misallocation or mismeasurement?misallocation or mismeasurement? mark bils, university of rochester...
TRANSCRIPT
Misallocation or Mismeasurement?
Mark Bils, University of Rochester and NBERPete Klenow, Stanford University and NBER
Cian Ruane, International Monetary Fund
January 2020
Any opinions and conclusions expressed herein are those of the author(s) and do notnecessarily represent the views of the U.S. Census Bureau. This research wasperformed at a Federal Statistical Research Data Center under FSRDC Project 1440.All results have been reviewed to ensure that no confidential information is disclosed.The views expressed in this paper are those of the authors and should not be attributedto the International Monetary Fund, its Executive Board, or its management.
1 / 65
Motivation
Widespread perception that resource misallocation an importantcause of low aggregate productivity (TFP) in EMDEs
Motivates structural reforms as a source of growth / convergence
Gains from resource reallocation are difficult to quantify, butgains are potentially huge
I Banerjee & Duflo (2005)I Restuccia & Rogerson (2008)I Hsieh & Klenow (2009, 2014)I Baqaee & Farhi (2019)
2 / 65
Hsieh and Klenow (2009)
Large dispersion in revenue/inputs (TFPR) across plants in India,China and U.S.
Interpret dispersion in TFPR as dispersion in marginal products⇒ quantify misallocation
Reducing resource misallocation in India and China to U.S.levels⇒ increase TFP by 40% and 50%
But dispersion in measured average products need not reflectdispersion in true marginal products
3 / 65
U.S. manufacturing in recent decades
Kehrig (2015) noticed rising TFPR dispersion
I Suggests falling allocative efficiency
For 1978–2013 we find it would imply:
I A drag on TFP growth of 1.7 percentage points per year
I 45 percent lower TFP (cumulatively by 2013)
Has misallocation really increased dramatically?
Or has mismeasurement and/or misspecification worsened?
4 / 65
U.S. allocative efficiency
5 / 65
What we do
Propose a way to estimate misallocation allowing for:
I Measurement error in revenue and inputs
I Misspecification due to overhead costs
Apply to:
I manufacturing plants in the U.S. 1978–2013
I manufacturing plants in India 1985–2013
Preview of results:
I No longer a severe decline in U.S. allocative efficiency
I For U.S. potential gains ∼ 49% rather than ∼ 123%
I For India, potential gains ∼ 89% rather than ∼ 111%
6 / 65
U.S. vs India corrected allocative efficiency
7 / 65
Others skeptical of misallocation
Adjustment costs
I Asker, Collard-Wexler and De Loecker (2014)
I Kehrig and Vincent (2019)
Overhead costs
I Bartelsman, Haltiwanger and Scarpetta (2013)
Variable production elasticities
I David and Venkateswaran (2019)
Measurement error and imputation
I Rotemberg and White (2019)
8 / 65
Outline
1 Illustrative example
2 Full model
3 TFPR dispersion in U.S. and Indian data
4 Estimating measurement and specification error
5 Corrected measures of misallocation
9 / 65
Simple model setup
Y =
(∑i Y
1− 1ε
i
) 1
1− 1ε , P =
(∑i P
1−εi
) 11−ε
Yi = Ai · Li
max(1− τYi
)PiYi − wLi
I Monopolistic competitor takes w, Y , and P as given
PiYi = PiYi + gi
Li = Li + fi
10 / 65
Simple model TFPR
Pi =
(ε
ε− 1
)×(τi ·
w
Ai
), where τi ≡
1
1− τYi
PiYiLi
∝ τi
TFPRi ≡PiYi
Li∝ τi ·
PiYiPiYi
· LiLi
Let ∆Xt ≡Xt −Xt−1
Xt−1for variable X
11 / 65
Identifying misallocation vs. dispersion in TFPR
∆P Y i = ∆Li ·τi
TFPRi
I assuming distortions and measurement error are fixed over time
I in which case ∆PiYi = ∆Li = (ε− 1) ∆Ai
We will generalize to allow:
I Sales R and a composite input I
I Shocks to distortions and to measurement errors
And regress ∆R on ∆I in different deciles of TFPR
I Measurement error should make coefficients fall with TFPR
I Will use this to estimate E (ln τi | ln TFPRi)
I Validate approach using simulations
12 / 65
Outline
1 Illustrative example
2 Full model
3 TFPR dispersion in U.S. and Indian data
4 Identifying measurement and specification error
5 Corrected misallocation
13 / 65
Full Model (Setup)
Closed economy, S sectors, Ns firms, L workers, K capital
Q =∏Ss=1Q
θss , Qs =
(∑Nsi Q
1− 1ε
si
) 1
1− 1ε
Qsi = Asi(Kαssi L
1−αssi )γsX1−γs
si
max Rsi − (1 + τLsi)wLsi − (1 + τKsi )rKsi − (1 + τXsi )Xsi
I Rsi ≡ PsiQsiI Monopolistic competitor takes input prices as given
C = Q−X , X =∑S
s
∑Nsi Xsi
14 / 65
Model (Aggregate TFP)
TFP ≡ C
L1−αKα
I where α ≡∑Ss=1 αsγsθs∑Ss=1 γsθs
TFP = T ×S∏s=1
TFP
θs∑Ss=1 γsθs
s
I TFPs ≡Qs
(Kαss L1−αs
s )γsX1−γss
I T = reflects sectoral distortions (set aside)
15 / 65
Model (Sectoral TFP)
Suppressing s here and whenever possible:
TFP =
[N∑i
Aε−1i
(τiτ
)1−ε] 1ε−1
τi ≡[(
1 + τLi)1−α (
1 + τKi)α]γ (
1 + τXi)1−γ
τ ≡[(
1 + τL)1−α (
1 + τK)α]γ (
1 + τX)1−γ
where 1 + τL ≡[∑N
i=1RiR
11+τLi
]−1and so on
16 / 65
Model (Sectoral TFP Decomposition)
TFP = AE · PD ·A ·N1ε−1
AE ≡ Allocative Efficiency
PD ≡ Productivity Dispersion
A ≡ Average productivity
N1ε−1 ≡ Variety
17 / 65
Model (Sectoral TFP Decomposition)
TFP =
[1
N
N∑i
(Ai
A
)ε−1 (τiτ
)1−ε] 1ε−1
︸ ︷︷ ︸AE=Allocative Efficiency
×
[1
N
N∑i
(Ai
A
)ε−1] 1ε−1
︸ ︷︷ ︸PD=Productivity Dispersion
×N1ε−1︸ ︷︷ ︸
Variety
× A︸︷︷︸Average Productivity
A =[
1N
∑Ni (Ai)
ε−1] 1ε−1 (power mean)
A =∏Ni=1A
1Ni (geometric mean)
18 / 65
Full Model (Sectoral TFP Decomposition)
TFP ≡ Q
(L1−αKα)γX1−γ
=
[1
N
N∑i
(Ai
A
)ε−1 (τiτ
)1−ε] 1ε−1
︸ ︷︷ ︸AE=Allocative Efficiency
×
[N∑i
(Ai)ε−1
] 1ε−1
︸ ︷︷ ︸Residual Productivity
τi ≡[(
1 + τLi)1−α (
1 + τKi)α]γ (
1 + τXi)1−γ
19 / 65
Aside: Intuition in a special case
If Ai and τi are jointly lognormal and τLi = τKi = τXi then
AE =ε
2· Var [ln(τi)]
20 / 65
Outline
1 Illustrative example
2 Full model
3 TFPR dispersion in U.S. and Indian data
4 Identifying measurement and specification error
5 Corrected misallocation
21 / 65
Indian Annual Survey of Industries (ASI)
Survey of Indian manufacturing plants
I Long panel 1985–2013
I Used in Hsieh and Klenow (2009, 2014)
Sampling frame
I All plants > 100 or 200 workers (45% of plant-years)
I Probabilistic if > 10 or 20 workers (55% of plant-years)
I ≈ 43,000 plants per year
Variables used
I Gross output (Ri), intermediate inputs (Xi), labor (Li), wage bill(wLi), and capital (Ki)
22 / 65
U.S. Longitudinal Research Database (LRD)
U.S. Census Bureau data on manufacturing plants
I Long panel, 1978–2013
I Used in Hsieh and Klenow (2009, 2014)
Sampling frame
I Annual Survey of Manufacturing (ASM) plants
I ∼ 50k plants per year with at least one employee
I Quinquennial sample for ∼ 34k plants, certainty for other ∼ 16k
Variables used
I Gross output (Ri), intermediate inputs (Xi), labor (Li), wage bill(wLi), and capital (Ki)
23 / 65
Data cleaning steps
Step Cleaning
1 Starting sample of plant-years2 Missing no key variables3 Common sector concordance4 Trim 1% of each left and right TFPR and TFPQ tail
1,806,000 plant-years for U.S. LRD
943,186 plant-years for Indian ASI
24 / 65
Inferring allocative efficiency a la Hsieh & Klenow (2009)
AE =
[N∑i
(TFPQi
TFPQ
)ε−1(TFPRiTFPR
)1−ε] 1ε−1
Ai = TFPQi =(Ri)
εε−1
(Kαi L
1−αi )γX1−γ
i
TFPRi =Ri
(Kαi L
1−αi )γX1−γ
i
25 / 65
Inferring allocative efficiency as in Hsieh & Klenow (2009)
AE =
[N∑i
(TFPQi
TFPQ
)ε−1(TFPRiTFPR
)1−ε] 1ε−1
TFPQ =[∑N
i TFPQε−1i
] 1ε−1
TFPR =(
εε−1
) [MRPL
(1−α)γ
](1−α)γ [MRPKαγ
]αγ [MRPX1−γ
]1−γ
I MRPK =
[∑i
RiR
1
MRPKi
]−1
and so on
I MRPKi =
(ε− 1
ε
)αγ
RiKi
and so on
26 / 65
Inferring aggregate AE
Aggregating within-sector allocative efficiencies:
AEt =
S∏s=1
AE
θst∑Ss=1 γsθst
st
Parameterization:
ε = 4 based on Redding and Weinstein (2016)
αs and γs inferred from sectoral cost-shares (r = .2)
θst inferred from sectoral shares of aggregate output
27 / 65
Indian allocative efficiency
28 / 65
U.S. allocative efficiency
29 / 65
Allocative efficiency in the U.S. relative to India
30 / 65
TFPR Dispersion in the U.S. and India over time
31 / 65
TFPQ Dispersion in the U.S. and India over time
32 / 65
Elasticity of TFPR wrt TFPQ in the U.S and India
33 / 65
Outline
1 Illustrative example
2 Full model
3 TFPR dispersion in U.S. and Indian data
4 Identifying measurement and specification error
5 Corrected misallocation
34 / 65
Measurement error and ∆TFPR
Recall TFPRi = τi ·RiRi· IiIi
∆TFPRi = ∆τi + ∆
(RiRi
)− ∆
(IiIi
)
∆ is the growth rate of a variable relative to the sector s mean.
Increases in Ri and Ii reduce the role of additive measurement error
But have no impact on the role of multiplicative measurement error
35 / 65
Previewing our empirical specification
We will regress revenue growth on input growth for a panel of plants:
∆Ri = λk + βk∆Ii + ei
i denotes plant
k denotes one of K bins of TFPR
∆ is the growth rate of a variable relative to the sector s mean.
Additive measurement error shows up as lower βk at higher TFPR’s
36 / 65
Measurement error: key assumptions
Only additive measurement error
Ii = Ii + fi; Ri = Ri + gi
I Conservative, as multiplicative also overstates TFPR differences
I Analogous to heterogeneous overhead costs
Common measurement error across inputs
Zero covariance between ln (τi) and ln
(RiRi· IiIi
)
37 / 65
Look at elasticity of revenue wrt inputs
Defineβk ≡
Covk
(∆R,∆I
)V ark
(∆I)
k indexes the level of TFPR (plant index i implicit)
βk is the population projection of ∆R on ∆I , at k–level TFPR
38 / 65
βk in the absence of measurement error
If f = 0 and g = 0, then:
∆I = ∆I = (ε− 1) ∆A − ε∆τ
∆R = ∆R = (ε− 1)(
∆A−∆τ)
βk = 1 + φk
where φk ≡Covk (∆τ,∆I)
V ark (∆I)
=−εV ark (∆τ) + (ε− 1)Covk (∆τ,∆A)
V ark (∆I)
39 / 65
βk in the absence of measurement error cont.
βk = 1 + φk
φk = elasticity of ∆τ wrt ∆I
If ∆τ is i.i.d., then does not project on TFPR
I ⇒ φk = φ
I ⇒ βk = β
If τ is stationary, then V ark (∆τ) larger at both high and lowvalues of ln(TFPR) = ln(τ), so smaller βk at TFPR extremes
40 / 65
βk in the presence of measurement error
βk ≈ Ek
(RI
R I
)(1 + φk) + ψk
≈ reflects for “small” changes: ∆A, ∆τ ,df
I,dg
R
Recall TFPR = τ · RR· II
Ek
(RI
R I
)captures role of both measurement errors in TFPR
Ek (τ) = Ek
(RI
R I
)· TFPRk
41 / 65
Measurement error in revenue and inputs, cont.
Now φk reflects elasticities of ∆τ and df
Iwrt ∆I
φk ≡Covk
(I
I∆τ − f ′−f
I, ∆I
)V ark
(∆I)
Added term ψk
ψk =1
V ark
(∆I)(Covk(RI
R I, ∆I
(∆I +
I
I∆τ − f ′ − f
I
))−
Ek
(∆I)Covk
(RI
RI,(
∆I +I
I∆τ − f ′ − f
I
))+ Covk
(g′ − gR
, ∆I
))
42 / 65
Recovering Ek (τ)
Ek (τ) = Ek
(RI
R I
)· TFPRk =
(βk − ψk1 + φk
)· TFPRk
If ∆τ , ∆A,df
Iand
dg
Rare i.i.d. =⇒ ψk = 0, φk = φ
Ek (τ) ∝ βk · TFPRk
Model simulations to correct for large shocks and φk, ψk
43 / 65
Getting dispersion in τ
Ek (τ) ∝ βk · TFPRk is τ dispersion that projects on TFPR
With measurement error, also τ dispersion ⊥ to TFPR
“Add back” dispersion, call term ε, in ln τ
I ε ⊥ ln TFPR
I ε ∼ N(0, σ2)
I σ2 chosen to hit var[ln(τ)] assuming ln(R I
R I
)⊥ ln τ
44 / 65
Dispersion in τ , continued
ln TFPR = ln τ − ln(R I
R I
)V ar
(ln τ
)=
V ar(
ln TFPR)− V ar
(ln
(RI
RI
))+ 2Cov
(ln τ, ln
(RI
RI
))
Assuming Cov(
ln τ, ln
(RI
RI
))= 0
I Eliminates last term in V ar(
ln τ)
I −V ar
(ln
(R I
R I
))= Cov
(ln TFPRk, Ek
(ln
(R I
R I
)))
45 / 65
Dispersion in τ , continued
As a result:
V ar
(ln τ
)=
V ar
(ln TFPR
)+ Cov
(ln TFPRk, Ek
(ln
(R I
R I
)))
I First term is data
I Second reflects how our βk estimates covary with TFPR
46 / 65
TFPR correction implementation
Regress revenue growth on input growth by decile of TFPR:I Divide 25+ year samples into 5/6-year windowsI Unbalanced panel of Indian and U.S. plantsI ≈ 28,000 / 6,000 plants per decile-window in U.S. / India
∆Ri = λk + βk∆Ii + ei
i denotes plant, k denotes decileI ∆Ri, ∆Ii and TFPR are deviations from sector-year averageI Use Tornqvist average of TFPR for constructing decilesI Regressions are cost-share weightedI Trim observations where TFPR changes by factor > 5
Merge βk estimates into non-panel sample by decile-window:
ln (τi) = ln(TFPRi) + ln(βk) + εi
47 / 65
βk’s wrt TFPRk, India and U.S.
Table: Coefficients βk for revenue growth on input growth by TFPR decile
k→ 1 2 3 4 5 6 7 8 9 10
India 1.09 1.04 1.03 1.00 1.01 0.99 1.00 0.99 0.95 0.88
U.S. 1.05 1.00 0.97 0.96 0.93 0.90 0.86 0.84 0.70 0.54
Source: Indian ASI 1985–2013 and U.S. LRD 1978–2013. TFPR deciles are constructed asTornqvist deviations from the (cost-weighted) sector-year average. Regressions weight by plant’sinput costs. Standard errors are clustered at the industry level, and are uniformly below 0.02.
48 / 65
Indian βk slopes wrt TFPRk
49 / 65
U.S. βk slopes wrt TFPRk
50 / 65
τ dispersion vs. TFPR dispersion
India
1985–1991 1992–1996 1997–2001 2002–2007 2008–2013
σ2τ/σ
2TFPR 0.68 0.76 0.74 0.69 0.71
U.S.
1978–1984 1985–1991 1992–1998 1999–2005 2006–2013
σ2τ/σ
2TFPR 0.40 0.43 0.38 0.32 0.28
51 / 65
Allocative efficiency in India
52 / 65
Allocative efficiency in the U.S.
53 / 65
Uncorrected vs. corrected gains from reallocation
INDIA1985–2013
Mean S.D.
Uncorrected gains 110.9% 17.3%
Corrected gains (estimates) 87.8% 13.8%
Shrinkage 21% 20%
54 / 65
Uncorrected vs. corrected gains from reallocation
U.S.1978–2013
Mean S.D.
Uncorrected gains 123.2% 59.7%
Corrected gains (estimates) 49.1% 12.2%
Shrinkage 60% 80%
55 / 65
Cumulative change in AE
U.S. INDIA1978–2013 1985–2013
Uncorrected -45% -1.5%
Corrected -16% -0.8%
56 / 65
Allocative efficiency: U.S. relative to India
57 / 65
Dispersion of U.S. TFPR
58 / 65
Elasticity of TFPR wrt TFPQ in the U.S.
59 / 65
Dispersion of TFPQ in the U.S.
60 / 65
Simulations to test the validity of our strategy
Ait = Ai · ait where ln(Ai) ∼ N(0, σ2A)
ait and τit follow
ln(xit) = ρx · ln(xit−1) + ηxit where ηxit ∼ N(0, σ2x)
fit follows
fit = ρf · fit−1 + ηfit · Iit where ηfit ∼ N(0, σ2f )
Use ε = 4, ρa = ρτ = ρf = 0.9
Estimate {σA, σa, στ , σf} by window to fit{σTFPR, σTFPQ, σ∆I
, projection of ln(βk) on ln(TFPRk)}
61 / 65
Simulation Results
India
1985–1991 1992–1996 1997–2001 2002–2007 2008–2013
σ2τ/σ
2TFPR (our correction) 0.64 0.76 0.76 0.68 0.71
σ2τ/σ
2TFPR (truth) 0.60 0.80 0.82 0.68 0.71
U.S.
1978–1984 1985–1991 1992–1998 1999–2005 2006–2013
σ2τ/σ
2TFPR (our correction) 0.36 0.41 0.34 0.32 0.25
σ2τ/σ
2TFPR (truth) 0.17 0.29 0.17 0.13 0.02
62 / 65
Takeaways from simulations
ln(β) vs. ln(TFPR) approach:
Does well at correcting for additive measurement error
I similar results when measurement error is in revenues
I tends to undercorrect when there is lots of measurement error
And further simulations show:
Does not correct at all for multiplicative measurement error
Does not correct at all for adjustment costs
63 / 65
Conclusion
Proposed a way to estimate true dispersion of marginal products
I Revenue growth is less sensitive to input growth when averageproducts overstate marginal products
I Requires measurement error be additive and ⊥ to distortions
Implemented on Indian ASI
I Potential gains from reallocation reduced by 15
I Time-series volatility reduced by 15
Implemented on U.S. LRD
I Potential gains from reallocation reduced by 35
I Time-series volatility reduced by 45
I No longer a sharp downward trend in allocative efficiency
I U.S. allocative efficiency predominantly higher than in India
64 / 65
Should the U.S. Census Bureau have noticed?
Arguably hard to see:
Variance of ln R and ln I rose 7.2% and 7.3% (1978–2013)
Correlation of ln R and ln I fell from 0.993 to 0.979
22–58 times higher variance for ln R and ln I than for ln TFPR
65 / 65