The Market Valuation of Innovation: The Case of Indian Manufacturing

Sunil KanwarDelhi School of Economics

Bronwyn H. HallUC 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)

(2)

(3)

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)

• 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 after- tax, purged of knowledge capital and other

intangible capital (and time dummies)

Table 1 Sample statistics (3,494 observations on 380 firms, 2001-2010)

Variable Mean Median Standard

Deviation Minimum Maximum Share

Variance Within††

𝑽𝑲𝑷Τ 4.36 3.23 3.43 0.16 19.82 0.265 𝑲𝑲 𝑲𝑷Τ 0.12 0.05 0.20 0.00 2.72 0.159 𝑲𝑲′ 𝑲𝑷Τ 0.17 0.06 0.32 0.00 5.39 0.181 𝑲𝑶𝑰 𝑲𝑷Τ 0.13 0.00 0.42 0.00 7.38 0.078 𝑺 𝑲𝑷Τ 0.00 –0.03 0.31 –1.94 2.02 0.427 𝑲𝑷 (M rupees) 1140.7† 1110.8 1.71 2.30 1,500,007 0.050 D (𝑲𝑶𝑰 = 0) 42.4% 0.052 Correlation Matrix 𝐥𝐧(𝑽𝑲𝑷)Τ 𝑲𝑲 𝑲𝑷Τ 𝑲𝑲′ 𝑲𝑷Τ 𝑲𝑶𝑰 𝑲𝑷Τ 𝑺 𝑲𝑷Τ 𝐥𝐧𝑲𝑷 𝐥𝐧(𝑽𝑲𝑷)Τ 1 𝑲𝑲 𝑲𝑷Τ 0.330 1 𝑲𝑲′ 𝑲𝑷Τ 0.338 0.906 1 𝑲𝑶𝑰 𝑲𝑷Τ 0.302 0.112 0.077 1 𝑺 𝑲𝑷Τ 0.391 –0.004 –0.140 –0.001 1 𝐥𝐧𝑲𝑷 –0.024 –0.131 –0.045 –0.039 0.004 1 Definitions: 𝑽 = Market value = Equity + Book Debt 𝑲𝑷 = Net fixed assets 𝑲𝑲 = Knowledge capital at 15% depreciation 𝑲𝑲′ = 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 (𝑽𝑲𝑷Τ ) (1) (2) (3) (4) (5) (6) Regressor NLLS NLLS NLLS NLLS NLLS,

lag RHS NLIV

𝑲𝑲 𝑲𝑷Τ 2.275*** 2.009*** 1.790*** 1.473*** 1.661*** 1.764*** (0.389) (0.375) (0.330) (0.336) (0.324) (0.329) [0.164] *** [0.140] *** [0.134] *** [0.114] *** [0.126] *** [0.137] *** (0.018) (0.018) (0.018) (0.019) (0.018) (0.018) 𝑲𝑶𝑰 𝑲𝑷Τ 0.988*** 0.817*** 0.974*** 0.815*** 0.640*** (0.224) (0.183) (0.191) (0.185) (0.145) [0.058] *** [0.052] *** [0.059] *** [0.051] *** [0.044] *** (0.009) (0.008) (0.008) (0.008) (0.008) D (𝑲𝑶𝑰= 𝟎) –0.028 –0.037 –0.004 –0.031 –0.083 (0.057) (0.053) (0.056) (0.055) (0.053) 𝑺 𝑲𝑷Τ 0.508*** 0.464*** 0.527*** 0.709*** (0.103) (0.101) (0.095) (0.031) ln 𝑲𝑷 0.020 0.020 0.012 0.015 0.013 0.012 (0.015) (0.015) (0.014) (0.015) (0.014) (0.016) Industry d.v. No No No Yes No No Year FEs Yes Yes Yes Yes Yes Yes 𝑹𝟐 0.199 0.267 0.318 0.383 0.286 0.270 Standard Error

0.608 0.582 0.561 0.536 0.571 0.579

Panel D-W 0.266 0.285 0.316 0.345 0.360 0.346 Observations 3494 3494 3494 3494 3114 3114 Firms 380 380 380 380 380 380 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 (𝑽𝑲𝑷Τ ) (1) (2) (3) (4) (5) (6) Regressor OLS OLS OLS OLS OLS,

lag RHS IV

𝑲𝑲 𝑲𝑷Τ 1.025*** 0.939*** 0.943*** 0.790*** 0.912*** 0.964*** (0.136) (0.129) (0.116) (0.118) (0.118) (0.120) [0.128] *** [0.117] *** [0.117] *** [0.098] *** [0.114] *** [0.118] *** (0.017) (0.016) (0.014) (0.015) (0.015) (0.015) 𝑲𝑶𝑰 𝑲𝑷Τ 0.368*** 0.368*** 0.393*** 0.392*** 0.385*** (0.062) (0.051) (0.055) (0.055) (0.053) [0.049] *** [0.049] *** [0.053] *** [0.053] *** [0.051] *** (0.008) (0.007) (0.007) (0.007) (0.007) D (𝑲𝑶𝑰= 𝟎) –0.079 –0.079 –0.054 –0.076 0.039 (0.051) (0.047) (0.047) (0.049) (0.050) 𝑺 𝑲𝑷Τ 0.704*** 0.633*** 0.686*** 0.500*** (0.076) (0.071) (0.074) (0.100) ln 𝑲𝑷 0.006 0.007 0.007 0.010 0.009 0.011 (0.015) (0.015) (0.013) (0.015) (0.014) (0.014) Industry d.v. No No No Yes No No Year Fes Yes Yes Yes Yes Yes Yes 𝑹𝟐 0.177 0.238 0.339 0.396 0.301 0.318 Standard Error

0.616 0.593 0.552 0.530 0.565 0.559

Panel D-W 0.265 0.282 0.364 0.385 0.413 0.335 Observations 3494 3494 3494 3494 3114 3114 Firms 380 380 380 380 380 380 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 (𝑽𝑲𝑷Τ ) (1) (2) (3) (4) (5) Regressor OLS with

industry fixed effects

OLS with random firm effects

OLS with firm fixed effects

OLS with firm fixed effects

GMM-SYS With lag 2+ instruments

Lagged dep. Var. 0.484*** 0.706*** (0.023) (0.036) 𝑲𝑲 𝑲𝑷Τ 0.785*** 0.688*** 0.428*** 0.315*** 0.302*** (0.117) (0.117) (0.140) (0.087) (0.071) 𝑲𝑶𝑰 𝑲𝑷Τ 0.413*** 0.353*** 0.250*** 0.192*** 0.146*** (0.054) (0.048) (0.064) (0.054) (0.028) 𝑺 𝑲𝑷Τ 0.631*** 0.428*** 0.352*** 0.239*** 0.251*** (0.071) (0.053) (0.051) (0.045) (0.056) ln 𝑲𝑷 0.011 –0.047*** –0.158*** –0.182*** –0.005 (0.015) (0.018) (0.042) (0.032) (0.014) LR coef: 𝑲𝑲 𝑲𝑷Τ 0.609*** 1.026*** (0.172) (0.221) LR coef: 𝑲𝑶𝑰 𝑲𝑷Τ 0.372*** 0.495*** (0.107) (0.091) Year Fes Yes Yes Yes Yes Yes Observations 3494 3494 3494 3114 3096 Firms 380 380 380 380 379 𝑹𝟐 0.395 0.372 0.381 0.522 Std. Err. Within 0.530 0.347 0.321 0.271 Share of variance across firms

0.566 0.602 0.737 0.662

AR(1) t-test 69.9*** 29.0*** 30.1*** 1.8*** Hansen test 255.1** (df) (206) AR(1) test –10.7*** (p-value) (0.000) AR(2) test 2.0** (p-value) (0.050) Note: Robust standard errors clustered on firm in parentheses. Hausman test for correlated effects: 𝝌𝟖𝟐 = 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 (𝑽𝑲𝑷Τ ) (1) (2) (3) (4) (5) (6) Estimation Method Regressor GMM-SYS

with lag 2+ instruments

GMM-SYS with lag 3+ instruments

GMM-SYS with lag 3/4 instruments

GMM-SYS with lag 2+ instruments

GMM-SYS with lag 3+ instruments

GMM-SYS with lag 3/4 instruments

Lagged dep. var. 0.706*** 0.694*** 0.677*** (0.036) (0.039) (0.045) 𝑲𝑲 𝑲𝑷Τ 0.991*** 0.711*** 0.668*** 0.302*** 0.326*** 0.238*** (0.174) (0.130) (0.144) (0.071) (0.094) (0.110) 𝑲𝑶𝑰 𝑲𝑷Τ 0.336*** 0.287*** 0.287*** 0.146*** 0.169*** 0.165*** (0.055) (0.073) (0.079) (0.028) (0.039) (0.039) 𝑺 𝑲𝑷Τ 0.793*** 0.802*** 0.821*** 0.251*** 0.181*** 0.203*** (0.115) (0.149) (0.153) (0.056) (0.073) (0.085) ln 𝑲𝑷 0.002 –0.024 0.018 –0.005 –0.011 0.001 (0.035) (0.035) (0.035) (0.014) (0.016) (0.019) LR coef: 𝑲𝑲 𝑲𝑷Τ 1.026*** 1.067*** 0.735*** (0.221) (0.286) (0.312) LR coef: 𝑲𝑶𝑰 𝑲𝑷Τ 0.495*** 0.553*** 0.510***

(0.091) (0.133) (0.125) Observations 3494 3494 3494 3096 3096 3096 Firms 380 380 380 379 379 379 Hansen test 279.3*** 224.1*** 165.1*** 255.1*** 220.2*** 155.1*** (df) (216) (184) (96) (206) (170) (95) AR(1) test –6.7*** –6.9*** –6.8*** –10.7*** –10.4*** –9.9*** (p-value) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) AR(2) test –1.0 –0.9 –0.9 2.0** 1.9* 1.9* (p-value) (0.328) (0.357) (0.365) (0.050) (0.065) (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 (𝑽𝑲𝑷Τ ) (1) (2) (3) (4) Pavitt Sector Regressor Supplier-

dominated Scale- intensive

Specialized- supplier

Science- based

𝑲𝑲 𝑲𝑷Τ 4.24 1.80*** 1.28*** 1.73*** (3.45) (0.65) (0.38) (0.50) [0.102] [0.093] *** [0.152] *** [0.155] *** (0.063) (0.025) (0.034) (0.034) 𝑲𝑶𝑰 𝑲𝑷Τ 2.74*** 0.73*** 0.83 1.51** (0.89) (0.18) (0.56) (0.64) [0.097] *** [0.077] *** [0.030] [0.055] ** (0.022) (0.013) (0.019) (0.016) 𝑺 𝑲𝑷Τ 0.41 0.50*** 0.48*** 0.48*** (0.46) (0.18) (0.13) (0.18) ln 𝑲𝑷 0.08 0.03 –0.10*** 0.03 (0.06) (0.02) (0.04) (0.02) Year Fes Yes Yes Yes Yes 𝑹𝟐 0.450 0.352 0.357 0.289 Std. Error 0.464 0.549 0.541 0.581 Panel D-W 0.350 0.329 0.343 0.294 Observations 316 1,235 690 1,253 Firms 32 134 78 136 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 two- tail test

Table A1 Observations by Industry and Pavitt sector

Pavitt sector Industry Obs

. Firms Mean

R&D growth

Mean ADV

growth (i) supplier-dominated Gems and jewellery 7 1 0.23% 4.81% (i) supplier-dominated Leather products 30 3 3.21% 0.60% (i) supplier-dominated Rubber products 20 2 0.91% –0.88% (i) supplier-dominated Textiles & textile products 259 26 1.42% –1.08% (ii) scale-intensive Domestic appliances 60 7 1.29% 4.11% (ii) scale-intensive Automobiles 101 12 1.59% 0.96% (ii) scale-intensive Cement 140 14 1.33% 0.78% (ii) scale-intensive Food & agri. products 352 39 0.89% 0.51% (ii) scale-intensive Glass and glassware 25 3 –1.97% 7.93% (ii) scale-intensive Metals & metal products 217 22 0.51% 1.03% (ii) scale-intensive Other consumer goods 30 3 –2.80% –0.17% (ii) scale-intensive Other construction products 171 18 0.62% 2.71% (ii) scale-intensive Paper & paper products 129 13 1.79% 0.08% (ii) scale-intensive Personal care 10 3 –2.92% 1.46% (iii) specialized supplier Automobile ancillaries 419 43 1.58% 1.18% (iii) specialized supplier Nonelectrical machinery 271 35 2.59% 1.94% (iv) science-based Chemicals 600 62 0.79% 0.08% (iv) science-based Electrical machinery 129 15 2.36% 2.32% (iv) science-based Electronics 68 8 1.39% 0.83% (iv) science-based Petroleum products 64 7 –0.36% 2.58% (iv) science-based Drugs & pharmaceuticals 268 31 2.72% 1.76% (iv) science-based Plastic products 124 13 1.21% 0.09% Total 3494 380 1.32% 1.00%

Table B2 GARCH Model for log(Sales)

Parameter (1) (2) (3) (4) (5) Parameters of Equation 8(a) 𝜷𝟏 0.999*** 1.000*** 1.002*** 1.002*** 1.001*** (0.002) (0.002) (0.001) (0.001) (0.001) Parameters of Equation 8(c) 𝜶𝟎 –3.070*** –2.980*** –5.030*** –4.600*** (0.190) (0.150) (0.480) (0.580) 𝜶𝟏 –0.064*** –0.078*** –0.321*** –0.384*** –0.235*** (0.026) (0.021) (0.072) (0.086) (0.076) 𝝅𝟎 0.904*** 0.636*** –0.050*** –0.049*** –0.056*** (0.348) (0.075) (0.003) (0.003) (0.003) 𝝅𝟏 –0.040 (0.049) 𝜸𝟎 1.063*** 1.029*** 1.075*** (0.008) (0.034) (0.008) 𝜸𝟏 0.005 (0.004) Year FEs In eq (8a) In eq (8a) In eq (8a) In eq (8a) In eq (8a) Industry FEs No No No No In eq (8c) Observations 2752 2752 2752 2752 2752 Log-likelihood 466.9 466.5 1172.7 1173.2 1216.5 Equations (8a)-(8c) in the text are reproduced below for convenience: 𝒚𝒊𝒕=𝝁𝒕+𝜷𝟏𝒚𝒊,𝒕−𝟏+𝜺𝒊𝒕 𝜺𝒊𝒕~𝜱(𝟎,𝒉𝒊𝒕) 𝒉𝒊𝒕= 𝐞𝐱𝐩൫𝝁𝒋+𝜶𝟏𝒙𝒊𝒕൯+ሺ𝝅𝟎+𝝅𝟏𝒙𝒊𝒕ሻ(𝜺ො��𝒊,𝒕−𝟏)𝟐+(𝜸𝟎+𝜸𝟏𝒙𝒊𝒕)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 (𝑽𝑲𝑷Τ ) Regressor (1) (2) (3) (4) 𝑲𝑲 𝑲𝑷Τ 0.959*** 0.945*** 0.925*** 1.227*** (0.110) (0.110) (0.120) (0.220) 𝑲𝑶𝑰 𝑲𝑷Τ 0.380*** 0.374*** 0.378*** 0.376*** (0.050) (0.050) (0.050) (0.050) 𝒉 † 5.790** –5.540 8.300*** (2.640) (8.310) (3.180) 𝒉 𝟐 203.6 (144.0) 𝒉 x (𝑲𝑲 𝑲𝑷Τ ) –13.240 (8.390) 𝑺 𝑲𝑷Τ 0.727*** 0.716*** 0.713*** 0.716*** (0.080) (0.078) (0.078) (0.078) ln 𝑲𝑷 0.009 0.013 0.012 0.014 (0.014) (0.014) (0.014) (0.014) Year Fes Yes Yes Yes Yes 𝑹𝟐 0.329 0.335 0.337 0.337 Std. Error 0.553 0.551 0.550 0.550 Panel D-W 0.351 0.329 0.345 0.294 Observations 3114 3114 3114 3114 Firms 380 380 380 380 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.