The cost efficiency of the U.S. small banks after the 2008 global financial crisis

Rezvanian, Rasoul; Mehdian, Seyed; Teclezion, Mussie Article The cost efficiency of the U.S. small banks after the 2008 global financial crisis Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Rezvanian, Rasoul; Mehdian, Seyed; Teclezion, Mussie (2024) : The cost efficiency of the U.S. small banks after the 2008 global financial crisis, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 12, Iss. 1, pp. 1-14, https://doi.org/10.1080/23322039.2024.2402558 This Version is available at: https://hdl.handle.net/10419/321605 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Economics & Finance ISSN: 2332-2039 (Online) Journal homepage: www.tandfonline.com/journals/oaef20 The cost efficiency of the U.S. small banks after the 2008 global financial crisis Rasoul Rezvanian, Seyed Mehdian & Mussie Teclezion To cite this article: Rasoul Rezvanian, Seyed Mehdian & Mussie Teclezion (2024) The cost efficiency of the U.S. small banks after the 2008 global financial crisis, Cogent Economics & Finance, 12:1, 2402558, DOI: 10.1080/23322039.2024.2402558 To link to this article: https://doi.org/10.1080/23322039.2024.2402558 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group Published online: 16 Sep 2024. Submit your article to this journal Article views: 587 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 GENERAL & APPLIED ECONOMICS | RESEARCH ARTICLE The cost efficiency of the U.S. small banks after the 2008 global financial crisis Rasoul Rezvanian a , Seyed Mehdian b and Mussie Teclezion a a Cofrin School of Business, University of Wisconsin, Green Bay, WI, USA; b School of Management, University of Michigan - Flint, Flint, MI, USA ABSTRACT This paper examines the relative cost efficiency of U.S. small banks after the 2008 Global Financial Crisis (2008 GFC). Using financial information from 10,495 of the same small banks operating from 2010 to 2021, we examine the after-effects of the recent global financial crises on the U.S. small banks. The study uses Data Envelopment Analysis (DEA) to calculate the overall efficiency using yearly and pooled data. The overall efficiency measure is then decomposed into allocative, technical, pure-technical, and scale efficiency to better understand the sources of small banks’inefficiencies. The results indicate that the overall efficiency of small banks operating in the U.S. after the 2008 GFC has been continuously low until 2021. The source of the low level of overall efficiency has been the low level of technical efficiency rather than allocative efficiency. In turn, the basis of the low level of technical efficiency has been pure technical and scale efficiency. Understanding the origins of cost inefficiencies in small banks has implications for micro and macro policymaking. Examining the underlying causes of cost inefficiencies in small banks after the financial crisis can inform policymakers in devising strategies to improve banks’cost efficiency. IMPACT STATEMENT This paper delves into the impact of the 2008 global financial crisis on the efficiency of small banks in the U.S. using Data Envelopment Analysis (DEA). Understanding the reasons for cost inefficiencies in small banks has implications for both micro and macro policy-making. By investigating the root causes of cost inefficiencies in small banks following the 2008 financial crisis, policymakers can develop strategies to improve small banks’cost efficiency. ARTICLE HISTORY Received 28 September 2023 Revised 25 July 2024 Accepted 3 September 2024 KEYWORDS U.S. small banks; efficiency; financial crisis JEL CLASSIFICATION G21; G29; C61 SUBJECTS Economics; Finance; Business, Management and Accounting 1. Introduction The financial system of any developed country is a cornerstone of its economic growth. Among the key players in this system, depository financial institutions, particularly commercial banking organizations, stand out due to their size and number. These banks serve as financial intermediaries, converting deposits into productive investments that fuel economic development. Given their pivotal role in macroeconomics, it’s imperative for policymakers to safeguard the stability and health of the banking system. Despite concerted efforts to bolster its safety, the banking industry has weathered several crises in recent decades, including the 1997 Southeast Asia Financial Crisis and the 2008 Global Financial Crisis (2008 GFC). The increasing interconnectedness of financial markets and the evolution of international payment systems have made any financial crisis a global threat. While all financial crises take a toll, the 2008 GFC is widely regarded as the most destructive, particularly for the global banking system. The 2008 GFC originated in the United States in 2007, swiftly spreading worldwide and enduring for over two years. Some analysts attribute the crisis to increased household borrowing, particularly for home purchases. It had a far-reaching impact on all sectors of the economy, especially the banking sector, which CONTACT Seyed Mehdian [email protected] Cofrin School of Business, University of Wisconsin, Green Bay, Wisconsin, USA. ß2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. COGENT ECONOMICS & FINANCE 2024, VOL. 12, NO. 1, 2402558 https://doi.org/10.1080/23322039.2024.2402558 had significantly heightened borrowing and loaded their asset portfolios with risky loans based on subprime lending. The bank management justified the increased borrowing by citing corporate finance theory, aiming to magnify the return on equity and benefit from tax advantages. However, a combination of falling real estate prices, highly leveraged balance sheets, and regulatory oversights laid the groundwork for the arrival of the 2008 GFC. The financial fallout significantly influenced the behavior of commercial banks’ management, as many grappled with mortgage defaults and suffered losses due to high foreclosure rates. During the crisis, policymakers from several developed economies responsible for the stability of the financial systems implemented a series of bold fiscal and monetary policies. They provided support to both depository and non-depository financial institutions. On the monetary policy side, they pursued an aggressive monetary expansion strategy by reducing interest rates to stimulate the economy. 1 On the fiscal policy side, policymakers implemented initiatives such as TARP in the USA. Additionally, banking sector managers undertook strategic changes to restructure and realign their portfolio holdings to reduce potential risks and enhance their banks’performance. The 2008 GFC ended in 2009, leaving significant financial disruptions. Although all commercial banks perform similar functions, their activities may vary depending on their size. 2 Small banks generally concentrate on the retail side of the business by attracting deposits from individuals and small businesses and making consumer and business loans to individuals and small businesses operating in their communities. 3 Small businesses are critical to the U.S. economy, representing most economic activities and more than half of the private sector workforce. Berger et al. (2004) argue that healthy community banks improve Small and Medium Enterprises (SMEs) financing. De Young et al. (2012) find that SMEs rely on community banks for financing. They also argue that small banks exacerbate economic downturns during the recession due to low diversification and limited access to the government safety net. Hendrik et al. (2015) also show theoretically and empirically that small regional banks are necessary funding providers in regions with low access to financing. Therefore, the soundness and effectiveness of the overall banking system and small banks are essential to policymakers responsible for the proper functioning of the economy. It is important to assess the strength and effectiveness of banks by analyzing their profit and cost efficiency. More cost-efficient banks tend to be more productive, profitable, and less susceptible to economic downturns. Therefore, it is essential to understand the primary factors influencing banking efficiency before, during, and after financial crises. Assaf et al. (2019) studied the performance of U.S. banks prior to and during, but not after, the 2008 GFC and concluded that cost efficiency is a better indicator of managerial quality. Our research contributes to the existing literature by examining the determinants of cost efficiency in small US banks after the 2008 GFC. This is significant because the findings offer valuable insights to banking management and policymakers in promoting safety and stability within the banking sector. 2. Review of literature In the past 35 years, a significant body of finance literature has emerged focusing on measuring the efficiency of banking firms. The first study on the banking efficiency frontier was conducted by Sherman and Gold (1985), and since then, numerous studies using different methodologies and input and output definitions have been carried out to address efficiency in the banking industry. 4 Berger and Humphrey (1997) comprehensively reviewed 130 papers on banking efficiency frontier techniques up to 1995. However, since then, there have been significant advances in banking efficiency literature in terms of the development of efficient frontier techniques and consideration of essential factors affecting banking efficiency. These developments have spurred researchers to continue their study of banking efficiency. Recent studies on this topic can be found in Ashton and Hardwick (2000), Casu and Molyneux (2001), Berger (2007), Fethi and Pasiouras (2010), Paradi and Zhu (2013), and Bhatia et al. (2018). The recent financial crisis has spurred researchers to explore its impact on the financial system and macroeconomics. Caprio and Honohan (2002) have highlighted that a crisis in the financial system, specifically within the banking industry, can lead to a widespread economic recession. Consequently, there has been a growing emphasis on examining the effects of the financial crisis on banking efficiency before and after the crisis. While most studies on the impact of the financial crisis on banking have focused on the 1997 Asian financial crisis, it’s essential to recognize that the nature and causes of the 1997 Asian financial crisis differ from those of the 2008 GFC. 5 2 R. REZVANIAN ET AL. There have been several published papers on the impact of the 2008 GFC on the banking industry. However, very few have focused on the effect of the crisis on small banks in the United States. In a study by Moradi-Motlagh and Babacan (2015), it was reported that the 2008 GFC negatively affected the efficiency of banks in Australia, with small banks experiencing a more severe impact. On the other hand, Gulati and Kumar (2016) found that the impact of the 2008 GFC on Indian banks was relatively mild and that the recovery was swift. Additionally, Mehdian et al. (2019) reported a negative impact of the 2008 GFC on the efficiency of large U.S. banks. This study aims to contribute to the existing literature by investigating the impact of the 2008 GFC on the efficiency of U.S. small banks. While numerous studies have examined banking efficiency, few have focused on the post-crisis efficiency of small banks. This study seeks to fill this gap by analyzing the factors influencing the cost efficiency of small banks following the 2008 GFC. The remainder of the paper is divided as follows: Section 3 describes the data and methodology, Section 4 discusses the empirical results, Section 5 presents the conclusions, and Section 6 addresses the limitations and outlines the future direction of the study. 3. Data and methodology 3.1. Data This study investigates the efficiency of small banks operating in the United States after the 2008 GFC. The data was collected from the consolidated Report of Condition (balance sheet) and Report of Income (income statement) published by the Federal Deposit Insurance Corporation (FDIC) website. The nature of financial transactions introduces complexity in defining input and output in bank efficiency and productivity studies. Two main definitions of input and output variables are commonly used in these studies: the intermediation and production approaches. The intermediation approach, introduced by Sealey and Lindley (1977), views banks as intermediaries of services, using inputs such as deposits, fixed assets, and employees to produce earning assets like loans and investments. The production approach, introduced by Benston (1965), considers banks as utilizing inputs such as fixed assets and employees to produce services like deposits and earning assets such as loans and investments. 6 For this study, we applied the intermediation approach, assuming that small banks provide intermediation services by collecting deposits from savers (both interestand non-interest-paying deposits and other liabilities) and then channeling these funds to deficit units of the economy through providing loans (such as Real Estate Loans, Commercial and Industrial Loans, and other loans) and investing in various investment securities. Under this approach, we define input and output variables as follows: Y1 ¼Commercial and industrial loans. Y2 ¼Real estate loans. Y3 ¼Other loans. Y4 ¼Total investment securities. X1 ¼Total Liabilities. X2 ¼Number of full-time equivalent employees. X3 ¼Premises and fixed assets. P1 ¼Unit price of interest ¼Total interest expenses/Total interest-bearing liabilities. P2 ¼Unit price of labor ¼Wages & benefits expenses/# of full-time equivalent employees. P3 ¼Unit price of fixed assets ¼Total expenses of fixed assets/Total fixed assets. TC ¼Total cost, the sum of total interest and non-interest expenses. TA ¼Total assets, as a measure of bank size, as included in the bank’s balance sheet. The measures of banking efficiency can vary significantly depending on the sample, input-output specifications, and methodology used. Ferrier and Lovell (1990) observed substantial differences in efficiency among different specifications. Bauer et al. (1998) found varying efficiency scores using five different methodologies. Mester (1997) argues that U.S. banks are too diverse for comparison with a common benchmark and rejects the hypothesis of a common cost function for all banks. We also posit that COGENT ECONOMICS & FINANCE 3 assuming all banks of all sizes have the same production technology is overly simplistic when measuring their cost efficiency using a single cost-efficient frontier. In this article, we utilize a large, high-quality sample of similar small banks that share the same objective and production model, allowing us to expect a common production technology. We utilized data from the FDIC Call Report to identify a selection of small FDIC-insured banks spanning from 2010 to 2021. Although there is no standard definition of “small banks,”we classified them as banks with total assets below $172,000,000. 7 Our focus on small banks stemmed from two key reasons: firstly, the unique composition of assets and liabilities of small banks sets them apart from medium and large banks, and secondly, we hypothesized that small banks might respond differently to financial crises compared to their larger counterparts. Additionally, we excluded very small banks with total assets below $73,000,000, as we believe they are situated in remote rural areas and possess distinct asset and liability profiles, making them less susceptible to global financial crises. We did not consider the structural aspects of the banking corporations or their geographic locations. Our analysis covers a 12-year period from 2010 to 2021. To maintain consistency, we only included small banks that were operational throughout this entire period, resulting in a total of 10,495 small banks. This translates to an average of approximately 875 small banks per year. Table 1 presents summary statistics for the outputs and inputs of these 10,495 small banks operating between 2010 and 2021. 8 The number of banks varied yearly, from 829 in 2021 to 895 in 2018. While yearly summary statistics for outputs and inputs are available, they are not included here for brevity but can be provided upon request. 9 4. Methodology In simple terms, efficiency is the comparison between the actual outputs produced by a set of inputs and the optimal outputs that could be produced by the same inputs (Coelli et al., 2005). The frontier efficiency methodology is used to compare actual output/input values with the optimal output/input values. In this study, we employ the frontier efficiency methodology to assess the efficiency of U.S. small banks post the 2008 GFC. This methodology encompasses both parametric and non-parametric techniques, both of which establish an efficient frontier from which individual bank efficiency is calculated. The parametric approach constructs the efficient frontier based on a specific production or cost function and allows for random error, whereas the non-parametric techniques do not assume a specific functional form and do not allow for random error. 10 Additionally, both methods can be input or output-oriented and are adaptable to analyze scale efficiency. In our analysis, we utilize a non-parametric approach originally introduced by Farrell (1957), further developed by Charnes et al. (1978), and extended by F€ are et al. (1985). This method involves creating input-oriented efficient frontiers through the solution of multiple Linear Programming (LP) problems, which allows us to calculate the efficiency of each bank. The solutions to these LPs yield five efficiency measures: Overall Efficiency (OE), Overall Technical Efficiency (TE), Allocative Efficiency (AE), Pure Technical Efficiency (PTE), and Scale Efficiency (SE). To determine a bank’s efficiency, we first solve a linear programming model to find the potential minimum total cost and then compute the OE for each bank i each year as follows: C i¼min px yizY xizX z0 (1) Table 1. Summary statistics of outputs, inputs, price of inputs, and total costs for the pooled sample; year 2010–2021 (N¼10,495). 2010–2021 Y1 Y2 Y3 Y4 X1 X2 X3 P1 P2 P3 TC Mean 21020 4166 2556 2056 50136 14.33 783 0.0064 66.17 0.6975 1424 Min 0 0 0 0 5090 2 0 0.0001 0.042 0 0.687 Max 311477 85441 120616 64299 281681 157 8993 0.0704 834.2 108 26852 STD 14544 4214 3550 5682 21921 7.54 903 0.0044 21.09 2.346 788 Y1 ¼Real Estate Loan, Y2 ¼Commercial and Industrial Loan, Y3 ¼Consumer Laon, 4 ¼Securities, X1 ¼Deposits, X2 ¼Labor, X3 ¼Premises and fixed assets, P1 ¼interest cost, P2 ¼Labor cost, P3 ¼Cost of premises and fixed assets, and TC ¼Total costs. 4 R. REZVANIAN ET AL. where C i is the potential minimum total cost of production of bank i, P is a vector of input prices, y i is a vector of outputs produced by bank i of dimension (1, m), x i is a vector of inputs utilized by bank i of dimension (1, n), Y is a matrix of observed outputs of all companies in the sample of dimension (m, N), X is a matrix of observed inputs of all companies in the sample of dimension (n, N), z is an intensity vector, N is the number of firms in the sample. Having the potential minimum total cost of production of bank i calculated (C i ), we then, the OE of bank i as: OE ¼C i=Ci To gain insights into the sources of inefficiency, the OE can be broken down into Overall TE and AE. To determine the TE of bank i in a given year (t ¼2010 …2021), the following linear programming problem is solved for each bank, for each year in the sample: min ki yizY kixizX z0 i¼1, ......:,N (2) where all variables are as defined earlier. k i is the measure of efficiency (overall technical efficiency, TE), estimated for bank i relative to a frontier that exhibits constant returns to scale (CRS). We have broken down this measure into two more efficiency measures to better understand the sources of overall technical inefficiency. The first measure is Pure Technical Efficiency (PTE), which assesses the bank’s efficiency relative to a frontier that demonstrates constant and variable returns to scale. The other efficiency measure, the SE measure, offers insights into whether the bank operates at constant returns to scale (optimal scale) or at increasing or decreasing returns to scale (sub-optimal scale). Formally, the TE of bank i can be expressed as: TEi¼PTEiSEi, Where SEiis ratio of TEito PTEi: To compute PTE, denoted by w i, for bank i, Eq. (2) is solved with an additional constraint that is PN i¼1zi¼1, then we have SE i ¼k i /w i . Bank i is called scale efficient if SE i ¼1. If 0 SE i <1, bank i is called scale inefficient. To sort the source of scale inefficiency of bank i, we resolve Eq. (2) after replacing PN i¼1zi¼1, by PN i¼1zi1, and obtaining an efficiency measure denoted by x. Following F€ are et al. (1985) and Turk Ariss et al. (2007), if bank i is not scale-efficient and x¼w, the source of scale inefficiency bank i is decreasing returns to scale (DRS). On the other hand, if bank i is not scale-efficient and x6¼ w, the source of scale inefficiency of this bank is because of increasing returns to scale (IRS). Finally, we compute AE, which is an indication of the deviation of the operation from the optimal input mix of resources as: AE i ¼OE i /TE i . We summarize the efficiency measures defined above as follows: OEi¼TEiAEi,TE i¼PTEiSEi, and then OEi¼PTEiSEiAEi To proceed with the methodology mentioned above, we have two approaches for measuring efficiency scores for individual banks: year-specific and pooled sample measures. First, we calculate each bank’s efficiency measures relative to each year’s frontier using the banks’inputs, outputs, and total cost for that particular year (2010 to 2021). These efficiency measures are referred to as "year-specific measures" because they are calculated relative to the corresponding year’s frontier. The efficient frontiers for each year are determined using the data for that specific year. The underlying assumption is that the yearly frontiers represent the available technology for all banks in the sample for that year. Isik and Hassan (2003) outlined two advantages of this approach. Firstly, it is more flexible and, therefore, more appropriate than estimating a single multiyear frontier for the banks in the sample. Secondly, it partially mitigates the problems related to the lack of random error in DEA by allowing an efficient bank in one COGENT ECONOMICS & FINANCE 5 year to be inefficient in another year based on the assumption that errors due to luck or data problems are not consistent over time in a given year. Next, we recalculate the efficiency measures of each bank by pooling the data for all years. We call these "pooled sample efficiency measures," calculated relative to the common frontiers from 2010 to 2021. The underlying assumption is that over the 12 years under study, all banks could have access to the best available technology, that is, they are facing common frontiers. Chen et al. (2015) raise concerns about using a single frontier that envelops all banks for all years. Using a single frontier for all years may underestimate the efficiency measures because banks are compared with the most efficient banks operating under the best available technology during the study. In this study, we will use both approaches. 5. Empirical results Table 1 provides the Summary Statistics of outputs, inputs, price of inputs, and total assets for 10,495 small banks from 2010-2021. After the 2008 GFC, earning assets (the sum of loans and investments) accounted for 56.1% of total assets. Among earning assets, real estate loans (Y1) represented the largest portion at 68.56% of earning assets and 35.20% of total assets, followed by commercial and industrial loans (Y2) at 13.43% of earning assets and 6.71% of total assets. The total cost per dollar of earning assets was $0.0481, with labor cost (P2X2) being the highest input cost at $0.0315 per dollar of earning assets, followed by the interest cost at $0.011 per dollar of earning assets. Based on the methodology and data outlined in section 3, we initially calculated the efficiency measures of small banks for each year from 2010 to 2021 using the annual efficient frontier. The summary statistics of efficiency measures are presented in Table 2, while Table 3 provides the same information for each year. It’s worth noting that the annual efficiency measures, as indicated by Tables 2 and 3, have consistently remained low. The average OE of the 10,495 small banks operating between 2010 and 2021 was 32.93%. Notably, the main contributor to this inefficiency has been low TE at 48.14%, rather than AE at 68.85%. A breakdown of TE into PTE and SE further reveals that the principal cause of the low TE has been both low PTE at 56.955% and SE at 57.77%. In summary, the primary factor contributing to the overall low efficiency of small banks has been the combination of higher pure technical and scale inefficiencies. The information in Table 3 shows the yearly statistics of efficiency measures for small banks from 2010 to 2021. Figure 1 provides a graphical representation of the same information. According to Table 3 and Figure 1, the operational efficiency (OE) measure of small banks has remained low for all 12 years after the 2008 GFC, ranging from a maximum of 36.29% in 2010 to a minimum of 29.98% in 2015. The primary cause of the low OE has been the low TE, which is, in turn, caused by a low level of PTE and SE. Next, in our analysis, we established a common efficiency frontier by consolidating data from the 12 years spanning 2010 to 2021, post the 2008 GFC. The summary statistics of efficiency measures are presented in Table 4, while Table 5 provides the same information for each year. As far as we know, there has not been a comparable study using a non-parametric technique to investigate the cost efficiency of U.S. small banks post-2008 GFC. However, Elyasiani and Mehdian (1995) researched the efficiency of small banks before and after the 1980s deregulation. They found that the efficiency of small banks declined during the post-deregulation era compared to the pre-deregulation era. Akhigbe and Table 2. Summary statistics of efficiency measures of the pooled sample (2010–2021, TN ¼10,495). OE AE TE PTE SE Mean 0.3293 0.6885 0.4814 0.5695 0.5777 Min 0.0881 0.1770 0.0960 0.1740 0.0901 Max11111 STD 0.1148 0.1582 0.1480 0.1536 0.1522 OE ¼Overall Efficiency, AE ¼Allocative Efficiency, TE ¼Overall Technical Efficiency, PTE ¼Pure Technical Efficiency, SE ¼Scale Efficiency, TN ¼Total Number of observations. 6 R. REZVANIAN ET AL. McNulty (2003) examined the comparative profit efficiency of small banks from 1990 to 1996 and concluded that small banks are more profit-efficient than larger banks. Based on Table 4, the average OE for the 10,495 small banks operating within the common efficient frontier from 2010 to 2021 was 25.52%. The primary contributors to this OE inefficiency were low TE (38.56%) rather than AE (67.23%) efficiency. Breaking down the TE into its components of PTE and SE reveals that the main reason for low TE is low PTE (47.75%) and SE (53.78%). When considering these Table 3. Summary statistics of yearly efficiency measures relative to yearly efficient frontier. OE AE TE PTE SE 2010 (N¼878) Mean 0.3629 0.6901 0.5270 0.6151 0.5887 Min 0.0960 0.2870 0.1120 0.2280 0.1125 Max 1 1 1 1 1 STD 0.1512 0.1423 0.1770 0.1692 0.1551 2011 (N¼877) Mean 0.3353 0.6947 0.4838 0.5801 0.5786 Min 0.0955 0.2830 0.1120 0.2150 0.1056 Max 1 1 1 1 1 STD 0.1471 0.14289 0.1744 0.1744 0.1581 2012 (N¼875) Mean 0.3085 0.6539 0.4769 0.5713 0.5412 Min 0.0900 0.2320 0.1150 0.2050 0.1004 Max 1 1 1 1 1 STD 0.1409 0.1583 0.1751 0.1743 0.1537 2013 (N¼879) Mean 0.3409 0.7162 0.4827 0.5765 0.5985 Min 0.089095 0.265 0.134 0.206 0.1169562 Max 1 1 1 1 1 STD 0.1429 0.1582 0.1757 0.1785 0.1631 2014 (N¼878) Mean 0.3578 0.7480 0.4831 0.5883 0.6138 Min 0.0987 0.2490 0.1440 0.2240 0.0987 Max 1 1 1 1 1 STD 0.1501 0.1583 0.1723 0.1757 0.1686 2015 (N¼867) Mean 0.2988 0.5156 0.5745 0.5805 0.5115 Min 0.0888 0.1140 0.1400 0.1400 0.1140 Max 1 1 1 1 1 STD 0.1488 0.1450 0.1820 0.1885 0.1433 2016 (N¼878) Mean 0.3352 0.6901 0.4897 0.5823 0.5779 Min 0.0887 0.1900 0.1280 0.2200 0.1182 Max 1 1 1 1 1 STD 0.1504 0.1643 0.1737 0.1740 0.1680 2017 (N¼879) Mean 0.3377 0.7167 0.4769 0.5619 0.6041 Min 0.0929 0.2010 0.1180 0.2140 0.1236 Max 1 1 1 1 1 STD 0.1502 0.1684 0.1718 0.1741 0.1686 2018 (N¼895) Mean 0.3344 0.7560 0.4464 0.5408 0.6214 Min 0.0914 0.1830 0.1140 0.1910 0.1086 Max 1 1 1 1 1 STD 0.1518 0.1570 0.1744 0.1813 0.1665 2019 (N¼895) Mean 0.3173 0.7239 0.4383 0.5463 0.5846 Min 0.0891 0.1780 0.0048 0.2110 0.1064 Max 1 1 1 1 1 STD 0.1462 0.1551 0.261 0.1813 0.1672 2020 (N¼865) Mean 0.3105 0.7510 0.4190 0.5255 0.5955 Min 0.0884 0.1980 0.1020 0.2070 0.1106 Max 1 1 1 1 1 STD 0.1458 0.1541 0.1796 0.1854 0.1660 2021 (N¼829) Mean 0.2895 0.5991 0.4810 0.5654 0.5125 Min 0.0914 0.1880 0.1070 0.2030 0.1203 Max 1 1 1 1 1 STD 0.1485 0.1676 0.1922 0.1946 0.1590 OE ¼Overall Efficiency, AE ¼Allocative Efficiency, TE ¼Overall Technical Efficiency, PTE ¼Pure Technical Efficiency, SE ¼Scale Efficiency, N¼Number of Observations per year. 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