The economic value of open government data: Micro evidence from corporate investment

With the progressive advancement of government data openness, scholarly attention has transitioned from evaluating the performance and determining the influencing factors of government data openness to examining its economic implications. The initial body of literature predominantly addressed the macroeconomic value of government data openness. The majority of these studies have concluded that government data openness positively contributes to local economic growth (Altayar, 2018; Attard et al., 2016; de Magalhães Santos, 2024; Hardy & Maurushat, 2017; Janssen et al., 2012; Leviäkangas & Molarius, 2020; McBride et al., 2019; Zeleti et al., 2016; Zuo et al., 2023). Nevertheless, the other literature indicates that the influence of government data openness on regional economic growth is heterogeneous across different regions (Zhao et al., 2022). Additionally, some other studies have demonstrated that government data openness positively contributes to entrepreneurial vitality (Hardy & Maurushat, 2017) and plays a significant role in advancing the reform of administrative streamlining and power delegation, as well as in optimizing the business environment (Park & Gil-Garcia, 2022).

The second category of literature investigates the microeconomic value derived from government data openness, specifically analyzing its impact on micro enterprises. The government data open platform not only diminishes the costs associated with the search, acquisition, and verification of data for market entities (Chen et al., 2023; Goldfarb & Tucker, 2019), but also facilitates enterprises in extracting valuable information from government data. This capability supports a range of economic activities that necessitate precise identification of business opportunities, such as site selection, innovation, and timely sales (Brynjolfsson & McElheran, 2016; Ghasemaghaei & Calic, 2019; Müller et al., 2018; Yan et al., 2023), and facilitates enterprises in estimating market demand to mitigate the supply-demand discrepancies arising from market misjudgments (Caputo et al., 2019; Cong et al., 2021). Additionally, the openness of government data can reduce information asymmetry between creditors and enterprises, thereby lowering the debt financing costs for enterprises (Xing et al., 2024). Several other papers have indicated that the openness of government data can enhance the quality of enterprise operations and total factor productivity by lowering operating costs, optimizing the business environment, revitalizing asset circulation, and improving the efficiency of resource allocation (Jetzek et al., 2019; Magalhaes & Roseira, 2020; Nikiforova & Lnenicka, 2021). Concurrently, the accessibility of government data is also found to facilitate the digital transformation of enterprises (Begany & Gil-Garcia, 2024).

According to the literature review above, the existing literature addresses the macroeconomic and microeconomic value of government data openness. However, few studies have explored the economic value of open government data from the perspective of corporate investment. Our study fills this gap by investigating the relationship between open government data and corporate investments.

Corporate investment decision making is a multifaceted process shaped by a variety of factors, including internal business management practices and external macroeconomic policies. The existing literature examines the determinants of corporate investment from both micro and macro perspectives. At the micro level, factors influencing corporate investment can be further categorized into (a) management-level factors, such as managerial discretion (Shen et al., 2022), management discussion and analysis (Durnev & Mangen, 2020); (b) human factors, such as CEO overconfidence (Malmendierm & Tate, 2005; Malmendierm et al., 2011), the role of lead independent director (Rajkovic, 2020), the governance role of multiple large shareholders(Jiang et al., 2018), motivated monitoring by institutional investors (Ward et al., 2020), (c) technical factor, such as digitalization (Chen & Jiang, 2024; Xu et al., 2023), analysts’ forecast quality(Chen et al., 2017); (d) information factors, such as financial reporting quality (Biddle et al., 2009; Chen et al., 2011; Roychowdhury et al., 2019), mandatory corporate social responsibility disclosure (Liu & Tian, 2021), carbon information (Lewandowski, 2017; Phan et al., 2022; Trinks et al., 2020). At the macro level, the existing literature has focused on the impact of digital finance (Tang & Geng, 2024), microenvironment uncertainty (Li et al., 2021), political uncertainty (Brandon & Youndgsuk, 2012), and policy uncertainty (Gulen & Ion, 2016; Wang et al., 2014) on corporate investment.

As mentioned above, in the investigation of the factors influencing corporate investment, the existing literature has predominantly focused on micro and macro factors. However, few studies have addressed the role of data elements in corporate investment. In the context of the digital economy, where data elements play a crucial role in enterprise decision-making, it has become imperative to analyze the impact of government data openness on corporate decision-making processes. Therefore, we empirically examine the impact of open government data on corporate investment in this paper. It will not only augment the existing body of literature concerning the economic value of open government data but also further explore the factors influencing corporate investment.

Corporate investment projects typically require substantial capital. Relying exclusively on internal funding may hinder the ability to sustain the normal operation of these projects, thereby necessitating the need for external financing. However, a prevalent issue of information asymmetry exists between external investors, such as financial institutions and enterprises, resulting in significant financing constraints for investment initiatives undertaken by enterprises (Roychowdhury et al., 2019; Yang et al., 2023). The implementation of open government data platforms, which provide large-scale public data free of charge, has significantly enhanced the information environment and facilitated the dismantling of inter-regional information barriers (Zeleti et al., 2016; Zhao et al., 2022). This initiative alleviates the temporal and spatial limitations on communication between enterprises and various stakeholders, thereby accelerating the transmission and sharing of information and ensuring more timely disclosure of corporate information (Xing et al., 2024). Corporate stakeholders leverage government data open platforms to enhance their capacity to access, process, and utilize information, thus mitigating the traditional issues of information asymmetry (Ahmad et al., 2023; Bellucci et al., 2023). The accessibility of government data facilitates a more comprehensive and precise understanding of the various dimensions of a city, including its economic, social, demographic, and market characteristics. This availability allows enterprises to mitigate the risks associated with inadequate or erroneous information during the investment decision-making process, thereby enhancing their confidence in investment and promoting increased investment activity (Bello, 2024; Gao et al., 2023). Simultaneously, the enhancement of government data transparency has augmented market transparency, allowing financing entities to gain a deeper understanding of industry development trends and competitive dynamics (Xing et al., 2024; Wang et al., 2024). Additionally, this also facilitates a more accurate assessment of the positioning and competitive advantages of financed enterprises, thereby bolstering investment confidence and mitigating risk perceptions. Consequently, it attracts a greater number of financing parties and assists enterprises in transforming information flows into external capital flows and alleviating corporate financing constraints (Zeleti et al., 2016; Zhao et al., 2022). Based on these considerations, the following hypothesis is proposed:

Previous research has demonstrated that financial constraints negatively impact corporate investments (Albuquerque, 2024; Howes, 2023; Jumah et al., 2023; Xu et al., 2023). Conversely, the availability of government data has been shown to mitigate information asymmetry and reduce financial constraints (Xing et al., 2024). Government agencies possess primary information regarding enterprises’ operational conditions through their regulatory activities. Utilizing the credit information-sharing mechanism, financial institutions can construct precise profiles of enterprise credit. This enables a more comprehensive understanding of the operational conditions and credit levels of enterprises, thereby reducing their financing costs and thresholds through innovative financial service models (deHaan et al., 2023; Luo et al., 2023). Additionally, it provides diversified financing options for enterprises and supports credit financing for enterprises in the real economy, particularly small and medium-sized enterprises. Given adequate funding, enterprises are more inclined to augment investment, particularly in innovative projects (Shaturaev, 2024). Additionally, the availability of government data facilitates enterprises in estimating market demand, mitigates the supply-demand imbalances resulting from market misjudgments, enhances managerial decision-making capabilities, strengthens risk assessment and predictive abilities, and improves enterprise profitability, thus relieving financing constraints.

Furthermore, to delve deeper into the mechanisms through which open government data impact corporate investment, this study deconstructs the concept of financing constraints. According to the theory of financing constraints, enterprises experiencing such constraints typically encounter higher financing costs and reduced financing availability (Kaplan & Zingales, 1997). This indicates that financing constraints encompass two dimensions: the scale of financing that can be obtained and the cost of financing that needs to be borne. In addition, the transparency of government data should be leveraged to improve financial outcomes, specifically by optimizing debt financing mechanisms. Consequently, this study hypothesizes that the openness of government data may influence corporate investments by affecting both the scale and cost of debt financing. This implies a logical progression wherein open government data results in either an increase in the scale of financing or a reduction in financing costs, thereby impacting corporate investment.

The availability of open government data can significantly enhance the scale of debt financing, lower associated costs, and improve enterprises’ access to investment funds via debt financing channels. This impact is manifested through three primary mechanisms. First, financial institutions leveraging big data to create digital inclusive finance can make more efficient and accurate assessments of enterprise operations, thereby reducing financing costs and barriers for enterprises through innovative financial solutions (Tomar & Periyasamy, 2024). In 2019, Shanghai introduced inclusive finance applications on an open data platform, consolidating data from eight governmental departments and making over 300 data items accessible to enhance bank credit mechanisms (Zhu & Guo, 2024). Therefore, the openness of government data has augmented the supply of credit funds in the financial system.

Second, classical financial theory posits that information asymmetry is intricately linked to corporate borrowing costs (Bellucci et al., 2023; Kong, 2023). The transparency of government data facilitates external funding providers in obtaining timely and precise insights into the operational dynamics of enterprises, thereby enabling them to effectively identify potential investment targets, mitigate information frictions, and decrease information costs. This bolsters the confidence of external funding providers and diminishes perceived risks, thus promoting increased investment activity (Wang et al., 2024).

Third, the increased transparency and availability of government data facilitated the entry of foreign banks. This promotes business competition among financial institutions and forces domestic banks to adjust their credit strategies. On the one hand, banks are anticipated to adopt a more proactive approach in information mining and screening, thereby broadening their business scope and customer base, reducing credit approval requirements, and enhancing approval efficiency (Lyu et al., 2023). On the other hand, in response to the competitive pressures induced by the entry of foreign banks, domestic financial institutions are likely to moderately lower their loan interest rates to maintain or expand their market share, consequently reducing the borrowing costs for enterprises (Jigeer & Koroleva, 2023; Yao & Song, 2023).

Therefore, we posit the following hypotheses: H2:

Open government data facilitate corporate investment by mitigating the overall financing constraints faced by enterprises.

The dependent variable is corporate investment, denoted by Invest. In alignment with previous research (Cheng et al., 2024; Crawford & Markarian, 2024; Deng et al., 2020; Li et al., 2021), we normalized corporate investment by total assets. Specifically, we quantify corporate investment expenditure as the ratio of net capital expenditure (cash expended on the acquisition and construction of fixed assets, intangible assets, and other long-term assets minus the net cash received from the disposal of fixed assets, intangible assets, and other long-term assets) to total assets.

The data open platform established by local governments serves as a crucial mechanism for facilitating the sharing of government data. The introduction of this platform offers an optimal quasi-natural experimental setting for examining the economic value of open government data. Consequently, the independent variable in this context is open government data, denoted by the variable open. We employ a staggered Difference-in-Differences model for empirical analysis, and then the variable open is operationalized as a dummy variable. It takes the value of 1 if the enterprise’s office is situated in a city that has implemented a government data open platform and 0 if it is not.

By referring to Deng et al. (2020) and Duchin et al. (2010), we identify the following control variables: firm size (Size), financial leverage (Lev), return on total assets (Roa), cash flow from operations (Cfo), firm age (Age), sales growth (Growth), equity balance (Balance), board independence (Independent), board size (Board), and CEO duality (Dual). Additionally, to mitigate the impact of macroeconomic development levels, we incorporate macroeconomic control variables at the city level, specifically including economic development level (lngdp) and government revenue (lnfinance). The variable definitions are listed in Table 2.

In accordance with the parallel trend assumption, it is posited that in the absence of the government data openness initiative, the investment trajectories of enterprises in both open and closed regions would exhibit similar patterns of change. However, the government’s data openness initiative has not been rescinded before, precluding direct verification through counterfactual analysis. To address this limitation, we employ a dynamic difference-in-differences model to examine the inter-group variation trends in corporate investment levels prior to government data openness. The analysis aims to determine whether there is a significant difference in the trends of investment level changes between enterprises located in cities with government data openness and those in cities without such openness before policy implementation. If no significant difference is observed, it indicates that the parallel trend assumption is satisfied. The dynamic difference-in-differences model is specified as follows: 2Investit=β0+βk∑k=−1310Eventitk+γControlsit+δi+θt+μc+ϑj+εit.{\rm{ Inves}}{{\rm{t}}_{it}} = {\beta _0} + {\beta _k}\sum\limits_{k = - 13}^{10} {{\rm{ }}Event_{it}^k} + \gamma Control{s_{it}} + {\delta _i} + {\theta _t} + {\mu _c} + {\vartheta _j} + {\varepsilon _{it}}.

In this context, EventitkEvent{\rm{ }}_{it}^k represents the temporal distance variable, indicating the number of years relative to the year of government data openness. When k equals 0, it denotes the year of data openness. Conversely, a negative value of k signifies years prior to openness, whereas a positive value indicates years following openness. The definitions of the remaining variables align with those in the basic difference-in-differences model. Specifically, Event0 denotes the year the government data open platform was introduced, EventN1 corresponds to the year preceding the platform’s launch, and EventP1 refers to the year following the platform’s launch. This nomenclature extends similarly to other variables. To mitigate multicollinearity among temporal distance variables, we designated the year preceding the initiation of government data openness as the reference period, thereby excluding EventN1 from the regression analysis. We performed parallel trend analyses by adjusting for control variables and fixed effects, utilizing data processed through the propensity score matching method. The results presented in Table 6 demonstrate that the coefficients for EventN2 through EventN13 are not statistically significant across all the columns. The analysis indicates that there is no statistically significant difference in investment level trends between enterprises located in cities with government data disclosure and those in cities without such disclosure prior to the implementation of data openness. This observation fulfills the parallel trend assumption inherent in the Difference-in-Differences (DID) model. As a result, the findings of this study are robust against the potential concerns of omitted variable bias and reverse causality, thereby supporting the notion that government data openness can effectively promote corporate investment.

Furthermore, we provide Figure 1, which depicts the findings within a 95% confidence interval. The data indicate that prior to the implementation of government data openness, there was no statistically significant difference in the investment behavior trends between affected and unaffected enterprises. However, subsequent to the introduction of open government data, a significant increase in the investment behavior of affected enterprises was observed, thereby providing additional support for the parallel trend hypothesis.

Figure 1.

Parallel trend test. This figure plots the parallel trend test based on the results from Column 3 of Table 5.

While Figure 1 initially confirms the parallel trend assumption, it is important to acknowledge that a growing body of recent research has highlighted potential biases in parameter estimation when using the traditional two-way fixed effects model, particularly due to heterogeneous treatment effects in the multi-period difference-in-differences framework (Goodman-Bacon, 2021). To address this issue and mitigate the influence of heterogeneous treatment effects on parameter estimation, this study adopts the methodology proposed by Borusyak et al. (2021) and utilizes the imputation estimator of heterogeneous treatment effects to reassess the dynamic impacts of introducing local public data open platforms on enterprise investment. As illustrated in Figure 2, the estimated coefficient of the primary explanatory variable exhibits a change in magnitude; however, it remains statistically insignificant at the 10% level prior to the implementation of government data open platforms and becomes significantly positive after the implementation of government data open platforms. Compared to Figure 1, no fundamental alterations are evident. This suggests that even after accounting for heterogeneous treatment effects, the parallel trend assumption of the difference-in-differences model remains valid.

Figure 2.

Parallel trend test with consideration of heterogeneous treatment effects.

While the model specifications for benchmark regression and parallel trend testing have partially mitigated concerns regarding endogeneity, there may still exist additional stochastic factors that influence corporate investment and contribute to estimation bias. To further isolate the impact of unobservable variables on corporate investment levels and to ensure that the findings of this study are attributable to the implementation of government data open platforms rather than extraneous factors, this study conducts additional verification through placebo testing. Given the challenges associated with directly measuring numerous factors at the city and temporal levels, a placebo test was implemented by randomly selecting cities and years with available open government data. Considering that 204 cities in the final sample had established open government data platforms, this study initially randomized the sample regions and designated the top 204 regions as pseudoopen government data regions. Subsequently, we randomly sorted the city-year samples and selected the first city-year sample at each city level. We merge the randomly selected cities and city-year samples, retaining only those city-year samples that correspond to the pseudo-government data open cities. Construct a pseudo-government data openness variable (_b[open]) and conduct a regression analysis. Finally, we document the regression coefficients for the _b[open] variable and the kernel density distribution of P-values. The placebo test conducted in this study was repeated 500 times. If the conclusions drawn from the benchmark regression are not influenced by omitted variables at the city and time levels, the estimated coefficient kernel density of the regression should approximate a normal distribution with a mean of zero. Figure 3 presents the kernel density distribution of the estimation coefficients and P-values for the explanatory variables in the placebo test. The kernel density of the estimated coefficients from the sample regression exhibited an approximately normal distribution, with a mean of zero. The majority of the estimated coefficients possess P-values exceeding the 10% statistical significance threshold of 0.1. Furthermore, all estimated coefficients fall below the benchmark regression coefficient of 0.003, as indicated by the red solid line on the right. These findings suggest that the assertion that government data openness enhances enterprise investment levels is not attributable to random factors, thereby indicating that the conclusions of this study have successfully passed the placebo test.

Figure 3.

Placebo test.

To mitigate the interference of selection bias, this study employed two matching methodologies to match each treatment group observation with a control group, followed by regression analysis on the matched samples. Initially, propensity score matching was utilized to address inter-group feature differences within samples. The nearest neighbor matching, radius matching and kernel matching are employed. Subsequently, a staggered Difference-in-Differences model is applied to estimate the impact of government data openness on corporate investment levels. The regression results after matching are shown in columns (1) to (3) of Table 7. It is evident that, irrespective of the nearest neighbor matching, radius matching and kernel matching method employed, the regression coefficient of government data openness remains significantly positive. This finding suggests that the benchmark regression results are not attributable to selection bias and demonstrate robustness.

Given that the propensity score matching (PSM) method is susceptible to sample loss, we adopt Hainmueller’s (2012) entropy balance method to mitigate the limitations associated with PSM and ensure the randomness and exogeneity of model testing. Consequently, the regression results obtained from the entropy balance matching method are presented in Column (4) of Table 7. The results indicate that the regression coefficient for government data openness remains significantly positive, thereby reaffirming the validity of the benchmark regression results.

Given the potential sensitivity of our conclusions to the measurement of corporate investments, we introduce three novel metrics: Invest2, Invest3, and Invest4. Specifically, Invest2 is defined as the ratio of cash expended by a company for the acquisition and construction of fixed assets, intangible assets, and other long-term assets to total assets at the end of the period. Invest3 is defined as the ratio of cash expended by a company for the acquisition and construction of fixed assets, intangible assets, and other long-term assets to total assets at the beginning of the period. Invest4 is defined as the quotient obtained by dividing the difference between the cash expenditures for the acquisition and construction of fixed assets, intangible assets, and other long-term assets, and the net cash inflows from the disposal of these assets by the total assets at the end of the reporting period. Table 8 presents the regression results using Invest2, Invest3, and Invest4 as the dependent variables. The findings indicate that irrespective of whether Invest2, Invest3, or Invest4 is employed to gauge the level of corporate investment, the coefficient for government data openness remains significantly positive. This suggests that government data openness consistently enhances corporate investment across different measures, thereby affirming the robustness and reliability of the research conclusions presented in this paper.

Under the hierarchical administrative management system in China, provincial governments exert a substantial influence on the development of cities within their jurisdiction. The initiation timelines of municipal government data open platforms often coincide with those of provincial government data open platforms. Consequently, provincial-level platforms may impact the online activities of municipal platforms within their jurisdiction, potentially resulting in biased identification and regression outcomes. To evaluate the robustness of the results, first, the fixed effects of provinces were controlled for based on the benchmark model. This approach accounts for the impact of provincial government data open platforms and other relevant factors on regression outcomes. Column (1) of Table 9 presents the findings of this analysis. The results indicate that even after controlling for the influence of provincial platforms, the investment promotion effect of government data openness remains significantly evident. Second, to assess the impact of the municipal government’s open data platform on a sample of prefecture-level cities, municipalities directly under the central government were excluded from the analysis. Column (2) of Table 9 presents the results of this analysis, which indicate that the regression coefficient for government data openness remains significantly positive. Finally, it is important to consider that the establishment timelines of provincial public data platforms overlap with those of prefecturelevel city public data platforms, potentially leading to biased identification and estimation results. To validate the robustness of the findings, this study employs the establishment of a provincial public data platform as a quasi-natural experiment and performs a difference-in-differences analysis akin to a benchmark regression. The corresponding results are presented in Column (3) of Table 9. The regression coefficient indicates that the effect of government data openness remained significantly positive, thereby affirming the robustness of the study’s conclusions.

To isolate the effects of other concurrent policies, we examined three representative initiatives: the Broadband China Pilot Policy, the National Big Data Comprehensive Experimental Zone Policy, and the Free Trade Zone Policy. The detailed outcomes are presented in Table 10. Column (1) illustrates the results after accounting for the Broadband China Pilot Policy. Column (2) demonstrates the findings after controlling for the National Big Data Comprehensive Experimental Zone Policy. Column (3) depicts the outcomes after considering the Free Trade Zone Policy. Column (4) presents the results after simultaneously controlling for all three policies. The findings suggest that the results remain robust even after accounting for the influence of these various policies.

To investigate whether the reduction in external financing costs serves as a mechanism through which government data openness influences corporate investment, we operationalize the mechanism variable financing cost (OutFin) as the ratio of interest payable to total liabilities. This hypothesis was tested using models (5) and (6). The regression results for the mechanism variable relative to the independent variable are presented in Column (1) of Table 13. The coefficient of the independent variable (Open) is significantly negative at the 5% level, suggesting that government data openness mitigates financial resource mismatch and reduces external financing costs. In addition, the regression analysis results examining the relationship between corporate investment levels and the mechanism variables are presented in Column (2) of Table 13. The coefficient of the independent variable (Open) is significantly positive at the 1% level, whereas the coefficient of corporate financing cost (OutFin) is significantly negative at the 1% level. This indicates that a reduction in financing costs can enhance corporate investment levels. Consequently, it can be inferred that the openness of government data can be substantiated by its effect of lowering enterprise financing costs and subsequently improving investment levels. Thus, Hypothesis 2a was empirically validated.

To examine whether the expansion of the financing scale serves as a mechanism through which government data openness influences corporate investment, this study primarily focuses on the increase in debt financing. It employs the volume of bank credit acquired as a metric for assessing the scale of corporate financing. Specifically, corporate bank credit is defined as follows: the volume of bank credit acquired (Loan) is calculated as the sum of long-term loans and short-term loans divided by total assets. The regression results pertaining to the mechanism variable (the volume of bank credit acquired) in relation to the explanatory variable (Open) are presented in Column (3) of Table 13. The coefficient for the explanatory variable is 0.0025, which is statistically significant at the 10% level, suggesting that increased government data openness is associated with a rise in the amount of credit accessible to enterprises. Additionally, the regression results concerning the enterprise investment level in relation to the mechanism variables are displayed in Column (4) of Table 13. We find that the coefficient of the explanatory variable (Open) is significantly positive at the 1% level, and the coefficient of financing scale (Loan) is likewise significantly positive at the 1% level. This suggests that an increase in credit acquisition can partially satisfy the financing needs of enterprises, provide financial support for enhancing investment, and elevate the level of corporate investment. Consequently, the mechanism of government data openness can be validated through its role in expanding the financing scale of enterprises and enhancing their investment levels. Thus, Hypothesis 2b was empirically validated.

Given that enterprises of varying ownership structures may exhibit different levels of data utilization on government open data platforms, it is observed that state-owned enterprises (SOEs) are more likely to find alignment between the fields and directions of government data openness and their development strategies. This alignment facilitates the rapid transformation of open data into foundational elements for investment decisions and the identification of new investment opportunities, in contrast to non-state-owned enterprises. Simultaneously, state-owned enterprises typically maintain a close relationship and cooperative dynamic with government departments, which confers inherent advantages in accessing government data, obtaining authorization for data use, and facilitating data access. This symbiotic relationship enables enterprises to swiftly and efficiently acquire and utilize government data for informed investment decisions and strategic business expansion. Furthermore, state-owned enterprises exhibit a comparatively higher tolerance for risk in their investment decisions and demonstrate greater capacity and willingness to invest in emerging sectors and projects that leverage government data openness. This propensity facilitates the transformation and application of achievements derived from government data openness. Consequently, it can be inferred that the influence of government data openness on enterprise investment levels is more pronounced in state-owned enterprises.

To examine whether the influence of government data openness on corporate investment levels is moderated by varying ownership structures, we segregated the sample into two subgroups: state-owned enterprises (SOEs) and non-state-owned enterprises (Non-SOEs), based on the nature of the actual controllers of listed companies. Model (1) was employed for group testing, and the results are presented in columns (1) and (2) of Table 14. The findings indicate that the significance of government data openness is not significant within the category of non-state-owned enterprises. Conversely, in the context of state-owned enterprises, the coefficient associated with government data openness is significantly positive, suggesting that the influence of government data openness on the investment levels of state-owned enterprises is more pronounced than that observed in non-state-owned enterprises.

The influence of government data openness on corporate investment across various industries may exhibit significant variability. High-technology enterprises, in contrast to their non-high-technology counterparts, demonstrate a heightened demand for information and knowledge. The availability of government data can provide these enterprises with valuable insights into technological development trends, market demand dynamics, and the status of competitors. Enterprises utilize government data to comprehend the latest technological trends and shifts in market demand within their industry. This enables them to promptly adjust their investment strategies and development priorities, thereby allocating resources to projects with greater market potential and technological feasibility. Consequently, this accelerates the transformation of innovative achievements into new products and fosters enterprise investment. It can be inferred that the impact of open government data on corporate investment is likely to be more pronounced in high-tech enterprises. To evaluate this inference, we categorized the sample into two subsamples: high-tech enterprises and non-high-tech enterprises, subsequently employing Model (1) for group testing. As evidenced by columns (3) and (4) of Table 14, the coefficient for government data openness is significantly positive at the 5% level within high-tech enterprises, whereas its influence is not statistically significant in the non-high-tech enterprise. This finding suggests that the effect of government data openness on corporate investment is more pronounced in high-tech enterprises.

Economic policy uncertainty not only heightens the unpredictability of a company’s future cash flows but also amplifies the risk of investment failure. Consequently, as macroeconomic uncertainty escalates, the demand for information among enterprises intensifies. On one hand, the transparency of government data can furnish enterprises with critical insights into macroeconomic trends, industry dynamics, and relevant policies and regulations, thereby enhancing their capacity to identify and assess potential risks and opportunities more effectively. On the other hand, government data openness can provide enterprises with critical information concerning industrial development, facilitating a deeper understanding of development trends, market saturation, and technological innovation trajectories. By synthesizing this data with their internal resources and capabilities, enterprises can strategically allocate investment capital to industries exhibiting substantial development potential and strong risk resilience, thereby reducing the probability of investment failure.

It can be inferred that the influence of government data openness on corporate investment is more pronounced under conditions of elevated macroeconomic uncertainty. To empirically examine this hypothesis, we employ the Economic Policy Uncertainty Index (EU) as a proxy for economic policy uncertainty, following the methodology outlined by Baker et al. (2016). The sample is stratified into high (EU-high) and low (EU-low) uncertainty groups based on the mean value of the macroeconomic uncertainty index. Subsequently, Model (1) was utilized to conduct the grouped analysis. The test results are presented in columns (5) and (6) of Table 14. The regression coefficient for the variable Open is not statistically significant in the samples characterized by low macroeconomic uncertainty. However, it is significantly positive in samples exhibiting high macroeconomic uncertainty. This finding suggests that the influence of open government data on corporate investment is more pronounced in enterprises experiencing high levels of macroeconomic uncertainty.

There exist notable disparities in data infrastructure, levels of economic development, data integration capabilities, and market demand across various regions, which may result in divergent effects of government data openness on corporate investment levels. Generally, the eastern region exhibits a relatively advanced data infrastructure and technological environment, facilitating easier access to and utilization of government data by enterprises. This enhanced accessibility is likely to improve production efficiency and competitiveness among enterprises, thereby fostering increased investment. In addition, the eastern region exhibits a comparatively advanced level of economic development, characterized by robust corporate investment and intense market competition. The openness of government data is likely to further stimulate enterprise innovation and enhance market competition, thereby improving investment efficiency and return on investment. Conversely, the central and western regions experience relatively slower economic development, with potentially weaker corporate investment demand. Consequently, we can infer that the influence of government data openness on corporate investment is more pronounced in the eastern region. We categorized cities into two groups, the East and the Midwest, and subsequently applied Model (1) for group testing. The regression results for the Eastern group are presented in Column (7) of Table 14, where the coefficient of the independent variable (Open) is significantly positive. In contrast, Column (8) displays the regression results for the central and western groups, where the coefficient of the independent variable (Open) is not significant. These findings suggest that the effect of government data openness on corporate investment is more pronounced in the eastern cities.