FEVE Crossroads

Understanding Obligación

This essay builds a model of the human connectivity offered by Spain’s railways, revealing the patterns between Spaniards and the democractic tension therein, with income analysis that explores the import of “Obligación de Servicio Público”. “Understanding Obligación” is the fourth essay in the sequence “Café Para Todos“, an exploration of the contemporary relationship between the railways and the people of Spain. The first essay, “Saving Ferroviarias“, reviews the broad policy context of Spain’s passenger railways, highlighting the residual tension between pre and post-democratic eras, the financial impetus to make the high speed network more viable, and the evolving policy paradigm of rationalisation. “Disassembling Trenes“, the second essay in the sequence, deconstructs Spain’s current passenger railways to expose the deceptions of AVE and nation therein. “Deconstructing Estaciones” provides a demographic analysis of Spain’s railway stations, that explores the unserved areas and probes the differences between regions. “Reanimating Regional” outlines the regional biases of Spanish railway connectivity, reassesses the role of Castilla in the national railway, and ponders the balance between actuality and perception inherent in Adolfo Suárez’s doctrine of “café para todos”.

Modelling Connectivity

Transport models have acquired a reputation for becoming impenetrably complicated, their results rendered as factual knowledge however internally flawed their logics actually are. Spanish policymaking has its own form of complexity, that in the relationships between people, and thus complex modelling risks being distilled down into simple statements in support of a pre-determined policy position. Instead this analysis tries to place greater emphasis on understanding, using only commonly agreed tokens (people, trains, distance), and making only practical assumptions that hopefully reflect “common sense”. To that end, a model of connectivity across Spain’s passenger railway network has been built in simple stages:

  1. Basic Connectivity – who is connected by train to who: A matrix of routes between municipalities with stations on which at least one train per day links the pair directly. Each pairing is multiplied by the municipal population of the destination, the result for all pairs from the origin then summed and attributed to the origin. The population of the origin municipality is added to the result, which is assumed to have perfect connectivity to itself – an assumption that only tangibly affects the overall connectivity score of the largest, and avoids cities attaining worse connectivity scores than the suburbs that connect to them (because those suburbs would gain the connectivity of the city’s population, while the city would not).
  2. Service Connectivity – who is connected to who by what frequency of train service: As basic connectivity above, except each route pairing is additionally multiplied by a factor representing service frequency, ( 1 – ( 1 / daily trains ) ), where daily trains is the total of both directions. This formula gives no value to the first train (which logically supposes no possibility of return), but thereafter values of each additional pair marginally, as half the value of the previous pair. Such weighting places emphasis on attaining the most basic level of service, as befits the non-urban regional networks that are the focus of this sequence of essays, while weighting high frequency metro services very marginally indeed.
  3. Hinterland Connectivity – who is connected to who by what frequency of train service, but where people use the station with the best ratio of connectivity to proximity, not necessarily the nearest station: For every municipality (both with and without stations), calculate the straight line distance from the centroid of the origin municipality to all municipalities with stations within 150 kilometres, and then find the municipality with the highest ( Service Connectivity of municipality * ( 1 / distance to municipality in kilometres ) ), assigning that calculated value to the initial origin municipality. This gravity model reflects the tendency of municipalities with many more trains to attract passengers from more distant markets. The assumed distance tapper is approximate, but generally succeeds in both re-assigning relatively poorly served municipalities that are close to a much better served neighbour (for example, a municipality 10 kilometres away would need to offer at least 10 times better Service Connectivity than a local station), and assigning people in municipalities without a station to the most attractive station in their proximity (the best served relative to distance). Every place in Peninsula Spain is within 100 kilometres of at least one station, and the 150 km buffer ensures a range of stations are considered, including provincial capitals.
  4. Connectivity Index – how does this connectivity compare to that of the average Spaniard: Hinterland Connectivity is expressed as a percentage of the average for all the municipalities scored (in the base case, those within 150 kilometres of at least one station, almost the entire population of Spain), with that average weighted by population: For example, if Madrid represented 7% of the total population of scored municipalities, Madrid’s score would count towards 7% of the overall average. This population weighting serves only to distribute the resulting indices around a meaningful average, where a connectivity index of 100 is what the average Spaniard (with a station within 150 kilometres) would obtain. The indices are thus entirely relative to other members of the population, reflecting policy themes of balance and equality.

The underlying dataset used is that described in the earlier essay, “Disassembling Trenes” – essentially a frequency-based matrix of all non-tourist rail passenger services within Spain on Friday 20 July 2018, alongside the municipal Padrón from the start of 2017. In the interest of transparency and the benefit of any other interested researchers, the raw network analysed is available in Geojson format – as is, neither supported nor maintained, and obviously without the associated computation described above. The analysed network can also be explored visually using Aquius. Frequency-based connectivity models are far faster to compute than those that process detailed schedules, and also far easier to edit – allowing the impact of a change in service pattern to be tested conceptually, without providing the kind of detailed schedule operational planners only produce after deciding to implement a network change. That flexibility to use connectivity models for network design was unfortunately lost during the development of these techniques in Britain in the early 2000s, ultimately because central government’s desire to understand connectivity surpassed their desire to assist those who might improve it, a rationale subsequently perpetuated in academia. Yet basic connectivity models remain powerful tools for both grand strategy and network tinkering, and in an environment with little or no interchangeable electronic schedule data (such as Spain) their deployment can add insight where otherwise there is none: Spanish railway interests produce plenty of good technical information, but remarkably little relates services to people, and much of what does is pre-occupied with appeasing the god of high speed.

The aim of this analysis is to understand the broad patterns by focusing on the key relationships, not to attempt to model every conceivable detail: Journey distance is ignored, but in practice the pattern of direct routes will tend to constrain destinations, while the tendency to lower frequencies on longer distance journeys renders remote destinations with poorer Service Connectivity. The availability of realistic return journeys is also ignored, but the probability of such return journeys is inherent in the overall service frequency. Interchange between trains is ignored, since as discussed in the essay “Disassembling Trenes“, interchange is not a dominant behaviour in most of Spain’s non-urban regional networks. Local interchange, especially between suburbs and better-served city centres, is factored into Hinterland Connectivity – the reduced connectivity with distance may be assumed a crude proxy for the reduced attractiveness of interchange. Hinterland Connectivity similarly manages the few branchlines (such as FEVE‘s Collanzo line in the Asturias) whose services require interchange to reach any major destination. Hinterland Connectivity takes no specific account of the availability of alternative modes of transport to reach the railway network, although its tendency is to link groups of people in relatively close proximity, groups who tend to establish transport links between one another. The factors used in Service and Hinterland Connectivity calculations may seem rather arbitrary – and would be for detailed microsimulation – but their use here is in the production of strategic aggregated comparators, where broad consistency of approach is more important than precise local calibration.

The connectivity of the islands and north-African autonomous cities – Balears, Canarias, Ceuta and Melilla – cannot be adequately reflected in a railway model of Spain, since even islands with railways can provide no direct connections beyond their own island. Overall Connectivity Indices include island and autonomous city municipalities within 150 kilometres of a municipality with a station, so can affect the overall average score and thus the index, but in practical terms the results for these municipalities are spurious and cannot be compared to Peninsula Spain. Analysis of the connectivity of (only) non-Renfe operators has a similar weakness because the networks of these operators do not generally connect to one another – for example, however well FGV serves Valenciana, it cannot be fairly compared to a national network that links Valenciana to other parts of Spain. Non-Renfe operators can be important to specific local municipalities, and are thus important within the most local analysis, but add only marginally to the overall connectivity of regions: Even in the provinces best served by non-Renfe operators, Madrid and Barcelona, such operators only add about 10% to the overall Connectivity Index. Lleida’s extremely high connectivity poses a particular challenge to the Pobla de Segur route, which offers a relatively infrequent service whose only major destination is Lleida, and thus provides far less direct connectivity than Lleida herself. While the route is modelled, the connectivity it offers is usurped by Hinterland Connectivity at many place close to Lleida, and even at Pobla de Segur the railway offers only a marginal connectivity advantage, hence is almost invisible in the Connectivity Indices for local municipalities. Analysis of Renfe’s “commercial” non-OSP products ignores local OSP journeys delivered as shared seats on commercial services, leaving those commercial services only to stop for the benefit of longer-distance passengers. This is an accurate reflection on current operations, but produces local quirks such as removing one of the links between Badajoz and Cáceres while retaining that between Badajoz and Madrid – with the net result of reducing slightly the overall (commercial) Connectivity Index of Badajoz. Such reduced connectivity is, however, a reasonable reflection on the marginal nature of the commercial service provided.

People Are Strange

Madrid has the highest railway connectivity in Spain – 476 for the region (where 100 is average) and 814 for the city. The next best connected city is the second highest populated – Barcelona with a Connectivity Index of 700. Connectivity does not, however, always correlate to local population – for example, Palencia’s 79 thousand people have similar connectivity (just under 430) to Sevilla’s 689 thousand residents, reflecting Palencia’s geographic position at the heart of the railway network of northern Spain. Of the largest Peninsula cities with a railway station (those with over a hundred thousand residents), only Granada’s Connectivity Index, of just 64, falls below the Spanish average. At the time of the research Granada had no direct train to anywhere outside Andalucía – the city’s connectivity having been reduced for 3 years pending the completion of LAV and the introduction of AVE services. Marbella, the only large Peninsula city without a railway station, attains a Connectivity Index of just 7: Marbella’s rail connection is modelled via Málaga, which has far better connectivity than the nearest station geographically, Fuengirola, but overall still represents an extremely unattractive option for most journeys from Marbella. Baides (near Sigüenza), the least populated municipality with a station in the analysed network: 200. The most “average” town happens to be La Robla (north of León), which is served by just two trains a day in each direction, one local, the other part of a long distance route to Barcelona that also carries local seats between the Asturias and León. Aside from the islands, the worst connected municipalities tend to be along the northern half of Portugal’s eastern border with Spain – from the far west of the province of Salamanca, down to Cedillo in Extremadura. Cedillo’s best railway connection is actually outside Spain, from Castelo Branco toward Lisbon, a connection which is not modelled.

The map below visualises the overall pattern of connectivity for Spain’s railways, where municipalities with higher Connectivity Index values are shaded with darker colours. As noted in the essay, “Deconstructing Estaciones“, the scale of municipal geography is larger in the south of Spain than in the north, so well-connected towns in the south visually spread over a greater area than otherwise equivalent towns in the north. The map serves to broadly confirm the expected pattern of connectivity. Subsequent numerical analysis will reveal the more subtle variation within, and ultimately provides a better description than the map itself.

Municipal Connectivity Index for Spain's Railways
Municipal Connectivity Index using all Spain’s Railways, with better connectivity coloured darker (base geography – municipalities shaded, with Autonomous Community and province boundaries in white – CC-BY 4.0 via Instituto Geográfico Nacional de España)

The table below lists Renfe’s Connectivity Index for every Peninsula province and Autonomous Community. Restricting the network to just that of Renfe makes little difference to the national pattern, because at this scale of analysis Renfe accounts for at least 90% of the connectivity that is achieved by all operators (that mapped above). The Connectivity Index of each municipality within has been weighted by its share of the province or Autonomous Community’s population. For example, the municipality of Almería contains 28% of the population of the province that bears its name, thus that municipality’s specific Connectivity Index counts toward 28% of the province’s total Connectivity Index. Renfe’s network has also been split into state-supported Obligación de Servicio Público (OSP) products and non-supported “commercial” products. The split is important because it formally defines which services government pays to support. In most, but not all cases, OSP services are supported directly by the central Spanish state – the main exception being trains within Catalunya. Calculating Connectivity Indices separately within each network (OSP and commercial), where each separate index averages to the same value of 100, allows a comparison between otherwise rather different networks. The comparison reveals how reliant different regions are on OSP support, a metric present below as “OSP Bias”, which is calculated as the difference between OSP and Commercial indices, expressed as a percentage of the Connectivity Index for both combined (All Renfe). Interpreted alongside the other indices, “OSP Bias” helps explore whether a province might need more or less support: For example, Almería shows a slight commercial bias, but below average connectivity, so perhaps needs more OSP supported services to address its limited connectivity. Granada has similarly poor connectivity from its OSP services, but its commercial services are even worse – primarily offered via Antequera pending a future AVE service to the provincial capital. Interestingly, simply adding the proposed daily return Talgo train from Granada to Madrid (assuming stops at Moreda, Linares and Alcazar) raises the province of Granada’s Renfe connectivity from 24 to 45, enough to give Granada parity with Almería – although not enough for parity with Spain, so will not entirely placate local frustrations. Meanwhile Córdoba attains very high levels of connectivity for commercial services, and perhaps warrants a reduction in OSP supported services relative to Almería. While reality is always more complex, the current balance in Córdoba might further explain Renfe’s reluctance to grant the city an (OSP) Cercanías service, an example discussed in the previous essay, “Deconstructing Estaciones“. Herein the most basic validation of any policy model, the prediction of current campaigns and their administrative reactions: It is here that connectivity analysis comes to life.

Renfe Connectivity and Public Support Bias by Peninsula Province/Community
Province and Community Connectivity Index (100 is “average”) OSP Bias
All Renfe OSP Only Commercial Only
Almería 49 30 46 -33%
Cádiz 105 72 120 -45%
Córdoba 185 90 229 -75%
Granada 24 38 8 +124%
Huelva 61 73 60 +23%
Jaén 78 106 45 +78%
Málaga 133 71 147 -57%
Sevilla 175 132 171 -22%
All Andalucía 116 83 119 -32%
Huesca 54 50 47 +5%
Teruel 38 67 6 +161%
Zaragoza 362 415 377 +11%
All Aragón 277 318 283 +12%
Asturias 153 68 181 -74%
Cantabria 82 51 88 -45%
Ávila 103 165 82 +80%
Burgos 218 222 269 -22%
León 176 108 225 -66%
Palencia 228 211 277 -29%
Salamanca 88 131 100 +36%
Segovia 120 130 132 -2%
Soria 71 125 6 +168%
Valladolid 232 273 273 +0%
Zamora 66 10 84 -112%
All Castilla y León 165 169 194 -15%
Albacete 194 178 236 -30%
Ciudad Real 155 149 168 -12%
Cuenca 96 109 105 +4%
Guadalajara 127 218 24 +153%
Toledo 63 110 13 +154%
All Castilla-La Mancha 122 146 105 +34%
Barcelona 260 291 232 +23%
Girona 73 83 46 +51%
Lleida 170 163 180 -10%
Tarragona 153 226 89 +89%
All Catalunya 225 256 196 +27%
Araba 257 287 293 -2%
Bizkaia 96 39 115 -79%
Gipuzkoa 134 149 145 +3%
All Euskadi 133 112 151 -30%
Badajoz 65 98 37 +93%
Cáceres 71 123 33 +127%
All Extremadura 67 107 36 +106%
Coruña, A 95 38 112 -78%
Lugo 93 23 118 -101%
Ourense 134 36 172 -101%
Pontevedra 129 46 157 -86%
All Galicia 111 39 135 -87%
Madrid 447 512 426 +19%
Murcia 183 96 221 -69%
Navarra 136 53 170 -87%
Rioja, La 137 54 172 -86%
Alacant 124 93 141 -39%
Castelló 168 111 201 -54%
València 196 239 211 +14%
All Valenciana 166 170 184 -9%

If Renfe’s network was perfectly equitable all the Connectivity Indices in the previous table would be 100. That they are not even close, not even when averaged into large geographical areas, represents a policy vulnerability: The inconsistent targeting of state support is a particular concern, because in some cases OSP connectivity appears to be enhancing an already above-average commercially-provided connectivity, while in other cases state support does little to improve upon the below-average connectivity provided by the commercial network. These are clearly not the patterns that define Renfe’s obligación, but they are the patterns of democratic expectation, a difference that inevitably creates tension in contemporary public policy.

Us and Them

Almost all the provinces with the lowest Connectivity Indices are well known for grumbling in public about their railway service – Granada, Extremadura, Teruel, Toledo – to name four of the most vocal, with Almería and Soria far from content, all provinces with well below average connectivity. However the pattern is more complex than places with the lowest connectivity simply complaining the loudest:

  • The stronger the “OSP Bias”, the more likely it appears that poor connectivity will become a local policy issue. For example, the province of Zamora, and to a degree Girona, have similarly poor overall Connectivity Indices to Extremadura and Toledo, except Zamora’s (and to lesser degree Girona’s) connectivity is strongly biased to the commercial network, while Extremadura and Toledo are highly dependant on OSP networks for their connectivity. There are several possible explanations: First, the state is more obviously responsible for OSP services than commercial services, although Renfe remains a state-owned company operating on state-funded infrastructure, so commercial services are not isolated from policy discourse. Another possibility is that perceptions of Renfe’s commercial products, not least AVE, are simply better, so poor connectivity with AVE is not perceived as poorly as it is modelled numerically. The final, and perhaps most obvious explanation is that OSP services tend to be more local than Renfe’s commercial services, so if the dominant perception is local, deficiencies in OSP connectivity are given greater policy importance than deficiencies in commercial connectivity.
  • Places that have never had a train service in the past tend to ignore their poor rail connectivity in the present. For example, the dearth of railway services on much of the Costa del Sol generates remarkably little political activism given the size of some of the towns affected. Likewise the province of Girona’s below average Connectivity Index is primarily because two thirds of its population have no direct access to a station – but while the reopening of the Olot line is occasionally mooted, similarly sized towns such as Lloret de Mar remain largely unconcerned about their reliance on interurban buses – even though a 5 kilometre extension of the Barcelona-Blanes railway would represent a modest proposal in the context of wider Spanish railway-building. The implication is that there is an implicit public acceptance that railways cannot be made available to all, but with no understanding of why, and thus the only reference point local people have for the possibility of a railway service is that there was one in the past. Thus poor connectivity in Huesca is expressed through Canfranc, even though improving the service to Canfranc itself would have little impact on the (domestic) connectivity of the whole province. The curiosity is that at national level, the civil engineering of LAV has visually demonstrated that almost everywhere in Spain can technically have a railway, yet quite different perceptions of what is possible seem to persist locally, based only on what has historically been demonstrated locally.

The actuality of locality once again taunts the perceived national role of the railway, a theme of the essay, “Disassembling Trenes“: The railway does not serve the connectivity needs of Spaniards fairly because only those with local experience of a railway know to expect one locally. Is Alta Velocidad Fast? first investigated the meaning of locality, and reached the conclusion that in contemporary Spain it meant somewhere smaller than an Autonomous Community, specifically somewhere whose most local tier of government is not dependant on globally financed debt. The inherent paradox, that railways are too expensive for localities, but localities are the only places they are expected to exist: That there is only a role for a local railway, but railways cannot be local.

Locality is not yet the sole measure, but there is evidence in the table above that the comparators are becoming more local. For example, overall connectivities in all the provinces of Valenciana are above the Spanish national average, however Alacant and Castelló enjoy far less state-supported connectivity than València, and thus each demands an expansion of its (state supported) Cercanías: Not strictly for parity with Spain – which both already exceed – but to better match their regional point of reference, València. Any dilution or diminution of a universal reference, such as the nation, weakens the ability to understand transport networks from universal comparators such a connectivity, and in practice makes the role of national entities, such as Renfe, far harder to manage.

Aragón provides a stark example of the national railway’s difficulty in managing balances within Autonomous Communities: Teruel Existe, one of the best known province-centric civic campaigns in Spain, was born of the poor connectivity quantified in the table above: After Granada, whose low Connectivity Index reflects a temporary loss of long-distance services, Teruel is the worst connected Peninsula province, with a Renfe Connectivity Index of just 38. Even the provincial capital is below average at 90 – one of the few such capitals to have neither direct train to Madrid nor Barcelona (the two most gravitationally attractive destinations), and the only provincial capital outside of Castilla with no recent history of “commercial” Renfe operations. Yet overall, Aragón is one of the best connected Autonomous Communities in Spain, second only to the Community of Madrid. Unfortunately for Teruel, Aragón’s high connectivity is almost entirely focused on Zaragoza, as province, but primarily as city. The city of Zaragoza alone contains 51% of Aragón’s entire population, which in the first instance explains why railways gravitate towards Zaragoza when serving an otherwise sparsely populated region – because the most efficient rail operations are generally those serving the highest densities of population. However the demographic dominance of Zaragoza inevitably infects the balance of political power within Aragón, rendering Zaragoza as a little Madrid, albeit with none of the convoluted counter-balances of Castilla. Teruel’s dearth of railway service is surely a function of Zaragoza’s surfeit, and thus at least partly a function of the structure of interaction between the national railway and more local tiers of modern Spain’s political administration.

As discussed in the essay, “Disassembling Trenes“, RENFE‘s true raison d’être was to manage Castilla, and while the Ebro Valley has a different geopolitical history, the dominance of Zaragoza should have been manageable on similar terms to Madrid. As described in the first essay, “Saving Ferroviarias“, Guadalhorce proposed many routes which were economically irrational, but which did add inter-regional links that had not been provided by the primarily-commercial concessionaires that built the original Spanish railway network. In attempting to build the infamous Baeza to Saint Girons transversal railway, and completing the Santander-Mediterranean axis, the state showed considerable determination to improve the connectivity of Teruel – even if Guadalhorce’s plans emphasised the connection of territory, not specifically people, and thus his railways added little practical passenger connectivity to the network: Neither of Guadalhorce’s lines directly connected Teruel to Madrid or Barcelona. The contemporary Renfe and ADIF manage a similarly ambitious and ultimately unaffordable tool of “cohesión territorial” called “AVE” – a tool as imperfect as “democracy” itself, best described as placing greater emphasis on connecting people. The most fundamental difference between then and now is surely the modern autonomic structure that democracy gave birth to: A structure under which Aragón should logically manage its internal differences internally, not expect national entities to counteract the region’s internal imbalances. The schizophrenic role of the national railways – national by perception, local by operation – makes their interaction with this modern administrative structure particularly challenging. Renfe’s national role may reasonably suppose only linking the biggest regional cities, here Zaragoza, to other parts of Spain – which, with the exception of a Zaragoza-Calatayud shared-seat Avant and a couple of Madrid trains extended to Huesca, is AVE‘s role in Aragón. Yet Renfe’s OSP services continue to link a lot of local stations within Aragón – even where, as in the case of Zaragoza-Teruel-Valencia, those trains also have an inter-regional role.

To add to this administrative confusion, the role of the national railway is not consistent between regions, a recurrent theme of this sequence of essays for which the earlier table provides further evidence: The Connectivity Indices of both Madrid and Barcelona are skewed toward their OSP supported networks, in spite of the cities being largest in Spain and thus with a natural tendency to generate commercial routes. Contrast that to the north coast, where every Autonomous Community from Galicia to Navarra is skewed toward commercial networks, and in some cases the connectivity delivered by the purely state-supported network is extremely poor: The people of Asturias may have the best direct access to Renfe stations, and those in Cantabria the best ratio of Renfe trains to population, but neither condition guarantees the national connections provided are good. Galicia, along with Navarra and La Rioja, demonstrates particularly low levels of state dependency, while attaining acceptably average levels of connectivity overall. This might be considered a reflection on the inappropriateness of rail to serve local journeys in relatively rural regions – that regions like Galicia only need rail for the long-distance travel, and those long distance services tend to be provided commercially. Such would imply equivalent national support of some alternative mode of transport to maintain a sense of equality, and if no such alternative bias is apparent an intriguing conjecture is raised: That Galicians are less dependant on, or accepting of, the physical manifestation of the state through railways within their localities than in certain other parts of Spain. That theme was introduced in the first essay, “Saving Ferroviarias“, which suggested the intensity of nature in the north-west fostered an even more focused idea of locality, and in spite of attempts to provide even more local stations, the state could not reasonably maintain the same (theological) influence on nature through railways as it could elsewhere. Regardless the explanation, Galicians evidently differ from the similarly peripheral Catalans, for whom the physical sphere remains the only common political arena for an otherwise virtualised society, and conflict over who provides the trains is a key part of the political balance between Catalunya and Spain – even if, as intimated by the analysis in the next section, Catalunya’s railways may not be as important to Catalans as they may seem when presented through the prism of the Independence politics.

So do specific regions of Spain need their local trains physically provided by the nation simply to maintain their political interaction with that state? And are those that do not at risk of disconnecting from the Spanish state entirely? Or would connectivity concerns in other sparsely populated regions, such as Extremadura, dissipate if (counter-intuitively) they lost all their existing local railway services and were instead offered only the long-distance connectivity of national perception? And if so, at what point does the national state stop supporting fundamentally local trains in regions even more closely aligned to the core than Extremadura, not least in Madrid?

Incoming Importance

If people are the most theoretically interchangeable token of equality, perhaps money is actually the most interchangeable token. The interchangeability implied by modern currency is arguably a myth – the physical currency of work is inflating far slower the virtual currency of optimism – but such myths continue to carry their own societal importance, with money serving a panoply of roles in associating different groups of people to one another. It is those associations that are analysed here: Money is a proxy for societal importance, and subsequent analysis should not be read with an egalitarian agenda. The function of modern European states requires them to maintain detailed records of the monetary income of their citizens, and Spain is no exception, rendering published income statistics reasonably accurate. Income is not wealth, merely a proxy for it, and the measure is increasingly problematic in ageing societies, where retirees record no income, but live on wealth previously accumulated: The “poorest” towns of Spain in terms of income often transpire to be beach resorts whose residential population is skewed to the elderly. Household income – the financial resources implicitly shared by those living in the same home – is an imperfect measure of the matrix of income dependency within Spain, which can often emphasise the extended family unit in the distribution of resources, not necessarily just those family members sharing the same building. However, the spatial separation of that building is particularly relevant to transport geography, that analysed here.

The European Urban Audit project has resulted in the publication of average household incomes for Spanish municipalities with at least 20,000 residents. The most recent complete Spanish dataset is from 2015. Two thirds of the Spanish population live in municipalities with at least 20,000 residents, and as described in the essay, “Deconstructing Estaciones“, rail provision is strongly skewed toward the most populated municipalities, rendering the geographic gaps in the Urban Audit dataset relatively unimportant in the context of railways: Filling those gaps with crude Autonomous Community income averages, weighted by household size, should not result in substantial distortions. The income assigned to each municipality is expressed as a proportion of the Spanish average of €27,200 to give a factor for Wealth, and the inverse calculation to give a factor for Poverty. For both factors, 100% equates to the average income. When aggregated into Autonomous Communities, the highest Wealth factor is only 129% (in Euskadi, the Basque Country) and the highest Poverty factor 124% (in Extremadura). However the range across municipalities is greater – 283% (Pozuelo de Alarcón) and 157% (Torrevieja) respectively. Each factor is multiplied by the 2017 (to maintain as much consistency with the prior connectivity analysis as possible) municipal Padrón, thereby weighting the population in accordance with its income. The resulting income-weighted populations are fed into the Connectivity Model described above, using otherwise identical connectivity calculations to produce two additional sets of connectivity indices: Wealth Connectivity emphasising the connections between the rich, and Poverty Connectivity emphasising the connections between the poor. A single comparator called Income Bias expresses the overall balance of those connectivities, calculated as ( Wealth Connectivity – Poverty Connectivity ) / Connectivity Index, where the Connectivity Index is that analysed in the previous sections of this essay (the average without any income factor). Positive Income Bias favours the connectivity of the wealthy, negative favours the poor. Income Bias expresses only the difference between these Wealth and Poverty Connectivities indices for each community, not the magnitude of the underlying indices.

The table below shows the resulting Income Biases by Autonomous Community, for the two major Renfe product groups, with the “OSP Skew” the difference between the income-biased connectivity of Renfe’s OSP and Commercial networks. The rationale of Income Bias analysis is simple: People that have a choice, broadly the wealthy, tend to live near what they and their immediate societal references consider important. Long-term their choices become apparent in demographic patterns, and thus given sufficient network stability, wealthy households gravitate toward places that are well-connected by rail only in proportion to the importance they attach to those railway connections. More broadly, positive Income Biases indicate an aspirational status, while negative biases evoke less desirability. While the demographic patterns aggregate many elements of consumer choice, not just railways, over such a large dataset any clear tendencies associated to the railway should become visible. Analysis of this data is more subjective than the raw connectivity in the previous sections, and should be interpreted with caution, however the data has been included because it suggests some interesting patterns which further help explain those patterns already discussed in this sequence of essays.

Income Bias of National Renfe Connectivity
Community Average Household Income (000 euros, 2015) Income Bias OSP Skew
All Renfe OSP Only Commercial Only
Andalucía 22.2 -7% -44% +1% -46%
Aragón 28.5 -17% -17% -7% -10%
Asturias 27.7 -15% -48% -7% -42%
Cantabria 26.1 -4% -34% +1% -35%
Castilla y León 26.0 -6% -3% -3% +1%
Castilla-La Mancha 22.9 +0% +5% -3% +9%
Catalunya 32.2 -15% -16% -8% -8%
Euskadi 35.1 -13% -18% -7% -11%
Extremadura 22.0 +3% -4% +8% -12%
Galicia 26.4 -2% -39% -1% -39%
Madrid 32.2 -20% -14% -14% +0%
Murcia 22.7 -9% -47% -4% -43%
Navarra 34.0 -6% -26% -4% -23%
La Rioja 28.5 -11% -37% -6% -31%
Valenciana 23.1 -13% -21% -6% -15%

Overall, the Income Biases tend slightly toward the negative, implying a slight tendency to unimportance. However the comparison of OSP and Commercial network Income Biases reveals substantial differences therein:

  1. Renfe’s Commercial network – that which is providing the stongest inter-regional connections – is consistently well balance between wealth and poverty: Acceptably average almost everywhere. And in so far as it is possible (due to its limited geographic coverage) to analyse the connectivity of the specific product called “AVE”, there is no evidence that its national income-biased connectivity is any different. This pattern is entirely reasonable, since the commercial network is primarily used for exceptional journeys, not the regular journeys most likely to factor into home location decisions.
  2. For Castilla, and to a lesser degree other inland regions of Spain, there is little or no difference between the connectivity income biases of the OSP and Commercial networks. While Castilla’s patterns are a recurrent theme of these essays, this statistic is particularly intriguing because the implication that all railways in Castilla carry a similar import reveals the societal depth of the singular perception of Castilla and nation.
  3. Madrid broadly follows the pattern of Castilla, but places lower absolute societal import on all railways. An equally intriguing statistic given Madrid’s central role in the national railway, and one that questions Renfe’s approach to market development in what should be one of its strongest market territories – a topic explored further in the next essay, “Reanimating Regional“.
  4. For the periphery the connectivity of the OSP network is strongly biased toward lower incomes – ergo state-supported railway services are simply not at all important societally. This bias is moderated in communities with extensive suburban railway networks, notably Catalunya, where railways are perhaps more likely to provide a commuter function.

Renfe is, however, primarily a local operator, and its OSP provisions particularly emphasise travel specifically within regions. The national connectivity model described above can instead be limited to connections only within the same Autonomous Community, or the same province, as the origin municipality, allowing an assessment of purely regional connectivity. These regional indices reflect how well people within a particular geographic area are connected to each other, not how well they are connected to major populations elsewhere in Spain, and consequently can produce very different results to the national model. The broad modelling approach is the same, but the construction of the regional index’s population weighting differs slightly, with each region weighted by its proportion of the total analysed population. Autonomous Communities which contain only one province attain different indices for the same internal network because the overall average changes – the comparison is to communities and provinces respectively. This method of analysis is explored further in the next essay, “Reanimating Regional“, but here serves to understand only the broad income biases. The supported network is defined as every service except those operated by Renfe commercially, since non-Renfe operated services are commonly funded through consortia of provincial public agencies, broadly equivalent to OSP. This supported network completely dominates provision within regions, so the prior comparison (of commercial and non) cannot usefully be repeated within regions. However the income biased connectivity of the purely supported network can be compared by scope: In addition to the nation, the income biased connectivity specifically within own Autonomous Community, and also that within own province. Each scope redefines the population connected, altering both the distribution of connections and the Income Biases therein. The results are summarised by community and shown below.

Income Bias of Peninsula Supported Services by Territorial Scope
Community Income Bias by Scope
National Community Province
Andalucía -45% +22% +9%
Aragón -18% +20% +10%
Asturias -50% +22% +10%
Cantabria -35% +8% -4%
Castilla y León -4% +18% +8%
Castilla-La Mancha +4% +8% 0%
Catalunya -17% -1% -4%
Euskadi -17% +24% +3%
Extremadura -5% +9% -5%
Galicia -40% +17% +6%
Madrid -14% +15% +3%
Murcia -49% +14% +3%
Navarra -28% +15% +4%
La Rioja -39% +20% +9%
Valenciana -26% +3% -7%

The Income Biases for travel within communities are tangibly more positive than those nationally. Provincial biases are more positive than the national, but always below those of the community. In short, the railways are important, but at primarily community scope. To understand this broad pattern, first consider that connectivity specifically within each community is almost always better than that nationally or within provinces: Passenger rail is simply a better match to geography on the scale of most Autonomous Communities. In comparison national journeys tend to be too distant to generate sufficient passenger volumes for rail (the exceptions are journeys only to the biggest cities), while journeys within provinces tend to be too local in their character for rail to serve effectively (the exceptions are the relatively urbanised provinces that are not themselves communities, such as Barcelona). These patterns do vary between different communities, as explored in the next essay, “Reanimating Regional“, but such variation has been neutralised in the data above, which compares income variation in the same overall population with the same overall services. The significance of better connectivity within communities is that it raises the functional importance of the railway – not just its likelihood of being used for regular travel, but also its perceived societal importance even by non-users – and the consequent strength of the political discourse surrounding its provision. That railways carry their greatest societal importance within Autonomous Communities is a theme of the next essay, “Reanimating Regional“.

The quirks in this pattern are revealing, for example:

  • The Castillas, along with Extremadura, retain the narrowest data spread across all three Income Biases, but there are subtle differences within, notably that Castilla y León leans more strongly toward community, while results for Castilla-La Mancha suggest a less tangible notion of community – a difference already seen in the balance of trains from each community to Madrid, explored further in the next essay, “Reanimating Regional“.
  • Catalunya, and to a lesser degree Valenciana, attach less importance to their regional trains than other communities. Intriguingly railways carry an overt political importance in both communities, yet relatively low actual importance as observed through income. The combination suggests the difference between perception and actuality in these communities is greater than elsewhere in Spain. Much might be inferred from such a statistic which might not be reasonable to so, but it surely helps understand why contemporary Catalan society is so adept at inhabiting two quite separate perceptions of a shared community.

The previous sections of this essay demonstrated that Renfe’s obligación is not specifically to equalise the connections between the people of Spain. This section has suggested the societal importance of delivering railway services within Autonomous Communities, services which are indeed the focus of most OSP provision. That alone does not define “obligación”, any more that it answers the question of why such obligation within communities is primarily national. But such reasoned logics are surely flawed: “Obligación” emerges as a piece of societal engineering that operates from within itself, and thus can possess no clear definition: Renfe’s obligación is best described as to maintain state, which in Castilla may imply nation, but elsewhere may be understood as something quite different. It follows that “Obligación de Servicio Público” cannot be understood outside its context, a context that varies across Spain. Of course because “obligación” operates from within itself, none within can grasp that OSP varies by context, so there can be no meaningful discourse about how or why. Such is the domain of lunatic extranjeros that cannot approach a society from within itself.

The next essay in this sequence is called “Reanimating Regional“. It outlines the regional biases of Spanish railway connectivity, reassesses the role of Castilla in the national railway, and ponders the balance between actuality and perception inherent in Adolfo Suárez’s doctrine of “café para todos”. Continue reading…