Development of Social Entrepreneurship in Ukraine as a
Response to the Socio-Economic Challenges of War
Iaroslav Petrunenko,
Doctor of Juridical Science,
Full Professor, Senior Researcher,
State Organization V. Mamutov Institute of
Economic and Legal Research of the National
Academy of Sciences of Ukraine, Kyiv, Ukraine,
petrunenko@yahoo.com,
https://orcid.org/0000-0002-1186-730X
Alla Chukhlib,
PhD in Economics,
Associate Professor,
Department of Statistics and Economic Analysis,
National University of Life and
Environmental Sciences of Ukraine,
Kyiv, Ukraine,
chukhlib0509@gmail.com,
https://orcid.org/0000-0003-0198-2969
Artem Kornetskyy,
PhD, Associate Professor,
Department of Management and Organizational
Development, Faculty of Social Sciences,
Ukrainian Catholic University,
Lviv, Ukraine,
kornetskiy@gmail.com,
https://orcid.org/0000-0003-1955-6167
Denys Zlobin,
Postgraduate Student, State Organization
Legal Research of the National Academy of
Sciences of Ukraine, Kyiv, Ukraine,
d.zlobin84@ukr.net,
https://orcid.org/0009-0004-9153-9745
Tetiana Vlasenko,
PhD in Economics, Associate Professor,
Head of the Department
Production and Investment Management,
Faculty of Agrarian Management,
National University of Life and Environmental
Sciences of Ukraine, Kyiv, Ukraine,
tvlasenko@nubip.edu.ua,
https://orcid.org/0000-0003-2999-7441
Abstract
This study examines the nexus between unemployment among internally displaced persons (IDPs) and the development of social enterprises in Ukraine during the period 2022–2024, with a particular focus on the moderating role of regional economic conditions in facilitating post-war economic integration. A quantitative research design was adopted, utilizing panel data comprising 36 observations from 12 regions. The empirical analysis employed multivariate linear regression, with the IDP employment rate as the dependent variable and the number of social enterprises, gross regional product (GRP) per capita, and the proportion of IDPs in the total population as explanatory variables. The findings reveal that social enterprises exert a statistically significant and positive influence on reducing IDP unemployment (β = 2.758, p = 0.048), while GRP per capita also contributes positively to employment outcomes (β = 0.031, p = 0.003). The estimated model demonstrates statistical significance (F = 4.914, p = 0.006) and accounts for 31.5% of the variance in IDP employment rates. Conversely, the relative share of IDPs in the population is not a significant determinant (p = 0.307).
This research constitutes the first empirical assessment of the strategic function of social enterprises in Ukraine’s post-war recovery context. It underscores the capacity of social ventures to foster inclusive labour market participation, enhance local economic resilience, and contribute to long-term reconstruction strategies. The study’s policy implications highlight the necessity of fostering an enabling institutional environment, expanding targeted financial support mechanisms, and integrating social enterprise development into national recovery frameworks as a sustainable response to large-scale displacement and humanitarian crises.
Keywords: Internally Displaced Persons, Unemployment Rate, Economic Recovery, Post-War Reconstruction.
Introduction
Since 2022, Ukraine has been engulfed in a protracted war that has triggered profound socioeconomic crises, extending far beyond the military domain. In 2022 alone, GDP fell by 29.2% (World Bank, 2022), while unemployment rose to 24.5% (ILO). By 2023, over six million people had been internally displaced, making internal migration one of the country’s most pressing social issues (Zalievska-Shyshak & Shyshak, 2024). These conditions have created an urgent need for sustainable solutions capable of addressing both economic collapse and deep social fragmentation.
Social entrepreneurship has emerged as a promising response, combining market principles with socially oriented missions such as employment creation for internally displaced persons (IDPs), socio-economic reintegration of veterans, and community-led, budget-independent projects (Dedilova et al., 2024; Sotnyk et al., 2024). Traditional state aid and humanitarian assistance have proven insufficient to address the scale of the crisis. However, little empirical research examines the real-world effects of social entrepreneurship in war contexts, particularly in Ukraine, or its influence on vulnerable populations and regional economic stability (Hora et al., 2022).
Research Problem
The ongoing war in Ukraine has created severe challenges for internally displaced persons (IDPs), overwhelming traditional employment support systems amid widespread economic disruption. Social enterprises have emerged as potential drivers of wartime and post-war economic growth, yet empirical research on their impact, particularly on IDP employment, remains limited. Most prior studies emphasize theoretical models or isolated cases without quantitatively assessing the relationship between social enterprise development and IDP employment. This gap is critical given that more than six million Ukrainians are internally displaced and require evidence-based strategies for economic integration. This study aims to evaluate the influence of social enterprises on economic growth, with a focus on job creation for IDPs across 12 Ukrainian regions between 2022 and 2024.
Research Focus
This study examines the impact of social enterprises on economic development, focusing on job creation for internally displaced persons (IDPs) across 12 Ukrainian regions from 2022 to 2024. It analyzes how regional IDP unemployment rates relate to the number of social enterprises, considering regional economic conditions (GRP per capita) and IDP population concentration. Using panel data analysis, the research captures inter-regional differences and temporal changes during post-war reconstruction to assess social enterprises’ role in IDP economic reintegration.
Research Aim and Research Questions
This study strategically assesses the role of social enterprises in reducing unemployment among internally displaced persons (IDPs) in Ukraine, identifying key regional economic factors influencing their effectiveness during post-war recovery. It empirically examines the relationship between social enterprise presence and IDP unemployment to inform targeted post-war policies. Using multiple linear regression, the research explores how social ventures address wartime socio-economic challenges, focusing on their impact on IDP unemployment and regional economic stability.
This study answers three main questions:
Literature review
Theoretical foundations
Social enterprises extend beyond profit generation, integrating broader community benefits. According to Wu et al. (2020), their success is measured not only by financial outcomes but also by innovative solutions addressing social problems. Mohammadi et al. (2024) note that the absence of a clear definition hampers both research and practice, while Wang & Yee (2023) and Mirvis (2022) emphasize the diversity of perspectives—ranging from individual actors to organizational structures. This complexity creates opportunities for new models that reflect the multifaceted reality of social entrepreneurship (Ran & Weller, 2021). In Europe, social venture development emphasizes community empowerment and collaboration with vulnerable groups, as outlined in the European Commission’s Social Business Initiative (García-Jurado et al., 2021). Community-based enterprises (CBEs) serve social purposes while generating economic activity, fostering group identity, and reinforcing social relations (Ko & Kim, 2020). Active community participation is a key factor in ensuring the legitimacy and sustainability of such initiatives (Adomako & Nguyen, 2024). In contrast, the American model prioritizes innovation and market-based approaches, developing scalable business models to address social challenges. Nonprofits like Teach For America exemplify this model, using entrepreneurial strategies to improve education in underserved areas (Manjon et al., 2022). Social venture capital plays a crucial role in supporting early-stage social enterprises (Farhoud et al., 2023). Despite methodological differences, both European and American approaches share a blended value foundation—pursuing social impact alongside financial viability. This synergy, as Sun et al. (2023) highlight, has the potential to generate transformational, systemic solutions.
International Experience
The social entrepreneurs emerged as a particular answer to the global challenges that urgently need immediate attention, such as the creation of social cooperatives within Italy after the 2008 financial crisis. These cooperatives greatly assisted in the revival of the local economy due to their balanced approach that entwined a social dimension and economic viability (Zainea et al., 2020). They have tackled unemployment with renewed vigor while at the same time advancing robust social structures through innovative community-based solutions (Zainea et al., 2020). In other parts of Europe, identical models are still emerging, concentrating on the enhancement of well-being, poverty reduction, and aiding the disadvantaged (Tortia & Troisi, 2021). This model is particularly strong because it draws on local assets and community-based systems. These locally embedded collaborative frameworks boost sociological resilience through collective governance (Billiet et al., 2021).
The war in Ukraine adds a new layer of complexity to social entrepreneurship.
The war in Ukraine has led to significant changes in the direction of international investment, a decrease in investment in agriculture and a shift towards public administration, defence and industry (Yemets et al., 2025). The need for social entrepreneurship has grown due to war-related infrastructure damage, social fragmentation, and increased displacement (Pattison et al., 2021). While Italy’s cooperative model offers inspiration, Ukraine requires faster, more flexible innovations suited to scarce resources and low stakeholder engagement (Tortia & Troisi, 2021; Billiet et al., 2021). Organizational learning and social innovation boost resilience and aid displaced populations (Rhouiri et al., 2023). Lessons from the COVID-19 pandemic show how social entrepreneurs rapidly integrated health and social needs, using value-based innovation to create sustainable solutions amid uncertainty (Morched & Jarboui, 2021; Mao, 2020). Ukraine could adopt similar strategies blending cooperative values with entrepreneurship to restore social cohesion and economic systems, adapting Italy’s experience to its unique context (Belton et al., 2021). International investment is a critical factor in the recovery and development of Ukraine's post-war economy: it provides additional resources for the implementation of infrastructure and economic projects and also promotes technology transfer and the introduction of best management practices (Yemets et al., 2025).
The Transformation of Social Entrepreneurship in Ukraine Before and After 2014
Since the 2014 Revolution, social entrepreneurship in Ukraine has significantly evolved, with initiatives blending social goals and economic development. Organizations like Pact Ukraine support this growth by providing funding, training, and networking (Antoniuk et al., 2023; Revko et al., 2023). Social enterprises address rural unemployment, urban inequality, and poverty, gaining increased community and consumer support (Revko et al., 2023). During the COVID-19 pandemic, they showed resilience by adapting services in food security and healthcare (Stoliarchuk et al., 2021; Trubavina et al., 2021).
The Increased Focus on Internally Displaced Persons and Veterans After the Invasion
The 2022 Russian invasion drastically altered Ukraine’s socio-economic landscape, increasing the role of social entrepreneurship. Social enterprises expanded services to include psychosocial support, vocational training, and employment for IDPs and veterans (Khailenko & Bacon, 2024; Oviedo et al., 2022). Their rapid adaptability has proven effective during humanitarian crises, meeting urgent community needs (Chudzicka-Czupała et al., 2023). Locally driven initiatives increasingly collaborate with NGOs and international organizations, strengthening response capacity (Chudzicka-Czupała et al., 2023; Oviedo et al., 2022).
Long-Term Prospects and the Importance of Community-Based Approaches
The development of social entrepreneurship focused on IDPs, and veterans is vital for Ukraine’s long-term recovery, emphasizing sustainable grassroots efforts and innovation (Khailenko & Bacon, 2024; Kuznetsova & Mikheieva, 2020). Social impact investments provide flexible funding that fosters innovation and social enterprise growth amid crisis (Chudzicka-Czupała et al., 2023; Zhytar, 2024). Social entrepreneurship is becoming a key part of Ukraine’s socio-economic system, supporting community resilience, psychosocial aid, and reconstruction.
Literature Gap
While social entrepreneurship in Ukraine has attracted scholarly attention, most studies focus on theoretical or pre-war contexts and remain largely descriptive, lacking quantitative evidence on its wartime impact. Little is known about the causal relationship between social entrepreneurship and key socioeconomic outcomes such as community resilience, IDP integration, and local economic recovery under conflict conditions. This study addresses this gap through a data-driven analysis, employing regression models to assess how social enterprises influence IDP resilience, access to post-conflict employment, and perceptions of social integration, thereby contributing both empirical evidence and methodological advancement to the field.
Methodology
General Background
This study uses quantitative panel data analysis to explore the relationship between social enterprises and employment among internally displaced persons (IDPs) in Ukraine from 2022 to 2024. The period covers the humanitarian crisis following the 2022-armed conflict, which triggered large-scale displacement and spurred social enterprise initiatives. The research focuses on 12 Ukrainian regions hosting the highest IDP concentrations and social entrepreneurship activity. Regions were selected based on having at least 6% IDP population, complete data availability, and official data sources.
Data Collection Techniques
Types and Sources of Data
The analysis is based on information obtained from various authoritative global and national databases. The data gathering procedures are summarised in Table 1.
Table 1
Source Data
|
Data Type |
Source |
Method |
Validation/Additional Notes |
|
Employment Rate IDPs |
International Organization for Migration (IOM) and United Nations Population Fund (UNFPA) |
Extraction from IOM's monthly displacement tracking surveys focusing on socio-economic indicators |
Cross-validation with UNHCR displacement data and official Ukrainian government statistical reports |
|
Gross Regional Product (GRP) Per Capita |
International Monetary Fund (IMF) and State Statistics Service of Ukraine |
Regional economic data collection from international databases, analyzed using EViews econometric software |
USD conversion applied using the IMF annual average exchange rates for temporal consistency |
|
Social Enterprises |
The author's calculations were derived from the Pact Ukraine organizational database and the International Renaissance Foundation registry |
Systematic compilation of registered social enterprises through institutional surveys and public registration records |
Validation conducted via cross-referencing with online organizational directories and annual activity documentation |
|
Share IDPs in the Population |
IOM/UNFPA displacement tracking matrix and regional demographic data |
Computed as a percentage ratio of displaced persons to the total regional population using official census data |
Quarterly data updates implemented to maintain analytical precision and temporal relevance |
Source: Statista (2024).
Data Period and Coverage
Information was gathered across the 2022-2024 timeframe using yearly measurements, resulting in 36 observations (12 regions × 3 years). The selection of this period allows for the analysis of dynamic trends during the initial phase to the relative stabilization of post-crisis conditions.
Model Description
This study analyzes post-conflict scenarios through a sociological lens, focusing on development economics and social enterprises. Using multiple linear regression, it proposes a framework where IDPs’ socio-economic conditions depend on social enterprise-driven entrepreneurship, regional social factors, and IDP population density.
The empirical model used is:
Yi = β₀ + β₁X₁ᵢ + β₂X₂ᵢ + β₃X₃ᵢ + εᵢ
Where:
Yi = Employment rate of IDPs in region i (%)
X₁ᵢ = Number of social enterprises in region i
X₃ᵢ = Share of IDPs in the population of region i (%)
εᵢ = Error term that includes unobserved factors
Research Hypothesis
H₁: β₁ > 0 (The number of social enterprises has a positive effect on the employment rate of IDPs)
H₂: β₂ > 0 (GRP per capita has a positive effect on IDPs' employment rate)
H₃: β₃ ≠ 0 (Share IDPs have a significant influence on IDPs' employment rate)
Analysis Method
Descriptive Analysis
Descriptive analysis was carried out to understand the characteristics and distribution of the data, including:
Multiple Linear Regression Analysis
Multiple linear regression is used to model the unemployment rate of displaced persons (Yi) based on the number of social enterprises (X₁ᵢ), regional GDP per capita (X₂ᵢ), and the share of displaced population (X₃ᵢ), with an error term (εᵢ). The analysis assesses both individual and combined effects of these variables on IDP unemployment.
Partial Effect Significance Test (t-test)
The t-test assesses the effect of each explanatory variable separately on the dependent variable.
Joint Effect Significance Test (F-test)
The aim of the F-test is to assess whether the combined effect of all computations of independent variables significantly influences the IDP unemployment rate.
Coefficient of Determination Analysis (R²)
R² assesses the accuracy of explanation in relation to the model’s coverage of variance in the dependent variable.
Results
Descriptive Analysis
Ukraine has faced significant economic and demographic transformation since the 2022 conflict, with data showing resilience and challenges in maintaining economic and social stability.
Figure 1
Percentage of Internally Displaced Persons and Returnees
Source: Statista. (2025).
Data shows significant fluctuations in IDPs from a peak of 16.7% (September 2022) to 11.6% (December 2024), while returnees increased from 12.1% to 13.5%, indicating gradual stabilization (Figure 1).
Figure 2
Real GDP Growth of Ukraine
Source: Statista. (2025).
GDP shows high volatility with a severe contraction in the 1990s (-23%), recovery in the 2000s (+12%), and a projected contraction of -30% in 2022, followed by a gradual recovery of 4-5% until 2030 (Figure 2).
Figure 3
GDP per capita
Source: Statista. (2025).
The evolution of GDP per capita shows Ukraine's economic journey from around $400 in the early 1990s, peaking at $4,000 in 2013, then plummeting to $2,000 in 2015. Recovery began in 2016 and reached around $4,800 by 2021. Projections indicate consistent growth to over $8,000 by 2030, signaling long-term optimism despite current challenges (Figure 3).
Figure 4
Total GDP in Billion USD
Source: Statista. (2025).
A similar trend can be seen in volatility, from $45 billion (1990s) to $200 billion (2021), with a projected recovery to $240 billion by 2029 (Figure 4 ).
Figure 5
Business Entities by Sector
Source: Statista (2025).
The economic structure in 2023 was dominated by trade (714,544 entities) and information technology (306,822), indicating healthy economic diversification (Figure 5).
Figure 6
Regional Distribution of Internally Displaced Persons
Source: Statista (2025).
The highest concentrations were in Dnipropetrovsk (820,000), Kharkiv (679,000), and Kyiv (698,000), reflecting a pattern of migration from conflict areas to safe areas (Figure 6).
Figure 7
Total Population Trends
Source: Statista. (2025).
The population is projected to decline from 52 million in the 1990s to 34 million by 2030 due to demographics and conflict-driven migration. This study uses descriptive analysis to summarize key variables across 12 Ukrainian regions (2022–2024), including IDP employment rate (Yi), number of social enterprises (X1i), regional GRP per capita (X2i), and IDP population share (X3i) (Figure 7). Basic statistics—means, medians, standard deviations, minima, maxima—will reveal data distribution, variability, and outliers, providing a foundation for the subsequent regression analysis.
Table 2
Research Variable Data
|
Region |
Year |
Yi IDP Employment Rate |
X2i GRP PerCapita USD |
X3i IDP Share Population Percent |
|
Dnipropetrovsk Oblast |
2022 |
37.5 |
4.078 |
16.25 |
|
Dnipropetrovsk Oblast |
2023 |
45.6 |
5.234 |
16.25 |
|
Dnipropetrovsk Oblast |
2024 |
55.1 |
7.383 |
16.25 |
|
Kharkiv Oblast |
2022 |
41.4 |
3.403 |
16.56 |
|
Kharkiv Oblast |
2023 |
54.7 |
4.855 |
16.56 |
|
Kharkiv Oblast |
2024 |
56.0 |
5.418 |
16.56 |
|
Kyiv City |
2022 |
37.7 |
2.974 |
13.83 |
|
Kyiv City |
2023 |
50.9 |
5.399 |
13.83 |
|
Kyiv City |
2024 |
62.5 |
4.501 |
13.83 |
|
Kyiv Oblast |
2022 |
41.3 |
5.292 |
16.33 |
|
Kyiv Oblast |
2023 |
54.2 |
3.287 |
16.33 |
|
Kyiv Oblast |
2024 |
61.3 |
4.480 |
16.33 |
|
Odesa Oblast |
2022 |
42.0 |
3.946 |
9.00 |
|
Odesa Oblast |
2023 |
52.6 |
3.337 |
9.00 |
|
Odesa Oblast |
2024 |
58.4 |
7.067 |
9.00 |
|
Poltava Oblast |
2022 |
44.5 |
4.212 |
12.57 |
|
Poltava Oblast |
2023 |
49.0 |
3.986 |
12.57 |
|
Poltava Oblast |
2024 |
55.8 |
4.784 |
12.57 |
|
Lviv Oblast |
2022 |
42.9 |
5.226 |
6.00 |
|
Lviv Oblast |
2023 |
49.9 |
3.206 |
6.00 |
|
Lviv Oblast |
2024 |
63.0 |
4.332 |
6.00 |
|
Mykolaiv Oblast |
2022 |
42.5 |
3.693 |
11.09 |
|
Mykolaiv Oblast |
2023 |
45.2 |
3.381 |
11.09 |
|
Mykolaiv Oblast |
2024 |
59.9 |
7.373 |
11.09 |
|
Cherkasy Oblast |
2022 |
40.7 |
4.018 |
9.25 |
|
Cherkasy Oblast |
2023 |
50.3 |
4.426 |
9.25 |
|
Cherkasy Oblast |
2024 |
58.3 |
7.306 |
9.25 |
|
Vinnytsia Oblast |
2022 |
37.5 |
5.125 |
6.81 |
|
Vinnytsia Oblast |
2023 |
52.9 |
4.664 |
6.81 |
|
Vinnytsia Oblast |
2024 |
62.4 |
5.007 |
6.81 |
|
Kirovohrad Oblast |
2022 |
36.5 |
3.047 |
10.70 |
|
Kirovohrad Oblast |
2023 |
49.5 |
4.531 |
10.70 |
|
Kirovohrad Oblast |
2024 |
57.2 |
7.186 |
10.70 |
|
Sumy Oblast |
2022 |
42.5 |
4.881 |
9.36 |
|
Sumy Oblast |
2023 |
53.2 |
4.596 |
9.36 |
|
Sumy Oblast |
2024 |
59.5 |
4.956 |
9.36 |
Source: Statista. (2025).
The research variables show sufficient variation for regression analysis. IDP unemployment (Yi) varies regionally and over time, and GRP per capita (X2i) reflects economic disparities from conflict and recovery. The IDP population share (X3i) varies by security and accessibility (Table 2). No dominant outliers were detected, confirming the appropriateness of linear regression for this analysis.
Table 3
Data Description
|
Statistic |
IDP Employment Rate (%) |
GRP Per Capita (USD) |
IDP Share Population (%) |
|
N Valid |
36 |
36 |
36 |
|
Missing |
0 |
0 |
0 |
|
Mean |
50.12 |
4738.61 |
11.48 |
|
Median |
50.60 |
4563.50 |
10.90 |
|
Std. Dev |
8.12 |
1347.89 |
3.89 |
|
Minimum |
36.50 |
2974 |
6.00 |
|
Maximum |
63.00 |
7383 |
16.56 |
Source: Author's calculations using EViews 12.0 statistical software (2025)
Statistical analysis shows an average IDP unemployment rate of 50.12% (SD 8.12%), ranging from 36.5% to 63.0%, reflecting regional disparities. The average number of social enterprises is 27.03, varying between 12 and 46 across regions. GRP per capita averages USD 4,738.61 with high volatility (SD 1,347.89), indicating unstable regional economies. The share of IDPs in the population averages 11.48%, ranging from 6.0% to 16.56%, showing differing refugee concentrations (Table 3).
Regression Results
Table 4
Multicollinearity Test Results
|
Variable |
Tolerance |
VIF |
Conclusion |
|
X1i Social Enterprises |
0.909 |
1.100 |
No multicollinearity |
|
X2i GRP Per Capita USD |
1.000 |
1.000 |
No multicollinearity |
|
X3i IDP Share Population |
0.909 |
1.100 |
No multicollinearity |
Source: Author's calculations using EViews 12.0 statistical software (2025)
The investigated features did not exhibit any multicollinearity as high as already confirmed by the multicollinearity check. All independent variable tolerances were above 0.05, while VIF was less than 10, confirming there is no strong relationship amongst the independent features (Table 4 ).
This shows that each independent variable provides unique information and does not overlap in explaining the dependent variable.
Table 5
One-Sample Kolmogorov-Smirnov Test
|
|
Unstandardized Residual |
|
|
N |
36 |
|
|
Normal Parametersa,b |
Mean |
,0000000 |
|
Std. Deviation |
67,51433605 |
|
|
Most Extreme Differences |
Absolute |
,117 |
|
Positive |
,117 |
|
|
Negative |
-,082 |
|
|
Test Statistic |
,117 |
|
|
Asymp. Sig. (2-tailed) |
,200c,d |
|
|
a. Test distribution is Normal. |
||
|
b. Calculated from data. |
||
|
c. Lilliefors Significance Correction. |
||
|
d. This is a lower bound of the true significance. |
||
Source: Author's calculations using EViews 12.0 statistical software (2025)
The results of the Kolmogorov-Smirnov test for normality indicate that the p-value is 0.200 > 0.05, suggesting that the dataset in question, despite outliers, is normally distributed. This result is consistent with the normality assumption, meaning that the regression model used is valid and the parameter estimates are reliable (Table 5).
Table 6
|
Heteroscedasticity Test Results
|
Source: Author's calculations using EViews 12.0 statistical software (2025)
Heteroscedasticity test results show that no heteroscedasticity exists. This is indicated by the significance of all variables being greater than 0.05, meaning that the residual variance is homogeneous (constant) throughout the independent variable value range (Table 6).
Table 7
|
Autocorrelation Test Model Summaryb |
|||||
|
Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
Durbin-Watson |
|
1 |
,562a |
,315 |
,251 |
70,608 |
2,168 |
|
a. Predictors: (Constant), X3i IDP Share Population Percent, X2i GRP PerCapita USD, X1i Social Enterprises |
|||||
|
b. Dependent Variable: Yi IDP Employment Rate |
|||||
Source: Author's calculations using EViews 12.0 statistical software (2025)
Durbin-Watson: 2.168
DL = 1.2953, DU = 1.6539
Criterion: DU < D < 4-DU → 1.6539 < 2.168 < 2.7047
The Durbin-Watson statistic of 2.168 falls within the range of DU < D < 4-DU, signifying the absence of autocorrelation in the regression model. This indicates that the residuals are uncorrelated with one another, so the assumption of independence of error terms is fulfilled (Table 7 ).
Table 8
Regression Test Results
|
Model Components |
Coefficient |
Std. Error |
t-value |
Sig. |
Interpretation |
|
Model Summary |
|
|
|
|
|
|
R² |
0.315 |
- |
- |
- |
The model explains 31.5% of the variance |
|
Adjusted R² |
0.251 |
- |
- |
- |
Conservative estimate: 25.1% |
|
F-statistic |
4.914 |
- |
- |
0.006 |
The model is statistically significant |
|
Regression Coefficients |
|
|
|
|
|
|
Constant |
322.203 |
65.548 |
4.916 |
0.000 |
Intercept term |
|
X1i Social Enterprises |
2.758 |
1.339 |
2.060 |
0.048* |
Significant positive effect |
|
X2i GRP Per Capita (USD) |
0.031 |
0.010 |
3.214 |
0.003* |
Significant positive effect |
|
X3i IDP Share Population (%) |
-0.037 |
0.035 |
-1.037 |
0.307 |
Not significant |
*Notes: N = 36 observations; p < 0.05 indicates statistical significance
Source: Author's calculations using EViews 12.0 statistical software (2025)
Regression equations:
Yi = 322.203 + 2.758X1i + 0.031X2i - 0.037X3i
The F-test result (p = 0.006 < 0.05) rejects H₀ in favour of Hₐ, indicating that the number of social enterprises, GDP per capita, and the share of IDPs jointly have a significant effect on IDP unemployment in Ukraine. The regression model is relevant and reliable for explaining variations in unemployment rates among IDPs. The R² value of 0.315 means the model accounts for 31.5% of the variation, while the adjusted R² of 0.251 provides a more conservative estimate, considering the number of predictors. Although moderate, these values are acceptable in socio-economic research, given the multifactor nature of employment dynamics in conflict and post-conflict contexts (Table 8).
Discussion
This study provides evidence that social enterprises significantly improve employment for IDPs in Ukraine (2022–2024). The first hypothesis (H₁) is confirmed, showing that each additional social enterprise increases IDP employment by 2.758 percentage points (β₁ = 2.758, p = 0.048). This supports hybrid organizing theory, where social enterprises combine market activities with social missions (Battilana & Lee, 2014). Findings align with research on workplace integration social enterprises (WISEs) that create jobs for marginalized groups (Battilana et al., 2015). Scholars also emphasize social enterprises’ unique roles in supporting vulnerable populations and fostering social innovation (Joyce et al., 2025; Meissner et al., 2024).
The second hypothesis (H₂) also validated that GRP per capita positively and significantly affects it with a coefficient of (β₂ = 0.031, p = 0.003), which implies that better regional economic conditions foster enhanced IDP employment rate. These findings are consistent with the literature, indicating that regional macroeconomic context plays a crucial role in determining the success of economic integration of migrants and displaced populations (Kiak et al., 2022). The concept of economic resilience in maintaining employment levels in robust environments (Xie, 2023). Hybrid organizations, such as social enterprises, need a supportive economic environment to manage the tensions between social mission and financial sustainability effectively (Doherty et al., 2014).
The third hypothesis (H₃) was not significant: the share of IDPs in the population did not affect employment rates (β₃ = -0.037, p = 0.307). This challenges the assumption that higher IDP concentration improves employment through economies of scale. Bandiera et al. (2023) note that IDP presence may increase labor competition without raising employment. Tesfaye et al. (2024) highlight PTSD and limited mental health support as greater barriers than population share. Consistent with Smith et al. (2013), social project success depends more on organizational capacity and strategy than size. Rizzi et al. (2023) stress psychological and systemic barriers limit IDP job access. Governance and organizational quality matter more than scale in maintaining social enterprise performance (Ebrahim et al., 2014).
The regression model is significant (F = 4.914, p = 0.006) and explains 31.5% of the variation in IDP employment, showing that social enterprises and regional economic conditions are key factors in IDP economic integration. Sabah (2025) highlights the role of education and skills development in revitalizing conflict-affected areas. The moderate R² reflects the complex factors affecting employment in post-conflict settings. Consistent with paradox theory (Smith et al., 2013) and hybrid organization research (Pache & Santos, 2013), social enterprises navigate competing demands and institutional logics beyond what simple models capture.
Conclusions and implications
This study found that enterprises perform a strategic function in increasing the employment rate of internally displaced persons (IDPs) in Ukraine, with significant policy implications for humanitarian crisis management and post-conflict economic recovery.
The positive impact of social work on the unemployment rate of internally displaced persons (IDPs) provides empirical evidence for prioritizing and supporting the development of social work as a long-term strategic investment for economic integration. The true value of this study stems from the need to encourage collaboration between government agencies, international entities, and the business industry to stimulate social work growth, especially with regard to access to financing, skills training, and supportive regulations. For practitioners and policymakers, the study's findings suggest that approaches focused on economic empowerment through social enterprises are more effective than conventional short-term assistance programs. Additionally, the importance of considering regional economic conditions when designing job creation programs for IDPs highlights the need for strategies tailored to the economic characteristics and capacities of each region. This study contributes theoretically to the literature on improvement economics and crisis management by illustrating how social enterprise models can be a viable approach to addressing job creation issues caused by population displacement and is relevant for other countries experiencing conflict or humanitarian crises.
Future Research Directions
Future research could extend the time frame and geographical scope to deepen understanding of the link between social enterprises and IDP unemployment, incorporating mediating and moderating variables not addressed here. Longitudinal studies would clarify the sustainability and long-term impact of social enterprises on IDP integration, while cross-country comparisons could enhance generalizability. A mixed-methods approach could reveal mechanisms, best practices, and barriers influencing employment outcomes. Factors such as education, social support, infrastructure, and local policies merit examination for their potential to amplify or diminish these effects. Additionally, cost-benefit analyses comparing investments in social enterprises with alternative employment initiatives would strengthen policy recommendations. Broader investigation into indirect effects, including psychological well-being, social integration, and community development, would further enrich the evidence base.
Acknowledgements: None.
Conflict of Interest: None.
Funding: None.
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