Do regional integration and trade linkages promote productivity spillovers? Evidence from the European Union

Hazwan Haini; Pang Wei Loon

doi:10.1080/1406099X.2023.2198284

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Do regional integration and trade linkages promote productivity spillovers? Evidence from the European Union

Haini, Hazwan; Wei Loon, Pang 2023-01-02 00:00:00 BALTIC JOURNAL OF ECONOMICS 2023, VOL. 23, NO. 1, 64–90 https://doi.org/10.1080/1406099X.2023.2198284 Do regional integration and trade linkages promote productivity spillovers? Evidence from the European Union Hazwan Haini and Pang Wei Loon School of Business and Economics, Universiti Brunei Darussalam, Bandar Seri Begawan, Brunei ABSTRACT ARTICLE HISTORY Received 6 July 2022 This study examines the productivity and eﬃciency spillovers in the Accepted 30 March 2023 presence of trade linkages in 27 European Union countries from 1990 to 2019. The European Union is one of the largest trading KEYWORDS blocs in the world and has implemented costly policies and European union (EU); reforms to improve productivity growth. Meanwhile, trade- Eurozone; spillovers; total induced productivity and eﬃciency spillovers have often been factor productivity (TFP); overlooked in the literature, and examining them could provide spatial Durbin model further clarity to the productivity puzzle. Using a spatial Durbin model and a bilateral trade matrix, this study estimates a spatial JEL CLASSIFICATION CODES stochastic production frontier model using data from the Penn C23; O49; C31; D24 World Table and the World Integrated Trade Solution. We decompose production frontier estimates to obtain the spillover eﬀects of total factor productivity growth and technical eﬃciency from a network of bilateral trading partners. Our results provide evidence of productivity and eﬃciency spillovers; however, the gains are uneven. Policy implications are discussed. 1. Introduction To promote sustainable development and regional integration, the European Union (EU) has implemented various policies, such as the National Productivity Boards and the Inno- vation Union policy, as well as various structural funding packages such as the Cohesion Funds (Pegkas et al., 2020; Webber et al., 2019). Yet, despite these costly policies and reforms, the EU has experienced a declining trend in productivity in recent years. A recent ECB Economic Bulletin report highlights that the EU experienced sluggish pro- ductivity growth even before the recent global ﬁnancial crisis of 2008 (ECB, 2017). Further- more, recent studies have found that weak investment and the slow recovery of employment levels – an indicator of resource misallocation – have contributed to low pro- ductivity (Claeys et al., 2022; Lopez-Garcia, 2021). This is concerning, as productivity is crucial for the region’ssustainable growth andpros- perity. Future productivity growth is suggested to depend on an economy’sability to diﬀuse technology, within and across countries, through interactions such as trade, foreign direct CONTACT Hazwan Haini hazwan.haini@ubd.edu.bn School of Business and Economics, Universiti Brunei Dar- ussalam, Bandar Seri Begawan, Brunei © 2023 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. BALTIC JOURNAL OF ECONOMICS 65 investment, and mobile factors of production (McGowan et al., 2015). Productivity measures, such as total factor productivity (TFP) growth, are dimensionless in nature and do not con- sider interactions between observations; however, economic interactions between nations play a crucial role in economic growth as they can capture knowledge spillovers and tech- nical diﬀusion as well as help to optimize resource allocation (Wu et al., 2019). As a result, considering economic interactions is critical in the European context, as cohesion policies and the elimination of intra-EU tariﬀs have increased trade and econ- omic interactions in the region (Pappalardo & Vicarelli, 2017). The EU is suggested to beneﬁt from many convergence mechanisms that arise from certain spillover eﬀects. Empirical studies on the region indicate that the diﬀusion of technology is vital for pro- ductivity growth (Banerji et al., 2015; Hafner, 2014; Harasztosi, 2016; Pegkas et al., 2020). Furthermore, other research has emphasized that the gains from spillovers may be uneven, especially for the Eurozone economies, which are suggested to beneﬁt from a currency union (Gunnella et al., 2021; Jagelka, 2013). This is also true for the Central and Eastern European (CEE) economies, which are relatively new members of the EU enlargement process and have beneﬁtted from technological content from their advanced counterparts (Antimiani & Costantini, 2013; Pina & Sicari, 2021). Therefore, the present study re-examines this issue by measuring TFP growth and tech- nical eﬃciency spillovers through trade linkages in 27 EU Member States from 1990 to 2019. This is of interest for various reasons: First, TFP and technical eﬃciency spillovers that arise from trade linkages have been overlooked in the literature. This is vital to con- sider as understanding the role of technical diﬀusion can provide insights into the declin- ing trend of productivity in the EU, particularly with the role of trade linkages. Second, it allows for a comparative examination of productivity and eﬃciency spillovers across the EU Member States. This allows one to assess whether the Eurozone countries beneﬁt more from spillovers or whether new Member States catch up in terms of productivity (Bardazzi & Ghezzi, 2018; Gunnella et al., 2021; Pina & Sicari, 2021). This study oﬀers several innovations over existing literature. First, studies that have exam- ined the spillover impact of productivity and eﬃciency have focused on geographical spatial spillover eﬀects (Glass et al., 2016;Haini, 2020). By contrast, this study captures the impact of spillovers from TFP and technical eﬃciency that occur through trade linkages. This is critical because, as distance becomes less of an obstacle, economies are being increasingly inte- grated globally and interconnected through trading activities. In fact, intra-trade in the EU has grown by 1200% in real terms (Badinger & Breuss, 2004). We provide new empirical evi- dence regarding the productivity puzzle in the EU, which has been demonstrated to be declining over time. Estimating a frontier that considers trade linkages allows one to examine whether technical spillovers occur, and whether productivity is higher in Eurozone economies. Likewise, we also provide new evidence regarding whether the CEE economies have beneﬁted from technical spillovers from their advanced counterparts. Finally, many studies have examined the impact of productivity by focusing on the spil- lover eﬀect of labour productivity or TFP growth, while others have examined the eﬃciency spillover eﬀects of frontier models (Carvalho, 2018; Glass & Kenjegalieva, 2019). The present study extends the literature by measuring the impact of technical eﬃciency spillovers that arise through a network of bilateral trading partners’ perform- ance through an eﬃciency multiplier. Similarly, this study’s approach diﬀers from the one suggested by Carvalho (2018), which employs Bayesian methods to examine 66 H. HAINI AND P. WEI LOON eﬃciency spillovers through the traditional production function model estimated using maximum likelihood. This allows us to obtain explicit estimates of eﬃciency spillovers, which could be of interest to policy makers. This study also employs a spatial Durbin stochastic production frontier model that uses a bilateral trade matrix, which is considered one of the most adequate connectivity matrices (Debarsy & Ertur, 2019). More speciﬁcally, we employ trade in goods exports from the Com- trade Database (World Integrated Trade Solution, 2020) to capture the extent of trade lin- kages between the EU Member States. Using this matrix, we estimate a spatial Durbin frontier using quasi-maximum likelihood methods, which consider endogenous spatial autoregressive and lag variables. We also employ data from the Penn World Table 10.0 (Feenstra et al., 2015) and use traditional frontier variables, such as output, capital, and labour. Furthermore, the spatial stochastic frontier model allows us to interpret the esti- mates like a standard production frontier model in addition to providing estimates of direct, indirect (spillover), and total parameters from the standard capital and labour inputs. Finally, the results can be decomposed to provide estimates of TFP growth, total technical change, technical eﬃciency change, and returns to scale change. In addition, we ﬁnd evidence of TFP growth and technical eﬃciency spillovers through trade linkages between the EU economies. In particular, our results suggest that indirect technical change and indirect technical eﬃciency provide an additional average spillover eﬀect of 1.01% and 18.50%, respectively. However, we also ﬁnd that the CEE economies beneﬁt less compared with our full sample of EU economies as they only beneﬁt from eﬃciency spillovers, while the Eurozone economies beneﬁt from both indirect eﬃciency and technical change spillovers. Finally, we demonstrate that diﬀusion eﬀects from indirect technical change are increasing over time, while direct technical change is declining. This follows the narrative of the EU’s persistently declining trend in pro- ductivity; however, it also provides evidence of successful policy implementation in terms of diﬀusing technical knowledge. The remainder of this paper is organized as follows: Section 2 presents our theoretical considerations and literature review; Section 3 describes the data sample, variables employed, and econometric strategy; and Section 4 presents the results and provides a brief discussion of them. Finally, Section 5 concludes the study with policy implications and directions for future research. 2. Theoretical considerations and literature review 2.1. Productivity growth and spillovers Improvements in productivity through innovation, economies of scale, and learning-by- doing can impact economic growth and violate the assumptions of the Solow (1956) model. As a result, two producers can have diﬀerent labour productivity even though they have the same inputs due to TFP growth (Syverson, 2011). In other words, productivity is eﬃciency in production and how much output is produced given a set of inputs. Since the growth of input factors cannot fully explain the growth of output, there must be other driving forces that explain growth (Nordhaus, 1969). Conse- quently, a decline in productivity is concerning for policy makers as productivity growth is suggested to be vital for sustainable prosperity in regions and countries (Krugman, 1994). BALTIC JOURNAL OF ECONOMICS 67 In addition, new growth theories have been introduced alongside endogenous models to explain the violations of traditional neoclassical growth models. These models include the new international economics theory, which provides a reformulation of the theories of trade and trade policy, and the new economic geography theory, which attempts to explain the spatial distribution of economic activity (Fujita et al., 1999). These theories extend the traditional neoclassical model as it generates long-run growth paths that resemble the Solow model with additional endogenous explanations for returns to scale, rate of technological change, savings, and population (Nijkamp & Poot, 1998). More speciﬁcally, the new growth theories highlight the growing importance of trade, capital ﬂows, diﬀusion of innovation, and migration at the interregional and international levels, suggesting that spatial interactions need to be explicitly considered. For example, factor mobility in capital and labour can have a disequilibrium eﬀect, while the diﬀusion of technology can aﬀect the growth paths of developing economies, where productivity growth can be driven by importing new technology (Nijkamp & Poot, 1998). Regarding trade, Grossman and Helpman (1991) provide an extensive discussion of the links between innovation, trade, and growth in an open economy, as knowledge spil- lovers between ﬁrms and sectors can amplify growth. Technological development is suggested to radiate from a regional source to its surrounding neighbours, which pro- motes technological progress (Wu et al., 2019). Thus, spatial interdependencies are vital to consider as factor mobility, innovation diﬀusion, and trade occur in open economies that are constantly interacting with one another. Several empirical papers have highlighted the spillover eﬀect of productivity and eﬃciency across regions and economic structures. Capello (2009) suggests that neigh- bouring regions can take advantage of capital and labour availability, while inter-industry linkages allow growth to spill over and be transmitted to neighbouring regions and econ- omies. Other research highlights the importance of trade linkages and spillover eﬀects for exporting ﬁrms, which can beneﬁt from agglomeration as it allows for the reduction of supply chain costs (e.g. by sharing a logistics centre), while an industrially dense economy allows for a specialized labour force (Harasztosi, 2016). Thus, trade connectivity and linkages are a critical source of knowledge and technical diﬀusion that can enhance productivity and often crosses regions and countries (Zhang et al., 2020). Similarly, several econometric papers have highlighted the importance of modelling spillovers in terms of geographic, economic, trade, and even linguistic distance (Debarsy & Ertur, 2019; Glass et al., 2013; Glass et al., 2016; Ho et al., 2013; Tientao et al., 2016; Wu et al., 2019). 2.2. Productivity in the EU Modelling trade linkages becomes more critical in the context of the EU as the region has grown considerably through its enlargement process. To provide some background, the EU was initially founded by Belgium, France, Italy, Luxembourg, the Netherlands, and Germany in 1958. It established the European Economic Community and the EU Customs Union, which enable Member States to operate within a single market (McCann & Ortega-Argilés, 2013). Today, the region consists of 27 Member States and has developed a single legal binding agreement known as the EU Acquis Communautaire, which consists of a uniﬁed economic and institutional setting throughout the region; moreover, it allows for the free movement of goods and services as well as of capital 68 H. HAINI AND P. WEI LOON and labour (Gehringer, 2013). As a result, the establishment of a European Common Market and the elimination of capital controls has allowed ﬁnancial development to ﬂourish through increased economic integration between Member States (Bordo & Rous- seau, 2012). However, since the turn of the century, growth in the annual gross domestic product (GDP) per capita has been stagnating, marred by the global ﬁnancial crisis of 2008 and the subsequent Eurozone crisis of 2012 (ECB, 2017). Similarly, productivity growth in the region has also followed a declining trend, as labour productivity has dramatically slowed from an average of 2.4% during 1970–1995 to an average of 1.4% during 1995– 2014 (Timmer et al., 2007). More recently, in 2018, the average labour productivity growth was approximately 1.1% (van Ark et al., 2018). The manufacturing sector is suggested to continue to contribute signiﬁcantly to the productivity growth of the EU, while the productivity of new Member States is converging with the founding members (Pina & Sicari, 2021). In addition, human capital development, infrastructure investment, research and development, and non-ICT capital deepening are found to be major determinants of TFP growth in the region (Strobel, 2012). For example, a recent study ﬁnds that research and development capital positively contributes to TFP growth in Eurozone countries (Pegkas et al., 2020), while another ﬁnds ﬁnancial markets to be a crucial determining factor for capital allocation and productivity in European economies (Claeys et al., 2022). Yet, it is suggested that the region still faces various issues, such as weak demand, a changing employment composition, a declining rate of technological progress and diﬀusion, as well as increasing resource misallocation (ECB, 2017). Recent studies have highlighted that declines in the EU are mainly due to a reduction in innovative activity (Oulton, 2018), and also that industry misallocation has doubled within the Eurozone economies (Dias et al., 2016). Therefore, the EU faces the challenge of addressing and developing future initiatives to reverse the decline in productivity growth and regional disparity across the region, as low productivity can restrict the region’s ability to develop further (Banerji et al., 2015). In response to these issues, the EU has implemented several initiatives. As the region has observed interregional disparities since the 1980s, it has introduced a variety of struc- tural and investment funds that support the EU Cohesion Policy. The aim is to reduce dis- parities in economic output and support cohesion objectives for new Member States to adjust to the single market environment (McCann & Ortega-Argilés, 2013). Furthermore, in March 2000, the European Council adopted the Lisbon Strategy to address the pro- ductivity slowdown that the EU has faced compared with the United States, which has higher productivity growth through the adoption of ICT technologies (Palazuelos & Fer- nández, 2010). The Lisbon Strategy aims to increase the EU’s competitiveness by develop- ing the region as a dynamic knowledge-based economy. Finally, more recently, the EU has adopted a more direct response to address the decline in productivity by establishing the National Productivity Boards in 2016 (Webber et al., 2019). In addition, the region has implemented the Innovation Union policy, which includes the development of a European Research Area, the aim of which is to develop and produce high-quality research and innovation to boost productivity (Pegkas et al., 2020). Other recent initiatives also focus on cooperation between EU Member States on innovative activity, such as Horizon 2020 and the Important Projects BALTIC JOURNAL OF ECONOMICS 69 of Common European Interest, which aims to promote further regional growth and con- vergence (Pina & Sicari, 2021). Finally, the recent COVID-19 pandemic led to the creation of NextGenerationEU, a 750- billion-euro recovery plan launched by the EU in response to the pandemic. The plan aims to provide ﬁnancial assistance to Member States to help them to recover from the economic and social impacts of the pandemic. The funds will be used to support the green and digital transition, invest in education and training, and support small and medium-sized enterprises (SMEs). This plan can promote productivity and eﬃciency in the region by boosting investment in innovation and research, supporting the digital and green transition, and providing support to SMEs. By investing in the digital infrastruc- ture and the skills required to support the digital transition, businesses can adopt digital technologies that streamline operations and improve communication, eventually leading to gains in productivity and eﬃciency. 2.3. Trade, Eurozone, and CEE economies While productivity has been declining, trade in the region has increased tremendously over the last few decades. In fact, from 1960 to 2000, a study ﬁnds that intra-EU trade grew by an impressive 1200% in real terms (Badinger & Breuss, 2004). Trade liberalization and income growth of the EU are suggested to have allowed trade to prosper, particularly through the elimination of the intra-EU tariﬀ. Hafner (2014) suggests that the enlargement process of the region has beneﬁted new Member States as they gain access to trade- related foreign technology from old Member States, particularly CEE countries. More crucially, while trade liberalization and income growth have beneﬁtted the growth of intra-EU trade, the impact of the euro on bilateral trade ﬂows is questionable. It must be noted that the EU is a political agreement, while the Eurozone is a monetary union aimed at increasing economic integration through a common currency. Scholars suggest that a common currency allows for greater unity, controls for volatility, allows for higher market transparency and competition, and provides higher macroeconomic stability (Frankel & Rose, 2002). In theory, the Eurozone economies should beneﬁt further from trade as they share a single market and a common currency, which should eliminate many informational and transactional costs. However, on the other hand, empirical studies that have examined the impact of the euro on trade at the macroeconomic and ﬁrm levels have suggested that the eﬀect is non- signiﬁcant (Bresser-Pereira & Rossi, 2015;Caﬁso, 2011; Figueiredo et al., 2016; Pappalardo & Vicarelli, 2017). Other researchers have found that the currency union unevenly beneﬁts new members of the Eurozone, particularly the relatively smaller and underdeveloped EU countries, and they have suggested that the euro has facilitated the establishment and expansion of production chains (Gunnella et al., 2021; Jagelka, 2013). Hence, the growth of intra-EU trade and the considerable variation across bilateral trade ﬂows – in both the EU and Eurozone economies – provide an interesting avenue for examining the extent of trade-induced productivity spillovers. Additionally, another strand of literature highlights the convergence eﬀect that arises from the EU, whereby newer Member States, particularly CEE economies, beneﬁt more than older and developed Member States. Previously, many CEE economies had fragile institutional settings with a high dependence on an unskilled labour force, and the 70 H. HAINI AND P. WEI LOON European enlargement process has led to increased export ﬂows and technological spil- lovers as well as further developed the institutional capacity of newer Member States (Antimiani & Costantini, 2013). Moreover, many CEE countries were part of the former Soviet bloc, in which they suﬀered from the poor utilization of their production capacity, and thus, convergence to their advanced counterparts was easier (Haini & Wei Loon, 2022; Lopez-Garcia, 2021). As such, it is unsurprising to observe strong growth and convergence from CEE countries, which have seen fewer losses in output compared with the EU average (Pina & Sicari, 2021). Thus, in addition to the Eurozone economies, the CEE economies provide an interesting case study for examining trade-induced productivity and eﬃciency spillovers as they are expected to absorb more technological progress from their developed counterparts. 3. Empirical methodology 3.1. Spatial Durbin production frontier model Consider Equation (1), which presents the standard stochastic frontier and includes the traditional outcome variable GDP, denoted as y , and the independent input variables it X (capital and labour). The composed error structure consists of two components, it namely the idiosyncratic error, n and time-varying technical eﬃciency, m . it it y = a + bX + n − m (1) it it it it However, Equation (1) is dimensionless in nature and does not consider the interactions between the observations, as the cross-sectional units are assumed to be independent. Yet, productivity and eﬃciency can spill over and be transmitted to other economies (Ertur & Koch, 2011). As a result, it is suggested that modelling a closed economy might not be valid and that spatial interdependencies must be considered to capture spil- lovers between economies (Ho et al., 2018). While several studies have examined growth spillovers, the literature on spatial stochastic frontier modelling is relatively scarce, only being developed in recent studies (Glass et al., 2016). Following Glass et al. (2016), the present study employs the spatial Durbin production frontier model estimated using maximum likelihood, as in Equation (2). The omission of the spatial autoregressive variable is suggested to lead to bias estimates, while the issue of spatial dependence is seldomly discussed in stochastic frontier modelling. The spatial dependence between the cross-sectional units is captured by the inclusion of a spatial weights matrix, denoted as w . The speciﬁcation of the matrix is explained in ij more detail below. Furthermore, the spatial frontier model is estimated using a three- step simulated maximum likelihood method (Filippini & Greene, 2016). The estimates of the spatial frontier are obtained in the ﬁrst stage, followed by the eﬃciency spillovers and returns to scale, and ﬁnally the TFP in the ﬁnal stage. i = 1, ... , N; t = 1, .. . T (2) In Equation (2), there are N countries indexed i = 1, ... , N over time T, indexed t = 1, ... , T. The dependent variable y represents the traditional stochastic frontier it variable, namely real GDP, while g represents a vector of exogenous independent it BALTIC JOURNAL OF ECONOMICS 71 input variables, namely real capital stock and labour. We include a vector of control vari- ables denoted by z and the time trend t and t to account for Hicks-neutral technological it change. To account for spatial dependence, w g represents a vector of the spatial ij jt j=1 lags of the exogenous input variables, while w z represents a vector of the spatial ij jt j=1 lags of the control variables. Finally, as the spatial Durbin model nests both the spatial autocorrelation and autoregression model, w y represents a vector of the spatial ij jt j=1 lag of the dependent variable from bilateral trading partners. However, following Glass and Kenjegalieva (2019), the local estimate for the time trend is omitted as it would be perfectly collinear with a row-normalised w . The intercept is denoted as a, while h , ij h , 4, c, z, u, and d are vectors of parameters that shift the stochastic frontier. Focusing on the spatial weights matrix, w , various methods exist for constructing it. ij However, many spatial weights are not derived from theory but are rather based on geo- graphical distance or features such as a distance or contiguity matrix (Debarsy & Ertur, 2019). More recent studies have employed other interaction matrices, which can be employed as a spatial weights matrix, such as bilateral trade ﬂows, genealogic distance, and linguistic distance (Ho et al., 2018). This is vital to consider as the misspeciﬁcation of the interaction matrix could lead to signiﬁcant biases. This study employs bilateral trade ﬂows as its spatial weights matrix, as it is suggested to be the most adequate inter- action matrix based on several robustness tests (Debarsy & Ertur, 2019). Meanwhile, the construction and intuition of the spatial weights matrix remain the same. In this case, w captures the spatial arrangement of the cross-sectional units and ij the strength of the spatial interaction based on pairwise trading partners. The diagonal elements of w are set to zero and row-normalised to have the unit sum ij w = 1, i = 1, ... , N. This preserves the spatial scaling of the data and transforms ij j=1 the spatial lag to be a weighted average of the bilateral trade observations. Thus, denot- ing I as the identity matrix, one can assume from Equation (2) that (I − dW) is non- N N singular, that the parameter space of z, u, and d is (r , 1) as it is row-normalised, and min that r represents the lower-limit characteristic root of w . min ij With the interaction matrix deﬁned, the production frontier model is estimated using the spatial Durbin speciﬁcation. However, LeSage and Pace (2009) highlight that the coeﬃcients of the independent variables in Equation (2) cannot be interpreted as elasti- cities, as the spatial spillover eﬀect arises through the marginal eﬀects of the independent variable interacting with the interaction matrix. Thus, to isolate the marginal eﬀects, Equation (2) can be reduced to Equation (3), which allows the estimated parameters to calculate the direct, indirect, and total marginal eﬀects: −1 −1 −1 y = (I − dW) X b + (I − dW) n − (I − dW) m (3) t N t N t N In Equation (3), the unit subscript i is dropped for the successive stacking of cross-sections. It is assumed that X is a matrix of stacked observations for g , z , g , z , and y . As out- t it it jt jt jt lined by LeSage and Pace (2009), Equation (3) can be diﬀerentiated to isolate the eﬀects 72 H. HAINI AND P. WEI LOON for the direct, indirect, and total marginal eﬀects. Consequently, the isolated eﬀects allow for estimates of spillover eﬀects arising from trading partners. The direct parameters con- sider the individual unit’s independent impact on the dependent variable, while the indir- ect parameters consider the spillover impact of trading partners. As such, the total parameters are the sum of direct and indirect spillovers. Glass et al. (2016) extend the approach to isolate marginal eﬀects by estimating the relative direct, relative indirect, and relative total eﬃciencies. Traditional technical eﬃciency scores in stochastic frontier modelling are generally bounded from the interval [0,1]. The inclusion of indirect eﬃciency allows scores to be greater than 1, as countries can beneﬁt from importing eﬃciency spillover from their trading partners. Glass and Ken- jegalieva (2019) call this the eﬃciency multiplier eﬀect. Finally, with technical eﬃciency deﬁned, we calculate TFP growth using standard components, such as the sum of change in technical eﬃciency, technical change, and returns to scale change following Kumar and Russell (2002). Additionally, we follow Glass et al. (2013) and extend this to include the spatial decomposition of TFP change, including direct and indirect technical change, total scale changes, and total technical eﬃciency change. We outline the decomposition in Appendix A1. 3.2. Data and variables This study employs an annual-level panel dataset of 27 EU Member States, namely Austria, Belgium, Bulgaria, Croatia, Cyprus, Czech, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, the Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, and Sweden. The data for the vari- ables are compiled from the Penn World Table 10.0 (Feenstra et al., 2015) from 1990 to 2019. The sample time-period is chosen based on data availability. All variables undergo the standard manipulation of data, such as log-transformation and mean-diﬀer- encing; thus, the ﬁrst-order input and time parameters can be interpreted as elasticities. Additionally, the bilateral trade matrix data w are compiled from the Comtrade Data- ij base (World Integrated Trade Solution, 2020). We employ the average imports and exports of country i from country j over the period 1990–2019 following Debarsy and Ertur (2019). Our bilateral trade ﬂows data are measured in nominal terms, as exports are deﬂated by two multilateral opposing terms and treating them into real data would not adequately capture the multilateral terms and would be inconsistent in terms of matching exports and imports (Brun, 2005). Equally critical is that our bilateral trade data from the Com- trade Database measure and capture trade in goods at the HS-6 level (5000 products). Although trade in services is becoming increasingly important in the EU, which has grown faster than trade in goods (Gunnella et al., 2021), trade in goods is still a large com- ponent of the EU and accounts for nearly €2,000 billion, while trade in services accounts for €910 billion in 2020 (World Integrated Trade Solution, 2020). As the spatial Durbin frontier model is an extension of the traditional stochastic fron- tier, the traditional variables used in frontier modelling are employed to calculate TFP growth. The dependent variable y represents real GDP, which is measured as output- it side real GDP at chained purchasing power parity in 2017 US$ million. Feenstra et al. (2015) recommend using output-side GDP to analyze productivity, as opposed to expen- diture-side GDP. Thus, we employ the rgdpo measure from the Penn World Table 10.0. BALTIC JOURNAL OF ECONOMICS 73 While labour productivity is usually captured using GDP per labour, we employ GDP at level terms with our capital and labour inputs to estimate a spatial stochastic production frontier. Moreover, g represents a vector of inputs that includes labour input, denoted as l, and it real capital stock at constant 2017 national prices (in 2017 US$ million), denoted as k. While Glass et al. (2016) use labour employed (emp in Penn World Table 10.0), we extend this and employ the average annual hours worked by persons engaged (avh in Penn World Table 10.0). This allows us to speciﬁcally focus on the substitutability of hours worked and capital employed, which extends the traditional frontier methodology. While traditional stochastic frontier models generally use the total number of labour employed (e.g. Glass et al. (2016), who use EU economies), we argue that there are increasing numbers of zero-hour and short-term contracts in the EU and, as a result, it may be misleading to use the total number of labour employed. Furthermore, we employ the rnna variable from the Penn World Table 10.0 as our capital input. Speciﬁcally, rnna consists of four assets, namely structures (including residential and nonresidential), machinery (including computers, communication equipment, and other machinery), transport equipment, and other assets (including software, other intellectual property, and cultivated assets; Feenstra et al., 2015). In addition, the interaction term between capital and labour and its squared terms are included following the literature. Furthermore, the time trend and its squared terms are included to capture Hicks-neutral technological change. Finally, z represents a vector it of control variables, including the ratio of government spending to GDP, denoted by gov (csh_g in Penn World Table 10.0 – the share of government consumption at current purchasing power parity); the ratio of net exports to GDP, denoted as exp (csh_x + csh_m in Penn World Table 10.0 – the sum of the share of merchandise exports and imports at current purchasing power parity); and a dummy variable for the CEE econom- ies, denoted as cee. Net exports are employed rather than trade openness as it signiﬁes imports as leakage (Glass et al., 2016). In addition, we include the share of government spending and net exports as part of our frontier model following the Barro-style regression (1991) and traditional growth account- ing aggregate demand models. The empirical literature on government size and GDP is inconclusive as some studies have found government size to be positive for GDP as it allows for the provision of infrastructure and human capital formation, while others have found government size to be detrimental as it distorts incentives and crowds out the private sector (Haini & Wei Loon, 2022). Moreover, the relationship between net exports and GDP is straightforward as increases in exports are generally favourable (Balassa, 1978). Table 1 presents the summary statistics of the variables employed at levels. Our full sample exhibits considerable heterogeneity across our input and output vari- ables. The average real GDP for the sample is approximately $518,830.70, while the inputs of real capital stock and the average annual hours worked have an average value of $2,535,834.00 and 1,758.69 h. We observe considerable variation in real GDP and real capital, while labour input displays less variation. When grouping our countries into Euro- zone and CEE economies, we observe that both real GDP and capital for the CEE econom- ies have lower averages than those of the full sample. However, the average labour input for CEE economies is larger. As expected, the Eurozone economies have a higher average output and input compared with the full sample. 74 H. HAINI AND P. WEI LOON Table 1. Summary statistics. Variable Description Mean SD Min. Max y Real GDP (2005 million US dollars at 2005 PPPs) 518,830.70 813,476.00 5,450.66 4,392,073.00 k Real capital stock (2005 million US dollars at 2,535,834.00 4,174,380.00 15,276.01 20,900,000.00 current PPPs) l Average annual hours worked by persons 1,758.69 193.20 1,380.60 2,277.38 engaged exp Exports of merchandise minus imports of −0.08 0.14 −0.61 0.39 merchandise as a share of GDP i.e. net trade openness gov Government spending as a share of GDP 0.21 0.06 0.08 0.46 y Central and Eastern European Economies (n = 188,365.80 219,988.50 14,015.03 1,211,305.00 k 300) 707,355.70 700,699.90 66,321.37 3,148,161.00 l 1,836.32 121.38 1,592.93 2,115.16 exp −0.07 0.06 −0.33 0.11 gov 0.27 0.05 0.16 0.46 y Eurozone Countries (n = 570) 617,395.50 942,330.90 5,450.66 4,392,073.00 k 3,135,475.00 4,829,964.00 15,276.01 20,900,000.00 l 1,804.90 172.67 1,495.46 2,277.38 exp −0.09 0.16 −0.61 0.39 gov 0.20 0.06 0.08 0.46 N = 810 observations from 27 EU countries from 1990 to 2019. Real GDP, real capital stock and labour employed are reported in levels and are log-transformed and standardized prior to the estimations. Meanwhile, we standardize the time-trend, net exports, and government spending. Standardization (mean 0, standard deviation 1) allows us to interpret the indirect and direct coeﬃcients as elasticities. Finally, the dummy variable cee refers to the Central and European Union Economies and includes Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovenia, and Slovakia. The Eurozone countries are Austria, Belgium, Cyprus, Estonia, Finland, France, Germany, Greece, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Portugal, Slovakia, Slovenia, and Spain. 4. Results and discussion Table 2 reports the coeﬃcients and parameters of the spatial Durbin production frontier, which includes the spillover eﬀects of the independent variable reported by the direct, indirect, and total parameters. The direct parameters of the spatial production frontier can be interpreted in the same manner as a nonspatial frontier (Glass & Kenjegalieva, 2019). Consequently, following the standard frontier literature, the monotonicity properties and condition must hold for the direct parameters of the production function. Monotonicity is a condition where an increase in inputs can never decrease the level of output, and it is important for theoretical consistency; furthermore, if this condition is violated, then the esti- mated results cannot be interpreted reasonably (Glass et al., 2016). We observe that the esti- mated direct parameters for k and l are both positive and signiﬁcant at the 1% level to output, which satisﬁes the monotonicity theory of frontier modelling. The model suggests that capital accounts for 55% of output while labour accounts for 42% of total output. In terms of our spatial dependency, we ﬁnd that the spatial dependence of the model is positive and signiﬁcant at the 1% level, as captured by the spatial lag coeﬃcient Wr.A higher and signiﬁcantly positive value for spatial dependence improves the ﬁt and log- likelihood of the model as it measures the average inﬂuence of observations by their bilat- eral trading partners’ observations (LeSage & Pace, 2009). Thus, one can assume that, as expected, cross-sectional dependency exists between the European trading partners. Our ﬁndings support the strand of literature that indicates the interconnectivity of trade in the EU (Bannò et al., 2015; Hafner, 2014; Harasztosi, 2016), as we ﬁnd our spatial rho to be positive and signiﬁcant to output. BALTIC JOURNAL OF ECONOMICS 75 Table 2. Spatial Durbin production frontier coeﬃcients and associated parameters. Variable Model Coeﬀ. Direct Parameter Indirect Parameter Total Parameter K 0.553*** 0.557*** 0.155 0.711*** (0.038) (0.039) (0.177) (0.178) l 0.425*** 0.428*** 0.529** 0.958*** (0.037) (0.037) (0.232) (0.226) k 0.132*** 0.137*** 0.226 0.363* (0.026) (0.025) (0.206) (0.203) l 0.020 0.027 0.583** 0.611** (0.021) (0.020) (0.253) (0.253) kl −0.149*** −0.166*** −1.238*** −1.404*** (0.043) (0.041) (0.449) (0.448) t 0.013*** 0.013*** 0.003* 0.016*** (0.004) (0.004) (0.002) (0.004) t 0.000*** 0.000*** 0.000 0.000*** (0.000) (0.000) (0.000) (0.000) kt −0.012*** −0.012*** 0.016 0.004 (0.002) (0.002) (0.011) (0.011) lt 0.009*** 0.009*** −0.003 0.005 (0.002) (0.001) (0.010) (0.010) exp 0.039*** 0.038*** −0.076** −0.038 (0.006) (0.006) (0.034) (0.033) gov −0.025*** −0.027*** −0.069*** −0.096** (0.006) (0.005) (0.025) (0.024) cee −0.161*** −0.153** 0.334 0.180 (0.061) (0.061) (0.439) (0.403) Wk 0.014 (0.142) Wl 0.347* (0.212) Wk 0.157 (0.165) 2 2 Wl 0.455** R within 0.919 (0.197) Wkl −0.962*** R between 0.989 (0.343) Wkt 0.015* R overall 0.986 (0.008) Wlt −0.004 AIC −1124.65 (0.008) Wexp −0.068** BIC −1003.46 (0.028) Wgov −0.051** Log-likelihood 1.269 (0.020) Constant −0.250* 0.201** (0.137) (0.102) Deﬁnition of variables are in Table 1. *, **, *** denote statistical signiﬁcance at the 10%, 5%, and 1% levels, respectively. Standard errors are in parenthesis. AIC and BIC denote the Akaike and Bayesian Information Criteria, respectively. While the interpretation of the direct and main coeﬃcients follows the traditional fron- tier literature, the theory of the spatial production frontier does not specify whether the indirect parameters should be positive or negative (Glass & Kenjegalieva, 2019). Our results indicate that the indirect eﬀect of k is nonsigniﬁcant to output. This implies that an average increase in the overall capital stock of bilateral trading partners in the EU will have no such eﬀect on GDP in a respective economy. This implies that real capital stock increases in pairwise trading partners do not really spill over in terms of output. A similar observation is made with the local spatial elasticity to capital as Wk is also nonsigniﬁcant to output. This contrasts with the idea that capital accumulation is connected to the process of structural change, particularly for EU regions with lower 76 H. HAINI AND P. WEI LOON levels of development (Filippetti & Peyrache, 2015). Our results imply that capital stock increases in trading partners in the EU are nonsigniﬁcant to output spillovers. Moreover, the indirect eﬀect of l is positive and signiﬁcant at the 5% level to output, while the local spatial elasticity to labour, Wl, is also positive and signiﬁcant at the 5% level to output. Our results imply that an increase in labour hours in a respective economy can potentially beneﬁt other trading partners. Intuitively, this is expected as higher levels of production through labour input increases can potentially be exported to trading partners in the form of goods. Moreover, our results loosely imply that dense labour forces can potentially lead to agglomeration and specialization, which can beneﬁt from trade linkages (Harasztosi, 2016). Previous studies demonstrate that that labour growth is constrained in many developing European countries (Claeys et al., 2022); as such, our results emphasize the importance of increasing labour growth to beneﬁt from output spillovers in a network of trading partners. We also ﬁnd that the direct eﬀect of k is positive and signiﬁcant at the 1% level, while the direct eﬀect of l is nonsigniﬁcant. This implies that there is an increasing returns to scale eﬀect with regards to capital, while labour growth may be constrained. In terms of our Hicks-neutral time trend, we ﬁnd that the direct and indirect eﬀect of our time-trend t is positive and signiﬁcant while the direct eﬀect of t is positive and signiﬁcant at the 1% level. This suggests that productivity growth may occur over time. Furthermore, the direct interaction between capital and labour kl is negative and signiﬁcant at the 1% level, which implies substitutability, and this eﬀect is greater in the indirect parameter. Moreover, the interaction between our inputs and time trend is interesting as kt exhibits a negative and signiﬁcant direct eﬀect, while lt exhibits a positive and signiﬁcant direct eﬀect. This implies that labour, over time, may be more important for output than capital. Addition- ally, the indirect eﬀects of kt and lt are nonsigniﬁcant. Finally, our control variables provide expected signs and signiﬁcance levels. We ﬁnd the direct eﬀect of net exports as a share of GDP (exp) to be positive and signiﬁcant at the 1% level, while the indirect eﬀect of net exports is negative and signiﬁcant at the 5% level. Intuitively, when neighbouring countries increase their exports, a respective country may potentially import more, which is a leakage on output. Moreover, we ﬁnd the direct and indirect eﬀect of government spending as a share of GDP (gov) to be nega- tive and signiﬁcant at the 1% level. Our results support the strand of literature that pos- tulates the crowding-out eﬀect from large governments, which are also susceptible to rent-seeking activities and the misallocation of resources (Haini & Wei Loon, 2022). Finally, our dummy variable cee is negative and signiﬁcant, which suggests that the CEE economies are associated with lower real GDP output, as expected from our summary statistics. Table 3 provides the decomposition of the total, direct, and indirect technical eﬃciency scores across EU Member States. As discussed in Section 3, the eﬃciency scores are unbounded as they consider a unit’s own net time-invariant, net time- varying and gross varying eﬃciency, calculated using a spatial multiplier matrix that passes through a unit’s ﬁrst order and bilateral trading partners units before rebounding back to the original unit. As a result, a unit can beneﬁt from increased performance as it can import eﬃciency from bilateral trading partners, which acts as an eﬃciency performance multiplier that leads to eﬃciency scores above 100% (Glass & Kenjegalieva, 2019). BALTIC JOURNAL OF ECONOMICS 77 Table 3. Averaged gross eﬃciency scores across countries. Total gross eﬃciency Indirect eﬃciency Direct eﬃciency Country scores scores scores Austria* 110.51% 18.38% 92.13% Belgium* 112.62% 18.46% 94.16% Bulgaria 97.13% 18.21% 78.91% Croatia 98.76% 18.44% 80.31% Cyprus* 104.55% 18.21% 86.34% Czech* 105.33% 18.49% 86.85% Denmark 110.90% 18.79% 92.12% Estonia* 113.58% 19.04% 94.54% Finland 113.60% 18.81% 94.79% France* 113.73% 18.05% 95.68% Germany* 110.11% 17.81% 92.30% Greece* 99.66% 18.40% 81.26% Hungary 108.94% 18.42% 90.51% Ireland* 114.55% 18.80% 95.76% Italy* 106.35% 18.38% 87.98% Latvia* 116.55% 18.86% 97.69% Lithuania* 112.03% 18.97% 93.06% Luxembourg* 115.48% 18.87% 96.61% Malta* 110.27% 18.54% 91.74% Netherlands* 113.70% 18.42% 95.28% Poland 116.40% 18.44% 97.96% Portugal* 89.68% 18.53% 71.15% Romania 98.09% 18.44% 79.65% Slovakia* 103.39% 18.46% 84.94% Slovenia* 108.14% 18.36% 89.78% Spain* 107.36% 18.18% 89.18% Sweden 114.21% 18.66% 95.55% Total Average 108.36% 18.50% 89.86% Total Average Central and Eastern Europe 107.96% 18.57% 89.39% Total Average Eurozone 108.82% 18.48% 90.34% Total Average Central and Eastern Europe – 109.84% 18.70% 91.14% Eurozone Total Average Central and Eastern Europe – 105.14% 18.38% 86.76% Non-Eurozone Gross time-varying scores are unbounded as it takes into account the own net time-invariant, own net time-varying and gross time-varying eﬃciency. This is calculated using the spatial multiplier matrix which passes through a provinces’ ﬁrst order and neighbouring countries and rebounds back to the unit. The scores above 1 suggests that the eﬃciency spillover is suﬃciently large and has pushed the eﬃciency of the respective unit beyond the best practice frontier. * denotes Eurozone countries and denotes Central and Eastern European countries. In addition, we observe that the total average eﬃciency score of the EU is approxi- mately 108.36%, while the total average for the CEE sample is slightly lower at 107.96% and that for the Eurozone countries is approximately 108.82%. The expectation is that the Eurozone countries will beneﬁt more from a uniﬁed currency, and our results suggest that most Eurozone countries outperform non-Eurozone members. However, it is important to note that some CEE countries belong to Eurozone members, such as Czech, Estonia, Latvia, Lithuania, Slovakia, and Slovenia, while Bulgaria, Hungary, Poland, and Romania are not part of the Eurozone. We ﬁnd that CEE members that are part of the Eurozone have a higher average gross eﬃciency score of 109.84% compared with 105.14%, with an indirect spillover of 18.70% compared with 18.38%. Since the eﬃciency multiplier is based on bilateral trade ﬂows, our results support the strand of literature that emphasizes the importance of the euro for trade (Antimiani & Costantini, 2013; Jagelka, 2013). Our ﬁndings also provide evidence of eﬃciency spillovers resulting from bilateral trade linkages, which support the role of trade in increasing the 78 H. HAINI AND P. WEI LOON technical eﬃciency of an economy as it beneﬁts from increased production and trade lin- kages (Debarsy & Ertur, 2019; Ho et al., 2013; Tientao et al., 2016). By contrast, Pina and Sicari (2021) emphasize that the CEE economies have been catch- ing up. Yet, our results indicate that the overall total average for these economies is below the total average in terms of total gross eﬃciencies. However, when we examine individ- ual countries, Latvia and Poland beneﬁt the most in terms of total gross eﬃciency scores, while Bulgaria, Romania, and Croatia do not beneﬁt from spillovers and have scores below 100%. As such, the CEE economies have heterogeneous eﬃciency scores across the sample. More crucially, we observe the indirect eﬃciency scores for the CEE economies to be larger on average (18.57%) than those of the Eurozone (18.48%) and our full sample (18.50%). Thus, the indirect eﬃciency spillovers imply that the CEE economies have a pseudo-convergence eﬀect. Finally, the spillover eﬀect is visualized in Figure 1, where many of the developed European economies can be observed to have higher levels of eﬃciency, while the CEE economies have relatively lower eﬃciency scores. Focusing on TFP growth, Table 4 presents the averaged TFP growth decompositions. The average TFP growth for the region is approximately 7.2%, which is rather high and diﬀers from the narrative that TFP growth is weak in EU Member States (Schiersch et al., 2015). Upon closer examination, when focusing only on direct technical change, we observe that average direct technical change is at 3.1%, which is relatively low, Figure 1. Averaged gross time-varying eﬃciency across countries. Source: Author’s compilation from Table 3. BALTIC JOURNAL OF ECONOMICS 79 Table 4. Averaged TFP growth decomposition across countries. Direct Indirect Total factor Total technical technical technical Returns to Country productivity change change change scale Austria* 1.084 1.049 1.035 1.036 0.999 Belgium* 1.117 1.051 1.066 1.028 1.038 Bulgaria 1.024 1.000 1.024 0.990 1.035 Croatia 1.051 1.033 1.018 1.012 1.006 Cyprus* 1.094 1.096 0.998 1.007 0.992 Czech* 1.066 1.021 1.045 1.006 1.039 Denmark 1.067 1.044 1.022 1.022 1.001 Estonia* 1.081 1.050 1.031 0.984 1.048 Finland 1.042 1.030 1.012 1.012 1.000 France* 1.078 1.010 1.068 1.031 1.037 Germany* 1.083 0.993 1.090 1.043 1.047 Greece* 1.051 1.035 1.016 1.018 0.998 Hungary 1.053 1.019 1.033 0.998 1.035 Ireland* 1.141 1.131 1.010 1.019 0.991 Italy* 1.040 1.012 1.028 1.032 0.995 Latvia* 1.092 1.058 1.035 0.989 1.045 Lithuania* 1.066 1.030 1.036 0.987 1.049 Luxembourg* 1.121 1.122 0.999 0.999 1.000 Malta* 1.111 1.133 0.978 0.990 0.988 Netherlands* 1.105 1.036 1.070 1.030 1.039 Poland 1.050 0.999 1.051 1.004 1.047 Portugal* 1.071 1.044 1.026 1.016 1.010 Romania 0.988 0.979 1.009 0.997 1.012 Slovakia* 1.062 1.027 1.035 0.995 1.039 Slovenia* 1.084 1.048 1.035 0.992 1.043 Spain* 1.071 1.027 1.045 1.027 1.018 Sweden 1.048 1.032 1.016 1.017 0.999 Total Average 1.072 1.041 1.031 1.010 1.020 Total Average Central and 1.057 1.023 1.033 0.994 1.039 Eastern Europe Total Average Eurozone 1.085 1.051 1.034 1.012 1.022 Total Average Central and 1.075 1.039 1.036 0.992 1.044 Eastern Europe – Eurozone Total Average Central and 1.029 0.999 1.029 0.997 1.032 Eastern Europe – Non- Eurozone Total factor productivity growth index is decomposed using a Malmquist index outlined in Section A1. The direct and indirect eﬀect is calculated using the spatial multiplier matrix which passes through a unit’s ﬁrst order and neighbour- ing units and rebounds back to the unit. * denotes Eurozone countries and denotes Central and Eastern European countries. while returns to scale is at 2.0%. Moreover, our results provide evidence of trade-induced technical spillovers with an average indirect technical change of 1.01%. Focusing on the Eurozone and CEE economies, we ﬁnd an average TFP growth of 8.5% and 5.7%, respect- ively. This implies that TFP growth is higher in Eurozone economies, supporting recent as well as earlier studies that have demonstrated the positive trade gains that arise from adoption of the euro (Antimiani & Costantini, 2013; Gunnella et al., 2021). Additionally, the lower average TFP growth from the CEE economies implies that output growth may be driven by capital deepening and labour inputs rather than TFP (Lopez-Garcia, 2021). In fact, we ﬁnd a negative average indirect technical change for the CEE economies, which implies that the CEE region does not beneﬁt from indirect tech- nical spillovers. Here, our intuition is that many CEE economies are developing and have lower levels of human capital formation and institutional capacity (Haini & Wei Loon, 2022); as such, they may ﬁnd it diﬃcult to absorb technical knowledge and know-how 80 H. HAINI AND P. WEI LOON from their trading partners. Many studies indicate that human capital formation is an important contributor to productivity and TFP growth, even in Europe as a whole (Álvarez-Ayuso et al., 2011). Thus, our ﬁndings loosely support previous empirical studies that demonstrate how countries can amplify, absorb, or block trade spillovers (Kireyev & Leonidov, 2018). When focusing on individual economies, we ﬁnd Ireland, Luxembourg, and Belgium to have the largest TFP growth, whereas Romania and Bulgaria have the lowest out of our full sample. Interestingly, many individual Eurozone countries perform higher than average, while the TFP growth for individual CEE economies is distributed across the full sample. In terms of direct technical change, we ﬁnd Germany, the Netherlands, and France to have the highest average, while Malta, Cyprus, and Luxembourg have the lowest. Similarly, in terms of indirect technical change, we ﬁnd Germany, Austria, and Italy to have the highest, while Estonia, Lithuania, and Latvia do not beneﬁt from technical spillovers. This is visualized in Figure 2, which reveals a higher concentration of TFP growth in the Western EU and Eurozone economies. Thus, our results support the strand of literature that postulates a positive eﬀect from the adoption of the euro (Anti- miani & Costantini, 2013; Gunnella et al., 2021; Jagelka, 2013), as we ﬁnd a higher average technological spillover induced from trade linkages. Additionally, when we compare the TFP growth rates of CEE economies that are part of the Eurozone with those of CEE economies that are not, we ﬁnd contrasting results. Figure 2. Averaged TFP growth decomposition across countries. Source: Author’s compilation from Table 4. BALTIC JOURNAL OF ECONOMICS 81 Table 5. Averaged TFP growth decompositions over time. Total factor Total technical Direct technical Indirect technical Returns to Year productivity change change change scale 1991 1.052 1.022 1.024 1.008 0.999 1992 1.047 1.021 1.023 1.008 0.995 1993 1.086 1.040 1.032 1.009 1.006 1994 1.095 1.039 1.031 1.009 1.017 1995 1.106 1.038 1.030 1.009 1.029 1996 1.096 1.038 1.029 1.009 1.021 1997 1.087 1.037 1.028 1.009 1.014 1998 1.091 1.036 1.027 1.009 1.019 1999 1.084 1.035 1.027 1.009 1.014 2000 1.071 1.034 1.026 1.009 1.002 2001 1.088 1.033 1.025 1.008 1.021 2002 1.069 1.033 1.024 1.008 1.004 2003 1.094 1.032 1.024 1.008 1.030 2004 1.115 1.031 1.023 1.008 1.053 2005 1.089 1.030 1.022 1.009 1.028 2006 1.125 1.030 1.020 1.009 1.065 2007 1.138 1.029 1.019 1.010 1.080 2008 1.139 1.029 1.018 1.011 1.081 2009 1.076 1.028 1.016 1.012 1.020 2010 1.087 1.027 1.015 1.012 1.033 2011 1.091 1.026 1.014 1.013 1.038 2012 1.077 1.026 1.012 1.013 1.026 2013 1.072 1.025 1.011 1.014 1.023 2014 1.076 1.024 1.010 1.014 1.028 2015 1.079 1.023 1.010 1.014 1.032 2016 1.110 1.023 1.009 1.014 1.065 2017 1.072 1.022 1.008 1.014 1.028 2018 1.072 1.021 1.007 1.014 1.030 2019 1.078 1.020 1.007 1.014 1.038 Total 1.072 1.041 1.031 1.010 1.020 Average Total factor productivity growth index is decomposed using a Malmquist index outlined in Section A1. The direct and indirect eﬀect is calculated using the spatial multiplier matrix which passes through a unit’s ﬁrst order and neighbour- ing units and rebounds back to the unit. * denotes Eurozone countries and denotes Central and Eastern European countries. Speciﬁcally, we ﬁnd that the TFP growth rates of CEE economies in the Eurozone to be 7.5% compared with 2.9% for those that are not part of the Eurozone. However, the indir- ect technical spillovers are less than 1 for both cases. The TFP growth diﬀerences between CEE economies that adopt the euro seem to be driven by returns to scale, as CEE econ- omies within the Eurozone observe a 4.4% increase compared with 3.2%. This highlights the gains from a shared currency. Finally, Table 5 presents the average TFP growth decomposition for the EU over time, while Figure 3 graphically presents the ﬁndings. We visually observe that returns to scale changes are a major driver of TFP growth in the region, while direct technical change exhi- bits a decline over time. This somewhat follows the narrative that the EU as a region is experiencing a decline in labour productivity (which in our case is measured by direct technical change; ECB, 2017), while we support studies that ﬁnd capital intensity and investment (returns to scale changes) to be one of the main contributors to productivity growth in the EU (Banerji et al., 2015; Lopez-Garcia, 2021). However, we also ﬁnd overall TFP growth to be increasing as such growth was 5.2% in 1991 and approximately 7.8% in 2019, while the average direct technical change was approximately 2.2% in 1991 and 2.0% in 2019. Returns to scale changes are one of the 82 H. HAINI AND P. WEI LOON Figure 3. Averaged TFP growth decomposition over time. Source: Author’s compilation from Table 5. main contributors to TFP growth; however, they exhibit ﬂuctuations over time. For example, returns to scale experienced a sharp drop in 2008 and 2009 as well as in 2016. While direct technical progress exhibits a gradual decline, returns to scale changes from input factors are more volatile. The signiﬁcant drop in 2008 and 2009 can be attributed to the global ﬁnancial crisis of 2008, while the drop in 2013 can be attributed to the Eurozone crisis of 2012, where many Eurozone members were unable to repay or reﬁnance their government debt. Our ﬁndings support the diminishing pro- ductivity growth that plagues the region as it experiences a decline in the rate of technical progress and an increase in input misallocation (ECB, 2017; Webber et al., 2019). It is suggested that the misallocation of capital and labour in the EU can restrict productivity growth (Claeys et al., 2022). On the other hand, we ﬁnd our indirect technical spillover to increase over time, albeit at a smaller average, from 0.8% in 1991–1.4% in 2019. While the spillover eﬀects that arise from indirect technical change is low, they provide evidence for the importance of trade linkages in promoting technical diﬀusion and knowledge spillovers (Wu et al., 2019). It is suggested that exporting ﬁrms beneﬁt from agglomeration and, since Europe as a region beneﬁts from the freer ﬂow of goods and services, exports can beneﬁt from sharing logis- tical links, innovation, and a matching labour force, which leads to productivity spillover eﬀects (Harasztosi, 2016). This may be an indicator of the success of cross-border cooperation and innovation policies that the EU has implemented, such as the Important Projects of Common European Interest (Pina & Sicari, 2021). In general, our results indicate that while there has been a decline in technical change, trade integration and linkages alongside capital intensity contribute to TFP growth in the region and result in productivity and eﬃciency spillovers. This echoes recent studies that have found that the decline in the EU is mainly due to the reduction in innovative activity (Oulton, 2018), which may indicate why our direct technical change is declining over time. BALTIC JOURNAL OF ECONOMICS 83 This is critical for the EU economies as innovation is a vital process, where less innovation means a permanently lower TFP growth, as our ﬁndings demonstrate. Moreover, the eﬀect of returns to scale changes varies as it is usually driven by capital intensity, which ﬂuctuates as capital accumulation returns to a steady-state trend (Claeys et al., 2022; Solow, 1956). Thus, we support studies that highlight the weak investment in the EU as a major contributor to lower productivity growth. 5. Conclusion A recent OECD report titled The Future of Productivity suggests that future productivity growth will depend on eﬀective technological diﬀusion through global connections, such as trade and factor mobility (McGowan et al., 2015). Furthermore, the EU has seen intra-EU trade increase substantially since its inception due to the introduction of the single market as well as other policies that promote regional integration, such as the Cohesion and Struc- tural Funds. As previous studies have tended to overlook the spillover impact of TFP growth and technical eﬃciency, the European context is an interesting case study to examine. Using a spatial Durbin stochastic frontier model and a bilateral trade matrix, we decom- pose the estimates and calculate TFP growth, direct and indirect technical change, direct and indirect eﬃciency change, and returns to scale change. Our results provide empirical evidence of trade-induced productivity and eﬃciency spillovers within the EU. We ﬁnd that economies can potentially beneﬁt from an additional indirect technical change of approximately 1.01% and an additional average eﬃciency spillover eﬀect of 18.50%, which allows countries to push their production frontier. Of equal importance, we ﬁnd that the Eurozone economies beneﬁt more from indirect technical change and indirect eﬃciency spillovers, while the CEE economies do not gain from indirect technical spillovers on average. Furthermore, we ﬁnd a higher average TFP growth rate for the Eurozone economies, which supports studies that emphasize the importance of the adoption of the euro (Gunnella et al., 2021). While our results provide evidence of technical diﬀusion, direct technical change is declining over time. While our estimation method is not a causal one, our study loosely follows the narrative of the persistently declining productivity trend in the region, which can be attributed to other structural factors (Claeys et al., 2022; Lopez-Garcia, 2021). 5.1. Policy implications Our ﬁndings have several policy implications. They imply the success of the EU enlarge- ment process and the economic and monetary union in the Eurozone economies. Our ﬁndings oﬀer evidence of technical spillovers, which suggests that the EU should continue to pursue cooperative and innovation policies to further gain from TFP growth. Recent initiatives such as Horizon 2020, an EU research and innovation programme aimed at pro- moting synergies and fostering cross-border networks, as well as the Important Projects of Common European Interest, should continue to be developed to promote further techni- cal spillovers. Similarly, post-COVID-19 initiatives such as the NextGenerationEU should be welcomed. Given the potential beneﬁts of NextGenerationEU in promoting productivity and eﬃciency in the region, it is crucial that the plan is implemented and supported by all 84 H. HAINI AND P. WEI LOON Member States. To ensure the success of the plan, policymakers must allocate funds eﬀec- tively and ensure that investments are made in a manner that maximizes their impact on economic growth and job creation. Policymakers should also prioritize support for SMEs as they are a major source of employment and economic growth in the region. Addition- ally non-Eurozone EU Members States should be encouraged to adopt the euro, as we ﬁnd evidence of higher TFP growth and technical spillovers. The adoption of the euro can facilitate technical diﬀusion by facilitating the establishment of production chains within the EU (Gunnella et al., 2021). However, since direct technical change is declining over time, the National Productivity Boards in the respective Member States should identify policies that can further drive technological progress as it can promote sustainable long-run growth. Moreover, the CEE economies should ensure that structural factors, such as institutional capacity and human capital formation, are adequate to beneﬁt from technical spillovers (Álvarez- Ayuso et al., 2011; Lopez-Garcia, 2021), as we ﬁnd that they do not gain much from trade-induced technical spillovers. 5.2. Directions for future research We recommend several directions for future research. This study focuses on productivity and eﬃciency spillovers from trade by examining the EU. Thus, since economies are increasingly integrated on a global basis, it might be interesting to examine the pro- ductivity and eﬃciency spillovers from a global perspective to estimate the eﬀects of intra- and extra-EU trade. This could provide greater context for the European pro- ductivity puzzle as it would allow one to estimate the indirect technical change that arises from trading partners within and outside of the EU. Second, it might be interesting to examine the productivity and eﬃciency spillovers through ﬁnancial ﬂows within the EU. As mentioned earlier, the interaction matrix models cross-sectional interdependence between observations, and previous studies have employed geographic distance, linguistic and cultural distance, and bilateral trade ﬂows. However, ﬁnancial ﬂows have been overlooked in the literature, and examining productivity and eﬃciency spillovers with a ﬁnancial ﬂow matrix could provide deeper insights into the EU productivity puzzle. As such, future studies could empirically examine whether ﬁnancial linkages can promote technical spillovers. Finally, future studies could also extend the interaction matrix to consider bilateral trade in services within the EU. Trade in services has grown faster than trade in goods over the last two decades (Gunnella et al., 2021) and measuring a bilateral trade ﬂow using services is relatively unexplored. This is due to missing data for the early 2000s as trade in goods was dominant. Nonetheless, if future studies attempt to overcome such missing data, a bilateral trade in services matrix may provide a deeper understanding of the European productivity puzzle. Notes 1. Carvalho (2018) employs the Bayesian estimator using the Markov chain Monte Carlo scheme proposed by Tsionas and Michaelides (2016). This is known as the spatial error stochastic fron- tier as it contains a spatial lag of the ineﬃciency term. While it estimates a nonspatial frontier BALTIC JOURNAL OF ECONOMICS 85 in the ﬁrst stage, Tsionas and Michaelides (2016) assume a half-normal ineﬃciency error term. These distributional assumptions make it diﬃcult to interpret the spatial variables when cal- culating for TFP growth as they do not have an economic interpretation. Moreover, Glass and Kenjegalieva (2019) estimate a spatial Durbin stochastic frontier model, which estimates a tra- ditional spatial Durbin frontier and then splits the error term into an idiosyncratic error and its respective eﬃciency components. 2. Similarly, previous spatial models calculated using the generalized method of moments approach (Fingleton, 2008) generally estimate a spatially moving average error term that does not have an economic interpretation in terms of eﬃciency or productivity. 3. The European Economic Community was largely replaced by the Euro and Eurozone area fol- lowing the revision of the Treaties in 1992 with the Maastricht Treaty. 4. For more information, see https://next-generation-eu.europa.eu/index_en 5. For example, two trading partners that export to one another will have exactly opposing numbers (e.g., what Austria exports to Belgium is equal to what Belgium imports from Austria). If one deﬂates this using GDP or CPI from the respective exporting and importing nations, the export and import values would be inconsistent. 6. We follow the traditional production function and calculation of TFP growth with the estimation of a spatially decomposed error term based on a bilateral trade matrix. While our error terms are spatial, we follow the traditional frontier production function, where TFP reﬂects shifts in the isoquants of a production function from capital and labour (Syverson, 2011). 7. We thank Reviewer 1 for the excellent suggestion. 8. The CEE countries are the Central and Eastern European economies, namely Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovenia, and Slovakia. 9. We do not observe constant returns to scale 1 − b in this case. 10. Note that capital stock includes structures, machinery, transport equipment, and other assets (Feenstra et al., 2015). 11. Scores below 100% imply that they do not beneﬁt from the eﬃciency spillover and cannot push beyond the optimal practice boundary. Acknowledgements The authors acknowledge the direction of the handling editor, Dr Swapnil Singh, and the sugges- tions provided by two anonymous referees. Funding This work is supported by the Universiti of Brunei Darussalam FIC Research Grant [UBD/RSCH/18/ FICBF(b)/2021/013]. Availability of data and material Data sharing is not applicable to this article as no new data were created or analyzed in this study. Code availability The econometric analysis was conducted using Stata14. Disclosure statement No potential conﬂict of interest was reported by the author(s). 86 H. HAINI AND P. WEI LOON Funding This work is supported by the Universiti of Brunei Darussalam FIC Research Grant [UBD/RSCH/18/ FICBF(b)/2021/013]. Notes on contributors Hazwan Haini is a lecturer in economics at the UBD School of Business and Economics in Universiti Brunei Darussalam. 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Spatial Economic Analysis, 15(1), 5–23. doi:10.1080/17421772.2019.1578402 Appendix A.1 Decomposition of the Indirect, Direct, and Total TFP Growth From the reduced form in Equation (3), it can be assumed that the total parameters from the time- −1 Tot varying technical eﬃciency is (I − dW) m = m , which estimates the vectors of total eﬃciencies. t t Therefore, the expected values of total relative eﬃciency can be denoted as −1 Tot Dir (I − dW) exp(m ) = j and this can be re-written as Equation (A1) below where j (i = j) N ij Ind denote direct eﬃciencies and j (i = j) denote indirect eﬃciencies (Glass et al., 2016). ij ⎛ ⎞ Dir Ind Ind ⎛ ⎞ j + j + ..+ j j 11 12 1N ⎜ ⎟ Ind Dir Ind ⎜ j ⎟ ⎜ j + j + ⎟ ..+ j 21 22 2N ⎜ ⎟ ⎜ ⎟ −1 (I − dW) = N ⎜ . ⎟ ⎜ ⎟ . + .+ ..+ . ⎝ ⎠ ⎜ ⎟ ⎝ ⎠ . + .+ ..+ . j Ind Ind Dir j + j + ..+ j N1 N2 NN (A1) ⎛ ⎞ Tot Tot ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎝ ⎠ Tot For the meantime, technical eﬃciency can be estimated using the modiﬁed Schmidt and Sickles (1984) approach, considering the relative best performing unit in each time period. Glass et al. (2016) further extends this and allows for the disaggregation of relative direct and relative indirect 90 H. HAINI AND P. WEI LOON eﬃciency as outlined in Equations (A2) and (A3). Dir N Ind j j ijt i=1 ijt Tot TE = + (A2) it max Tot Dir N Ind max Tot TE (j ) i it ijt TE j i it j=1 ijt Dir N Ind j j ijt j=1 ijt Tot TE = + (A3) jt max Tot Dir N Ind max Tot TE (j ) j jt ijt TE j j jt i=1 ijt Meanwhile, Equations (A2) and (A3) are unbounded and allows technical eﬃciency scores to be greater than 1, as it decomposes eﬃciency scores from the direct (own) relative eﬃciency and indir- ect (spillover) relative eﬃciency. The intuitive interpretation is that units with technical eﬃciency greater than 1 beneﬁts from importing eﬃciency from its trading partners. Consequently, it is suggested that the spillover acts as an eﬃciency multiplier and that the network of bilateral trading partners performance can enhance a unit’seﬃciency (Debarsy & Ertur, 2019; Glass & Kenje- galieva, 2019). 1 1 Tot Tot Dir Dir Ind Ind TFP = [lnTE − lnTE ] + [ht − ht ] + [ht − ht ] it+1 it+1 it it+1 it it+1 it 2 2 R Dir 1 x r,it+1 Dir Dir Dir Dir + ((hx SF ) + (hx SF ))ln r,it+1 it+1 r,it it Dir 2 x r,it r=1 Ind Ind Ind Ind Ind r,it+1 + ((hx SF ) + (hx SF ))ln (A4) r,it+1 it+1 r,it it Ind 2 x r=1 r,it Finally, the standard components of TFP change (denoted as TFP ) are the sum of change in tech- it+1 Tot Tot Dir Dir nical eﬃciency (denoted as lnTE − lnTE ), technical change (denoted as ht − ht and it+1 it it+1 it Ind Ind ht − ht ) and the returns to scale change (denoted as it+1 it Dir Ind R R x x r,it+1 r,it+1 Dir Dir Dir Dir Ind Ind Ind Ind ((hx SF ) + (hx SF ))ln and ((hx SF ) + (hx SF ))ln ) follow- r,it+1 it+1 r,it it r,it+1 it+1 r,it it Dir Ind x x r=1 r=1 r,it r,it ing Kumar and Russell (2002) shown in Equation (A4). In simpliﬁed terms, TFP = TE + t + SF. Simi- larly, Glass et al. (2013) extends this to include the spatial decomposition of TFP change to include direct and indirect technical change in addition to the non-spatial decomposition as shown by the superscript Ind or Dir which indicates direct and indirect coeﬃcients respectively. Dir Ind Additionally, SF and SF refers to the scale changes and extends the approach introduced by it it Dir Dir Ind Ind Caves et al. (1982), while ht − ht presents the direct technical change and ht − ht pre- it+1 it it+1 it sents the indirect technical change. Likewise, the intuition with productivity spillovers can be inter- preted like eﬃciency spillovers. In this case, the productivity spillovers results from importing and exporting productivity through bilateral trade linkages. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Baltic Journal of Economics Taylor & Francis http://www.deepdyve.com/lp/taylor-francis/do-regional-integration-and-trade-linkages-promote-productivity-sbt78lMxtR

Do regional integration and trade linkages promote productivity spillovers? Evidence from the European Union

Haini, Hazwan; Wei Loon, Pang

Baltic Journal of Economics , Volume 23 (1): 27 – Jan 2, 2023

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Do regional integration and trade linkages promote productivity spillovers? Evidence from the European Union

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This study examines the productivity and efficiency spillovers in the presence of trade linkages in 27 European Union countries from 1990 to 2019. The European Union is one of the largest trading blocs in the world and has implemented costly policies and reforms to improve productivity growth. Meanwhile, trade-induced productivity and efficiency spillovers have often been overlooked in the literature, and examining them could provide further clarity to the productivity puzzle. Using a...

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Publisher: Taylor & Francis
ISSN: 2334-4385
eISSN: 1406-099X
DOI: 10.1080/1406099X.2023.2198284
Publisher site: See Article on Publisher Site

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BALTIC JOURNAL OF ECONOMICS 2023, VOL. 23, NO. 1, 64–90 https://doi.org/10.1080/1406099X.2023.2198284 Do regional integration and trade linkages promote productivity spillovers? Evidence from the European Union Hazwan Haini and Pang Wei Loon School of Business and Economics, Universiti Brunei Darussalam, Bandar Seri Begawan, Brunei ABSTRACT ARTICLE HISTORY Received 6 July 2022 This study examines the productivity and eﬃciency spillovers in the Accepted 30 March 2023 presence of trade linkages in 27 European Union countries from 1990 to 2019. The European Union is one of the largest trading KEYWORDS blocs in the world and has implemented costly policies and European union (EU); reforms to improve productivity growth. Meanwhile, trade- Eurozone; spillovers; total induced productivity and eﬃciency spillovers have often been factor productivity (TFP); overlooked in the literature, and examining them could provide spatial Durbin model further clarity to the productivity puzzle. Using a spatial Durbin model and a bilateral trade matrix, this study estimates a spatial JEL CLASSIFICATION CODES stochastic production frontier model using data from the Penn C23; O49; C31; D24 World Table and the World Integrated Trade Solution. We decompose production frontier estimates to obtain the spillover eﬀects of total factor productivity growth and technical eﬃciency from a network of bilateral trading partners. Our results provide evidence of productivity and eﬃciency spillovers; however, the gains are uneven. Policy implications are discussed. 1. Introduction To promote sustainable development and regional integration, the European Union (EU) has implemented various policies, such as the National Productivity Boards and the Inno- vation Union policy, as well as various structural funding packages such as the Cohesion Funds (Pegkas et al., 2020; Webber et al., 2019). Yet, despite these costly policies and reforms, the EU has experienced a declining trend in productivity in recent years. A recent ECB Economic Bulletin report highlights that the EU experienced sluggish pro- ductivity growth even before the recent global ﬁnancial crisis of 2008 (ECB, 2017). Further- more, recent studies have found that weak investment and the slow recovery of employment levels – an indicator of resource misallocation – have contributed to low pro- ductivity (Claeys et al., 2022; Lopez-Garcia, 2021). This is concerning, as productivity is crucial for the region’ssustainable growth andpros- perity. Future productivity growth is suggested to depend on an economy’sability to diﬀuse technology, within and across countries, through interactions such as trade, foreign direct CONTACT Hazwan Haini hazwan.haini@ubd.edu.bn School of Business and Economics, Universiti Brunei Dar- ussalam, Bandar Seri Begawan, Brunei © 2023 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. BALTIC JOURNAL OF ECONOMICS 65 investment, and mobile factors of production (McGowan et al., 2015). Productivity measures, such as total factor productivity (TFP) growth, are dimensionless in nature and do not con- sider interactions between observations; however, economic interactions between nations play a crucial role in economic growth as they can capture knowledge spillovers and tech- nical diﬀusion as well as help to optimize resource allocation (Wu et al., 2019). As a result, considering economic interactions is critical in the European context, as cohesion policies and the elimination of intra-EU tariﬀs have increased trade and econ- omic interactions in the region (Pappalardo & Vicarelli, 2017). The EU is suggested to beneﬁt from many convergence mechanisms that arise from certain spillover eﬀects. Empirical studies on the region indicate that the diﬀusion of technology is vital for pro- ductivity growth (Banerji et al., 2015; Hafner, 2014; Harasztosi, 2016; Pegkas et al., 2020). Furthermore, other research has emphasized that the gains from spillovers may be uneven, especially for the Eurozone economies, which are suggested to beneﬁt from a currency union (Gunnella et al., 2021; Jagelka, 2013). This is also true for the Central and Eastern European (CEE) economies, which are relatively new members of the EU enlargement process and have beneﬁtted from technological content from their advanced counterparts (Antimiani & Costantini, 2013; Pina & Sicari, 2021). Therefore, the present study re-examines this issue by measuring TFP growth and tech- nical eﬃciency spillovers through trade linkages in 27 EU Member States from 1990 to 2019. This is of interest for various reasons: First, TFP and technical eﬃciency spillovers that arise from trade linkages have been overlooked in the literature. This is vital to con- sider as understanding the role of technical diﬀusion can provide insights into the declin- ing trend of productivity in the EU, particularly with the role of trade linkages. Second, it allows for a comparative examination of productivity and eﬃciency spillovers across the EU Member States. This allows one to assess whether the Eurozone countries beneﬁt more from spillovers or whether new Member States catch up in terms of productivity (Bardazzi & Ghezzi, 2018; Gunnella et al., 2021; Pina & Sicari, 2021). This study oﬀers several innovations over existing literature. First, studies that have exam- ined the spillover impact of productivity and eﬃciency have focused on geographical spatial spillover eﬀects (Glass et al., 2016;Haini, 2020). By contrast, this study captures the impact of spillovers from TFP and technical eﬃciency that occur through trade linkages. This is critical because, as distance becomes less of an obstacle, economies are being increasingly inte- grated globally and interconnected through trading activities. In fact, intra-trade in the EU has grown by 1200% in real terms (Badinger & Breuss, 2004). We provide new empirical evi- dence regarding the productivity puzzle in the EU, which has been demonstrated to be declining over time. Estimating a frontier that considers trade linkages allows one to examine whether technical spillovers occur, and whether productivity is higher in Eurozone economies. Likewise, we also provide new evidence regarding whether the CEE economies have beneﬁted from technical spillovers from their advanced counterparts. Finally, many studies have examined the impact of productivity by focusing on the spil- lover eﬀect of labour productivity or TFP growth, while others have examined the eﬃciency spillover eﬀects of frontier models (Carvalho, 2018; Glass & Kenjegalieva, 2019). The present study extends the literature by measuring the impact of technical eﬃciency spillovers that arise through a network of bilateral trading partners’ perform- ance through an eﬃciency multiplier. Similarly, this study’s approach diﬀers from the one suggested by Carvalho (2018), which employs Bayesian methods to examine 66 H. HAINI AND P. WEI LOON eﬃciency spillovers through the traditional production function model estimated using maximum likelihood. This allows us to obtain explicit estimates of eﬃciency spillovers, which could be of interest to policy makers. This study also employs a spatial Durbin stochastic production frontier model that uses a bilateral trade matrix, which is considered one of the most adequate connectivity matrices (Debarsy & Ertur, 2019). More speciﬁcally, we employ trade in goods exports from the Com- trade Database (World Integrated Trade Solution, 2020) to capture the extent of trade lin- kages between the EU Member States. Using this matrix, we estimate a spatial Durbin frontier using quasi-maximum likelihood methods, which consider endogenous spatial autoregressive and lag variables. We also employ data from the Penn World Table 10.0 (Feenstra et al., 2015) and use traditional frontier variables, such as output, capital, and labour. Furthermore, the spatial stochastic frontier model allows us to interpret the esti- mates like a standard production frontier model in addition to providing estimates of direct, indirect (spillover), and total parameters from the standard capital and labour inputs. Finally, the results can be decomposed to provide estimates of TFP growth, total technical change, technical eﬃciency change, and returns to scale change. In addition, we ﬁnd evidence of TFP growth and technical eﬃciency spillovers through trade linkages between the EU economies. In particular, our results suggest that indirect technical change and indirect technical eﬃciency provide an additional average spillover eﬀect of 1.01% and 18.50%, respectively. However, we also ﬁnd that the CEE economies beneﬁt less compared with our full sample of EU economies as they only beneﬁt from eﬃciency spillovers, while the Eurozone economies beneﬁt from both indirect eﬃciency and technical change spillovers. Finally, we demonstrate that diﬀusion eﬀects from indirect technical change are increasing over time, while direct technical change is declining. This follows the narrative of the EU’s persistently declining trend in pro- ductivity; however, it also provides evidence of successful policy implementation in terms of diﬀusing technical knowledge. The remainder of this paper is organized as follows: Section 2 presents our theoretical considerations and literature review; Section 3 describes the data sample, variables employed, and econometric strategy; and Section 4 presents the results and provides a brief discussion of them. Finally, Section 5 concludes the study with policy implications and directions for future research. 2. Theoretical considerations and literature review 2.1. Productivity growth and spillovers Improvements in productivity through innovation, economies of scale, and learning-by- doing can impact economic growth and violate the assumptions of the Solow (1956) model. As a result, two producers can have diﬀerent labour productivity even though they have the same inputs due to TFP growth (Syverson, 2011). In other words, productivity is eﬃciency in production and how much output is produced given a set of inputs. Since the growth of input factors cannot fully explain the growth of output, there must be other driving forces that explain growth (Nordhaus, 1969). Conse- quently, a decline in productivity is concerning for policy makers as productivity growth is suggested to be vital for sustainable prosperity in regions and countries (Krugman, 1994). BALTIC JOURNAL OF ECONOMICS 67 In addition, new growth theories have been introduced alongside endogenous models to explain the violations of traditional neoclassical growth models. These models include the new international economics theory, which provides a reformulation of the theories of trade and trade policy, and the new economic geography theory, which attempts to explain the spatial distribution of economic activity (Fujita et al., 1999). These theories extend the traditional neoclassical model as it generates long-run growth paths that resemble the Solow model with additional endogenous explanations for returns to scale, rate of technological change, savings, and population (Nijkamp & Poot, 1998). More speciﬁcally, the new growth theories highlight the growing importance of trade, capital ﬂows, diﬀusion of innovation, and migration at the interregional and international levels, suggesting that spatial interactions need to be explicitly considered. For example, factor mobility in capital and labour can have a disequilibrium eﬀect, while the diﬀusion of technology can aﬀect the growth paths of developing economies, where productivity growth can be driven by importing new technology (Nijkamp & Poot, 1998). Regarding trade, Grossman and Helpman (1991) provide an extensive discussion of the links between innovation, trade, and growth in an open economy, as knowledge spil- lovers between ﬁrms and sectors can amplify growth. Technological development is suggested to radiate from a regional source to its surrounding neighbours, which pro- motes technological progress (Wu et al., 2019). Thus, spatial interdependencies are vital to consider as factor mobility, innovation diﬀusion, and trade occur in open economies that are constantly interacting with one another. Several empirical papers have highlighted the spillover eﬀect of productivity and eﬃciency across regions and economic structures. Capello (2009) suggests that neigh- bouring regions can take advantage of capital and labour availability, while inter-industry linkages allow growth to spill over and be transmitted to neighbouring regions and econ- omies. Other research highlights the importance of trade linkages and spillover eﬀects for exporting ﬁrms, which can beneﬁt from agglomeration as it allows for the reduction of supply chain costs (e.g. by sharing a logistics centre), while an industrially dense economy allows for a specialized labour force (Harasztosi, 2016). Thus, trade connectivity and linkages are a critical source of knowledge and technical diﬀusion that can enhance productivity and often crosses regions and countries (Zhang et al., 2020). Similarly, several econometric papers have highlighted the importance of modelling spillovers in terms of geographic, economic, trade, and even linguistic distance (Debarsy & Ertur, 2019; Glass et al., 2013; Glass et al., 2016; Ho et al., 2013; Tientao et al., 2016; Wu et al., 2019). 2.2. Productivity in the EU Modelling trade linkages becomes more critical in the context of the EU as the region has grown considerably through its enlargement process. To provide some background, the EU was initially founded by Belgium, France, Italy, Luxembourg, the Netherlands, and Germany in 1958. It established the European Economic Community and the EU Customs Union, which enable Member States to operate within a single market (McCann & Ortega-Argilés, 2013). Today, the region consists of 27 Member States and has developed a single legal binding agreement known as the EU Acquis Communautaire, which consists of a uniﬁed economic and institutional setting throughout the region; moreover, it allows for the free movement of goods and services as well as of capital 68 H. HAINI AND P. WEI LOON and labour (Gehringer, 2013). As a result, the establishment of a European Common Market and the elimination of capital controls has allowed ﬁnancial development to ﬂourish through increased economic integration between Member States (Bordo & Rous- seau, 2012). However, since the turn of the century, growth in the annual gross domestic product (GDP) per capita has been stagnating, marred by the global ﬁnancial crisis of 2008 and the subsequent Eurozone crisis of 2012 (ECB, 2017). Similarly, productivity growth in the region has also followed a declining trend, as labour productivity has dramatically slowed from an average of 2.4% during 1970–1995 to an average of 1.4% during 1995– 2014 (Timmer et al., 2007). More recently, in 2018, the average labour productivity growth was approximately 1.1% (van Ark et al., 2018). The manufacturing sector is suggested to continue to contribute signiﬁcantly to the productivity growth of the EU, while the productivity of new Member States is converging with the founding members (Pina & Sicari, 2021). In addition, human capital development, infrastructure investment, research and development, and non-ICT capital deepening are found to be major determinants of TFP growth in the region (Strobel, 2012). For example, a recent study ﬁnds that research and development capital positively contributes to TFP growth in Eurozone countries (Pegkas et al., 2020), while another ﬁnds ﬁnancial markets to be a crucial determining factor for capital allocation and productivity in European economies (Claeys et al., 2022). Yet, it is suggested that the region still faces various issues, such as weak demand, a changing employment composition, a declining rate of technological progress and diﬀusion, as well as increasing resource misallocation (ECB, 2017). Recent studies have highlighted that declines in the EU are mainly due to a reduction in innovative activity (Oulton, 2018), and also that industry misallocation has doubled within the Eurozone economies (Dias et al., 2016). Therefore, the EU faces the challenge of addressing and developing future initiatives to reverse the decline in productivity growth and regional disparity across the region, as low productivity can restrict the region’s ability to develop further (Banerji et al., 2015). In response to these issues, the EU has implemented several initiatives. As the region has observed interregional disparities since the 1980s, it has introduced a variety of struc- tural and investment funds that support the EU Cohesion Policy. The aim is to reduce dis- parities in economic output and support cohesion objectives for new Member States to adjust to the single market environment (McCann & Ortega-Argilés, 2013). Furthermore, in March 2000, the European Council adopted the Lisbon Strategy to address the pro- ductivity slowdown that the EU has faced compared with the United States, which has higher productivity growth through the adoption of ICT technologies (Palazuelos & Fer- nández, 2010). The Lisbon Strategy aims to increase the EU’s competitiveness by develop- ing the region as a dynamic knowledge-based economy. Finally, more recently, the EU has adopted a more direct response to address the decline in productivity by establishing the National Productivity Boards in 2016 (Webber et al., 2019). In addition, the region has implemented the Innovation Union policy, which includes the development of a European Research Area, the aim of which is to develop and produce high-quality research and innovation to boost productivity (Pegkas et al., 2020). Other recent initiatives also focus on cooperation between EU Member States on innovative activity, such as Horizon 2020 and the Important Projects BALTIC JOURNAL OF ECONOMICS 69 of Common European Interest, which aims to promote further regional growth and con- vergence (Pina & Sicari, 2021). Finally, the recent COVID-19 pandemic led to the creation of NextGenerationEU, a 750- billion-euro recovery plan launched by the EU in response to the pandemic. The plan aims to provide ﬁnancial assistance to Member States to help them to recover from the economic and social impacts of the pandemic. The funds will be used to support the green and digital transition, invest in education and training, and support small and medium-sized enterprises (SMEs). This plan can promote productivity and eﬃciency in the region by boosting investment in innovation and research, supporting the digital and green transition, and providing support to SMEs. By investing in the digital infrastruc- ture and the skills required to support the digital transition, businesses can adopt digital technologies that streamline operations and improve communication, eventually leading to gains in productivity and eﬃciency. 2.3. Trade, Eurozone, and CEE economies While productivity has been declining, trade in the region has increased tremendously over the last few decades. In fact, from 1960 to 2000, a study ﬁnds that intra-EU trade grew by an impressive 1200% in real terms (Badinger & Breuss, 2004). Trade liberalization and income growth of the EU are suggested to have allowed trade to prosper, particularly through the elimination of the intra-EU tariﬀ. Hafner (2014) suggests that the enlargement process of the region has beneﬁted new Member States as they gain access to trade- related foreign technology from old Member States, particularly CEE countries. More crucially, while trade liberalization and income growth have beneﬁtted the growth of intra-EU trade, the impact of the euro on bilateral trade ﬂows is questionable. It must be noted that the EU is a political agreement, while the Eurozone is a monetary union aimed at increasing economic integration through a common currency. Scholars suggest that a common currency allows for greater unity, controls for volatility, allows for higher market transparency and competition, and provides higher macroeconomic stability (Frankel & Rose, 2002). In theory, the Eurozone economies should beneﬁt further from trade as they share a single market and a common currency, which should eliminate many informational and transactional costs. However, on the other hand, empirical studies that have examined the impact of the euro on trade at the macroeconomic and ﬁrm levels have suggested that the eﬀect is non- signiﬁcant (Bresser-Pereira & Rossi, 2015;Caﬁso, 2011; Figueiredo et al., 2016; Pappalardo & Vicarelli, 2017). Other researchers have found that the currency union unevenly beneﬁts new members of the Eurozone, particularly the relatively smaller and underdeveloped EU countries, and they have suggested that the euro has facilitated the establishment and expansion of production chains (Gunnella et al., 2021; Jagelka, 2013). Hence, the growth of intra-EU trade and the considerable variation across bilateral trade ﬂows – in both the EU and Eurozone economies – provide an interesting avenue for examining the extent of trade-induced productivity spillovers. Additionally, another strand of literature highlights the convergence eﬀect that arises from the EU, whereby newer Member States, particularly CEE economies, beneﬁt more than older and developed Member States. Previously, many CEE economies had fragile institutional settings with a high dependence on an unskilled labour force, and the 70 H. HAINI AND P. WEI LOON European enlargement process has led to increased export ﬂows and technological spil- lovers as well as further developed the institutional capacity of newer Member States (Antimiani & Costantini, 2013). Moreover, many CEE countries were part of the former Soviet bloc, in which they suﬀered from the poor utilization of their production capacity, and thus, convergence to their advanced counterparts was easier (Haini & Wei Loon, 2022; Lopez-Garcia, 2021). As such, it is unsurprising to observe strong growth and convergence from CEE countries, which have seen fewer losses in output compared with the EU average (Pina & Sicari, 2021). Thus, in addition to the Eurozone economies, the CEE economies provide an interesting case study for examining trade-induced productivity and eﬃciency spillovers as they are expected to absorb more technological progress from their developed counterparts. 3. Empirical methodology 3.1. Spatial Durbin production frontier model Consider Equation (1), which presents the standard stochastic frontier and includes the traditional outcome variable GDP, denoted as y , and the independent input variables it X (capital and labour). The composed error structure consists of two components, it namely the idiosyncratic error, n and time-varying technical eﬃciency, m . it it y = a + bX + n − m (1) it it it it However, Equation (1) is dimensionless in nature and does not consider the interactions between the observations, as the cross-sectional units are assumed to be independent. Yet, productivity and eﬃciency can spill over and be transmitted to other economies (Ertur & Koch, 2011). As a result, it is suggested that modelling a closed economy might not be valid and that spatial interdependencies must be considered to capture spil- lovers between economies (Ho et al., 2018). While several studies have examined growth spillovers, the literature on spatial stochastic frontier modelling is relatively scarce, only being developed in recent studies (Glass et al., 2016). Following Glass et al. (2016), the present study employs the spatial Durbin production frontier model estimated using maximum likelihood, as in Equation (2). The omission of the spatial autoregressive variable is suggested to lead to bias estimates, while the issue of spatial dependence is seldomly discussed in stochastic frontier modelling. The spatial dependence between the cross-sectional units is captured by the inclusion of a spatial weights matrix, denoted as w . The speciﬁcation of the matrix is explained in ij more detail below. Furthermore, the spatial frontier model is estimated using a three- step simulated maximum likelihood method (Filippini & Greene, 2016). The estimates of the spatial frontier are obtained in the ﬁrst stage, followed by the eﬃciency spillovers and returns to scale, and ﬁnally the TFP in the ﬁnal stage. i = 1, ... , N; t = 1, .. . T (2) In Equation (2), there are N countries indexed i = 1, ... , N over time T, indexed t = 1, ... , T. The dependent variable y represents the traditional stochastic frontier it variable, namely real GDP, while g represents a vector of exogenous independent it BALTIC JOURNAL OF ECONOMICS 71 input variables, namely real capital stock and labour. We include a vector of control vari- ables denoted by z and the time trend t and t to account for Hicks-neutral technological it change. To account for spatial dependence, w g represents a vector of the spatial ij jt j=1 lags of the exogenous input variables, while w z represents a vector of the spatial ij jt j=1 lags of the control variables. Finally, as the spatial Durbin model nests both the spatial autocorrelation and autoregression model, w y represents a vector of the spatial ij jt j=1 lag of the dependent variable from bilateral trading partners. However, following Glass and Kenjegalieva (2019), the local estimate for the time trend is omitted as it would be perfectly collinear with a row-normalised w . The intercept is denoted as a, while h , ij h , 4, c, z, u, and d are vectors of parameters that shift the stochastic frontier. Focusing on the spatial weights matrix, w , various methods exist for constructing it. ij However, many spatial weights are not derived from theory but are rather based on geo- graphical distance or features such as a distance or contiguity matrix (Debarsy & Ertur, 2019). More recent studies have employed other interaction matrices, which can be employed as a spatial weights matrix, such as bilateral trade ﬂows, genealogic distance, and linguistic distance (Ho et al., 2018). This is vital to consider as the misspeciﬁcation of the interaction matrix could lead to signiﬁcant biases. This study employs bilateral trade ﬂows as its spatial weights matrix, as it is suggested to be the most adequate inter- action matrix based on several robustness tests (Debarsy & Ertur, 2019). Meanwhile, the construction and intuition of the spatial weights matrix remain the same. In this case, w captures the spatial arrangement of the cross-sectional units and ij the strength of the spatial interaction based on pairwise trading partners. The diagonal elements of w are set to zero and row-normalised to have the unit sum ij w = 1, i = 1, ... , N. This preserves the spatial scaling of the data and transforms ij j=1 the spatial lag to be a weighted average of the bilateral trade observations. Thus, denot- ing I as the identity matrix, one can assume from Equation (2) that (I − dW) is non- N N singular, that the parameter space of z, u, and d is (r , 1) as it is row-normalised, and min that r represents the lower-limit characteristic root of w . min ij With the interaction matrix deﬁned, the production frontier model is estimated using the spatial Durbin speciﬁcation. However, LeSage and Pace (2009) highlight that the coeﬃcients of the independent variables in Equation (2) cannot be interpreted as elasti- cities, as the spatial spillover eﬀect arises through the marginal eﬀects of the independent variable interacting with the interaction matrix. Thus, to isolate the marginal eﬀects, Equation (2) can be reduced to Equation (3), which allows the estimated parameters to calculate the direct, indirect, and total marginal eﬀects: −1 −1 −1 y = (I − dW) X b + (I − dW) n − (I − dW) m (3) t N t N t N In Equation (3), the unit subscript i is dropped for the successive stacking of cross-sections. It is assumed that X is a matrix of stacked observations for g , z , g , z , and y . As out- t it it jt jt jt lined by LeSage and Pace (2009), Equation (3) can be diﬀerentiated to isolate the eﬀects 72 H. HAINI AND P. WEI LOON for the direct, indirect, and total marginal eﬀects. Consequently, the isolated eﬀects allow for estimates of spillover eﬀects arising from trading partners. The direct parameters con- sider the individual unit’s independent impact on the dependent variable, while the indir- ect parameters consider the spillover impact of trading partners. As such, the total parameters are the sum of direct and indirect spillovers. Glass et al. (2016) extend the approach to isolate marginal eﬀects by estimating the relative direct, relative indirect, and relative total eﬃciencies. Traditional technical eﬃciency scores in stochastic frontier modelling are generally bounded from the interval [0,1]. The inclusion of indirect eﬃciency allows scores to be greater than 1, as countries can beneﬁt from importing eﬃciency spillover from their trading partners. Glass and Ken- jegalieva (2019) call this the eﬃciency multiplier eﬀect. Finally, with technical eﬃciency deﬁned, we calculate TFP growth using standard components, such as the sum of change in technical eﬃciency, technical change, and returns to scale change following Kumar and Russell (2002). Additionally, we follow Glass et al. (2013) and extend this to include the spatial decomposition of TFP change, including direct and indirect technical change, total scale changes, and total technical eﬃciency change. We outline the decomposition in Appendix A1. 3.2. Data and variables This study employs an annual-level panel dataset of 27 EU Member States, namely Austria, Belgium, Bulgaria, Croatia, Cyprus, Czech, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, the Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, and Sweden. The data for the vari- ables are compiled from the Penn World Table 10.0 (Feenstra et al., 2015) from 1990 to 2019. The sample time-period is chosen based on data availability. All variables undergo the standard manipulation of data, such as log-transformation and mean-diﬀer- encing; thus, the ﬁrst-order input and time parameters can be interpreted as elasticities. Additionally, the bilateral trade matrix data w are compiled from the Comtrade Data- ij base (World Integrated Trade Solution, 2020). We employ the average imports and exports of country i from country j over the period 1990–2019 following Debarsy and Ertur (2019). Our bilateral trade ﬂows data are measured in nominal terms, as exports are deﬂated by two multilateral opposing terms and treating them into real data would not adequately capture the multilateral terms and would be inconsistent in terms of matching exports and imports (Brun, 2005). Equally critical is that our bilateral trade data from the Com- trade Database measure and capture trade in goods at the HS-6 level (5000 products). Although trade in services is becoming increasingly important in the EU, which has grown faster than trade in goods (Gunnella et al., 2021), trade in goods is still a large com- ponent of the EU and accounts for nearly €2,000 billion, while trade in services accounts for €910 billion in 2020 (World Integrated Trade Solution, 2020). As the spatial Durbin frontier model is an extension of the traditional stochastic fron- tier, the traditional variables used in frontier modelling are employed to calculate TFP growth. The dependent variable y represents real GDP, which is measured as output- it side real GDP at chained purchasing power parity in 2017 US$ million. Feenstra et al. (2015) recommend using output-side GDP to analyze productivity, as opposed to expen- diture-side GDP. Thus, we employ the rgdpo measure from the Penn World Table 10.0. BALTIC JOURNAL OF ECONOMICS 73 While labour productivity is usually captured using GDP per labour, we employ GDP at level terms with our capital and labour inputs to estimate a spatial stochastic production frontier. Moreover, g represents a vector of inputs that includes labour input, denoted as l, and it real capital stock at constant 2017 national prices (in 2017 US$ million), denoted as k. While Glass et al. (2016) use labour employed (emp in Penn World Table 10.0), we extend this and employ the average annual hours worked by persons engaged (avh in Penn World Table 10.0). This allows us to speciﬁcally focus on the substitutability of hours worked and capital employed, which extends the traditional frontier methodology. While traditional stochastic frontier models generally use the total number of labour employed (e.g. Glass et al. (2016), who use EU economies), we argue that there are increasing numbers of zero-hour and short-term contracts in the EU and, as a result, it may be misleading to use the total number of labour employed. Furthermore, we employ the rnna variable from the Penn World Table 10.0 as our capital input. Speciﬁcally, rnna consists of four assets, namely structures (including residential and nonresidential), machinery (including computers, communication equipment, and other machinery), transport equipment, and other assets (including software, other intellectual property, and cultivated assets; Feenstra et al., 2015). In addition, the interaction term between capital and labour and its squared terms are included following the literature. Furthermore, the time trend and its squared terms are included to capture Hicks-neutral technological change. Finally, z represents a vector it of control variables, including the ratio of government spending to GDP, denoted by gov (csh_g in Penn World Table 10.0 – the share of government consumption at current purchasing power parity); the ratio of net exports to GDP, denoted as exp (csh_x + csh_m in Penn World Table 10.0 – the sum of the share of merchandise exports and imports at current purchasing power parity); and a dummy variable for the CEE econom- ies, denoted as cee. Net exports are employed rather than trade openness as it signiﬁes imports as leakage (Glass et al., 2016). In addition, we include the share of government spending and net exports as part of our frontier model following the Barro-style regression (1991) and traditional growth account- ing aggregate demand models. The empirical literature on government size and GDP is inconclusive as some studies have found government size to be positive for GDP as it allows for the provision of infrastructure and human capital formation, while others have found government size to be detrimental as it distorts incentives and crowds out the private sector (Haini & Wei Loon, 2022). Moreover, the relationship between net exports and GDP is straightforward as increases in exports are generally favourable (Balassa, 1978). Table 1 presents the summary statistics of the variables employed at levels. Our full sample exhibits considerable heterogeneity across our input and output vari- ables. The average real GDP for the sample is approximately $518,830.70, while the inputs of real capital stock and the average annual hours worked have an average value of $2,535,834.00 and 1,758.69 h. We observe considerable variation in real GDP and real capital, while labour input displays less variation. When grouping our countries into Euro- zone and CEE economies, we observe that both real GDP and capital for the CEE econom- ies have lower averages than those of the full sample. However, the average labour input for CEE economies is larger. As expected, the Eurozone economies have a higher average output and input compared with the full sample. 74 H. HAINI AND P. WEI LOON Table 1. Summary statistics. Variable Description Mean SD Min. Max y Real GDP (2005 million US dollars at 2005 PPPs) 518,830.70 813,476.00 5,450.66 4,392,073.00 k Real capital stock (2005 million US dollars at 2,535,834.00 4,174,380.00 15,276.01 20,900,000.00 current PPPs) l Average annual hours worked by persons 1,758.69 193.20 1,380.60 2,277.38 engaged exp Exports of merchandise minus imports of −0.08 0.14 −0.61 0.39 merchandise as a share of GDP i.e. net trade openness gov Government spending as a share of GDP 0.21 0.06 0.08 0.46 y Central and Eastern European Economies (n = 188,365.80 219,988.50 14,015.03 1,211,305.00 k 300) 707,355.70 700,699.90 66,321.37 3,148,161.00 l 1,836.32 121.38 1,592.93 2,115.16 exp −0.07 0.06 −0.33 0.11 gov 0.27 0.05 0.16 0.46 y Eurozone Countries (n = 570) 617,395.50 942,330.90 5,450.66 4,392,073.00 k 3,135,475.00 4,829,964.00 15,276.01 20,900,000.00 l 1,804.90 172.67 1,495.46 2,277.38 exp −0.09 0.16 −0.61 0.39 gov 0.20 0.06 0.08 0.46 N = 810 observations from 27 EU countries from 1990 to 2019. Real GDP, real capital stock and labour employed are reported in levels and are log-transformed and standardized prior to the estimations. Meanwhile, we standardize the time-trend, net exports, and government spending. Standardization (mean 0, standard deviation 1) allows us to interpret the indirect and direct coeﬃcients as elasticities. Finally, the dummy variable cee refers to the Central and European Union Economies and includes Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovenia, and Slovakia. The Eurozone countries are Austria, Belgium, Cyprus, Estonia, Finland, France, Germany, Greece, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Portugal, Slovakia, Slovenia, and Spain. 4. Results and discussion Table 2 reports the coeﬃcients and parameters of the spatial Durbin production frontier, which includes the spillover eﬀects of the independent variable reported by the direct, indirect, and total parameters. The direct parameters of the spatial production frontier can be interpreted in the same manner as a nonspatial frontier (Glass & Kenjegalieva, 2019). Consequently, following the standard frontier literature, the monotonicity properties and condition must hold for the direct parameters of the production function. Monotonicity is a condition where an increase in inputs can never decrease the level of output, and it is important for theoretical consistency; furthermore, if this condition is violated, then the esti- mated results cannot be interpreted reasonably (Glass et al., 2016). We observe that the esti- mated direct parameters for k and l are both positive and signiﬁcant at the 1% level to output, which satisﬁes the monotonicity theory of frontier modelling. The model suggests that capital accounts for 55% of output while labour accounts for 42% of total output. In terms of our spatial dependency, we ﬁnd that the spatial dependence of the model is positive and signiﬁcant at the 1% level, as captured by the spatial lag coeﬃcient Wr.A higher and signiﬁcantly positive value for spatial dependence improves the ﬁt and log- likelihood of the model as it measures the average inﬂuence of observations by their bilat- eral trading partners’ observations (LeSage & Pace, 2009). Thus, one can assume that, as expected, cross-sectional dependency exists between the European trading partners. Our ﬁndings support the strand of literature that indicates the interconnectivity of trade in the EU (Bannò et al., 2015; Hafner, 2014; Harasztosi, 2016), as we ﬁnd our spatial rho to be positive and signiﬁcant to output. BALTIC JOURNAL OF ECONOMICS 75 Table 2. Spatial Durbin production frontier coeﬃcients and associated parameters. Variable Model Coeﬀ. Direct Parameter Indirect Parameter Total Parameter K 0.553*** 0.557*** 0.155 0.711*** (0.038) (0.039) (0.177) (0.178) l 0.425*** 0.428*** 0.529** 0.958*** (0.037) (0.037) (0.232) (0.226) k 0.132*** 0.137*** 0.226 0.363* (0.026) (0.025) (0.206) (0.203) l 0.020 0.027 0.583** 0.611** (0.021) (0.020) (0.253) (0.253) kl −0.149*** −0.166*** −1.238*** −1.404*** (0.043) (0.041) (0.449) (0.448) t 0.013*** 0.013*** 0.003* 0.016*** (0.004) (0.004) (0.002) (0.004) t 0.000*** 0.000*** 0.000 0.000*** (0.000) (0.000) (0.000) (0.000) kt −0.012*** −0.012*** 0.016 0.004 (0.002) (0.002) (0.011) (0.011) lt 0.009*** 0.009*** −0.003 0.005 (0.002) (0.001) (0.010) (0.010) exp 0.039*** 0.038*** −0.076** −0.038 (0.006) (0.006) (0.034) (0.033) gov −0.025*** −0.027*** −0.069*** −0.096** (0.006) (0.005) (0.025) (0.024) cee −0.161*** −0.153** 0.334 0.180 (0.061) (0.061) (0.439) (0.403) Wk 0.014 (0.142) Wl 0.347* (0.212) Wk 0.157 (0.165) 2 2 Wl 0.455** R within 0.919 (0.197) Wkl −0.962*** R between 0.989 (0.343) Wkt 0.015* R overall 0.986 (0.008) Wlt −0.004 AIC −1124.65 (0.008) Wexp −0.068** BIC −1003.46 (0.028) Wgov −0.051** Log-likelihood 1.269 (0.020) Constant −0.250* 0.201** (0.137) (0.102) Deﬁnition of variables are in Table 1. *, **, *** denote statistical signiﬁcance at the 10%, 5%, and 1% levels, respectively. Standard errors are in parenthesis. AIC and BIC denote the Akaike and Bayesian Information Criteria, respectively. While the interpretation of the direct and main coeﬃcients follows the traditional fron- tier literature, the theory of the spatial production frontier does not specify whether the indirect parameters should be positive or negative (Glass & Kenjegalieva, 2019). Our results indicate that the indirect eﬀect of k is nonsigniﬁcant to output. This implies that an average increase in the overall capital stock of bilateral trading partners in the EU will have no such eﬀect on GDP in a respective economy. This implies that real capital stock increases in pairwise trading partners do not really spill over in terms of output. A similar observation is made with the local spatial elasticity to capital as Wk is also nonsigniﬁcant to output. This contrasts with the idea that capital accumulation is connected to the process of structural change, particularly for EU regions with lower 76 H. HAINI AND P. WEI LOON levels of development (Filippetti & Peyrache, 2015). Our results imply that capital stock increases in trading partners in the EU are nonsigniﬁcant to output spillovers. Moreover, the indirect eﬀect of l is positive and signiﬁcant at the 5% level to output, while the local spatial elasticity to labour, Wl, is also positive and signiﬁcant at the 5% level to output. Our results imply that an increase in labour hours in a respective economy can potentially beneﬁt other trading partners. Intuitively, this is expected as higher levels of production through labour input increases can potentially be exported to trading partners in the form of goods. Moreover, our results loosely imply that dense labour forces can potentially lead to agglomeration and specialization, which can beneﬁt from trade linkages (Harasztosi, 2016). Previous studies demonstrate that that labour growth is constrained in many developing European countries (Claeys et al., 2022); as such, our results emphasize the importance of increasing labour growth to beneﬁt from output spillovers in a network of trading partners. We also ﬁnd that the direct eﬀect of k is positive and signiﬁcant at the 1% level, while the direct eﬀect of l is nonsigniﬁcant. This implies that there is an increasing returns to scale eﬀect with regards to capital, while labour growth may be constrained. In terms of our Hicks-neutral time trend, we ﬁnd that the direct and indirect eﬀect of our time-trend t is positive and signiﬁcant while the direct eﬀect of t is positive and signiﬁcant at the 1% level. This suggests that productivity growth may occur over time. Furthermore, the direct interaction between capital and labour kl is negative and signiﬁcant at the 1% level, which implies substitutability, and this eﬀect is greater in the indirect parameter. Moreover, the interaction between our inputs and time trend is interesting as kt exhibits a negative and signiﬁcant direct eﬀect, while lt exhibits a positive and signiﬁcant direct eﬀect. This implies that labour, over time, may be more important for output than capital. Addition- ally, the indirect eﬀects of kt and lt are nonsigniﬁcant. Finally, our control variables provide expected signs and signiﬁcance levels. We ﬁnd the direct eﬀect of net exports as a share of GDP (exp) to be positive and signiﬁcant at the 1% level, while the indirect eﬀect of net exports is negative and signiﬁcant at the 5% level. Intuitively, when neighbouring countries increase their exports, a respective country may potentially import more, which is a leakage on output. Moreover, we ﬁnd the direct and indirect eﬀect of government spending as a share of GDP (gov) to be nega- tive and signiﬁcant at the 1% level. Our results support the strand of literature that pos- tulates the crowding-out eﬀect from large governments, which are also susceptible to rent-seeking activities and the misallocation of resources (Haini & Wei Loon, 2022). Finally, our dummy variable cee is negative and signiﬁcant, which suggests that the CEE economies are associated with lower real GDP output, as expected from our summary statistics. Table 3 provides the decomposition of the total, direct, and indirect technical eﬃciency scores across EU Member States. As discussed in Section 3, the eﬃciency scores are unbounded as they consider a unit’s own net time-invariant, net time- varying and gross varying eﬃciency, calculated using a spatial multiplier matrix that passes through a unit’s ﬁrst order and bilateral trading partners units before rebounding back to the original unit. As a result, a unit can beneﬁt from increased performance as it can import eﬃciency from bilateral trading partners, which acts as an eﬃciency performance multiplier that leads to eﬃciency scores above 100% (Glass & Kenjegalieva, 2019). BALTIC JOURNAL OF ECONOMICS 77 Table 3. Averaged gross eﬃciency scores across countries. Total gross eﬃciency Indirect eﬃciency Direct eﬃciency Country scores scores scores Austria* 110.51% 18.38% 92.13% Belgium* 112.62% 18.46% 94.16% Bulgaria 97.13% 18.21% 78.91% Croatia 98.76% 18.44% 80.31% Cyprus* 104.55% 18.21% 86.34% Czech* 105.33% 18.49% 86.85% Denmark 110.90% 18.79% 92.12% Estonia* 113.58% 19.04% 94.54% Finland 113.60% 18.81% 94.79% France* 113.73% 18.05% 95.68% Germany* 110.11% 17.81% 92.30% Greece* 99.66% 18.40% 81.26% Hungary 108.94% 18.42% 90.51% Ireland* 114.55% 18.80% 95.76% Italy* 106.35% 18.38% 87.98% Latvia* 116.55% 18.86% 97.69% Lithuania* 112.03% 18.97% 93.06% Luxembourg* 115.48% 18.87% 96.61% Malta* 110.27% 18.54% 91.74% Netherlands* 113.70% 18.42% 95.28% Poland 116.40% 18.44% 97.96% Portugal* 89.68% 18.53% 71.15% Romania 98.09% 18.44% 79.65% Slovakia* 103.39% 18.46% 84.94% Slovenia* 108.14% 18.36% 89.78% Spain* 107.36% 18.18% 89.18% Sweden 114.21% 18.66% 95.55% Total Average 108.36% 18.50% 89.86% Total Average Central and Eastern Europe 107.96% 18.57% 89.39% Total Average Eurozone 108.82% 18.48% 90.34% Total Average Central and Eastern Europe – 109.84% 18.70% 91.14% Eurozone Total Average Central and Eastern Europe – 105.14% 18.38% 86.76% Non-Eurozone Gross time-varying scores are unbounded as it takes into account the own net time-invariant, own net time-varying and gross time-varying eﬃciency. This is calculated using the spatial multiplier matrix which passes through a provinces’ ﬁrst order and neighbouring countries and rebounds back to the unit. The scores above 1 suggests that the eﬃciency spillover is suﬃciently large and has pushed the eﬃciency of the respective unit beyond the best practice frontier. * denotes Eurozone countries and denotes Central and Eastern European countries. In addition, we observe that the total average eﬃciency score of the EU is approxi- mately 108.36%, while the total average for the CEE sample is slightly lower at 107.96% and that for the Eurozone countries is approximately 108.82%. The expectation is that the Eurozone countries will beneﬁt more from a uniﬁed currency, and our results suggest that most Eurozone countries outperform non-Eurozone members. However, it is important to note that some CEE countries belong to Eurozone members, such as Czech, Estonia, Latvia, Lithuania, Slovakia, and Slovenia, while Bulgaria, Hungary, Poland, and Romania are not part of the Eurozone. We ﬁnd that CEE members that are part of the Eurozone have a higher average gross eﬃciency score of 109.84% compared with 105.14%, with an indirect spillover of 18.70% compared with 18.38%. Since the eﬃciency multiplier is based on bilateral trade ﬂows, our results support the strand of literature that emphasizes the importance of the euro for trade (Antimiani & Costantini, 2013; Jagelka, 2013). Our ﬁndings also provide evidence of eﬃciency spillovers resulting from bilateral trade linkages, which support the role of trade in increasing the 78 H. HAINI AND P. WEI LOON technical eﬃciency of an economy as it beneﬁts from increased production and trade lin- kages (Debarsy & Ertur, 2019; Ho et al., 2013; Tientao et al., 2016). By contrast, Pina and Sicari (2021) emphasize that the CEE economies have been catch- ing up. Yet, our results indicate that the overall total average for these economies is below the total average in terms of total gross eﬃciencies. However, when we examine individ- ual countries, Latvia and Poland beneﬁt the most in terms of total gross eﬃciency scores, while Bulgaria, Romania, and Croatia do not beneﬁt from spillovers and have scores below 100%. As such, the CEE economies have heterogeneous eﬃciency scores across the sample. More crucially, we observe the indirect eﬃciency scores for the CEE economies to be larger on average (18.57%) than those of the Eurozone (18.48%) and our full sample (18.50%). Thus, the indirect eﬃciency spillovers imply that the CEE economies have a pseudo-convergence eﬀect. Finally, the spillover eﬀect is visualized in Figure 1, where many of the developed European economies can be observed to have higher levels of eﬃciency, while the CEE economies have relatively lower eﬃciency scores. Focusing on TFP growth, Table 4 presents the averaged TFP growth decompositions. The average TFP growth for the region is approximately 7.2%, which is rather high and diﬀers from the narrative that TFP growth is weak in EU Member States (Schiersch et al., 2015). Upon closer examination, when focusing only on direct technical change, we observe that average direct technical change is at 3.1%, which is relatively low, Figure 1. Averaged gross time-varying eﬃciency across countries. Source: Author’s compilation from Table 3. BALTIC JOURNAL OF ECONOMICS 79 Table 4. Averaged TFP growth decomposition across countries. Direct Indirect Total factor Total technical technical technical Returns to Country productivity change change change scale Austria* 1.084 1.049 1.035 1.036 0.999 Belgium* 1.117 1.051 1.066 1.028 1.038 Bulgaria 1.024 1.000 1.024 0.990 1.035 Croatia 1.051 1.033 1.018 1.012 1.006 Cyprus* 1.094 1.096 0.998 1.007 0.992 Czech* 1.066 1.021 1.045 1.006 1.039 Denmark 1.067 1.044 1.022 1.022 1.001 Estonia* 1.081 1.050 1.031 0.984 1.048 Finland 1.042 1.030 1.012 1.012 1.000 France* 1.078 1.010 1.068 1.031 1.037 Germany* 1.083 0.993 1.090 1.043 1.047 Greece* 1.051 1.035 1.016 1.018 0.998 Hungary 1.053 1.019 1.033 0.998 1.035 Ireland* 1.141 1.131 1.010 1.019 0.991 Italy* 1.040 1.012 1.028 1.032 0.995 Latvia* 1.092 1.058 1.035 0.989 1.045 Lithuania* 1.066 1.030 1.036 0.987 1.049 Luxembourg* 1.121 1.122 0.999 0.999 1.000 Malta* 1.111 1.133 0.978 0.990 0.988 Netherlands* 1.105 1.036 1.070 1.030 1.039 Poland 1.050 0.999 1.051 1.004 1.047 Portugal* 1.071 1.044 1.026 1.016 1.010 Romania 0.988 0.979 1.009 0.997 1.012 Slovakia* 1.062 1.027 1.035 0.995 1.039 Slovenia* 1.084 1.048 1.035 0.992 1.043 Spain* 1.071 1.027 1.045 1.027 1.018 Sweden 1.048 1.032 1.016 1.017 0.999 Total Average 1.072 1.041 1.031 1.010 1.020 Total Average Central and 1.057 1.023 1.033 0.994 1.039 Eastern Europe Total Average Eurozone 1.085 1.051 1.034 1.012 1.022 Total Average Central and 1.075 1.039 1.036 0.992 1.044 Eastern Europe – Eurozone Total Average Central and 1.029 0.999 1.029 0.997 1.032 Eastern Europe – Non- Eurozone Total factor productivity growth index is decomposed using a Malmquist index outlined in Section A1. The direct and indirect eﬀect is calculated using the spatial multiplier matrix which passes through a unit’s ﬁrst order and neighbour- ing units and rebounds back to the unit. * denotes Eurozone countries and denotes Central and Eastern European countries. while returns to scale is at 2.0%. Moreover, our results provide evidence of trade-induced technical spillovers with an average indirect technical change of 1.01%. Focusing on the Eurozone and CEE economies, we ﬁnd an average TFP growth of 8.5% and 5.7%, respect- ively. This implies that TFP growth is higher in Eurozone economies, supporting recent as well as earlier studies that have demonstrated the positive trade gains that arise from adoption of the euro (Antimiani & Costantini, 2013; Gunnella et al., 2021). Additionally, the lower average TFP growth from the CEE economies implies that output growth may be driven by capital deepening and labour inputs rather than TFP (Lopez-Garcia, 2021). In fact, we ﬁnd a negative average indirect technical change for the CEE economies, which implies that the CEE region does not beneﬁt from indirect tech- nical spillovers. Here, our intuition is that many CEE economies are developing and have lower levels of human capital formation and institutional capacity (Haini & Wei Loon, 2022); as such, they may ﬁnd it diﬃcult to absorb technical knowledge and know-how 80 H. HAINI AND P. WEI LOON from their trading partners. Many studies indicate that human capital formation is an important contributor to productivity and TFP growth, even in Europe as a whole (Álvarez-Ayuso et al., 2011). Thus, our ﬁndings loosely support previous empirical studies that demonstrate how countries can amplify, absorb, or block trade spillovers (Kireyev & Leonidov, 2018). When focusing on individual economies, we ﬁnd Ireland, Luxembourg, and Belgium to have the largest TFP growth, whereas Romania and Bulgaria have the lowest out of our full sample. Interestingly, many individual Eurozone countries perform higher than average, while the TFP growth for individual CEE economies is distributed across the full sample. In terms of direct technical change, we ﬁnd Germany, the Netherlands, and France to have the highest average, while Malta, Cyprus, and Luxembourg have the lowest. Similarly, in terms of indirect technical change, we ﬁnd Germany, Austria, and Italy to have the highest, while Estonia, Lithuania, and Latvia do not beneﬁt from technical spillovers. This is visualized in Figure 2, which reveals a higher concentration of TFP growth in the Western EU and Eurozone economies. Thus, our results support the strand of literature that postulates a positive eﬀect from the adoption of the euro (Anti- miani & Costantini, 2013; Gunnella et al., 2021; Jagelka, 2013), as we ﬁnd a higher average technological spillover induced from trade linkages. Additionally, when we compare the TFP growth rates of CEE economies that are part of the Eurozone with those of CEE economies that are not, we ﬁnd contrasting results. Figure 2. Averaged TFP growth decomposition across countries. Source: Author’s compilation from Table 4. BALTIC JOURNAL OF ECONOMICS 81 Table 5. Averaged TFP growth decompositions over time. Total factor Total technical Direct technical Indirect technical Returns to Year productivity change change change scale 1991 1.052 1.022 1.024 1.008 0.999 1992 1.047 1.021 1.023 1.008 0.995 1993 1.086 1.040 1.032 1.009 1.006 1994 1.095 1.039 1.031 1.009 1.017 1995 1.106 1.038 1.030 1.009 1.029 1996 1.096 1.038 1.029 1.009 1.021 1997 1.087 1.037 1.028 1.009 1.014 1998 1.091 1.036 1.027 1.009 1.019 1999 1.084 1.035 1.027 1.009 1.014 2000 1.071 1.034 1.026 1.009 1.002 2001 1.088 1.033 1.025 1.008 1.021 2002 1.069 1.033 1.024 1.008 1.004 2003 1.094 1.032 1.024 1.008 1.030 2004 1.115 1.031 1.023 1.008 1.053 2005 1.089 1.030 1.022 1.009 1.028 2006 1.125 1.030 1.020 1.009 1.065 2007 1.138 1.029 1.019 1.010 1.080 2008 1.139 1.029 1.018 1.011 1.081 2009 1.076 1.028 1.016 1.012 1.020 2010 1.087 1.027 1.015 1.012 1.033 2011 1.091 1.026 1.014 1.013 1.038 2012 1.077 1.026 1.012 1.013 1.026 2013 1.072 1.025 1.011 1.014 1.023 2014 1.076 1.024 1.010 1.014 1.028 2015 1.079 1.023 1.010 1.014 1.032 2016 1.110 1.023 1.009 1.014 1.065 2017 1.072 1.022 1.008 1.014 1.028 2018 1.072 1.021 1.007 1.014 1.030 2019 1.078 1.020 1.007 1.014 1.038 Total 1.072 1.041 1.031 1.010 1.020 Average Total factor productivity growth index is decomposed using a Malmquist index outlined in Section A1. The direct and indirect eﬀect is calculated using the spatial multiplier matrix which passes through a unit’s ﬁrst order and neighbour- ing units and rebounds back to the unit. * denotes Eurozone countries and denotes Central and Eastern European countries. Speciﬁcally, we ﬁnd that the TFP growth rates of CEE economies in the Eurozone to be 7.5% compared with 2.9% for those that are not part of the Eurozone. However, the indir- ect technical spillovers are less than 1 for both cases. The TFP growth diﬀerences between CEE economies that adopt the euro seem to be driven by returns to scale, as CEE econ- omies within the Eurozone observe a 4.4% increase compared with 3.2%. This highlights the gains from a shared currency. Finally, Table 5 presents the average TFP growth decomposition for the EU over time, while Figure 3 graphically presents the ﬁndings. We visually observe that returns to scale changes are a major driver of TFP growth in the region, while direct technical change exhi- bits a decline over time. This somewhat follows the narrative that the EU as a region is experiencing a decline in labour productivity (which in our case is measured by direct technical change; ECB, 2017), while we support studies that ﬁnd capital intensity and investment (returns to scale changes) to be one of the main contributors to productivity growth in the EU (Banerji et al., 2015; Lopez-Garcia, 2021). However, we also ﬁnd overall TFP growth to be increasing as such growth was 5.2% in 1991 and approximately 7.8% in 2019, while the average direct technical change was approximately 2.2% in 1991 and 2.0% in 2019. Returns to scale changes are one of the 82 H. HAINI AND P. WEI LOON Figure 3. Averaged TFP growth decomposition over time. Source: Author’s compilation from Table 5. main contributors to TFP growth; however, they exhibit ﬂuctuations over time. For example, returns to scale experienced a sharp drop in 2008 and 2009 as well as in 2016. While direct technical progress exhibits a gradual decline, returns to scale changes from input factors are more volatile. The signiﬁcant drop in 2008 and 2009 can be attributed to the global ﬁnancial crisis of 2008, while the drop in 2013 can be attributed to the Eurozone crisis of 2012, where many Eurozone members were unable to repay or reﬁnance their government debt. Our ﬁndings support the diminishing pro- ductivity growth that plagues the region as it experiences a decline in the rate of technical progress and an increase in input misallocation (ECB, 2017; Webber et al., 2019). It is suggested that the misallocation of capital and labour in the EU can restrict productivity growth (Claeys et al., 2022). On the other hand, we ﬁnd our indirect technical spillover to increase over time, albeit at a smaller average, from 0.8% in 1991–1.4% in 2019. While the spillover eﬀects that arise from indirect technical change is low, they provide evidence for the importance of trade linkages in promoting technical diﬀusion and knowledge spillovers (Wu et al., 2019). It is suggested that exporting ﬁrms beneﬁt from agglomeration and, since Europe as a region beneﬁts from the freer ﬂow of goods and services, exports can beneﬁt from sharing logis- tical links, innovation, and a matching labour force, which leads to productivity spillover eﬀects (Harasztosi, 2016). This may be an indicator of the success of cross-border cooperation and innovation policies that the EU has implemented, such as the Important Projects of Common European Interest (Pina & Sicari, 2021). In general, our results indicate that while there has been a decline in technical change, trade integration and linkages alongside capital intensity contribute to TFP growth in the region and result in productivity and eﬃciency spillovers. This echoes recent studies that have found that the decline in the EU is mainly due to the reduction in innovative activity (Oulton, 2018), which may indicate why our direct technical change is declining over time. BALTIC JOURNAL OF ECONOMICS 83 This is critical for the EU economies as innovation is a vital process, where less innovation means a permanently lower TFP growth, as our ﬁndings demonstrate. Moreover, the eﬀect of returns to scale changes varies as it is usually driven by capital intensity, which ﬂuctuates as capital accumulation returns to a steady-state trend (Claeys et al., 2022; Solow, 1956). Thus, we support studies that highlight the weak investment in the EU as a major contributor to lower productivity growth. 5. Conclusion A recent OECD report titled The Future of Productivity suggests that future productivity growth will depend on eﬀective technological diﬀusion through global connections, such as trade and factor mobility (McGowan et al., 2015). Furthermore, the EU has seen intra-EU trade increase substantially since its inception due to the introduction of the single market as well as other policies that promote regional integration, such as the Cohesion and Struc- tural Funds. As previous studies have tended to overlook the spillover impact of TFP growth and technical eﬃciency, the European context is an interesting case study to examine. Using a spatial Durbin stochastic frontier model and a bilateral trade matrix, we decom- pose the estimates and calculate TFP growth, direct and indirect technical change, direct and indirect eﬃciency change, and returns to scale change. Our results provide empirical evidence of trade-induced productivity and eﬃciency spillovers within the EU. We ﬁnd that economies can potentially beneﬁt from an additional indirect technical change of approximately 1.01% and an additional average eﬃciency spillover eﬀect of 18.50%, which allows countries to push their production frontier. Of equal importance, we ﬁnd that the Eurozone economies beneﬁt more from indirect technical change and indirect eﬃciency spillovers, while the CEE economies do not gain from indirect technical spillovers on average. Furthermore, we ﬁnd a higher average TFP growth rate for the Eurozone economies, which supports studies that emphasize the importance of the adoption of the euro (Gunnella et al., 2021). While our results provide evidence of technical diﬀusion, direct technical change is declining over time. While our estimation method is not a causal one, our study loosely follows the narrative of the persistently declining productivity trend in the region, which can be attributed to other structural factors (Claeys et al., 2022; Lopez-Garcia, 2021). 5.1. Policy implications Our ﬁndings have several policy implications. They imply the success of the EU enlarge- ment process and the economic and monetary union in the Eurozone economies. Our ﬁndings oﬀer evidence of technical spillovers, which suggests that the EU should continue to pursue cooperative and innovation policies to further gain from TFP growth. Recent initiatives such as Horizon 2020, an EU research and innovation programme aimed at pro- moting synergies and fostering cross-border networks, as well as the Important Projects of Common European Interest, should continue to be developed to promote further techni- cal spillovers. Similarly, post-COVID-19 initiatives such as the NextGenerationEU should be welcomed. Given the potential beneﬁts of NextGenerationEU in promoting productivity and eﬃciency in the region, it is crucial that the plan is implemented and supported by all 84 H. HAINI AND P. WEI LOON Member States. To ensure the success of the plan, policymakers must allocate funds eﬀec- tively and ensure that investments are made in a manner that maximizes their impact on economic growth and job creation. Policymakers should also prioritize support for SMEs as they are a major source of employment and economic growth in the region. Addition- ally non-Eurozone EU Members States should be encouraged to adopt the euro, as we ﬁnd evidence of higher TFP growth and technical spillovers. The adoption of the euro can facilitate technical diﬀusion by facilitating the establishment of production chains within the EU (Gunnella et al., 2021). However, since direct technical change is declining over time, the National Productivity Boards in the respective Member States should identify policies that can further drive technological progress as it can promote sustainable long-run growth. Moreover, the CEE economies should ensure that structural factors, such as institutional capacity and human capital formation, are adequate to beneﬁt from technical spillovers (Álvarez- Ayuso et al., 2011; Lopez-Garcia, 2021), as we ﬁnd that they do not gain much from trade-induced technical spillovers. 5.2. Directions for future research We recommend several directions for future research. This study focuses on productivity and eﬃciency spillovers from trade by examining the EU. Thus, since economies are increasingly integrated on a global basis, it might be interesting to examine the pro- ductivity and eﬃciency spillovers from a global perspective to estimate the eﬀects of intra- and extra-EU trade. This could provide greater context for the European pro- ductivity puzzle as it would allow one to estimate the indirect technical change that arises from trading partners within and outside of the EU. Second, it might be interesting to examine the productivity and eﬃciency spillovers through ﬁnancial ﬂows within the EU. As mentioned earlier, the interaction matrix models cross-sectional interdependence between observations, and previous studies have employed geographic distance, linguistic and cultural distance, and bilateral trade ﬂows. However, ﬁnancial ﬂows have been overlooked in the literature, and examining productivity and eﬃciency spillovers with a ﬁnancial ﬂow matrix could provide deeper insights into the EU productivity puzzle. As such, future studies could empirically examine whether ﬁnancial linkages can promote technical spillovers. Finally, future studies could also extend the interaction matrix to consider bilateral trade in services within the EU. Trade in services has grown faster than trade in goods over the last two decades (Gunnella et al., 2021) and measuring a bilateral trade ﬂow using services is relatively unexplored. This is due to missing data for the early 2000s as trade in goods was dominant. Nonetheless, if future studies attempt to overcome such missing data, a bilateral trade in services matrix may provide a deeper understanding of the European productivity puzzle. Notes 1. Carvalho (2018) employs the Bayesian estimator using the Markov chain Monte Carlo scheme proposed by Tsionas and Michaelides (2016). This is known as the spatial error stochastic fron- tier as it contains a spatial lag of the ineﬃciency term. While it estimates a nonspatial frontier BALTIC JOURNAL OF ECONOMICS 85 in the ﬁrst stage, Tsionas and Michaelides (2016) assume a half-normal ineﬃciency error term. These distributional assumptions make it diﬃcult to interpret the spatial variables when cal- culating for TFP growth as they do not have an economic interpretation. Moreover, Glass and Kenjegalieva (2019) estimate a spatial Durbin stochastic frontier model, which estimates a tra- ditional spatial Durbin frontier and then splits the error term into an idiosyncratic error and its respective eﬃciency components. 2. Similarly, previous spatial models calculated using the generalized method of moments approach (Fingleton, 2008) generally estimate a spatially moving average error term that does not have an economic interpretation in terms of eﬃciency or productivity. 3. The European Economic Community was largely replaced by the Euro and Eurozone area fol- lowing the revision of the Treaties in 1992 with the Maastricht Treaty. 4. For more information, see https://next-generation-eu.europa.eu/index_en 5. For example, two trading partners that export to one another will have exactly opposing numbers (e.g., what Austria exports to Belgium is equal to what Belgium imports from Austria). If one deﬂates this using GDP or CPI from the respective exporting and importing nations, the export and import values would be inconsistent. 6. We follow the traditional production function and calculation of TFP growth with the estimation of a spatially decomposed error term based on a bilateral trade matrix. While our error terms are spatial, we follow the traditional frontier production function, where TFP reﬂects shifts in the isoquants of a production function from capital and labour (Syverson, 2011). 7. We thank Reviewer 1 for the excellent suggestion. 8. The CEE countries are the Central and Eastern European economies, namely Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovenia, and Slovakia. 9. We do not observe constant returns to scale 1 − b in this case. 10. Note that capital stock includes structures, machinery, transport equipment, and other assets (Feenstra et al., 2015). 11. Scores below 100% imply that they do not beneﬁt from the eﬃciency spillover and cannot push beyond the optimal practice boundary. Acknowledgements The authors acknowledge the direction of the handling editor, Dr Swapnil Singh, and the sugges- tions provided by two anonymous referees. Funding This work is supported by the Universiti of Brunei Darussalam FIC Research Grant [UBD/RSCH/18/ FICBF(b)/2021/013]. Availability of data and material Data sharing is not applicable to this article as no new data were created or analyzed in this study. Code availability The econometric analysis was conducted using Stata14. Disclosure statement No potential conﬂict of interest was reported by the author(s). 86 H. HAINI AND P. WEI LOON Funding This work is supported by the Universiti of Brunei Darussalam FIC Research Grant [UBD/RSCH/18/ FICBF(b)/2021/013]. Notes on contributors Hazwan Haini is a lecturer in economics at the UBD School of Business and Economics in Universiti Brunei Darussalam. 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Spatial Economic Analysis, 15(1), 5–23. doi:10.1080/17421772.2019.1578402 Appendix A.1 Decomposition of the Indirect, Direct, and Total TFP Growth From the reduced form in Equation (3), it can be assumed that the total parameters from the time- −1 Tot varying technical eﬃciency is (I − dW) m = m , which estimates the vectors of total eﬃciencies. t t Therefore, the expected values of total relative eﬃciency can be denoted as −1 Tot Dir (I − dW) exp(m ) = j and this can be re-written as Equation (A1) below where j (i = j) N ij Ind denote direct eﬃciencies and j (i = j) denote indirect eﬃciencies (Glass et al., 2016). ij ⎛ ⎞ Dir Ind Ind ⎛ ⎞ j + j + ..+ j j 11 12 1N ⎜ ⎟ Ind Dir Ind ⎜ j ⎟ ⎜ j + j + ⎟ ..+ j 21 22 2N ⎜ ⎟ ⎜ ⎟ −1 (I − dW) = N ⎜ . ⎟ ⎜ ⎟ . + .+ ..+ . ⎝ ⎠ ⎜ ⎟ ⎝ ⎠ . + .+ ..+ . j Ind Ind Dir j + j + ..+ j N1 N2 NN (A1) ⎛ ⎞ Tot Tot ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎝ ⎠ Tot For the meantime, technical eﬃciency can be estimated using the modiﬁed Schmidt and Sickles (1984) approach, considering the relative best performing unit in each time period. Glass et al. (2016) further extends this and allows for the disaggregation of relative direct and relative indirect 90 H. HAINI AND P. WEI LOON eﬃciency as outlined in Equations (A2) and (A3). Dir N Ind j j ijt i=1 ijt Tot TE = + (A2) it max Tot Dir N Ind max Tot TE (j ) i it ijt TE j i it j=1 ijt Dir N Ind j j ijt j=1 ijt Tot TE = + (A3) jt max Tot Dir N Ind max Tot TE (j ) j jt ijt TE j j jt i=1 ijt Meanwhile, Equations (A2) and (A3) are unbounded and allows technical eﬃciency scores to be greater than 1, as it decomposes eﬃciency scores from the direct (own) relative eﬃciency and indir- ect (spillover) relative eﬃciency. The intuitive interpretation is that units with technical eﬃciency greater than 1 beneﬁts from importing eﬃciency from its trading partners. Consequently, it is suggested that the spillover acts as an eﬃciency multiplier and that the network of bilateral trading partners performance can enhance a unit’seﬃciency (Debarsy & Ertur, 2019; Glass & Kenje- galieva, 2019). 1 1 Tot Tot Dir Dir Ind Ind TFP = [lnTE − lnTE ] + [ht − ht ] + [ht − ht ] it+1 it+1 it it+1 it it+1 it 2 2 R Dir 1 x r,it+1 Dir Dir Dir Dir + ((hx SF ) + (hx SF ))ln r,it+1 it+1 r,it it Dir 2 x r,it r=1 Ind Ind Ind Ind Ind r,it+1 + ((hx SF ) + (hx SF ))ln (A4) r,it+1 it+1 r,it it Ind 2 x r=1 r,it Finally, the standard components of TFP change (denoted as TFP ) are the sum of change in tech- it+1 Tot Tot Dir Dir nical eﬃciency (denoted as lnTE − lnTE ), technical change (denoted as ht − ht and it+1 it it+1 it Ind Ind ht − ht ) and the returns to scale change (denoted as it+1 it Dir Ind R R x x r,it+1 r,it+1 Dir Dir Dir Dir Ind Ind Ind Ind ((hx SF ) + (hx SF ))ln and ((hx SF ) + (hx SF ))ln ) follow- r,it+1 it+1 r,it it r,it+1 it+1 r,it it Dir Ind x x r=1 r=1 r,it r,it ing Kumar and Russell (2002) shown in Equation (A4). In simpliﬁed terms, TFP = TE + t + SF. Simi- larly, Glass et al. (2013) extends this to include the spatial decomposition of TFP change to include direct and indirect technical change in addition to the non-spatial decomposition as shown by the superscript Ind or Dir which indicates direct and indirect coeﬃcients respectively. Dir Ind Additionally, SF and SF refers to the scale changes and extends the approach introduced by it it Dir Dir Ind Ind Caves et al. (1982), while ht − ht presents the direct technical change and ht − ht pre- it+1 it it+1 it sents the indirect technical change. Likewise, the intuition with productivity spillovers can be inter- preted like eﬃciency spillovers. In this case, the productivity spillovers results from importing and exporting productivity through bilateral trade linkages.

Journal

Baltic Journal of Economics – Taylor & Francis

Published: Jan 2, 2023

Keywords: European union (EU); Eurozone; spillovers; total factor productivity (TFP); spatial Durbin model; C23; O49; C31; D24

References

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Trade performances and technology in the enlarged European Union
What has determined the rapid post-war growth of intra-EU trade?
Exports and economic growth
Structural reforms in the EU – policy prescriptions to boost productivity
The nature of export spillovers on trade: An analysis at NUTS 3 level in Italy
Trade, competitiveness and investment: An empirical assessment
Economic growth in a cross section of countries
Historical evidence on the finance-trade-growth nexus
Sovereignty, the exchange rate, collective deceit, and the euro crisis
Has distance died? Evidence from a panel gravity model
Rose effect versus border effect: The Euro’s impact on trade
Spatial spillovers and regional growth: A cognitive approach
Efficiency spillovers in Bayesian stochastic frontier models: Application to electricity distribution in New Zealand
The economic theory of index numbers and the measurement of input, output, and productivity
Interaction matrix selection in spatial autoregressive models with an application to growth theory
Misallocation and productivity in the lead up to the Eurozone crisis
The slowdown in Euro area productivity in a global context
A contribution to the theory and empirics of Schumpeterian growth with worldwide interactions
The next generation of the penn world table
The effect of the Euro on the bilateral trade distribution
Labour productivity and technology gap in European regions: A conditional frontier approach
Persistent and transient productive inefficiency: A maximum simulated likelihood approach
A generalized method of moments estimator for a spatial panel model with an endogenous spatial lag and spatial moving average errors
An estimate of the effect of common currencies on trade and income
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Productivity growth decomposition using a spatial autoregressive frontier model
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A spatial autoregressive stochastic frontier model for panel data with asymmetric efficiency spillovers
Endogenous product cycles
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Spatial productivity and efficiency spillovers in the presence of transient and persistent efficiency: Evidence from China’s provinces
Examining the impact of government spending on the finance-growth nexus: Evidence from post-communist economies
Export spillovers in Hungary
Growth spillover through trade: A spatial dynamic panel data approach
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Bilateral trade and the eurozone: Evidence from new member countries
Network effects of international shocks and spillovers
The myth of Asia’s miracle
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Redesigning and reforming European regional policy
Spatial perspectives on new theories of economic growth
An economic theory of technological change
Productivity and the great recession
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Euro introduction and the behaviour of Italian exporting firms
Does domestic and foreign R&D capital affect total factor productivity? Evidence from eurozone countries
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New evidence on the sources of EU countries’ productivity growth–industry growth differences from R&D and competition
What determines productivity?
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APA
Haini, H., & Wei Loon, P. (2023). Do regional integration and trade linkages promote productivity spillovers? Evidence from the European Union. Baltic Journal of Economics, 23(1), 64-90.
MLA
Haini, Hazwan, and Pang Wei Loon. "Do regional integration and trade linkages promote productivity spillovers? Evidence from the European Union." Baltic Journal of Economics 23.1 (2023): 64-90.

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Do regional integration and trade linkages promote productivity spillovers? Evidence from the European Union

Do regional integration and trade linkages promote productivity spillovers? Evidence from the European Union

Do regional integration and trade linkages promote productivity spillovers? Evidence from the European Union

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