A Note on Data Revisions of Aggregate Hours Worked Series: Implications ...€¦ · A Note on Data...

A Note on Data Revisions of Aggregate Hours Worked Series:

Implications for the Europe-US Hours Gap∗

Alexander BickArizona State University

Bettina BruggemannMcMaster University

Nicola Fuchs-SchundelnGoethe University Frankfurt, CEPR and CFS

March 29, 2017

Abstract

In this note we document that the OECD and the Conference Board’s Total Economy Database substan-tially revised their measures of hours worked over time. Relying on the data used by Rogerson (2006)and Ohanian et al. (2008), we find a Europe-US hours per person gap of -18% for the year 2003. Usingthe most recent releases of the same data yields for the year 2003 a gap that is 40% smaller, namelya gap of only -11%. Using labor force survey data, which are less subject to data revisions, we find aEurope-US hours gap of -19%.

∗Email: [email protected], [email protected] and [email protected]. We thank Valerie Ramey for help-ful comments and encouraging us to write this note. The authors gratefully acknowledge financial support from the Cluster ofExcellence “Formation of Normative Orders” at Goethe University and the European Research Council under Starting Grant No.262116. All errors are ours.

1 Introduction

Rogerson (2006) documents large differences in hours worked per person among OECD countries for the

early 2000s. Specifically, based on data from the OECD and the Total Economy Database (TED), he finds

that hours worked per person in the 16 European countries in his sample are substantially lower than in

the US. In this note, we show that using the most recent releases of the same data yields a Europe-US

hours worked per person gap that is about 40% smaller than the one he found. Since the data used by

Rogerson (2006) were not published along with his study, we replicate his results using the data from his

companion paper with Ohanian and Raffo (Ohanian et al., 2008) available on the website of the Journal of

Monetary Economics. The implied Europe-US hours worked per person gap is -17.6% for the year 2003,

the latest available year in their data set. Using the most recent release of the same data yields for the year

2003 a gap of only -11.0%, thus 37.5% lower. We document that these drastically different results mainly

originate from revisions of the hours worked per employed series used in the calculation of hours worked

per person. Next to Rogerson (2006) and Ohanian et al. (2008), the implied Europe-US hours per person

gaps in Prescott (2004) and McDaniel (2011) are also subject to such revisions.1 Using labor force survey

data, which are less subject to data revisions, we find a Europe-US hours gap of -19%. We further show that

the different formulas used to calculate hours worked per person in these papers only have negligible effects

on the estimated Europe-US hours per person gap.

2 Literature Overview

In this section we provide a brief overview of how hours worked per person have been calculated in the

literature and which data sources have been used. The upper panel of Table 1a lists the main papers ex-

plaining hours worked differences between a large set of European countries and the US. Column 2 states

the formula used in each paper to calculate hours worked per person, while column 3 lists the respective

time period covered. The first four papers calculate hours worked per person by multiplying average hours

worked per employed with various measures of the civilian employment to population ratio. The first three

papers calculate the latter as civilian employment without an age limit over the population aged 15 to 64,

with Prescott (2004) also including non-civilian employment. Ragan (2013) restricts the employment to

population ratio to the age group 25 to 64. Henceforth, we use the term employment rate interchangeably

for these employment to population ratios. McDaniel (2011) differs from these papers by directly dividing

total hours worked by the population aged 15 to 64. We exclude Alesina et al. (2005), and Faggio and Nick-

ell (2007) from Table 1 because their measure of hours worked per employed are based on data published

in a special section of the OECD’s Economic Outlook 2004. These data are not part of the OECD’s general

database and are as such not subject to their usual maintenance and revisions. Moreover, their measure of

hours worked per employed was obtained from labor force surveys, which are usually not subject to larger

1Ragan (2013) is an exception. While the underlying data used by her were subject to non-negligible revisions on the countrylevel, the average Europe-US hours gap is nearly left unchanged.

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Table 1: Overview of the Macro Literature on Cross-Country Differences in Hours Worked per Person

(a) Hours Worked per Person Formulas and Time Period Covered

Reference Hours Worked per Person Period

Prescott (2004) Avg. Hours per Employed × Civilian + Non-civilian Empl.Population 15-64 Avg. 1970-73 & 1993-96

Rogerson (2006) Avg. Hours per Employed × Civilian EmploymentPopulation 15-64 2003

Ohanian et al. (2008) Avg. Hours per Employed × Civilian EmploymentPopulation 15-64 1956-2004

Ragan (2013) Avg. Hours per Employed × Civilian Employment 25-64Population 25-64 Avg. 1998-2003

McDaniel (2011) Total HoursPopulation 15-64 1960-2004

Bick et al. (2017)

NIPA Total HoursTotal Employment × Total Employment

Population 15-64 1983-2015LFS Civilian Hours

Civilian Employment × Civilian EmploymentCivilian Population 15-64

(b) Data Sources

Reference Hours Employment Population

Prescott (2004) OECD Labour Database - Labour Force Statistics

Rogerson (2006) TED OECD Labor Market Database Statistics

Ohanian et al. (2008) TED [2008]Various issues of the OECD’s

Economic Outlook and Main Economic Indicators

Ragan (2013) OECD Labor Market Statistics

McDaniel (2011) TED [2007] OECD

Bick et al. (2017)

NIPA (OECD) OECD NA Database∗ [2016] OECD ALFS† [2016]

NIPA (TED) TED [2016] OECD ALFS† [2016]

LFS National Labor Force Surveys [2016]

∗ OECD National Accounts Database; † OECD Labour Database - Labour Force Statistics - Annual Labour Force Statistics

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revisions.

In Bick et al. (2017) we construct internationally comparable hours worked measures for the US and 18

European countries on a more disaggregate level, e.g. by gender and education, using national labor force

surveys (LFS). Such detailed measures of hours worked have not been available so far. In Bick et al. (2017)

we discuss in detail the strategy how we achieve comparability of hours worked across countries and over

time, a task far from trivial.2 We also contrast the aggregate hours implied by the LFS data with those from

the National Income and Product Accounts (NIPA). For both types of data sources, we use conceptually

the same formula, see the lower panel of Table 1a. Here, we intentionally use the product formulation, i.e.

hours per employed times the respective employment to population ratio, because it highlights that in our

calculation employment cancels out, as in McDaniel (2011). For the first four studies listed in Table 1a

this is not necessarily the case. We do not know whether the denominator in average hours per employed

referred to total or civilian employment in older releases of the data. Thus, it may or may not be equal to the

numerator in the respective employment to population ratio. We elaborate on this further below.

The upper panel of Table 1b states the data sources exactly as specified in each of the five papers.

Numbers in parentheses refer to the year of the publication of each paper, whereas numbers in brackets

refer to the year of the data release if provided by the authors in the respective paper. Employment and

population figures are always taken from the OECD, while average hours worked per employed are either

from the OECD or from different releases of the Total Economy Database (TED), which in earlier years was

maintained jointly by the Conference Board and the Groningen Growth and Development Centre.

The lower panel of Table 1b states the data sources used in Bick et al. (2017). We report here two

NIPA measures, which only differ in their source for hours per employed. We take total hours and total

employment either from the OECD’s “National Accounts Database”, downloaded in March 2016, or from

the May 2016 TED release. We denote both measures by NIPA because for most years and countries both

data sources report exactly the same numbers. We normalize both NIPA measures with the population aged

15 to 64 from the OECD’s “Annual Labor Force Statistics”, downloaded as well in August 2016. The

calculation of our LFS measure of hours worked per person uses for hours worked, employment, and the

population only information from the national labor force surveys.3 We downloaded CPS data for the US

in August 2016 from the NBER’s web page, and use the ELFS as provided to us by Eurostat in 2014. The

LFS also undergo revisions, but these are minor revisions concentrated on certain quarters and countries,

and only for a few variables at a time.4

2Hours per employed and weeks worked in the OECD Economic Outlook 2004, which is used by Alesina et al. (2005), andFaggio and Nickell (2007) are constructed in a similar way as those in Bick et al. (2017). Both approaches are based on nationallabor force surveys, and use external data sources for annual leave and public holidays to estimate weeks worked per year. The keydifference lies in the treatment of weekly hours lost (relative to usual hours worked) for reasons other than annual leave and publicholidays. In our approach these reduce weekly hours worked per employed but do not affect weeks worked per year, whereas in theOECD Economic Outlook 2004 it is the other way around.

3To ensure the comparability across countries and over time, we need to make an adjustment using external data sources forpublic holidays and annual leave to overcome differences in the sets of weeks sampled across countries and over time. For details,we refer the reader to Bick et al. (2017).

4The first draft of this note was written in August 2016. Since then both the OECD and TED have updated their data. There

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Both the OECD and TED also report average hours worked per employed as a separate variable, which

are used in the papers by Prescott (2004), Rogerson (2006), Ohanian et al. (2008), and Ragan (2013). For

the TED, this is simply total hours worked divided by total employment from the TED. The OECD’s average

annual hours worked per employed series can be found in the OECD’s “Labour Database” under the category

“Labour Force Statistics”. Similarly to the TED, this estimate is equal to total hours worked divided by total

employment from the OECD’s “National Account Database”, although there are small differences for few

countries in some years. We are not entirely sure whether in earlier releases of the data hours worked

per employed were similarly calculated as total hours divided by total employment; in the Septemter 2006

release of the TED hours worked per employed are defined as “total hours divided by persons engaged”.

In our analysis of the effects of data revisions, we draw for civilian employment on data from the

OECD’s “Labour” database under the section “Labour Force Statistics”. Two data series are available:

one under the category “LFS by sex and age” (LFSsa) and one under the category “Annual Labour Force

Statistics” (ALFS). As we show further below, the differences between LFSsa and ALFS employment are

small for most countries. The difference with total employment from NIPA data is however non-negligible

for both series. Hence at least for the most recent release of the data, employment does not cancel out in

the formulas used by the first four papers in Table 1a. We come to this conclusion because average hours

worked per employed directly available from the OECD and TED are largely based on total hours and total

employment, as discussed above. If we add non-civilian employment, which is available for a subset of

countries in the “Annual Labour Force Statistics”, to civilian employment from LFSsa or ALFS, we do not

arrive at total employment from NIPA, such that for the formula used by Prescott (2004) employment does

not cancel out either.

3 Evaluating Differences in Labor Supply Measures Originating from Dif-ferent Data Releases

In this section, we evaluate the role of revisions between different data releases for the measurement of

hours worked based on OECD and TED data. Since the data used by Rogerson (2006) for the year 2003

were not published along with his study, we conduct our comparison based on the data used in his companion

paper with Ohanian and Raffo (Ohanian et al. (2008)). These are available on the website of the Journal

of Monetary Economics. Note that Ohanian et al. (2008) focus only on the cross-country comparisons of

trends. Therefore, in principle our study relates to the paper by Rogerson (2006), but we use the data from

Ohanian et al. (2008) for the analysis.5 We restrict our attention to the US and the set of 15 European

countries which are part of the Ohanian et al. (2008) and part of the Bick et al. (2017) sample.6 We report

were no major revisions in those releases relative to the data available in August 2016. Hence, we still refer to the data used as the“Current” release even though they are not the most current any longer.

5Using the data from Ohanian et al. (2008) for the year 2003 yields the same results as presented in Table 1 in Rogerson (2006).6We drop Finland from the Ohanian et al. (2008) sample, and the Czech Republic, Hungary and Poland from the Bick et al.

(2017) sample.

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Table 2: Employment Rate and Avg. Annual Hours per Employed Differences for the Year 2003

Employment Rate Avg. Hours per EmployedCountry eORR ∆ALFS, 2016 Rel. ∆LFSsa, 2016 Rel. HE

ORR ∆2006 Rel. ∆2011 Rel. ∆2013 Rel. ∆2016 Rel.

Austria 68.7 −0.4 −0.1 1498.5 1.1 15.3 18.5 19.0Belgium 58.6 2.1 2.1 1618.9 0.0 −2.7 −2.7 −2.5Denmark 74.6 1.4 1.5 1519.0 2.1 2.2 2.2 −2.5France 62.5 3.3 0.8 1428.6 0.2 7.3 3.1 3.9Germany 63.2 2.4 3.0 1441.4 −0.2 −0.2 −0.4 −1.2Greece 59.7 −1.1 −1.4 1929.0 0.0 9.0 9.0 8.4Ireland 66.0 0.5 0.9 1652.7 0.0 14.5 14.2 14.2Italy 56.3 0.9 2.4 1608.7 0.0 13.5 13.5 12.9Netherlands 73.2 1.1 −2.0 1352.1 4.1 3.6 3.6 5.5Norway 75.5 0.0 0.8 1336.3 0.1 4.6 4.8 5.1Portugal 71.2 1.4 2.0 1702.2 0.0 13.6 13.6 10.8Sweden 72.8 0.0 2.0 1553.4 0.6 1.8 1.8 1.8Spain 57.8 3.9 4.4 1798.5 0.0 −5.2 −4.4 −2.4Switzerland 83.9 −0.2 −5.0 1537.0 0.0 6.7 5.8 5.9United Kingdom 72.3 0.0 0.4 1623.9 0.0 3.3 3.3 2.8

Mean Absolute 67.7 1.2 1.9 1573.3 0.6 6.9 6.7 6.6

US 70.9 0.4 0.4 1817.1 −1.2 −5.9 −5.9 −1.9

∆ALFS, 2016 Rel. and ∆LFSsa, 2016 Rel. are the percentage deviation of the ALFS and LFSsa employment rates (each taken from themost current data release) from the ORR employment rate for the year 2003. The employment rate is measured as civilian employ-ment over the population 15 to 64.∆Y Rel. is the percentage deviation of average hours per employed from the TED release in year Y from the ORR average hours peremployed (TED release 2008) for the year 2003.

all results for 2003, the latest year available in the Ohanian et al. (2008) data set with information for all

variables. In our conclusion, we briefly discuss the remaining papers in Table 1.

Rogerson (2006) and Ohanian et al. (2008) calculate hours worked per person as the product of hours

worked per employed from the TED and the employment rate from the OECD. In a first step, we investigate

the effect of different data releases on each component separately. Then, we look at how hours worked per

person are affected by the different releases and how important each margin is in shaping such potential

differences. We want to stress again that in all these comparisons, the numbers are reported for the year

2003 but are based on data released in different years.

3.1 Employment Rates and Hours Worked per Employed

The first column of Table 2 lists the employment rate (eORR) using the Ohanian et al. (2008) data. The next

two columns show the percentage difference between the employment rates based on civilian employment

from the OECD’s ALFS and LFSsa, respectively, and the ORR employment rate. In each case the employ-

ment rate is given by civilian employment over the population 15 to 64 for the year 2003 as available in

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August 2016 from the OECD’s website. For example, in Spain, the country with the largest differences,

the ALFS employment rate is 3.9% (2.3 percentage points) higher than the ORR employment, whereas the

LFSsa employment rate is 4.4% (2.6 percentage points) higher than the ORR employment rate. On average

in the European countries the absolute difference between the ALFS or LFSsa employment rate and the

ORR employment rate is 1.2% or 1.9% in Europe and 0.4% in the US for both OECD series. While the

differences with the ORR employment rate are already not that large on average, the differences between the

ALFS and LFSsa employment rates are even smaller as can be indirectly inferred from comparing columns

2 and 3 (Switzerland is the only exception.)

The fourth column of Table 2 lists the average annual hours worked per employed using the Ohanian

et al. (2008) data, which come from the 2008 release of the TED. While for civilian employment from the

OECD we only have access to the most current release of the data, we have several data releases from the

TED available. Columns 5 to 8 show the percentage difference between average hours worked per employed

from these different releases of the TED, each time compared to the 2008 release. While the 2011 release of

the TED and all subsequent releases are available on the Conference Board’s website, the September 2006

release was shared with us by Cara McDaniel. For many countries there are no differences at all between the

2006 release of the TED and the 2008 release of the TED, i.e. the data available from Ohanian et al. (2008).

The mean absolute difference is only 0.6%, and the largest difference is present for the Netherlands: average

hours worked per employed in the 2006 release exceed those from the 2008 release by 4.1%. This changes

drastically when we compare the 2011 release with the 2008 release. More than half of the countries have

(absolute) differences that are larger than 4.1% (the largest difference between the 2006 and 2008 release).

Austria, Ireland, Italy and Portugal display double-digit percentage differences. The mean absolute average

amounts to 6.9% for the European countries, with most countries having a positive difference. In contrast,

the average hours per employed for the US from the 2011 release are 5.9% lower than those from the 2008

release. The large differences with the 2008 release persist for the European countries for the 2013 and

2016 releases, even though there are some substantial changes between those more recent releases (e.g., for

France between 2011 and 2013, or Denmark between 2013 and 2016). For the US the 2016 release is much

closer to the 2008 release than the 2011 and 2013 releases are. These results already make clear that data

revisions affect the measurement of hours worked per employed substantially, but the measurement of the

employment rate only to a small degree. We will back this up more formally further below.

3.2 Hours Worked per Person

We now use the formula in Rogerson (2006) and Ohanian et al. (2008) to calculate hours per person for the

year 2003. We compare the hours per person directly obtained from Ohanian et al. (2008), labeled as HORR,

to those using the most recent data releases, labeled as HCurrent Rel.. Specifically, we calculate the latter by

multiplying average hours worked per employed from the current release of the TED (May 2016 release)

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Table 3: Hours Worked per Person Differences for the Year 2003

% of Dev. Explained byCountry HORR HCurrent Rel. ∆Current Rel. ∆e ∆HE

Austria 1028.8 1220.0 18.6 −2.1 102.1Belgium 948.2 943.7 −0.5 −444.4 544.4Denmark 1133.6 1121.2 −1.1 −128.0 228.0France 892.5 957.8 7.3 44.9 55.1Germany 910.7 921.6 1.2 198.6 −98.6Greece 1152.3 1235.5 7.2 −15.4 115.4Ireland 1090.8 1251.4 14.7 3.3 96.7Italy 905.4 1031.3 13.9 6.6 93.4Netherlands 989.9 1056.0 6.7 16.6 83.4Norway 1008.9 1060.2 5.1 0.0 100.0Portugal 1211.2 1360.6 12.3 11.0 89.0Sweden 1131.0 1151.5 1.8 −0.2 100.2Spain 1039.0 1054.0 1.4 270.0 −170.0Switzerland 1289.7 1363.0 5.7 −2.9 102.9United Kingdom 1174.0 1206.4 2.8 −1.1 101.1

Mean 1060.4 1129.0 6.5

US 1287.5 1268.2 −1.5 −26.7 126.7

∆Current Rel. is the percentage deviation of HCurrent Rel. from HORR, i.e. HCurrent Rel.−HORRHORR

. The decomposition in columns 4 and 5 isconstructed as follows:

HCurrent Rel. −HORR =eALFS ×HECurrent Rel. − eORR ×HE

ORR

HCurrent Rel. −HORR =eALFS

(HE

Current Rel. −HEORR

)+HE

ORR (eALFS − eORR)

1 =eALFS

(HE

Current Rel. −HEORR)

HCurrent Rel. −HORR︸︷︷︸=∆HE (Col. 5)

+HE

ORR (eALFS − eORR)

HCurrent Rel. −HORR︸︷︷︸=∆e (Col. 4)

(1)

∆e is the fraction of HCurrent Rel. −HORR accounted for by differences between the ALFS and ORR employment rate. ∆HE is thefraction of HCurrent Rel. −HORR accounted for by differences between hours per employed from the 2016 TED release and the2008 TED release. Note that this decomposition is not unique. We weight the hours per employed difference by eALFS and theemployment rate difference by HE

ORR. Using as weights eORR and HECurrent Rel. leaves the results virtually unchanged.

with the ALFS employment rate (downloaded in August 2016 from the OECD’s website).7 As a reminder,

we report the hours for the year 2003, based on data released in different years. The first column of Table 3

shows HORR, the second columns shows HCurrent Rel., and the third column shows the percentage deviation of

HCurrent Rel. from HORR. For the European countries, hours per person using the most currently available data

are on average 6.5% larger than using the data from Ohanian et al. (2008), while for the US hours worked

per person are 1.5% lower in the most currently available data. The last two columns show what fraction of

7We use the ALFS employment rate rather than the LFSsa employment rate because the former shows a smaller mean absolutedifference with the employment rate used by Ohanian et al. (2008).

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Table 4: Hours Worked per Person Relative to the US for the Year 2003

Country ORR Current Rel. NIPA (TED) NIPA (OECD) LFS

Austria −20.1 −3.8 −4.9 −3.5 −17.0Belgium −26.4 −25.6 −25.3 −24.3 −30.5Denmark −12.0 −11.6 −12.2 −10.9 −14.5France −30.7 −24.5 −23.2 −22.1 −27.2Germany −29.3 −27.3 −21.4 −20.2 −25.9Greece −10.5 −2.6 −1.4 0.0 −15.3Ireland −15.3 −1.3 −2.1 −0.7 −17.0Italy −29.7 −18.7 −11.3 −10.0 −31.9Netherlands −23.1 −16.7 −15.5 −14.3 −23.0Norway −21.6 −16.4 −15.6 −14.4 −19.5Portugal −5.9 7.3 6.3 7.9 −8.2Sweden −12.2 −9.2 −7.9 −6.6 −17.1Spain −19.3 −16.9 −14.1 −12.8 −23.8Switzerland 0.2 7.5 5.7 7.2 −4.1United Kingdom −8.8 −4.9 −5.8 −4.5 −14.6

Mean −17.6 −11.0 −9.9 −8.6 −19.3

the hours per person difference is accounted for by differences in the employment rate and by differences in

hours per employed. Equation (1) Table 3 states the formula for these calculations. For Belgium, Denmark,

Germany, Sweden, and Spain, this decomposition is less informative because the (weighted) differences in

hours worked per employed and employment rates are divided by the small difference in hours worked per

person (less than 2% in absolute value). We therefore do not show the mean over all European countries.

For the remaining countries, the hours per employed differences account for 55% in France, and at least

83% (Netherlands) in the other countries of the hours per person difference for the year 2003 between the

ORR data based on the 2008 TED release and those using the most current release from 2016.

4 The Europe-US Hours per Person Gap: The Effect of Different Data Re-leases and Formulas

In this section, we analyze the role of the use of different data releases and of different formulas for the

measurement of the Europe-US hours gap for the year. In addition, we contrast those findings with the

Europe-US hours gap constructed with the LFS by Bick et al. (2017).

Table 4 shows in the first two columns the hours per person gap relative to the US for HORR (using the

original Ohanian et al. (2008) data, i.e. column 1 in Table 3) and for HCurrent Rel. (using the latest release of

the same data sources used by Ohanian et al. (2008), i.e. column 2 in Table 3) for the year 2003. Constructing

hours per person as in Rogerson (2006) and Ohanian et al. (2008), but using the most current release yields

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a much smaller gap of -11.0% between Europe and the US, compared to -17.6% based on the original ORR

data. As shown in Table 3, this is mostly driven by major revisions of the hours per employed data from the

TED between the 2008 release and later releases. Column 3 in turn shows hours worked per person using

the NIPA formula McDaniel (2011) and Bick et al. (2017) and the TED data. This implies that employment

refers to total employment in hours per employed and the employment rate. Hence, the only difference

between columns 2 and 3 is that the employment figures cancel out in column 3 but not in 2. This has only

a small effect on the Europe-US hours gap of on average 1.1 percentage points. Germany and Italy stand

out with large differences. Column 4 shows our calculations if we use the OECD hours rather than the TED

hours. The Europe-US hours gaps differ only by 1.3 percentage points, which is mostly driven by a higher

estimate of hours per person in the US in the TED. As the last column of Table 4 shows, it turns out that the

Europe-US hours gap in the original Ohanian et al. (2008) data set is quite similar to the one we find in LFS

data.

In Bick et al. (2017) we provide a detailed discussion of the potential forces behind the different NIPA

and LFS estimates using the most recent OECD release. LFS and NIPA data differ conceptually along two

dimensions. First, LFS data cover only civilian, non-institutionalized residents aged 15 and older, while the

NIPA does not impose these restrictions to ensure that the labor inputs are consistent with the measurement

of gross domestic output (GDP). Second, the NIPA estimates are usually constructed in country-specific

ways from multiple data sources (administrative data, social security data, employer surveys, labor force

surveys, census data, etc.). We show suggestive evidence that the differences in population coverage are not

very important. For the US, Abraham et al. (2013) investigate which features of the underlying data sources

drive the differences between NIPA and LFS employment estimate, and Eldridge et al. (2004) and Frazis

and Stewart (2010) do the same for hours works per employed. While the details are specific to the US,

these papers highlight advantages and disadvantages of household survey data used in the LFS estimates

vs. administrative data used in the NIPA estimates. The combination of multiple data sources might deliver

more accurate estimates of employment and hours worked for a given country. The downside is that the

cross-country comparability suffers, despite the efforts to harmonize measurement through the Systems of

National Accounts, see Fleck (2009). In fact, the OECD remarks on its website that “The [hours worked]

data are intended for comparisons of trends over time; they are unsuitable for comparisons of the level of

average annual hours of work for a given year, because of differences in their sources” and recommends

using employment rates based on labor force surveys for cross-country comparisons: “National Labor Force

Surveys are the best way to capture unemployment and employment according to the ILO guidelines that

define the criteria for a person to be considered as unemployed or employed... While data from LFS make

international comparisons easier compared to a mixture of survey and registration data, there are some

differences across countries in coverage, survey timing, etc, that may affect international comparisons of

labour market outcomes.”.8 The approach in Bick et al. (2017) deals with one of the main differences in the

8Both quotes we retrieved from the OECD’s website on March 10, 2017: http:

//stats.oecd.org/Index.aspx?DataSetCode=ANHRS and http://www.oecd.org/els/emp/

basicstatisticalconceptsemploymentunemploymentandactivityinlabourforcesurveys.htm.

9

http://stats.oecd.org/Index.aspx?DataSetCode=ANHRS

http://stats.oecd.org/Index.aspx?DataSetCode=ANHRS

http://www.oecd.org/els/emp/basicstatisticalconceptsemploymentunemploymentandactivityinlabourforcesurveys.htm

http://www.oecd.org/els/emp/basicstatisticalconceptsemploymentunemploymentandactivityinlabourforcesurveys.htm

cross-country comparability of the LFS, namely the survey timing.9

If one is interested in hours worked for different demographic subgroups, the LFS is the only option.

For aggregate applications only, researchers should be aware of the differences between the LFS and NIPA

data on the one hand and the potential for major revisions of the latter over time.

5 Conclusion

In this note we compare the effect of different formulas used in the macro literature for calculating hours

worked per person and the effect of different data releases on the hours estimates. In doing so, we focus on

the data provided by Ohanian et al. (2008). We show that the revisions of hours worked per employed in

the TED have a large impact on the conclusions drawn. Put differently, if Rogerson (2006) would have had

today’s release of the data, he would have found an about 40% lower Europe-US hours gap (-11.0%) than

what he found with the data available in the mid 2000s (-17.6%). Using labor force survey data, which are

less subject to data revisions, we find a Europe-US hours gap of -19%.

Since McDaniel (2011) uses the 2007 release of the TED data, the facts in her paper are affected by

revisions in a similar way as those in Rogerson (2006) and Ohanian et al. (2008). Ragan (2013) in turn

uses average hours per employed from the OECD and is thus not affected by any revisions of the TED data.

However, the OECD average hours worked per employed also underwent revisions. For the (smaller) set of

European countries in her data, the average absolute difference in hours worked per employed between the

release used by her and the most recent release of the same data is 3.8%, see Table A.2. This is a smaller

difference than between the different releases of the TED, but still substantial. Moreover, the revisions by

the OECD are not due to a change of the guidelines in the System of National Accounts (SNA 93 vs. SNA

2008). The OECD provides on its website NIPA employment and NIPA hours under both guidelines. For

the most recent release, the differences are small for most countries with the exception of the Netherlands,

Portugal, and Spain, see Table A.3. Finally, like Ragan (2013), Prescott (2004) uses hours per employed

directly from the OECD. Using the same set of countries (US, France, Italy, Germany, and the UK) and time

period (1993-96) as he does, we qualitatively reconfirm our finding from the comparison with Rogerson

(2006) and Ohanian et al. (2008) for the year 2003; for a detailed discussion, see the Appendix. Relying

on the most recent data releases from the OECD or TED yields a smaller Europe-US hours gap than when

using data from the same sources available to researchers in the early 2000s.

Finally, as we show in Appendix B, the data revisions also affect the measurement of time trends in

hours worked per person. While the secular decrease in European hours worked per person between 1956

and 2003 is present both in the data by Ohanian et al. (2008) and using the most current release of the same

data, the decline in the sample of European countries is 6.1 percentage points, or in relative terms 27%,

9Other reasons impeding the comparability across time and countries of the LFS, which we cannot adjust for, are the revisionof population figures used for population adjustment on the basis of new population censuses, as well as changes in the samplingdesign, and content or order of the questionnaire. For details, see http://ec.europa.eu/eurostat/statistics-explained/index.php/EU_labour_force_survey (retrieved on March 10, 2017).

10

http://ec.europa.eu/eurostat/statistics-explained/index.php/EU_labour_force_survey

http://ec.europa.eu/eurostat/statistics-explained/index.php/EU_labour_force_survey

smaller in the current release. In the US in turn, hours worked per person increase by 3.6% using the most

recent release of the data, and only by 0.4% in the data used by Ohanian et al. (2008).

References

Abraham, K. G., J. Haltiwanger, K. Sandusky, and J. R. Spletzer (2013). Exploring Differences in Employ-ment between Household and Establishment Data. Journal of Labor Economics 31(2), S129–S172.

Alesina, A., E. Glaeser, and B. Sacerdote (2005). Work and Leisure in the United States and Europe: WhySo Different? NBER Macroeconomics Annual 20, 1–64.

Bick, A., B. Bruggemann, and N. Fuchs-Schundeln (2017). Hours Worked in Europe and the US: New Data,New Answers. Working Paper.

Eldridge, L. P., M. E. Manser, and P. F. Otto (2004). Alternative measures of supervisory employee hoursand productivity growth. Monthly Labor Review April, 9–28.

Faggio, G. and S. Nickell (2007). Patterns of Work Across the OECD. Economic Journal 117, F416–F440.

Fleck, S. E. (2009, May). International Comparisons of Hours Worked: An Assessment of Statistics.Monthly Labor Review 132(5), 3–31.

Frazis, H. and J. Stewart (2010). Why Do BLS Hours Series Tell Different Stories About Trends in HoursWorked? BLS Working Paper 433.

McDaniel, C. (2011). Forces Shaping Hours Worked in the OECD, 1960-2004. American Economic Jour-nal: Macroeconomics 3, 27–52.

Ohanian, L., A. Raffo, and R. Rogerson (2008). Long-Term Changes in Labor Supply and Taxes: Evidencefrom OECD Countries, 1956-2004. Journal of Monetary Economics 55, 1353–1362.

Prescott, E. C. (2004). Why Do Americans Work So Much More Than Europeans? Federal Reserve Bankof Minneapolis Quarterly Review 28, 2–13.

Ragan, K. (2013). Taxes, Transfers and Time Use: Fiscal Policy in a Household Production Model. Ameri-can Economic Journal: Macroeconomics 5(1), 168–192.

Rogerson, R. (2006). Understanding Differences in Hours Worked. Review of Economic Dynamics 9,365–409.

11

A Appendix – Cross-Section

A.1 Comparison with Prescott (2004)

Table A.1 shows average hours worked per person over the years 1993 to 1996 relative to the US for the

four European countries included in Prescott (2004). The first column restates the numbers from his paper,

while the second column provides the calculation using his formula but the most current releases from the

same data sources he used for the years 1993 to 1996. Note that in contrast to our previous analysis, and in

line with Prescott (2004), employment refers here to civilian plus non-civilian employment. Moreover, we

use in column 2 here as Prescott (2004) the average hours worked per employed from the OECD’s “Labour

Database” and not total hours divided by total employment from the “National Account Database”. Recall

however that for most countries and years the latter are used to calculate the former and thus both coincide

for the most recent release of the data.

As for our previous analysis with the Ohanian et al. (2008) data, the implied Europe-US hours per person

gap becomes much smaller (by 4 percentage points) once we use the most currently released data. In the

spirit of the exercise in the main body of this paper, we should compare column 2 to hours per person using

our NIPA formula with the OECD NIPA data. Unfortunately, NIPA hours and employment by the OECD

do not go sufficiently back in time to do so. We therefore show first in column 3 the calculation when we

replace the OECD hours per employed with those from the TED. We stick however to the formula used

by Prescott (2004) such that employment in the employment rate is civilian plus non-civilian employment.

Column 4 uses the TED data but our NIPA formula where employment cancels out. Comparing columns 2

and 3 shows that using the most recent TED hours worked per employed implies an even smaller hours gap.

Similar to before, a comparison between columns 3 and 4 shows that the gap becomes even smaller when

employment cancels out. Yet, the differences caused by changes in data releases between columns 1 and 2

are substantially larger than the ones caused by changes in the formula between columns 2, 3, and 4. While

we are not able to conduct the analysis for Prescott (2004) as carefully as for Rogerson (2006) and Ohanian

et al. (2008) because of the lack of data, our main conclusions remains intact. Using the most recent data

releases from the OECD or TED yields a smaller Europe-US hours gap than using the releases available to

researchers in the early 2000s. Moreover, the Europe-US hours gap is similar between Prescott (2004) and

our LFS hours.

Table A.1: Hours Worked per Person Relative to the US: Avg. over the Years 1993-96

Country HPrescott H = eALFS ×HEOECD H = eALFS ×HE

T ED NIPA (TED) LFS

France −32.0 −25.0 −23.8 −23.8 −26.8Germany −25.0 −25.0 −23.8 −21.4 −22.4Italy −36.0 −27.1 −25.8 −20.9 −37.5United Kingdom −12.0 −11.5 −10.7 −11.3 −15.8

Mean −26.2 −22.1 −21.1 −19.3 −25.6

12

A.2 Tables

Table A.2: Hours Worked per Employed for the Years 1998-2003: Ragan (2013) vs. OECD 2016 Release

Country HERagan HE

2016 Rel. ∆2016 Rel.

Belgium 1590.0 1584.8 −0.3Denmark 1548.0 1482.3 −4.2France 1584.0 1531.2 −3.3Germany 1468.0 1453.7 −1.0Italy 1853.0 1845.9 −0.4Norway 1441.0 1441.3 0.0Portugal 1776.0 1901.7 7.1Sweden 1609.0 1626.3 1.1Spain 1813.0 1757.2 −3.1United Kingdom 1709.0 1700.8 −0.5

Mean (Absolute) 1587.0 1639.1 3.8

US 1831.0 1825.5 −0.3

∆2016 Rel. is the percentage deviation of average hours per employed from the OECD Release in year 2016 from the OECD Releaseused by Ragan (2013).

Table A.3: OECD’s SNA 93 vs. SNA 2008 for the Year 2003

%-Deviation SNA 93 from SNA 2008Country Employment Rate Hours per Employed Hours per Person

Austria −0.6 −0.3 −0.8Belgium 0.2 0.2 0.4Denmark 0.8 −1.3 −0.4France −0.1 −0.8 −0.9Germany −0.8 1.0 0.2Greece 0.2 0.4 0.6Ireland 0.0 0.0 0.0Italy −0.4 0.6 0.1Netherlands −0.9 −3.3 −4.2Norway −0.2 −0.2 −0.4Portugal 1.0 2.4 3.5Sweden 0.0 0.0 0.0Spain −2.1 −2.1 −4.2Switzerland 0.8 −0.8 0.0United Kingdom −0.2 0.0 −0.2

Mean -0.1 -0.3 -0.4

US 0.0 −0.2 −0.2

13

B Appendix – Time-Series

The revisions do not only affect the level estimates of employment and hours but also time-series trends.

Similar to the cross-sectional results, the impact of the revisions is larger for hours worked per employed

than for employment rates. Table B.1 shows the percentage point change in the employment rate between

1956 and 2003 in the Ohanian et al. (2008) data and for the 2016 release of civilian employment from the

ALFS. On average the trends in the European countries are basically the same, which is also true for the US.

There are however a few countries which stand out. In Denmark and Portugal, the most recently available

data suggest an increase that is 5.1 and 7.3 percentage points, respectively, larger than in the Ohanian et al.

(2008) data. In Greece in turn, the most recent release suggests a decrease of 3.3 percentage compared to

an increase of 8.8 percentage points in the Ohanian et al. (2008) data. The different trends for these three

countries between the two data releases originate from differences in the employment rate in the early years

of the sample. Using 1970 as starting year rather than 1956 would yield essentially the same change in the

employment rates. The smaller differences between the two releases in the remaining countries are driven by

differences between the two releases at the end of the observation period. Table B.2 compares hours worked

per employed from the data by Ohanian et al. (2008) with those from the 2016 release of the TED. While

the revision did not change the overall secular pattern of decreasing hours worked per employed over the

period 1956 to 2003, in the most current release hours per employed in Europe fell by 5.1 percentage points

(or about one fifth) less than in the 2008 release, see Table B.1. These differences are not concentrated on a

few countries. Only for Germany, Norway, Sweden, and Spain is the difference of the trend between the two

data releases smaller than 2 percentage points. Section B.3 shows the full time-trends. Finally, Table B.3

combines the information in Tables B.1 and B.2 and shows differences in the time trends in hours worked

per person between the two data releases. Overall, the European countries display a 6.1 percentage point

lower decline in hours worked per person using the most recent release (-16.6% vs. -22.7%). In the US in

turn, the increase is 3.2 percentage points larger in the most recent release of the data (3.6% vs. 0.4%).

14

B.1 Tables

Table B.1: Time Trends in Employment Rates for Different Releases of OECD Data

% Point Change between 1956 and 2003Country ∆eORR ∆eALFS, 2016 Rel. ∆eALFS, 2016 Rel. −∆eORR

Austria 1.6 1.0 −0.5Belgium 1.2 2.4 1.2Denmark 4.8 9.9 5.1France −4.3 −2.2 2.1Germany −4.5 −3.0 1.5Greece 8.8 −3.3 −12.1Ireland 0.5 1.1 0.6Italy −5.6 −4.8 0.8Netherlands 13.7 13.6 0.0Norway 12.2 12.2 0.0Portugal 7.3 14.5 7.3Sweden −0.7 1.8 2.4Spain −1.5 0.8 2.3Switzerland 5.9 5.4 −0.5United Kingdom 3.2 3.2 0.0

Mean 2.8 3.5Mean Absolute 2.4

US 9.1 9.4 0.3

15

Table B.2: Time Trends in Hours Worked per Employed for Different Releases of TED Data

% Change between 1956 and 2003Country ∆HE

ORR ∆HECurrent Rel. ∆HE

Current Rel. −∆HEORR

Austria −28.1 −14.3 13.8Belgium −30.7 −22.8 7.8Denmark −32.9 −25.9 7.0France −29.4 −33.1 −3.7Germany −35.8 −37.4 −1.6Greece −14.8 −7.7 7.2Ireland −30.1 −22.7 7.5Italy −18.1 −15.4 2.7Netherlands −34.4 −25.2 9.2Norway −32.5 −32.0 0.4Portugal −24.5 −12.9 11.6Sweden −20.1 −19.1 1.0Spain −11.8 −10.5 1.3Switzerland −23.0 −16.4 6.6United Kingdom −24.2 −18.2 6.0

Mean -26.0 -20.9Mean Absolute 5.8

US −12.5 −10.1 2.4

16

Table B.3: Time Trends in Hours Worked per Person for Different Releases of TED/OECD Data

% Change between 1956 and 2003Country ∆HORR ∆HCurrent Rel. ∆HCurrent Rel. −∆HORR

Austria −26.4 −12.9 13.5Belgium −29.2 −19.6 9.7Denmark −28.4 −14.8 13.5France −33.9 −35.3 −1.4Germany −40.1 −40.2 −0.1Greece −0.2 −12.5 −12.4Ireland −29.6 −21.4 8.2Italy −25.5 −22.0 3.5Netherlands −19.4 −8.3 11.1Norway −19.4 −18.9 0.5Portugal −16.0 9.0 25.0Sweden −20.8 −17.0 3.8Spain −14.0 −9.4 4.6Switzerland −17.3 −10.7 6.6United Kingdom −20.7 −14.4 6.3

Mean -22.7 -16.6Mean Absolute 8.0

US 0.4 3.6 3.2

17

B.2 Employment Rates 1956-2003: Different Releases of Civilian Employment by the OECD

Figure B.1: Austria

(a) Levels

6264

6668

70Em

ploy

men

t Rat

e in

%

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

ORR [2008 Release] ALFS, 2016 Release

(b) Normalized to 1 in 1956

.9.9

51

1.05

Nor

mal

ized

Em

ploy

men

t Rat

e

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


Figure B.2: Belgium

(a) Levels

5254

5658

6062

Empl

oym

ent R

ate

in %

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


(b) Normalized to 1 in 1956.9

.95

11.

05N

orm

aliz

ed E

mpl

oym

ent R

ate

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


Figure B.3: Denmark

(a) Levels

6570

7580

Empl

oym

ent R

ate

in %

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.95

11.

051.

11.

151.

2N

orm

aliz

ed E

mpl

oym

ent R

ate

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


18

Figure B.4: France

(a) Levels

5860

6264

6668

Empl

oym

ent R

ate

in %

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.85

.9.9

51

Nor

mal

ized

Em

ploy

men

t Rat

e

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


Figure B.5: Germany

(a) Levels

6062

6466

6870

Empl

oym

ent R

ate

in %

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.95

11.

05N

orm

aliz

ed E

mpl

oym

ent R

ate

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


Figure B.6: Greece

(a) Levels

5055

6065

Empl

oym

ent R

ate

in %

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.8.9

11.

11.

2N

orm

aliz

ed E

mpl

oym

ent R

ate

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


19

Figure B.7: Ireland

(a) Levels

5055

6065

70Em

ploy

men

t Rat

e in

%

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.8.8

5.9

.95

11.

05N

orm

aliz

ed E

mpl

oym

ent R

ate

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


Figure B.8: Italy

(a) Levels

5055

6065

Empl

oym

ent R

ate

in %

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.85

.9.9

51

Nor

mal

ized

Em

ploy

men

t Rat

e

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


Figure B.9: Netherlands

(a) Levels

5055

6065

7075

Empl

oym

ent R

ate

in %

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.8.9

11.

11.

21.

3N

orm

aliz

ed E

mpl

oym

ent R

ate

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


20

Figure B.10: Norway

(a) Levels60

6570

7580

Empl

oym

ent R

ate

in %

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.91

1.1

1.2

Nor

mal

ized

Em

ploy

men

t Rat

e

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


Figure B.11: Portugal

(a) Levels

5560

6570

75Em

ploy

men

t Rat

e in

%

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.91

1.1

1.2

1.3

Nor

mal

ized

Em

ploy

men

t Rat

e

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


Figure B.12: Spain

(a) Levels

4550

5560

Empl

oym

ent R

ate

in %

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.7.8

.91

Nor

mal

ized

Em

ploy

men

t Rat

e

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


21

Figure B.13: Sweden

(a) Levels

7075

8085

Empl

oym

ent R

ate

in %

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.95

11.

051.

11.

15N

orm

aliz

ed E

mpl

oym

ent R

ate

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


Figure B.14: Switzerland

(a) Levels

7075

8085

90Em

ploy

men

t Rat

e in

%

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



51

1.05

1.1

1.15

Nor

mal

ized

Em

ploy

men

t Rat

e

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


Figure B.15: United Kingdom

(a) Levels

6466

6870

72Em

ploy

men

t Rat

e in

%

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.9.9

51

1.05

Nor

mal

ized

Em

ploy

men

t Rat

e

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


22

Figure B.16: United States

(a) Levels

6065

7075

Empl

oym

ent R

ate

in %

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



51

1.05

1.1

1.15

1.2

Nor

mal

ized

Em

ploy

men

t Rat

e

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


23

B.3 Hours Worked per Employed 1956-2003: Different Releases of TED Data

Figure B.17: Austria

(a) Levels

1400

1600

1800

2000

2200

Hou

rs W

orke

d pe

r Em

ploy

ed

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

ORR [2008 Release] 2016 Release


.7.8

.91

Nor

mal

ized

Hou

rs W

orke

d pe

r Em

ploy

ed

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


Figure B.18: Belgium

(a) Levels

1600

1800

2000

2200

2400

Hou

rs W

orke

d pe

r Em

ploy

ed

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.8.9

1N

orm

aliz

ed H

ours

Wor

ked

per E

mpl

oyed

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


Figure B.19: Denmark

(a) Levels

1400

1600

1800

2000

2200

Hou

rs W

orke

d pe

r Em

ploy

ed

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.6.7

.8.9

1N

orm

aliz

ed H

ours

Wor

ked

per E

mpl

oyed

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


24

Figure B.20: France

(a) Levels

1400

1600

1800

2000

2200

Hou

rs W

orke

d pe

r Em

ploy

ed

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.7.8

.91

Nor

mal

ized

Hou

rs W

orke

d pe

r Em

ploy

ed

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


Figure B.21: Germany

(a) Levels

1400

1600

1800

2000

2200

Hou

rs W

orke

d pe

r Em

ploy

ed

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.7.8

.91

Nor

mal

ized

Hou

rs W

orke

d pe

r Em

ploy

ed

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


Figure B.22: Greece

(a) Levels

1900

2000

2100

2200

2300

Hou

rs W

orke

d pe

r Em

ploy

ed

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.8.8

5.9

.95

1N

orm

aliz

ed H

ours

Wor

ked

per E

mpl

oyed

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


25

Figure B.23: Ireland

(a) Levels

1600

1800

2000

2200

2400

Hou

rs W

orke

d pe

r Em

ploy

ed

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.7.8

.91

Nor

mal

ized

Hou

rs W

orke

d pe

r Em

ploy

ed

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


Figure B.24: Italy

(a) Levels

1600

1800

2000

2200

Hou

rs W

orke

d pe

r Em

ploy

ed

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.85

.9.9

51

Nor

mal

ized

Hou

rs W

orke

d pe

r Em

ploy

ed

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



(a) Levels

1400

1600

1800

2000

2200

Hou

rs W

orke

d pe

r Em

ploy

ed

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.6.7

.8.9

1N

orm

aliz

ed H

ours

Wor

ked

per E

mpl

oyed

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


26

Figure B.26: Norway

(a) Levels14

0016

0018

0020

0022

00H

ours

Wor

ked

per E

mpl

oyed

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.7.8

.91

Nor

mal

ized

Hou

rs W

orke

d pe

r Em

ploy

ed

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



(a) Levels

1600

1800

2000

2200

2400

Hou

rs W

orke

d pe

r Em

ploy

ed

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.75

.8.8

5.9

.95

1N

orm

aliz

ed H

ours

Wor

ked

per E

mpl

oyed

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


Figure B.28: Spain

(a) Levels

1700

1800

1900

2000

2100

Hou

rs W

orke

d pe

r Em

ploy

ed

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.85

.9.9

51

1.05

Nor

mal

ized

Hou

rs W

orke

d pe

r Em

ploy

ed

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


27

Figure B.29: Sweden

(a) Levels

1500

1600

1700

1800

1900

2000

Hou

rs W

orke

d pe

r Em

ploy

ed

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.75

.8.8

5.9

.95

1N

orm

aliz

ed H

ours

Wor

ked

per E

mpl

oyed

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



(a) Levels

1500

1600

1700

1800

1900

2000

Hou

rs W

orke

d pe

r Em

ploy

ed

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



5.8

.85

.9.9

51

Nor

mal

ized

Hou

rs W

orke

d pe

r Em

ploy

ed

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



(a) Levels

1600

1800

2000

2200

Hou

rs W

orke

d pe

r Em

ploy

ed

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.75

.8.8

5.9

.95

1N

orm

aliz

ed H

ours

Wor

ked

per E

mpl

oyed

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


28


(a) Levels

1700

1800

1900

2000

2100

Hou

rs W

orke

d pe

r Em

ploy

ed

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



5.9

.95

1N

orm

aliz

ed H

ours

Wor

ked

per E

mpl

oyed

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


29

B.4 Hours Worked per Person 1956-2003: Different Releases of TED and OECD Data

Figure B.33: Austria

(a) Levels

1000

1100

1200

1300

1400

Hou

rs W

orke

d pe

r Per

son

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.7.8

.91

Nor

mal

ized

Hou

rs W

orke

d pe

r Per

son

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


Figure B.34: Belgium

(a) Levels

800

1000

1200

1400

Hou

rs W

orke

d pe

r Per

son

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.8.9

1N

orm

aliz

ed H

ours

Wor

ked

per P

erso

n

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


Figure B.35: Denmark

(a) Levels

1000

1200

1400

1600

Hou

rs W

orke

d pe

r Per

son

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.7.8

.91

1.1

Nor

mal

ized

Hou

rs W

orke

d pe

r Per

son

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


30

Figure B.36: France

(a) Levels

800

1000

1200

1400

1600

Hou

rs W

orke

d pe

r Per

son

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.6.7

.8.9

1N

orm

aliz

ed H

ours

Wor

ked

per P

erso

n

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


Figure B.37: Germany

(a) Levels

800

1000

1200

1400

1600

Hou

rs W

orke

d pe

r Per

son

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.7.8

.91

Nor

mal

ized

Hou

rs W

orke

d pe

r Per

son

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


Figure B.38: Greece

(a) Levels

1000

1100

1200

1300

1400

Hou

rs W

orke

d pe

r Per

son

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.8.8

5.9

.95

11.

05N

orm

aliz

ed H

ours

Wor

ked

per P

erso

n

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


31

Figure B.39: Ireland

(a) Levels

1000

1200

1400

1600

Hou

rs W

orke

d pe

r Per

son

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.6.7

.8.9

1N

orm

aliz

ed H

ours

Wor

ked

per P

erso

n

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


Figure B.40: Italy

(a) Levels

800

1000

1200

1400

Hou

rs W

orke

d pe

r Per

son

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.8.9

1N

orm

aliz

ed H

ours

Wor

ked

per P

erso

n

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



(a) Levels

800

900

1000

1100

1200

Hou

rs W

orke

d pe

r Per

son

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.6.7

.8.9

1N

orm

aliz

ed H

ours

Wor

ked

per P

erso

n

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


32

Figure B.42: Norway

(a) Levels10

0011

0012

0013

00H

ours

Wor

ked

per P

erso

n

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.8.8

5.9

.95

1N

orm

aliz

ed H

ours

Wor

ked

per P

erso

n

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



(a) Levels

1100

1200

1300

1400

1500

Hou

rs W

orke

d pe

r Per

son

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.8.9

11.

1N

orm

aliz

ed H

ours

Wor

ked

per P

erso

n

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


Figure B.44: Spain

(a) Levels

800

900

1000

1100

1200

1300

Hou

rs W

orke

d pe

r Per

son

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.7.8

.91

1.1

Nor

mal

ized

Hou

rs W

orke

d pe

r Per

son

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


33

Figure B.45: Sweden

(a) Levels

1100

1200

1300

1400

1500

Hou

rs W

orke

d pe

r Per

son

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.8.8

5.9

.95

1N

orm

aliz

ed H

ours

Wor

ked

per P

erso

n

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



(a) Levels

1200

1300

1400

1500

1600

Hou

rs W

orke

d pe

r Per

son

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.85

.9.9

51

Nor

mal

ized

Hou

rs W

orke

d pe

r Per

son

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



(a) Levels

1100

1200

1300

1400

1500

Hou

rs W

orke

d pe

r Per

son

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.75

.8.8

5.9

.95

1N

orm

aliz

ed H

ours

Wor

ked

per P

erso

n

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


34


(a) Levels

1100

1200

1300

1400

Hou

rs W

orke

d pe

r Per

son

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005



.95

11.

051.

1N

orm

aliz

ed H

ours

Wor

ked

per P

erso

n

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005


35

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