Chapter 1. Recent trends in productivity, employment and wages

The statistical data for Israel are supplied by and under the responsibility of the relevant Israeli authorities. The use of such data by the OECD is without prejudice to the status of the Golan Heights, East Jerusalem and Israeli settlements in the West Bank under the terms of international law.

Labour productivity growth remains weak

Labour productivity growth in the OECD area remains weak and well below pre-crisis rates. Since 2010, annual growth in labour productivity has slowed to 0.9%, about half the rate recorded in the 2000-2005 pre-crisis period (Figure 1.1). Post-crisis labour productivity growth has also slowed in countries with relatively low labour productivity levels (Figure 2.7), undermining the pace of convergence towards higher labour productivity levels. A similar picture emerges for emerging economies, in particular in Brazil, the Russian Federation and South Africa (Figure 1.2), who have fallen further behind the productivity frontier as productivity has contracted in recent years.

Figure 1.1. Labour productivity growth in the OECD
GDP per hour worked, percentage rate at annual rate
Figure 1.1. Labour productivity growth in the OECD

Note: Data for all other countries are presented in the Annex 1.A.

Source: OECD Productivity Statistics (database), http://dx.doi.org/10.1787/pdtvy-data-en, March 2019.

 StatLink http://dx.doi.org/10.1787/888933967908

Figure 1.2. Labour productivity growth in BRIICS
GDP per person employed, percentage change at annual rate
Figure 1.2. Labour productivity growth in BRIICS

Source: OECD Productivity Statistics (database), http://dx.doi.org/10.1787/pdtvy-data-en, March 2019.

 StatLink http://dx.doi.org/10.1787/888933967927

The post-crisis slowdown in productivity growth affects all major sectors, but particularly manufacturing where productivity growth rates remain well below last-decade’s rates in most countries (Figure 1.3). Indeed, in Australia, Israel, the United Kingdom and the United States, productivity gains in manufacturing have been negligible since 2010. In Ireland, the relocation of firms with significant intellectual property assets and aircraft leasing companies, led to a significant increase in labour productivity in 2015.

Figure 1.3. . Labour productivity in manufacturing
GDP per hour worked, percentage change at annual rate
Figure 1.3. . Labour productivity in manufacturing

Note: For Japan, Korea and the United States, labour productivity is measured as gross value added per person employed, as national accounts estimates of hours worked by main economic activity are not currently available.

Source: OECD Productivity Statistics (database), http://dx.doi.org/10.1787/pdtvy-data-en, March 2019.

 StatLink http://dx.doi.org/10.1787/888933967946

In the services sector, the picture has been more varied (Figure 1.4). In Central and Eastern European OECD economies, for example, the catch-up process has helped sustain relatively robust growth, picking up strongly in Poland and Slovenia in the most recent years. However, productivity growth remains weak in most other economies, indeed sclerotic in some, such as Italy and Greece. Even in countries where it has improved in recent years, such as the United States, it remains weak.

Figure 1.4. . Labour productivity in business services excluding real estate
GDP per hour worked, percentage change at annual rate
Figure 1.4. . Labour productivity in business services excluding real estate

Notes: Business services exclude real estate activities. For Japan, Korea and the United States, labour productivity is measured as gross value added per person employed, as national accounts estimates of hours worked by main economic activity are not currently available.

Source: OECD Productivity Statistics (database), http://dx.doi.org/10.1787/pdtvy-data-en, March 2019.

 StatLink http://dx.doi.org/10.1787/888933967965

Multifactor productivity growth remains below pre-crisis rates

Like labour productivity growth, multifactor productivity growth also remains weak across most OECD economies, and although some countries, including the United Kingdom and the United States, have seen a pick-up in recent years, growth remains well below pre-crisis rates (Figure 1.5). Multifactor productivity growth has also improved in Italy, Portugal and Spain, outpacing pre-crisis rates, but it remains weak.

Figure 1.5. Growth in multifactor productivity
Percentage change at annual rate
Figure 1.5. Growth in multifactor productivity

Source: OECD Productivity Statistics (database), http://dx.doi.org/10.1787/pdtvy-data-en, March 2019.

 StatLink http://dx.doi.org/10.1787/888933967984

Employment rates have climbed to historic highs

Weak productivity growth restricts the potential for economic growth and improvements in living standards. Unlike in the pre-crisis period, where productivity was the main driver of economic growth, since 2010, contributions from labour utilisation, i.e. hours worked per head of population, have been the main driver of growth in GDP per capita in many countries (Figure 1.6), with the contributions increasing significantly in nearly all countries. In Italy, New Zealand and Portugal, and to a lesser extent the Netherlands and Spain, GDP per capita growth was almost entirely driven by labour utilisation.

Figure 1.6. Growth in GDP per capita, labour productivity and labour utilisation
Total economy, percentage rate at annual rate
Figure 1.6. Growth in GDP per capita, labour productivity and labour utilisation

Source: OECD Productivity Statistics (database), http://dx.doi.org/10.1787/pdtvy-data-en, March 2019.

 StatLink http://dx.doi.org/10.1787/888933968003

Post-crisis increases in labour utilisation have been largely driven by increasing employment almost across the board (Figure 1.7), with employment rates climbing to historic highs in most countries (Figure 1.8) (OECD, 2018b). However, demographic changes, in particular population ageing, limit the potential for labour utilisation to act as a sustainable source of economic growth (OECD, 2017).

Figure 1.7. Sources of growth in labour utilisation after the crisis
Total economy, percentage rate at annual rate
Figure 1.7. Sources of growth in labour utilisation after the crisis

Sources: OECD Productivity Statistics (database), http://dx.doi.org/10.1787/pdtvy-data-en, February 2019.

 StatLink http://dx.doi.org/10.1787/888933968022

Figure 1.8. Employment rates
Employment to 15-64 aged population ratio, all persons, percentage
Figure 1.8. Employment rates

Sources: OECD Employment and Labour Market Statistics (database), https://doi.org/10.1787/lfs-data-en, and OECD Main Economic Indicators (database), https://doi.org/10.1787/data-00052-en, April 2019.

 StatLink http://dx.doi.org/10.1787/888933968041

Changes in average hours worked per person have contributed less to the pick-up in labour utilisation in recent years but of note is the fact that the long-term decline in average hours worked has begun to stabilise in many countries and reverse in some, such as the United Kingdom and the United States (Figure 1.9).

Figure 1.9. Average hours worked per worker
Indices, 2000=100
Figure 1.9. Average hours worked per worker

Source: OECD Productivity Statistics (database), http://dx.doi.org/10.1787/pdtvy-data-en, March 2019.

 StatLink http://dx.doi.org/10.1787/888933968060

Box 1.1. Challenges in measuring working time and international labour productivity gaps

Historically, comparisons of productivity across countries have shown substantial gaps, even between similar-sized economies at a similar stage of development. However, a new OECD study (Ward et al., 2018) reveals smaller gaps when differences in how countries measure labour input, and in particular, average hours worked are accounted for.

In the national accounts framework, for productivity measures, labour input is most appropriately defined by the total number of hours actually worked by all persons engaged in production, i.e. employees and self-employed (OECD, 2001). Hours worked include all hours effectively used in production, whether paid or not, but they exclude hours not used in production (e.g. annual and sickness leave), even if some compensation is received for them. In practice, countries adopt one of two methods to estimate average hours worked for productivity estimates:

  • the direct method, which takes actual hours worked self-reported by respondents in surveys, generally labour force surveys (LFS); and

  • the component method, which starts from contractual, paid or usual hours per week from establishment surveys, administrative sources or, indeed, the LFS, with adjustments for absences (e.g. public holidays, annual, sickness and maternity leave) and paid and unpaid overtime and indeed other adjustments that are necessary to align with concepts of output in the national accounts, for example concerning cross-border workers.

While the “direct” approach appeals due to its simplicity, it depends heavily on respondent recall, cannot account for respondents’ self-reporting bias, and, moreover, assumes a perfect alignment of workers and measures of output. The component approach is more complex, but it systematically attempts to address these issues. To give some sense of the potential impact of these different approaches on the international comparability of hours worked, the OECD has used the LFS and complementary sources to estimate national hours worked using both a direct approach and a simplified component method.

The results provide strong evidence that response bias and a lack of exhaustive adjustments to align with the underlying conceptual production boundary, lead to systematic upward biases in estimates based on the direct method, which are, in turn, always higher than those compiled using the simplified component approach. Figure 1.10 compares official estimates of hours worked in countries’ national accounts with the OECD simplified component method estimates for those countries that currently use a direct method and make minimal or no additional adjustments.

The corollary of an over-estimation of hours worked is an under-estimation of labour productivity levels. Figure 1.11 below presents the impact of the adjustments shown in Figure 1.10 above on labour productivity levels, referenced to the United States. Overall, the results point to a reduction in relative productivity gaps of around 10 percentage points compared with current official estimates in many countries.

Figure 1.10. Average annual hours worked per worker, selected OECD countries, 2016
Figure 1.10. Average annual hours worked per worker, selected OECD countries, 2016

Source: Ward, A., M. Zinni and P. Marianna (2018), "International productivity gaps: Are labour input measures comparable?", OECD Statistics Working Papers, No. 2018/12, OECD Publishing, Paris, https://doi.org/10.1787/5b43c728-en.

 StatLink http://dx.doi.org/10.1787/888933968079

Figure 1.11. International productivity gaps, 2016
GDP per hour worked, current prices and current PPPs, total economy, United States = 100
Figure 1.11. International productivity gaps, 2016

Source: Ward, A., M. Zinni and P. Marianna (2018), "International productivity gaps: Are labour input measures comparable?", OECD Statistics Working Papers, No. 2018/12, OECD Publishing, Paris, https://doi.org/10.1787/5b43c728-en.

 StatLink http://dx.doi.org/10.1787/888933968098

While the results reveal biases in international comparisons of productivity levels, it does not follow that the same holds for international comparisons of productivity growth rates; growth rate estimates would only be distorted if the impact of the adjustments had significantly and disproportionally changed over time. Indeed, implementing the simple component approach reveals no systematic bias in growth rates.

Minor differences do occur however, and, so, to avoid introducing differences with national estimates of productivity growth (and those that can be derived from the countries’ national accounts data), the OECD has taken estimates of average hours actually worked (levels) using the simplified component method in 2016 as a benchmark, and projected series forwards and backwards using official (national) growth rates.

At this stage, based on the data available to the OECD, the implementation of the simplified component method has been applied to the following countries: Austria, Estonia, Finland, Greece, Latvia, Lithuania, Poland, Portugal, Sweden and the United Kingdom. These changes have been incorporated into the OECD Productivity Statistics (database) and the OECD Average annual hours actually worked per worker dataset as of the end of January 2019, along with corresponding metadata, and are now incorporated in the labour productivity levels available in this publication.

Most new jobs have been created in low productivity and low paid activities

Compounding the impact of weak productivity growth on material well-being, is the fact that in most OECD economies most jobs have been created in lower labour productivity activities, weighing down, in turn, on overall labour productivity levels (Table 1.1) (OECD, 2018a). Some countries, such as Belgium, Finland, Italy and Spain, have even seen net job destruction in industries with above average labour productivity levels.

In many OECD economies the three sectors generating the largest employment gains between 2010 and 2017 had below average labour productivity, with accommodation and food, and health and residential care activities featuring highly in many economies (Table 1.2). In Belgium, Canada, Italy, Portugal and Sweden, the top three sectors, all with below average labour productivity in 2010, accounted for 40% of total employment creation in the economy between 2010 and 2017, while in the Netherlands, this share was close to 55%. Only a few countries, such as Czech Republic and Poland, had a single sector with above average labour productivity among the three activities with the largest net employment creation. On the other hand, most economies had at least one sector with above average labour productivity among the top three sectors that lost most jobs over the same period; two sectors in the case of Germany and the United Kingdom, and all three in the case of Sweden and the United States (Table 1.3).

Since labour compensation levels typically correlate highly with labour productivity levels, more jobs in low labour productivity activities has also meant more jobs with below average wages in most economies (Table 1.1), weighing down on average salaries in the economy as a whole. For example, the top three sectors with the largest employment gains between 2010 and 2017 in France, Germany and the United Kingdom accounted for one third of total employment creation and paid below average wages (Table 1.2). Similarly, in most countries, in at least one of the top three sectors showing the largest job destruction, wages were above total economy’s average; all three in Sweden and the United States (Table 1.3). However, in some countries, one or more of the top three sectors with the largest employment gains had above average productivity and above average wages, as was the case for example of computer programming, related consultancy activities and other information services in Poland and Spain.

Table 1.1. Change in employment over the period 2010-2017 (or latest available year)
Thousands of persons; percentage of total employment in 2010 (in brackets)

 

Industries that in 2010 had labour productivity

Industries that in 2010 had labour compensation

Country

above average

below average

above average

below average

AUT

59 [1.4%]

258 [6.3%]

152 [3.7%]

164 [4.0%]

BEL

-20 [-0.5%]

270 [6.0%]

69 [1.5%]

181 [4.1%]

CAN

163 [0.9%]

510 [2.9%]

451 [2.6%]

221 [1.3%]

CZE

161 [3.2%]

128 [2.5%]

252 [5.0%]

37 [0.7%]

DEU

701 [1.7%]

1 921 [4.7%]

939 [2.3%]

1 683 [4.1%]

DNK

43 [1.5%]

90 [3.2%]

67 [2.4%]

66 [2.4%]

ESP

-483 [-2.5%]

354 [1.8%]

-413 [-2.1%]

285 [1.4%]

EST

26 [4.7%]

42 [7.7%]

38 [7.0%]

30 [5.4%]

FIN

-5 [-0.2%]

66 [2.7%]

18 [0.7%]

43 [1.7%]

FRA

212 [0.8%]

529 [2.0%]

77 [0.3%]

664 [2.5%]

GBR

568 [1.9%]

2 265 [7.7%]

1 012 [3.5%]

1 821 [6.2%]

GRC

-98 [-2.1%]

-461 [-9.8%]

-180 [-3.8%]

-379 [-8.1%]

HUN

396 [10.0%]

171 [4.3%]

395 [9.9%]

173 [4.4%]

IRL

67 [3.6%]

192 [10.2%]

111 [5.9%]

148 [7.9%]

ISL

7 [4.3%]

25 [15.4%]

15 [9.4%]

17 [10.4%]

ITA

-110 [-0.4%]

170 [0.7%]

-55 [-0.2%]

115 [0.5%]

LTU

67 [5.4%]

49 [3.9%]

74 [5.9%]

41 [3.3%]

LVA

31 [3.7%]

12 [1.4%]

33 [3.9%]

9 [1.1%]

NLD

53 [0.6%]

267 [3.0%]

16 [0.2%]

304 [3.5%]

NOR

16 [0.6%]

158 [6.1%]

111 [4.3%]

63 [2.4%]

POL

513 [3.3%]

195 [1.3%]

536 [3.5%]

173 [1.1%]

PRT

-54 [-1.1%]

-168 [-3.4%]

-31 [-0.6%]

-191 [-3.9%]

SVK

54 [2.5%]

148 [6.8%]

79 [3.6%]

123 [5.7%]

SVN

12 [1.3%]

13 [1.4%]

30 [3.1%]

-4 [-0.5%]

SWE

34 [0.8%]

345 [7.7%]

89 [2.0%]

290 [6.4%]

USA

3 893 [2.8%]

8 312 [5.9%]

2 636 [1.9%]

9 569 [6.8%]

Notes: Average labour productivity and average labour compensation per employee are measured as gross value added per person employed and compensation per employee in the total economy of the country. Data for Canada refer to the period 2010-2013 and shows thousands of jobs created over that period. Data for France, Germany, Italy, Latvia, Lithuania, Norway, Poland, Portugal, Sweden and the United States refer to 2010-2016. Information provided for the United States follows a broader industry breakdown and comparisons with other countries needs some caution. Figures in the table refer to net jobs created/lost in a given sector over the period.

Sources: OECD Productivity Statistics (database), http://dx.doi.org/10.1787/pdtvy-data-en, and OECD National Accounts Statistics (database), http://dx.doi.org/10.1787/na-data-en, March 2019.

 StatLink http://dx.doi.org/10.1787/888933968117

Table 1.2. Net employment creation between 2010 and 2017 (or latest available year)
Three sectors with largest net employment creation, largest 15 OECD economies (where data exist)

Country

Sectors with largest net job creation between 2010 and 2017

Jobs created (net), in number of persons

Net job creation in the sector, % of total net job creation between 2010 and 2017

Labour productivity of the sector in 2010, % of total economy labour productivity

Compensation per employee in the sector in 2010, % of compensation per employee in the economy

AUT

I_55_56 (Accommodation and food service activities)

38 600

10%

75%

66%

 

P85 (Education)

36 200

9%

84%

117%

 

Q_87_88 (Residential care activities; social work activities without accommodation)

33 400

9%

42%

69%

BEL

M_69_70 (Legal and account activities; activities of head offices; management consultancy activities)

55 300

16%

102%

178%

 

N_80_82 (Security and investigation; services to buildings and landscape; office administrative and support)

51 200

15%

50%

79%

 

Q_87_88 (Residential care activities; social work activities without accommodation)

48 400

14%

48%

86%

CAN

G47 (Retail trade, except of motor vehicles and motorcycles)

141 450

18%

43%

52%

 

F_41_42_43 (Construction)

132 350

17%

97%

114%

 

I_55_56 (Accommodation and food service activities)

56 200

7%

32%

42%

CZE

C29 (Manufacture of motor vehicles, trailers and semi-trailers)

53 900

12%

147%

115%

 

P85 (Education)

37 400

8%

76%

105%

 

C25 (Manufacture of fabricated metal products, except machinery and equipment)

33 900

8%

72%

89%

DEU

Q86 (Human health activities)

434 000

14%

77%

94%

 

Q_87_88 (Residential care activities; social work activities without accommodation)

426 000

14%

37%

59%

 

N_80_82 (Security and investigation; services to buildings and landscape; office administrative and support)

257 000

8%

39%

50%

ESP

I_55_56 (Accommodation and food service activities)

185 600

19%

93%

87%

 

J_62_63 (Computer programming, consultancy and related activities; information service activities)

93 200

10%

114%

137%

 

S96 (Other personal service activities)

74 700

8%

44%

53%

FRA

Q86 (Human health activities)

141 000

11%

86%

95%

 

N78 (Employment activities)

137 000

11%

52%

79%

 

Q_87_88 (Residential care activities; social work activities without accommodation)

135 000

11%

46%

66%

GBR

I_55_56 (Accommodation and food service activities)

379 800

12%

40%

47%

 

N_80_82 (Security and investigation; services to buildings and landscape; office administrative and support)

282 400

9%

43%

49%

 

M_69_70 (Legal and account activities; activities of head offices; management consultancy activities)

265 800

8%

94%

93%

HUN

O84 (Public administration and defence; compulsory social activity)

68 300

9%

102%

119%

 

M_69_70 (Legal and account activities; activities of head offices; management consultancy activities)

64 300

9%

152%

205%

 

G46 (Wholesale trade, except of motor vehicles and motorcycles)

62 000

9%

162%

176%

ITA

I_55_56 (Accommodation and food service activities)

218 400

20%

68%

76%

 

T_97_98 (Activities of households as employers; production activities of private households for own use)

102 700

10%

21%

35%

 

N78 (Employment activities)

97 900

9%

46%

75%

NLD

N78 (Employment activities)

208 000

35%

41%

61%

 

I_55_56 (Accommodation and food service activities)

78 000

13%

39%

44%

 

G47 (Retail trade, except of motor vehicles and motorcycles)

44 000

7%

43%

45%

POL

C29 (Manufacture of motor vehicles, trailers and semi-trailers)

90 000

7%

96%

86%

 

O84 (Public administration and defence; compulsory social activity)

82 000

7%

93%

146%

 

J_62_63 (Computer programming, consultancy and related activities; information service activities)

78 000

6%

156%

158%

PRT

Q_87_88 (Residential care activities; social work activities without accommodation)

26 300

14%

51%

70%

 

I_55_56 (Accommodation and food service activities)

24 550

13%

85%

79%

 

N_80_82 (Security and investigation; services to buildings and landscape; office administrative and support)

24 600

13%

50%

65%

SWE

Q_87_88 (Residential care activities; social work activities without accommodation)

85 000

19%

55%

82%

 

P85 (Education)

55 000

12%

54%

75%

 

I_55_56 (Accommodation and food service activities)

49 000

11%

44%

63%

USA

Q_86_87_88 (Human health and social work activities)

1 682 000

13%

55%

82%

 

I_55_56 (Accommodation and food service activities)

1 388 000

11%

36%

44%

 

M_69_to_75 (Professional, scientific and related activities)

1 286 000

10%

146%

196%

Notes: Average labour productivity and average labour compensation per employee are measured as gross value added per person employed and compensation per employee in the total economy of the country. Data for Canada refer to 2010-2013 and show thousands of jobs created over that period. Data for France, Germany, Italy, Poland, Portugal, Sweden and the United States refer to 2010-2016. Information provided for the United States follows a broader industry breakdown and comparisons with other countries needs some caution.

Sources: OECD Productivity Statistics (database), http://dx.doi.org/10.1787/pdtvy-data-en, and OECD National Accounts Statistics (database), http://dx.doi.org/10.1787/na-data-en, March 2019.

 StatLink http://dx.doi.org/10.1787/888933968136

Table 1.3. Net employment destruction between 2010 and 2017 (or latest available year)
Three sectors with largest net employment destruction, largest 15 OECD economies (where data exist)

Country

Sectors with largest net job destruction between 2010 and 2017

Jobs destroyed (net), in number of persons

Net job destruction in the sector, % of total net job destruction between 2010 and 2017

Labour productivity of the sector in 2010, % of total economy labour productivity

Compensation per employee in the sector in 2010, % of compensation per employee in the economy

AUT

A01 (Crop and animal production, hunting and related service activities)

-28 850

43%

24%

46%

 

K64 (Financial service activities, except insurance and pension funding)

-9 500

14%

168%

169%

 

C_31_32 (Manufacture of furniture, other manufacturing)

-4 600

7%

78%

84%

BEL

K64 (Financial service activities, except insurance and pension funding)

-8 800

9%

297%

212%

 

C_13_15 (Manufacture of textiles, wearing apparel; leather and related products)

-7 600

8%

81%

99%

 

C29 (Manufacture of motor vehicles, trailers and semi-trailers)

-6 800

7%

113%

142%

CAN

N_80_82 (Security and investigation; services to buildings and landscape; office administrative and support)

-21 750

19%

51%

73%

 

O84 (Public administration and defence; compulsory social activity)

-12 350

11%

115%

141%

 

C_13_15 (Manufacture of textiles, wearing apparel; leather and related products)

-7 560

7%

54%

75%

CZE

F_41_42_43 (Construction)

-69 150

46%

74%

86%

 

G47 (Retail trade, except of motor vehicles and motorcycles)

-27 300

18%

55%

69%

 

O84 (Public administration and defence; compulsory social activity)

-9 900

7%

109%

127%

DEU

O84 (Public administration and defence; compulsory social activity)

-173 000

36%

95%

124%

 

J58 (Publishing activities)

-47 000

10%

99%

95%

 

A01 (Crop and animal production, hunting and related service activities)

-41 000

8%

43%

54%

ESP

F_41_42_43 (Construction)

-512 300

46%

107%

104%

 

T_97_98 (Activities of households as employers; production activities of private households for own use)

-76 400

7%

28%

44%

 

K64 (Financial service activities, except insurance and pension funding)

-59 400

5%

235%

214%

FRA

F_41_42_43 (Construction)

-101 000

19%

88%

102%

 

G45 (Wholesale and retail trade and repair of motor vehicles and motorcycles)

-44 000

8%

77%

91%

 

T_97_98 (Activities of households as employers; production activities of private households for own use)

-42 000

8%

29%

58%

GBR

O84 (Public administration and defence; compulsory social activity)

-259 100

58%

99%

119%

 

K64 (Financial service activities, except insurance and pension funding)

-61 200

14%

297%

231%

 

C18 (Printing and reproduction of recorded media)

-31 200

7%

75%

92%

HUN

A01 (Crop and animal production, hunting and related service activities)

-29 250

19%

49%

63%

 

H49 (Land transport and transport via pipelines)

-20 400

13%

64%

69%

 

C13_15 (Manufacture of textiles, wearing apparel; leather and related products)

-17 460

11%

29%

41%

ITA

F_41_42_43 (Construction)

-354 400

35%

73%

91%

 

O84 (Public administration and defence; compulsory social activity)

-131 100

13%

130%

148%

 

C23 (Manufacture of other non-metallic mineral products)

-57 500

6%

87%

103%

NLD

F_41_42_43 (Construction)

-54 000

20%

90%

120%

 

Q_87_88 (Residential care activities; social work activities without accommodation)

-38 000

14%

40%

68%

 

O84 (Public administration and defence; compulsory social activity)

-36 000

13%

128%

137%

POL

A01 (Crop and animal production, hunting and related service activities)

-319 000

65%

20%

86%

 

F_41_42_43 (Construction)

-50 000

10%

107%

89%

 

C_13_15 (Manufacture of textiles, wearing apparel; leather and related products)

-32 000

7%

40%

48%

PRT

F_41_42_43 (Construction)

-159 900

39%

65%

75%

 

A01 (Crop and animal production, hunting and related service activities)

-103 500

25%

15%

44%

 

O84 (Public administration and defence; compulsory social activity)

-29 900

7%

129%

159%

SWE

C26 (Manufacture of computer, electronic and optical products)

-22 000

29%

276%

144%

 

C28 (Manufacture of machinery and equipment n.e.c.)

-8 000

11%

125%

108%

 

C17 (Manufacture of paper and paper products)

-5 000

7%

157%

128%

USA

O84 (Public administration and defence; compulsory social activity)

-296 000

80%

148%

184%

 

C26 (Manufacture of computer, electronic and optical products)

-66 000

18%

384%

328%

 

C19 (Manufacture of coke and refined petroleum products)

-10 000

3%

670%

144%

Notes: Average labour productivity and average labour compensation per employee are measured as gross value added per person employed and compensation per employee in the total economy of the country. Data for Canada refer to the period 2010-2013 and shows thousands of jobs created over that period. Data for Belgium, France, Germany, Italy, Poland, Portugal, Sweden, the United Kingdom and the United States refer to 2010-2016. Information provided for the United States follows a broader industry breakdown and comparisons with other countries needs some caution.

Sources: OECD Productivity Statistics (database), http://dx.doi.org/10.1787/pdtvy-data-en, and OECD National Accounts Statistics (database), http://dx.doi.org/10.1787/na-data-en, March 2019.

 StatLink http://dx.doi.org/10.1787/888933968155

Wage growth has recovered in many countries but remains below pre-crisis rates in most countries

The sharp increase in unemployment rates at the height of the crisis was followed by a significant slowdown in wage growth in many countries (OECD, 2018b). While unemployment rates are now below, or close to, pre-crisis levels in many countries – with record lows in some - wage growth remains sluggish. Growth in real wages, adjusted for inflation (using the consumer price index), has improved almost across the board in recent years compared with the early recovery period, but remains below pre-crisis rates in two thirds of OECD countries (Figure 1.12). In many countries, e.g. Ireland, Portugal, and the United Kingdom, recent growth in real wages has come on the back of declines in purchasing power in the years following the crisis. However, in many other countries, real wages have barely increased (e.g. Finland, the Netherlands) or even declined (e.g. Canada, Belgium) in recent years.

Figure 1.12. Growth in real wages before and after the crisis
Compensation per hour worked (employees), total economy, CPI all items-deflated, percentage change at annual rate
Figure 1.12. Growth in real wages before and after the crisis

Note: Data for Korea refer to real average compensation per employee.

Sources: OECD Productivity Statistics (database), http://dx.doi.org/10.1787/pdtvy-data-en, OECD National Accounts Statistics (database), http://dx.doi.org/10.1787/na-data-en, and OECD Main Economic Indicators (database), https://doi.org/10.1787/data-00052-en, March 2019.

 StatLink http://dx.doi.org/10.1787/888933968174

Sluggish wage growth is in part a function of slowing productivity growth (OECD, 2018b), but in many countries there are signs that the post-crisis decoupling of wage and productivity growth is beginning to unwind (Figure 1.13) (OECD, 2017; OECD, 2018a), in particular in those economies where employment rates are high.

Figure 1.13. Growth in labour productivity and average labour compensation per hour, total economy
Percentage change at annual rate
Figure 1.13. Growth in labour productivity and average labour compensation per hour, total economy

Notes: The 2014-2018 period corresponds to 2014-2018 or latest available year. For Korea, labour productivity refers to gross value added volume per person employed and labour compensation refers to average real compensation per employee.

Sources: OECD Productivity Statistics (database), http://dx.doi.org/10.1787/pdtvy-data-en, OECD National Accounts Statistics (database), http://dx.doi.org/10.1787/na-data-en, and OECD Main Economic Indicators (database), https://doi.org/10.1787/data-00052-en, March 2019.

 StatLink http://dx.doi.org/10.1787/888933968193

Recent increases in wages may in turn help to boost productivity growth. In some economies, such as Canada, the United Kingdom and the United States, slack in the labour market appears to have allowed firms to defer investment decisions (Figure 1.14) and instead meet increased demand through higher (including temporary) labour utilisation, especially with labour costs lagging investment costs (Figure 1.15). With labour costs beginning to rise in many countries, however, firms may begin to reconsider investment decisions, but political uncertainties, trade tensions and the erosion of business and consumer confidence (OECD, 2019a), may continue to weigh down on the recovery in investment (Figure 1.14) and productivity growth.

Figure 1.14. Investment rates
Total economy, gross fixed capital formation by asset type as a percentage of GDP
Figure 1.14. Investment rates

Sources: OECD National Accounts Statistics (database), http://dx.doi.org/10.1787/na-data-en, IBGE and ROSSTAT, March 2019.

 StatLink http://dx.doi.org/10.1787/888933968212

Figure 1.15. Growth differential between labour costs and investment prices
Selected OECD countries, total economy, percentage change at annual rate
Figure 1.15. Growth differential between labour costs and investment prices

Sources: OECD Productivity Statistics (database), http://dx.doi.org/10.1787/pdtvy-data-en, and OECD National Accounts Statistics (database), http://dx.doi.org/10.1787/na-data-en, March 2019.

 StatLink http://dx.doi.org/10.1787/888933968231

Conclusions

Ten years after the global financial crisis productivity growth remains weak in most economies. Economic growth over this period has been sustained by job creation, but with employment rates climbing to historic highs in many economies the contribution from higher employment will inevitably begin to wane. Higher employment rates appear, at least in part, to be a post-crisis effect. The slack in labour markets and downward pressure on wages may have allowed firms to defer investment decisions and instead increase employment, undermining, in turn, the potential for investment driven productivity growth. The fact that most of these new jobs have been in activities with relatively low productivity and low wages has compounded the impact of lower investment. With labour costs beginning to rise in many countries, firms may begin to reconsider investment decisions, but political uncertainties, trade tensions and the erosion of business and consumer confidence may continue to weigh down on investment. As higher employment cannot be an indefinite source of growth, policies to kick-start the investment engine, to capitalise on mega-trends such as efficiencies and economies of scale provided by the digital transformation, or to stimulate growth in higher productivity activities, are essential (OECD, 2019a; OECD, 2019c).

References

OECD (2019a), Going Digital: Shaping Policies, Improving Lives, OECD Publishing, Paris, https://doi.org/10.1787/9789264312012-en.

OECD (2019b), OECD Economic Outlook, Interim Report March 2019, OECD Publishing, Paris, https://doi.org/10.1787/69e962c6-en.

OECD (2019c), OECD SME and Entrepreneurship Outlook, OECD Publishing, Paris, forthcoming.

OECD (2018a), OECD Compendium of Productivity Indicators 2018, OECD Publishing, Paris, https://doi.org/10.1787/pdtvy-2018-en.

OECD (2018b), OECD Employment Outlook 2018, OECD Publishing, Paris, https://doi.org/10.1787/empl_outlook-2018-en.

OECD (2017), OECD Compendium of Productivity Indicators 2018, OECD Publishing, Paris, https://doi.org/10.1787/pdtvy-2017-en.

OECD (2001), Measuring Productivity – OECD Manual: Measurement of Aggregate and Industry-level Productivity Growth, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264194519-en.

Ward, A., M. Zinni and P. Marianna (2018), “International productivity gaps: Are labour input measures comparable?”, OECD Statistics Working Papers, No. 2018/12, OECD Publishing, Paris, https://doi.org/10.1787/5b43c728-en.

Annex 1.A
Annex Figure 1.A.1. Labour productivity growth in the OECD
GDP per hour worked, percentage rate at annual rate
Annex Figure 1.A.1. Labour productivity growth in the OECD

Source: OECD Productivity Statistics (database), http://dx.doi.org/10.1787/pdtvy-data-en, March 2019.

 StatLink http://dx.doi.org/10.1787/888933968250

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