Title
The contribution of labor reallocation to aggregate productivity growth:
Evidence from Canada, France and the United States
Version: 30/04/09

AuthorBenoit Dostie, HEC Montréal
Ali Fakih, HEC Montréal
Lars Vilhuber, Cornell University

Short Summary
A large literature attempts to quantify how factor reallocation contributes toproductivity growth. Whereas it was usually thought that most productivity
growth came from innovation in existing firms, recent research underscores
the importance of firm entry/exit dynamics (e.g. David and Haltiwanger(1992)). At the same time, many studies find that labor reallocation ispersistent across sectors. For example, Stewart (2002), using CPS data from1976 to 2001, estimates that 50% of aggregate productivity growth isattributable to labor reallocation. However, both Bartelsman and Doms
(2000) and Foster, Haltiwanger and Krizan (2000), using firm-level data fromthe U.S., find lesser contributions (0-25%). Lentz and Mortensen (2009) use agrowth model to decompose aggregate productivity growth into threecomponents (1) labor reallocation, (2) innovation within existing firms and (3)firm entry/exit dynamics. In their model, firms are assumed to beheterogenous in their ability to introduce new production processes orinnovations. At entry, the ability to innovate is supposed to be a stochastic
variable. But firms that innovate become more productive and attract
workers from other firms. Thus, the introduction of new production processes
at the establishment level leads to labor reallocation across firm and acrossindustries. It is possible to estimate the contribution of each term toaggregate productivity growth using the method of simulated moments(McFadden (1989)). The estimation procedure relies on finding a set ofparameters that replicate as closely as possible computed moments from

data on employment, sales and wages. Using Danish data, they find thatlabor reallocation contributes a significantly higher percentage of aggregate
productivity growth (55%).
In this article, we assess how general the Lentz and Mortensen results arewith respect to the contribution of labor reallocation to aggregate
productivity growth, by taking their model to a broader selection of data
sets, including data very similar to previous U.S. studies, as well as for
Canada and France. Results will also be compared to those obtained using
alternative productivity decomposition methods proposed by Baily, Hultenand Campbell (1992)) and by Griliches and Regev (1995)).
The US data is constructed from the Longitudinal Business Database (LBD)
and its links to the Annual Survey of Manufacturers (ASM) and thequinquennial Census of Manufacturers (CM) for 1973-2007. The ASM and CM
are accessible to researchers in the U.S. Census Bureau Research DataCenters. For Canada and France, we use the firm-level component of twolinked employer-employee data sets from Canada and France. French datacovers the time period from 1978 to 1996 on 1,142,738 firms obtained
through the EAE (Enquête Annuelle d'Entreprises), an annual survey of firms.
This is the same source for firm-level data as the one used by Abowd andKramarz (1999a). The Canadian data stem from the Workplace and Employee
Survey 1999-2006 (WES). The WES is a restricted-access data set available
in the Statistics Canada network of Research Data Centers (RDC). WES has
been conducted annually since 1999. In this study, we use all 8 years ofavailable data (1999-2006). Abowd and Kramarz (1999b) classify WES as asurvey in which both the sample of workplaces and the sample of workers
are cross-sectionally representative of the target population. Because it does
not come from administrative data, our sample of firms is much smaller inthe case of Canada and comprises about 6,000 workplaces. In the case ofCanada, we are also in the process of securing access to T2/LEAP, anadministrative-level firm database constructed from firm tax records. This isa much larger data base and comparisons of the results between WES andT2/LEAP would be useful to assess the advantage and inconvenient of survey
versus administrative data.
Preliminary results for Canada using the WES data indicate that firm
dynamics explain 33% of aggregate productivity growth while innovation
within firm explains an additional 38%. These results indicate that thecontinual process by which labor is allocated to growing or new firms
explains more than 75% of productivity growth. Results for France and the USare pending. If labor reallocation is found to explain a larger fraction of
aggregate productivity growth in the U.S. than in France, this would
corroborate similar evidence, for instance by Bartelsman, Scarpetta andSchivardi (2005) who find that firm size at entry is lower in the U.S. than inEurope, but subsequent productivity growth for these firms is faster in theU.S. than Europe. The final conclusion will relate the results to theinstitutional features of the labor market in the three countries will be made.
Our findings are of relevance to public policy. On the one hand, if innovation
within firms is the more important driver of productivity, policies that
increase incentives related to R&D are likely to be the best way to increase
productivity growth. However, if labor reallocation is the dominant factor,
policies that emphasize flexibility and dynamism in a well-functioning labormarket have the scope to be important drivers for productivity. Finally, if
most productivity growth is coming from new firms, policies that encourageand promote entrepreneurship are likely to have the highest productivity
gains.