The Market Valuation of Innovation:
The Case of Indian Manufacturing
Sunil Kanwar
Delhi School of Economics
Bronwyn H. Hall
UC Berkeley, NBER, IFS, and NIESR
1. Motivation
Innovation prime motive force behind
economic growth
Firms spend large amounts of scare
resources on innovative activities
Desirable to know whether financial markets
value innovation-intensive firms differentially
Persuasive evidence that developed country
stock markets value innovative activity by
firms
Can we expect the same in less developed
economies? Major reason for incredulity is
the fact that predominant share of
intellectual capital is generated in a handful
of developed economies
A few developing countries do some innovation
(Bogliacino et al. 2012) – process patents,
utility models, smaller innovations. Often for
imitation and diffusion, but generate profits
and hence market value
Hence, stock market’s valuation of innovation
also relevant in developing country context:
• Are more innovative firms valued more
highly than less innovative ones?
• Is market valuation responsive to the
quality of innovation spending?
• Is market value responsive to market
risk?
• Does the market value-innovation relation
vary across industries; and if so, how?
We explore such issues in the context of
manufacturing industries in India.
2. Prior literature
Very informative, but mostly pertains to
developed countries:
Griliches (1981); Bloom and Van Reenen
(2002); Hall, Jaffe and Trajtenberg (2005);
Greenhalgh and Rogers (2006); Griffiths and
Webster (2006); Hall and Oriani (2006);
exception Chadha and Oriani (2010)
Our study broad-bases the available
evidence by providing further evidence on a
developing country, namely India.
Number of distinguishing features, including
the context - mostly no product patents;
few process patents, limited to certain
industries; utility models never an option
Far from obvious that stock market would
value such innovation as does occur
3. The Model
(1)
𝑉 = 𝑝(𝐾𝑃 + 𝛽𝐾𝐾 + 𝛾𝐾𝑂𝐼 + 𝛿𝑆)𝜎
𝑉
ln
𝐾𝑃
𝑖𝑡
𝐾𝐾
= 𝜌 ln(𝐾𝑃 )𝑖𝑡 + 𝜎 ln 1 + 𝛽
𝐾𝑃
𝑖𝑡
4. Sample and Variables
Firm-level data for Indian manufacturing
sector (‘Prowess‘; CMIE)
Sample: 380 firms, 3494 observations, over
2001-2010, covering 22 industries (mostly
2-digit, some 3-digit levels):
Auto ancillaries, automobiles, cement,
chemicals, (other) construction material,
(other) consumer goods, domestic
appliances, drugs and pharmaceuticals,
electrical machinery, electronics, food and
agro-products, gems and jewellery, glass
and glassware, leather and leather
products, metals, non-electrical machinery,
paper and paper products, personal care,
petroleum, plastics and plastic products,
rubber and rubber products, and textiles
and textile products.
Variables:
•
Market Value (V): equity + debt
•
Physical capital (Kp): net fixed assets
•
Knowledge capital (KK): capitalized value
of R&D expenditure; perpetual inventory
method (15% depreciation rate)
𝑲𝑲𝒕 = 𝟏 − 𝜽 𝑲𝑲 𝒕−𝟏 + 𝑹𝑫𝒕
•
Other intangible capital (KOI): capitalized
value of advertising expenditure; perpetual
inventory method (30% depreciation rate)
•
DUM (adv = 0); invariably insignificant
•
Quality of capital (S): real profit aftertax, purged of knowledge capital and other
intangible capital (and time dummies)
Table 1
Sample statistics (3,494 observations on 380 firms, 2001-2010)
Variable
𝑽 𝑲𝑷
𝑲𝑲 𝑲𝑷
𝑲′𝑲 𝑲𝑷
𝑲𝑶𝑰 𝑲𝑷
𝑺 𝑲𝑷
𝑲𝑷 (M rupees)
D (𝑲𝑶𝑰 = 0)
Mean
Median
Standard
Deviation
Minimum
Maximum
Share
Variance
Within††
4.36
0.12
0.17
0.13
0.00
1140.7†
42.4%
3.23
0.05
0.06
0.00
–0.03
1110.8
3.43
0.20
0.32
0.42
0.31
1.71
0.16
0.00
0.00
0.00
–1.94
2.30
19.82
2.72
5.39
7.38
2.02
1,500,007
0.265
0.159
0.181
0.078
0.427
0.050
0.052
Correlation Matrix
𝑲′𝑲 𝑲𝑷
𝑲𝑶𝑰 𝑲𝑷
𝑺 𝑲𝑷
𝐥𝐧 𝑲𝑷
1
0.004
1
𝐥𝐧⁡
( 𝑽 𝑲𝑷 ) 𝑲𝑲 𝑲𝑷
𝐥𝐧⁡
( 𝑽 𝑲𝑷 )
𝑲𝑲 𝑲𝑷
𝑲′𝑲 𝑲𝑷
𝑲𝑶𝑰 𝑲𝑷
𝑺 𝑲𝑷
𝐥𝐧 𝑲𝑷
1
0.330
0.338
0.302
0.391
–0.024
1
0.906
0.112
–0.004
–0.131
1
0.077
–0.140
–0.045
1
–0.001
–0.039
Definitions:
𝑽 = Market value = Equity + Book Debt
𝑲𝑷 = Net fixed assets
𝑲𝑲 = Knowledge capital at 15% depreciation
𝑲′𝑲 = Knowledge capital at 30% depreciation
𝑲𝑶𝑰 = Advertising capital at 30% depreciation
𝑺 = Quality of capital = Profit surprise
†
Geometric mean
††
Within-firm variance as a proportion of total variance (controlling for overall year
means)
Table 2
Nonlinear Regressions
Dependent Variable: ln (𝑽 𝑲𝑷 )
(1)
NLLS
(2)
NLLS
(3)
NLLS
(4)
NLLS
(5)
NLLS,
lag RHS
(6)
NLIV
2.275***
(0.389)
[0.164] ***
(0.018)
2.009***
(0.375)
[0.140] ***
(0.018)
0.988***
(0.224)
[0.058] ***
(0.009)
–0.028
(0.057)
ln 𝑲𝑷
0.020
(0.015)
0.020
(0.015)
1.790***
(0.330)
[0.134] ***
(0.018)
0.817***
(0.183)
[0.052] ***
(0.008)
–0.037
(0.053)
0.508***
(0.103)
0.012
(0.014)
1.473***
(0.336)
[0.114] ***
(0.019)
0.974***
(0.191)
[0.059] ***
(0.008)
–0.004
(0.056)
0.464***
(0.101)
0.015
(0.015)
1.661***
(0.324)
[0.126] ***
(0.018)
0.815***
(0.185)
[0.051] ***
(0.008)
–0.031
(0.055)
0.527***
(0.095)
0.013
(0.014)
1.764***
(0.329)
[0.137] ***
(0.018)
0.640***
(0.145)
[0.044] ***
(0.008)
–0.083
(0.053)
0.709***
(0.031)
0.012
(0.016)
Industry d.v.
Year FEs
𝑹𝟐
Standard
Error
Panel D-W
Observations
Firms
No
Yes
0.199
0.608
No
Yes
0.267
0.582
No
Yes
0.318
0.561
Yes
Yes
0.383
0.536
No
Yes
0.286
0.571
No
Yes
0.270
0.579
0.266
3494
380
0.285
3494
380
0.316
3494
380
0.345
3494
380
0.360
3114
380
0.346
3114
380
Regressor
𝑲𝑲 𝑲𝑷
𝑲𝑶𝑰 𝑲𝑷
D (𝑲𝑶𝑰 = 𝟎)
𝑺 𝑲𝑷
Note: Robust standard errors clustered on firm in parentheses below each coefficient
Elasticity at the means in square brackets, with its standard error below it
In column (5), all right hand side (RHS) variables are lagged one year
In column (6), the instruments are the right hand side variables lagged one year
*** **
, and * denote significance at the 1%, 5% and 10% levels, for a two-tail test
Table 3
Linear Regressions
Dependent Variable: ln (𝑽 𝑲𝑷 )
(1)
OLS
(2)
OLS
(3)
OLS
(4)
OLS
(5)
OLS,
lag RHS
(6)
IV
1.025***
(0.136)
[0.128] ***
(0.017)
0.939***
(0.129)
[0.117] ***
(0.016)
0.368***
(0.062)
[0.049] ***
(0.008)
–0.079
(0.051)
ln 𝑲𝑷
0.006
(0.015)
0.007
(0.015)
0.943***
(0.116)
[0.117] ***
(0.014)
0.368***
(0.051)
[0.049] ***
(0.007)
–0.079
(0.047)
0.704***
(0.076)
0.007
(0.013)
0.790***
(0.118)
[0.098] ***
(0.015)
0.393***
(0.055)
[0.053] ***
(0.007)
–0.054
(0.047)
0.633***
(0.071)
0.010
(0.015)
0.912***
(0.118)
[0.114] ***
(0.015)
0.392***
(0.055)
[0.053] ***
(0.007)
–0.076
(0.049)
0.686***
(0.074)
0.009
(0.014)
0.964***
(0.120)
[0.118] ***
(0.015)
0.385***
(0.053)
[0.051] ***
(0.007)
0.039
(0.050)
0.500***
(0.100)
0.011
(0.014)
Industry d.v.
Year Fes
𝑹𝟐
Standard
Error
Panel D-W
Observations
Firms
No
Yes
No
Yes
No
Yes
Yes
Yes
No
Yes
No
Yes
0.177
0.616
0.238
0.593
0.339
0.552
0.396
0.530
0.301
0.565
0.318
0.559
0.265
0.282
0.364
0.385
0.413
0.335
3494
380
3494
380
3494
380
3494
380
3114
380
3114
380
Regressor
𝑲𝑲 𝑲𝑷
𝑲𝑶𝑰 𝑲𝑷
D (𝑲𝑶𝑰 = 𝟎)
𝑺 𝑲𝑷
Note: Robust standard errors clustered on firm in parentheses
Elasticity at the means in square brackets, with its standard error below it
In column (5), all right hand side variables are lagged one year
In column (6), the instruments are the right hand side variables lagged one year
*** **
, and * denote significance at the 1%, 5% and 10% levels, for a two-tail test
Table 4
Regressions with Firm Effects
Dependent Variable: ln (𝑽 𝑲𝑷 )
Regressor
(1)
OLS with
industry
fixed effects
(2)
OLS with
random
firm effects
(3)
OLS with
firm
fixed effects
(4)
OLS with
firm
fixed effects
(5)
GMM-SYS
With lag 2+
instruments
0.428***
(0.140)
0.250***
(0.064)
0.352***
(0.051)
–0.158***
(0.042)
0.484***
(0.023)
0.315***
(0.087)
0.192***
(0.054)
0.239***
(0.045)
–0.182***
(0.032)
0.706***
(0.036)
0.302***
(0.071)
0.146***
(0.028)
0.251***
(0.056)
–0.005
(0.014)
1.026***
(0.221)
0.495***
(0.091)
Yes
3096
379
Lagged dep. Var.
𝑲𝑲 𝑲𝑷
𝑲𝑶𝑰 𝑲𝑷
𝑺 𝑲𝑷
ln 𝑲𝑷
0.785***
(0.117)
0.413***
(0.054)
0.631***
(0.071)
0.011
(0.015)
0.688***
(0.117)
0.353***
(0.048)
0.428***
(0.053)
–0.047***
(0.018)
Yes
3494
380
0.395
0.530
0.566
Yes
3494
380
0.372
0.347
0.602
Yes
3494
380
0.381
0.321
0.737
0.609***
(0.172)
0.372***
(0.107)
Yes
3114
380
0.522
0.271
0.662
69.9***
29.0***
30.1***
1.8***
LR coef: 𝑲𝑲 𝑲𝑷
LR coef: 𝑲𝑶𝑰 𝑲𝑷
Year Fes
Observations
Firms
𝑹𝟐
Std. Err. Within
Share of variance
across firms
AR(1) t-test
Hansen test
(df)
AR(1) test
(p-value)
AR(2) test
(p-value)
255.1**
(206)
–10.7***
(0.000)
2.0**
(0.050)
Note: Robust standard errors clustered on firm in parentheses.
Hausman test for correlated effects: 𝝌𝟐𝟖 = 137.0 (𝒑-value = 0.000).
Instruments in col. (5) are lags 2 and earlier (level and differenced) of the dependent
and independent variables.
*** **
, and * denote significance at the 1%, 5% and 10% levels, for a two-tail test.
Table B1
GMM-SYS regressions
Dependent Variable: ln (𝑽 𝑲𝑷 )
Regressor
(1)
(2)
GMM-SYS
with lag 2+
instruments
GMM-SYS
with lag 3+
instruments
(3)
(4)
Estimation Method
GMM-SYS GMM-SYS
with lag 3/4 with lag 2+
instruments instruments
Lagged dep. var.
𝑲𝑲 𝑲𝑷
𝑲𝑶𝑰 𝑲𝑷
𝑺 𝑲𝑷
ln 𝑲𝑷
0.991***
(0.174)
0.336***
(0.055)
0.793***
(0.115)
0.002
(0.035)
0.711***
(0.130)
0.287***
(0.073)
0.802***
(0.149)
–0.024
(0.035)
0.668***
(0.144)
0.287***
(0.079)
0.821***
(0.153)
0.018
(0.035)
LR coef: 𝑲𝑲 𝑲𝑷
LR coef: 𝑲𝑶𝑰 𝑲𝑷
Observations
Firms
Hansen test
(df)
AR(1) test
(p-value)
AR(2) test
(p-value)
3494
380
279.3***
(216)
–6.7***
(0.000)
–1.0
(0.328)
3494
380
224.1***
(184)
–6.9***
(0.000)
–0.9
(0.357)
3494
380
165.1***
(96)
–6.8***
(0.000)
–0.9
(0.365)
(5)
(6)
GMM-SYS
with lag 3+
instruments
GMM-SYS
with lag 3/4
instruments
0.706***
(0.036)
0.302***
(0.071)
0.146***
(0.028)
0.251***
(0.056)
–0.005
(0.014)
0.694***
(0.039)
0.326***
(0.094)
0.169***
(0.039)
0.181***
(0.073)
–0.011
(0.016)
0.677***
(0.045)
0.238***
(0.110)
0.165***
(0.039)
0.203***
(0.085)
0.001
(0.019)
1.026***
(0.221)
0.495***
(0.091)
1.067***
(0.286)
0.553***
(0.133)
0.735***
(0.312)
0.510***
(0.125)
3096
379
255.1***
(206)
–10.7***
(0.000)
2.0**
(0.050)
3096
379
220.2***
(170)
–10.4***
(0.000)
1.9*
(0.065)
3096
379
155.1***
(95)
–9.9***
(0.000)
1.9*
(0.065)
Note: Robust standard errors in parentheses
Instruments are lags (level and differenced) of dependent and independent variables –
In columns (1) and (4) they include lag 2 and earlier values,
In columns (2) and (5) lag 3 and earlier values, and
In columns (3) and (6) lags 3 and 4 only.
*** **
, and * denote significance at the 1%, 5% and 10% levels, for a two-tail test
Economic Significance:
Elasticity w.r.t Knowledge Capital: 0.14
(Hall-Oriani 2006)
France: 24%
Germany: 22%
Italy:
18%
US:
42%
UK:
24%
Semi-elasticity w.r.t Knowledge Capital: 1.75
France: 0.66
Germany: 0.56
Italy:
0.94
US:
0.80
UK:
1.92
Evidence of under-investment
Sectoral Heterogeneity (Pavitt 1984)
1: supplier dominated - leather, textiles &
textile products, rubber, gems & jewellery
2: production intensive (scale intensive) –
automobiles, cement, (other) construction
material, (other) consumer goods, domestic
appliances, food & agro-products, glass &
glassware, metals & metal products,
personal care, paper & paper products
3: production intensive (specialised suppliers)
- automobile ancillaries, non-electrical
machinery
4: science-based - chemicals, drugs &
pharmaceuticals, electrical machinery,
electronics, petroleum products, & plastic
products
Table 5
Nonlinear Regressions by Pavitt Sector
Dependent Variable: ln (𝑽 𝑲𝑷 )
(1)
Regressor
Supplierdominated
(3)
Pavitt Sector
ScaleSpecializedintensive
supplier
𝑲𝑲 𝑲𝑷
4.24
(3.45)
[0.102]
(0.063)
2.74***
(0.89)
[0.097] ***
(0.022)
0.41
(0.46)
0.08
(0.06)
1.80***
(0.65)
[0.093] ***
(0.025)
0.73***
(0.18)
[0.077] ***
(0.013)
0.50***
(0.18)
0.03
(0.02)
1.28***
(0.38)
[0.152] ***
(0.034)
0.83
(0.56)
[0.030]
(0.019)
0.48***
(0.13)
–0.10***
(0.04)
1.73***
(0.50)
[0.155] ***
(0.034)
1.51**
(0.64)
[0.055] **
(0.016)
0.48***
(0.18)
0.03
(0.02)
Yes
0.450
0.464
0.350
316
32
Yes
0.352
0.549
0.329
1,235
134
Yes
0.357
0.541
0.343
690
78
Yes
0.289
0.581
0.294
1,253
136
𝑲𝑶𝑰 𝑲𝑷
𝑺 𝑲𝑷
ln 𝑲𝑷
Year Fes
𝑹𝟐
Std. Error
Panel D-W
Observations
Firms
(2)
(4)
Sciencebased
Notes:
Robust standard errors clustered on firm in parentheses
Elasticity at means in square brackets, with standard error below it
*** **
, and * denote significance at 1%, 5% and 10% levels, for twotail test
Table A1
Observations by Industry and Pavitt sector
Pavitt sector
Industry
Obs
.
Firms
(i) supplier-dominated
(i) supplier-dominated
(i) supplier-dominated
(i) supplier-dominated
(ii) scale-intensive
(ii) scale-intensive
(ii) scale-intensive
(ii) scale-intensive
(ii) scale-intensive
(ii) scale-intensive
(ii) scale-intensive
(ii) scale-intensive
(ii) scale-intensive
(ii) scale-intensive
(iii) specialized supplier
(iii) specialized supplier
(iv) science-based
(iv) science-based
(iv) science-based
(iv) science-based
(iv) science-based
(iv) science-based
Total
Gems and jewellery
Leather products
Rubber products
Textiles & textile products
Domestic appliances
Automobiles
Cement
Food & agri. products
Glass and glassware
Metals & metal products
Other consumer goods
Other construction products
Paper & paper products
Personal care
Automobile ancillaries
Nonelectrical machinery
Chemicals
Electrical machinery
Electronics
Petroleum products
Drugs & pharmaceuticals
Plastic products
7
30
20
259
60
101
140
352
25
217
30
171
129
10
419
271
600
129
68
64
268
124
3494
1
3
2
26
7
12
14
39
3
22
3
18
13
3
43
35
62
15
8
7
31
13
380
Mean
R&D
growth
0.23%
3.21%
0.91%
1.42%
1.29%
1.59%
1.33%
0.89%
–1.97%
0.51%
–2.80%
0.62%
1.79%
–2.92%
1.58%
2.59%
0.79%
2.36%
1.39%
–0.36%
2.72%
1.21%
1.32%
Mean
ADV
growth
4.81%
0.60%
–0.88%
–1.08%
4.11%
0.96%
0.78%
0.51%
7.93%
1.03%
–0.17%
2.71%
0.08%
1.46%
1.18%
1.94%
0.08%
2.32%
0.83%
2.58%
1.76%
0.09%
1.00%
Table B2
GARCH Model for log(Sales)
Parameter
(1)
(2)
(3)
(4)
(5)
Parameters of Equation 8(a)
𝜷𝟏
0.999***
(0.002)
1.000***
(0.002)
1.002***
(0.001)
1.002***
(0.001)
1.001***
(0.001)
Parameters of Equation 8(c)
𝜶𝟎
𝜶𝟏
𝝅𝟎
𝝅𝟏
–3.070***
(0.190)
–0.064***
(0.026)
0.904***
(0.348)
–0.040
(0.049)
–2.980***
(0.150)
–0.078***
(0.021)
0.636***
(0.075)
𝜸𝟎
–5.030***
(0.480)
–0.321***
(0.072)
–0.050***
(0.003)
–4.600***
(0.580)
–0.384***
(0.086)
–0.049***
(0.003)
1.063***
(0.008)
1.029***
(0.034)
0.005
(0.004)
1.075***
(0.008)
In eq (8a)
No
2752
1172.7
In eq (8a)
No
2752
1173.2
In eq (8a)
In eq (8c)
2752
1216.5
𝜸𝟏
Year FEs
Industry FEs
Observations
Log-likelihood
In eq (8a)
No
2752
466.9
In eq (8a)
No
2752
466.5
–0.235***
(0.076)
–0.056***
(0.003)
Equations (8a)-(8c) in the text are reproduced below for convenience:
𝒚𝒊𝒕 = 𝝁𝒕 + 𝜷𝟏 𝒚𝒊,𝒕−𝟏 + 𝜺𝒊𝒕
𝜺𝒊𝒕 ~ 𝜱(𝟎, 𝒉𝒊𝒕 )
𝒉𝒊𝒕 = 𝐞𝐱𝐩 𝝁𝒋 + 𝜶𝟏 𝒙𝒊𝒕 + (𝝅𝟎 + 𝝅𝟏 𝒙𝒊𝒕 )(𝜺𝒊,𝒕−𝟏 )𝟐 + (𝜸𝟎 + 𝜸𝟏 𝒙𝒊𝒕 )𝒉𝒊,𝒕−𝟏
where 𝒚 is log(sales), 𝒙 is log(𝑲𝑷 ), 𝒋 is the industry to which the 𝒊𝒕𝒉 firm
belongs, 𝝁𝒕 are the year dummies, and 𝝁𝒋 are the industry dummies.
Table 6
Market Value Regressions Allowing for Uncertainty
Dependent Variable: ln (𝑽 𝑲𝑷 )
Regressor
(1)
(2)
(3)
(4)
𝑲𝑲 𝑲𝑷
0.959***
(0.110)
0.380***
(0.050)
0.945***
(0.110)
0.374***
(0.050)
5.790**
(2.640)
0.925***
(0.120)
0.378***
(0.050)
–5.540
(8.310)
203.6
(144.0)
1.227***
(0.220)
0.376***
(0.050)
8.300***
(3.180)
𝑲𝑶𝑰 𝑲𝑷
𝒉†
𝒉𝟐
0.727***
(0.080)
0.009
(0.014)
0.716***
(0.078)
0.013
(0.014)
0.713***
(0.078)
0.012
(0.014)
–13.240
(8.390)
0.716***
(0.078)
0.014
(0.014)
Yes
0.329
0.553
0.351
3114
380
Yes
0.335
0.551
0.329
3114
380
Yes
0.337
0.550
0.345
3114
380
Yes
0.337
0.550
0.294
3114
380
𝒉 x (𝑲𝑲 𝑲𝑷 )
𝑺 𝑲𝑷
ln 𝑲𝑷
Year Fes
𝑹𝟐
Std. Error
Panel D-W
Observations
Firms
Notes:
OLS regressions. Robust standard errors clustered on firm in
parentheses
† Industry sales variance estimated as shown in Appendix B,
Table B3.
*** **
, and * denote significance at 1%, 5% and 10% levels, for
two-tail test
Conclusions
Where most firms do not patent, or have
utility models, we find that:
•
Stock market places greater value on
more innovative firms, ceteris paribus
•
Rate of return appears to be larger than
that in developed countries, excepting UK
•
•
•
•
Depreciation rate too high? Probably
not
Firms underinvest in R&D. Probably
R&D-intensive firms valued more for
option value of R&D programmes
Market value-innovation relation appears
to vary between supplier-dominated &
other industry groups, but few firms in
former group, & differences insignificant.