Entrepreneurship and Unemployment in Türkiye: Regional Evidence on Schumpeter and Refugee Effects Under Economic and Financial Constraints

Abstract

Sustainable regional development requires understanding how entrepreneurship and unemployment co-evolve. This study investigates this relationship across Türkiye’s 26 Nomenclature of Territorial Units for Statistics 2 regions over the 2007–2024 period, testing the Schumpeter (pull) and Refugee (push) effects with controls for regional economic and financial determinants. Using the Dynamic Common Correlated Effects estimator, which accounts for cross-sectional dependence and slope heterogeneity across regions, the analysis provides evidence supporting both effects, while revealing that neither effect emerges instantaneously. The Schumpeter effect operates with an approximately one-year lag, reflecting the time new ventures require to complete organizational formation and generate net labor demand, with a creative destruction dynamic appearing from the second year onward. The Refugee effect materializes within one to two years, as unemployed individuals exhaust formal job search alternatives before turning to necessity entrepreneurship. Critically, the findings identify banking sector intermediation efficiency, rather than aggregate credit volume, as a more consistent financial channel for sustainable labor market outcomes, and document a pattern consistent with jobless growth, in which regional output expansion has not systematically translated into unemployment reduction. These results call for employment- and entrepreneurship-linked policy instruments that are timed to the lag structure of both effects and targeted at transforming necessity-driven activities into sustainable, high-value-added structures, rather than merely incentivizing firm entry. Aligning regional financial intermediation with employment creation can foster long-term socio-economic sustainability and promote sustainable regional development.

1. Introduction

The complex interaction between entrepreneurship and unemployment is one of the central macroeconomic debates at the heart of economic growth, employment generation, and regional development policies. Understanding this dynamic nexus is critical for achieving socio-economic sustainability and forms the foundation of sustainable regional development by ensuring inclusive economic growth and decent work opportunities. In economic theory, individuals’ choices between being an employee, unemployed, or self-employed have traditionally been framed around Occupational Choice Theory [1]. Within this framework, the literature explains the bidirectional causality between entrepreneurship and unemployment through two fundamental effects. The first is the Schumpeter Effect (pull effect), which posits that innovative firms enter the market through creative destruction, creating new employment and reducing unemployment; the second is the Refugee Effect (push effect), which argues that labor market contractions and rising unemployment compel individuals to start their own businesses as a rational survival strategy [2,3].

While studies conducted in developed countries generally demonstrate the simultaneous presence of both Schumpeter and Refugee effects [4,5], empirical evidence regarding the operation of these effects in emerging markets such as Türkiye remains mixed and inconclusive. In the Turkish literature, while some studies find significant causality from unemployment to entrepreneurship and thus support the Refugee effect [6,7], others detect that unemployment reduces or renders insignificant the effect on entrepreneurship due to financial inadequacies [3,8], arguing that the Refugee effect does not operate. The primary reason for these inconsistencies is that aggregate time-series analyses at the national level mask the profound socioeconomic development disparities between the country’s sub-regions and the macro-financial conditions prevailing in those markets. Indeed, the literature emphasizes that individuals’ entrepreneurial decisions and the interaction between these two variables do not unfold in isolation but within a complex ecosystem determined by factors such as regional economic welfare, liquidity constraints, and credit availability [9,10].

Motivated by this, the primary aim of this study is to test the bidirectional causal relationship between entrepreneurship and unemployment and potential time lags in light of economic and financial determinants at the regional level, using annual data for Türkiye’s 26 Nomenclature of Territorial Units for Statistics-2 (NUTS-2) regions over the 2007–2024 period. Two separate econometric models are constructed to test the Schumpeter and Refugee effects individually. To control for inter-regional asymmetries and financial access disparities, the models include regional economic variables such as gross domestic product (GDP) per capita, GDP per capita growth, and gross value added (GVA), alongside regional financial control variables such as credit volume per capita and the loan-to-deposit ratio (LDR), which reflects the banking sector’s funding capacity. The Dynamic Common Correlated Effects (DCCE) estimator, which accounts for cross-sectional dependence (CSD) and slope heterogeneity among regions, is employed for model estimation.

This study aims to make a unified contribution to the literature by demonstrating how regional financial intermediation efficiency moderates the time-lagged realization of both effects. By shifting the focus from national aggregates to Türkiye’s 26 NUTS-2 regions, the analysis reveals that the transition between entrepreneurship and net employment is not instantaneous but structurally delayed. Methodologically, the application of the DCCE estimator successfully isolates these regional dynamics from unobserved macroeconomic shocks, providing robust evidence that the operational channel for sustainable labor market outcomes lies in targeted financial intermediation rather than raw credit expansion.

The remainder of the paper is organized as follows. Section 2 reviews the theoretical mechanisms underlying both effects. Section 3 discusses the role of economic and financial factors. Section 4 presents the data and methodology. Section 5 reports the empirical results. Section 6 discusses the results within the structural context of the Turkish economy. Section 7 presents the policy implications and Section 8 concludes with the future research agenda.

2. Theoretical Background

At the individual level, the decision to become an entrepreneur rather than remain an employee or unemployed is shaped by three interacting factors: human capital endowment, the opportunities offered by the prevailing macroeconomic environment, and the financial constraints to which the individual is exposed [1,3,9].

In the economics and entrepreneurship literature, this dynamic interaction between regional labor markets and entrepreneurial activity exhibits not a unidirectional and static causality but a bidirectional structure that can influence each other simultaneously or with different time lags [4,11]. The literature examines this complex structure within the framework of two opposing theoretical mechanisms that operate with fundamentally different motivations yet complement each other in regional economies [2,12,13]. Beyond the direction of causality, these two mechanisms differ fundamentally in the nature and quality of the entrepreneurial activity they generate. The Schumpeter effect is associated with opportunity-based activity that creates sustainable employment, while the Refugee effect produces a necessity-driven adaptation mechanism with limited growth potential and short survival horizons [14,15].

Macro-level analyses conducted with national data can cause these two opposing mechanisms to obscure each other, producing incomplete and misleading results. Indeed, while Thurik et al. [2] empirically demonstrate that both effects operate simultaneously at the national level, recent regional studies reveal that this interaction exhibits significant variation according to spatial dynamics and regional economic performance [4,16,17]. In particular, O’Leary [17] has demonstrated that the direction of causality can change depending on regional development levels, rendering the regional level the appropriate unit of analysis in economies like Türkiye where substantial inter-regional socioeconomic development disparities are well-documented [7,18,19,20].

The following subsections examine each mechanism in turn, tracing its theoretical foundations, empirical transmission channels, and the time-lag structure through which its labor market effects materialize.

2.1. Schumpeter Effect

Rooted in Schumpeter’s [21] The Theory of Economic Development and frequently conceptualized in the literature as the ‘prosperity-pull effect’ or ‘entrepreneurship effect,’ the Schumpeter effect focuses on the proactive role of entrepreneurship in economic growth and employment creation [22]. From the Schumpeterian perspective, entrepreneurs who enter the market with new products, production processes, and organizational innovations are the principal actors of the ‘creative destruction’ process. This process disrupts the static economic equilibrium by eliminating inefficient, technologically obsolete firms through the entry of innovative new firms, directing resources toward more productive areas [21,23]. Therefore, in the Schumpeter framework, causality runs from entrepreneurship to unemployment; increases in entrepreneurship are expected to create new employment areas at the macro and regional levels, thereby reducing unemployment rates [2,3,4,11,12].

The manifestations of the Schumpeter effect in labor markets occur through complex transmission channels rather than a one-dimensional employment increase. While newly established firms first create direct labor demand, they also intensify competitive pressure in the market, encouraging productivity growth and improving resource allocation [24]. In addition, the Knowledge Spillover Theory of Entrepreneurship developed by Acs et al. [25,26] argues that innovative entrepreneurship creates significant indirect employment growth through knowledge spillovers and regional supply chain integration. This type of activity, generally classified in the literature as ‘opportunity entrepreneurship’ driven by qualified human capital, R&D, and innovation, functions particularly in technologically strong central regions as a ‘pull factor’ that attracts skilled labor and investment [14,16,27,28].

Modern empirical literature emphasizes that the positive impact of the Schumpeter effect on employment does not emerge instantly but manifests with a distinct ‘time lag.’ Fritsch and Mueller [29] showed that new firm formation’s employment impact follows a negative trajectory during the first five years due to crowding-out and market selection, turning positive after this incubation period and peaking in years seven to eight. Similarly, in a comprehensive panel data analysis of 23 Organisation for Economic Co-operation and Development (OECD) countries by Thurik et al. [2], the unemployment-reducing effect of entrepreneurship was found to maximize after an 8-year lag, and this entrepreneurship effect was found to be far stronger than the refugee effect. Although short-term employment losses may occur due to disruptive market competition initially, in the medium and long run, the competition, innovation, and structural growth created by entrepreneurship permanently reduce unemployment [24,29].

International studies emphasizing the importance of regional dynamics [13,17] demonstrate that this effect clearly operates in the long run particularly in socioeconomically strong and developed central regions. Empirical studies conducted in the Turkish context also support this theoretical framework. Özerkek and Doğruel [30] revealed that increases in entrepreneurship using data from 1970 to 2013 reduce unemployment in the long run. Apaydın [3], in an ARDL analysis conducted with 2000–2016 data, identified a negative and unidirectional causality from entrepreneurship to unemployment, providing evidence consistent with the Schumpeter effect at the national level. On the other hand, in a recent panel data analysis of Türkiye by Moiz and Ileri [7], the Schumpeter effect was found to be insignificant at the NUTS-2 level, but at the NUTS-3 level, it significantly reduced unemployment with 4- and 5-year time lags. Determining the time intervals at which the Schumpeterian employment creation process reaches its optimum in sub-regions, using methods that account for CSD, remains a central debate in the empirical literature.

2.2. Refugee Effect

In contrast to the optimistic picture of the Schumpeter Effect, which emphasizes employment creation and economic growth promotion, the approach known in the literature as the ‘Refugee Effect’ frames the direction of causality from labor market contractions to entrepreneurship. Theoretically grounded by Oxenfeldt [31] and later econometrically formulated by Blau [32] and Evans and Leighton [33,34] within the framework of Income Choice Theory, this effect treats entrepreneurship as a compulsory occupational choice [3]. It is also referred to in the literature as ‘recession-push,’ ‘unemployment-push,’ or the ‘shopkeeper effect’, and posits that rising unemployment rates substantially lower the probability of an individual finding satisfactory employment in the formal sector and thereby reduce the opportunity cost of starting an independent business [2,6].

Individuals deprived of alternative employment opportunities and facing declining expected wage income are pushed toward self-employment by the drive for economic survival. This situation is associated with the concept of ‘necessity entrepreneurship’, a low-value-added activity with no high growth ambition, rather than opportunity entrepreneurship supported by qualified human capital and innovation [14,28]. Fairlie [35], in his study on the U.S., provided evidence supporting this push effect by finding that the rates at which individuals who lose their jobs during economic recessions turn to entrepreneurship increase at a statistically significant level. Similarly, Blanchflower [36], in a study for OECD countries, demonstrated that increases in unemployment rates raise self-employment rates. However, the quality and sustainability of this effect have been subject to intense criticism in the literature. Since these businesses, established out of necessity, typically have low management skills, limited innovation capacity, and insufficient financial infrastructure, their survival rates in the market are quite short [37]. This constrains the transformation of unemployment pressure into long-term, sustained economic growth [5,38,39].

There are also strong theoretical and empirical objections to the operation of the Refugee Effect. Hurst and Lusardi [10] and Johansson [40], who find a negative relationship between unemployment and entrepreneurship and thus reject the push effect, explain this through ‘liquidity constraints’ experienced during periods of economic contraction. During periods of high unemployment, the general macroeconomic decline in demand and the lack of seed capital needed by unemployed individuals to start their own businesses prevent the taking entrepreneurial risks. Therefore, the existence and magnitude of the Refugee Effect vary depending on countries’ macroeconomic welfare levels, institutional quality, and the credit opportunities offered by financial markets.

Empirical findings in the literature regarding whether the Refugee Effect operates in the context of the Turkish economy are quite mixed, and there is no definitive consensus among researchers [3,6,7,8]. Çetintaş et al. [6], applying Toda-Yamamoto causality analysis with monthly data from 2010 to 2017, found a unidirectional and positive causality from the unemployment rate to the number of newly opened companies, arguing that the Refugee Effect holds in Türkiye. In contrast, Kum and Karacaoğlu [8] found that increases in the unemployment rate decrease entrepreneurial activity in Türkiye; Apaydın [3] found that unemployment has no statistically significant effect on entrepreneurship and, citing insufficient capital accumulation, rejected this effect. Moiz and Ileri [7] detected the presence of the push effect in their recent study, though no consensus has yet emerged in the literature. Considering the macroeconomic vulnerabilities’ reflections on regional credit markets, the difficulties individuals face in accessing financial resources during periods of high unemployment emerge as the greatest structural barrier that could prevent the Refugee Effect (necessity entrepreneurship) from operating in sub-regions of developing countries such as Türkiye [3,8,9,10].

Based on the theoretical mechanisms reviewed above, two hypotheses guide the empirical analysis. H1 (Schumpeter Effect): entrepreneurial activity reduces regional unemployment, with the employment-creating effect materializing with a lag rather than instantaneously, reflecting the organizational formation and market entry processes required before new firms generate net labor demand. H2 (Refugee Effect): rising regional unemployment increases entrepreneurial activity through a necessity-driven push mechanism, with the transition to self-employment occurring with a delay as individuals exhaust formal job search alternatives. This process depends on the financial accessibility of the regional economy, as discussed in the following section.

3. Economic and Financial Determinants of Entrepreneurship-Unemployment Nexus

The interaction between entrepreneurship and unemployment is not limited solely to micro-level rational occupational choices or the theoretical causalities of the ‘Schumpeter’ and ‘Refugee’ effects. As frequently emphasized in the literature, this complex and bidirectional relationship does not occur in a sterile vacuum [5]. Rather, this mechanism exists in a multidimensional ecosystem directly shaped by a country’s or region’s macroeconomic welfare level, business cycles, institutional quality, and financial architecture [15,41].

Particularly in developing countries characterized by deep structural, economic, and demographic heterogeneity, macroeconomic and financial conditions emerge as the key factors that either catalyze or suppress this cyclical interaction. In broad terms, economic factors such as economic growth, per capita income growth, and regional demand shape both the motivations for individuals to capitalize on market opportunities and the push and pull forces driving entrepreneurial activity. Financial factors such as credit volume determine the risk barriers and resource constraints that a potential entrepreneur will face in turning these opportunities into action [5,9,10,15]. Therefore, these environmental dynamics that determine the direction and intensity of causality between regional labor markets and the entrepreneurship ecosystem are detailed below under two fundamental axes: economic and financial determinants.

3.1. Economic Determinants

Economic growth, business cycles, and overall macroeconomic performance are the most fundamental dynamics determining the direction of the relationship between entrepreneurship and unemployment [42]. As emphasized by endogenous growth models [43], during economic expansion periods when prosperity and regional per capita income are high, increasing consumer demand directly stimulates innovative opportunity entrepreneurship by creating new profit opportunities in the market. This strengthens the Schumpeter effect, exerting downward pressure on regional unemployment [41]. In contrast, during economic contraction periods, declining market demand weakens the Schumpeter effect; rising unemployment rates can trigger the Refugee effect by pushing individuals toward necessity entrepreneurship in order to survive [28,44]. However, firms established during recession periods have very short survival periods due to the contraction in general market demand, and their capacity to structurally resolve overall unemployment is, at best, modest [5].

When considered within the framework of occupational choice theory, regional welfare and wage levels constitute the greatest opportunity cost of starting an independent business [1]. During periods of rapid economic growth, capacity increases by institutional and large-scale firms may raise labor demand and guaranteed employment opportunities, potentially crowding out micro-scale individual entrepreneurship tendencies (crowding-out effect). This is consistent with the U-shaped regional development model empirically supported by Wennekers et al. [42]. In this context, quantitative entrepreneurship rates that show a declining tendency in the early stages of economic growth begin to rise again (in an opportunity and innovation-oriented manner) as regional economic output surpasses a certain welfare threshold and transitions to a knowledge economy. On the other hand, in crisis periods or macroeconomic instability environments, the decline in expected wage incomes substantially lowers the opportunity cost, potentially directing unemployed individuals toward necessity entrepreneurship [38].

Regional market potential, demographic structure, and spatial agglomeration economies are also critical factors causing this relationship to differentiate at the regional level. Densely populated urban regions (core regions) offer a favorable ecosystem for new entrepreneurs through knowledge spillovers and large consumer markets [45,46]. Furthermore, the literature positions regional per capita income and economic growth momentum as the most fundamental macroeconomic forces determining total market demand and the profitability expectations of investments [5,15]. In regions with high per capita income (regional welfare), increased purchasing power and consumption capacity function as a direct catalyst stimulating opportunity entrepreneurship (the Schumpeter effect). Recent analyses conducted on Türkiye also support these macroeconomic dynamics; in addition to increases in the industrial production index [6], regional human capital endowments such as qualified education levels and patent applications are found to have a complementary and strong effect on new firm formation [7]. In this context, it is evident that the interaction between entrepreneurship and unemployment is substantially shaped by regional income (welfare) levels, market size, and macroeconomic production conditions.

3.2. Financial Determinants

The effectiveness of financial markets in initiating entrepreneurial activities, ensuring their sustainability, and absorbing regional unemployment is recognized in the literature as an endogenous engine of economic growth [47]. The depth of the financial system and the efficiency of intermediation mechanisms accelerate regional development by channeling savings into productive investments [48]. However, the processes by which potential entrepreneurs, especially unemployed individuals, access external financing are explained in the literature through the theory of ‘liquidity constraints.’ According to this framework, outlined by Evans and Jovanovic [9] and Hurst and Lusardi [10], asymmetric information problems and collateral deficiencies in capital markets significantly restrict individuals’ access to banking system credits. In regions with insufficient financial access and limited credit channels, the necessary initial capital cannot be obtained, thus hindering both the Schumpeterian process of creative destruction and preventing the refugee effect, which encourages unemployed individuals to pursue entrepreneurship [40,49]. Indeed, studies conducted on Türkiye have also shown that individuals’ lack of financial resources suppresses entrepreneurship and can render the push effect invalid [8].

Credit volume per capita and the LDR, the most fundamental indicators of regional financial deepening and the active funding capacity in the market, showing the extent to which banks can transfer the savings they collect to the real sector, constitute the lifeblood of the entrepreneurship ecosystem. Beyond indicating active funding capacity, LDR also captures critical dimensions of the banking sector’s regional risk appetite and local funding structures. Variations in this ratio reflect the willingness of banks to intermediate local deposits into regional real sector investments under varying conditions of macroeconomic uncertainty. Key studies in the literature demonstrate that the expansion of regional credit volume and ease of access to credit directly break down the financial barriers to market entry by loosening the liquidity constraints faced by potential entrepreneurs [9,10]. Indeed, in the Turkish context, Çetintaş et al. [6] demonstrated a strong bidirectional causal relationship between commercial credits and the number of newly opened companies, suggesting that open credit channels mutually reinforce entrepreneurship.

However, this positive cycle can reverse during periods of macroeconomic instability and financial fragility. Particularly during financial crises or when non-performing loans (NPLs) increase in the banking system, banks’ risk appetite narrows, leading to declining LDRs and tighter credit rationing. This contraction in per capita credit volume and difficulties in accessing funding not only block new firm formations (the Refugee effect) but also lead to the downsizing or bankruptcy of existing businesses, further amplifying the regional unemployment spiral [50,51]. In this context, regional fixed capital investments beyond bank credits and alternative financing channels function as vital financial buffers that support new firm formations and mitigate liquidity shocks by reflecting capital accumulation [15].

4. Data and Methodology

4.1. Data and Variable Definitions

To control for economic and financial differences among regions, the models include regional economic variables (GDP per capita, GDP per capita growth, gross value added) and regional financial control variables (credit volume per capita and the LDR). The natural logarithm of GDP per capita and credit per capita is used. Economic variables were obtained from the Turkish Statistical Institute (TurkStat) [52], and financial variables from the Banking Regulation and Supervision Agency (BRSA) [53].

The self-employment rate within total employment (including employers and own-account workers) is used as the entrepreneurship indicator. This variable was calculated using household labor force statistics for the regions obtained from the Turkish Statistical Institute [52]. Regional unemployment rate data obtained from [52] is used as the unemployment indicator.

While the self-employment rate is a standard proxy in the empirical literature, it is important to acknowledge a theoretical limitation: this variable aggregates both opportunity-driven and necessity-driven ventures. Consequently, the estimated coefficients in our models reflect the net dominance of these aggregate effects within the regions, and any interpretative distinction between the Schumpeter and Refugee effects relies on the specific temporal and financial dynamics identified, rather than a direct disaggregation of the dependent variable.

Descriptive statistics for all variables are presented in

Table 1. Variable definitions and descriptive statistics.

Variables	Definition	Obs	Mean	Std. Dev.	Min	Max
unemp	Unemployment rate	468	10.466	4.390	3.4	33.5
ent	Entrepreneurship rate	468	24.699	5.526	13.036	42.192
ln_pcgdp	Log of GDP per capita	468	10.371	1.111	8.404	13.596
pcgdp_growth	GDP per capita growth	468	5.459	7.459	−20.190	37.447
gva	Regional gross value added (2009 = 100)	468	153.387	41.908	94.6	315.7
ldr	Loan-to-deposit ratio	468	132.887	44.431	53.524	341.384
ln_creditpc	Log of credit per capita	468	9.424	1.295	5.976	12.828

.

4.2. Methodology

Regional labor markets and entrepreneurship ecosystems are not isolated: macroeconomic shocks, financial fluctuations, and employment incentives in any of Türkiye’s 26 NUTS-2 regions simultaneously affect others through spatial spillovers and inter-regional trade links. When this situation, conceptualized in panel data econometrics as CSD, is ignored, traditional estimators produce biased and asymptotically inconsistent results [54].

In this framework, CSD is examined separately for each model. To detect CSD, Pesaran’s [55] CD and Friedman’s [56] tests are applied, and the results are presented in

Table 2. Cross-sectional tests results.

	Pesaran CD Test
Model	Pesaran’s Test	p-Value	Avg. Absolute Value
1. Model (unemp Dependent)	10.473	0.0000 ***	0.319
2. Model (ent Dependent)	3.321	0.0009 ***	0.308
	Friedman’s Test of Cross Sectional Independence
Model	Friedman’s Test	p-Value
1. Model (unemp Dependent)	76.036	0.0000 ***
2. Model (ent Dependent)	41.189	0.0219 **

*** p < 0.01, ** p < 0.05.

.

Examining Table 2, the null hypothesis (H₀) of ‘no CSD among units’ is strongly rejected for both the Schumpeter model (Model 1), where unemployment is the dependent variable and the Refugee model (Model 2), where entrepreneurship is the dependent variable. According to the Pesaran CD test results, the probability (p-value) values obtained for Model 1 (p = 0.0000) and Model 2 (p = 0.0009) are smaller than the 1% significance level. This strong finding is also consistent with the Friedman test results, confirming that the null hypothesis is rejected at the 1% and 5% levels for Model 1 (p = 0.0000) and Model 2 (p = 0.0219), respectively.

Given the high levels of CSD detected, traditional methods that assume regional independence are inappropriate. The analysis, therefore, employs second-generation unit root tests for stationarity and the DCCE estimator for model estimation, both of which account for inter-regional common shocks.

To account for CSD, the stationarity properties of the variables are examined using second-generation panel unit root tests: the Cross-Sectionally Augmented Dickey–Fuller (CADF) and Pesaran Cross-Sectionally Augmented IPS (CIPS) tests [57]. These methods augment standard ADF regressions with cross-sectional averages to filter out unobserved common factors and spatial spillovers, avoiding the spurious stationarity problem of first-generation tests.

After determining the stationarity levels of the series, the next step is to test for a long-run equilibrium relationship among the variables. Because Table 2 shows strong CSD among the NUTS-2 regions, first-generation residual-based tests such as Pedroni and Kao would yield asymptotically biased results [58]. We therefore employ the Westerlund [59] error-correction-based panel cointegration test, which utilizes a bootstrapping procedure to generate robust p-values, thereby filtering out spatial spillovers and unobserved common macroeconomic shocks [60].

With cointegration established, the analysis proceeds to estimate the Schumpeter and Refugee effects. The incubation period and financing processes inherent in these effects require lagged values of entrepreneurship and unemployment variables in the empirical models. Slope heterogeneity across NUTS-2 regions additionally motivates a heterogeneous-coefficient estimator [61,62].

To address these limitations, the DCCE estimator developed by Chudik and Pesaran [62] is used. DCCE incorporates structural differences among Türkiye’s NUTS-2 regions and uses cross-sectional averages of model variables and their lags as proxy variables, filtering unobserved common factors and national macroeconomic shocks affecting the regions.

The Schumpeter (Model 1) and Refugee (Model 2) DCCE equations constructed to test the study’s fundamental research questions are presented in Equations (1) and (2), respectively.

Model 1 (Schumpeter Effect Equation):

unemp(i,t) = α_1i + λ_1i·unemp(i,t − 1) + β_10i·ent(i,t) + β_11i·ent(i,t − 1) + β_12i·ent(i,t − 2) + γ_1i·X(i,t) + Σδ_1il·Z⁻(t − l) + ε_1it

(1)

Model 2 (Refugee Effect Equation):

ent(i,t) = α_2i + λ_2i·ent(i,t − 1) + β_20i·unemp(i,t) + β_21i·unemp(i,t − 1) + β_22i·unemp(i,t − 2) + γ_2i·X(i,t) + Σδ_2il·W⁻(t − l) + ε_2it

(2)

In the equations, the index i represents the NUTS-2 regions (i = 1, 2, …, 26), and the index t represents the time dimension (t = 2007, 2008, …, 2024). The λ parameters in both models reflect the dependence of the dependent variables on their own past dynamics and the partial adjustment process of the system in response to shocks.

In the study, the explanatory variables are structured in a distributed lag form to enable a step-by-step analysis of the time-spread effects of the bidirectional macroeconomic cycle between entrepreneurship and unemployment conceptualized by Thurik et al. [2]. Accordingly, in Model 1, to measure the creative destruction and employment creation capacity (Schumpeter effect), the entrepreneurship variable is included in the model together with its simultaneous level value (ent(i,t)), its one-period lagged value (ent(i,t − 1), denoted L.ent in the regression tables), and its two-period lagged value (ent(i,t − 2), denoted L2.ent in the regression tables). Model 2 mirrors this structure symmetrically: the unemployment rate enters at level (unemp(i,t)), with one-period lag (L.unemp), and with two-period lag (L2.unemp) to capture the necessity and push dynamics. The choice of a maximum of two-period lags for the core variables is structurally dictated by the temporal dimension of the panel dataset (T = 18, covering 2007–2024). The DCCE framework natively consumes substantial degrees of freedom by incorporating cross-sectional averages and their corresponding lags to effectively filter out unobserved common factors. Extending the lag structure further within a T = 18 horizon would rapidly deplete the remaining degrees of freedom, thereby compromising the reliability and asymptotic consistency of the parameter estimates.

The vector X(i,t) in the models represents the set of macroeconomic and financial control variables affecting the regional labor force and entrepreneurship ecosystem.

The Z⁻(t − l) and W⁻(t − l) components represent the cross-sectional averages of all dependent and independent variables in Model 1 and Model 2, respectively, and their lags of length pT. These terms successfully filter the CSD (spatial interactions and common shocks) among NUTS-2 regions, ensuring that the residuals (ε(i,t)) are independently distributed [62]. Finally, since the DCCE estimator is based on the Mean Group approach [63], it allows parameters to vary at the regional level according to the socioeconomic dynamics of each region and provides a consistent and unbiased average of these heterogeneous coefficients [64]. All panel data tests and dynamic econometric estimations in this study were performed using Stata 19 software.

5. Results

Before proceeding to the formal findings of the econometric analyses (unit root, cointegration, and DCCE model estimation), it is of great importance to visually examine the historical trajectory of the macroeconomic relationship between variables. Accordingly, the trajectories of entrepreneurship and unemployment rates obtained from the cross-sectional averages of Türkiye’s NUTS-2 regions over the 2007–2024 period are presented in Figure 1.

Examining Figure 1, it is clearly observed that the upward and downward trends of entrepreneurship and unemployment rates do not always move synchronously. The fact that the turning points (peaks and troughs) of the series follow each other at different times indicates that the interaction between these two variables may be a delayed rather than instantaneous process. This visual finding provides important preliminary evidence that the relationship between variables needs to be examined not with static econometric models but with a dynamic approach that accounts for past period effects (lags).

5.1. Panel Unit Root Test Results

The study first tests whether the variables are stationary. Due to CSD, the stationarity of the series is examined using the second-generation unit root tests: CADF and CIPS.

Examining the CADF test findings presented in

Table 3. CADF unit root test results.

Variable	Test Level	Z[t-bar]	p-Value
unemp	Level	−3.342	0.000
ent	Level	−2.921	0.002
ln_pcgdp	Level	−4.088	0.000
pcgdp_growth	Level	−8.164	0.000
gva	Level	0.071	0.528
gva	1st Difference	−3.656	0.000
ldr	Level	−2.555	0.005
ln_creditpc	Level	−1.650	0.049

, it is observed that the Z[t-bar] statistics of all variables except gva are statistically significant at the 1% and 5% significance levels. This finding indicates that these series do not contain a unit root and are stationary at their level values (I(0)). In contrast, while the gva variable does not exhibit stationarity at its level value (p = 0.528), it is demonstrated to follow an I(1) process by becoming stationary at the 1% significance level when first differenced.

Examining the CIPS test results in

Table 4. CIPS unit root test results.

Variable	Test Level	CIPS Statistic	10%	5%	1%
unemp	Level	−2.326	−2.07	−2.15	−2.32
ent	Level	−2.610	−2.07	−2.15	−2.32
ln_pcgdp	Level	−2.561	−2.07	−2.15	−2.32
pcgdp_growth	Level	−4.155	−2.07	−2.15	−2.32
gva	Level	−1.868	−2.07	−2.15	−2.32
gva	1st Difference	−3.811	−2.07	−2.15	−2.32
ldr	Level	−2.362	−2.07	−2.15	−2.32
ln_creditpc	Level	−1.998	−1.47	−1.58	−1.76

, full consistency with the CADF findings is observed. The calculated CIPS test statistics for the variables unemp, ent, ln_pcgdp, pcgdp_growth, ldr, and ln_creditpc are found to be larger in absolute value than the 1% and 5% critical values proposed by Pesaran [57]. This supports the hypothesis that these variables are stationary. The gva variable, which falls below the critical values at its level value, becomes stationary when first differenced, exceeding all critical values, as in the CADF results.

Consequently, the common findings from both CADF and CIPS tests indicate that the panel dataset exhibits a mixed structure in which I(0) and I(1) integrated series coexist. Although the majority of the variables are stationary at levels, the presence of an I(1) variable (gross value added) strictly necessitates a cointegration analysis to rule out spurious regression dynamics. This heterogeneous stationarity structure methodologically dictates the preference for the Westerlund test, which is robust to mixed integration orders, ensuring that the identified long-run relationships remain theoretically valid and statistically sound.

5.2. Panel Cointegration Test Results

After determining the integration degrees of the variables, the next stage is to determine whether a long-run equilibrium relationship (cointegration) exists among the series. For this purpose, the Westerlund cointegration test, which accounts for CSD and heterogeneity in the overall panel, is used. The null hypothesis (H₀) of this test assumes that there is no cointegration relationship between the variables.

Accordingly, to more reliably reveal the long-run relationship between the variables, the cointegration analysis is conducted within two different model specifications. In the first stage, bivariate models are estimated to examine the direct relationship between the core variables. In the second stage, control variables that may affect this relationship are also included in the models, multivariate models are constructed, and the analyses are repeated. This approach enables assessment of the robustness of the obtained findings and testing of the consistency of the long-run relationship between variables under alternative model specifications.

Examining

Table 5. Westerlund panel cointegration test results (bivariate model).

Model	Statistic	p-Value
1. Model (unemp Dependent)	−3.9170	0.0000
2. Model (ent Dependent)	7.1084	0.0000

, the test statistics for Model 1 (unemployment as dependent variable) and Model 2 (entrepreneurship as dependent variable) are both statistically significant at the 1% level (p = 0.0000). Within this framework, the H₀ hypothesis of ‘no cointegration’ is strongly rejected for both models.

Table 6. Westerlund panel cointegration test results (multivariate model with control variables).

Model	Statistic	p-Value
1. Model (unemp Dependent)	−2.6534	0.0040
2. Model (ent Dependent)	−2.4011	0.0082

presents the Westerlund cointegration test results obtained within the scope of the multivariate model in which control variables are additionally included alongside the core model. This model specification allows assessment of whether the long-run relationship between unemployment and entrepreneurship remains robust when economic and financial factors are controlled.

The test statistics for Model 1 (unemployment as dependent variable) and Model 2 (entrepreneurship as dependent variable) are both statistically significant at the 1% level. This finding also indicates the rejection of the null hypothesis and provides evidence for a long-run relationship between the variables.

When Table 5 and Table 6 are evaluated together, while the inclusion of control variables in the model leads to partial differences in the magnitude of the test statistics, the fundamental finding regarding the existence of the cointegration relationship remains unchanged. This demonstrates that the long-run relationship between unemployment and entrepreneurship specific to Türkiye’s NUTS-2 regions is not unique to the bivariate structure but remains robust when economic and financial factors are controlled. Therefore, the results reveal that the findings are robust and exhibit consistency under different model specifications. This result completes the econometric foundation required for applying the DCCE approach, which performs coefficient estimation by preserving the long-run information set in the level values of variables.

5.3. Dynamic Common Correlated Effects Estimator Results

In line with the determined methodological framework, the bidirectional interaction between entrepreneurship and unemployment is analyzed using the DCCE estimator. At this stage, with factors such as CSD and heterogeneity among the series controlled, coefficient estimations are performed for both core effects (Schumpeter and Refugee). Accordingly, the results of Model 1, which tests the effect of entrepreneurship on unemployment, are first discussed; then the results of Model 2, which examines the reflections of unemployment on entrepreneurship, are presented.

Table 7. DCCE estimation (Schumpeter model).

Variables	(1) unemp	(2) unemp	(3) unemp	(4) unemp	(5) unemp	(6) unemp
L.unemp	0.258 ***	0.224 ***	0.246 ***	0.224 ***	0.231 ***	0.201 ***
	(0.0518)	(0.0546)	(0.0531)	(0.0560)	(0.0582)	(0.0579)
ln_pcgdp	−4.446 ***	−4.048 ***	−3.764 ***	−3.253 ***	−3.569 ***	−2.781 ***
	(0.799)	(0.934)	(0.847)	(0.817)	(0.936)	(0.958)
pcgdp_growth	−0.120 ***	−0.103 ***	−0.108 ***	−0.0967 ***	−0.121 ***	−0.0955 ***
	(0.0179)	(0.0156)	(0.0175)	(0.0164)	(0.0180)	(0.0162)
gva	0.0912 ***	0.0956 ***	0.0879 ***	0.0887 ***	0.124 ***	0.112 ***
	(0.0195)	(0.0197)	(0.0236)	(0.0231)	(0.0243)	(0.0265)
ldr	−0.0543 ***	−0.0515 ***	−0.0537 ***	−0.0480 ***	−0.0515 ***	−0.0447 ***
	(0.00678)	(0.00813)	(0.00755)	(0.00982)	(0.00775)	(0.00951)
ln_creditpc	0.905	0.266	0.0741	−0.576	−1.065	−1.758
	(0.888)	(1.298)	(0.737)	(0.993)	(1.242)	(1.261)
ent		0.00253		0.0105		0.0547
		(0.100)		(0.102)		(0.105)
L.ent			−0.197 *	−0.201 *		−0.232 **
			(0.104)	(0.110)		(0.107)
L2.ent					0.0590 ***	0.0551 ***
					(0.0122)	(0.0140)
Observations	442	442	442	442	442	442
R-squared	0.196	0.177	0.170	0.156	0.178	0.140
No. of groups	26	26	26	26	26	26
F Statistic	5.86	5.24	5.48	4.81	5.19	4.28
p-Value	0.00	0.00	0.00	0.00	0.00	0.00

*** p < 0.01, ** p < 0.05, * p < 0.1. Standard errors in parentheses.

presents the DCCE estimation results testing the mechanism known in the literature as the ‘Schumpeter effect’, which examines the effect of entrepreneurship on unemployment. This model specification aims to analyze whether entrepreneurial activities create new employment areas and thus reduce unemployment, in other words, whether entrepreneurship constitutes a ‘pull force’ in the labor market.

The simultaneous level value of entrepreneurship (ent), the core explanatory variable, is statistically insignificant across all specifications. This indicates that new firm formations do not create an immediate (within the same year) net employment increase in regional labor markets. However, the coefficient of the one-period lagged entrepreneurship variable (L.ent) included in the model is negative and statistically significant (−0.197, −0.201, and −0.232 in specifications 3, 4, and 6, respectively). This finding indicates that the Schumpeter effect operates in Türkiye’s NUTS-2 regions, but that the employment creation process requires approximately a one-year ‘incubation period.’

On the other hand, the positive and statistically significant (0.0590 and 0.0551) coefficient of the two-period lagged entrepreneurship variable (L2.ent) is noteworthy. This plausibly reflects a potential displacement dynamic of the Schumpeterian ‘creative destruction’ process, a possible explanation which is evaluated structurally in Section 6.

Examining the control variables, the fact that the first lag of the dependent variable (L.unemp) is positive and significant at the 1% level in all columns demonstrates that regional unemployment in Türkiye has strong path dependence and a hysteresis effect. The macroeconomic welfare indicators GDP per capita (ln_pcgdp) and economic growth (pcgdp_growth) reduce unemployment strongly and at the 1% significance level, consistent with expectations. This demonstrates that regional economic expansion continues to be the primary driver of employment in labor markets. Of particular significance is the positive and significant effect of gva on unemployment, a counterintuitive result that runs against the standard expectation that output growth reduces unemployment. This finding suggests that increases in regional value added do not automatically translate into employment growth, pointing to an investment composition driven by capital-intensive productivity gains rather than labor absorption, or to structural transformation dynamics in which labor exits shrinking sectors faster than growing ones can absorb it. This coefficient points to a dynamic consistent with a jobless growth pattern. However, since sectoral composition is not directly analyzed, this interpretation warrants caution. The policy implications of this potential pattern are examined in depth in Section 6.

Evaluating the role of financial markets on regional employment, ldr produces a consistently negative and significant coefficient (approximately −0.05) in all estimations. This result demonstrates that the banking system’s capacity to transfer the savings it collects to the real sector through the regional credit mechanism (financial intermediation efficiency) functions as a critical liquidity buffer in breaking the unemployment spiral. The statistical insignificance of credit per capita (ln_creditpc) alongside the significant effect of ldr suggests that the efficiency of fund conversion and regional risk appetite may play a more consistent role in job creation than the absolute amount of credit alone.

Following the findings obtained in Table 7,

Table 8. DCCE estimation (Refugee model).

Variables	(1) ent	(2) ent	(3) ent	(4) ent	(5) ent	(6) ent
L.ent	0.109 **	0.0685	0.0491	0.0152	0.102 **	0.0352
	(0.0505)	(0.0619)	(0.0611)	(0.0687)	(0.0495)	(0.0654)
ln_pcgdp	3.789 ***	3.815 ***	4.734 ***	4.700 ***	4.386 ***	5.374 ***
	(0.684)	(0.634)	(0.696)	(0.713)	(0.871)	(0.956)
pcgdp_growth	0.0161	0.0149	0.00808	−0.000619	0.00987	0.00259
	(0.0138)	(0.0129)	(0.0152)	(0.0148)	(0.0140)	(0.0167)
gva	−0.0271 *	−0.0266 *	−0.0275	−0.0197	−0.0243	−0.0221
	(0.0156)	(0.0161)	(0.0168)	(0.0177)	(0.0166)	(0.0180)
ldr	0.00522	0.00781	0.0150 *	0.0171 *	0.00933	0.0230 **
	(0.00772)	(0.00813)	(0.00841)	(0.00975)	(0.00933)	(0.0104)
ln_creditpc	−4.549 ***	−4.676 ***	−5.442 ***	−5.734 ***	−5.344 ***	−6.327 ***
	(0.962)	(0.933)	(1.036)	(1.039)	(1.154)	(1.220)
unemp		0.0202		−0.0128		0.0378
		(0.0602)		(0.0687)		(0.0711)
L.unemp			0.134 ***	0.166 ***		0.159 ***
			(0.0457)	(0.0513)		(0.0560)
L2.unemp					0.0822 ***	0.0716 **
					(0.0307)	(0.0325)
Observations	442	442	442	442	442	442
R-squared	0.077	0.070	0.070	0.062	0.067	0.053
No. of groups	26	26	26	26	26	26
F Statistic	17.05	14.90	15.01	13.35	15.67	12.42
p-Value	0.00	0.00	0.00	0.00	0.00	0.00

*** p < 0.01, ** p < 0.05, * p < 0.1. Standard errors in parentheses.

analyzes the effect of unemployment on entrepreneurship in order to evaluate the reverse directional dynamics of the entrepreneurship–unemployment relationship. In this framework, the approach referred to in the literature as the ‘Refugee Effect’ is tested, and whether unemployment compulsorily directs individuals toward entrepreneurship is examined.

Examining the DCCE estimation results reported in Table 8, the coefficient of the simultaneous unemployment rate (unemp) is statistically insignificant. However, the coefficients of the one-period (L.unemp) and two-period (L2.unemp) lagged unemployment rates included in the model as lags take positive values at the 1% and 5% significance levels, respectively. These findings support the necessity of entrepreneurship arguments in the literature, demonstrating that the Refugee effect is operative. Furthermore, the emergence of the positive effect not simultaneously but with 1- and 2-year lags indicates that individuals who become unemployed exhaust their alternative job search processes before turning to entrepreneurship and need a certain period of time to accumulate sufficient seed capital. The structural financial dimension of this delay is examined in Section 6.

Examining the control variables, the consistently positive and significant coefficient of GDP per capita (ln_pcgdp) at the 1% level (ranging from 3.78 to 5.37) in all specifications is particularly notable. This reflects that as regional welfare increases, general consumer demand is stimulated and new profit opportunities emerging in the market encourage individuals toward entrepreneurship. On the other hand, the weakly negative and statistically borderline significant values produced by the gva variable in specifications 1 and 2 may reflect that regions with higher output levels offer relatively more stable formal employment opportunities, reducing the necessity pressure that drives individuals toward self-employment, consistent with the opportunity cost mechanism of Occupational Choice Theory.

Evaluating the effect of financial indicators on entrepreneurship, credit per capita (ln_creditpc) produces consistently negative and significant results at the 1% level in all models. The fact that expansion in regional credit volume reduces self-employment rates provides critical evidence regarding the financial barriers to the Refugee effect. This indicates that aggregate credit expansion appears to disproportionately benefit established institutional firms, likely reducing the necessity pressure that drives unemployed individuals toward self-employment, a dynamic we explore structurally in Section 6. The positive and significant results of ldr in some specifications (3, 4, and 6) suggest that the increase in the banking sector’s funding efficiency (intermediation capacity) is an element that supports the entrepreneurship ecosystem, albeit limitedly. The geographic dimension of this credit misallocation and its implications for regional entrepreneurship conditions are analyzed in Section 6.

6. Discussion

The empirical findings of this study suggest that both the Schumpeter and Refugee effects operate simultaneously across Türkiye’s 26 NUTS-2 regions over the 2007–2024 period, yet neither effect materializes instantaneously. While this temporal structure is broadly consistent with the international literature, its specific manifestation in the Turkish regional context carries interpretive depth that warrants examination against the structural characteristics of the Turkish entrepreneurship and financial ecosystem.

6.1. The Schumpeter Effect and the Small and Medium Enterprises Dominated Economy

The one-year incubation lag identified for the Schumpeter effect is theoretically meaningful when placed against the structural composition of Turkish entrepreneurship. According to TurkStat [65], small and medium enterprises (SMEs) constitute 99.6% of all enterprises and account for 68.5% of total employment, yet contribute only 41.2% of value added, a productivity gap that reflects their operational characteristics. Critically, 54.8% of manufacturing SMEs operate in the low-technology class, compared to 41.5% for large firms, while only 0.7% of micro enterprises reach the high-technology threshold [65]. The annual SME birth rate of 15.9% indicates high entry-exit dynamics, yet newly born firms account for only 7.0% of total SME employment in the year of their founding, a figure that directly illustrates why the employment-creating capacity of new ventures cannot materialize within the same year of establishment. Completing administrative registration, fulfilling Social Security Institution (SGK) obligations, building organizational capacity, and generating sufficient market demand to hire employees formally all require time, a process consistent with the time-lag framework established by Thurik et al. [2] and Fritsch and Mueller [29]. The approximately one-year lag identified in our results is therefore not a statistical artifact but a structural feature of an economy where the path from firm entry to net employment contribution passes through a necessary operational transition phase.

The positive and significant coefficient on the two-period lagged entrepreneurship variable adds an important qualification. It points to the creative destruction dimension of the Schumpeter process: from approximately the second year onward, newly competitive entrants begin displacing less efficient incumbents, generating temporary structural unemployment as a by-product of market renewal. Given that Turkish SMEs are predominantly concentrated in trade and services sectors [65], these displacement effects are likely concentrated in traditional labor-intensive industries where competitive entry is relatively easy, but survival margins are thin, and where innovative entrants can rapidly render existing low-productivity firms unviable.

6.2. The Refugee Effect and Financial Exclusion

The evidence supporting the Refugee effect with 1–2 year lags is consistent with a sequential labor market adjustment process in which unemployed individuals first exhaust formal job search before necessity entrepreneurship becomes the rational alternative. However, the delay is not purely behavioral, as it also has a structural financial dimension that is directly evidenced by our results. Despite representing 99.6% of all enterprises [65], SMEs receive only 27% of total banking sector credits, while commercial and corporate firms absorb 48% of total credit [66]. Within the SME category itself, the average loan extended to micro enterprises stands at only 409 thousand Turkish Lira (TL), compared to 12,184 million TL for medium-sized enterprises. This nearly 30-fold gap reflects the systematic exclusion of the smallest, most financially vulnerable firms from meaningful credit access [66]. The strong negative effect of credit per capita on entrepreneurship identified in our model is therefore not a paradox but a reflection of this structural misallocation: Theoretically, this finding is consistent with the ‘credit rationing’ and ‘collateral-based lending’ mechanisms. During phases of aggregate credit expansion, banking institutions in Türkiye prioritize established, collateral-rich firms to mitigate information asymmetries and credit risk. This selective expansion strengthens the formal labor absorption capacity of the corporate sector, which in turn reduces the ‘necessity pressure’ that drives unemployed individuals toward self-employment. As aggregate credit expands, it primarily reinforces existing employment structures rather than providing seed capital for new, high-risk necessity ventures. The delay in the Refugee effect likely reflects the time needed to accumulate informal savings or mobilize family resources in the absence of accessible formal credit, a process that is inherently slower and more precarious than financing through bank-intermediated startup credit.

6.3. The Jobless Growth Pattern

Perhaps the most policy-relevant finding of this study is the positive cross-sectional relationship between regional gross value added and unemployment. As a supplementary descriptive exercise, analysis of the panel dataset reveals a positive regional correlation of +0.55 between average GVA levels and average unemployment rates across the 26 NUTS-2 regions over the study period (Authors’ calculations, [52,53]). In other words, regions with higher output growth have not systematically achieved lower unemployment, a pattern consistent with a jobless growth dynamic in which regional economic expansion is driven by capital-intensive or productivity-intensive sectors that generate value without proportionate labor absorption. A structural transformation effect may also be at work: as regional economies modernize and shift away from agriculture and traditional manufacturing, labor is displaced from shrinking sectors faster than growing sectors can absorb it, producing transitional unemployment alongside productive entry dynamics. The current study cannot disentangle these mechanisms at the sectoral level due to the absence of sectoral disaggregation, a limitation noted in the conclusion, but the cross-sectional correlation and the regression coefficient together provide an indirect indication that the employment content of regional growth in Türkiye is heterogeneous.

6.4. Credit Intermediation Efficiency vs. Credit Volume

The contrasting findings on the two financial variables in the model deserve an integrated interpretation. LDR exerts a consistent negative effect on unemployment and a positive effect on entrepreneurship in several specifications, indicating that banking sector intermediation efficiency matters for both labor market outcomes and entrepreneurial activity. This suggests that the efficiency with which the banking system converts locally mobilized savings into productive lending, alongside regional risk appetite, appears to be a highly relevant factor, potentially more so than the absolute volume of credit in a regional economy. By contrast, the credit per capita variable suppresses entrepreneurship, pointing to a distributional problem rather than a volume problem, and extending the liquidity constraint mechanism identified in Section 5 to the geographic dimension. According to BRSA [66], the geographic distribution of total banking sector credits is highly concentrated: the Marmara region alone accounts for 44% of all credits, while Eastern Anatolia receives only 2%. This geographic concentration closely mirrors the deposit distribution, with Marmara holding 50% of total deposits while Eastern and Southeastern Anatolia together hold only 4% [66]. Within our panel dataset, the credit per capita gap between the most and least financially integrated NUTS-2 regions is nearly tenfold: İstanbul averages 83,356 TL per capita over the 2007–2024 period, compared to just 8443 TL in Van, Muş, Bitlis, and Hakkari, a ratio of approximately 9.9 to 1 (Authors’ calculations, [52,53]). This pronounced geographic concentration means that aggregate credit expansion in the national banking system translates very differently into regional entrepreneurship conditions: in western regions, expanding credit deepens institutional firm capacity; in eastern regions, even limited credit growth is insufficient to overcome the collateral and information barriers faced by necessity entrepreneurs.

6.5. The West–East Dimension and Türkiye’s Socio-Economic Development Ranking Survey

To further contextualize the regional heterogeneity documented in the panel data, the 2025 Socio-Economic Development Ranking Survey (SEGE) of the Republic of Türkiye Ministry of Industry and Technology is drawn upon here, which classifies Türkiye’s 26 NUTS-2 regions into four development tiers based on 52 socioeconomic variables covering demography, employment, education, health, financial access, competitiveness, innovation capacity, and quality of life [67]. The tier distribution is: six regions in Tier 1 (most developed), seven in Tier 2, nine in Tier 3, and four in Tier 4 (least developed). Cross-referencing this official classification with the panel data reveals a striking and systematic structural gradient. In the Tier 1 regions comprising İstanbul, Ankara, İzmir, Kocaeli-Sakarya-Düzce-Bolu-Yalova, Antalya-Isparta-Burdur, and Bursa-Eskişehir-Bilecik, the average credit per capita amount is 51,313 TL and the higher education rate is 20.7% (share of population aged 6 and over with university-level education or above, aggregated from provincial data for the year 2024). In sharp contrast, in the four Tier 4 regions (Ağrı-Kars-Iğdır-Ardahan, Van-Muş-Bitlis-Hakkari, Şanlıurfa-Diyarbakır, and Mardin-Batman-Şırnak-Siirt), the average credit per capita amount is only 10,430 TL and the higher education rate is 12.2% (Authors’ calculations, [52,53,67,68]). The credit ratio between Tier 1 and Tier 4 regions is 4.9 to 1, and the higher education ratio is 1.7 to 1, which emphasizes that the West–East divide in Türkiye is not a simple income gradient but a compounding of financial exclusion and human capital disadvantage that is simultaneously and officially measured in the SEGE framework. Tier 4 regions also record the highest average unemployment in the panel at 15.0%, reaching 21.3% in the Mardin-Batman-Şırnak-Siirt region, which is the highest in the 2007–2024 panel. Notably, the unemployment gradient across tiers is non-linear: Tier 2 and Tier 3 regions record the lowest average unemployment in the panel (8.8% and 9.2%, respectively), while Tier 1 regions show relatively elevated unemployment (11.3%), likely reflecting structural labor supply pressure from internal migration into major urban centers. Tier 4 average unemployment is some 6.2 percentage points above the Tier 2 average and 3.7 percentage points above the Tier 1 average (Authors’ calculations, [52]).

A further important finding from the SEGE-tier cross-tabulation is the entrepreneurship paradox: Tier 2, Tier 3, and Tier 4 regions all display higher average self-employment rates (26.6%, 28.5% and 24.7% respectively) than Tier 1 regions (19.9%), yet this is not a sign of entrepreneurial dynamism (Authors’ calculations, [52,53]). Rather, it reflects the predominance of subsistence-oriented necessity entrepreneurship in regions where formal wage employment is structurally scarce, a pattern consistent with the Refugee mechanism, the regression results capture. High self-employment in low-development NUTS-2 regions is therefore a labor market symptom, not an indicator of productive Schumpeterian activity. The SEGE tier classification suggests, in line with the panel regression results, that the compounding of financial exclusion, educational disadvantage, and low market density in Tier 4 regions creates a structural environment in which neither entrepreneurship efficiently absorbs unemployment nor unemployment efficiently generates viable self-employment. The mean group DCCE estimates must be read as averages concealing considerable tier-specific heterogeneity, a point that future spatially disaggregated analyses should address explicitly.

6.6. Comparison with Prior Turkish Evidence

The evidence supporting the Schumpeter effect at the NUTS-2 level diverges from Moiz and Ileri [7], who find this effect insignificant at NUTS-2 but operative at NUTS-3 with longer lags. This divergence likely reflects both methodological differences and the broader time horizon of the current study: the DCCE estimator employed here controls simultaneously for CSD and slope heterogeneity, which are known sources of bias in macro panels when ignored. Rather than a contradiction, this is better read as evidence that the Schumpeter effect in Türkiye is sensitive to the level of spatial aggregation and the econometric methodology, underscoring the importance of methodological choices in regional entrepreneurship research.

7. Policy Implications

The empirical findings of this study strongly align with the critical strand of literature cautioning against treating entrepreneurship as a universal ‘magic bullet’ for regional development. As Shane [69] argues, encouraging more people to become entrepreneurs can be a bad public policy if it merely stimulates low-growth, marginal businesses rather than high-value innovation. Furthermore, our evidence from Türkiye’s less developed NUTS-2 regions directly corroborates Bögenhold’s [70] assertion that an excess of self-employment is often an indicator that a large parcel of the regional population is being structurally neglected, reacting to labor market failures rather than engaging in productive venture creation. Recognizing that not all entrepreneurship yields sustainable macroeconomic benefits is crucial for effective intervention. In this context, three specific policy implications follow directly from our findings.

First, the one-year Schumpeter lag and the positive GVA-unemployment relationship suggest that the timing and conditioning of public firm support could be better aligned with employment dynamics. Presidential Decree [71] provides multiple support instruments, but only one of these, the Social Security premium employer-share support under Article 18, is directly tied to additional employment. While this support scales regionally along SEGE-aligned tiers, it activates upon investment completion certification rather than operational survival. Our findings suggest that the relevant trigger should be year-two operational survival, when the Schumpeter employment effect actually materializes, rather than capital-commitment milestones.

Second, the credit per capita finding calls not for credit expansion but for credit redirection toward the segment the Refugee model identifies as most financially constrained: necessity entrepreneurs who establish firms after exhausting a formal job search. The Small and Medium Enterprises Development and Support Administration (KOSGEB) Entrepreneurship Support Program currently provides non-repayable founding support of 10,000–20,000 Turkish lira for firms aged zero to one year, which is insufficient for working capital. The business development component covers larger investment costs, but at 80 percent repayable, which is particularly burdensome for entrepreneurs who have depleted their savings during the one to two years of unemployment, as the Refugee lag documents. Several public banks currently offer thematic Credit Guarantee Fund (KGF) schemes, which provide up to one to three million Turkish lira with an 80 percent KGF guarantee and simplified eligibility criteria based on firm age, owner age, and educational background. All such products, however, target opportunity-driven entrepreneurs in knowledge-intensive sectors. No equivalent product currently exists for necessity entrepreneurs. A KGF micro-enterprise Portfolio Guarantee System (PGS) was previously operational across 43 provinces, providing working capital guarantees to micro-enterprises in high-unemployment provinces without requiring individual creditworthiness assessment. This program is no longer listed as an active instrument. Designing a thematic KGF product modeled on that structure but with eligibility defined by unemployment registration history with the Social Security Institution rather than age or educational background and geographically concentrated in high-unemployment NUTS-2 regions, would fill this gap within the proven institutional architecture. No new institution is required: only a new thematic program agreement between KGF and the public banks that carry a development mandate, extending a mechanism that already operates for opportunity entrepreneurs to the necessity entrepreneur segment, as the Refugee effect identifies.

Third, the LDR finding reframes the financial dimension of entrepreneurship policy from a credit volume problem to an intermediation efficiency problem. In low-income regions, the deposit base is structurally thin because incomes are low, not because banking infrastructure is absent. The development lending mandates of public deposit banks are the relevant instruments here: these institutions have retail branch networks in lower-tier regions, collect local deposits, and carry an explicit public development mission that commercial banks do not. Directing their SME lending capacity toward high-unemployment NUTS-2 regions where local deposit mobilization is structurally thin would improve regional LDR through the intermediation channel the model identifies as the more consistent lever for unemployment reduction than aggregate credit volume.

8. Conclusions

This study examines the dynamic and bidirectional relationship between entrepreneurship and unemployment across Türkiye’s 26 NUTS-2 regions, within a framework that integrates regional economic and financial determinants. Using the DCCE estimator, the findings are consistent with the simultaneous operation of both the Schumpeter and Refugee effects, while revealing a clearly time-lagged adjustment process.

The results show that the employment-generating impact of entrepreneurship does not materialize immediately. Instead, the Schumpeter effect emerges with an approximate one-year lag, reflecting the time required for new firms to establish organizational capacity and generate labor demand. Beyond this initial phase, evidence of a creative destruction dynamic appears, indicating that market entry may also induce transitional employment losses through competitive displacement. In the reverse direction, the Refugee effect is also supported, but with a delay of one to two years. This lag structure suggests that individuals do not transition into self-employment immediately upon becoming unemployed; rather, necessity entrepreneurship emerges only after formal job search options are exhausted and minimal financial resources are mobilized.

A key contribution of the study lies in identifying the financial mechanism underlying these dynamics. The results demonstrate that credit volume alone does not stimulate entrepreneurship and may, in fact, suppress necessity-driven entry by reinforcing the position of established firms. In contrast, financial intermediation efficiency, proxied by the LDR, plays a decisive role in reducing unemployment by improving the allocation of funds to productive uses. These findings highlight a structural misalignment between financial expansion and entrepreneurial accessibility. The analysis also provides evidence consistent with a jobless growth pattern, whereby increases in regional output are not systematically associated with reductions in unemployment. This suggests that the composition of growth, particularly its concentration in capital-intensive activities, limits its capacity to absorb labor, especially in structurally disadvantaged regions.

Taken together, the findings carry several implications for policy design. First, entrepreneurship support mechanisms should be aligned with the observed lag structure, prioritizing post-entry firm survival and employment creation rather than initial firm formation alone. Second, financial policies should focus on improving access to credit for new and small-scale entrepreneurs, rather than expanding aggregate credit volumes that disproportionately benefit incumbent firms. Third, regionally differentiated strategies are necessary to address the structural constraints faced by less developed regions, where high self-employment rates reflect necessity rather than productive entrepreneurial activity.

Overall, the study demonstrates that entrepreneurship and unemployment are linked through a delayed and financially mediated adjustment process, the effectiveness of which depends critically on regional economic conditions and the structure of financial systems. Ensuring the efficient functioning of this mechanism is a core requirement for sustainable regional development. By addressing structural financial constraints and mitigating the risks of ‘jobless growth’, regional economies can transition from temporary, necessity-driven self-employment toward long-term socio-economic sustainability. Nevertheless, this study has one principal limitation that defines the agenda for future research. The absence of sectoral disaggregation prevents disentangling the contributions of different industries to the jobless growth pattern and the creative destruction dynamic. Future studies incorporating sectoral heterogeneity would substantially deepen the interpretation of both the GVA-unemployment relationship and the differential strength of the Schumpeter effect across regions.