Does halo effect of innovative firms moderate the impact of working capital efficiency on firm value? Evidence from India

Figures & data

Table 1. Classification of industries into innovative and non-innovative categories

Innovative industries	No. of firms	Non—innovative industries	No. of firms
Automobiles & Parts	16	Construction Materials	15
Chemicals	12	FMCG	21
Electronic & Electrical Equipment	1	Mobile Telecom	5
Health Care	4	Beverages	3
Pharmaceuticals	16	Media	7
Software & Comp Services	11	Tobacco	3
Technology Hardware	1	Utilities	17
Oil & Gas	1	Personal Care	16
Household Goods	12	Real Estate	7
Mining & Metals	3	Transportation & Logistics	6
Industrial Engineering	1
Paper Industries	1
General Industrial	2
Total	100	Total	100

Note: Based on OECD Classification (Grupp, 1995)

Table 2. Variable description

Sl. No.	Variables	Acronyms	Type of variable	Measurement
Dependent variable
1	Firm Value	FV	Dependent	(Market Capitalization + Total Debt—Cash & Cash Equivalents)/EBITDA
Independent explanatory variables
2	Cash Conversion Cycle-	CCC	Proxy for working capital management efficiency	Accounts Receivables Days+ Inventory Holding days—Accounts Payable Days. (ARD + IHD—APD)
3	Accounts Receivable Days	ARD	Proxy for receivables management efficiency	Accounts Receivables (amount) divided by sales, multiplied by 365 days. (AR/Sales) X 365
4	Inventory Holding Days	IHD	Proxy for inventory management efficiency	Inventory (amount) divided by Cost of Sales, multiplied by 365 days. (INV/COS) X 365
5	Accounts Payable Days	APD	Proxy for payables management efficiency	Accounts Payables (amount) divided by Cost of Sales, multiplied by 365 days. $\left(\mathrm{A}\mathrm{P}/\mathrm{C}\mathrm{O}\mathrm{S}\right)\times 365$
6	R&D Intensity	RD	R&D expenditure proxy for firm innovativeness/ effectiveness	R & D $\mathrm{E}\mathrm{x}\mathrm{p}\mathrm{e}\mathrm{n}\mathrm{s}\mathrm{e}\mathrm{s}/\mathrm{S}\mathrm{a}\mathrm{l}\mathrm{e}\mathrm{s}\times 100$ (in %)
Control variables
7	Firm Size	SZ	A measure of firm size	Natural logarithm of Sales
8	Leverage	LEV	A measure of the financial leverage of the firm	Total debt divided by total shareholder’s equity
9	Working Capital Ratio	WCR	A measure of the liquidity of the firm	Current Assets/Current Liabilities
10	Growth in Sales	GRW	A proxy for the growth of the firm	(Current year sales—previous year sales)/previous year sales
11	Age of the Firm	AGE	A measure of the maturity of the firm in its lifecycle	Natural log transformed age of the firm in years since incorporation
12	Net Profit Margin	NPM	A measure of the profitability of the firm	Profit after Tax/Sales

Table 3. Descriptive statistics

Variable	Obs.	Mean	Median	Max	Min	Std. Dev.	Skewness	Kurtosis
FV	1000	21.5213	17.2000	382.7600	2.9800	21.1689	7.5985	104.5275
CCC	1000	80.8802	43.0000	5248.0000	−483.0000	274.4372	9.5756	144.1490
ARD	1000	51.8960	41.0000	446.0000	0.00	48.4589	2.5564	15.6402
IHD	1000	106.8220	62.0000	6085.0000	0.00	287.4548	11.7709	204.4345
APD	1000	77.8378	59.0000	901.0000	1.00	72.8131	4.1411	30.7853
RD	1000	0.9556	0.0800	22.1300	0.00	2.4146	4.3958	26.0978
SZ	1000	8.2365	8.1450	12.8300	3.8000	1.3303	0.1731	3.6218
LEV	1000	0.4600	0.2200	8.7800	0.0000	0.6997	3.7937	29.5156
WCR	1000	1.4614	1.2600	9.9800	0.0500	0.9795	3.0552	19.4968
GRW	1000	14.1377	9.9400	1929.4800	−70.3900	72.2406	21.4459	532.4795
AGE	1000	3.5414	3.5000	4.7600	1.1000	0.6418	−0.4616	3.2100
NPM	1000	10.1290	9.1800	80.3200	−340.0600	21.3267	8.4259	131.7767

Data Source: Capitaline Database, Computation by Authors.

Table 4. Correlation among variables

	FV	CCC	ARD	IHD	APD	RD	SZ	LEV	WCR	GRW	AGE	NPM
FV	1.0000
CCC	0.0570*	1.0000
ARD	−0.0077	0.1517***	1.0000
IHD	0.0531*	0.9669***	0.0572*	1.0000
APD	−0.0102	0.1490***	0.3194***	0.3417***	1.0000
RD	−0.0060	0.0146	0.2332***	−0.0226	0.0110	1.0000
SZ	−0.1997***	−0.1999***	−0.0648**	−0.1881***	−0.0325	0.1323***	1.0000
LEV	−0.0942***	0.0228	0.0558*	0.0562*	0.1731***	−0.1184***	0.0477	1.0000
WCR	−0.0108	0.1392***	0.1972***	0.0672**	−0.1283***	0.0818***	−0.1859***	−0.3270***	1.0000
GRW	−0.0238	−0.0071	−0.0230	−0.0188	−0.0631**	−0.0299	−0.0420	0.0112	0.0156	1.0000
AGE	0.0010	0.0226	−0.1344***	0.0352	−0.0354	0.0541*	0.1248***	−0.2146***	−0.0124	−0.1372***	1.0000
NPM	0.0421	0.1990***	0.0311	0.1695***	−0.0600*	0.0858***	−0.0707**	−0.2155***	0.3218***	0.0314	0.0265	1.0000

Note: *, **, *** indicates significance at 10%, 5%, and 1% level, respectively.

Data Source: Capitaline Database, Computation by Authors.

Table 5. Impact of WCM efficiency and R&D intensity on firm value for sample of all firms

Dependent variable FV, sample of all firms, sample size 1000 firm-years
	Eq-1 (OLS)	Eq-1 (GMM-DPD)	Eq-2 (OLS)	Eq-2 (GMM-DPD)	Eq-3 (OLS)	Eq-3 (GMM-DPD)	Eq-4 (OLS)	Eq-4 (GMM-DPD)
Variable	Coefficient	Coefficient	Coefficient	Coefficient	Coefficient	Coefficient	Coefficient	Coefficient
$\mathrm{F}{\mathrm{V}}_{\mathrm{t}-1}$		0.2189		0.2237		0.2553		0.2237
CCC	−0.0354***	−0.1191**	−0.0356***	−0.1253**
ARD					0.0436	−0.1186	0.0443	−0.3425**
IHD					−0.0381***	−0.1105***	−0.0379***	−0.1428**
APD					0.0566***	0.0674	0.0556***	0.1839
RD	1.0440	−0.4494	0.7983	−2.1048	0.8641	0.5962	0.2230	20.0022
$\mathrm{C}\mathrm{C}\mathrm{C}\times \mathrm{R}\mathrm{D}$			0.0026	0.0085
$\mathrm{A}\mathrm{R}\mathrm{D}\times \mathrm{R}\mathrm{D}$							−0.0033	0.0576
$\mathrm{I}\mathrm{H}\mathrm{D}\times \mathrm{R}\mathrm{D}$							0.0079	−0.0982
$\mathrm{A}\mathrm{P}\mathrm{D}\times \mathrm{R}\mathrm{D}$							0.0003	−0.0099
GRW	−0.0231***	−0.0204	−0.0230***	−0.0430	−0.0208**	−0.0498	−0.0210**	−0.0949
LEV	−1.0924	−3.0146	−1.0951	−3.7889	−2.3963	−1.6328	−2.4317	13.0043
NPM	−0.0736*	0.1102	−0.0735*	0.1255	−0.0864**	0.1017	−0.0869**	0.0803
SZ	−2.4967	−20.2434	−2.4094	−19.3331	−2.7695	−10.0205	−2.5554	−8.9769
WCR	−1.9634	−2.5300	−1.9946	−1.2125	−1.8523	−5.4386	−1.8270	−36.6366
AGE	−5.8387	35.3939	−5.8628	35.8177	−2.4954	26.3125	−3.1964	−1.3907
C	69.0697		68.5047		54.7231		55.6171
J-Statistics		4.1153		3.9910		6.0535		1.4773
Prob of J-stat		0.5329		0.5507		0.3010		0.9157
Adj ${\mathrm{R}}^2$	0.3744		0.3739		0.380108		0.3781
F-Statistics	3.8335***		3.8137***		3.8759***		3.8124***
DW-Stat	2.4724		2.4698		2.4710		2.4722

Note: Equations 1(1) $F{V}_{it}=\alpha +{\beta }_{1}CC{C}_{it}+{\chi }_{it}R{D}_{it}+\hspace{0.17em}\sum _j{\kappa }_i{X}_{it}^j+{\mu }_i+{\tau }_t+{\epsilon }_{it}$(1) to 4 are estimated using Panel OLS with fixed cross-section and period effects for the entire sample. Dynamic Panel Arellano-Bond n-step estimator (with dummy variables to remove period fixed effects, difference to remove cross-sectional fixed effects and using all explanatory variables as instruments) has been used to find robust estimates. Sargan-Hansen J-stat (${\mathrm{H}}_0$: GMM Overidentifying restrictions are valid, i.e., instruments are valid) and its p-values are reported to support validity of the GMM estimates. *, **, *** indicates significance at 10%, 5%, and 1% level, respectively.

Data Source: Capitaline Database, Computation by Authors

Table 6. Impact of WCM efficiency and R&D intensity on firm value for sample of innovative firms

Dependent variable FV, sample of innovative firms, sample size 500 firm-years
	Eq-1 (OLS)	Eq-1 (GMM-DPD)	Eq-2 (OLS)	Eq-2 (GMM-DPD)	Eq-3 (OLS)	Eq-3 (GMM-DPD)	Eq-4 (OLS)	Eq-4 (GMM-DPD)
Variable	Coefficient	Coefficient	Coefficient	Coefficient	Coefficient	Coefficient	Coefficient	Coefficient
$\mathrm{F}{\mathrm{V}}_{\mathrm{t}-1}$		0.5920		−0.8957*		−0.6852		−0.8214*
CCC	−0.0062	0.1355	−0.0104	0.1169
ARD					0.0069	0.0481	0.0001	0.1105
IHD					0.0438	0.2034	0.0262	0.1769
APD					0.0402*	0.0753	0.0257	−0.1577
RD	0.6754	−2.4530	0.4353	7.4726	0.4223	0.1740	−1.0854	−0.3485
$\mathrm{C}\mathrm{C}\mathrm{C}\times \mathrm{R}\mathrm{D}$			0.0017	−0.0249
$\mathrm{A}\mathrm{R}\mathrm{D}\times \mathrm{R}\mathrm{D}$							0.0021	−0.0299
$\mathrm{I}\mathrm{H}\mathrm{D}\times \mathrm{R}\mathrm{D}$							0.0069	−0.0030
$\mathrm{A}\mathrm{P}\mathrm{D}\times \mathrm{R}\mathrm{D}$							0.0033	0.0334
GRW	−0.0061	−0.6120	−0.0059	0.7717*	−0.0046	0.5596	−0.0050	0.6080
LEV	0.0864	−31.9482	0.3656	−51.9594	−1.9449	−78.7498	−1.4873	−54.0310
NPM	0.0498**	0.0963	0.0499**	0.2894**	0.0403*	0.3676**	0.0450**	0.3085
SZ	−7.2114***	42.4547	−7.0365**	−70.5235*	−6.2599**	−59.1503	−5.6716**	−62.3977
WCR	−1.6686	18.8903	−1.6293	3.8039	−1.306	14.4704	−1.1737	3.7749
AGE	4.5978	−52.8754	4.7543	70.9106	8.6937	75.3032	7.3781	79.0241
C	65.5087		63.6457		36.3790		39.1536
J-Statistics		3.4161		2.7024		3.2193		3.5567
Prob of J-stat		0.6361		0.7458		0.6662		0.6148
Adj ${\mathrm{R}}^2$	0.4795		0.4785		0.4853		0.4848
F-Statistics	5.1414***		5.0877***		5.1639***		5.0487***
DW-Stat	2.0803		2.0858		2.1720		2.2030

Note: Equations 1(1) $F{V}_{it}=\alpha +{\beta }_{1}CC{C}_{it}+{\chi }_{it}R{D}_{it}+\hspace{0.17em}\sum _j{\kappa }_i{X}_{it}^j+{\mu }_i+{\tau }_t+{\epsilon }_{it}$(1) to 4 are estimated using Panel OLS with fixed cross-section and period effects for the sample of innovative firms. Dynamic Panel Arellano-Bond n-step estimator (with dummy variables to remove period fixed effects, difference to remove cross-sectional fixed effects, and using all explanatory variables as instruments) has been used to find robust estimates. Sargan-Hansen J-stat (${\mathrm{H}}_0$: GMM Overidentifying restrictions are valid, i.e., instruments are valid) and its p-values are reported to support validity of the GMM estimates. *, **, *** indicates significance at 10%, 5%, and 1% level, respectively.

Data Source: Capitaline Database, Computation by Authors.

Table 7. Impact of WCM efficiency and R&D intensity on firm value for sample of non-innovative firms

Dependent variable FV, sample of innovative firms, sample size 500 firm-years
	Eq-1 (OLS)	Eq-1 (GMM-DPD)	Eq-2 (OLS)	Eq-2 (GMM-DPD)	Eq-3 (OLS)	Eq-3 (GMM-DPD)	Eq-4 (OLS)	Eq-4 (GMM-DPD)
Variable	Coefficient	Coefficient	Coefficient	Coefficient	Coefficient	Coefficient	Coefficient	Coefficient
$\mathrm{F}{\mathrm{V}}_{\mathrm{t}-1}$		0.2432*		0.2459		0.2317		0.1962
CCC	−0.0303***	−0.1060**	−0.0304***	−0.1433**
ARD					0.0309	−0.2750	0.0277	0.0672
IHD					−0.0321***	−0.1494**	−0.0328***	−0.1029*
APD					0.0501*	0.0339	0.0551*	0.1619
RD	1.0062	−4.0635	12.2317	296.4592	1.0527	−1.7779	17.3790	102.8427
$\mathrm{C}\mathrm{C}\mathrm{C}\times \mathrm{R}\mathrm{D}$			0.0727	1.7657
$\mathrm{A}\mathrm{R}\mathrm{D}\times \mathrm{R}\mathrm{D}$							−0.0415	−0.0736
$\mathrm{I}\mathrm{H}\mathrm{D}\times \mathrm{R}\mathrm{D}$							0.0146	−0.3635
$\mathrm{A}\mathrm{P}\mathrm{D}\times \mathrm{R}\mathrm{D}$							−0.0791	−0.4547
GRW	−0.0881***	0.0602	−0.0878***	0.3098	−0.0828***	−0.1626	−0.0825***	0.1211
LEV	−2.5770	−11.5726	−2.6300	−28.3444	−2.9901	−16.5127	−3.0831	−18.0074
NPM	−0.8626***	−0.5739	−0.8699***	−0.8436	−0.8601***	−0.2795	−0.8690***	−0.3341
SZ	7.7405	−37.9070	7.9656	−79.2124	7.0403	−17.9497	7.2150	−24.0185
WCR	−1.9917	−1.5258	−1.8887	23.1956	−1.7384	−7.7708	−1.6086	1.3664
AGE	−12.3439	74.3843	−15.4128	87.4547	−9.4658	17.7018	−13.0654	29.5713
C	21.7217		29.0127		13.2645		22.8714
J-Statistics		4.4634		2.7323		5.0381		6.9633
Prob of J-stat		0.4848		0.7412		0.4112		0.2234
Adj ${\mathrm{R}}^2$	0.4143		0.4158		0.4143		0.4133
F-Statistics	4.1805***		4.1715***		4.1232***		4.0298***
DW-Stat	2.4931		2.4869		2.48652		2.4866

Note: Equations 1(1) $F{V}_{it}=\alpha +{\beta }_{1}CC{C}_{it}+{\chi }_{it}R{D}_{it}+\hspace{0.17em}\sum _j{\kappa }_i{X}_{it}^j+{\mu }_i+{\tau }_t+{\epsilon }_{it}$(1) to 4 are estimated using Panel OLS with fixed cross-section and period effects for the sample of innovative firms. Dynamic Panel Arellano-Bond n-step estimator (with dummy variables to remove period fixed effects, difference to remove cross-sectional fixed effects, and using all explanatory variables as instruments) has been used to find robust estimates. Sargan-Hansen J-stat (${\mathrm{H}}_0$: GMM Overidentifying restrictions are valid, i.e., instruments are valid) and its p-values are reported to support validity of the GMM estimates. *, **, *** indicates significance at 10%, 5%, and 1% level, respectively.

Data Source: Capitaline Database, Computation by Authors.

Table 8. Robustness Test: Effect of WCM efficiency on firm value for samples of high R&D and low R&D firms

	Dependent variable FV, sample of high R&D firms, sample size 500 firm-years				Dependent Variable FV, sample of low R&D firms, sample size 500 firm-years
	Eq-1 (OLS)	Eq-1 (GMM-DPD)	Eq-2 (OLS)	Eq-2 (GMM-DPD)	Eq-1 (OLS)	Eq-1 (GMM-DPD)	Eq-2 (OLS)	Eq-2 (GMM-DPD)
Variable	Coefficient	Coefficient	Coefficient	Coefficient	Coefficient	Coefficient	Coefficient	Coefficient
$\mathrm{F}{\mathrm{V}}_{\mathrm{t}-1}$		−0.5408***		−0.6110***		0.0784		0.0177
CCC	0.0211	−0.0211	0.0298	0.0116	−0.0367***	−0.1214***	−0.0369***	−0.1176**
RD	0.7519	−4.6633	0.9248*	−0.4513	8.4980	−55.8704	−19.9184	−99.0069
$\mathrm{C}\mathrm{C}\mathrm{C}\times \mathrm{R}\mathrm{D}$			−0.0023	−0.0192			0.3736	0.6767
GRW	0.0100	0.5566	0.0072	0.4710	−0.0233*	0.2418	−0.0232*	0.2495
LEV	−0.7680	−42.0883	−0.8347	−28.3200	−1.4691	−16.4061	−1.5392	−13.2400
NPM	−0.4515***	−1.2932*	−0.4482***	−1.2312*	−0.0467	0.2750*	−0.0483	0.2383
SZ	−5.4526	−85.4287**	−5.7584*	−70.0114*	−2.5363	−47.9768	−2.7490	−47.3493
WCR	−2.1482*	−8.0683	−2.1760*	−6.5261	−1.4846	2.7388	−1.5331	1.9333
AGE	−7.6813	101.9133	−7.7182	63.6799	−4.2252	84.5008	−4.3385	79.2573
C	102.8464*		105.1935*		63.6471		65.9468
J-Statistics		2.7256		3.3953		2.1389		2.6873
Prob of J-stat		0.7422		0.6393		0.8296		0.7481
Adj ${\mathrm{R}}^2$	0.5967		0.5963		0.3134		0.3121
F-Statistics	7.6523***		7.5820***		3.0517***		3.0218***
DW-Stat	2.3298		2.3327		2.4811		2.4835

Note: As check for robustness of the method and classification of firms into Innovative and Non-Innovative categories, equations 1(1) $F{V}_{it}=\alpha +{\beta }_{1}CC{C}_{it}+{\chi }_{it}R{D}_{it}+\hspace{0.17em}\sum _j{\kappa }_i{X}_{it}^j+{\mu }_i+{\tau }_t+{\epsilon }_{it}$(1) and 2(2) $F{V}_{it}=\alpha +{\beta }_{1}CC{C}_{it}+{\beta }_2\left(CC{C}_{it}\times R{D}_{it}\right)+{\chi }_{it}R{D}_{it}+\sum _j{\kappa }_i{X}_{it}^j+{\mu }_i+{\tau }_t+{\epsilon }_{it}$(2) are estimated using Panel OLS (with fixed cross-section and period effects) and with Dynamic Panel Arellano-Bond n-step estimator (with dummy variables to remove period fixed effects, difference to remove cross-sectional fixed effects, and using all explanatory variables as instruments) for the samples of High R&D and Low R&D firms. Sargan-Hansen J-stat (${\mathrm{H}}_0$: GMM Overidentifying restrictions are valid, i.e., instruments are valid) and its p-values are reported to support validity of the GMM estimates. *, **, *** indicates significance at 10%, 5%, and 1% level, respectively.

Data Source: Capitaline Database, Computation by Authors

Table 9. Test for optimal or threshold WCM efficiency in determining firm value for samples of innovative and non-innovative firms

	Dependent variable FV, sample of innovative firms, sample size 500 firm-years		Dependent variable FV, sample of non-innovative firms, sample size 500 firm-years
	Eq-7 (OLS)	Eq-7 (GMM-DPD)	Eq-7 (OLS)	Eq-7 (GMM-DPD)
Variable	Coefficient	Coefficient	Coefficient	Coefficient
$\mathrm{F}{\mathrm{V}}_{\mathrm{t}-1}$		0.4692		0.4484*
MEDPOS	0.0219	0.3124	−0.0325***	−0.1708**
MEDNEG	0.0328	−0.0224	−0.0333	0.5774
RD	0.6243	−5.0722	1.1228	−14.9732
GRW	−0.0054	−0.4206	−0.0882***	−0.2575
LEV	−1.4441	−44.7666	−2.4849	−13.6355
NPM	0.0428*	0.0598	−0.8332***	−0.4268
SZ	−7.5681***	21.6315	5.5476	−13.1438
WCR	−1.3883	17.4321	−2.2277	0.1535
AGE	5.7802	−24.4714	−11.0314	−13.9157
C	62.5698		35.9627
J-Statistics		3.3640		2.3098
Prob of J-stat		0.6441		0.8048
Adj ${\mathrm{R}}^2$	0.4825		0.4178
F-Statistics	5.1547***		4.1978***
DW-Stat	2.1043		2.4643

Note: This table tests for the presence of optimal/threshold WCM efficiency that determines firm value for Innovative and Non-Innovative samples of firms. Equations 5(5) $MEDPO{S}_{its}=\mathrm{m}\mathrm{a}\mathrm{x}\left(CC{C}_{its}-MEDIANCC{C}_s,0\right)$(5) is estimated separately using Panel OLS (with fixed cross-section and period effects) and with Dynamic Panel Arellano-Bond n-step estimator (with dummy variables to remove period fixed effects, difference to remove cross-sectional fixed effects, and using all explanatory variables as instruments) for the samples of Innovative and Non-Innovative firms. Sargan-Hansen J-stat (${\mathrm{H}}_0$: GMM Overidentifying restrictions are valid, i.e., instruments are valid) and its p-values are reported to support validity of the GMM estimates. *, **, *** indicates significance at 10%, 5%, and 1% level, respectively.

Data Source: Capitaline Database, Computation by Authors.

Grupp, H. (1995). Science, high technology and the competitiveness of EU countries. Cambridge Journal of Economics, 19(1), 209–223. https://doi.org/10.1093/oxfordjournals.cje.a035304

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