## cr1843

## Source details

**Canonical URL:** [cr1843](https://www.imf.org/-/media/files/publications/cr/2018/cr1843.pdf)

## Other formats

- [Markdown version](/-/media/files/publications/cr/2018/cr1843.pdf.md)
- [Structured JSON version](/-/media/files/publications/cr/2018/cr1843.pdf.json)

---

### A. Introduction and key context
- Nominal wage growth in the UK has remained subdued since the financial crisis despite tightening labor market conditions.
- Headline unemployment rate fell from about 8 percent in 2010 to 4.3 percent in 2017Q3—the lowest level since 1975.
- Labor force participation has increased and is now equal to its pre-crisis peak.
- Average weekly hours of work have recovered to the average level in 2003–2007.
- Main factors behind the slowdown in wage growth: weak productivity growth, labor market slack (both in the UK and the EU), and low inflation.

### B. Determinants of wage growth
- Labor productivity:
  - Labor share as percent of GDP has been close to 64 percent over time.
  - Real output per worker (labor productivity) and real product wage are analyzed.
- Labor market slack:
  - Unemployment rate historically signals tightness but flexible work arrangements require broader measures.
- Global conditions:
  - EU labor market slack considered an additional determinant of UK wage pressures.
- Expected inflation and uncertainty:
  - Expected inflation and uncertainty about medium-term growth prospects influence nominal wage dynamics.

### C. Assessing labor market slack — broader measures and evidence
- Broader measures highlighted:
  - Involuntary part-time workers: share doubled from 9.4 percent in 2007 to 18 percent in 2013, then fell to 12 percent recently.
  - Share of self-employed and zero-hour workers increased above pre-crisis levels.
  - Underutilization (percent of employment) considered for zero-hour and part-time underemployment.
- Transition dynamics:
  - Transition probability from self-employment to regular employment is procyclical.
  - Transition rate from self-employment to regular employment increased in 2016–17 to about 3.5 percent from an average of 3 percent after the crisis.
- Alternative measures of slack used in regressions:
  - Headline unemployment rate.
  - Adjusted unemployment rate capturing unemployed, inactive, and self-employed (weights based on average probability of finding a regular job between 2005 and 2017). On average, 3 percent of self-employed workers have taken a regular job each quarter, compared to 22 percent of those unemployed.
  - Hiring rate: those finding new jobs every period over total employment.
  - Involuntary part-time employment as a share of the labor force added separately.
- Equilibrium considerations:
  - Adjusted unemployment rate gap computed as difference between actual rate and time-varying equilibrium underemployment rate (estimated by a Kalman filter).
  - Evidence suggests equilibrium unemployment rate has declined over time due to rising educational attainment and tax and benefit reforms.

### D. Box 1 — Could high self-employment share affect wage growth?
- Compositional shift to greater self-employment would structurally reduce wage growth because self-employed typically have lower wage income.
- Blanchflower and Shadforth (2007): self-employed (without their own workers) saw median weekly income drop by about a 20 percent in real terms between 2007–08 and 2014–15.
- Pension coverage in 2010/11: 21 percent of self-employed had pensions compared to 50 percent of employees.
- Share of self-employed who employ staff: 17 percent in the UK versus 44 percent in Germany (Hatfield 2015).
- High share of self-employed can negatively influence wage growth for regular employees through competition for tasks and vacancies.
- Transition evidence: transitions from self-employment to regular employment increased in 2016–17 (to about 3.5 percent from an average of 3 percent after the crisis).

### E. Empirical strategy and data
- Long-run real wage specification:
  - Long-run real wage (ݓ௧ோ) determined by labor productivity (ܲܮ௧) following ݓ௧ோ = ߙ௅ோ,ଵ + ߚ௅ோ,ଵ ܲܮ௧ + ߝ௅ோ,௧ = ݓ௧∗ + ߝ௅ோ,௧.
- Short-run nominal wage dynamics (error-correction model):
  - ∆ݓ௧ே = ߙௌோ,ଵ + ߱ଵ (ݓ௧ିଵோ − ݓ௧ିଵ∗) + ߚௌோ,ଵ ݈݇ܿܽݏ௧ + ߚௌோ,ଶ ∆ܲܮ௧ି௜ + ߚௌோ,ଷ ߨ௧௧ି௜ா + ߚௌோ,ସ ܺ௧ି௜ + ߝௌோ,௧.
  - Short-run determinants include lagged error term, measures of labor market slack (݈݇ܿܽݏ௧), lags of inflation expectations (ߨ௧௧ି௜ா), lagged productivity growth (∆ܲܮ௧ି௜), and other factors (ܺ௧ି௜) including growth uncertainty and EU labor market conditions.
- Data and sample:
  - Quarterly data over 2000Q1–2017Q2 used (weekly earnings data available since 2000).
  - Long-run wage equation estimated by fully modified least squares; short-run equation follows a general-to-specific approach.
- Variable definitions:
  - Wages: average weekly earnings from Office of National Statistics (excludes self-employed earnings).
  - Nominal wage deflated by GDP deflator to obtain real wage.
  - Labor productivity: real Gross Value Added (GVA) per worker.

### F. Summary statistics and unit root results (selected)
- Summary statistics (Obs, Mean, Max, Min, Std. Dev):
  - Nominal wage (yoy change): Obs 660, Mean 0.03, Max 0.05, Min 0.00, Std. Dev 0.01
  - Labor productivity (yoy change): Obs 660, Mean 0.01, Max 0.04, Min -0.05, Std. Dev 0.02
  - Unemployment rate: Obs 700, Mean 0.06, Max 0.01, Min 0.04, Std. Dev 0.08
  - Adj. unemployment rate (percent): Obs 700, Mean 0.09, Max 0.01, Min 0.08, Std. Dev 0.10
  - Adj. unemployment rate gap (ppt): Obs 700, Mean 0.00, Max 0.01, Min -0.01, Std. Dev 0.02
  - Hiring rate (percent): Obs 630, Mean 0.06, Max 0.00, Min 0.04, Std. Dev 0.06
  - Share of involuntary PT workers: Obs 700, Mean 0.03, Max 0.01, Min 0.02, Std. Dev 0.05
  - Inflation expectations (5-year ahead derived from government securities): Obs 700, Mean 0.03, Max 0.00, Min 0.00, Std. Dev 0.04
  - EU unemployment gap: Obs 700, Mean 0.33, Max 0.98, Min -1.80, Std. Dev 2.18
  - Growth uncertainty (standard deviation of one-year ahead growth forecast from consensus): Obs 700, Mean 0.50, Max 0.16, Min 0.25, Std. Dev 0.94
- Unit root testing:
  - Real product wage: level ADF t-stat -2.630 (trend and intercept), first difference ADF -7.635 (intercept).
  - Productivity (MA,8q): level ADF t-stat -2.473 (trend and intercept), first difference ADF -1.825 (no intercept).
  - First differences are stationary per ADF and PP tests.

### G. Empirical findings (overview and detailed results)
- Long-run wage–productivity relationship:
  - Estimated coefficient on labor productivity trend: 0.86 (0.06)***.
  - Constant: -2.50 (0.29)***.
  - Observations: 70.
  - Adjusted R-squared: 0.93.
  - S.E. of regression: 0.01.
  - Cointegration tests:
    - Engle-Granger tau-statistic: -3.09; Probability: 0.10.
    - Phillips-Ouliaris tau-statistic: -3.08; Probability: 0.11.
    - Engle-Granger z-statistic: 16.38; Probability: 0.09.
    - Phillips-Ouliaris z-statistic: -15.95; Probability: 0.10.
  - Hansen instability Lc statistics: 0.07; Probability: >0.2.
  - Interpretation: Coefficient close to one suggests a tight long-run relationship; real wages did not fall significantly during the crisis but wage growth slowed and by 2013 real wages started to lag productivity. As of 2017Q2, real wage was broadly in line with the estimated long-run equilibrium value.
- Short-run determinants (private sector average weekly regular pay growth):
  - Error-correction term (ecm (t-4)):
    - Model (1): -0.25*** (0.09)
    - Model (2): -0.21*** (0.07)
    - Model (3): -0.20*** (0.07)
    - Model (4): -0.22*** (0.07)
  - Labor market slack measures (selected coefficients):
    - Headline unemployment (t-4): -0.59*** (0.06) [model (1)].
    - Adj. unemployment rate (t-4): -0.37*** (0.13) [model (2)].
    - Adj. unemployment rate gap (t-4): -0.32** (0.13) [model (3)].
    - Hiring rate (t-4): 0.46** (0.18) [model (4)].
    - Involuntary part-time employment (t-4): -0.28** (0.11) [model (1)]; -0.34*** (0.11) [model (2)]; -0.29** (0.11) [model (3)].
  - Other short-run drivers:
    - Inflation expectation (t-4): 0.66*** (0.18) [model (1)]; 0.34** (0.14) [model (2)]; 0.34** (0.15) [model (3)]; 0.36** (0.16) [model (4)].
    - Productivity growth (t-1): 0.28*** (0.05) [model (1)]; 0.21*** (0.04) [model (2)]; 0.23*** (0.04) [model (3)]; 0.21*** (0.04) [model (4)].
    - EU unemployment gap (t): -0.44*** (0.10) [model (1)]; -0.43*** (0.10) [model (2)]; -0.54*** (0.09) [model (4)].
    - Growth uncertainty (t-2): -2.43*** (0.47) [model (1)]; -2.41*** (0.49) [model (2)]; -2.02*** (0.60) [model (4)].
  - Model fit:
    - Observations: 66 [models (1), (2), (3)]; 59 [model (4)].
    - R-squared: Model (1): 0.78; Model (2): 0.89; Model (3): 0.89; Model (4): 0.88.

### H. In-sample forecast performance and historical decomposition
- In-sample forecasts:
  - Model 1 consistently overpredicts wage growth.
  - Forecast performance improves significantly when controlling for the share of involuntary part-time workers and other factors (model 3).
  - Rest of analysis based on results from model 3.
- Historical decomposition (contributors to nominal wage growth deviation from mean):
  - Key contributors to post-crisis slowdown in nominal wage growth:
    - Low productivity growth (LP).
    - Weak labor market conditions (slack) in the UK and the EU.
    - Increased uncertainty about future economic growth (depressed wage growth around the referendum and recent quarters).
    - Low inflation in 2015–16 contributed to depressing wages during 2016–17.
  - More recently: diminished slack in the labor market provided a modest boost to wages, offset by greater uncertainty and weak lagged inflation.

### I. Wage outlook and scenarios
- Baseline assumptions:
  - Labor markets remain relatively tight, with the unemployment rate slightly below the estimated equilibrium level.
  - Share of involuntary part-time workers drops to pre-crisis levels.
  - Growth uncertainty dissipates once a broad agreement on post-EU relationship is reached.
  - Inflation expectations and the EU labor market gap remain unchanged at their 2017Q2 level.
  - Productivity growth recovers to about 1 percent in the medium term.
- Baseline projection:
  - Annual nominal wage growth expected to pick up from 2¼ percent in 2017Q2 to between 2¾ and 3 percent in 2018.
- Risks and scenario on self-employment transition:
  - Observed transition of self-employed to regular employment: 3.5 percent in 2017 vs 3 percent average post-crisis.
  - If transition rate doubles to 6 percent, wage growth could be lower by about 1 percentage point due to a larger pool of labor competing for vacancies.
- Other risks:
  - Growth uncertainty may remain elevated even after the UK leaves the European Union.
  - If productivity growth fails to pick up as projected, wage growth would remain weak.

### J. Conclusions and policy implications (wage dynamics)
- Explanation for the disconnect between falling unemployment (40-year low) and subdued wage growth:
  - Persistent weak productivity growth.
  - Well-anchored inflation expectations.
  - Greater effective labor market slack than suggested by the headline unemployment rate.
- Broader measures of underemployment (involuntary part-time unemployment, inactive and self-employed people seeking regular jobs) indicate higher slack than unemployment rate alone.
- Policy implications:
  - Persistent labor market tightness should prompt some firming of wage growth in the coming year, other things equal.
  - Mild increase in unit labor costs would help bring domestically generated inflation in line with the inflation target.
  - Sustainable wage recovery requires productivity growth to recover.
  - Reducing Brexit-related uncertainty would help firms make long-term decisions affecting wages.

### K. Regional disparities in labor productivity (United Kingdom)
- National and trend statistics:
  - In 2015, GDP per hour worked was between 20 and 25 percent lower than in France, Germany and the United States.
  - UK productivity growth declined from 2.1 percent during 2000–07 to 0.5 percent during 2010–16.
- Spatial disparities:
  - Only two regions at the NUTS 1 level show productivity levels above the country average; 70 percent of employment is in regions with productivity levels below average.
  - The UK’s highest productivity region (London) is nearly 60 percent more productive than its lowest.
  - Regional disparities are large compared to other advanced economies and have not shown signs of convergence over the last decade.
- Sectoral and structural observations:
  - Wealthier regions have higher productivity across most industry sectors, suggesting region-wide characteristics drive comparative advantage.

### L. Human capital and skills: current situation and policy levers
- Human capital:
  - On aggregate, adults in the UK have lower literacy and numeracy skills than the OECD average; the proportion of younger people in England with low skills is higher than the proportion among people nearing retirement age.
  - The share of those aged 15–19 enrolled in education is below the OECD average.
  - Large regional variation in workforce skills correlates strongly with regional disparities in labor productivity.
- Education participation and quality:
  - Early-age education participation is high and homogeneous across regions.
  - Participation at advanced education levels varies; participation in education for students aged 17 in Wales is notably lower.
  - PISA scores for Wales are below UK and OECD averages; this is the fourth consecutive PISA cycle with Welsh students performing worse than the UK average.
  - Ofsted (2016): more secondary schools assessed as “less than good” in the North and Midlands compared with the South and East of England.
- Sorting:
  - Six months after graduation, London employs 22 percent of all working new university graduates.
- Policy levers:
  - Basic skills: reforms include national curriculum and GCSE reforms; since 2013 math and English study programs for 16–19-year-olds who have not achieved minimum grade.
  - Technical skills: UK ranks 16th out of 20 OECD countries for the proportion with technical qualifications; initiatives such as T-levels could help.
  - Teacher shortages: government missed recruitment targets for the last four years; Department for Education has “weak understanding of the extent of local teacher supply shortages.”
  - Continued reform in Wales recommended per OECD (2014, 2017) guidance.

### M. Labor mobility, housing, and regional adjustment
- Barriers to labor mobility reduce effectiveness as a regional adjustment mechanism; highly productive regions tend to have net outflows instead of inflows.
- Housing prices and regulations impact internal migration:
  - If the South East had the regulatory restrictiveness of the North East, house prices in the South East would have been roughly 25 percent lower in 2008 (Hilber and Vermeulen 2015).
- Housing regulations and income convergence (46 English counties, 1979–2008):
  - Regression results: R2 = 0.77.
  - Avg. real earnings of a male worker in ’79 (in log): -0.004* (spec. 1); -0.005* (spec. 2).
  - Housing regulations: 0.02*** (both specs).
  - Housing regulations * Avg. real earnings in ’79: 0.22*** (both specs).
  - Interpretation: tighter housing regulations dampen speed of convergence across counties.
- Policy implications:
  - Boosting housing supply can improve labor mobility and growth (easing planning restrictions, mobilizing unused publicly-owned lands, incentives for local authorities).

### N. Investment, access to finance, and R&D
- Capital formation:
  - Aggregate public investment and perceived quality of infrastructure traditionally below peers.
  - ONS estimate of regional GFCF for 2000–2013 (scaled by regional GDP) does not show more productive regions invest more; e.g., London invests a lower fraction of its GDP than Wales or Northern Ireland.
- Access to finance:
  - UK SME access to finance index is above the EU average, but constraints remain: lack of competition in banking, excessive short-termism, under-supply of late-stage venture capital, low appetite for equity investment among SMEs.
  - Venture capital, equity investment, and crowdfunding concentrated in London and the South East.
- R&D:
  - Expenditure on R&D (private and public) has typically been lower in the UK than in other advanced economies.
  - London and the South-East account for a large share of innovation investment.
- Improving adoption of innovation in lagging regions:
  - Key factors: workforce skills, investment in appropriate R&D, investment in physical capital and ICT.
  - “Regional champions” play a significant role for productivity growth.
- Box 2 empirical findings:
  - Data: over 104 thousand firms, years 2008–2013.
  - Distance from regional frontier coefficients: 0.27***, 0.27***, 0.25***, 0.24*** (specs 1–4).
  - Capital per worker: 0.03***, 0.04***, 0.04***, 0.03***.
  - Number of employees: -0.01***, -0.02***, -0.02**, -0.01**.
  - Interpretation: distance to the regional frontier matters more for firm-level productivity growth than distance to the national frontier.

### O. Agglomeration effects and connectivity
- Agglomeration positively correlated with productivity (NUTS 3, England):
  - Agglomeration Index: 0.2314** (spec 1), 0.1294** (spec 2).
  - Share of establishments with +50% staff with level 4 qualifications: 0.0248***, 0.0224***, 0.0271*** across specs.
  - Population density coefficients small but significant.
  - Observations: 100; R-squared across specs: 0.268, 0.485, 0.343, 0.504, 0.459.
- Policy implications:
  - Excessive planning restrictions that limit city size likely reduce agglomeration benefits.
  - Improvements in connectivity within and between cities likely raise productivity; congestion is high in rail network and some smaller cities (e.g., Leeds and Birmingham) congestion is higher than in London (HMT 2017).
  - Major infrastructure projects (Northern Powerhouse Rail, Midlands Rail Hub) aim to increase connectivity and agglomeration effects.

### P. Role of government, decentralization, and regional policy
- Perceived quality of local governments correlates with productivity (European Quality of Government Index).
- Public spending per head: below UK average in England; above in Scotland, Wales, and Northern Ireland. Within England, lowest in South East and highest in London.
- Fiscal decentralization tends to reduce regional disparities (OECD 2016); UK has a relatively high share of local revenues and expenditures in total public revenues and expenditures from a cross-country perspective.
- Government initiatives:
  - Fiscal decentralization: assessing options for greater local control and piloting greater retention of business rates revenue by local government.
  - Governance decentralization: introduction of Local Enterprise Partnerships (LEPs), and city and local growth devolution deals since 2011.
- Risks and considerations:
  - Devolution could give rise to fiscal risks in transition; local government capacity may need building (IMF UK Fiscal Transparency Evaluation 2016).
  - Planning restrictions may correct market failures but can be harmful when they disregard market outcomes or the balance of costs and benefits.
- Box 3 — governance evolution and industrial strategy:
  - LEPs: voluntary partnerships between local authorities and businesses, decide local priorities, apply for Growth Funds.
  - Devolution Deals: transfer powers and funding to combined authorities; most require an elected mayor; elected mayors can add up to two percent on business rates with LEP agreement.
  - Industrial strategy white paper (November): combines horizontal and vertical policy proposals; recommendation for evidence-based evaluation, stable institutional framework, and potentially a law or long-lasting mandate for industrial policy.

### Q. Framing and policy priorities
- Large and long-standing disparities in labor productivity across UK regions—two regions (London and the South East) have very high productivity; most other regions lag substantially.
- Key drivers of regional disparities: differences in human capital; agglomeration effects; housing market frictions; uneven R&D and innovation adoption.
- Structural reform priorities differ by local needs:
  - Address congestion and housing restrictions in more successful regions.
  - Increase human capital and improve transport connectivity in underperforming regions.
- Evidence-based evaluation and long-term consistent implementation are crucial because many reforms (e.g., education) have long gestation lags.

*Source: cr1843 - References _______________________________________________________________________________ 15*

### References _______________________________________________________________________________ 15

### References _______________________________________________________________________________ 15

### A. Introduction and key context
- Nominal wage growth in the UK has remained subdued since the financial crisis despite a tightening of labor market conditions.
- Headline unemployment rate fell from about 8 percent in 2010 to 4.3 percent in 2017Q3—the lowest level since 1975.
- Labor force participation has increased and is now equal to its pre-crisis peak.
- Average weekly hours of work have recovered to the average level in 2003–2007.
- The chapter identifies the main factors behind the slowdown in wage growth as weak productivity growth, labor market slack (both in the UK and the EU), and low inflation.

### B. Determinants of wage growth
- Labor productivity growth:
  - Labor share as percent of GDP has been close to 64 percent over time.
  - Real output per worker (labor productivity) and real product wage are presented in log points (chart reference).
- Labor market slack:
  - The unemployment rate historically has signaled labor market tightness but flexible work arrangements necessitate broader measures.
- Global conditions:
  - EU labor market slack is considered an additional determinant of UK wage pressures.
- Expected inflation and uncertainty:
  - Expected inflation and uncertainty about medium-term growth prospects influence nominal wage dynamics.

### C. Assessing labor market slack — broader measures and evidence
- Broader measures highlighted:
  - Involuntary part-time workers: share doubled from 9.4 percent in 2007 to 18 percent in 2013, then fell to 12 percent recently.
  - Share of self-employed and zero-hour workers has increased above pre-crisis levels.
  - Underutilization in the labor market (percent of employment) chart referenced for zero-hour and part-time underemployment.
- Transition dynamics:
  - Transition probability from self-employment to regular employment is procyclical.
  - Panel regression (2001–15) shows probability of moving to regular employment increases when the economy recovers.
  - Transition rate from self-employment to regular employment increased in 2016–17 to about 3.5 percent from an average of 3 percent after the crisis.
- Alternative measures of slack used in the analysis:
  - Headline unemployment rate.
  - An adjusted unemployment rate capturing unemployed, inactive, and self-employed (weights based on average probability of finding a regular job between 2005 and 2017).
    - On average, 3 percent of self-employed workers have taken a regular job each quarter, compared to 22 percent of those unemployed.
  - Hiring rate: those finding new jobs every period (existing workers, inactive, or unemployed) over total employment.
  - Involuntary part-time employment as a share of the labor force added separately in regressions.
- Equilibrium considerations:
  - The adjusted unemployment rate gap is computed as the difference between the actual rate and a time-varying equilibrium underemployment rate (estimated by a Kalman filter).
  - Evidence suggests the equilibrium unemployment rate has declined over time due to rising educational attainment and tax and benefit reforms.

### D. Box 1 — Could high self-employment share affect wage growth?
- Key observations on self-employment and wage dynamics:
  - Compositional shift towards greater self-employment would structurally reduce wage growth because self-employed workers typically have lower wage income.
  - Blanchflower and Shadforth (2007): self-employed (without their own workers) saw median weekly income drop by about a 20 percent in real terms between 2007–08 and 2014–15.
  - Pension coverage in 2010/11: 21 percent of self-employed had pensions compared to 50 percent of employees.
  - Share of self-employed who employ staff: 17 percent in the UK versus 44 percent in Germany (Hatfield 2015).
  - High share of self-employed can negatively influence wage growth for regular employees through competition for tasks and vacancies.
  - Empirical transition data: transitions from self-employment to regular employment increased in 2016–17 (to about 3.5 percent from an average of 3 percent after the crisis).

### E. Empirical strategy and data
- Model framework:
  - Long-run real wage (ݓ௧ோ) is assumed determined by labor productivity (ܲܮ௧), following an equation of the form ݓ௧ோ = ߙ௅ோ,ଵ + ߚ௅ோ,ଵ ܲܮ௧ + ߝ௅ோ,௧ = ݓ௧∗ + ߝ௅ோ,௧.
  - Short-run nominal wage dynamics specified as an error-correction model:
    - ∆ݓ௧ே = ߙௌோ,ଵ + ߱ଵ (ݓ௧ିଵோ − ݓ௧ିଵ∗) + ߚௌோ,ଵ ݈݇ܿܽݏ௧ + ߚௌோ,ଶ ∆ܲܮ௧ି௜ + ߚௌோ,ଷ ߨ௧௧ି௜ா + ߚௌோ,ସ ܺ௧ି௜ + ߝௌோ,௧.
    - Short-run determinants include lagged error term, measures of labor market slack (݈݇ܿܽݏ௧), lags of inflation expectations (ߨ௧௧ି௜ா), lagged productivity growth (∆ܲܮ௧ି௜), and other factors (ܺ௧ି௜) including growth uncertainty and EU labor market conditions.
- Data and sample:
  - Quarterly data over 2000Q1–2017Q2 used (weekly earnings data available since 2000).
  - Long-run wage equation estimated by fully modified least squares; short-run equation follows a general-to-specific approach.
- Variable definitions and choices:
  - Wages: average weekly earnings from Office of National Statistics (excludes self-employed earnings).
  - Nominal wage deflated by GDP deflator to obtain real wage.
  - Labor productivity defined as real Gross Value Added (GVA) per worker.

### F. Summary statistics and unit root results (selected)
- Summary statistics (Obs, Mean, Max, Min, Std. Dev):
  - Nominal wage (yoy change): Obs 660, Mean 0.03, Max 0.05, Min 0.00, Std. Dev 0.01
  - Labor productivity (yoy change): Obs 660, Mean 0.01, Max 0.04, Min -0.05, Std. Dev 0.02
  - Unemployment rate: Obs 700, Mean 0.06, Max 0.01, Min 0.04, Std. Dev 0.08
  - Adj. unemployment rate (percent): Obs 700, Mean 0.09, Max 0.01, Min 0.08, Std. Dev 0.10
  - Adj. unemployment rate gap (ppt): Obs 700, Mean 0.00, Max 0.01, Min -0.01, Std. Dev 0.02
  - Hiring rate (percent): Obs 630, Mean 0.06, Max 0.00, Min 0.04, Std. Dev 0.06
  - Share of involuntary PT workers: Obs 700, Mean 0.03, Max 0.01, Min 0.02, Std. Dev 0.05
  - Inflation expectations (5-year ahead derived from government securities): Obs 700, Mean 0.03, Max 0.00, Min 0.00, Std. Dev 0.04
  - EU unemployment gap: Obs 700, Mean 0.33, Max 0.98, Min -1.80, Std. Dev 2.18
  - Growth uncertainty (standard deviation of one-year ahead growth forecast from consensus): Obs 700, Mean 0.50, Max 0.16, Min 0.25, Std. Dev 0.94
- Unit root testing:
  - Real product wage: level ADF t-stat -2.630 (trend and intercept), first difference ADF -7.635 (intercept).
  - Productivity (MA,8q): level ADF t-stat -2.473 (trend and intercept), first difference ADF -1.825 (no intercept).
  - The null hypothesis for the ADF and PP tests is non-stationary; first differences are stationary.

### G. Empirical findings (overview)
- Long-run relationship:
  - The long-run wage equation is estimated using fully modified least squares (results presented in Table 3 in source).
- Short-run dynamics:
  - Error-correction models estimated using 2000Q1–2017Q2 quarterly data; a general-to-specific approach applied for short-run specification.
- Main factors identified as influencing recent wage dynamics:
  - Low labor productivity growth.
  - Significant labor market slack in the UK and the EU until recently (with broader underemployment measures showing additional slack).
  - Low actual and expected inflation.
  - Uncertainty about the growth outlook.

*Source: cr1843 - References _______________________________________________________________________________ 15*

### 11. As expected, the estimated coefficient on productivity in the long run equation is close

### cr1843 - 11. As expected, the estimated coefficient on productivity in the long run equation is close

### Long-run wage–productivity relationship
- Estimated coefficient on labor productivity trend: 0.86 (0.06)***.
- Constant: -2.50 (0.29)***.
- Observations: 70.
- Adjusted R-squared: 0.93.
- S.E. of regression: 0.01.
- Cointegration tests:
  - Engle-Granger tau-statistic: -3.09; Probability: 0.10.
  - Phillips-Ouliaris tau-statistic: -3.08; Probability: 0.11.
  - Engle-Granger z-statistic: 16.38; Probability: 0.09.
  - Phillips-Ouliaris z-statistic: -15.95; Probability: 0.10.
- Hansen instability Lc statistics: 0.07; Probability: >0.2.
- Interpretation:
  - Coefficient close to one suggests a tight long-run relationship between predicted and actual real wage growth.
  - Real wages did not fall significantly during the crisis (downward wage rigidity), but wage growth slowed and by 2013 real wages started to lag productivity. As of 2017Q2, real wage was broadly in line with the estimated long-run equilibrium value.

### Short-run determinants of wage growth (private sector average weekly regular pay growth)
- Error-correction term (ecm (t-4)):
  - Model (1): -0.25*** (0.09)
  - Model (2): -0.21*** (0.07)
  - Model (3): -0.20*** (0.07)
  - Model (4): -0.22*** (0.07)
- Labor market slack measures and coefficients (selected):
  - Headline unemployment (t-4): -0.59*** (0.06) [model (1)].
  - Adj. unemployment rate (t-4): -0.37*** (0.13) [model (2)].
  - Adj. unemployment rate gap (t-4): -0.32** (0.13) [model (3)].
  - Hiring rate (t-4): 0.46** (0.18) [model (4)].
  - Involuntary part-time employment (t-4):
    - -0.28** (0.11) [model (1)]
    - -0.34*** (0.11) [model (2)]
    - -0.29** (0.11) [model (3)]
- Other short-run drivers:
  - Inflation expectation (t-4):
    - 0.66*** (0.18) [model (1)]
    - 0.34** (0.14) [model (2)]
    - 0.34** (0.15) [model (3)]
    - 0.36** (0.16) [model (4)]
  - Productivity growth (t-1):
    - 0.28*** (0.05) [model (1)]
    - 0.21*** (0.04) [model (2)]
    - 0.23*** (0.04) [model (3)]
    - 0.21*** (0.04) [model (4)]
  - EU unemployment gap (t):
    - -0.44*** (0.10) [model (1)]
    - -0.43*** (0.10) [model (2)]
    - -0.54*** (0.09) [model (4)]
  - Growth uncertainty (t-2):
    - -2.43*** (0.47) [model (1)]
    - -2.41*** (0.49) [model (2)]
    - -2.02*** (0.60) [model (4)]
- Model fit:
  - Observations: 66 [models (1), (2), (3)]; 59 [model (4)].
  - R-squared:
    - Model (1): 0.78.
    - Model (2): 0.89.
    - Model (3): 0.89.
    - Model (4): 0.88.

### In-sample forecast performance and historical decomposition
- In-sample forecasts comparing model 1 and model 3:
  - Model 1 consistently overpredicts wage growth.
  - Forecast performance improves significantly when controlling for the share of involuntary part-time workers and other factors (model 3).
  - Rest of analysis based on results from model 3.
- Historical decomposition (contributions to nominal wage growth, deviation from mean):
  - Key contributors to post-crisis slowdown in nominal wage growth:
    - Low productivity growth (LP).
    - Weak labor market conditions (slack) in the UK and the EU.
    - Increased uncertainty about future economic growth (depressed wage growth around the referendum and recent quarters).
    - Low inflation in 2015–16 contributed to depressing wages during 2016–17.
  - More recently: diminished slack in the labor market provided a modest boost to wages, offset by greater uncertainty and weak lagged inflation.

### Wage outlook and scenarios
- Baseline assumptions:
  - Labor markets remain relatively tight, with the unemployment rate slightly below the estimated equilibrium level.
  - Share of involuntary part-time workers drops to pre-crisis levels.
  - Growth uncertainty dissipates once a broad agreement on post-EU relationship is reached.
  - Inflation expectations and the EU labor market gap remain unchanged at their 2017Q2 level.
  - Productivity growth recovers to about 1 percent in the medium term.
- Baseline projection:
  - Annual nominal wage growth expected to pick up from 2¼ percent in 2017Q2 to between 2¾ and 3 percent in 2018.
- Risks and scenario on self-employment transition:
  - Observed transition of self-employed to regular employment: 3.5 percent in 2017 vs 3 percent average post-crisis.
  - If transition rate doubles to 6 percent, wage growth could be lower by about 1 percentage point due to a larger pool of labor competing for vacancies.
- Other risks:
  - Growth uncertainty may remain elevated even after the UK leaves the European Union.
  - If productivity growth fails to pick up as projected, wage growth would remain weak.

### Conclusions and policy implications (wage dynamics)
- Puzzle: disconnect between falling unemployment (40-year low) and subdued wage growth explained by:
  - Persistent weak productivity growth.
  - Well-anchored inflation expectations.
  - Greater effective labor market slack than suggested by headline unemployment rate.
- Broader measures of underemployment (involuntary part-time unemployment, inactive and self-employed people seeking regular jobs) indicate higher slack than the unemployment rate alone.
- Policy implications:
  - Persistent labor market tightness should prompt some firming of wage growth in the coming year, other things equal.
  - Mild increase in unit labor costs would help bring domestically generated inflation in line with the inflation target.
  - Sustainable wage recovery requires productivity growth to recover.
  - Reducing Brexit-related uncertainty would help firms make long-term decisions affecting wages.

### Regional disparities in labor productivity (United Kingdom)
- National and trend statistics:
  - In 2015, GDP per hour worked was between 20 and 25 percent lower than in France, Germany and the United States.
  - UK productivity growth declined from 2.1 percent during 2000–07 to 0.5 percent during 2010–16.
- Spatial disparities:
  - Only two regions at the NUTS 1 level show productivity levels above the country average; 70 percent of employment is in regions with productivity levels below average.
  - The UK’s highest productivity region (London) is nearly 60 percent more productive than its lowest.
  - Productivity gap within regions is greater at higher territorial disaggregation (NUTS 3).
  - Regional disparities are large compared to other advanced economies and have not shown signs of convergence over the last decade.
- Sectoral and structural observations:
  - Wealthier regions have higher productivity across most industry sectors, suggesting region-wide characteristics (not a single sector) drive comparative advantage.
- Policy-relevant findings on channels behind disparities:
  - Strengthening education and training is likely important to address regional disparities in productivity.
  - High housing prices and regulatory constraints affect internal migration and may reduce its effectiveness as a convergence mechanism.
  - Aggregate public investment in the UK has traditionally been below peers, but evidence does not suggest investment in physical capital and SME access to finance are particularly worse in underperforming regions.
  - Investment in research and development (R&D) in the UK lags peers and is uneven across regions; improving adoption of innovations in underperforming regions is crucial.
  - Agglomeration effects play a significant role; major infrastructure projects (Northern Powerhouse Rail, Midlands Rail Hub) aim to increase connectivity and agglomeration outside London and the South East.
  - Foreign direct investment and exposure to international competition can stimulate regional performance; openness to trade and FDI matters for productivity gains.
  - Decentralization of governance could improve policy responsiveness to local economic conditions.
- Framing:
  - Regional disparities may reflect both persistent geographic/agglomeration advantages and frictions (labor mobility, finance access, technology diffusion, uneven public goods) that policy can address to promote convergence.
  - Policies should be judged by impact on growth and inclusion rather than solely by narrowing gaps between particular regions.

*Source: IMF staff calculations and analysis as presented in the provided content.*

### 6.      There is scope to improve educational outcomes and increase human capital in the UK.

### 6.      There is scope to improve educational outcomes and increase human capital in the UK.

### Human capital and skills: current situation and regional variation
- Human capital is a key determinant of economic growth.
- On aggregate, adults in the UK have lower literacy and numeracy skills than the OECD average; the proportion of younger people in England with low skills is higher than the proportion among people nearing retirement age.
- The share of those aged 15–19 that are enrolled in education is below the OECD average.
- Large variations in the skills composition of the local workforce across UK regions correlate strongly with regional disparities in labor productivity.
- Workforce skills in a region depend on:
  - production of skills in that region through education and training, and
  - movement of workers in and out of the region (sorting effect).
- Participation:
  - Early-age education participation is high and homogeneous across UK regions.
  - Regional differences in participation are more pronounced at advanced levels of education; participation in education for students aged 17 in Wales is notably lower than other regions.
- Quality:
  - Comparing education quality across UK regions is difficult due to limited comparable data.
  - PISA scores for Wales are below UK and OECD averages; this is the fourth consecutive PISA cycle in which Welsh students performed worse than the UK average.
  - Within England, Ofsted’s annual report (2016) indicates more secondary schools assessed as “less than good” in the North and Midlands compared with the South and East of England.
- Sorting:
  - Six months after graduation, London employs 22 percent of all working new university graduates.
  - London attracts and retains a larger share of high achievers.
  - Sorting effects account for a considerable fraction of observed spatial differences in wages (D’Costa and Overman, 2014).

### Policy levers to strengthen education and training
- Basic skills:
  - Areas with high concentrations of workers lacking basic skills are key problems for productivity.
  - Recent reforms: national curriculum and GCSE reforms to assure core literacy and numeracy; since 2013 math and English study programs provided to all 16 to 19-year-old students who have not, by age 16, achieved at least minimum grade.
  - Effectiveness of recent government initiatives to improve basic skills should be kept under review.
- Technical skills:
  - The UK ranks 16th out of 20 OECD countries for the proportion with technical qualifications (HM Government Industrial Strategy Green Paper, 2017).
  - Initiatives such as T-level technical qualifications could benefit young people who do not attend university and who are more prevalent in low productivity regions.
  - Expansion in vocational training requires continuous monitoring to ensure good quality.
- Teacher shortages:
  - The government missed recruitment targets for the last four years; indicators suggest teacher shortages are growing nationally (NAO 2016).
  - The Department for Education has a “weak understanding of the extent of local teacher supply shortages and whether they are being resolved locally.”
  - Underperforming schools in disadvantaged areas find it hard to attract high-quality teachers.
- Continued reform in Wales:
  - Wales’ 2011 school improvement reform targeted literacy, numeracy, and reducing the impact of deprivation.
  - OECD (2014) recommended: differentiated teaching, simplifying targeted funding, investing in support staff; raising teaching profession status and improving initial teacher training; creating a coherent assessment and evaluation framework; long-term policy perspective.
  - OECD (2017) stock-taking: progress in some areas, recommended sustaining commitment, deepening investments, and strengthening implementation.

### Labor mobility, housing, and regional adjustment
- Barriers to labor mobility may reduce its effectiveness as a regional adjustment mechanism; internal flows for England and Wales show highly productive regions tend to have net outflows instead of inflows.
- Housing prices (and regulations) significantly impact internal migration patterns:
  - Bilateral gross flows analysis between regions in England and Wales shows house prices are negatively related to worker movement between regions.
  - Hilber and Vermeulen (2015): housing prices are significantly (causally) affected by housing regulations.
  - If the South East had the regulatory restrictiveness of the North East, house prices in the South East would have been roughly 25 percent lower in 2008.
- Housing regulations and income convergence (46 English counties, 1979–2008):
  - Specification follows Ganong and Shoag (2015); dependent variable: average annual growth rate of real weekly earnings of a male worker in 1979–2008.
  - Key regression results (coverage: 46 English counties; R2 = 0.77; robust standard errors):
    - Avg. real earnings of a male worker in ’79 (in log): -0.004* (spec. 1); -0.005* (spec. 2).
    - Housing regulations: 0.02*** (both specs).
    - Housing regulations * Avg. real earnings in ’79: 0.22*** (both specs).
  - Interpretation: the negative coefficient on starting earnings suggests income convergence; the positive and highly significant interaction term indicates tighter housing regulations dampen the speed of convergence across counties.
- Policy implications:
  - Efforts to boost housing supply can improve labor mobility and growth.
  - Recommended measures include easing planning restrictions, mobilizing unused publicly-owned lands for construction, and providing incentives for local authorities to facilitate residential development.

### Determinants of internal migration (empirical specification and results)
- Empirical focus: bilateral gross migration flows between NUTS 3 regions of England and Wales; Tobit model estimated for adults above 30 years old.
- Specification captures effects of: productivity levels, housing prices (or house-price-to-earnings), unemployment rates, and distance.
- The estimation provides evidence that:
  - Migrants are drawn by strong labor markets (positive effect of productivity and low unemployment at destination).
  - High house prices and greater distance discourage migration.
  - Results are robust to clustering standard errors by region of origin, expanding age sample to 30–59, and excluding regions at the 5 percent tails of productivity or house prices.

### Investment and access to finance
- Capital formation:
  - Aggregate public investment in the UK and perceived quality of UK infrastructure assets has been traditionally below that of peer countries.
  - An ONS estimate of regional gross fixed capital formation (GFCF) for 2000–2013, scaled by regional GDP, does not show that more productive regions invest more; e.g., London invests a lower fraction of its GDP than Wales or Northern Ireland.
- Access to finance:
  - The UK SME access to finance index is above the EU average, but constraints remain:
    - Lack of competition in the UK banking system, excessive short-termism, under-supply of late-stage venture capital, and low appetite for equity investment and alternative funding among SMEs.
  - Scaling bank lending to SMEs by region’s GDP does not suggest SMEs in productive regions have better access to bank finance.
  - Evidence suggests venture capital, equity investment, and crowdfunding are more concentrated in London and the South East.

_Italic: Source: IMF staff summary of chapter "6.      There is scope to improve educational outcomes and increase human capital in the UK." from cr1843 PDF._

### 14.      Investment in research and development (R&D) in the UK lags that of peers and is

### 14.      Investment in research and development (R&D) in the UK lags that of peers and is

### R&D investment and regional disparities
- Expenditure on R&D (both private and public) has typically been lower in the UK than in other advanced economies.
- At the regional level, overall R&D investment tends to be lower in places with low productivity.
- London and the South-East account for a large share of investment in innovation and represent the country’s “frontier” region.
- Source data for expenditure charts: Eurostat Regional Statistics at the NUTS 2 level.

### Improving adoption of innovation in lagging regions
- Technological adoption raises productivity as less productive firms learn from frontier firms and imitate processes.
- Diffusion of innovations from frontier to lagging regions requires adaptation to industry- and region-specific circumstances and may be constrained by local skills endowments.
- Key factors improving a region’s ability to adopt new technologies:
  - skills of the workforce;
  - investment in appropriate R&D;
  - investment in physical capital, including information and communications technologies and new machinery (OECD 2016).
- “Regional champions” play a significant role for productivity growth within each region (see Box 2).

### Box 2 — Productivity Growth and the Role of the Regional Frontier: empirical findings
- Estimation adapts Andrews et al. (2015) framework to test how firm productivity growth relates to regional frontier and national frontier performance.
- Regional frontier defined as top 5 percent of firms with highest TFP in each year (NUTS2 level); national frontier includes 100 most productive firms in the UK.
- Distance from regional frontier measured as the difference between median TFP level of frontier firms and the given firm’s TFP.
- Data covers over 104 thousand firms in years 2008–2013.
- Key regression findings (dependent variable: firm-level total factor productivity (TFP) annual growth rate):
  - Distance from regional frontier: 0.27***, 0.27***, 0.25***, 0.24*** (in specifications (1)–(4)).
  - Distance from regional to national frontier: 3e-7, 4e-7, 6e-7, 9e-7.
  - Growth at the regional frontier: -0.004, -0.007, -0.003, 0.04.
  - Growth at the national frontier: 0.15, 0.15, 0.13, 0.07.
  - Capital per worker: 0.03***, 0.04***, 0.04***, 0.03***.
  - Number of employees: -0.01***, -0.02***, -0.02**, -0.01**.
  - Firm’s age: -0.0003**, -0.0004**, -0.0003**, -0.0005**.
  - Industry fixed effects: YES (all specifications).
  - Year fixed effects: YES (all specifications).
  - Excluding crisis years: NO, YES, YES, YES.
  - Excluding London: NO, NO, YES, YES.
  - Accounting for regional price differences: NO, NO, NO, YES.
- Robustness: results robust to using labor productivity as explanatory variable and to extending regional frontier to 10 percent of most productive firms.
- Interpretation: distance to the regional frontier matters more for firm-level productivity growth than distance to the national frontier, implying regional frontier firms facilitate diffusion of innovation.

### International competition, exports, and FDI
- Exporting firms have systematically higher levels of productivity than domestically-oriented firms, on average by around a third (Haldane 2017).
- Foreign-owned firms in the UK are typically more productive than domestic companies; their productivity advantage has increased since the crisis.
- Regions with a larger presence of foreign-owned companies tend to be more productive.
- External-facing firms introduce new technologies and management practices and intensify competitive pressures.
- Policy implication: openness to trade and foreign direct investment is important for boosting regional productivity; policies should build conditions to attract FDI (e.g., large pool of skilled labor).

### Agglomeration effects
- Concentrations of economic activity generate benefits: larger supply of workers, easier access to inputs and suppliers, creation of knowledge spillovers.
- Positive and significant correlation between productivity and agglomeration levels (results reported at NUTS 3 level, England only; Northern Ireland data missing).
- Agglomeration measurement: sum of local population plus nearby populations inversely weighted by distance up to 45 kilometers (pairwise route kilometers from Eurostat).
- Key empirical table results (selected coefficients and model diagnostics):
  - Agglomeration Index: 0.2314** (spec 1), 0.1294** (spec 2).
  - Share of Establishments with +50% Staff with level 4 qualifications: 0.0248***, 0.0224***, 0.0271***.
  - Population density - inhabitant per square metre: 0.0001***, 0.0000**.
  - Constant terms: 3.6926***, 3.1233***, 3.7924***, 3.2402***, 3.1642*** across specifications.
  - Observations: 100 (all specifications).
  - R-squared: 0.268, 0.485, 0.343, 0.504, 0.459 across specifications.
  - NUTS 1 Dummies: YES (all specifications).
  - Robust pval in parentheses; significance: *** p<0.01, ** p<0.05, * p<0.1.
- Policy-relevant implications:
  - Excessive city planning restrictions that limit city size likely reduce agglomeration benefits.
  - Improvements in connectivity within and between cities likely raise productivity; congestion is high in the rail transport network (OECD 2017), and in some smaller cities (e.g., Leeds and Birmingham) congestion is higher than in London (HMT 2017).
  - Major infrastructure projects (e.g., Northern Powerhouse Rail, Midlands Rail Hub) aim to increase connectivity and agglomeration effects.
  - Agglomeration benefits increase with concentration/connection of higher fractions of high skilled workers and decline with distance.

### Role of government, decentralization, and regional policy
- Perceived quality of local governments correlates with productivity levels (European Quality of Government Index).
- Public spending per head (NUTS 1 level): below UK average in England; above in Scotland, Wales, and Northern Ireland. Within England, public spending per head is lowest in South East and highest in London.
- Government spending on transport typically larger in regions with higher productivity; transport expenditure and average commuting trip length are reported in sterling and minutes.
- Fiscal decentralization tends to reduce regional disparities (OECD 2016); measured by share of sub-central government taxes (or revenue) in total taxes (revenues).
- The UK has a relatively high share of local revenues and expenditures in total public revenues and expenditures from a cross-country perspective.
- Government initiatives (England):
  - Fiscal decentralization: government assessing options for greater local control of collected resources and piloting greater retention of business rates revenue by local governments.
    - Note: adequate equalization mechanisms are important to ensure poorer subnational governments can meet devolved responsibilities.
  - Governance decentralization: since 2011, introduction of local enterprise partnerships, and city and local growth devolution deals.
- Risks and considerations:
  - Devolution could give rise to fiscal risks in transition; local government capacity may need building to manage new fiscal responsibilities (IMF UK Fiscal Transparency Evaluation 2016).
  - Planning restrictions may correct market failures, but can be harmful when they disregard market outcomes or the balance of costs and benefits.

*Source: IMF staff analysis as presented in the chapter excerpt.*

### Box 3.  The Evolving Governance Framework for Regional Development in England

### Box 3.  The Evolving Governance Framework for Regional Development in England

### Evolution of governance arrangements after the crisis
- Growing consensus on the need for enhanced decentralization to improve policy effectiveness (Overman 2015,17; Gardiner et al. 2016, Pike 2015).
- The RDAs were closed in 2010 and substituted with governance arrangements at a sub-regional scale to benefit from local knowledge and local accountability (BIS 2010).
- Local governance arrangements introduced include Local Enterprise Partnerships (LEPs), and city and local growth devolution deals.

### Local Enterprise Partnerships (LEPs)
- LEPs are voluntary partnerships between local authorities and businesses established instead of the RDAs.
- LEPs decide on local priorities for physical infrastructure investments and apply for funding from Growth Funds.
- LEPs bring strategic decision-making on economic development policies (e.g. transport) to a scale above local authorities but beneath regions.

### City and local growth (devolution) deals
- City Deals (agreed between 2011 and 2014) provided cities with additional funding to be used flexibly but did not transfer general powers to local authorities.
- Devolution Deals transfer power over specific policy areas and funding from central government to combined authorities (legally recognized partnerships between councils that make decisions on certain policy areas affecting the whole area).
- The devolved powers and the level of funding varies across the combined authorities.
- All devolution deals, with the exception of Cornwall (which is smaller in scope and breadth than other mayoral deals), require an elected mayor to promote accountability.
- Elected mayors will have the power to add a supplement of up to two percent on business rates, with the agreement of the relevant LEP.
- The government is assessing options for giving local governments greater control over the resources they collect, and is piloting approaches to greater retention of business rates revenue by local government.

### Outstanding questions and assessment needs
- It is still early to assess the effectiveness of these recent decentralization deals; for instance, the four most recent combined authorities (out of nine) were established in 2016–17 and the first mayoral elections took place in last May.
- Key evaluative questions to assess progress:
  - Does the fact that devolution deals are still ultimately made by the central government constrain local decision-making (Overman 2015,17)?
  - Which functions and instruments are best aligned at different levels of government?
  - Is there a need for a comprehensive UK-wide strategy?
- Lessons from local experimentation should inform which functions to localize across the board.
- Is there a need for further fiscal devolution? A relatively centralized dispersion of funds from the Regional Growth Fund and other sources of local growth funding (such as infrastructure investment) may constrain local decision-making (Overman 2015, 17).
- The Transport Committee of the House of Commons (2014) suggested it is questionable whether bidding for pots of central government money allocated via rules set by central government rules amounts to genuine devolution.
- Devolution Deals currently give only limited financial powers to local areas.

### Industrial strategy (Section C)
- The government’s industrial strategy identifies driving growth across the whole country as a key pillar; the white paper released in November includes a combination of horizontal (economy-wide) and vertical (targeted towards specific sectors or localities) policy proposals.
- Many initiatives target improving skills and addressing chronic underinvestment in infrastructure and R&D and infrastructure spending.
- Policy intervention should be based on an understanding of the market friction it seeks to address, particularly for interventions targeting specific sectors or localities.
  - A set of transparent rules for intervention, and evidence-based evaluation both ex-ante and ex-post, could guard against the risk of arbitrary policy intervention driven by vested interests (Banks 2015, Crafts 2017, and Valero 2017).
- A strong institutional framework is important to give the industrial strategy stability and protection from the political cycle.
  - The LSE Growth Commission (2017) recommended industrial policy be given a new law or long-lasting mandate, independent decision-making or oversight, and enhanced transparency and accountability.
- Selective industrial policy is currently generally limited by the EU state aid framework, but this may potentially change when the UK leaves the EU.

### Conclusions on regional disparities and policy priorities (Sections D, paras 23–25)
- There are large and long-standing disparities in labor productivity across UK regions; regional disparities are large compared to other advanced economies.
- Two regions, London and the South East, have very high levels of productivity, while productivity levels are low elsewhere.
- The UK’s lowest productivity regions underperform the least productive regions in other advanced economies.
- Wealthier regions have higher productivity across most industry sectors, suggesting comparative advantage is due to cross-cutting regional characteristics rather than particular types of economic activity.
- Key drivers of regional disparities:
  - Differences in human capital levels.
  - Agglomeration effects.
- Major infrastructure projects (e.g., Northern Powerhouse Rail and the Midlands Rail Hub) aim to increase connectivity to achieve agglomeration effects outside London and the South East.
- Housing prices and regulatory constraints impact internal migration, possibly reducing the effectiveness of relocation of labor as a regional convergence mechanism.
- Investment in research and development in the UK lags peers and is uneven across regions; improving under-performing regions’ ability to adopt innovations is crucial to enable catch-up.
- Government spending per capita does not differ systematically across regions, but fiscal centralization is high in the UK relative to other countries.
- Continued decentralization of governance arrangements could improve policy responsiveness to local economic conditions.
- Structural reform priorities differ by local needs:
  - Addressing congestion and housing restrictions is important for more successful regions.
  - Other regions should aim to increase human capital and improve transport connectivity.
- Evidence-based evaluation is crucial to assess effectiveness of reforms.
- Many reforms have long gestation lags; e.g., education reforms can take decades to yield positive effects, so securing consistent long-term implementation is important.

### Annex I — Does sectoral composition explain regional disparities?
- Regional aggregate disparities could be driven by different sectoral productivity or by different industry presence (regional employment shares).
- The most productive regions at the NUTS 1 level tend to have higher productivity across most sectors, suggesting regional industry structure may play a relatively small role.
- A decomposition exercise at NUTS 3 level separates a pure productivity index and an occupational composition index (following Rice et al. 2006):
  - The pure productivity index takes each region’s sectoral productivity levels and weights them by country average sectoral employment shares.
  - The occupational composition index takes each region’s sectoral employment shares and weights them by country average sectoral productivity levels.
- Empirical result:
  - Correlation of the pure productivity index with productivity levels is 0.99.
  - Correlation of the compositional index with productivity levels is 0.33.
  - These correlations confirm the compositional index plays a less relevant role in explaining regional discrepancies.
- Chart note: Output per job by region and sector (UK average = 100 in each sector; 2015) shows London and South East above other regions across most sectors listed (Agriculture, mining, utilities supply; Manufacturing; Construction; Trade; Transportation; Accommodation and food; Information and communication; Financial services; Real estate; Professional and scientific services; Administrative and support services; Other services).

*Source: Box 3. The Evolving Governance Framework for Regional Development in England (continued / concluded), cr1843.*

---


_Source: https://www.imf.org/-/media/files/publications/cr/2018/cr1843.pdf_