Structural Parameter Calibration And Estimation
The parameters employed in this study are determined through two primary methodologies: calibration and estimation. First, parameter calibration is informed by existing literature, ensuring alignment with established research. Second, Bayesian estimation techniques are applied to quarterly data sourced from the Bank of Korea’s economic statistics, facilitating the derivation of empirically grounded and context-specific parameters. Regarding calibration, parameter values are derived from the context of South Korea and referenced to authoritative literature within the country. For instance, following He and Teng (2024), the discount factor (\({\rm{\beta }}\)) is set at 0.97, while He (2024) sets the risk aversion coefficient (\({\rm{\sigma }}\)) at 2, and Li et al. (2023) informs the choice of the elasticity of substitution (\({\rm{\phi }}\)) at 3.12. South Korea’s recent emphasis on strengthening its cybersecurity strategy, particularly in protecting critical infrastructure and communication networks, plays a critical role in calibrating parameters related to cybercrime. In this study, the parameter \({\rm{\psi }}\), which captures the utility loss or negative impact of cybercrime on households, is calibrated at 0.05. This value reflects the severity of disruptions caused by cybercrime to household welfare and aligns with South Korea’s proactive measures to minimize the economic and social costs of cyber threats, especially in essential sectors. Kim and Seong (2022) provide additional context, showing that crime prevention projects in specific regions have effectively reduced crime rates, implying that well-targeted investments can significantly lower economic losses associated with cybercrime. In South Korea, where cybercrime exerts substantial pressure on businesses and households, the government has allocated substantial resources to prevention efforts. Based on this, the parameter \({{\rm{\kappa }}}_{1}\) is calibrated to reflect the effectiveness of such investments, set at 0.1 to quantify their impact on mitigating economic damage caused by cyberattacks. Similarly, following He (2023), the capital share parameter (\({\rm{\alpha }}\)) is set at 0.5.
Ebert (2020) highlights South Korea’s vulnerability to transnational cybercrime and cyber-espionage, exacerbated by North Korean-originated attacks. Park et al. (2022) detail the economic losses incurred, including data breaches, ransomware, and financial fraud. In this context, the parameter \({{\rm{\kappa }}}_{2}\), representing the ratio of cybercrime-related losses to the size of the economy, is set at 0.05. Building on Kshetri (2016) and Mohasseb et al. (2020), cybercrime costs are often expressed as a percentage of GDP or corporate revenue. Accordingly, the parameter \({{\rm{\kappa }}}_{3}\), reflecting the impact of cybercrime on firm productivity, is set at 0.01 to capture production disruptions caused by cyberattacks. This value is consistent with broader findings in the literature on the macroeconomic effects of cybercrime. The effectiveness of South Korea’s cybersecurity investments is represented by the parameter \({\rm{\theta }}\), which reflects the marginal utility derived from government spending on cybercrime prevention. Drawing on the most recent cybersecurity strategy and the studies by Song et al. (2021) and Ku (2021), this study sets \({\rm{\theta }}\) at 0.1, illustrating the government’s capacity to reduce cybercrime’s economic disruptions through strategic investments, thereby strengthening overall economic resilience. Finally, the persistence of cybercrime shocks, represented by the parameter \({\rm{\xi }}\), is crucial in assessing the duration and recurrence of such incidents. Given South Korea’s advanced digital infrastructure and the nature of recurring cyberattacks, \({\rm{\xi }}\) is calibrated at 0.75 to reflect the sustained impact of cybercrime on the economy, factoring in both the frequency of attacks and the response speed of government and businesses. For a more intuitive understanding of the calibration parameters discussed in this paper, their basic information is summarized in Table 1.
This study employs Bayesian estimation techniques, utilizing quarterly data from the Bank of Korea’s Economic Statistics System for the period spanning Q1 2000 to Q4 2023. This analysis centers on South Korea’s GDP and the consumer price index (CPI), which are derived based on key macroeconomic indicators. Following the methodology outlined by He and Wang 2022), both GDP and CPI are transformed logarithmically to eliminate long-term trends, and first-order differences are subsequently calculated. The resulting series is standardized by multiplying by 100, yielding a dataset of 92 observations. In the Bayesian framework, the prior distribution of the parameter vector, denoted as \({\rm{p}}({{\rm{\theta }}}_{{\rm{M}}}\left|{\rm{M}}\right.)\), is established for the model under consideration. The likelihood function, conditional on the model and its parameters, is represented as \({\rm{L}}({{\rm{\theta }}}_{{\rm{M}}}\left|{{\rm{Y}}}_{{\rm{T}}},{\rm{M}}\right.)\), with \({\rm{p}}({{\rm{Y}}}_{{\rm{T}}}\left|{{\rm{\theta }}}_{{\rm{M}}},{\rm{M}}\right.)\) indicating the probability density function for the observed data. The dataset up to period \({\rm{T}}\) is denoted as \({{\rm{Y}}}_{{\rm{T}}}\), while the probability density function \({\rm{p}}(\cdot )\) may follow distributions such as gamma, beta, generalized beta, normal, inverse gamma, shifted gamma, or uniform. The marginal density function of the data, given the model, is expressed in Eq. (11). This marginal density function integrates over the full range of parameter values, \({{\rm{\theta }}}_{{\rm{M}}}\), providing a comprehensive evaluation of the model’s fit to the observed data. This step is fundamental within the Bayesian approach, allowing for the prior distributions to be updated with the observed data. The resulting posterior distributions provide the most likely estimates of the model’s parameters, offering insights grounded in the available empirical evidence.
$${\rm{p}}\left({{\rm{Y}}}_{{\rm{T}}}\left|{{\rm{\theta }}}_{{\rm{M}}},{\rm{M}}\right.\right)={\int }_{{{\rm{\theta }}}_{{\rm{M}}}}^{1}{\rm{p}}\left({{{\rm{\theta }}}_{{\rm{M}}},{\rm{Y}}}_{{\rm{T}}}\left|{\rm{M}}\right.\right){{\rm{d}}}_{{{\rm{\theta }}}_{{\rm{M}}}}={\int }_{{{\rm{\theta }}}_{{\rm{M}}}}^{1}{\rm{p}}\left({{\rm{Y}}}_{{\rm{T}}}\left|{{\rm{\theta }}}_{{\rm{M}}},{\rm{M}}\right.\right){\rm{p}}\left({{\rm{\theta }}}_{{\rm{M}}}\left|{\rm{M}}\right.\right){{\rm{d}}}_{{{\rm{\theta }}}_{{\rm{M}}}}$$
(11)
Bayes’ theorem is applied to derive the posterior density, denoted as \({\rm{p}}({{\rm{Y}}}_{{\rm{T}}}\left|{{\rm{\theta }}}_{{\rm{M}}},{\rm{M}}\right.)\), which is formulated as the product of the likelihood function and the prior density. This relationship is formally defined in Eqs. (12) and (13). The posterior density establishes a probabilistic framework that integrates both the prior knowledge of the parameter vector \({{\rm{\theta }}}_{{\rm{M}}}\) and the empirical information obtained from the observed data \({{\rm{Y}}}_{{\rm{T}}}\). By combining the likelihood—representing the probability of the observed data given the parameters—with the prior distribution, the posterior distribution provides a more refined estimate of the parameters. This approach allows the model to incorporate observed evidence while maintaining consistency with the prior beliefs, yielding a comprehensive update to parameter estimates within the structure of the specified model \({\rm{M}}\).
$${\rm{p}}({{\rm{\theta }}}_{{\rm{M}}}\left|{{\rm{Y}}}_{{\rm{T}}},{\rm{M}}\right.)=\frac{{\rm{p}}({{\rm{Y}}}_{{\rm{T}}}\left|{{\rm{\theta }}}_{{\rm{M}}},{\rm{M}}\right.){\rm{p}}({{\rm{\theta }}}_{{\rm{M}}}\left|{\rm{M}}\right.)}{({{\rm{Y}}}_{{\rm{T}}}\left|{\rm{M}}\right.)}$$
(12)
$${\rm{p}}({{\rm{\theta }}}_{{\rm{M}}}\left|{{\rm{Y}}}_{{\rm{T}}},{\rm{M}}\right.)=\frac{{\rm{L}}({{\rm{\theta }}}_{{\rm{M}}}\left|{{\rm{Y}}}_{{\rm{T}}},{\rm{M}}\right.){\rm{p}}({{\rm{\theta }}}_{{\rm{M}}}\left|{\rm{M}}\right.)}{{\int }_{{{\rm{\theta }}}_{{\rm{M}}}}^{1}{\rm{p}}({{\rm{Y}}}_{{\rm{T}}}\left|{{\rm{\theta }}}_{{\rm{M}}},{\rm{M}}\right.){\rm{p}}({{\rm{\theta }}}_{{\rm{M}}}\left|{\rm{M}}\right.){{\rm{d}}}_{{{\rm{\theta }}}_{{\rm{M}}}}}$$
(13)
The posterior kernel is defined as the numerator of the posterior density, denoted by \({\rm{k}}({{\rm{\theta }}}_{{\rm{M}}}\left|{{\rm{Y}}}_{{\rm{T}}},{\rm{M}}\right.)\equiv {\rm{L}}({{\rm{Y}}}_{{\rm{T}}}\left|{{\rm{\theta }}}_{{\rm{M}}},{\rm{M}}\right.){\rm{p}}({{\rm{\theta }}}_{{\rm{M}}}\left|{\rm{M}}\right.){\rm{A}}\). This kernel represents the product of the likelihood function and the prior distribution, capturing the combined influence of both the observed data and prior beliefs on the parameter estimates. The posterior distribution of the parameter vector for the model is proportional to the posterior density, which establishes a direct relationship between the model parameters and the observed data. This proportionality is formally outlined in Eq. (14), providing the mathematical basis for the posterior distribution within the Bayesian framework. This formulation highlights how the posterior distribution integrates prior knowledge with empirical evidence to refine the parameter estimates.
$${\rm{p}}\left({{\rm{\theta }}}_{{\rm{M}}}\left|{{\rm{Y}}}_{{\rm{T}}},{\rm{M}}\right.\right)\propto {\rm{L}}\left({{\rm{\theta }}}_{{\rm{M}}}\left|{{\rm{Y}}}_{{\rm{T}}},{\rm{M}}\right.\right){\rm{p}}\left({{\rm{\theta }}}_{{\rm{M}}}\left|{\rm{M}}\right.\right)$$
(14)
The distribution outlined above is defined by standard measures of central tendency, including the mean, median, and mode, as well as dispersion metrics such as the standard deviation and selected percentiles. Once the model is fully specified and the necessary data is obtained, the likelihood function can be estimated using advanced techniques. For linear models, the Kalman filter is typically employed, while nonlinear models often utilize particle filters. These methodologies facilitate efficient estimation of the likelihood function, even in complex and high-dimensional contexts. The resulting statistical measures, crucial for interpreting the model’s results, are presented in Table 2.
Simulating the economic effects of cybercrime
This subsection employs an impulse response function framework to analyze the complex interactions between key economic sectors—households, firms, government, and the cybercrime sector—under the influence of a cybercrime shock. The framework facilitates a comprehensive examination of how cybercrime shocks propagate through the economy, impacting critical macroeconomic variables such as consumption, output, labor supply, wages, and returns on capital. By modeling these shocks as exogenous disturbances, the analysis captures not only the direct effects of cybercrime on household and firm decision-making but also underscores the pivotal role of government interventions in mitigating these adverse impacts. The simulation results, presented in Fig. 1, provide crucial insights into the macroeconomic consequences of cybercrime shocks and their persistence over time, offering a deeper understanding of the dynamics between sectors in the presence of cyber threats.
Simulation results of cybercrime shock.
The simulation results depicted in Fig. 1 illustrate the extensive macroeconomic effects of cybercrime shocks. Key economic variables—consumption, output, labor supply, wages, and the return on capital—exhibit substantial fluctuations in response to these shocks, highlighting the broad-reaching impact of cyberattacks on the economic system. Following a cybercrime shock, consumption and output decline markedly, labor supply is disrupted, and both wages and returns on capital experience significant volatility. These findings reflect the transmission mechanisms through which cybercrime shocks disrupt firm production and household behavior, thereby influencing the central drivers of macroeconomic performance. In terms of consumption, the results in Fig. 1 indicate that cybercrime shocks directly reduce household consumption capacity. This outcome is closely linked to financial losses and personal data breaches caused by cyberattacks, which significantly diminish household utility. Theoretically, this can be explained by the inclusion of the cybercrime utility loss coefficient ψ in the utility function, which quantifies the negative effects of cybercrime on household welfare. This reduction in consumption, as reflected in the simulation results, is particularly pronounced in the context of South Korea, where households rely heavily on digital infrastructure—a primary target of cybercrime—thus making financial losses and data breaches especially impactful. From the perspective of output, cybercrime-induced disruptions to the firm sector lead to reduced production efficiency, as demonstrated by the significant decline in output in Fig. 1. In the theoretical model, cyberattacks impose additional repair costs on firms, diminishing their production efficiency and adversely affecting the supply side of the economy. South Korean firms, as integral components of the global digital economy, rely on complex communication networks and information systems, making them particularly vulnerable to cybercrime shocks. These disruptions are reflected in reduced production efficiency and increased operational costs, a phenomenon that aligns with the findings of Wolff (2022) and Bhakhri et al. (2024), who similarly analyzed the substantial financial losses stemming from cyberattacks, including data breaches and ransomware, which significantly increased firms’ costs.
In the labor market, cybercrime shocks lead to a contraction in labor supply, thereby affecting market-clearing. The simulation results suggest that the shock to firm production is mirrored by a reduction in labor demand, as firms adjust their inputs and reduce labor demand in response to cyberattacks. Macroeconomic theory suggests that when labor demand decreases, labor supply is similarly affected, resulting in a decline in wages. This is corroborated by the simulation results in Fig. 1, where wages decrease following a cybercrime shock. These results are consistent with the findings of Kovalchuk et al. (2021) and Ohrimenco et al. (2021), who emphasize the disruptive effects of cybercrime on firm productivity and subsequent adjustments in labor demand. Regarding the return on capital, Fig. 1 reveals a decline in returns following a cybercrime shock. This is explained by the capital return function in the theoretical model, where cyberattacks negatively impact firm production and capital investment. Firms must allocate additional resources for repair costs and cybersecurity measures, which reduces capital returns. In South Korea, firms have made significant investments in response to cyberattacks, leading to reduced returns on capital. This finding is consistent with the work of Paek et al. (2021) and Kim (2022), who highlighted the substantial economic losses South Korea has incurred in responding to transnational cybercrime and cyber-espionage, particularly in terms of capital market impacts. When comparing the simulation results of this study with those of prior research, several points of innovation and distinction can be identified. First, similar to the findings of earlier studies, this analysis demonstrates the significant negative impact of cybercrime on output and firm production. However, the primary innovation of this study lies in its more comprehensive exploration of the government’s role in combating cybercrime, particularly in how cybersecurity investments help mitigate economic volatility. Studies such as those by Kang and Lee (2017) and Kim et al. (2019) underscore the effectiveness of crime prevention design projects in reducing crime rates, and this study extends that concept to cybercrime. The results show that government investment in cybersecurity can substantially reduce the frequency and severity of cyberattacks, thereby mitigating their negative economic impacts.
Second, unlike previous studies, which primarily focus on the direct financial losses incurred by firms, this study places greater emphasis on the utility losses experienced by households as a result of cybercrime. By introducing the parameter ψ, this study quantifies the effects of cybercrime on household consumption behavior and highlights the long-term negative consequences for household welfare. This perspective complements existing research on cybercrime by illustrating how household behavior contributes to the broader macroeconomic effects of cybercrime shocks. Finally, while earlier studies such as Kshetri (2010) and Smith et al. (2019) identified the destructive impact of cybercrime on firm productivity, this study distinguishes itself by employing a dynamic stochastic general equilibrium model to simulate the dynamic effects of cybercrime shocks. The model traces the transmission pathways of these shocks across multiple economic variables over time, revealing the persistent impacts of cybercrime on the overall economy. By incorporating the parameter ζ, this study captures the inertia of cybercrime shocks—a dimension that has been insufficiently explored in prior research. In conclusion, this study simulates the macroeconomic effects of cybercrime shocks using an impulse response function framework, validating some findings from the existing literature while offering new insights through an analysis of government interventions and the economic effects of cybersecurity investments. The results provide a valuable theoretical foundation for policymakers seeking to address the growing threat of cybercrime, particularly in the context of the South Korean economy.
Simulating the economic effects of government investment in cybercrime prevention measures
Before presenting the simulation results of government investment in cybercrime prevention measures, it is crucial to outline the economic rationale that underpins the model. Government investments in cybersecurity are not merely a response to immediate cyber threats, but also serve as a strategic tool to bolster the overall resilience of the economy. These investments aim to mitigate the adverse spillovers of cybercrime on households and firms by reducing the frequency, severity, and persistence of cyberattacks. The model developed in this study incorporates government intervention as an exogenous shock, directly influencing key macroeconomic variables, including consumption, output, labor supply, wages, and returns on capital. This framework provides a rigorous analysis of how government spending on cybersecurity prevention contributes to economic stability and growth. The following subsection discusses the simulation results presented in Fig. 2, which demonstrate the extent to which these preventive measures can offset the negative economic impacts of cybercrime, thereby promoting long-term economic resilience.
Simulation results of government investment in cybercrime prevention measures shock.
Figure 2 illustrates the macroeconomic impact of government investment in cybercrime prevention measures on key variables, highlighting the dynamic effects on consumption, output, labor supply, wages, and returns on capital. The simulation results underscore the critical role of government intervention in mitigating the adverse economic effects of cybercrime. These findings demonstrate how strategic investments in cybersecurity reduce the frequency, severity, and persistence of cyberattacks, ultimately enhancing economic stability. From the perspective of consumption, Fig. 2 reveals that government investment in cybersecurity significantly enhances household consumption levels. As government spending on cybersecurity infrastructure increases, the frequency and severity of cyberattacks decline, reducing the risks of financial losses and data breaches faced by households. Consequently, household consumption capacity and utility improve. This outcome is consistent with established macroeconomic theory, which posits that public investment, by enhancing security and reducing uncertainty, can boost household consumption and welfare. In the case of South Korea, where households are highly dependent on digital infrastructure, the negative impact of cybercrime on consumption is particularly pronounced, making government investment in cybersecurity all the more crucial. In terms of output, government investment in cybersecurity substantially improves firm productivity. The simulation results suggest that defensive government investment mitigates productivity losses and reduces the costs associated with repairing cyberattack damage, thereby increasing overall output levels. This can be explained by an improvement in the production function, where firms are able to allocate more resources to productive activities rather than diverting them toward damage control and cybersecurity expenditures. For South Korea, a country heavily reliant on its digital economy, enhanced cybersecurity investment plays a pivotal role in boosting firm competitiveness and supporting economic growth.
The labor market also benefits significantly from government investment in cybersecurity. The simulation results indicate that the reduction in cybercrime and the corresponding improvement in firm productivity lead to an increase in labor demand. This increase is reflected in the labor market equilibrium, as rising demand from firms results in an expansion of labor supply and an increase in wages. This finding aligns with classical economic theory, which suggests that an increase in the demand for productive inputs leads to adjustments in labor supply and wage levels. For a highly developed economy like South Korea, the reduction in cybercrime may alleviate labor market pressures and promote both employment and wage growth. Regarding returns on capital, government investment in cybersecurity reduces the costs firms incur in repairing cyberattack-related damage, indirectly improving the return on capital. As indicated by the simulation results, as government investment in cybersecurity increases, the negative impact of cybercrime on firms’ returns on capital diminishes, enabling firms to realize higher returns on their investments. This observation is consistent with capital market theory, which holds that a reduction in operational risks tends to improve the return on capital investments. In South Korea, where cybercrime incurs substantial repair and defense costs for firms, increased government investment in cybersecurity alleviates financial burdens and enhances capital market performance. A comparison of these simulation results with prior studies further highlights the innovation and unique contributions of this study. Similar to the findings of Dupont (2017) and Park et al. (2019), this study confirms the significant negative impact of cybercrime on firms and households. However, the distinctive feature of this study lies in its deeper exploration of the role of government in combating cybercrime. Specifically, the study demonstrates how government investment in cybersecurity mitigates economic volatility by improving firm productivity and enhancing household utility. Additionally, the effectiveness of crime prevention initiatives has been well-documented by Morgan and Homel (2013) and Beato and Silveira (2014), particularly in reducing crime rates. This study extends that analysis to the realm of cybercrime, showing that government investment in cybersecurity can significantly reduce the frequency and intensity of cyberattacks, thereby mitigating their adverse economic impacts.
Moreover, this study introduces the parameter θ, which quantifies the marginal utility of government cybersecurity investment, a concept that has been rarely addressed in the existing literature. By incorporating this parameter, the study highlights how government investment effectively mitigates the impact of cybercrime on firms and households, thereby strengthening the economy’s overall resilience to external shocks. Compared with the studies by Renaud et al. (2020) and Kianpour et al. (2021), this research places further emphasis on the importance of government intervention in the context of cybersecurity. While previous studies have primarily focused on the impact of cybercrime on firm productivity, this study adopts an impulse response function framework to simulate the dynamic effects of cybercrime shocks. It traces the transmission of these shocks across various economic variables over time, revealing the long-term positive effects of government cybersecurity investment on the broader economy. In conclusion, this study, through an impulse response function framework, simulates the macroeconomic benefits of government cybersecurity investment. The findings validate the importance of government intervention in addressing cybercrime and offer valuable theoretical support for policymakers. In the face of the growing threat of cybercrime, these results demonstrate that government investment can significantly enhance economic resilience and foster sustainable long-term growth.
Forecast error variance decomposition
This section presents a detailed variance decomposition analysis of the estimated model, aiming to identify the external factors contributing to fluctuations in key macroeconomic variables in South Korea. The analysis specifically focuses on assessing the relative contribution of three types of shocks—technology shocks, cybercrime shocks, and shocks arising from government investment in cybercrime prevention measures—to the variance of critical economic variables, including consumption, output, labor supply, wages, returns on capital, and government spending. By decomposing the forecast error variance, the analysis provides a clearer understanding of the role each shock plays in driving the dynamics of these variables across different time horizons. Adopting the methodology proposed by Smets and Wouters (2003), the study defines the short term as spanning 1 to 4 quarters (1 year), the medium term as 10 quarters (2.5 years), and the long term as 100 quarters (25 years). These time distinctions allow for a more nuanced analysis of how each shock impacts the economy in the short, medium, and long run. By doing so, the variance decomposition sheds light on the persistence and significance of these shocks over time, revealing important insights about their effects on economic stability and growth. The results, presented in Fig. 3, offer valuable insights into the dynamic interactions between technology, cybercrime, and government intervention in cybercrime prevention. They illustrate the proportion of total variance attributable to each shock for different macroeconomic variables, highlighting the relative importance of each type of disturbance. The analysis is crucial in identifying the shocks that predominantly influence short-term volatility compared to those that have a more lasting, long-term impact on economic performance. Understanding these dynamics is particularly relevant for policymakers aiming to design effective strategies to mitigate the adverse effects of cybercrime while leveraging technological advancements and government interventions to support sustainable growth.
Results of forecast error variance decomposition (e1 technology shock; e2 cybercrime shock; e3 government investment in cybercrime prevention measures shock).
Figure 3 illustrates the effects of technology shocks, cybercrime shocks, and government investment in cybercrime prevention on key macroeconomic variables, such as consumption, output, labor supply, wages, returns on capital, and government spending. Utilizing forecast error variance decomposition, the analysis clearly outlines the contribution of each type of shock to fluctuations in these variables across short, medium, and long-term horizons. The results provide valuable insights into the sources of macroeconomic volatility and offer critical implications for the design of policies aimed at addressing external disruptions. In the short term, technology shocks exert a significant influence on all key variables, particularly driving fluctuations in output and returns on capital. This outcome is consistent with macroeconomic theory, which suggests that technological advancements can lead to rapid gains in productivity, resulting in notable short-term effects on economic performance. However, as time progresses, the influence of technology shocks diminishes, particularly in the long run, where the impact of cybercrime shocks and government investment shocks becomes more pronounced. For South Korea, a nation heavily dependent on technological innovation and digital development, the strong short-term effects of technology shocks reflect the characteristics of its technology-intensive economic structure. Cybercrime shocks, in contrast, have a more notable impact on consumption and labor supply in both the short and medium term, primarily due to the direct effects of cyberattacks on households and businesses, which lead to disruptions in labor supply and wage dynamics. This finding corresponds closely with the reality faced by South Korea, where increasingly frequent cyberattacks—especially targeting critical infrastructure and communication networks—have had a profound effect on household consumption and firm productivity. The simulation results from Fig. 3 further suggest that the persistence of cybercrime shocks can have long-term implications for economic variables, demonstrating the sustained adverse effects of cybercrime on the broader economy. Without sufficient defensive measures, cybercrime has the potential to erode firm productivity and household welfare over extended periods.
On the other hand, government investment in cybersecurity yields positive economic effects, particularly in the medium and long term. As government spending on cybersecurity infrastructure increases, the frequency and severity of cyberattacks decline, leading to improvements in firm productivity and a gradual recovery in household consumption. Figure 3 reveals that government investment shocks significantly improve wages and labor market conditions in the medium term, which is consistent with economic theory. Public investment not only reduces uncertainty but also enhances economic security, thereby fostering growth. In South Korea, where the government has strengthened its cybersecurity defenses, investments in cybersecurity infrastructure have mitigated the economic damage caused by cyberattacks, playing a crucial role in ensuring long-term economic stability. A comparison of the forecast error variance decomposition results with previous studies highlights several key innovations. While studies such as Lee (2020) similarly identify the negative impact of cybercrime shocks on consumption, output, and labor supply, this study provides a more in-depth analysis of the positive role of government investment in cybersecurity. In particular, it highlights how sustained public investment can mitigate the economic repercussions of cybercrime, thereby strengthening economic resilience. Moreover, unlike the analyses provided by Park et al. (2016) and Kianpour et al. (2021), this study employs an impulse response function framework to explore in greater detail how government investment stabilizes the labor market and boosts wages, counterbalancing the negative effects of cybercrime. This perspective, relatively rare in existing literature, expands the theoretical understanding of the role of government intervention in cybersecurity. In conclusion, the forecast error variance decomposition results demonstrate the differential effects of various shocks on key macroeconomic variables, with particular emphasis on how government investment provides long-term stability to the economy. The analysis underscores the importance of government intervention in addressing cybercrime, offering valuable insights for policymakers aiming to enhance economic resilience and ensure sustainable growth.
Discussion
This present investigation contributes significantly to the literature by providing an in-depth exploration of the macroeconomic impacts of cybercrime within the specific context of South Korea, a technologically advanced economy frequently targeted by sophisticated cyber threats. By utilizing a tailored dynamic stochastic general equilibrium model that incorporates Bayesian estimation and impulse response functions, the study advances beyond the traditional sector-specific analyses, offering a holistic examination of the interplay among household, firm, government, and cybercrime sectors. This comprehensive integration allows for nuanced insights into how cybercrime propagates through interconnected economic channels, manifesting not only in direct financial losses but also in broader systemic disruptions affecting consumption patterns, productivity metrics, and returns on capital. This study’s methodological approach provides a robust analytical framework capable of capturing the intricate temporal dynamics and persistence of cybercrime shocks. While previous literature has largely addressed immediate impacts of cyber incidents, the inclusion of parameters specifically calibrated to South Korea enhances the model’s ability to simulate long-term adjustments and policy responses realistically. However, despite the rigorous calibration to the South Korean context—reflecting its advanced digital infrastructure, proactive government cybersecurity measures, and significant exposure to international cyber threats—the direct generalizability of these findings to other economic contexts remains constrained. Different nations vary widely in terms of technological maturity, cybersecurity governance structures, institutional capabilities, and geopolitical vulnerability. Thus, caution must be exercised when applying insights from the South Korean model to economies with substantially different digital and institutional landscapes. Nevertheless, the structural design of our analytical model inherently offers flexibility, enabling adaptation through recalibration tailored to distinct economic and institutional contexts. This adaptability presents an opportunity for broader applicability and comparative analyses, particularly for economies undergoing rapid digital transformations or those with emerging digital sectors facing increasing cyber threats. Consequently, future studies are encouraged to utilize this foundational model framework to examine economies with varying degrees of technological integration and institutional resilience, thereby enhancing cross-national policy relevance and understanding of differential cybercrime impacts. Furthermore, this analysis explicitly acknowledges and systematically addresses potential methodological concerns raised regarding the exogenous treatment of cybercrime shocks. While limitations in available empirical data have constrained the capacity for extensive econometric validations, such as generalized method of moments or sensitivity analyses, the study transparently recognizes these constraints within the conclusions. Future research incorporating richer datasets and advanced econometric techniques would substantially enhance the robustness and causal interpretability of findings, potentially offering greater insights into the endogenous interplay between cybercrime occurrences and policy interventions. Lastly, this study’s policy implications provide strategic insights uniquely suited to South Korea’s economic and institutional realities, emphasizing the feasibility and practicality of proposed interventions. By suggesting incremental policy measures that strategically integrate public-private partnerships and targeted fiscal incentives, the analysis pragmatically addresses potential constraints related to fiscal capacity and private-sector cooperation. Future research might further refine these policy recommendations by exploring detailed scenario modeling under varying economic and fiscal conditions, thereby facilitating their adoption and implementation in diverse contexts.
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