The global financial crisis that began in 2007 triggered by the collapse of the U.S. subprime mortgage market, resulted in one of the worst post-war economic recessions worldwide. Although public finances were severely impacted, several countries managed to ensure their sustainability and recovery. However, Greece experienced a decade-long recession of unprecedented magnitude and duration among modern developed economies, losing approximately 20% of real per capita Gross Domestic Product (GDP) between 2007 and 2017 (see Avramidis et al., 2021 ; Chodorow-Reich et al., 2019 ; Giannoulakis & Sakellaris, 2021 ; Gourinchas et al., 2017 ).

Greece faced three quasi-simultaneous and interlinked shocks: a sovereign debt crisis, a banking crisis, and a sudden stop (see the seminal paper of Gourinchas et al., 2017 , which provides the first systematic analysis of the Greek economic crisis). Consequently, the Greek economy became even less competitive than other European Union (EU) and Organization for Economic Cooperation and Development (OECD) countries, which led to low productivity (see Alogoskoufis & Featherstone, 2021 ; Chodorow-Reich et al., 2019 ; Genakos, 2018 ; Katsoulakos et al., 2017 ; Pelagidis & Mitsopoulos, 2014 ). Therefore, the Greek economic crisis presents a unique opportunity to examine the factors that influence productivity and economic growth in a small open economy operating within a currency union during a crisis of unparalleled magnitude and duration.

This paper addresses the following question: How severely was productivity impacted in the aftermath of the crisis? This inquiry holds particular significance for investors, business professionals, and policymakers since enhancing productivity is essential for a country’s economic growth, which, in turn, leads to economic recovery. While there is a growing body of literature that measures productivity at the firm level and elucidates its importance for economic growth (see, for example, Bournakis & Mallick, 2018 ; Li & Liu, 2011 ; Rath, 2018 ), to the best of our knowledge, there is limited empirical evidence available for Greece (see Belegri-Roboli & Michaelides, 2006 ; Bournakis, 2011 ; Halkos & Tzeremes, 2007 ; Voutsinas & Tsamadias, 2013 ). However, none of these studies offer evidence regarding productivity growth in the aftermath of the crisis.

The aim of this paper is threefold: First, to measure productivity at the firm level and identify its primary drivers. Second, to assess productivity growth and its underlying sources, revealing valuable policy-related insights that could contribute to sustained growth in Greece. Finally, to investigate the role of firm size. In particular, we utilize detailed firm-level data and differentiate between various forms of capital, including financial capital services, which represents a novel approach in applied modeling. We adopt a stochastic production frontier model with time-varying inefficiency, enabling us to disentangle the components and gain a comprehensive understanding of the factors influencing productivity changes among 9054 Greek firms from 1996 to 2017¹.

Related to our work is the study of Genakos (2018) , which investigates the role of managerial practices in understanding productivity differences across firms and across countries and notes that “it is almost as if there are two types of firms inside Greece (i.e. “two Greeces”): one that is outdated, inefficient, and ignorant of its own quality of management and one that is up-to-date, efficient and is trying to improve itself and achieving world standards” (p. 888). His paper does not directly measure the productivity in Greece, though. In our paper, management is more related to efficiency or efficiency change.

The term “Total Factor Productivity (TFP) growth”, first introduced by Solow (1957) , also called “the Solow residual”, is defined as the growth rate of output that cannot be explained by the changes of traditional inputs (labor and capital). However, this index does not allow for efficiency change which if there exists, it contributes to productivity change. The study of Kumbhakar et al. (2000) was the first to address the estimation and decomposition of TFP change within a stochastic frontier framework that permits efficiency change which is a fundamental assumption in this paper (see Cornwell et al., 1990 ; Kumbhakar, 1990 ). Assuming that technical inefficiency remains constant over time implies that firms or economies never learn. This assumption may be unrealistic in a competitive market or in a long panel framework. To account for time-varying technical inefficiency, one must make assumptions about the functional form of this term (see Kumbhakar, 1990 ; Battese & Coelli, 1992 ; Lee & Schmidt, 1993 ; Kumbhakar & Wang, 2005 ).

We use two popular approaches, as introduced by Kumbhakar (1990) and Battese and Coelli (1992) . Our findings indicate that, on average, productivity growth remains positive, although it has decreased significantly since the subprime crisis. This outcome is primarily attributed to technical change. The role of efficiency change also appears to be significant, but its magnitude depends on the specific model used for efficiency estimation. Furthermore, we observe substantial variations in productivity among different firm size categories, indicating that larger firms generally exhibit higher average productivity. However, in the aftermath of the crisis, these differences are nearly eradicated.

In conclusion, despite a significant decline in productivity resulting from the shock of the financial crisis, it has managed to remain in positive territory. This suggests that certain policies have been effective in maintaining positive productivity growth. However, it is worth noting that these results have led to the narrowing of the productivity gap among firms that previously contributed to productivity growth and, consequently, economic growth in the past. In our view, these findings indicate that if policymakers were to tailor their reforms to the specific needs of different firm size categories, productivity growth could potentially act as a catalyst for economic growth and expedite the path to economic recovery.

The remainder of this paper is organized as follows. Section 2 presents the model specification and the method we use to estimate productivity growth and its component. In Section 3, we describe the data and Section 4 presents the results. Our concluding remarks are presented in Section 5.

We consider the following production frontier model y i t = f ( x i t , t ; β ) exp ( ε i t ) , where the error term ε i t = v i t − u i t is the difference of v i t the two-sided error component, and u i t , the nonnegative time-varying technical inefficiency component. The output of firm i at time t, y i t is produced using a vector of inputs x i t ∈ ℝ + Κ through a production function where t is the time trend used here as a proxy for technical change. A time-varying stochastic production frontier is assumed, originally proposed by Aigner et al. (1977) and Meeusen and van den Broeck (1977) in a cross-sectional framework:

ln y i t = β 0 + β 01 t + 1 2 β 02 t 2 + ∑ k = 1 K β k ln x k i t + 1 2 ∑ k = 1 K ∑ j = 1 K β k j ln x k i t ln x j i t     + ∑ k = 1 K β 1 k t ln x k i t + v i t − u i t (1)

for i = 1 , ⋯ , N , t = 1 , ⋯ , T .

We also assume that:

v i t ∼ i i d N ( 0 , σ v 2 ) (2)

u i t = G ( t ) u i (3)

u i ∼ i i d   N + ( μ , σ u 2 ) (4)

where G ( t ) ≥ 0 is function of time t. For G ( t ) , we use two functional forms, one proposed by Kumbhakar (1990) :

G ( t ) = ( 1 + exp ( γ t + δ t 2 ) ) − 1 (5)

and the other by Battese and Coelli (1992) :

G ( t ) = exp ( γ ( t − T ) ) (6)

We estimate the parameters using maximum likelihood and obtain estimates for productivity change and its sources (detailed derivations are presented in the Appendix A and parameter estimates are available on request):

T F ˙ P i t = T C i t + ( e i t − 1 ) ∑ k = 1 K ( e i t k e i t ) x ˙ k i t + T E C i t (7)

where

T C ^ i t = β ^ 01 + β ^ 02 t + ∑ k = 1 K β ^ 1 k ln x k i t (8)

e ^ i t k = β ^ k + β ^ 1 k t + ∑ j = 1 K β ^ k j ln x j i t (9)

e ^ i t = ∑ k = 1 K e ^ i t k (10)

The estimation of efficiency change (TEC) depends on the choice of G ( t ) so, following Kumbhakar (1990) (Equation (5)), we obtain:

T E C ^ i t = − u ^ i ( γ ^ + 2 δ ^ t ) exp ( γ ^ t + δ ^ t 2 ) ( 1 + exp ( γ ^ t + δ ^ t 2 ) ) − 2 (11)

If we adopt Battese and Coelli’s (1992) approach (Equation (6)), we have:

T E C ^ i t = − u ^ i γ ^ exp ( γ ^ ( t − T ) ) (12)

where u ^ i = E ( u i | ε i ) of the distribution of u i | ε i , where ε i = ( ε i 1 , ⋯ , ε i T ) ′ .

The data is an unbalanced panel of 9054 firms for the period 1996-2017. We include companies from several sectors except for banks and financial institutions that are active in Greece during the period under investigation. The data was obtained from the database of ICAP, a Greek private research company that collects detailed business information, which includes accounts and ratios from the firms’ annual financial statements². We measure output y i t as the sum of final and semifinal products. The inputs include labor l i t , measured as the number of employees and different types of capital: Services from use of buildings, x 1 i t , the services of mechanical equipment x 2 i t , the services of intangible assets x 3 i t , materials x 4 i t , services of financial capital x 5 i t and other inputs x 6 i t . To the best of our knowledge, use of such detailed data at the firm level is quite novel. Summary statistics are presented in Table 1 and industry classification (SIC) in Table 2, respectively.

Note: All variables, except for l, are measured in euros. Labor is defined as the number of employees. We use a sample of 42,391 observations and 9054 firms.

Note: The total number of firms is 9054.

In this section, we present the results of our analysis. Using the method of maximum likelihood, we estimate the parameters of production frontier and derive the sources of productivity change and its components. As we described in Section 2, we use two different functional forms for G ( t ) and hence, we have two models; Model 1 stands for the approach proposed by Kumbhakar (1990) and Model 2 stands for the approach proposed by Battese and Coelli (1992) .

We estimate a stochastic production frontier model with time-variant inefficiency that allows us to estimate productivity change and disentangle its sources. Utilizing detailed firm-level data for Greece, our findings reveal that, on average, productivity growth remains positive, albeit significantly reduced since the subprime crisis. This is primarily driven by technical change, though the role of efficiency change is also substantial, depending on the specific efficiency estimation model used. These results challenge the conventional belief that the crisis severely damaged the productive foundations of the Greek economy, demonstrating that there is still potential for both efficiency enhancements and technical advancements. Even in the aftermath of the crisis, there exists a group of firms that exhibit growth. Additionally, our research demonstrates the significant influence of firm size on productivity growth, although this impact appears to weaken in the post-crisis period. We observe a partial elimination of differences in growth experiences among firm size categories.

Compared to Genakos (2018) , our results indicate although it may be true that there is “evidence demonstrating the low ranking of firms in the Greek economy compared with other EU and OECD countries” (Genakos, 2018: p. 896) , it does not necessarily be true that productivity is low, especially as Genakos (2018) does not present direct productivity-related evidence. Our work contributes to this discussion by showing that, apart from the role of management, which in our paper is more related to efficiency and efficiency change, other factors are responsible for both: 1) the dramatic decline in productivity growth, and 2) the fact that this productivity growth is still positive (although unevenly distributed across firms) despite institutional rigidities and other exogenous factors (Bloom & Van Reenen, 2007, 2010) . An interesting extension of this paper is the evaluation of productivity growth among different sectors. However, we leave an investigation of this important question to future research.

We would like to thank the participants at the 2019 Conference of the Association of Southern European Economic Theorists (ASSET) for their valuable comments and suggestions. Authors acknowledge financial support from the project co-financed by Greece and the European Union (European Social Fund), through the Operational Program “Human Resources Development, Education and Lifelong Learning 2014-2020” (MIS-5006583). All remaining errors and omissions are our own.

The authors declare no conflicts of interest regarding the publication of this paper.

Tsionas, M. G., Vidali, M. E., Leledakis, G. N., & Tasiopoulos, A. E. (2023). Whither Greece? Productivity before and after the Subprime Crisis. Theoretical Economics Letters, 13, 1780-1801. https://doi.org/10.4236/tel.2023.137103

Assume the following production frontier y i t = f ( x i t , t ; β ) exp ( ε i t ) . By taking logs and differentiating with respect to time t we obtain:

y ˙ i t = ∂ ln f ( x i t , t ; β ) ∂ t + ∑ k = 1 K ∂ ln f ( x i t , t ; β ) ∂ x k i t ∂ x k i t ∂ t − ∂ u i t ∂ t

y ˙ i t = T C + ∑ k = 1 K ∂ f ( x i t , t ; β ) ∂ x k i t 1 f ( x i t , t ; β ) ∂ x k i t ∂ t + T E C

y ˙ i t = T C + ∑ k = 1 K e k x ˙ k i t + T E C

where e k = ∂ f ( x , t ; β ) ∂ x k x k f ( x , t ; β ) are the elasticities of output with respect to each of the inputs⁴, x ˙ k i t = ∂ ln x k i t ∂ t . TC stands for technological change and TEC for Efficiency change. Following Kumbhakar and Lovell (2000: p. 283) ,

T F ˙ P = y ˙ − ∑ k = 1 K s k x ˙ k = T C + ∑ k = 1 K ( e k − s k ) x ˙ k + T E C

By adding and subtracting the term ∑ k = 1 K ( e k e ) x ˙ k , we obtain

T F ˙ P = T C + ( e − 1 ) ∑ k = 1 K ( e k e ) x ˙ k + ∑ k = 1 K ( ( e k e ) − s k ) x ˙ k + T E C

where e = ∑ k = 1 K e k is the scale elasticity and provides a measure of returns to scale characterizing the production frontier, s k = w k x i / E , the observed expenditure share, E = ∑ k = 1 K w k x i the total expenditure and w k > 0 for k = 1 , ⋯ , K are the input prices. So, according to Kumbhakar and Lovell (2000: p. 284) , we assume that s k = e k / e and finally we obtain a measure that involves only quantity information (Equation (7)):

T F ˙ P = T C + ( e − 1 ) ∑ k = 1 K ( e k e ) x ˙ k i t + T E C

Note: The TFP growth rate and its components are based on Equations (7) to (12). Analytical calculations are provided in the appendix. The estimates are averaged across firms and years. From 1996 to 2008, the sample size is 13,061 while, from 2009 to 2017, it is 20,170, respectively.

Note: The TFP growth rate and its components are based on Equations (7) to (12). Analytical calculations are provided in the appendix. The estimates are averaged across firms and years. From 1996 to 2009, the sample size is 15,579 while, from 2010 to 2017, it is 17,652, respectively.