## _wp16114 - Appendix 1 and Chapter 21) — Summary

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---

### I. Introduction — purpose and scope
- Objective:
  - Quantify empirically the short- to medium-term impact of abrupt reductions in barriers to entry on sector-level output, employment, prices, investment, and productivity in five non-manufacturing industries: air transport, electricity and gas, rail and road transport, postal and courier activities, and telecommunications.
- Reform shocks:
  - Identified from a new database compiling large regulatory and legislative measures for 26 countries starting from the 1970s (Duval et al., 2016).
- Outcomes and methodology:
  - Outcomes: sectoral real value added, prices, employment, gross fixed capital formation, and labor productivity from the OECD STAN database (disaggregated at ISIC Rev.4 level where available).
  - Methodology: construct a country-sector-time panel and estimate dynamic impacts over five years post-reform using the local projection method (Jordà, 2005; Teulings and Zubanov, 2014); address omitted variable bias via country-time fixed effects and IV techniques.
- Three main empirical findings highlighted:
  - Major reductions in barriers to entry yield large increases in output and labor productivity over the medium term, concomitant with a relative price decline; sectoral employment and investment are essentially unaffected, implying output gains primarily reflect higher total factor productivity.
  - Effects become statistically significant two to three years after the reform, as prices start dropping and productivity and output increase significantly.
  - No evidence of any short-term cost from reform, including under weak macroeconomic conditions.

### II. Data sources and reform identification
- STAN database:
  - Coverage: annual sector-level inputs, output, and prices for OECD countries; dataset spans the period 1970-2011, with varying coverage across countries, years, and sectors.
  - Variables used: real value added, prices (value-added deflators), employment, gross fixed capital formation, labor productivity (defined as ratio of real value added to total employment).
  - All variables expressed in natural logarithm in econometric analysis.
  - Sectoral price deflator constructed as the logarithm of the ratio between the sectoral price deflator and the GDP deflator.
- OECD ETCR indicators:
  - Coverage: measures product market regulation in seven network industries from 1975 to 2013.
  - Structure: over 700 qualitative and quantitative indicators aggregated into sub-indicators (barriers to entry, public ownership, vertical integration, market structure, price controls).
  - Scale: scores range from zero to six, with a lower value indicating a less stringent degree of regulation.
- Identification of major reform shocks:
  - A policy action qualifies as a major reform if one of three criteria is met:
    - (i) OECD Economic Survey uses strong normative language (e.g., “major reform”);
    - (ii) policy action is mentioned repeatedly across editions and/or retrospective summaries;
    - (iii) OECD indicator of regulatory barriers to entry displays a very large change (in the 5th percentile of the distribution of the change); when only (iii) is met, other sources are searched to identify the underlying action.
  - Mapping to STAN industries:
    - Air transport, postal and courier services, telecommunications: reform shock variable = 1 in reform year, 0 otherwise.
    - Electricity and gas; rail and road transport: reform shocks aggregated using weights ¾ and ¼ to map to STAN industries “Electricity, gas, steam and air conditioning supply” and “Land transport and transport via pipelines”. This yields reform shock values of 0.75, 0.25, 1 or 0 depending on sub-sector combinations.
- Counts and sample:
  - Final dataset: country-sector-time panel covering 26 countries and 5 sectors over 1975 to 2011; panel is unbalanced.
  - Reform shocks constructed: total of 234 reform shocks comprising:
    - 60 in electricity and gas,
    - 52 in rail- and road transport,
    - 36 in air transport,
    - 32 in postal services,
    - 54 in telecommunications.
- Additional sources:
  - Historical list of competition-relevant EU directives used for instrumentation.
  - Country macro variables (real GDP and GDP deflator) from World Bank WDI and IMF WEO.

### III. Stylized facts from descriptive analysis
- Historical patterns:
  - All five network industries experienced liberalization over the past three decades, with variation in timing, speed, and scope.
  - Major progress: airline and telecommunications industries (limited remaining room for reform in most AEs).
  - More scope remaining: postal services, electricity and gas, and to a lesser extent land transport.
  - Cross-country dispersion of regulatory settings declined over time, indicating convergence.
- Timing of reform waves:
  - Early movers: United States and United Kingdom in late 1970s and early 1980s.
  - Peak waves: 1990s and 2000s; some resurgence post-global financial and euro area crises.
- Descriptive DID illustrative result:
  - Real value added increases by more than 16 per cent in cumulative terms over the 5 years following the reform (relative to non-reforming observations).
  - Caveat: the descriptive exercise does not address full dynamics nor omitted variable bias.

### IV. Empirical setup — local projections and identification
- Local projection specification (for j = 0, 1, ..., 5):
  - y_{c,s,t+j} – y_{c,s,t-1} = β_j * reform_{c,s,t} + Σ γ_k * controls_{c,s,t+k} + λ_{ct} + λ_{cs} + trend_s + ε_{cst}
  - y = log of variable of interest (output, employment, investment, relative value-added deflator, labor productivity).
  - Controls include all past and future reform shocks to address autocorrelation of reform shocks.
  - Fixed effects: country-time (λ_{ct}); country-industry (λ_{cs}); trend_s: industry-specific linear time trend.
  - Standard errors clustered at the country-sector level.
- Endogeneity concerns and IV strategy:
  - Instruments:
    - Initial stance of regulation: three-year lagged value of OECD indicator of stringency in the country-sector.
    - Peer pressure: number of other countries in the sample that carried out a major reform in the same sector over the past three years.
    - EU directive transposition pressure: sector time-varying dummy = 1 if an EU directive for the sector was adopted over the past five years (t-4 to t) and has not yet resulted in a reform in the country.
- Estimation approach for endogenous bounded reform variable:
  - Reform variable values: {0, 0.25, 0.75, 1}.
  - Wooldridge’s two-step procedure:
    - Step 1: estimate an ordinal probit model of the reform variable using the exogenous instruments.
    - Step 2: use the predicted value from the probit as the instrument in a standard IV (2SLS) estimation of the local projection equation.

### V. Key methodological notes and dataset conditioning
- Panel conditioning:
  - Baseline regressions drop any country-sector time series for which the dependent variable is not available for at least three years preceding the first liberalization shock. This removes approximately 10% of the dataset.
- Variable construction specifics:
  - Labor productivity = VALK / EMPN (variables in logs).
  - Reform coding for aggregate sectors uses ¾ and ¼ weights approximating average shares in value added for subcomponents.
- Time coverage for key sources:
  - STAN: 1970-2011.
  - OECD ETCR indicators: 1975-2013.
  - Final country-sector sample used in analysis: 1975-2011.

### IV. Econometric results — Baseline OLS and IV (major numerical findings)
- Baseline OLS: dynamics of estimated Reform Shock (Table 1, OLS):
  - t: 0.62 (standard error (1.36))
  - t+1: 2.98 (2.21)
  - t+2: 4.54 (3.20)
  - t+3: 7.31* (3.69)
  - t+4: 10.09** (4.66)
  - Sample sizes (Table 1): N: 2269 (t), 2181 (t+1), 2093 (t+2), 2005 (t+3), 1917 (t+4)
  - Interpretation:
    - Coefficient positive but statistically insignificant in reform year; grows over time, exceeding 7 percent and significant at 10% after four years; cumulative effect about 10% after five years and significant at 5%.
- OLS impulse responses and component breakdowns (Figure 4):
  - Relative value-added deflator falls markedly and rapidly; significant in the second year and levels off at about 8-9% after three years.
  - Value-added decomposition: only labor productivity shows a positive, statistically significant effect after five years.
  - Gross fixed capital formation: no statistically significant effect.
  - Interpretation: real value-added gains primarily reflect an increase in total factor productivity; deregulation does not reduce employment in the short term.
- Reform size interaction (Table 2): Reform Shock × Change (ETCR change on 0 to 6 scale):
  - t: 0.42 (0.83)
  - t+1: 2.05* (1.16)
  - t+2: 2.97* (1.53)
  - t+3: 3.94** (1.66)
  - t+4: 5.46** (2.22)
  - Interpretation: a liberalization equivalent to a two-point reduction in the ETCR indicator increases sectoral output by about 11 (=5.5*2) percent over 5 years; about three fourths of impact from improved labor productivity, remainder from employment increase.
- Instrumental Variable results (Table 3: Effect of Reform Shock on Real Value Added):
  - t: -1.13 (1.86)
  - t+1: 2.09 (2.98)
  - t+2: 3.46 (3.84)
  - t+3: 9.26** (4.00)
  - t+4: 13.66*** (5.08)
  - Sample sizes and first-stage F-statistics (Table 3):
    - N: 2264 (t), 2181 (t+1), 2093 (t+2), 2005 (t+3), 1917 (t+4)
    - 1st Stage F: 243.54 (t), 228.05 (t+1), 205.49 (t+2), 177.80 (t+3), 206.74 (t+4)
  - Interpretation:
    - IV magnitudes substantially larger than OLS; point estimates peak at almost 14 percent after five years.
    - Impulse responses qualitatively similar to OLS but amplified for employment and labor productivity; labor productivity effect larger and more statistically significant than employment effect.
    - No evidence of short-term employment costs; estimated impact on employment is negative in first year but not statistically significant.
  - Methodological note:
    - F-statistics reported are from regression of actual reform variable on predicted reform variable; conventional “rules of thumb” do not apply directly. Only one instrument enters the 2SLS first stage (the predicted probability).
- Interactions:
  - Business cycle:
    - Reform shock interacted with Auerbach and Gorodnichenko (2012) smooth transition function (γ = 1.5); results show no significant impact of business cycle position on effect of entry deregulation.
  - Employment Protection Legislation (EPLR) interaction (Table 4):
    - Reform Shock:
      - t: -0.13 (1.48)
      - t+1: 2.60 (2.08)
      - t+2: 2.44 (2.27)
      - t+3: 4.00 (2.51)
      - t+4: 4.84 (2.92)
    - Reform Shock × EPLR:
      - t: 4.28 (3.39)
      - t+1: 10.66** (5.11)
      - t+2: 7.13 (6.35)
      - t+3: 11.16 (6.92)
      - t+4: 16.39** (7.67)
    - Sample sizes (Table 4): N = 2321 (t), 2245 (t+1), 2152 (t+2), 2059 (t+3), 1966 (t+4)
    - Interpretation:
      - Employment effect of entry deregulation is larger where employment protection regulation is more stringent.
      - Positive interaction appears to capture a permanent effect, remaining statistically significant beyond the fifth year (not shown).

### V. Concluding remarks — summary conclusions and policy implications
- Main quantitative conclusions:
  - Sizeable average impact on real value added in deregulated industries in the order of 10% to 14% after five years depending on specification.
  - Effect accompanied by an increase in labor productivity and a relative price decline.
  - Impact on sectoral employment is weak; investment essentially unaffected — output gains primarily reflect higher total factor productivity.
- Additional findings:
  - Tentative evidence of positive short-term net job creation in deregulated sectors in countries with more restrictive employment protection legislation.
  - No short-term costs of reform detected; no evidence that gains are weaker when deregulation occurs amid weak overall demand.
- Policy implication:
  - Findings support intensifying product market reform efforts in advanced economies amid weak growth.

### Appendix — Selected variable definitions and data coverage
- Key variable definitions:
  - PRDK: Production (gross output) in volume. Source: OECD STAN Structural Analysis Database.
  - VALK: Value added in volume.
  - VALP: Value Added deflator. Sectoral price levels transformed into log-deviation from the GDP deflator (World Bank).
  - GFCK: Gross fixed capital formation in volume.
  - EMPN: Number of persons engaged (total employment).
  - LPRD: Labor Productivity (calculated as VALK / EMPN; variables in logs for empirical analysis).
  - ETCR: OECD indicators of regulation in energy, transport and communications summarising regulatory provisions in seven sectors and six dimensions.
  - GDP: Real GDP. Source: IMF WEO database.
  - GDPP: GDP deflator. Source: World Bank WDI.
  - EPLR: OECD Employment Protection Legislation index for regular employment (0 to 6 scale).
- Time coverage:
  - STAN: 1970-2011.
  - OECD ETCR indicators: 1975-2013.
  - Final country-sector sample used in analysis: 1975-2011.

*Source: _wp16114 - Appendix 1 and Chapter 21) (PDF).*

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

### _wp16114 - References

### Tables
- 1. Baseline OLS Regression: Effect of Reform Shock on Real Value Added..............  14
- 2. Effect on Real Value Added Accounting for the Size of the Reform Shock............  16
- 3.      IV Regression: Effect of Reform Shock on Real Value Added............................ 18
- 4. Effect of Reform Shock on Real Value Added: The Role of Employment  
         Protection         Legislation...........................................................................         20         

### Figures
- 1.      Barriers to Entry in Network Industries......................................................... 9
- 2.      Histogram of Barriers to Entry Reforms....................................................... 10
- 3.      A Descriptive “difference-in-differences” Measure of the Impact of Reform....   ......11
- 4.      Baseline OLS Regressions – Impulse Response Functions................................. 15
- 5.      Comparison of Reforming and Non-reforming Observations over Pre-reform  
 and Post-reform periods.......................................................................... 17
- 6.  IV Regressions – Impulse Response Functions............................................... 18

*Source: _wp16114 - References (PDF).*

### Appendix 1

### Appendix 1

### I. Introduction — purpose and scope
- Objective: quantify empirically the short- to medium-term impact of abrupt reductions in barriers to entry on sector-level output, employment, prices, investment, and productivity in five non-manufacturing industries: air transport, electricity and gas, rail and road transport, postal and courier activities, and telecommunications.
- Reform shocks: identified from a new database compiling large regulatory and legislative measures for 26 countries starting from the 1970s (Duval et al., 2016).
- Outcomes: sectoral real value added, prices, employment, gross fixed capital formation, and labor productivity from the OECD STAN database (disaggregated at ISIC Rev.4 level where available).
- Methodology: construct a country-sector-time panel and estimate dynamic impacts over five years post-reform using the local projection method (Jordà, 2005; Teulings and Zubanov, 2014); address omitted variable bias via country-time fixed effects and IV techniques.
- Three main empirical findings highlighted in the paper:
  - Major reductions in barriers to entry yield large increases in output and labor productivity over the medium term, concomitant with a relative price decline; sectoral employment and investment are essentially unaffected, implying output gains primarily reflect higher total factor productivity.
  - Effects become statistically significant two to three years after the reform, as prices start dropping and productivity and output increase significantly.
  - No evidence of any short-term cost from reform, including under weak macroeconomic conditions.

### II. Data sources and reform identification
- STAN database:
  - Coverage: annual sector-level inputs, output, and prices for OECD countries; dataset spans the period 1970-2011, with varying coverage across countries, years, and sectors.
  - Variables used: real value added, prices (value-added deflators), employment, gross fixed capital formation, labor productivity (defined as ratio of real value added to total employment).
  - All variables expressed in natural logarithm in econometric analysis.
  - Note: total factor productivity is ignored given capital stock measurement issues and short-term focus.
  - Sectoral price deflator constructed as the logarithm of the ratio between the sectoral price deflator and the GDP deflator.
- OECD ETCR indicators:
  - Coverage: measures product market regulation in seven network industries from 1975 to 2013.
  - Structure: over 700 qualitative and quantitative indicators aggregated into sub-indicators (barriers to entry, public ownership, vertical integration, market structure, price controls).
  - Scale: scores range from zero to six, with a lower value indicating a less stringent degree of regulation.
- Identification of major reform shocks:
  - Source: Duval et al. (2016) database built from documented legislative and regulatory actions in OECD Economic Surveys and additional country-specific sources.
  - A policy action qualifies as a major reform if one of three criteria is met:
    - (i) OECD Economic Survey uses strong normative language (e.g., “major reform”);
    - (ii) policy action is mentioned repeatedly across editions and/or retrospective summaries;
    - (iii) OECD indicator of regulatory barriers to entry displays a very large change (in the 5th percentile of the distribution of the change); when only (iii) is met, other sources are searched to identify the underlying action.
  - Mapping to STAN industries:
    - Air transport, postal and courier services, telecommunications: reform shock variable = 1 in reform year, 0 otherwise.
    - Electricity and gas; rail and road transport: reform shocks aggregated using weights ¾ and ¼ to map to STAN industries “Electricity, gas, steam and air conditioning supply” and “Land transport and transport via pipelines”. This yields reform shock values of 0.75, 0.25, 1 or 0 depending on sub-sector combinations.
- Counts and sample:
  - Final dataset: country-sector-time panel covering 26 countries and 5 sectors over 1975 to 2011; panel is unbalanced.
  - Countries listed in the dataset include: Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Estonia, Finland, France, Greece, Germany, Hungary, Iceland, Ireland, Italy, Luxembourg, Mexico, Japan, Korea, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States. (Note: list appears in source.)
  - Reform shocks constructed: total of 234 reform shocks comprising:
    - 60 in electricity and gas,
    - 52 in rail- and road transport,
    - 36 in air transport,
    - 32 in postal services,
    - 54 in telecommunications.
- Additional sources:
  - Historical list of competition-relevant EU directives used for instrumentation.
  - Country macro variables (real GDP and GDP deflator) from World Bank WDI and IMF WEO.

### III. Stylized facts from descriptive analysis
- Historical patterns:
  - All five network industries experienced liberalization over the past three decades, with variation in timing, speed, and scope.
  - Major progress: airline and telecommunications industries (limited remaining room for reform in most AEs).
  - More scope remaining: postal services, electricity and gas, and to a lesser extent land transport.
  - Cross-country dispersion of regulatory settings declined over time, indicating convergence.
- Timing of reform waves:
  - Early movers: United States and United Kingdom in late 1970s and early 1980s.
  - Peak waves: 1990s and 2000s; some resurgence post-global financial and euro area crises.
  - Example: 10 identified air transport reforms in 1993 related to adoption of the single European market for aviation (Third Aviation Liberalization Package, June 1992).
- Simple difference-in-differences (illustrative) results:
  - Constructed a DID measure comparing cumulative growth over [R; R+5] vs [R-5; R] between reforming and non-reforming observations.
  - Findings: reforming observations experienced substantial pick-up in growth rates of real value added, employment, and investment, and a decline in relative price of value added.
  - Quantitative example from the descriptive DID: real value added increases by more than 16 per cent in cumulative terms over the 5 years following the reform (relative to non-reforming observations).
  - Caveat: descriptive exercise does not address full dynamics nor omitted variable bias—these are addressed in econometric analysis.

### IV. Empirical setup — local projections and identification
- Local projection specification:
  - For j = 0, 1, ..., 5 estimate:
    - y_{c,s,t+j} – y_{c,s,t-1} = β_j * reform_{c,s,t} + Σ γ_k * controls_{c,s,t+k} + λ_{ct} + λ_{cs} + trend_s + ε_{cst}
  - Where:
    - y = log of variable of interest (output, employment, investment, relative value-added deflator, labor productivity).
    - Controls include all past and future reform shocks to address autocorrelation of reform shocks.
    - Fixed effects: country-time (λ_{ct}) to control for economy-wide shocks; country-industry (λ_{cs}) to control for structural country-industry factors.
    - trend_s: industry-specific linear time trend (used instead of industry-year fixed effects to avoid absorbing reform variation clustered in particular years).
  - Standard errors clustered at the country-sector level.
- Endogeneity concerns and IV strategy:
  - Potential endogeneity sources: reforms endogenous to sector growth prospects; measurement error in reform variable.
  - Instruments used (three types):
    - Initial stance of regulation: three-year lagged value of OECD indicator of stringency in the country-sector.
    - Peer pressure: number of other countries in the sample that carried out a major reform in the same sector over the past three years.
    - EU directive transposition pressure: sector time-varying dummy = 1 if an EU directive for the sector was adopted over the past five years (t-4 to t) and has not yet resulted in a reform in the country (captures transposition delay).
  - Rationale: instruments considered exogenous to country-sector present and future outcomes and affect domestic reform only through scope/pressure channels.
- Estimation approach for endogenous bounded reform variable:
  - Reform variable can take values {0, 0.25, 0.75, 1} (ordinal).
  - Follow Wooldridge’s two-step procedure for endogenous dummy/ordinal regressors:
    - Step 1: estimate an ordinal probit model of the reform variable using the exogenous instruments as explanatory variables.
    - Step 2: use the predicted value from the probit as the instrument in a standard IV (2SLS) estimation of the local projection equation.
  - Justification: procedure suited for endogenous dummy/ordinal variable models (Heckman, 1978; Wooldridge, 2010).

### V. Key methodological notes and dataset conditioning
- Panel conditioning:
  - Baseline regressions drop any country-sector time series for which the dependent variable is not available for at least three years preceding the first liberalization shock. This removes approximately 10% of the dataset but does not qualitatively change results.
  - For the descriptive DID in Figure 3, only simultaneous shocks to both sub-components (electricity and gas; rail and road) are considered for simplicity.
- Variable construction specifics:
  - Labor productivity defined as ratio of real value added to total employment.
  - Reform coding for aggregate sectors uses ¾ and ¼ weights approximating average shares in value added for subcomponents where disaggregated historical data are available.
- Time coverage for key sources:
  - STAN: 1970-2011 (coverage varies).
  - OECD ETCR indicators: 1975-2013.
  - Final country-sector sample used in analysis: 1975-2011.

*Source: Appendix 1 of the paper (content unit: _wp16114 - Appendix 1).*

### Chapter 21): i) there is no need to account for the fact that a generated instrument is used; ii)

### _wp16114 - Chapter 21): i) there is no need to account for the fact that a generated instrument is used; ii)

### IV. ECONOMETRIC RESULTS — Overview
- Presents baseline OLS and IV regression results for real value-added in the deregulated industry over the five years following a major reform.
- Extensions: variation of reform impact across the business cycle; interactions between product market reform and stringency of employment protection legislation.

### A. Baseline OLS Results — Key findings
- Main specification: dependent variable is the log difference of real value added between year t-1 (the year preceding the shock) and year t+j. Cluster robust standard errors reported.
- Dynamics of estimated Reform Shock (Table 1, OLS):
  - t: 0.62 (standard error (1.36))
  - t+1: 2.98 (2.21)
  - t+2: 4.54 (3.20)
  - t+3: 7.31* (3.69)
  - t+4: 10.09** (4.66)
- Sample sizes (Table 1):
  - N: 2269 (t), 2181 (t+1), 2093 (t+2), 2005 (t+3), 1917 (t+4)
- Statistical notes:
  - *,**,*** denote significance at the 10, 5, and 1% levels respectively.
  - Estimated coefficient positive but statistically insignificant in reform year; grows over time, exceeding 7 percent and significant at 10% after four years; cumulative effect about 10% after five years and significant at 5%.
- Impulse responses and component breakdowns (Figure 4):
  - Relative value-added deflator (sector vs aggregate GDP deflator) falls markedly and rapidly; significant in the second year and levels off at about 8-9% after three years.
  - Value-added decomposition into employment and productivity: both react similarly, but only labor productivity shows a positive, statistically significant effect after five years.
  - Gross fixed capital formation: no statistically significant effect (estimates fluctuate around zero with large standard errors).
  - Interpretation: real value-added gains primarily reflect an increase in total factor productivity; deregulation does not reduce employment in the short term.
- Reform size interaction (Table 2):
  - Reform Shock × Change (ETCR change on 0 to 6 scale) estimates:
    - t: 0.42 (0.83)
    - t+1: 2.05* (1.16)
    - t+2: 2.97* (1.53)
    - t+3: 3.94** (1.66)
    - t+4: 5.46** (2.22)
  - Interpretation: a liberalization equivalent to a two-point reduction in the ETCR indicator increases sectoral output by about 11 (=5.5*2) percent over 5 years; about three fourths of impact from improved labor productivity, remainder from employment increase.
  - Statistical significance reduced relative to narrative dummy indicator approach.

### B. Instrumental Variable Results — Key findings
- Endogeneity addressed by instrumenting reform shock with three country-sector-level instruments:
  - lagged regulatory stance;
  - number of other reforming countries in the sector over the past three years;
  - existence of an EU Directive not yet transposed into national law.
- Ordered probit predicts occurrence of a reform; Wooldridge two-step procedure uses predicted value as instrument in 2SLS.
- IV estimates (Table 3: Effect of Reform Shock on Real Value Added):
  - t: -1.13 (1.86)
  - t+1: 2.09 (2.98)
  - t+2: 3.46 (3.84)
  - t+3: 9.26** (4.00)
  - t+4: 13.66*** (5.08)
- Sample sizes and first-stage F-statistics (Table 3):
  - N: 2264 (t), 2181 (t+1), 2093 (t+2), 2005 (t+3), 1917 (t+4)
  - 1st Stage F: 243.54 (t), 228.05 (t+1), 205.49 (t+2), 177.80 (t+3), 206.74 (t+4)
- Interpretation:
  - IV magnitudes substantially larger than OLS; point estimates peak at almost 14 percent after five years.
  - Impulse responses (Figure 6): qualitatively similar to OLS but somewhat amplified for employment and labor productivity; labor productivity effect larger and more statistically significant than employment effect.
  - No evidence of short-term employment costs from reductions in barriers to entry; estimated impact on employment is negative in first year but not statistically significant.
- Methodological note:
  - F-statistics reported are from regression of actual reform variable on predicted reform variable; conventional “rules of thumb” (e.g., Staiger and Stock 1997) do not apply directly. Only one instrument enters the 2SLS first stage (the predicted probability), so no Hansen statistic reported.

### C. Interactions — Business cycle and employment protection
- Business cycle interaction:
  - Reform shock interacted with Auerbach and Gorodnichenko (2012) smooth transition function of aggregate GDP growth (tends toward 1 in recessions, 0 in expansions; γ = 1.5).
  - Results (not reported) show no significant impact of business cycle position on effect of entry deregulation.
- Employment Protection Legislation (EPLR) interaction (Table 4):
  - EPLR: OECD indicator for regular contracts on 0 to 6 scale; interaction uses pre-reform (t-1) EPLR.
  - Estimates (Table 4):
    - Reform Shock:
      - t: -0.13 (1.48)
      - t+1: 2.60 (2.08)
      - t+2: 2.44 (2.27)
      - t+3: 4.00 (2.51)
      - t+4: 4.84 (2.92)
    - Reform Shock × EPLR:
      - t: 4.28 (3.39)
      - t+1: 10.66** (5.11)
      - t+2: 7.13 (6.35)
      - t+3: 11.16 (6.92)
      - t+4: 16.39** (7.67)
  - Sample sizes (Table 4): N = 2321 (t), 2245 (t+1), 2152 (t+2), 2059 (t+3), 1966 (t+4)
  - Interpretation:
    - Employment effect of entry deregulation is larger where employment protection regulation is more stringent.
    - Positive interaction appears to capture a permanent effect, remaining statistically significant beyond the fifth year (not shown).

### V. CONCLUDING REMARKS — Summary conclusions and policy implications
- Data and scope:
  - Focused on major reform episodes in historically protected non-manufacturing industries; five network industries in twenty-six countries over three decades.
  - Used three-dimensional panel and instrumentation with external instruments to control for macro shocks and omitted variable bias.
- Main quantitative conclusions:
  - Sizeable average impact on real value added in deregulated industries in the order of 10% to 14% after five years depending on specification.
  - Effect accompanied by an increase in labor productivity and a relative price decline.
  - Impact on sectoral employment is weak; investment essentially unaffected — output gains primarily reflect higher total factor productivity.
- Additional findings:
  - Tentative evidence of positive short-term net job creation in deregulated sectors in countries with more restrictive employment protection legislation.
  - No short-term costs of reform detected; no evidence that gains are weaker when deregulation occurs amid weak overall demand.
- Policy implication:
  - Findings support intensifying product market reform efforts in advanced economies amid weak growth.

### Appendix — Variable definitions (selected)
- PRDK: Production (gross output) in volume. Source: OECD STAN Structural Analysis Database.
- VALK: Value added in volume.
- VALP: Value Added deflator. Sectoral price levels transformed into log-deviation from the GDP deflator (World Bank).
- GFCK: Gross fixed capital formation in volume.
- EMPN: Number of persons engaged (total employment).
- LPRD: Labor Productivity (calculated as VALK / EMPN; variables in logs for empirical analysis).
- ETCR: OECD indicators of regulation in energy, transport and communications summarising regulatory provisions in seven sectors and six dimensions (Barriers to entry, Public ownership, Market structure, Vertical integration, Price controls, Constraints on business operation). OECD.
- GDP: Real GDP. Source: IMF WEO database.
- GDPP: GDP deflator. Source: World Bank WDI.
- EPLR: OECD Employment Protection Legislation index for regular employment (0 to 6 scale).

*Source: _wp16114 - Chapter 21): i) there is no need to account for the fact that a generated instrument is used; ii) (PDF).*

### REFERENCES

### _wp16114 - REFERENCES

### Product market regulation, deregulation, and productivity
- Aghion, P., P. Askenazy, R. Bourlès, G. Cette, and N. Dromel, 2009a, “Education, Market Rigidities and Growth,” Economics Letters, 102(1), pp. 62–5.
- Aghion, P., R. Blundell, R. Griffith, P. Howitt, and S. Prantl, 2009b, “The Effects of Entry on Incumbent Innovation and Productivity,” Review of Economics and Statistics, 91(1), pp. 20–32.
- Aghion, P., R. Blundell, R. Griffith, P. Howitt, and S. Prantl, 2004, “Entry and Productivity Growth: Evidence from Microlevel Panel Data,” Journal of the European Economic Association, 2(2-3), pp. 265–76.
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- Alesina, A., S. Ardagna, G. Nicoletti, and F. Schiantarelli, 2005, “Regulation and Investment,” Journal of the European Economic Association, 3(4), pp.791-825.
- Barone, G. and F. Cingano, 2011, “Service Regulation and Growth: Evidence from OECD Countries,” Economic Journal, 121(555), pp. 931–57.
- Bourlès, R., G. Cette, J. Lopez, J. Mairesse, and G. Nicoletti, 2013, “Do Product Market Regulations In Upstream Sectors Curb Productivity Growth? Panel Data Evidence For OECD Countries,” The Review of Economics and Statistics, 95(5), pp. 1750–68.
- Conway, P. and G. Nicoletti G., 2006, “Product Market Regulation in Non-Manufacturing Sectors of OECD Countries: Measurement and Highlights,” OECD Economics Department Working Papers, No. 530.
- Conway, P., D. de Rosa, F. Steiner and G. Nicoletti, 2006, “Product Market Regulation and Productivity Convergence,” OECD Economic Studies, 43, pp. 39–76.
- Koske, I., I.Wanner, R. Bitetti, and O. Barbiero, 2015, “The 2013 Update of the OECD Product Market Regulation Indicators: Policy Insights for OECD and Non- OECD Countries,” OECD Economics Department Working Papers, No. 1200.
- Nicoletti, G. and S.Scarpetta, 2003, “Regulation, Productivity and Growth: OECD Evidence,” Economic Policy, 18 (36), pp. 9–72.

### Structural reforms, labor markets, and macroeconomic effects
- Bassanini, A., 2015, “A Bitter Medicine? Short-term Employment Impact of Deregulation in Network Industries,” IZA Discussion Paper No. 9187, July.
- Bassanini, A. and R. Duval, 2009, “Unemployment, Institutions, and Reform Complementarities: Reassessing the Aggregate Evidence for OECD Countries,” Oxford Review of Economic Policy 25, 40–59, Spring.
- Bertrand, M. and F. Kramartz, 2002, “Does Entry Regulation Hinder Job Creation? Evidence from the French Retail Industry?” Quarterly Journal of Economics, 117 (4), pp. 1369–1413.
- Blanchard, O. and F. Giavazzi, 2003, “Macroeconomic Effects of Regulation and Deregulation in Goods and Labor Markets,” Quarterly Journal of Economics, 118(3), pp. 879–907.
- Bordon, A-R., C. Ebeke, and K. Shirono, 2016, “When Do Structural Reforms Work? On the Role of the Business Cycle and Macroeconomic Policies,” IMF Working Paper, No. 62.
- Bouis, R., O. Causa, L. Demmou, R. Duval, and A. Zdzienicka, 2012, “The Short-Term Effects of Structural Reforms: an Empirical Analysis,” OECD Economics Department Working Paper, No. 950.
- Bouis, R. and R. Duval, 2011, “Raising Potential Growth After the Crisis: A Quantitative Assessment of the Potential Gains from Various Structural Reforms in the OECD Area and Beyond,” OECD Economics Department Working Paper, No. 835.
- Cacciatore, M. and G. Fiori, 2015, “The Macroeconomic Effects of Goods and Labor Market Deregulation,” Review of Economic Dynamics, 20(1), pp. 1–24.
- Cacciatore, M., R. Duval, F. Ghironi and G. Fiori, 2016a, “Short-Term Pain for Long-term Gain: Market Deregulation and Monetary Policy in Small Open Economies,” Journal of International Money and Finance, forthcoming.
- Cacciatore, M., R. Duval, F. Ghironi and G. Fiori, 2016b, “Market Reforms in the Time of Imbalance,” Journal of Economic Dynamics and Control, forthcoming.
- Everaert, L. and W. Schule, 2008, “Why it Pays to Synchronize Structural Reforms in the Euro Area across Markets and Countries,” IMF Staff Papers, 55 (2), pp. 356–66.
- Fiori, G., G. Nicoletti, S. Scarpetta, and F. Schiantarelli, 2012, “Employment Outcomes and the Interaction between Product and Labor Market Deregulation: Are They Substitutes or Complements?” Economic Journal, 122 (558), pp. 79–104.
- Gal, P., and A. Hijzen, 2016, “The Short-term Impact of Product Market Reforms: A Cross-country Firm-level Analysis,” IMF Working Paper, forthcoming.
- Gomes, S., P. Jacquinot, M. Mohr, and M. Pisani, 2011, “Structural Reforms and Macroeconomic Performance in the Euro Area Countries: A Model-based Assessment,” ECB Working Paper, No. 1323.
- Inklaar, R., M. Timmer, and B. Van Ark, 2008, “Market Services Productivity Growth across Europe and the US,” Economic Policy, 23(53), pp.139–94.
- Skuterud, M., 2005, “The Impact of Sunday Shopping on Employment and Hours in the Retail Industry: Evidence from Canada,” European Economic Review, 49(8), pp. 1953–78.

### Monetary, fiscal policy, and macroeconomic interactions
- Auerbach, A. and Y. Gorodnichenko, 2012, “Measuring the Output Responses to Fiscal Policy,” American Economic Journal: Economic Policy, 4(2), pp. 1–27.
- Draghi, M., 2015, “Monetary Policy and Structural Reforms in the Euro Area.” Speech, Prometeia40, Bologno, December 14th, available at https://www.ecb.europa.eu/press/key/date/2015/html/sp151214.en.html
- Eggertsson, G., A. Ferrero and A. Raffo, 2014, “Can Structural Reforms Help Europe?” Journal of Monetary Economics, 61(C), pp. 2–22.
- Romer, C. and D. Romer, 2015, “New Evidence on the Impact of Financial Crises in Advanced Countries,” NBER Working Paper No. 21021, National Bureau of Economic Research, Cambridge, Massachusetts.
- Romer, C. and D. Romer, 2010, “The Macroeconomic Effects of Tax Changes: Estimates Based on a New Measure of Fiscal Shocks.” American Economic Review 100 (3), pp. 763–801.
- Romer, C. and D. Romer, 2004, “A New Measure of Monetary Shocks: Derivation and Implications.” American Economic Review 94 (4), pp. 1055–84.
- Everaert, L. and W. Schule, 2008, “Why it Pays to Synchronize Structural Reforms in the Euro Area across Markets and Countries,” IMF Staff Papers, 55 (2), pp. 356–66.
- Duval, R., D. Furceri, J. Jalles, and H. Nguyen, 2016, “A New Narrative Database of Product and Labor Market Reforms in Advanced Economies,” IMF Working Paper, forthcoming.

### Firm-level, industry, and sectoral analyses
- Bertrand, M. and F. Kramartz, 2002, “Does Entry Regulation Hinder Job Creation? Evidence from the French Retail Industry?” Quarterly Journal of Economics, 117 (4), pp. 1369–1413.
- Bourlès, R., G. Cette, J. Lopez, J. Mairesse, and G. Nicoletti, 2013, “Do Product Market Regulations In Upstream Sectors Curb Productivity Growth? Panel Data Evidence For OECD Countries,” The Review of Economics and Statistics, 95(5), pp. 1750–68.
- Ebell, M., and C. Haefke, 2009, “Product Market Deregulation and the U.S. Employment Miracle,” Review of Economic Dynamics, 12(3), pp. 479–504.
- Fang, L., and R. Rogerson, 2011, “Product Market Regulation and Market Work: A Benchmark Analysis,” American Economic Journal: Macroeconomics, 3(2), pp. 163–88.
- Felbermayr, G. and J. Prat, 2011, “Product Market Regulation, Firm Selection, and Unemployment,” Journal of the European Economic Association, 9(2), pp. 278–317.
- Gal, P., and A. Hijzen, 2016, “The Short-term Impact of Product Market Reforms: A Cross-country Firm-level Analysis,” IMF Working Paper, forthcoming.

### Econometric methods, measurement, and theory
- Arpaia, A., R. Werner, J. Varga, and J. in’t Veld, 2007, “Quantitative Assessment of Structural Reforms: Modeling the Lisbon Strategy,” European Commission Economic Papers, No. 282.
- Greene, W., 2002, “The Bias of the Fixed Effects Estimator in Non-Linear Models,” New York University, unpublished manuscript.
- Heckman, J., 1978, “Dummy Endogenous Variables in a Simultaneous Equations System,” Econometrica, 46 (4), pp. 931–60.
- Jordà, Ò., 2005, “Estimation and Inference of Impulse Responses by Local Projections,” American Economic Review, 95(1), pp. 161–182.
- Jordà, Ò. and A. Taylor, 2013, “The Time for Austerity: Estimating the Average Treatment Effect of Fiscal Policy,” NBER Working Paper No. 19414, National Bureau of Economic Research, Cambridge, Massachusetts.
- Leibenstein, H., 1966, “Allocative Efficiency vs. ‘X-Efficiency’,” American Economic Review, 56 (3), pp. 392–415.
- Rajan, R. and L. Zingales, 1998, “Financial Dependence and Growth,” American Economic Review, 88(3), pp. 559–86.
- Staiger, D. and J. Stock, 1997, “Instrumental Variable Regression with Weak Instruments,” Econometrica, 65(3), pp. 557–86.
- Stock, J., and M. Watson. 2007, “Why Has U.S. Inflation Become Harder to Forecast?” Journal of Money, Credit and Banking 39 (S1): 3–33.
- Teulings, C. and N. Zubanov, 2014, “Is Economic Recovery a Myth? Robust Estimation of Impulse Responses,” Journal of Applied Econometrics, 29(3), pp. 497–514.
- Wooldridge, J., 2010, Econometric Analysis of Cross Section and Panel Data, The MIT Press, Cambridge, Massachusetts, 2nd edition.
- Heckman, J., 1978, “Dummy Endogenous Variables in a Simultaneous Equations System,” Econometrica, 46 (4), pp. 931–60.

### IMF, OECD, and institutional contributions
- Cubeddu, L., A. Culiuc, G. Fayad, Y. Gao, K. Kochhar, A. Kyobe, C. Oner, R. Perrelli, S. Sanya, E. Tsounta, Z. Zhang, 2014, “Emerging Markets in Transition: Growth Prospects and Challenges,” IMF Staff Discussion Note, 14/6.
- Dabla-Norris, E., G. Ho, K. Kochhar, A. Kyobe, and R. Tchaidze, 2013, “Anchoring Growth: The Importance of Productivity-Enhancing Reforms in Emerging Market and Developing Economies,” IMF Staff Discussion Note, No. 13/8.
- Dabla-Norris, E., S. Guo, V. Haksar, K. Kochhar, K. Wiseman and A. Zdzienicka, 2015, “The New Normal: A Sector-Level Perspective on Productivity Trends in Advanced Economies,” IMF Staff Discussion Note, No. 15/03.
- International Monetary Fund, 2016, “Time for a Supply-Side Boost? Macroeconomic Effects of Labor and Product Market Reforms in Advanced Economies,” World Economic Outlook, Chapter 3, April, Washington.
- International Monetary Fund, 2015, “Where Are We Headed? Perspectives on Potential Output,” World Economic Outlook, Chapter 3, April, Washington.
- Organisation for Economic Cooperation and Development (OECD), 2016, Economic Policy Reforms: Going for Growth, Paris.
- Koske, I., I.Wanner, R. Bitetti, and O. Barbiero, 2015, “The 2013 Update of the OECD Product Market Regulation Indicators: Policy Insights for OECD and Non- OECD Countries,” OECD Economics Department Working Papers, No. 1200.

* _wp16114 - REFERENCES_*

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