Do Crises Catalyze Creative Destruction?
Firm Level Evidence From Indonesia
Mary Hallward-Driemeier and Bob Rijkers
ACES/AEA, January 8, 2012
Do crises facilitate or hamper a more
efficient allocation of resources?
• COMPETING THEORETICAL PARADIGMS:
– The “Cleansing” Hypothesis:
Crises accelerate the Schumpetarian (1939) process of creative
destruction (Caballero and Hammour 1994).
• They weed out unproductive firms.
• And free up resources for more productive uses
– The “Scarring” Hypothesis:
Crises obstruct the reallocative process (Barlevy, 2002; Ouyang 2009)
• They exacerbate market imperfections (e.g. credit or labor markets)
• And destroy potentially productive firms
– Implications for appropriate policy response: is there a tradeoff
between minimizing short-term impacts and maximizing long-run
growth prospects?
Related Literature
• Empirical evidence Is ambiguous
– Not clear whether firm- and job turnover are pro- or countercyclical
(Davis and Haltiwanger, 1990, 1992; Caballero and Davis, 1995; Boeri, 1996)
– Weak evidence that plant productivity rises during downturns
(Griliches and Regev, 1995, Bailey et al. 1998; Davis et al, 1996)
– Market imperfections may be particularly pernicious during crises
(Bergoeing et al., 2005, Blalock and Gertler, 2006, Gallego and Tessadda, 2009)
• Few micro-level studies of the impact of crises on resource allocation
– Exceptions, again with ambiguous findings:
• Evidence for scarring: Japan’s Banking Crisis
(Nishimura et al, 2005)
• Evidence for cleansing: Chile during debt crisis; Uruguay during the Argentine peso crisis
(Tybout and Liu 1995: Casacuberta and Gandelsman: 2009)
This Paper
• Examines the impact of the East Asian Crisis on manufacturing
plant dynamics in Indonesia
Complementary analyses
• Aggregate productivity decompositions
•
Contributions due to entry, exit, within-plant adjustment and reallocation
between plants
• Plant-level regressions of determinants of exit and of employment
growth
• Examine plant heterogeneity in adjustment patterns
• Disentangle the role of productivity and other plant characteristics
• Allow for variation in impact over time
• Examine role of regulations that varied over time and location
• Separate effects of economic crisis and political crisis
Testable hypotheses
If crises are cleansing:
•
•
Aggregate productivity decompositions should show:
– Increased contribution of reallocation between firms, and positive correlation
between productivity and growth
– Relatively larger contributions of entry
Plant-level
– Survival: stronger relationship between firm productivity and survival
– Growth: productive firms shed proportionately fewer jobs during the crisis and
expand faster after the crisis
What accounts for the observed patterns?
•
•
Plant characteristics: size, sector, need for credit
Policy environment: e.g. labor regulations
Is the political crisis more cleansing?
Suharto fell in the spring of 2008, but time alone is not enough to distinguish the
political crisis. Data can identify those where a Suharto family member has an
ownership stake, and/or is on the board (Mobarak and Purbasari, 2008).
- Does productivity play a stronger role in connected firms?
- Are the general results robust to the exclusion of connected firms?
Data




BPS’s Indonesian Annual Manufacturing Census
 Covers all manufacturing firms ≥20 employees
 Entry: first entry into the survey
 Exit: last exit
 Sample: 1991-2001
 Detailed information on employment, inputs and outputs, industrial
classification and ownership.
Productivity measures:
 Real value added per worker
 TFP: OLS, Solow residual, Ackerberg Caves and Frazer (2006)
 Non-stationarity (and persistence) of series limits the applicability
of some estimation procedures, e.g. Arellano-Bond
Political connectedness:
 Mobarak and Purbasari (2008) measures of Suharto family ownership
and membership on boards
Regulations
 Minimum wages, set at the provincial level, World Bank Jakarta Office
Plant Entry and Exit
Exit rates were higher during the crisis.
Peak in 2001 reflects the economic slowdown, but is magnified by splits in
provinces where plants were seen to exit and reenter (results robust to
excluding 2001)
Aggregate Jobs Flows
Excess churning and net job destruction during the crisis, 1997-98
Decomposing Productivity Growth:
Within-Adjustment Dominates
FHK Decomposition of the growth of real value added per worker
 P    it 1 pit    it ( pit 1  P t 1 )    it  pit
iC
iC
“within”
iC
“between”
“cross
   it ( P t  P t 1 )    it ( pit  P t )    it 1 ( pit 1  P t 1 )
iN
“proportionate entry”
iN
iX
“disproportionate entry”
“exit”
Do see increasing cross and proportionate entry. But negative contribution of exit.
Firm Survival: Econometric Strategy
Discrete-time logistic survival model (Cox, 1972)
S (t | x)   (1  (t | x))
i
i
j 1
tj
j


 (ti )
log  (ti )  log 0 (ti )   ' xti xti 
 exp(  ' xti xti )
0 (ti )
“Proportional Hazards”
Our strategy: Interact explanatory variables over time to examine how the determinants of
survival vary over time:





it (t )  log 0 (t )   xit xit   Crisis*xi Crisis * xit   Re cov xi Re cov* xit 
 P pit   Crisis Crisis * pit   Re cov Re cov* pit  vit
P
P
Competing Hypotheses
H 0 : exp  'Crisis*TFPt  1
Crises have no differential impact on creative destruction (in the short-run)
HA1 : exp  'Crisis*TFPti  1
Crises accelerate creative destruction (in the short-run)
i
HA2 : exp  'Crisis*TFPt  1 Crises attenuate creative destruction (in the short-run)
i
The crisis attenuated the link between productivity and exit
ln(V/L)
Crisis*ln(V/L)
Recovery*ln(V/L)
TFP (Solow)
Crisis*TFP (Solow)
Recovery*TFP (Solow)
TFP (Solow)
Logistic Survival model:
Baseline Model
Odds Ratios: Relative Probability of Exit
Value-Added
TFP -Solow
coef/se
coef/se
0.834***
(0.011)
1.187***
(0.025)
0.990
(0.020)
0.755***
(0.033)
1.556***
(0.090)
1.111*
(0.069)
Crisis*TFP (Solow)
Recovery*TFP (Solow)
Province Dummies
Period Dummies
Industry Dummies
N
Pseudo R2
Yes
Yes
Yes
153,115
0.075
Yes
Yes
Yes
95,966
0.072
TFP- ACF
coef/se
0.724***
(0.034)
1.144**
(0.071)
1.075
(0.059)
Yes
Yes
Yes
73,196
0.058
Results are robust
3 Year Window
Survival Period (from
ln(V/L)
– to)
TFP (Solow)
VA
coef/seTFP (Solow)coef/se
Pre-Crisis
1993-1996
0.791***
(0.019)
TFP (Solow)
coef/se
coef/se
Crisis
1996-1999
0.939***
(0.019)
Pre-Crisis
1993-1996
Crisis
1996-1999
0.757***
(0.052)
1.009
(0.066)
Crisis
1996-2001
0.885***
(0.016)
Pre-Crisis
1991-1996
Crisis
1996-2001
0.723***
(0.040)
1.055
(0.068)
5 Year Window
Survival Period (from
ln(V/L)
– to)
TFP (Solow)
Pre-Crisis
1991-1996
0.811***
(0.017)
Robustness tests passed but not presented:
–
–
–
–
Additional controls (in years for which they are available): self-reported constraints,
training, R&D intensity, foster parent company, education, minimum wages.
Using lagged productivity as a proxy for true productivity
Including anomalous observations
Excluding outliers
Are Credit Constraints Driving the Attenuation Effect?
• Hypothesis: Firms that are credit constrained or face greater needs for
financing should be disproportionately hurt (exit more, face greater
attenuation effect)
• Cannot measure financial constraints directly
– Across sectors: use measure of financial dependence
• Use Rajan-Zingales (1998) measure of dependence on external credit, Braun (2003)
measure of asset tangibility
– Within sectors: exploit information on the composition of investment
financing.
• Compare: firms that financed investment with loans versus financed by other means
• With depreciation, those with loans could be disproportionately hurt rather than
protected by having credit. If productive firms received loans in dollars and then exited
during the crisis, this could account for the attenuation. But results hold excluding
them.
• Result: Changing credit conditions amplified exit, those in sectors with greater
need for financing experienced higher rates of exit.
• But do not account for the observed attenuation
– Attenuation result remains even including financial dependence
– And protective role of productivity is actually higher during the crisis in sectors with greater
financial dependence – consistent with the cleansing hypothesis.
– Attenuation was strongest for firms that lacked access to credit to start with.
Firms in sectors more dependent on external finance were more likely to exit
… yet the attenuation effect was weakest in these sectors
RZ_
fin_dependence
Crisis*
RZ_fin_dependence
Recovery*
RZ_fin_dependence
ln(V/L)
Crisis*ln(V/L)
Recovery*ln(V/L)
RZ*ln(V/L)
Crisis*RZln(V/L)
Recovery*RZln(V/L)
TFP (Solow)
A Financial dependence (RZ)
Baseline
Extended
0.988
0.313***
(0.070)
(0.108)
1.329**
6.727***
(0.148)
(3.818)
0.745***
1.198
(0.077)
(0.567)
0.836***
0.819***
(0.011)
(0.012)
1.160***
1.194***
(0.026)
(0.029)
0.991
0.999
(0.021)
(0.023)
1.168***
(0.053)
0.805***
(0.059)
0.939
(0.059)
Crisis*TFP (Solow)
Recovery*TFP (Solow)
RZ*TFP (Solow)
Baseline
1.184**
(0.092)
1.167
(0.156)
0.446***
(0.067)
Extended
0.914
(0.349)
1.049
(0.696)
0.169**
(0.133)
0.780***
(0.029)
1.592***
(0.098)
1.173**
(0.081)
0.759***
(0.040)
1.578***
(0.138)
1.052
(0.112)
1.126
(0.196)
1.028
(0.270)
1.549
(0.531)
Yes
95,966
0.071
Crisis*RZ*TFP (Solow)
Recovery*RZ*TFP (Solow)
Controls
N
Pseudo R2
Yes
153,115
0.074
Yes
153,115
0.074
Yes
95,966
0.071
Labor market rigidities?
• Labor markets were relatively flexible during
Suharto
• Minimum wages set at provincial level
• MW were rising steadily during the 1990s
• Results:
– Interaction terms of plant productivity and provincial
minimum wage is >1; more productive firms are more
likely to exit when MW are high – and this is more
pronounced during the crisis
– Including MW also reduces the overall attenuation
results.
Labor market regulations are partial explanation
VA
ln(V/L)
Crisis*ln(V/L)
Recovery*ln(V/L)
MW*ln(V/L)
Levels
coef/se
0.820***
(0.011)
1.189***
(0.026)
1.040*
(0.021)
MW*Crisis*ln(V/L)
MW*Recovery*ln(V/L)
TFP (Solow)
TFP (Solow)
Interacted
coef/se
0.847***
(0.014)
1.049
(0.036)
1.005
(0.023)
1.194***
(0.062)
1.176***
(0.064)
0.819***
(0.049)
Crisis*TFP (Solow)
Recovery*TFP (Solow)
MW*TFP (Solow)
MW*Crisis*TFP
(Solow)
MW*Recovery*TFP
(Solow)
Minimum Wage (log)
Crisis*Minimum Wage
(log)
Recovery*Minimum
Wage (log)
Controls
N
Pseudo R2
0.821
(0.111)
0.958
(0.144)
0.168***
(0.028)
Yes
153,115
0.076
0.215***
(0.088)
0.795
(0.134)
0.189***
(0.033)
Yes
153,115
0.076
TFP (Solow)
Levels
Interacted
coef/se
coef/se
0.753***
(0.031)
1.555***
(0.094)
1.111
(0.074)
0.725*
(0.124)
1.247
(0.262)
0.117***
(0.035)
Yes
95,966
0.074
0.746***
(0.034)
1.417***
(0.144)
1.137*
(0.081)
0.950
(0.137)
1.489**
(0.296)
0.492**
(0.154)
0.794
(0.280)
1.060
(0.252)
0.130***
(0.041)
Yes
95,966
0.074
Political crisis was more cleansing
C Suharto Family Member on the Board and/or JSX connection (A&B)
Extended
Baseline
Extended
Baseline
ln(V/L)’96
0.798***
0.804***
(0.028)
(0.028)
TFP (Solow)
1.160
1.113
(0.160)
(0.107)
Connected (JSX/Suharto)
1,871.488*
1.390
227.982**
1.622
(0.482)
(0.594)
(576.428)
(7,284.427)
Connected, (JSX/Suharto)
0.565**
* ln(V/L)’96
(0.164)
Connected (JSX/Suharto)
0.046
* TFP (Solow)
(0.093)
Controls
Yes
Yes
Yes
Yes
N
11,877
11,877
6,957
6,957
Pseudo R2
Yes
Yes
0.078
0.081
Connected firms were far more likely to exit post-Suharto, but less so for
productive firms.
Employment Growth
Estimating Equation

d ln L   ' xt xti1
i



  'Crisis*xti1 Crisis * xti1   'Re cov xti1 Re cov* xti1   'Post Re cov xti1 Postrec * xti1
  'Crisis Crisis   'Re cov Re cov  ' PostrecPost Re cov Postre cov  ti
NB: testing strategy is analogous to that for the survival model
Competing Hypotheses
H 0 :  'Crisis*TFPt  0 Crises has no differential impact on the reallocative efficiency of employment growth
i
HA1 :  'Crisis*TFPti  0 Crises improves the reallocative efficiency of employment growth
HA 2 :  'Crisis*TFPt  0 Crises weakens the reallocative efficiency of employment growth
i
The association between employment growth and productivity
weakened over time and did not recover post-crisis
ln(V/L)
Crisis*ln(V/L)
Recovery*ln(V/L)
TFP (Solow)
OLS
coef/se
0.023***
(0.001)
-0.005**
(0.002)
-0.001
(0.002)
Employment Growth
Dependent Variable: ∆lnL
Ln (V/L)
empvafe
FE
coef/se
0.009***
(0.002)
0.002
(0.002)
0.004**
(0.002)
Crisis*TFP (Solow)
Recovery*TFP (Solow)
Plant controls
Province Dummies
Period Dummies
Industry Dummies
N
R2
Adjusted R2
Yes
Yes
Yes
Yes
138,997
0.029
0.029
Yes
Yes
Yes
Yes
138,997
0.219
0.219
TFP (Solow)
OLS
coef/se
FE
coef/se
0.004
(0.003)
-0.004
(0.005)
-0.004
(0.004)
Yes
Yes
Yes
Yes
88,530
0.025
0.024
0.025***
(0.005)
-0.018***
(0.006)
-0.021***
(0.006)
Yes
Yes
Yes
Yes
88,530
0.213
-0.048
Conclusions
• The crisis was excessively punishing in the short-run
– Excess job losses
– Attenuation of the link between productivity, survival and growth
– Although some improvement in relative productivity of entrants
• Changing credit market conditions amplified exit, but do not fully account for
the observed attenuation.
– Attenuation was strongest amongst firms that lack access to credit to start with
• Political crisis exhibited more cleansing
– Post-Suharto, previously connected firms were more likely to exit, but more
productive ones were more likely to survive
• Reallocation dynamics were not permanently scarred
– The link between productivity and survival is restored post-crisis
– Yet, the link between productivity and employment growth remains weaker than it
had been pre-crisis
• Perhaps because of more stringent labor market regulation?
Broader Lessons from Indonesia
 Indonesia of interest as a large ‘tiger cub’. But are these
likely to be relevant elsewhere?
 Crisis was marked by large fall in demand and tightening of
credit markets
 The extent of the depreciation is significant.
 Crisis are more prevalent in developing countries, although
current situation shows lessons can be relevant for a larger
set of countries
 Shock was particularly large
 But not unprecedented – or unmatched.
 Cleansing hypothesis may be more easily rejected if there
are large market frictions
 Labor markets were fairly flexible under Suharto, although
regulations were tightened after his fall