What Determines Productivity

TL;DR: The authors surveys and evaluates recent empirical work addressing the question of why businesses differ in their measured productivity levels, and lays out what I see are the major questions that research in the area should address going forward.

Abstract: Economists have shown that large and persistent differences in productivity levels across businesses are ubiquitous. This finding has shaped research agendas in a number of fields, including (but not limited to) macroeconomics, industrial organization, labor, and trade. This paper surveys and evaluates recent empirical work addressing the question of why businesses differ in their measured productivity levels. The causes are manifold, and differ depending on the particular setting. They include elements sourced in production practices -- and therefore over which producers have some direct control, at least in theory -- as well as from producers' external operating environments. After evaluating the current state of knowledge, I lay out what I see are the major questions that research in the area should address going forward. (JEL D24, G31, L11, M10, O30, O47)

Summary

1. Introduction

• T hanks to the massive infusion of detailed production activity data into economic study over the past couple of decades, researchers in many fields have learned a great deal about how firms turn inputs into outputs.
• Productivity, the efficiency with which this conversion occurs, has been a topic of particular interest.
• The particulars of these studies have varied depending on the researchers' specific interests, but there is a common thread.
• The range's standard deviation across four-digit industries is 0.173, so several industries see much larger productivity differences among their producers.

1.2 The Question of "Why?"

• Given the important role that productivity differences play in these disparate literatures, the facts above raise obvious and crucial questions.
• But we've also learned more about what the authors don't know, and this is guiding the ways in which the productivity literature will be moving.
• Furthermore, for obvious reasons, I will focus on research that has been done since Bartelsman and Doms (2000) was written.
• These are elements of businesses' external environments that can affect productivity levels.
• This section briefly reviews what productivity is conceptually, how it is measured in practice, and how productivity differences among producers of similar goods might be supported in equilibrium.

2.1 Productivity in Concept

• Simply put, productivity is efficiency in production: how much output is obtained from a given set of inputs.
• Labor productivity is the most common measure of this type, though occasionally capital or even materials productivity measures are used.
• Because of this, researchers often use a productivity concept that is invariant to the intensity of use of observable factor inputs.
• Higher-TFP producers will produce greater amounts of output with the same set of observable inputs than lower-TFP businesses and, hence, have isoquants that are shifted up and to the right.
• The literature has made progress when it can explain systematic influences on output across production units that do not come from changes in observable inputs like standard labor or capital measures.

2.2 Measuring Productivity

• While productivity is relatively straightforward in concept, a host of measurement issues arise when constructing productivity measures from actual production data.
• In that case, producers' measured productivity levels may reflect less about how efficient they are and more about the state of their local output market.
• Capital is typically measured using the establishment or firm's book value of its capital stock.
• On top of all these considerations, one makes these input measurement choices in the context of knowing that any output driven by unmeasured input variations (due to input quality differences or intangible capital, for example) will show up as productivity.
• The input index in the TFP denominator can be constructed similarly for general production functions.

2.3 A Model of Within-Industry Productivity Dispersion

• The ubiquity of this dispersion suggests there must be some real economic force at work, rather than it simply being an artifact of measurement or odd chance.
• In a heterogeneous-cost Cournot oligopoly, D will contain the parameters of the industry demand curve and the productivity levels of the industry's producers, as these are sufficient to determine the Nash equilibrium outputs and therefore revenues of each producer i.
• The assumptions on the shape of R imply that, given the industry state D, each producer has a unique optimal employment level L i * that is increasing in its productivity level.
• Increases in the average productivity level across plants (coming from parameter changes that increase A) will thus expectedly translate into higher aggregate industry productivity-the ratio of total industry output to total industry inputs.
• Further, even this simple structure hints at how the dynamics of reallocation-a focus of some of the literature discussed below-might work.

3. Productivity and the Plant or Firm

• This section discusses factors that directly impact productivity at the micro level by operating within the plant or firm.
• They are akin to forces that would allow firms in the model of the previous section to raise their A i draw, though most likely at a cost.
• I have broken up the discussion of direct productivity impacts by category for the sake of exposition.
• It's good to keep in mind that some forces can overlap these categories, and multiple mechanisms can act in concert.
• I will point out many of these acrosscategory links as the discussion goes along.

3.1 Managerial Practice/Talent

• Researchers have long proposed that managers drive productivity differences.
• They and their team surveyed managers from over 700 mediumsized firms in the United States, United Kingdom, France, and Germany.
• Importantly, therefore, Bloom and Van Reenen document that higher-quality management practices (and higher scores) are correlated with several measures of productivity and firm performance, including labor productivity, TFP, return on capital, Tobin's Q, sales growth, and the probability of survival.
• These papers have elucidated some interesting details about the productivity effects of these practices.
• This study could go a long way toward establishing whether or not a causal link exists.

3.2 Higher-Quality General Labor and Capital Inputs

• Management is an unmeasured input in most production functions, and hence is embodied in the productivity measure.
• Newer work using matched employeremployee datasets, which allow individual workers to be tracked across plants or firms over time, has offered evidence on the importance of labor quality.
• Capital can also vary in quality in ways not captured with standard measures.
• This seems to be an area desperate for further evidence, given its potential importance.
• Interestingly, his estimates of each technology's parameters suggest that capital-augmenting productivity is the primary driver of labor productivity growth under lean processes, while Hicks-neutral TFPtype productivity drives growth in the traditional technology plants.

3.3 Information Technology and R&D

• While the research described above indicates that input heterogeneity matters, the productivity effects of a particular type of capital-information technology (IT)-have been the subject of intense study.
• These studies document that IT-related productivity gains-both spectacular productivity growth in IT-producing industries and more modest changes in IT-using industries-play an important role in explaining aggregate U.S. productivity growth over the past couple of decades.
• They link their management practices data discussed above to data on IT usage to test for particular mechanisms through which this productivity advantage arises.
• A new technology's net productivity benefit to the adopter depends on the difference between the increased production the new technology facilitates and its acquisition cost.
• There are many reasons why more productive firms might do more R&D, suggesting that some of the causation may go the other way.

3.5 Product Innovation

• Innovations in product quality may not necessarily raise the quantity of output (measured in some physical unit) per unit input, but they can increase the product price and, therefore, the firm's revenue per unit input.
• This is captured in standard revenue-based productivity measures since they reflect price variations across an industry's plants or firms.
• About one-third of this comes from entry and exit channels.
• Nevertheless, given the breadth of the study's coverage and its result that correlations exist, more research in this area would be worthwhile.
• At the very least, these results indicate that productivity growth accompanies expansion of the variety of products a firm offers.

3.6 Firm Structure Decisions

• A lot of the micro productivity literature uses the establishment (e.g., factory, store, or office) as the unit of analysis.
• Silke J. Forbes and Mara Lederman (2011) look at how vertical integration affects airline performance.
• They find that vertically integrated plants have higher productivity levels than their nonintegrated industry cohorts, but most of this difference reflects selection of high-productivity plants into vertical structures rather than a causal impact of integration on productivity.
• Their work was spurred on in part by the extensive finance literature on the "diversification discount," the term for the oft-measured negative correlation between a firm's financial returns and the number of business lines it operates.
• They support their efficient allocation argument by showing that conglomerate firms' most productive plants are in their largest segments, and segments of a given rank are more productive in larger firms.

4. External Drivers of Productivity Differences

• The previous section discussed factors that operate within the firm to determine productivity levels.
• This section focuses instead on how producers' operating environments can influence productivity levels and growth.
• While distinct in theory and empirical implementation from the accounting decompositions, such "gap methods" have the same conceptual goal: to separately measure how much aggregate productivity growth comes from businesses becoming more efficient themselves and how much comes from reallocation of economic activity to more efficient producers.
• Holmes, Levine, and Schmitz suppose that adopting a productivity-enhancing practice involves disruption costs: a temporary period where costs are actually higher than before any technological change was made.
• Elements of a firm's market environment can affect the firm's incentives to chase that moving target.

4.1 Productivity Spillovers

• Producer practices can have spillover effects on the productivity levels of other firms.
• Higher productivity correlations among "nearby" producers are predicted by many theories of spillovers.
• On the other hand, the ubiquity of large and persistent productivity differences within industries suggests that any such emulation/spillover process is far from perfect.
• The crucial research questions of these studies, then, are the size of knowledge transfers, what features influence this size, and the channels through which the spillovers operate.
• Bloom, Schankerman, and Van Reenen (2007) point out that spillovers can cut two ways: technological spillovers can benefit everyone, but there can also be market-stealing effects on the product market side.

4.2 Competition

• Pressures from threatened or actual competitors can affect productivity levels within an industry.
• Competition drives productivity through two key mechanisms; this section discusses examples of research into both.
• The first is Darwinian selection among producers with heterogeneous productivity levels.
• It also raises the productivity bar that any potential entrant must meet to successfully enter.

4.2.1 Intramarket Competition

• A general indicator that product-market competition is enhancing productivity is a positive correlation between productivity and producer growth and survival.
• Syverson (2004a) investigates the connection between competition and productivity in a case study of the ready-mixed concrete industry, which is well suited for this type of investigation.
• Differences in competitiveness across markets should therefore be related to the density of concrete producers in the market.
• He follows U.S. iron ore mining during the period the industry was first facing competition from foreign producers.
• The industry's average labor productivity had been roughly constant at two tons of ore per worker-hour for several decades preceding 1980.

4.2.2 Trade Competition

• As seen in Schmitz's results for the iron ore industry, the presence-or even just the threat-of imports from abroad is another form of competitive pressure.
• The paper demonstrates that sectors facing new import competition saw faster productivity growth over her 1979-86 sample period than sectors producing primarily nontradables.
• Multiple studies using producer microdata have found comparable results in other settings.
• That is, exporters are almost inevitably more productive than their nonexporting industry counterparts, but most studies have found that this correlation largely reflects selection rather than a causal impact of exporting on productivity.

4.3 Deregulation or Proper Regulation

• Poorly regulated markets can create perverse incentives that reduce productivity.
• Farmers received a flat payment per ton of sugar contained in their beets, so their optimal response was to simply grow the largest beets possible.
• Beyond these case studies, recent work has also taken a broader look at how product market regulations impact productivity at the micro level.
• They document broad-based productivity growth in plants after privatization but they also find considerable variation in the size of the impacts across countries, with more than 15 percent average TFP growth in Romania but a slightly negative impact in Russia.

4.4 Flexible Input Markets

• I discussed above how competition increases productivity.
• If one thinks of competition as flexibility in product marketsin more competitive markets, it's easier for consumers to shift their purchases from one producer to another-it is logical to suppose that flexible input markets might also raise productivity levels.
• Such gaps can be caused by any one of a number of market distortions, like market power, taxes, or the firing costs that are the object of the study.
• Efficiency increases if labor inputs are moved from low-to high-gap plants because the net change in marginal product caused by the input shift outstrips the change in wage costs.
• Their model indicates that in the absence of distortions, plants' revenue-based TFP levels (TFP measured using revenues as an output measure rather than quantities) should be equal.

5. Big Questions

• That is a brief summary of what the authors know about the causes of productivity differences at the micro level and why they would want to know these causes.
• I want to emphasize that while the discussion draws out major themes of that body of knowledge, it really only just scratches the surface of the literature.
• I think a fair reading of the discussion above would say that the authors have learned a lot about productivity since the Bartelsman and Doms (2000) survey.
• Many pressing issues and open questions remain.
• I will briefly lay out what I see to be the major questions about productivity that the research agenda should address.

5.1 What Is the Importance of Demand?

• But productivity as actually measured in producer microdata generally reflects more than just supply-side forces.
• Because producer-specific prices are unobserved in most businesslevel microdata, output is typically measured by revenue divided by an industry-level deflator.
• A new strand of research has begun to extend the productivity literature to explicitly account for such idiosyncratic demand effects as well.
• The work to this point indicates that demand factors are indeed important.
• The scope of issues that this new line of research has addressed is still small, however.

5.2 What Is the Role of (or Hope for)

• Clearly, many of the productivity drivers discussed above can be influenced by government policies.
• Several policy-related questions are prime targets for research.
• Research has typically compared the effects of policy reforms to a null of no reform, but perhaps an equally important comparison is among possible reform alternatives.
• There could be economic reasons for this.

5.3 Which Productivity Drivers Matter

• The research described above has framed which factors might explain variation in productivity levels.
• The relative quantitative importance of each, however, is still unclear.
• Of course, it's quite likely that the quantitative impact of factors varies across industries or markets.
• Research that ties observable attributes of the industry's technology or demand structure to the quantitative importance of productivity-influencing factors would be an incredible advance in their ability to explain productivity growth.

5.4 What Factors Determine Whether

• In many settings above, there was a prominent distinction between aggregate productivity growth coming from "within" (productivity growth at a given plant or firm) and "between" (reallocation-based selection across existing businesses or entry and exit) sources.
• Aggregate productivity growth in the retail sector seems to be almost exclusively from reallocation, at least in the United States.
• But of course the literature has covered nowhere near the full span of sectors and economies.
• More importantly, the authors do not yet have a good model of what sectoral features (again on either the supply or demand side) might determine the relative importance of each.
• Answering questions like this would go a long way to developing their understanding of how micro productivity differences drive the aggregate productivity movements.

5.5 What Is the Role of Misallocation as a Source of Variation in Emerging

• Productivity differences explain much of the per capita income variation across countries.
• On the other hand, the result also has discouraging elements.
• While research has identified misallocation as a source of the problem, it hasn't really pinned down exactly what distortions create gaps between the social marginal benefits and costs of inputs across production units.
• It is hard to implement policies that close these gaps and the variation between them (i.e., reallocate inputs more efficiently) without knowing the nature of the gaps in the first place.
• That said, there has been some early progress on this front.

5.6 What Is the Importance of Higher

• Some of the work above, particularly that focusing on the role of IT capital, suggests that the variance of productivity outcomes might be increasing at a very broad level.
• The value of this option increases with a mean-preserving spread in outcomes.
• There is some evidence that this is happening, but the literature has yet to show this definitively.
• Historical evidence would be very informative here.

5.7 Can We Predict Innovation Based on Market Conditions?

• Here I speak of innovation broadlyproduct and process innovation, measured or unmeasured by formal R&D numbers.
• This question is in some ways a corollary to the one above about quantifying and predicting the split between within-producer and between-producer productivity growth.

5.8 The Nature of Intangible Capital

• Many of the primary drivers of productivity naturally create persistence in productivity levels at plants and firms.
• Understanding how such intangible capital stocks are built and sustained would shed light on many productivity-related issues for this reason.
• Such insights would also speak toward active literatures on the subject in macroeconomics and finance.

5.9 Management Versus Managers

• Understanding these issues might also help to pin down the causal nature of management practices.
• If good management practices reflect in large part the fact that they are what good managers do, then the causal impact might be limited.

5.10 A Plea for Data

• Data availability is not a research question, but it is crucial for answering the questions posed above.
• Virtually everything discussed in this survey the authors now know because detailed data on production practices was available.
• But many of these datasets were originally collected by statistical agencies for the purpose of constructing aggregates.
• Their ability to offer insights into what happens at the micro level was in many ways a happy externality.
• Now that the authors know the value of the knowledge that such information can generate, economists should push for more directed efforts to measure business-level production practices.

6. Conclusion

• The research into the productivity differences across businesses has come a long way since Bartelsman and Doms (2000) surveyed the literature a decade ago.
• The authors know more about what causes the measured differences in productivity, and how factors both internal and external to the plant or firm shape the distribution.
• These insights have been applied to research questions in numerous fields.
• Fortunately, I see no sign that the rate at which researchers accumulate knowledge in this area is slowing.
• I am excited to see what the next several years bring in this research agenda, as the content of the next decade's survey unfolds.

Full text

Journal of Economic Literature 2011, 49:2, 326–365
http:www.aeaweb.org/articles.php?doi
=
10.1257/jel.49.2.326
326
1. Introduction
T
hanks to the massive infusion of detailed
production activity data into economic
study over the past couple of decades,
researchers in many elds have learned a
great deal about how rms turn inputs into
outputs. Productivity, the efciency with
which this conversion occurs, has been a
topic of particular interest. The particulars
of these studies have varied depending on
the researchers’ specic interests, but there
is a common thread. They have documented,
virtually without exception, enormous and
persistent measured productivity differences
across producers, even within narrowly
dened industries.
The magnitudes involved are striking.
Chad Syverson (2004b) nds that within four-
digit SIC industries in the U.S. manufactur-
ing sector, the average difference in logged
total factor productivity (TFP) between an
industry’s 90th and 10th percentile plants
is 0.651. This corresponds to a TFP ratio of
e
0.651
= 1.92. To emphasize just what this
number implies, it says that the plant at the
90th
percentile of the productivity distribu-
tion makes almost twice as much output with
the same measured inputs as the 10th per-
centile plant. Note that this is the average
90–10 range. The range’s standard deviation
across four-digit industries is 0.173, so sev-
eral industries see much larger productiv-
ity differences among their producers. U.S.
manufacturing is not exceptional in terms of
productivity dispersion. Indeed, if anything,
What Determines Productivity?
C S
*
Economists have shown that large and persistent differences in productivity levels
across businesses are ubiquitous. This nding has shaped research agendas in a num-
ber of elds, including (but not limited to) macroeconomics, industrial organization,
labor, and trade. This paper surveys and evaluates recent empirical work address-
ing the question of why businesses differ in their measured productivity levels. The
causes are manifold, and differ depending on the particular setting. They include ele-
ments sourced in production practices—and therefore over which producers have
some direct control, at least in theory—as well as from producers’ external operat-
ing environments. After evaluating the current state of knowledge, I lay out what I
see are the major questions that research in the area should address going forward.
( JEL D24, G31, L11, M10, O30, O47)
*
University of Chicago and National Bureau of Eco-
nomic Research. I thank Eric Bartelsman, Nick Bloom,
Roger Gordon, John Haltiwanger, Chang-Tai Hsieh, Ariel
Pakes, Amil Petrin, John Van Reenen, and anonymous
referees for helpful comments. This work is supported by
the NSF (SES-0519062 and SES-0820307), and both the
Stigler Center and the Centel Foundation/Robert P. Reuss
Faculty Research Fund at the University of Chicago Booth
School of Business.

327
Syverson: What Determines Productivity?
it is small relative to the productivity varia-
tion observed elsewhere. Chang-Tai Hsieh
and Peter J. Klenow (2009), for example,
nd even larger productivity differences in
China and India, with average 90–10 TFP
ratios over 5:1.
1
These productivity differences across pro-
ducers are not eeting, either. Regressing
a producer’s current TFP on its one-year-
lagged TFP yields autoregressive coefcients
on the order of 0.6 to 0.8 (see, e.g., Árpád
Ábrahám and Kirk White 2006 and Foster,
Haltiwanger, and Syverson 2008). Put sim-
ply, some producers seem to have gured out
their business (or at least are on their way),
while others are woefully lacking. Far more
than bragging rights are at stake here: another
robust nding in the literature—virtually
invariant to country, time period, or indus-
try—is that higher productivity producers are
more likely to survive than their less efcient
industry competitors. Productivity is quite lit-
erally a matter of survival for businesses.
1.1 How Micro-Level Productivity
Variation and Persistence Has
Inuenced Research
The discovery of ubiquitous, large, and per-
sistent productivity differences has shaped
research agendas in a number of elds. Here
are some examples of this inuence, though
1
These gures are for revenue-based productivity mea-
sures; i.e., where output is measured using plant revenues
(deated across years using industry-specic price indexes).
TFP measures that use physical quantities as output mea-
sures rather than revenues actually exhibit even more
variation than do revenue-based measures as documented
in Lucia Foster, John Haltiwanger, and Syverson (2008).
Hsieh and Klenow (2009) also nd greater productivity
dispersion in their TFP measures that use quantity proxies
to measure output (actual physical quantities are not avail-
able for most producers in their data). Even though it is
only a component of revenue-based TFP (the other being
the producer’s average price), quantity-based TFP can be
more dispersed because it tends to be negatively corre-
lated with prices, as more efcient producers sell at lower
prices. Thus revenue-based productivity measures, which
combine quantity-based productivity and prices, tend to
understate the variation in producers’ physical efciencies.
by no means is it meant to be a comprehen-
sive accounting. They speak to the breadth
of the impact that answers to this paper’s title
question would have.
Macroeconomists are dissecting aggregate
productivity growth—the source of almost all
per capita income differences across coun-
tries—into various micro-components, with
the intent of better understanding the sources
of such growth. Foster, Haltiwanger, and C.
J. Krizan (2001), for example, overview the
substantial role of reallocations of economic
activity toward higher productivity produc-
ers (both among existing plants and through
entry and exit) in explaining aggregate pro-
ductivity growth. Hsieh and Klenow (2009)
ask how much larger the Chinese and Indian
economies would be if they achieved the
same efciency in allocating inputs across
production units as does the United States.
Models of economic uctuations driven by
productivity shocks are increasingly being
enriched to account for micro-level patterns,
and are estimated and tested using plant-
or rm-level productivity data rather than
aggregates (e.g., Jeffrey R. Campbell and
Jonas D. M. Fisher 2004, Eric J. Bartelsman,
Haltiwanger, and Stefano Scarpetta 2009,
and Marcelo Veracierto 2008). Micro pro-
ductivity data have also been brought to bear
on issues of long-run growth, income conver-
gence, and technology spillovers. They offer
a level of resolution unattainable with aggre-
gated data.
In industrial organization, research has
linked productivity levels to a number of
features of technology, demand, and market
structure. Examples include the effect of
competition (Syverson 2004a and James A.
Schmitz 2005), the size of sunk costs (Allan
Collard-Wexler 2010), and the interaction of
product market rivalry and technology spill-
overs (Nicholas Bloom, Mark Schankerman,
and John Van Reenen 2007). Another line of
study has looked at the interaction of rms’
organizational structures with productivity

Journal of Economic Literature, Vol. XLIX (June 2011)
328
levels (e.g., Vojislav Maksimovic and Gordon
Phillips 2002, Antoinette Schoar 2002, and
Ali Hortaçsu and Syverson 2007, 2011).
Labor economists have explored the
importance of workers’ human capital in
explaining productivity differences (John M.
Abowd et al. 2005 and Jeremy T. Fox and
Valérie Smeets 2011), the productivity effects
of incentive pay (Edward P. Lazear 2000),
other various human resources practices
(Casey Ichniowski and Kathryn Shaw 2003),
managerial talent and practices (Bloom and
Van Reenen 2007), organizational form
(Luis Garicano and Paul Heaton 2007), and
social connections among coworkers (Oriana
Bandiera, Iwan Barankay, and Imran Rasul
2009). There has also been a focus on the
role of productivity-driven reallocation on
labor market dynamics via job creation and
destruction (Haltiwanger, Scarpetta, and
Helena Schweiger 2008).
Perhaps in no other eld have the produc-
tivity dispersion patterns noted above had
a greater inuence on the trajectory of the
research agenda than in the trade literature.
Theoretical frameworks using heterogeneous-
productivity rms like Jonathan Eaton and
Samuel Kortum (2002) and Marc J. Melitz
(2003) are now the dominant conceptual
lenses through which economists view trade
impacts. In these models, the trade impacts
vary across producers and depend on their
productivity levels in particular. Aggregate
productivity gains come from improved selec-
tion and heightened competition that trade
brings. A multitude of empirical studies have
accompanied and been spurred by these
theories (e.g., Nina Pavcnik 2002, Andrew
B. Bernard, J. Bradford Jensen, and Peter K.
Schott 2006, and Eric A. Verhoogen 2008).
They have conrmed many of the predicted
patterns and raised questions of their own.
1.2 The Question of “Why?”
Given the important role that produc-
tivity differences play in these disparate
literatures, the facts above raise obvious and
crucial questions. Why do rms (or factories,
stores, ofces, or even individual production
lines, for that matter) differ so much in their
abilities to convert inputs into output? Is it
dumb luck or instead something—or many
things—more systematic? Can producers
control the factors that inuence productiv-
ity or are they purely external products of the
operating environment? What supports such
large productivity differences in equilibrium?
A decade ago, when Bartelsman and Mark
Doms (2000) penned the rst survey of the
micro-data productivity literature for this
journal, researchers were just beginning to
ask the “Why?” question. Much of the work
to that point had focused on establishing facts
like those above—the “What?” of productiv-
ity dispersion. Since then, the literature has
focused more intensely on the reasons why
productivity levels are so different across
businesses. There has denitely been prog-
ress. But we’ve also learned more about what
we don’t know, and this is guiding the ways
in which the productivity literature will be
moving. This article is meant to be a guide to
and comment on this research.
I begin by setting some boundaries. I have
to. A comprehensive overview of micro-
founded productivity research is neither
possible in this format nor desirable. There
are simply too many studies to allow ade-
quate coverage of each. First, I will focus on
empirical work. This is not because I view
it as more important than theory. Rather,
it affords a deeper coverage of this impor-
tant facet of a giant literature and it better
reects my expertise. That said, I will sketch
out a simple heterogeneous-productivity
industry model below to focus the discus-
sion, and I will also occasionally bring up
specic theoretical work with particularly
close ties to the empirical issues discussed.
Furthermore, for obvious reasons, I will
focus on research that has been done since
Bartelsman and Doms (2000) was written.

329
Syverson: What Determines Productivity?
Even within these boundaries, there
are more studies than can be satisfactorily
described individually. I see this article’s role
as ltering the broader lessons of the lit-
erature through the lens of a subset of key
studies. The papers I focus on here are not
necessarily chosen because they are the rst
or only good work on their subject matter,
but rather because they had an archetypal
quality that lets me weave a narrative of
the literature. I urge readers whose inter-
ests have been piqued to more intensively
explore the relevant literatures. There is far
more to be learned than I can convey here.
A disclaimer: some of my discussion con-
tains elements of commentary. These opin-
ions are mine alone and may not be the
consensus of researchers in the eld.
I organize this article as follows. The
next section sketches the conceptual back-
ground: what productivity is, how it is often
measured in practice, and how differences
in productivity among producers of similar
goods might be sustained in equilibrium.
Section 3 looks at inuences on productivity
that operate primarily within the business.
This can be at the rm level, plant level, or
even on specic processes within the rm.
Many of these inuences may potentially
be under the control of the economic actors
inside the business. In other words, they can
be “levers” that management or others have
available to impact productivity. Section 4
focuses on the interaction of producers’ pro-
ductivity levels and the markets in which
they operate. These are elements of busi-
nesses’ external environments that can affect
productivity levels. This impact might not
always be direct, but they can induce pro-
ducers to pull some of the levers discussed
in section 3, indirectly inuencing observed
productivity levels in the process. They may
also be factors that affect the amount of pro-
ductivity dispersion that can be sustained
in equilibrium and inuence observed pro-
ductivity differences through that channel.
Section 5 discusses what I see as the big
questions about business-level productivity
patterns that still need to be answered. A
short concluding section follows.
2. Productivity—What It Is, How It Is
Measured, and How Its Dispersion
Is Sustained
This section briey reviews what produc-
tivity is conceptually, how it is measured in
practice, and how productivity differences
among producers of similar goods might be
supported in equilibrium. Deeper discus-
sions on the theory of productivity indexes
can be found in Douglas W. Caves, Laurits
R. Christensen, and W. Erwin Diewert
(1982) and the references therein. More
detail on measurement issues can be found
in the large literature on the subject; see, for
example, G. Steven Olley and Ariel Pakes
(1996), Zvi Griliches and Jacques Mairesse
(1998), Richard Blundell and Stephen R.
Bond (2000), James Levinsohn and Amil
Petrin (2003), and Daniel C. Ackerberg et
al. (2007). Examples of models that derive
industry equilibria with heterogeneous-pro-
ductivity producers include Boyan Jovanovic
(1982), Hugo A. Hopenhayn (1992), Richard
Ericson and Pakes (1995), Melitz (2003),
Marcus Asplund and Volker Nocke (2006),
and Foster, Haltiwanger, and Syverson
(2008).
2.1 Productivity in Concept
Simply put, productivity is efciency in
production: how much output is obtained
from a given set of inputs. As such, it is
typically expressed as an output–input
ratio. Single-factor productivity measures
reect units of output produced per unit of
a particular input. Labor productivity is the
most common measure of this type, though
occasionally capital or even materials produc-
tivity measures are used. Of course, single-
factor productivity levels are affected by the

Journal of Economic Literature, Vol. XLIX (June 2011)
330
intensity of use of the excluded inputs. Two
producers may have quite different labor
productivity levels even though they have the
same production technology if one happens
to use capital much more intensively, say
because they face different factor prices.
Because of this, researchers often use a
productivity concept that is invariant to the
intensity of use of observable factor inputs.
This measure is called total factor productiv-
ity (TFP) (it is also sometimes called mul-
tifactor productivity). Conceptually, TFP
differences reect shifts in the isoquants of a
production function: variation in output pro-
duced from a xed set of inputs. Higher-TFP
producers will produce greater amounts of
output with the same set of observable inputs
than lower-TFP businesses and, hence, have
isoquants that are shifted up and to the
right. Factor price variation that drives fac-
tor intensity differences does not affect TFP
because it induces shifts along isoquants
rather than shifts in isoquants.
TFP is most easily seen in the often-used
formulation of a production function where
output is the product of a function of observ-
able inputs and a factor-neutral (alterna-
tively, Hicks-neutral) shifter:
Y
t
= A
t
F( K
t
, L
t
, M
t
),
where Y
t
is output, F(·) is a function of
observable inputs capital K
t
, labor L
t
, and
intermediate materials M
t
, and A
t
is the
factor-neutral shifter. In this type of formu-
lation, TFP is A
t
. It captures variations in
output not explained by shifts in the observ-
able inputs that act through F(·).
2
2
I use a multiplicatively separable technology shift
to make exposition easy, but TFP can be extracted
from a general time-varying production function Y
t
=
G
t
(A
t
, K
t
, L
t
, M
t
). Totally differentiating this production
function gives:
d Y
t
=
∂ G
_
∂ A
d A
t
+
∂ G
_
∂K
d K
t
+
∂ G
_
∂L
d L
t
+
∂ G
_
∂M
d M
t
.
TFP is, at its heart, a residual. As with all
residuals, it is in some ways a measure of our
ignorance: it is the variation in output that
cannot be explained based on observable
inputs. So it is fair to interpret the work dis-
cussed in this survey as an attempt to “put a
face on” that residual—or more accurately,
“put faces on,” given the multiple sources
of productivity variation. The literature has
made progress when it can explain system-
atic inuences on output across produc-
tion units that do not come from changes in
observable inputs like standard labor or capi-
tal measures.
2.2 Measuring Productivity
While productivity is relatively straight-
forward in concept, a host of measurement
issues arise when constructing productiv-
ity measures from actual production data.
Ironically, while research with micro pro-
duction data greatly expands the set of
answerable questions and moves the level of
analysis closer to where economic decisions
are made than aggregate data does, it also
raises measurement and data quality issues
more frequently.
The rst set of issues regards the output
measure. Many businesses produce more
than one output. Should these be aggregated
to a single output measure, and how if so?
Further, even detailed producer microdata
do not typically contain measures of output
quantities. Revenues are typically observed
instead. Given this limitation of the data, the
standard approach has been to use revenues
(deated to a common year’s real values
using price deator series) to measure out-
put. While this may be acceptable, and even
desirable, if product quality differences are
fully reected in prices, it can be problematic
Without loss of generality, we can choose units to nor-
malize ∂ G/∂A = 1. Thus when observed inputs are xed
(dK
t
= dL
t
= dM
t
= 0), differential shifts in TFP, dA
t
, cre-
ate changes in output d Y
t
.

Frequently asked questions

Q1. What are the contributions mentioned in the paper "What determines productivity?" ?

T to the massive infusion of detailed production activity data into economic study over the past couple of decades, researchers in many fields have learned a great deal about how firms turn inputs into outputs. 

Q2. What future works have the authors mentioned in the paper "What determines productivity?" ?

The authors know more about what causes the measured differences in productivity, and how factors both internal and external to the plant or firm shape the distribution. These insights have been applied to research questions in numerous fields. 

Q3. What are the dominant conceptual lenses through which economists view trade impacts?

Theoretical frameworks using heterogeneousproductivity firms like Jonathan Eaton and Samuel Kortum (2002) and Marc J. Melitz (2003) are now the dominant conceptual lenses through which economists view trade impacts. 

Q4. What is the potential selection bias when a panel is used?

There is also potential selection bias when a panel is used, since less efficient producers—those with low ωt—are more likely to exit from the sample. 

Q5. What is the implication of equal revenue TFP across plants?

Their model’s implication of equal revenue TFP across plants stems from the standard efficiency condition that inputs’ marginal revenue products are equated across all uses, and the fact that marginal products are proportional to average products for a Cobb–Douglas production function without fixed costs. 

Q6. What does she show that when a conglomerate diversifies, the plants it buys?

She shows that when a conglomerate diversifies, the plants it buys actually experience productivity growth, suggesting that they are in fact being reallocated to more capable management (there will be more on the reallocation of productive inputs below). 

Q7. How much of the knowledge stock depreciates each year?

Forgetting is quantitatively important in this setting: Benkard estimates that almost 40 percent of the knowledge stock depreciates each year.