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Productivity, convergence and policy: a study of OECD countries and industries

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Abstract

This paper analyses trends in labour productivity and its underlying determinants in a panel of OECD countries from 1979 to 2002. Data Envelopment Analysis (DEA) is used to estimate a Malmquist measure of multifactor productivity (MFP) change. We decompose the growth in labour productivity into (i) net technological change (ii) input biased technical change (IBTC) (iii) efficiency change and (iv) capital accumulation. We analyse the effect of each of these factors in the transition towards the equilibrium growth paths of both labour productivity and per capita GDP for the OECD countries, controlling for the effects of different policies and institutions. The results indicate that on average gaps in productivity or income levels are narrowing down although there is no evidence to suggest that the entire OECD area comprises a single convergence “club”. Using kernel estimation methods we find that that labour productivity and per capita GDP are settling toward a twin peak (bimodal) distribution. Panel unit root tests over an extended (1960–2001) period provide general support for the convergence hypothesis. Analysis of the contributions of productivity growth within industries and sectoral composition changes show that aggregate productivity change is predominantly driven by ‘net’ within sector effects with very little contribution emerging from sectoral shifts (the ‘in-between’ static or dynamic effects resulting from higher or above average productivity industries gaining employment shares or low productivity industries losing shares).

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Notes

  1. Note that both TCH and KCH measures are path dependent (i.e., they are generally different dependent upon whether the shift in the frontier is evaluated at the base or current period observation for TCH and whether it is measured along the current- or base-frontier for KCH) unless technological change is Hicks neutral. As Kumar and Russell (2002) emphasise this “arbitrariness is endemic to the basic task of measuring technological change.” We reconcile this problem by using Fisher ideal geometric averages of the two alternative measures of TCH and KCH. This is reflected in (2) as well as in (4) and (6).

  2. Note that the frontier is defined in terms of the ‘best practice’ of the countries in the sample and therefore performance measures are relative. Different MFP productivity measures are likely to obtain in relation to different groupings of countries. We assume that technology is potentially transferable across the sample of countries. Note that each country is compared to the frontier segment with the same input mix. A similar approach but in a parametric setting is adopted by Hultberg et al. (1999). As in Hultberg et al. we allow for the possibility that countries may overtake each other during the transition to their steady state.

  3. New Zealand did not deregulate its labour market until 1992 when the Employment Contracts Act (ECA) came into effect; it has been argued (see, for example, Hansen and Margaritis 1993) that labour market rigidities may have aggravated the adjustment costs during transition from a highly protected to a market driven economy. The ECA was repealed in 1999 and was replaced by a more centralised system of employment relations.

  4. It is likely that Portugal’s labour market, one of the most rigid in the OECD, imposes higher costs of adjustment and therefore exacerbates the problems firms face with adopting or developing new technology (see Scarpetta et al. 2002). In the 1965 to 1998 period the capital to labour ratio in Portugal increased by fivefold while output per worker increased at about half this rate. Contrast this with the case of Ireland where labour productivity outpaced the rate of capital productivity over the same period.

  5. Note that convergence in distribution or σ-convergence provide evidence of convergence but they do not necessarily support the convergence hypothesis as they not address the issue of β-convergence; namely, that lower per capita income (or per unit of labour) countries will catch-up with the rich countries through the process of technology transfer or through higher (marginal) capital productivity. They do not also address the issue of mobility patterns or intra-distribution dynamics (see Bianchi 1997).

  6. The results of standard unit root tests indicate that the individual (log) productivity and output variables are non-stationary I(1) series.

  7. The Levin et al. (2002) panel unit root test rejects the non-stationary null hypothesis for agriculture with a p-value of 0.047. The same test also rejects the null for construction, low tech manufacturing and wholesale and retail trade. The Im et al. (2003) test does not reject the unit root null for financial services (p-value = 0.354).

Abbreviations

MFP:

Multifactor Productivity (Malmquist Index Measure)

ECH:

Efficiency Change

TCH:

Technological Change

OBTC:

Output Biased Technical Change

IBTC:

Input Biased Technical Change

MTC:

Net Magnitude Component of Technical Change

KCH:

Capital Accumulation (Measured by the change in the capital-labour ratio)

Y:

Purchasing Power Parity (PPP) adjusted real GDP

L:

Employment (Annual hours worked)

s:

Saving Rate

y:

Y/L

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Acknowledgements

We acknowledge financial support from the New Zealand Foundation for Research, Science and Technology. A version of this paper was presented at the 2004 Asian Pacific Productivity Conference at the University of Queensland and the 4th International Symposium of DEA at Aston University. We would like to thank three anonymous referees for their comments and suggestions and Gary Feng and Sha Sha Guo for excellent research assistance.

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Authors and Affiliations

  1. Department of Finance, Faculty of Business, AUT, 46 Wakefield St, Auckland, 1010, New Zealand

    Dimitris Margaritis

  2. Department of Economics, Oregon State University, Corvallis, OR, 97331-3612, USA

    Rolf Färe & Shawna Grosskopf

Authors
  1. Dimitris Margaritis
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  2. Rolf Färe
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Correspondence to Dimitris Margaritis.

Data appendix

Data appendix

The main source of aggregate output (Y) and employment (L) data for most countries is the Total Economy Database of Groningen University (see http://www.ggdc.net). The (PPP adjusted) real capital stock series (K) are taken from the GGDC Total Economy Growth Accounting Database. The industry output (value-added) and employment data is from GGDC’s 60-Industry database. The data for Australia and New Zealand are from the Statistics New Zealand and ABS databases.

The policy indicators used in this study are taken from Nicoletti et al. (1999). They cover different regulatory areas (e.g. state control, barriers to trade and entrepreneurship, administrative regulation) and summarise information on 156 general-purpose and industry-specific regulations into broad economy-wide indicators that describe the regulatory situation in each country in 1998. These indicators are a relative measure of the strictness of regulatory provisions across different OECD countries. In this sense they comprise a measure of relative friendliness of regulations to the market mechanism and are not purported to assess the overall quality of the regulatory regime or its suitability in achieving the stated policy goals.

1.1 The following indicators are used

  • Bar_trade = barriers to international trade and investment.

  • State_ctrl = state control; a summary indicator for the relative size and scope of public ownership and state involvement in business operations and market controls.

  • Adm_reg = administrative regulation; a summary indicator for general regulatory and administrative barriers, inclusive of barriers to business start-ups, compliance costs and barriers to competition.

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Margaritis, D., Färe, R. & Grosskopf, S. Productivity, convergence and policy: a study of OECD countries and industries. J Prod Anal 28, 87–105 (2007). https://doi.org/10.1007/s11123-007-0044-8

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