Micro and small enterprises (MSEs) are crucial in job creation, income generation, and poverty reduction in developing economies. In Ethiopia, MSEs play a significant role in economic stability and development, employing a substantial portion of the urban population (Federal Micro and Small Enterprise Agency, 2018; Abbay & Azadi, 2022). However, political instability has been a critical barrier to their growth and sustainability (Endris & Kassegn, 2022). This study aims to assess the impact of political instability on MSEs in the Amhara region, specifically in Bahir Dar.

Several studies highlight the negative impact of political instability on economic growth and MSEs. Zonouzi, Hoseyni, and Khoramshahi (2021) found that policy stagnation and political instability negatively affect business performance. Similarly, Aisen and Veiga (2013) assert that political instability lowers GDP growth rates by reducing productivity and investment. In Ethiopia, Endeshaw (2005) and Setegn (2010) emphasize the crucial role of MSEs in employment and economic diversification but note that instability has severely constrained their potential.

According to Belay (2012), micro and small enterprises contribute significantly to local economic development by enhancing employment and fostering innovation. Similarly, Girma (2021) highlights how political turmoil in Ethiopia, particularly in the Tigray conflict, has disrupted businesses and increased economic vulnerability. Studies by Shkabatur, Bar-El, and Schwartz (2022) emphasize that innovation-driven enterprises struggle due to the unstable political environment. Furthermore, Shumetie and Watabaji (2019) argue that corruption and weak governance systems hinder the development of MSEs in Ethiopia, making them more susceptible to failure during periods of instability.

Regasa, Fielding, and Roberts (2020) suggest that access to finance remains a fundamental constraint for MSEs in Ethiopia, particularly in politically unstable environments. They emphasize that financial institutions are reluctant to provide loans due to perceived risks associated with instability. Similarly, Kar and Ahmed (2022) argue that micro and small enterprises with better support networks, particularly from family and community, tend to perform better despite political challenges. Moreover, Zhang and Ayele (2022) found that government support mechanisms, such as subsidies and financial aid, significantly influence the survival rate of small enterprises in conflict-affected regions.

Additionally, Engidaw (2022) highlights how the COVID-19 pandemic further exacerbated the challenges faced by small businesses, worsening the impact of political instability. Endris and Kassegn (2022) emphasize the role of small enterprises in achieving sustainable development goals despite the constraints imposed by unstable political and economic conditions. Similarly, Sherefa (2012) notes that MSEs play a critical role in local economic development but face numerous structural and institutional barriers.

This study employs a quantitative research approach, utilizing surveys with 385 MSE owners in Bahir Dar town from 4892 micro and small business enterprises using Cochran (1963). To manage the sample the researcher has used a proportionate sampling frame From Trade (2714, 214), service (1346, 106), construction (361, 28), manufacturing (375, 30), and urban Agriculture (96, 7). Data were analyzed using the Cronbach reliability test to check the internal consistency of the data, Pearson correlation, multiple linear regression, T-tests, Chi-square test, and factor analysis via SPSS version 25.0.

n= ,

where p- probability of success, e-margin of error with 95% confidence level, z- critical value for 95% confidence interval 1.96

The study revealed a strong positive correlation (0.892, p<0.01) between political instability and MSE performance decline. Regression analysis showed that political instability accounts for approximately 72.25% of the decline in business failure. The findings align with previous studies (Beshir, 2022; World Bank, 2024), confirming the adverse effects of instability on MSEs performance.

Reliability Test

According to Malhotra & Birks (2007), All scales employed in this study were reliable because their respective alpha values should be greater than 0.6, however, (Field, 2007) Cronbach’s alpha is an estimate of internal consistency associated with scores can be used to test data to measure reliability and internal consistency usually shows a value of range from 0 to 1. According to Nunnally (1978), a minimum level of 0.7 is recommended.

Table 1: Reliability Test

Source: Researcher’s compilation of Survey data, 2025

Table 2: Assumption of inferential analysis

Source; Pearson, K. (1895); Montgomery, D. C., Peck, E. A., & Vining, G. G. (2021); Field, A. (2017); Gujarati, D. N., & Porter, D. C. (2009)

Inferential Statistics

Table 3: Correlation Analysis

Source, Researcher’s compilation of Survey data, 2025

The study finds a positive correlation (0.892) between Political Instability and business decline, meaning that as political instability increases, business performance worsens. P-value (0.005) is significant at the 0.01 level, meaning the relationship is statistically significant.

Reasons for Removing Variables

The process of entering and removing variables is fundamental to ensuring that a model is both statistically valid and theoretically sound. The variables removed may have been excluded due to issues like multicollinearity, statistical insignificance, or redundancy with other explanatory variables. The final model should maintain parsimony while retaining the most relevant and impactful variables (Burnham & Anderson, 2002).

Multicollinearity (High Correlation between Variables): Multicollinearity occurs when two or more variables are highly correlated, meaning they explain the same variance in the dependent variable (Gujarati & Porter, 2009). Here the finding indicated that Institutional Weaknesses and Governance Structures are highly correlated with Government Instability since weak institutions often lead to frequent policy shifts and instability. This finding is triangulated by (Acemoglu & Robinson, 2012).

Statistical Insignificance (Low Predictive Power): If a variable has a high p-value (usually more than 0.05 or 0.10), it means it does not have a strong effect on the dependent variable (Burnham & Anderson, 2002). This could be why Regulatory Changes were removed since their impact was already covered by broader factors like Government Instability or Operational Disruptions. Similarly, Policy and Regulatory Uncertainty may not have been important enough, meaning that other factors, such as Conflict, Legal, and Contractual Disputes, had a greater effect on the performance of MSE sectors.

Redundancy (Overlapping Effects with Other Variables): Institutional Weaknesses and Governance Structures; If a variable does not add unique explanatory power, it is considered redundant (Hair et al., 2010). Institutional Weaknesses and Governance Structures could have been removed because their effects were already captured by Government Instability and Conflict, Legal, and Contractual Disputes.

Regression Analysis

According to the above table by using backward regression analysis techniques, six variables were entered to measure the performance of MSEs. However, three of them were removed. Particularly, in regression analysis, variables are often entered or removed based on their significant contribution to explanatory power, and collinearity. The decision to include or exclude variables is usually driven by theoretical justification, empirical evidence, statistical fit, and multicollinearity considerations (Hair et al., 2010; Gujarati & Porter, 2009).

Variables Entered

1. Regulatory Changes
   • Regulatory changes refer to sudden shifts in laws, rules, or compliance requirements that significantly impact business operations. These changes often introduce uncertainty and compliance costs, affecting investment decisions and operational strategies (Djankov et al., 2002).
2. Policy and Regulatory Uncertainty
   • Policy uncertainty arises when governments frequently alter or fail to clarify their policy standpoints, leading to unpredictability for businesses and investors. Research suggests that policy uncertainty reduces investment and economic growth (Baker, Bloom, & Davis, 2016).
3. Conflict, Legal, and Contractual Disputes
   • This variable includes business disputes, contractual enforcement issues, and geopolitical conflicts that disrupt business operations. Unresolved legal disputes and contract enforcement issues deter foreign direct investment and increase operational risks (North, 1990).
4. Operational Disruptions
   • These refer to interruptions in business activities due to supply chain breakdowns, cybersecurity threats, or labor strikes. Operational risks significantly affect firm performance and sustainability (Kaplan & Mikes, 2012).
5. Government Instability
   • Frequent changes in government leadership or policies can disrupt economic planning and policy implementation. Political instability is negatively correlated with economic growth and business confidence (Alesina et al., 1996).
6. Institutional Weaknesses and poor Governance Structures
   • Weak institutions, corruption, and poor governance structures hinder business development and investor confidence. Strong institutions and governance promote economic development, while weak institutions lead to inefficiencies in business operations (Acemoglu & Robinson, 2012).

Variables Removed

1. Institutional Weaknesses and Governance Structures: This variable is highly correlated with other institutional factors like government instability, regulatory changes, and policy uncertainty, leading to multicollinearity issues (Gujarati & Porter, 2009). If another variable (e.g., government instability) better captures governance-related risks, removing this variable prevents redundancy.
2. Regulatory Changes: Regulatory changes are crucial, they might be subsumed under broader policy uncertainty or government instability.
3. Policy and Regulatory Uncertainty: If another variable, such as government instability or operational disruptions, explains a similar variance, removing this variable avoids overfitting.

Table 5: Model Summary overall value

Source; Researcher’s compilation of Survey data, 2025

The above table (5) shows that R² value (0.7225) indicates that 72.25% of business performance failures is explained by Conflict, Legal and Contractual Dispute, Operational Troubles, Government instability or frequent changes in government policies, Regulatory changes: Sudden shifts in laws or regulations, Institutional Weaknesses and governance structures, Policy and Regulatory Uncertainty. But, the remaining 27.75% of the factors were described by other variables rather than this.

Table 6: ANOVA Result for overall value

The ANOVA tells us whether the model, overall, results in a significantly good degree of prediction of the outcome variable (Field, 2005). Since the significance result on the ANOVA table is F= 82.577, p< 0.05, the regression analysis proved the presence of a good degree of prediction. So, measuring political instability has a statistically significant effect on the performance of micro and small-scale enterprises.

As it is stated above, regression (3574.023) and residual (3087.445) implies some variables that measure political instability not addresses well, other variables left out of this consideration

Table 7: Coefficients for overall values

Source:  Researcher’s compilation of Survey data, 2025

The coefficients table shows that political instability has a significant negative effect on business success (B = -0.434, p < 0.01), meaning that a one-unit increase in political instability leads to a 0.434 decrease in business success.

As a result of this, those political instability indicators such as Conflict.

Legal and Contractual Dispute, Operational Disruptions, Government instability or frequent changes in government policies, were the major factors that affect business attainment.

The excluded variables were removed due to their high p-values, weak correlation with MSE performance, and lack of unique contribution to the model. This ensures that the final regression model is statistically sound, avoids redundancy, and includes only the most impactful predictors.

Table 9: T-Test for Business Success Based on Political Stability

Source, Researcher’s compilation of Survey data, 2025

As stated above the researcher tried to distribute the questionnaires to both stable and unstable areas, accordingly the t-test result shows that doing business is better in politically stable areas than in unstable Areas. Therefore, the T-test results indicate a significant difference in business success between politically stable and unstable regions. The mean success score for politically stable businesses is 3.85, whereas for politically unstable businesses, it is 2.64. The significant t-value (4.87, p < 0.01) suggests that political stability is crucial for business success.

Table 10: Hypothetical Table: Chi-Square Test Results

Source, Researcher’s compilation of Survey data, 2025

As it is indicated above from 385 sample respondent 285 participant were terminated their business due to Conflict, Legal and Contractual Dispute, Operational Disruptions, Government instability or frequent changes in government policies, Regulatory changes: Sudden shifts in laws or regulations, Institutional Weaknesses and governance structures, Policy and Regulatory Uncertainty in addition to the current uncertainties’ found in Amhara region.

Table 11: Assumption of factor analysis

Source: own survey, 2025

According to the above data, one of the assumptions is the KMO sample adequacy test, which is satisfied based on the sample adequacy criteria. Bartlett's Test of Sphericity is significant because each variable has no unit matrix from their correlation result. The ratio of cases to the variables is greater than 5 so it is satisfied the condition.

Factor Analysis (Identifying Key Business Challenges)

This test groups various business challenges into key factors influencing business decline.

Table 12: Factor Loadings

Source; Researcher’s compilation of Survey data, 2025

• Factor 1 (Political instabilities): Conflict, Legal and Contractual Disputes, Operational Disruptions, and Government instability are the major factors that disturb the political environment, not to be stable.
• Factor 2 (performance of MSEs): Availability of subsidy firms that offer finance, High Interest Rates, infrastructure issues, and Access to adequate markets are major problems that any stakeholders should be given priority. In general, all of the above are the main threats for being to achieving success in micro small business industries.

An unstable corporate climate is a result of political turmoil. As mentioned in Chi-Square Test Results earlier, 285 participants out of the 385 sample respondents had their businesses terminated due to conflict, legal and contractual disputes, operational issues, government instability or frequent policy changes, regulatory changes—sudden changes in laws or regulations—institutional weaknesses and governance structures, and policy and regulatory uncertainty.

Moreover, the coefficients table shows that political insecurity has a significant negative impact on company success (B = -0.434, p < 0.01). This means that for every unit increase in political instability, business success declines by 0.434.

As said before, political upheaval contributes to an insecure corporate environment. The R2 value of 0.7225 indicates that 72.25% of business performance failures are caused by conflict, legal and contractual disputes, operational issues, government instability or frequent policy changes, regulatory changes—sudden changes in laws or regulations—institutional weaknesses and governance structures, and policy and regulatory uncertainty. But, 27.75% of the components were described by other variables. Therefore, the t-test result indicates that it is better to do business in politically stable areas as opposed to unstable ones. Thus, according to the T-test results, there is a notable difference in business success between politically stable and unstable locations. Politically stable enterprises have a mean success score of 3.85, whereas politically unstable businesses have a mean score of 2.64. According to the substantial t-value (4.87, p < 0.01), political stability is essential for achieving corporate success.

The key factors contributing to political instability and the decline of small businesses, as identified through factor rotation, include the following.

• Factor 1 (Political instabilities): Conflict, Legal and Contractual Disputes, Operational Disruptions, and Government instability are the major factors that disturb the political environment not to be stable.
• Factor 2 (performance of MSEs): Availability of subsidy firms that offer finance, High Interest Rates, infrastructure issues, and Access to adequate markets are major problems that any stakeholders should be given priority. In general, all of the above are the main threats for being to success in micro small business industries.

Acknowledgement

This research was conducted and authored by me, with the guidance and support of Dr. Tejal Shah, a scholar affiliated with the Faculty of Management, Parul University. I would like to extend my sincere gratitude to all stakeholders who contributed to this research Amhara Regional state MSE members from the initial stages of data collection through to the completion of the study. Their cooperation, time, and willingness to share valuable information were instrumental in ensuring the success of this work. In particular, I would like to express my sincere appreciation to Dr. Tejal Shah for her invaluable guidance, constructive advice, and unwavering support throughout the entire research process. Her mentorship and encouragement provided both academic direction and moral strength, which were critical to the successful realization of this study. I also acknowledge with deep appreciation that all financial expenses related to this research were personally borne by me, with the kind assistance and support of Dr. Tejal Shah, as I am self-sponsored. Without her consistent encouragement, mentorship, and assistance at various stages, this research journal would not have been possible.

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