Technological innovation represents the cornerstone of modern economic development and serves as the primary engine driving sustained prosperity, competitiveness, and societal advancement in contemporary knowledge-based economies, where nations that successfully cultivate robust innovation ecosystems consistently demonstrate superior economic performance characterized by enhanced productivity growth, diversified economic structures, and greater resilience to external shocks (Cetin et al., 2025; Mohamed et al., 2022). The fundamental importance of innovation becomes particularly evident when we consider that technological progress not only facilitates the creation of new products, processes, and services, but also generates positive spillover effects throughout the economy, fostering human capital accumulation, knowledge diffusion, and structural transformation that collectively contribute to long-term economic growth, as emphasized by the endogenous growth theory developed by pioneering scholars such as (Romer, 1990) and (Aghion & Howitt, 1990). Understanding the determinants of technological innovation has thus become a critical area of research, with scholars identifying numerous factors ranging from human capital and infrastructure investments to institutional quality and policy frameworks. However, among these various determinants, the role of political stability has emerged as particularly crucial, given its fundamental influence on the institutional environment within which innovation activities take place (Wang et al., 2024). Political instability, encompassing phenomena such as frequent government changes, policy uncertainty, civil unrest, weak governance structures, and institutional breakdown, creates an environment of uncertainty that can significantly undermine the long-term investments essential for technological innovation, particularly research and development (R&D) activities that are characterized by high sunk costs, extended gestation periods, and uncertain returns, making them exceptionally sensitive to political risk and policy volatility (Huang & Wen, 2025; Nadeem et al., 2020). The theoretical foundation for understanding this relationship rests on the uncertainty channel, whereby political instability increases uncertainty about future policies, property rights, and regulatory frameworks, leading rational economic actors to reduce investment in productive activities, especially those requiring sustained long-term commitments, such as innovation projects, while simultaneously encouraging capital flight and brain drain as skilled researchers and entrepreneurs seek more stable environments for their activities (Dimitraki & Emmanouilidis, 2025). The empirical literature examining the nexus between political instability and technological innovation has evolved considerably in recent years, with early studies focusing primarily on the broader relationship between institutions and economic outcomes. However, more recent research has begun to provide nuanced insights into the specific mechanisms through which political instability affects innovation performance. (Wang et al., 2024) conducted a comprehensive analysis demonstrating that political instability significantly reduces innovation in renewable energy technologies, particularly in countries with robust technological capabilities. Their findings revealed that the adverse effects are transmitted through multiple channels, including reduced foreign direct investment flows that typically bring technology transfer, decreased government R&D expenditure due to fiscal constraints and shifting policy priorities, and the migration of skilled human capital to more stable environments. Similarly, (Nadeem et al., 2020) provide important evidence on the innovation-politics nexus by examining the combined effects of foreign aid and political instability on innovation outcomes, revealing that aid, political instability, and terrorism all adversely impact innovation performance, thereby highlighting the complex interplay between political factors and technological advancement. The heterogeneous effects of political instability across different country groups have received increasing scholarly attention, with (Sabir et al., 2019) demonstrating that developed countries possess stronger institutional frameworks and deeper financial markets that can better withstand political shocks, while developing countries remain more vulnerable to the disruptive effects of political instability due to their weaker institutions, limited resources, and greater dependence on government support for innovation activities. This differential impact suggests that the relationship between political instability and innovation may not be uniform across countries with varying levels of institutional development and economic sophistication, which has important implications for policy design and international development strategies. (Wang et al., 2024) further explored this theme by examining the role of education, political stability, and information and communication technology (ICT) in shaping technological innovation across Belt and Road Initiative (BRI) countries. They found that political stability plays a crucial positive role in fostering innovation outcomes, while their comprehensive analysis covering the period 2004-2020 provided additional evidence for the importance of stable political environments in supporting technological advancement. The literature also reveals interesting insights into the specific mechanisms through which political instability affects innovation, suggesting that instability reduces innovation through decreased foreign direct investment flows that bring essential technology transfer and knowledge spillovers (Ali et al., 2023), reduced government R&D expenditure as resources are diverted to address immediate political concerns rather than long-term development objectives (Gao et al., 2023), and the creation of brain drain effects as skilled researchers and entrepreneurs migrate to more stable environments where they can pursue their activities with greater security and predictability (Jafarov, 2025). Despite this growing body of research, several important gaps remain in our understanding of the political instability-innovation relationship. These include the lack of comprehensive cross-country analyses that examine the relationship across diverse economic contexts and institutional settings, the absence of consensus on appropriate measurement approaches for both political instability and innovation outcomes, limited attention to heterogeneous effects across different types of countries, poor understanding of the temporal dynamics of this relationship, and insufficient insights into potential non-linear relationships where moderate levels of instability might spur innovation through creative destruction mechanisms while extreme instability uniformly reduces innovation outcomes. Against this backdrop, this study aims to provide a comprehensive examination of the relationship between political instability and technological innovation using a large cross-country dataset spanning 73 countries, including both developed and developing economies, over the period 2010-2024, The primary research objective is to investigate how political instability affects technological innovation outcomes while accounting for various economic, demographic, and institutional factors that may influence this relationship. This study addresses several specific research questions designed to advance our understanding of this complex relationship. First, what is the overall impact of political instability on technological innovation across countries when controlling for relevant economic and institutional variables? Second, do the effects of political instability on innovation differ systematically between developed and developing countries? If so, what factors account for these differences? Third, how do various control factors, such as economic development levels, population dynamics, industrialization patterns, educational investments, and foreign direct investment flows, moderate the political instability-innovation relationship? Based on the theoretical framework derived from uncertainty theory and the empirical evidence reviewed in the recent literature, we formulate our primary hypothesis that political instability has a negative impact on technological innovation (H1). This hypothesis is grounded in the understanding that political instability creates an environment of policy uncertainty that discourages the long-term investments essential for R&D and innovation activities. This relationship is expected to be particularly strong given the characteristics of innovation investments, including their irreversible nature, extended payback periods, and dependence on stable institutional frameworks for success. Additionally, we propose a secondary hypothesis that the negative impact of political instability on technological innovation is stronger in developing countries than in developed countries (H2), reflecting the differential institutional capacities whereby developed countries typically possess more robust institutions, deeper financial markets, stronger rule of law, and greater resources that may provide some insulation against the adverse effects of political instability. This study makes several important contributions to the existing literature by providing one of the most comprehensive cross-country analyses of the political instability-innovation relationship through the examination of 73 countries over a 15-year period encompassing both developed and developing economies. It employs a robust methodological approach using two-way fixed effects estimation to control for unobserved country-specific and time-specific factors that may confound the relationship, explicitly examining heterogeneous effects between developed and developing countries to provide insights into how institutional development mediates this relationship. It utilises a comprehensive set of control variables drawn from various streams of literature, including economic development indicators, demographic factors, industrialization measures, educational investments, and foreign direct investment flows. Finally, it generates important policy implications for governments, international organizations, and development agencies by documenting the adverse effects of political instability on innovation and identifying differential impacts across country groups. The findings of this study contribute to a deeper understanding of the institutional determinants of innovation and provide valuable evidence-based insights for policymakers seeking to foster innovation-driven economic development in the face of political challenges, which is becoming increasingly critical as countries around the world grapple with rising political polarization, institutional instability, and the pressing need to maintain technological competitiveness in a rapidly evolving global economy where innovation capacity increasingly determines national economic success and societal well-being.
The remainder of this paper is organised as follows: Section 2 discusses the literature; Section 3 discusses the sample data, variables, and empirical specifications. Section 4 presents the empirical analysis, including the heterogeneous analysis, robustness checks, and an additional test. Section 5 concludes and discusses the policy implications of the study.
The relationship between political instability and technological innovation is grounded in several complementary theoretical frameworks that help us understand why stable political environments are crucial for innovative activities. At its core, institutional theory provides the foundation for understanding that institutions, defined as the formal and informal rules that govern economic and social interactions, create the environment within which innovation activities take place (North, 1990). This theoretical foundation emphasises that stable and predictable institutional frameworks reduce transaction costs, provide clear property right protections, and create the certainty necessary for long-term investments, particularly in research and development activities that are characterised by high uncertainty and extended payback periods.
Building on institutional theory, uncertainty theory offers a more specific mechanism through which political instability affects innovation outcomes. Political uncertainty, whether manifested through frequent government changes, policy reversals, civil unrest, or weak governance structures, creates an environment in which firms and entrepreneurs cannot reliably predict future policy directions, regulatory frameworks, or the security of their investments (Bloom et al., 2007). This uncertainty is particularly damaging for innovation activities because R&D investments typically require sustained commitment over multiple years, involve substantial sunk costs, and depend heavily on stable intellectual property protections and supportive policy environments. Recent empirical studies have demonstrated that political uncertainty significantly reduces corporate R&D investment, with firms postponing or cancelling innovation projects when political environments become unstable (Jens, 2017)
However, the literature also reveals complexities in this relationship. Interestingly, some recent studies have identified what researchers term the "bright side" of political uncertainty, suggesting that moderate levels of political uncertainty may stimulate certain types of innovation activities. Research using close gubernatorial elections as a quasi-natural experiment has documented a positive effect of political uncertainty on firm-level R&D, in contrast to the existing literature documenting the negative impacts of political uncertainty on capital investment. This finding suggests that while physical capital investment may decline during periods of political uncertainty, firms may increase R&D spending as a strategic response to uncertainty, possibly to build competitive advantages or hedge against political risks.
The endogenous growth theory framework, developed by scholars such as (Aghion & Howitt, 1990; Romer, 1990), provides additional theoretical grounding by emphasising the role of knowledge accumulation and technological progress in driving economic growth. This theory suggests that innovative activities generate positive spillovers throughout the economy, making them particularly valuable for long-term development. However, these spillovers depend critically on the presence of stable institutions that can support knowledge diffusion, protect intellectual property rights, and maintain collaborative networks essential for innovation ecosystems.
The theoretical foundation for following hypothesis rests on the convergence of three complementary theoretical frameworks that collectively explain why political instability undermines technological innovation. From an institutional theory perspective, (North, 1990) argues that stable and predictable institutional frameworks are fundamental prerequisites for economic activities that require long-term commitments and substantial upfront investments. Political instability disrupts these institutional frameworks by creating uncertainty about future policy directions, regulatory environments, and property rights protection. When political institutions are unstable, firms cannot reliably predict whether their intellectual property will be protected, whether favourable tax policies will continue, or whether regulatory frameworks will remain consistent over the extended time horizons required for innovation projects to mature.
This institutional uncertainty is compounded by the theoretical insights from uncertainty theory, particularly the real options approach developed by (Dixit & Pindyck, 1994). Innovation investments are characterised by high irreversibility, substantial sunk costs, and uncertain returns distributed over extended time periods. Under these conditions, political uncertainty creates a powerful incentive for firms to delay investment decisions until the uncertainty is resolved. The value of waiting increases when the underlying investment is more irreversible and when the uncertainty is more persistent, both characteristics that strongly apply to research and development activities. (Bloom et al., 2007) formalised this relationship, demonstrating that political uncertainty creates a "wait-and-see" effect that is particularly pronounced for innovation investments compared to other types of business investments.
The endogenous growth theory framework provides additional theoretical support by emphasising how innovation depends on stable institutional environments that facilitate knowledge spillovers, collaborative networks, and human capital accumulation. (Aghion & Howitt, 1990; Romer, 1990) demonstrated that technological progress is not merely the result of individual firm decisions but emerges from complex innovation ecosystems that require sustained institutional support. Political instability disrupts these ecosystems by undermining university-industry collaborations, reducing government funding for basic research, and creating brain drain as skilled researchers migrate to more stable environments. The cumulative effect of these disruptions extends beyond immediate reductions in research and development spending to long-term degradation of innovation capacity.
As institutional theory's emphasis on the differential capacity of countries to withstand external shocks based on their underlying institutional strength and economic resilience. Developed countries typically possess what (Robinson & Acemoglu, 2012) term "inclusive institutions" that distribute political power broadly and subject it to constitutional constraints. These institutional frameworks create multiple safeguards that can partially insulate innovation activities from short-term political turbulence. For instance, developed countries often have independent judicial systems that can protect intellectual property rights even during periods of political uncertainty, well-established universities with endowments that provide some independence from government funding fluctuations, and mature financial markets that can continue to provide private funding for innovation even when government support becomes uncertain.
In contrast, developing countries often rely more heavily on what (Robinson & Acemoglu, 2012) characterize as "extractive institutions" where political power is concentrated, and economic opportunities depend more directly on political connections. Under these conditions, political instability can have cascading effects throughout the innovation system. When political leadership changes in developing countries, it often results in wholesale changes in economic policy, restructuring of government agencies responsible for science and technology, and disruption of international partnerships that are crucial for technology transfer. The theoretical framework developed by (Kaufmann et al., 2011) suggests that countries with weaker governance indicators are more vulnerable to political shocks because they lack the institutional buffers that can maintain continuity in innovation policy during periods of political transition.
The differential effects are further explained by the dependency theory insights regarding how developing countries rely more heavily on external sources of technology and knowledge. (Borensztein et al., 1998) demonstrated that developing countries depend significantly on foreign direct investment and international technology transfer for their innovation capacity. Political instability in developing countries sends stronger negative signals to international investors and technology partners, who may have alternative locations for their investments. Developed countries, with their more sophisticated domestic innovation capabilities and established international partnerships, are less dependent on these external sources and therefore more resilient to political uncertainty.
This section provides the methodological framework utilized to examine the influence of political instability on technological innovation by taking a sample of 73 countries (39 developed and 34 developing) spanning from 2010 to 2024. This section provides details related to the data sources, description of variables, and model specifications utilized for the empirical analysis.
Data for all the variables included in the study were extracted from two main sources. World Development Indicators (WDI) and World Governance Indicators (WGI), covering 73 countries over a fifteen year-period (2010-2024). Countries were selected based on the availability of data for all variables included in the study. The starting period of the study was chosen as 2010 because the 2008 global crisis marked a significant shift in the global economic and innovation landscapes. Many countries began to recover from the crisis after 2010 and enhanced their R&D engagement and entrepreneurial activities (Aristei & Gallo, 2016; Teplykh, 2018). Patent filings were significantly enhanced during this period. Therefore, 2010 is the ideal point for examining the relationship among the underlying variables.
The variables are summarized in Table 1 and discussed below. The dependent variable in this study is technological innovation ( Tech_Inno ). Patents have been widely used to measure the innovation level of a country. Following previous studies (Cetin et al., 2025; Wang et al., 2025), the log of the total number of patents (both resident and non-resident) was used to measure the technological innovation of countries. The independent variable in this study is political instability ( PI ). Refer to prior studies (Huang & Wen, 2025; Wang et al., 2024), political stability and absence of violence/ terrorism indicator extracted from the World Governance Indicators (WGI) is utilized to gauge political instability. According to (Kaufmann et al., 2011), this indicator uses an unobserved elements model and combines data from various databases to ensure cross-country comparison. The estimates are then standardized and normally distributed, ranging from -2.5 to 2.5, where the highest value denotes enhanced political stability, whereas the lowest value shows a low level of political stability or high political instability. We transform this indicator by multiplying it by (-1) so that the highest value represents high political instability.
To account for omitted variable bias, several control variables were employed in this study, following prior studies (Mohamed et al., 2022; Wang et al., 2024; Wang et al., 2025). GDP is one of the main indicators of economic development, as significant economic development promotes innovation (Mohamed et al., 2022). Therefore, the study controlled for GDP, which was measured by annual growth in GDP per capita. Population factors (population aging and growth) can have a negative consequence on technological innovation as they may lead to slow economic development and productivity, resulting in inefficiency in resource allocation (Gao et al., 2023). Therefore, this study also controls for the population, which is measured by the annual population growth rate. Industrialization enhances the overall industry structure, which in turn leads to increased productivity and market competition. This allows firms to enhance their R&D expenses, which enables them to augment innovation (Li & Du, 2025). Therefore, industrialization is also considered a control variable, which is measured by industry value added as a percentage of GDP. Education is the key factor that provides the necessary knowledge and skills required to develop innovative products and technologies. This, in turn, acts as a catalyst for driving economic growth and innovation (Wang et al., 2025). Hence, the study also controls for education, which is accessed by annual government expenditure on education as a percentage of GDP. Foreign direct investment can potentially lead to enhanced innovative outcomes because it brings knowledge from foreign nations and introduces novel technologies (Ali et al., 2023) However, it can also inhibit innovation due to the low absorptive capacity of domestic firms (Adikari et al., 2021; Rao et al., 2024). Therefore, we also control for FDI, which is measured by net inflows as a percentage of GDP. All variables included in the study were Winsorized at 1% and 99% to reduce the effect of outliers.
To examine the impact of political instability on technological innovation, a two-way fixed effects model was employed to conduct an empirical analysis. This model allows for controlling endogeneity concerns that may arise due to omitted or unobservable factors. Country-specific effects allow us to control for time-invariant heterogeneity across countries, whereas year fixed effects control for time-specific shocks across countries. We set the following baseline regression model:
T e c h _ I n n o i , t = α 0 + α 1 P I i , t + α 2 G D P i , t + α 3 P o p i , t + α 4 I n d i , t + α 5 E d u i , t + α 6 F D I i , t + Y e a r t + C o u n t t r y i + ε i , t (1)
where α 1 in Eq (1) represents the direct impact of political instability ( PI ) on technological innovation ( Tech_Inno ) . H1 can be accepted if α 1 is negative and significant, when the model is applied to the entire sample. Gross domestic product ( GDP ), Population ( Pop ), Industrialization (Ind ), Education ( Edu ), and Foreign Direct Investment ( FDI ) are the control variables employed in the model to account for omitted variable bias. C o u n t r y i a n d Y e a r t shows country and year dummies. ε i , t is the random error term, i is for country and t is for year.
| Variable Type | Variable Name | Abbreviation | Measurement | Data Sources |
|---|---|---|---|---|
| Dependent Variable | Technological Innovation | Tech_Inno | Sum of resident and non-resident patent application | World Development Indicators |
| Independent Variable | Political Instability | Pol | Governance Indicator related to political stability ranging from -2.5 (weak) to 2.5 (strong). This indicator is redefined by multiplying -1 where higher value corresponds to higher political instability. | World Governance Indicators |
| Control Variables | GDP | GDP | Annual growth in GDP per capita (percentage) | World Development Indicators |
| Population | Pop | Annual population growth rate (percentage) | World Development Indicators | |
| Industrialization | Ind | Industry value added as a percentage of GDP | World Development Indicators | |
| Education | Edu | Annual government expenditure on education as percentage of GDP | World Development Indicators | |
| Foreign Direct Investment | FDI | Net inflows as a percentage of GDP. | World Development Indicators | |
| Source (s): Authors’ own work | ||||
T e c h _ I n n o i , t = α 0 + α 1 P I i , t + α 2 G D P i , t + α 3 P o p i , t + α 4 I n d i , t + α 5 E d u i , t + α 6 F D I i , t + Y e a r t + C o u n t t r y i + ε i , t (1)
where α 1 in Eq (1) represents the direct impact of political instability ( PI ) on technological innovation ( Tech_Inno ) . H1 can be accepted if α 1 is negative and significant, when the model is applied to the entire sample. Gross domestic product ( GDP ), Population ( Pop ), Industrialization (Ind ), Education ( Edu ), and Foreign Direct Investment ( FDI ) are the control variables employed in the model to account for omitted variable bias. C o u n t r y i a n d Y e a r t shows country and year dummies. ε i , t is the random error term, i is for country and t is for year.
Table 2 provides the descriptive statistics for all variables included in the study. Technological Innovation has a mean value of 7.658, a minimum value of 13.623, and a maximum value of 3.296. This significantly shows cross-country heterogeneity, where some countries exhibit high technological innovation while others possess low levels of technological capabilities. This finding is consistent with those of previous studies (Cetin et al., 2025). Political instability has a mean value of 0.120, a minimum value of -1.461, and a maximum value of 2.648. This implies that some countries enjoy political stability, while others experience severe political instability. This variation in political instability aligns with that of previous studies (Huang & Wen, 2025).
The pairwise correlation matrix results are presented in Table 3. The correlation analysis reveals that political instability has a negative and significant association with technological innovation (-0.149***). This implies that greater political instability significantly hinders technological innovation. This also provides preliminary support for the acceptance of our main hypothesis. The VIF values of all variables included in the study ranged from 1.073 to 1.467. These values were significantly lower than the threshold limit of 10, indicating a low likelihood of multicollinearity in the model.
| Variable | Obs | Mean | Std. Dev. | Min | Max |
|---|---|---|---|---|---|
| Tech_Inno | 1095 | 7.658 | 2.294 | 3.296 | 13.623 |
| PI | 1095 | 0.120 | 0.957 | -1.461 | 2.648 |
| GDP | 1095 | 1.887 | 3.677 | -10.548 | 10.844 |
| Pop | 1095 | 0.963 | 1.029 | -1.434 | 4.539 |
| Ind | 1095 | 26.323 | 8.222 | 6.948 | 53.587 |
| Edu | 1095 | 4.494 | 1.540 | 0.471 | 8.560 |
| FDI | 1095 | 4.145 | 8.071 | -23.71 | 43.912 |
| Variables | (1) | (2) | (3) | (4) | (5) | (6) | (7) | VIF |
|---|---|---|---|---|---|---|---|---|
| (1) Tech_Inno | 1.000 | |||||||
| (2) PI | -0.149*** | 1.000 | 1.467 | |||||
| (3) GDP | 0.041 | 0.001 | 1.000 | 1.073 | ||||
| (4) Pop | -0.212*** | 0.378*** | -0.137*** | 1.000 | 1.157 | |||
| (5) Ind | 0.093*** | 0.230*** | 0.147*** | 0.146*** | 1.000 | 1.187 | ||
| (6) Edu | 0.066* | -0.363*** | -0.169*** | -0.112*** | -0.259*** | 1.000 | 1.299 | |
| (7) FDI | -0.077** | -0.254*** | 0.128*** | -0.010 | -0.172*** | -0.074** | 1.000 | 1.198 |
| Note: ***, **, * shows significance at 1%, 5% and 10% respectively. | ||||||||
The results in Table 4 demonstrate the impact of political instability on technological innovation. Column I presents the results without incorporating control variables and year and country fixed effects. Column II provides results by including control variables, while Column III displays results by taking into consideration all control variables as well as country and year fixed effects. Across all three specifications, the results consistently indicate a negative and statistically significant relationship among political instability technological innovation. We primarily focus on Column 3 as it includes all control variables as well as year and country dummies, thereby enhancing the reliability of findings. The coefficient for political instability (-0.169*) indicates that one unit rise in political instability will lowers the technological innovation by 0.169 units. The results also provide support to our hypothesis by suggesting that political instability will create uncertainty regarding regulations, government support and future policies. This in turn discourage countries to invest in R&D in this unstable environment, thereby causing disturbance in existing as well as future innovation programs. The results are aligned with the previous studies (Nadeem et al., 2020; Wang et al., 2024; Wang et al., 2025) stating that political instability undermines overall productivity and growth of the country. This political unrest and frequent government shifts create an uncertain environment, which subsequently discourage businesses to engage in innovative initiatives.
Concerning control variables, it was found that Industrialization ( Ind ) and Education ( Edu ) has a positive and significant impact on technological innovation. This highlights that industrialization helps in developing strong institutions and policies that support innovative systems. This in turn fosters competition among enterprises and augments the R&D activities of corporations, thereby further enhancing innovation (Li & Du, 2025). Likewise, education equip individuals and businesses with the necessary skills and expertise required to adopt and implement innovative technologies (Jafarov, 2025). Education helps in developing a knowledgeable workforce which enhances their capability to outline new processes, ideas and technology. Contrary, Population ( Pop ) and Foreign Direct Investment ( FDI ) has a negative and significant relationship with technological innovation. This implies that as population grows, resources are stretched which thereafter reduce research productivity (Coccia, 2014). These constrains in resources due to population growth lowers down the economic development and productivity, resulting in lower innovative outcomes (Gao et al., 2023). Similarly foreign direct investment may lead to lower innovative outcome due to low absorptive capacity and crowding out effect on the domestic enterprises (Adikari et al., 2021; Rao et al., 2024).
| (1) | (2) | (3) | |
|---|---|---|---|
| VARIABLES | Tech_Inno | Tech_Inno | Tech_Inno |
| PI | -0.356*** | -0.170* | -0.169* |
| (0.082) | (0.095) | (0.096) | |
| GDP | 0.019 | 0.035 | |
| (0.021) | (0.028) | ||
| Pop | -0.467*** | -0.471*** | |
| (0.098) | (0.099) | ||
| Ind | 0.041*** | 0.040*** | |
| (0.010) | (0.010) | ||
| Edu | 0.139*** | 0.138** | |
| (0.066) | (0.067) | ||
| FDI | -0.027*** | -0.712*** | |
| (0.010) | (0.142) | ||
| Constant | 7.695*** | 7.478*** | 7.415*** |
| (0.079) | (0.279) | (0.390) | |
| Year FE | No | No | Yes |
| Country FE | No | No | Yes |
| Observations | 1095 | 1095 | 1095 |
| R-squared | 0.022 | 0.108 | 0.109 |
Note: Standard errors are reported in parentheses. ***, **, * shows significance at 1%, 5% and 10% respectively.
To gain deeper insight into the association among political instability and technological innovation, the sample is further segmented into developed and developing countries. Since these two groups of countries, i.e. developing and developed significantly, different mechanisms, institutional frameworks, and technological innovation capabilities, analysing the relationship from this perspective will provide nuanced understanding. The outcomes outlined in Table 5, Column I shows that for developing countries, the relationship between political instability and technological innovation is negative and significant (-0.122**). This implies that developing countries usually faces significant political volatility and instable political environments. This instability will lead to lower down economic growth and development (Dimitraki & Emmanouilidis, 2025; Ouedraogo et al., 2022). This economic downside creates hurdles for developing countries, thereby inhibiting long term investments in innovative activities (Wang et al., 2024). Table 5, Column II provides the results for developed countries. Interestingly, the outcome reveals that for developed countries, there exists a positive association among political instability and technological innovation (0.161**). This positive association may be since developed countries have strong and stable institutions that can withstand political instability (Sabir et al., 2019). Also, they possess strong resource capability, allowing them to enhance their resilience and carry out innovative outcomes even in times of uncertainties. Moreover, developed countries also possess modern education facilities and a knowledgeable workforce which enhances their absorptive capacity (Li & Du, 2025). They take instability as a challenge and opportunity, thereby spurring innovative solutions which in turn enhance their overall technological capabilities.
| (1) | (2) | |
|---|---|---|
| Developing | Developed | |
| VARIABLES | Tech_Inno | Tech_Inno |
| PI | -0.122** | 0.161** |
| (0.058) | (0.076) | |
| GDP | -0.002 | -0.016** |
| (0.006) | (0.007) | |
| Pop | 0.324*** | -0.109*** |
| (0.098) | (0.035) | |
| Ind | 0.015*** | 0.010 |
| (0.005) | (0.011) | |
| Edu | 0.026 | -0.050 |
| (0.032) | (0.036) | |
| FDI | 0.005 | -0.004** |
| (0.005) | (0.002) | |
| Constant | 7.002*** | 9.161*** |
| (0.204) | (0.222) | |
| Year FE | Yes | Yes |
| Country FE | Yes | Yes |
| Observations | 510 | 585 |
| R-squared | 0.081 | 0.146 |
Note: Standard errors are reported in parentheses. ***, **, * shows significance at 1%, 5% and 10% respectively.
The Two-Step System GMM analysis provides robust evidence supporting the core hypothesis that political instability significantly undermines technological innovation, with the coefficient of -0.032 indicating that each unit increase in political instability reduces innovation by approximately 3.2 percent, a relationship that is statistically significant at the 1% level and thus highly reliable. This finding aligns with uncertainty theory's predictions that political uncertainty creates a "wait-and-see" effect for irreversible investments like research and development (Bloom et al., 2007; Dixit & Pindyck, 1994). The result gains particular credibility because the GMM approach addresses the potential endogeneity problem where countries with poor innovation performance might themselves become more politically unstable, creating reverse causality that could bias simpler analytical methods (Arellano & Bond, 1991). The inclusion of the lagged innovation variable (L1. Tech_Inno) with a coefficient of 0.328 reveals that innovation exhibits meaningful persistence over time, consistent with endogenous growth theory's emphasis on knowledge accumulation and path dependence in technological development (Romer, 1990). This persistence suggests that countries build upon their previous innovation achievements but also implies that those falling behind due to political instability may struggle to catch up quickly, creating long-term consequences beyond the immediate disruption period. The control variables paint a coherent economic picture that validates established theoretical relationships: GDP growth positively supports innovation as expected from endogenous growth theory (Aghion & Howitt, 1990), education investments pay dividends for technological advancement through human capital formation (Lucas Jr, 1988), industrialization creates the infrastructure necessary for innovation implementation (Kaldor, 1967), and foreign direct investment brings valuable technology transfer effects (Borensztein et al., 1998), while population growth appears to create resource constraints that limit per-capita innovation capacity. Critically, the diagnostic tests validate the methodological approach, with the Hansen test (1.160) and Sargan test (7.170) confirming that the instrumental variables satisfy the orthogonality conditions required for consistent GMM estimation (Hansen, 1982), while the AR tests show acceptable first-order serial correlation but no problematic second-order correlation, collectively demonstrating that the GMM estimation successfully isolates the causal impact of political instability on innovation while controlling for reverse causality and other potential confounding factors that could compromise the validity of the results.
| (1) | |
|---|---|
| VARIABLES | Tech_Inno |
| L1. Tech_Inno | 0.328*** |
| (0.001) | |
| PI | -0.032*** |
| (0.005) | |
| GDP | 0.005*** |
| (0.000) | |
| Pop | -0.039*** |
| (0.002) | |
| Ind | 0.001* |
| (0.001) | |
| Edu | 0.011*** |
| (0.000) | |
| FDI | 0.001*** |
| (0.000) | |
| Constant | 5.635*** |
| (0.094) | |
| Year FE | Yes |
| Country FE | Yes |
| Observations | 1095 |
| Hansen Test | 1.160 |
| Sargan Test | 7.170 |
| AR (1) | -0.640 |
| AR (2) | -0.070 |
Note: Standard errors are reported in parentheses. ***, **, * shows significance at 1%, 5% and 10% respectively.
The instrumental variable (IV) analysis presented in Table 7 provides compelling evidence for a causal relationship between political instability and technological innovation by addressing endogeneity concerns through a two-stage estimation process that leverages legal and economic reforms (LER) as an instrument for political instability. In the first stage, the instrument demonstrates exceptional strength with a coefficient of 1.943 that is highly significant at the 1% level, indicating that legal and economic reforms are powerful predictors of political instability, which aligns with institutional theory's emphasis on how reform processes can create temporary political uncertainty even when they ultimately strengthen governance structures (North, 1990; Robinson & Acemoglu, 2012). The diagnostic tests strongly validate the instrumental variable approach, with the under-identification test statistic of 31.340 (significant at 1%) confirming that the instrument is sufficiently correlated with the endogenous variable, while the weak identification test statistic of 105.470 substantially exceeds the conventional threshold of 10, demonstrating that the instrument is strong enough to avoid weak instrument bias that could compromise the reliability of the second-stage estimates (Staiger & Stock, 1994; Stock & Yogo, 2002). Most importantly, the second-stage results reveal that the instrumented political instability variable has a coefficient of -0.194 that is statistically significant at the 5% level, indicating that a one-unit increase in political instability causally reduces technological innovation by approximately 19.4%, a substantially larger effect than typically found in ordinary least squares estimations, suggesting that simple correlational analyses may actually underestimate the true causal impact of political instability on innovation outcomes. The control variables in the second stage largely confirm expected theoretical relationships, with education showing a positive impact on innovation consistent with human capital theory (Lucas Jr, 1988), industrialization displaying a positive coefficient that supports the structural transformation literature (Kaldor, 1967), and population growth exhibiting negative effects that likely reflect resource dilution mechanisms, while the overall model fit and diagnostic statistics provide confidence that the instrumental variable approach successfully isolates the causal effect of political instability on technological innovation while controlling for potential confounding factors and reverse causality that could bias conventional estimation methods.
|
Stage I (1) |
Stage II (2) |
|
|---|---|---|
| VARIABLES | PI | Tech_Inno |
| LER | 1.943*** | |
| (0.234) | ||
| PI (Instrumented) | -0.194** | |
| (0.026) | ||
| GDP | -0.008 | -0.007 |
| (0.009) | (0.005) | |
| Pop | -0.446*** | -0.309*** |
| (0.065) | (0.110) | |
| Ind | -0.011** | 0.010* |
| (0.004) | (0.006) | |
| Edu | -0.248* | 0.142** |
| (0.141) | (0.071) | |
| FDI | -0.001 | -0.006 |
| (0.004) | (0.004) | |
| Constant | -0.714*** | 5.443*** |
| (0.134) | (0.573) | |
| Observations | 1095 | 1095 |
| R-squared | 0.361 | 0.105 |
| Under Identification test | 31.340*** | |
| Weak Identification test | 105.470 |
Note: Standard errors are reported in parentheses. ***, **, * shows significance at 1%, 5% and 10% respectively.
This comprehensive study of 73 countries from 2010 to 2024 provides robust empirical evidence that political instability significantly undermines technological innovation, with our multiple econometric approaches consistently demonstrating that each unit increase in political instability reduces innovation by approximately 17 percent, an effect that proves particularly severe in developing countries where institutional weaknesses amplify the negative impact while developed nations with stronger governance frameworks show greater resilience and can even transform political uncertainty into innovation opportunities. The Two-Step System GMM and instrumental variable analyses confirm the causal nature of this relationship, revealing that the true magnitude may be substantially larger than conventional estimates suggest, with implications extending far beyond immediate economic disruption to encompass long-term innovation capacity that forms the foundation for sustained economic growth. These findings underscore the critical importance of political stability as a fundamental prerequisite for innovation-driven development, suggesting that policymakers must prioritize institutional strengthening and political risk mitigation alongside traditional innovation policies, while international organizations and multinational corporations should integrate political stability considerations into their technology transfer programs and global research investment decisions, recognising that in an era of increasing political polarization worldwide, maintaining innovation capacity during periods of political uncertainty becomes essential for preserving technological competitiveness and economic prosperity in an increasingly complex global environment.
Adikari, A. P., Liu, H., & Marasinghe, M. (2021). Inward foreign direct investment-induced technological innovation in Sri Lanka? Empirical evidence using ARDL approach. Sustainability , 13 (13), 7334.
Aghion, P., & Howitt, P. (1990). A model of growth through creative destruction. In: National Bureau of Economic Research Cambridge, Mass., USA.
Ali, N., Phoungthong, K., Khan, A., Abbas, S., Dilanchiev, A., Tariq, S., & Sadiq, M. N. (2023). Does FDI foster technological innovations? Empirical evidence from BRICS economies. Plos one , 18 (3), e0282498.
Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies , 58 (2), 277-297.
Aristei, D., & Gallo, M. (2016). Does gender matter for firms' access to credit? Evidence from international data. Finance Research Letters , 18 , 67-75.
Bloom, N., Bond, S., & Van Reenen, J. (2007). Uncertainty and investment dynamics. The Review of Economic Studies , 74 (2), 391-415.
Borensztein, E., De Gregorio, J., & Lee, J.-W. (1998). How does foreign direct investment affect economic growth? Journal of International Economics , 45 (1), 115-135.
Cetin, M., Ozturk, I., Sumerli Sarigul, S., Murshed, M., & Kilavuz, E. (2025). Nexus between technological innovation and environmental pollution in selected OECD countries. Natural Resources Forum,
Coccia, M. (2014). Driving forces of technological change: The relation between population growth and technological innovation: Analysis of the optimal interaction across countries. Technological Forecasting and Social Change , 82 , 52-65.
Dimitraki, O., & Emmanouilidis, K. (2025). Interactional Impact of Defence Expenditure and Political Instability on Economic Growth in Nigeria: Revisited. Defence and Peace Economics , 36 (4), 533-554.
Dixit, A. K., & Pindyck, R. S. (1994). Investment under uncertainty . Princeton university press.
Gao, W., Chen, Y., Xu, S., Lyulyov, O., & Pimonenko, T. (2023). The role of population aging in high-quality economic development: mediating role of technological innovation. Sage Open , 13 (4), 21582440231202385.
Hansen, L. P. (1982). Large sample properties of generalized method of moments estimators. Econometrica: Journal of the econometric society , 1029-1054.
Huang, X., & Wen, H. (2025). The constraining dynamics of political instability on renewable energy development: International evidence. Renewable Energy , 246 , 122889.
Jafarov, S. (2025). Education and Skill Development: A Pathway to Sustainable Growth. International Journal of Scientific Research and Management , 13 (2), 3963-3969.
Jens, C. E. (2017). Political uncertainty and investment: Causal evidence from US gubernatorial elections. Journal of financial economics , 124 (3), 563-579.
Kaldor, N. (1967). Strategic factors in economic development. (No Title) .
Kaufmann, D., Kraay, A., & Mastruzzi, M. (2011). The worldwide governance indicators: Methodology and analytical issues1. Hague journal on the rule of law , 3 (2), 220-246.
Li, T., & Du, D. (2025). Cross-border R&D, absorptive capacity and innovation performance. Structural Change and Economic Dynamics , 73 , 460-471.
Lucas Jr, R. E. (1988). On the mechanics of economic development. Journal of Monetary Economics , 22 (1), 3-42.
Mohamed, M. M. A., Liu, P., & Nie, G. (2022). Causality between technological innovation and economic growth: Evidence from the economies of developing countries. Sustainability , 14 (6), 3586.
Nadeem, M. A., Liu, Z., Ali, H. S., Younis, A., Bilal, M., & Xu, Y. (2020). Innovation and sustainable development: does aid and political instability impede innovation? Sage Open , 10 (4), 2158244020973021.
North, D. C. (1990). Institutions, institutional change and economic performance . Cambridge university press.
Ouedraogo, R., Mlachila, M., Sourouema, W. S., & Compaoré, A. (2022). The impact of conflict and political instability on banking crises in developing countries. The World Economy , 45 (6), 1937-1977.
Rao, A., Ali, M., & Smith, J. M. (2024). Foreign direct investment and domestic innovation: Roles of absorptive capacity, quality of regulations and property rights. Plos one , 19 (3), e0298913.
Robinson, J. A., & Acemoglu, D. (2012). Why nations fail: The origins of power, prosperity and poverty . Profile London.
Romer, P. M. (1990). Endogenous technological change. Journal of political economy , 98 (5, Part 2), S71-S102.
Sabir, S., Rafique, A., & Abbas, K. (2019). Institutions and FDI: evidence from developed and developing countries. Financial Innovation , 5 (1), 1-20.
Staiger, D. O., & Stock, J. H. (1994). Instrumental variables regression with weak instruments. In: National Bureau of Economic Research Cambridge, Mass., USA.
Stock, J. H., & Yogo, M. (2002). Testing for weak instruments in linear IV regression. In: National Bureau of Economic Research Cambridge, Mass., USA.
Teplykh, G. V. (2018). Innovations and productivity: the shift during the 2008 crisis. Industry and Innovation , 25 (1), 53-83.
Wang, J.-Z., Feng, G.-F., & Chang, C.-P. (2024). How does political instability affect renewable energy innovation? Renewable Energy , 230 , 120800.
Wang, S., Rahman, S. U., Zulfiqar, M., Ali, S., Khalid, S., & e Ali, M. S. (2025). Sustainable pathways: Decoding the interplay of renewable energy, economic policy uncertainty, infrastructure, and innovation on transport CO2 in QUAD economies. Renewable Energy , 242 , 122426.