Digital financial development and inefficient investment: a study based on the dual perspectives of resource and governance effects

The study investigates whether and how digital finance mitigates firms’ inefficient investment—defined as deviations of actual investment from its optimal level, manifesting as underinvestment or overinvestment. Prior work highlights external institutions (policy environment, political resources, regional institutional development) and internal governance (managers/shareholders) as key determinants of investment efficiency, operating through financing constraints, information asymmetry, and agency costs. The paper posits that digital finance—by expanding access, reducing costs, and leveraging data analytics—can alleviate financing constraints, reduce information asymmetry, and strengthen governance, thereby improving investment efficiency. It develops the dual mechanisms of a resource effect (improved access to precise, low-cost financial services) and a governance effect (enhanced monitoring and reduced agency conflicts) and tests the hypothesis that digital finance significantly reduces inefficient investment among Chinese listed firms.

Existing research on investment efficiency emphasizes two strands: (1) external institutional influences (policy environment, political connections/resources, and regional institutional development) that guide and constrain firm investment behavior; and (2) internal governance factors involving managers and shareholders, drawing on upper echelons and information asymmetry theories, to curb inefficient investment. Market imperfections generate financing constraints, and weak governance heightens information asymmetry and agency costs, jointly causing under- or overinvestment. While prior studies document economic effects of digital finance on households, innovation, entrepreneurship, financialization, and risk-taking, the role of digital finance in mitigating firms’ inefficient investment through resource and governance channels remains underexplored. This study addresses that gap by linking digital finance to reduced financing constraints, lower information asymmetry and agency costs, and improved investment efficiency.

Data and sample: The sample comprises Chinese non-financial A-share firms listed in Shanghai and Shenzhen from 2008–2019. Data cleaning excluded firms with asset-liability ratios >1 or <0, ST/ST*/PT firms, and those with missing key variables. Continuous variables were winsorized at the 1st and 99th percentiles. The Digital Inclusive Finance Index is available from 2011–2019; the final analyzed sample contains 19,780 firm-year observations. Key variables: Inefficient investment (INVEFF) follows Biddle et al. (2009). For each year and industry, estimate Invest_it = β0 + β1 SalesGrowth_{t−1} + ε_it, where Invest is capital investment and SalesGrowth is the prior period sales growth. INVEFF is the absolute value of the residuals; larger values indicate greater inefficiency. Residuals are split into overinvestment (OVER) and underinvestment (UNDER). Digital finance (DWF): Measured using the municipal-level Digital Inclusive Finance Index (Peking University Institute of Digital Finance and Ant Group), divided by 100 for interpretability. The index covers coverage breadth, usage depth, and digitization of inclusive finance. Provincial-level indicators are used for robustness. Controls: Firm size (Size), leverage (Lev), performance (ROA), ownership concentration (Top1), board size (Board), number of committees (Comnum), state ownership (State), listing age (Age), cash ratio (Cash), stock returns (Ret), and fixed asset ratio (PPE). Industry and year fixed effects are included. Empirical specification: INVEFF = α0 + α1 DWF + Controls + Industry FE + Year FE + ε. A significantly negative α1 supports the hypothesis that digital finance mitigates inefficient investment. Endogeneity strategies: (1) Heckman two-stage: First-stage probit predicting high digital finance dummy (DWF_A) using the share of other same-industry firms in high-DWF regions (Other-DWF) as an instrument; the inverse Mills ratio (IMR) enters the second stage. (2) Instrumental variables: Provincial internet penetration (NET) as instrument for DWF. Robustness checks: Alternative DWF measurement using provincial index (PDWF); alternative dependent variable constructions (e.g., INVEFF1, OVER1, UNDER1); excluding municipalities directly under the central government; excluding Growth Enterprise Market (GEM) firms; including industry-by-year fixed effects. Mechanism tests: Interaction models examine resource and governance channels: financing constraints (KZ index per Kaplan and Zingales, 1997), agency costs (management expense ratio per Ang et al., 2000), and information transparency (stock price synchronicity per Liu et al., 2023). Models include interaction terms KZ*DWF, Agency*DWF, and Trans*DWF, with standard controls and fixed effects.

- Benchmark results: Digital finance (DWF) significantly reduces inefficient investment. Coefficients on DWF are negative and statistically significant across specifications (e.g., −0.005 at the 1% level). Economic magnitude: a one–standard-deviation increase in DWF (0.661) reduces INVEFF by about 8.47% relative to its mean (−0.005*0.661/0.039). - Endogeneity: Heckman correction yields significant IMR and preserves the negative, significant effect of DWF on INVEFF, OVER, and UNDER. IV estimates using provincial internet penetration (NET) show strong first stages and maintain negative, significant effects of DWF. - Robustness: Results hold when replacing municipal with provincial DWF (PDWF), using alternative measures of inefficient investment (INVEFF1/OVER1/UNDER1), excluding municipalities directly under the central government, removing GEM firms, and adding industry×year fixed effects. Coefficients on DWF remain negative and significant at least at the 10% (often 5% or 1%) level. - Mechanisms: Resource effect—financing constraints (KZ) are positively associated with underinvestment; interaction KZ*DWF is significantly negative (e.g., at 1%), indicating digital finance alleviates underinvestment induced by financing constraints. Governance effect—agency costs are positively related to inefficient investment; Agency*DWF is significantly negative (5%), implying digital finance mitigates inefficiency arising from agency problems. Information asymmetry—higher information transparency is associated with lower inefficient investment; interaction Trans*DWF is significant (reported positive at 5%) and interpreted as digital finance mitigating inefficiency linked to information asymmetry. - Heterogeneity: The mitigating effect of DWF is stronger where regional institutional development (marketization) is higher (e.g., coefficient −0.009, 1% significance) and is insignificant in regions with lower institutional development; the difference across groups is significant at 5%. By ownership, the effect is pronounced for non-state-owned firms (e.g., −0.007, 1% significance) and insignificant for state-owned firms; group difference significant at 5%. - Over vs. underinvestment: DWF significantly reduces both overinvestment and underinvestment (1% significance), with a more pronounced effect on overinvestment (difference significant at 10%). - Digital finance dimensions: Coverage breadth, usage depth, and digital support (digitization degree) each contribute to lowering inefficient investment. - Investment levels: Digital finance increases firms’ willingness and ability to invest, improving overall investment levels beyond merely reducing inefficiency.

The findings confirm the central hypothesis that digital finance improves firm investment efficiency by mitigating both under- and overinvestment. By expanding access to tailored, low-cost financial services, digital finance eases financing constraints (resource effect), allowing value-creating projects to proceed. Simultaneously, enhanced data analytics and monitoring curb managerial opportunism and reduce information asymmetry (governance effect), restraining wasteful investments and aligning resources with project risk. The stronger effects observed in regions with better institutional development suggest complementarities between digital finance and formal institutional quality. The more pronounced impact on non-state-owned firms indicates that digital finance especially benefits firms facing tighter external financing frictions and weaker traditional governance support. Together, the results underscore digital finance as a mechanism that simultaneously relaxes resource constraints and strengthens external governance, thereby raising investment efficiency and overall investment activity.

This paper demonstrates that digital finance significantly mitigates inefficient investment among Chinese listed non-financial firms, operating through resource (alleviating financing constraints) and governance (reducing agency problems and information asymmetry) effects. The impact is stronger in regions with higher institutional development and among non-state-owned firms and is more pronounced for overinvestment than underinvestment. All three dimensions of digital finance—coverage breadth, usage depth, and digitization—contribute to the effect, and digital finance increases firms’ investment willingness and capacity. Contributions include: (1) extending the literature on digital finance by documenting its role in improving firm investment efficiency through dual mechanisms; (2) enriching research on determinants of inefficient investment by showing digital finance reduces financing constraints, information asymmetry, and agency costs; and (3) offering policy implications that strengthening digital financial infrastructure and supervisory frameworks can complement corporate governance and promote efficient investment. Future research could further explore causal channels at finer spatial and firm-level granularities and assess generalizability beyond China as digital finance ecosystems evolve.