3.1. Measuring Financing Constraints on Sustainable R&D Investment
How to define the approach to study sustainability of R&D investment is the key to distinguish this research from other R&D investment research. Most scholars have agreed that research on the sustainability of R&D investment is only reflected in the context of financing constraints. The logic is that, only when investment activities face external financing constraints, it is necessary to consider the issue of uninterrupted and sustainability of investment. Therefore, for the existing study of sustainability of R&D investment [
1,
2,
3,
4,
5,
6,
7,
8], the scholars must use equations such as Euler equation to prove that R&D investment being in the context of financing constraints. Therefore, we first define the measurement of financing constraints.
Since the seminal contribution by Fazzari et al. [
41], the standard approach for testing for financial constraints has been to examine the cash flow sensitivity of investment [
42]. However, it should be noted that the positive link between cash flow and investment within Tobin’s Q equation, which was previously interpreted as an indicator of financial constraints, has been challenged by researchers in recent years. For example, a positive coefficient of cash flow may be caused by investment demand but not financial constraints [
43], and the measurement error of Tobin’s Q may lead to unreliable estimates [
44]. In addition, the application of Tobin’s Q is demanding on the efficiency of the capital market [
45,
46], which is not appropriate for developing countries.
The Euler equation is a structural model derived from the dynamic optimization of the “Euler condition“ under the assumption of symmetric, quadratic adjustment costs relating the current investment to last period’s investment and the marginal product of capital for imperfectly competitive firms. Compared with Tobin’s Q equation, the Euler equation offers several advantages. First, the critique from Kaplan and Zingales [
43] has yet to be proven theoretically in a dynamic multi-period setting with investment adjustment costs [
10]. Second, the Euler equation controls for the impact of the expectation of future profitability on current investments, and the estimated coefficients of lagged or current financial variables can be interpreted in a more straightforward manner [
4]. Lastly, the Euler equation does not require any information on firms’ market value or dividends [
10]. Therefore, we examined R&D sensitivity to political connections by estimating the Euler equation, which is widely used in the research of financial constraints and investment [
4,
5,
6,
10,
13].
3.2. Empirical Model and Variables
Following existing research [
1,
47], we used a dynamic investment model based of Euler equation to test the presence of financing constraints:
Innov represents the delta or difference in R&D investment, measured by R&D expenditures. We divide this index by K, which represents the total assets of the firm, to eliminate the impact of different sizes of firms. Y stands for the net output, which we use prime operating revenue to measure. CF stands for the cash flow, which we use net cash flow in the operating activities to measure. The subscript i and t represent individual and time, respectively, where fi refers to the individual effect, dt refers to the time effect and εi,t refers to a random error term. We also use annual dummy variables to control annual fixed effect.
Bond and Meghir [
47] held the view that, if the investment follows its optimal path,
β1 should be more than 1,
β2 should be less than −1,
β3 should be positive, and
β4 should be negative. The coefficient of CF(
β4) can reflect the situation of financing constraints because, once that situation occurs, as stated by the “Pecking Order” theory, enterprises will use more internal funds for investment because the cost of internal financing is less than the cost of external financing. Thus, a firm is financially constrained if the coefficient of CF(
β4) is significantly positive [
1]. This criterion was also used by Ratti et al. [
48] and Chan et al. [
49]. We established the regression in Equation (2) to examine Hypothesis 1a, which discusses the easing effect of political connections on R&D investment.
In the regression in Equation (2), the meanings of the Innov, K, Y, CF,
fi,
dt and
εi,t are the same as those in the regression in Equation (1), and Govern stands for political connections. This paper encodes the chairmen’s political connections. (Although studies outside China often choose to encode the political connections of CEOs, Chinese researchers have found that the legal representative of the listed enterprise is its chairman of the board, and the chairman is also the entrepreneur and the critical decision maker. Researchers have also found that the chairman of the board is the actual controller of the enterprise. Therefore, it is appropriate to choose the chairman of the board as the entrepreneur in the case study of China.) When analyzing the resumes of chairmens of the board, we let Govern = 1 if the chairman has served in the government or has served as a member of the National People’s Congress or the National Committee of Chinese People’s Political Consultative Conference; otherwise, we let Govern = 0. If political connections can help relieve external financial constraints, then, according to Laeven [
1], the firm will be less dependent on internal capital during the innovation, and β
5 will be significantly negative.
To examine Hypothesis 1b, the easing effect of political connections is different at different levels of political connections, and we therefore establish the regressions in Equations (3)–(5) to examine the influence caused by different levels of political connections after referring to the existing method.
Loc_Cen is the critical variable and represents the level difference of political connections. We let Loc_Cen = 1 if the chairman has not served in the central government or served as a member of the National People’s Congress or the National Committee of Chinese People’s Political Consultative Conference but has served in local government or has served as a member of the Local People’s Congress or the Local Committee of Chinese People’s Political Consultative Conference. By contrast, we let Loc_Cen = −1 if the chairman has not served in local government or served as a member of the Local People’s Congress or the Local Committee of Chinese People’s Political Consultative Conference but has served in the central government or served as a member of the National People’s Congress or the National Committee of Chinese People’s Political Consultative Conference. Otherwise, we let the Loc_Cen = 0. If β6, the coefficient of Loc_Cen, is significantly greater than 0, we can conclude that it is the local political connections and not the central political connections that help ease financial constraints; if β6 is significantly smaller than 0, we can conclude the opposite; and if the coefficient is not significant, we can conclude that the easing effect is not influenced by differing levels of political connections.(In the regression in Equation (3), the coefficient of easing effect of local political connections equals Govern + Loc_Cen, while the coefficient of easing effect of central political connections equals Govern-Loc_Cen. The difference between the two coefficients is 2*Loc_Cen. Moreover, if we leave out the Govern variable and only keep the Loc_Cen variable, the empirical results will remain unchanged.)
Based on the the regression in Equation (3), we establish the regressions in Equations (4) and (5) to do robustness tests on the different effects of political connections in different levels. Cengn indicates whether the firm has central political connections. We let Cengn = 1 if the chairman of the board has served in the central government or served as a member of National People’s Congress or National Committee of Chinese People’s Political Consultative Conference. Otherwise, we let Cengn = 0. Similarly, Locgn indicates whether the firm has local political connections. We let Locgn = 1 if the chairman of the board has served in the local government or served as a member of Local People’s Congress or Local Committee of Chinese People’s Political Consultative Conference. Otherwise, we let Locgn = 0. We expect that, if level differences of the easing effect of political connections on R&D investment exist,
β5 in the regressions in Equations (4) and (5) will be significantly different.
The policy burden of firms in China includes both a strategic policy burden and a social policy burden [
39,
50]. The strategic policy burden requires firms to invest in the capital-intensive industry pushed by government. In this case, these firms usually have more fixed assets under a certain operating income. The social policy burden refers to keeping redundant workers in order to help the government to solve employment problems and maintain social stability. We use Equation (6) to measure the optimal capital intensity (Intenc) determined by the relevant economic endowments [
51]. This measurement method has been accepted widely in China.
The variable Intenc represents capital intensity, measured by the ratio of net fixed assets to numbers of employees. The variable Size represents the size of the firm, measured by the natural logarithm of total assets. The variable Debt represents the asset–liability ratio, measured by the ratio of total liabilities to total assets. The variable Growth represents the growth of the firm, measured by the growth rate of operating income. Roa represents the return on assets of the firm. Capital represents the asset structure of the firm, measured by the ratio of current assets to total assets. Year represents an annual dummy variable, Region represents an area dummy variable, and Industry represents an industry dummy variable. Residual δ represents the difference of the actual capital intensity of the firm and the optimal capital intensity measured by the equation (6). When the value of δ is positive, reflecting that fixed assets of the firm are too high, then the firm assumes strategic policy burden. By contrast, when the value of δ is negative, reflecting that firms hire redundant employees relative to the assets needed, then the firm is assuming a social policy burden. Finally, we use the absolute value of δ as indicator of measuring the policy burden of firms (Burden).
We use the product term (Innov/K
t−1*Locgn
t*Burden
t) in the regression in Equation (7) to test Hypothesis 2. We expect that the greater the policy burden that the firm undertakes, the more effective the easing effect of local political connections will be. (To ensure the completeness and tightness of this theory, we only report the product term, Innov/K
t−1*Locgn
t*Burden
t, in the regression in Equation (7). However, according to social exchange theory, in order to make the political connections have an effect, enterprises need to undertake some responsibilities in return. This rule is also applied to general political connections and central political connections. The coefficients of the product term, Innov/K
t−1*Govern
t*Burden
t and Innov/K
t−1*Cengn
t*Burden
t, are also significant.) Therefore,
β7 should be significantly negative.
Finally, to test Hypothesis 3, we distinguish the ownership of the firm (Ownship) according to the type of the actual controller. We let Ownship = 0 if the firm is a non-private firm, the actual controller of which will have a close relationship with the government, such as a government agency, a public institution, state-owned firm or a collective firm. We let Ownship = 1 if the firm is a private firm, the actual controller of which may not have a close relationship with the government, such as an individual proprietorship or a foreign-funded firm. We consider that after dividing the firms into these two different groups, the regression in Equation (1), which reflects the effect of external financial constraints on R&D investments, the regression in Equation (5), which reflects the easing effect of local political connections and the regression in Equation (7), which reflects that firms need to undertake policy burdens to ensure the easing effect of local political connections should have significant differences.