2.1 Introduction
Elections of the post-financial crisis period were not kind to parties on the mainstream European left. Although experiencing a slight rebound in a few post-COVID elections, social democratic parties nonetheless remain substantially weaker than they were two decades ago. Next to this electoral decline of the moderate left, more economically and politically radical right populist parties as well as both green/left-libertarian and populist radical left parties have scored impressive victories.
While most of this volume examines the changing partisan affiliations of voters and their relation to social democratic party strategies in different contexts, this chapter takes a bird’s eye view and examines aggregate regional patterns of electoral realignment. It finds that shifts in voting patterns from moderate to radical parties manifest themselves in distinctive configurations across geographically varied regions. This regional variation reveals the structural dilemma in which social democratic parties find themselves, namely that they face different competitors across places. The combination of the transition to knowledge-based growth with the social sorting of voters places competing pressures on the social democratic mobilization strategy. Many newer left voters who are both culturally liberal and economically progressive have sorted themselves into vibrant metropolitan areas, while many of their past core voters with more moderate positions – at least on the cultural dimension – are residing in what are now lagging areas from the vantage point of vantage point of the knowledge society. The geographical dilemma evidenced in this chapter underlines two key contentions of the entire volume: it has become difficult for social democratic parties to devise programmatic appeals that effectively and successfully resonate simultaneously in their different distinctive core constituencies; and radical parties of the Left and Right – especially in proportional electoral systems – are more successful in proposing such distinctive programs.
I argue that social democrats’ traditional ‘national’ (i.e. regionally untargeted) policy approaches are increasingly less effective at holding together cross-regional support. Non-regionally targeted policies such as improved pensions and unemployment insurance are clearly still effective and popular redistributive instruments for the Left. However, in an era in which geographic regions (a) prioritize different economic policies and (b) cultural sorting creates different ‘second dimension’ demands, social democratic parties find themselves vulnerable to competitors that can offer more targeted appeals on both dimensions – particularly in proportional electoral systems.
A first empirical implication of this claim, which I develop in the chapter, is that social democratic parties increasingly struggle to either maintain historical regional strongholds or to capture more voters in economic regions where they have been hitherto weak, leading to cross-regional losses.
A second empirical implication is that social democratic parties lose to different parties in different places. Their cross-regional losses rest on different competitive dynamics within countries. Regions with more or less exposure to the knowledge economy and urban and non-urban areas have different political leanings. The result is a fragmentation of the political space. Cities in knowledge dense areas trend more to the green, left-libertarian and non-centrist left parties, while urban voting for populist parties is (relatively) higher in declining industrial cities. By contrast, suburbs, rural areas and towns have experienced a growth in both moderate and populist right voting, especially in post-industrial knowledge economies. The result is that different challenger parties mobilize voters across regions in ways that cost social democratic parties.
Third, as the new political geography meets the Left’s historic mobilization structures, it creates different competitive threats across countries. Where social democratic parties historically mobilized more urban areas – as they did in many European countries – then these new dynamics mean that they are more threatened by left challenger parties. Where the Left had a more agrarian or suburban base, then the rise of the Moderate and in some cases populist Radical Right, poses more of a threat.
While there is evidence from other work – which I review later – that regional dynamics are not entirely compositional (i.e. that localized geographic shocks/experiences have effects over and above the type of individuals who live within an area), this paper cannot adjudicate between contextual and compositional effects as drivers of variation. The argument of the chapter, however, does not hinge on this distinction. The core claim is that geographic changes reinforce existing accounts of individual cleavages and help explain why social democratic parties struggle to hold together political coalitions despite offering quite popular policy programs. Differences in economic growth models and legacies of mobilization help to explain varying patterns of social democratic adjustment in ways that complement the individual-level analysis.
The chapter provides a largely descriptive contribution to the volume, looking to trace patterns of adjustment across place. But this descriptive contribution rests on an underlying theorization, congruent with the framework of the volume, that sees medium-term competitive challenges for social democratic parties as varying across political economies. To show these trends, it draws on an original dataset of electoral results at the highly localized level.
2.2 Why Regions Matters: The Importance of Political Geography
This brief chapter cannot do justice to the extensive work on political geography (Rickard Reference Rickard2020). However, I want to highlight two broad approaches in this literature, which both point to the importance of geographic dynamics in shaping parties’ strategic options.
First, there is a large literature on the importance of geographically based institutions, such as the electoral system (Persson and Tabellini Reference Persson and Tabellini2005; Rodden Reference Rodden2019), federalism (Rodden Reference Rodden2002; Beramendi Reference Beramendi2012), and structure of local government (Trounstine Reference Trounstine2018; Freemark et al. Reference Freemark, Steil and Thelen2020) in shaping the distributive and mobilizing trade-offs for political parties. This literature suggests that parties have greater strategic incentives to provide policies targeted to voters in geographic areas that are electorally competitive than those that are not (Jusko Reference Jusko2017). While the Left was historically weaker in majoritarian system (Iversen and Soskice Reference Iversen and Soskice2006), there was nonetheless substantial variation, following, in part, from the spatial distribution of workers. Here, parties on the Left faced trade-offs between appealing to their base among workers and winning districts where the marginal voter was not necessarily a worker.
However, geographic trade-offs do not just emerge in majoritarian systems. Parties may face them in proportional systems if shared geographic experiences create strategic incentives for some parties to mobilize voters as geographic groups.
This claim brings us to a second literature, which comes from economic geography. This literature suggests that local economic experiences can vary in ways that are not entirely compositional – that is, there are both ‘agglomeration effects’ in local economies (e.g. successful firms attract other successful firms) and local macro-economic effects to shocks (e.g. the closure of plant can spill over into other areas) (Rosenthal and Strange Reference Rosenthal, Strange, Henderson and Thisse2004; Autor and Dorn Reference Autor and Dorn2013; Autor et al. Reference Autor, Dorn and Hanson2013). These contextual economic effects mean that people with similar jobs (e.g. factory workers, hairdressers and childcare workers) can have different economic experiences across places.
These geographic experiences can matter electorally if parties mobilize voters based on them. There are clear historical examples of this strategy. Most Scandinavian countries, for instance, had successful rural parties that mobilized agrarian voters with particular economic and cultural interests. These parties both drew on the support of those directly involved in agriculture (e.g. farmers) but also those that shared economic interests with voters in these areas (e.g. small business people). Classic work on the origins of the party systems stressed both the ways that traditional urban–rural cleavages materialized as political ones and how new industrial cleavages overlay onto geographical areas (Lipset and Rokkan Reference Lipset, Rokkan, Lipset and Rokkan1967).
Where varied economic geography combined with geographically oriented institutions, historically it created particular partisan and economic dynamics, as Rodden (Reference Rodden2019) outlines for the US case. In the majoritarian systems, nationalized parties had to broker cross-regional deals to hold together their base, often providing more targeted local expenditure or concessions. Katznelson (Reference Katznelson2013) shows, for instance, how Democrats in the US, in constructing early welfare policies through New Deal programs, provided racist regionally based economic concessions to secure the support of Southern Democrats.
In Europe’s largely proportional systems, the Left had more ability to pursue broad based policies emphasizing national solutions (e.g. welfare, pensions) that reduced geographic mobilization. Social democratic parties mobilized voters largely through policies aimed at securing the interests of the industrial working class, including expansive Keynesian policies, support for trade unions and labour rights and a growing welfare state (Hibbs Reference Hibbs1977). As winning elections required moving beyond narrow class appeals, social democratic parties both looked to create broader cross-class or cross-regional coalitions through these policies. The success of the Nordic social democratic parties lay, in many ways, in institutionalizing both, cutting into rural parties’ strength in the countryside by promising generous (national) welfare policies, which also appealed to parts of the urban middle class. The more tenuous position of some Continental social democratic parties followed in part from a less institutionalized approach, with Christian democratic parties playing a key role in mobilizing the working class in many industrial and agricultural regions.
Section 2.2.1 argues that Europe’s economic regions have undergone a dual change in the last thirty years, both towards a post-industrial economic model generally and urban growth model. These geographic shifts are central to Rodden’s (Reference Rodden2019) account of the transformation of the Left in majoritarian electoral systems. The places the Left mobilizes may be the same – urban areas – but the people are different, creating new forms of support. In majoritarian systems, Rodden argues that the Left’s voters are increasingly concentrated in cities, giving them an electoral disadvantage. In this account, the Left faces fewer penalties to different geographic distributions of support in Europe. However, the following sections argue that in even in proportional systems, the transition to knowledge economy can put pressure on social democratic parties’ traditional national economic and mobilizing strategies. The result is that changes in the economic and social geography contribute to new political divides. I first start with outlining the geographic transformations and then turn to their political implications.
2.2.1 The Dual Geographic Transformation of the Knowledge Economy
Defining the ‘knowledge economy’ is complex, but it generally involves a shift to both higher-skilled forms of production involving new technological innovations (e.g. ICT) and high-skilled forms of service provision (e.g. finance, business services) (Boix Reference Boix2019; Hope and Martelli Reference Hope and Martelli2019; Iversen and Soskice Reference Iversen and Soskice2019). The growth of the knowledge economy has meant both a shift in the underlying economic structure and, through mass educational expansion and changes in the nature of the work, dramatic changes in the class structure (Oesch Reference Oesch2008b, Reference Oesch2013).
These economic and class transformations have not been geographically flat. Indeed, changes in the nature demands for skill, and the subsequent distribution of types of work (and their associated cultural preferences), have taken on an increasingly varied geographic character, both across and within countries.
On aggregate, much high-skilled employment growth in the last decades has occurred in both the public sector and in what Eckert et al. (Reference Eckert, Ganapati and Walsh2019) label ‘skilled tradeable’ services. While the former often occurs across geographic regions, the latter is less evenly geographically distributed both across Europe’s higher-level geographic regions and within these broad regions across cities and suburbs – leading to what I label the ‘dual economic transformation’ of Europe’s regions.
To take broad economic regions first. People live and work in areas that make or do different things. For instance, in the United States, the city of Houston, is part of the broader economic area of Houston–Woodland–Sugarland, an economic area that is historically more dependent on petroleum natural resource extraction. By contrast, the city of Detroit, located in Detroit–Warren–Dearborn area, is historically an industrial producer of automobiles and other manufactured goods. San Jose, part of the Silicon Valley area of California, has, for the last decades, been a leader in new technology firms. The broader economic structure of Houston, or Detroit, or Silicon Valley then, depends in part on competitiveness of the US energy, automobile and technology sectors (Boix Reference Boix2019).
The same is true in Europe – both within and across countries. Historically, there are distinct regional economies that generally have different economic strengths even within a given country (e.g. Herrigel Reference Herrigel2000). In the face of post-industrial economic change, some regions of Europe have moved more extensively towards employment in the knowledge economy than others.
To show this outcome, I follow much of the literature in European regional political economy and look at NUTS-2 regions. NUTS-2 regions are high level units, with between 500,000 and 3,000,000 inhabitants. NUTS units are both the basis of the distribution of parts of European structural funds (Becker et al. Reference Becker, Egger and von Ehrlich2010) and other work shows that they are meaningful economically and political relevant units (Colantone and Stanig Reference Colantone and Stanig2018b; Rodríguez-Pose Reference Rodríguez-Pose2018).
Figure 2.1 develops a measure of the regional structure of the knowledge economy across European NUTS-2 regions, using three indicators: the share of the 25–64 year-old population with a higher degree (defined as ISCED 5–8), the number of patents per 1,000 population and the share of employment in the NUTS-2 region in finance and business services (all data taken from Eurostat). Each indicator is averaged across decades, and rescaled to run 0-1, with the regions at the 99th percentile and above scored as 1. Each of the three components counts equally in the overall index, which is itself rescaled to run 0-1. This regional index correlates at the national level with the World Bank’s 2005 Knowledge Economy Index at 0.82.Footnote 1 Figure 2.1 shows the distribution of regions across and within European countries (unfortunately, data on Switzerland is not available).
Figure 2.1 Knowledge economy index
What Figure 2.1 shows is that in many countries, there are regions that are extremely knowledge intensive – including in parts of UK, the Scandinavian countries, France, the Netherlands and Belgium. But that within these same countries, there are also much less knowledge-intensive regions. The growth of knowledge-intensive regions is, in some cases, associated with greater overall regional divergence, but in other regional divergence remains flat (Rosés and Wolf Reference Rosés and Wolf2019). However, where large and regionally redistributive welfare states have limited growing regional divergence – such as in the Scandinavian countries or the Netherlands – Figure 2.1 shows that many of the underlying structural trends are still present. By contrast, there are fewer knowledge-intensive regions in the European south and lower variation in the more industrial Germany and Austria.
However, there is also variation within regions. To return to the example of Houston, Detroit and San Jose. Within these broader economic areas, some people live in the centre of the core city, some live in a rich inner suburb or an (often poorer) exurb, and some live in an outlying rural area that is not directly linked to the city.
New economic sectors – finance, and parts of the knowledge economy are linked in particular to urban conurbations, with capital cities experiencing particular growth in these sectors (Odendahl et al. Reference Odendahl, Springford, Johnson and Murray2019). This sorting of high-skilled work into urban areas has both fuelled inequality among the high skilled – with workers in high-skilled jobs in high-skilled firms located, often, in high-skilled areas – being particular winners (Song et al. Reference Song, Price, Guvenen, Bloom and Von Wachter2019); at the same time, the sorting of high-skilled work into urban areas has had further knock on effects on other forms of inequality, such as housing wealth.
By contrast, both less knowledge-intensive industrial regions and new outlying suburban areas bordering knowledge-intensive areas may have higher concentrations of poverty or economic duress; the latter often including areas housing the lower-paid worker who service major cities but without the associated gains of urban growth (Kneebone and Garr Reference Kneebone and Garr2010). These areas have been hard hit by job losses and weaker rates of economic growth than the knowledge-intensive urban core (Rickard Reference Rickard2020).
Figure 2.2 draws a on different way of thinking about geography, based on the relationship between local units and cities. Many economists studying local economic effects examine areas defined ‘commuter zones’ or ‘travel to work areas’ rather than high level regions, as these areas are more closely linked to common economic experiences (Autor et al. Reference Autor, Dorn and Hanson2013). Figure 2.2 draws on an original dataset of highly localized data of educational attainment (Gingrich, McArthur and Cuibus Reference Gingrich, McArthur and Cuibus2023), aggregated to urban areas defined by commuter zone (broken apart by core city and suburbs) vis-à-vis those not attached to a commuter zone (rural and towns). The precise measures are discussed in the data section later.
Figure 2.2 Urban and rural areas
What Figure 2.2 shows is that in both knowledge and less knowledge-intensive regions, there are differences in the educational concentration of the population across cities and rural areas. In the Anglo-American economies and, to a lesser extent, the Scandinavian economies, economic growth in the last decades has generically benefited higher-skilled workers and particularly higher-skilled workers in either urban core areas or areas with high pre-existing levels of human capital (Moretti Reference Moretti2012). In cultural terms, Maxwell (Reference Maxwell2019) finds that major cities have become magnets for higher-skilled and culturally liberal voters, and substantial cultural sorting has occurred. Where the knowledge economy is less developed, either due to ongoing industrial or agricultural production, the gaps between the city and countryside are less stark, both because there are fewer centralizing urban pressures (i.e. de-concentrated forms of growth remain viable) and the gaps in economic and cultural experiences between the city and the countryside are less stark. Nonetheless, here too we see differences.
In other words, the geography of voters’ experiences differ both across types of economic growth models (knowledge-economy intensive or not) and their proximity to economic centres.
2.2.2 Changing Political Alignments
Do these shifts matter? Early work on the knowledge economy and party system change argued that changes in the class structure and nature of knowledge economy growth both created new pressures for social democratic parties. I argue later, that as the above-mentioned geographic shifts emerged, they magnified these difficulties.
The rise of knowledge-intensive work put pressure on social democrat’s traditional strategy of mobilization via broad based economic policy. Early work argued that the transition to the knowledge economy put increasing constraints on fiscal Keynesian demand side policies, limiting social democratic consumption spending, for example for pensions, health, and unemployment, while also making ‘supply side’ investment policies in skills to elevate the bottom half of the income distribution more imperative (Boix Reference Boix1998). At the same time, the rise of a new category of educated professionals – particularly in the sociocultural fields (education, culture, and health/wellness) – created a broad constituency calling for a societal liberalization in addition to demands for economic security and redistribution (Kitschelt Reference Kitschelt1994).
Social democratic parties that continued to primarily appeal to working-class voters on economic grounds were thus likely to face electoral decline. However, for modernizing social democratic parties, weaving together a coalition of their core base of voters with the new professional strata was increasingly difficult in the changing competitive space.
Subsequent work has vindicated many of these early claims. The changing class composition of the electorate has prompted a dramatic class realignment on the Left and Right (Kriesi et al. Reference Kriesi, Grande, Lachat, Dolezal, Bornschier and Frey2006, Reference Kriesi, Grande, Lachat, Dolezal, Bornschier and Frey2008; Kitschelt and Rehm Reference Kitschelt and Rehm2014, Reference Kitschelt, Rehm, Beramendi, Häusermann, Kitschelt and Kriesi2015; Gingrich and Häusermann Reference Gingrich and Häusermann2015; Häusermann and Kriesi Reference Häusermann, Kriesi, Beramendi, Häusermann, Kitschelt and Kriesi2015; Oesch and Rennwald Reference Oesch and Rennwald2018); the distinctiveness of traditional social democratic appeals – and policy – has declined (Huber and Stephens 2015; Raess and Pontusson Reference Raess and Pontusson2015), while moderation on new issues, particularly so-called ‘social investment’ has been critical to gaining new voters (Beramendi et al. Reference Beramendi, Häusermann, Kitschelt, Kriesi, Beramendi, Häusermann, Kitschelt and Kriesi2015; Abou-Chadi and Wagner Reference Abou-Chadi and Wagner2019) it has often been blocked by traditional labour market ‘insiders’ (Rueda Reference Rueda2005, Reference Rueda2007; Häusermann and Schwander Reference Schwander2012; Schwander Reference Schwander2012; Lindvall and Rueda Reference Lindvall and Rueda2014). The result has been an ongoing decline of social democratic parties and a competitive space that is increasingly fragmented not only across parties but also the dimensions of political competition.
However, as Boix (Reference Boix2019) and Iversen and Soskice (Reference Iversen and Soskice2019) argue, as these generic changes in skills and cultural values took on a geographic character that reinforced these broad shifts, sharpening the trade-offs for social democratic parties.
The growth of knowledge-intensive cities has the potential to further fragment the interests of working- and middle-class citizens across high- and low-productivity areas, making a singular policy appeal – whether on the demand or the supply side – increasingly difficult. This fragmentation follows from the geographic challenges that knowledge economy growth creates.
In one regard, regional economic divides are nothing new. In the postwar period, large parties, both on the Left and the Right, had to address deep economic disparities – Northern and Southern Italy, rural Wales and Manchester, and Stockholm and Northern Sweden, were all historically highly economically diverse. Both social and Christian democrats looked to appeal across regions with broad nationally based policies – with the expansion of the non-geographically targeted welfare state a method for doing so.
However, with the advent of knowledge society, there may be greater trade-offs. On the economic side, supply-side investments are likely to be less effective in lower productivity areas, absent some support for stimulating local growth. A redistributive supply-side policy (or ‘social investment’) is unlikely to meet the needs of these regions, where there are few high-paying employers demanding skills and where traditional industry is in decline. These deteriorating regions, therefore, demand social consumption policies – in the form of early retirement packages, higher pensions and greater unemployment payments – that fuel heterogeneity and disparity of wage earner demands across regions with different economic conditions. While working-class voters and professionals in knowledge-intensive cities often demand basic redistribution, they face specific pressure due to housing costs, childcare and transportation, making them more supportive of investment (Häusermann et al. Reference Häusermann, Pinggera, Ares and Enggist2019). Those in rural areas and outer suburbs, by contrast, face more pressure in terms of anaemic job creation, infrastructure investment and population aging. Creating a single economic package to address both – although not impossible (this was the attempt of US President Biden’s new revived industrial policy via the CHIPS and Science Act and Inflation Reduction Act) – often runs up against voters’ generic concerns about high taxation and social spending.
The same is likely to be the case for societal and cultural concerns with ecology, restrictions on automobile traffic, sociocultural pluralization and proliferation of art and entertainment. While urban areas and industrial districts – which Section 2.2.3 shows were often the traditional strongholds for the Left – were historically wealthy, the nature of production in these places has shifted away from traditional heavy industry with skilled crafts and manufacturing jobs, converting them into knowledge-intensive service job areas populated by professionals with tertiary education. As Rodden (Reference Rodden2019) shows, these shifts in the class structure produce a new cadre of urban voters increasingly willing to vote for the Left, but whose economic and sociocultural priorities may differ from those of the older outer suburban and industrial voters on questions of urban development and transportation, immigration, gender and climate issues. Taken together, these shifts mean that social democratic parties have a hard time to develop a unified strategy that appeals to all of these groups.
Put differently, there is a spatial disparity of the potential social democratic electorate between the new knowledge-intensive metropolitan areas and the more peripheral suburban and rural areas, but there is also a social and political disparity within each of these spaces between different potential constituencies which social democratic parties can no longer reach with the same appeals. There are unifying national issues left – such as support for an encompassing and generous national public pension system – but regional and group divides on other issues may overwhelm these bridging strategies.
2.2.3 New Geographic Competition
How geography shapes social democratic party strategies depends on the structure of party competition. This competition, in turn, is likely to vary across electoral system. Where electoral systems organize representation based on small geographical single-member constituencies with plurality or majority electoral formula, the strategic dilemmas of social democracy are often particularly intense.
In majoritarian countries, such as the US, Canada, UK and Australia, the electoral system magnifies geographic changes. While majoritarian systems insulate social democratic parties from new competitors, these electoral rules are associated both with both sharper geographic divides and a clearer transmission belt between geographic divisions and political conflict. As Iversen and Soskice (Reference Iversen and Soskice2019) argue, in (largely Anglo) countries with single-member district plurality systems parties have engaged little in redistributive policies that make regions reaping the benefits of the knowledge economy share them with those that do not. Majoritarian systems then, offer social democratic parties a particular dilemma. While the first-past-the-post system has protected them from shedding large proportions of votes to smaller radical left and green/left-libertarian parties, growing regional economic variation divides the social democratic base, making win-win urban-non-urban working-class coalitions hard to achieve. This pressure is compounded by urban malapportionment. As Rodden (Reference Rodden2019) argues, urban voters often deliver inefficient, over-sized electoral majorities to left parties, but few majorities in more suburban, peripheral districts. The result is that moderate left parties here have an increasingly urban base of victorious districts won with appeals conducive to attract professionals. But this makes it hard to win mixed suburban and rural districts with more working-class voters, with whom social democratic appeals that rally urban professionals will not resonate strongly.
Proportional systems would seem to avoid these problems. In these countries, the overall level of regional variation (and individual earning inequality) is much lower, with both larger welfare states, and more dispersed models of skill investment (through vocational training) continuing to redistribute resources and opportunities to more peripheral areas and the electoral system does not mechanically enhance urban–rural geographic disparities. Indeed, in such systems redistributive coalitions are more effectively viable (Iversen and Soskice Reference Iversen and Soskice2019), which may also cement more even interregional transfers (Beramendi Reference Beramendi2012). Moreover, proportional systems offer no (dis)advantage to concentrated support, hence they are not producing under-representation of left-party votes due to malapportionment across districts (Rodden Reference Rodden2019).
While systems of proportional representation enable parties to craft more cross-regional coalitions, and thereby reduce their electoral dilemma to serve different social constituencies, they also increase this dilemma in a different fashion. Because they pose low entry barriers to new contenders, such novel parties may seize upon locally concentrated political demands (De Vries and Hobolt Reference De Vries and Hobolt2020). After decades of cultural and economic sorting, parties representing a cross-region compromise face challenges from parties that are more directly targeted to the interests of particular areas. Social democratic parties can be outflanked in growing cities on the left by green-left parties catering primarily to sociocultural professionals, while in suburbs by parties of the Moderate Right or the Radical Right that pick up on traditional social democratic core voters’ lack of enthusiasm for libertarian societal innovations.
The result is that maintaining broad electoral support requires Social Democrats to compete with different demands in distinctive competitive spaces, both more or less knowledge-intensive areas and more or less rural and urban areas. Even when social democratic parties are broadly popular, they increasingly face distinct regional competitors that are more locally popular. The result is a loss on multiple fronts.
Table 2.1 lays out four different configurations. The competitive threat from the Green Left is likely to be strongest in knowledge-intensive cities (particularly those without a strong industrial past), which compositionally have more new professionals. In rural areas and outer suburbs of knowledge-intensive areas, particularly those with an industrial past, there will be more old-fashioned left voters disposed to follow Moderate Right or the Radical Right populist appeals to counter the rising influence of Green Left parties. Social democrats will be engulfed here by competition from all partisan directions.
Table 2.1 Competitive patterns
| Urban | Suburbs and rural | |
|---|---|---|
| Strong knowledge economy | GL more competitive | RR more competitive |
| Weak knowledge economy | RL more competitive | MR more competitive |
Less knowledge-intensive urban and rural areas follow different patterns. Here, many wealthy rural semi-industrial areas remain, where moderate right parties continue to have strong support. There are many fewer large urban non knowledge-intensive areas (e.g. Naples) but many mid-sized cities fall into this category (e.g. the French mid-sized city of Dijon scores as much less knowledge intensive than Grenoble). In these areas, radical left parties, where they exist, have often mobilized in both cities and suburbs, picking up on young educated voters frustrated with their lack of economic opportunities.
In other words, the geographic shifts are part of the broader well-theorized class realignment around parties. Moving towards the urban strata of knowledge society professionals may alienate working-class wage earners from social democracy. The rise of knowledge society has the potential to magnify the tensions between both within-region as well as cross-regional divisions over economic and societal issue preferences, making it hard for social democratic parties to address these tensions with a single unifying appeal.
Table 2.1 summarizes the likely patterns of party system configuration, but glosses over complexities and overlaps. Consider industrially declining urban areas surrounded by a thriving knowledge economy. These may mitigate party alignments attributed to either strong or weak urban knowledge economies. The cells in Table 2.1 thus suggest relative (not absolute) patterns of competition, compared to other urban regional types.
In conjunction, then, the claim here is that in knowledge-intensive areas social democratic parties face a growing divergence between the competitive space across urban and non-urban voting base, with challenger parties targeting these particular geographic strongholds in different ways. In less knowledge-intensive areas, social democratic parties face the competitive challenge of retaining support in adjusting urban areas where Radical Left and Radical Right populists may be growing in support, while competing with more traditional parties in the rural periphery. Thus, even in proportional systems, Social Democrats face a challenge in holding together cross-geographic coalitions under the umbrella of a single mainstream left appeal.
2.2.4 Regimes of Vulnerability?
What do the above-mentioned claims imply for social democratic parties’ overall electoral vulnerability? It is difficult to generalize to the country level, given that there are many moving parts across competitive systems, however, a few implications from the preceding discussion emerge.
Historically, these parties mobilized voters in particular places. If social democratic parties face different competitive threats across economic structures, then all else equal, the extent of these threats to overall performance will depend on how vested they are in particular geographic economic structures. Structural changes thus may have different aggregate implications for Social Democrats depending on their historic strongholds of mobilization.
For instance, social democratic parties that historically mobilized in cities, will face more threat from the Green Left in knowledge economies than the Radical Right. But, where they historically mobilized in the countryside, they might face more relative threat from the Radical Right. Indeed, while Abou Chadi and colleagues’ chapter on vote switching suggest that the electoral threat of social democratic voters switching to the Radical Right is generally overstated, it may be that there are specific contexts, such as Sweden where mobilization was historically more rural and where knowledge economy divides are growing, the Radical Right does pose a threat to social democratic parties. In less knowledge-intensive economies, social democratic parties may face fewer competitive threats where they are strong in rural areas, however, those with stronger traditional supporting in urban areas or suburbs are likely to face more threat from new radical left parties. These features suggest that the optimal competitive strategies for social democratic parties vary across place: trying to outcompete the Green Left may be less crucial to them in Sweden than in the Netherlands, for instance.
These structural shifts are not fully determinative. Where social democratic parties mobilize cities or rural areas effectively, they may prevent the rise of competitors on the supply side. Latent vulnerability is not always manifest.
2.3 Examining Processes of Change
Section 2.2 suggested that it is increasingly difficult for social democratic parties to hold cross-regional alliances together, but that patterns of change vary across economic regions and the macro-implications of change vary across historic structures of mobilization. To test these arguments, I draw on an original dataset of election results at the level of local area units (LAU), the base unit for European geographic hierarchies for the 1980s to present.Footnote 2
This dataset has greater geographical coverage and much more granular election results than Kollman et al. (Reference Kollman, Hicken, Caramani and Backer2010), but spans a shorter time period. The LAU are generally municipalities, although the scope of geographic disaggregation varies widely across countries – from highly aggregated units in the UK to highly disaggregated units in France. LAU have the advantage that they can be matched to the OECD and Eurostat ‘functional urban areas’, which are equivalent to commuter zones that have been harmonized across Europe. The LAU are fully embedded in NUTS units. As such, in measuring electoral outcomes at the LAU level, I can directly examine localized electoral outcomes along the two dimensions outlined in Table 2.1 – by the extent of the knowledge economy and the type of urban area.Footnote 3 All analyses weight the results by the national share of voters in the unit to adjust for highly varied unit sizes.
The previous discussion made three claims. First, in proportional systems, that social democratic parties were likely to lose voters over time as it became more difficult to hold together cross-place electoral appeals, that is we would see a weakening of their appeal across areas, not a geographic realignment. Second, that this weakening rests on the geographically varied party competitive dynamics, outlined in Table 2.2. Third, the aggregate effect of these shifts on social democratic parties’ competitive position depends on the historic mobilization structure. I use the disaggregated electoral data to test each of these claims.
Table 2.2 Voting by types of regions
| (1) | (2) | (3) | (4) | (5) | |
|---|---|---|---|---|---|
| SD | MR | GL | RL | RR | |
| Knowledge index | −0.215Footnote *** | 0.107Footnote *** | 0.00117 | −0.0302 | 0.0958Footnote * |
| (0.0417) | (0.0343) | (0.0219) | (0.0395) | (0.0496) | |
| Suburbs | −0.00831 | −0.0271 | −0.0190Footnote ** | 0.0371Footnote *** | 0.0222Footnote *** |
| (0.0198) | (0.0250) | (0.00889) | (0.00997) | (0.00715) | |
| Mid and major cities | 0.0202 | −0.0247 | −0.0521Footnote *** | 0.0335Footnote ** | 0.0438Footnote *** |
| (0.0259) | (0.0285) | (0.0152) | (0.0136) | (0.0113) | |
| Suburbs × KE | 0.0211 | 0.0413 | 0.0496Footnote *** | −0.0484Footnote *** | −0.0594Footnote *** |
| (0.0357) | (0.0412) | (0.0168) | (0.0153) | (0.0183) | |
| Cities × KE | 0.0177 | −0.0279 | 0.142Footnote *** | 0.00317 | −0.124Footnote *** |
| (0.0484) | (0.0531) | (0.0353) | (0.0242) | (0.0319) | |
| Baseline share industry | 0.00828 | −0.0297 | −0.129Footnote *** | −0.135Footnote *** | 0.171Footnote ** |
| (0.0486) | (0.0538) | (0.0351) | (0.0306) | (0.0736) | |
| Employment in agriculture | −0.0244 | 0.229Footnote *** | −0.107Footnote *** | −0.114Footnote *** | 0.0502 |
| (0.0690) | (0.0669) | (0.0313) | (0.0296) | (0.0723) | |
| Log voters | 0.000391 | −0.00233 | 0.00560Footnote *** | −0.00162 | −0.00192 |
| (0.00276) | (0.00377) | (0.00168) | (0.00247) | (0.00249) | |
| GDP per capita | 0.00278 | −0.0120Footnote *** | 0.00254 | −0.00592Footnote ** | 0.000727 |
| (0.00343) | (0.00348) | (0.00180) | (0.00254) | (0.00286) | |
| Constant | 0.320Footnote *** | 0.347Footnote *** | 0.0940Footnote *** | 0.150Footnote *** | 0.0587 |
| (0.0421) | (0.0401) | (0.0180) | (0.0293) | (0.0566) | |
| Observations | 154,917 | 154,917 | 146,963 | 60,436 | 144,697 |
* 0.10, **0.05, and ***0.01.
I begin with the first claim, conducting a series of descriptive analyses, using the LAU dataset to show examine geographic patterns of electoral decline among social democratic parties: I show that they have lost voters across different types of areas and regions, without compensating for these losses in new areas.
To examine the second claim that social democratic parties face distinct competitors across areas, I follow two approaches. I first turn to the LAU dataset to measure changing party competitive patterns across economic regions and urban types. I match each LAU to several features of the NUTS-3 region, such as GDP per capita, share of employment in industry and agriculture. I further match each LAU to the NUTS-2 level knowledge economy indicators outlined in Table 2.1. For these analyses, I distinguish three types of areas, rural and towns that are non-commuter zone areas, outer suburbs of large and small cities and large and small cities. I combine large and small cities into a single category, despite important differences between them, because there are very few non-knowledge-intensive large cities. This three category classification allows me to distinguish those living in core urban areas, outlying urban areas and rural areas, across different types of broad regional structures and look at trends in electoral outcomes. I examine vote shares across the LAU for the five main party families examined in this volume, using the coding strategy outlined in the introduction.
Observing structural ‘challenges’ is often difficult. As most voters are creatures of habit, and systems of mobilization and partisanship, especially among older voters, are often entrenched, the link between both structural change and political outcomes can be strongly temporally lagged. Both Karreth et al. (Reference Karreth, Polk and Allen2013) and Evans and Tilley (Reference Evans and Tilley2017) show that at the individual level, an underlying weakening of support for social democratic parties often precedes defection. A similar effect can occur geographically. There may be a latent weakening of support for a party in a particular place before there is a large outright defection of voters. In order to tease out the longer-term structural weakening of social democratic support across different places, I conduct a second analysis focusing changes in local electoral outcomes in the post-great financial crisis period. Given the extent of economic distress and accompanying challenger entry that posed a shock to most systems, if structural changes are altering trade-offs in ways that vary across places but take time to manifest themselves, we would expect to see the financial crisis to be a particular important turning point for areas (and people) that have experienced longer-term underlying weakening of support. I thus look at changes in support across parties in this context.
Finally, to examine the third claim about overall vulnerability of social democratic parties, I look at the same dependent variables, now interacting the knowledge-urban configurations with their historic mobilizing structures. This analysis allows us to tease out the different underlying threats to social democratic parties across places.
2.3.1 Where Did They Lose?
The first claim mentioned earlier is that social democratic parties, over time, lose in nearly all geographic areas, without picking up new areas. To examine the question of losses descriptively, Figure 2.3 plots the vote shares for the main social democratic party in the early 1980s against their vote shares in post-2010 for the most disaggregated unit available in sixteen countries (data at this level of disaggregation is only available from early the 1990s in France and unified Germany). The small number of units for which data in the 1980s is missing are assigned the average in the NUTS-3 region. For each country, the solid black line represents the relationship between the early 1980s vote share and post-2010 vote share by local unit, and the dashed black line is a 45° line (i.e. the early 1980s vote share plotted against itself). In nearly all cases, we see either a uniform cross-regional decline (as in Sweden), or an accentuated loss of support in historically strong regions (i.e. the dashed and black lines cross much closer towards the origin). In France and Germany, for instance, the traditional strongholds have shifted substantially away from the PS and SPD, respectively. In Italy, there has a been an even dramatic decline for the Moderate Left – driven by the rise of new populist radical right and radical left parties in both cases – even the traditionally strong ‘red belt’. In Greece, the historically strong PASOK, now KINAL, shows an even more extreme collapse in regional support. Thus, while there remains a high correlation in historic vote share and current vote share, social democratic parties have almost universally lost traditional strongholds without gaining new ones – in other words, we are not seeing geographic realignment in their support.
Figure 2.3 Voting in the early 1980s and 2010s
The patterns discussed earlier suggest, descriptively, that social democratic decline is in large part driven by both these parties’ inability to maintain voters in traditional strongholds and to build new forms of regional support. In many ways, this outcome is not surprising. Benedetto et al. (Reference Benedetto, Hix and Mastrorocco2020) find an on aggregate relationship between declining industrial shares and long run social democratic support, a finding that resonates with the above-mentioned long line of work at the individual level theorizing the relationship between changing class structures and a political dilemma of social democracy (Kitschelt Reference Kitschelt1994). The industrial structure – and its associated organization of economic and political life in trade unions – that gave birth to social democratic parties has changed, and so too have traditionally strong social democratic regions (Rodden Reference Rodden2019).
However, when viewed from another angle, the patterns are somewhat surprising. Social democrat’s traditional strongholds varied substantially across place from the wealthier industrial and urban regions of Germany, Italy and Switzerland to the poorer and more sparsely populated regions of Northern Scandinavia and Finland. Moreover, the transformation of these regional economies has been profound – and yet vote shares in many European countries correlate at above 0.80 in the 2010s to the 1980s. The continuity of geographic support then is as striking as the general overall losses in the post-financial crisis era. In other words, social democratic parties have lost everywhere, including their past strongholds, without picking up new areas. This trend is relatively universal across countries.
2.3.2 Whom Did They Lose To?
To whom have social democratic parties lost voters? In order to examine these trends, I begin descriptively, looking at urban–rural divisions – using the highly localized data aggregated to functional urban areas – across the included European countries. Figure 2.5 shows that across Europe, mainstream social democratic parties historically had a more urban base, with more substantial support in major and small cities, and to a lesser extent suburbs, than in rural areas and towns. Over time, this support has collapsed across all areas, with marginally more resilience in smaller cities than other areas. The Moderate Right shows the reverse geographic pattern, with stronger support in rural areas and towns and suburbs, with historically less relative support in major and smaller cities. It too has experienced across the board decline, albeit less dramatic decline in the post-2010 period. When we look at competitor parties on the Left and Right however, a starker geographic pattern emerges. Green and left-libertarian parties and radical left parties have a strong urban core, particularly in Europe’s major cities. Right populists, by contrast, while gaining in all geographic categories, have gained much less in major and small cities than outlying areas and suburbs. On aggregate then, there are stark urban–rural divides in new parties, with suburbs often looking more ‘rural’ than their associated cities.
Figure 2.4 Party family vote share by urban type
Figure 2.5 Regional patterns of competition, post 2010
There are some key exceptions to these trends. In the post-2010 period, the non-mainstream Left grew substantially in support in Spain and Greece, compressing geographic differences; and, both M5S and Lega in Italy have a strong urban base, including in the major cities and inner suburbs. In both the Netherlands and France, ‘major cities’ do not differ that much from the rest of the population because they are internally heterogenous – Amsterdam and Paris are very different from Rotterdam and Marseille. However, as Figure 2.4 shows, on aggregate, green/left-libertarian and radical left parties have picked up disproportionately in urban areas, whereas populist radical right parties have gained outside them.
Does the structure of the knowledge economy shape these urban–rural partisan divides? To provide a suggestive test of the claims, in Table 2.2 I run a series of linear regressions, regressing vote share for Social Democrats, the Moderate Right, Green Left, Radical Left, and Radical Right at the LAU level, on the NUTS-2 knowledge economy index (Figure 2.1) interacted with the three-part urban classification. Each model includes a control for logged population in the LAU, and NUTS-3 historic industrial structure in employment (1980 baseline), employment share in agriculture (averaged over the decade) and GDP per capita. All context data is from Eurostat-ARDECO (2018). In each model, I include election-specific fixed effects – meaning that individual areas are being compared only to other areas in their country for a given election. I further cluster the standard errors by election and all analyses are weighted by population in the unit. Table 2.2 shows the results, with Figure 2.5 showing the results graphically.
We see that social democratic parties, on average, have a lower vote share in knowledge-intensive regions, and the gap between cities and other areas are larger in these regions. When the UK (with its majoritarian system) is excluded, the patterns are similar but the standard errors on the city*KE interaction are larger. The Moderate Right, by contrast, does worse in knowledge-intensive urban regions (compared to rural areas and suburbs), an effect that is somewhat reduced in size when the UK is excluded, but remains statistically significant. Green and left-libertarian parties do substantially better in urban areas in knowledge-intensive regions compared to both other types of areas and urban areas in non-knowledge-intensive regions. These results are not just from large cities, green and left parties have the strongest base in mid-sized cities. Here, the exclusion of the UK magnifies these effects (particularly as, in this volume, the SNP, with a more mixed geographic base is considered a green left party). Radical left parties only exist in several countries, and here suburbs differ across knowledge and non-knowledge-intensive regions, with a higher urban gradient but no differences across knowledge-intensive areas. Finally, the knowledge index is positively associated with radical right voting, and this effect is driven by rural areas and suburbs, which are much more likely to support radical right parties in knowledge-intensive regions. The Appendix replicates these results, here looking at the patterns of interaction with the 1980s industrial structure, rather than the knowledge economy measure. We see somewhat congruent patterns, with social democratic parties performing better in industrial cities than other areas, with the Moderate Right performing worse in industrial cities. Green and left-libertarian parties do best in less industrial cities, with radical left parties performing less well in industrial suburbs and rural areas. Finally, the Radical Right performs better in all types of industrial areas, with a weaker gap between cities and suburbs and rural areas here (additional analysis shows that this is particularly driven by mid-sized cities).
In short, largely in line with Table 2.1, the competitive space around party mobilization varies across both urban types and economic regions, creating distinct patterns of competition. The heightening of structural pressures created new geographic pressures, combined with tipping point of the financial crisis, pushed Europe’s regions into different directions. In cities, social democratic parties must compete with green and left-libertarian or radical left parties for both middle- and working-class citizens, in the country side, with the Radical and Moderate Right. The results here suggest ongoing difficulties in creating a cross-regional left coalition.
2.3.3 Historic Mobilization Meets New Pressures
What do these dynamics mean for aggregate differences across countries? Understanding how the regional dynamics translate into different national strategic threats, requires tracing the relative threat different competitors face.
Historically, social democratic parties had different areas of strength across countries. To measure historic patterns then across all the European countries, I return to the LAU dataset, which includes election results from the 1980s (and from 1993 for France and 1994 for Germany). Here I average voting through all elections 1980s measuring disproportionality in social democratic parties’ share across towns and rural areas, suburbs and cities. Disproportionality is measured as the proportion of a party’s total vote that comes from a type of geographic area relative to the population share of that area. For instance, about 54 per cent of Sweden’s population lived in rural areas and towns in the 1980s, but about 58 per cent of SAP voters were town dwellers (35 per cent of Swedes were city dwellers, compared to 31 per cent of SAP voters). This situation creates some mild rural disproportionality in Sweden. By contrast, in Austria, 46 per cent of SPO voters lived in towns, compared to 49.5 per cent of the population, creating a more urban base. The difference between these numbers is the rural disproportionality measure. For Italy, I count the Communist Party as the main social democratic party in the 1980s. Figure 2.6 plots this disproportionality measure on the x-axis, against total vote share for the party through the decade on the y-axis. While the differences are not large, we nonetheless see consistently different patterns of mobilization across place.
Figure 2.6 Mobilization in the 1980s
To find out how these historic structures intersect with economic change, I conduct one final analysis. Here I combine the knowledge index and the urban categorization, splitting the top and bottom half of those on the knowledge index, and further splitting urban areas from suburbs and rural areas. The result is four groups, urban more-knowledge-intensive local areas (Urban-KE), urban less-knowledge-intensive areas (Urban Non-KE), and rural and suburban more-knowledge-intensive areas (Rural-KE), and rural and suburban less-knowledge-intensive areas (Rural-Non KE). I then interact this categorization with the share social democratic parties received in the local area in the 1980s, with urban KE regions as the baseline. Each model uses country-year fixed effects and country-year clustered standard errors, and all results are weighted by the LAU vote share. This approach examines whether the places that social democratic parties traditionally mobilized have been more or less vulnerable to particular types of competitors, giving us hints at the aggregate threat they face.
Table 2.3 shows the results, with Figure 2.7 demonstrating the results graphically. We see that not surprisingly, social democratic parties do better in all types of places that they historically mobilized, with no major differences across the urban regional types. They do marginally better in urban areas that they were historically weak in, suggesting that on aggregate they have picked up some urban support outside of their core areas. When the UK is excluded, we see a small, but significant positive effect on vote share in rural strongholds, suggesting that outside of the UK, they have been marginally more effective at holding their rural and suburban strongholds. However, the effect size is small and the standard errors large, suggesting heterogeneous patterns.
Table 2.3 Voting by types of historic and contemporary regional structures
| (1) | (2) | (3) | (4) | (5) | |
|---|---|---|---|---|---|
| SD | MR | GL | RL | RR | |
| SD share in 1980s | 0.547Footnote *** | −0.555Footnote *** | −0.0776 | 0.000139 | 0.0119 |
| (0.0468) | (0.0343) | (0.0525) | (0.0358) | (0.0356) | |
| Urban Non-KE | 0.106Footnote *** | −0.165Footnote *** | −0.0598Footnote ** | 0.0698Footnote ** | 0.0424Footnote *** |
| (0.0307) | (0.0207) | (0.0244) | (0.0327) | (0.0140) | |
| Rural-Sub. KE | −0.0417Footnote *** | 0.0274Footnote ** | −0.0425Footnote ** | −0.0353Footnote ** | 0.0443Footnote ** |
| (0.0138) | (0.0113) | (0.0164) | (0.0127) | (0.0213) | |
| Rural-Sub. Non-KE | 0.0166 | −0.0356Footnote ** | −0.0548Footnote *** | −0.00792 | 0.0317Footnote ** |
| (0.0163) | (0.0162) | (0.0191) | (0.0147) | (0.0142) | |
| Urban Non-KE × share | −0.193Footnote *** | 0.480Footnote *** | 0.0897 | −0.217Footnote ** | −0.0774Footnote * |
| (0.0699) | (0.0540) | (0.0630) | (0.0890) | (0.0413) | |
| Rural-suburb KE × share | 0.0756Footnote * | 0.0297 | 0.0340 | 0.0430 | −0.0592 |
| (0.0406) | (0.0346) | (0.0463) | (0.0319) | (0.0449) | |
| Rural-suburb Non-KE × share | −0.0222 | 0.204Footnote *** | 0.0825 | −0.0842Footnote ** | −0.0474 |
| (0.0425) | (0.0420) | (0.0536) | (0.0398) | (0.0351) | |
| Baseline share industry | −0.000928 | −0.0452 | −0.159Footnote *** | −0.0693Footnote * | 0.198Footnote ** |
| (0.0298) | (0.0534) | (0.0419) | (0.0352) | (0.0767) | |
| Employment in agriculture | 0.108Footnote *** | 0.161Footnote *** | −0.122Footnote *** | −0.101Footnote *** | 0.0321 |
| (0.0352) | (0.0433) | (0.0310) | (0.0213) | (0.0581) | |
| Log voters | −0.00446Footnote ** | 0.00232 | 0.00688Footnote *** | −0.000990 | −0.00207 |
| (0.00173) | (0.00216) | (0.00198) | (0.00193) | (0.00276) | |
| GDP per capita | 0.00213 | −0.0121Footnote *** | 0.00713Footnote *** | −0.00654Footnote * | 0.000837 |
| (0.00165) | (0.00293) | (0.00151) | (0.00351) | (0.00326) | |
| Constant | 0.0867Footnote *** | 0.503Footnote *** | 0.149Footnote *** | 0.145Footnote *** | 0.0734Footnote * |
| (0.0200) | (0.0301) | (0.0231) | (0.0321) | (0.0367) | |
| Observations | 149,094 | 149,094 | 141,158 | 55,065 | 138,986 |
* 0.10, **0.05, and ***0.01.
Figure 2.7 Historic mobilization and regional type
The Moderate Right shows the reverse pattern to social democratic parties. These parties perform best in the regions where social democratic parties were traditionally weak, especially rural areas, and are weak in historic social democratic urban knowledge strongholds. Green and left-libertarian parties are strongest in urban knowledge centres both in traditionally strong and weak social democratic areas. Only rural and suburban knowledge-intensive areas are more prone to radical left voting if they were social democratic strongholds, for other areas there is no strong association. Finally, while the previous analysis suggested rural knowledge-intensive regions were key to radical right parties’ strength, we see that this strength is attenuated when the Left had a stronger presence in the region.
These results suggest, in line with Section 2.2, that the aggregate threat to social democratic parties may vary based on historic mobilizing structures but not always in straightforward ways. Social democratic parties do, however, face different challenges in their historic areas based on their structure, with less relative challenge from the Moderate Right (although absolute moderate right voting remains the highest competitive group in all areas), strong challenge from the Green Left, and a more mixed pattern with the Radical Right.
2.4 Conclusion
This chapter has argued that geographic shifts reinforce the individual changes studied elsewhere in this volume. The structural economic and social transformations of the knowledge economy have created new geographic experiences, pushing Europe’s regions into different directions politically. In cities, particular knowledge-intensive ones, social democratic parties must compete with other left parties for both highly educated professionals and working-class voters, In the countryside, they face strong Radical Right and Moderate Right competitors. As voters are increasingly comfortable with defection this balancing act is more difficult, and party positioning to address it more constrained. The results here suggest ongoing difficulties in creating a cross-regional Left under the social democratic umbrella. What that implies for party strategy then, varies across countries. Where social democratic parties were historically strong in cities, then the advantages of green and left-libertarian parties in these areas make these parties a more important competitor. Where social democratic parties have rural advantages, they may both dampen right populist voting and be vulnerable to it, suggesting a different strategic challenge in mobilization.
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