Firm Dynamics and Productivity
Eric Bartelsman*
July 13, 2010
Warwick University
*Vrije Universiteit Amsterdam; Tinbergen Institute;.IZA.
1
Overview
•
•
•
•
Identification of policy effects
Cross-country firm-level data
From macro to micro and back
Examples:
–  The role of allocation and selection
–  Endogenous choice of innovation strategy
2
Identification of Policy Effects
•  What is effect of policy
–  Directly on firm-level decisions (ceteris
paribus)
–  On interactions in markets
•  Indirectly on firm-level decisions
–  On aggregate economy
•  What methods are available for
identification?
3
Data for identification
•  Firm-level: (natural) experiments
–  Micro behavior can be studied (Ceteris
paribus)
•  Aggregate (industry) level
–  Identification is difficult
•  Cross-country panels
–  have variation in policy changes
4
The challenge of cross
-country analysis
•  Macro data
–  e.g. SNA, PWT
–  Difficult to identify effects (e.g. 2 million regressions)
•  Sectoral data
–  e.g. OECD-STAN; Unido
–  aggregate sectors obscure causal mechanism
•  Meta-analysis of results from micro
studies
–  A challenge to control for data, method, and context
–  Little within-country variation in policy (e.g. before and after)
–  No GE effects
•  Multi-country longitudinal micro dataset
–  Generally not possible (disclosure)
–  EUROSTAT attempting to build EU panel
From firm data to macro indicators
Longitudinal
Micro Data
Single country
Surveys,
Business Registers
• SC LMD
N.A.
Multiple
countries
National Accounts
Industry Data
Macro and
Sectoral
Timeseries
• DMD
EUKLEMS/
STAN/Unido
Linked
Indicators
6
Mismatch between theory and
empirics
•  Growth Theory: (e.g. Romer, 1989) Firm-level
behavior, applied to macro/sectoral data (e.g.
growth accounting)
•  IO-style theory (R&D races, competition and
growth, innovation input/output/effect-CDM)
has macro-policy implications but is applied to
micro datasets.
•  New work bridging the gap (Kortum-Klette,
Melitz, Restuccia-Rogerson, MortensonPissarides)
7
Why linked firm-level indicators and
aggregate-level analysis may help answer
questions
•  Heterogeneous agents at micro level
Diversity in firm-level strategies
Frictions, uncertainty, expectations
•  Market selection
Sales and input growth, conditional on productivity
and economic ‘environment’
•  Combination of firm-level productivity impact
and market share evolution gives total impact
on industry productivity
8
Firm-level, cross-country comparisons
•  Policy environment affects all firms in country
(and industry) in the same manner
•  Cross-country firm-level comparisons may
provide means to observe/identify the impacts
of policy changes
•  Firm-level datasets generally cannot be
combined across countries
9
Distributed Micro Data Research
  OECD sample
 Demographics (entry/exit) for 10 countries
 Productivity decompositions for 7 countries
 Survival analysis 7 countries
  World Bank sample
 Same variables, 14 of Central and Eastern Europe, Latin
America and South East Asia
  EU Sample (10 countries), updates and a few new countries
 Productivity decompositions
 Sample Stats and correlations by quartile
 Eurostat/ONS Sample (13 EU countries), ICT-impacts
project.
 Linked BR-PS-EC-(CIS)
Data sources
•  Business registers for firm demographics
–  Firm level, at least one employee, 2/3-digit industry
•  Enterprise surveys
–  Production Statistics
–  R&D Surveys
–  Other firm-level surveys
•  Countries:
–  About 25-30 so far. Eurostat, OECD, WB
•  Data are disaggregated by:
–  industry (2-3 digit);
–  size classes 1-9; 10-19; 20-49; 50-99; 100-249; 250-499; 500+ (for OECD
sample the groups between 1 and 20 and the groups between 100 and 500 are
combined)
–  Time (late 1980s – early 2000s)
Indicators Collected
•  Moments of productivity distributions
–  Std Deviation, quartiles
•
•
•
•
Size distribution (employment) of firms
Gross job flows
Firm entry and exit
Decompositions of productivity levels and
growth
–  Olley-Pakes decomposition of productivity levels
–  Dynamic within/between decompositions including
contribution of entry and exit
12
Network
Researcher
Using Distributed Micro Data Analysis
Policy Question
Research Design
Program Code
Publication
Metadata
Cross-country
Tables
NSOs
Network
members
Provision of metadata.
Approval of access.
Disclosure analysis
13
Steps undertaken
•
•
•
•
Build network (e.g. Eurostat countries)
Collect meta-data
Prepare firm-level datasets
Do until 
–  Run code
–  Analyse cross-country datasets
–  Clean firm-level datasets
–  Fix/change code
•  End
14
Availability of features in firmlevel data
15
Lessons learned
•  Countries with experience in firm-level require very little
work to iterate
•  Countries which have ‘external’ micro-level users have
better data quality and fewer problems
•  Countries where statistician/economist has computer
skills have fewer problems
•  Investment in firm-level data preparation needed to
participate in network has large payoffs to NSO
–  If knowledge, meta-data, and data cleaning are codified
16
Statistical Issues
•  How to harmonize across countries?
•  What about measurement error?
•  What are the properties of linked datasets
–  Assume that linking occurs perfectly through
Business Register and unique firm-id
•  When to use sample (re-)weighting?
•  Who says ‘you can never be too thin…’?
–  Which indicators are fairly robust to sample
size
17
Cross-country Comparisons
•  Harmonization
–  Sample frames; Variable definitions;
Classifications; Aggregation Methods
•  Make comparisons that ‘control’ for
errors
–  Exploit the different dimensions of the data (size,
industry, time)
–  Use difference in differences techniques
•  Even in absence of measurement error,
interpretation of cross-country
indicators requires careful analysis
Measurement Error
•  Three sources of error potentially affect
comparability of indicators built from
firm level data:
–  Classical Error of firm-level measure
–  Errors in observed firms (sample)
–  Method of Aggregation of Indicator
Computation of sample re-weight
20
Results of data linking analysis
•  Info on first and second moments of PS
and linked PS-EC are reasonably robust
•  Sample reweighting of linked PS-EC gives
results that are similar to weighted PS
•  Regression coefficients are robust to
smaller set of obs in linked sub-sample
21
Descriptive Statistics
•  Firm size
•  Firm demographics:
•
Employment and # of firms for entry, exit, continuers: by
industry and size class
•  Firm survival :
•
Employment and # of survivors, by cohort, industry, year
•  Static and dynamic analysis of allocative
efficiency:
•
•
•
Decompositions of productivity (entry/exit/continuer)
Higher moments, covariances, means by quartile
In presentation, focus on 2 and 4
Interpretation of Gross Turnover
•  Theoretical explanations
–  Entry explained by ‘push’ and ‘pull’ factors
–  Exit barriers may effect characteristics of
exiting firm more than number of exits
•  Measurement errors
–  Conceptual differences in measure (e.g.
labor)
–  Differences in underlying data sources
Evidence of firm turnover
Total business sector, firms with at least 1
employee
Total business sector, firms with at least 20
employees
•  No major differences
across OECD countries,
especially after controlling
for sector and size effects
•  But large differences in
size at entry
•  Large net entry in
transition economies: filling
the gaps (?)
Gross and net firm turnover: how the time dimension sheds light on the evolution
of market forces in transition economies
Entry rate by size: how the size dimension may shed light on the nature of firm
dynamics
•  Monotonic decline in entry
rate by size in US
•  Less clear link between size
and entry rate in other EU
countries;
•  Any role for entry costs ?
Evolution of Average manuf firm-size (top quartile)
Estonia
Latvia
160
140
120
100
80
60
40
20
0
1994
1996
1998
2000
year
3rd quartile
2002
2004
2006
Average Firm Size Manuf
Average Firm Size Manuf
160
140
120
100
80
60
40
20
0
1995
Average Firm Size, Manuf
Average Firm Size, Manuf
100
80
60
40
20
0
1990
1992
year
1994
2003
2005
2nd quartile
Sweden
120
1988
2001
3rd quartile
2nd quartile
140
1986
1999
year
Mexico
1984
1997
1996
1998
2000
2002
90
80
70
60
50
40
30
20
10
0
1988 1990 1992 1994 1996 1998 2000 2002 2004 2006
year
27
2nd quartile
2nd quartile
Overview
•
•
•
•
Identification of policy effects
Cross-country firm-level data
From macro to micro and back
Examples:
–  The role of allocation and selection
–  Endogenous choice of innovation strategy
28
Policy Example: Boosting
Innovation
•  How does policy affect the economy at
micro and macro level
•  How to model effects
•  How to take model to the data
29
Optimal Innovation
Innovation, productivity and growth
from micro to macro
•  The idea of creation of diversity (supply) and
selection (demand) to model innovation is quite
useful.
•  However, two problems:
–  Outcome of evolutionary process has no ‘value’.
There is no mechanism to decide whether we need
to intervene in process, and how.
–  Creation side has ‘forethought’ (Prometheus).
Feedback from envisioned future outcome matters
(‘General Equilibrium’, Animal Spirits)
Optimal Innovation
An aside: Innovations and taste
Time
Too much of a good thing?
•  Slippage between individual incentives and aggregate
needs
–  Spillovers
–  Stepping on toes
–  Business Stealing
•  Non-transitivity
–  New, in cycles….
•  Network externalities
–  Spillovers on demand side, learning/demonstration effects
•  Path Dependent traps
–  How to get rid of the internal combustion engine?
Cites: Solow, Romer; Jones and Williams
Innovative Activity, Knowledge
Growth and Impact
Innovative input
Innovative output
Innovative impact
Innovative Activity, Knowledge
Growth and Impact
Crepon, Duguet and Mairesse
Policy levers in Innovation
Human capital
formation
R&D subsidies
Spillovers,
adoptive capacity
Labor and capital markets
Micro to macro
Micro choices
Market Selection
- Entry/Exit
- Innovation Strategy
- Factor inputs
- Product output
- Competition
- Policy Environment
- Tech. environment
Macro
impact of
innovation
38
Innovation and Market Interaction
Policy Levers
•  Allocation of resources across firms
•  Demand and supply conditions affect firm-level input
decisions
•  Selection
•  Factors affecting Entry and exit decisions
•  Choice of innovation strategy
•  Intangible investments
•  Technology adoption
40
Firm-level, cross-country comparisons
•  Policy environment affects all firms in country (and
industry) in the same manner
•  Cross-country firm-level comparisons may provide
means to observe/identify the impacts of policy
changes through the three paths:
•  Allocation of resources across firms
•  Selection (entry/exit)
•  Choice of innovation strategy (externalities, intangibles)
41
Some micro evidence that:
Source: simulated data
Source: Erik Brynjolfsson
Results: ICT Impacts Project
If micro correlation were negative, macro
effect would still be positive, if:
and
Cite: model calibration from Bartelsman, Haltiwanger and Scarpetta
:
Cross Country Differences in
Productivity: The Role of
Allocative Efficiency
Eric Bartelsman, John Haltiwanger
and Stefano Scarpetta*
*Vrije Universiteit Amsterdam, Tinbergen Institute and IZA; University of Maryland,
NBER, and IZA; OECD and IZA.
47
Motivation
•  Recent work bringing together two separate literatures:
–  Large and persistent differences in productivity across countries
–  Large and persistent differences in productivity within industries
within countries
•  Are these connected?
•  Potentially – misallocation hypothesis
–  Distortions impact allocation of activity within industries
–  Our take – distortions impact size/productivity relationship within
industries
•  Size/productivity positive covariance a core prediction of canonical
models of firm heterogeneity and size distribution
48
Recent Literature
•  Misallocation hypothesis not new
–  Banerjee and Duflo (2003) discuss literature in handbook paper
•  New firm level datasets permit rich analysis.
•  Tractable model recently developed by Restuccia and
Rogerson (2008)
•  Implemented empirically for China, India, U.S. by Hsieh
and Klenow (2009)
–  Focus on comparisons of dispersion in productivity
•  Our contribution:
–  Multi-country, multi-moment evidence and analysis
–  Suggests more robust moment theoretically and empirically is
size/productivity covariance
–  Distortions impact selection (who produces) as well as allocation
among existing producers (Hsieh and Klenow focus on latter).
49
What are these distortions?
•  Working conjecture:
–  Emerging and transition economies have market structure and
institutions that distort allocative efficiency
•  Distortions:
–  Restrictions on competition
–  Subsidies (explicit) or quotas/rationing of allocation to insiders
/incumbents/favored businesses
–  Credit constraints to young and small businesses
–  Bribes and corruption (unevenly applied)
–  Doing Business inefficiencies:
»  Costly to start up a business
»  Costly to adjust employment
»  Poor or inefficient infrastructure (telephones, roads, electricity)
–  Again this hypothesis not new, but can now look at in richer
ways with firm level datasets
50
Harmonized cross-country
moments from firm level datasets
•  Measure STD(TFPR), STD(RLP) and
Olley-Pakes (1996) decomposition within
industries for 8 countries (U.S., Western
European and Transition Economies).
–  TFPR (revenue TFP), RLP (revenue labor
productivity), OP covariance
–  Full dataset has 20+ countries
•  Not all of the measures we focus on within
industries are available (in particular, STD(TFPR))
51
52
Covariance Between Size and
Productivity?
•  Olley and Pakes (1996) static decomposition:
  The first term is the unweighted average of firm-level productivity
  The second term (OP cross term) reflects allocation of resources: do firms with higher
productivity have greater market share.
  OP (1996) showed second term increased rapidly in U.S. telecommunications equipment
industry after deregulation
  Second term is summary measure of covariance between size and productivity
  By construction, cross term takes out country effects in productivity levels, so abstracts
from some aspects of measurement error
  We use log based measures of productivity in our implementation
  Decomposition relatively easy to compute (e.g., does not require longitudinal linkages)
53
54
55
56
57
Evidence from cross-country firm
-level data
•  Large dispersion of productivity across firms
within narrowly defined sectors.
•  STD(TFPR)<STD(LPR) – a puzzle in simple models
•  No systematic relationship in magnitudes across countries
•  Size and productivity distributions tend to be
positively related especially in U.S. and Western
Economies
–  Systematically lower in transition economies
•  Over time, dispersion moments don’t change
much but covariance moments increase in
transition
58
A Model of “Mis”-Allocation (Based on Restuccia and Rogerson (2008) (and
similar to Hsieh and Klenow (2009))
•  Households maximize utility, supply labor inelastically
•  Firms maximize profits
–  Ex ante, firms do not know productivity or distortion but know joint
distribution.
–  Pay entry fee, learn their permanent draws of productivity and distortions
–  Productivity and distortion draws are firm-specific and have a permanent
time-invariant component
–  Add two key features:
•  Overhead labor
•  Subject to transitory productivity and distortion shocks but following frictions:
–  Each period must make decision on how much capital and whether to produce/exit
before observing transitory shock
–  Capital is quasi-fixed, labor absorbs transitory shocks
–  Very low (permanent) productivity/high distortion firms don’t produce
–  Size distribution due to idiosyncratic shocks, differentiated products,
decreasing returns and other frictions
–  Dispersion in LP (correlated with TFP) due to interaction of overhead
labor, quasi-fixed capital and transitory shocks
59
A Model of “Mis”-Allocation (Based on Rogerson and Restuccia (2007) (and
similar to Hsieh and Klenow (2007))
Consumers supply labor inelastically and maximize utility:
Firms maximize profits where:
Our innovations: Overhead labor, quasi-fixed capital and transitory shocks
Yields dispersion in TFPR and LPR even in absence of distortions
60
Entry/Selection
Ex Ante Joint Distribution
Exogenous probability of exiting in each period given by λ
61
Aggregate Relationships and
Steady State Equilibrium
Resources expended on entry/exit impact consumption
and welfare
Free entry condition and equilibrium in labor market
62
Calibration
•  Chosen parameters (based on literature)
•  Calibrated parameters:
–  f = overhead fraction; ce = entry cost; variance of
permanent and transitory productivity shocks
•  Targets:
–  Moments from US from Table 1, and US firm-survival
rates
•  55 percent of U.S. Entrants survive after 5 years
63
Calibration (2)
•  We manage to match OP covariance and
survival exactly, and get quite close on
LPR and TFPR dispersion in US
–  Can’t match all moments exactly due to
nonlinear relationship between parameters
and moments
•  Overhead labor is crucial in matching OP
covariance and getting Std(RLP)>STD
(TFPR)
64
Non distorted economy responsiveness to overhead labor
65
Relationship Between Labor Productivity and Employment: No Institutional
Distortions, Permanent and Transitory Shocks, Quasi-fixed capital
66
Relationship Between Productivity and Employment: Correlated Scale
Distortions, Permanent and Transitory Shocks, Quasi-fixed capital
67
68
69
All of these effects are missing in Hsieh and Klenow
70
Using distortions to match OP-gap
71
Observations
•  Size-productivity relationship is strong in advanced economies and
and has increased substantially over time in transition economies
•  In a model with frictions, shocks and idiosyncratic distortions we can
match cross-country patterns of size-productivity relationship from
firm-level data sets.
•  Transitory shocks, quasi-fixed capital interacting with overhead labor
yields substantial labor productivity dispersion and OP(LP) gap
•  Idiosyncratic distortions lower consumption through selection
(survival) and allocation effects
•  Distortions that are correlated with productivity are particularly bad for
OP-gap, productivity, and welfare
•  While distortions should increase dispersion in TFP, the model does
not match actual data
–  Measurement of TFP difficult
–  Maybe other factors (endogenous TFP dispersion, transitory dynamics)
are important
•  For example, large differences in productivity dispersion by firm size and firm
age – firm size and age distributions differ across countries for a number of
reasons
72
Final Remarks
•  Misallocation has potential for accounting for
(some) variation in productivity and
consumption per capita
•  Are there summary measures that can serve as
diagnostics?
–  OP decomposition shows one potentially important
margin: size/productivity covariance
–  This is not the only relevant margin but still an
important margin
–  Impact of distortions on selection important to take
into account
–  Productivity dispersion is a less robust moment
empirically and theoretically
73
Endogenous Productivity
Dispersion
•  In Hsieh and Klenow, dispersion in
productivity is sign of misallocation
•  High dispersion could be a sign of
adoption of risky technology
•  Policy environment may distort choice of
innovation and/or technology adoption
74
Source: Erik Brynjolfsson
Interquartile range of firm growth distribution
(pct-points)
ICT and variability of
outcomes
Percentage of workers with broadband access
Source: Eurostat ICT-Impacts Project
76
Impact of Policy
on ICT and Productivity
•
•
Firms have capabilities and desire to ‘try’ new ways of meeting market
demand
Since mid-1990s, this experimentation is often through ICT
Firms’ desire to experiment depend on carrot and stick
Leveraging of successful investment through scale increase is
an enormous carrot
•
Policy affects resource reallocation
And thus indirectly firm’s choice for innovation strategy
Choice of Innovation Strategy
•  Experimental vs incremental/follower strategy.
–  Follower payoff: π
–  Experimenter payoff: Π
•  Experiment payoff with probability: p.
•  Relative payoff π/Π depends on uncertainty:
–  Π(p) with Π’<0.
•  When experiment fails: reconfigure, try again
–  Partial exit costs PEX. (Firms continue to experiment)
–  Total exit cost TEX (Firms give up experimentation)
•  Exogenous exit hazard x for all producing firms
78
Payoffs for Experimentation
pΠ
p
(1-p)p(Π-Px)
p
1-p
1-p
(1-p)2(-Px-Tx)
79
Testable implications
•  With more experimentation average productivity
is higher
•  With higher exit costs, experimentation is lower,
especially at frontier
•  So:
–  Exit costs lower productivity more in those sectors
where potential gain from experimentation is higher
–  Exit costs lower experimentation, and more so near
frontier
80
Index of Employment Protection
(source: OECD)
81
Empirical specification
•  Main regression: TFP effect of exit costs
•  Frontier indicators (by industry for US or UK):
–  Top quartile productivity relative to mean
–  Standard deviation of productivity
–  Adoption of Broadband
82
Exit costs and productivity
Dependent var: Log(VA)
Log(VA)
Log(VA)
Regressor:
Log: Kit,Knit,Hours
***
***
***
EPL
.47
.34
.46
(0.02)
(0.14)
(0.19)
---
-1.18
-1.13
(3.07)
(3.08)
EPL x Rank
Rank variable
---
Top quartile prod.
relative to mean
Broadband-use
Num. obs.
7032
6790
7031
R-sq
.97
.97
.97
Innovation strategy and employment
•  Mortensons-Pissarides type model with 2
sectors
–  1: safe sector, known technology
–  2: risky sector, draw from prod. distribution
•  With firing costs, option of closing down
conditional on bad draw is more expensive
–  So, fewer jobs created in risky sector
84
Exit costs and employment
Dependent var: Labor share in sector
Labor share in sector
Regressor:
EPL
.02
.02
(0.74)
(0.74)
-0.82
-0.84
(10.30)
(10.55)
Rank variable
Top quartile prod/mean
Broadband-use
Num. obs.
5518
5518
R-sq
.84
.84
EPL x Rank
Exit costs and productivity
•  Productivity is reduced in industries that
have potential gain from experimentation
•  Employment share in innovative industries
is reduced
•  => High exit costs lower aggregate
productivity
86
A Research agenda for innovation and
productivity using micro and macro data
•  Discover incentives, at decision making level, of
incentives of supply and use of innovative output.
•  Look at costs and benefits. Take into account expectations
(General Eq. effects)
•  Look at interactions between actors
•  Understand process of knowledge accumulation
•  Depreciation, obsolescence
•  Spillovers, diffusion, appropriability, non-exhaustiveness
•  Individual vs global knowledge stocks
•  Estimate impact of knowledge stock on output/
welfare
•  Non-transitivity of ‘newness’
•  Costs of churn
87