Regional digital finance and inefficient corporate investments: Empirical evidence from China

Nisar, Asad; Li, Haolin; Shah, Syed Sadaqat Ali; Rafique, Rabia Article Regional digital finance and inefficient corporate investments: Empirical evidence from China Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Nisar, Asad; Li, Haolin; Shah, Syed Sadaqat Ali; Rafique, Rabia (2024) : Regional digital finance and inefficient corporate investments: Empirical evidence from China, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 12, Iss. 1, pp. 1-17, https://doi.org/10.1080/23322039.2024.2390943 This Version is available at: https://hdl.handle.net/10419/321570 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Economics & Finance ISSN: 2332-2039 (Online) Journal homepage: www.tandfonline.com/journals/oaef20 Regional digital finance and inefficient corporate investments: Empirical evidence from China Asad Nisar, Haolin Li, Syed Sadaqat Ali Shah & Rabia Rafique To cite this article: Asad Nisar, Haolin Li, Syed Sadaqat Ali Shah & Rabia Rafique (2024) Regional digital finance and inefficient corporate investments: Empirical evidence from China, Cogent Economics & Finance, 12:1, 2390943, DOI: 10.1080/23322039.2024.2390943 To link to this article: https://doi.org/10.1080/23322039.2024.2390943 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group Published online: 19 Aug 2024. Submit your article to this journal Article views: 1024 View related articles View Crossmark data Citing articles: 2 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 FINANCIAL ECONOMICS | RESEARCH ARTICLE Regional digital finance and inefficient corporate investments: Empirical evidence from China Asad Nisar a , Haolin Li b , Syed Sadaqat Ali Shah a and Rabia Rafique a a School of Finance, Central University of Finance and Economics, Beijing, PR China; b Farmer School of Business, Miami University, Oxford, OH, USA ABSTRACT Using data of 1,457 Chinese A-share listed enterprises from 2012 to 2021, this study investigates the impact of regional digital finance development on inefficient corporate investments. Data for core explanatory variables, including Digital Finance and Breadth, were obtained from the Peking University Digital Finance Research Center, while firm-level variables’data were sourced from CSMAR. The baseline results show that regional digital finance development significantly reduces inefficient corporate investments in China. These findings are further supported by a series of robustness tests. Additionally, we identify two mechanisms through which regional digital finance development mitigates inefficient corporate investments: alleviating financing constraints and increasing cash flow circulation. The mitigation effects of digital finance are more pronounced in state-owned firms, firms with strong governance, firms located in western regions, firms in areas with a high degree of marketization, and firms in innovative and competitive industries. Overall, this study offers significant insights for developing countries, suggesting that regional digital finance development can enhance firms’resource allocation efficiency. IMPACT STATEMENT By analyzing data of Chinese A-share listed firms over 2012-2021, the study discovers that digital finance alleviates financing constraints and enhances cash flow circulation, thereby optimizing resource allocation. This research highlights the strategic role of digital finance in fostering more efficient investment decisions, with broader implications for economic development and policy-making in developing countries. ARTICLE HISTORY Received 30 March 2024 Revised 3 August 2024 Accepted 6 August 2024 KEYWORDS Digital finance; inefficient corporate investments; under-investments; overinvestments; financing constraints; cash flow circulations SUBJECTS Finance; Economics; Business, Management and Accounting 1. Introduction Digital finance with cutting-edge technologies has the potential to promote financial inclusion, inclusive growth, and efficiency of financial services (Ozili, 2018;Siddik&Kabiraj,2020). The advanced form of information technology has permeated the financial industry, improving the accuracy and efficiency of financial support, expanding the availability and scope of financial services, and accelerating the expansion of financial business (Makina, 2019;Morgan,2022). Consequently, enterprises of every size are developing digitization strategies to revamp their current operational processes and structures, and to optimize their competitiveness (Chen et al., 2019). Employing digital finance, which includes services like digital mobile payments, online lending, and online financial services, has gained prominence to address corporate and individual needs. Digital finance with its micro-perspective allows firms to increase corporate risk-taking (Tian et al., 2022), boost corporate innovation (Zhang et al., 2023), reduce bankruptcy risk (Ji et al., 2022), enhance corporate green investments (Ding et al., 2023), and allow firms to make efficient capital allocation decisions (Fan & Chen, 2022). It alleviates financing constraints (Li et al., 2023), reduces information asymmetry (Kong et al., 2022), and opens new financing channels to fund business operations (Han & Gu, 2021). Investment is the CONTACT Rabia Rafique [email protected] School of Finance, Central University of Finance and Economics, 39 College South Road, Haidian District, Beijing 100081, PR China. ß2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. COGENT ECONOMICS & FINANCE 2024, VOL. 12, NO. 1, 2390943 https://doi.org/10.1080/23322039.2024.2390943 fundamental pillar for growth, development, and value expansion of any enterprise, and there are several frictional factors, including agency costs, financing constraints, information asymmetry, and cognitive biases of decision makers, tend to influence investment behaviors of managers, and lead to inefficient corporate investments (Almeida & Campello, 2007; Childs et al., 2005; Fazzari et al., 1988; McDonald & Siegel, 1986; Myers & Majluf, 1984;Wang,2010). Inefficient corporate investments are classified as over-investments and under-investments. Over-investments occur when managers allocate cash flows to these projects which have negative net present value, while under-investments occur when managers neglect those projects with positive net present value. These both forms of investments reflect inefficiency in resource allocation and therefore might not contribute to increasing corporate value. However, existing literature lacks conclusive evidence that resolving financing constraints or agency problems can definitively solve investment inefficiency. Traditional finance allocates resources inefficiently because of information asymmetry, financing constraints, and market imperfections (Fan & Chen, 2022). In this context, digital finance, as an emerging financial development tool, can help firms reduce inefficient investments by reducing information asymmetry and easing financing constraints. By employing digital technologies, digital finance gains a more efficient integration of user information compared to traditional finance, thereby helping to decrease the information asymmetry that exists between parties involved in a credit transaction (Yao & Yang, 2022). These technological innovations can help firms determine their investment needs; digital finance boosts corporate innovations, which include both technological and non-technological innovations (Khan et al., 2023). In this way, digital finance enriches firms with potential investment opportunities and allows them to select the most viable investment choices, thus reducing inefficiency in corporate investments with the help of digital tools (Puschmann, 2017). Most of the previous studies on digital finance development focused on its macro-level outcomes, such as its influence on financial stability (Risman et al., 2021), household consumption (Li et al., 2020), and financing constraints (Chen & Zhang, 2021). There are only a few studies focused on the micro-level benefits of digital finance (Ding et al., 2022;Jietal.,2022;Kongetal.,2022;Tian&Shao,2023); however, none of these focused on investigating the role of digital finance in reducing inefficient corporate investments. So, recognizing the benefits delivered by digital finance development and the existing research gap, this study aims to achieve these research objectives: (1) to what extent digital finance development reduces inefficient corporate investments; (2) how digital finance development alleviates financing constraints and increases cash flow circulations to discourage inefficient corporate investments; and (3) how firm-level, industry-level, and regional-level characteristics influence the impact of digital finance development on inefficient corporate investments. Analyzing the impact of digital finance on inefficient corporate investments is significantly magnified when considering China’s distinct position as an economic powerhouse with a superficial financial ecosystem. The potential impact of digital finance in impeding inefficient investments becomes a crucial question with far-reaching implications when the Chinese government is steering the country toward a market-oriented economy. China, as the world’s largest exporter with a share of 18% of global exports, has a major role in the global supply chain (Lunness, 2023). The production lines and factories of China, often termed as the ‘world’sfactory,’are woven deeply into the global supply chain fabric. So, any business distress challenge in the form of inefficient investments to Chinese firms can disrupt the global supply chain. The ripple effects of inefficient investments would send shockwaves through global supply chain disruptions in the form of impacting everything from the availability of raw materials to consumer goods. By considering the importance of this domain, this paper examines how digital finance can serve as a safeguard against the disruptions that could threaten the smooth operations of the global supply ecosystem. Theoretically, digital finance with its comprehensive impact (L. Liu et al., 2022), may help firms to reduce inefficient investments through different possible mechanisms. There are two mechanisms considered by this paper through which digital finance can reduce inefficient investments: easing financing constraints and helping cash flow circulations. First, digital finance may reduce financing constraints and help enterprises to extend a firm’s access to capital. Via platforms of crowdfunding, peer-to-peer lending, and digital payment systems, firms may gain faster and more affordable access to funding. Thus, by alleviating the financing constraints for firms (Li et al., 2023), digital finance facilitates an efficient resource allocation paradigm and allows them to invest in profitable investment opportunities. The second mechanism by which digital finance reduces inefficient investments is to enhance cash flow circulations within the business process and accelerate corporate financialization (Jiang et al., 2022). Firms adopting digital 2 A. NISAR ET AL. finance technologies become more efficient at circulating cash in a manner that generates better returns. It facilitates the rapid movement of funds to enhance liquidity and enables them to respond efficiently to operational needs and investment opportunities. This paper uses inefficient investment data of Chinese listed firms and the “Peking University Digital Inclusive Finance Index”(Guo et al., 2020), to conduct an empirical investigation on the above issue. The benchmark results suggest that digital finance development significantly reduces the inefficient corporate investments of Chinese listed companies. Specifically, an increase of 1% in digital finance leads to a reduction in inefficient investment by 3.2465%. The results remain robust after using the 2SLS endogenous test, replacing the explanatory variable, and using a one-lagged period for the explanatory variables. Mechanism analysis shows that digital finance reduces inefficient corporate investments by easing financing constraints and increasing cash flow circulations. Firm-level heterogeneity analysis shows that digital finance has greater inhibitory effects on firms with SOE ownership rights and strong governance. Regional-level heterogeneity analysis shows that firms located in the western region and in areas with a high marketization degree are more exposed to the benefits of digital finance to reduce their inefficient investments. Industry-level heterogeneity analysis shows there are greater inhibitory effects of digital finance for firms in innovative industries and less-heavily polluted industries compared to firms in non-innovative and heavily polluted industries. This study makes significant contributions to the existing literature on the micro-level effects of digital finance. First, it bridges the research gap by presenting micro-level empirical evidence on how regional digital finance development influences inefficient corporate investments, an area that is relatively unexplored compared to the macro-level outcomes of regional digital finance development (Chen et al., 2019; Chen et al., 2021; Ding et al., 2022). Second, this study provides a nuanced understanding by examining the effects of regional digital finance development on the classification of inefficient corporate investments into under-investments and over-investments, a sub-classification of inefficient investments unexplored in the literature (Lv & Xiong, 2022). Third, this study investigates the channels through which digital finance affects corporate investment inefficiencies, such as alleviating financing constraints and improving cash flow circulation (Li et al., 2023; Wu & Huang, 2022). Fourth, by incorporating firm-level, regional-level, and industry-level heterogeneity analyses, this study offers deeper insights into how the benefits of regional digital finance vary across firms of different sizes, ownership structures, governance strengths, and those in high marketization regions, innovative industries, and competitive sectors (Guo et al., 2020; Huang et al., 2023). Last, this study underscores the potential of digital finance as a strategic tool to aid China’s transition to a market-oriented economy, with significant implications for the global supply chain and economic development (Tang et al., 2020). The remaining structure of this paper is as follows: Section 2 presents the theoretical discussion and develops some hypotheses. Section 3 discusses the research design, including the empirical model, main variables, variable measurement, data sources, and descriptive statistics. Section 4 presents the baseline regression results, a series of robustness tests, mechanism analysis tests, and heterogeneity tests at the firm-level, industry-level, and regional-level. Section 5 concludes the paper with potential policy implications. 2. Theoretical analysis and hypotheses development There are significant social and economic developments led by digital finance, as the crucial element to boost the digital economy (Goldfarb & Tucker, 2019). Inefficient corporate investments as a business distress challenge have gained much attention from industry and academia, and are considered a barrier to economic growth. The People’s Bank of China has indicated that it is necessary to use digital finance for optimizing the credit process, alleviating financing problems, enhancing the financial systems’ability, and reducing firms’financing costs (Fullerton & Morgan, 2022). On the basis of these grounds, we may argue that digital finance can ease financing constraints and discourage inefficient corporate investments. In a perfect market economy, the weight of marginal benefits and costs determines investment decisions. Stein (2003) states that the availability of investment options influences corporate investment performance. However, agency costs (Childs et al., 2005; Wang, 2010), information asymmetry (Fazzari et al., 1988; Myers & Majluf, 1984), and financing constraints (Almeida & Campello, 2007; Hirth & Viswanatha, 2011; McDonald & Siegel, 1986) may lead firms to make inefficient investments. Digital finance, with its ability to eliminate spatial and temporal barriers, reduces information asymmetry (Tian & Shao, 2023), COGENT ECONOMICS & FINANCE 3 and alleviates financing constraints (Alber & Dabour, 2020; Gomber et al., 2017; Khan et al., 2018). Additionally, Fin-tech boosts corporate innovation in the form of technological and non-technological innovations (Chen et al., 2019; Ding et al., 2022). These technological innovations assist firms in identifying their investment needs, enriching them with a variety of available investment opportunities, and encouraging them to select more advantageous opportunities with high investment value (Puschmann, 2017; Xu et al., 2023), thereby increasing corporate investment efficiency. So, drawing on information asymmetry theory, it may be argued that digital finance reduces information asymmetry to help firms make well-informed decisions, and thus reduces inefficient corporate investments (Tian & Shao, 2023). Digital finance development has a profound impact on corporate investment decisions, and thus potentially leads to a reduction in the volume of inefficient corporate investments. Al-Smadi (2023) suggests that digital finance development enhances financial inclusion and increases access to capital, which can further reduce information asymmetry and improve information efficiency. However, this increased access to funds may also encourage firms to undertake excessive investments due to managerial overconfidence or misallocation of resources (Fan & Chen, 2022). Empirical studies support this dual effect, indicating that while digital finance may streamline financial operations and increase capital allocation efficiency, it may simultaneously result in overinvestment or underinvestment depending on a firm’s governance and corporate structure (Lin et al., 2023). Drawing on the theoretical framework explained by information asymmetry theory and the dual effect of digital finance presented by prior empirical studies, there is a need to investigate the impacts of digital finance development on inefficient corporate investments, so we propose the following hypothesis; H 1 : Digital finance reduces inefficient corporate investments for Chinese listed firms. This paper postulates that digital finance eases firms’financing constraints, optimizes their financing access, and thus reduces their inefficient investments. Financing constraints for firms can be in form of limited access to external capital, which lead to high costs and stringent borrowing conditions, imposed by traditional financial institutions (Kaplan & Zingales, 1997). Inefficient corporate investments are directly linked with the functioning of capital markets and credit markets (Beck & Demirguc-Kunt, 2006). In other words, it can be stated that if a firm faces a fund deficiency that hampers its operations, it moves toward external financing, which incurs high costs and barriers. Therefore, financing constraints are significant barriers preventing firms from obtaining low-cost external financing, which consequently leads them to inefficient investments (Brown & Petersen, 2009). The digital finance system introduces new financing channels which promote fair market competition and reduce information asymmetry between all market participants (Qu & Zhu, 2023). Digital finance overcomes the issues associated with traditional finance services that are not inclusive enough, and helps firms make timely decisions on the basis of abundant information availability. Digital platforms introduced by digital finance utilize big data analytics to assess the creditworthiness of firms more accurately, thereby reducing information asymmetry between borrowers and lenders (Chen & Yoon, 2022). This improvement in credit assessment processes allows even small and mediumsized enterprises (SMEs) to acquire necessary funds at lower costs, which might not be offered by traditional banking systems (Li et al., 2020). J. Liu et al. (2022) demonstrate that digital finance development fosters a more competitive lending environment, encouraging traditional banks to innovate and mitigate their borrowing costs. The rapid development of digital finance in China has been instrumental in enhancing financial inclusion and reducing regional disparities in financing access (Guo et al., 2020). By alleviating financing constraints, digital finance helps firms undertake profitable investments that they might have otherwise missed, thereby reducing the prevalence of inefficient corporate investments (Li et al., 2021). Drawing on these views, this paper proposes that digital finance improves firms’access to funds and reduces transaction costs, thereby helping them to make more efficient investment decisions and curb inefficient corporate investments. Therefore, we postulate this hypothesis; H 2 : Digital finance reduces inefficient corporate investments by alleviating financing constraints. Digital finance enables firms to quickly access funds, allowing them to secure funds according to their investment needs by absorbing social capital into financing markets (Tang et al., 2020). Thus, with digital finance, firms are not restricted to internal capital reserves, as there is a possibility to obtain market 4 A. NISAR ET AL. capital prompting them to increase their investments to enhance investment performance (Han & Gu, 2021). Digital finance can aid firms in optimizing capital allocation efficiency and enhancing control over investment opportunities by increasing cash flow circulations. It improves internal capital adequacy and encourages an abundance of cash flows which provide a stable source of funding (L. Liu et al., 2022), for firms to reinvest and curb inefficient investments. Free cash flow theory argues that easy access to cash leads to wasteful spending; digital finance streamlines financing processes and improves transparency of cash flows (Dhumale, 2003). Digital finance, with data analytics tools, can help firms allocate cash funds adequately, monitor investment performance, and optimize cash reinvestments in a timely manner. Thus, drawing on these theoretical grounds, we propose this hypothesis; H 3 : Digital finance reduces inefficient corporate investments by increasing cash flow circulations. 3. Research design 3.1. Empirical model This paper tests the effect of digital finance on inefficient corporate investments by using the degree of inefficient investment presented by previous literature (Biddle et al., 2009; Chen et al., 2016; Richardson, 2006). The basic empirical model is constructed as: InInvit ¼b0þb1DgF it þbnControls it þviþktþcjþeijn (1) where i,t, and jrepresent firm, year, and industry respectively. InInvit as the dependent variable denotes the degree of firm’s inefficient investments. DgF, as the core explanatory variable represents digital finance index of the city in which firm is registered. Controls denotes the set of control variables, while e represents the error term. This study fixed the firm, industry, and year effects in benchmark regression model, and it mainly considers the firm-specific characteristics as the control variables. Moreover, regional-level variables have been employed to perform heterogeneity analysis. Additionally, this study examines the two potential mechanism channels of regional digital finance development: enterprise financing constraints (KZ) and enterprise cash flow circulations (Cash). These both are incorporated as the mediating variables into our baseline empirical model. The empirical models to test the mediating effects of financing constraints (KZ) will be constructed as: KZit ¼b0þb1DgF it þbnControls it þviþktþcjþeijn (2) InInvit ¼b0þb1DgF it þb2KZ it þbnControls it þviþktþcjþeijn (3) where SAit represents the financing constraints experienced by an enterprise iin year t. These models will test how regional digital finance development influence inefficient corporate investments through incorporating the effects of cash flow circulations (Cash): Cashit ¼b0þb1DgF it þbnControls it þviþktþcjþeijn (4) InInvit ¼b0þb1DgF it þb2Cash it þbnControls it þviþktþcjþeijn (5) where Cashit represents cash reinvestment ratio of firm iin year t. 3.2. Definition of variables 3.2.1. Inefficient investment Instead of measuring inefficient investment degree by ourselves, we have used inefficient investment degree estimated by CSMAR. Following previous literature on firm-level investment decisions (Biddle et al., 2009; Chen et al., 2016; Richardson, 2006), CSMAR measures firm inefficient investment by estimating the expected investment with the following model; Newinv i,t¼aþbXi,t−1þXIndustry þXYear þei,t(6) where explained variable is Newinv i,twhich denotes firm i’s actual new investment in year t. The matrix Xincludes economic determinants (TobinQ, Cash, Lev, Listage, Ret, Size) of firm-level investment decisions in year t-1. We also control industry fixed effects (PIndustry) and year fixed effects (PYear). COGENT ECONOMICS & FINANCE 5 ei,tdenotes the error term. The fitted value from the regression estimates the expected investment, while the residual represents the unexplained investment, indicating inefficient corporate investments 1 .A positive (negative) residual suggests that the actual investment exceeds (falls short of) the expected investment, corresponding to overinvestment (underinvestment) (Gomariz & Ballesta, 2014). The absolute value of the residual measures the deviation of real investment from the expected investment, reflecting the degree of inefficient corporate investments in a given firm-year. Consequently, our primary measure of inefficient investment InInv, is defined as the absolute value of the residual. We then differentiate between types of inefficient corporate investments with two additional measures. Underinvest quantifies underinvestment as the absolute value of the residual for samples with negative residuals, equating to zero otherwise. Overinvest, on the other hand, measures overinvestment as the absolute value of the residual for samples with positive residuals, also equating to zero otherwise. In the robust tests, we apply the same measures for underinvestment and overinvestment, but based on underinvestment and overinvestment subsamples, respectively. 3.2.2. Regional digital finance The Digital Inclusive Financial Development Index as a prevalent metric to assess digital finance development in China. The digital finance research department at Peking University has compiled this index in collaboration with Ant Financial Services Group, which is a well-recognized and credible digital finance company in China (Guo et al., 2020). The transaction data of this enterprise have the advantage of comprehensive index composition, solid pertinence, nationwide user coverage, and low error. There are three sub-dimensions of this index including breadth, depth, and digitization degree. This paper uses city level main digital inclusive financial development index (Def.) and sub-dimension of breadth (Brdth) to perform regression analysis and confirm the robustness of findings. 3.2.3. Control variables Following literature (Ding et al., 2023; Han & Gu, 2021; Huang et al., 2023; Ji et al., 2022; Puschmann, 2017), this study includes firm-specific control variables of firm profitability, tangibility, firm size, stock yield, cash flow status, growth and age. These control variables have direct linkage with the dependent variable to get the influence of the digital finance. 3.3. Data sources Data for core explanatory variables DgF and Brdth are obtained from Peking University Digital Finance Inclusion Index (Guo et al., 2020). Firm-level variables’data is sourced from CSMAR. Data for city and provincial level variables are sources from China Statistical Yearbook. Data on industry classification is obtained from CSRC industry classifications. The data used in this paper cover 1,457 Chinese A-share listed companies on Shenzhen Stock Exchange and Shanghai Stock Exchange, and distributed across 366 cities and 31 provinces in China. The latest version of digital finance index covering the period is 2011-2021 is used in this paper. The time interval of this paper is from 2012-2021, which is selected on the basis of rich data availability for the maximum number of firms. 3.4. Summary statistics The definitions and descriptive statistics of all variables of this study including the explained variable; inefficient investments (In_inv), explanatory variables; digital finance index (DgF), Digital finance_breadth (Brdth), and control variables; profitability (ROA), tangibility, (Tang), firm size (F_size), stock yield (Yield), cash flow (Cash), assets growth (Growth), and firm age (Age), are shown in Table 1. Inefficient corporate investment has mean value of -4.177, with standard deviation of 10.405, which suggests notable variability in the degree of inefficient corporate investments across the Chinese listed firms. Conversely, the explanatory variable, DgF has mean value of 2.266 with standard deviation of 0.654, suggesting the modest variability in digital finance development across the prefectures and cities in which firms are registered. The same pattern is observed for the supporting explanatory variable Brdth. Additionally, descriptive statistics for all other control variables fall within acceptable ranges. 6 A. NISAR ET AL. 4. Results and discussion 4.1. Benchmark regression Benchmark regression results for digital finance and inefficient corporate investments are reported in Table 2.TheDgF coefficient is −3.2465, which is statistically significant at 1%, suggesting that digital finance reduces inefficient investments for Chinese listed firms. This influence is further verified by Brdth with regression coefficient of −2.3677. This shows that digital finance allows Chinese listed firms to reduce their inefficient investments. Hence, H 1 is accepted. These results further show that profitability (ROA), Firm Size (F_size), Stock Yield (Yield), and Growth (Growth)as control variables also negatively influence inefficient corporate investments. This paper further classifies inefficient corporate investments into over-investments and under-investments, and reestimates the empirical model. Columns (1) and (2) of Table 3 report the results for the overinvestments, and columns (3) and (4) in same table report the results for the under-investments. In both cases, digital finance significantly reduces inefficient corporate investments as DgF and Brdth coefficients are negative and statistically significant. However, digital finance’sinfluenceis more pronounced in reducing the degree of inefficient corporate investments for those making under-investments. Overall, it can be claimed that digital finance helps the firms to reduce the Table 1. Descriptive statistics. Variable Symbol Definition Mean Std. dev Min Max Inefficient investment degree In_Inv The degree of inefficient corporate investments. −4.177 10.405 −860.99 −0.001 Digital inclusive finance index DgF The digital inclusive finance index of the city in which the firm is registered. 2.266 0.654 0.620 3.597 Digital finance_breadth Brdth The breadth index of firm’s registered city. 2.258 0.659 0.401 3.718 Profitability ROA Net profit/Total assets 0.028 0.137 −4.782 7.446 Tangibility Tang Tangible assets / Total assets 0.231 0.178 0.000 0.954 Firm size F_size Natural logarithm of total assets 22.619 1.404 14.940 28.640 Stock yield Yield Firm’s annual stock yield. 0.153 0.512 −0.806 7.355 Cash flow Cash Net cash flow from operating activities / Total assets at beginning of the period 0.052 0.099 −4.050 1.209 Asset growth Growth The annual change in total assets. 0.308 6.961 −0.899 529.944 Firm age Age The difference between firm’s listing year and fiscal year. 14.442 6.496 2 31 Table 2. The benchmark regression results. Variables (1) (2) In_Inv In_Inv DgF −3.2465 (−3.64) Brdth −2.3677 (−2.48) ROA −7.9885 −8.0250 (−12.59) (−12.64) Tang 6.8666 6.8150 (5.9) (5.85) F_size −1.3569 −1.4178 (−6.54) (−6.87) Yield −0.9742 −0.8183 (−5.94) (−4.85) Cash 29.4373 29.5195 (30.14) (30.23) Growth −0.0846 −0.0851 (−6.94) (−6.98) Age 0.9818 0.7897 (5.17) (3.95) Constant 16.9851 19.1130 (3.7) (4.22) Firm, Industry and Year FE Yes Yes R-Squared 0.154 0.181 Obs. 14,570 14,570 Notes: Robust t-statistic are presented in parentheses; ,, and , denote significant levels of 1%, 5% and 10%, respectively. COGENT ECONOMICS & FINANCE 7 these firms could motivate them to utilize the benefits of digital finance, thereby helping to build an inclusive market. Despite the significant findings, this study has certain limitations that warrant attention and highlight areas for future research. First, the sample of this study is limited to Chinese listed firms, which may not reflect the global applicability of the results. Future researchers could extend this analysis by focusing on other countries and regions to explore the broader implications of digital finance development on inefficient corporate investments. Second, this study has used a regional level digital finance index as a proxy measure of digital finance development and examined its connection with corporate level scenarios. In the future, if data on digital finance at the firm level become available, research could connect firm-level digital finance with firm-level corporate inefficiency issues. Third, this study is primarily focused on the mechanisms of easing financing constraints and increasing cash flow circulations, although there could be other mechanisms that influence the nexus of digital finance and inefficient corporate investments, which could be considered by future researchers. Lastly, given the rapid evolution of digital finance, continuous updates and longitudinal studies are essential to understand its long-term impacts and dynamic interactions with corporate investments. Notes 1. Inefficient corporate investments include both under-investments and over-investments, meaning that when firms experience information asymmetries in capital markets, and thus they are not well informed to make timely investment decisions. Due to these constraints, firms either are reluctant to make investments or they make higher investments than the actual profit potential of investment projects. In both cases, they remain inefficient in garnering the actual benefits of investments, thereby resulting in corporate investment inefficiencies. 2. KZ-index is measured by following the Kaplan and Zingales (1997). 3. Following Industry Classification (2012 Edition), C29, C39, I and M are defined as innovative industries, while other industries are considered as non-innovative industries. 4. Specifically, SEC’s Industry Classification (2012 Edition) considers industries with industry codes B, C25, C31, C32, C36, C37, D, E48, G53, G54, G55, G56, I63, I64, K, and R as regulated industries, while other industries as competitive industries. CRediT authorship contribution statement Asad Nisar: Conceptualization, Methodology, Data Analysis, Writing; Haolin Li: Validation, Review, Proof-Reading; Syed Sadaqat Ali Shah: Data Curation, Investigation, Review and Editing; Rabia Rafique: Resources, Data Curation, Supervision. Disclosure statement No potential conflict of interest was reported by the author(s). About the authors Asad Nisar is a doctoral student at the Central University of Finance and Economics, Beijing, P.R. China. He is focused on the integration of technology in business, economics, and environmental domains. His research interests include regional digital finance, digital transformation in business, artificial intelligence, resource productivity, renewable energy, climate change, fiscal policy, and public debt management. Haolin Li is an undergraduate student majoring in Finance at Miami University. He is interested in digital finance, fintech, and other fields, and his research directions include regional digital finance, big data analytics, and green finance. Syed Sadaqat Ali Shah recently graduated from the Central University of Finance and Economics, Beijing, P.R. China. He is currently serving as an Assistant Professor at Beijing International Studies University, Beijing, P.R. China. His research directions include fiscal policy management, public debt management, climate change, energy transition, resource productivity and the integration of technology in finance and economics. 14 A. NISAR ET AL. Rabia Rafique is a doctoral student at the Central University of Finance and Economics, Beijing, P.R. China. She possesses advanced knowledge in econometrics, adept at addressing the complexities of econometric techniques. Her research interests include regional digital finance development, fiscal policy management, public debt management, green innovation, climate change, and energy efficiency. ORCID Asad Nisar http://orcid.org/0009-0004-4010-6800 Rabia Rafique http://orcid.org/0009-0002-9464-4845 Data availability statement Firm-level data is sourced from CSMAR and authors don’t have authority to share due to copyright issue. Digital Inclusive Financial Development Index data is sourced Peking University, and authors don’t have the right to share it. References Alber, N., & Dabour, M. (2020). The dynamic relationship between FinTech and social distancing under COVID-19 pandemic: Digital payments evidence. 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