Factors Driving Venture Capital Investments in India

Abstract

This study aims to investigate what factors determine venture capital investments in India. The study selects the most relevant variables categorised as the Ease of Doing Business Index (EODBI), institutional indicators and macroeconomic variables. The EODBI has been introduced for the first time in the venture capital literature, intending to capture the business environment of India. Three regression models with three different dependent variables report the ambiguous results for domestic venture capital funds (DVCF), foreign venture capital investments and total venture capital investments. The individual measure EODB, representing the overall rating of doing business in India, is not favourable for domestic funds but favours foreign investors. Enforcing contracts and trading across borders respond positively to DVCF, while registered property moves differently. Resolving insolvency positively affects all three types of investments, benefiting young investors and young entrepreneurs by exploiting venture capital. This study can be refined in several ways in terms of future research, but the biggest challenge will be collecting enough data to derive meaningful results.

Keywords

Venture capital ease of doing business macroeconomic variables World Wide Governance Indicators HAC autocorrelation

Introduction

The venture capital market started in the late 1990s in India, and now it has become a benchmark for financing the start-ups such as Flipkart, Paytm and Razorpay. This increasing trend of start-ups in India attracts more attention of alternative investors like venture capital. Venture capital is the money of invention which is invested in young businesses of no historical background. Venture capital is an important source of alternative investments which plays a vital role in financing a business that might otherwise have difficulty meeting capital requirements (Gompers & Lerner, 2001). It is by now recognised globally that venture capital is beneficial for the growth and development of an economy. The survival and growth of venture capital industry required an investment-friendly economic and political system. Despite wide recognition, the amount invested through venture capital broadly differs across countries. The pattern of investment also varies based on the industry. This study aims to investigate what factors determine venture capital in India.

The study selects the most relevant variables categorised as Ease of Doing Business Index (EODBI), institutional indicators (INST), and macroeconomic variables (ECO). EODBI has been introduced for the first time in the literature on venture capital and intends to capture India’s business environment. Three regression models were estimated using Newey–West heteroscedasticity and autocorrelation (HAC), which corrects serial correlation and is known for its added benefit of eliminating heteroscedasticity problems.

The study reports ambiguous results for DVCF, foreign venture capital investments (FVCI) and total venture capital investments (TVCI). The individual measure EODB indicating overall ease of doing business in India is not favourable for domestic funds but favourable for foreign investors. Enforcing contracts and trading across borders (TAB) respond positively to DVCF, while registered property moves differently. Resolving insolvency positively affects all three types of investments, which can be considered beneficial for young investors and young entrepreneurs for exploiting venture capital. However, rules of protecting minority investors and paying of taxes are not favourable for all three types of investments and need the attention of the researcher and the policymaker. GDP and inflation significantly affect foreign and total investment but fail to explain DVCF. Interestingly, a negative relationship is found between unemployment and DVCF, which is again the point of concern for the academician and the researchers. Among the institutional variables, regulatory quality has a significant positive effect on domestic and foreign investors. The government effectiveness positively influences foreign and TVCI, while it is a point of concern for the DVCF, which records a negative relationship. Control of corruption determines none of the venture capital activity in India. These results are potentially important for understanding and promoting venture capital investments in India.

This study can be refined in several different ways in terms of future research, but the biggest challenge will be collecting enough data to derive meaningful results. At first, increasing the number of observations will significantly improve the reliability of the findings. Moreover, stage financing has also attracted the attention of the researcher, but unavailability of data restricted us to simple investments without any categorisation of funds according to the stage. The other factors such as capital gains tax, labour rigidities, capital market, foreign direct investments and mutual funds can also be included in the study for a wider recognition of venture capital.

Literature

Oino (2014) shows in his study that fast-growing economies, a well-developed stock market and a robust legal system attract private equity market. More precisely, the study demonstrates that the lending interest rate significantly influences private equity and has an ambiguous effect depending on the preponderance of supply side or demand side. Félix et al. (2013) found that venture capital investment nurtured well in countries with vibrant M&A markets despite under-developed IPO markets. The findings also show that the unemployment and TEA index negatively affects venture capital activity. Diaconu (2012), in his study in Romania between 2000 and 2010, found that gross expenditure on R&D affects the venture activity at early and expansion stage. Moreover, interest rate has a negative impact, but market capitalisation (representing capital market liquidity) and the taxes rates have no effect. The study by Bonini and Alkan (2011) shows IPO markets, interest rates, corporate income tax rates and R&D spending are meaningful factors in explaining the cross-country variation in levels of investment for both early-stage and overall venture capital activity. Khoury et al. (2015) investigate how political hazard risks and the legal system affect venture capital activity at different stages of investments. Regression results on 433 VC transactions across 13 Latin American countries show the negative relationship between investment size and political hazards. Lower quality legal systems overspill investments. Moreover, in moderated institutional dimensions, VC-staging strategies are invisible in the lower quality legal systems, and middle and later stage ventures receive the largest investments. However, this pattern changes with the improvement of the legal system; larger investments go to early-stage ventures. Within the framework of the Vector Error Correction Model, Füss and Schweizer (2011) found that venture capital activity is positively related to industrial production, exit channel and long-term interest. Conversely, short-term interest rates evidence the negative relationship.

Additionally, the Granger causality test shows the casual effect of industrial production to venture capital while venture capital returns cause the exit route. Stimel (2012) provided the evidence of the decrease of venture capital activity in an economy with a high inflation rate. The negative effect is higher on start-up and early-stage ventures but less negative for matured ventures. However, this effect is positive for late-stage ventures, indicating these firms’ acceleration towards initial public offerings (IPOs) to avoid any additional increase in inflation. Romain and Potterie (2003) estimated a generalised least square regression on a panel dataset of 16 major OECD countries from 1990 to 1998. Using a panel data set of 16 major OECD countries, they found that short-term and long-term interest positively influence venture capital activity, and a stronger effect is on the demand side. However, this effect is found to be opposite for the supply side. According to, labour market performance is the essential variable that accounts for seed, start-up and expansion of business. Jeng and Wells (2000) conducted a similar study on a panel dataset of 21 countries, confirming that labour market rigidities negatively affect early-stage venture capital while no effect is found for IPOs on early-stage venture capital. Jagwani (2000) shows that capital gains tax is stronger on the demand side than on the supply side of venture capital. Kortum and Lerner (2001), through patented inventions in the United States across 20 industries over three decades, determine how venture capital spur innovation. The study shows that venture capital activity in an industry is associated with significantly higher patenting rates than R&D. were the first to determine factors that affect venture capital investments. They have captured the effect by examining the fundraising information of independent venture capital organisations. The multivariate and logit regression results show that higher GDP, higher R&D and lower capital gains tax lead to more venture capital activity. This effect, however, appears more significant in demand for venture capital. The study also found that the early-stage activity is performed by individual venture organisations where technology-based start-ups are more prevalent.

Variables Description

This section provides the theoretical arguments on variables used in the study, characterised into three broad categories: EODBI, institutional factors and financial macroeconomic variables. The study focuses on how venture capital investments function under different economic conditions, that is, the environment of doing business, political and legal conditions and macroeconomic fluctuations.

Ease of Doing Business Index

The entrepreneurial environment differs across countries, (Guiso et al., 2006) largely depends on rules of governance, economic conditions and government policies. A business-friendly environment is crucial for a nation’s economic growth and development (Djankov et al., 2006; Gillanders & Whelan, 2010). The efficiency and effort required for setting up a business in a particular nation are represented by the EODBI. EODBI comprises different indicators, particularly an aggregate distance to frontier (DTF) score that fetches the level of environment of doing business. The current study argues that ease of business motivates individuals to become entrepreneurs, creating the demand for capital and increasing venture capital activity. On the other hand, striving to do business may affect otherwise.

The study has considered eight EODBI parameters, including EODB, representing the overall environment of doing business. The performance level of each parameter is assessed by DTF scores. A higher DTF score indicates a better or more friendly business environment (Natarajan & Raza, 2017).

Starting a business (SAB) is an indicator which is measured by considering the paid-up minimum capital requirement, number of procedures, time, and cost for small- to medium-sized business units. India ranked 155 and 151 out of 133 economies in 2016 and 2017. It reflects that India lags in a business-friendly environment compared to other economies. Protecting minority investors (PMI) is an essential issue as some company insiders use corporate assets for personal gain; a party-related benefit is the most common example. The oppression of minority shareholders, often referred to by different names, squeeze-outs, freeze-outs and washouts are just a few. Venture capitalists essentially take advantage of the ingenuity and passion of start-up company founders and early-stage angel investors by reducing the value of their shares by enormous amounts Hence, PMI, that is, angel investors and entrepreneurial firms, will positively influence venture capital activity. Registering property (REGPRP) is essential in today’s world as investment, productivity and growth are affected by its entitlement rights. The economies are worldwide evident that property owners with registered titles are more likely to attract domestic and foreign investors. Since the venture capital is invested in a business of no history or background, a business established on a registered property can quickly secure the VC funding. Resolving insolvency (INSOL) ensures the smooth functioning of business operations and prevents premature liquidation of sustainable businesses. It discourages lenders from issuing high-risk loans and stops managers and shareholders from taking imprudent loans and making reckless financial decisions. However, imposing bankruptcy laws without knowing the consequences on entrepreneurs and finance providers may result in low entrepreneurial activity and, hence, a decline in venture capital investments. Enforcing contracts measures the time and cost involved in resolving commercial disputes through a local court and ensures the effective quality of judicial processes. Economies with a more efficient judiciary system have more developed credit markets, resulting in growth in small firms. Improving the business environment through enforcement contracts can increase the supply of alternative investors like venture capital. Trading across borders records the time and cost associated with the logistical process of exporting and importing goods. Domestic firms shall have more exposure in international markets than those selling their products domestically. Economic integration removes trade barriers and allows goods, services, capital, labour and technology to move freely between member countries. The firms with these characteristics attract more investors to their ventures. Paying of taxes (POT) checks the administrative burden of paying taxes and other mandatory contributions of small- and medium-sized businesses. It directly affects the investment and growth of small- and medium-sized firms as the amount they spend on paying taxes can meet the working capital requirement. Economies ranked better in paying taxes tend to perceive tax rates and tax administration as less obstacle to business firms and often receive large amounts of external and internal funding.

Institutional Indicators

A well-governed economic system is beneficial for the growth and development of a nation. In a high-quality legal system, transactions are more organised, predictable and transparent, promotion of entrepreneurship is more consistent (Armour & Cumming, 2006; Judge et al., 2008; Kaplan et al., 2007; Lee et al., 2007). Alternatively, a weak legal system results in the expropriation of property or resources, the inconvertibility of currencies, discriminatory taxation, etc. (Howell & Chaddick, 1994). It deters investments through high transaction costs (Coase, 1995), misappropriation of investments, or the misrepresentation of firm quality. These risks collectively limit venture capital investments in firms residing in such risky legal environments.

Institutional indicators represent a nation’s governing economic system, comprising six broad dimensions. Voice and accountability, political stability and absence of violence/terrorism, government effectiveness, regulatory quality, rule of law, control of corruption. This study argues that an economy with a strong and effective governance economic system assumes to have higher venture capital activity. Three indicators were chosen and the rest were removed from the analysis due to insignificant results.

Government effectiveness (GVTEF) represents the quality of public services and civil services and the degree of its independence from political pressures, etc. Regulatory quality (REGQ) indicates the ability of the government to formulate and implement sound policies and regulations and protect private and public sectors from unfair competitive practices, price controls, discriminatory tariffs, excessive protections, discriminatory taxes, etc. Control of corruption measures how public power is exercised for private gain and private interests.

Economic Variables

Macroeconomic variables such as interest rates, GDP, corporate tax, stock market, inflation rate and unemployment rate (UNEMP) are the most frequently used variables in venture capital literature (see Black & Gilson, 1998; Cumming, 2014; Gompers et al., 2008; Jeng & Wells, 2000). This study considered GDP, interest rate, inflation and unemployment. GDP growth rate develops venture capital activity by generating more investment opportunities (Füss & Schweizer, 2011; Gompers et al., 1998). High inflation rates make venture capital more attractive for the VC fund managers (Sahlman, 1990). However, economies with high inflation have low or weak demand for venture capital. In an equilibrium model, UNEMP and venture capital react ambiguously and move positively on the demand side and negatively on the supply side (Félix et al., 2013). Moreover, unemployment due to the unavailability of jobs or low entrepreneurial activity can be controlled by creating new entrepreneurial firms. Hence, more venture capital investments. On the other hand, unemployment due to business failures lowers the incentives to become an entrepreneur and, thus, low venture capital activity.

Data and Methodology

Data

The study employs quarterly data for 2006:Q4 to 2015:Q4, retrieved from different sources. The quarterly figures for real GDP, inflation rate, interest lending rate and the UNEMP were retrieved from the International and Financial Statistics database.

The quarterly investments of DVCF and FCVI are retrieved from the official website of SEBI. The annual data on Ease of Doing Business Index (EODBI) indicators and institutional indicators were retrieved from the Doing Business Database and Database of Worldwide Governance Indicators. These two databases are maintained by the World Bank Group and are considered the authentic data source. The annual figure was transformed into quarterly using the methodology of the linear match last in E-Views 8.

Method

The study has estimated three regression models with three different dependent variables. Models 1 and 2 use DVCF and FVCI as dependent variables. Model 3 uses the TVCI, the summation of DVCF and FVCI, as a dependent variable. The same set of independent variables is used that determines venture capital investments in each model.

As a first step, stationarity was checked taking log converted values in Augmented Dickey–Fuller (ADF) unit root test. Some variables were stationary at level and first difference, while most independent variables became stationary at the second difference. Although observations from the same country possibly are correlated, they were heteroskedastic in nature. The reason may be the different methodology adopted for measuring each dimension of the three constructs. For example, EODBI and institutional indicators are measured on surveyed data, while economic variables are calculated using real values.

Since the study employs time-series observations on venture capital investments and the independent variables, the results may be affected by serial correlation in the error terms. The initial estimates based on ordinary least-squares (OLS) estimates show the potential presence of serial correlation. Even though it was not so clear from the plot of the residuals, Durbin–Watson confirms the presence of serial correlation. Durbin–Watson statistics in Model 1 is above 2, establishing the existence of negative serial correlation. Models 2 and 3 have a positive serial correlation as Durbin–Watson statistics are below 2. As a diagnostic, the residuals of each regression model were again tested using the Breusch–Godfrey Serial Correlation LM test. The p-value was less than 5%, indicating that each regression model has a serial correlation.

This urged us to switch to the Newey–West estimation method known as Newey–West HAC. It corrects serial correlation and is known for its added benefit of eliminating heteroscedasticity problems. Bartlett kernel fixed bandwidth and pre-whitening lag 1 was set in the model due to a small number of observations and a large number of independent variables. However, a different bandwidth is not expected to change the estimates in any noticeable way. As Wooldridge notes, the heteroscedasticity robust standard errors are not very different from the non-robust forms. The test statistics for statistical significance of coefficients are generally unchanged, and the robust standard errors are often larger than their counterparts. The estimated regression model is as follows:

Y_{i} = α + β_{1} {EODBI}_{i} + β_{2} {INST}_{i} + β_{3} {ECO}_{i} + ε_{i},

where Y represents domestic, foreign and total venture capital investments for each model. α is the constant, β is the coefficient of each independent variable, i is time series and ε denotes error terms. EODBI denotes Ease of Doing Business Index, INST denotes institutional indicators and ECO is macroeconomic variables.

Descriptive Statistics

Table 1 highlights descriptive statistics of log-converted values of dependent and independent variables. Total investments (M = 4.67, SD = 0.18), surpassed FVCI (M = 4.46, SD = 0.22) and DVCF (M = 4.24, SD = 0.15). Among EDOBI variables, TAB (M = 1.80, SD = 0.03) is the most preferred activity, and enforcing contracts (M = 1.42, SD = 0.02) is the least qualified variable. Among institutional variables, government effectiveness (M = 1.72, SD = 0.03) and control of corruption (M = 1.59, SD = 0.04) are most effective variables. GDP (M = 4.71, SD = 0.18) is the most developed variables as compared to other macroeconomic variables, where unemployment (M = 0.96, SD = 0.04) secured the lowest mean.

Table 1.

Descriptive Statistics of the Dependent and the Independent Variables.

Variables	M	Median	Max	Min	SD	Skewness	Kurtosis
DVCF	4.24	4.25	4.45	3.88	0.15	−0.50	2.48
FVCI	4.46	4.53	4.66	3.86	0.22	−1.22	3.54
TVCI	4.67	4.73	4.86	4.17	0.18	−1.02	3.29
EODB	1.67	1.67	1.71	1.58	0.03	−0.94	3.20
ENF	1.42	1.41	1.51	1.41	0.02	3.22	12.28
INSOL	1.44	1.43	1.51	1.39	0.04	0.63	2.29
PMI	1.77	1.77	1.87	1.75	0.03	2.06	7.29
REGPRP	1.76	1.77	1.78	1.70	0.02	−1.48	5.17
SAB	1.71	1.72	1.80	1.47	0.08	−1.06	3.84
TAB	1.80	1.81	1.82	1.73	0.03	−1.24	2.93
POT	1.63	1.60	1.75	1.52	0.09	0.22	1.30
COC	1.59	1.58	1.68	1.52	0.04	0.24	1.94
GVTEF	1.72	1.74	1.76	1.66	0.03	−0.80	2.05
REGQ	1.59	1.60	1.66	1.53	0.04	−0.10	1.81
GDP	4.71	4.72	5.00	4.41	0.18	−0.07	1.69
ILR	1.04	1.02	1.12	0.92	0.05	0.16	2.24
INF	2.03	2.03	2.17	1.87	0.10	−0.06	1.67
UNEMP	0.96	0.96	1.03	0.86	0.04	−0.15	2.24
Observation	37	—	—	—	—	—	—

Source: The authors.

Notes: DVCF, investment by domestic venture capital funds; FVCI, foreign venture capital investments; TVCI, total investments made by domestic and foreign funds; EODB, ease of doing business; ENF, enforcing contracts; INSOL, resolving insolvency; PMI, protecting minority investors; REGPRP, registering property, SAB, starting a business; TAB, trading across border, POT, payment of taxes; COC, control of corruption; GVTEF, government effectiveness; REGQ, regulatory quality; GDP, gross domestic product; ILR, interest lending rates; INF, inflation rates; UNEMP, unemployment rates.

Skewness is a measure of symmetry, or more precisely, the lack of symmetry. Kurtosis predicts heavy-tailed or light-tailed relative to a normal distribution. Skewness should be close to 0 for a normally distributed variable, and kurtosis should not exceed 3. The range of skewness diverges from 0, and the kurtosis showed heavy tailed as the value for some variables exceeds 3. The small sample size is one of the limitations, which can be normalised by increasing the sample size assuming no change in the results.

Spearman Rank Correlation Matrix of Independent

Table 2 highlights the correlation matrix of explanatory variables indicating no significant risk of multi-collinearity. This validates the idea of selecting variables according to the evidence presented by Cumming et al. (2010). Since the study has employed a large number of exogenous variables, it needs further control for multi-collinearity risk. Variance inflation factors (VIF) analyses were used in a parallel set of standard OLS. The study reports the highest VIF value, which does not exceed 7, with various recommendations as an acceptable level published in the literature. Perhaps most commonly, a value of 10 has been recommended as the maximum level of VIF (e.g., Hair et al., 2010; Neter et al., 1989).

Table 2.

Spearman Rank Correlation Matrix of Independent.

Variables

1. EODB

–

2. ENF

0.11

–

3. INSOL

0.00

−0.26

–

4. PMI

0.07

0.38*

−0.27

–

5. PRPREG

0.06

0.18

0.08

−0.20

–

6. SAB

−0.07

0.04

0.42*

−0.11

0.17

–

7. TAB

0.17

0.29

−0.05

−0.27

0.34*

0.29

–

8. POT

0.02

−0.28

0.43*

−0.19

−0.02

−0.32*

−0.10

–

9. COC

0.28

0.56**

−0.17

0.09

0.11

−0.46*

0.21

0.18

–

10. GVTEF

0.27

−0.02

−0.15

0.31

0.20

−0.03

−0.02

−0.40*

−0.19

–

11. REGQ

0.22

0.14

−0.32

0.37*

0.18

0.08

−0.14

−0.34

−0.39*

0.60**

–

12. GDP

0.03

−0.26

0.04

−0.44**

0.17

−0.03

0.19

0.25

0.02

−0.14

−0.11

1.00

–

13. ILR

0.07

−0.37*

−0.16

0.11

−0.43*

−0.36*

−0.46*

0.21

−0.10

−0.38*

0.03

-0.09

–

14. INF

−0.56**

−0.13

0.01

−0.50**

0.10

0.08

0.13

0.26

−0.25

−0.36*

−0.19

0.10

−0.26

–

15. UNEMP

0.15

0.08

−0.08

−0.10

0.03

−0.05

0.27

−0.03

−0.07

0.07

0.14

0.32

0.05

−0.29

Source: The authors.

Note: *Significant at 0.05; **significant at 0.01.

Unit Root Tests for Stationarity

Table 3 presents the results of the ADF unit root test for dependent and independent variables at levels I(0), first differences I(1), and the second difference I(2). DVCF becomes stationary at first difference while FVCI and TVCI are stationary at levels. Independent variables SAB and GDP become stationary at first difference. Independent variables EODB, ENF, INSOL, PMI, REGPRP, TAB, POT, ILR, INF, UNEMP, COC, GVTEF and REGQ were non-stationary at level and the first difference, achieved stationarity at the second difference. I(0), I(1) and I(2) nature of the variables indicates that there is no co-integration (no long-run relationship) among the variables.

Table 3.

Augmented Dickey–Fuller Test Results for Unit Roots.

	t-Statistics I(0)		t-Statistics I(1)		t-Statistics I(2)
	Intercept	Trend & Intercept	Intercept	Trend & Intercept	Intercept	Trend & Intercept
DVCF	−1.50(0.52)	−3.83(0.03)**	−5.25(0.00)*	−5.25(0.00)*	–	–
FVCI	−4.64(0.00)*	−3.75(0.02)**	–	–	–	–
TVCI	−4.81(0.00)*	−3.68(0.04)**	–	–	–	–
EODB	−1.43(0.56)	−3.83(0.03)**	−2.48(0.13)	−1.93(0.62)	−5.64(0.00)*	−5.94(0.00)*
ENF	0.01(0.95)	−0.09(0.99)	−0.81(0.80)	−1.59(0.78)	−6.09(0.00)*	−6.27(0.00)*
INSOL	−0.35(0.91)	−1.89(0.64)	−1.19(0.67)	−2.08(0.54)	−7.16(0.00)*	−7.02(0.00)*
PMI	0.86(0.99)	−0.87(0.95)	−1.03(0.73)	−1.77(0.70)	−6.01(0.00)*	−6.11(0.00)*
REGPRP	−0.98(0.75)	1.06(1.00)	0.25(0.97)	−0.97(0.94)	−5.65(0.00)*	−6.12(0.00)*
SAB	−1.25(0.64)	−4.05(0.02)**	−3.87(0.01)**	5.25(0.00)*	–	–
TAB	−2.19(0.21)	−0.89(0.95)	−1.29(0.63)	−2.66(0.26)	−5.69(0.00)*	−5.76(0.00)*
POT	−1.22(0.65)	−2.71(0.24)	−2.26(0.19)	−1.98(0.59)	−5.77(0.00)*	−5.90(0.00)*
GDP	−1.59(0.48)	−2.00(0.58)	−6.10(0.00)*	−6.08(0.00)*	–	–
ILR	−2.32(0.17)	−2.90(0.17)	−2.61(0.10)	−2.55(0.30)	−5.91(0.00)*	−5.85(0.00)*
INF	−2.18(0.22)	−0.98(0.93)	−1.17(0.68)	−1.62(0.76)	−5.86(0.00)*	−6.48(0.00)*
UNEMP	−2.81(0.07)	−2.94(0.16)	−2.78(0.07)	−2.84(0.19)	−5.90(0.00)*	−5.90(0.00)*
COC	−1.33(0.60)	−0.45(0.98)	−2.06(0.26)	−2.78(0.21)	−5.84(0.00)*	−5.75(0.00)
GVTEF	−2.28(0.18)	−1.86(0.65)	−1.39(0.58)	−1.56(0.79)	−5.88(0.00)*	−6.10(0.00)
REGQ	−2.07(0.26)	−2.63(0.09)	−1.38(0.58)	−1.18(0.90)	−5.87(0.00)*	-7.02(0.00)

Source: The authors.

Notes: *Significant at 0.01 level; **significant at 0.05 level. P-values are given in parentheses.

Results of Multiple Linear Regression

Domestic Venture Capital Funds

Table 4 presents regression results of DVCF. Domestic venture capital funds is positively related to ENF, INSOL and TAB, while the negative effect is recorded for EODB, PMI, PRPREG, SAB and POT. The results show that enforcing contracts, resolving insolvency and TABs positively influence the investment of DVCF. The results also show that the rules regarding doing a business, protection of minority investors, property registration, SAB and payment of taxes negatively influence the investment flows. The negative effect of these indicators needs the government’s attention to provide an efficient environment for alternative investments. Among the Institutional variables, COC and GVTEF are negatively related, whereas REGQ has a positive relationship. The negative effect of COC and GVTEF indicates that India lacks control over corruption and effectively manages the business environment. Alternative investments like venture capital efficiently function in a defective environment. The positive effect of regulatory quality indicates that India has sound policies and regulations that permit and promote the private sector to develop, leading to an increase in venture capital activity. Among the macroeconomic variables, only UNEMP is significant and negatively related to DDVCF. This indicates that the UNEMP goes down with the increase of domestic investors. In other words, when the flow of venture capital increases in the Indian economy, it reduces unemployment due to the deficiency of financial capital in businesses.

Table 4.

Model 1 Domestic Venture Capital Funds.

Dependent Variable: DVCF
Method: Least squares
Sample (adjusted): 2007Q2 2015Q4
Included observations: 35 after adjustments
HAC standard errors and covariance (pre-whitening with lags = 1, Bartlett; Kernel, Newey–West fixed bandwidth = 4.0000)
Variables	Coefficient	Std. Error	t-Statistics	Prob.
EODB	−306.7893	67.77778	−4.526400	0.00
ENF	269.7585	60.95718	4.425377	0.00
INSOL	153.9015	49.66048	3.099073	0.01
PMI	−280.0431	100.5693	−2.784580	0.01
REGPRP	−324.1749	72.12376	-4.494703	0.00
SAB	−9.724367	2.419000	−4.019994	0.00
TAB	481.0449	112.1615	4.288860	0.00
POT	−38.89804	18.29722	−2.125899	0.05
GDP	1.864576	1.589814	1.172826	0.26
ILR	−23.67754	13.40426	−1.766419	0.09
INF	135.9145	128.3952	1.058564	0.30
UNEMP	−126.4390	30.69167	−4.119653	0.00
COC	−48.32338	29.69466	−1.627342	0.12
GVTEF	−225.7304	85.56261	−2.638189	0.02
REGQ	377.1940	98.94977	3.811975	0.00
C	0.042009	0.024048	1.746912	0.10
R-squared	0.594781	Mean-dependent var		0.012830
Adjusted R-squared	0.274871	S.D.-dependent var		0.056438
SE of regression	0.048059	Akaike info criterion		−2.929389
Sum squared residual	0.043884	Schwarz criterion		−2.218373
Log-likelihood	67.26431	Hannan–Quinn criteria		−2.683946
F-statistic	1.859213	Durbin–Watson stat		2.722427
Prob (F-statistic)	0.101049	Wald F-statistic		535355.6
Prob (Wald F-statistic)	0.000000

Source: The authors.

Foreign Venture Capital Investments

Table 5 highlights the regression results, which show that EODB and INSOL are positively related to FVCI while PMI, SAB and POT have a negative relationship. The findings show that rules regarding PMI, SAB and paying of taxes are the most concern in the case of foreign investors. Institutional indicators GVTEF and REGQ, including INF and GDP from the macroeconomic variables, positively influence foreign investments.

Table 5.

Model 2 Foreign Venture Capital Investments.

Dependent Variable: FVCI
Method: Least squares
Sample (adjusted): 2007Q2 2015Q4
Included observations: 35 after adjustments
HAC standard errors and covariance (pre-whitening with lags = 1, Bartlett; Kernel, Newey–West fixed bandwidth = 4.0000)
Variables	Coefficient	Std. Error	t-Statistic	Prob.
EODB	626.6724	192.7682	3.250912	0.00
ENF	124.3985	226.7498	0.548616	0.59
INSOL	334.7951	73.79423	4.536874	0.00
PMI	−478.2892	127.5508	−3.749795	0.00
PRPREG	−10.72270	148.0938	−0.072405	0.94
SAB	−14.24274	4.517100	−3.153073	0.01
TAB	144.4675	132.5642	1.089792	0.29
POT	−175.5865	60.14179	−2.919542	0.01
GDP	6.501269	3.421509	1.900117	0.07
ILR	−0.583976	15.77542	−0.037018	0.97
INF	1537.241	272.7700	5.635665	0.00
UNEMP	31.61508	23.37750	1.352372	0.19
COC	29.43945	156.8705	0.187667	0.85
GVTEF	175.6302	80.39823	2.184504	0.04
REGQ	289.6993	132.1363	2.192428	0.04
C	4.828010	0.003728	1295.125	0.00
R-squared	0.923685	Mean-dependent var		4.489534
Adjusted R-squared	0.863437	S.D.-dependent var		0.177384
SE of regression	0.065551	Akaike info criterion		−2.308585
Sum squared residual	0.081643	Schwarz criterion		−1.597569
Log-likelihood	56.40024	Hannan–Quinn criteria		−2.063142
F-statistic	15.33123	Durbin–Watson stat		1.184617
Prob (F-statistic)	0.000000	Wald F-statistic		100222.6
Prob (Wald F-statistic)	0.000000

Source: The authors.

Total Venture Capital Investment

Table 6 show that total venture capital investment is positively related to EODB and INSOL, while PMI, SAB and POT are negatively related. This effect is the same as in the case of foreign investments. INF and GDP among the macroeconomic variables and GOVEFF from the Institutional variables are positively significant.

Table 6.

Model 3 Total Venture Capital Investments Activity.

Dependent variable: TVCI
Method: Least squares
Sample (adjusted): 2007Q2 2015Q4
Included observations: 35 after adjustments
HAC standard errors and covariance (pre-whitening with lags = 1, Bartlett; Kernel, Newey–West fixed bandwidth = 4.0000)
Variables	Coefficient	Std. Error	t-Statistic	Prob.
EODB	544.2165	48.00442	11.33680	0.00
ENF	−2.050859	1.486073	−1.380053	0.18
INSOL	219.6286	95.46586	2.300598	0.03
PMI	−453.7242	163.7873	−2.770205	0.01
PRPREG	−3.044316	90.73524	−0.033552	0.97
SAB	−8.766513	2.240166	−3.913332	0.00
TAB	−42.76177	150.9434	−0.283297	0.78
POT	−148.0091	54.59151	−2.711212	0.01
GDP	7.693975	3.359323	2.290335	0.03
ILR	12.09402	19.19851	0.629946	0.54
INF	1202.256	256.6241	4.684893	0.00
UNEMP	50.32308	50.85077	0.989623	0.33
COC	108.4233	72.08159	1.504175	0.15
GVTEF	212.2399	78.61423	2.699765	0.01
REGQ	212.0079	215.8195	0.982339	0.34
C	7.858856	2.121711	3.704018	0.00
R-squared	0.913157	Mean dependent var		4.691394
Adjusted R-squared	0.844597	SD dependent var		0.150115
SE of regression	0.059177	Akaike info criterion		−2.513189
Sum squared resid	0.066537	Schwarz criterion		−1.802173
Log-likelihood	59.98081	Hannan–Quinn criteria		−2.267746
F-statistic	13.31906	Durbin–Watson stat		1.241815
Prob (F-statistic)	0.000000	Wald F-statistic		4325523
Prob (Wald F-statistic)	0.000000

Source: The authors.

Table 7.

Summary of Results.

Variables	DVCF	FVCI	TVCI
Ease of Doing Business Index
EODB	−	+	+
ENF	+	NS	NS
INSOL	+	+	+
PMI	−	−	−
REGPRP	−	NS	NS
SAB	−	−	−
TAB	+	NS	NS
POT	−	−	−
Macroeconomic variable
GDP	NS	+	+
ILR	NS	NS	NS
INF	NS	+	+
UNEMP	−	NS	NS
Institutional factors
COC	NS	NS	NS
GVTEF	−	+	+
REGQ	+	+	NS

Source: The authors.

Robustness of the Results

As a robustness check of the results, the R-squared value of Model 1 (R = 0.59) indicates normally fit data in the regression. The R-squared value of Model 2 (R = 0.92) and model 3 (R = 0.91) represents a tight fit data in the regression. Moreover, the probability value of the non-robust F-statistic (p-value = 0.00) and the robust Wald F-statistic (p-value = 0.000) shows that non-intercept coefficients are jointly statistically significant. It indicates that most of the independent variables are significant to explain the dependent variable. Furthermore, the residuals of each regression model were checked using a histogram (see Figures 1 –3). The p-value of Jerrque–Bera statistics is significantly different from zero, indicating normally distributed residuals. Hence, the obtained results meet most of the assumptions of the robustness check.

Figure 1.

Source: The authors.

Figure 2.

Source: The authors.

Figure 3.

Source: The authors.

Conclusion

India has become the third largest start-up ecosystem globally after the United States and China. This increasing trend of start-ups in India attracts more attention of alternative investors like venture capital. Venture capital is the money of invention which is invested in young businesses of no historical background. It is by now recognised globally that venture capital is beneficial for the growth and development of an economy. The survival and growth of venture capital industry need an investment-friendly economic and political system. This study aims to investigate what factors determine venture capital investments in India. We have selected the most relevant variables categorised as EODBI, INST and ECO. EODBI has been introduced for the first time in the literature on venture capital, intends to capture India’s business environment. We have estimated three regression models with three different dependent variables. In Models 1 and 2, DVCF and FVCI have been used as dependent variables, respectively. In Model 3, TVCI, the summation of DVCF and FVCI, is used as a dependent variable. We have used the same set of independent variables that determine venture capital investments in each model.

The initial estimates based on OLS show the potential presence of serial correlation, urged us to switch to Newey–West estimation method known as Newey–West HAC. It corrects serial correlation and is known for its added benefit of eliminating heteroscedasticity problems.

The study reports ambiguous results for DVCF, FVCI and TVCI. The individual measure EODB indicating overall ease of doing business in India is not favourable for domestic funds but favourable for foreign and total venture capital investments. This suggests that the DVCF should invite more FCVI when the ease of doing business is not favourable for the domestic funds. Interestingly, indicator SAB negatively affects all three types of investments. This indicates that rules regarding SAB need improvement to develop venture capital activity. Enforcing contracts and TABs respond positively to DVCF, while registered property moves differently. Furthermore, these three indicators are not significant for determining foreign and total investments. Resolving insolvency positively affects all three types of investments, which can be considered beneficial for young investors and young entrepreneurs for exploiting venture capital. However, rules of protecting minority investors and paying of taxes are not favourable for all three types of investments and need the attention of the researcher and the policymaker.

GDP and inflation show a significant positive effect on foreign and total investment but fail to explain DVCF. Furthermore, inflation rate determines none of the venture capital activity. Interestingly, unemployment and DVCF have a negative relationship, again the point of concern for the academician and the researchers. Regulatory quality has a significant positive effect on domestic and foreign investors among the institutional indicators. The government effectiveness positively influences foreign and total venture capital investment, while it is a point of concern for DVCF and has a negative relationship. Control of corruption determines none of the venture capital activity in India. These results are potentially important for understanding and promoting venture capital investments in India. The foreign and domestic players in the venture capital industry need to focus very closely on the variables discussed in the study. Indicators ease of doing business, protecting minority investors, registered property, SAB, paying of taxes, UNEMP and government effectiveness are concern for the DVCF. It also needs the attention of the government and other regulatory bodies controlling the venture capital industry. Foreign venture capital investments efficiently growing in the present scenario still need to focus on rules for SAB and TAB. Total investments, which is the summation of domestic and foreign investors or the investment made by domestic funds through foreign investors, have the issues of minority interest, norms on SAB and paying of taxes. The findings are beneficial to carry out venture capital activities in India (Table 7).

This study can be refined in several different ways in terms of future research, but the biggest challenge will be collecting enough data to derive meaningful results. At first, increasing the number of observations will significantly improve the reliability of the findings. Moreover, investigating the effect of these variables by employing stage financing data on venture capital investments. The stage financing has also attracted the attention of the researcher, but unavailability of data restricted us to simple investments without any categorisation of funds according to the stage. The other factors such as capital gains tax, labour rigidities, capital market, foreign direct investments and mutual funds can also be included in the study for wider recognition of venture capital.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

ORCID iD

M. Tanzeem Raza

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