/workincentives, Duval (2003), Brandt, Burniaux, and Duval (2005).

Net pension replacement rates (b4)

OECD (2005, p. 52) presents net pension replacement rates for individuals at various multiples of average individual earnings in the economy. We consider the data for individuals with average earnings.

Appendix 2: Robustness test of the results reported in Table 6

In this Appendix we report the detailed results of two robustness tests. In both tests we simulate the combined fiscal policy shocks of Table 6. In the first test we start from the US as benchmark. Simulated effects are in Table A2-1. In the second test we leave the assumption of an open economy. We re-calibrated our model assuming a closed economy and an endogenous real interest rate. The model generates an annual real interest rate in equilibrium of 4.2%. Simulated policy effects are in Table A2-2.

Table A2-1

Effects of fiscal shocks equal to 2% of output, compensated by a change in another fiscal policy variable – benchmark: US.

(1)(2)(3)(4)(5)(6)(7)
Change in policy variable(a)Δτ1=Δτ2=Δτ3=–2.9Δτ3=–11.3Δb1=Δb2=Δb3a=Δb3b=–9.4Δb1=Δb2=Δb3a=Δb3b=–9.4Δb1=Δb2=Δb3a =Δb3b=–9.4Δb1=Δb2=Δb3a=Δb3b=–9.4Δgy=+2.0
Compensating change(b)Δτc=4.4Δτc=4.6Δτ1=Δτ2=Δτ3=–2.8Δτ3=–10.9Δτc=–4.2Δgy=2.6Δgc=–1.5
Effect(c):
Δn10.02–2.071.91–0.041.90–1.33–2.53
Δn20.03–0.431.861.451.842.270.36
Δn30.042.772.384.872.342.950.50
ΔR(d)0.000.180.130.290.120.160.00
Δe0.011.56–0.351.12–0.362.712.40
Δn(c, e)0.03–0.062.031.942.001.24–0.58
ΔN/N(f)0.04–0.093.183.033.141.95–0.91
Δ annual growth rate(c)0.000.10–0.020.07–0.020.280.24

(a) change in policy variable, in percentage points.

(b) compensating change, in percentage points.

(c) difference in percentage points between new steady state and benchmark, except ΔN/N.

(d) change in optimal effective retirement age in years.

(e) change in (weighted) aggregate employment rate in hours.

(f) change in volume of employment in hours, in %.

Table A2-2

Effects of fiscal shocks equal to 2% of output, compensated by a change in another fiscal policy variable – closed economy, benchmark: six core euro area countries.

(1)(2)(3)(4)(5)(6)(7)
Change in policy variable(a)Δτ1=Δτ2=Δτ3=–2.9Δτ3=–13.2Δb1=Δb2=Δb3a =Δb3b=–9.3Δb1=Δb2=Δb3a =Δb3b=–9.3Δb1=Δb2=Δb3a=Δb3b=–9.3Δb1=Δb2=Δb3a=Δb3b=–9.3Δgy=+2.0
Compensating change(b)Δτc=4.9Δτc=6.0Δτ1=Δτ2=Δτ3=–3.3Δτ3=–12.1Δτc=–5.1Δgy=3.1Δgc=–1.7
Effect(c):
Δn1–0.69–2.163.152.043.901.72–1.58
Δn20.33–0.932.821.712.462.810.25
Δn30.834.675.048.284.163.69–0.37
ΔR(d)0.080.430.610.900.530.480.00
Δe0.671.81–0.670.18–1.410.931.68
Δn(c, e)0.130.273.573.733.442.70–0.55
ΔN/N(f)0.240.506.516.806.274.91–1.01
Δ annual growth rate(c)0.050.13–0.050.01–0.110.220.23
Δ annual interest rate(c)–0.080.31–0.010.350.080.340.18

(a) change in policy variable, in percentage points.

(b) compensating change, in percentage points.

(c) difference in percentage points between new steady state and benchmark, except ΔN/N.

(d) change in optimal effective retirement age in years.

(e) change in (weighted) aggregate employment rate in hours.

(f) change in volume of employment in hours, in %.

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Received: 2012-9-27
Accepted: 2013-8-27
Published Online: 2013-10-12
Published in Print: 2013-01-01

©2013 by Walter de Gruyter Berlin Boston

Articles in the same Issue

  1. Masthead
  2. Masthead
  3. Advances
  4. How have global shocks impacted the real effective exchange rates of individual euro area countries since the euro’s creation?
  5. Employment by age, education, and economic growth: effects of fiscal policy composition in general equilibrium
  6. Overeducation and skill-biased technical change
  7. Strategic wage bargaining, labor market volatility, and persistence
  8. Households’ uncertainty about Medicare policy
  9. Contributions
  10. Deconstructing shocks and persistence in OECD real exchange rates1)
  11. A contribution to the empirics of welfare growth
  12. Development accounting with wedges: the experience of six European countries
  13. Implementation cycles, growth and the labor market
  14. International technology adoption, R&D, and productivity growth
  15. Bequest taxes, donations, and house prices
  16. Business cycle accounting of the BRIC economies
  17. Privately optimal severance pay
  18. Small business loan guarantees as insurance against aggregate risks
  19. Output growth and unexpected government expenditures
  20. International business cycles and remittance flows
  21. Effects of productivity shocks on hours worked: UK evidence
  22. A prior predictive analysis of the effects of Loss Aversion/Narrow Framing in a macroeconomic model for asset pricing
  23. Exchange rate pass-through and fiscal multipliers
  24. Credit demand, credit supply, and economic activity
  25. Distortions, structural transformation and the Europe-US income gap
  26. Monetary policy shocks and real commodity prices
  27. Topics
  28. News-driven international business cycles
  29. Business cycle dynamics across the US states
  30. Required reserves as a credit policy tool
  31. The macroeconomic effects of the 35-h workweek regulation in France
  32. Productivity and resource misallocation in Latin America1)
  33. Information and communication technologies over the business cycle
  34. In search of lost time: the neoclassical synthesis
  35. Divorce laws and divorce rate in the US
  36. Is the “Great Recession” really so different from the past?
  37. Monetary business cycle accounting for Sweden
https://doi.org/10.1515/bejm-2012-0057
Keywords for this article
employment by age; endogenous growth; taxes; fiscal policy; human capital production; overlapping generations.; JEL Classification Codes: E62, J22, O41

Articles in the same Issue

  1. Masthead
  2. Masthead
  3. Advances
  4. How have global shocks impacted the real effective exchange rates of individual euro area countries since the euro’s creation?
  5. Employment by age, education, and economic growth: effects of fiscal policy composition in general equilibrium
  6. Overeducation and skill-biased technical change
  7. Strategic wage bargaining, labor market volatility, and persistence
  8. Households’ uncertainty about Medicare policy
  9. Contributions
  10. Deconstructing shocks and persistence in OECD real exchange rates1)
  11. A contribution to the empirics of welfare growth
  12. Development accounting with wedges: the experience of six European countries
  13. Implementation cycles, growth and the labor market
  14. International technology adoption, R&D, and productivity growth
  15. Bequest taxes, donations, and house prices
  16. Business cycle accounting of the BRIC economies
  17. Privately optimal severance pay
  18. Small business loan guarantees as insurance against aggregate risks
  19. Output growth and unexpected government expenditures
  20. International business cycles and remittance flows
  21. Effects of productivity shocks on hours worked: UK evidence
  22. A prior predictive analysis of the effects of Loss Aversion/Narrow Framing in a macroeconomic model for asset pricing
  23. Exchange rate pass-through and fiscal multipliers
  24. Credit demand, credit supply, and economic activity
  25. Distortions, structural transformation and the Europe-US income gap
  26. Monetary policy shocks and real commodity prices
  27. Topics
  28. News-driven international business cycles
  29. Business cycle dynamics across the US states
  30. Required reserves as a credit policy tool
  31. The macroeconomic effects of the 35-h workweek regulation in France
  32. Productivity and resource misallocation in Latin America1)
  33. Information and communication technologies over the business cycle
  34. In search of lost time: the neoclassical synthesis
  35. Divorce laws and divorce rate in the US
  36. Is the “Great Recession” really so different from the past?
  37. Monetary business cycle accounting for Sweden
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