Innovation efficiency and Firm value: evidence from the U.S.
corporate
Supervisor: Dr. Nassima Selmane
Student name: Siman Zhai
Student number: 3626652
Study Programme : MSc IFM
Date: 10th February 2021
Field Key words: innovation efficiency, firm value, global diversification
Abstract
1. Introduction
Innovation has received considerable attention over the last two decades. As Freeman and Soete (1997, p. 5) claimed that 'in the most fundamental sense the winning of new knowledge is the basis of human civilization'. Knowledge is the essential energy for economic development (Alfred & Marshall, 1897).
With the rapid development of the economy, many firms in developed countries, such as the US, aggressively conduct research and development (R&D) projects with other countries and domestically, especially within technological firms (Ang, 2020; Kogan et al., 2017; Lei et al., 2018). There have been several longitudinal studies involving the effect of innovation efficiency on a firm’s performance and future return. Hirshleifer et al. (2013) expounded that IE signifies the firm’s ability to generate output. In the innovation context, the output refers to the number of the patent or patent citations per dollar of R&D. Furthermore, more recent studies demonstrated that as US firms gradually increase the ratio of cash to assets or stockpile cash, more capital becomes available for investment in R&D (Petera & Taylor, 2016; Lyandres & Palazzo, 2016; Lei et al., 2018). Such large corporations generally possesse high RQ due to economies of scale (Knott & Vieregger, 2020). Within empirical research, the IE portfolio reveals that high IE groups have the highest operating performance and stock return. However, there is an opposite conclusion that a multinational corporation (MNC) may not display obvious superior levels of innovation efficiency compared with local firms or small firms (Gao and Chou, 2015; Cohen, 2010). MNCs, with their unique firm characteristics such as financial flexibility, geographical diversification and complexity of management, have to invest more capital and be willing to accept a relatively low return from their R&D projects (Lei et al., 2018; Brandão-Marques et al., 2019; Gao & Chou, 2015).
In other words, those factors have a pronounced effect on innovation efficiency. Previous work has only focused on patent-based or citation-based methods to address innovation efficiency. But these measures may not give a substantial outcome, due to less coverage of financial data, weak reliability or difficult to obtain patient data. The first objective is to address the relationship between innovation efficiency and firm value. The second objective is to examine how global diversification affects innovation efficiency and firm value across countries. Thus, our main research question is:
Whether global diversification affects the relationship between innovation efficiency and firm value.
lower risk rate on capital market and abundant external funding sources than domestic firms, but it concurrently led to information asymmetry and expensive management and operating costs (Gao & Chou, 2015; Bruno & Shin, 2014; Desai et al., 2007; Jiang, 2107). The findings also broaden the emerging literature on the effect of intangible assets on economic development, especially as they relate to MNCs (Kogan et al.; Pinkowitz et al., 2015).
The paper proceeds in sections. Section 2 provides a review of existing literature on innovation, innovation efficiency, research quotient and firm value, as well as related theories of MNCs and Schumpeterian. It ends with a review of studies of the characteristics of global diversification. Section 3 describes data and method. The results are presented in section 4. We draw a conclusion in the last section, section 5.
2.Literature review 2.1Innovation efficiency
(university, uniformity reliability and robustness) of RQ are indispensable in the innovation efficiency context. RQ inherently incorporates all three properties (university, uniformity and reliability) because the estimation is fully based on standard financial data so that any firm doing R&D is possible to calculate their RQ. Thus, RQ could serve a firm-level innovation – efficiency measure.
Unfortunately, the patent-based and citation-based measures would appear to have been an obvious flaw. The patent data are not always available to gather, especially in some international markets. The patent data is probably obtained from the NBER patent database maintained by Hall et. All (2011). This database is only updated to 2006. In other words, recent data are not available to collect. Besides, patent counts may be universal and uniform. To protect shield intellectual property, firms are unlikely to be to exposure their trading and work on R&D file patents. It is also possible that some patients may not successfully translate to commercialisation.
2.2Innovation efficiency and firm value
Multinational firms, with their larger size and a 'bundle of resources', are more efficient in innovation than domestic firms. Barney (1986) proposed a resource-based view that reveals resources owned by firms may lead to competitive advantages. MNCs utilise their competitive strengths in implementing and exploiting R&D effort, which reduces the expense of R&D as well as generates considerable revenue (Gao & Chou, 2015). In other words, based on the multinational model, large firms seem to enjoy the economy of scale and minimal transportation costs. Henderson and Cockburn (1996) asserted that the scope economies are taken from the internal and external knowledge spillovers by diverse portfolios of research projects. Furthermore, R&D acts on the firm’s learning capability that assimilates knowledge from its environment (Aghion & Jaravel, 2015). As a result, knowledge spillover and R&D are complementary. Besides, corporations amortise the fixed cost per innovation over R&D outputs. Moreover, firms invest in R&D on efficient and competitive capital markets according to efficient market hypotheses (EMH). It is possible to expect that the corporates merge target firms at the discounted market price for risk and obtain plenty of returning thanks to a great number of patents (Hirshleifer et al., 2013). In other words, efficient investing in R&D could produce more income.
With more financial flexibility, larger firms can shift cash to intangible assets (e.g. patent) and gain investment opportunities (Leia et al., 2018). As a consequence, those firms can invest more and increase output relative to smaller firms. These studies suggest that MNCs, with their geographical diversity, have easier access to international sources of capital and own unique product-market exposure (Desai et al., 2008). Regarding incumbents investment in R&D via cross-border M&A, it is directly related to an increase in the value of firms (Albuquerque et al., 2019). Generally, MNCs or large firms outperform local or small firms for R&D productivity and the ability to increase their value. However, the opposite conclusion also has been drawn from several works of literature. Specifically, Cooper et al. (2019) emphasised that small or local firms take advantage of governance to facilitate R&D productivity. Albuquerque et al. (2019) noted that governance change, following cross-border M&A, resulted in spillover to the local economy and local non-target firms. Whatever the size of the local firms, they could exploit their characteristics and achieve greater return from R&D investment. Although, it is puzzling that evidence on the aggregate effect of IE on firm value is mixed. Hirshleife et al. (2013) believed that innovation efficiency linked to positive firm value. They used Fama and MacBeth regression (1973) and gave empirical evidence that a higher innovative efficiency measure is obvious positive with ROA, cash flows and equity market-to-book controlling tax shields, industry, abnormal earnings and so on, where some elements are important for Tobin’s Q as a proxy of firm value. Fama and MacBeth (1973) conducted an empirical test of the relationship between average return and risk for common stocks. Specifically, the firms fell into three groups based on their degree of IE (the 33rd and 66th percentiles of the IE measures), which consisted of low, middle and high portfolios, respectively. Based on the above discussion, I hypothesiz that:
H1: There is a positive relationship between innovation efficiency and firm value. 2.3 Global diversification and innovation efficiency
rivals. In addition to parent firms located in developed countries, they possess natural and unique advantages, such as stronger patent protection and sufficient compensation for risk (Qian, 2007; Acemoglu et al., 2004). Aghion, Akcigit and Howitt (2015) assumed patent protection and innovation based on the Schumpeterian growth framework and confirmed that patent protection encouraged innovation as well as increased the profits for neck-and-neck firms. In short, patent protection enhances the post-innovation rents, which is in line with the Schumpeterian effect. Further, if the affiliate located in emerging countries meets with a financial crisis, in the sense of, globally diversified firms are possible to alleviate their loss. Desai et al. (2007) confirmed that affiliates have access to parent equity during currency depreciation. This implies that geographically diversified corporations have a more effortless approach to foreign sources of funding (debt and equity) compared to domestic firms, even during economic depression (Desai et al., 2007). In this case, globally diversified firms have stable cash flow and financial stability, which assists on-going R&D projects and lowers the probability of cutting R&D. Besides, innovative output is efficiently to commercialise and rent extracts from new innovation due to wider markets.
Knowledge-intensive activity also accelerates technical and scientific innovation. US firms recently reduced cash stockpiling and shifted to R&D investment (patents and brands) (Lei et al.,2018; Albuquerque et al., 2019). Parent firms typically spread the knowledge to their subsidiaries, often located in different countries. The varied geographical space expedites the diffusion of such spillover (Fritsch & Franke, 2003). The effect of spillover becomes operative on assorted modes of sustainable activities in innovation (Hajek & Stejskal, 2018). Hence, wider geographical distribution of knowledge-based projects plays an important role in innovation.
H2a: global diversification strengthens the relationship between IE and firm value
Moreover, the complexity, in terms of management, coordination and operation, induces high costs. For instance, geographically diversified firms with less transparency between headquarter and divisional managers provoke agency problems. Previous studies in this area of research have reported positive side effects and negative side effects simultaneously. As supported by Hitt et al. (1994), international diversification directly facilitates innovation, IE and R&D intensity, due to a firm’s capabilities and its country's circumstance. While information asymmetry and complexity are important issues for globally diversified firms. In this sense, growth in international diversification impedes the development of innovation and lessens firm performance (Hitt, Hoskisson, and Kim, 1997). Accordingly, the relationship between global diversification and IE has not been strongly established. Therefore, I develop hypothesis as following:
H2b: global diversification weakens relationship between IE and firm value. 3. Methodology
3.1 Sample and Data collection
To construct my sample. I obtain original data from Compustat that includes all available firms from the period 2006 to 2015. Primary, there are 40 countries with 2793 unique firms, but I only focus on US firms because the number of US firms occupied major fraction on the full sample. Accounting and financial data are captured from Compustat North America. The IE refers to a firm's R&D productivity. RQ is adapted to measure the effectiveness of a firm's R&D. If the firm participates in the innovation activities, we could find the firm's RQ on Research Quotient database. Since the RQ reporting was ended in 2015, we prefer to collect recent 10 years data. Thus, we choose 2015 as the last year and backward 10 years periods with reliable data for the time series research.
We further impose some requirement on the merged sample: (1) exclude firms in financials and utilities(SIC codes 4900-4949 and 6000-6999); (2) exclude firms with missing or negative value of RQ, Total Q, cash flow, assets and sales. I wintorize all regression variables at 1% and 99% level to alleviate the concern of extreme outliers. Through this study, firm- level mult ivariate analyses enter year and industry fixed effects and all standard errors are clustered with firm and year. Overall, the sample consists of 41,636 firm-year observations with 2087 unique companies.
Previous studies may use the accounting measures of performance. Wernerfelt and Montgomery (1988) consider that such measurement creates strong bias an estimation due to failing to reckon differences in systematic risk, disequilibrium effects, tax law and especially accounting conventions regarding R&D. Whereas innovation is rooted in the R& D. Accounting-based performance indicator is not appropriate for this study. For example, ROA as a ratio analysis (performance) measures the overall effectiveness of management in general profits of its average total assets. Wernerfelt and Montgomery (1998) state that the accounting rates of return may not take into account the accounting conventions regarding R&D so that this accounting measure is distorted and the prediction of estimation is biased on the large extent. Tobin's Q ratio is a capital market-based indicator is extensively applied to measure the company value, which defines as the capital market value of the firm divided by the replacement value of its assets (Wernerfelt and Montgomery, 1998). However, tobins'q is not appropriate as a proxy of firm value in the innovation context. As Peter and Taylor (2015) enacts tobins'q exclude around 30%-40% intangible capital. IE is directly related to innovative output (revenue) and innovative input (R&D expenditure, capital and labor) based on the notion of RQ. Traditionally, IE is measured via R&D input (R&D expenditure) scaled by outputs (patent or patent citation). When the intangible capital occupied a large proportion of total capital, there is a key measurement error from omitting intangibles. Total q will be served as proxy measuring value of firms in this paper. Including intangible capital in the measure q ratio is correspondence with innovation context. Firstly, total q adds intangibles in the dominator of calculating the formula listed below. It considers intangible assets like innovative products and patents, which are essential for our measurement. Further, total q recognizes R&D expenditure in knowledge capital. Especially, in the GAAP rule, R&D spending is identified as an expense rather than an investment so that such spending will not record as an asset on the balance sheet. Third, total q proxy may be more close to the true q than tobins q. this physical q ratio mainly focuses on firms' physical assets (eg. PP&E) and ignore the intangible assets. Therefore, total q is relatively appropriate as a proxy rather than the tobins'q in innovation and investment background. According to total q theory, the firm's total capital consists of its physical and intangible capital that is the sum of knowledge capital and organizational capital (Peter and Taylor, 2015).
Mktcap: the market value of outstanding equity Debt: book value of outstanding debt
AC: current assets of the firm
PP&E: book value of property, plant and equipment Intan: replacement cost of the firm's intangible capital Independent variable: IE
Considerable literature denoted innovation efficiency as the ratio of R&D expenditure or R&D capital which is from the NBER patent database developed by Hall, Jaffe and Trajtenberg in 2001 (Berry, 2014). Experts primarily perform the patient-based measure that is the number of patents scaled by R&D capital as well as the citation-based measure is the number of each patent's citation scaled by the number of citations for all other patients. But lots of firms may not prepare the R&D patents in any given year so that patent counts fail. Besides, this database offers information from 1976 to 2006, which may lack relatively new data. On the basis of disadvantaged argument, patient-based measure is universal and reliable for IE. So, it will not employs.
RQ will be used in this paper to measure the efficiency of a firm's R&D. It refers to percentage increases in revenue from a one per cent increase in R&D expenditure (WRDS, 2017). In other words, it also displays a firm's ability to generate profits from its R&D investment. Besides, Knott (2012) highlights that the RQ works better than other measures due to its three essential properties (universality, uniformity and reliability). It is conformable with the findings of Cooer, Knott and Yang (2019). They deem that research quotient is befitting measure to identify the innovation efficiency at the firm-level due to the high level of coverage, test consistency and low correlation with other measures. Furthermore, this new metric for R&D productivity can link R&D spending to firm growth and market value, derive a firm's optional R&D spending and then predicts monthly returns (WRDS, 2016).
𝑌I,t = 𝐴𝑖, 𝐾α𝑖,𝑡 𝐿β𝑖,𝑡 𝑅γ𝑖,𝑡−1 𝑆δ𝑖,𝑡−1 𝐷φ𝑖,𝑡𝑒𝑖,𝑡
WRDS RQ manual (2017) describes upon characters as follow: Yi,t is output, Ai, is a firm fixed effect, Ki,t is capital, Li,t is labour, Ri,t-1 is lagged R&D, Si,t- 1 is lagged spillovers, Di,t is advertising,
Control variable
As was noted in the literature to this paper, global diversification is closely related to IE. A firm is identified as globally diversified if there is the information disclosure of any foreign sales or income (Jang, 2017; Aabo et al.2015). Simply, the measure of global diversification is based on MNCs dummy variable and foreign sales ratio. These two proxies are both used in the later analyses. In line with the former research (Gao and Chou, 2015), firm-level control variables include in regressions: Assets to sales (ATS), logarithm of assets (Size), debt to assets ratio (Lev), edit to sales (EBIT), total sales (Sale), capital expenditure to assets (CapEx), equity market-to-book ratio (MB) and cash flow (CF). Leverage, size, EBIT, MB and sales are associated with firm value and investment (Rizqia and Sumiati, 2013). The corporate sizes determine the investment of innovation and also serves as the collateral for the loan. Leverage serves as firm performance signal. capital expenditure represents the financial risk firms take. Sales and EBIT indicate ability to generate profits.
3.2 Regression Specification 3.2.1 Global Diversification
The following regression models will utilize to examine the effect of global diversification on the relationship between IE and firm value, while controlling for year fixed effect.
Total Q it = 𝛽0+ 𝛽1RQ𝑖𝑡+ 𝛽2𝐶𝐹𝑖𝑡+ 𝛽3𝑆𝑖𝑧𝑒𝑖𝑡+ 𝛽4𝐿𝑒𝑣𝑖𝑡+𝛽5𝑀𝐵𝑖𝑡+ 𝛽6𝐶𝑎𝑝𝑒𝑥𝑖𝑡+
𝛽7𝐸𝐵𝐼𝑇𝑖𝑡+ 𝛽8ATS𝑖𝑡+ 𝛽9IndDiv𝑖𝑡+ ∑ Year F. E.𝑡 + ∑ Industry F. E.𝑗 +
𝜀𝑖𝑡 (1)
operating income before interest and tax ratio respectively; Size is the logarithm of total assets; ATS is the ratio of asset to sales; IndDiv refers to industry diversification and is a dummy variable ; εit is the error term.
Total Q it = 𝛽0+ 𝛽1RQ𝑖𝑡+ 𝛽1𝑅𝑄𝑖𝑡∗ 𝛽2 𝐹𝑆𝑖𝑡+ 𝛽2 𝐹𝑆𝑖,𝑡+ 𝛽3𝐶𝐹𝑖𝑡+ 𝛽4𝑆𝑖𝑧𝑒𝑖𝑡+ 𝛽5𝐿𝑒𝑣𝑖𝑡+
𝛽6𝑀𝐵𝑖𝑡+ 𝛽7𝐶𝑎𝑝𝑒𝑥𝑖𝑡+ 𝛽8𝐸𝐵𝐼𝑇𝑖𝑡+ 𝛽8ATS𝑖𝑡+ ∑ Year F. E.𝑡 + ∑ Industry F. E.𝑗 + 𝜀𝑖𝑡 (2)
Where i,t and j are the subscripts for each firm, year and industry, respectively ; total q is the ratio of the market value of a company divided by its total capital that includes physical and intangible capital; RQ is the measure of R&D productivity; CF is the cash flow; FS denotes the multinational firms’ foreign sales ratio; Lev, CapEx and EBIT are the total debt ratio, capital expenditure ratio and operating income before interest and tax ratio respectively; Size is the logarithm of total assets; ATS is the ratio of asset to sales; εit is the error term.
3.3 Descriptive statistics
Figure 1 Average RQ and Total Q
Figure 1 shows the tendency of RQ and Total Q from 2006 to 2015. Initially, in 2006, total Q has a high value (around 1.4) and peaks in 2007. However, its value sharply decreases below 1.0 in 2008. Then total Q gradually increase and slightly fluctuate between 2009 to 2015. In contrast to total Q, the dash line of RQ displays smoothly during10 years and drop steadily in the last few years. Underlying this graph, the implication reveals that the firm value and IE have no similar changes.
Table 1 summary statistics
Panel A Definitions of variables
Variables Definitions Source
Total Q The improved ratio of Tobins’q proxy Compustat
CF Ratio of cash and equivalents divided by assets net of cash Compustat Size The natural logarithm of book value of assets in USD Compustat Leverage The ratio of (book value of total long-term debt +
short-term debt) to book value of total assets
Compustat
ATS The ratio of asset to sales
MB market-to-book of equity Compustat
EBIT The ratio of earnings before interest and tax deflated by sales
Compustat
Capex The ratio of capital expenditures to book value of total assets
Compustat
Foreign sales ratio The ratio of foreign sales divided by total sales Compustat Industrially diversified Dummy variable taking the value of 1 if more than one
business segments reports as the multi -segments, otherwise 0
Compustat
Panel B Descriptive statistics of all firms.
This table shows summary statistics of IE measures and firm characteristics. firms partitions based on their global diversification statues. RQ is the output elasticity of RD at the firm-level; CF is the ratio of cash add market security divided by asset; EBIT is the earnings before interest and tax; Sales are the total sales; Capex is the capital expenditure to assets; MB refers to market value of equity to book value of equity; Lev is the ratio of total debt divided by total assets; Size is the logarithm of total assets and ATS refers to the ratio of asset to sales. Sample period is from 2006 to 2015.
Variable N Mean Median SD Min Max
(1) (2) (3) (4) (5) (6) Total Q 41636 1.276 0.882 1.605 0.025 19.07 RQ 41636 0.054 0 0.068 0 0.241 Size 41636 7.256 7.231 1.886 0.076 12.13 Lev 41636 0.234 0.218 0.188 0 1 MB 41636 37.19 27.35 36.45 0 255.8 Capex 41636 0.049 0.032 0.055 0 0.431 ATS 41636 1.345 1.104 1.221 0.273 83.22 EBIT 41636 0.120 0.100 0.087 0.005 0.531 CF 41636 0.256 0.108 0.739 0 29.82
and minimum on Total Q and size. While the IE measures, Capex and EBIT have not observed the significant gap in the range of maximum and minimum. MB has the largest values such as mean than other variables.
4. Results
4.1 Innovation efficiency and firm value Table 2 The innovation and firm value
This table shows the impact of IE on firm’s value proxied by Total Q. Dependent variable is Total Q. RQ is the output elasticity of RD at the firm-level; RD intensity is the RD expenditure to its revenue; EBIT is the earnings before interest and tax; Sales are the total sales; Capex is the capital expenditure to assets; MB refers to market value of equity to book value of equity; Lev is the ratio of total debt divided by total assets; Size is the logarithm of total assets. ATS refers to the ratio of asset to sales. Firm level (year – firm two way) clustered standard error are reported in parentheses. Sample period is from 2006 to 2015.
*** Indicates significance at the 1 % level. ** Indicates significance at the 5% level. * Indicates significance at the 10% level.
Dependent Variable Total Q (1) (2) (3) (4) RQ -1.080** -1.870*** -1.873*** -1.636* [0.338] [0.408] [0.406] [0.517] Size -0.111*** -0.070*** -0.053*** -0.055** [0.012] [0.015] [0.014] [0.016] Lev -1.080*** -1.706*** -1.538*** -1.496*** [0.188] [0.194] [0.184] [0.226] Capex -1.719** -1.810** -1.427** -2.083** [0.580] [0.573] [0.520] [0.602] ATS 0.043 0.201* 0.180 0.226 [0.054] [0.093] [0.099] [0.133] EBIT 7.201*** 5.237*** 4.989*** 4.683** [0.583] [0.587] [0.607] [0.814] MB 0.019*** 0.018*** 0.017*** [0.001] [0.001] [0.002] CF 0.242** 0.286 [0.086] [0.154] IndDiv -0.237** [0.069] Constant 1.531*** 0.784*** 0.622*** 0.869*** [0.099] [0.106] [0.104] [0.134] Adjusted R2 0.203 0.381 0.392 0.421 Observations 63084 41661 41636 29071
Industry fixed effects Yes Yes Yes Yes
Table 2 reports the effect of IE on firm’s value from columns (1) to column (4). The RQ has a coefficient estimate of -1.080 (0.338) in first column and in other columns, coefficient estimate of RQ are all negative and significant, which indicates a significantly negative relation between firm value and IE. This is not consistent with the idea that having high efficiency of innovation activities promotes growth in value of the firm even if some variables are controlled. So hypothesis 1 is rejected.
Moreover, size, leverage, Capex and EBIT remain obvious in four columns. Heavily leverage and capital expenditure are harmful the firm’s value. Smaller size firm may have greater contribution to firm value. More earnings increase the market value of firms. MB and CF also have positive effect on the firm value due to the positive signs of coefficient estimates of MB and CF. Lastly, the industry diversification reduces the firm value, which is an alternative channel to impact valuation.
To deeper explore effect of IE on firm value, the firm is categorized as industrially diversified and concentrated. In this case, the effect of IE could be distinguished from sample firms. The firm with a single segment defines as the industrially concentrated and with more segment firms are industrially diversified. The analyse is performed on a subsample of 29,071 firm-year observations with 2087 firms over the period from 2013 to 2015. Because it is not available to gather historical data in Compustat industry segment file.
Table 3 Robustness tests
This table reports the impact of IE on value for single- and multi- segments firms. A firm is classified as industrially diversified if it reports more than one business segment in Compustat. Dependent variable is Total Q. RQ is the output elasticity of RD at the firm-level; CF is the ratio of cash add market security divided by asset; EBIT is the earnings before interest and tax; Sales are the total sales; Capex is the capital expenditure to assets; MB refers to market value of equity to book value of equity; Lev is the ratio of total debt divided by total assets; Size is the logarithm of total assets; ATS refers to the ratio of asset to sales. Firm level (year – firm two way) clustered standard error are reported in parentheses. Sample period is from 2013 to 2015.
*** Indicates significance at the 1 % level. ** Indicates significance at the 5% level. * Indicates significance at the 10% level.
Industry diversified firms Industrially concentrated firms
(1) (2)
[0.555] [1.124] Size -0.067** 0.009 [0.016] [0.048] Lev -1.279** -2.274** [0.237] [0.544] Capex -2.391* -1.298 [0.803] [0.976] ATS 0.227 0.224 [0.135] [0.144] EBIT 4.929** 4.558*** [1.147] [0.713] MB 0.015*** 0.023*** [0.002] [0.003] CF 0.232 0.306 [0.254] [0.199] Constant 0.696** 0.440 [0.161] [0.257] Adjusted R2 0.425 0.418 Observations 24428 4642
Industry fixed effects Yes Yes
Year fixed effects Yes Yes
Results are presented in table 3, suggesting that IE is associated with a lower value for concentrated firms. However, the coefficient estimates of IE are not significant and relatively in industrially diversified firm value. Especially, the coefficient of RQ is -0.916(0.555) in columns (1) for the industrially diversified firm and -5.175(1.124) in columns (2) for industrially concentrated firms. This is consistent with Seru (2014) and Denis et al (2002) notion that firm value damaged caused by industrial diversification as well as Hitt et al (1994) claims that MNCs complexity of operating hinders the development of innovation and firm’s performance.
4.2 Innovation efficiency and diversification 4.2.1 Global diversification as a moderator
It has been noted that MNCs possess international human resource, knowledge, external funding sources and economy of scale to implement R&D projects (Gao and Chou, 2015). Here, if a firm is defined by MNCs, there is a fraction of foreign sales from their affiliate or foreign subsidiaries. Since one of the aims is to investigate how continuous changing of MNCs value results from IE. To measure the effect of global diversification, I follow Aabo et al., (2015) using the overseas sales ratio as a proxy for geographical diversified firms.
Innovation efficiency and global diversification
This table explores how the impact of global diversification impact the IE and firm value synchronous. Dependent variable is Total Q. RQ is the output elasticity of RD at the firm-level; CF is the ratio of cash add market security divided by asset; EBIT is the earnings before interest and tax; Sales are the total sales; Capex is the capital expenditure to assets; MB refers to market value of equity to book value of equity; Lev is the ratio of total debt divided by total assets; Size is the logarithm of total assets. Foreign sales ratio is the foreign sales divided by total sales. ATS refers to the ratio of asset to sales. Firm level (year – firm two way) clustered standard error are reported in parentheses. Sample period is from 2013 to 2015.
*** Indicates significance at the 1 % level. ** Indicates significance at the 5% level. * Indicates significance at the 10% level.
Dependent Variable Total Q (1) (2) (3) (4) RQ -1.106*** -1.061** -1.883*** -1.388** [0.338] [0.334] [0.410] [0.439] Size -0.112*** -0.086*** -0.055*** -0.052*** [0.012] [0.012] [0.015] [0.015] Lev -1.075*** -0.787*** -1.530*** -1.531*** [0.189] [0.178] [0.186] [0.186] Capex -1.712** -1.155* -1.410** -1.412** [0.583] [0.562] [0.525] [0.529] ATS 0.044 0.018 0.180 0.181 [0.054] [0.056] [0.099] [0.099] EBIT 7.162*** 6.649*** 4.925*** 4.970*** [0.595] [0.586] [0.602] [0.603] FS ratio 0.283 0.346 0.326 1.355 [0.573] [0.532] [0.679] [1.063] CF 0.429*** 0.242** 0.241** [0.081] [0.086] [0.086] MB 0.018*** 0.018*** [0.001] [0.001] RQ * FS ratio -16.981* [8.304] Constant 1.538*** 1.237*** 0.635*** 0.594*** [0.097] [0.105] [0.105] [0.102] Adjusted R2 0.203 0.234 0.392 0.393 Observations 63084 63053 41636 41636
Industry fixed effects Yes Yes Yes Yes
Year fixed effects Yes Yes Yes Yes
columns. In other words, having greater IE is the unfavourable improving value of cross-broader corporate. Last, the foreign sales ratio is positive and insignificant associated with total Q, suggesting that the growth of the firm’s value links to the geographical diversification. Overall, international diversification strengthens the relationship between IE and firm value. Hypothesis 2a could accept and hypothesis 2b is rejected.
5. Conclusion
This dissertation is the result of an investigation related to the IE and valuation of firms as well as the interaction of global diversification and IE. Global diversification plays an important role in moderating the relation between IE and the firm’s value. The outcomes of regressions provide robust evidence to partly support the hypotheses. Basically, IE has a negative impact on firm value. Hypothesis 1 could not be accepted. Then, further research reveals that international diversification could strengthen the relationship between IE and the firm’s value. As stated by preceding research, multinational firms with their characteristics are likely to have higher or lower R&D efficiency compared to their domestic counterparts. On the one hand, international diversified firms generally have larger size, leverage, IE and investment in R&D. The empirical results confirm that the RQ, size, leverage and capital expenditure decrease the firm value on the extend. On the other hand, a higher degree of global diversification is associated with higher firm value. Besides, it is interesting that single operating firms with higher IE than diversified operating firms, significantly lessen their value. This indicates the complex operation does impair firm valuation, whereas globally diversified corporates have more firm value.
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Appendix
Country code Observation number Country code Observation number USA 2309 IND 4 CHN 105 ITA 4 ISR 64 NOR 4 CAN 46 FIN 4 JPN 30 BEL 3 GBR 29 ANT 2 DEU 21 ESP 2 FRA 21 RUS 2 NLD 21 BMU 2 CHE 16 IDN 1 IRL 16 PHL 1 TWN 13 ISL 1 AUS 11 ARG 1 HKG 11 MEX 1 SWE 10 CYM 1 BRA 9 NZL 1 SGP 8 DNK 1 KOR 6 CUW 1 ZAF 5 AUT 1 LUX 4 BHS 1
RQ Total Q Lev ATS MB Size EBIT Capex CF RQ 1 Total Q -0.0676 1 Lev -0.118 -0.156 1 ATS -0.0644 0.178 -0.0825 1 MB 0.0790 0.428 0.0190 0.0728 1 Size 0.128 -0.198 -0.00110 -0.183 -0.0691 1 EBIT -0.00450 0.340 0.0322 0.376 0.237 0.0928 1 Capex -0.179 -0.0399 0.0236 0.0458 -0.0470 -0.0399 0.166 1 CF -0.0397 0.253 -0.161 0.328 0.130 -0.225 0.142 -0.130 1
