Abstract
Selected Issues
Drivers of UK Wage Growth1
Since the financial crisis, nominal wage growth has been much weaker than during the previous decade. This chapter finds that the main factors behind the slowdown include weak productivity growth, labor market slack (both in the UK and the EU), and low inflation. We consider broader measures of labor market slack including a newly constructed measure of underemployment, aiming to capture labor market pressures more accurately than the headline unemployment rate. This measure signals limited labor market slack in 2017, which should support wage growth going forward. However, a sustained recovery in wages would require a recovery in productivity growth.
A. Introduction
1. Nominal wage growth in the UK has remained subdued in recent years despite a significant tightening of labor market conditions. After several years of robust employment growth, the headline unemployment rate has fallen from about 8 percent in 2010 to 4.3 percent in 2017Q3—the lowest level since 1975. The share of long term unemployment has also declined. At the same time, labor force participation has increased and is now equal to its pre-crisis peak.2 Moreover, average weekly hours of work have recovered to the average level in 2003–2007. Howeover, nominal wage growth has recovered only modestly and is still well below its pre-crisis average. This has renewed the debate on the strength of the link between labor market conditions and wages (see Haldane, 2017).
Unemployment and Wage Growth
(Percent)
Citation: IMF Staff Country Reports 2018, 043; 10.5089/9781484341759.002.A001
Sources: Haver.2. This chapter aims to explain UK’s wage dynamics in recent years. A new measure of underemployment for the UK is used in the analysis to capture the impact of self-employed workers on labor market slack. It is then complemented with the share of involuntary part-time workers to give a broader view of labor market slack. Key factors that have affected the dynamics of wages since the crisis include low labor productivity growth, significant labor market slack in the UK and the EU until recently, low actual and expected inflation, and uncertainty about the growth outlook.
B. Determinants of Wage Growth
3. Nominal wage growth is determined by the interaction of a number of factors. Some are structural in nature, other are cyclical:
Labor productivity growth—the growth in output per worker—is a key driver of real wage growth. As productivity increases (which could happen as firms invest in new machines and adopt a better technology, for example), the incentives to expand production and hire new workers improve, which should eventually translate into rising pressure on wages. In the UK, the labor share as percent of GDP has been close to 64 percent over time, which suggests a broadly stable relationship between labor compensation and workers’ productivity.
Labor market slack—the gap between headline and equilibrium unemployment—also has an important influence on wages. During the expansionary phase of the business cycle, firms seek to hire more workers to meet increased demand for output, which leads to lower unemployment and eventually to higher wages as the supply of qualified workers diminishes. The reverse happens during downturns. Historically, the unemployment rate has been a good indicator of labor market tightness. More recently, due to the changing nature of work arrangements, one has to look at a broader range of measures to form a view on the state of the labor market. This topic is discussed in the next section.
With globalization, goods, capital, and labor move more freely across borders, so global labor market conditions also matter for domestic wages. In our analysis we consider measures of labor market slack in the EU as an additional determinant of wage pressures in the UK.
Expected inflation is another important factor determining nominal wages. In a simple world with no business cycle fluctuations, constant non-wage costs, and no monopoly power, pay raises should be approximately equal to productivity growth plus expected inflation.
Finally, uncertainty about medium term growth prospects can also influence hiring decisions and wage dynamics. At a time of pessimism or uncertainty about the future, firms would be less willing to hire full-time employees or pay better wages to attract more qualifies workers (even if current demand is strong). At the same time, workers may be less willing to switch jobs or seek a wage increase.
Real Product Wage and Labor Productivity
(Log points)
Citation: IMF Staff Country Reports 2018, 043; 10.5089/9781484341759.002.A001
Sources: Haver and Fund staff calculations.1/ Real output per worker.2/ Average weekly private sector regular wages deflated by GDP deflator.Labor Share
(Percent of GDP)
Citation: IMF Staff Country Reports 2018, 043; 10.5089/9781484341759.002.A001
Sources: Bank of England and Fund staff calculatins.C. Assessing Labor Market Slack
4. Historically, the unemployment rate has been a good indicator of slack, but with the rise of more flexible forms of employment, a broader assessment of labor market conditions may be warranted. For example, the share of part-time employees who would prefer fulltime jobs—involuntary part-time workers— doubled from 9.4 percent in 2007 to 18 percent in 2013, before falling back to 12 percent recently. Involuntary part-time workers may have little wage-bargaining power and may prioritize job security over higher wages. Moreover, the share of self-employed workers and zero-hour workers3 has increased above pre-crisis levels. Some of these workers may prefer to be in regular employment, and could seek to return to it as the economy recovers. Therefore, cyclical pressures on the labor market can perhaps be assessed better by looking at changes in broader measures of underemployment.
Underutilization in the Labor Market
(Percent of employment)
Citation: IMF Staff Country Reports 2018, 043; 10.5089/9781484341759.002.A001
Sources: Haver.5. Analysis of labor flows suggest that the transition rate from self-employment to regular employment is procyclical. Although the increase in self-employment has a structural component (some workers may have an incentive to become self-employed due to the nature of their work and/or the relatively more favorable tax treatment of the self-employed, see Tatomir 2015), our analysis suggests that it has a cyclical component as well. The share of self-employment rises during recessions (transition from unemployment to self-employment increases). In addition, transitions from self-employment to employment increase as economic conditions improve (see chart). Panel regression using data from 2001–15 (following Rees and Shah 1986) shows that the probability of moving to regular employment increases when the economy recovers. This is consistent with anecdotal evidence that some self-employed have been forced out of regular employment as firms try to avoid the legal obligations that come with an employment contract, such as meeting minimum wage requirements, national insurance contributions, statutory sick and holiday pay, and fair dismissal. A recent study by Tomlinson and Corlett (2017) suggests that about 60 percent of the self-employed are in the “precarious” sectors, where they are more likely to be underemployed. Overall, this evidence suggests that self-employment could affect the degree of labor market slack and wages (see Box 1).
Transition Probability of Self-employed Workers to Regular Employment and Output Gap
(percent)
Citation: IMF Staff Country Reports 2018, 043; 10.5089/9781484341759.002.A001
Sources: Fund staff calculations.Could High Self-Employment Share Affect Wage Growth?
A compositional shift towards greater self-employment would structurally reduce wage growth, as self-employed workers typically have lower wage income. On average, self-employed workers receive lower labor income, although there is a significant variance, with fat tails at both ends of the income distribution (Hatfield 2015). Moreover, Blanchflower and Shadforth (2007) show that self-employed (without their own workers) have seen their median weekly income drop by about a 20 percent in real terms between 2007–08 and 2014–15, while both employees and self-employed with workers have seen milder declines. Self-employed workers are not bounded by the national minimum requirement, and are less likely to have made contributions to a private pension scheme: in 2010/11 only 21 percent had pensions compared to 50 percent of employees. Few self-employed in the UK employ staff of their own: only 17 percent of self-employed in the UK have workers compared to 44 percent in Germany (Hatfield 2015).
In addition, high share of self-employed may have a negative influence on wage growth for regular employees. Since self-employed workers have lower wages on average, they could compete with regular employees for a given task, reducing economy-wide wage pressures. Empirically, transitions from self-employment to regular employment increase when economy recovers, so some of the self-employed workers will compete with the unemployed for vacancies, thus delaying the pace of wage recovery. Indeed, the transition rate from self-employment to regular employment has increased in 2016–17 (to about 3.5 percent from an average of 3 percent after the crisis).
| Panel fixed effect VARIABLES |
Probability of self-employed workers to employee |
Probability of unemployed to employee |
||
|---|---|---|---|---|
| Unemployment rate | −0.04*** | −0.09*** | ||
| (0.01) | (0.03) | |||
| Regional unemployment | −0.02* | −0.07** | ||
| (0.01) | (0.03) | |||
| age | 0.03*** | 0.03*** | 0.06*** | 0.05** |
| (0.01) | (0.01) | (0.02) | (0.02) | |
| age squared | −0.0001* | −0.0002* | −0.0004 | −0.0005* |
| (8.93e-05) | (8.93e-05) | (0.0003) | (0.0003) | |
| Eduction level | −0.004 | −0.005 | −0.004 | −0.004 |
| (0.006) | (0.006) | (0.02) | (0.02) | |
| Single | 0.02 | 0.02 | 0.03 | 0.03 |
| (0.02) | (0.02) | (0.07) | (0.08) | |
| Constant | −0.72*** | −0.61*** | −0.50 | −0.40 |
| (0.21) | (0.21) | (0.39) | (0.40) | |
| Fixed effect | Yes | Yes | Yes | Yes |
| Observations | 6,031 | 5,991 | 2,200 | 2,191 |
| R-squared | 0.011 | 0.009 | 0.027 | 0.023 |
| Number of pid | 1,847 | 1,842 | 1,444 | 1,439 |
| Panel fixed effect VARIABLES |
Probability of self-employed workers to employee |
Probability of unemployed to employee |
||
|---|---|---|---|---|
| Unemployment rate | −0.04*** | −0.09*** | ||
| (0.01) | (0.03) | |||
| Regional unemployment | −0.02* | −0.07** | ||
| (0.01) | (0.03) | |||
| age | 0.03*** | 0.03*** | 0.06*** | 0.05** |
| (0.01) | (0.01) | (0.02) | (0.02) | |
| age squared | −0.0001* | −0.0002* | −0.0004 | −0.0005* |
| (8.93e-05) | (8.93e-05) | (0.0003) | (0.0003) | |
| Eduction level | −0.004 | −0.005 | −0.004 | −0.004 |
| (0.006) | (0.006) | (0.02) | (0.02) | |
| Single | 0.02 | 0.02 | 0.03 | 0.03 |
| (0.02) | (0.02) | (0.07) | (0.08) | |
| Constant | −0.72*** | −0.61*** | −0.50 | −0.40 |
| (0.21) | (0.21) | (0.39) | (0.40) | |
| Fixed effect | Yes | Yes | Yes | Yes |
| Observations | 6,031 | 5,991 | 2,200 | 2,191 |
| R-squared | 0.011 | 0.009 | 0.027 | 0.023 |
| Number of pid | 1,847 | 1,842 | 1,444 | 1,439 |
6. This chapter considers three alternative measures of labor market slack:
Headline unemployment rate. This is the most commonly used measure of labor market slack in the literature.
An adjusted unemployment rate, capturing the fact that some self-employed individuals and people outside the labor force seek regular employment. In the spirit of Kudlyak and Lange (2014), the adjusted unemployment rate is calculated as the weighted average of the unemployed, inactive, and self-employed. The weighs are calculated as the average probability of finding a regular job between 2005 and 2017 for each group.4 On average, 3 percent of self-employed workers have taken a regular job each quarter, compared to 22 percent of those unemployed. The adjusted unemployment rate broadly tracks headline unemployment, but has diverged more recently as the share of self-employed workers has increased. In the regressions, we use alternatively the underemployment rate or the underemployment gap – the difference between the actual rate and a time-varying equilibrium underemployment rate (estimated by a Kalman filter). There is evidence that the equilibrium unemployment rate has declined over time due to rising educational attainment in the labor force, and tax and benefit reforms that have changed incentives to move from unemployment to employment (Saunders 2017).
The hiring rate is defined as those finding new jobs every period (existing workers, inactive, or unemployed) over total employment. Wage negotiations occur when a worker finds a new job and as the hiring rate improves, wage pressures may increase.
Involuntary part-time employment as a share of the labor force is added as a separate variable to the wage regressions to compliment the above measures of labor market slack.
Unemployment and Adjusted Unemployment Rate
Citation: IMF Staff Country Reports 2018, 043; 10.5089/9781484341759.002.A001
Sources: Fund staff calculations.Contribution to Adjusted Unemployment Rate
(percent of working age population)
Citation: IMF Staff Country Reports 2018, 043; 10.5089/9781484341759.002.A001
Sources: ONS and fund staff calculations.D. Empirical Strategy and Data
7. This chapter uses an error-correction model (ECM) for wage dynamics. Theory suggests that, over the long run, labor compensation will move in line with labor productivity, thus we assume that the long-run real wage
8. In the short run, nominal wages may temporarily deviate from productivity, driven by labor market developments and other factors. The short term nominal wage dynamics equation includes the lagged error term from the long run equation, measures of labor market slack (slackt), lags of inflation expectations
9. Table 1 provides summary statistics for the key variables. There are several different measures for wages. 6 This chapter uses average weekly earnings from the Office of National Statistics, since they exclude earnings of the self-employed (which may be driven by factors other than regular wages). The nominal wage is deflated by GDP deflator to get the real wage. Labor productivity is defined as real Gross Value Added (GVA) per worker. Table 2 shows that real wages and productivity are non-stationary, but their first differences are stationary.
United Kingdom: Summary Statistics for Selected Indicators
5-year ahead inflation expectation derived from government securities.
Standard deviation of one-year ahead growth forecast from consensus. Sources: Haver, Eurostat, Consensus Forecast, and fund staff calculations.
United Kingdom: Summary Statistics for Selected Indicators
| Obs | Mean | Max | Min | Std. Dev | |
|---|---|---|---|---|---|
| Nominal wage (yoy change) | 66 | 0.03 | 0.05 | 0.00 | 0.01 |
| Labor productivity (yoy change) | 66 | 0.01 | 0.04 | −0.05 | 0.02 |
| Unemployment rate | 70 | 0.06 | 0.01 | 0.04 | 0.08 |
| Adj. unemployment rate (percent) | 70 | 0.09 | 0.01 | 0.08 | 0.10 |
| Adj. unemployment rate gap (ppt) | 70 | 0.00 | 0.01 | −0.01 | 0.02 |
| Hiring rate (percent) | 63 | 0.06 | 0.00 | 0.04 | 0.06 |
| Share of involuntary PT workers | 70 | 0.03 | 0.01 | 0.02 | 0.05 |
| Inflation expectations 1/ | 70 | 0.03 | 0.00 | 0.00 | 0.04 |
| EU unemployment gap | 70 | 0.33 | 0.98 | −1.80 | 2.18 |
| Growth uncertainty 2/ | 70 | 0.50 | 0.16 | 0.25 | 0.94 |
5-year ahead inflation expectation derived from government securities.
Standard deviation of one-year ahead growth forecast from consensus. Sources: Haver, Eurostat, Consensus Forecast, and fund staff calculations.
United Kingdom: Summary Statistics for Selected Indicators
| Obs | Mean | Max | Min | Std. Dev | |
|---|---|---|---|---|---|
| Nominal wage (yoy change) | 66 | 0.03 | 0.05 | 0.00 | 0.01 |
| Labor productivity (yoy change) | 66 | 0.01 | 0.04 | −0.05 | 0.02 |
| Unemployment rate | 70 | 0.06 | 0.01 | 0.04 | 0.08 |
| Adj. unemployment rate (percent) | 70 | 0.09 | 0.01 | 0.08 | 0.10 |
| Adj. unemployment rate gap (ppt) | 70 | 0.00 | 0.01 | −0.01 | 0.02 |
| Hiring rate (percent) | 63 | 0.06 | 0.00 | 0.04 | 0.06 |
| Share of involuntary PT workers | 70 | 0.03 | 0.01 | 0.02 | 0.05 |
| Inflation expectations 1/ | 70 | 0.03 | 0.00 | 0.00 | 0.04 |
| EU unemployment gap | 70 | 0.33 | 0.98 | −1.80 | 2.18 |
| Growth uncertainty 2/ | 70 | 0.50 | 0.16 | 0.25 | 0.94 |
5-year ahead inflation expectation derived from government securities.
Standard deviation of one-year ahead growth forecast from consensus. Sources: Haver, Eurostat, Consensus Forecast, and fund staff calculations.
United Kingdom: Unit Root Tests
United Kingdom: Unit Root Tests
| Variable | ADF | Phillips-Perron | |||
|---|---|---|---|---|---|
| Testing Levels | t-ADF | 0.05 | Adj t-stat | 0.05 | Characteristics |
| Real product wage | −2.630 | −3.476 | −2.624 | −3.476 | Trend and intercept |
| Productivity (MA,8q) | −2.473 | −3.478 | −1.996 | −3.477 | Trend and intercept |
| Testing first differences | |||||
| Real product wage | −7.635 | −2.909 | −7.780 | −2.905 | Intercept |
| Productivity (MA,8q) | −1.825 | −1.946 | −1.989 | −1.946 | no intercept |
United Kingdom: Unit Root Tests
| Variable | ADF | Phillips-Perron | |||
|---|---|---|---|---|---|
| Testing Levels | t-ADF | 0.05 | Adj t-stat | 0.05 | Characteristics |
| Real product wage | −2.630 | −3.476 | −2.624 | −3.476 | Trend and intercept |
| Productivity (MA,8q) | −2.473 | −3.478 | −1.996 | −3.477 | Trend and intercept |
| Testing first differences | |||||
| Real product wage | −7.635 | −2.909 | −7.780 | −2.905 | Intercept |
| Productivity (MA,8q) | −1.825 | −1.946 | −1.989 | −1.946 | no intercept |
10. Error correction models are estimated using quarterly data over the period 2000Q1–2017Q2. The sample is constrained by the fact that weekly earnings data are only available since 2000. The long run wage equation is estimated by fully modified least squares and the results are presented in Table 3. A general-to-specific approach is adopted for the short run equation.
United Kingdom: LR Cointegration Relationship: Real Wage
United Kingdom: LR Cointegration Relationship: Real Wage
| Variables | p | |
|---|---|---|
| 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 | Phillips-Ouliaris | |
| tau-statistic | −3.09 | −3.08 |
| Probability | 0.10 | 0.11 |
| z-statistic | −16.38 | −15.95 |
| Probability | 0.09 | 0.10 |
| Hansen Instability | ||
| Lc statistics | 0.07 | |
| Probability | >0.2 |
United Kingdom: LR Cointegration Relationship: Real Wage
| Variables | p | |
|---|---|---|
| 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 | Phillips-Ouliaris | |
| tau-statistic | −3.09 | −3.08 |
| Probability | 0.10 | 0.11 |
| z-statistic | −16.38 | −15.95 |
| Probability | 0.09 | 0.10 |
| Hansen Instability | ||
| Lc statistics | 0.07 | |
| Probability | >0.2 |
E. Empirical Findings
11. As expected, the estimated coefficient on productivity in the long run equation is close to one, suggesting a tight relationship between predicted versus actual real wage growth (see text chart). It is interesting to note that real wages did not fall significantly during the crisis, suggesting some downward wage rigidity. However, 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.
Real Wage and Estimated Long-Run Equilibrium
(Log points)
Citation: IMF Staff Country Reports 2018, 043; 10.5089/9781484341759.002.A001
Sources: ONS and Fund staff calculations.12. The results from the second equation suggest that labor market slack is a significant determinant wage growth in the short run. All different measures of labor market slack are significant and have the expected sign. The results suggest that a one percent increase in underemployment (measured by the adjusted unemployment rate, the adjusted unemployment gap, or the hiring rate) reduces wage growth by about 0.35 percent. However, to fully account for domestic labor market slack, one also needs to consider the share of involuntary part-time workers, where one percentage point increase reduces wage growth by about 0.3 percent. Moreover, labor market slack in the EU has a statistically significant impact on wage growth.7 Increased uncertainty about future economic growth is found to depress wage growth, which could account for the weak wage growth since the referendum. As expected, the coefficient on the error correction term is negative and significant, suggesting a correction toward equilibrium over time.
13. In-sample forecast points to the importance of controlling for wage drivers beyond headline unemployment. The figures below compare the in-sample forecast performance of models 1 and 3 from Table 4.8 The forecast performance improves significantly when controlling for the share of involuntary part-time workers and other factors (model 1 consistently overpredicts wage growth). Thus, in the rest of the paper, the discussions are based on results from model 3.
United Kingdom: Private Sector Average Weekly Regular Pay Growth
United Kingdom: Private Sector Average Weekly Regular Pay Growth
| (1) | (2) | (3) | (4) | |
|---|---|---|---|---|
| ecm (t-4) | −0.25*** | −0.21*** | −0.20*** | −0.22*** |
| (0.09) | (0.07) | (0.07) | (0.07) | |
| Headline unemployment (t-4) | −0.59*** | |||
| (0.06) | ||||
| Adj. unemployment rate (t-4) | −0.37*** | |||
| (0.13) | ||||
| Adj. unemployment rate gap (t-4) | −0.32** | |||
| (0.13) | ||||
| Hiring rate (t-4) | 0.46** | |||
| (0.18) | ||||
| Involuntary part time employment (t-4) | −0.28** | −0.34*** | −0.29** | |
| (0.11) | (0.11) | (0.11) | ||
| Inflation expectation (t-4) | 0.66*** | 0.34** | 0.34** | 0.36** |
| (0.18) | (0.14) | (0.15) | (0.16) | |
| Productivity growth (t-1) | 0.28*** | 0.21*** | 0.23*** | 0.21*** |
| (0.05) | (0.04) | (0.04) | (0.04) | |
| EU unemployment gap (t) | −0.44*** | −0.43*** | −0.54*** | |
| (0.10) | (0.10) | (0.09) | ||
| Growth uncertainty (t-2) | −2.43*** | −2.41*** | −2.02*** | |
| (0.47) | (0.49) | (0.60) | ||
| Constant | 0.04*** | 0.07*** | 0.04*** | 0.01 |
| (0.01) | (0.01) | (0.01) | (0.01) | |
| Observations | 66 | 66 | 66 | 59 |
| R-squared | 0.78 | 0.89 | 0.89 | 0.88 |
United Kingdom: Private Sector Average Weekly Regular Pay Growth
| (1) | (2) | (3) | (4) | |
|---|---|---|---|---|
| ecm (t-4) | −0.25*** | −0.21*** | −0.20*** | −0.22*** |
| (0.09) | (0.07) | (0.07) | (0.07) | |
| Headline unemployment (t-4) | −0.59*** | |||
| (0.06) | ||||
| Adj. unemployment rate (t-4) | −0.37*** | |||
| (0.13) | ||||
| Adj. unemployment rate gap (t-4) | −0.32** | |||
| (0.13) | ||||
| Hiring rate (t-4) | 0.46** | |||
| (0.18) | ||||
| Involuntary part time employment (t-4) | −0.28** | −0.34*** | −0.29** | |
| (0.11) | (0.11) | (0.11) | ||
| Inflation expectation (t-4) | 0.66*** | 0.34** | 0.34** | 0.36** |
| (0.18) | (0.14) | (0.15) | (0.16) | |
| Productivity growth (t-1) | 0.28*** | 0.21*** | 0.23*** | 0.21*** |
| (0.05) | (0.04) | (0.04) | (0.04) | |
| EU unemployment gap (t) | −0.44*** | −0.43*** | −0.54*** | |
| (0.10) | (0.10) | (0.09) | ||
| Growth uncertainty (t-2) | −2.43*** | −2.41*** | −2.02*** | |
| (0.47) | (0.49) | (0.60) | ||
| Constant | 0.04*** | 0.07*** | 0.04*** | 0.01 |
| (0.01) | (0.01) | (0.01) | (0.01) | |
| Observations | 66 | 66 | 66 | 59 |
| R-squared | 0.78 | 0.89 | 0.89 | 0.88 |
In-sample Projection of Nominal Wage Growth
(yoy percent change)
Citation: IMF Staff Country Reports 2018, 043; 10.5089/9781484341759.002.A001
Sources: Fund staff calculation.In-sample Projection of Nominal Wage Growth
(yoy percent change)
Citation: IMF Staff Country Reports 2018, 043; 10.5089/9781484341759.002.A001
Sources: Fund staff calculation.14. Historical decomposition suggests labor productivity and labor market slack (in the UK and the EU) are the most important drivers of wage growth, explaining well the slowdown in nominal wage growth post-crisis. The text figure below shows the contribution of different factors to wage growth (in deviations from the sample average). Low productivity growth (LP) and weak labor market conditions (slack) contributed significantly to the slowdown in wages since the financial crisis. In addition, uncertainty about future economic growth appear to have weighed on wage growth during the crisis, as well as in recent quarters (while optimism about growth prospects supported wages during the period of steady recovery 2011–16). Low inflation in 2015–16 also played a role in depressing wages during 2016–17. More recently, slack in the labor market has diminished, providing a modest boost to wages (although the effects have been offset by greater uncertainty and weak lagged inflation).
Contribution to Nominal Wage Growth
(percentage points, deviation from mean)
Citation: IMF Staff Country Reports 2018, 043; 10.5089/9781484341759.002.A001
Sources: Fund staff calculations.F. Wage Outlook
15. Looking ahead, nominal wage growth should strengthen, reflecting improvements in labor markets. In the baseline, labor markets are expected remain relatively tight, with the unemployment rate slightly below the estimated equilibrium level and the share of involuntary part-time workers dropping to pre-crisis levels. This would help raise wages temporarily above the level implied by productivity growth. Growth uncertainty (which has surged after the Brexit vote) is assumed to dissipate once a broad agreement on the shape of the future economic relationship with the EU is reached. In addition, the baseline projection assumes that inflation expectations and the EU labor market gap remain unchanged at their 2017Q2 level, and productivity growth recovers to about 1 percent in the medium term. Under these assumptions, annual nominal wage growth is expected to pick up from 2¼ percent in 2017Q2 to between 2¾ and 3 percent in 2018.
16. However, this baseline projection is subject to significant risks. On the upside, a greater share of self-employed workers could start seeking regular jobs. We have already seen an increase in the rate at which the self-employed move to regular employment (to 3.5 percent in 2017 from 3 percent average post-crisis). If the 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. In addition, uncertainty about the rate of future growth may remain elevated for some time, even after the UK leaves the European Union. Ultimately, the main determinant of wage growth would be productivity growth – if it fails to pick up as projected, wage growth would remain weak.
Adjusted Unemployment Rate under Different Scenarios
(percent of working age population)
Citation: IMF Staff Country Reports 2018, 043; 10.5089/9781484341759.002.A001
Sources: Fund staff calculations.1/ Assume transition of self-employed workers to regular employment is 0.2/ Assume self-employed workers are 60 percent less likely to become regular employed compared with unemployed workers.Nominal Wage Growth under Different Scenarios Relative to Baseline
(Percentage points)
Citation: IMF Staff Country Reports 2018, 043; 10.5089/9781484341759.002.A001
Sources: Fund staff calculations.1/ Assume transition of self-employed workers to regular employment is 0.2/ Assume self-employed workers are 60 percent less likely to become regular employed compared with unemployed workers.G. Conclusions
17. Recent labor market developments in the UK appear to point to a disconnect between unemployment and wages. While the unemployment rate has fallen to a 40-year low, wage growth continues to growth at a subdued pace. The analysis in this paper suggests that this puzzle is explained by persistent weak productivity growth and well-anchored inflation expectations, as well as by greater effective labor market slack than suggested by the headline unemployment rate. Broader measures of underemployment—accounting for involuntary part-time unemployment, inactive and self-employed people seeking regular jobs—suggest that slack in the labor market was higher than implied by the unemployment rate in recent years. Models using these broader measures capture well the observed wage dynamics.
18. Persistent tightness of the labor market should prompt some firming of wage growth in the coming year, everything else equal. A mild increase in unit labor costs would help bring domestically generated inflation in line with the inflation target. Of course, the actual outcome for wage growth would also depend on the extent to which Brexit-related uncertainty dissipates, so firms can more easily make long-term decisions. More generally, wage growth will recover in a sustainable way only once productivity growth recovers.
References
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Prepared by Jiaqian Chen (EUR) and Weicheng Lian (RES).
This may partly reflect reforms on the pension entitlement age which has pushed up the participation of old aged workers (HMT, 2011).
People in employment on contracts where they are not guaranteed any hours in a given week.
The period is selected based on data availability.
Labor compensation is highly correlated with wages over time.
Other wage measures include labor cost index from Eurostat, wage and salaries from National Accounts, labor compensation from gross domestic product.
This finding in consistent with results in Chapter 2 of the 2017 April WEO, which notes significant cross-border spillovers of labor market conditions.
Comparison with other models is available upon request.
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Annex I. Does Sectoral Composition Explain Regional Disparities?
Regional aggregate disparities could in principle be driven by different sectoral productivity or by different industry presence (measured by regional employment shares) across regions. An extreme example of the former is when the distribution of workers across sectors is the same in all regions, but productivity levels differ across regions for each sector. An extreme example of the latter is when sectoral productivity is equal across regions (yet different across sectors) but some regions have a larger share of workers occupied in high productivity sectors.
The most productive regions at the NUTS 1 level of aggregation tend to have higher productivity across most sectors, suggesting that the regional industry structure may play a relatively small role in explaining productivity differences across regions.
Output per job by region and sector
(UK average = 100 in each sector; 2015)
Citation: IMF Staff Country Reports 2018, 043; 10.5089/9781484341759.002.A001
This intuition is tested using a decomposition exercise using data at the NUTS 3 level. Regional productivity levels are decomposed into 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.
Indeed, the correlation of the pure productivity index with productivity levels is close to 1 (0.99) and is much higher than that of the compositional index (0.33), confirming that the latter plays less relevant role in explaining regional discrepancies.
Prepared by Nicolas Arregui and Lucyna Gornicka (both EUR).
The European Union Nomenclature of Territorial UNITS (NUTS) classification is used in this analysis.
The persistence in regional disparities implies that these are unlikely the reason behind the “productivity puzzle” (i.e. the flattening in productivity growth in recent years).
“Improving the economic performance of every country and region of the UK is an essential element of [the Government’s] objective, firstly for reasons of equity, but also because unfulfilled economic potential in every region must be released to meet the overall challenge of increasing the UK’s long-term growth rate.” (HMT 2001).
“It is helpful to remember that we ultimately care about the effect of policies on people more than on places.” (Overman 2015).
Importantly, the more recent endogenous economic growth theories in which long run growth depends on the creation of technological knowledge do not predict convergence across regions with different starting positions.
See, for instance, Keller (2000), and Girma and Wakelin (2000).
Other potential factors include the differential regional impact of successive structural shocks, such as via trade or technology.
See, for instance, Mankiw, Romer and Weil (1992), Benhabib and Spiegel (1994), Aghion and Howitt (1998), Temple (2000), and Bassanini and Scarpetta (2001).
See OECD (2017). For instance, in the OECD Survey of Adult Skills, England and Northern Ireland have some of the highest proportions of adults scoring at or below the two lowest scores (out of six) in numeracy.
See, for instance, Rice et al (2006) and Webber et al (2009).
The percentage of four-year-olds in early childhood and primary education in the UK is one of the highest among OECD countries (OECD EAG 2016)
McNally (2015) suggests that, due to the regional differences in how GCSEs are taught, it is more informative to use international tests when making regional comparisons than the often-used measure of “percentage of pupils attaining five or more GCSEs at grades A*-C (including English and math).”
The picture is much more homogeneous for primary schools.
Taking a different view, McCann (2016) argues that small differences in the quality of interregional migrants across regions prove that sorting cannot explain the interregional inequalities observed in the UK. However, he only studies the quality of cross-regional migrants with a graduate degree, while not looking at other groups of migrants.
Expanding the pool of skilled labor to which a region has access may require a wide range of policies including housing and transport, as discussed in the following sections.
The report recognizes that government has launched initiatives such as National Teaching Service to help improve certain underperforming schools. However, it also highlights the fact that the DfE’s teacher supply model is not being used to estimate the need for teachers at a local or regional level, leaving the school system to sort out the gaps.
Also, over time, regional mobility has been declining at the same time differences in productivity between regions have increased (Resolution Foundation 2017).
Additionally, migration flows data shows that skilled workers have a higher propensity to move than low-skilled workers, who are very unlikely to move between regions. Areas with higher unemployment may be within travelling distance of labor markets with high levels of vacancies (HMT 2000). Market failures in workers’ skills acquisition may therefore have consequences for labor mobility, highlighting that education and training policies may have important follow-on effects.
High net internal outflows in London may also be indicative of international migrants arriving in London before moving elsewhere.
In particular, local regulatory constraints are found to increase the elasticity of house prices to changes in local earnings.
The paper disentangles the impact of housing regulations from the effect of local scarcity of developable land, which is found to be important in highly-urbanized areas only.
Ganong and Shoag (2015) show that, in the US, increasingly strict housing restrictions and rising house prices in highly productive areas have worked as a barrier to interregional migration of low-skilled workers and have slowed regional income convergence. Hsieh and Moretti (2017) find that the resulting labor misallocation had a considerable negative effect on US GDP.
Recent budgets commit additional (and increasing) spending to accelerate new housing supply over the coming years. This includes the creation of a Housing Infrastructure Fund to finance infrastructure targeted at unlocking new private house building in the areas where housing need is greatest. Funds are allocated to local government on a competitive basis. A Housing White Paper published earlier this year explores additional reforms to increase housing supply.
Numbers should be taken only as indicative as the data are experimental (i.e. not national statistics).
Firm level data for listed firms does not suggest that regions with low productivity regions have systematically lower regional median capital expenditures (to total assets). However, listed firms are not likely representative of the broader firm population.
In the context of Brexit, less productive regions in the UK are more exposed to the loss of funding from the European structural funds and lending from the European Investment Bank (see OECD 2017).
Several measures have been implemented in recent years to increase effective competition in banking (see Annex 3 in Roland and Valero (2015) for a summary).
However, there is evidence that other sources of finance, such as venture capital, equity investment, and crowdfunding, vary more significantly across regions and are concentrated in London and the South East (HMT 2017).
Foreign-owned companies account for a larger share of top-performing firms in the UK than suggested by their share in total firm population.
The analysis measures agglomeration level at the NUTS 3 as the sum of local population plus nearby populations inversely weighted by distance up to 45 kilometers. Pairwise distances in route kilometers at the NUTS 3 level are obtained from Eurostat. Alternative measures of agglomeration may use travel times instead of distance for the weighting, or population density (see Rice et al. 2006).
MIER (2009) establishes a similar result using firm-level data.
Planning restriction may potentially play a beneficial role by correcting market failures. The issue is when restrictions disregard market failures or the balance between costs and benefits of interventions.
The Transforming Cities Fund introduced as part of the Autumn 2017 commits £1.7 billion to supporting intra-city transport, by improving connectivity and reducing congestion.
Over half of total public spending in devolved administrations (Scotland, Wales, and Northern Ireland) is allocated following the Barnett Formula. Although the annual increment in funds is made on the basis of recent population figures, the baseline—accumulated over the last thirty years—does not reflect today’s population in the devolved administrations. The Barnett Formula is mechanical, and takes no account of the relative needs of the devolved administrations.
The European Quality of Government Index (EQI) is a survey-based governance indicator available at the regional level within the EU (Charron et al. 2014). The data focus on both perceptions and experiences with public sector corruption, along with the extent to which citizens believe various public sector services are impartially allocated and of good quality. Over 85 thousand respondents are surveyed on the extent to which they perceive and experience corruption, quality, and impartiality in such services as education, healthcare services, and law enforcement, among other public sector functions.
OECD (2016) finds that the poorest, underperforming regions benefit the most from fiscal decentralization.
The devolved administrations of Wales, Scotland, and Northern Ireland have a relatively high degree of autonomy in most areas of government, but together account for a small percentage of total population. In contrast, England is very centralized (see OECD 2017).
Devolution could give rise to fiscal risks, at least in the transition, and local government capacity might need to be built to discharge any new fiscal responsibilities granted if these risks are to be contained (IMF UK Fiscal Transparency Evaluation 2016).
Selective industrial policy is currently generally limited by the EU state aid framework, but this may potentially change when the UK leaves the EU.
