Aggregate Hours Worked in OECD Countries: New Measurement and Implications for Business Cycles

Board of Governors of the Federal Reserve System International Finance Discussion Papers Number 1039 December 2011 Aggregate Hours Worked in OECD Countries: New Measurement and Implications for Business Cycles Lee. E. Ohanian Andrea Raffo NOTE: International Finance Discussion Papers are preliminary materials circulated to stimulate discussion and critical comment. References to International Finance Discussion Papers (other than an acknowledgment that the writer has had access to unpublished material) should be cleared with the author or authors. Recent IFDPs are available on the Web at www.federalreserve.gov/pubs/ifdp/. This paper can be downloaded without charge from the Social Science Research Network electronic library at www.ssrn.com.

Aggregate Hours Worked in OECD Countries: New Measurement and Implications for Business Cycles (cid:3) Lee E. Ohanian y UCLA, NBER, Hoover Institution Andrea Ra⁄o z Board of Governors of the Federal Reserve System Abstract We build a dataset of quarterly hours worked for 14 OECD countries. We document that hours are as volatile as output, that a large fraction of labor adjustment takes place along the intensive margin, and that the volatility of hours relative to output has increased over time. We use these data to reassess the Great Recession and prior recessions. The Great Recession in many countries is a puzzle in that labor wedges are small,whilethoseintheU.S.GreatRecession-andthoseinpreviousEuropeanrecessions-aremuchlarger. JEL classi(cid:133)cation: E32, F44, J20 Keywords: Hours Worked, Great Recession, Labor Wedge We thank our discussants T. Van Rens, L. Fang, and G. Olivei, as well as R. Rogerson, Y. Chang, T. Cooley, M. Bils, D. (cid:3) Dobrislav, and participants at the 77th Meeting of the Carnegie-Rochester Conference on Public Policy "Advances in Labor MarketDynamics"forveryusefulsuggestions. WealsothankseminarparticipantsattheFederalReserveBoard,2011Midwest Macroeconomics Meetings, Ohio State University, Queens University, 2011 SCIEA Meetings, Bank of Italy, and 2011 SED Meetings for comments. Michelle Olivier, Giang Ho and Gonzalo Llosa provided outstanding research assistance. The views in this paper are solely the responsibility of the authors and should not be interpreted as re(cid:135)ecting the views of the Board of Governors ofthe FederalReserve System or ofany otherperson associated with the FederalReserve System. Corresponding authors: Prof. Lee E. Ohanian, UCLA, Department of Economics, 405 Hilgard Avenue, Los Angeles, CA y 90024. Phone: (310) 825-1979. E-mail: ohanian@econ.ucla.edu Email: andrea.ra⁄o@frb.gov z

1 Introduction Documenting and assessing cyclical (cid:135)uctuations in hours worked has been a central focus of business cycle research since at least Kydland and Prescott [1982] and Hansen [1985], and the very di⁄erent labor market outcomesobservedduringthe2008-09recessionacrosscountrieshavegeneratedincreasedinterestincyclical labor (cid:135)uctuations. However, the literature typically focuses on the U.S. due to the very limited availability of systematic measures of aggregate hours worked in other countries. Thus, what is known about cyclical changes in labor input and productivity in other countries is largely based on measures of employment, rather than hours worked (see for example Backus, Kehoe, and Kydland, [1995], BKK henceforth). The fact that international studies of (cid:135)uctuations omit the intensive labor margin means not only that cross-country comparisons of cyclical changes in labor are limited, but that labor policy analyses are limited as well. Speci(cid:133)cally, Hopenhayn and Rogerson(cid:146)s [1993] analyses of hiring and (cid:133)ring costs suggested to many economiststhatEuropeanlabormarketsprovideanexcellentopportunityforevaluatingandquantifyingthe impact of these policies, but studies of (cid:135)uctuations along these lines have thus far been limited because of the lack of data on the intensive margin. This paper addresses these shortcomings by constructing a new dataset for total hours worked at the quarterly frequency which covers 14 OECD countries and spans the last (cid:133)fty years. The dataset draws on a variety of international sources, including data from national statistical o¢ ces, establishment surveys, and household surveys. There are three contributions. First, this paper provides the most comprehensive, international database of quarterly total hours worked. Second, we document and compare cyclical (cid:135)uctuations betweentheU.S.andothercountriesintotalhoursandlaborproductivity,aswellasinthelaborwedge(the deviation of the marginal product of labor from the marginal rate of substitution between consumption and leisure) and in the productivity wedge (the deviation of output from the combination of capital and labor input). Third, the paper analyzes the Great Recession using these new data. We construct these measures consistently across countries according to national income and product account principles, with a focus on measuring hours worked, rather than hours paid for. The paper then uses these measures to compare business cycle properties of total hours worked across OECD countries, focusing on three questions: (1) How do accepted business cycle features in OECD countries change when total hours are used as labor input? (2) What fraction of (cid:135)uctuations in output and labor across countries is accounted for by labor versus productivity wedges? (3) How does the Great Recession in other OECD 1

countries compare to that in the U.S.? This last question is important, as Ohanian [2010] documents large di⁄erences in this recession between the U.S. and other advanced economies. He (cid:133)nds that productivity is close to trend in the US, and that the U.S. Great Recession is due to a very large decline in labor input and a historically large labor wedge. In contrast, the Great Recession in the other G7 countries is the consequence of large productivity declines, with only small employment declines and no labor wedge. Since there is evidence that labor input may have declined considerably through declines in hours per worker in these other countries, we use hours worked to re-assess Ohanian(cid:146)s analysis of the Great Recession, as the small changes in Western European employment may simply re(cid:135)ect large (cid:133)ring costs or other di⁄erences in labor markets.1 Our main (cid:133)ndings contrast signi(cid:133)cantly with commonly held views in some cases, and raise signi(cid:133)cant puzzles in other cases. Speci(cid:133)cally, we show that employment is a poor proxy for labor input in many OECD countries, as changes in hours per worker are about as large as changes in employment. We also (cid:133)nd that employment-based labor wedges are much too large in Europe, given high European (cid:133)ring costs, while hours-based labor wedges are comparatively too small. Finally, we (cid:133)nd that the Great Recession is a substantial puzzle in Europe, as both employment-based and hours-based labor wedges are nearly zero in many European countries. This stands in sharp contrast to labor wedges in the U.S. during the Great Recession, or labor wedges in other European recessions, both of which are an order of magnitude larger. The paper is organized as follows. Section 2 describes the data sources and the approach we use to construct the hours measures. Section 3 compares standard business cycle features of hours, employment, and productivity across countries. Section 4 uses the business cycle accounting approach developed by Cole and Ohanian [2002], and Chari, Kehoe, and McGrattan [2007], to construct labor and productivity wedges using both employment and hours for recessions since 1960, with a speci(cid:133)c focus on assessing the relative importance of these wedges during the Great Recession. Section 5 concludes. 2 Data We collected national accounts series for nominal output and its components from the OECD-Economic Outlook and de(cid:135)ate them using their speci(cid:133)c price de(cid:135)ators. Total hours worked (H) is constructed as the product of hoursworked per worker (h) and employment (E), normalized by the size of the population aged 1See,for instance,Burda and Hunt [2011]. 2

16-64 years (P): E H =h (1) (cid:3) P Labor productivity (LP) is the ratio between real output and total hours worked, Y LP = (2) H Employment and population data are from national statistical o¢ ces and the OECD-Economic Outlook database. Wenextpresentourmethodologytoconstructourseriesforhoursperworker(h),whichrepresents one of the main contributions of the paper. Appendix A presents country-speci(cid:133)c data sources and details. 2.1 Construction of hours per worker O¢ cial series for quarterly hours worked per worker are typically short and their comparability across countries is problematic, even in advanced OECD economies. In our dataset, only the United States data begins in 1960, while data in many of the other countries start in the mid- to late-1970s. Moreover, the underlying surveys used to construct these series, whether using establishments or labor force surveys, are not uniform across countries and, in some cases, are not consistent in the same country at di⁄erent dates. Establishmentssurveyshavebeenconductedinmanycountriesataquarterlyorevenmonthlyfrequency since the 1960s, but they often collect hours paid rather than hours worked. Thus, these survey data do not accountfordi⁄erencesacrosscountriesinimportantfeaturesoflaborcontractssuchaspaidvacationorsick days. In addition, establishment surveys do not sample all sectors of the economy, as the government sector is often omitted. Laborforcesurveystendtobemorecomprehensivesincetheydirectlysampleindividuals,buttheysu⁄er from several shortcomings as well. It is well-known that these surveys present an upward bias for hours worked per worker due to self-reporting. Moreover, there are methodological di⁄erences across countries in the construction of these surveys which also a⁄ect the concept of working time measured, thus undermining their comparability2. Finally, in many countries labor force surveys have been conducted only at an annual frequency until very recently. Given these data limitations, it is not suprising that the literature on international business cycles has focused on employment as the standard measure of labor input. One key contribution of this paper is to 2For instance, some countries do not include in their questionnaires a distinction between contractual hours and hours not worked because ofillness or holidays. 3

provide researchers with a standardized dataset of total hours worked, including both the intensive and the extensive margin. Our methodology to construct quarterly series of hours per worker consists of three elements. First, we obtain a dataset of hours worked per worker that has been adjusted to take into account cross-country variation in variables such as sick days and holidays, but is available only at annual frequency. Second, we construct a dataset of quarterly indicators for hours worked per worker comprised of the o¢ cial series extended back in time using information from establishment surveys published by the International Labor Organization (ILO). Third, we adjust our quarterly indicators to ensure that they feature statistical properties of the higher quality annual series. We next discuss the details of our procedure. The Conference Board and the Groningen Growth and Development Centre (GGDC) have produced estimates of hours worked per worker that are comparable across countries, but are only available annually. These series, which are from their Total Economy Database (TED), are adjusted to re(cid:135)ect most sources of cross-country variation in hours worked, including contracted length of the workweek, statutory holidays, paid vacation and sick days, and days lost due to strikes, and are consistent with NIPA measures of output. The TED dataset covers a large sample of developed and developing countries, in many cases starting as early as 1950, and is currently the benchmark source of data for analysis of long-run changes in total hours worked across countries (see Rogerson [2006], Ohanian et al. [2008], Rogerson and Shimer [2010]). We construct a dataset of quarterly indicators of hours worked per worker as follows. For all countries in our sample, we collect quarterly series of hours worked per worker that are consistent with the national accounts from national agencies. We refer to these data as the o¢ cial series. Since these series do not cover theentiresampleperiod,weextendthembacktotheearly1960susingmeasuresofhoursworkedperworker collected from ILO and, in a few instances, the OECD Main Economic Indicators (MEI)3. Although both publications are based on information from establishment surveys, we opted for adopting the ILO series where possible for several reasons. First, the ILO series often measure total hours actually worked, and not just hours paid for. Second, the ILO series cover the non-agricultural sector (i.e. manufacturing, mining and quarrying, construction, commerce, transport and services) whereas the OECD-MEI series typically cover the manufacturing sector only. Third, the ILO series have statistical properties in terms of trend and variability that are closer to the o¢ cial series. 3In particular, we used several historical issues of the ILO Bulletin of Labor Statistics and the ILO International Labour Review to import these data into electronic format. We generally used the latest available vintage of data for each series, smoothing breaks due to changes in the survey methodology using interpolation. 4

Weextendtheo¢ cialseriestothe1960sbyestimatingacountry-speci(cid:133)cstatisticalrelationshipbetween theo¢ cialandtheILOseriesandthenbackcastingtheo¢ cialseriesusingtheestimatedmodelandtheILO data. ToensurethattheestimatedOLScoe¢ cientsarenota⁄ectedbyextremevalues,weremoveoutliersin theILOseriesfollowingtheapproachofIglewiczandHoaglin[1993]. Speci(cid:133)cally, we constructthemodi(cid:133)ed Z-score test statistic x x M =0:6745 t (cid:0) (3) t median(x x) t j (cid:0) j b where x t is the (cid:133)rst di⁄erence of the logarithm of the ILO sberies, x is the median growth rate, and median(x x)isthemedianabsolutedeviation. Wethenidentifyasoutliersthoseobservationsforwhich j t (cid:0) j b M >3:48;thatcorrespondstoaprobabilityof0.0005inastandardnormaldistribution, andreplacethem j t j b with an interpolation that uses both the preceeding and following observations. Notably, our test statistic identi(cid:133)es at most 4 observations as outliers in each country. We then estimate an econometric model of the level of the o¢ cial series hi as a function of a constant t (c); current and lagged values of the ILO series hi , and a time trend: (cid:0) (cid:1) t k (cid:0) (cid:16) (cid:17) e hi =c+(cid:12) hi+:::(cid:12) hi +(cid:13)t+"i (4) t 0 t k t k t (cid:0) e e We estimate country-speci(cid:133)c models using all the observations available for overlapping quarters up to 1984Q4, since there is considerable evidence that the volatility of output declined markedly after 1984 (the GreatModeration). Wedonotincludeindicatorsofactivityamongtheregressorsbecauseseveraleconomists havedocumentedthatthevolatilityoftotalhoursintheUnitedStateshasincreasedrelativetothevolatility of output over time. Including such indicators would thus impose a (cid:133)xed relationship between output and labor input that may be strongly at variance with the data. We select the number of lags (k) using Akaike and Schwarz information criteria and perform Lagrange Multiplier tests on the residuals to test for serial correlation. Overall, this estimation produced adjusted R2 between 0.55 (in the case of Australia, whose speci(cid:133)cation does not include a time trend) and 0.98 (in the case of France and Germany, with a time trend included only in the speci(cid:133)cation for Germany). This estimation is applied to Australia, Canada, France, Germany, Italy, Japan, Norway, and Sweden. Since the o¢ cial series for Austria, Finland, Ireland, and Korea start after 1982, we use the entire sample to estimate our statistical model for these countries. No estimation is applied to the United States (the BLS series we use begins in 1947) and the United Kingdom (for which only the o¢ cial series, which starts in 5

1971, is available).4 The (cid:133)nal step involves adjusting the quarterly indicators of hours worked per worker so that they conform with the annual series obtained from the TED dataset. We follow Denton [1971] as it is commonly implemented by national statistical o¢ ces.5 This method minimizes the weighted adjustments imposed on the constructed quarterly indicators subject to the constraint that the sum of the quarterly adjusted series equals the value of the annual TED series: Min(x z)A(x z) (5) 0 x (cid:0) (cid:0) s:t: x=y (6) X where y is the annual TED series, z is the quarterly indicators we construct using the o¢ cial and the ILO series,xistheadjustedquarterlyseriesthatwewilluseinouranalysis,andA=D D isawieghtingmatrix. 0 Note that using the identity matrix to weight the observations would evenly distribute the discrepancy between the annual and (the sum of the) quarterly series across quarterly observations, thus introducing discrete jumps at the start of each year. Denton shows that a penalty function based on the di⁄erence between the (cid:133)rst di⁄erence of the two series [(cid:1)(x z)]2 or the proportional (cid:133)rst di⁄erence of the two (cid:0) series 1[(cid:1)(x z)]2 does not su⁄er fro (cid:16) m X this shortcom (cid:17) ing.6 These two approaches yield very similar z (cid:0) (cid:16)X (cid:17) resultsandwepresentresultsusingtheproportional(cid:133)rstdi⁄erencespeci(cid:133)cation. Table1showsthecountries and their time periods considered in our sample. [Insert Table 1 here] 2.2 Testing the accuracy of the methodology This section tests the quality of the data construction methodology by comparing actual hours in the U.S., Germany, and Japan to constructed hours from these countries. All three of these countries have o¢ cial data for the early years, as the U.S. data covers the entire period, German data begins in 1970 and Japan(cid:146)s data begins in 1968. 4Eurostat produces a series of hours worked per worker in Spain which starts in 1995. However, we were not able to (cid:133)nd consistentsurveydatacoveringthepreviousyears. Thus,weoptedtoincludeSpainonlyintheanalysisoftheGreatRecession. 5For instance, the BLS regularly uses this methodology to derive quarterly estimates of U.S. series (e.g. manufacturing output). 6For a more articulated discussion on the implications of alternative weighting matrix, see Denton (1971). For a broader discussionaboutinterpolationmethods,thereaderisinvitedtochecktheHandbookofQuarterlyNationalAccountsCompilation. 6

Figure 1 plots the constructed US data together with the o¢ cial data. We construct a U.S. series by applying the procedure presented in Section 2.1. That is, we (cid:133)rst collect U.S. hours worked per worker in the non-agricultural sector for the period 1960Q1-1984Q4 from the ILO and correct for outliers using the modi(cid:133)ed Z-score statistics M . We next estimate a relationship, following equation (4), between the o¢ cial t (BLS) series and the ILO series over the period 1975Q1-1984Q4. We then use the estimated coe¢ cients and the ILO series to backcast the o¢ cial series from 1974Q4 to 1960Q1. Finally, we adjust this extended series, that now covers the whole sample 1960Q1-2010Q4, to conform with the TED annual data using Denton(cid:146)s procedure. [Insert Figure 1 here] This procedure produces constructed hours worked per worker for the US that matches the o¢ cial hours series very well. Denton(cid:146)s adjustment accurately generates the level of the series while the ILO data largely reproduce the cyclical properties of the series. Table 2 provides additional evidence on the quality of the constructed data by comparing the cyclical properties of the constructed and o¢ cial US hours series, and also for those in Germany and Japan. Statistics are for three measures: (cid:133)rst di⁄erence, HP-(cid:133)lter with a smoothing parameter of 1600, and four-quarter changes of the natural logarithm of the hours series. [Insert Table 2 here] Thestandarddeviationofthecyclicalcomponentsoftheconstructedseriesforthesecountriesiscloseto thestandarddeviationoftheo¢ cialseries. Theconstructedandtheo¢ cialseriesarealsohighlycorrelated. This evidence indicates that our procedure provides an empirically accurate description for the cyclical patterns of hours per worker across these countries. 2.3 Di⁄erences between Fluctuations at the Quarterly and Annual Frequency Since most business cycle research for the countries presented here is conducted with annual data, it is natural to ask if business cycles have di⁄erent features when measured with quarterly data. This section presents peak-to-trough statistics for real GDP and total hours worked for both data frequencies. The time period for each analysis is based on data availability which di⁄ers across countries. [Insert Table 3 here] 7

Table 3 compares the average peak-to-trough changes in output and total hours worked for all NBER business cycle recession dates for the U.S. at both quarterly and annual frequency. We also include peak-totrough changes for France, Germany, Italy, their average (Euro), and the UK using ECRI recession dates. This comparison shows that there is considerably more business cycle volatility in the quarterly data. For instance, the average US recession features a decline of 1.6 percent for GDP and 2.8 percent for total hours workedinthequarterlydata, whereasthesesamestatisticsareonly0.7percentand2.2percentrespectively intheannualdata. Therearealsolargepeak-to-troughdi⁄erencesbetweenthesefrequenciesintheEuropean countries,witha1percentdeclineinrealGDPanda3percentdeclineinhoursworkedinquarterlydatafor the Euro countries, compared to a roughly unchanged real GDP and a 2.6 percent decline in hours worked at the annual frequency. These di⁄erences in volatility arise because annual data invariably smooths the quarterly variation and becausepeaksandtroughstendtooccuratdi⁄enttimesduringthecalendaryear. Forexample,apeakthat occurs at the start of a calendar year impacts measurement di⁄erently than a peak that occurs at the end of a calendar year. We also compared (cid:135)uctuation statistics at the two frequencies using HP (cid:133)ltered data, which allows us to compare di⁄erences in co-movement as well.7 We used the standard smoothing parameter of 1600 for quarterlydataandasmoothingparameterof100fortheannualdata,astypicallydoneintheliterature(see, forinstance,CooleyandOhanian[1991],andRogersonandShimer[2010]). Thereareconsiderabledi⁄erences in the volatilities and correlations between the two frequences in the HP (cid:133)ltered data. In particular, the correlation between total hours and output, among other variables, is signi(cid:133)cantly lower in the quarterly data than in the annual data. These di⁄erences were smaller when we used a smoothing parameter of 10 for the annual data, as recommended by Ravn and Uhlig [2002], although this parameter value has yet to become the benchmark for annual business cycle analysis. In summary, these data suggest that quarterly business cycle measurement di⁄ers considerably from annual data. 2.4 Cross-Country Di⁄erences in Employment Protection Beforeturningtotheanalysis,wenotethatthesecountrieshaveverydi⁄erentlabormarketinstitutionsand regulationsthata⁄ectthecostofhiringand(cid:133)ringworkers. Theseregulationsaretypicallycalledemployment protection legislation. Cross-country di⁄erences in this legislation di⁄erentially impacts the incentives for 7Correlations and volatilities from HP (cid:133)ltered data are available upon request. 8

employers to adjust labor input along the intensive margin compared to the extensive margin. The OECD produces employment protection rankings for OECD countries that measures the strength of these policies, and we summarize the OECD(cid:146)s ranking here [OECD, 2008]. The OECD index is based primarily on (i) the strength of protection of permanent workers against individual dismissal, (ii) the speci(cid:133)c requirements for collective worker dismissal, and (iii) regulations on temporary employment contracts. Several of the European countries studied here have relatively high levels of employment protection. Speci(cid:133)cally, Spain ranks 4th highest in protection (out of 40 countries), while France is 6th, Norway 11th, Germany is 13th, and Italy is 15th. At the other end of the distribution, the UK ranks 38th, Canada 39th, andtheU.S.hastheleastamountofemploymentprotectionofthese40countries. Theselargecross-country di⁄erences in employment protection will provide a useful approach in interpreting the (cid:133)ndings below. 3 Labor Input and Labor Productivity over the Business Cycle This section presents standard business cycle facts about labor input, measured as total hours worked per adult,andlaborproductivity,measuredasoutputperhour,acrosscountriesandovertime. Wealsocompare these statistics to the corresponding measures constructed with employment rather than hours, as typically done in the literature. Throughout the analysis, these statistics refer to the cyclical component of the data obtained after applying the HP (cid:133)lter with smoothing parameter of 1600 to the logged series8. The most striking (cid:133)ndings are that labor input is much more volatile than previously considered, that this volatility changes considerably over time, and that hours-based labor productivity is procyclical but, in contrast to employment-based productivity, signi(cid:133)cantly negatively correlated in some cases with hours worked. Taken together, these facts suggest that employment, which is commonly used in the literature, is a poor proxy for labor input, and that standard international equilibrium models, such as BKK, cannot plausibly account for the observed volatility of hours and the comovement between labor productivity and hours reported here. 3.1 Hours Worked: Volatility and Correlation Figure 2 presents the standard deviation of total hours worked relative to the standard deviation output for all countries in our dataset over the entire period. Hours worked is almost as volatile as output, as the 8We also reproduced all the tables using the (cid:133)rst di⁄erence operator, four-quarter changes, and the BP (cid:133)lter. Results are relatively robust to the various (cid:133)ltering procedure and are available upon request. 9

average of this ratio of standard deviations is about 0.9. There is also substantial variation across countries, ranging from 0.65 in Japan to 1.6 in Norway. Table 4 shows this volatility separately for the period 1985-2007, often referred to as the "Great Moderation", and the period before that. The table also compares these volatility statistics to the volatility of employment reported in BKK, which is a standard reference in the international business cycle literature. [Insert Figure 2 and Table 4 here] Totalhoursworkedinthepre-1984periodare,onaverage,three-quartersasvolatileasoutput. Moreover, theintensivemarginisaboutasimportantastheextensivemargin,withthestandarddeviationofhoursper workerintheEurocountriesandinJapanaslargeasthestandarddeviationofemployment. These(cid:133)ndings contrast not only with volatility statistics based only on employment, but also with the statistics based on U.S. hours data as reported in Hansen [1985], Kydland [1995], Cho and Cooley [1994], and Hall [2009], in which the volatility of hours per worker is less than half of the volatility of employment.9More generally, theseresultsareconsistentwiththeviewthatdi⁄erentlabormarketinstitutions,suchasdi⁄erencesinhiring and (cid:133)ring costs and work-sharing arrangements, may signi(cid:133)cantly a⁄ect the extent to which European and Japanese (cid:133)rms adjust labor input along the extensive versus the intensive margin.10 Table 4 also shows that the volatility of total hours worked has increased dramatically during the Great Moderation,whentotalhourshavebecomeasvolatileasoutput. Therelevanceofthisobservationistwofold. First, it extends recent (cid:133)ndings of Gal(cid:236) and Gambetti [2010] (GG hanceforth) and Gal(cid:236) and Van Rens [2011] (GVR henceforth) for the United States to a broader set of countries, thus warranting a general explanation forwhythisisthecase. Speci(cid:133)cally,GGandGVRinterprettheU.S.evidenceasconsistentwithanincrease in labor market (cid:135)exibility due to U.S.-speci(cid:133)c changes in policies or institutions. Our (cid:133)nding, in contrast, suggests that the Great Moderation requires an explanation that applies to most high income countries.11 Second, this increase in the relative volatility of total hours is at variance with a strong version of the "good luck" interpretation of the Great Moderation (see Stock and Watson [2002]). In particular, a proportional reduction of the variance of all shocks, as proposed by this interpretation, would preserve the ratio of 9Incidentally, we note that since we are reporting standard deviations (of HP residuals), the volatility of the intensive and theextensivemargindonotsumuptothevolatilityoftotalhoursworked. Whenwecomputethevarianceoftheseseriesusing the (cid:133)rst di⁄erence operator, for which terms are additive up to a covariance term, we (cid:133)nd that the two margins account for nearly 50 percent ofthe variance oftotalhours worked. 10Recentwork by Fang and Rogerson [2010],forinstance,showsthat,in steady state,higher(cid:133)ring costsinduce (cid:133)rmsto cut employmentand increasehoursperworkeras,from theperspectiveoftheproduction function,thetwomarginsaresubstitute inputs. 11Barnichon[2010]arguesthatlowerlabormarketfrictionscannotaccountforthelargeincreaseinhoursworked,suggesting that otherstructuralchanges might be quantitatively responsible for this change in volatility. 10

volatilities across variables. While there is evidence that the volatility of consumption and investment have not changed much relative to the volatility of output, this is not the case for hours worked. We next analyze how labor input covaries with output and how the extensive and intensive margin are related over the cycle. Figure 3 presents the correlation of labor input with output over the entire period (panel a) and in the pre- and post-1984 period (panel b). Total hours are procyclical, but the magnitude of this correlation varies across countries. In particular, labor input is strongly procyclical in Canada, U.K., and U.S., but less so in the Euro countries and in Japan. Moreover, labor input has generally become more correlated with output in the post-1984 period, but this correlation has decreased somewhat in the United States.12 [Insert Figure 3 here] Panels(c)and(d)showthathoursperworkerandemploymentareonlyweaklycorrelatedoverthecycle. In particular, this correlation is roughly zero in Euro countries over the whole sample, and has weakened signi(cid:133)cantly over time, becoming negative after 1984. These cyclical correlations suggest that labor input is highly synchronized with output (cid:135)uctuations in Canada, U.K. and U.S., but less so in the Euro countries and Japan. In addition, the adjustment along the intensive and the extensive margin in the United States is almost contemporaneous, whereas in Euro countriesittakesplacewithdi⁄erentleadsandlags. Thisresultpointstothefactthatlabormarketrigidities, which tend to be higher in the latter group of countries, may signi(cid:133)cantly a⁄ect the timing of adjustment along the two margins. 3.2 Cyclical Labor Productivity This subsection describes the behavior of labor productivity and how the features of productivity di⁄er depending on whether employment or our measure of hours is used to construct productivity. Figure 4 summarizes the cyclical behavior of productivity over time. [Insert Figure 4 here] The full bars in panel (a) show that labor productivity is generally procyclical between 1960 and 2007, with an average correlation of about 0.5. For comparison, we also show the employment-based measure of 12GG also document this observation forthe U.S. 11

laborproductivity,whichissigni(cid:133)cantlymoreprocyclicalthanthehours-basedmeasure. Thisisparticularly the case in Canada and the United States, and to a lesser extent in the United Kingdom. Panel (b) shows how the correlation of the hours-based measure of productivity changed over time. There are important di⁄erences across countries, with this correlation falling over time in the U.K. and the U.S., but remaining essentially unchanged in Euro countries and Japan. Panel (c) shows that the correlation between the hours-based measures of labor productivity and the hours-based measures of labor input (full bars) is negative across countries. This feature of the data is remarkable because it is di¢ cult to reconcile with standard real business cycle theories where the cycle is driven only by productivity shocks. In addition, employment-based labor productivity tends to be much less negatively correlated with employment-based labor input, thus con(cid:133)rming that using employment as a proxy for labor input might distort basic empirical facts. Panel(d)showsthatthehours-basedcorrelationbetweenlaborinputandlaborproductivityhasdeclined somewhat in the post-1984 period. While it is well established that labor productivity and hours are only weaklycorrelatedintheU.S.,andthatthiscorrelationhasdeclinedsigni(cid:133)cantlyduringtheGreatModeration (see, for instance, GG and Barnichon [2010]), our evidence suggests that these patterns are common across industrial countries. 3.3 Implications of Hours and Productivity Data for Theory Thisanalysisindicatesthataccountingforthesedatarequiresmodelsthatgeneratemuchhigherlaborinput volatilitythanexistingmodels,andthatdonotgenerateastrongcorrelationbetweenlaborandproductivity. To see this, note that international real business cycles models, such as BKK reproduce about half of the volatility observed in the data (see top of Table 4). But models that deliver larger labor responses to shocks than BKK, such as Hansen [1985] and Greenwood et al [1988] typically generate a counterfactually high correlation between labor input and labor productivity. 13 These data also have implications for accounting for the Great Moderation. Speci(cid:133)cally, several researchers have concluded that the relaxation of borrowing constraints are an important factor in the decline in output volatility (see Campbell and Hercowitz [2006] and Iacoviello and Pavan [2010]). But at the same time,thesetheoriesalsoimplyareductioninthevolatilityofhoursworked,aswealthe⁄ectsbecomestronger 13The Hansen formulation is commonly used in closed economy settings, while Greenwood et al preferences are commonly used in open economy settings (see Ra⁄o [2008]). 12

over time. These data do not support this implication of the borrowing contraint hypothesis. 4 Business Cycle Diagnostics Cole and Ohanian [2002] and Chari, Kehoe, and McGrattan [2002, 2007] present a diagnostic methodology forbroadlyevaluatingclassesoftheoriesof(cid:135)uctuations. Thisprocesshasbeenusedimplicitlyinoneformor anotherbymuchoftherealbusinesscycleliterature,includingKydlandandPrescott[1982],andinanalyses thatfocusonchannelsotherthanproductivity,includingHall[1997],ColeandOhanian[2004],Gali,Gertler, and Lopez-Salido [2007], Shimer [2009, 2010], and Mulligan [2008]. Thissectionusesthenewhoursdatatore-assessdiagnosticwedgesofthisframework. We(cid:133)rstconstruct these wedges using the standard measure of employment as labor input, and then compare them to wedges constructed using the measures of hours worked reported earlier in this paper as the measure of labor input. Thisprocessinvolvesusingtimeseriesdataonoutput,consumption,investment,andlaborinputtomeasure wedges from the (cid:133)rst order conditions in a parameterized optimal growth model, and then use those wedges as diagnostics for developing theories of (cid:135)uctuations. 4.1 The Diagnostic Framework The theoretical framework is given as follows. Preferences are: maxE 1 (cid:12)t ln(C ) B L 1+1 " (7) 0 t (cid:0) 1+ 1 t t=0 (cid:26) " (cid:27) X and the technology, resource constraint and the law of motion for capital are given by: AK(cid:18)L1 (cid:18) =Y =C +I +G ; (8) t t(cid:0) t t t t (1+g)K =(1 (cid:14))K +I ; (9) t+1 t t (cid:0) where the variables are, respectively, per-capita measures of consumption (C), fraction of time devoted to marketactivities(L),capitalstock(K);realoutput(Y);investment(I);andgovernmentspending(G):The variable A denotes is productivity parameter and g is the exogenous growth rate of technology, respectively: All per-capita variables are detrended at a two percent annual rate. 13

The parameters are chosen as follows. We set (cid:12) to 0:99; (cid:18) = 0:36; (cid:14) = 0:0175;g = 0:005; and "; which represents the Frisch labor supply elasticity, is 1. Note that the Frisch elasticity is constant regardless of the steady state level of hours worked. Typically, this framework is used to construct four deviations, or wedges: (1) a productivity wedge, which is the ratio between output and the Cobb-Douglas aggregator of capital and hours worked (i.e. the Solow Residual), (2) a labor wedge, which is the di⁄erence between the marginal rate of substitution between consumption and leisure and the marginal product of labor, (3) a capital market wedge, which is the di⁄erence between the intertemporal marginal rate of substitution and the return to capital, and (4) a resourceconstraintwedgethatmeasureschangesintheallocationofoutputbetweenconsumption,investment, and government spending. We will focus on productivity and labor wedges as these are typically the most important quantitatively in terms of accounting for (cid:135)uctuations. These deviations are given as: Y Z = t (10) t AK(cid:18)L1 (cid:18) t t(cid:0) X = B C tL 1+1 " (11) t (1 (cid:18)) Y t t (cid:0) The wedges are analyzed using HP (cid:133)ltered series for data through the end of 2007. We separately analyzethe2008-09recessionfrom2008:1- through2009:4, whichisthedateofthehourstroughoftheU.S. recession, and allows us to compare our (cid:133)ndings to those of Ohanian [2010], who measured these deviations in the U.S. using both hours and employment, but only employment for the other G7 countries (Canada, France, Germany, Italy, Japan, and the United Kingdom). 4.2 Cyclical Features of Labor and Productivity Wedges in OECD Countries: 1960 - 2007 Since the labor supply elasticity plays a key role in shaping the reponse of total hours to changes in wages, we begin by reporting the volatility of the cyclical components of the labor wedge for di⁄erent values of this parameter. We then compare the volatility and the correlation with output of the labor and productivity wedges constructed using total hours worked and alternatively employment as measures of labor input respectively. We focus on HP (cid:133)ltered logged data to facilitate comparison with existing business cycle 14

studies.14 The analysis will show that the wedges are large, they (cid:135)uctuate signi(cid:133)cantly over the business cycle, they di⁄er considerably depending on whether they are measured with total hours or employment, and their properties di⁄er across countries. These (cid:133)ndings also identify signi(cid:133)cant puzzles about the nature of European labor market (cid:135)uctuations given the size of European (cid:133)ring costs and how those costs a⁄ect incentives to adjust between the intensive versus extensive margin. 4.2.1 The Volatility of the Labor Wedge and its Components Table5presentsthepropertiesofthedetrendedlaborwedgefordi⁄erentlaborsupplyelasticities. Standard growththeory,inwhichthemarginalrateofsubstitutionbetweenconsumptionandleisureisalwaysequated to the wage, implies that movements in the consumption-ouput ratio, C=Y, should be exactly o⁄set by 1+1 changes in the labor input component, L " for the utility function presented here. Thus, the labor wedge t arises when movements in these variables do not o⁄set each other. The table also reports statistics on C=Y and labor input. 15 [Insert Table 5 here] The table shows that the labor wedge is highly volatile, about twice as volatile as either C=Y or L for the benchmark elasticity of one. The volatility of the two components, C=Y and L; is very similar across countries with the exception of the US, in which there is relatively much less consumption volatility and much more labor volatility. The volatility of the labor wedge increases for lower labor supply elasticities, becoming nearly 50 percent more volatile for an elasticity of 0.5. Moreover, the volatility of the labor wedge has declined markedly in both the US and in Euro countries over time, but has changed very little, on average, across OECD countries. The table also shows that the labor wedge is primarily related to movements in labor, rather than C=Y: Speci(cid:133)cally, the correlation between the wedge and labor is around 0.9, while its correlation with C=Y is close to zero in a number of countries. These (cid:133)ndings suggest that competitve and/or frictionless models of the labor market can account for only a relatively small fraction of cyclical changes in hours in these 14We also construct cyclicalwedges using other (cid:133)lters, such as (cid:133)rst di⁄erence, four-quarter changes, and band-pass. Results are available upon request. 15Foran analysis oftrend changes in the labor wedge and its component,see Ohanian et al. [2008]. 15

OECD countries. To see this, note that in such a model, changes in labor input are accounted for entirely by changes in C=Y : L 1+1 " = B C t (12) t (1 (cid:18)) Y t (cid:0) Thus,themodelpredictsthatthevolatilityoflaborisboundedabovebythevolatilityofconsumptionas the Frisch elasticity becomes in(cid:133)nite. However, labor input is typically more volatile in these countries than C=Y. This suggests that research should focus considerably more on the role of labor market imperfections in understanding (cid:135)uctuations. 4.2.2 Cyclical Propeties of Labor and Productivity Wedges [Insert Table 6 here] Table 6 shows the volatility of labor and productivity wedges relative to the volatility of output for both measures of labor input. The most striking (cid:133)nding is that the relative di⁄erence in the size of the hours and employment labor wedges are about the same across countries. Speci(cid:133)cally, the di⁄erence in the Euro countries,inwhichemploymentprotectionisveryhigh,andthusthereshouldberelativelymoreadjustment along the intensive margin, is 1.43 for the hours labor wedge compared to 1.11 for the employment labor wedge. However, the di⁄erence between these wedges in the US, in which there is comparatively little employment protection is 1.68 compared to 1.35, which is almost the same percentage di⁄erence as in the Euro countries. The fact that employment protection is much higher in Europe strongly suggests that there should be a signi(cid:133)cantly larger di⁄erence between hours and employment labor wedges in these countries relative to the US. Table 6 also shows the cross-correlations between the hours-based and employment-based wedges and output between 4 lags and 4 leads. For the US, the labor wedge is procyclical, as the contemporaneous correlation is 0.77 for the hours-based wedge (and 0.66 for the employment based wedge) which means that the wedge between the marginal rate of substitution and the marginal product of labor widens when the economyisbelowtrend,andnarrowswhentheeconomyisabovetrend. Thelaborwedge-outputcorrelations acrossalllagsandleadsrangebetween0.30to0.72forhoursand0.21to0.68foremployment. Productivity is strongly contemporaneously procyclical, as the correlation for the two measures of TFP is 0.78 and 0.85, respectively. 16

The cyclical correlation of the labor wedge with output is not as strong in Europe compared to the US (and also varies considerably across European countries, not shown). The labor wedge in Euro countries is contemporaneoulsy much less procyclical than in the U.S., with a correlation ranging between 0.42 for the hours-based wedge and 0.27 for the employment-based. In contrast, the cyclicality of the labor wedge in the UK is closer to that of the US, where the contemporaneous correlation is 0.64 (hours-based) and 0.46 (employment-based). ThecyclicalpatternoftheproductivitywedgeinEuropeisverysimilartothatintheUS(and,moregenerally, very similar across countries in Europe, not shown). The correlation of the hours-based productivity wedge is 0.88 and the correlation for the employment-based productivity wedge is 0.92. Insummary,thisanalysissuggeststhatthesewedgesarelargerinEuropethanintheUS,thatthecylical pattern of the labor wedge is quite di⁄erent in Europe, and that the cyclical pattern of productivity in Europe is similar to that in the US. 4.3 Labor and TFP Wedges in the OECD During the Great Recession ThissectionreportsthelaborandproductivitywedgesfortheGreatRecession. Wepursuethisanalysissince Ohanian[2010](cid:133)ndsthatthesewedges-measuredusingonlyemploymentaslaborinput-di⁄erremarkably between the U.S. and the other advanced countries during the Great Recession. Ohanian documented that the U.S. Great Recession is largely due to a very large decline in labor input associated with an historically large labor wedge, and that productivity is close to trend. In contrast, other G7 countries are virtually the opposite of that in the US, with much smaller employment declines, large productivity declines, and no quantitatively important labor wedge. Ohanian suggests that these di⁄erent patterns pose a challenge for the widely held view that all of these recessions were the result of similar banking crises that operated through the same economic channels. But Ohanian(cid:146)s (cid:133)ndings are entirely based on employment. The fact that (cid:133)ring costs are higher in several of the other G7 compared to the US suggests that labor input adjustment should have taken place much more on theintensivemargin,andthatOhanian(cid:146)suseofemploymentwouldgeneratedownard-biasedSolowResiduals and upward-biased labor wedges in the European countries. We therefore reassess this analysis using hours rather than employment. Our main (cid:133)nding is that the this puzzle is perhaps even more striking, as using total hours, which should be a much better measure of labor input given (cid:133)ring costs, does not materially change Ohanian(cid:146)s results. 17

Table7showsoutput,labor,andlaborandproductivitywedgesacrosscountriesfortheGreatRecession. We(cid:133)rstconsiderOhanian(cid:146)scomparisonoftheUStoaWesternEuropeanaverageofFrance,Germany,Italy, UK, Austria, Finland, Norway, and Sweden. The table shows that the labor wedge is much larger in the US than in these European countries. Speci(cid:133)cally, the US hours-labor wedge is -14.9 percent in 2009:4. In contrast, the hours-based labor wedge is only -2.7 percent on average for the Western European countries studied by Ohanian. [Insert Table 7 here] The European labor wedges are also very small during the Great Recession compared to other postwar recessionsinthesecountries. Theaveragepeak-to-troughhours-laborwedgefortheaboveEuropeancountries between 1960 and 2007 was about 6 percent, while output fell about 5 percent in these recessions (see Appendix B for details). If this pre-Great Recession relationship between the labor wedge and output also heldduringtheGreatRecession, then, giventheobservedoutputdeclineofnearly11percent, theEuropean labor wedge would have been around 11 percent, about four times larger than observed. We next assess the quantitative importance of these labor and productivity wedges using the model economy described and parameterized in section 4.1. We conduct two analyses. First, we feed in the productivitywedgesintothemodeleconomyfrom2007:4toeachcountry(cid:146)srespectivetroughinhoursworked, and calculates optimal path for labor, output, consumption, and investment relative to their trend values. Second, we replace the productivity wedge with the labor wedge and conduct a symmetric analysis to the previous one. Notsurprisingly,thelaborwedgeaccountsforverylittleoftheGreatRecessioninEurope. Table8shows the percentage of trough output and labor accounted for by the model described in Section 4.1 in response to the labor wedge and the productivity wedge measured in the data, using both employment and hours worked as labor input. TheUShours-laborwedgeaccountsforalmostallofUSoutput,whereastheEuropeanhours-laborwedge accounts for only about 10 percent of the drop in European output. In contrast, Table 8 also shows the relative contribution of productivity for the US and Europe. Productivity explains only about 20 percent of the drop in output, and almost none of the drop in hours in the US, whereas productivity accounts on average for almost all of the decline in output and hours in the European countries. 18

[Insert Table 8 here] The relevance of these (cid:133)ndings is twofold. First, the large di⁄erences in the size and importance of labor and productivity wedges during the Great Recession across countries pose a challenge for the common view thatthecoincidentrecessionsin2008-2009weretheconsequenceofverysimilarbankingcrisesthatdepressed economies through the same channels. Second, since many researchers and policymakers hold the view that the U.S. Great Recession was generated by a large (cid:133)nancial shock, our (cid:133)ndings also highlight the need for models in which (cid:133)nancial shocks depress labor and output through an increase in the labor wedge. Recent research by Arellano, Bai, and Kehoe [2011], Lopez [2011], and Perri and Quadrini [2011], develop models in which (cid:133)nancial shocks depress labor and output by operating through the labor wedge. Table 8 also shows labor and productivity wedges for some OECD countries that were not analyzed by Ohanian [2010]. In particular, both Spain and Ireland have very large labor wedges. This similarity betweentheUS,Spain,andIrelandsuggestsanewavenueforunderstandingcross-countryexperiences. One possibility relates to the housing market. Speci(cid:133)cally, some have argued that in the US, very large housing pricedeclines,coupledwithgovernmentpoliciesdesignedtocushiontheimpactoffallingpricesonborrowers, including mortgage modi(cid:133)cation programs, changed the incentives for unemployed individuals to take new jobs or for homeowners to relocate from relatively depressed areas to areas with better job prospects (see Mulligan [2008] and Herkenho⁄and Ohanian [2011]). We present some limited evidence on the relationship between the labor market and housing. Figure 5 shows indicators in housing activity, measured in terms of changes in house prices and in employment in the construction sector, for the US and a number of other countries. It is interesting that the three countries which have large labor wedges - the US, Spain, and Ireland - have also experienced very large correction in housing activities, with marked housing price and employment declines.16 These (cid:133)ndings suggest an interesting avenue for future research by developing theories that relate a widening labor wedge to sectoral dislocation in construction. [Insert Figure 5 here] 16We discount the fact that the Spanish housing price series does not fall as much as those in Ireland and the US, as there are concerns about the measurement ofthe Spanish housing price series (see Fernandez-Villaverde and Ohanian (2010)). 19

5 Summary and Conclusions Labor (cid:135)uctuations are a central focus of business cycle research, but this research has been limited significantly by the fact that typically only employment, rather than total hours worked, is available for many OECD countries. This paper has constructed quarterly time series of total hours worked for 17 OECD countries, with a focus on constructing hours that are consistent with national income and product account constructs. These hours measures provide new data that can shed light on a number of questions, including changes in the nature and sources of (cid:135)uctuations over time, how changes in (cid:133)scal and monetary policy have impacted (cid:135)uctuations over time, and how changes in labor market regulations have impacted (cid:135)uctuations over time. The results reported here stand in sharp contrast to many common views about cyclical labor market dynamics. Speci(cid:133)cally, these new data indicate that employment is a poor proxy for cyclical labor input, and consequently that employment provides a poor measure of productivity, as in many OECD countries about50percentoflaboradjustmentoccursalongtheintensivemargin. Employment(cid:135)uctuationsinmuchof WesternEuropeappeartobemuchtoohighcomparedtotheUS,givenmuchhigherhiringand(cid:133)ringcostsin Europe. And given the large (cid:135)uctuations in European hours, employment-based labor wedge (cid:135)uctuations in Europe appear to be too high, and hours-based labor wedge (cid:135)uctuations appear to be too low. Our (cid:133)ndings also have implications for the international Great Recession. Speci(cid:133)cally, there is a common view that the Great Recession across countries was the result of very similar responses to very similar banking crises. The (cid:133)ndings presented here contrast with that view, as Western European recessions feature very small labor wedges compared to the US, measured either with employment or hours, and instead feature much larger productivity shocks than the US. Thedatapresentedherewillaidfutureresearchinaddressingthesepuzzles,withafocusonunderstanding whytheintensivemarginadjustmentisnotlargerduringEuropeanrecessions,whylaborwedgesaresosmall in many European countries during the Great Recession, and why they are so large in the US. 20

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Table 1. Hours Worked per Worker: Sample Australia 1970-2010 Italy 1960-2010 Austria 1965-2010 Japan 1960-2010 Canada 1960-2010 Korea 1970-2009 Finland 1960-2010 Norway 1960-2010 France 1960-2010 Sweden 1975-2010 Germany 1960-2010 UK 1971-2010 Ireland 1960-2010 U.S. 1960-2010 25

Table 2. Assessing OR [2011] Procedure X: Hours per worker constructed following OR [2011] procedure Y: Official series of hours per worker First Difference 4-Quarter Changes HP Residuals Std. Deviation(X)/Std. Deviation(Y) U.S. 1.2 1.1 1.1 Germany 0.9 1.0 1.0 Japan 0.8 0.9 0.9 Correlation(X,Y) U.S. 0.7 0.9 0.9 Germany 0.9 1.0 1.0 Japan 0.8 1.0 1.0 NB. In all cases, series have been logged and adjusted using Denton [1971] procedure. Regressions are estimated over the sampe 1975-1984. Statistics refer to the following country-specific samples: U.S. (1960Q1-1984Q4), Germany (1970Q1-1984Q4), and Japan (1968Q1-1984Q4).

Table 3. Output and Hours at Trough, Deviations from Peak (Average, All Recessions) Output Total Hours Quarterly Annual Quarterly Annual United States -1.6 -0.7 -2.8 -2.2 France -0.6 0.1 -1.5 -1.4 Germany -0.9 -0.2 -4.8 -3.9 Italy -1.5 -0.1 -2.9 -2.3 Euro -1.0 -0.1 -3.1 -2.6 United Kingdom -4.1 -2.2 -4.2 -4.2 Note: For the U.S., recessions refer to NBER dates, excluding the 1960-1961 episode. For other countries, ECRI dates.

Table 4. Volatility of Labor Input (Standard Deviation Relative to Output) Canada Euro* Japan U.K. U.S . OECD Mean BKK [1995] 1970:Q1-1990:Q2 Data (Employment) 0.86 0.53 0.36 0.68 0.61 0.64 Benchmark Model 0.49 OR [2011] 1960:Q1-1984:Q4 Total Hours 1.03 0.75 0.67 0.67 0.84 0.81 Hours per worker 0.27 0.48 0.47 0.31 0.25 0.44 Employment 0.89 0.51 0.31 0.54 0.71 0.63 1985:Q1-2007:Q4 Total Hours 0.91 0.78 0.71 1.18 1.23 1.02 Hours per worker 0.40 0.54 0.55 0.49 0.39 0.58 Employment 0.65 0.70 0.36 0.85 0.97 0.80 NB. Statistics refer to reisiduals of the HP-filter. * Euro is the average of France, Germany, and Italy.

Table 5. Elements of the Labor Wedge (a) Standard Deviation Canada Euro Japan U.K. U.S. OECD Mean 1960:Q1-2007:Q4 e = 0.5 3.65 2.90 2.75 3.91 3.91 3.97 e = 1.0 2.34 1.96 1.85 2.65 2.51 2.73 e = 2.0 1.73 1.52 1.46 2.04 1.83 2.16 C/Y 1.17 0.93 1.22 0.99 0.72 1.34 L 1.38 1.01 1.03 1.32 1.43 1.34 1960:Q1-1984:Q4 e = 1.0 2.69 2.18 1.97 2.76 2.73 2.68 C/Y 1.07 1.08 1.49 1.14 0.89 1.43 L 1.50 1.14 1.08 1.22 1.59 1.27 1985:Q1-2007:Q4 e = 1.0 1.89 1.69 1.71 2.58 2.25 2.73 C/Y 1.27 0.74 0.81 0.78 0.48 1.21 L 1.23 0.85 0.97 1.37 1.22 1.39 (b) Correlation Canada Euro Japan U.K. U.S. OECD Mean 1960:Q1-2007:Q4 C/Y -0.17 0.15 0.15 0.19 -0.36 0.22 L 0.91 0.89 0.81 0.94 0.97 0.87 1960:Q1-1984:Q4 C/Y -0.18 0.12 0.24 0.46 -0.39 0.37 L 0.94 0.89 0.74 0.91 0.97 0.82 1985:Q1-2007:Q4 C/Y -0.17 0.21 -0.06 -0.07 -0.32 0.08 L 0.86 0.90 0.91 0.96 0.98 0.90 Note: Statistics refer to the HP-detrended component of the labor wedge for different Frisch labor supply elasticities (e). C/Y is the HP-detrended consumption to output ratio and L is HP-detrended total hours worked.

Table 6. Properties of the Labor and Productivity Wedges (a) Standard Deviation relative to output Canada Euro Japan U.K. U.S. OECD Mean Hours-based labor wedge 1.66 1.43 1.19 1.76 1.68 1.78 Empl.-based labor wedge 1.38 1.11 0.74 1.31 1.35 1.47 Hours-based productivity wedge 0.66 0.82 0.81 0.82 0.61 0.77 Empl.-based productivity wedge 0.75 0.87 0.94 0.88 0.72 0.81 (b) Cross-correlation with output -4 -3 -2 -1 0 1 2 3 4 Canada Hours-based labor wedge 0.34 0.44 0.56 0.67 0.66 0.58 0.44 0.27 0.10 Empl.-based labor wedge 0.33 0.42 0.49 0.55 0.50 0.43 0.31 0.17 0.03 Hours-based productivity wedge -0.16 0.05 0.25 0.49 0.76 0.70 0.61 0.53 0.41 Empl.-based productivity wedge -0.10 0.12 0.33 0.58 0.84 0.77 0.67 0.55 0.41 Euro Hours-based labor wedge 0.21 0.31 0.39 0.42 0.42 0.37 0.28 0.17 0.06 Empl.-based labor wedge 0.36 0.42 0.44 0.39 0.27 0.26 0.15 0.07 -0.04 Hours-based productivity wedge -0.04 0.14 0.36 0.57 0.88 0.68 0.54 0.39 0.22 Empl.-based productivity wedge -0.07 0.13 0.36 0.60 0.92 0.72 -0.58 0.41 0.25 U.K. Hours-based labor wedge 0.58 0.67 0.72 0.72 0.64 0.54 0.40 0.22 0.06 Empl.-based labor wedge 0.55 0.59 0.58 0.54 0.46 0.37 0.22 0.04 -0.09 Hours-based productivity wedge -0.13 0.09 0.30 0.54 0.83 0.72 0.63 0.54 0.37 Empl.-based productivity wedge 0.46 0.66 0.78 0.83 0.77 0.63 0.44 0.25 0.09 U.S. Hours-based labor wedge 0.46 0.66 0.78 0.83 0.77 0.63 0.44 0.25 0.09 Empl.-based labor wedge 0.59 0.74 0.80 0.79 0.66 0.52 0.31 0.11 -0.05 Hours-based productivity wedge -0.26 -0.09 0.19 0.48 0.78 0.76 0.68 0.52 0.38 Empl.-based productivity wedge -0.23 -0.03 0.26 0.56 0.85 0.82 0.73 0.57 0.42 OECD Hours-based labor wedge 0.29 0.39 0.46 0.49 0.46 0.40 0.30 0.18 0.07 Mean Empl.-based labor wedge 0.40 0.44 0.45 0.42 0.32 0.28 0.17 0.08 -0.03 Hours-based productivity wedge -0.12 0.06 0.27 0.49 0.78 0.65 0.54 0.42 0.26 Empl.-based productivity wedge -0.14 0.06 0.29 0.52 0.84 0.67 0.57 0.43 0.28 NB. Statistics refer to residuals of the HP-filter (smoothing parameter is 1600).

Table 7. Great Recession, Deviation from Peak (US Hours Trough) Data Labor Wedge Productivity Wedge Output Hours Empl. Hours Empl. Hours Empl. United States -7.3 -8.4 -6.8 -14.9 -11.9 -1.4 -2.5 Canada -8.0 -4.8 -2.9 -4.9 -1.1 -5.1 -6.3 Euro -8.4 -2.9 -1.3 -2.8 0.3 -6.2 -7.2 United Kingdom -9.9 -3.3 -2.4 -4.0 -2.2 -7.4 -7.9 Western Europe* -9.4 -3.7 -2.3 -2.7 0.1 -6.9 -7.7 Spain -9.1 -8.4 -10.0 -17.2 -20.1 -4.5 -3.4 Ireland -18.2 -15.8 -12.7 -23.7 -18.1 -9.3 -11.4 Japan -8.3 -4.7 -0.7 -5.3 2.7 -5.0 -7.5 Korea -2.4 -5.0 -1.1 -9.9 -2.4 -0.5 -3.0 * France, Germany, Italy, UK, Austria, Finland, Norway, Sweden.

Table 8. Great Recession, Deviation from Peak (Hours Trough) Data Predicted, Model 1 Predicted, Model 2 Predicted, Model 3 Predicted, Model 4 Output Hours Empl. Output Hours Output Empl. Output Hours Output Empl. U.S. -7.3 -8.4 -6.8 -6.8 -9.8 -5.1 -7.5 -2.0 -0.3 -3.5 -0.6 Canada -8.0 -4.8 -2.9 -2.2 -3.1 -0.5 -0.7 -7.0 -1.6 -8.7 -2.0 Euro -8.4 -2.9 -1.3 -1.2 -1.8 0.1 0.1 -8.6 -2.0 -9.9 -2.3 UK -9.9 -3.3 -2.4 -1.7 -2.4 -0.9 -1.3 -10.2 -2.4 -10.9 -2.5 Western Europe* -9.4 -3.7 -2.3 -1.2 -1.7 0.1 0.0 -9.5 -2.2 -10.7 -2.5 Spain -9.1 -8.4 -10.0 -8.0 -11.4 -9.2 -13.3 -6.1 -1.4 -4.6 -1.0 Ireland -18.2 -15.8 -12.7 -11.4 -16.4 -8.1 -11.9 -13.3 -2.8 -16.2 -3.5 Japan -8.3 -4.7 -0.7 -2.3 -3.3 1.2 1.6 -7.0 -1.5 -10.4 -2.3 Korea -2.4 -5.0 -1.1 -4.5 -6.2 -1.0 -1.4 -0.8 -0.1 -4.3 -0.8 NB. Euro is the average of France, Germany, and Italy. * France, Germany, Italy, U.K., Austria, Finland, Norway, Sweden. ** Model 1=labor wedge, hours; Model 2= labor wedge, employment; Model 3=productivity wedge, hours; Model 4=productivity wedge, employment.

Figure 1. Testing the OR [2011] Procedure using U.S. Data 1900 Official OR 2011 1850 1800 1750 1700 1650 1960 1965 1970 1975 1980 1985

Figure 2. Volatility of Labor Input (1960-2007) STD(Labor Input)/STD(Output) 2.0 1.8 1.6 1.4 1.2 1.0 Average 0.8 0.6 0.4 0.2 0.0 JAP GER ITA AUT IRE FRA AUS KOR USA FIN ESP UK CAN SWE NOR

Figure 3. Cyclical Properties of Total Hours Worked (1960-2007) (a) Correlation with Output (b) Changes in Correlation with Output 1.00 1.00 0.75 0.75 0.50 0.50 0.25 0.25 0.00 0.00 pre-1984 post-1984 -0.25 -0.25 Canada Euro Japan U.K. U.S. Mean Canada Euro Japan U.K. U.S. Mean (c) Correlation: Hours per worker and Employment (d) Changes in Correlation: Hours per worker and Employment 0.75 0.75 0.50 0.50 0.25 0.25 0.00 0.00 -0.25 -0.25 pre-1984 post-1984 -0.50 -0.50 Canada Euro Japan U.K. U.S. Mean Canada Euro Japan U.K. U.S. Mean

Figure 4. Cyclical Properties of Labor Productivity (1960-2007) (a) Correlation with Output (b) Changes in Correlation with Output 1.00 1.00 0.75 0.75 0.50 0.50 0.25 0.25 0.00 0.00 Hours-based Empl.-based pre-1984 post-1984 -0.25 -0.25 Canada Euro Japan U.K. U.S. Mean Canada Euro Japan U.K. U.S. Mean (c) Correlation with Labor Input (d) Changes in Correlation with Total Hours 0.50 0.50 0.25 0.25 0.00 0.00 -0.25 -0.25 -0.50 -0.50 Hours-based Empl.-based pre-1984 post-1984 -0.75 -0.75 Canada Euro Japan U.K. U.S. Mean Canada Euro Japan U.K. U.S. Mean

Figure 5. Housing Sector During the Great Recession A. Nominal House Prices (index, 2007:Q1=100) 120 US Canada Western Europe* Spain Ireland 110 100 90 80 70 60 2007 2008 2009 2010 B. Employment in Construction Sector (index, 2007:Q1=100) 120 110 100 90 US Canada Western Europe* 80 Spain Ireland 70 60 50 40 2007 2008 2009 2010 *Western Europe includes: Finland, France, Germany, Italy, Netherlands, Norway, Sweden, and U.K.