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# Domestic or export: What is basic at the NUTS 2 regional level? A spatial endogenous regional growth model applied in the EU

Pascal Ricordel

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Pascal Ricordel. Domestic or export: What is basic at the NUTS 2 regional level? A spatial endogenous regional growth model applied in the EU. Growth and Change, 2024, 55 (1), 10.1111/grow.12707. hal-04841821

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ORIGINAL ARTICLE

growth and change

WILEY

# Domestic or export: What is basic at the NUTS 2 regional level? A spatial endogenous regional growth model applied in the EU

Pascal Ricordel

EDEHN, Université Le Havre-Normandie,
Le Havre, France

**Correspondence**

Pascal Ricordel.

Email: pascal.ricordel@univ-lehavre.fr

**Abstract**

A common statement found in regional policy reports is that regional growth is an “export or die” issue. However, the succession of disruptions in the international supply chain has highlighted the crucial roles of domestic activities, local markets and short supply chains, turning the environmental and resilience challenge present in growth policy into a “domestic or die” issue. Recent regional growth theories have seriously questioned export activity as the only way in which to drive regional growth and have highlighted the crucial role of the domestic sector. However, no empirical study has assessed the roles of the domestic and export sectors in growth during this troubled economic period, despite the usefulness of this information for nonbiased policy decisions. Using a spatial endogenous regional growth model as a framework, we investigate the role of the domestic sector during the 1999–2014 period for 263 European Union (EU) Nomenclature of Territorial Units for Statistics (NUTS) regions. The results stress the importance of domestic productivity for regional growth during this period, which is characterized by three economic shocks, thereby elucidating the importance of domestic productivity for competitiveness and resilience issues.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2024 The Authors. Growth and Change published by Wiley Periodicals LLC.

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KEYWORDS

domestic export sectors, economic base theory, endogenous growth model, regional Dutch disease, regional growth, regional resilience, spatial model

# 1 | INTRODUCTION

The COVID-19 pandemic and the need for a new “build back better” stimulus plan have become pivotal to the way in which regional planners and policy makers now consider their credo. Prior to these shocks, specialization and clusterization for competitiveness and attractiveness led to an “export or die” doctrine that was dominant in the European Union (EU) regional growth strategy (Lisbon strategy, 2010, EU, 2020 strategy). With the disruptions generated by COVID shocks, the emphasis on resilience and environmental issues (OECD, 2021) brings a new vision of growth focussed on the circular economy (Bourdin & Torre, 2020) and supply security that involves sectorial diversity, short supply chains, local resources, and innovation in local small and medium enterprises (SMEs). New sources of productivity growth with a focus on territorial production and regional reindustrialization are needed (Capello & Cerisola, 2022). This is prone to confer much more consideration and virtue to the local economy and domestic sector, as illustrated by the key pivotal principles for building social-ecological resilience of Dufourmont et al. (2020) that are all implicitly related to domestic activity such as construction, repair and maintenance. However, this new vision embraces a “domestic or die” doctrine that sharply contrasts with the former “export or die” doctrine. This raises an interesting research question: what is crucial for growth in turbulent periods for Nomenclature of Territorial Units for Statistics (NUTS 2) metropolitan-sized regions, domestic or export sectors?

The regional literature on growth provides key theoretical arguments on the pivotal role of the domestic sector in the dynamics of growth that challenge the economic base model (EBM) introduced in the 1930s for regions of metropolitan size—NUTS 2—and that consider the export sector and external income as a unique source of regional growth. Arguments for the domestic sector are first brought by the theory of regional balances of payments that stresses that a region's growth performance is constrained by its balance of payment positions (Thirlwall, 1980) and the substitution of domestic purchases with external purchases (Blumenfeld, 1955; Christofakis & Gkouzos, 2014; Rutland & O'Hagan, 2007; Tiebout, 1956). The second theory arguing for the domestic sector, endogenous regional growth theory, encompasses the cumulative growth effects generated by the initial level of domestic capital (Kaldor, 1970) and questions the exogenous nature of regional exports in the EBM when, for two contiguous regions, interregional exports are crucially dependent on their mutual domestic activity (Baumont et al., 2006; Olfert & Stabler, 1999; Ozyurt & Dees, 2018; Tiebout, 1956, 1960). Seeking to identify the regional factors of growth, the regional competitiveness theory is the third to confer a crucial role to domestic forces, such as the “local rivalry” in the diamond model of Porter (1998), the “domestic productivity” and “domestic skills” in the hat model (Martin, 2003), or the “SME's structure” in the pyramidal model (Gardiner et al., 2004). Regional resilience is the most recent theory to emerge, and it posits decisive functional arguments for the domestic sector. Focussing on the capacity for a region to absorb disturbance and reorganize a new path of growth (Boschma, 2015; Martin et al., 2016; Martin & Sunley, 2015), the domestic market plays a crucial dynamic role as an incubator milieu (Rodriguez-Pose & Crescenzi, 2008) or a consumption base (Markusen, 2007) for export activities to shift from one lagging export sector to a successful one. Domestic activities are also crucial in short supply circuits and reuse cycles for the implementation of a circular economy
advocated for a more resilient and more sustainable supply (OECD, 2021) that requires reindustrialization (Capello & Cerisola, 2022) and territorialization (Bourdin & Torre, 2020). The productivity of the domestic sector is crucial in resisting an economic shock consequent to the emergence of a new export sector (Brincokova, 2016), which is called the Dutch disease (DD) phenomenon, with a key “learning” spillover process from export to the domestic sector (Sachs & Warmer, 1995).

The literature converges to justify a crucial position of the domestic sector in regional growth, which contradicts the classical base theory. However, previous studies are limited by the heterogeneous nature of their frameworks and by the lack of empirical studies deciding what is crucial between domestic and export, the last study being from Gardiner et al. (2004) in the competitiveness framework with inconclusive results. We want to contribute to this literature by measuring the domestic and export sector productivity in regional growth in a context of turbulence using a spatial endogenous growth model. A set of two hypotheses can be drawn from the regional literature: the first and main hypothesis is related to the role of the domestic sector in regional growth and can be formulated as “*the domestic sector productivity is a significant positive determinant in regional growth at least as important as export sector productivity*”. Investigated during a chaotic period of recessions, this hypothesis confers a resilience capacity to the domestic sector, as growth is a traditional univariate indicator of resilience (Cellini & Torrisi, 2014; Martin & Sunley, 2015). A corollary hypothesis can also be drawn from the literature relative to the ability of the domestic sector to better resist the recessionary period of a shock by limiting laid off (followed by a recovery or renewal period of rehire abilities, Martin et al., 2016). Focussing on this resistance dimension with an immediate effect on unemployment (Mussida & Pastore, 2012; Patuelli et al., 2012; Psycharis et al., 2014), the hypothesis is formulated as “*the domestic sector productivity is a significant negative determinant in unemployment associated with a shock, at least as important as export sector productivity*”.

To test these hypotheses, we applied a spatial endogenous growth model to a panel of 263 EU NUTS 2 regions from the 28 EU countries (Figure 1) studied during the tumultuous period of 1999–2014, which features three business cycles associated with three economic shocks in 1999, 2008, and 2010 (Figure 2).

<figure><img src="image_4.png" /><figcaption>FIGURE 1 EU NUTS 2 regions in 2010.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**FIGURE 2** The EU GDP per capita from 1999 to 2014.</figcaption></figure>

An endogenous growth model associated with panel data stresses the cumulative phenomenon associated with regional growth, as highlighted by Kaldor (1970) and Thirlwall (1980, 2013) in the regional endogenous growth theory. Our model is enriched with spatial effects related to externalities from neighbouring regions, a key specification for metropolitan regions according to the balance of payment theory (Olfert & Stabler, 1999; Tiebout, 1956, 1960), as demonstrated by LeSage & Reed for US regions (1989) and Ozyurt and Dees (2018) for EU NUTS 2 regions. Among the different possible techniques for differentiating and measuring exports from domestic activities, we have privileged the allotment of activities and secured the robustness of this selected method by testing the hypothesis of “the larger the region size is, the larger the domestic sector size is” over the 263 regions of our sample (ranging from 500,000 to 11 million inhabitants). The generalized method of moments (GMM) following the Arellano and Bond process (1991) is used for our estimations to alleviate many of the problems associated with endogeneity, autocorrelation, reverse causality between variables of interest, time inconsistency, institutional and structural time invariant factors and unobserved space specific effects (Capolupo, 2009). For every domestic or export sector, a spatial Durbin model (Elhorst method) measures the relation between the sectoral productivity growth rate and regional growth. For the relationship between sectoral productivity growth and unemployment growth, a spatial autoregressive model (SAR) is estimated, and the endogenous spatial dimension is restricted to the unemployment variable only. Control variables related to the surveys by Beckes (2015) and Glaeser and Gottlieb (2009), namely, population size, interregional migration pattern, capital investment, transport, education, agglomeration and dispersion pattern, skill composition, and research and development (R&D) spending, are introduced into the models.

This paper is organized as follows. Section 2 presents the literature review and the theoretical background of our empirical study. Section 3 addresses the methodology and the econometrics used to measure the base and domestic sector roles. Section 4 presents the results, and Section 5 presents a discussion and implications for policy guidelines.
## 2 | LITERATURE REVIEW AND THEORETICAL BACKGROUND

The research question of what sector is crucial for growth at the NUTS regional level has been investigated by five theoretical corpora: the EBM, introduced in the 1950s, clearly states that regional growth is associated with export only. The four others, namely, the balance of payments theory, the endogenous growth theory, the competitiveness theory and the resilience theory, have all questioned and rejected the EBM conclusion, elucidating a more significant role for the domestic sector as a source of regional growth. While limited by their heterogeneous frameworks, they are a fertile background for guiding the construction of an empirical model and key hypotheses to decide the research question.

### 2.1 | The EBM

Seminally stated by Cantillon (1755) and popularized by the geographer Sombart (1916), who both observed that the development of “places” depends on the external revenue that flows into it, the EBM was introduced at the metropolitan regional level by Hildebrand and Mace (1950) and North (1955) in a Keynesian-oriented subnational form. In its simplest presentation,¹ the total economic activity $E_T$ is assumed to be dichotomous, with a distinction between basic economic activities $E_B$ (activities devoted to the production of goods and services ultimately sold to consumers outside) and domestic economic activity $E_D$, which includes activities involved in producing goods and services produced and consumed locally:

$$E_T = E_B + E_D \qquad (1)$$

The identification of “basic” with “export” activities conveys the theory that the export sector is primary and crucial in regional growth and that the domestic sector identified as “nonbase” is ancillary and secondary. The implicit assumption is that domestic activities are endogenous and depend on exogenous basic activities, so the income injected from outside into the regional economy and the potential for developing locally oriented industries are in proportion to the size of a region's export base with a multiplier effect²:

$$E_D = f(E_B) \qquad (2)$$

Well-shaped for small areas, as empirically demonstrated by Davezies (2008) in the case of employment zones in France (more or less associated with regions the NUTS 3 size), the theory is clearly unshaped for regions of a country size (NUTS 1) for which the domestic sector represents most of the economic activity and is consequently crucial for growth and development (Krugman, 1994). For regions of metropolitan size (NUTS 2), which are intermediary between NUTS 1 and NUTS 3 size, the EBM and its conveyed representation of a nonbase, noncrucial, passive and ancillary domestic sector in growth is rejected by the four following theories.

### 2.2 | The regional balance of payments theory

If regional specialization in exports is a key driver of economic growth (Storper et al., 2015), its real value is constrained by the regional balance of payments (Blumenfeld, 1955; Thirlwall, 1980; Tiebout, 1956). Regional growth depends on the ratio of income elasticities of demand for export and import (Thirlwall's law) and of the possibility of substituting local purchases with external purchases
(Christofakis & Gkouzos, 2014; Rutland & O'Hagan, 2007; Tiebout, 1956). The domestic sector for metropolitan-sized regions is thus theoretically related to the ability to replace imports through local production, which Tiebout (1956) named the cost issue. There is no benefit for a region to foster exports if import or domestic costs rise. Indeed, a new export activity is unlikely to improve the regional balance of payments when this new activity lies in a branch plant of an outside firm and involves working with imported materials and when the share of its locally sold products removes local resources with which it used to work. In this case, the effect of new exports on the balance of payments is negative, and the replacement of local production by imports both increases specialization and depresses regional growth (Blumenfeld, 1955). When locally produced goods and services can be substituted for imports, growth induces further growth in a cumulative way, and a better position is reached. This theoretical point is also stressed by Isard (1975) and Porter (1998)'s vision of a regional basic industry that is likely to attract customers and providers who substitute some domestic activities to import. In this context, the configuration of regional places concerning local amenities or skilled labour (Vollet et al., 2018) is likely to foster the import substitution effect and a sectoral dynamic (Rutland & O'Hagan, 2007; Tiebout, 1956).

According to this theory, the balance of payment is the best indicator for identifying exports from domestic regional activity. Unfortunately, the limitation of this theory lies in the absence of any regional statistics (except for Canada) for the use of this indicator; thus, three other methods, the allotment method, the location quotient and the added value, can be used. The allotment method relates exports to manufacturing, wholesale trade, banking and insurance, transportation, administrative office, hotel and amusement and federal and state employment; it also relates the domestic sector to retail trade, professional and personal services, local transportation and utilities, construction, local government, business and repair services, real estate and local banking. This rough approximation lies within the hypothesis of compensation. What is considered an export in a domestic activity is exactly compensated for by what is considered domestic in the export-related sector. Although this is a raw method (used by Gardiner et al., 2004), Blumenfeld (1955) asserted that it is preferable to the location quotient and the value-added method. The location quotient method relies on the ratio of national/regional employment to identify the specialization sector. This method is not suitable for a balance of payment approach, as it relies on the assumption of trade uniformity between regional and national scales. International trade statistics show that the same categories of goods are imported and exported in a country, and a survey of results using this method is confusing (Richardson, 1985). According to Blumenfeld (1955), the only clear distinction is when specialization is very strong, however the location method is not truly needed in this context. The difficulty in using the value-added method is that the export/domestic distinction is very dependent on the supply chain organization. The Blumenfeld parable of the flour mill elucidates this issue. If we consider a mill that exports its production, the grain that is grown locally and is then sold to the local market and then to the local mill does, by definition, consist of domestic activities. If the mill industry integrates grain activity, the whole value added becomes an export. Considering theoretical arguments, we opted for the allotment method in our empirical study and measured the robustness of the sectoral delimitation by testing whether a larger metropolitan-sized region corresponds to a larger domestic sector in our sample.

## 2.3 | The regional endogenous growth theory

For metropolitan-sized regions, theoretical arguments in favour of an endogenous growth process were introduced at the same time as the EBM and echoed empirical observations from planners; in 1954, the US Chamber of Commerce noted, “Industrial growth stimulates the remainder of the local
economy and existence of the community makes possible industrial growth”. Endogenous growth theory stresses the cumulative growth phenomenon in the vein of the Kaldor model (1970), which was developed further by Dixon and Thirlwall (1975) and Thirlwall (2013). Regions with a capital lag are likely to experience lower growth than more developed regions; the same is true for regions with a technological lag (Baumont et al., 2006), an innovation capacity lag (Grossman & Helpman, 1991) or an education lag (Lucas, 1988). The initial levels of physical and human capital are thus key factors in endogenous growth theory. Endogenous growth theory refers to the Lucas (1988) and Barro (1991) framework that stresses technical progress and human capital factors by incorporating into the estimation the initial value of the endogenous variable, education level and R&D effort. However, regional endogenous growth theory is not a unified theory, as endogeneity lacks a clear direction. For example, Krugman (1989) stressed that regional growth determines export and import elasticities in the opposite direction as that suggested by Thirlwall. The same is true for Verdoorn's law, which suggests a reverse causality between growth and productivity under the rationale that growth fosters economies of scale. Nonetheless, endogenous growth theory is clearly related to the domestic issue. Cumulative growth is thus theorized by indigenous development and technical progress (Armstrong & Taylor, 2000). Rodriguez-Pose and Crescenzi (2008) suggested an empirical model whereby regional economic performance depends on three main factors: internal innovation effort, socioeconomic local factors conducive to innovation, and spatially bound knowledge spillovers.

The endogenous nature of regional growth is not limited to investment, technical progress and human capital factors but rather encompasses spatial effects. Indeed, the endogenous nature of export activities with significant neighbour spillovers was quoted by Tiebout (1956, 1960) in the context of metropolitan regions and empirically confirmed by Olfert and Stabler (1999) and Moretti (2010). Spatial spillovers have been demonstrated by LeSage and Reed for US regions (1989) and by Dall'Erba and Le Gallo (2005), Bourdin (2013) and Ozyurt and Dees (2018) for EU NUTS 2 regions. The concept of spatial dependence intuitively refers to Tobler's first law of geography (1979), which states that “everything is related to everything, but near things are more related than distant things”. Regional growth depends not only on the determinants in the region (initial GDP, population, human capital, etc.) but also on the characteristics of the neighbouring economy (Ertur & Koch, 2007). Thus, a regional growth model must be geographically contextualized (Dissart & Vollet, 2011; Guimaraes et al., 2014; Mulligan & Vias, 2011; Ruault & Proulhac, 2014) to address spatial effects. This theoretical background is of great importance, and we refer to the empirical studies on spatial dependence in the EU NUTS 2 region from Dall'Erba and Le Gallo (2008), Bourdin (2013) and Ozyurt and Dees (2018) to introduce spatial dependence into the endogenous growth model.

## 2.4 | Regional competitiveness theory background

The creation and enlargement of the EU during the nineties and the new millennium has increased the interest in regional competitiveness, which is broadly defined as “*the ability to produce goods and services that meet the test of international (or interregional) markets, while citizens enjoy a standard of living that is both rising and sustainable*” (Tyson, 1992, p. 1). Until the 2008 financial crisis, this issue generated a constellation of theories that have been reported by Beckes (2015) and Glaeser and Gottlieb (2009), highlighting that beyond the sectoral productivity that has been identified as a key growth factor (Gardiner et al., 2004; Krugman, 1994; Martin et al., 2016; Porter, 1998; Tsiapa et al., 2018), there are several other key determinants that can be used as control variables: population size, interregional migration pattern, capital investment, transport, education, agglomeration and dispersion pattern, skill composition, and R&D spending. Many other structural factors
have been identified in the growing literature, such as sectoral composition (Fingleton et al., 2012; Martin et al., 2016), specialization and competitive advantage (Porter, 2001; Storper, 2013; Storper et al., 2015), institutional governance arrangements and supply-side factors (Bristow & Healey, 2015; Kilkenny & Partridge, 2009; Martin et al., 2016; Porter, 1998; Vollet et al., 2018), and human and territorial capital (Bristow & Healey, 2015; Bristow & Wells, 2005; Fratesi & Perucca, 2018; Rizzi et al., 2018).

From this very extensive and heterogeneous background, three key models emerge: the diamond model (Porter, 1998), the regional competitiveness hat model (Martin, 2003), and the competitiveness pyramidal model (Gardiner et al., 2004). All three models point to the domestic sector and inner forces as key drivers in the competitiveness issue and position sectoral productivity as the main factor of long-run regional growth. The diamond model focuses on the more intense rivalry between local firms in competition for the home market. The idea that the domestic sector operates in a protected market without the pressure of competition is implicitly rejected, and the cluster concept conveys the inverse idea that there is inner competition for domestic resources and cooperation for exports. Factor conditions, local institutions and related support industry and firm structure and rivalry are three of the four attributes of the diamond model. With the double diamond model, Porter demonstrates that competitiveness theoretically depends on both domestic and foreign diamonds and puts forwards productivity and location as the microeconomic background of prosperity (Porter, 2001). In the regional hat model of Martin (2003), regional competitiveness is founded on a diversity of factors that differently influence sectoral and market performance according to their local or export nature. The division in the domestic and export sectors is thus legitimate. In the pyramidal regional growth model of Gardiner et al. (2004), the sectorial structure is among the eight sources that impact labour productivity and the employment rate, which are the two revealed competitiveness components that explain regional performance. Despite the limitations of heterogeneous frameworks associated with this theory, a central point that is decisive for our study is that each of these models focuses on sectoral structure, productivity and innovation (often related) as part of the theoretical background, which clearly suggests the use of domestic and export sector productivity as a key factor of regional growth to test to decide the research question of what is crucial.

## 2.5 | Regional resilience theory background

The chaos of the 2008 financial crisis, the increasing threat of climate effects (Rose, 2017) and disruptions generated by the COVID-19 pandemic have substituted the resilience issue for the competitiveness issue. Resilience theory is very different from competitiveness theory in the sense that the domestic sector is clearly associated with functionality in dampening a shock and finding new growth paths (Cortright, 2002; Rutland & O'Hagan, 2007; Vollet et al., 2018). The resilience concept is still under debate (Christopherson & Michie, 2010) but can be understood as “*the capacity of a system to absorb disturbances and reorganize while undergoing change*” (Bristow & Healey, 2015; Simmie & Martin, 2010). In a short-term approach also called “engineering resilience”, resilience is seen as “*the capacity of an economy to reduce vulnerability, resist shocks and recover quickly*” (OECD), meaning to bounce back from a shock, while in an evolutionary perspective also called “ecological resilience”, resilience is a long-term process and the recovery is replaced by the renewal concept seen as the capacity to create new growth paths (Boschma, 2015; Giannakis & Bruggeman, 2015). In both perspectives, some specific functionalities are devoted to the domestic background, such as facilitating the renewal of the export base when the vicissitudes of the business cycle make a former export sector uncompetitive. Local business, mostly structurally composed of SMEs, thus has a key role for a
metropolitan region in absorbing a shock and driving the economy towards a new export trajectory, as highlighted in recent studies referring to the innovation and R&D dimensions. While domestic innovation is often associated with low levels of technology, technology and innovation at the first step of development are often a domestic matter. The domestic sector is associated with the incubator milieu within innovative firms in the early stages of development (Rodriguez-Pose & Crescenzi, 2008). Local consumption can drive the development of new domestic products that are later widely exported. Beer and the development of microbreweries are an example of this (Cortright, 2002).

The resilient role of the domestic sector is also highlighted when a booming new export sector emerges and negatively impacts other export sectors, as was the case with the regional DD phenomenon (Papyrakis & Raveh, 2014). The emergence of a new export sector is likely to create a shock generating a resource transfer and a price increase in the domestic sector through a higher demand that finally makes the former export sector uncompetitive and depresses regional growth (Corden & Neary, 1982). Productivity in the export and domestic sectors and the transfer of skills (learning by doing (LBD) hypothesis) across sectors intermediated by the domestic sector thus have a key role in the disease transmission or eradication process (Brincokova, 2016; Gylfason et al., 1999; Sachs & Warmer, 1995; Torvik, 2001).

The potential of regional resilience associated with the domestic sector is also related to the growing volunteer substitution from consumers of external purchases with local purchases, as stressed by Rutland and O'Hagan (2007) with a proposed measure of "localness". "Localness" is thus a new trend in consumer preference that meets the need for short supply chains and a circular supply model whose importance in the regional growth dynamic is demonstrated by Dissart and Vollet (2011), Ruault and Proulhac (2014), Bourdin and Torre (2020). Resilience theory clearly suggests that domestic sector productivity is a key determinant in the capacity of a region to resist disturbance in the recessionary period, and bounce back or renew in the recovery period, justifying both the main and the corollary hypothesis we are going to test in the study. Despite limitations from its heterogeneous frameworks, the focus on functional aspects of the domestic sector is very useful in the discussion of our results and suggestions for policy guidelines.

Beyond the limits caused by their heterogeneous framework and the lack of empirical studies, the last four theories focus on the same direction supporting the idea that the domestic sector productivity is crucial in regional growth during chaotic time.

## 3 | STUDY FRAMEWORK AND METHODOLOGY

Investigating a panel of 263 regions from 1999 to 2014, we estimate an endogenous growth model that addresses the cumulative phenomenon associated with regional growth, as highlighted by Kaldor (1970) and Thirlwall (1980, 2013). We start from a dynamic growth model based on Barro (1991) with fixed individual and time effects as follows:

$$g_{it} = \mu + \alpha_{i} + g_{it-1}\beta_{1} + X_{it}\beta_{2} + Y_{it}\beta_{3} + \gamma_{t} + \varepsilon_{it} \quad (3)$$

where $g_{it}$ is the real growth rate of GDP per capita in region I, $X$ is the sectorial productivity growth rate (domestic or basic), and $Y$ is a set of common variables controlling for other regional growth determinants, as identified in the background literature. Meanwhile, $\alpha_i$ captures the unobserved region-specific effects, $\gamma_t$ captures the time effects, and $\varepsilon_{it}$ represents the corresponding disturbance terms, where $\varepsilon_{it} \sim N(0, \sigma^2 I)$.
The model is augmented with spatial effects from neighbouring regions. To account for spatial dependence in the growth process, a SAR is frequently used, as follows:

$$g_{it} = \mu + \alpha_i + \rho W g_{it} + g_{it-1}\beta_1 + X_{it}\beta_2 + Y_{it}\beta_3 + \gamma_i + \varepsilon_{it} \quad (4)$$

$\rho W g_{it}$ represents the spatially lagged dependent variable that captures the weighted average of its neighbouring growth rate for a region. W in the equation is the weight matrix that captures the spatial dependence across regions, and its coefficient $\rho$ indicates the size of the growth spillovers. However, three other spatial specifications exist with the following generic forms:

Spatial Error Model (SEM):

$$Y = X\beta_1 + Y\beta_2 + u \text{ with } u = \lambda Wu + v$$

Spatial Durbin Model (SDM):

$$Y = \rho W Y + X\beta_1 + Y\beta_2 + \theta_1 W X + \theta_2 W Y + u$$

Spatial Autoregressive Combined Model (SAC):

$$Y = \rho W Y + X\beta_1 + Y\beta_2 + \theta_1 W X + \theta_2 W Y + u \text{ with } u = \lambda Wu + v$$

Therefore, we face three methodological issues: delimiting the domestic from the export sector for setting up the *X* variable, identifying the spatial dependence pattern (**the matrix W**) associated with the EU regions, and identifying **the correct spatial model specification**.

The study framework is as follows (see Paragraph 3.1 and 3.2 for detailed methodology): concerning the sectoral assignment, we opt for the allotment method used by Gardiner et al. (2004) to delimit domestic from export activities for the six aggregated sectors for which NUTS 2 regional data exist (see the disaggregated sectors in Table 1). Concerning the spatial dependence pattern, Moran's I statistic and the Akaike information criterion (AIC) (see Appendix A1) led to the selection of a standardized inverse distance matrix with 250 kms as the critical cut-off distance ($W_{d250}$) among different

<table><caption>TABLE 1 Sectoral correspondence.</caption><thead><tr><th colspan="2">Basic sector</th><th colspan="2">Domestic sector</th></tr><tr><th>Disaggregated sector</th><th>NACE rev 2 code</th><th>Disaggregated sector</th><th>NACE rev 2 code</th></tr></thead><tbody><tr><td>Agriculture, forestry and fishing</td><td>A</td><td>Construction</td><td>F</td></tr><tr><td>Industry, excluding construction</td><td>B-E</td><td>Wholesale, retail, transport, accommodation and food services, information and communication</td><td>G-J</td></tr><tr><td>Financial and business services</td><td>K-N</td><td>Nonmarkets services</td><td>O-U</td></tr></tbody></table>

Source: Gardiner et al. (2004).
patterns. Concerning the spatial specification, the Elhorst process (2010) for spatial selection diagnosis (see Appendix A1) led to the selection of a SDM, so the model estimated in our study is as follows:

$$g_{it} = \mu + \alpha_i + \rho W_{d250} g_{it} + g_{it-1} \beta_1 + X_{it} \beta_2 + Y_{it} \beta_3 + W_{d250} X_{it} \theta_1 + W_{d250} Y_{it} \theta_2 + \gamma_t + \varepsilon_{it} \quad (5)$$

where $W_{d250}X$ and $W_{d250}Y$ are the spatial lag matrices associated with the values of the explanatory variables from neighbouring observations. Another model focussing on unemployment, with an SAR specification, is also estimated to better account for the resistance dimension in our study during this period.

The Arellano-Bover/Blundel-Bond linear dynamic panel-data estimation with the GMM technique is used to obtain robust regressors. Associated with fixed effects to deal with region- and time-period-specific effects, this technique eradicates the risk of endogenous bias (reverse causality), serial autocorrelation and omitted variable bias (Capolupo, 2009). Sectorial productivity and investment are declared endogenous in the GMM process to consider the influence of growth on productivity growth (Verdoorn's law) and capital formation (which is common in the endogenous growth model).

For every domestic or export sector (separately estimated because of strong collinearity), the model measures the relation between the sectoral productivity growth rate and regional growth. The control variables selected in the model are derived from the surveys by Beckes (2015) and Glaeser and Gottlieb (2009) and consist of population, urbanization level, interregional migration pattern, investment, transport, education, and R&D spending. The use of a fixed-effect (FE) panel data model eradicates the risk of bias generated by the omission of structural factors, as identified in the theoretical literature associated with regional growth (see Section 2).

The 1999–2014 period selected for the study is characterized by a succession of three economic shocks (Figure 2) with a recession in GDP per capita. This period allows us to address the issue of the domestic sector productivity role in a resilience context, as the enlargement of the EU in 2004 to 10 more countries followed by Romania and Bulgaria three years later created important disruptions in export sectors and opportunities for nascent new export sectors. Impact on growth is a traditional univariate measure of resilience from both the “bounce back” (engineering resilience) and renewal perspectives (ecological resilience). Employment is another traditional univariate measure of resilience (Annoni et al., 2019; Doran & Fingleton, 2016). However, when focussing on the resistance dimension associated with the recessionary period of shocks (without considering the recovery or renewal periods), the unemployment variable associated with job loss is privileged (Mussida & Pastore, 2012; Patuelli et al., 2012; Psycharis et al., 2014), and the unemployment rate is also a determinant of an individual's likelihood of employment in the post shock period (Doran & Fingleton, 2016). To test the corollary hypothesis of the role of the domestic sector in resisting shock, we investigate the relationship between regional unemployment rate growth as an endogenous and sectoral productivity with some modification in the model specification, as explained in Section 3.2.

The panel of the 263 NUTS 2 regions³ studied during the 1999–2014 period is nearly the whole population of the metropolitan regions of the 28 EU countries. The NUTS 2 level consists of regions with areas commonly ranging from 800,000 to 3,000,000 inhabitants.⁴ This level is accurate for studying regional economic policies because the area boundaries are defined according to economic, cultural, and administrative cohesiveness. We use the NUTS 2 regions of 2013 to maintain data cohesiveness throughout our period of study (a new NUTS 2 panel with 281 regions was issued in 2016). Our study relies on a cylinder panel for the 263 EU NUTS 2 regions from 1999 to 2014.
Data are obtained from the Eurostat and Cambridge Econometrics databases. The Cambridge Econometrics regional database provides a consistent time series for productivity with disaggregation into six sectors at the NUTS 2 and NUTS 3 levels.

## 3.1 | Sectoral and spatial framework

Concerning the sectorial framework, export and domestic activities are identified by using the sectoral allotment method (see the theoretical discussion in Section 2.2). The allotment technique is a classification of a whole economic sector either in basic or domestic activities. It was used for the first time in the 1920s for the New York regional plan and in the 1930s by Hoyt and then by Gardiner et al. (2004). The sectors are thus assigned as shown in Table 1.

The basic sector consists of agriculture, industry, and financial and business service activities, while the domestic sector consists of construction, wholesale, retail, transport, accommodation and food services, information and communication (WRTAFIC), and nonmarket services. This assignment method is far from perfect (Vollet et al., 2018). For example, some accommodation and food service activities whose sector is classified as domestic could be considered base-oriented when provided by international companies such as Novotel or Starbucks. In the same way, some agricultural activities are oriented towards the domestic market. The method relies on the compensation hypothesis, which states that basic activities included statistically in the domestic sector are exactly compensated for by domestic activities included in the base sector.

This method is, however, privileged considering that there is a very simple way to assess its accuracy for our study: testing the correlation between the region size and the domestic sector size. Given the base concept, the accuracy of the allotment method can be measured by testing whether statistically, the larger the region size is, the larger the domestic sector size. Using the differential of the 263 regions (ranging in our sample from 500,000 to 11 million inhabitants), the correlation between the domestic sector gross value-added share and the region size is estimated.

Two measures can reflect region size: population and population multiplied by the area. The two regressions processed on the NUTS 2 sample using a maximum likelihood regressor are shown in Table 2.

Table 2 provides very significant results: $\beta$ = 0.291 with $\sigma^2$ = 0.07 when size is the population variable, which is statistically significant at the 1‰ level, and $\beta$ = 0.374 with $\sigma^2$ = 0.17 when size is the population*area variable, which is statistically significant at the 1% level. The sectoral allotment used in our study is therefore empirically robust.

The spatial framework in our study is justified by the spatial autocorrelation detected by Moran's I statistics applied to the residuals of ordinary least squares (OLS) regression using regional GDP per capita (see spatial diagnosis in Appendix A1). Geographically weighted panel regression (GWPR) is now regarded as a better way to design spatial dependence (Fotheringham, 2009; Gibbons & Overman, 2012) with spatial heterogeneity associated with proximity represented by a variable coefficient. However, this treatment reduces the number of degrees of freedom and, in the context

<table><caption>TABLE 2 Regressions of domestic gross value-added on two measures of regional size.</caption><thead><tr><th>Variables</th><th>Coefficients</th><th>Prob > chi2</th></tr></thead><tbody><tr><td>Dependent: Domestic GVA share</td><td></td><td></td></tr><tr><td>Population (unit = 1 million people)</td><td>0.291*** (0.0704)</td><td>0.000</td></tr><tr><td>Area*Pop (unit = 1 million people*100 km²)</td><td>0.374** (0.170)</td><td>0.0279</td></tr></tbody></table>
of 263 regions, the accuracy of the estimators. The focus on the role of the domestic sector at the regional level as a whole suggests a regression over the entire area of interest (the 263 regions); thus, we opted for a global spatial econometrics approach. The spatial dependence structure in the context of economic growth in the EU NUTS 2 region was studied by Dall'Erba and Le Gallo (2005), Bourdin (2013) and Ozyurt and Dees (2018). To maintain exogeneity and because of the presence of islands, only a geographical inverse-distance matrix is accurate. Dall'Erba and Le Gallo used a square inverse distance matrix to consider gravity. However, Bourdin (2013) and Ozyurt and Dees (2018) both referred to spatial interaction that linearly declines with distance with a cut-off, meaning that no spatial effects are supposed beyond the cut-off point. Ozyurt and Dees referred to a 50-km and 100-km cut-off distance, while Bourdin (2013), who addressed the important variable area unit problem that is serious in the NUTS 2 context, selected a 300-km cut-off. Based on these empirical studies and after exploring different cut-offs associated with the lowest AIC, we concluded that the accurate spatial dependence in our model is a standardized inverse-distance weighted matrix limited to nearest neighbours with 250 km as the critical cut-off distance ($W_d$250).⁵ Beyond 250 km, the influence of the neighbour is conceived as null. The W matrix is thus defined as follows:

$$W \begin{cases} w_{ij} = 0 & \text{if } i = j \\ w_{ij} = 1/d_{ij} & \text{if } d_{ij} \le \bar{d} \\ w_{ij} = 0 & \text{if } d_{ij} > \bar{d} \end{cases}$$

where $d_{ij}$ is the great circle distance between the centroids of Regions i and j, and $\bar{d}$ is the critical distance cut-off of 250 km, after which the spatial effect is considered negligible; that is, $W_{ij} = 0$ if $d_{ij} > \bar{d}$.⁶ Centroids between i and j for measuring the distance are used, and we refer to Bouayad-Agha et al. (2018) to integrate the connection between islands and the continent (the United Kingdom, Ireland, etc.).

## 3.2 | Model identification and variable construction

### 3.2.1 | Model specification process

The Elhorst specification process (2010) is applied to identify the model for estimation from among the SAR, the SEM, the spatial lag exogenous (SLX) or the SDM. According to the process, $\rho = 0$ and $\lambda \neq 0$ (see Appendix A1); then, a likelihood ratio test associated with the common factor hypothesis ($\theta = -\rho\beta$) related to the SDM specification is performed, which leads to the rejection of $H_0$ ($\theta + \rho\beta = 0$) to select an SDM instead of an SEM (Floch & Le Saout, 2018). To choose between an SDM or SAC specification, the AIC and the Bayesian information criterion (BIC)⁷ are used, which leads to the selection of the SDM (see Appendix A1).

The identification of the SDM as the correct specification is in line with the study of Annoni et al. (2019) concerning the regional growth in the EU for NUTS 2 regions where an SDM specification is selected. It is also in line with the LeSage and Fisher (2008) statement that the SDM specification seems a natural choice in applied regional growth regression models, given the conjunction of the presence of spatial dependence in the error terms of the OLS regression coupled with the existence of omitted spatially dependent variables correlated with included variables.
Given the nature of the panel data, the presence of unobserved heterogeneity as a result of omitted variables is assumed to influence the growth process.⁸ This point associated with the Hausman test results applied to a nonspatial estimation and the consideration that we study nearly the whole population of EU regions led us to opt for a FE model instead of a random effect model.

The econometric specification for the SDM model is as follows:

$$ 
\begin{aligned}
g_{it} = & \rho W_{dij} g_{jt} + \beta_1 g_{it-1} + \beta_2 X_{d/bit} + \sum_{n=3}^{9} \alpha_{\beta_n} Y_{nit} \\ 
& + \theta_2 W_{dij} X_{d/bit} + \sum_{n=3}^{9} \theta_n W_{dij} Y_{nit} + \mu_i + \gamma_t + u_{it} \\ 
& i = 1, \dots, 263 \\ 
& t = 2000, \dots, 2014
\end{aligned}
$$

where:

$g_i$ is the per capita GDP growth of region i Gdpcapita

$g_j$ is the per capita GDP growth of region j

$X_{d/bi}$ is the domestic/basic sector productivity for region i Proddom/Prodbas

$Y_{3i}$ is the population level in region i Population

$Y_{4i}$ is the foreign and interregional migration level in region i Migration

$Y_{5i}$ is the investment level in region i Invest

$Y_{6i}$ is the education level in region i Education

$Y_{7i}$ is the urbanization level for region i Urban Density

$Y_{8i}$ is the transport infrastructure level for region i Transport

$Y_{9i}$ is the R&D spending level for region i R&D

Furthermore, $\rho$, $\Sigma\beta$, and $\Sigma\theta$ are the regressors attached to each independent variable;

$\mu_i$ represents the region fixed effects;

$\gamma_t$ represents the time fixed effects;

$W_{dij}$ is the normalized ($n \times n$) inverse-distance spatial weight matrix restricted to neighbours within a 250-km range; and

$\varepsilon_{it}$ is the error term with the standard assumptions that $\varepsilon \sim N(0, \sigma_e^2)$ and $E(\varepsilon_{it} \varepsilon_{js}) = 0$ for $i \neq j$ and/or $t \neq s$ apply in that case.

The variance inflation factor (VIF) establishes that there is multicollinearity that is too strong among the following variables: domestic productivity (VIF = 8.19) and basic productivity (VIF = 8.16), density (VIF = 46.87) and population (VIF = 43.66). Therefore, to eradicate the collinearity risk, domestic and basic productivity are estimated separately (Models 1 and 2), and the population variable is dropped from the regression that is finally processed with 6 control variables. All variables are log transformed. The GMM regression with the robust Arellano-Bover/Blundel-Bond system estimator is used. This is an extension of the robust Arellano-Bond one-step estimator that fits dynamic panel data estimation. In the system, lagged differences are used as instruments for the level equation, in addition to the moment conditions of lagged levels as instruments for the difference equations. This estimator is designed for datasets with many panels and few periods. For productivity
and investment, we cannot assume that there are strict exogeneity conditions in the regression because of Verdoorn's law and the endogenous capital hypothesis that supports endogenous growth theory, in which the capital level explains the growth level. Both variables are declared to be predetermined. For the difference equation, five instruments of GMM-type (the endogens) and 31 instruments of standard type are used, while for the level equation, five instruments of GMM-type (the endogens) and one of standard type are used. Sargan-Hansen tests of overidentification do not support rejecting the $H_0$ correct specification.

The signs of every coefficient in the regression are expected to be positive, as the theory predicts a positive relationship between regional growth and all its determinants. Our focus on the resistance issue led us to broaden our study to the unemployment variable as an endogen, as it is a largely accepted dimension of resistance to a shock. Thus, we also regress regional unemployment on sectoral productivity and control variables but do so in an SAR model framework, as we hypothesize herein that spatial spillover effects associated with unemployment are bound to the endogenous variable only.

### 3.2.2 | Variable construction

The methodology for every endogenous and interest variable construction is as follows:

*Real GDP per capita growth rate (g)*—Annual percentage change in regional growth in regional gross domestic product per capita (constant 2005 EU€). The endogenous variable is expressed as the differential of the natural logarithmic form of regional annual GDP. Source Eurostat (nama_10r_2g-vagr.xls).

*Unemployment*—Annual unemployment rate for the NUT S2 region. Source: Eurostat (lfst_r_lfu3rt.xls).

*Real export and domestic sectoral productivity (Xb and Xd, respectively)*—Natural logarithmic form of labour productivity measured⁹ according to the following formula:

$$ \text{Labour productivity} = \frac{\text{Real Gross Value Added}}{\text{Total worked hours}} $$

Gross value added is assessed in constant 2005 EU€. We refer to the Cambridge Econometrics data source and methodology for obtaining labour productivity for the 6 sectorial disaggregation. $X_b$ and $X_d$ are introduced separately in the regression because of collinearity (VIF > 5).

The control variables (Y) in the model are selected from among those identified in the survey of Beckes (2015) and Glaeser and Gottlieb (2009): population size, migration patterns, regional capital investment, tax policies, transport, education, agglomeration and dispersion patterns, skill composition, geographic proximity, and concentration of economic activity. NUTS 2 regional data from Eurostat exist for seven of those variables, which are expressed in the following natural logarithmic forms in the model:

*Population (Y₁)*—Source: Cambridge Econometrics. However, this variable (per million inhabitants) is dropped from the regression because of strong collinearity with the urbanization level variable (VIF > 5).

*Foreign and interregional migration (Y₂)*—The data are taken from the Eurostat file “lfst_r_lfe2ecomm” using the following calculus method¹⁰:
$$Y_{2it} = \frac{\text{foreign migration}_{it} + \text{interregional immigration}_{it} + \text{interregional emigration}_{it}}{\text{regional population}_{it}}$$

*Investment level (Y₃)*—Gross fixed capital formation as a percentage of regional GDP. Source: Cambridge Econometrics.

*Education (Y₄)*—Percentage of higher-education students¹¹ in the 20- to 24-year-old population for every region. Human capital is thus introduced through the role played by universities in regional competitiveness,¹² as stressed by the cluster and institutional approaches. Source: Eurostat (educ_regind, R03_2).

*Urbanization level (Y₅)*—Number of households (per hundred) by urbanization level (city, town or suburb, rural).¹³ Source: Eurostat (lfst_r_lfsd2hh).

*Transport infrastructure density (Y₆)*—Sum of road, rail, and navigable inland waterway networks in kilometres per 1000 square-km area. Source: Eurostat (“tran_r_net” and “demo_r_d3area” when density data are missing,¹⁴ the distribution key is thus the population¹⁵).

*R&D (Y₇)*—Gross domestic expenditures on R&D per 100 inhabitants (constant 2005 EU €). Source: OECD and Eurostat for NUTS 2 regions. “Descriptive statistics of all variables, including source, are provided in Appendix A2”.

# 4 | RESULTS

## 4.1 | Results associated with regional growth

The results of the regression for the domestic sector and the export sector are presented in Table 3, with 14 observations for each of the 263 regions, resulting in 3682 total observations:

The results for Model 1 clearly show that domestic sector productivity has a very large impact on regional growth (β for proddom = 0.25; σ² = 0.02; statistically significant at the 1% level). Given the log-log nature of the model, the coefficient is expressed in elasticity, meaning that during the study period, an increase of 1% in regional domestic sector productivity led to an increase of 0.25% in regional economic growth. During the same period, the results for Model 2 show that export sector productivity has a positive but far lesser impact on regional growth (β for prodbas = 0.07; σ² = 0.02; statistically significant at the 1% level), meaning that an increase of 1% in the regional export sector leads to an increase of 0.07% in regional economic growth. This result is astonishing; during the period, the economic growth impact of the basic sector is three and a half-fold weaker than that of the domestic sector. The hypothesis that at the NUTS 2 regional level, the domestic sector is a crucial sector during economic turmoil periods is wholly validated¹⁶. This surprising impact and differential with the export sector impact places the statute of the domestic sector in a crucial position regarding regional growth and gives credit to theories investigating the DD phenomenon. All control variables except for education (also migration and transport in Model 2) are significant with the expected signs except for urban density. Only the migration variable is unstable between the two models, but the effect seems to be captured by the neighbouring effect. The results provide evidence for the existence of spatial spillover effects in regional economic growth, especially for domestic productivity, migration and investment, which exert some significant influence on regional growth. Domestic productivity from neighbours has a strong significant negative impact with β = -0.82 (σ² = 0.21), which is in line
<table><caption>TABLE 3 SDM-GMM growth model regressions.</caption><thead><tr><th>Gdpcapita (growth)</th><th>Model 1<br>SDM FE domestic sector<br>β</th><th>Model 2<br>SDM FE export sector</th></tr></thead><tbody><tr><td>Gdpcapita (L1 growth)</td><td>-0.0037 (0.0342)</td><td>0.02561 (0.0369)</td></tr><tr><td>Gdpcapita (L1 level)</td><td>-0.3108*** (0.0194)</td><td>-0.2058*** (0.0213)</td></tr><tr><td>Proddom</td><td>0.2537*** (0.0207)</td><td></td></tr><tr><td>Prodbas</td><td></td><td>0.0721*** (0.0207)</td></tr><tr><td>Migration</td><td>-0.0294** (0.0137)</td><td>0.0103 (0.0092)</td></tr><tr><td>Invest</td><td>0.1394*** (0.0316)</td><td>0.1247*** (0.0321)</td></tr><tr><td>Education</td><td>0.0118 (0.0098)</td><td>0.0137 (0.0142)</td></tr><tr><td>Urban density</td><td>-0.0283*** (0.0040)</td><td>-0.0288*** (0.0051)</td></tr><tr><td>Transport</td><td>0.0397* (0.0235)</td><td>0.0387 (0.0272)</td></tr><tr><td>R&D</td><td>0.0093*** (0.0025)</td><td>0.0118** (0.0037)</td></tr><tr><td>Constant</td><td>-0.1530*** (0.0472)</td><td>-0.1245* (0.0658)</td></tr><tr><td>Wd250 ↓</td><td>ρ</td><td></td></tr><tr><td>DIGdpcapita</td><td>1.7841*** (0.3594)</td><td>2.0545*** (0.4628)</td></tr><tr><td>Wd250 ↓</td><td>θ</td><td></td></tr><tr><td>Gdpcapita</td><td>0.7322*** (0.2058)</td><td>0.2598 (0.2884)</td></tr><tr><td>Proddom</td><td>-0.8272*** (0.2112)</td><td></td></tr><tr><td>Prodbas</td><td></td><td>0.3816 (0.2348)</td></tr><tr><td>Migration</td><td>0.5957*** (0.1747)</td><td>0.6716*** (0.1991)</td></tr><tr><td>Invest</td><td>-0.4541* (0.2592)</td><td>-0.8145*** (0.2600)</td></tr><tr><td>Education</td><td>-0.0584 (0.0480)</td><td>-0.1218** (0.0560)</td></tr><tr><td>Urban density</td><td>-0.0032 (0.0488)</td><td>-0.0034 (0.0563)</td></tr><tr><td>Transport</td><td>-0.0242 (0.1456)</td><td>-0.0631 (0.1632)</td></tr><tr><td>R&D</td><td>0.0327 (0.0234)</td><td>0.0130 (0.0285)</td></tr><tr><td>Year</td><td></td><td></td></tr><tr><td>2001</td><td>-0.0003</td><td>0.0039</td></tr><tr><td>2002</td><td>-0.0004</td><td>0.0034</td></tr><tr><td>2003</td><td>0.0007</td><td>0.0051**</td></tr><tr><td>2004</td><td>0.0049***</td><td>0.0073***</td></tr><tr><td>2005</td><td>0.0018</td><td>0.0039**</td></tr><tr><td>2006</td><td>0.0076***</td><td>0.0075***</td></tr><tr><td>2007</td><td>0.0076***</td><td>0.0068***</td></tr><tr><td>2008</td><td>0.0026*</td><td>0.0017</td></tr><tr><td>2009</td><td>-0.0122***</td><td>-0.0133***</td></tr><tr><td>2010</td><td>-0.0008</td><td>-0.0029*</td></tr><tr><td>2011</td><td>0.0012</td><td>-0.0025*</td></tr><tr><td>2012</td><td>-0.0041***</td><td>-0.0061***</td></tr><tr><td>2013</td><td>-0.0032***</td><td>-0.0036***</td></tr></tbody></table>

(Continues)
<table><caption>TABLE 3 (Continued)</caption><thead><tr><th>Gdpcapita (growth)</th><th>Model 1<br>SDM FE domestic sector</th><th>Model 2<br>SDM FE export sector</th></tr></thead><tbody><tr><td>Statistics</td><td></td><td></td></tr><tr><td>N</td><td>3682</td><td>3682</td></tr><tr><td>R2 adjusted</td><td>0.54</td><td>0.52</td></tr></tbody></table>

with the regional balance theory that highlights the role of this factor in reducing imports. Neighbour investment also plays a significant negative role in growth, with $\beta = -0.81$ ($\sigma^2 = 0.26$) for Model 2, suggesting that investment from neighbours depresses growth opportunity for a side region. The migration level in a neighbouring region has a significant positive impact on growth, with $\beta = 0.59$ ($\sigma^2 = 0.17$), which is easily understood, as migration is also a source of promotion for the economic relationship between regions. These results validate the main hypothesis drawn from the literature that “the domestic sector productivity is a significant positive determinant in regional growth at least as important as export sector productivity”. With its role being investigated during a turbulent period of recessions and recoveries, this study confirms the domestic sector as a key in resilience.

## 4.2 | Results for regional unemployment

Focussing on the resistance dimension (capacity to absorb a shock), the sectorial impact on unemployment instead of economic growth is estimated during this chaotic period. The model is no longer related to endogenous growth theory, and an SAR model is estimated with the spatial endogenous restricted to the unemployment variable. The results of the regression for the domestic sector and the export sector with 127 instruments are presented in Table 4, with 14 observations for each of the 263 regions, resulting in 3682 total observations:

The results clearly show that domestic sector productivity has a significant impact on reducing unemployment growth during the period, with $\beta = -0.57$ ($\sigma^2 = 0.16$), meaning that a rise of 1% in domestic productivity lowers unemployment growth by 0.57%. A striking point is that export sector productivity has no significant impact on unemployment, as if all impact is only devoted to the domestic sector. This result provides a serious hint as to the potential of the domestic sector to resist a shock and validates the corollary hypothesis that “the domestic sector productivity is a significant negative determinant in unemployment associated with a shock, at least as important as export sector productivity”. A theoretical explanation for these empirical results could be the very role of the domestic sector in productivity transfer when export sector activities encounter turbulence in a positive or negative way. The period studied is particular to EU construction, with the anchorage of eastern countries in the EU. Some export sectors have seen their position reshuffled in either a boom or a decline in the EU region. Such fast changes could likely depress the whole regional economy (DD) if the domestic sector does not play a role in productivity transfer for the declining export sector. We claim that this is what our results show; that is, this transfer explains the resistance role of the domestic sector during the period. A final point about the results is the lack of a significant role of the control variables except for migration. Concerning this variable, migration level is shown to play a positive role in reducing unemployment.
<table><caption>TABLE 4 SAR-GMM unemployment model regressions.</caption><thead><tr><th>Unemployment (growth)</th><th>Model 1<br>SAR FE domestic sector</th><th>Model 2<br>SAR FE export sector</th></tr></thead><tbody><tr><td>W<sub>250</sub> Unemployment (growth)</td><td>1.2552*** (0.2414)</td><td>1.2852 (0.2441)</td></tr><tr><td>Gdpcapita (L1 level)</td><td>0.2007 (0.1955)</td><td>-0.0852 (0.2248)</td></tr><tr><td>Proddom</td><td>-0.5767*** (0.1603)</td><td></td></tr><tr><td>Prodbas</td><td></td><td>-0.0064 (0.1970)</td></tr><tr><td>Migration</td><td>-0.4564** (0.1593)</td><td>-0.4847*** (0.1574)</td></tr><tr><td>Invest</td><td>-0.1806 (0.2616)</td><td>-0.1087 (0.2610)</td></tr><tr><td>Education</td><td>0.1622 (0.0994)</td><td>0.1625* (0.0949)</td></tr><tr><td>Urban density</td><td>-0.0668 (0.0968)</td><td>0.0495 (0.0875)</td></tr><tr><td>Transport</td><td>0.0283 (0.1610)</td><td>0.0020 (0.1504)</td></tr><tr><td>R&D</td><td>0.0445 (0.0384)</td><td>0.0384 (0.0386)</td></tr><tr><td>Constant</td><td>0.5318 (0.7324)</td><td>0.3928 (0.7214)</td></tr><tr><td>Year</td><td></td><td></td></tr><tr><td>2002</td><td>0.1199***</td><td>0.1187***</td></tr><tr><td>2003</td><td>0.0832***</td><td>0.0787**</td></tr><tr><td>2004</td><td>0.1058***</td><td>0.1065***</td></tr><tr><td>2005</td><td>0.0586***</td><td>0.0623**</td></tr><tr><td>2006</td><td>0.0069</td><td>0.0111</td></tr><tr><td>2007</td><td>-0.0417***</td><td>-0.0339**</td></tr><tr><td>2008</td><td>0.0217</td><td>0.0357**</td></tr><tr><td>2009</td><td>0.2405***</td><td>0.2568***</td></tr><tr><td>2010</td><td>0.1010***</td><td>0.1176***</td></tr><tr><td>2011</td><td>0.0318</td><td>0.0403*</td></tr><tr><td>2012</td><td>0.0813***</td><td>0.0936***</td></tr><tr><td>2013</td><td>0.0737***</td><td>0.0039</td></tr><tr><td>2014</td><td>-0.0029</td><td></td></tr><tr><td>Statistics</td><td></td><td></td></tr><tr><td>N</td><td>3682</td><td>3682</td></tr><tr><td>R2 adjusted</td><td>0.63</td><td>0.59</td></tr></tbody></table>

## 5 | DISCUSSION, CONCLUDING REMARKS AND POLICY IMPLICATIONS

Investigating the issue of what is truly basic between the export sector and the domestic sector in an economically chaotic time, our study empirically shows that the domestic sector was crucial (“basic”) at the regional level for growth and resilience in the EU during the 1999–2014 period. Therefore, concepts that stress the local market as an inner force, as a resilience and substitution force, as a start-up nursery, and as a global economic platform that are conveyed in recent regional growth theories have a key role in metropolitan-sized region growth. With three and half-fold more growth potential than the export sector, our study shows the preeminent role of the domestic sector in a regional economy. A strong differential between the export and domestic sectors is also shown in the specific context of
unemployment, which proves the preeminent role in the resistance to a shock dimension. Thus, the terminology of what is basic at the regional level (and for this level only) should be re-evaluated amid calls for discussion and theoretical explanations.

First, it appears that the results sound like a “domestic or die” issue in economic performance and resilience. However, if our study confirms that the EBM model that highlights the export sector as the main growth source is disqualified for metropolitan-sized region studies, our study does not capture the dynamic domestic/export framework. Thus, re-establishing the domestic/export framework for metropolitan-size regions is likely to be very useful for this aim. If we contextualize our study, this period is characterized by the enlargement of the EU to new eastern regions, meaning that the period featured high capital mobility with a fast change in sectorial structure. Clearly, our results should not be considered as a final point on the domestic or export issue; rather, they should help raise questions about the great potential of the domestic sector in regional growth and resilience in the specific context of the EU during this period.

Second, the great potential of the domestic sector in growth and resilience can be related to recent resilience theories that emphasize the domestic sector as functional; also, considering the context of the period and the EU, guidelines can be suggested by the literature on the resource curse phenomenon that has refined the DD model of Corden and Neary (1982). This corpus uses the domestic/export framework to highlight the dynamic between export and the domestic sector through resource transfer and factor mobility. Developed at the national level in the context of the DD phenomenon, the LBD hypothesis developed by Torvik (2001) can be mobilized to understand what happened at the regional scale in the context of the EU during our study period. The LBD hypothesis assumes that both the export and domestic sectors have endogenous productivity, with both sectors contributing to the LBD and learning spillovers between sectors. The resource curse (or DD) occurs when a resource boom reduces the international competitiveness of a whole economy. The phenomenon was identified in 1977 for the Dutch economy and has paved the way for the recent concept of the resource curve (bib_SMS_2013Sala-i-Martin & Subramanian, 2013). A spending effect and a resource movement effect operate as identified by Corden and Neary (1982) and both generate structural transformation through the reallocation of productive factors across sectors in the economy and the subsequent change in the sectoral shares of value added in the GDP. In the context of the EU, the enlargement of the market to the eastern regions has hurt all traditional industrial export sectors while favouring the new export sector. A transfer of skills and resources has occurred from the declining export sector to the new export booming sector and from the declining export sector to the domestic sector. This reallocation of resources during sectorial transformation generates a loss of skills and know-how in the declining export sector that can serve as a bumper crop when a transfer of knowledge arises towards the domestic sector that benefits from rising productivity. Considering both LBD effects and technological gaps and assuming labour mobility, Cherif (2013) observed an increase in unemployment and poverty in the declining sector. The domestic sector can play a crucial role in recovering part of the LBD and thus foster growth and resilience. Foreign exchange gifts with new emerging export sectors for regions in the EU may have contributed to reducing productivity in all export sectors as a whole and increasing sectorial productivity in the domestic sector. This can likely explain the weak impact of the export sector on growth and the very large impact of the domestic sector on growth. This analysis highlights the fact that productivity in the domestic sector is a key source of resilience when fast structural transformation occurs due to an economic shock. Theoretical support exists from influential papers (Krugman, 1989; Sachs & Warmer, 1995; Torvik, 2001; Van Wijbergen, 1984) that demonstrate both an interest in keeping the domestic/export pattern for regional studies and a focus on the resource curse phenomenon at the regional level. The resource curse as a regional phenomenon
was demonstrated in the case of Canadian regions (Papyrakis & Raveh, 2014), but more evidence is needed in the context of the EU at the NUTS 2 level.

As the first concluding remark, the empirical results confirm the key role of the domestic sector in regional competitiveness and resilience at the NUTS 2 level; thus, we can derive serious implications that domestic sector productivity plays both the role of a shock buffer during the recessionary period of a shock and the role of an incubator for export sectorial transformation during the renewal period when the region set up a new growth path. Attention should be given to the domestic sector, whose role in the regional economy is often ignored or minimized, as stressed by Krugman (1994), Tiebout (1956) or Blumenfeld (1955) and resilience theory.

As a second concluding remark, the results and theoretical guidelines suggest the need for additional investigations into the functional role of the domestic sector associated with a declining and a booming export sector. A new three-sector framework is thus worth investigating to study the accuracy of intersectorial skills transfer following an economic shock. This specific point highlights the limits of our research. Investigating a research question expressed in a binary mode, our economic framework, while robust, is designed to deal with a simple measurement. More refinement in the sectorial allotment is needed for studying functionalities. In regard to resilience, our research delivers serious hints but not real evidence of the role of the domestic sector. For that, the use of an employment variable or a composite index should be a better endogen in line with resilience studies (Fingleton et al., 2012; Martin & Sunley, 2015). However robust our spatial model is, more refinement in the treatment of spatial heterogeneity and the range of spatial dependence (Annoni et al., 2019) is needed to go further in the resilience issue. Moreover, time periods need to be broken down to elucidate the role of the domestic sector in renewal periods (Martin et al., 2016). Those aims are beyond the scope of our study which is the first to shed a clear interesting light on the crucial statutes of the domestic sector that is under debate in this bring back better period.

As a third concluding remark, the dangerous obsession with the EBM and the export base should be treated as follows. On the one hand, regional policy deciders and advisers argue that globalization and world trade are dominant (“arguing against globalization is like arguing against gravity” Kofi Annan); on the other hand, the very role of the domestic sector in regional growth is demonstrated by empirical results. Imbued in a cultural political economy approach (Sum & Jessop, 2001) and a discursive mechanism (Bristow, 2010; Jessop, 2008), policy deciders and advisers still consider the EBM as a universal model and confuse competitiveness that is clearly rooted in domestic productivity with competition that focuses on export market share (Bristow, 2005; Kitson et al., 2004; Krugman, 1994). This proclivity to put forwards the export sector in regional growth is a deviance (Bristow, 2005); the construction of an imaginary economy in which the domestic role is absent bears a risk of bias, missteps and policy errors.

Policy implications can be derived from our results and discussion. Growth and resilience are explained by the very role of domestic productivity and the intersectorial transfer of resources, capital and skills. Any policy that favours a rise in global productivity by favouring infrastructure, education, and skills enhancement benefits growth and resilience (this has been the case with the EU regional policy until now). Any policy that favours the export sector to the detriment or without consideration of the domestic sector is misleading. In chaotic times, the local sector must be favoured in the same way as new emerging sectors, and there should be mobility from the declining export sector to the domestic sector to minimize LBD loss and favour the transfer of productivity from one sector to another. A key for improving policy efficiency during this chaotic time lies in our understanding of the sectoral dynamics and a better integration of the spatial dimension in policies.
ACKNOWLEDGMENTS

The author is very grateful to the two anonymous reviewers for their valuable suggestions.

CONFLICT OF INTEREST STATEMENT

The author declares no conflicts of interest

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available in datab browser at https://ec.europa.eu/
eurostat/datab browser. These data were derived from the following resources available in the public
domain: - datab browser for eurostat, https://ec.europa.eu/eurostat/datab browser.

ENDNOTES

<footnote n="1">¹ See Tiebout (1960) for a more completed EBM income-oriented formalization.</footnote>

<footnote n="2">² With Y_T and Y_B defined as the income generated from E_T and E_B, the export base multiplier is k_B = ΔY_T/ΔY_B = 1/(1-b) where b is the propensity to generate domestic income from regional income, which is related to the propensity to local spendings.</footnote>

<footnote n="3">³ Based on their distance from the EU area or due to an overly artificial segmentation, the French overseas territories of Guadeloupe (FRA1), Martinique (FRA2), French Guiana (FRA3), Reunion (FRA4), and Mayotte (FRA5), the Spanish territories of the Canary Islands (ES63), Ceuta (ES64), and Melilla (ES70), and the Portuguese territories of the Azores Archipelago (PT20) and Madeira (PT30) are excluded from the study.</footnote>

<footnote n="4">⁴ Although it has 11,000,000 inhabitants, the Île-de-France region is not dropped from our study.</footnote>

<footnote n="5">⁵ Extensively overloading the paper with a presentation of a neighbour weight matrix methodology seems inappropri-
ate here. The reader can refer to Ahmad (2019), Dall'Erba and Le Gallo (2008) or Ozyurt and Dees (2018) for more
precision.</footnote>

<footnote n="6">⁶ We have also processed the regression with all other different proximity matrices and confirmed the stability of the main result of the study relative to the role of the domestic sector productivity in the GDP.</footnote>

<footnote n="7">⁷ The AIC and BIC can be viewed as measures that combine fit and complexity. Fit is measured negatively as -2 * ln(likelihood); the lower the value is, the better the fit. Complexity is measured positively, either by 2 * k(AIC) or ln(N) * k(BIC). Given that two models fit the same data, the model with the smaller information criterion value is considered better.</footnote>

<footnote n="8">⁸ The Hausman tests for the nonspatial estimation of FE and random effects indicate that FE is the preferred model.</footnote>

<footnote n="9">⁹ All data are from the Cambridge Econometrics database except for the data for Croatia (HR), which are from Eurostat (name_10r_2emhrw.tsv). Eurostat also provided the data used to replace the missing data on “hours worked” in the Cambridge Econometrics dataset; exceptions are for missing data for the year 1999, which are replaced by year 2000 data.</footnote>

<footnote n="10">¹⁰ National migration data are only available at the NUTS 0 or NUTS 1 levels. National migration is calculated on the basis of the NUTS 2 regional population. The incomplete NUTS 2 data are included if they exist and the remaining data (migration at the NUTS 0 or NUTS 1 level less migration at the NUTS 2 level for regions with data) are included according to the ratio of the NUTS 2 population to the remaining population at the NUTS 1 level (the NUTS 1 population less the NUTS 2 population) when there are data. For Cyprus (CY), data from Malta (MT) are used. As there are no data for Greece (EL), an instrumental method is used: migration from Bulgaria, the nearest Balkan country, is used to calculate migration from Greece on the basis of the ratio of the Greek population to the Bulgarian population at the NUTS 2 level.</footnote>

<footnote n="11">¹¹ Levels 5 and 6 in the CITE classification</footnote>

<footnote n="12">¹² For the years 2013 and 2014, mean growth over the last four years is used. For observations that are missing NUTS 2-level data, the data are replaced according to the ratio NUTS 2/NUTS 0, as the relation between NUTS 2 and NUTS</footnote>
0 appears to be very stable. Example: Suppose AT11 = ?, AT1 (NUTS 0) = 80, and the mean ratio NUTS 2/NUTS 0 is 0.7. The missing data point for AT11 (NUTS 2) is then 80 * 0.7 = 56.

¹³ We use the density of the whole area as a proxy if data are missing for the level of urbanization.

¹⁴ For missing data, a projection is made based on the data from the closest available year (infrastructure does not easily disappear from 1 year to another). For Germany (GE) and the United Kingdom, only NUTS 0 data exist, which is a problem. The NUTS 0 distribution key is used at the NUTS 2 level for the surface area of navigable inland waterways, as geographical constraints are dominant.

¹⁵ A final problem occurs in the road and rail network data for Greece (EL) and Portugal (PT). Density data for the road and rail networks between 2000 and 2011 come from “knoema.com”, with projections for missing years. The distribution key for the NUTS 2 region is the population (which follows from the idea that small high-density areas are also highly dense in terms of infrastructure).

¹⁶ Having processed the regression with different model specifications, SAC, SEM, SAR and with different kind of neighbour matrices, we notice the robustness of the result with impact on growth ranging from 0.21 to 0.37 according to the model specification for domestic sector productivity, while the impact ranges from 0.07 to 0.14 for basic sector productivity. The differential in impact between both sectors is also confirmed from two and a half fold to three and a half fold.

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# APPENDIX

## APPENDIX A1 | Spatial Diagnosis and Model Configuration

<table><thead><tr><th></th><th>W<sub>d</sub>100</th><th>W<sub>d</sub>200</th><th>W<sub>d</sub>250</th><th>W<sub>d</sub>300</th></tr></thead><tbody><tr><td>Moran's I</td><td>3.45***</td><td>3.15***</td><td>3.07***</td><td>2.91***</td></tr><tr><td>Global Moran MI</td><td>0.2123***</td><td>0.1884***</td><td>0.1861***</td><td>0.1846***</td></tr><tr><td>AIC</td><td>-9510</td><td>-10156</td><td>-11456</td><td>-11324</td></tr></tbody></table>

*p* < 0.05; **p* < 0.01; ***p* < 0.001.

Source: The author.

*Spatial weight matrix selection between different cut-off distances of 100, 200, 250 and 300 (Moran's I Test for Spatial Autocorrelation in OLS Regression (SAR) and Akaike Information Criteria)*

According to the AIC, the lower the AIC value is, the better the model fit. The AIC leads to the selection of Wd250 as the better spatial dependence structure.

<table><tbody><tr><td>Spatial lag (ρ)</td><td></td></tr><tr><td>LM test (Anselin)</td><td>4.8151**</td></tr><tr><td>Robust LM test</td><td>0.0006</td></tr><tr><td>Error lag (λ)</td><td></td></tr><tr><td>LM test (Burridge)</td><td>337.2898***</td></tr><tr><td>Robust LM test</td><td>332.4753***</td></tr><tr><td>Common factor (θ + ρβ = 0)</td><td></td></tr><tr><td>LR test</td><td>Chi2(8) = 59.74<br>Prob > Chi2 = 0.000</td></tr></tbody></table>
Configuration selection between SAR, SEM and SDM

According to the robust LM tests, we cannot reject Hypothesis H₀: ρ = 0, and we reject Hypothesis H₀: λ = 0, meaning that λ ≠ 0. According to the common factor test, we reject Hypothesis H₀: θ + ρβ = 0; according to the Elhorst process, the correct specification is thus the SDM.

Configuration selection between SDM and SAC using AIC and BIC tests

<table><thead><tr><th></th><th>SDM</th><th>SAR</th></tr></thead><tbody><tr><td>AIC</td><td>-13970.36</td><td>-13135.47</td></tr><tr><td>BIC</td><td>-13906.91</td><td>-13078.36</td></tr></tbody></table>

The lower the information is, the better the fit is. The SDM is thus selected.

APPENDIX A2 | Summary Statistics of the Variables

<table><thead><tr><th>Variable</th><th>Sources</th><th>Mean</th><th>Standard Deviation</th><th>Minimum</th><th>Maximum</th><th>Observation</th></tr></thead><tbody><tr><td rowspan="3">Real GDP per capita growth</td><td rowspan="3">Eurostat nama_10r_2gvagr</td><td>Overall</td><td>2.93</td><td>0.69</td><td>0.46</td><td>4.59</td><td>N = 4208</td></tr><tr><td>Between</td><td></td><td>0.68</td><td>0.80</td><td>4.49</td><td>n = 263</td></tr><tr><td>Within</td><td></td><td>0.09</td><td>2.42</td><td>3.21</td><td>T = 16</td></tr><tr><td rowspan="3">Unemployment rate</td><td rowspan="3">Eurostat lfst_r_lfu3rt. xls</td><td>Overall</td><td>8.74</td><td>4.18</td><td>1.80</td><td>27.50</td><td>N = 4208</td></tr><tr><td>Between</td><td></td><td>3.04</td><td>4.22</td><td>15.31</td><td>n = 263</td></tr><tr><td>Within</td><td></td><td>2.88</td><td>1.26</td><td>22.75</td><td>T = 16</td></tr><tr><td rowspan="3">Real export productivity</td><td rowspan="3">Cambridge econometrics</td><td>Overall</td><td>3.34</td><td>0.82</td><td>0.05</td><td>5.06</td><td>N = 4208</td></tr><tr><td>Between</td><td></td><td>0.81</td><td>0.64</td><td>4.94</td><td>n = 263</td></tr><tr><td>Within</td><td></td><td>0.13</td><td>2.74</td><td>3.81</td><td>T = 16</td></tr><tr><td rowspan="3">Real domestic productivity</td><td rowspan="3">Cambridge econometrics</td><td>Overall</td><td>2.96</td><td>0.63</td><td>0.59</td><td>4.27</td><td>N = 4208</td></tr><tr><td>Between</td><td></td><td>0.63</td><td>0.92</td><td>4.21</td><td>n = 263</td></tr><tr><td>Within</td><td></td><td>0.06</td><td>2.51</td><td>3.46</td><td>T = 16</td></tr><tr><td rowspan="3">Population (per million)</td><td rowspan="3">Cambridge econometrics</td><td>Overall</td><td>1.87</td><td>1.50</td><td>0.02</td><td>12.06</td><td>N = 4208</td></tr><tr><td>Between</td><td></td><td>1.50</td><td>0.02</td><td>11.58</td><td>n = 263</td></tr><tr><td>Within</td><td></td><td>0.86</td><td>1.07</td><td>2.41</td><td>T = 16</td></tr><tr><td rowspan="3">Migration foreign and interregional</td><td rowspan="3">Eurostat lfst_r_ lfe2ecomm.xls</td><td>Overall</td><td>5.98</td><td>0.60</td><td>0.44</td><td>6.78</td><td>N = 4208</td></tr><tr><td>Between</td><td></td><td>0.60</td><td>0.84</td><td>6.69</td><td>n = 263</td></tr><tr><td>Within</td><td></td><td>0.06</td><td>5.09</td><td>6.78</td><td>T = 16</td></tr><tr><td rowspan="3">Investment</td><td rowspan="3">Cambridge econometrics</td><td>Overall</td><td>8.62</td><td>1.04</td><td>4.87</td><td>11.73</td><td>N = 4208</td></tr><tr><td>Between</td><td></td><td>1.02</td><td>5.34</td><td>11.61</td><td>n = 263</td></tr><tr><td>Within</td><td></td><td>0.18</td><td>7.40</td><td>9.42</td><td>T = 16</td></tr><tr><td rowspan="3">Education</td><td rowspan="3">Eurostat educ_ regind_R03_2.xls</td><td>Overall</td><td>3.55</td><td>0.80</td><td>0.78</td><td>5.51</td><td>N = 4208</td></tr><tr><td>Between</td><td></td><td>0.77</td><td>1.43</td><td>5.05</td><td>n = 263</td></tr><tr><td>Within</td><td></td><td>0.20</td><td>1.80</td><td>4.69</td><td>T = 16</td></tr></tbody></table>
<table><thead><tr><th>Variable</th><th>Sources</th><th></th><th>Mean</th><th>Standard Deviation</th><th>Minimum</th><th>Maximum</th><th>Observation</th></tr></thead><tbody><tr><td rowspan="3">Urbanization level (100 households)</td><td rowspan="3">Eurostat lfst_r_ lfsd2hh.xls</td><td>Overall</td><td>7.70</td><td>6.15</td><td>0.11</td><td>50.07</td><td>N = 4208</td></tr><tr><td>Between</td><td></td><td>6.15</td><td>0.12</td><td>48.36</td><td>n = 263</td></tr><tr><td>Within</td><td></td><td>0.58</td><td>2.10</td><td>11.99</td><td>T = 16</td></tr><tr><td rowspan="3">Transport density</td><td rowspan="3">Eurostat trans_r_net.xls demo_r_d3area.xls</td><td>Overall</td><td>6.90</td><td>1.05</td><td>2.48</td><td>10.49</td><td>N = 4208</td></tr><tr><td>Between</td><td></td><td>1.04</td><td>2.81</td><td>10.48</td><td>n = 263</td></tr><tr><td>Within</td><td></td><td>0.16</td><td>5.57</td><td>7.67</td><td>T = 16</td></tr><tr><td rowspan="3">R&D (per 100 inhabitants)</td><td rowspan="3">OECD and Eurostat</td><td>Overall</td><td>3.67</td><td>3.95</td><td>0.04</td><td>27.68</td><td>N = 4208</td></tr><tr><td>Between</td><td></td><td>3.85</td><td>0.08</td><td>22.06</td><td>n = 263</td></tr><tr><td>Within</td><td></td><td>0.93</td><td>1.47</td><td>26.05</td><td>T = 16</td></tr></tbody></table>