RDP 2007-04: Productivity Growth: The Effect of Market Regulations Appendix A: Data Description and Sources

TFP growth and technology gap: Construction of these variables mainly follows Griffith et al (2000). TFP growth in the business sector in country i at time t is given by the superlative index^[25]

where: Y_it is real business sector output; L_it is aggregate hours worked, which is the product of business sector employment and average hours per employee; and K_it is the real business sector capital stock. Both Y_it and K_it are rebased where necessary to a common year and then converted to US dollars using purchasing power parity (PPP) exchange rates. The rebasing uses implicit price deflators for aggregate output and private non-residential investment from the OECD Economic Outlook No 78 database. Rebased output is converted to US dollars using the 2,000 PPPs over GDP from the OECD. The rebased capital stocks are converted to US dollars using 2,000 PPPs over investment constructed by multiplying price indices for investment expenditure from Penn World Tables 6.1 by exchange rates from the OECD. The labour share of income is estimated by adding an approximation of labour's share of gross mixed income to compensation of employees:

where: α_it is labour's share of income in country i at time t; CoE_it is compensation of employees; SE_it is the number of self-employed people; E_it is total employment and GDP_it is aggregate nominal GDP. We approximate average compensation of employees with CoE_it/(E_it – SE_it) because the numbers of wage and salary earners are not available for all countries over a long enough time period. All the data are annual and are sourced from the OECD Economic Outlook No 78 database. Exceptions are estimates of New Zealand business sector employment, which are quarterly data from the OECD Economic Outlook No 77 database, and components of labour's share of income, which are annual OECD data sourced from Thomson Financial.

The technology gap is calculated as

where tfpgap_it is the technology gap for country i at time t, and TFP_it and TFP_Lt are the levels of TFP in country i and the technological leader at time t. The level of TFP is given by the index

where the output and capital stock have been converted to a common currency as described above. Variables with a bar are geometric means for all countries at time t, and σ_it is given by

where α_it is the LSI for country i at time t. The levels of TFP constructed with this index are comparable across countries, so the TFP leader can be identified and TFP growth in the leader (the variable Δtfp_Lt in Equation (1)) calculated using Equation (A1).

Product market regulations: Originally from Nicoletti et al (2001); we use the updated version presented in Conway and Nicoletti (2006). Countries are classified on a 0–6 scale from least to most restrictive for each regulatory and market feature of the seven non-manufacturing industries: airlines, railways, road, gas, electricity, post and telecommunications. Depending on the industry, the features covered are: barriers to entry, public ownership, market structure, vertical integration and price controls. Aggregate indicators for each country are simple averages of indicators for the seven industries and the time series run from 1975–2003. These data are different from the commonly cited economy-wide indicators, which are only available for 1998 and 2003 (Nicoletti, Scarpetta and Boylaud 2000; Conway, Janod and Nicoletti 2005). As the time series index is highly correlated with the economy-wide measure of product market regulation for the years where the two overlap, it is arguably a useful time-series proxy for the stance of economy-wide regulation (Conway and Nicoletti 2006).

Working days lost to labour disputes per thousand employed: Constructed from the number of working days lost (from the International Labour Organization) and the level of employment. The exceptions are: Australia – OECD Main Economic Indicators (MEI); Belgium – Eurostat; Canada – MEI; France – Eurostat; Germany – data from 1993 onwards from Eurostat; Netherlands – Eurostat; US – MEI. Employment data from OECD Economic Outlook, sourced from Datastream. Data are smoothed using a backward-looking three-year-moving average.

Extended employment protection legislation (EPL) index: We follow Blanchard and Wolfers' (1999) method of backcasting the EPL index to create a long time series for this indicator. Briefly, we backcast using the growth rates of a proxy, which is a weighted average of scaled data on severance and notice periods for a blue-collar worker with 10 years service. Except for the following differences, we follow the method outlined in Blanchard and Wolfers' Appendix:

• We backcast the EPL in Nicoletti et al (2000) rather than in the OECD Employment Outlook (1999). The only difference between the two series is the choice of weights; Nicoletti and Scarpetta use factor analysis to derive the weights while the weights in the Outlook are chosen subjectively. The use of statistically derived weights seems preferable ex ante, but Nicoletti et al show that the two series are broadly similar and differences in the summary indicator are small. When constructing the weighted average of severance and notice periods we use the relevant weights calculated from Table 12 in Nicoletti et al rather than those from the Outlook.
• We use Addison, Teixeira and Grosso's (2000) corrected and updated version of Lazear's (1990) dataset on severance and notice pay, which has been used in a number of papers by its authors and others. There are few missing values in this updated dataset and we do not attempt to fill them.
• We begin backcasting the EPL using growth rates in the proxy series from 1984 rather than from 1979. There are two countries (Denmark and the US) whose EPL for the ‘late 1980s’ from Nicoletti et al (2000) is positive but whose proxy EPL from Addison and Grosso is zero for all years 1956–2004. We choose to set the pre-1985 score for these countries as zero in our backcast EPL.

Union density: Proportion of wage and salary earners who are union members expressed as a percentage; both the numerator and denominator from the OECD. Trade union membership can be reported either by trade unions (‘administrative data’) or employees as part of labour force or other surveys (‘survey data’). For all countries in our sample except Australia, Canada, the Netherlands, Sweden, the UK and the US, only administrative data are available. For the remaining countries, survey data become available some time during our estimation period. Although survey data are preferable, we are wary of simply moving from administrative to survey data when the year in which the data source changes corresponds with a historically large change in calculated union density, as seems to be the case for Australia, Canada and the US. For these countries, we combine the two series in a way that avoids a historically large change in union density, for example by back- or forward-casting using the growth rates of administrative or survey data, respectively. For the Netherlands, Sweden and the UK we move from administrative to survey data without adjustment as this has no noticeable effect on calculated union density.

Average years of schooling: Geometric interpolation of the average years of schooling data constructed by de la Fuente and Doménech (2002). These data are a revised and partially extended version of the series in de la Fuente and Doménech (2000). Average years of schooling are observed every five years from 1960–1995, with the exception of France, Japan, Spain and the UK, for which there is no observation for 1995. We construct these observations by assuming that average years of schooling grew at their 1985–1990 rates over 1990–1995.

Employment to working-age population: Business sector employment as a percentage of the population aged 15–64, both from the OECD Economic Outlook No 78 database.

ICT expenditure: Total nominal expenditure on information technology (hardware, software and services) and telecommunications (equipment and services) as a percentage of aggregate nominal GDP. Source: WITSA (2000, 2002, 2004).

Output gap: Difference between natural logarithms of actual and trend business sector output, where trend business sector output is real business sector GDP from the OECD Economic Outlook No 78 database, smoothed with a Hodrick-Prescott filter using a smoothing parameter of 100. When smoothing we included the forecasts for real business sector output for the years 2005–2007; these forecasts are published in the OECD Economic Outlook No 78 database.

Research and development (R&D) expenditure: Nominal expenditure on research and development by the business enterprise sector as a percentage of GDP from the OECD's Main Science and Technology Indicators (MSTI): 2006/2 edition.

Footnote

This type of index is desirable because it can be derived directly from a flexible functional form. See OECD (2001) for more details. [25]