UNIVERSITA’ DEGLI STUDI DI PISA
Scuola di Economia e Management
Corso di Laurea Magistrale in Economics
COMMUTING IN EUROPE
AN INTER-REGIONAL ANALYSIS ON ITS
DETERMINANTS AND SPATIAL EFFECTS
Relatore: Prof. Angela PARENTI
Tesi di Laurea
Chiara CASTELLI
Matr. n. 561621
Index
Introduction ... 1
Chapter 1 Literature Review ... 3
1.1 Individual Characteristics ... 3
1.2 Household Characteristics ... 6
1.3 Job Characteristics ... 7
1.4 Macro (Regional) Determinants ... 9
Chapter 2 Data and Descriptive Statistics ... 14
2.1 Data ... 14
2.2 The Model ... 17
2.2.1 The dependent variable ... 17
2.2.2 1st Group of Regional Characteristics based on Aggregated Individual Socio- Demographic Variables ... 18
2.2.3 2nd Group of Regional Characteristics based on Aggregated Individual Job Features ... 19
2.2.4 3rd Group of Regional Characteristics based on External Data ... 21
2.3 Descriptive Statistics ... 23
Chapter 3 Panel Analysis ... 32
3.1 Methodology ... 32
3.2 Findings ... 37
3.2 A Closer Look on Unemployment ... 45
Chapter 4 Spatial Analysis ... 46
4.1 Methodology ... 46
4.2 Findings ... 53
4.2.1 Global and Local Moran’s I ... 53
4.2.2 The Spatial Durbin Model ... 57
Chapter 5 Conclusions and Policy Implications... 64
1
Introduction
A car stacked in the traffic jam, a head popping out from a bus window reading a book, a woman carrying her coffeepot and patiently waiting on an anonymous subway platform or a man trying to make his way in the crowded morning sidewalk, these are just few pictures that come to our minds when thinking about commuting.
Despite any personal judgments classifying it either beneficial or damaging on humans’ wellbeing, it undoubtedly characterizes modern societies and influences millions of lives. The shift towards the post-industrial economic framework, characterized by the development of transport and communication infrastructures together with the digital revolution, have redefined the relationship between work and home for a countless number of people. At personal level, the decision to commute for longer time or to reach more distant destinations can depend on some job characteristics (such as being employed in particular industries or on temporary contracts) as well as some induvial attributes (i.e. the attained education level, age and income) however, under a wider prospective, commuting can also be seen as a social phenomenon where the regular flow of travelers moving in and out of a specific geographical area shapes not only its local mobility channels (especially with respect to the development of both transportation and communication systems) but also the entire urban structure of the territory, thus driving the creation and expansion of cities and metropolitan areas1. Still, the study of any urban-related
phenomenon, hence commuting, should not be restricted to the sole recognition of what factors determine the upraise and fall of the investigated trend and include an additional question on whether the behavior of a unit, here corresponding to a geographical territory, interacts with the surrounding spatial context.
Nonetheless, a major part of todays’ literature on workplace mobility seem to underestimate the important role of spatiality in economics research thus neglecting some meaningful and realistic insights on the occurring dynamics. Considering this lack as an opportunity, it will be interesting to combine the traditional research aim of identification of what individual and macroeconomics aspects have effect on outbound commuting flows with a second question considering the potential effects of the spatial context, focusing the analysis at regional level.
In order to do so, complex methodologies need to be implemented and it is easy to understand how this has limited past researchers in developing their studies under wider perspectives. Fortunately, today’s new-born technologies allow modern social scientists to rely on machines that are able to compute difficult algorithms on enormous data loads in only few minutes and thanks to the latest technological
1According to the European Commission (2019) the majority of the global population (55%) already live in urban areas and the proportion is expected to rise to 68% by 2050 as reported by the United Nation Department of Economic and Social Affairs (2018).
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developments, the analysis will be able to cover both traditional and spatial aspects of regional commuting.
In order to briefly introduce the topic, the First Chapter will summarize the main findings in the existing literature while the Second Chapter will describe the dataset and the proposed model for the analysis, together with some observations on selected descriptive statistics. Moving to the empirical part, the third Chapter will consider the Fixed Effects framework on Panel Data in order to identify the major drivers of regional outbound commuting while Chapter Four will explore the spatial interaction among units first testing its existence by two summarizing statistics, namely the Morans’ I Indexes, and secondly by applying a spatial econometric model to data, that is the Spatial Durbin Model, in order to include the potential effects that neighbouring behaviours might exercise on the regional workplace mobility. In light of the outcoming results, a conclusive Chapter will collect the major findings and offer some policy advices and present the future work agenda.
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Chapter 1
Literature Review
The first Chapter will summarise the major findings of previous research on the topic with particular emphasis on individual socio-demographic and job attributes as well as on household characteristics and macro-economic variables at regional level. A summarizing table of the literature can be found at the end of the chapter, where every relevant variable is classified by author/s and coefficient sign that the researchers found significant with respect to commuting, which in turn will be the expected outcome of the next analysis.
1.1 Individual Characteristics
There is consistent evidence accounting higher propensity to commute and for longer distances for male workers with respect to their female counterpart (MacDonald, 1999; McLafferty & Preston, 1997; Sandow & Westin, 2010). However, there might be something more to say about gender differences in commuting as regards its association with other social and job characteristics. Starting from the social environment, an important factor differently affecting genders is the childcare responsibility as it has long been established in many developed countries that men commute longer than women and that fathers travel furthest to work while mothers travel least (Dex, Clark, & Taylor, 1995 and Grieco, Pickup, & Whipp, 1989; Turner & Niemeier, 1997). Despite some increases in fathers’ childcare responsibilities and increasing female employment participation rates, there are still significant differences between mothers’ and fathers’ working and commuting patterns and also between those with or without childcare responsibilities. Working hours are also affected by the birth of a child and Paull (2008) shows how this event has little effect on a father’s work time (whose working hours may even augment), but considerable effect on the mother’s (reducing their work time).
Another way in which working hours and commuting times are inter-related is through wages and other job characteristics. Madden (1981) argues that lower commuting times for women are due to their lower wage rates and shorter working hours resulting in a lower return in terms of earnings per commuting time (a phenomenon usually known as gender pay gap). Low wages are often associated with part-time work and with mothers more likely to work part-time, hence low remuneration may also influence their commuting times. Van Ommeren and Dargay (2006) found higher wages to be associated with higher commuting speeds, so lower wages might be associated with shorter commutes and this may particularly affect women with childcare responsibilities with Waldfogel (2007) describing the ‘family penalty’ faced by mothers in terms of lower wages, as women with children earn less than other women, while the reverse is the case for men, with fathers earning more than non-fathers (Booth & van Ours, 2008). Ong and Blumenberg (1998) argue that limited skills and low wages are more important than gender boundaries in determining travel-to-work. Moreover, another job related factor influencing women shorter commutes may reside in occupational segregation as industries employing relatively more women
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are the ones where jobs are usually more geographically evenly spread (Halfacree, 1995) whereas “male-dominated” industries have similar commuting times for women and men (Hanson & Pratt,1995). Another interesting factor that is important to consider when investigating differences in commuting patterns by sex is related to race/income/transport-mode groupings as previous research on gender commuting differences have been found to be small or absent among Hispanic and Afro-american U.S. commuters who use the same travel mode (McLafferty & Preston, 1997,Doyle & Taylor, 2000) and
Sultana (2003) shows that regardless of socio‐economic or occupational status, black females in Atlanta face a general spatial discrepancy with respect to white females living in more central-areas which leads the first ones to commute longer.
Workers are expected to perceived commuting costs in different ways as they age also because the older a worker is the higher his/her Working Experience and Human Capital accumulation is especially in relation to the same work-place or at least to the same sector and these factors usually lower the willingness to accept longer distance jobs (Booth, Francesconi & Garcia‐Serrano, 1999; Topel & Ward, 1992). However, older workers are expected to be home‐owners and have family obligations to take care of, which make the cost of permanent mobility much higher compared to young workers (Romani, Surinach, & Artis, 2003; van Ham, Mulder & Hooimeijer, 2001) that in turn might be more likely to accept higher commuting costs since they know that these costs are temporary (Van Ommeren, Rietveld, & Nijkamp, 1997). Other interesting findings in literature has linked different commuting behaviours not only with respect to age but also in relation to the gender dimension as in Benito and Oswald (2000) mixed effects of age are found with women’s travel-to-work times reducing sharply with age while that of men increased (after controlling for other job and individual characteristics).
Another crucial individual characteristics in order to understand commuting is represented by education, a widely included element in empirical analysis always identified in positive correlation with the individual propensity to travel-to-work for longer distances and time (McQuaid & Chen, 2011; Romaní ,Suriñach & Artiís,2003).The reasons why education can help explaining variation in workers’ travel behaviours are many.
First of all, investments in human capital can strongly influence both job and home location as higher educated workers are more likely to own rather than rent houses so the residential mobility is likely to be lower, which is translated in longer commutes. On the labor market, more educated people are usually more willing to travel longer distances to realize their professional expectations (Groot, de Groot and Veneri,2012) as well as they are ,on average, paid more than low-skilled workers so they can afford higher housing prices and live in residential and low-dense populated suburbs. However, education might have a specific role in terms of commuting behavior that goes beyond its effect on wages. First, the spatial job-search area for more educated workers is wider if compared to the one for lower educated individuals as they are relatively more likely find a fulfilling job when travelling further compared to lower educated workers (van Ham et al.,2001; van Ham & Hooimeijer, 2009). Moreover, the high-educated job market
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is usually concentrated in large urban centers (Borsch & Supan,1990) while low-educated workers earn competitive wages (Simpson, 1992) that are similar almost everywhere and their utility can be maximized by choosing a job as close as possible to where they live. In light of this, a second reason for the positive effect of higher education on working mobility might be that highly educated workers are also more willing to use public transport since they have the possibility to carry out part of their work during the trip and therefore lowering the perceived economic and non- costs of commutes, considering that they might also benefit on some monetary compensation for using public transport instead of private vehicles thanks to their stronger bargaining power (Romani et al.,2003) or to their higher share of employment in the public sector institutions such as Universities, where usage of public transportation is often strongly promoted.
Among the individual characteristics, It is easy to image house ownership can deeply influence individuals’ and households’ choices on mobility and migration and previous research shows increasing commuting propensity and length (in terms of time and distance) for those owning a house rather than renting it, as it is easier for tenants to change residence and move near to their workplace (Zax & Kain,1991) especially when the previous length of residence is not lasting for long term as people who are, in different ways, deeply settled in a geographical area are very likely to be immobile and to remain residents of that place (Fischer & Malmberg, 2001). A stronger place attachment might also influence homeowners to be more reluctant to move in response to changes in labour markets opportunities (Kantor, Nijkamp & Rouwendal, 2012) because of the higher non‐monetary costs perceived.
Finally, looking at commuting as a cause and not as a consequence, another interesting aspect to consider is the effect that travel-to-work can have on the individual quality of life. The commuter lifestyle presents both rewards and costs. On one side, frequent travelling (most of the time) towards metropolis can generate a weakening of identity and linkage with the original community and to the family (Nuvolati,2007; Bunker et al. 1992), an exposure to criminality, health problems such as stress and air pollution issues (Putnam, 2010; Gatersleben & Uzzel, 2007) but also transportation costs. These negative effects seem to appear particularly damaging for women due to their greater responsibility for family and home care (Roberts, Hudgson, & Dolan,2011; Costa, Pickup and Di Martino,1988). On the other side, commuting improves economic conditions, job opportunities, personal satisfaction and even cultural curiosity thanks to the chance to build social relationships not only with the own local environment but also with the fascinating city (Nuvolati,2007; Lyons, Jain, & Holley, 2007) or, simply, it gives some time to enjoy a relaxing break after a working day.
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1.2 Household Characteristics
The major drivers influencing commuting patterns at household level are represented by partners’ cohabitation and childcare responsibilities. With respect to the former, married workers show the tendency to prefer commutes rather than moves as in case of relocation one spouse is likely to lose his/her job (van Ham,2001; Flowerdew, 1992; Green, 1997). Moreover, if traditional commuting model assume single-wage owner households (Sultana, 2006) usually translated into higher time flexibility for married males due to the fact that the partner deals with domestic housework (van Ham et al., 2001; Van Ommeren et al., 1997) the growing female participation in the labour market leads to the increase of dual-earner households that have been identified as an obstacle to the job-housing balance concept because of their constrained ability to choose a residential location near both workplaces (Sultana, 2005). Moreover, there is evidence suggesting that members of two-worker households travel the same or less than one-worker households (Surprenant‐Legault, Patterson & El‐Geneidy, 2013) where one partner commute distance has a positive impact on individual commute distance, suggesting complementarity for partners’ distances. However, this does not imply that dual-commuters households do not trade-off commute distance but they rather try to decrease the joint travel distance (Flowerdew, 1992; Green, 1997). Finally, evidence is also found in favour of more sensibility of dual-owners households with respect to wives’ earnings rather than husbands’ ones, but only when children are not present (Mok, 2007).
Moving to parenting responsibilities, the birth of a child may result in a household moving to a more suburban location due to factors such as schooling quality with a corresponding increase in commuting time (Crane & Takahashi,2009). However, childbirth might affect fathers differently with respect to mothers since there is evidence supporting the thesis that the presence of school-aged children has no effect on men’s commuting times but it is associated with shorter commuting time for women, because of the perceived higher value of leisure time due to childcare responsibilities (Roberts & Taylor,2016; McQuaid, Chen, 2011). Nevertheless, an opposite effect is observed in case of single-parents household
as Preston et all. (1993) pointed out in a study, showing that the presence of school-age children significantly increase commuting times of single mothers with variation depending on ethnicity (with white women commuting longer than other races) and place of residence (where ethnic differences result less determinant in suburbs with respect to the city center).
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1.3 Job Characteristics
Job characteristics have primary importance in determining commuting patterns. Starting with working status, van Ommeren & van der Straaten (2008) assume that commutes of self-employed individuals are the result of a search process for vacant workplaces whereas employees search for vacant jobs and since the arrival rate of workplaces is much higher than the arrival rate of jobs then it is reasonable to infer that self-employed workers minimize their commute whereas employees may have to accept jobs far away, as also found by Giuliano (1998). Moreover, industries at high concentration of self-employment might be more spatially decentralized and this further corroborates the previous findings (Romani et al.,2003;Stutzer and Frey,2008).
Another important role is played by job tenure, whose increase is translated into a greater firm attachment and sector specialization but also into shorter time before retirement and these all make job changes more costly both in monetary and non-monetary terms, also considering that during their careers workers may had the possibility to relocate near their workplace (van Ham et al.,2001). Wage rates also influence commuting especially if combined to individuals’ housing situation as a job change in lower paid workers is more likely to cause a longer commute rather than a change of residence due to the inability to afford more central housing (McQuaid,Chen,2011). Housing location and job availability mismatches also emerges as strong influence for commuting (Sultana,2002) suggesting that higher quality housing growth close to job-rich communities are fundamental in order to economize working mobility.
Full-time occupation is also find as a further push to commute for longer time (Hong, Lee, Mc Donald,2002; McQuaid,Chen,2011) and the reasons behind are many. First, part-time turn over presents higher rates and second, workers are usually younger, low wage and/or female and these are all factors previously targeted as negative influences for longer commutes (Salmieri,2009;Giuliano,1998; Dijst&Schwanen,2002). A greater working flexibility is another significant factor influencing pendular work as working from home induces people to accept longer travelling time usually concentrated in certain days of the week (sometimes of the month) as found in de Vos, Meijers et all. (2017).
In the mainstream literature, the type of contract a worker is hired on also drives the willingness to commute with an increasing effect. In particular, temporary employees tend to commute more than permanent ones (Parenti & Tealdi,2015) due to the limited period of their contracts which might or might not turn into a renewal at the end (a situation that rarely occurs in Southern Europe, according to the
European Commission,2010). In addition, an higher perceived uncertainty on future employment can entangle temporary workers to their current residence even if it implies longer commutes in the meantime (Rouwendal and Meijer,2001) despite the fact that being hired for fixed time happens more frequently for women (OECD,2007; Alach & Casey,2004).
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Moving on, in line with the previous findings related to education the occupational level presents similar effect as several studies show a positive relationship between high skilled jobs and propensity to commute (van Ham et al.,2001;Finland Statistical Office,2017) along with specific economic sectors that are likely to extend travel time to work, again in strong correlation with the attained education level (i.e. engineering, architecture, financial studies etc.). The size of the hiring firm may also have an effect on commuting as evidence shows that large business induce workers to travel longer distance to reach their workplace (Scherer,1976). In part, this may be due to the ability of larger firms to recruit from a larger territory but they are also more able than smaller companies to afford payback schemes for employees’ transportation costs (Paci et al.,2007).
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1.4 Macro (Regional) Determinants
The spatial context affects residents’ mobility in several ways but one of the most important influences concerns the local labour market features and, among all, job uncertainty. More precisely, empirical studies shows that the likelihood of inter-regional mobility to other labour markets increases with regional unemployment that is further boosted when there has been a previous individual experience of migration or commuting (Eliasson et al.,2010;Roberts & Taylor,2017;Crane,1996). Therefore, the economic investment and consequent specialization of a region not only influence its own labour market but also the one in the surrounding network (Reggiani et all.,2011) so it is important for policy makers at the regional level to be aware of the functional interdependencies between regions, in particular when these regions are not functional in labour markets’ terms (Cörvers & Hensen,2003). Given the scarce access to local job opportunities, workers might decide to commute in order to escape the resulting underemployment from spatial mismatch (Preston & McLafferty, 1999;van Ham et al.,2001). Nonetheless, regional disparities in labour context occurs not only opportunity wise but also in terms of monetary reward (Bentivogli & Pagano,2003;Muellbauer & Cameron,1998) with relatively higher wage rate areas attracting workers from a larger territory with respect to the average (Reggiani et al.2011). Generally, job uncertainty is not uniquely determined by unemployment rates and instead it interacts with many different phenomena such as temporary work and housing costs. With respect to the former, it is reasonable to assume the existence of a positive correlation between the usage of temporary contracts with job uncertainty which in turn represents a push factor that drives workers away in search of better job opportunities (Parenti & Tealdi,2019).
At regional level, this can be translated with a higher probability to cross borders for those who live in regions with a strong temporary contracts’ culture. Moreover, an increasing proportion of temporary contracts can cause a reaction with opposite sign for the individual willingness to live close to the workplace that is, given the high turnover rate characterizing temporary work then workers cannot make long-term plans with respect to where it is more convenient to live (van Ham et al., 2001). The economic regional specialization is thus very important as temporary contracts are particularly employed in specific economic sectors (mainly the ones of seasonal nature such as agriculture and tourism) and this might influence as well the individual propensity to commute (Gagliarducci,2005). As previously introduced, housing cost has a major role in individuals’ choice of permanent location since commuting is, at least for contiguous regions, often an alternative to migration (Muellbauer&Cameron,1998; Reitsma&Vergoossen,1988). It is not infrequent for high job-opportunity regions to have prohibitive house prices (Romani et al., 2003) thus making travel-to-work a more attractive option, even in its extend of long-distance commute (Allen,2014).
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Furthermore, the decision to commute instead of migrate is strongly influenced by the quality of transportation and communication infrastructures that can reduce perceived travelling time and distances with great extent especially when combined with high urbanization rates (Zhu et al.,2017) . During the past century, the steep decline in transportation costs that has been registered both for goods as well as for people movement (Glaeser & Kohlhase,2005) together with the technological innovation of new transportation modes such as the high speed railway has amplified the possibilities of travelling for commuting with significant changes in workers labour mobility (Guirao, Campa et al.,2018) even though when dealing with public transport some characteristics of the service such as its frequency and its ticket cost are essential determinants in users’ mobility choices (Lättman et al.,2016).
Finally, on a regional perspective, the macro-regional clusters based on by the economic specialization of contiguous territories (or agglomeration economies) can also drive the aggregated commuting propensity either towards a potential renaissance of rural areas that can benefit from spatial spillover created by the economic growth of their urban neighbours (Partridge, Ali, & Olfert, 2010) or towards the cline of these agricultural areas when people choose migration over commuting (Artz, Kim et al.,2016).
11 Table 1.1 – Literature Review and expected signs
Variable Description Literature Expected Sign
Individual Characteristics
Male Gender MacDonald, 1999; McLafferty & Preston, 1997; Sandow
&Westin, 2010.
+
Partner Cohabitation Partner van Ham,2001;Flowerdew, 1992; Green, 1997. +
Children Cohabitation Children Crane & Takahashi,2009. +
Age Classes: Age 16-24
Age 25-34
Age 35-49
Age 50 and over
Education Level:
Age between 16 and 24
Age between 25 and 34
Age between 35 and 49
Age equal or more than 50
Booth, Francesconi, & Garcia‐Serrano, 1999; Topel & Ward, 1992; Romani, Surinach, & Artis, 2003; van Ham, Mulder, & Hooimeijer, 2001. - - - + Primary Education Secondary Education Tertiary Education Elementary Education
Middle schooling and secondary upper Education Post-secondary and University Education
Ronald W. McQuaid, Tao Chen, 2011; Javier Romaní , Jordi Suriñach & Manuel Artiís,2003.
-
-
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Variable Description Literature Expected Sign
House ownership Ownership of the residence Zax & Kain,1991; Fischer & Malmberg, 2001 +
Skilled Job High skilled job van Ham et al.,2001;Finland Statistical Office,2017 +
Job Characteristics
Employee Working status Giuliano,1998; Romani et al.,2003;Stutzer and Frey,2008 +
Job Tenure Contract Length van Ham et al.,2001. -
Temporary Contract Type of Contract Parenti, Tealdi,2015; Rouwendal, Meijer,2001. +
Full-time Full-time working activity Hong, Lee, Mc Donald,2002; McQuaid,Chen,2011. +
Firm Size: Small Firm
Medium Firm
Large Firm
Firm employing <20 workers
Firm employing 20-49 workers
Firm employing ≥50 workers
Scherer,1976;Paci et al.,2007.
-
-
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Variable Description Literature Expected Sign
Regional Characteristics
Unemployment Unemployment rate Eliasson et al.,2010;Roberts & Taylor,2017;Crane,1996 +
Wage Wage rate Bentivogli&Pagano,2003;Muellbauer&Cameron,1998;Reggiani et
al.2011
-
Temporary Contracts Regional share of temporary contracts
Parenti & Tealdi,2019; van Ham et al., 2001 +
Level of Urbanisation Regional Level of Urbanisation Zhu et al.,2017 +
Primary sector
Secondary sector
Tertiary sector
Knowledge sector
Regional Share of Primary sector
Regional Share of Secondary sector
Regional Share of Secondary sector
Regional Share of Secondary sector Gagliarducci,2005 + NA + NA
Transport quality Quality of Transportation infrastructures
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Chapter 2
Data and Descriptive Statistics
After the previous overlook on what the existing literature has highlighted as driving factors of commuting, the second Chapter will present the Dataset and the Model that will constitute the grounds of both Panel and Spatial Analysis within the next Chapters. The conclusive section on Descriptive Statistics will help to gain some useful insights on the dataset before going to the heart of the empirical part.
2.1 Data
In the next chapters analysing workers’ outflows across EU regions, a central role will be played by the European Union Labour Force Survey (EU-LFS) that is a large household sample survey on labour participation of people aged 15 and over as well as people outside the labour force and covers the 28 Member States of the European Union plus three members of the European Free Trade Association (EFTA) namely Iceland, Norway and Switzerland. The data collection is available from year 1983 onwards, where surveys are first conducted by each national statistics institute across Europe and then they are centrally processed by Eurostat, which harmonises data at European level.
The survey provides demographic and socio-economic information at individual level with particular focus on employment and job characteristics. Moreover, it also supplies information on both current residence and workplace of the interviewed that allows to capture work mobility (express through commuting) whenever the NUTS nomenclature2 of workplace differs from the residential one.
The initial focus of the study was to consider only NUTS 2 territories within the European continent, unfortunately, at this aggregating level data were missing for several countries and in order to preserve their information the choice was to involve their smallest available NUTS level (for United Kingdom, Austria and Germany the aggregation considers NUTS1 while for the Netherlands, Switzerland and Lithuania aggregation is done at NUTS0). Furthermore, there are countries whose nomenclature remains the same at all three NUTS levels (namely Estonia, Luxembourg and Latvia). There are also territories that have been intentionally excluded from the analysis as they showed exclusively internal
2 The NUTS (Nomenclature of Territorial Units for Statistics) classification is a geocode standard for referencing the subdivisions of countries for statistical purposes. The standard, adopted in 2003, is developed and regulated by the European Union, thus providing detailed information only for EU members as well as Norway and Switzerland. For each EU country, a hierarchy of three NUTS levels is established by Eurostat in agreement with each member state however the subdivisions of some levels do not necessarily correspond to administrative divisions within countries. In smaller states, where the entire country would be placed on the NUTS 2 or even NUTS 3 level (ex. Luxembourg), the regions at levels 1, 2 and 3 are identical to each other (and also to the entire country), but are coded with the appropriate length codes levels 1, 2 and 3.
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commuting (thus risking to unbalance estimates) or a change of internal borders3 (hence returning
inaccurate data) or again an unreliable weighting design (i.e. the Greek case).
The final sample (Map 2.1) includes 26 countries - 24 EU members plus 2 EFTA members - classified at various NUTS levels for a total of 195 territories representing citizens’ residence or “Origin”4. Moreover,
since some ELFS information are missing for both Norway and Switzerland regions (i.e. Annual Compensation of Employees and the Number of Households by region) data are added from the Swiss Labour Force Survey (SLFS) and from the Household and Families section of the Norwegian Statistics Office when necessary.
Map 2.1 – The final sample of 195 Origins
Starting from ELFS individual surveys containing information on sex, education, age, type of household, working status and job characteristics (such as profession, type of contract and job tenure) the dataset is enriched by other contextual variables including regional Long-term and Youth Unemployment Rates (Eurostat) in order to check for specific effects that different unemployment measures might have on commuting. In addition, the National House Price Index (Bank of International Settlements) is
3 For the majority of these cases (Cyprus, Malta ,ES70-Canarias and Iceland) the exclusive internal commuting can be explained by the very peripheric position that these countries have within the European continent while Slovenia was excluded because of an internal boundaries change occurred in 2010. Other omitted territories are EU member states’ overseas territories (i.e. French territories in Africa).
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considered in order to capture any potential difference among regional outbound flows due to price discrepancies in real estate markets and finally two regional indexes related to Road and Railway Quality (Eurostat) are included to measure the impact of transport infrastructure on commuting. From here, only individuals having an active working status (identified by the variable ILOSTAT) will be weighted and aggregated at the designated NUTS level. Unfortunately, the initial target for the inclusion of only NUTS2 areas had to be abandoned due to the scarce availability of good quality data over years so that various aggregation levels were considered at the end. Consequently, the choice on the time interval was finally for data from 2007 to 2017 in order to preserve the opportunity to highlight the effects of the 2008 financial crisis. In doing so, the analysis will consider an additional distinction of data in two subperiods named Recession and Post-Crisis, whose division is motivated by Figure 2.1 below. Here, the inclusion of two important economic trends based on the regional levels of GDP and unemployment rate for the sampled territories shows a remarkable divergence of the two especially during the 2007-2011 interval (i.e. the Recession period) while from 2012, despite an initial increase of the unemployment rate, the two lines seem to follow opposite trends that gradually confirm a slow recovery of the economic system.
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2.2 The Model
2.2.1 The dependent variable
The dependent variable of this analysis will be the regional (and, in some cases, macro-regional or national) share of cross-border commuters. As previously said, for each paired country-year dataset only people having an occupation are considered and their identification is given by the “Employed” modality of the ILOSTAT variable, which follows the International Labour Organisation (ILO) definition of Working Activity5. After only the employed population is taken into account, then the regional share of
inter-regional commuters of a given year is found first creating an individual dummy that highlights whether residence and work place are different and then the NUTS aggregation is performed through the formula 𝑆ℎ𝑟_𝐶𝑜𝑚𝑚𝑡𝑖 = ∑ 𝜔r W𝑑r 𝑐 R r=1
where for every resident r then 𝑑𝑟𝑐 { 1 𝑤𝑜𝑟𝑘𝑝𝑙𝑎𝑐𝑒 ≠ 𝑟𝑒𝑠𝑖𝑑𝑒𝑛𝑐𝑒0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 is the individual dummy, 𝜔𝑟 is the
unique individual design weight and 𝑊 = ∑ 𝜔𝑅1 𝑟 is the regional sum of weights related to the
𝑟 = 1, … , 𝑅 working individuals. Therefore, the annual region’s i share of cross-border workers is given by the weighted mean value 𝑆ℎ𝑟_𝐶𝑜𝑚𝑚𝑡𝑖.
Every economic research aims to understand what driving factors and dynamics lie behind the investigated social phenomenon and that is why models are often written as equations where the right-hand side is supposed to explain the left one. Here, the element of interest is going to be represented by the previously computed commuting share while the other side is going to be filled by different variables over several aspects (such as socio-economic, demographic and geographic characteristics) that literature identifies as potential triggers influencing commuters behaviours. This will lead to the initial econometric model
𝑆ℎ𝑟_𝐶𝑜𝑚𝑚𝑡𝑖,𝑡= 𝛼 + 𝑋𝑖,𝑡𝛽 + 𝜀𝑖,𝑡
where the outcome variable 𝑆ℎ𝑟_𝐶𝑜𝑚𝑚𝑡𝑖,𝑡 of region i in year t is here defined by the regional set of
variables X (i.e. regressors) as well as by the error term 𝜀𝑖,𝑡 that has a central role in determining what
framework can be used in order to estimate the strength of the relationships between regressors and explained variable. In particular, the error terms across regions and time are here assumed to be random variables that are identically and independently distributed (i.i.d.) since this property allows to consider
5 Persons employed in the sense of the ILO are those who worked for any amount of time, if only for one hour, in the course of the reference week. This notion is different from that of employment in the sense of the population census, which concerns persons having declared they had a job on the census form. The notion of employment in the sense of the ILO is broader than that in the sense of the population census as some people may consider that occasional jobs are not worth declaring in the census.
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data points to be generated by the same probability distribution. In addition, the probability distribution of the exogenous random part is assumed to be a bell-shaped Gaussian distribution with 𝜀 ~ 𝑁(0; 𝜎2) ∀ 𝑖, 𝑡 .
Moving more into details, a last introductory step will now describe the explanatory variables in the regional set X by grouping them into three main categories: the regional socio-demographic and job related characteristics, both derived by aggregating individual information, as well as other specific regional features gained from external data.
2.2.2 1
stGroup of Regional Characteristics based on Aggregated Individual
Socio- Demographic Variables
Regional Share of males : Regional share of active male population. According to the literature
(MacDonald, 1999; McLafferty & Preston, 1997; Sandow & Westin, 2010), the expected sign of its relationship with cross-border commuting is positive.
Regional Share of cohabitating partners : Share of cohabitating couples at regional level. This variable is
gained from the individual dummy that switches on whenever the interviewed shares the residence with his/her partner and it is used instead of the more traditional Martial Status in order to get a complete picture by including all kinds of couples living together (being or being not married). Previous studies found different dynamics for dual-earner households with respect to the single ones (among all Sultana, 2005; Surprenant‐Legault, Patterson & El‐Geneidy, 2013) however ELFS does not allow to track partners’ working status therefore a more general assumption in favour of a positive effect of commuting is considered.
Regional Share of households with in-house offspring : Regional share of households with children living
at home (not controlling for their age) gained from the individual dummy capturing offspring in-house presence for those who are parents. The dataset also provides a variable identifying the number of 00-14 years old persons in the household which might be useful for a robustness check. According to the literature (Crane & Takahashi, 2009), a positive sign is here expected.
Regional Population Distribution by Age : Shares of regional population strata by four age-classes,namely
individuals between 15 and 24 years old, 25-34, 35-49 and 50 and over. These groups are gained by the individual information on age. According to the previous research (Romani, Surinach & Artis,2003; van Ham,Mulder & Hooimeijer,2001)an increasing propensity to commute is found for elderly workers.
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Regional Population Distribution by Education : Regional population shares by educational attainment
where the individual variable indicates the highest achieved ISCED6 level and the regional aggregation
computes shares of residents being at Primary (elementary schooling), Secondary (lower and upper higher schooling) and Tertiary (university schooling) level. Here, the expected sign is positive for an increasing education level (Ronald W. McQuaid, Tao Chen, 2011; Romaní, Suriñach & Artiís, 2003).
2.2.3 2
ndGroup of Regional Characteristics based on Aggregated Individual Job Features
Regional Job Tenure Length : Average value of regional contracts’ length based on the weighted mean ofindividual job tenures expressed in months. For an increasing value of the outcoming regional job tenure, a lower propensity to commute is expected (in line with the individual studies of van Ham et al., 2001).
Regional Share of Full-Time Contracts: Regional share of working residents in full-time occupation.
Starting from the individual variable detecting full-time working activity then the regional share is gained via aggregation (using the weighted average). According to the literature (Hong, Lee, Mc Donald, 2002;
McQuaid & Chen, 2011) a positive correlation with the dependent variable should emerge.
Regional Share of Temporary Contracts: Regional share of temporary contracts. Following the previous
full-time feature, ELFS provides also an individual question that highlights whether the interviewed is hired on a contract of temporary duration. From here, the weighted mean value gives the regional estimate that previous research identifies as push factor for longer commutes (Parenti & Tealdi, 2019).
Regional Employment Distribution by Firm size : Regional Employment distribution by firm size defined
by the number of hired workers. Among the individual questions on job occupation, a specific variable asks to every employed person for the total number of co-workers in his/her firm. Given this piece of information, the regional aggregation classifies companies into Small, Medium and Large firms with cuts at 20, 50 and more than 50 employees. Following the literature based on individual agents (Scherer, 1976) it will be interesting to see whether aggregated values present the same dynamics, that is for an increasing share of employment in large business than the relationship with the explained variable is positive.
6 The ISCED classification - International Standard Classification of Education - was developed by UNESCO in the mid-1970s and was first revised in 1997. Further reviews of schooling levels were undertaken during years. For period 2007-2017, information on education are based on ISCED 97 until 2013 and ISCED 2011 from 2014. In order to allow for comparison, the latest version (for a total of 8 levels) has been converted to ISCE 97 version (6 levels).
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Regional Employment Distribution by Economic Sectors: Regional shares of Employment by economic
activities and industrial sectors. The survey asks for the NACE7 code of the occupation thus providing t
he individual job categories. However, aggregation is based on industrial sectors rather than professions because of the variety and frequent changes in their definitions during the considered time period and four main economic sectors are identified as follows:
Primary Sector includes the employment in Agriculture and Forestry, Fishing, Mining and
Quarrying industries;
Secondary Sector comprehends the employment in Manufacturing, Electricity and Gas, Water
Supply, Construction, Vehicles Repairing and Wholesale & Retail Trade activities8; Tertiary Sector includes the employment in Hospitality (namely Hotels and Restaurants),
Logistic and Storage, Real Estate, Administration and Business Support, Public
Administration, Recreation and Households Employees sectors;
Knowledge sector considers the employment in Information and Communication, Education,
Social Health, Finance and Consulting activities as well as Professionals such as lawyers, architects, engineers, medical practitioners etc. This last sector focuses on those industries characterized by the intensive use of technology and/or human capital in their activities. A particular interest for its potential effect on commuting is justified by several studies that link individuals belonging to this category as more keen to commute.
Previous research highlights a higher propensity towards mobility for specific job categories, particularly concentrated in the Knowledge sector (de Vos, van Ham et al., 2019) as well as for high skilled jobs (van Ham et al., 2001;Finland Statistical Office, 2017). However, the previous classification will focus on the regional specialisation in different economic sectors rather than on the individual occupation and this might lead to different results.
7 NACE acronym (Nomenclature générale des Activités économiques dans les Communautés Européennes or Statistical classification of economic activities in the European Communities) is used to designate the various statistical classifications of economic activities developed since 1970 in the European Union. Statistics produced on the basis of NACE are comparable at European and at world level. The use of NACE is mandatory within the European Statistical System. Here, the available information give the first level of classification (i.e. section).
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2.2.4 3
rdGroup of Regional Characteristics based on External Data
Regional Unemployment Rate Differential: Regional index that compares unemployment rates across
NUTS. In order to get a more realistic picture, instead of including regional unemployment rates the differential measure expresses a certain region’s unemployment level with respect to the average of the other ones. Therefore, starting from the regional unemployment rates then for every region the corresponding Unemployment Rate Differential is computed as ratio of its annual rate of unemployment over the weighted average of the unemployment rates of all the other regions (or potential destinations) where weights are based on the distance between NUTS centroids. In particular, for every region at the numerator, all paired distances with the regions at the denominator are first inverted and then row-standardised to get weights. Following the literature on the unemployment effect on work mobility (Eliasson et al., 2010;Roberts & Taylor, 2017;Crane, 1996) the relationship is expected to be positive as the lack of job opportunities is a notorious push factor towards migration in all its forms.
Regional Wage Differential : Similarly to the previous case, this Regional Index measures the income
dimension based on the regional value of the Annual Compensation of Employees9. The Index
is given by the ratio between the specific regional compensation at the numerator over the weighted average of the corresponding compensations of the other regions where weights are derived as before. Unfortunately, data are not available for Switzerland but the Helvetic national office of Statistics provides for the Annual Gross Income of Employees, here used in order to overdue the problem. According to Reggiani et al. (2011) higher wage rates should attract workers from larger territories.
Regional Road quality : The index is computed following Parenti et al (2019) and dividing the regional
highway length (in kilometers) by the area extension (per thousand kilometer squared) using Eurostat data. A symmetrical measure is also available with respect to the railway network length so that the corresponding Railway quality index is used instead of the first one in order to check for the effect of transport infrastructure on commuting, which are expected to be positive in both cases (Guirao, Campa et al., 2018).
Regional Level of Urbanisation : There are different measurements for population density and here,
following J. Makarov et al (2007),urbanisation is expressed as the national share of households for areas exceeding 500 individuals/km². Differently from the authors, the threshold for detecting urban areas has been lowered to 300 individuals as the original threshold returned a very poor subset of territories (not a very surprising result considering that,on average, the European population density is 120 indv./km²)10. 9 Eurostat identifies the Compensation of employees (at current prices) as the total remuneration, in cash or in kind, payable by an employer to an employee in return for work done by the latter during the accounting period. Compensation of employees consists of wages and salaries, and of employers' social contributions expressed in millions of euro.
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Moreover, the information on both Norway and Switzerland are missing so that the respective households shares and population densities are gained by the respective official national statistics websites, where data are available as open source. For increasing urbanization levels, a higher propensity to commute can be reasonably deduced (Zhu et al., 2017).
National House Price Level : The variable identifies the national house price for every included years, as
regional prices are not available. Data come from the International Bank of Settlement (IBS) which uses price indexes in order to capture any change in the real estate market with respect to a reference year (here 2010). Evidence is again in favour of a positive relationship with commuting that constitutes a more attractive option instead of relocation at increasing housing costs (Allen, 2014).
Regional Geographic Characteristics: A set of specific dummies is created in order to classify each region
of the sample by some geographical features such as being a Capital-City Region11 (Capital), an External
Region that shares its borders with at least one region belonging to another EU member (External), a Bordering Region that shares its boundaries with at least a non-EU member (Border) or an Internal Region, whose borders confine only with other regions belonging to the same country (Internal). There are many factors that can differently influence commuting according to the geographic position of the specific territory. For example, it is usual for capital cities and their outskirts to be relatively densely populated, hence urbanized, and this can attract commuters from longer distances (Zhu et al., 2017) or, in case of contiguity for regions belonging to different countries, the language barriers can represent a huge obstacle to cross-border commuting (Matha & Wintr, 2009).
11 The set includes Bruxelles, Berlin, Bucarest, Budapest, Madrid, Lisbon, Stockholm, London and Prague. These territories not only are capital cities but also NUTS2 regions.
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2.3 Descriptive Statistics
Before moving to the analytical part, it might be useful to take a closer look at the dataset through a descriptive summary. A first picture of EU regional commuting shares is given by Figure 2.2 below,
where the plotted variable over 2007-2017 (grey line) shows a general upward trend of particular acceleration in the second half of the period - especially from 2012 onwards - while the first part exhibits an erratic movement around 2009, the heart of the financial crack. Finally, the dotted red line corresponds to the temporal mean value of 5.9% (C.I.: 5,8 - 6,0 %), that is the average proportion of regional cross border commuters with respect to the total amount of employed individuals (either in self or in wage employment) for the period considered.
Figure 2.2- 95% C.I. Regional Cross-border Commuting in EU regions over 2007-2017
Other useful insights can be highlighted from the cross-border mobility distribution that is given by Figure 2.3 below, which considers both initial and final years. A first interesting observation is gained through the comparison of the two maps that shows how, in general, regions maintain their initial position with respect to the sample distribution despite a global increase of the corresponding quantiles values \(with the exception of the last one) hence confirming the positive trend of Figure 2.2. Moreover, first in the maps and even more clearly in the dot plots, several satellite regions12 present many of the
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highest cross-border shares, either comparing to National or to European levels13.
Throughout the decade, the regions with the highest proportion of cross-border commuters are those surrounding the city of Brussels (namely the Province Brabant-Wallon or BE31 and the Province Vlaams-Brabant or BE24) whose shares over 40% confirm the results of the European Commission (2015).
Figure 2.3- Regional Cross-border Commuting Distribution
Figure 2.3a : 2007
Figure 2.3b : 2017
13 To cite the most important: DE40-Brandeburg (surrounding area of Berlin), BE31- Province Brabant-Wallon and BE24- Province Vlaams-Brabant (surrounding area of Brussels), DK02-Zealand (surrounding area of Copenhagen),ES42-Castilla-La Mancha (surrounding area of Madrid), UKH0-East of England (surrounding area of London), FR22-Picardie (surrounding area of Paris) and CZ02-Central Bohemia Region (surrounding area of Prague).
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Moving into details and taking again years 2007 and 2017 as references, Table 2.1 (at the end of the section) summarises some statistical measures that can help in describing data. At first glance, it is easy to observe that many variables show remarkable standard deviations as well as outlying extremes (namely maximum and minimum values) due to the large heterogeneity of the European regions. That is, starting from the regional cross-border shares then the presence of outliers is proven by the remarkable differences between Third Quintile (or 75th Percentile) to Maximum value occurring in both reference years thus
supporting the idea for the existence of significant disparity within the sample, as previously observed. Other interesting dynamics seem to occur when considering the employment distribution by economic activities, as plotted in Figure 2.4 below. Here, the predominance of post-industrial industries, that represent the leading sectors for the majority of the European regions, is partially hidden by the separation into two different categories (i.e. Tertiary and Knowledge sectors) and the highest share of employment is consequently registered by the Secondary sector. Despite its supremacy, the persistent decline of industrial employment motivates the rise of the employment in technological industries (as shown in the figure) and also, with lower magnitude, in rural employment as indicated in the corresponding mean values of Table 2.1. Interestingly, the graph gives the opportunity to capture the relationship among Knowledge (dotted red line) and Tertiary sectors (dotted brown line) which seems to indicate a common positive trend, despite the initial asymmetrical movement.
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The strong presence of highly-skilled jobs in post-industrial activities, usually taken by highly educated individuals, motivates the need of a deeper overlook on Education and its distribution over time. That is, looking at the mean values of the corresponding shares in Table 2.1 the growth of employment in technological industries is here confirmed by an upraise of the Tertiary level in the European sample, as the corresponding share registered an increase over time of almost 8 percentage points, in contrast to the decline of the other attainment levels – respectively of –6 (Secondary level) and –2,8 (Primary level) percentage points – and despite the persistent predominance of secondary level over time.
With respect to socio-demographic variables, a last important aspect to discuss concerns the sample age structure that can be observed once again in Table 2.1. The picture coming out from the corresponding rows shows that while the two youngest classes (namely 16-24 and 25-34 y.o. individuals) registered a decrease in the corresponding shares - either referring to its mean value as well as to the other statistical measurements - the elder classes present constant (35-49 class) and increasing values (50 and over class), in line with the “aging” hypothesis of an older European population (European Commission, 2018). The longer life expectancy is often synonym of better living conditions and solid welfare system but it can also imply low fertility rates, with significant impact on the labour force composition (that is a higher female and older workers participation to the active population). However, it is important to recall that these results only refer to the working part of the population so that, in line with the previous findings on education, the increasing proportion of young adults that invest in their education and shift their entrance in the job market can explain - at least a part of - the resulting lower shares of young working classes. Moving to job characteristics, the variable capturing the weekly working hours shows an almost unchanged mean value of 37 hours in its time series. The tendency towards a full-time occupation for the majority of workers is also confirmed by the regional share of Full-time jobs whose mean value lies above 80% at the beginning as well as at the end of the decade.
With respect to job uncertainty, two variables here indicate whether contract terms have become more volatile. A first measure involves the regional share of temporary contracts, which kept a constant mean value around 15% (against the alternative of being employed in a permanent job) while a second measure of job uncertainty considers the unemployment rate differential, previously described in the Model Section. Looking at the corresponding rows in Table 1, the registered increase in its mean value implies that on average the disparity between the regional unemployment rates with respect to the closest neighbours has increased over time. Interestingly, the minimum value differs in the two years and finds first Rogaland (FIN04) and then the region of Central Transdanubia (HU21) at the bottom of the distribution whereas the maximum value is found for the Spanish region of Extremadura (ES43) , one of the territories that is still suffering for the tragic leftovers of the financial crisis.
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Literature finds in wage rates an important push factor on individual commuting and that is the reason for the inclusion of the regional wage differential in the dataset, whose measures seem to remain constant in 2007 as well as in 2017 (i.e. mean value and standard deviation). Looking at the sample, its maximum values are found for some regions of northern Europe namely the Île de France (FR10), the North Rhein-Westfalen (DEA0), the Netherlands (NL00) and Switzerland (CH00) that represent the locations with the relative highest wage income compared to the surrounding areas. However, the apparent generosity of these territories comes usually along with the unaffordability of the local house prices and this encourages workers to live in less expensive areas and accept longer commutes (Muellbauer & Cameron, 1998; Reitsma & Vergoossen, 1988). In Figure 2.5 below, a time series on the sample national House Price Index (HPI) allows to get a clearer picture on the property values dynamics as it highlights a rocketing trend starting from 2013, in contrast to the initial decline over the first 5 years and in line with the risky markets conditions of those times.
Figure 2.5 – Sample average national House Price Index (HPI) over decade 07-17 (2010=100)
Finally, moving to the employment distribution based on business size, the general increase in the shares of large companies corresponds to the decrease of small business employment thus supporting the hypothesis of Bourletidis & Triantafyllopoulos (2014) that stands in favour of the idea that the financial crisis had particularly damaging effects for small and medium enterprises because of their limited financial resources, that usually force them to depend on banks’ lending policies.
28 Table 2.1- Descriptive Statistics for 2007 and 2017
Variable N Mean St. Dev. Min Pctl(25) Pctl(75) Max
Shr_Commt
2007 195 0.055 0.068 0.0001 0.018 0.065 0.457
2017 195 0.063 0.069 0.000 0.024 0.078 0.453
Share Male Population
2007 195 0.552 0.031 0.489 0.532 0.561 0.674 2017 195 0.541 0.027 0.486 0.525 0.554 0.649 Share Cohabitating Partners 2007 169 0.664 0.058 0.442 0.634 0.705 0.762 2017 168 0.670 0.040 0.542 0.646 0.699 0.763 Share In-House Offspring 2007 169 0.302 0.049 0.183 0.272 0.335 0.447 2017 168 0.300 0.048 0.197 0.267 0.329 0.509 Number of 00-14 in the household 2007 169 0.738 0.484 0.378 0.513 0.710 3.642 2017 168 0.674 0.313 0.385 0.504 0.676 2.366 Share Native 2007 195 0.932 0.072 0.538 0.902 0.993 1.000 2017 195 0.895 0.088 0.441 0.851 0.965 1.000 Age Classes : Share 16-24 2007 195 0.097 0.027 0.034 0.077 0.116 0.190 2017 195 0.076 0.030 0.030 0.052 0.097 0.154 Share 25-34 2007 195 0.244 0.038 0.162 0.212 0.274 0.329 2017 195 0.214 0.031 0.140 0.191 0.237 0.313 (Continues)
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Variable N Mean St. Dev. Min Pctl(25) Pctl(75) Max
Share 35-49 2007 195 0.408 0.034 0.322 0.382 0.434 0.485 2017 195 0.391 0.044 0.304 0.350 0.425 0.481 Share 50over 2007 195 0.251 0.037 0.170 0.224 0.275 0.373 2017 195 0.319 0.040 0.176 0.290 0.350 0.428 Education Level : Share Primary 2007 195 0.058 0.093 0.000 0.004 0.074 0.570 2017 195 0.030 0.052 0.000 0.004 0.033 0.347 Share Secondary 2007 195 0.659 0.121 0.277 0.588 0.747 0.897 2017 195 0.605 0.107 0.335 0.527 0.681 0.847 Share Tertiary 2007 195 0.277 0.093 0.091 0.198 0.335 0.565 2017 195 0.361 0.103 0.141 0.284 0.427 0.641 Economic Sectors : Share Primary 2007 195 0.012 0.021 0.000 0.001 0.011 0.120 2017 195 0.054 0.060 0.000 0.020 0.074 0.467 Share Secondary 2007 195 0.441 0.088 0.243 0.380 0.504 0.674 2017 195 0.379 0.077 0.194 0.327 0.435 0.590 Share Tertiary 2007 195 0.339 0.067 0.196 0.291 0.372 0.582 2017 195 0.361 0.103 0.141 0.284 0.427 0.641 (Continues)
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Variable N Mean St. Dev. Min Pctl(25) Pctl(75) Max
Share Knowledge 2007 195 0.209 0.055 0.113 0.166 0.252 0.370 2017 195 0.290 0.081 0.095 0.233 0.353 0.479 Job Tenure 2007 195 120.655 18.081 82.551 106.270 135.890 165.519 2017 195 128.315 20.166 80.708 112.644 144.736 176.843 Usual Working Hours
2007 195 37.120 2.675 29.588 35.816 38.599 43.312
2017 195 36.790 2.402 30.334 35.516 38.599 41.870 Share Full-Time Jobs
2007 195 0.843 0.083 0.537 0.780 0.907 0.987 2017 195 0.824 0.088 0.493 0.749 0.910 0.994 Share Temporary Contracts 2007 195 0.149 0.090 0.004 0.083 0.192 0.413 2017 195 0.150 0.079 0.001 0.090 0.199 0.361 Firms Distribution :
Share Small Size
2007 195 0.316 0.173 0.005 0.227 0.349 1.000
2017 195 0.261 0.080 0.005 0.203 0.319 0.447
Share Medium Size
2007 195 0.163 0.045 0.000 0.152 0.189 0.255
2017 195 0.177 0.031 0.090 0.153 0.201 0.253
Share Large Size
2007 195 0.403 0.129 0.000 0.343 0.491 0.677
2017 195 0.435 0.093 0.140 0.367 0.506 0.640
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Variable N Mean St. Dev. Min Pctl(25) Pctl(75) Max
Houseprice 2007 195 104.519 12.327 87.820 97.300 111.140 182.670 2017 195 113.856 21.946 83.660 98.170 131.950 177.560 Urbanisation Level 2007 195 0.030 0.092 0 0 0 1 2017 195 0.033 0.094 0 0 0 1 Roadquality 2007 195 0.024 0.028 0.000 0.004 0.032 0.223 2017 195 0.027 0.030 0.000 0.007 0.035 0.254 Railquality 2007 195 0.080 0.113 0.000 0.033 0.091 1.121 2017 195 0.080 0.112 0.000 0.033 0.091 1.126 Unemployment Differential 2007 195 0.982 0.429 0.284 0.660 1.204 2.692 2017 195 0.958 0.484 0.307 0.616 1.176 2.722 Wage Differential 2007 195 0.508 0.734 0.010 0.126 0.589 5.100 2017 195 0.509 0.748 0.012 0.136 0.586 4.429 Georaphical Dummies Coastline NORTH 195 0.241 0.429 0 0 0 1 Coastline SOUTH 195 0.195 0.397 0 0 0 1 Capital 195 0.046 0.210 0 0 0 1 External 195 0.441 0.498 0 0 1 1 Border 195 0.077 0.267 0 0 0 1 Internal 195 0.133 0.341 0 0 0 1
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Chapter 3
Panel Analysis
The Chapter starts with a brief explanation of the applied Methodology and proceeds with a second section dedicated to results. Finally, a conclusive section offers an overview on different Unemployment measures that have been subsequently applied to the model in order to further explore the relationship among out-bound commuting and unemployment. It should be recalled that this analysis is based on correlations, which give the direction and magnitude of a relationship between two variables however they should not be confused with cause-and-effect relationships.
3.1 Methodology
The dataset previously described belongs to the class of Panel or Longitudinal Data, meaning that the same sample units are under observation over time. That is, starting from the generic linear model:
𝑦𝑖 = 𝛼 + 𝛽𝑖⟙ xi+ 𝑢𝑖𝑡 (1)
where i = 1,…,N indexes individuals, y represents the dependent (or outcome) variable, x is the set of independent variables (or regressors) accounted for explaining the variation of y through the estimation of the coefficients in β and u represents the random disturbance term of 0 mean. Adding the time index t will lead to the new specification that is
𝑦𝑖𝑡 = 𝛼 + 𝛽𝑖𝑡 ⟙𝑥
𝑖𝑡+ 𝑢𝑖𝑡 (2)
where the so called longitudinal property can be observed. In order to contextualise the previous definitions to this specific research, the dependent variable y will be the Regional Share of Cross-border commuters previously created through aggregation whereas the sample of units will be formed by the 195 European regions14. The set of explanatory variables accounted to influence regional commuting over the
period 2007-2017 will be the set of socio-demographic and economic determinants previously found in Chapter 2.
Switching again to the theoretical framework, further explanations might be useful before moving to results. One of the main issues in Panel Data concerns homogeneity that is whether it is reasonable to consider individuals as fully comparable, namely with no parameters distinction or 𝛼𝑖𝑡= 𝛼 and 𝛽𝑖𝑡= 𝛽
for all i,t . When this holds, then Equation (2) becomes
𝑦𝑖𝑡 = 𝛼 + 𝛽⟙𝑥𝑖𝑡+ 𝑢𝑖𝑡 (3)
14 However, even though the sample majority is based on NUTS2 elements, some territories are included at NUTS1 (Austria, Germany and United Kingdom) and NUTS0 levels (Switzerland and the Netherlands and Lithuania) whereas Luxemburg, Latvia, Estonia share same notation for the three levels.
