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The Sensitivity of Firms’ Investment to Uncertainty and Cash Flow: Evidence From Listed State-Owned Enterprises and Non-State-Owned Enterprises in China:

The Sensitivity of Firms’ Investment to Uncertainty and Cash Flow: Evidence From Listed... This study examines the association between various uncertainties and corporate investment and further investigates this association between state-owned enterprises (SOEs) and non-state-owned enterprises (non-SOEs). Moreover, this study analyzes the indirect effects of uncertainty on corporate investment through cash flow. The current research uses an unbalanced panel data of Chinese nonfinancial listed firms for the period 1999–2016. To control endogeneity issues, this study applies a robust two-step system generalized method of moments (GMM) technique to estimate the model. Empirical findings indicate that market-based and firm-specific uncertainties have positive effects, whereas economic policy and CAPM- based uncertainties have negative effects on corporate investment. Furthermore, results indicate that the effects of market- based, CAPM-based, and firm-specific uncertainties (economic policy uncertainty) were less (more) prominent for SOEs. Additional analyses show that cash flow stimulates the effect of firm-specific uncertainty on SOEs’ investment, whereas it weakens the influence of CAPM-based uncertainty (economic policy uncertainty) on investment of non-SOEs (SOEs). Moreover, cash flow attenuates the market uncertainty effect on investment. Keywords uncertainty, investment, cash flow, state-owned enterprises, China stimulates investment by extending Hartman’s discrete-time Introduction result to a continuous setup. Caballero (1991) proves that Researchers have exerted substantial effort in attempting to under the assumptions of nonconstant returns to scale pro- understand the nature of the uncertainty–investment relation- duction technology and imperfect competition, the result of ship at the firm and market levels. However, the nature of Hartman–Abel can be opposite (i.e., the negative uncer- this relationship is still inconclusive (i.e., uncertainty may tainty–investment nexus). Previous studies have also pro- positively or negatively affect the investment) from the vided evidence that uncertainty positively influences empirical and theoretical perspectives. investment (Baum et al., 2008; Ma, 2015; Shaoping, 2008). Real options theory (Bernanke, 1983; Dixit & Pindyck, 1994) states that uncertainty adversely affects investment in terms of irreversible capital by obtaining maximum informa- 1 School of Economics and Management, Dalian University of Technology, tion, which resulted from waiting. Hartman (1972) consid- Dalian, P.R. China School of Business Administration, Dongbei University of Finance and ers the convex function of the marginal product of capital Economics, Dalian, P.R. China and argues that firms invest substantially in a high degree of International Institute of Islamic Economics, International Islamic uncertainty. The marginal product of capital is a convex University, Islamabad, Pakistan function of stochastic variables under the assumptions of Corresponding Author: constant returns to scale production technology, perfect com- Xuezhi Qin, School of Economics and Management, Dalian University petition, risk-neutral firms, and reversibility of adjustment of Technology, No.2 Linggong Road, Ganjingzi District, Dalian, Liaoning cost function. To support the aforementioned argument, Abel 116024, P.R. China. (1983) and Caballero (1991) confirm that uncertainty Email: qinxz0994@sina.com Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). 2 SAGE Open Although researchers have considerably focused on state-owned banks, thereby making obtaining loans from investigating how uncertainty influences investment in state-owned banks comparatively easy for SOEs. For exam- developed economies (Bloom et al., 2007; Gulen & Ion, ple, SOEs obtain discounted loans from banks to generate 2015; Kang et al., 2014; Rashid, 2011), only a few studies funds for investment (some of their interest payments are have explored the phenomena for firms operating in transi- subsidized by the government) because the government tion economies. Moreover, the literature on uncertainty and encourages and finance them to develop further (Bo & investment association for various natures of firms is limited Zhang, 2002). By contrast, non-SOEs face problems in if nonexistent. The current study seeks to expand the litera- obtaining loans from banks and rely primarily on their inter- ture by exploring the investment and uncertainty association nal funds (Guariglia & Yang, 2016). Kang et al. (2000) argue for a transition economy, namely, China. that firms with close relationships with banks can facilitate Prior studies have shown that potential lenders in a highly and improve investment policies, thereby increasing share- uncertain environment may be less or unable to determine holders’ wealth. Guariglia and Mateut (2016) document that the credit worthiness of firms, thereby limiting these firm’s firms with political connections can easily access external capability to raise funds from external sources. In such an financing compared with those that are not politically con- environment, lenders demand a high risk premium to provide nected. Therefore, we assume that in a transition economy, funds, which leads firms to become liquidity constrained. In fluctuations in various uncertainties influence the investment the case of internal fund deficiency, state-owned enterprises behavior of firms, which may differ across firms of different (SOEs) generally rely on state-owned banks and can access nature. credit easily. By contrast, the non-SOE counterpart substan- Using Chinese nonfinancial firms during the period tially relies upon their self-generated funds (Guariglia & 1999–2016, we show significant and positive (negative) Yang, 2016; Khan et al., 2019). Hence, we expect that uncer- impact of market and firm-specific uncertainties (CAPM- tainty significantly affects investment, either solely or based and economic policy uncertainties) on investment. through variations in cash-flow, which may vary across firms Furthermore, the results show that the impact of market- of different nature. Previous studies have investigated the based, CAPM-based, and firm-specific uncertainties (eco- influence of cash flow on firms’ investment by introducing nomic policy uncertainty) on investments are less (more) cash flow in the basic investment model. However, no previ- prevalent for SOEs. Additional analyses show that cash flow ous research has examined whether cash flow has a stimulat- strengthens the investment of SOEs when firm-specific ing or mitigating effect on the uncertainty–investment uncertainty is high, whereas it weakens the negative impact relationship between Chinese SOEs and non-SOEs. This of CAPM-based uncertainty (economic policy uncertainty) study also fills in this gap by studying the association among on the investments of non-SOEs (SOEs). Moreover, cash the uncertainty, cash flow, and investment behavior of SOEs flow has a mitigating impact on the investment of both types and non-SOEs separately. of firms under a high market uncertainty. Our findings are This study examines the uncertainty–investment relation- robust to several diagnostic tests and alternative proxies for ship for Chinese firms for two reasons. First, as a transition investment, market, and firm-specific uncertainties. economy, China is moving from a command-based to a mar- The current study provides three key contributions. First, ket-based economy, and the economy is under a high policy unlike previous studies (Baum et al., 2008, 2010; Dibiasi uncertainty (Wang, Chen, & Huang, 2014). Therefore, it will et al., 2018; Wang et al., 2014; Xu et al., 2010), we investi- influence firms’ costs, sales, and earnings. China is adopting gate the effects of economic policy uncertainty, along with and practicing free-market principles, thereby creating many CAPM, market, and firm-related uncertainties on corporate investment opportunities for listed firms. However, firms investment. Therefore, the current research offers important may face variations from different aspects because the capi- contributions to the literature by studying the effects of four tal market is underdeveloped and the market remains in the forms of uncertainties on investment. Second, this study transition process. Therefore, uncertainty will likely have a examines the differential impact of different forms of uncer- significant influence on corporate decisions, particularly tainties on the investments of SOEs and non-SOEs. Our find- those related to investments. In particular, investment is ings indicate that the uncertainty–investment relationship often irreversible and costly in terms of uncertainty. Policy varies for firms of different nature. Finally, we contribute to changes can influence the investment behaviors of firms and the literature by identifying the factors that can strengthen or make the environment uncertain in which firms operate. The weaken the uncertainty–investment association. We explore prior literature clearly indicates that policy uncertainty has that cash flow is one of the attributes that can moderate the an adverse influence on corporate investment and, therefore, association between investment and uncertainty. reduces economic growth. Second, the nature of ownership The remainder of this article is organized as follows. (i.e., SOEs and non-SOEs) is one of the distinctive attributes Section “Literature Review and Hypothesis Development” of Chinese firms. Compared with non-SOEs, SOEs evidently reviews the literature and develops hypotheses. Section suffer more from various government policies (Fan et al., “Data, Variables Measurement, and Descriptive Statistics” 2013). In addition, SOEs can access the “policy lending” by explains the data set and measures the variables. Section Khan et al. 3 “Econometric Model” presents the econometric model. Australian mining industry and stimulate investment. Section “Empirical Findings” reports the empirical findings. Shaoping (2008) argues that the higher the risk, the more the Finally, Section “Conclusion” provides the conclusion. investment will result, thereby resulting in higher reinvest- ment. The aforementioned study finds a significant positive association between firm-specific uncertainty and investment Literature Review and Hypothesis for Chinese firms. Khan et al. (2019) report that Chinese firms Development increase their capital investment with an increase in firm-spe- Researchers have empirically investigated the nature of the cific and market-based uncertainties. We use the preceding uncertainty and investment association, although the theo- discussion to argue that China, as an emerging economy, faces retical relationship between uncertainty and investment high uncertainty. Moreover, firms show risk-taking behavior remains inconsistent (Wang et al., 2014). Nickell (1978) and invest substantially under high uncertainty owing to com- finds that the attitude of firms in making investment deci- petition among firms. Therefore, we can assume that a high sions toward uncertainty may be positive or negative and firm-specific uncertainty will stimulate firms’ behavior toward argues that risk-averse (risk-taker) firms invest less (more) in a high investment. Thus, we hypothesize that: a highly uncertain environment. Shaoping (2008) argues that uncertainty–investment relationship depends on modeling, Hypothesis 1: Firm-specific uncertainty positively affects specific assumptions, and sample type. corporate investment. Bernanke (1983) and Dixit and Pindyck (1994) propose real options theory of irreversible investment decisions under The prior literature has provided evidence that an increase in uncertainty. Several studies have supported this theory by market uncertainty stimulates investment. Baum et al. (2008) suggesting a negative uncertainty–investment association for show that market uncertainty induces U.S. firms’ investment. various countries. For example, Leahy and Whitcd (1996), The aforementioned study argues that firms invest more in Kang et al. (2014), and Gulen and Ion (2015) for the United response to an increase in market uncertainty. Hence, they States; Bloom et al. (2007) and Rashid (2011) for the United may have a high opportunity to expand their presence in the Kingdom; Ma (2015) for Australia; and Rashid and Saeed market. Shaoping (2008) discusses that market uncertainty (2017) for Pakistan. Xu et al. (2010), Wang et al. (2014), An enhances investment of Chinese firms. Xu et al. (2010) find et al. (2016), and Khan et al. (2019) show the negative uncer- positive market uncertainty and investment association. tainty–investment relationship for Chinese firms. Bloom Therefore, we use the preceding discussions to argue that et al. (2007) and (Ma, 2015) argue that a high level of uncer- market uncertainty can increase the firm-specific invest- tainty weakens firms’ response of irreversible investment to ments of Chinese firms. Hence, we hypothesize as follows: demand uncertainty. Baum et al. (2008) document a negative effect of CAPM-based and firm-specific uncertainties on Hypothesis 2: Market uncertainty positively affects cor- investment, whereas market-based uncertainty has a stimu- porate investment. lating impact. They also show that investment is more affected by firm-specific uncertainty than market-based Although we have argued that market and firm-specific uncertainty. Moreover, Rashid (2011) reports the significant uncertainties increase investment, we assume that invest- negative effects of both forms of uncertainty on private ments decrease because of CAPM-based uncertainty. It is firms’ investment. Rashid and Saeed (2017) find that because prior studies have reported that CAPM-based uncer- Pakistani firms decrease investments when they face a high tainty negatively affects investment. Baum et al. (2008) pro- market or firm-related uncertainty. Khan et al. (2019) indi- vide evidence for the negative effect of CAPM-based cate a negative influence of economic policy and CAPM uncertainty on U.S. firm’s investment. Baum et al. (2010) uncertainties on Chinese firms’ investment behavior. Xu also report that manufacturing firms in the United States et al. (2010) present a negative influence of uncertainty on decrease their investment when the CAPM-based uncer- Chinese firms’ investment and further show that this effect is tainty increases. Dixit and Pindyck (1994) determine that positively moderated by government control. uncertainty can significantly influence firms’ investment Hartman (1972) considers the convex function of the mar- decisions, particularly in a situation where the substantial ginal product of capital and explains that firms make substan- sunk cost is involved in fixed capital investments. Firms tial investments in response to high uncertainty. Caballero have a low likelihood to invest in an uncertain environment (1991) uses market structure and returns to scale as bases to under irreversibility, thereby possibly increasing sunk costs. explain that under the assumptions of constant returns to scale Therefore, uncertainty in the presence of irreversibility will production technology and perfect competition, the convex reduce capital formation. Thus, we consider real options the- function of the marginal product of capital leads to a positive ory and the prior literature and argue that CAPM-based association between uncertainty and investment. Ma (2015) uncertainty has a negative influence on the capital invest- reports that Chinese ownership and exchange rate costs are ment of Chinese firms. Accordingly, we propose the follow- positively associated with the investment behavior of the ing hypothesis: 4 SAGE Open Hypothesis 3: CAPM-based uncertainty negatively which can mitigate the impact of uncertainty. Therefore, we affects corporate investment. expect that firm-specific uncertainty is less likely to influ- ence SOEs than other firms. Accordingly, we hypothesize as We also assume that Chinese firms reduce investments when follows: there is high policy uncertainty. Prior research has shown that policy uncertainty leads to a decline in firms’ capital Hypothesis 5a: The positive association between firm- investment (Baker et al., 2016; Bhattacharya et al., 2017; specific uncertainty and corporate investment is weak for Gulen & Ion, 2015; Julio & Yook, 2012). Gulen and Ion SOEs. (2015) find that uncertainty in policy reduces investments, and this impact is more prominent for financially constrained SOEs may obtain resources and benefits from the govern- firms and those operating in less competitive industries. ment when a high market uncertainty exists. Wang et al. Kang et al. (2014) indicate that policy uncertainty reduces (2017) report that macroeconomic uncertainty negatively investments. Governments in developing countries signifi- influences R&D investment for Chinese firms. The afore- cantly influence economic activities, which ultimately affect mentioned study shows that market-based shocks have a share performance, financing choices, and firm value (Firth mitigating effect on firms without political connections and et al., 2013). The Chinese government also intervenes in eco- has no effect on politically connected firms. Moreover, they nomic activities and plays a key role. When economic policy argue that when market uncertainty is high, firms with politi- uncertainty exists, firms have no adequate information on cal connections have considerable opportunities and advan- changes in government policies. In particular, firms have no tages to obtain resources from the government, thereby information on the main direction of industrial development mitigating the influence of uncertainty. However, non-SOEs in the future or which industry the government will support. are strongly affected by market uncertainty owing to mini- In this situation, when a high policy uncertainty exists, firms mal political connections. Therefore, the influence of market bear the risk of irreversible investment in intangible assets uncertainty is strong for non-SOEs. Accordingly, we hypoth- and choose the option of waiting to invest (Bhattacharya esize that: et al., 2017). Therefore, we consider the preceding discus- sion and argue that economic policy uncertainty can Hypothesis 5b: The positive association between market adversely influence investment. Thus, we hypothesize that: uncertainty and corporate investment is weak for SOEs. Hypothesis 4: Economic policy uncertainty negatively Baum et al. (2008) indicate that the U.S. manufacturing affects corporate investment. firms’ investment is adversely affected by the CAPM-based uncertainty. However, they report a stimulating effect of We expect that uncertainty plays an essential role in firms’ CAPM-based uncertainty in interaction with cash flow. investment decisions operating in transition economies. Given that CAPM-based uncertainty is the interaction Uncertainties can influence firms from many aspects and are between market and firm-related uncertainties, the previous relative shocks for the firms. However, SOEs in the transi- literature has shown that non-SOEs’ investment is more tion economy have never faced demand uncertainty, and they influenced by firm-specific and market-based uncertainties are not affected by uncertainty in factor markets in a transi- compared with that of SOEs. Consistent with our prior argu- tion economy. Bo and Zhang (2002) find an insignificant ments, we argue that the investment behavior of non-SOEs influence of demand and supply uncertainties on investment compared with that of SOEs is more influenced by CAPM- for state enterprises of the Chinese machinery industry, based uncertainty. Thus, we hypothesize as follows: whereas the investment of collective enterprises is positively affected by labor cost uncertainty. The aforementioned Hypothesis 5c: The negative association between CAPM- research finds no evidence supporting accelerator theory of based uncertainty and corporate investment is weak for investment for their sample firms. Khan et al. (2019) deter- SOEs. mine that the influence of firm-specific uncertainty is strong for non-SOEs. Government intervention is common in transition economies. Managers of SOEs are partially autonomous in making The investment behavior of SOEs is “pro-policy” because of investment decisions because the contract responsibility sys- their relationship with the government. That is, when the pol- tem links many of these enterprises with the government. icy aims to stimulate the economy, SOEs increase their When high firm-specific uncertainty exists, SOEs can obtain investments and vice versa. Wang et al. (2014) show that additional resources on the basis of government ownership firms lower their investments as the economic policy uncer- (Jebran et al., 2019). By contrast, non-SOEs are highly tainty increases, and this impact is comparatively strong for dependent on their internal resources to compete in the mar- SOEs. Wang et al. (2017) find that the policy uncertainty ket. The government can provide privileges, such as subsi- impact on R&D investment is negative for Chinese firms. dies, tax incentives, and favorable loan policies, to SOEs, They further report that policy uncertainty only influences Khan et al. 5 Figure 1. Theoretical model. R&D of firms with political connections but has no impact on variables as instruments to use a two-step robust system gen- nonpolitically connected firms. Morck et al. (2013) report eralized method of moments (GMM) model. After the initial that compared with non-SOEs, the investment behavior of screening, the final sample comprised 17,258 observations SOEs in China considerably affected by changes in economic (7,738 for SOEs and 9,520 for non-SOEs) for 1,791 firms (561 policies. Hence, we hypothesize as follows: for SOEs and 1,230 for non-SOEs). All variables are win- sorized at the upper and lower one-percentile to control the Hypothesis 5d: The negative association between eco- potential effects of outliers. nomic policy uncertainty and corporate investment is weak for non-SOEs. Variables Measurement We present the aforementioned hypotheses in a theoretical Measuring corporate investment (Inv). We follow Ding et al. model, assuming that CAPM-based, market-based, firm-spe- (2016) and An et al. (2016) and define investment as firms’ cific, and economic policy uncertainties affect the Chinese current year’s net fixed assets, minus the previous year’s net firms’ investment behavior, and this relationship may differ fixed assets, plus the current year’s depreciation, and scaled for companies of different nature of ownership and is shown by the previous year’s total assets. in Figure 1. Measuring firm-specific uncertainty (η). We follow prior stud- ies (Baum et al., 2008; Shaoping, 2008) and measure the Data, Variables Measurement, firm-specific uncertainty by estimating the variance of firms’ and Descriptive Statistics daily stock return for each year. The use of variance of stock returns as a measure of uncertainty is based on the presump- Data tion that stock prices contain information that correspond to This study initially considered all A-share Chinese listed firms’ underlying fundamentals. Investors perceive firms’ firms during the period 1999–2016. We categorized the sam- overall environment by the stock return. Therefore, the vola- ple into SOEs and non-SOEs to empirically test uncertainty tility of stock returns can be used to measure the and investment association. The financial data were obtained uncertainty. from the China Stock Market and Accounting Research Database (CSMAR). Measuring market-based uncertainty (ε). We follow Wang We only considered all nonfinancial firms and excluded et al. (2017) and Baum et al. (2009) and use the GARCH financial firms. Furthermore, we excluded the data for missing model to proxy for market uncertainty. Market uncertainty is observations on control variables. We included data for com- measured using the conditional variance attained from the panies with at least three consecutive years for accurate calcu- estimation of the GARCH model for the stock market return lation of uncertainty and to appropriately use the endogenous of Chinese publicly traded firms. 6 SAGE Open Table 1. Descriptive Statistics. Firms Statistics Inv η ε ν epu Cf Lev Tobin’s Q Sg Size Full sample Observations 17,258 17,258 17,258 17,258 17,258 17,258 17,258 17,258 17,258 17,258 Mean 0.0663 0.0965 0.0169 1.1560 155.9901 0.0565 0.4484 3.1718 0.1243 21.6520 P25 0.0097 0.0293 –0.4344 0.9779 98.8882 0.0331 0.2874 1.5817 –0.0157 20.8543 Median 0.0346 0.0433 –0.0774 1.1446 124.3563 0.0568 0.4442 2.3195 0.1184 21.5163 P75 0.0884 0.0684 0.3980 1.3201 181.2867 0.0866 0.5976 3.7036 0.2609 22.2644 SD 0.1043 0.2688 0.6450 0.2790 82.8965 0.0646 0.2175 2.6561 0.3414 1.1489 SOEs Observations 7,738 7,738 7,738 7,738 7,738 7,738 7,738 7,738 7,738 7,738 Mean 0.0721 0.1044 0.0288 1.1076 142.2919 0.0571 0.4961 2.5467 0.1229 22.0055 P25 0.0108 0.0269 –0.3715 0.9644 83.5514 0.0322 0.3515 1.4030 –0.0102 21.1173 Median 0.0364 0.0403 –0.2688 1.1182 123.6349 0.0552 0.4980 1.9409 0.1138 21.8255 P75 0.0922 0.0659 0.3980 1.2581 179.0405 0.0866 0.6379 2.9665 0.2517 22.7176 SD 0.1130 0.2988 0.7006 0.2318 76.2114 0.0617 0.2012 1.9508 0.3038 1.2398 Non-SOEs Observations 9,520 9,520 9,520 9,520 9,520 9,520 9,520 9,520 9,520 9,520 Mean 0.0616 0.0901 0.0073 1.1954 167.1243 0.0561 0.4096 3.6799 0.1254 21.3648 P25 0.0089 0.0313 –0.4344 0.9916 113.8974 0.0338 0.2381 1.8067 –0.0207 20.7142 Median 0.0333 0.0454 –0.0774 1.1718 127.6239 0.0582 0.3990 2.6976 0.1231 21.2866 P75 0.0854 0.0702 0.2349 1.4037 181.2867 0.0867 0.5532 4.3816 0.2690 21.9386 SD 0.0963 0.2414 0.5959 0.3066 86.3745 0.0669 0.2224 3.0201 0.3691 0.9796 Mean t-statistics –6.5929*** –3.4862*** –2.1815** 20.8190*** 19.7917*** –1.0735 –26.5067*** 28.5213*** 0.4922 –37.9192*** difference test Note. This table presents the descriptive statistics and the estimates for the mean-difference test. Inv = investment; η = firm-specific uncertainty; ε = market-based uncertainty; ν = CAPM-based uncertainty; epu = economic policy uncertainty; Cf = cash flow ratio; Lev = leverage, Tobin’s Q ratio; Sg = sales growth; Size = firm size; SOEs = state-owned enterprises. Significance level at 1% and 5% are represented by *** and **, respectively. Measuring CAPM-based uncertainty (ν). To quantify the more than non-SOEs possibly because the former is sup- CAPM-based uncertainty, we follow prior studies (Baum ported by the government. For the uncertainty measures, we et al., 2010; Leahy & Whitcd, 1996) and estimate the risk of find a significant difference among the mean values of mar- an individual firm by using the covariance between firms’ ket uncertainty, firm-specific uncertainty, and economic pol- daily stock returns and the value-weighted index of the icy uncertainty for SOEs and non-SOEs in the sample period. Shanghai Stock Exchange (SSE)/The Shenzhen Stock Compared with non-SOEs, SOEs have high average values Exchange (SZSE). and high variation for firm-specific and market uncertainty. For other uncertainties, we obtain high mean values and high Measuring economic policy uncertainty (epu). Baker et al. (2016) variabilities for non-SOEs. These figures indicate different develop an index (i.e., BBD index) to measure the economic levels of uncertainties for both types of firms, even within policy uncertainty for the United States. Their index for eco- the same industry. nomic policy uncertainty has been used in many studies and We further perform t-test to examine the significant dif- found to be a suitable proxy for the real economic policy ferences between SOEs and non-SOEs. The estimates for the uncertainty (Bloom et al., 2018; Dibiasi et al., 2018; Leduc & t-test are reported in Table 1, which shows that the t-statistics Liu, 2016). Following the same methodology, they constructed are significant for all variables (except cash flow and sales epu indices for other countries, which include Canada, Austra- growth). These findings suggest that the statistics of our lia, Europe, India, and China. The current study also opts for main variables (e.g., investment and various uncertainties) this index as a proxy for economic policy uncertainty. significantly vary across SOEs and non-SOEs. Descriptive Statistics Econometric Model Table 1 reports the descriptive statistics for the full sample, Baseline Model SOEs, and non-SOEs. The mean (median) value of invest- We use the following regression equation to examine the ment for the full sample is 6.63% (3.46%). We observe from association between different forms of uncertainty and the mean value of investment of SOEs (non-SOEs) approxi- investment: mately 7.21% (6.16%). This result suggests that SOEs invest Khan et al. 7 Inv =+ ββ Inv + βη + ββ  + ν () () () () InvI =+ ββ nv + ββ Cf + ηβ + Cf it 01 2 3 4 () () () it −− 1 it 1 it −− 1 it 1 it 01 it −12 3 4 it −− 1 it 1 it −1 + β () epu + ββ () Cf + () Lev 5 6 7 × η + + ββ  + Cf × + βν iit −− 1 it 1 it −1 () () () () () 5 6 7 it −1 it −− 1 it 11 it −− it 1 + ββ Tobins ′ QS + g + β Size ++ ff + () () () (2) 8 9 1 10 it it it −− 1 it 1 it −1 + βν Cf × + ββ epuC + f () () () () 8 9 10 it −− 1 it 1 1 it −− 1 it 1 (1) × epuC +∑ β ontrol + . () iiti −1 t it −1 where i denotes firm, t denotes time, and Inv is the invest- We examine the indirect effects of firm-specific, market- ment. We include the first lagged investment in our model based, CAPM-based, and economic policy uncertainties on because it significantly affects the current investment (Bloom the investment of SOEs and non-SOEs by investigating the et al., 2007). Moreover, η, ε, ν, and epu represent the firm- significance of β , β , β , and β , respectively. The signifi- specific, market-based, CAPM-based, and economic policy 4 6 8 10 cance level of these coefficients shows that different forms of uncertainties, respectively. Cf represents the cash flow ratio, uncertainties affect firms’ investment with the change in the computed as the ratio of net profits and depreciation to total level of firms’ cash flow. assets (Cleary, 2005; Lima Crisóstomo et al., 2014; Phan, 2018). Lev denotes the leverage of a firm, measured as the ratio of total liabilities to total assets (Bai et al., 2014; Chow Estimation Technique et al., 2018). Tobin’s Q is the ratio of the sum of the market To estimate the preceding models, we use the dynamic panel value of equity and total liabilities to lagged total assets data (DPD) approach to control the problem of endogeneity. (Wang et al., 2017). Sg stands for the growth of sales mea- Given that we jointly determine firms’ investment decision sured as the log of the first difference of total sales during a with cash flow and leverage, reverse causality is likely to year (Pukthuanthong et al., 2013; Rashid & Saeed, 2017). occur because investment may also affect the leverage and Size denotes firms’ size in terms of total assets (Bai et al., cash flow of firms or the uncertainty, thereby possibly affect- 2014; Chow et al., 2018). f and f represent the industry and i t ing firms’ leverage and cash flow. Therefore, we use a two- time fixed-effect, respectively. Finally,  denotes the error it step robust system GMM technique to minimize endogeneity term. In this model, the coefficients β , ββ ,, and β are 2 34 5 issues and consider the panel nature of our data (Arellano & of primary interest. That is, we expect ββ and to be posi- Bover, 1995; Blundell & Bond, 1998; Roodman, 2006). To tive, while ββ and are negative. control for the industry and time effects, the system GMM technique enables us to combine the level equation of vari- Differential Effects of Uncertainty on able with the equation in differences of variables as we use Investment for SOEs and Non-SOEs the lags of variables and the lags of first difference as instru- ments. We include time and industry dummies in all estima- We divide the full sample into SOEs and non-SOEs. Out of tions and use them as additional instruments. 1,791 firms, 561 are SOEs and 1,230 are non-SOEs. Thereafter, we estimate separate models for both groups to examine whether the impact of different types of uncertain- Empirical Findings ties on the investment behavior of SOEs is statistically dif- Baseline Model ferent from that of non-SOEs. We estimate Equation 1 separately for SOEs and non-SOEs. Table 2 presents the regression results for the baseline model (Equation 1). Column (1) reports the standard investment model, which includes the lagged dependent variable (Inv ) it−1 Indirect Effects of Uncertainty Through Cash and control variables. The coefficients for the lagged invest- Flow on Investment for SOEs and Non-SOEs ment and cash flow are significantly positive, which is consis- tent with Baum et al. (2010), Gulen and Ion (2015), and Julio Baum et al. (2010) empirically examine the link among and Yook (2012). Consistent with the literature (Ma, 2015; uncertainty, investment behavior, and cash flow of manufac- Wang et al., 2017), we find an adverse and highly significant turing firms in the United States. A high level of cash flow effect of leverage and firm size on investment. Moreover, the can stimulate or mitigate the investment activities of firms. coefficients for Tobin’s Q and Sg are not statistically different Given that the investment opportunities of a firm depend on from zero. Therefore, we find no evidence in support to the its financial condition and level of cash flow generated, the accelerator theory of investment, which corroborates with uncertainty will have an indirect effect through cash flow, in Baum et al. (2010) and Bo and Zhang (2002). Furthermore, addition to its direct impact on firms’ investment. This sec- diagnostic tests are provided at the end of each table. We tion empirically investigates the effects of various forms of apply the Arellano and Bond (1991) test under the null uncertainties on their own and in interaction with cash flow hypothesis that there is no serial correlation among the resid- on the investment of SOEs and non-SOEs. We include the uals. Moreover, m1 and m2 stand for the first- and second- interactions of cash flow with uncertainty measures as ordered serial correlations, respectively. We reject (accept) follows: 8 SAGE Open Table 2. Effect of Uncertainty on Investment in Full Sample. Variables (1) (2) (3) (4) (5) (6) (7) Inv 0.0323** 0.0320** 0.0466** 0.0007 0.0350* 0.0004 0.0608** t−1 (0.0142) (0.0161) (0.0230) (0.0174) (0.0203) (0.0187) (0.0257) ηt 0.0157** 0.0081* 0.0139** 0.0236** −1 (0.0078) (0.0042) (0.0057) (0.0097) εt 0.0868*** 0.0314** 0.0106** 0.0005 −1 (0.0315) (0.0130) (0.0049) (0.0062) νt –0.0358* –0.0029 −1 (0.0199) (0.0298) epu –0.0004*** –0.0002** t−1 (0.0001) (8.7e–05) Cf 0.1783** 0.2464*** 0.1885 0.1866** 0.6493*** –0.0534 0.8250*** t−1 (0.0823) (0.0892) (0.1521) (0.0825) (0.1333) (0.0806) (0.1920) Lev –0.0821*** –0.1243*** –0.0747** –0.1521*** –0.0714* –0.0147 –0.2266*** t−1 (0.0204) (0.0303) (0.0339) (0.0318) (0.0380) (0.0366) (0.0432) Tobin’s Q –0.0010 –0.0042*** –0.0041 0.0025 –0.0015 –0.0017 0.0099*** t−1 (0.0008) (0.0011) (0.0031) (0.0021) (0.0024) (0.0023) (0.0030) Sg 0.0155 –0.0067 0.0121 –0.0163 –0.0532 0.0527** –0.2165*** t−1 (0.0120) (0.0149) (0.0237) (0.0204) (0.0328) (0.0264) (0.0259) Size –0.0172** –0.0189*** 0.0114 –0.0024 –0.0141* 6.3e–05 0.0397*** t−1 (0.0075) (0.0028) (0.0127) (0.0066) (0.0075) (0.0116) (0.0055) Constant 0.4240** 0.5227*** –0.2689 0.2736 0.3886** 0.2048 –0.6898*** (0.1751) (0.0647) (0.2908) (0.1890) (0.1516) (0.3253) (0.1148) Industry Yes Yes Yes Yes Yes Yes Yes Year Yes Yes Yes Yes Yes Yes Yes Observations 15,803 15,803 15,803 15,803 15,803 15,803 15,803 Firms 1,791 1,791 1,791 1,791 1,791 1,791 1,791 Diagnostic tests m1 –16.95 –17.45 –17.17 –17.78 –17.67 –16.60 –14.96 p value .000 .000 .000 .000 .000 .000 .000 m2 1.34 0.99 1.07 0.93 0.58 0.31 0.92 p value .181 .323 .286 .353 .561 .757 .355 J-statistic 212.36 88.27 130.72 83.83 139.69 71.55 134.54 p value .261 .200 .166 .606 .175 .832 .142 Note. Standard errors are in parentheses. Inv = investment; η = firm-specific uncertainty; ε = market-based uncertainty; ν = CAPM-based uncertainty; epu = economic policy uncertainty; Cf = cash flow ratio; Lev = leverage, Tobin’s Q ratio; Sg = sales growth; Size = firm size. Significance level at 1%, 5%, and 10% are represented by ***, **, and *, respectively. the null hypothesis for m1 (m2). The J-statistic is the Hansen to show the same statistically significant and stimulating test of overidentifying restrictions, showing the validity of impact on investment. instruments used in the estimations. By considering the brev- Column (5) shows that the coefficient of firm-specific ity, we did not explain these tests in the discussion. uncertainty has increased again, whereas the coefficient of Columns (2) and (3) show that firm-specific and market- market uncertainty has decreased substantially. Both types of based uncertainties significantly positively affect investment uncertainties have the same positive and significant effect. (uncertainty coefficients are statistically significant at 5% and However, we can observe the negative influence of CAPM- 1%, respectively). These results corroborate the assumptions based uncertainty on firms’ investment. This result supports of Hartman (1972) and Caballero (1991) and the prior litera- real options theory. Hence, Hypothesis 3 is supported. ture (Shaoping, 2008; Xu et al., 2010), which argued that In Column (6), we find that economic policy uncertainty more the risk, the more the investment and therefore, higher reduces investment. This result is consistent with Wang et al. reinvestment. Thus, Hypotheses 1 and 2 are supported. (2014) and Kang et al. (2014) and supports theory of irre- In Column (4), we include firm-specific and market-based versible investment and the option value of waiting to invest uncertainties in the same equation. Although the coefficients (Bernanke, 1983; Dixit & Pindyck, 1994). Thus, Hypothesis for both forms of uncertainties have decreased, they continue 4 is confirmed. Khan et al. 9 Table 3. Effect of Uncertainty on Investment in State-Owned Enterprises. Variables (1) (2) (3) (4) (5) (6) (7) Inv 0.0431** 0.0470** 0.0380* 0.0404* 0.0379* –0.2807 0.0955 t−1 (0.0186) (0.0196) (0.0202) (0.0213) (0.0216) (0.1975) (0.1299) ηt 0.0079 0.0035 0.0028 0.0041 −1 (0.0096) (0.0094) (0.0094) (0.0089) εt –0.0033 –0.0030 –0.0023 –0.0191 −1 (0.0025) (0.0027) (0.0027) (0.0119) νt 0.0236 0.0147 −1 (0.0176) (0.0544) epu –0.0004** –0.0004*** t−1 (0.0002) (0.0001) Cf 0.4936*** 0.4925*** 0.5799*** 0.5719*** 0.5778*** 0.5338* 0.6967*** t−1 (0.1065) (0.1067) (0.1770) (0.1785) (0.1802) (0.3080) (0.2223) Lev 0.0471 0.0458 0.0271 0.0247 0.0248 –0.1122 0.0074 t−1 (0.0397) (0.0397) (0.0441) (0.0452) (0.0457) (0.1880) (0.0725) Tobin’s Q –0.0021 –0.0023 –0.0006 –0.0009 –0.0009 0.0160** 0.0130** t−1 (0.0019) (0.0020) (0.0027) (0.0028) (0.0028) (0.0072) (0.0064) Sg –0.0179 –0.0222 –0.0186 –0.0195 –0.0172 –0.0626 –0.2914*** t−1 (0.0150) (0.0160) (0.0203) (0.0205) (0.0207) (0.0596) (0.0380) Size –0.0180*** –0.0180*** –0.0182*** –0.0181*** –0.0197*** 0.0524** 0.0371*** t−1 (0.0039) (0.0039) (0.0039) (0.0041) (0.0042) (0.0228) (0.0130) Constant 0.4107*** 0.4113*** 0.4184*** 0.4176*** 0.4263*** –0.5607 –0.6086** (0.0736) (0.0729) (0.0735) (0.0741) (0.0742) (0.4904) (0.2765) Industry Yes Yes Yes Yes Yes Yes Yes Year Yes Yes Yes Yes Yes Yes Yes Observations 7,389 7,389 7,389 7,389 7,389 7,389 7,389 Firms 561 561 561 561 561 561 561 Diagnostic tests m1 –12.37 –12.35 –12.55 –12.53 –12.54 –1.86 –5.31 p value .000 .000 .000 .000 .000 .063 .000 m2 1.34 1.42 1.31 1.35 1.28 –1.01 0.83 p value .179 .156 .189 .176 .202 .315 .408 J-statistic 189.91 188.95 142.85 142.78 139.82 51.18 151.44 p value .292 .291 .285 .266 .304 .277 .107 Coefficients differences test (χ ) SOEs non-SOEs η = 14.47*** 14.15*** 7.78*** 9.26*** SOEs non-SOEs ε = 14.97*** 11.17*** 7.64*** 8.93*** SOEs non-SOEs ν = 12.11*** 9.44*** SOEs non-SOEs epu = 17.02*** 7.84*** Note. Standard errors are in parentheses. Inv = investment; η = firm-specific uncertainty; ε = market-based uncertainty; ν = CAPM-based uncertainty; epu = economic policy uncertainty; Cf = cash flow ratio; Lev = leverage, Tobin’s Q ratio; Sg = sales growth; Size = firm size. Significance level at 1%, 5%, and 10% are represented by ***, **, and *, respectively. In the last column of Table 2, we combine all forms of Chinese manufacturing firms decrease their investment when uncertainties in our investment model and find that the coef- epu increases. ficient of firm-specific uncertainty has increased and remains significant at 5%. This result suggests that firms’ investment Differential Effects of Uncertainty on increases with an increase in firm-specific uncertainty. The Investment for SOEs and Non-SOEs signs of market-based and CAPM-based uncertainties remain the same. However, their coefficients are not statistically sig- Tables 3 reports the results of the uncertainties and invest- nificant. The coefficient of economic policy uncertainty has ment association for SOEs. Column (1) presents similar decreased in absolute value but remains negative and statisti- results to those in Table 2 in terms of signs and significance cally significant at 5%. This result supports the claim that levels for the lagged investment and control variables. The 10 SAGE Open exception is leverage, which is not different from zero. economic policy uncertainty, have no significant effects on Columns (2) and (3) show no significant effect of firm- SOEs’ investment. We likewise consider cash flow in isola- specific and market-based uncertainties on corporate invest- tion only and not in interaction with uncertainties terms. ment, respectively. The coefficients of firm-specific and Table 5 presents the estimates for Equation 2 by adding an market-based uncertainties remain statistically insignificant interaction term of cash flow with each form of uncertainty. if we combine them in Columns (4) and (5), along with the Column (1) shows an insignificant coefficient of firm-spe- CAPM-based uncertainty. The results indicate that managers cific uncertainty in isolation, while it has an exacerbating of SOEs have no incentives to react to uncertainty, which influence with cash flow interaction. This result indicates may be caused by the contract responsibility system between that cash flow enhances the association between investment firms and the government. Column (6) shows that economic and firm-specific uncertainty. policy uncertainty reduces SOEs’ investments. Column (7) Column (2) presents the findings for market-based uncer- shows no significant change in terms of the sign and signifi- tainty and its interaction term. The coefficient of market- cance level of uncertainties, thereby indicating that except based uncertainty is insignificant, while the interaction term economic policy uncertainty, no other forms of uncertainty appears to have a significant negative influence on invest- influence SOEs’ investment. ment. The results show that cash flow can decrease the effect Thereafter, we apply the Chow test to investigate the sta- of market-based uncertainty on investment. This may sug- tistical difference in the coefficients of uncertainties across gest that managers of SOEs are considerably cautious in SOEs and non-SOEs. The results reported in Table 3 show highly uncertain market, as pointed out by prior studies, such significant chi-square values for all uncertainty coefficients. as Bloom et al. (2007) and Baum et al. (2010). The findings confirm that the coefficients of uncertainties The signs and significance of the market- and firm-spe- across SOEs and non-SOEs significantly vary. cific uncertainties, along with their interactions, remain Table 4 presents the findings for non-SOEs. The results in unchanged when we combine them in Column (3). However, Column (1) are consistent with the full sample. Furthermore, the coefficients of CAPM-based uncertainty and interaction Columns (2) and (3) show that firm-specific and market- of the CAPM-based uncertainty in Column (4) are not sig- based uncertainties positively affect investment. From these nificantly different from zero. These results are consistent results, we can infer that non-SOEs’ investment behavior with the findings in Table 3 and suggest that market, CAPM, appears to be risk-taking by investing substantially in a high and firm-specific uncertainties (in isolation) have no mean- degree of uncertainty. These findings support Hypotheses 5a ingful impact on SOEs’ investment. This result may be and 5b. The coefficient of firm-specific uncertainty becomes caused by the contract responsibility system, and managers insignificant when we combine both forms of uncertainties of SOEs do not show risk-taking behavior in making deci- in Column (4), and with CAPM-based uncertainty in Column sions to invest. (5). These coefficients indicate that firm-specific uncertainty In Column (5), we determine that economic policy uncer- has no significant impact on non-SOEs’ investment, whereas tainty reduces SOEs’ investment. However, the coefficient of market-based uncertainty has a stimulative impact, with a the interaction term is significantly positive, which implies negative effect of CAPM-based uncertainty. This result sup- that cash flow mitigates the influence of economic policy ports Hypothesis 5c. When comparing the results of Column uncertainty. These findings are consistent with those of Wang (6) in Table 4 with those of Table 3, we determine that both et al. (2014). types of firms are negatively influenced by economic policy Column (6) provides the results for the combined impact uncertainty. However, the coefficient is considerably low in of uncertainties and their interaction with cash flow. The absolute value for non-SOEs, thereby indicating that the signs and significance remains the same for economic policy investment behavior of non-SOEs is minimally influenced uncertainty and its interaction term, whereas all other forms by economic policy uncertainty. Our findings corroborate of uncertainties and interactions become insignificant. those of Wang et al. (2014) and Wang et al. (2017) and sup- Table 6 presents the indirect effect of various forms of port the theory of irreversible investment. This result verifies uncertainties through cash flow on non-SOEs’ investment Hypothesis 5d. In Column (7), we find that the coefficient of behavior. Column (1) shows the exacerbating and significant epu is the same in sign and value, whereas the coefficients of effect of the firm-specific uncertainty and cash flow in isola- all other forms of uncertainties become statistically insignifi- tion, with no meaningful interaction term. Column (2) pres- cant. These findings show that when non-SOEs face epu, ents the significant coefficients of market-based uncertainty their risk-taking behavior changes, thereby resulting in a and its interaction with cash flow. The negative coefficient for reduction of their investment in uncertain environments. the interaction term implies that cash flow weakens the impact of market-based uncertainty. The signs and significance of the market-based uncertainty and its interaction term remain Indirect Effects of Uncertainty: Does Uncertainty unchanged in Column (3), while the coefficients of firm-spe- Affect the Firm’s Investment Through Cash Flow? cific uncertainty and its interaction term are not statistically Table 3 shows that cash flow has a significantly positive significant. In Column (4), we include the CAPM-based coefficient, whereas all forms of uncertainties, except for uncertainty and its interaction with cash flow. The main effect Khan et al. 11 Table 4. Effect of Uncertainty on Investment in Non-State-Owned Enterprises. Variables (1) (2) (3) (4) (5) (6) (7) Inv 0.0537*** 0.0515*** 0.0414* 0.0365 0.0497** 0.1964 0.0624** t−1 (0.0180) (0.0176) (0.0230) (0.0231) (0.0250) (0.1474) (0.0266) ηt 0.0079** 0.0116 –0.0008 0.0128 −1 (0.0039) (0.0118) (0.0065) (0.0115) εt 0.1009*** 0.0998*** 0.0195*** 0.0011 −1 (0.0387) (0.0384) (0.0075) (0.0072) νt –0.0671*** 0.0071 −1 (0.0216) (0.0265) epu –0.0002** –0.0002** t−1 (8.1e–05) (8.6e–05) Cf 0.2629*** 0.1946*** 0.0059 –0.0070 0.4744*** 0.7599*** 0.5636*** t−1 (0.1014) (0.0693) (0.1085) (0.1104) (0.1648) (0.2247) (0.1619) Lev –0.0683*** –0.0839*** –0.0772*** –0.0819*** –0.0979** –0.2277*** –0.2057*** t−1 (0.0238) (0.0180) (0.0289) (0.0292) (0.0388) (0.0532) (0.0372) Tobin’s Q 3.2e–05 –0.0002 0.0032 0.0039 –0.0036 0.0076** 0.0068** t−1 (0.0011) (0.0007) (0.0029) (0.0030) (0.0028) (0.0035) (0.0031) Sg –0.0115 –0.0141 0.0071 0.0078 –0.0214 –0.1590*** –0.1215*** t−1 (0.0130) (0.0128) (0.0234) (0.0237) (0.0297) (0.0334) (0.0204) Size –0.0252*** –0.0281*** 0.0078 0.0102 –0.0472*** 0.0337*** 0.0346*** t−1 (0.0064) (0.0048) (0.0135) (0.0139) (0.0100) (0.0076) (0.0056) Constant 0.6006*** 0.6695*** –0.2319 –0.2824 1.1098*** –0.5673*** –0.6588*** (0.1255) (0.0947) (0.3060) (0.3133) (0.2082) (0.1565) (0.1095) Industry Yes Yes Yes Yes Yes Yes Yes Year Yes Yes Yes Yes Yes Yes Yes Observations 8,414 8,414 8,414 8,414 8,414 8,414 8,414 Firms 1,230 1,230 1,230 1,230 1,230 1,230 1,230 Diagnostic tests m1 –11.93 –11.78 11.56 –11.49 –12.10 –5.41 –11.88 p value .000 .000 .000 .000 .000 .000 .000 m2 0.48 0.43 0.44 0.46 –0.05 0.90 0.76 p value .633 .666 .660 .649 .962 .370 .445 J-statistic 205.29 268.95 128.28 130.72 133.01 88.20 140.67 p value .146 .121 .205 .150 .317 .356 .308 Note. Standard errors are in parentheses. Inv = investment; η = firm-specific uncertainty; ε = market-based uncertainty; ν = CAPM-based uncertainty; epu = economic policy uncertainty; Cf = cash flow ratio; Lev = leverage, Tobin’s Q ratio; Sg = sales growth; Size = firm size. Significance level at 1%, 5%, and 10% are represented by ***, **, and *, respectively. is significantly negative, whereas the interaction term coeffi- uncertainty and market-based uncertainty in isolation or cient was significantly positive, thereby implying that cash through cash flow. flow weakens the effect of the CAPM-based uncertainty. However, the positive effects of market and firm-related Robustness Tests uncertainties remain consistent, whereas their interaction terms lack significance. Column (5) shows that non-SOEs’ We conducted several robustness checks for the validity of our investment is not influenced by economic policy uncertainty results. First, we use an additional measure of investment, in isolation or through cash flow. Column (6) presents the which is defined as the ratio of expenditures on the purchase of combined impact of all uncertainties and their interaction fixed tangible assets during a year to total assets. Table 7 pres- terms. Firm-specific uncertainty has a significant positive ents the results. We obtain consistent results in terms of sign impact, with no meaningful interaction term. The signs and and significance for coefficients of variables to those reported significance of the CAPM-based uncertainty and its interac- in Table 2. tion remain unchanged. However, results show that non- Second, we use an alternative measure for firm-specific SOEs’ investment is not influenced by economic policy uncertainty (Equation 3), which is defined as the first-order 12 SAGE Open Table 5. Indirect Effect of Uncertainty Through Cash Flow on Investment in State-Owned Enterprises. Variables (1) (2) (3) (4) (5) (6) Inv 0.0595*** 0.0318* 0.0467** 0.0446** 0.0185 0.2128 t−1 (0.0197) (0.0176) (0.0200) (0.0203) (0.0235) (0.1682) Cf 0.3429*** 0.4217*** 0.4240*** 0.8353** 0.0732 0.4228 t−1 (0.0929) (0.0818) (0.0959) (0.3723) (0.2477) (0.7635) ηt –0.0241 –0.0310 –0.0224 0.0212 −1 (0.0194) (0.0208) (0.0182) (0.0343) Cf × ηt 0.4955* 0.5149* 0.4549* –0.1042 −1 (0.2597) (0.2749) (0.2541) (0.5440) εt 0.00247 0.0032 0.0024 0.0004 −1 (0.0034) (0.0037) (0.0038) (0.0112) Cf × εt –0.1027** –0.1001* –0.0958* 0.0067 −1 (0.0522) (0.0592) (0.0573) (0.1727) νt 0.0120 0.0270 −1 (0.0229) (0.0371) Cf × νt –0.4046 –0.6245 −1 (0.3340) (0.6216) epu –0.0002* –0.0003* t−1 (0.0001) (0.0002) Cf × epu 0.0041** 0.0061** t−1 (0.0019) (0.0029) Lev 0.0256 0.0122 0.0059 –0.0036 –0.0420 –0.0169 t−1 (0.0322) (0.0403) (0.0337) (0.0319) (0.0442) (0.0656) Tobin’s Q –0.0025 0.0008 –0.0003 0.0006 –0.0005 –0.0007 t−1 (0.0017) (0.0019) (0.0025) (0.0023) (0.0018) (0.0024) Sg –0.0151 –0.0041 –0.0089 –0.0109 –0.0077 –0.0190 t−1 (0.0149) (0.0142) (0.0158) (0.0154) (0.0232) (0.0358) Size –0.0173*** –0.0141*** –0.0132*** –0.0106*** –0.0102 –0.0189** t−1 (0.0033) (0.0034) (0.0032) (0.0031) (0.0074) (0.0087) Constant 0.4149*** 0.3387*** 0.3246*** 0.2595*** 0.3079** 0.4515** (0.0643) (0.0609) (0.0617) (0.0603) (0.1498) (0.1790) Industry Yes Yes Yes Yes Yes Yes Year Yes Yes Yes Yes Yes Yes Observations 7,389 7,389 7,389 7,389 7,389 7,389 Firms 561 561 561 561 561 561 Diagnostic tests m1 –12.40 –12.43 –12.51 –12.43 –12.55 –3.75 p value .000 .000 .000 .000 .000 .000 m2 1.57 1.46 1.40 1.50 0.51 1.21 p value .116 .144 .162 .134 .611 .226 J-statistic 261.20 284.64 264.35 282.41 127.56 66.46 p value .155 .151 .116 .162 .238 .139 Note. Standard errors are in parentheses. Inv = investment; η = firm-specific uncertainty; ε = market-based uncertainty; ν = CAPM-based uncertainty; epu = economic policy uncertainty; Cf = cash flow ratio; Lev = leverage, Tobin’s Q ratio; Sg = sales growth; Size = firm size. Significance level at 1%, 5%, and 10% are represented by ***, **, and *, respectively. autoregressive model AR(1) for total sales normalized by from the estimation of the AR(1) model of the total sales in a capital stock for each firm (Baum et al., 2016; Bo & Zhang, five-period rolling window with a minimum of 3 years of 2002; Caglayan & Rashid, 2014): residual data. Our findings reported in Table 8 remain con- sistent with those presented in Table 2. sales sales     Finally, we follow Caglayan and Rashid (2014) and =+ ββ + . (3)   01   it K K     Rashid (2011) and estimate the GARCH model by using the it it −1 annual data of the T-bills rate for the period 1997–2016 to The uncertainty proxy for each year is measured by calcu- measure market uncertainty. Our findings in Table 8 provide lating the moving standard deviation of the residuals obtained consistent results to our prior findings. Overall, the results of Khan et al. 13 Table 6. Indirect Effect of Uncertainty Through Cash Flow on Investment in Non-State-Owned Enterprises. Variables (1) (2) (3) (4) (5) (6) Inv 0.0723*** 0.0613*** 0.0478** 0.0458*** 0.0484** 0.0455* t−1 (0.0189) (0.0213) (0.0197) (0.0177) (0.0194) (0.0270) Cf 0.2218*** 0.0667 0.1373* –0.0429 0.2652** –0.5641 t−1 (0.0779) (0.0826) (0.0824) (0.2048) (0.1350) (0.5671) ηt 0.0196* 0.0015 0.0164* 0.0540* −1 (0.0111) (0.0085) (0.0086) (0.0316) Cf × ηt –0.1349 –0.0426 –0.1460 –0.6404 −1 (0.1617) (0.1130) (0.1262) (0.3953) εt 0.0984*** 0.0301** 0.0148* 0.0348 −1 (0.0320) (0.0136) (0.0079) (0.0432) Cf × εt –0.0748* –0.0805** –0.0199 –0.0810 −1 (0.0382) (0.0356) (0.0400) (0.0896) νt –0.0590*** –0.1722*** −1 (0.0164) (0.0468) Cf × νt 0.3167* 0.7983* −1 (0.1817) (0.4606) epu –1.6e–05 0.0003 t−1 (5.9e–05) (0.0004) Cf × epu 0.0004 0.0028 t−1 (0.0008) (0.0019) Lev –0.0791*** –0.0723*** –0.0743*** –0.0822*** –0.0616*** –0.1458** t−1 (0.0166) (0.0233) (0.0216) (0.0226) (0.0224) (0.0594) Tobin’s Q 0.0011 0.0013 0.0005 0.0001 –7.4e–05 0.0139*** t−1 (0.0011) (0.0020) (0.0015) (0.0014) (0.0012) (0.0032) Sg –0.0191 0.0084 0.0144 –0.0108 –0.0151 –0.0826*** t−1 (0.0118) (0.0179) (0.0167) (0.0125) (0.0136) (0.0305) Size –0.0227*** 0.0093 –0.0059 –0.0233*** –0.0260*** 0.0297*** t−1 (0.0032) (0.0089) (0.0058) (0.0056) (0.0059) (0.0073) Constant 0.5521*** –0.2530 0.1753 0.6066*** 0.6168*** –0.4784*** (0.0683) (0.2116) (0.1220) (0.1078) (0.1129) (0.1593) Industry Yes Yes Yes Yes Yes Yes Year Yes Yes Yes Yes Yes Yes Observations 8,414 8,414 8,414 8,414 8,414 8,414 Firms 1,230 1,230 1,230 1,230 1,230 1,230 Diagnostic tests m1 –11.67 –12.00 –11.79 –11.62 –11.83 –11.60 p value .000 .000 .000 .000 .000 .000 m2 0.99 0.79 0.40 0.30 0.35 0.21 p value .321 .430 .688 .763 .728 .833 J-statistic 319.26 167.14 203.35 326.24 223.35 116.97 p value .107 .417 .274 .240 .114 .284 Note. Standard errors are in parentheses. Inv = investment; η = firm-specific uncertainty; ε = market-based uncertainty; ν = CAPM-based uncertainty; epu = economic policy uncertainty; Cf = cash flow ratio; Lev = leverage, Tobin’s Q ratio; Sg = sales growth; Size = firm size. Significance level at 1%, 5%, and 10% are represented by ***, **, and *, respectively. these sections suggest that our main results are insensitive to 1999–2016. This study further tests whether the impact of alternative measures of investment and uncertainties. uncertainty on investment varies across SOEs and non- SOEs. Furthermore, the current research investigated the influence of cash flow on the uncertainty–investment rela- Conclusion tionship. By controlling for the time and industry-fixed This study investigates the impact of the four forms of uncer- effects and considering the potential endogeneity problem, tainties on corporate investment, using an unbalanced panel this study uses a robust two-step system GMM technique to data of 1,791 firms listed on the SSE/SZSE for the period of estimate the model. 14 SAGE Open Table 7. Robustness Check Using Alternative Measure of Investment in Full Sample. Variables (1) (2) (3) (4) (5) (6) Inv 0.7310*** 0.7154*** 0.7310*** 0.7335*** 0.6521*** 0.6586*** t−1(F.A/T.A) (0.0250) (0.0214) (0.0250) (0.0252) (0.0294) (0.0306) ηt 0.0079*** 0.0079*** 0.0082*** –0.0006 −1 (0.0029) (0.0029) (0.0030) (0.0129) εt 0.0092** 0.0198** 0.0220** –0.0034 −1 (0.0043) (0.0085) (0.0088) (0.0064) νt –0.0320** –0.0156 −1 (0.0153) (0.0241) epu –0.0002** –0.0002** t−1 (0.0001) (9.6e–05) Cf –0.0470 –0.1048 –0.0470 –0.0959 0.0213 0.0069 t−1 (0.0656) (0.0786) (0.0656) (0.0720) (0.0559) (0.0472) Lev –0.0328 –0.0438* –0.0328 –0.0668** 0.0178 0.0296 t−1 (0.0266) (0.0257) (0.0266) (0.0332) (0.0305) (0.0251) Tobin’s Q –0.0031** –0.0035** –0.0031** –0.0025* –0.0024 –0.0035*** t−1 (0.0014) (0.0015) (0.0014) (0.0014) (0.0017) (0.0011) Sg 0.0019 0.0139 0.0019 –0.0042 –0.0052 –0.0067 t−1 (0.0167) (0.0144) (0.0167) (0.0162) (0.0151) (0.0136) Size –0.0038 0.0070 –0.0038 –0.0027 0.0117 0.0085 t−1 (0.0053) (0.0069) (0.0053) (0.0054) (0.0106) (0.0085) Constant 0.1563 –0.0606 0.1563 0.1930 0.2069 0.1407 (0.1327) (0.1442) (0.1327) (0.1302) (0.3246) (0.2403) Industry Yes Yes Yes Yes Yes Yes Year Yes Yes Yes Yes Yes Yes Observations 15,803 15,803 15,803 15,803 15,803 15,803 Firms 1,791 1,791 1,791 1,791 1,791 1,791 Diagnostic tests m1 –17.21 –16.73 –17.21 –17.13 –15.91 –15.78 p value .000 .000 .000 .000 .000 .000 m2 –1.54 –1.41 –1.54 –1.47 –1.54 –1.50 p value .123 .158 .123 .142 .123 .133 J-statistic 100.10 164.18 100.10 94.10 69.41 109.40 p value .198 .331 .178 .283 .874 .498 Note. Standard errors are in parentheses. Inv = investment; η = firm-specific uncertainty; ε = market-based uncertainty; ν = CAPM-based uncertainty; epu = economic policy uncertainty; Cf = cash flow ratio; Lev = leverage, Tobin’s Q ratio; Sg = sales growth; Size = firm size. Significance level at 1%, 5%, and 10% are represented by ***, **, and *, respectively. The empirical findings indicate that market- and firm- on SOEs’ investment and mitigates the adverse effect of specific uncertainties (economic policy and CAPM-based CAPM-based uncertainty (economic policy uncertainty) on uncertainties) have a positive (negative) effect on corporate investment of non-SOEs (SOEs). Moreover, cash flow atten- investment. The findings further indicate that the investment uates the effects of market uncertainty on investment of decisions undertaken by SOEs do not respond to market- SOEs and non-SOEs. Thus, the findings signify that cash based, CAPM-based, and firm-specific uncertainties. By flow is an important factor that influences the uncertainty– contrast, non-SOEs respond to market-based and firm-spe- investment association. Our results remain robust, consider- cific uncertainties (CAPM-based uncertainty) positively ing the potential endogeneity problems, and alternative (negatively). The results provide evidence that economic proxies for investment, firm-specific, and market-based policy uncertainty has a negative impact on firms’ invest- uncertainties. ment. Moreover, the investment behavior of SOEs is more The findings would help firm managers, investors, and sensitive to economic policy uncertainty than that of non- policymakers to understand the uncertainty–investment SOEs. Furthermore, the results show that the influence of association in a transition economy, such as China. The cash flow on investment can be exacerbating or mitigating, results of this study facilitate an improved understanding of depending on the underlying uncertainty. In particular, the how different kinds of uncertainties affect the investment cash flow exacerbates the impact of firm-specific uncertainty behavior of Chinese SOEs and non-SOEs. Given that China Khan et al. 15 Table 8. Robustness Test Using Alternative Measure of Uncertainty in Full Sample. Variables (1) (2) (3) (4) (5) (6) Inv 0.1773* 0.0482*** 0.1439 0.0897 0.0545 0.0422* t−1 (0.1055) (0.0150) (0.1101) (0.0660) (0.0386) (0.0229) ηt 0.0486** 0.0442* 0.0458** 0.0505 −1(S.D_Sales) (0.0229) (0.0237) (0.0220) (0.1090) εt 0.0099** 0.0031 –0.0019 0.0114 −1(t-bill) (0.0050) (0.0065) (0.0042) (0.0099) νt –0.0113** 0.0271 −1 (0.0053) (0.0250) epu –0.0004*** –0.0001** t−1 (0.0001) (5.3e–05) Cf 0.1663 0.3985*** 0.3140* 0.2363*** 0.6010* 0.1389** t−1 (0.1632) (0.1202) (0.1791) (0.0695) (0.3551) (0.0651) Lev –0.0832 –0.0231 –0.1450** –0.0973*** –0.2819*** –0.0784 t−1 (0.0516) (0.0197) (0.0615) (0.0220) (0.1073) (0.0575) Tobin’s Q –0.0009 –0.0041*** –0.0002 –0.0004 0.0113* –0.0042** t−1 (0.0017) (0.0013) (0.0019) (0.0009) (0.0064) (0.0020) Sg –0.0115 0.0037 –0.0285 –0.0177 –0.2207*** –0.0431** t−1 (0.0185) (0.0134) (0.0214) (0.0129) (0.0335) (0.0193) Size –0.0164*** –0.0156*** 0.0066 0.0052 0.0523*** 0.0242*** t−1 (0.0041) (0.0030) (0.0095) (0.0034) (0.0108) (0.0038) Constant 0.4291*** 0.4030*** –0.0099 0.0115 –0.7855*** –0.6871*** (0.1069) (0.0663) (0.1879) (0.0674) (0.2644) (0.2403) Industry Yes Yes Yes Yes Yes Yes Year Yes Yes Yes Yes Yes Yes Observations 12,334 15,803 12,334 12,334 15,803 12,334 Firms 1,687 1,687 1,687 1,687 1,687 1,687 Diagnostic tests m1 –5.54 –17.79 –5.24 –7.74 –13.31 –15.26 p value .000 .000 .000 .000 .000 .000 m2 1.63 1.24 1.26 1.34 1.02 0.77 p value .104 .214 .207 .181 .310 .442 J-statistic 133.04 139.98 127.07 338.12 49.3 53.38 p value .179 .127 .247 .107 .174 .903 Note. Standard errors are in parentheses. Inv = investment; η = firm-specific uncertainty; ε = market-based uncertainty; ν = CAPM-based uncertainty; epu = economic policy uncertainty; Cf = cash flow ratio; Lev = leverage, Tobin’s Q ratio; Sg = sales growth; Size = firm size. Significance level at 1%, 5%, and 10% are represented by ***, **, and *, respectively. is moving from a command-based to a market-based econ- Acknowledgment omy, firms face substantial policy uncertainty, which nega- We are thankful to the editor and anonymous reviewers for many tively affects their investment behavior. Therefore, firms can constructive comments and suggestions. be encouraged to invest more by a significant reduction in policy uncertainty. Furthermore, a substantial cash flow can Declaration of Conflicting Interests mitigate the negative impact of CAPM-based and economic The author(s) declared no potential conflicts of interest with respect policy uncertainties. to the research, authorship, and/or publication of this article. The findings of this study are beneficial and informative with regard to the understanding of uncertainty–investment Funding relationship, and the role of cash flow in mitigating the nega- The author(s) disclosed receipt of the following financial support tive effect of uncertainty. 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The Sensitivity of Firms’ Investment to Uncertainty and Cash Flow: Evidence From Listed State-Owned Enterprises and Non-State-Owned Enterprises in China:

Khan, Muhammad Arif; Qin, Xuezhi; Jebran, Khalil; Rashid, Abdul
SAGE Open , Volume 10 (1): 1 – Jan 29, 2020
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Abstract

This study examines the association between various uncertainties and corporate investment and further investigates this association between state-owned enterprises (SOEs) and non-state-owned enterprises (non-SOEs). Moreover, this study analyzes the indirect effects of uncertainty on corporate investment through cash flow. The current research uses an unbalanced panel data of Chinese nonfinancial listed firms for the period 1999–2016. To control endogeneity issues, this study applies a robust two-step system generalized method of moments (GMM) technique to estimate the model. Empirical findings indicate that market-based and firm-specific uncertainties have positive effects, whereas economic policy and CAPM- based uncertainties have negative effects on corporate investment. Furthermore, results indicate that the effects of market- based, CAPM-based, and firm-specific uncertainties (economic policy uncertainty) were less (more) prominent for SOEs. Additional analyses show that cash flow stimulates the effect of firm-specific uncertainty on SOEs’ investment, whereas it weakens the influence of CAPM-based uncertainty (economic policy uncertainty) on investment of non-SOEs (SOEs). Moreover, cash flow attenuates the market uncertainty effect on investment. Keywords uncertainty, investment, cash flow, state-owned enterprises, China stimulates investment by extending Hartman’s discrete-time Introduction result to a continuous setup. Caballero (1991) proves that Researchers have exerted substantial effort in attempting to under the assumptions of nonconstant returns to scale pro- understand the nature of the uncertainty–investment relation- duction technology and imperfect competition, the result of ship at the firm and market levels. However, the nature of Hartman–Abel can be opposite (i.e., the negative uncer- this relationship is still inconclusive (i.e., uncertainty may tainty–investment nexus). Previous studies have also pro- positively or negatively affect the investment) from the vided evidence that uncertainty positively influences empirical and theoretical perspectives. investment (Baum et al., 2008; Ma, 2015; Shaoping, 2008). Real options theory (Bernanke, 1983; Dixit & Pindyck, 1994) states that uncertainty adversely affects investment in terms of irreversible capital by obtaining maximum informa- 1 School of Economics and Management, Dalian University of Technology, tion, which resulted from waiting. Hartman (1972) consid- Dalian, P.R. China School of Business Administration, Dongbei University of Finance and ers the convex function of the marginal product of capital Economics, Dalian, P.R. China and argues that firms invest substantially in a high degree of International Institute of Islamic Economics, International Islamic uncertainty. The marginal product of capital is a convex University, Islamabad, Pakistan function of stochastic variables under the assumptions of Corresponding Author: constant returns to scale production technology, perfect com- Xuezhi Qin, School of Economics and Management, Dalian University petition, risk-neutral firms, and reversibility of adjustment of Technology, No.2 Linggong Road, Ganjingzi District, Dalian, Liaoning cost function. To support the aforementioned argument, Abel 116024, P.R. China. (1983) and Caballero (1991) confirm that uncertainty Email: qinxz0994@sina.com Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). 2 SAGE Open Although researchers have considerably focused on state-owned banks, thereby making obtaining loans from investigating how uncertainty influences investment in state-owned banks comparatively easy for SOEs. For exam- developed economies (Bloom et al., 2007; Gulen & Ion, ple, SOEs obtain discounted loans from banks to generate 2015; Kang et al., 2014; Rashid, 2011), only a few studies funds for investment (some of their interest payments are have explored the phenomena for firms operating in transi- subsidized by the government) because the government tion economies. Moreover, the literature on uncertainty and encourages and finance them to develop further (Bo & investment association for various natures of firms is limited Zhang, 2002). By contrast, non-SOEs face problems in if nonexistent. The current study seeks to expand the litera- obtaining loans from banks and rely primarily on their inter- ture by exploring the investment and uncertainty association nal funds (Guariglia & Yang, 2016). Kang et al. (2000) argue for a transition economy, namely, China. that firms with close relationships with banks can facilitate Prior studies have shown that potential lenders in a highly and improve investment policies, thereby increasing share- uncertain environment may be less or unable to determine holders’ wealth. Guariglia and Mateut (2016) document that the credit worthiness of firms, thereby limiting these firm’s firms with political connections can easily access external capability to raise funds from external sources. In such an financing compared with those that are not politically con- environment, lenders demand a high risk premium to provide nected. Therefore, we assume that in a transition economy, funds, which leads firms to become liquidity constrained. In fluctuations in various uncertainties influence the investment the case of internal fund deficiency, state-owned enterprises behavior of firms, which may differ across firms of different (SOEs) generally rely on state-owned banks and can access nature. credit easily. By contrast, the non-SOE counterpart substan- Using Chinese nonfinancial firms during the period tially relies upon their self-generated funds (Guariglia & 1999–2016, we show significant and positive (negative) Yang, 2016; Khan et al., 2019). Hence, we expect that uncer- impact of market and firm-specific uncertainties (CAPM- tainty significantly affects investment, either solely or based and economic policy uncertainties) on investment. through variations in cash-flow, which may vary across firms Furthermore, the results show that the impact of market- of different nature. Previous studies have investigated the based, CAPM-based, and firm-specific uncertainties (eco- influence of cash flow on firms’ investment by introducing nomic policy uncertainty) on investments are less (more) cash flow in the basic investment model. However, no previ- prevalent for SOEs. Additional analyses show that cash flow ous research has examined whether cash flow has a stimulat- strengthens the investment of SOEs when firm-specific ing or mitigating effect on the uncertainty–investment uncertainty is high, whereas it weakens the negative impact relationship between Chinese SOEs and non-SOEs. This of CAPM-based uncertainty (economic policy uncertainty) study also fills in this gap by studying the association among on the investments of non-SOEs (SOEs). Moreover, cash the uncertainty, cash flow, and investment behavior of SOEs flow has a mitigating impact on the investment of both types and non-SOEs separately. of firms under a high market uncertainty. Our findings are This study examines the uncertainty–investment relation- robust to several diagnostic tests and alternative proxies for ship for Chinese firms for two reasons. First, as a transition investment, market, and firm-specific uncertainties. economy, China is moving from a command-based to a mar- The current study provides three key contributions. First, ket-based economy, and the economy is under a high policy unlike previous studies (Baum et al., 2008, 2010; Dibiasi uncertainty (Wang, Chen, & Huang, 2014). Therefore, it will et al., 2018; Wang et al., 2014; Xu et al., 2010), we investi- influence firms’ costs, sales, and earnings. China is adopting gate the effects of economic policy uncertainty, along with and practicing free-market principles, thereby creating many CAPM, market, and firm-related uncertainties on corporate investment opportunities for listed firms. However, firms investment. Therefore, the current research offers important may face variations from different aspects because the capi- contributions to the literature by studying the effects of four tal market is underdeveloped and the market remains in the forms of uncertainties on investment. Second, this study transition process. Therefore, uncertainty will likely have a examines the differential impact of different forms of uncer- significant influence on corporate decisions, particularly tainties on the investments of SOEs and non-SOEs. Our find- those related to investments. In particular, investment is ings indicate that the uncertainty–investment relationship often irreversible and costly in terms of uncertainty. Policy varies for firms of different nature. Finally, we contribute to changes can influence the investment behaviors of firms and the literature by identifying the factors that can strengthen or make the environment uncertain in which firms operate. The weaken the uncertainty–investment association. We explore prior literature clearly indicates that policy uncertainty has that cash flow is one of the attributes that can moderate the an adverse influence on corporate investment and, therefore, association between investment and uncertainty. reduces economic growth. Second, the nature of ownership The remainder of this article is organized as follows. (i.e., SOEs and non-SOEs) is one of the distinctive attributes Section “Literature Review and Hypothesis Development” of Chinese firms. Compared with non-SOEs, SOEs evidently reviews the literature and develops hypotheses. Section suffer more from various government policies (Fan et al., “Data, Variables Measurement, and Descriptive Statistics” 2013). In addition, SOEs can access the “policy lending” by explains the data set and measures the variables. Section Khan et al. 3 “Econometric Model” presents the econometric model. Australian mining industry and stimulate investment. Section “Empirical Findings” reports the empirical findings. Shaoping (2008) argues that the higher the risk, the more the Finally, Section “Conclusion” provides the conclusion. investment will result, thereby resulting in higher reinvest- ment. The aforementioned study finds a significant positive association between firm-specific uncertainty and investment Literature Review and Hypothesis for Chinese firms. Khan et al. (2019) report that Chinese firms Development increase their capital investment with an increase in firm-spe- Researchers have empirically investigated the nature of the cific and market-based uncertainties. We use the preceding uncertainty and investment association, although the theo- discussion to argue that China, as an emerging economy, faces retical relationship between uncertainty and investment high uncertainty. Moreover, firms show risk-taking behavior remains inconsistent (Wang et al., 2014). Nickell (1978) and invest substantially under high uncertainty owing to com- finds that the attitude of firms in making investment deci- petition among firms. Therefore, we can assume that a high sions toward uncertainty may be positive or negative and firm-specific uncertainty will stimulate firms’ behavior toward argues that risk-averse (risk-taker) firms invest less (more) in a high investment. Thus, we hypothesize that: a highly uncertain environment. Shaoping (2008) argues that uncertainty–investment relationship depends on modeling, Hypothesis 1: Firm-specific uncertainty positively affects specific assumptions, and sample type. corporate investment. Bernanke (1983) and Dixit and Pindyck (1994) propose real options theory of irreversible investment decisions under The prior literature has provided evidence that an increase in uncertainty. Several studies have supported this theory by market uncertainty stimulates investment. Baum et al. (2008) suggesting a negative uncertainty–investment association for show that market uncertainty induces U.S. firms’ investment. various countries. For example, Leahy and Whitcd (1996), The aforementioned study argues that firms invest more in Kang et al. (2014), and Gulen and Ion (2015) for the United response to an increase in market uncertainty. Hence, they States; Bloom et al. (2007) and Rashid (2011) for the United may have a high opportunity to expand their presence in the Kingdom; Ma (2015) for Australia; and Rashid and Saeed market. Shaoping (2008) discusses that market uncertainty (2017) for Pakistan. Xu et al. (2010), Wang et al. (2014), An enhances investment of Chinese firms. Xu et al. (2010) find et al. (2016), and Khan et al. (2019) show the negative uncer- positive market uncertainty and investment association. tainty–investment relationship for Chinese firms. Bloom Therefore, we use the preceding discussions to argue that et al. (2007) and (Ma, 2015) argue that a high level of uncer- market uncertainty can increase the firm-specific invest- tainty weakens firms’ response of irreversible investment to ments of Chinese firms. Hence, we hypothesize as follows: demand uncertainty. Baum et al. (2008) document a negative effect of CAPM-based and firm-specific uncertainties on Hypothesis 2: Market uncertainty positively affects cor- investment, whereas market-based uncertainty has a stimu- porate investment. lating impact. They also show that investment is more affected by firm-specific uncertainty than market-based Although we have argued that market and firm-specific uncertainty. Moreover, Rashid (2011) reports the significant uncertainties increase investment, we assume that invest- negative effects of both forms of uncertainty on private ments decrease because of CAPM-based uncertainty. It is firms’ investment. Rashid and Saeed (2017) find that because prior studies have reported that CAPM-based uncer- Pakistani firms decrease investments when they face a high tainty negatively affects investment. Baum et al. (2008) pro- market or firm-related uncertainty. Khan et al. (2019) indi- vide evidence for the negative effect of CAPM-based cate a negative influence of economic policy and CAPM uncertainty on U.S. firm’s investment. Baum et al. (2010) uncertainties on Chinese firms’ investment behavior. Xu also report that manufacturing firms in the United States et al. (2010) present a negative influence of uncertainty on decrease their investment when the CAPM-based uncer- Chinese firms’ investment and further show that this effect is tainty increases. Dixit and Pindyck (1994) determine that positively moderated by government control. uncertainty can significantly influence firms’ investment Hartman (1972) considers the convex function of the mar- decisions, particularly in a situation where the substantial ginal product of capital and explains that firms make substan- sunk cost is involved in fixed capital investments. Firms tial investments in response to high uncertainty. Caballero have a low likelihood to invest in an uncertain environment (1991) uses market structure and returns to scale as bases to under irreversibility, thereby possibly increasing sunk costs. explain that under the assumptions of constant returns to scale Therefore, uncertainty in the presence of irreversibility will production technology and perfect competition, the convex reduce capital formation. Thus, we consider real options the- function of the marginal product of capital leads to a positive ory and the prior literature and argue that CAPM-based association between uncertainty and investment. Ma (2015) uncertainty has a negative influence on the capital invest- reports that Chinese ownership and exchange rate costs are ment of Chinese firms. Accordingly, we propose the follow- positively associated with the investment behavior of the ing hypothesis: 4 SAGE Open Hypothesis 3: CAPM-based uncertainty negatively which can mitigate the impact of uncertainty. Therefore, we affects corporate investment. expect that firm-specific uncertainty is less likely to influ- ence SOEs than other firms. Accordingly, we hypothesize as We also assume that Chinese firms reduce investments when follows: there is high policy uncertainty. Prior research has shown that policy uncertainty leads to a decline in firms’ capital Hypothesis 5a: The positive association between firm- investment (Baker et al., 2016; Bhattacharya et al., 2017; specific uncertainty and corporate investment is weak for Gulen & Ion, 2015; Julio & Yook, 2012). Gulen and Ion SOEs. (2015) find that uncertainty in policy reduces investments, and this impact is more prominent for financially constrained SOEs may obtain resources and benefits from the govern- firms and those operating in less competitive industries. ment when a high market uncertainty exists. Wang et al. Kang et al. (2014) indicate that policy uncertainty reduces (2017) report that macroeconomic uncertainty negatively investments. Governments in developing countries signifi- influences R&D investment for Chinese firms. The afore- cantly influence economic activities, which ultimately affect mentioned study shows that market-based shocks have a share performance, financing choices, and firm value (Firth mitigating effect on firms without political connections and et al., 2013). The Chinese government also intervenes in eco- has no effect on politically connected firms. Moreover, they nomic activities and plays a key role. When economic policy argue that when market uncertainty is high, firms with politi- uncertainty exists, firms have no adequate information on cal connections have considerable opportunities and advan- changes in government policies. In particular, firms have no tages to obtain resources from the government, thereby information on the main direction of industrial development mitigating the influence of uncertainty. However, non-SOEs in the future or which industry the government will support. are strongly affected by market uncertainty owing to mini- In this situation, when a high policy uncertainty exists, firms mal political connections. Therefore, the influence of market bear the risk of irreversible investment in intangible assets uncertainty is strong for non-SOEs. Accordingly, we hypoth- and choose the option of waiting to invest (Bhattacharya esize that: et al., 2017). Therefore, we consider the preceding discus- sion and argue that economic policy uncertainty can Hypothesis 5b: The positive association between market adversely influence investment. Thus, we hypothesize that: uncertainty and corporate investment is weak for SOEs. Hypothesis 4: Economic policy uncertainty negatively Baum et al. (2008) indicate that the U.S. manufacturing affects corporate investment. firms’ investment is adversely affected by the CAPM-based uncertainty. However, they report a stimulating effect of We expect that uncertainty plays an essential role in firms’ CAPM-based uncertainty in interaction with cash flow. investment decisions operating in transition economies. Given that CAPM-based uncertainty is the interaction Uncertainties can influence firms from many aspects and are between market and firm-related uncertainties, the previous relative shocks for the firms. However, SOEs in the transi- literature has shown that non-SOEs’ investment is more tion economy have never faced demand uncertainty, and they influenced by firm-specific and market-based uncertainties are not affected by uncertainty in factor markets in a transi- compared with that of SOEs. Consistent with our prior argu- tion economy. Bo and Zhang (2002) find an insignificant ments, we argue that the investment behavior of non-SOEs influence of demand and supply uncertainties on investment compared with that of SOEs is more influenced by CAPM- for state enterprises of the Chinese machinery industry, based uncertainty. Thus, we hypothesize as follows: whereas the investment of collective enterprises is positively affected by labor cost uncertainty. The aforementioned Hypothesis 5c: The negative association between CAPM- research finds no evidence supporting accelerator theory of based uncertainty and corporate investment is weak for investment for their sample firms. Khan et al. (2019) deter- SOEs. mine that the influence of firm-specific uncertainty is strong for non-SOEs. Government intervention is common in transition economies. Managers of SOEs are partially autonomous in making The investment behavior of SOEs is “pro-policy” because of investment decisions because the contract responsibility sys- their relationship with the government. That is, when the pol- tem links many of these enterprises with the government. icy aims to stimulate the economy, SOEs increase their When high firm-specific uncertainty exists, SOEs can obtain investments and vice versa. Wang et al. (2014) show that additional resources on the basis of government ownership firms lower their investments as the economic policy uncer- (Jebran et al., 2019). By contrast, non-SOEs are highly tainty increases, and this impact is comparatively strong for dependent on their internal resources to compete in the mar- SOEs. Wang et al. (2017) find that the policy uncertainty ket. The government can provide privileges, such as subsi- impact on R&D investment is negative for Chinese firms. dies, tax incentives, and favorable loan policies, to SOEs, They further report that policy uncertainty only influences Khan et al. 5 Figure 1. Theoretical model. R&D of firms with political connections but has no impact on variables as instruments to use a two-step robust system gen- nonpolitically connected firms. Morck et al. (2013) report eralized method of moments (GMM) model. After the initial that compared with non-SOEs, the investment behavior of screening, the final sample comprised 17,258 observations SOEs in China considerably affected by changes in economic (7,738 for SOEs and 9,520 for non-SOEs) for 1,791 firms (561 policies. Hence, we hypothesize as follows: for SOEs and 1,230 for non-SOEs). All variables are win- sorized at the upper and lower one-percentile to control the Hypothesis 5d: The negative association between eco- potential effects of outliers. nomic policy uncertainty and corporate investment is weak for non-SOEs. Variables Measurement We present the aforementioned hypotheses in a theoretical Measuring corporate investment (Inv). We follow Ding et al. model, assuming that CAPM-based, market-based, firm-spe- (2016) and An et al. (2016) and define investment as firms’ cific, and economic policy uncertainties affect the Chinese current year’s net fixed assets, minus the previous year’s net firms’ investment behavior, and this relationship may differ fixed assets, plus the current year’s depreciation, and scaled for companies of different nature of ownership and is shown by the previous year’s total assets. in Figure 1. Measuring firm-specific uncertainty (η). We follow prior stud- ies (Baum et al., 2008; Shaoping, 2008) and measure the Data, Variables Measurement, firm-specific uncertainty by estimating the variance of firms’ and Descriptive Statistics daily stock return for each year. The use of variance of stock returns as a measure of uncertainty is based on the presump- Data tion that stock prices contain information that correspond to This study initially considered all A-share Chinese listed firms’ underlying fundamentals. Investors perceive firms’ firms during the period 1999–2016. We categorized the sam- overall environment by the stock return. Therefore, the vola- ple into SOEs and non-SOEs to empirically test uncertainty tility of stock returns can be used to measure the and investment association. The financial data were obtained uncertainty. from the China Stock Market and Accounting Research Database (CSMAR). Measuring market-based uncertainty (ε). We follow Wang We only considered all nonfinancial firms and excluded et al. (2017) and Baum et al. (2009) and use the GARCH financial firms. Furthermore, we excluded the data for missing model to proxy for market uncertainty. Market uncertainty is observations on control variables. We included data for com- measured using the conditional variance attained from the panies with at least three consecutive years for accurate calcu- estimation of the GARCH model for the stock market return lation of uncertainty and to appropriately use the endogenous of Chinese publicly traded firms. 6 SAGE Open Table 1. Descriptive Statistics. Firms Statistics Inv η ε ν epu Cf Lev Tobin’s Q Sg Size Full sample Observations 17,258 17,258 17,258 17,258 17,258 17,258 17,258 17,258 17,258 17,258 Mean 0.0663 0.0965 0.0169 1.1560 155.9901 0.0565 0.4484 3.1718 0.1243 21.6520 P25 0.0097 0.0293 –0.4344 0.9779 98.8882 0.0331 0.2874 1.5817 –0.0157 20.8543 Median 0.0346 0.0433 –0.0774 1.1446 124.3563 0.0568 0.4442 2.3195 0.1184 21.5163 P75 0.0884 0.0684 0.3980 1.3201 181.2867 0.0866 0.5976 3.7036 0.2609 22.2644 SD 0.1043 0.2688 0.6450 0.2790 82.8965 0.0646 0.2175 2.6561 0.3414 1.1489 SOEs Observations 7,738 7,738 7,738 7,738 7,738 7,738 7,738 7,738 7,738 7,738 Mean 0.0721 0.1044 0.0288 1.1076 142.2919 0.0571 0.4961 2.5467 0.1229 22.0055 P25 0.0108 0.0269 –0.3715 0.9644 83.5514 0.0322 0.3515 1.4030 –0.0102 21.1173 Median 0.0364 0.0403 –0.2688 1.1182 123.6349 0.0552 0.4980 1.9409 0.1138 21.8255 P75 0.0922 0.0659 0.3980 1.2581 179.0405 0.0866 0.6379 2.9665 0.2517 22.7176 SD 0.1130 0.2988 0.7006 0.2318 76.2114 0.0617 0.2012 1.9508 0.3038 1.2398 Non-SOEs Observations 9,520 9,520 9,520 9,520 9,520 9,520 9,520 9,520 9,520 9,520 Mean 0.0616 0.0901 0.0073 1.1954 167.1243 0.0561 0.4096 3.6799 0.1254 21.3648 P25 0.0089 0.0313 –0.4344 0.9916 113.8974 0.0338 0.2381 1.8067 –0.0207 20.7142 Median 0.0333 0.0454 –0.0774 1.1718 127.6239 0.0582 0.3990 2.6976 0.1231 21.2866 P75 0.0854 0.0702 0.2349 1.4037 181.2867 0.0867 0.5532 4.3816 0.2690 21.9386 SD 0.0963 0.2414 0.5959 0.3066 86.3745 0.0669 0.2224 3.0201 0.3691 0.9796 Mean t-statistics –6.5929*** –3.4862*** –2.1815** 20.8190*** 19.7917*** –1.0735 –26.5067*** 28.5213*** 0.4922 –37.9192*** difference test Note. This table presents the descriptive statistics and the estimates for the mean-difference test. Inv = investment; η = firm-specific uncertainty; ε = market-based uncertainty; ν = CAPM-based uncertainty; epu = economic policy uncertainty; Cf = cash flow ratio; Lev = leverage, Tobin’s Q ratio; Sg = sales growth; Size = firm size; SOEs = state-owned enterprises. Significance level at 1% and 5% are represented by *** and **, respectively. Measuring CAPM-based uncertainty (ν). To quantify the more than non-SOEs possibly because the former is sup- CAPM-based uncertainty, we follow prior studies (Baum ported by the government. For the uncertainty measures, we et al., 2010; Leahy & Whitcd, 1996) and estimate the risk of find a significant difference among the mean values of mar- an individual firm by using the covariance between firms’ ket uncertainty, firm-specific uncertainty, and economic pol- daily stock returns and the value-weighted index of the icy uncertainty for SOEs and non-SOEs in the sample period. Shanghai Stock Exchange (SSE)/The Shenzhen Stock Compared with non-SOEs, SOEs have high average values Exchange (SZSE). and high variation for firm-specific and market uncertainty. For other uncertainties, we obtain high mean values and high Measuring economic policy uncertainty (epu). Baker et al. (2016) variabilities for non-SOEs. These figures indicate different develop an index (i.e., BBD index) to measure the economic levels of uncertainties for both types of firms, even within policy uncertainty for the United States. Their index for eco- the same industry. nomic policy uncertainty has been used in many studies and We further perform t-test to examine the significant dif- found to be a suitable proxy for the real economic policy ferences between SOEs and non-SOEs. The estimates for the uncertainty (Bloom et al., 2018; Dibiasi et al., 2018; Leduc & t-test are reported in Table 1, which shows that the t-statistics Liu, 2016). Following the same methodology, they constructed are significant for all variables (except cash flow and sales epu indices for other countries, which include Canada, Austra- growth). These findings suggest that the statistics of our lia, Europe, India, and China. The current study also opts for main variables (e.g., investment and various uncertainties) this index as a proxy for economic policy uncertainty. significantly vary across SOEs and non-SOEs. Descriptive Statistics Econometric Model Table 1 reports the descriptive statistics for the full sample, Baseline Model SOEs, and non-SOEs. The mean (median) value of invest- We use the following regression equation to examine the ment for the full sample is 6.63% (3.46%). We observe from association between different forms of uncertainty and the mean value of investment of SOEs (non-SOEs) approxi- investment: mately 7.21% (6.16%). This result suggests that SOEs invest Khan et al. 7 Inv =+ ββ Inv + βη + ββ  + ν () () () () InvI =+ ββ nv + ββ Cf + ηβ + Cf it 01 2 3 4 () () () it −− 1 it 1 it −− 1 it 1 it 01 it −12 3 4 it −− 1 it 1 it −1 + β () epu + ββ () Cf + () Lev 5 6 7 × η + + ββ  + Cf × + βν iit −− 1 it 1 it −1 () () () () () 5 6 7 it −1 it −− 1 it 11 it −− it 1 + ββ Tobins ′ QS + g + β Size ++ ff + () () () (2) 8 9 1 10 it it it −− 1 it 1 it −1 + βν Cf × + ββ epuC + f () () () () 8 9 10 it −− 1 it 1 1 it −− 1 it 1 (1) × epuC +∑ β ontrol + . () iiti −1 t it −1 where i denotes firm, t denotes time, and Inv is the invest- We examine the indirect effects of firm-specific, market- ment. We include the first lagged investment in our model based, CAPM-based, and economic policy uncertainties on because it significantly affects the current investment (Bloom the investment of SOEs and non-SOEs by investigating the et al., 2007). Moreover, η, ε, ν, and epu represent the firm- significance of β , β , β , and β , respectively. The signifi- specific, market-based, CAPM-based, and economic policy 4 6 8 10 cance level of these coefficients shows that different forms of uncertainties, respectively. Cf represents the cash flow ratio, uncertainties affect firms’ investment with the change in the computed as the ratio of net profits and depreciation to total level of firms’ cash flow. assets (Cleary, 2005; Lima Crisóstomo et al., 2014; Phan, 2018). Lev denotes the leverage of a firm, measured as the ratio of total liabilities to total assets (Bai et al., 2014; Chow Estimation Technique et al., 2018). Tobin’s Q is the ratio of the sum of the market To estimate the preceding models, we use the dynamic panel value of equity and total liabilities to lagged total assets data (DPD) approach to control the problem of endogeneity. (Wang et al., 2017). Sg stands for the growth of sales mea- Given that we jointly determine firms’ investment decision sured as the log of the first difference of total sales during a with cash flow and leverage, reverse causality is likely to year (Pukthuanthong et al., 2013; Rashid & Saeed, 2017). occur because investment may also affect the leverage and Size denotes firms’ size in terms of total assets (Bai et al., cash flow of firms or the uncertainty, thereby possibly affect- 2014; Chow et al., 2018). f and f represent the industry and i t ing firms’ leverage and cash flow. Therefore, we use a two- time fixed-effect, respectively. Finally,  denotes the error it step robust system GMM technique to minimize endogeneity term. In this model, the coefficients β , ββ ,, and β are 2 34 5 issues and consider the panel nature of our data (Arellano & of primary interest. That is, we expect ββ and to be posi- Bover, 1995; Blundell & Bond, 1998; Roodman, 2006). To tive, while ββ and are negative. control for the industry and time effects, the system GMM technique enables us to combine the level equation of vari- Differential Effects of Uncertainty on able with the equation in differences of variables as we use Investment for SOEs and Non-SOEs the lags of variables and the lags of first difference as instru- ments. We include time and industry dummies in all estima- We divide the full sample into SOEs and non-SOEs. Out of tions and use them as additional instruments. 1,791 firms, 561 are SOEs and 1,230 are non-SOEs. Thereafter, we estimate separate models for both groups to examine whether the impact of different types of uncertain- Empirical Findings ties on the investment behavior of SOEs is statistically dif- Baseline Model ferent from that of non-SOEs. We estimate Equation 1 separately for SOEs and non-SOEs. Table 2 presents the regression results for the baseline model (Equation 1). Column (1) reports the standard investment model, which includes the lagged dependent variable (Inv ) it−1 Indirect Effects of Uncertainty Through Cash and control variables. The coefficients for the lagged invest- Flow on Investment for SOEs and Non-SOEs ment and cash flow are significantly positive, which is consis- tent with Baum et al. (2010), Gulen and Ion (2015), and Julio Baum et al. (2010) empirically examine the link among and Yook (2012). Consistent with the literature (Ma, 2015; uncertainty, investment behavior, and cash flow of manufac- Wang et al., 2017), we find an adverse and highly significant turing firms in the United States. A high level of cash flow effect of leverage and firm size on investment. Moreover, the can stimulate or mitigate the investment activities of firms. coefficients for Tobin’s Q and Sg are not statistically different Given that the investment opportunities of a firm depend on from zero. Therefore, we find no evidence in support to the its financial condition and level of cash flow generated, the accelerator theory of investment, which corroborates with uncertainty will have an indirect effect through cash flow, in Baum et al. (2010) and Bo and Zhang (2002). Furthermore, addition to its direct impact on firms’ investment. This sec- diagnostic tests are provided at the end of each table. We tion empirically investigates the effects of various forms of apply the Arellano and Bond (1991) test under the null uncertainties on their own and in interaction with cash flow hypothesis that there is no serial correlation among the resid- on the investment of SOEs and non-SOEs. We include the uals. Moreover, m1 and m2 stand for the first- and second- interactions of cash flow with uncertainty measures as ordered serial correlations, respectively. We reject (accept) follows: 8 SAGE Open Table 2. Effect of Uncertainty on Investment in Full Sample. Variables (1) (2) (3) (4) (5) (6) (7) Inv 0.0323** 0.0320** 0.0466** 0.0007 0.0350* 0.0004 0.0608** t−1 (0.0142) (0.0161) (0.0230) (0.0174) (0.0203) (0.0187) (0.0257) ηt 0.0157** 0.0081* 0.0139** 0.0236** −1 (0.0078) (0.0042) (0.0057) (0.0097) εt 0.0868*** 0.0314** 0.0106** 0.0005 −1 (0.0315) (0.0130) (0.0049) (0.0062) νt –0.0358* –0.0029 −1 (0.0199) (0.0298) epu –0.0004*** –0.0002** t−1 (0.0001) (8.7e–05) Cf 0.1783** 0.2464*** 0.1885 0.1866** 0.6493*** –0.0534 0.8250*** t−1 (0.0823) (0.0892) (0.1521) (0.0825) (0.1333) (0.0806) (0.1920) Lev –0.0821*** –0.1243*** –0.0747** –0.1521*** –0.0714* –0.0147 –0.2266*** t−1 (0.0204) (0.0303) (0.0339) (0.0318) (0.0380) (0.0366) (0.0432) Tobin’s Q –0.0010 –0.0042*** –0.0041 0.0025 –0.0015 –0.0017 0.0099*** t−1 (0.0008) (0.0011) (0.0031) (0.0021) (0.0024) (0.0023) (0.0030) Sg 0.0155 –0.0067 0.0121 –0.0163 –0.0532 0.0527** –0.2165*** t−1 (0.0120) (0.0149) (0.0237) (0.0204) (0.0328) (0.0264) (0.0259) Size –0.0172** –0.0189*** 0.0114 –0.0024 –0.0141* 6.3e–05 0.0397*** t−1 (0.0075) (0.0028) (0.0127) (0.0066) (0.0075) (0.0116) (0.0055) Constant 0.4240** 0.5227*** –0.2689 0.2736 0.3886** 0.2048 –0.6898*** (0.1751) (0.0647) (0.2908) (0.1890) (0.1516) (0.3253) (0.1148) Industry Yes Yes Yes Yes Yes Yes Yes Year Yes Yes Yes Yes Yes Yes Yes Observations 15,803 15,803 15,803 15,803 15,803 15,803 15,803 Firms 1,791 1,791 1,791 1,791 1,791 1,791 1,791 Diagnostic tests m1 –16.95 –17.45 –17.17 –17.78 –17.67 –16.60 –14.96 p value .000 .000 .000 .000 .000 .000 .000 m2 1.34 0.99 1.07 0.93 0.58 0.31 0.92 p value .181 .323 .286 .353 .561 .757 .355 J-statistic 212.36 88.27 130.72 83.83 139.69 71.55 134.54 p value .261 .200 .166 .606 .175 .832 .142 Note. Standard errors are in parentheses. Inv = investment; η = firm-specific uncertainty; ε = market-based uncertainty; ν = CAPM-based uncertainty; epu = economic policy uncertainty; Cf = cash flow ratio; Lev = leverage, Tobin’s Q ratio; Sg = sales growth; Size = firm size. Significance level at 1%, 5%, and 10% are represented by ***, **, and *, respectively. the null hypothesis for m1 (m2). The J-statistic is the Hansen to show the same statistically significant and stimulating test of overidentifying restrictions, showing the validity of impact on investment. instruments used in the estimations. By considering the brev- Column (5) shows that the coefficient of firm-specific ity, we did not explain these tests in the discussion. uncertainty has increased again, whereas the coefficient of Columns (2) and (3) show that firm-specific and market- market uncertainty has decreased substantially. Both types of based uncertainties significantly positively affect investment uncertainties have the same positive and significant effect. (uncertainty coefficients are statistically significant at 5% and However, we can observe the negative influence of CAPM- 1%, respectively). These results corroborate the assumptions based uncertainty on firms’ investment. This result supports of Hartman (1972) and Caballero (1991) and the prior litera- real options theory. Hence, Hypothesis 3 is supported. ture (Shaoping, 2008; Xu et al., 2010), which argued that In Column (6), we find that economic policy uncertainty more the risk, the more the investment and therefore, higher reduces investment. This result is consistent with Wang et al. reinvestment. Thus, Hypotheses 1 and 2 are supported. (2014) and Kang et al. (2014) and supports theory of irre- In Column (4), we include firm-specific and market-based versible investment and the option value of waiting to invest uncertainties in the same equation. Although the coefficients (Bernanke, 1983; Dixit & Pindyck, 1994). Thus, Hypothesis for both forms of uncertainties have decreased, they continue 4 is confirmed. Khan et al. 9 Table 3. Effect of Uncertainty on Investment in State-Owned Enterprises. Variables (1) (2) (3) (4) (5) (6) (7) Inv 0.0431** 0.0470** 0.0380* 0.0404* 0.0379* –0.2807 0.0955 t−1 (0.0186) (0.0196) (0.0202) (0.0213) (0.0216) (0.1975) (0.1299) ηt 0.0079 0.0035 0.0028 0.0041 −1 (0.0096) (0.0094) (0.0094) (0.0089) εt –0.0033 –0.0030 –0.0023 –0.0191 −1 (0.0025) (0.0027) (0.0027) (0.0119) νt 0.0236 0.0147 −1 (0.0176) (0.0544) epu –0.0004** –0.0004*** t−1 (0.0002) (0.0001) Cf 0.4936*** 0.4925*** 0.5799*** 0.5719*** 0.5778*** 0.5338* 0.6967*** t−1 (0.1065) (0.1067) (0.1770) (0.1785) (0.1802) (0.3080) (0.2223) Lev 0.0471 0.0458 0.0271 0.0247 0.0248 –0.1122 0.0074 t−1 (0.0397) (0.0397) (0.0441) (0.0452) (0.0457) (0.1880) (0.0725) Tobin’s Q –0.0021 –0.0023 –0.0006 –0.0009 –0.0009 0.0160** 0.0130** t−1 (0.0019) (0.0020) (0.0027) (0.0028) (0.0028) (0.0072) (0.0064) Sg –0.0179 –0.0222 –0.0186 –0.0195 –0.0172 –0.0626 –0.2914*** t−1 (0.0150) (0.0160) (0.0203) (0.0205) (0.0207) (0.0596) (0.0380) Size –0.0180*** –0.0180*** –0.0182*** –0.0181*** –0.0197*** 0.0524** 0.0371*** t−1 (0.0039) (0.0039) (0.0039) (0.0041) (0.0042) (0.0228) (0.0130) Constant 0.4107*** 0.4113*** 0.4184*** 0.4176*** 0.4263*** –0.5607 –0.6086** (0.0736) (0.0729) (0.0735) (0.0741) (0.0742) (0.4904) (0.2765) Industry Yes Yes Yes Yes Yes Yes Yes Year Yes Yes Yes Yes Yes Yes Yes Observations 7,389 7,389 7,389 7,389 7,389 7,389 7,389 Firms 561 561 561 561 561 561 561 Diagnostic tests m1 –12.37 –12.35 –12.55 –12.53 –12.54 –1.86 –5.31 p value .000 .000 .000 .000 .000 .063 .000 m2 1.34 1.42 1.31 1.35 1.28 –1.01 0.83 p value .179 .156 .189 .176 .202 .315 .408 J-statistic 189.91 188.95 142.85 142.78 139.82 51.18 151.44 p value .292 .291 .285 .266 .304 .277 .107 Coefficients differences test (χ ) SOEs non-SOEs η = 14.47*** 14.15*** 7.78*** 9.26*** SOEs non-SOEs ε = 14.97*** 11.17*** 7.64*** 8.93*** SOEs non-SOEs ν = 12.11*** 9.44*** SOEs non-SOEs epu = 17.02*** 7.84*** Note. Standard errors are in parentheses. Inv = investment; η = firm-specific uncertainty; ε = market-based uncertainty; ν = CAPM-based uncertainty; epu = economic policy uncertainty; Cf = cash flow ratio; Lev = leverage, Tobin’s Q ratio; Sg = sales growth; Size = firm size. Significance level at 1%, 5%, and 10% are represented by ***, **, and *, respectively. In the last column of Table 2, we combine all forms of Chinese manufacturing firms decrease their investment when uncertainties in our investment model and find that the coef- epu increases. ficient of firm-specific uncertainty has increased and remains significant at 5%. This result suggests that firms’ investment Differential Effects of Uncertainty on increases with an increase in firm-specific uncertainty. The Investment for SOEs and Non-SOEs signs of market-based and CAPM-based uncertainties remain the same. However, their coefficients are not statistically sig- Tables 3 reports the results of the uncertainties and invest- nificant. The coefficient of economic policy uncertainty has ment association for SOEs. Column (1) presents similar decreased in absolute value but remains negative and statisti- results to those in Table 2 in terms of signs and significance cally significant at 5%. This result supports the claim that levels for the lagged investment and control variables. The 10 SAGE Open exception is leverage, which is not different from zero. economic policy uncertainty, have no significant effects on Columns (2) and (3) show no significant effect of firm- SOEs’ investment. We likewise consider cash flow in isola- specific and market-based uncertainties on corporate invest- tion only and not in interaction with uncertainties terms. ment, respectively. The coefficients of firm-specific and Table 5 presents the estimates for Equation 2 by adding an market-based uncertainties remain statistically insignificant interaction term of cash flow with each form of uncertainty. if we combine them in Columns (4) and (5), along with the Column (1) shows an insignificant coefficient of firm-spe- CAPM-based uncertainty. The results indicate that managers cific uncertainty in isolation, while it has an exacerbating of SOEs have no incentives to react to uncertainty, which influence with cash flow interaction. This result indicates may be caused by the contract responsibility system between that cash flow enhances the association between investment firms and the government. Column (6) shows that economic and firm-specific uncertainty. policy uncertainty reduces SOEs’ investments. Column (7) Column (2) presents the findings for market-based uncer- shows no significant change in terms of the sign and signifi- tainty and its interaction term. The coefficient of market- cance level of uncertainties, thereby indicating that except based uncertainty is insignificant, while the interaction term economic policy uncertainty, no other forms of uncertainty appears to have a significant negative influence on invest- influence SOEs’ investment. ment. The results show that cash flow can decrease the effect Thereafter, we apply the Chow test to investigate the sta- of market-based uncertainty on investment. This may sug- tistical difference in the coefficients of uncertainties across gest that managers of SOEs are considerably cautious in SOEs and non-SOEs. The results reported in Table 3 show highly uncertain market, as pointed out by prior studies, such significant chi-square values for all uncertainty coefficients. as Bloom et al. (2007) and Baum et al. (2010). The findings confirm that the coefficients of uncertainties The signs and significance of the market- and firm-spe- across SOEs and non-SOEs significantly vary. cific uncertainties, along with their interactions, remain Table 4 presents the findings for non-SOEs. The results in unchanged when we combine them in Column (3). However, Column (1) are consistent with the full sample. Furthermore, the coefficients of CAPM-based uncertainty and interaction Columns (2) and (3) show that firm-specific and market- of the CAPM-based uncertainty in Column (4) are not sig- based uncertainties positively affect investment. From these nificantly different from zero. These results are consistent results, we can infer that non-SOEs’ investment behavior with the findings in Table 3 and suggest that market, CAPM, appears to be risk-taking by investing substantially in a high and firm-specific uncertainties (in isolation) have no mean- degree of uncertainty. These findings support Hypotheses 5a ingful impact on SOEs’ investment. This result may be and 5b. The coefficient of firm-specific uncertainty becomes caused by the contract responsibility system, and managers insignificant when we combine both forms of uncertainties of SOEs do not show risk-taking behavior in making deci- in Column (4), and with CAPM-based uncertainty in Column sions to invest. (5). These coefficients indicate that firm-specific uncertainty In Column (5), we determine that economic policy uncer- has no significant impact on non-SOEs’ investment, whereas tainty reduces SOEs’ investment. However, the coefficient of market-based uncertainty has a stimulative impact, with a the interaction term is significantly positive, which implies negative effect of CAPM-based uncertainty. This result sup- that cash flow mitigates the influence of economic policy ports Hypothesis 5c. When comparing the results of Column uncertainty. These findings are consistent with those of Wang (6) in Table 4 with those of Table 3, we determine that both et al. (2014). types of firms are negatively influenced by economic policy Column (6) provides the results for the combined impact uncertainty. However, the coefficient is considerably low in of uncertainties and their interaction with cash flow. The absolute value for non-SOEs, thereby indicating that the signs and significance remains the same for economic policy investment behavior of non-SOEs is minimally influenced uncertainty and its interaction term, whereas all other forms by economic policy uncertainty. Our findings corroborate of uncertainties and interactions become insignificant. those of Wang et al. (2014) and Wang et al. (2017) and sup- Table 6 presents the indirect effect of various forms of port the theory of irreversible investment. This result verifies uncertainties through cash flow on non-SOEs’ investment Hypothesis 5d. In Column (7), we find that the coefficient of behavior. Column (1) shows the exacerbating and significant epu is the same in sign and value, whereas the coefficients of effect of the firm-specific uncertainty and cash flow in isola- all other forms of uncertainties become statistically insignifi- tion, with no meaningful interaction term. Column (2) pres- cant. These findings show that when non-SOEs face epu, ents the significant coefficients of market-based uncertainty their risk-taking behavior changes, thereby resulting in a and its interaction with cash flow. The negative coefficient for reduction of their investment in uncertain environments. the interaction term implies that cash flow weakens the impact of market-based uncertainty. The signs and significance of the market-based uncertainty and its interaction term remain Indirect Effects of Uncertainty: Does Uncertainty unchanged in Column (3), while the coefficients of firm-spe- Affect the Firm’s Investment Through Cash Flow? cific uncertainty and its interaction term are not statistically Table 3 shows that cash flow has a significantly positive significant. In Column (4), we include the CAPM-based coefficient, whereas all forms of uncertainties, except for uncertainty and its interaction with cash flow. The main effect Khan et al. 11 Table 4. Effect of Uncertainty on Investment in Non-State-Owned Enterprises. Variables (1) (2) (3) (4) (5) (6) (7) Inv 0.0537*** 0.0515*** 0.0414* 0.0365 0.0497** 0.1964 0.0624** t−1 (0.0180) (0.0176) (0.0230) (0.0231) (0.0250) (0.1474) (0.0266) ηt 0.0079** 0.0116 –0.0008 0.0128 −1 (0.0039) (0.0118) (0.0065) (0.0115) εt 0.1009*** 0.0998*** 0.0195*** 0.0011 −1 (0.0387) (0.0384) (0.0075) (0.0072) νt –0.0671*** 0.0071 −1 (0.0216) (0.0265) epu –0.0002** –0.0002** t−1 (8.1e–05) (8.6e–05) Cf 0.2629*** 0.1946*** 0.0059 –0.0070 0.4744*** 0.7599*** 0.5636*** t−1 (0.1014) (0.0693) (0.1085) (0.1104) (0.1648) (0.2247) (0.1619) Lev –0.0683*** –0.0839*** –0.0772*** –0.0819*** –0.0979** –0.2277*** –0.2057*** t−1 (0.0238) (0.0180) (0.0289) (0.0292) (0.0388) (0.0532) (0.0372) Tobin’s Q 3.2e–05 –0.0002 0.0032 0.0039 –0.0036 0.0076** 0.0068** t−1 (0.0011) (0.0007) (0.0029) (0.0030) (0.0028) (0.0035) (0.0031) Sg –0.0115 –0.0141 0.0071 0.0078 –0.0214 –0.1590*** –0.1215*** t−1 (0.0130) (0.0128) (0.0234) (0.0237) (0.0297) (0.0334) (0.0204) Size –0.0252*** –0.0281*** 0.0078 0.0102 –0.0472*** 0.0337*** 0.0346*** t−1 (0.0064) (0.0048) (0.0135) (0.0139) (0.0100) (0.0076) (0.0056) Constant 0.6006*** 0.6695*** –0.2319 –0.2824 1.1098*** –0.5673*** –0.6588*** (0.1255) (0.0947) (0.3060) (0.3133) (0.2082) (0.1565) (0.1095) Industry Yes Yes Yes Yes Yes Yes Yes Year Yes Yes Yes Yes Yes Yes Yes Observations 8,414 8,414 8,414 8,414 8,414 8,414 8,414 Firms 1,230 1,230 1,230 1,230 1,230 1,230 1,230 Diagnostic tests m1 –11.93 –11.78 11.56 –11.49 –12.10 –5.41 –11.88 p value .000 .000 .000 .000 .000 .000 .000 m2 0.48 0.43 0.44 0.46 –0.05 0.90 0.76 p value .633 .666 .660 .649 .962 .370 .445 J-statistic 205.29 268.95 128.28 130.72 133.01 88.20 140.67 p value .146 .121 .205 .150 .317 .356 .308 Note. Standard errors are in parentheses. Inv = investment; η = firm-specific uncertainty; ε = market-based uncertainty; ν = CAPM-based uncertainty; epu = economic policy uncertainty; Cf = cash flow ratio; Lev = leverage, Tobin’s Q ratio; Sg = sales growth; Size = firm size. Significance level at 1%, 5%, and 10% are represented by ***, **, and *, respectively. is significantly negative, whereas the interaction term coeffi- uncertainty and market-based uncertainty in isolation or cient was significantly positive, thereby implying that cash through cash flow. flow weakens the effect of the CAPM-based uncertainty. However, the positive effects of market and firm-related Robustness Tests uncertainties remain consistent, whereas their interaction terms lack significance. Column (5) shows that non-SOEs’ We conducted several robustness checks for the validity of our investment is not influenced by economic policy uncertainty results. First, we use an additional measure of investment, in isolation or through cash flow. Column (6) presents the which is defined as the ratio of expenditures on the purchase of combined impact of all uncertainties and their interaction fixed tangible assets during a year to total assets. Table 7 pres- terms. Firm-specific uncertainty has a significant positive ents the results. We obtain consistent results in terms of sign impact, with no meaningful interaction term. The signs and and significance for coefficients of variables to those reported significance of the CAPM-based uncertainty and its interac- in Table 2. tion remain unchanged. However, results show that non- Second, we use an alternative measure for firm-specific SOEs’ investment is not influenced by economic policy uncertainty (Equation 3), which is defined as the first-order 12 SAGE Open Table 5. Indirect Effect of Uncertainty Through Cash Flow on Investment in State-Owned Enterprises. Variables (1) (2) (3) (4) (5) (6) Inv 0.0595*** 0.0318* 0.0467** 0.0446** 0.0185 0.2128 t−1 (0.0197) (0.0176) (0.0200) (0.0203) (0.0235) (0.1682) Cf 0.3429*** 0.4217*** 0.4240*** 0.8353** 0.0732 0.4228 t−1 (0.0929) (0.0818) (0.0959) (0.3723) (0.2477) (0.7635) ηt –0.0241 –0.0310 –0.0224 0.0212 −1 (0.0194) (0.0208) (0.0182) (0.0343) Cf × ηt 0.4955* 0.5149* 0.4549* –0.1042 −1 (0.2597) (0.2749) (0.2541) (0.5440) εt 0.00247 0.0032 0.0024 0.0004 −1 (0.0034) (0.0037) (0.0038) (0.0112) Cf × εt –0.1027** –0.1001* –0.0958* 0.0067 −1 (0.0522) (0.0592) (0.0573) (0.1727) νt 0.0120 0.0270 −1 (0.0229) (0.0371) Cf × νt –0.4046 –0.6245 −1 (0.3340) (0.6216) epu –0.0002* –0.0003* t−1 (0.0001) (0.0002) Cf × epu 0.0041** 0.0061** t−1 (0.0019) (0.0029) Lev 0.0256 0.0122 0.0059 –0.0036 –0.0420 –0.0169 t−1 (0.0322) (0.0403) (0.0337) (0.0319) (0.0442) (0.0656) Tobin’s Q –0.0025 0.0008 –0.0003 0.0006 –0.0005 –0.0007 t−1 (0.0017) (0.0019) (0.0025) (0.0023) (0.0018) (0.0024) Sg –0.0151 –0.0041 –0.0089 –0.0109 –0.0077 –0.0190 t−1 (0.0149) (0.0142) (0.0158) (0.0154) (0.0232) (0.0358) Size –0.0173*** –0.0141*** –0.0132*** –0.0106*** –0.0102 –0.0189** t−1 (0.0033) (0.0034) (0.0032) (0.0031) (0.0074) (0.0087) Constant 0.4149*** 0.3387*** 0.3246*** 0.2595*** 0.3079** 0.4515** (0.0643) (0.0609) (0.0617) (0.0603) (0.1498) (0.1790) Industry Yes Yes Yes Yes Yes Yes Year Yes Yes Yes Yes Yes Yes Observations 7,389 7,389 7,389 7,389 7,389 7,389 Firms 561 561 561 561 561 561 Diagnostic tests m1 –12.40 –12.43 –12.51 –12.43 –12.55 –3.75 p value .000 .000 .000 .000 .000 .000 m2 1.57 1.46 1.40 1.50 0.51 1.21 p value .116 .144 .162 .134 .611 .226 J-statistic 261.20 284.64 264.35 282.41 127.56 66.46 p value .155 .151 .116 .162 .238 .139 Note. Standard errors are in parentheses. Inv = investment; η = firm-specific uncertainty; ε = market-based uncertainty; ν = CAPM-based uncertainty; epu = economic policy uncertainty; Cf = cash flow ratio; Lev = leverage, Tobin’s Q ratio; Sg = sales growth; Size = firm size. Significance level at 1%, 5%, and 10% are represented by ***, **, and *, respectively. autoregressive model AR(1) for total sales normalized by from the estimation of the AR(1) model of the total sales in a capital stock for each firm (Baum et al., 2016; Bo & Zhang, five-period rolling window with a minimum of 3 years of 2002; Caglayan & Rashid, 2014): residual data. Our findings reported in Table 8 remain con- sistent with those presented in Table 2. sales sales     Finally, we follow Caglayan and Rashid (2014) and =+ ββ + . (3)   01   it K K     Rashid (2011) and estimate the GARCH model by using the it it −1 annual data of the T-bills rate for the period 1997–2016 to The uncertainty proxy for each year is measured by calcu- measure market uncertainty. Our findings in Table 8 provide lating the moving standard deviation of the residuals obtained consistent results to our prior findings. Overall, the results of Khan et al. 13 Table 6. Indirect Effect of Uncertainty Through Cash Flow on Investment in Non-State-Owned Enterprises. Variables (1) (2) (3) (4) (5) (6) Inv 0.0723*** 0.0613*** 0.0478** 0.0458*** 0.0484** 0.0455* t−1 (0.0189) (0.0213) (0.0197) (0.0177) (0.0194) (0.0270) Cf 0.2218*** 0.0667 0.1373* –0.0429 0.2652** –0.5641 t−1 (0.0779) (0.0826) (0.0824) (0.2048) (0.1350) (0.5671) ηt 0.0196* 0.0015 0.0164* 0.0540* −1 (0.0111) (0.0085) (0.0086) (0.0316) Cf × ηt –0.1349 –0.0426 –0.1460 –0.6404 −1 (0.1617) (0.1130) (0.1262) (0.3953) εt 0.0984*** 0.0301** 0.0148* 0.0348 −1 (0.0320) (0.0136) (0.0079) (0.0432) Cf × εt –0.0748* –0.0805** –0.0199 –0.0810 −1 (0.0382) (0.0356) (0.0400) (0.0896) νt –0.0590*** –0.1722*** −1 (0.0164) (0.0468) Cf × νt 0.3167* 0.7983* −1 (0.1817) (0.4606) epu –1.6e–05 0.0003 t−1 (5.9e–05) (0.0004) Cf × epu 0.0004 0.0028 t−1 (0.0008) (0.0019) Lev –0.0791*** –0.0723*** –0.0743*** –0.0822*** –0.0616*** –0.1458** t−1 (0.0166) (0.0233) (0.0216) (0.0226) (0.0224) (0.0594) Tobin’s Q 0.0011 0.0013 0.0005 0.0001 –7.4e–05 0.0139*** t−1 (0.0011) (0.0020) (0.0015) (0.0014) (0.0012) (0.0032) Sg –0.0191 0.0084 0.0144 –0.0108 –0.0151 –0.0826*** t−1 (0.0118) (0.0179) (0.0167) (0.0125) (0.0136) (0.0305) Size –0.0227*** 0.0093 –0.0059 –0.0233*** –0.0260*** 0.0297*** t−1 (0.0032) (0.0089) (0.0058) (0.0056) (0.0059) (0.0073) Constant 0.5521*** –0.2530 0.1753 0.6066*** 0.6168*** –0.4784*** (0.0683) (0.2116) (0.1220) (0.1078) (0.1129) (0.1593) Industry Yes Yes Yes Yes Yes Yes Year Yes Yes Yes Yes Yes Yes Observations 8,414 8,414 8,414 8,414 8,414 8,414 Firms 1,230 1,230 1,230 1,230 1,230 1,230 Diagnostic tests m1 –11.67 –12.00 –11.79 –11.62 –11.83 –11.60 p value .000 .000 .000 .000 .000 .000 m2 0.99 0.79 0.40 0.30 0.35 0.21 p value .321 .430 .688 .763 .728 .833 J-statistic 319.26 167.14 203.35 326.24 223.35 116.97 p value .107 .417 .274 .240 .114 .284 Note. Standard errors are in parentheses. Inv = investment; η = firm-specific uncertainty; ε = market-based uncertainty; ν = CAPM-based uncertainty; epu = economic policy uncertainty; Cf = cash flow ratio; Lev = leverage, Tobin’s Q ratio; Sg = sales growth; Size = firm size. Significance level at 1%, 5%, and 10% are represented by ***, **, and *, respectively. these sections suggest that our main results are insensitive to 1999–2016. This study further tests whether the impact of alternative measures of investment and uncertainties. uncertainty on investment varies across SOEs and non- SOEs. Furthermore, the current research investigated the influence of cash flow on the uncertainty–investment rela- Conclusion tionship. By controlling for the time and industry-fixed This study investigates the impact of the four forms of uncer- effects and considering the potential endogeneity problem, tainties on corporate investment, using an unbalanced panel this study uses a robust two-step system GMM technique to data of 1,791 firms listed on the SSE/SZSE for the period of estimate the model. 14 SAGE Open Table 7. Robustness Check Using Alternative Measure of Investment in Full Sample. Variables (1) (2) (3) (4) (5) (6) Inv 0.7310*** 0.7154*** 0.7310*** 0.7335*** 0.6521*** 0.6586*** t−1(F.A/T.A) (0.0250) (0.0214) (0.0250) (0.0252) (0.0294) (0.0306) ηt 0.0079*** 0.0079*** 0.0082*** –0.0006 −1 (0.0029) (0.0029) (0.0030) (0.0129) εt 0.0092** 0.0198** 0.0220** –0.0034 −1 (0.0043) (0.0085) (0.0088) (0.0064) νt –0.0320** –0.0156 −1 (0.0153) (0.0241) epu –0.0002** –0.0002** t−1 (0.0001) (9.6e–05) Cf –0.0470 –0.1048 –0.0470 –0.0959 0.0213 0.0069 t−1 (0.0656) (0.0786) (0.0656) (0.0720) (0.0559) (0.0472) Lev –0.0328 –0.0438* –0.0328 –0.0668** 0.0178 0.0296 t−1 (0.0266) (0.0257) (0.0266) (0.0332) (0.0305) (0.0251) Tobin’s Q –0.0031** –0.0035** –0.0031** –0.0025* –0.0024 –0.0035*** t−1 (0.0014) (0.0015) (0.0014) (0.0014) (0.0017) (0.0011) Sg 0.0019 0.0139 0.0019 –0.0042 –0.0052 –0.0067 t−1 (0.0167) (0.0144) (0.0167) (0.0162) (0.0151) (0.0136) Size –0.0038 0.0070 –0.0038 –0.0027 0.0117 0.0085 t−1 (0.0053) (0.0069) (0.0053) (0.0054) (0.0106) (0.0085) Constant 0.1563 –0.0606 0.1563 0.1930 0.2069 0.1407 (0.1327) (0.1442) (0.1327) (0.1302) (0.3246) (0.2403) Industry Yes Yes Yes Yes Yes Yes Year Yes Yes Yes Yes Yes Yes Observations 15,803 15,803 15,803 15,803 15,803 15,803 Firms 1,791 1,791 1,791 1,791 1,791 1,791 Diagnostic tests m1 –17.21 –16.73 –17.21 –17.13 –15.91 –15.78 p value .000 .000 .000 .000 .000 .000 m2 –1.54 –1.41 –1.54 –1.47 –1.54 –1.50 p value .123 .158 .123 .142 .123 .133 J-statistic 100.10 164.18 100.10 94.10 69.41 109.40 p value .198 .331 .178 .283 .874 .498 Note. Standard errors are in parentheses. Inv = investment; η = firm-specific uncertainty; ε = market-based uncertainty; ν = CAPM-based uncertainty; epu = economic policy uncertainty; Cf = cash flow ratio; Lev = leverage, Tobin’s Q ratio; Sg = sales growth; Size = firm size. Significance level at 1%, 5%, and 10% are represented by ***, **, and *, respectively. The empirical findings indicate that market- and firm- on SOEs’ investment and mitigates the adverse effect of specific uncertainties (economic policy and CAPM-based CAPM-based uncertainty (economic policy uncertainty) on uncertainties) have a positive (negative) effect on corporate investment of non-SOEs (SOEs). Moreover, cash flow atten- investment. The findings further indicate that the investment uates the effects of market uncertainty on investment of decisions undertaken by SOEs do not respond to market- SOEs and non-SOEs. Thus, the findings signify that cash based, CAPM-based, and firm-specific uncertainties. By flow is an important factor that influences the uncertainty– contrast, non-SOEs respond to market-based and firm-spe- investment association. Our results remain robust, consider- cific uncertainties (CAPM-based uncertainty) positively ing the potential endogeneity problems, and alternative (negatively). The results provide evidence that economic proxies for investment, firm-specific, and market-based policy uncertainty has a negative impact on firms’ invest- uncertainties. ment. Moreover, the investment behavior of SOEs is more The findings would help firm managers, investors, and sensitive to economic policy uncertainty than that of non- policymakers to understand the uncertainty–investment SOEs. Furthermore, the results show that the influence of association in a transition economy, such as China. The cash flow on investment can be exacerbating or mitigating, results of this study facilitate an improved understanding of depending on the underlying uncertainty. In particular, the how different kinds of uncertainties affect the investment cash flow exacerbates the impact of firm-specific uncertainty behavior of Chinese SOEs and non-SOEs. Given that China Khan et al. 15 Table 8. Robustness Test Using Alternative Measure of Uncertainty in Full Sample. Variables (1) (2) (3) (4) (5) (6) Inv 0.1773* 0.0482*** 0.1439 0.0897 0.0545 0.0422* t−1 (0.1055) (0.0150) (0.1101) (0.0660) (0.0386) (0.0229) ηt 0.0486** 0.0442* 0.0458** 0.0505 −1(S.D_Sales) (0.0229) (0.0237) (0.0220) (0.1090) εt 0.0099** 0.0031 –0.0019 0.0114 −1(t-bill) (0.0050) (0.0065) (0.0042) (0.0099) νt –0.0113** 0.0271 −1 (0.0053) (0.0250) epu –0.0004*** –0.0001** t−1 (0.0001) (5.3e–05) Cf 0.1663 0.3985*** 0.3140* 0.2363*** 0.6010* 0.1389** t−1 (0.1632) (0.1202) (0.1791) (0.0695) (0.3551) (0.0651) Lev –0.0832 –0.0231 –0.1450** –0.0973*** –0.2819*** –0.0784 t−1 (0.0516) (0.0197) (0.0615) (0.0220) (0.1073) (0.0575) Tobin’s Q –0.0009 –0.0041*** –0.0002 –0.0004 0.0113* –0.0042** t−1 (0.0017) (0.0013) (0.0019) (0.0009) (0.0064) (0.0020) Sg –0.0115 0.0037 –0.0285 –0.0177 –0.2207*** –0.0431** t−1 (0.0185) (0.0134) (0.0214) (0.0129) (0.0335) (0.0193) Size –0.0164*** –0.0156*** 0.0066 0.0052 0.0523*** 0.0242*** t−1 (0.0041) (0.0030) (0.0095) (0.0034) (0.0108) (0.0038) Constant 0.4291*** 0.4030*** –0.0099 0.0115 –0.7855*** –0.6871*** (0.1069) (0.0663) (0.1879) (0.0674) (0.2644) (0.2403) Industry Yes Yes Yes Yes Yes Yes Year Yes Yes Yes Yes Yes Yes Observations 12,334 15,803 12,334 12,334 15,803 12,334 Firms 1,687 1,687 1,687 1,687 1,687 1,687 Diagnostic tests m1 –5.54 –17.79 –5.24 –7.74 –13.31 –15.26 p value .000 .000 .000 .000 .000 .000 m2 1.63 1.24 1.26 1.34 1.02 0.77 p value .104 .214 .207 .181 .310 .442 J-statistic 133.04 139.98 127.07 338.12 49.3 53.38 p value .179 .127 .247 .107 .174 .903 Note. Standard errors are in parentheses. Inv = investment; η = firm-specific uncertainty; ε = market-based uncertainty; ν = CAPM-based uncertainty; epu = economic policy uncertainty; Cf = cash flow ratio; Lev = leverage, Tobin’s Q ratio; Sg = sales growth; Size = firm size. Significance level at 1%, 5%, and 10% are represented by ***, **, and *, respectively. is moving from a command-based to a market-based econ- Acknowledgment omy, firms face substantial policy uncertainty, which nega- We are thankful to the editor and anonymous reviewers for many tively affects their investment behavior. Therefore, firms can constructive comments and suggestions. be encouraged to invest more by a significant reduction in policy uncertainty. Furthermore, a substantial cash flow can Declaration of Conflicting Interests mitigate the negative impact of CAPM-based and economic The author(s) declared no potential conflicts of interest with respect policy uncertainties. to the research, authorship, and/or publication of this article. The findings of this study are beneficial and informative with regard to the understanding of uncertainty–investment Funding relationship, and the role of cash flow in mitigating the nega- The author(s) disclosed receipt of the following financial support tive effect of uncertainty. 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SAGE Open – SAGE

Published: Jan 29, 2020

Keywords: uncertainty; investment; cash flow; state-owned enterprises; China

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