Abstract
We argue that the conventional NAIRU (non-accelerating inflation rate of unemployment) model is a special case of a larger model of equilibrium unemployment, in which demand, investment, and endogenous technological progress do have lasting effects on steady-inflation unemployment. It follows that the labor market policy prescriptions (i.e. to drastically deregulate), following from the conventional NAIRU model, cannot be generalized. Empirical support for the extended model is provided by an econometric analysis for 20 OECD countries (1984–2004): demand factors are the dominant determinants of OECD unemployment.
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Notes
We acknowledge that the measurement of unemployment and its cross-country comparison is problematic over a long period, because unemployment rates, even the ILO standardized one, are not good measures of unused labor supply, since in many countries some unemployed people are hidden in other categories (e.g. disability, early retirement, subsidized employment, prison). This may potentially be a serious limitation of our analysis as well as of earlier studies, but “in the absence of any serious discussion of the relevant numbers in all the countries” [Nickell et al. 2005, p. 2] we proceed by assuming that its impact on the conclusions is limited.
In so doing, we build on earlier work by Rowthorn [1995; 1999], Galbraith [1997], Gordon [1997], Arestis and Biefang-Frisancho Mariscal [1998], Ball [1999], Galbraith and Garcilazo [2004], Karanassou and Snower [2004], and Arestis et al. [2007], who have all written on the impact of aggregate demand on long-term unemployment.
This means that, in the underlying bargaining process, if α 2<1, a unit increase in productivity growth will increase the price-determined real-wage growth by one unit, but it will not affect the bargained wage curve to same extent. As a result, bargained real-wage growth goes up by less than one unit and the NAIRU declines.
The empirical critique of the robustness of the case against labor market regulation has impelled the OECD [2004, p. 81] to admit that the evidence of the impact of employment protection legislation on unemployment rates remains “mixed” and that the evidence of a positive impact of high aggregate wages and/or wage compression on unemployment is “somewhat fragile”, while it is accepted [on p. 165] that the effects of collective bargaining “appears to be contingent upon other institutional and policy factors that need to be clarified to provide robust policy evidence.”
In contrast, if the cost sensitivity of export demand is high and investment demand is highly sensitive to changes in the profit share, while σ π −σ W ≈0, the economy will be profit-led, that is, an increase in real wage growth relative to productivity growth will reduce output growth.
In a short- and medium-run context, we could assume that real wage growth is fixed by bargaining. As a result, income growth, productivity growth, and unemployment become a function of real wage growth (and the other exogenous variables including net public expenditure, exports, and the real interest rate).
The EPL index reflects (i) procedural inconveniences that the employer faces when trying to dismiss employees; (ii) notice and severance pay provisions; and (iii) prevailing standards of and penalties for unfair dismissal [see OECD 1999].
We do not include a variable for technology diffusion or technological catching up in the productivity growth regressions, because the potential for catching up is generally held to have become exhausted among the OECD countries after 1980. We included the share of ICT capital in total capital stock in the estimated equation (29) but obtained no statistically significant coefficients; hence, in the reported estimations, this variable has not been included.
Their preferred approach is a static fixed effects model in first differences with data averaged over five-year periods during 1960–1998 for 18 OECD countries; these results appear in their Table 11; we have rescaled their coefficient estimate to fit the scale of our EPL measure [1–4].
According to estimates by Baker et al. [2005a], the average rate of unemployment in 19 OECD countries increased from 2.8 percent during 1960–1980 to 7.9 percent during 1980–2000.
Over a longer period of time (than the period covered by our analysis), the impact of fiscal policy on unemployment will be smaller because of requirements of fiscal solvency. We thank a referee for pointing this out.
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Acknowledgements
We are indebted to Lance Taylor, Malcolm Sawyer, and Peter Auer for insightful comments on an earlier draft. In addition, we acknowledge, with thanks, the critical comments and suggestions from three anonymous referees.
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Appendix
Appendix
Derivation of the productivity regime Equation (4)
Consider the following increasing-returns-to-scale CES production function:
where x is the Gross Domestic Product (GDP) measured at constant prices, k the economy's fixed capital stock (at constant prices), l the number of hours worked (in a year) by the labor force, a an “efficiency” parameter, δ the distribution parameter, ρ the substitution parameter, and h the returns-to-scale parameter (h>1 corresponding with increasing returns to scale). Denoting the price of capital by Π, the elasticity of capital-labor substitution σ is defined as
From the first-order condition ∂x/∂l=W/p, where W is the (nominal) wage rate and p is the GDP deflator, and using definition (A.2), it follows that labor productivity, λ, is equal to
Log differentiating (A.3) and dividing through by λ gives us an expression for the proportional growth rate of labor productivity:
If we assume that
and if we substitute (A.5) into (A.4), we obtain:
where w=W/p. (A.6) is the productivity regime equation (4) used in the text. Note that (i) (h−1)p/h(p+1)=β 1 is the Kaldor–Verdoorn elasticity, which is economically meaningful only if h>1; (ii) σ=β 2 is the coefficient of wage-led technological change, and (iii) (σp/h)α 1=β 3 is the coefficient giving the impact on the degree of labor market regulation productivity growth.
Data sources and definitions
The countries in the sample are:7
The period of analysis is 1984–2004. The full data set is available in Storm and Naastepad [2007b].
Employment Protection Legislation (EPL) index: the index used is the average of the indices for 1989 and 1999 as provided by Nicoletti et al. [2000]. The index has a range (0–4). Data on the Benefit Duration index (for the period 1980–1987; range 0–1.1), Collective Bargaining Coverage (in 1994), and the Coordination index (1980–1987; range 1–3) are from Nickell et al. [2005], as are the data on Union Density, Total Labor Taxes, and the Replacement Ratio. Estimates of the Management Ratio are based on ILO data on the occupational composition of OECD labor forces. Data on Earnings Inequality and Average Tenure are from the OECD Economic Outlook Database. Real GDP growth (at factor cost and at market prices), net public expenditure growth, and export growth are from the OECD Economic Outlook Database. Labor productivity growth is defined as the average annual growth rate of real GDP (at factor cost) per hour worked. Data on hours worked are from the GGDC Total Economy Database (http://www.eco.rug.nl/ggdc), University of Groningen, and the Conference Board. Real wage growth is the average annual growth rate of real compensation per employee per hour worked; data on real compensation per employee are from the OECD Economic Outlook Database. Unemployment rates are the OECD standardized unemployment rates and conform to the ILO definition. Real interest rates are defined as long-term nominal interest rates less the rate of inflation (defined in terms of the GDP deflator); the data source is the OECD Economic Outlook Database.
Factor analysis
We used factor analysis to reduce seven measures of labor market regulation to one factor, which serves as a meaningful indicator of the extent of Labor Market Regulation (LMR). The eigenvalue of this first factor is 3.47 (>1) and this (unrotated) factor solution represents 88.7 percent of the variance in the data. We report only one factor, because the eigenvalues of the second (and third) factor were well below 1, namely 0.586 and 0.192, respectively, and they lacked substantive interpretation (unlike the first factor). The factor loadings and factor scoring coefficients are shown below; the latter were used to calculate each country's score on the factor LMR. Because of missing data on dimensions of labor market regulation, Greece and Ireland are not included in the factor analysis.8
The resulting LMR score for 18 OECD countries for two periods (1984–1994 and 1994–2004) is presented in Figure A1 See Storm and Naastepad [2007b] for the complete database and full analysis.
Labor market regulation (LMR) factor score 1984–1994 and 1994–2004. Source: Storm and Naastepad [2007b].
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Storm, S., Naastepad, C. The NAIRU, Demand and Technology. Eastern Econ J 35, 309–337 (2009). https://doi.org/10.1057/eej.2008.15
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DOI: https://doi.org/10.1057/eej.2008.15