## 6. Australian Industries MFP Levels (relative to U.S.)

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### Introduction and objective
- Study objective: examine Australia’s productivity performance from the perspective of productivity best practices diffusion across OECD countries by analyzing (i) multi-factor productivity (MFP) diffusion and (ii) diffusion of ICT capital, and assess the role of policies in fostering diffusion.
- Time focus and context: Australia experienced a long expansion since the end of the 1991 recession with labor productivity increasing by close to 30 percent (comparison made to the U.S. over the same period). The ICT revolution has diffused to Australia; Australia undertook major labor and product market reforms and prudent monetary and fiscal policies over the 1990s and early 2000s.

### Key empirical questions
- Do Australia’s labor and product market reforms explain the sustained productivity acceleration observed since 1991?
- How do product market regulations (PMR), labor market flexibility (measured by the strictness of Employment Protection Legislation, EPL), human capital, and R&D affect:
  - diffusion of MFP best practices across industries and countries?
  - ICT capital deepening (ICT capital to labor ratios)?

### Main findings — summary
- Product market reforms have a significant and positive impact on MFP, particularly in industries that use ICT capital goods more intensively, but the estimated magnitude is insufficient alone to explain the Australian productivity acceleration observed in the 1990s.
- Labor market flexibility is associated with faster productivity gains in industries that are more human capital intensive; the magnitude of this estimated effect is large.
- Countries with more flexible labor markets experienced faster ICT capital deepening.
- Domestic R&D intensity and human capital crucially affect technological diffusion even after controlling for PMR and EPL.
- Combined labor and product market reforms could have induced productivity gains of the order of magnitude observed in Australia in the 1990s.

### Australian productivity performance and structural facts
- Aggregate labor productivity increased by about 30 percent between 1990 and 2006.
- Labor productivity gains concentrated mainly in market services: wholesale trade, retail trade, business services, transport.
- Sectoral patterns:
  - Manufacturing: contribution declining steadily.
  - Agriculture and mining: cyclical patterns with no clear long-run trend.
- ICT investment trends:
  - ICT capital to labor ratio growth in sample: 13.9 percent on average.
  - Non-ICT capital to labor ratio growth in sample: 2.6 percent on average.
  - Australia experienced much faster ICT capital accumulation than other sample countries: Australia’s ICT capital deepening estimated at 3.5 percentage points above the sample average of 13.9 percent.
- Labor market reforms timeline highlights:
  - 1991: decentralization of bargaining agreements at enterprise level.
  - 1993: expansion and acceleration of enterprise bargaining.
  - 1996: introduction of individual contracts via Australian Workplace Agreements and reduced powers of the Australian Industrial Relations Commission.
  - Result: Australia characterized by low corporatism, decentralized wage bargaining, flexible employment protection legislation.
- Product market reforms:
  - Phased tariff reductions starting in 1988 leading to negligible tariffs by end of the 1990s.
  - Deregulation and restructuring of air, coastal transport, telecommunications; commercialization and privatization of public enterprises.
  - National Competition Policy (1995–2000) reduced anti-competitive regulations and reformed transport and utilities sectors.
  - OECD Regulatory Impact Indicator shows Australian PMR falling over 1980–2002, reaching a level slightly below the U.S. by the end of the period.

### Data, methodology, and empirical specification
- Production function: constant returns to scale with two capital types (ICT K and non-ICT K) and labor services L; technology parameter A is Hicks-neutral MFP.
- MFP diffusion modeled with an ADL(1,1) co-integrated process comparing country-industry MFP to the technology-frontier country F (leader may vary by industry and time).
- Short-run dynamics for MFP and ICT capital-labor ratio specified; long-run marginal impact of covariates X on MFP gap derived as (1/(1−β1))⋅λ.
- Key datasets:
  - EU KLEMS and IGA (GGDC) growth-accounting databases for industry-level inputs and outputs.
  - OECD STAN, OECD ANBERD 2 & 3 for R&D, OECD bilateral trade for trade variables.
  - Countries: Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Italy, Japan, Netherlands, Spain, Sweden, the U.K., U.S.
- Sector coverage: market economy industries; public-sector-related sectors excluded.
- Growth accounting: perpetual inventory for capital stocks; capital services aggregation with two-year average shares; labor services by hours and skill composition; Tornqvist aggregation across industries.
- International comparability: gross output PPPs used to avoid biases from GDP PPPs; notable industry-level price differences across countries.

### Empirical results — convergence and magnitudes
- Descriptive averages (1980–2003):
  - Industry labor productivity growth (sample average): 2.8 percent.
  - MFP growth (sample average): 1.3 percent.
  - Distance to MFP leader (sample average): 39 percent (Australia’s average distance: 43 percent).
- Convergence regression (Table 5, Panel A):
  - For a typical industry with a 40 percent gap to the leader, diffusion implies MFP growth of 0.21 percentage points annually on average (computed as: -0.0095*Log(0.6)).
  - Given observed sample annual MFP growth of 1.3 percent, diffusion explains about 15 percent of observed MFP growth on average.
  - Convergence of MFP levels has significantly slowed over the past ten years.
  - Australia-specific effect: convergence led to annual MFP growth of about 0.4 percent in Australia; convergence in Australia occurred mainly after 1990.
  - In Australia, MFP in ICT-using sectors grew 0.1 percentage points faster than Australia’s own average.
- ICT capital-labor ratio convergence (Table 5, Panel B):
  - Sample average ICT capital-labor ratio growth: 13.9 percent.
  - Australia’s ICT capital deepening: 3.5 percentage points above sample average (consistent with summary statistics), implying Australia ICT deepening ≈ 17.4 percent.
- Product market regulation (PMR) effects (Table 6):
  - Average PMR effect not robustly significant when entered linearly or non-linearly.
  - Robustly significant positive effect of product market deregulation in ICT-using industries; no effect in other industries.
  - Effects robust to controls for human capital and ICT capital externalities.
- Labor market flexibility (EPL) effects (Table 7 and Table 8):
  - Strongly negative correlation between EPL index (more stringent EPL) and industry-level MFP growth.
  - Negative association strongly significant in high-skill industries but not in low-skill industries.
  - Labor market flexibility particularly important for MFP growth in human-capital-intensive industries.
  - Both labor market flexibility for high-skill industries and product market deregulation for ICT-using industries have contributed to boosting MFP growth across OECD countries; combined reforms help explain the “Australian effect.”
  - Share of skilled workers in labor compensation remains significantly associated with faster MFP growth after 1990.
- ICT capital deepening determinants (Table 9):
  - Countries with more flexible labor markets experienced faster ICT capital deepening.
  - Result robust to controls for previous year’s ICT share in total capital and initial ICT capital-labor ratio.
  - PMR effects on ICT deepening ambiguous: become insignificant when accounting for EPL or when including industry-year fixed effects.
- Quantitative magnitudes highlighted:
  - Diffusion explains 0.21 percentage points MFP growth for a 40 percent gap; about 15 percent of observed 1.3 percent average MFP growth.
  - Australia convergence implied ~0.4 percent annual MFP growth.
  - Australia ICT deepening: sample average 13.9 percent + Australia-specific 3.5 percentage points = Australia ICT deepening ≈ 17.4 percent.
  - Australia’s ICT-using-sector MFP growth exceeded Australia’s average by 0.1 percentage points.

### Robustness and sensitivity
- Results robust when:
  - Using labor productivity growth instead of MFP growth (Appendix 1).
  - Dropping countries one-by-one (Appendix 2): main coefficients and t-statistics stable, not driven by any single outlier country.
  - Alternative averaging (3-year averages) reported in Appendix 3; three-year averaging smooths noise but may reduce precision and hide accelerations/decelerations.
- Controls and fixed effects:
  - Country fixed effects included to control for unobserved country factors and measurement differences.
  - Industry-year fixed effects included to control for industry-wide trends and leader MFP growth.
  - Results on human capital and R&D remain significant after controlling for PMR and EPL in many specifications.

### Interpretation and policy implications
- Product market deregulation promotes MFP diffusion particularly in ICT-using industries; however, PMR alone does not fully account for Australia’s productivity acceleration.
- Labor market flexibility is especially important for productivity in high-skill, innovation-intensive industries and for facilitating ICT capital deepening.
- Domestic R&D intensity and human capital are essential complements to market deregulation for effective technology diffusion; technology diffusion depends on a wide range of factors beyond PMR and EPL.
- Policy implication: a combination of labor and product market reforms, together with policies to enhance human capital and R&D, can generate productivity gains consistent with those observed in Australia in the 1990s.

### D. Do Reforms Explain Australia’s Productivity Performance?
- Observed MFP growth:
  - MFP growth averaged 1.2 percent each year between 1991 and 2003.
  - MFP growth averaged 0.4 percent between 1980 and 1990.
- Product market reform (PMR) changes in ICT-using industries:
  - PMR index in Australia went down from 0.22 to 0.18 between the early 1990s and 2003.
  - Given an average gap to the leader of about 40 percent, regression-based predicted MFP growth acceleration from PMR reforms is 0.1 percent on average each year.
  - Catch-up effect due to gap widening in the 1980s (from 42 percent to 47 percent) implies an additional acceleration of MFP growth of 0.07 percent.
  - Combined implication: less than 20 percent of Australia’s productivity acceleration is explained by product market reforms and convergence; product market deregulations alone cannot explain Australia’s productivity acceleration.
- Labor market flexibility (EPL) effects:
  - Regression results imply labor market flexibility effects are of the order of observed MFP growth differentials and may better explain MFP catch-up within industries.
  - Panel B (1990–2003) comparison:
    - Four countries with most flexible labor markets in 2001 (the US, the UK, Australia and Canada) experienced MFP growth of 1.13 percent in high skill industries.
    - Countries with less flexible labor markets (Germany, Spain and Italy) experienced annual MFP growth of 0.68 percent in high skill industries.
    - Estimated coefficient on the EPL variable predicts an MFP growth differential of 0.58 percentage points for flexible-labor-market countries relative to inflexible ones.
    - Observed MFP growth differential between the two groups is 0.44 percent, similar in magnitude to the predicted 0.58 percent.
- Synthesis:
  - The impact of product market reforms was not negligible, but other factors must have also contributed to the productivity performance.
  - Labor market deregulation in the 1990s is a strong candidate to explain Australia’s specific MFP experience.

### E. Other Determinants of MFP Convergence: The Role of Human Capital and R&D
- Robustness:
  - Table 11 shows key results on labor market flexibility and product market deregulation remain significant when controlling for R&D intensity and trade-related variables.
- R&D intensity:
  - Strong and significant association between R&D intensity and MFP growth is uncovered.
  - Evidence points to a strong indirect benefit of R&D, but no direct significant association between R&D intensity and MFP growth after controlling for the indirect effect.
  - Indirect effect proxied by interaction between R&D intensity and relative level of MFP: domestic R&D enhances technological transfer by increasing domestic absorptive capacity.
  - Endogeneity concerns:
    - Direct effect of R&D intensity becomes insignificant when lagged 3 years or more; indirect effect via adoption of foreign technologies remains strongly significant even after 5 years.
  - Magnitude estimates:
    - If Australian firms increased their R&D intensity from 1.8 percent to the sample average of 3.6 percent, MFP growth would increase by 0.04 percent annually.
    - An increase of R&D intensity by one standard deviation to 8.9 percent would increase MFP growth by about 0.1 percent annually.
- Human capital:
  - Effect of human capital becomes strongly significant when controlling for R&D effects, supporting human capital externalities at the industry level.
  - Economic significance:
    - An increase in the share of high-skill workers in total labor compensation by one standard deviation (13 percent) would lead to an acceleration of industry MFP growth of 0.5 percent annually.
  - Australian context: younger cohorts are more educated than older cohorts, narrowing the gap in educational attainment.
- Trade-related factors:
  - Controls for export orientation and import-weighted R&D intensity of U.S. industries show no significant and robust correlations with MFP growth in this sample.

### V. CONCLUSION
- Summary of Australia’s experience:
  - Since the early 1990s Australia has been an outlier: long economic expansion, strong productivity gains, and extensive reforms in labor and product markets.
  - Combined, these reforms may largely explain Australia’s productivity performance.
- Broader findings for OECD countries:
  - Product market reforms significantly affect MFP growth in industries that use ICT intensively.
  - Labor market flexibility has a strong and positive impact on MFP in sectors more intensive in human capital and tends to foster accumulation of ICT capital.
  - R&D is important for the speed of technological diffusion; evidence of human capital externalities at the industry level is found.
  - Caveat: more R&D—one input among many—would not necessarily always result in higher productivity.

*Source: IMF Working Paper content unit provided in the input.*

### References..............................................................................................................

### _wp0804 - References..............................................................................................................

### Tables
- 1.    Sectoral Decomposition ..............................................................................................13
- 2.    Summary Statistics (1980–2003) ................................................................................28
- 3.    Correlations.................................................................................................................29
- 4.    Multi-Factor Productivity Leaders..............................................................................30
- 5.    Convergence of Australian Industries' Technology Level in a Panel  
       of OECD Countries.....................................................................................................32
- 6.    Impact of Product Market Regulations of MFP Growth ............................................34
- 7.    Impact of Labor Market Institutions on MFP Growth................................................35
- 8.    Disentangling the Effects of Product and Labor Market Institutions  
       on MFP Growth ..........................................................................................................36
- 9.    Impact of Labor and Product Market Institutions on ICT Capital Deepening ...........37
- 10.  Predicted Impact of Product and Labor Market Reforms ...........................................38
- 11.  Controlling for Other Determinants of MFP Growth .................................................39

### Figures
- 1. Australia's Productivity Performance ..............................................................................7
- 2. Investments in Information and Communication Technologies ......................................8
- 3. Sectoral Contributions to Real GDP Growth...................................................................8
- 4. Employment Protection Legislations in OECD Countries ..............................................9
- 5. Product Market Reforms in Australia ............................................................................10

*https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2008/_wp0804.pdf*

### 6. Australian Industries MFP Levels (relative to U.S.)......................................................31

### _wp0804 - 6. Australian Industries MFP Levels (relative to U.S.)......................................................31

### Introduction and objective
- Study objective: examine Australia’s productivity performance from the perspective of productivity best practices diffusion across OECD countries by analyzing (i) multi-factor productivity (MFP) diffusion and (ii) diffusion of ICT capital, and assess the role of policies in fostering diffusion.
- Time focus and context: Australia experienced a long expansion since the end of the 1991 recession with labor productivity increasing by close to 30 percent (comparison made to the U.S. over the same period). The ICT revolution has diffused to Australia; Australia undertook major labor and product market reforms and prudent monetary and fiscal policies over the 1990s and early 2000s.

### Key empirical questions
- Do Australia’s labor and product market reforms explain the sustained productivity acceleration observed since 1991?
- How do product market regulations (PMR), labor market flexibility (measured by the strictness of Employment Protection Legislation, EPL), human capital, and R&D affect:
  - diffusion of MFP best practices across industries and countries?
  - ICT capital deepening (ICT capital to labor ratios)?

### Main findings — summary
- Product market reforms have a significant and positive impact on MFP, particularly in industries that use ICT capital goods more intensively, but the estimated magnitude is insufficient alone to explain the Australian productivity acceleration observed in the 1990s.
- Labor market flexibility is associated with faster productivity gains in industries that are more human capital intensive; the magnitude of this estimated effect is large.
- Countries with more flexible labor markets experienced faster ICT capital deepening.
- Domestic R&D intensity and human capital crucially affect technological diffusion even after controlling for PMR and EPL.
- Combined labor and product market reforms could have induced productivity gains of the order of magnitude observed in Australia in the 1990s.

### Australian productivity performance and structural facts
- Aggregate labor productivity increased by about 30 percent between 1990 and 2006.
- Labor productivity gains concentrated mainly in market services (wholesale trade, retail trade, business services, transport).
- Sectoral patterns:
  - Manufacturing: contribution declining steadily.
  - Agriculture and mining: cyclical patterns with no clear long-run trend.
- ICT investment trends:
  - ICT capital to labor ratio growth in sample: 13.9 percent on average.
  - Non-ICT capital to labor ratio growth in sample: 2.6 percent on average.
  - Australia experienced much faster ICT capital accumulation than other sample countries (Australia’s ICT capital deepening estimated at 3.5 percentage points above the sample average of 13.9 percent; see regression results).
- Labor market reforms timeline highlights:
  - 1991: decentralization of bargaining agreements at enterprise level.
  - 1993: expansion and acceleration of enterprise bargaining.
  - 1996: introduction of individual contracts via Australian Workplace Agreements and reduced powers of the Australian Industrial Relations Commission.
  - Result: Australia characterized by low corporatism, decentralized wage bargaining, flexible employment protection legislation.
- Product market reforms:
  - Phased tariff reductions starting in 1988 leading to negligible tariffs by end of the 1990s.
  - Deregulation and restructuring of air, coastal transport, telecommunications; commercialization and privatization of public enterprises.
  - National Competition Policy (1995–2000) reduced anti-competitive regulations and reformed transport and utilities sectors.
  - OECD Regulatory Impact Indicator shows Australian PMR falling over 1980–2002, reaching a level slightly below the U.S. by the end of the period.

### Data, methodology, and empirical specification
- Production function: constant returns to scale with two capital types (ICT K and non-ICT K) and labor services L; technology parameter A is Hicks-neutral MFP.
- MFP diffusion modeled with an ADL(1,1) co-integrated process comparing country-industry MFP to the technology-frontier country F (leader may vary by industry and time).
- Short-run dynamics for MFP and ICT capital-labor ratio specified; long-run marginal impact of covariates X on MFP gap derived as (1/(1−β1))⋅λ.
- Key datasets:
  - EU KLEMS and IGA (GGDC) growth-accounting databases for industry-level inputs and outputs.
  - OECD STAN for additional growth accounting, OECD ANBERD 2 & 3 for R&D, OECD bilateral trade for trade variables.
  - Countries: Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Italy, Japan, Netherlands, Spain, Sweden, the U.K., U.S.
- Sector coverage: market economy industries; public-sector-related sectors excluded.
- Growth accounting follows Jorgenson et al. (2005) using perpetual inventory for capital stocks, capital services aggregation with two-year average shares, labor services by hours and skill composition, Tornqvist aggregation across industries.
- International comparability: gross output PPPs (Timmer et al. (2007c)) used to avoid biases from GDP PPPs; notable industry-level price differences across countries (examples given for Australia’s relative industry prices).

### Empirical results — convergence and magnitudes
- Descriptive averages (1980–2003):
  - Industry labor productivity growth (sample average): 2.8 percent.
  - MFP growth (sample average): 1.3 percent.
  - Distance to MFP leader (sample average): 39 percent (Australia’s average distance: 43 percent).
- Convergence regression (Table 5, Panel A):
  - For a typical industry with a 40 percent gap to the leader, diffusion implies MFP growth of 0.21 percentage points annually on average (computed as: -0.0095*Log(0.6)).
  - Given observed sample annual MFP growth of 1.3 percent, diffusion explains about 15 percent of observed MFP growth on average.
  - Convergence of MFP levels has significantly slowed over the past ten years.
  - Australia-specific effect: convergence led to annual MFP growth of about 0.4 percent in Australia (back-of-the-envelope using coefficients in column (3)); convergence in Australia occurred mainly after 1990.
  - In Australia, MFP in ICT-using sectors grew 0.1 percentage points faster than Australia’s own average (based on Australia dummy in column (3)).
- ICT capital-labor ratio convergence (Table 5, Panel B):
  - Sample average ICT capital-labor ratio growth: 13.9 percent.
  - Australia’s ICT capital deepening: 3.5 percentage points above sample average (consistent with summary statistics).
  - Australia’s convergence in ICT capital-labor ratio explains most of average convergence effect for ICT capital.
- Product market regulation (PMR) effects (Table 6):
  - Average PMR effect not robustly significant when entered linearly or non-linearly.
  - Robustly significant positive effect of product market deregulation in ICT-using industries; no effect in other industries.
  - Effects robust to controls for human capital and ICT capital externalities.
- Labor market flexibility (EPL) effects (Table 7 and Table 8):
  - Strongly negative correlation between EPL index (more stringent EPL) and industry-level MFP growth.
  - Negative association is strongly significant in high-skill industries (above-country-median share of high-skill workers) but not in low-skill industries.
  - Labor market flexibility particularly important for MFP growth in human-capital-intensive industries.
  - Table 8: both labor market flexibility for high-skill industries and product market deregulation for ICT-using industries have contributed to boosting MFP growth across OECD countries; combined reforms help explain the “Australian effect” from Table 5 (column (7)).
  - Share of skilled workers in labor compensation remains significantly associated with faster MFP growth after 1990.
- ICT capital deepening determinants (Table 9):
  - Countries with more flexible labor markets experienced faster ICT capital deepening.
  - Result robust to controls for previous year’s ICT share in total capital and initial ICT capital-labor ratio.
  - PMR effects on ICT deepening ambiguous: become insignificant when accounting for EPL or when including industry-year fixed effects, suggesting PMR associations may reflect industry-specific factors rather than PMR causation.
- Quantitative magnitudes highlighted in text:
  - Diffusion explains 0.21 percentage points MFP growth for a 40 percent gap; this is about 15 percent of observed 1.3 percent average MFP growth.
  - Australia convergence implied ~0.4 percent annual MFP growth.
  - Australia ICT deepening: sample average 13.9 percent + Australia-specific 3.5 percentage points = Australia ICT deepening ≈ 17.4 percent (text presents 3.5 percentage points above the 13.9 percent average).
  - Australia’s ICT-using-sector MFP growth exceeded Australia’s average by 0.1 percentage points.

### Robustness and sensitivity
- Results robust when:
  - Using labor productivity growth instead of MFP growth (Appendix 1).
  - Dropping countries one-by-one (Appendix 2): main coefficients and t-statistics stable, not driven by any single outlier country.
  - Alternative averaging (3-year averages) reported in Appendix 3; three-year averaging smooths noise but may reduce precision and hide accelerations/decelerations.
- Controls and fixed effects:
  - Country fixed effects included to control for unobserved country factors and measurement differences.
  - Industry-year fixed effects included to control for industry-wide trends and leader MFP growth.
  - Results on human capital and R&D remain significant after controlling for PMR and EPL in many specifications.

### Interpretation and policy implications (as presented in the paper)
- Product market deregulation promotes MFP diffusion particularly in ICT-using industries; however, PMR alone does not fully account for Australia’s productivity acceleration.
- Labor market flexibility is especially important for productivity in high-skill, innovation-intensive industries and for facilitating ICT capital deepening.
- Domestic R&D intensity and human capital are essential complements to market deregulation for effective technology diffusion; technology diffusion depends on a wide range of factors beyond PMR and EPL.
- Policy implication suggested: a combination of labor and product market reforms, together with policies to enhance human capital and R&D, can generate productivity gains consistent with those observed in Australia in the 1990s.

*Source: IMF Working Paper content unit provided in the input.*

### section III.

### section III.

### D. Do Reforms Explain Australia’s Productivity Performance?

- Question: whether product and labor market reforms can explain Australia’s long period of high productivity growth.
- Empirical comparison: actual versus predicted MFP performances using regression estimates (Table 10).
- Observed MFP growth:
  - MFP growth averaged 1.2 percent each year between 1991 and 2003.
  - MFP growth averaged 0.4 percent between 1980 and 1990.
- Product market reform (PMR) changes in ICT-using industries:
  - PMR index in Australia went down from 0.22 to 0.18 between the early 1990s and 2003.
  - Given an average gap to the leader of about 40 percent, regression-based predicted MFP growth acceleration from PMR reforms is 0.1 percent on average each year (using the change in end of period PMR indices to estimate the MFP acceleration).
  - Catch-up effect due to gap widening in the 1980s (from 42 percent to 47 percent) implies an additional acceleration of MFP growth of 0.07 percent.
  - Combined implication: less than 20 percent of Australia’s productivity acceleration is explained by product market reforms and convergence; product market deregulations alone cannot explain Australia’s productivity acceleration.
- Labor market flexibility (EPL) effects:
  - Regression results imply labor market flexibility effects are of the order of observed MFP growth differentials and may better explain MFP catch-up within industries.
  - Panel B comparison (1990–2003) between most flexible and least flexible labor market countries:
    - Four countries with most flexible labor markets in 2001 (the US, the UK, Australia and Canada) experienced MFP growth of 1.13 percent in high skill industries during that period.
    - Countries with less flexible labor markets (Germany, Spain and Italy) experienced annual MFP growth of 0.68 percent in high skill industries.
    - Estimated coefficient on the EPL variable predicts an MFP growth differential of 0.58 percentage points for flexible-labor-market countries relative to inflexible ones.
    - Observed MFP growth differential between the two groups is 0.44 percent, which is of the order of magnitude of the predicted 0.58 percent.
- Synthesis:
  - The impact of product market reforms was not negligible, but other factors must have also contributed to the productivity performance.
  - Labor market deregulation in the 1990s is a strong candidate to explain Australia’s specific MFP experience.

### E. Other Determinants of MFP Convergence: The Role of Human Capital and R&D

- Robustness:
  - Table 11 shows key results on labor market flexibility and product market deregulation remain significant when controlling for R&D intensity and trade-related variables.
- R&D intensity:
  - Strong and significant association between R&D intensity and MFP growth is uncovered (consistent with Griffith et al. (2004)).
  - Evidence points to a strong indirect benefit of R&D, but no direct significant association between R&D intensity and MFP growth after controlling for the indirect effect.
  - Indirect effect proxied by interaction between R&D intensity and relative level of MFP: suggests domestic R&D enhances technological transfer by increasing domestic absorptive capacity.
  - Endogeneity concerns:
    - R&D intensity may reflect expected future productivity performance or industry structure/business environment.
    - Controlling for industry-specific co-movements partially addresses concerns.
    - Direct effect of R&D intensity becomes insignificant when lagged 3 years or more; two non-mutually exclusive interpretations: (a) initial effect driven by endogeneity, (b) direct effect materializes during the first two years.
    - Indirect effect via adoption of foreign technologies remains strongly significant even after 5 years, suggesting it is less likely driven by endogeneity.
  - Magnitude estimates from Table 11:
    - If Australian firms increased their R&D intensity from 1.8 percent to the sample average of 3.6 percent, MFP growth would increase by 0.04 percent annually.
    - An increase of R&D intensity by one standard deviation to 8.9 percent would increase MFP growth by about 0.1 percent annually.
- Human capital:
  - Effect of human capital becomes strongly significant when controlling for R&D effects, supporting human capital externalities at the industry level and indirect effects on labor productivity beyond input composition effects.
  - Economic significance:
    - An increase in the share of high-skill workers in total labor compensation by one standard deviation (13 percent) would lead to an acceleration of industry MFP growth of 0.5 percent annually.
  - Australian context: younger cohorts are more educated than older cohorts, narrowing the gap in educational attainment (Dolman et al. (2007) and Davis and Rahman (2006)).
- Trade-related factors:
  - Controls for export orientation (share of exports in industry value-added) and import-weighted R&D intensity of U.S. industries (as a measure of technology diffusion through trade) show no significant and robust correlations with MFP growth in this sample.
  - Despite prior literature finding strong effects of technology diffusion through trade, this sample does not find significant positive effects.

### V. CONCLUSION

- Summary of Australia’s experience:
  - Since the early 1990s Australia has been an outlier: long economic expansion, strong productivity gains, and extensive reforms in labor and product markets.
  - Combined, these reforms may largely explain Australia’s productivity performance.
- Broader findings for OECD countries:
  - Product market reforms significantly affect MFP growth in industries that use ICT intensively.
  - Labor market flexibility has a strong and positive impact on MFP in sectors more intensive in human capital and tends to foster accumulation of ICT capital.
  - R&D is important for the speed of technological diffusion; evidence of human capital externalities at the industry level is found.
  - Caveat: more R&D—one input among many—would not necessarily always result in higher productivity.

*Source: _wp0804 - section III.*

### REFERENCES

### _wp0804 - REFERENCES

### Academic and Institutional References
- List of cited works includes papers and reports by:
  - Acemoglu, D.; Aghion, P.; Zilibotti, F. (2006), “Distance to Frontier, Selection and Economic Growth,” Journal of the European Economic Association, Vol. 4, No. 1, pp. 37–74.
  - Acharya, R., and W. Keller (2007), “Technology Transfer Through Imports,” CEPR Discussion Paper No. 6296.
  - Aghion, P., Bloom, N., Griffith, R., Blundell, R., and Howitt, P. (2005), “Competition and Innovation: An Inverted U Relationship,” Quarterly Journal of Economics, Vol. 120 No. 2, pp. 701–28.
  - Aghion, P., Blundell, R., Griffith, R., Howitt, P., and S. Prantl (2006), “The Effects of Entry on Incumbent Innovation and Productivity” (unpublished; Cambridge, Massachusetts: Harvard University).
  - Australian Bureau of Statistics (2007), “Experimental Estimates of Industry Multifactor Productivity,” Information Paper (5260.0.55.001).
  - Banks, G. (2005), “Structural Reform Australian-Style: Lessons for Others?” Presentation to the IMF, World Bank and OECD, May 2005, Productivity Commission, Canberra.
  - Bertola, G. (1994), “Flexibility, Investment and Growth,” Journal of Monetary Economics, Vol. 34, No. 2, pp. 215–38.
  - Cameron, G., Proudman J., S. Redding (2005), “Technological Convergence, R&D, Trade and Productivity Growth,” European Economic Review, Vol. 49, pp. 775–807.
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### Tables and Key Summary Statistics (selected items from source)
- Table 2. Summary Statistics (1980–2003) — sample of regression (1) Table 5
  - Observations and means (selected):
    - MFP growth: Obs 448, Mean 11.3%, Std. Dev. 3.8%
      - MFP growth - Australia: Obs 325, Mean 1.5%, Std. Dev. 3.8%
      - MFP growth - United States: Obs 383, Mean 1.1%, Std. Dev. 3.9%
    - Distance to MFP frontier: Obs 448, Mean 139.3%, Std. Dev. 19.0%
      - Distance to frontier - Australia: Obs 325, Mean 43.4%, Std. Dev. 17.8%
      - Distance to frontier - United States: Obs 383, Mean 36.7%, Std. Dev. 17.9%
    - Labor productivity growth: Obs 448, Mean 12.8%, Std. Dev. 4.0%
      - Labor productivity growth - Australia: Obs 325, Mean 3.1%, Std. Dev. 4.5%
      - Labor productivity growth - United States: Obs 383, Mean 2.8%, Std. Dev. 4.0%
    - ICT capital deepening: Obs 448, Mean 113.9%, Std. Dev. 7.5%
      - ICT capital deepening - Australia: Obs 325, Mean 17.7%, Std. Dev. 6.4%
      - ICT capital deepening - United States: Obs 383, Mean 15.7%, Std. Dev. 6.5%
    - Non-ICT capital deepening: Obs 448, Mean 12.6%, Std. Dev. 3.1%
      - Non-ICT capital deepening - Australia: Obs 325, Mean 2.4%, Std. Dev. 3.6%
      - Non-ICT capital deepening - United States: Obs 383, Mean 2.2%, Std. Dev. 2.9%
    - R&D intensity: Obs 294, Mean 43.6%, Std. Dev. 8.2%
      - R&D intensity - Australia: Obs 236, Mean 1.8%, Std. Dev. 2.6%
      - R&D intensity - United States: Obs 254, Mean 3.8%, Std. Dev. 6.0%
    - ICT capital compensation (in percent of total compensation): Obs 391, Mean 15.9%, Std. Dev. 13.5%
    - High skill labor compensation (in percent of total labor compensation): Obs 392, Mean 10.9%, Std. Dev. 11.1%
    - Hours worked by high skill workers (in percent of total hours worked): Obs 389, Mean 4.4%, Std. Dev. 8.7%
    - Annual percent change in share of hours worked by high skill workers: Obs 448, Mean 12.6%, Std. Dev. 12.6%
    - Employment protection legislation index: Obs 448, Mean 12.24, Std. Dev. 1.18
    - Product market regulation (PMR): Obs 421, Mean 40.22, Std. Dev. 20.18
    - Annual percent change in PMR: Obs 421, Mean -1.7%, Std. Dev. 3.2%
    - ICT using industries: Obs 448, Mean 29.4%
    - ICT producing industries: Obs 448, Mean 8.7%
    - Other industries: Obs 448, Mean 62.0%

- Table 3. Correlations (note: p-values are in italics in source)
  - Examples of correlation coefficients and reported p-values (as presented):
    - Correlation between MFP growth and Labor productivity growth: 0.9021 (p-value 0.00)
    - Correlation between MFP growth and ICT capital deepening: 0.0018 (p-value 0.86)
    - Correlation between ICT capital deepening and share of ICT capital in total capital comp.: 0.94 (p-value 0.00)
    - Other entries and corresponding p-values are reported in the table (note: p-values formatted in italics in source).

- Table 4. Multi-Factor Productivity Leaders (selected snapshots)
  - Agriculture:
    - 1990: No. 1 Netherlands; No. 2 United States; No. 3 Belgium
    - 2003: No. 1 Germany; No. 2 Denmark; No. 3 Belgium
  - Mining:
    - 1990: No. 1 Denmark; No. 2 Belgium; No. 3 Australia
    - 2003: No. 1 Denmark; No. 2 United Kingdom; No. 3 Australia
  - Total Manufacturing:
    - 1990: No. 1 United States; No. 2 Belgium; No. 3 Germany
    - 2003: No. 1 United States; No. 2 Sweden; No. 3 Finland
  - Electrical and Optical Equipment, Wholesale & Retail Trade, Transport & Storage, Business Services: leaders reported for 1990 and 2003 in table.

- Figures: “Figure 6. Australian Industries MFP levels (relative to U.S.)”
  - Plots in source show "Gap to U.S. TFP Level (in percent)" over years (1980–2002) for industries including AGRICULTURE, MINING, TOTAL MANUFACTURING, CONSTRUCTION, WHOLESALE & RETAIL TRADE, TRANSPORT & STORAGE, BUSINESS SERVICES. Specific plotted axis ticks are given (e.g., AGRICULTURE: -50% to 0%; MINING: -4% to 14%; etc.). Source attributed as "author's calculations."

### Regression Results and Coefficients (selected reported estimates)
- Table 5. Convergence of Australian Industries’ Technology Level — Panel A. Multi-Factor Productivity (selected coefficients)
  - Dependent variable: annual MFP growth
  - Example coefficients (full sample unless indicated):
    - TFP level, relative to leader (t-1): -0.0095 [3.11]*** (column (1))
    - TFP level, relative to leader (t-1): -0.020 [5.74]*** (column (2))
    - ICT using in Australia: 0.01 [2.64]*** (one specification)
  - Observations reported (examples): 448, 1275, 845, 4472, 1626, 4481 (as in table); R-squared values reported such as 0.02, 0.01 (note: R-squared exclude industry-year fixed effects per source).
  - Note: Robust t statistics in brackets; * significant at 10%; ** significant at 5%; *** significant at 1%.

- Table 5. Panel B. ICT capital deepening (selected coefficients)
  - Dependent variable: annual growth in ICT capital-labor ratio
  - Example coefficients:
    - ICT capital-labor ratio, relative to leader (t-1): -0.006 [4.27]*** (column (1))
    - ICT capital-labor ratio, relative to leader (t-1): -0.003 [2.15]** (column (2))
    - Share of ICT capital in total capital (t-2): -0.007 [3.56]*** (column (1))
    - Australia relative to average (dummy): 0.035 [3.61]*** (one specification)
  - Observations reported (examples): 4518, 4378, 4378; R-squared examples: 0.27, 0.03.

- Table 6. Impact of Product Market Regulations on MFP Growth (selected coefficients)
  - Dependent variable: annual MFP growth (1990-2003)
  - Example coefficients:
    - TFP level, relative to leader (t-1): -0.010 [3.25]*** (column (1))
    - TFP level, relative to leader (t-1) Interacted with PMR: 0.008 [1.81]* (one specification)
    - Sectoral PMR (t-1) in ICT using: -0.028 [2.21]** (one specification)
    - High skill labor share (t-1): 0.0006 [2.86]*** (one specification)
  - Observations vary per column; R-squared examples: 0.02, 0.03, 0.0349.

- Table 7. Impact of Labor Market Institutions on MFP Growth (selected coefficients)
  - Dependent variable: annual MFP growth (1990-2003)
  - Example coefficients:
    - TFP level, relative to leader (t-1): -0.01002 [3.18]*** (column example)
    - EPL: -0.002 [-2.02]** (EPL main effect in one specification)
    - EPL in High Skill industries: -0.0023 [2.36]** (reported as significant)
    - High skill labor share (t-1): 0.00039 [2.44]** (one specification)
  - Observations reported: 4481, 448, 1448, etc.; R-squared values reported such as 0.00, 0.02, 0.03.

- Table 8. Disentangling the Effects of Product and Labor Market Institutions on MFP Growth (selected coefficients)
  - Dependent variable: annual MFP growth (1990-2003)
  - Example coefficients:
    - TFP level, relative to leader (t-1) Interacted with PMR in ICT using industries: 0.015 [2.45]** (one specification)
    - EPL: -0.001 to -0.002 (various columns with t-statistics reported)
    - High skill share (t-1): coefficients include 0.000450 [2.75]*** in a column.
  - Observations: 3679 across multiple columns; R-squared examples: 0.01–0.04.

- Table 9. Impact of Labor and Product Market Institutions on ICT Capital Deepening (selected coefficients)
  - Dependent variable: annual growth in ICT capital-labor ratio
  - Example coefficients:
    - ICT capital-labor ratio, relative to leader (t-1): coefficients include 0.006 [4.2]***, 0.004 [3.3]***.
    - Share of ICT capital in total capital (t-1): -0.006 [-2.92]***, -0.008 [-5.08]***.
    - Sectoral PMR: -0.038 [-5.55]*** in one specification; 0.070 [3.12]*** in another specification (sectoral variation).
    - EPL: -0.022 [-10.82]*** (one specification).
  - Observations examples: 4116, 4378, 4116, 4116; R-squared examples: 0.02, 0.13, 0.14, 0.27.

- Table 10. Predicted Impact of Product and Labor Market Reforms (select reported figures)
  - Panel B. Predicted impact of labor market flexibility on MFP growth in high skill industries across OECD countries:
    - Change in annual MFP : Actual 0.58%
    - Predicted 1/ : 0.59
    - 0.9% reported under heading Annual MFP growth EPL index PMR (period end)
    - Countries at bottom quartile EPL: Australia, Canada, the United Kingdom and the United States; countries at the top quartile EPL: Germany, Italy and Spain.
  - Panel A. Predicted impact of product market reforms on MFP growth in Australian ICT using industries:
    - Example numbers: 0.22, 0.18, 0.4%, 0.10%, 0.07%, 0.17%, 1.2%, 0.44% (reported in table as various impacts and shares).

- Table 11. Controlling for Other Determinants of MFP Growth (selected coefficients)
  - Dependent variable: annual MFP growth (1990-2003)
  - Example coefficients:
    - TFP level, relative to leader (t-1): -0.00878 [1.96]*; -0.01983 [3.97]***; -0.02009 [4.04]*** in various columns.
    - TFP level, relative to leader (t-1) Interacted with PMR in ICT using industries: 0.034 [3.16]***; 0.028 [2.59]** (examples).
    - EPL in high skill industries: -0.0019 [3.37]*** (column (1) example).
    - High skill share (t-1): 0.00042 [2.30]** (column (1) example).
    - R&D intensity (t-1): 0.00144 [2.21]** (one specification).
    - Interacted effects of TFP level and R&D intensity reported as statistically significant (e.g., [5.59]***, [6.13]***, [4.59]*** for interaction terms).
  - Observations across columns: 2398, 3679, 2377, 1657; R-squared examples: 0.03–0.06.
  - Country fixed effects and industry-time fixed effects: YES across reported specifications; robust t statistics clustered by country-year per source.

### Notes on Reporting Conventions in Source
- Robust t statistics are reported in brackets for regression tables.
- Significance notation: * significant at 10%; ** significant at 5%; *** significant at 1%.
- Notes in tables state: "TFP levels relative to leader are expressed in Logs." and "Regressions are on the full sample (1980-2003), unless indicated."
- R-squared values in some tables exclude industry-year fixed effects (explicitly noted in source).
- p-values in Table 3 are formatted in italics in the source.

*Source: _wp0804 - REFERENCES (content unit provided).*

### Appendix I. The Impact of Produc

### Appendix I. The Impact of Produc
t and Labor Market Institutions on Labor Productivity

### Regression results: annual labor productivity growth (1990-2003)
- Dependent variable: annual labor productivity growth.
- Models (1)–(9) — key coefficients and statistics:
  - Labor productivity level, relative to leader (t-1):
    - (1) -0.015  [-5.37]***
    - (2) -0.004  [1.32]
    - (3) -0.014  [5.12]***
    - (4) -0.019  [-6.28]***
    - (5) -0.019  [6.27]***
    - (6) -0.017  [4.09]***
    - (7) -0.018  [5.52]***
    - (8) -0.016  [3.93]***
    - (9) -0.018  [-5.86]***
  - Labor productivity level, relative to leader (t-1) interacted with PMR:
    - (2) 0.011  [2.14]**
    - (3) 0.010  [1.50]
    - (4) 0.013  [1.23]
  - Labor productivity level, relative to leader (t-1) interacted with PMR in ICT using industries:
    - (2) 0.029  [4.41]***
    - (3) 0.026  [3.08]***
    - (4) 0.029  [2.76]***
  - Labor productivity level, relative to leader (t-1) interacted with PMR in ICT producing industries:
    - (2) -0.008  [0.79]
    - (3) -0.005  [0.25]
    - (4) -0.010  [-0.95]
  - Labor productivity level, relative to leader (t-1) interacted with PMR in other industries:
    - (2) 0.009  [1.23]
    - (3) 0.009  [0.91]
    - (4) 0.008  [0.98]
  - EPL:
    - Reported as -0.002 in several specifications (values shown without t-stat brackets).
    - EPL (alternative) 0.0006 [0.48] (labeled Low skill industries)
  - High skill share (t-1):
    - (1) 0.0003  [2.06]**
    - (2) 0.0002  [0.93]
    - (3) 0.0004  [2.12]*
    - (4) 0.0003  [2.2]**
    - (5) 0.0004  [2.49]**
    - (6) 0.0006  [3.28]***
    - (7) 0.0004  [1.96]*
    - (8) 0.0006  [2.26]**
    - (9) 0.0004  [2.19]**
  - ICT share (t-1):
    - (1) 0.015  [1.98]*
    - (2) 0.013  [0.97]
    - (3) 0.012  [0.86]
    - (4) 0.017  [2.25]**
    - (5) 0.017  [2.29]**
    - (6) 0.024  [2.51]**
    - (7) 0.014  [1.00]
    - (8) 0.023  [1.80]*
    - (9) 0.014  [1.14]
- Sample sizes and fit:
  - Observations:
    - (1) 3942
    - (2) 3942
    - (3) 3942
    - (4) 3724
    - (5) 3724
    - (6) 2397
    - (7) 3724
    - (8) 2397
    - (9) 3724
  - R-squared:
    - (1) 0.03
    - (2) 0.01
    - (3) 0.04
    - (4) 0.04
    - (5) 0.04
    - (6) 0.04
    - (7) 0.04
    - (8) 0.04
    - (9) 0.04
  - Fixed effects:
    - Country fixed effects: YES in all except (2) where NO.
    - Industry-time fixed effects: YES in all specifications.
- Notes:
  - Robust t statistics in brackets, observations clustered by country-year.
  - * significant at 10%; ** significant at 5%; *** significant at 1%.
  - TFP levels relative to leader are expressed in Logs.
  - Regressions are on the full sample (1980-2003), unless indicated.

### Robustness: Dropping countries one by one (Appendix II)
- Dependent variable: (implied) annual MFP/TFP growth; regressions on full sample (1980-2003) unless indicated.
- Selected coefficients when dropping individual countries (columns (1)–(5)):
  - TFP level, relative to leader (t-1):
    - (1) -0.010  [3.11]***
    - (2) -0.013  [-3.77]***
    - (3) -0.014  [-3.82]***
    - (4) -0.01093  [3.12]***
    - (5) -0.0112  [3.19]***
  - TFP level, relative to leader (t-1) interacted with PMR:
    - (1) 0.009  [1.87]*
    - (2) 0.006  [1.31]
  - TFP level, relative to leader (t-1) interacted with PMR in ICT using industries:
    - (1) 0.018  [3.04]***
  - TFP level, relative to leader (t-1) interacted with PMR in ICT producing industries:
    - (1) 0.006  [0.65]
  - TFP level, relative to leader (t-1) interacted with PMR in other industries:
    - (1) 0.005  [0.65]
  - EPL (selected values reported as -0.001, -0.002 with High skill industries t-stats):
    - High skill industries: [3.16]***, [3.70]***
  - High skill share (t-1):
    - (1) 0.0002  [1.27]
    - (2) 0.0003  [1.52]
    - (3) 0.00015  [0.78]
    - (4) 0.00016  [1.17]
  - ICT capital share (t-1):
    - (1) 0.0125  [1.92]*
    - (2) 0.012  [1.85]*
    - (3) 0.00389  [0.31]
    - (4) 0.004  [0.62]
- Observations and fit in selected robustness runs:
  - Observations: 4481, 3539, 3539, 3892, 3679 (various columns).
  - R-squared: 0.02, 0.03, 0.03, 0.02, 0.02.
  - Country fixed effects: YES; Industry-time fixed effects: YES.
- Sensitivity summaries (highest and smallest p-value cases):
  - Highest p-value cases (least significant coefficient) — examples:
    - Coefficient -0.008, T-stat [-2.44]**, Country dropped: Germany.
    - Coefficient 0.007, T-stat [1.32], Country dropped: Australia.
    - Coefficient 0.012, T-stat [1.95]*, Country dropped: Denmark.
  - Smallest p-value cases (most significant coefficient) — examples:
    - Coefficient -0.011, T-stat [-3.41]***, Country dropped: United States.
    - Coefficient 0.014, T-stat [2.27]**, Country dropped: Japan.
    - Coefficient 0.021, T-stat [3.52]***, Country dropped: Belgium.
- Notes:
  - Robust t statistics in brackets, observations clustered by country-year.
  - * significant at 10%; ** significant at 5%; *** significant at 1%.
  - Table reports, for each variable of interest, the coefficient, t statistics, and the country dropped when the coefficient is the least significant (highest p value) and the most significant (lowest p value).

### Regressions with 3-year averages (Appendix III)
- Dependent variable: 3 years average MFP growth (overlapping panel). Dependent variable is the 3-year moving average growth rate of MFP growth (years t+1, t and t-1).
- Sample period reported: 1990-2003 in column headers.
- Key coefficients (columns (1),(2),(3),(6),(7),(8)):
  - TFP level, relative to leader (t-2):
    - (1) -0.016  [4.28]***
    - (2) -0.017  [4.18]***
    - (3) -0.018  [4.21]***
    - (6) -0.018  [4.30]***
    - (7) -0.018  [4.34]***
    - (8) -0.014  [2.85]***
  - TFP level, relative to leader (t-2) interacted with PMR:
    - (1) 0.010  [1.78]*
    - (2) 0.009  [1.57]
  - Sectoral PMR (t-2):
    - (reported values) 0.0005  [0.04]; 0.001  [0.08]; -0.0012  [0.10]; -0.0006  [0.05]; -0.0032  [0.21]
  - TFP level, relative to leader (t-2) interacted with PMR in ICT using industries:
    - (reported) 0.013  [1.76]*; 0.014  [1.79]*; 0.017  [1.81]*
  - TFP level, relative to leader (t-2) interacted with PMR in ICT producing industries:
    - (reported) 0.003  [0.21]; 0.002  [0.12]; -0.010  [0.54]
  - TFP level, relative to leader (t-2) interacted with PMR in other industries:
    - (reported) 0.009  [1.59]; 0.008  [1.27]; 0.010  [1.30]
  - EPL (in high skill industries):
    - EPL coefficients: -0.0016, -0.0017, -0.0018 with High skill industries t-stats [2.98]***, [3.02]***, [2.52]**
- Sample sizes and fit:
  - Observations (reported across columns): 4255, 4002, 4002, 4002, 2400, 2400, 2400, 2260, 7 (as shown in table fragments).
  - R-squared reported: 0.05 for many columns, and 0.07 in one specification.
  - Fixed effects: Country fixed effects YES; Industry-time fixed effects YES in all reported specifications.
- Notes:
  - Robust t statistics in brackets, observations are clustered by country-industry.
  - * significant at 10%; ** significant at 5%; *** significant at 1%.
  - TFP levels relative to leader are expressed in Logs.
  - Regressions are on the full sample (1980-2003), unless indicated.

*Appendix I–III tables and notes as presented in the source PDF.*

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