Claude Diebolt
email: cdiebolt@unistra.fr

Ralph Hippe

Remoteness equals backwardness? Human capital and market access in the European regions: insights from the long run

Keywords: Human Capital, New Economic Geography, Regional Development, Market Access. JEL Codes: I21, N33, N93, R11

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Introduction

Human capital is generally perceived to be a key factor for today's knowledge-driven economies. This is particularly true for Europe and the European Union. For this reason, the Council of the European Union highlights that "[e]ducation and training have made a substantial contribution towards achieving the long-term goals of the Lisbon strategy for growth and jobs" (Council of the European Union 2009, C 119/2). Still, the EU is facing important challenges in its regional policy. Although the EU has aimed to decrease economic and social inequalities over the last decades, there still remain important differences between and within countries. The current economic crisis has further widened previous convergence tendencies. Similarly, education is not equally distributed in space.

Thus, how can one explain these differences? One possible explanation advanced by theory and in particular by models from New Economic Geography (NEG) is that consumer markets play an important role in the distribution of economic development. These models have already been tested empirically for the last decades (e.g., [START_REF] Braudel | Civilisation materielle, économic el capitalisme, XVe-XVIIIe siècle. Le temps du monde[END_REF][START_REF] Faíña | Market access and human capital accumulation: the European Union case[END_REF], Head and Mayer 2011) and have confirmed the predictions provided by these rather recently developed NEG models. A particular case including human capital formation is presented by [START_REF] Redding | Distance, Skill Deepening and Development: Will Peripheral Countries Ever Get Rich?[END_REF]. The authors develop a theoretical NEG model showing that remoteness from large consumer markets gives disincentives to individuals to increase their human capital. For this reason, this 'penalty of remoteness' explains worldwide inequalities in human capital accumulation. Subsequent empirical studies have also confirmed the predictions of the model for the European regions for the last couple of years (e.g., [START_REF] López-Rodríguez | Human Capital Accumulation and Geography: Empirical Evidence from the European Union[END_REF].

Nevertheless, (to our knowledge) there has not yet been any empirical evidence for the long-term evolution of market access and human capital at the EU or European regional level. This empirical evidence, however, appears particularly important to understand the changes that have shaped today's European regions in the long run. This may considerably enlarge the recent analyses for the short term which may be only part of a much larger long-term process.

For these reasons, this paper explores for the first time the importance of market access for the spatial distribution of human capital in the European regions in the past. We combine and adapt several databases to create a new unique dataset. More specifically, we use two different human capital indicators at different points in time to test the robustness of our analysis. First, we employ regional numeracy estimates for 1850. The age heaping method enables us to estimate numeracy (e.g., [START_REF] A'hearn | Quantifying Quantitative Literacy: Age Heaping and the History of Human Capital[END_REF][START_REF] Hippe | Regional Inequality in Human Capital Formation in Europe, 1790 -1880[END_REF], Hippe 2012a). Second, we use literacy as an alternative human capital proxy. Literacy is certainly the most employed indicator for human capital in the past. Therefore, we can check the overall numeracy results by using regional literacy outcomes in 1930. Both indicators also allow to better model the proposed theory than alternative historical education variables. In addition, as has been proposed by the recent literature, we exploit data on the distribution and size of cities in Europe to model historical market access.

The results show that market access has a significant negative influence in OLS, Tobit and IV regressions. In the latter case we use distance to Luxembourg and area size of European countries as instrumental variables. In sum, the 'penalty of remoteness' hypothesis theoretically advanced by [START_REF] Redding | Distance, Skill Deepening and Development: Will Peripheral Countries Ever Get Rich?[END_REF] is confirmed by our historical data. This result implies that the 'penalty of remoteness' is not a current trend but has existed for long time spans, the present being only a very special case of a larger phenomenon.

The paper is structured as follows. First, we consider the literature on human capital formation in the European regions in the past and the main contributions of New Economic Geography. Then, we briefly present the underlying theoretical NEG framework which has been originally proposed by [START_REF] Redding | Distance, Skill Deepening and Development: Will Peripheral Countries Ever Get Rich?[END_REF]. Subsequently, the data and the econometric specifications are discussed. In the fourth section we show the results and their policy implications. The final section concludes.

Related literature 2.1 Regional human capital formation in Europe, today and in the past

Human capital formation in the European regions has attracted the attention from many researchers (e.g., Badinger and Tondl 2003[START_REF] Braudel | Civilisation materielle, économic el capitalisme, XVe-XVIIIe siècle. Le temps du monde[END_REF][START_REF] Faíña | Market access and human capital accumulation: the European Union case[END_REF][START_REF] Sterlacchini | R&D, higher education and regional growth: Uneven linkages among European regions[END_REF]. For example, Rodríguez-Pose and Tselios (2011) use Exploratory Spatial Data Analysis to test the spatial distribution of educational attainment in western Europe between 1995 and 2010. They find that educational attainment is strongly correlated with inequality and that regions tend to cluster in space. Proximity plays an important role for educational attainment even today. Moreover, there are noticeable differences between the north and the south of western Europe and the urban and rural communities.

However, as the authors state, "[t]he geography of education, especially at [the] subnational level, is a huge black box" (Rodríguez-Pose and Tselios 2011, p. 358). If this is still true for today, one can imagine how the situation is for the past. New evidence on the regional distribution of human capital in Europe in the past has recently been provided by [START_REF] Hippe | Regional Inequality in Human Capital Formation in Europe, 1790 -1880[END_REF]. They use the age heaping method to calculate numeracy estimates, i.e., whether individuals are able to count or calculate (e.g., [START_REF] A'hearn | Quantifying Quantitative Literacy: Age Heaping and the History of Human Capital[END_REF][START_REF] Crayen | Global Trends in Numeracy 1820-1949 and its Implications for Long-Run Growth[END_REF]. They show that regional numeracy values steadily improved almost everywhere in Europe during the 19 th century. Leaders in numeracy were countries in Scandinavia, in central Europe and the United Kingdom. Many of the regions in these parts of Europe had already very high numeracy values at the beginning of the 19 th century.

In contrast, Southern and Eastern Europe importantly lagged behind. They needed many more decades to attain similar levels, in part even until the beginning of the first or the second half of the 20 th century. In addition, regional differences in numeracy were quite striking in most of these countries. For example, a core-periphery pattern characterised Spain. The regions in the north of Madrid had the highest numeracy levels while those at the southern periphery (Andalusia) and in the north-western periphery (Galicia) followed with a large gap. In contrast, a north-south gap is visible in Italy. The lowest numeracy values were calculated for regions in the Balkans and the Caucasus. Still, most countries and regions were able to improve the numeracy levels of their population during the 19 th century. In consequence, regional inequalities in numeracy diminished over that period.

Numeracy is one possibility to measure human capital in the past but there are also other proxies. However, one has to note that no other measure is available at such a scale to that time period. Nevertheless, one can focus on individual countries to check the validity of the numeracy data. In fact, the most important tendencies in numeracy can also be detected when employing other indicators. For instance, [START_REF] Cinnirella | Landownership Concentration and the Expansion of Education[END_REF] use data on Prussian counties in the 19 th century. Taking a look at the data one can see that the counties of Poznan province had the lowest enrolment rates (6-14 year olds), confirming the lower numeracy levels in the study by [START_REF] Hippe | Regional Inequality in Human Capital Formation in Europe, 1790 -1880[END_REF].

Another country example is provided by Felice's (2012) recent study on the regions of Italy during the 19 th and the 20 th century. In contrast to the above-mentioned studies, he uses a specifically constructed human capital measure which takes into account both enrolment rates and literacy. He shows that regional differences in human capital peaked around 1871 but diminished during the next decades. Northern regions had a distinct lead to other regions, followed by central and ultimately southern and island regions.

Furthermore, Núñez' (1992) Spanish literacy data underline a core-periphery structure similar to the one highlighted by numeracy. Finally, the correspondence of regional numeracy and literacy data has been put emphasised by [START_REF] Hippe | How to measure human capital? The relationship between numeracy and literacy[END_REF], using data for a number of European countries around the beginning of the 19 th century and adding more recent data for current developing countries in Latin America, Asia and Africa during the second half of the 20 th century.

Economic geography and market access in Europe

Economic geography has become an important field in economics over the last years.

Economic geography models enable to understand why economic activity and individuals cluster in space (e.g., [START_REF] Krugman | Increasing returns and economic geography[END_REF]. In other words, they allow to clarify the reasons for the existence of urban agglomerations, e.g., Tokyo and Mexico City, and areas with concentrated activity, such as the Manufacturing Belt in the United States and the Blue Banana in Europe. Accordingly, the regional distribution of GDP per capita in Europe is quite unequal.

The spectacular growth of urban agglomerations, particularly in developing countries, further shows that economic geography is an important factor for the distribution of the population in the past, today and probably in the future. Given these facts, it is not astonishing that policy makers are faced with the question of how to deal with these inequalities. Economic geography, and in particular New Economic Geography (NEG), have gained attention due to the process of European integration and its consequences for regional inequalities [START_REF] Fujita | The Spatial Economy: Cities, Regions and International Trade[END_REF]. Although integration within the EU has led to increasing convergence across members states, within member state inequality has had the tendency to accentuate [START_REF] Faiña | Cohesion policy and transportation, Chapter 21[END_REF] Thus, the developments of NEG have substantially improved the understanding of the concentration of economic activities and of market potential in geographical space.

More specifically, they capture the effect of the 'pecuniary economies' which are transmitted through the price system. They have had a revolutionary impact in integrating space structures into the standard economic models considering demand and supply factors. Nevertheless, they only provide a partial explanation of agglomeration patterns.

They are less suited to capture the spillover effects from technology and knowledge (and others) which are crucial to comprehend the agglomeration into cities and clusters. These, however, are characteristics of Urban Economics. In consequence, the specific characteristics of the investigated problem determine which explanation is appropriate in each individual case. For example, the "NEG stresses the role of spatial linkages" (Brakman, Garretsen and Van Marrewijk 2009, p. 777) that exist across large spatial entities, while Urban Economics is more concerned with the local production conditions, emphasizing the importance of knowledge (and other) spillovers [START_REF] De Vries | California dreamin': The feeble case for cluster policies[END_REF][START_REF] Combes | Agglomeration and the adjustment of the spatial economy[END_REF][START_REF] Fingleton | The empirical performance of the NEG with reference to small areas[END_REF]).

Moreover, Head and Mayer (2004) have found a crucial asymmetry in NEG models' predictive power concerning spatial evolution and income inequalities. As Faiña et al. (2016, p. 349) note, "NEG predictions on spatial dynamics (through computer simulations) point to instability and breaking points [in tendencies], which do not fit with the stability of spatial patterns and urban hierarchy exhibited by empirical data. On the contrary, whenever a centre-periphery spatial structure exists, inequalities in salaries and income between central and peripheral areas fit well with available evidence and can be explained by market access differentiation (the so-called nominal wage equation)." Yet this relationship cannot be considered to be deterministic, because there are clear exceptions to this rule. For example, the "Scandinavian countries have been able to overcome to a large extent the main handicaps of peripheral areas" (Faiña et al. 2016, p. 349).

In fact, NEG offers much more than possible explanations for the distribution and concentration of economic activity in space -it offers a framework of analysis suitable for application to the study and evaluation of many different situations with different problems, which require specific combinations, and thus tailor-made policy measures.

Additionally, there are further theories which take a non-deterministic approach to policies on regional development and consider the economic expectations of different actors. For example, [START_REF] Redding | The low-skill, low-quality trap[END_REF] model combines the two strands in which endogenous growth theory has been split (see [START_REF] Faiña | Cohesion policy and transportation, Chapter 21[END_REF]. The first strand focuses on RDI and human capital, while the other sees the innovation process à la Schumpeter as being the most important driver behind growth. His model concentrates on the mutually reinforcing interplay of investments in human capital and in RDI. Furthermore, it considers the interactions of skill development at the individual level and quality-enhancing RDI investments by firms. These interactions lead to various equilibria which have already been shown in the empirical literature: either high human capital and high RDI investment outcomes or low ones. Depending on the equilibrium this will then lead to high or low growth rates. The expectations and strategic behavior of agents are the major driving force behind the selection of the equilibrium. Thus, governments can take an important role in regional development by promoting policies that guide these expectations into the right direction so that the economy may end up in the high growth equilibrium and not get stuck in low growth. In particular, these policies can consist of specific subsidies that enhance the expectations on the positive return of individual human capital and firm level RDI investments. In consequence, a region can boost sustainable growth by increasing both mutually reinforcing investment types at the same time (see [START_REF] Faiña | Cohesion policy and transportation, Chapter 21[END_REF].

Furthermore, the concept of market access or market potential plays a very important role in many economic geography models (e.g., [START_REF] Crozet | Do migrants follow market potentials? An estimation of a new economic geography model[END_REF][START_REF] Redding | Economic geography and international inequality[END_REF][START_REF] Hanson | Market potential, increasing returns and geographic concentration[END_REF][START_REF] Niebuhr | Market access and regional disparities[END_REF]). Having a good access to large markets is deemed to be a fundamental economic advantage of a region.

The notion of market access follows the idea to apply physical laws to human and economic movements. More specifically, human interactions are considered to follow gravitational principles, which could at the earliest predecessors of the concept explain lower demand in remote areas. The concept itself goes back to [START_REF] Harris | The market as a factor in the localization of industry in the United States[END_REF]. Technically speaking, market potential is constructed using volume measures (e.g., number of inhabitants, number of transactions) which are weighted by the inverted distance (see [START_REF] Faiña | Cohesion policy and transportation, Chapter 21[END_REF]. Later applications include [START_REF] Clark | Industrial location and economic potential in Western Europe[END_REF] and, in particular, [START_REF] Keeble | Regional accessibility and economic potential in the European Community[END_REF] (see also [START_REF] Niebuhr | Market access and regional disparities[END_REF]. The latter authors show that market potential is lowest in periphery regions. The highest market potential, in contrast, was found in north-western Europe, including West Germany and the Benelux countries. In this way, mapping market potentials allows to condense and visualise huge amount of spatial information which supports regional planning [START_REF] Faiña | Cohesion policy and transportation, Chapter 21[END_REF].

However, studies à la Harris also attracted criticism for not taking enough account of supply/demand structures [START_REF] Faiña | Cohesion policy and transportation, Chapter 21[END_REF]. They also lacked solid theoretical foundations. These were only later provided by NEG, so that newer studies are able to test the implications of these theoretical models. Initially, these were country studies (e.g., [START_REF] Roos | Wages and market potential in Germany[END_REF][START_REF] Brakman | The spatial distribution of wages: estimating the Helpman-Hanson model for Germany[END_REF][START_REF] Mion | Spatial externalities and empirical analysis: the case of Italy[END_REF][START_REF] Ottaviano | Market potential and productivity: Evidence from Finnish regions[END_REF] which generally emphasise the importance of market access. More recently, new studies also take a European approach (e.g., Head andMayer 2006, Niebuhr 2006) and generally confirm the hypotheses set up by NEG.

Market access may also have effects on the accumulation of human capital.

Human capital is clearly an important economic factor which may enable higher growth rates and lead to convergence or divergence processes. However, the incentive for individuals to invest in their human capital and their geographic location are not independent. In particular, higher market access may encourage human capital accumulation. This hypothesis has been validated by a range of publications focussing on the worldwide national level (e.g., [START_REF] Redding | Distance, Skill Deepening and Development: Will Peripheral Countries Ever Get Rich?[END_REF] and later on the European regional level. In particular, the latter include the work by López-Rodríguez and co-authors (e.g., [START_REF] López-Rodríguez | New Economic Geography And Educational Attainment Levels In The European Union[END_REF][START_REF] Faíña | Market access and human capital accumulation: the European Union case[END_REF][START_REF] López-Rodríguez | Human Capital Accumulation and Geography: Empirical Evidence from the European Union[END_REF]. The results of these papers clearly indicate that human capital levels (as approximated by educational attainment levels) decrease when moving from NUTS 2 regions with high market access to those with low market access in the year 2000.

Another factor influencing human capital accumulation is labour mobility. Yet, labour mobility has been quite low in Europe, even during the most recent years (e.g., Barslund et al 2015) (and even more so in comparison to the US). But it may be higher for skilled labour, thus being a potential factor driving agglomeration in human capital, as labour may move to the more advanced regions with higher human capital. But even at the level of tertiary education these flows are rather limited. For example, learning mobility in higher education in the EU is on average still low, although it has increased during the last years (see [START_REF] Flisi | Learning Mobility[END_REF]. Certainly, one can expect labour mobility to be still much lower if we go back in time, particularly when considering time periods before World War II.

In sum, market access has been shown to be a crucial factor influencing human capital formation in the present time. However, has this always been the case? Is it a more general pattern that has persisted until the present time? This paper contributes to answer this question by analysing econometrically the importance of market access in the long run.

Theoretical model

The proposed NEG model has originally been developed by [START_REF] Fujita | The Spatial Economy: Cities, Regions and International Trade[END_REF]. This model has two sectors, i.e., agriculture and manufacturing. However, the model does not take into account human capital accumulation. This factor has only been added by [START_REF] Redding | Distance, Skill Deepening and Development: Will Peripheral Countries Ever Get Rich?[END_REF]. Their model focuses on the interaction between human capital and input-output linkages, taking account of transport costs and assuming increasing returns to scale. One of their main results is that countries that are remotely located from main markets have to face higher trade costs and a decrease in the skill premium than other countries if one assumes that manufactures are relatively more skill intensive than agricultural goods. In this way, the effect of a remote location has the same consequences as a reduction in the relative price level of manufactures. Due to the assumption that the required skills in the manufacturing sector are higher than in agriculture, skilled workers face a fall in their relative wages. Thus, the incentive for an unskilled worker to invest in human capital and become skilled is decreased.

Because the main contribution of this paper is empirical, we only briefly present some foundations and results of [START_REF] Redding | Distance, Skill Deepening and Development: Will Peripheral Countries Ever Get Rich?[END_REF] model. 3 We adapt the model to the context of this paper by explicitly considering regions (instead of countries as in the original model). First, we consider the preferences and the endowments that have to be modelled. Accordingly, Europe is constituted by i∈ {1 , … , R } regions. Every region is characterised by an endowment of L i consumers. Every consumer has one single unit of labour. The supply of this unit of labour is inelastic, i.e., there is no disutility. Consumer preferences are identical for all L i . Consumption is restricted to two types of goods: first, the production of the agricultural sector is limited to one homogenous good. Second, the manufacturing sector produces a range of differentiated manufactures. The preferences follow a standard utility function in Cobb-Douglas form.

Let us now define the production technologies involved in the two sectors. In the first sector, the produced agricultural good is homogeneous. Production is set within the framework of perfect competition and is characterised by constant returns to scale. In the second sector, the production of the differentiated manufactured goods is characterised by increasing returns to scale and uses a combination of the two types of labour (skilled, unskilled) and of the intermediate inputs of manufactured goods.

In the next step, we introduce endogenous investment in human capital into the model. It is assumed that a conversion from an unskilled to a skilled worker is possible.

Denoting an individual as z, this conversion incurs a fixed cost of education Ω i ( z ) units in terms of unskilled labour. The underlying idea is that real resources are consumed to become skilled which results in the fact that the education cost is a proportion of the wage of unskilled labour. Moreover, the quantity of unskilled labour that is needed to become skilled is dependent on two factors. In particular,

Ω i ( z )= h i a(z )
, where h i denotes the overall environment provided by institutions and government policies that have repercussions on the education cost and a ( z ) denotes the individual's personal ability. This ability is subject to human biology. Thus, an individual z will only take the decision to invest in human capital if

w i S -w i U ≥ h i a(z) w i U , (1)
i.e., if education costs are lower than (or equal to) the difference between the wages of a skilled (w i S ¿ and an unskilled (w i U ¿ worker. The equation defines an implicit critical value for a above which all individuals choose to invest in human capital. This value a i ¿ giving the supply of skills in equilibrium is

a i ¿ = h i ( w i S w i U -1) . ( 2
)
An individual having the ability a i ¿ does neither prefer to become skilled nor to remain unskilled but is indifferent to both options. Therefore, this equation is the 'skill indifference condition'. Only if an individual has an ability above a i ¿ he will choose to get further education.

After defining the producer equilibrium and profit maximisation, we can obtain the zero-profit conditions. Joining them with the skill indifference condition, we obtain the equilibrium relationship that exists between the geographical location of a region and endogenous investments in human capital. In particular, the equilibrium equations show that if the equilibrium market access of i, MA i , decreases, if the manufacturing sector is assumed to be skill intensive with regard to the agricultural sector and if the region is incompletely specialised, then the equilibrium moves to a new equilibrium with lower skilled wages but higher unskilled wages. This implies that the critical ability level a i ¿ increases. This change induces a lower supply of skilled and a higher supply of unskilled labour.

More specifically, the decrease of MA i has led to a smaller size of the skill intensive manufacturing sector. The reduction in size means that there are now more skilled workers in the market than there is demand for them in agriculture. Therefore, the wages of skilled workers decrease whereby their relative wages in comparison to the ones of unskilled workers fall. In this way, remoteness leads to smaller incentives to invest in human capital. This means that the model predicts a positive relationship between market access and human capital investment.

While this basic two sector model has so far been used to explain the current economic geography of Europe, it appears even more appropriate for the past. Clearly, whether to switch from agriculture to manufacturing is not a policy question for most European countries anymore.

Data and methodology

We test the theoretical model by the use of different datasets. In particular, we use regional numeracy and literacy as our human capital proxies. First, we employ numeracy as a proxy [START_REF] Crayen | Global Trends in Numeracy 1820-1949 and its Implications for Long-Run Growth[END_REF]. The underlying formula of the ABCC Index is

ABCC ¿ =125-125 ׿ ( 3
)
where i denotes a region, j the number of years, n the number of individuals and t the time period. Its formula illustrates that one calculates the share of age observations ending on '0' and '5' in relation to all observations. One takes into account all ages between 23 and 72, the standard in the numeracy literature. The ABCC with its limits of 0 and 100 is comparable to other share indexes, in particular literacy.

The human capital data have been taken from the new and large database provided by [START_REF] Hippe | Regional Inequality in Human Capital Formation in Europe, 1790 -1880[END_REF]. These data are based on original historical census data. The advantage of this measurement method is that it always takes into account the entire population and not, as other historical proxies of human capital (e.g., signature rates), only parts of it. For this reason, it is representative for the whole population and is not prone to biases that naturally reside in more partial indicators. In this way, we are able to measure the regional distribution of basic numeracy from Portugal to Russia. In total, there are 298 regions in our dataset (see Table 1 for descriptive statistics).4

Literacy ¿ = ∑ j=10 N rw j ,¿ / ∑ j=10 N n j ,¿ , ¿ ¿ (4)
where rw denotes the ability to read and write and N is the total number of years.

In other words, literacy is the share of individuals (10+ years) who are able to read and write in a region at a given point in time. Data stem originally from [START_REF] Kirk | Europe's Population in the Interwar Years[END_REF] and have been adapted for the purposes of this paper.

Both numeracy and literacy have the advantage that they are share variables. In fact, the proposed NEG model divides individuals into skilled and unskilled workers. A defined level of numeracy is nothing more than the share of numerate to innumerate, a level of literacy is the share of literate to illiterate individuals. We can take the simple but straight forward assumption that an unskilled worker is innumerate (in 1850) or illiterate (in 1930). Similarly, it is reasonable to assume that an innumerate person can decide to become numerate and an illiterate one to become literate. The endogenous investment assumption in the model, from unskilled to skilled workers, can thus be illustrated by our indicators. These parallels show the correspondence between our empirical specification and the underlying theoretical model. In addition, we have been able to collect regional data for these human capital proxies in the past, whereas data with a similar degree of regional precision are not available for other proxies for most of Europe.

Moreover, the data on urbanisation are provided by two different sources. For 1850, we use the data provided by [START_REF] Bairoch | La population des villes européennes : 800-1850 : banque de données et analyse sommaire des résultats[END_REF]. It is, alongside with a similar database by De Vries (1984), the standard database on urbanisation in the long run. In fact, the data trace back the cities of Europe until the year 800, starting from 1850. For a general geographical illustration of the data for 1850 see Figure 1. London is by far the largest European city, followed by Paris. Cities are quite dense in most of Europe, except Scandinavia and Eastern Europe, where Russia's capital, St. Petersburg, is the most important city. Cities are included if they fulfil a minimum threshold of population size between 800 and 1800. This threshold is 5000 inhabitants. In total, there are 2201 cities in our database. We excluded two observations because they were geographical outliers so that we have used the remaining 2199 cities for our calculations.5

Figure 1 Location and size of European cities, 1850

Source: Own graphical presentation of data provided by [START_REF] Bairoch | La population des villes européennes : 800-1850 : banque de données et analyse sommaire des résultats[END_REF]. Size of cities is shown in thousand inhabitants. [START_REF] Moriconi-Ebrard | Geopolis, pour comparer les villes du monde[END_REF] and are compatible to the Bairoch et al. database. 7 It is a worldwide database 6 We are very well aware of the fact that World War II affected important portions of regional populations which may have a biasing effect on our estimates. However, authors such as [START_REF] Martí-Henneberg | Empirical Evidence of Regional Population Concentration in Europe, 1870-2000[END_REF] show that population concentrations are highly correlated at the regional level between 1870 and 2000 which suggests that data from 1950 are still a good approximation for 1930. 7 Moriconi-Ebrard's (1994) database includes agglomeration data from 1950 to 1990.

with a threshold of 10000 inhabitants in 1990, so that the entire database includes up to 26000 worldwide agglomerations. A graphical illustration of these data for Europe shows their general resemblance to those in 1850 (see Figure 2). While London is still the most populous European city, there have been increases in population elsewhere. For example, Russia's new capital, Moscow, is now significantly larger than St. Petersburg and other capitals and agglomerations such as Athens show an increased importance in the European urban landscape. However, the overall picture that we get from 1950 is quite similar to the 1850 data.

Furthermore, market access has been calculated with population data in the recent literature (e.g., López-Rodríguez et al. 2005). 8 Population potential also appears to be the best available proxy in historical European applications.9 It is a standard way of representing changes in the pattern in which cities are distributed in space. It allows to identify the relative location of a city within a greater network of other cities. Two factors are essential in the evaluation process: first, the size of the population of cities. Second, the distance of a city to the other regions in the network. In practice, one adds to the population size of a city the population sizes of the other cities, each time divided by their distances to the original city. This is done for every city in the data. In this way, a potential value is assigned to each city. To be more precise, the mathematical formula, which is a development of the classic concept of population potentials first introduced by Harris, is (see [START_REF] López-Rodríguez | New Economic Geography And Educational Attainment Levels In The European Union[END_REF]:

MA i =Po i + Po i D i ,1 + …+ Po n D i , n =Po i + ∑ j ≠i , j=1 N Po j D i , j , (5)
where MA i stands for the market potential at i, Po i is the population of i and D i,j is the distance that exists between i and j, each i and j representing individual nodes.

For the econometric specification of the relationship between investment in human capital and market access, we first test a standard OLS regression model as used by the literature. The basic framework is the following:

ln (HC ¿¿ i)= β 0 + β 1 ln ( MA i ) +ε i , ¿ (6)
where HC is the respective human capital indicator (i.e., numeracy or literacy; in logarithmic terms), MA is the market access (in logarithmic terms), i is a region and ε are the unexplained residuals. The basic OLS framework is later complemented by Tobit and instrumental variable regressions.

In addition, note that 'region' stands for a NUTS region in our case. NUTS is the official Nomenclature for Territorial Units of Statistics which has been developed by the European Union. It comprises all countries of the EU, EFTA and Candidate Countries of the EU. For countries outside this area, e.g., Russia, we used the current administrative division. This allows us to make our data comparable to current data and other research.

Given the fact that market access and distance involves point data (cities and the central point of each region, respectively), the NUTS level can be attributed without any further difficulties. The case is different for human capital data which were available only for the historical regions. In this case, we developed the correspondence of these historical regions to current regions as best as possible. Because we have often more detailed data than needed for this study (e.g., the départements in France, the provincias in Spain or the Bezirks-Hauptmannschaften in Cisleithania (i.e., the Austrian part of Austria-Hungary before its disestablishment), the possible biases are importantly reduced because we can easily aggregate our data from province or county level. As a standard, we use NUTS 2 as the basic unit of analysis which is also the standard unit in most other contributions in our area. 10 In this way, we are able to create a unique dataset for the European regions in 1850 and 1930.

Results

We first present our results for numeracy in 1850 and subsequently check their overall London, the shape of the triangle is not surprising because these were the two most populated cities of Europe in 1850. Still, the figure highlights that they were not isolated from other population hubs but were the centre of a greater accumulation of population in western Europe. This can be explained by the long-term geographic change of economic importance from northern Italy to this area, as has also been postulated by [START_REF] Braudel | Civilisation materielle, économic el capitalisme, XVe-XVIIIe siècle. Le temps du monde[END_REF].

This is also in line with the concept of the existence of a "blue banana" which has been put forward by [START_REF] Brunet | Lignes de force de l'espace européen[END_REF], a concentration of population and economic active from northern Italy over the course of the Rhine River until the UK and even Ireland.

Figure 3 Population potential in Europe in 1850

Note: Graphical representation using natural breaks (Jenks) with 32 classes. Values decrease from the highest to the lowest value in the following broad order of colours: white, pink, blue, green, yellow, orange and red. Source: Own calculations, city data provided by [START_REF] Bairoch | La population des villes européennes : 800-1850 : banque de données et analyse sommaire des résultats[END_REF].

In general, the more one distances oneself from the centre in western Europe, the In the next step, we investigate the relationship between market access and human capital. To this end, we plot market access against the ABCC (Figure 4). Unfortunately, the ABCC has already achieved its maximum level of 100 in several countries. This is why there are a number of regions that are limited by the upper bound. Nevertheless, there is a clear relationship between market access and the ABCC.

Figure 4

ABCC and market access, 1850 To test this relationship econometrically, we perform different regression models, always including country dummies. As OLS is the most basic and standard estimation method, we begin with OLS regressions. Subsequently, we will also test alternative models that incorporate issues concerning the scale of the dependent variable (i.e., Tobit models) and regarding endogeneity (i.e., instrumental variable models). The results of the baseline OLS regressions are shown in Table 2. Market access has a highly significant positive effect on numeracy at the 5 % level (p-value = 0.018, column 1). 11 A 1 % increase in market access increases numeracy by 0.08 %, a sizeable effect. To compare our results for market access with distance to Luxembourg as proposed in the literature, we also computed this distance (in natural logarithm) and show the results in column 2 (and also in the subsequent steps). 12 Distance to Luxembourg is negatively significantly correlated to the ABCC at the 1 % level. However, we have seen in the scatter plot that there are a number of regions that have already achieved the upper bound of 100 ABCC points in 1850. This given upper limit may bias our results because some of these regions would have had higher numeracy values if the limit was not existing. For this reason, we take this fact explicitly into account by running the same regressions with the Tobit model. The Tobit model incorporates the problem of upper or lower bounds in its estimations. The lower bound is not important in our case, but the upper limit is. Thus, in total, there are 39 regions which are right-censored by the model. 13 The results when using the alternative Tobit model are shown in columns 3 to 4. The coefficient of market access increases in the new specification (to 0.10, columns 3), which is also true for distance to Luxembourg (column 4). Overall, the Tobit model confirms the robustness of our former results. Nevertheless, it is still possible that our results are biased by endogeneity. In fact, one can imagine that market access is correlated with alternative variables that may have a significant influence on numeracy. Thus, to be able to identify whether there is causality between market access and numeracy, we also perform instrumental variable regressions.

AL AM AT11 AT12 AT13 AT21 AT22 AT31 AT32 AT33 AT34 AZ BA BE10 BE21 BE22 BE23 BE24 BE25 BE32 BE33 BE34 BE35 BG32 BG33 BG34 BG41 BG42 BY001 BY002 BY003 BY004 BY006 CH01 CH02 CH03 CH04 CH05 CH06 CH07 CZ02 CZ03 CZ04 CZ05 CZ06 CZ07 DE11 DE12 DE13 DE14 DE21 DE22 DE23 DE24 DE25 DE26 DE27 DE41 DE42 DE60 DE71 DE72 DE73 DE80 DE91 DE92 DE93 DE94 DEA1 DEA2 DEA3 DEA4 DEA5 DEB1 DEB2 DEB3 DEC0 DED1 DED2 DED3 DEE0 DEF0 DEG0 DK01 DK04 DK05 EE EE00 ES11 ES12 ES13 ES21 ES22 ES23 ES24 ES30 ES41 ES42 ES43 ES51 ES52 ES53 ES61 ES62 FR10 FR21FR22 FR23 FR24 FR25 FR26 FR30 FR41 FR42 FR43 FR51 FR52 FR53 FR61 FR62 FR63 FR71 FR72 FR81 FR82 FR83 GE GR14 GR21 GR22 GR23 GR24 GR25 GR30 GR42 HR01 HR02 HR03 HU10 HU21 HU22 HU23 HU31 HU32 HU33 IE01 IE02 ITC1 ITC2 ITC3 ITC4 ITD1 ITD2 ITD3 ITD4 ITD5 ITE1 ITE2 ITE4 ITF1 ITF2 ITF3 ITF4 ITF5 ITF6 ITG1 ITG2 LT00 LU00 LV00 MD MK00 NL11 NL12 NL21 NL22 NL31 NL32 NL33 NL34 NL41 NL42 NO01 NO03 NO05 NO06 PL11 PL12 PL21 PL22 PL31 PL33 PL34 PL41 PL42 PL43 PL51 PL52 PL61 PL63 PT11 PT15 PT16 PT17 PT18 RO11 RO21 RO42 RU11 RU12 RU13 RU14 RU15 RU16 RU17 RU18 RU19 RU1A RU1C RU1D RU1F RU1G RU1H RU21 RU22 RU25 RU26 RU27 RU28 RU2A RU31 RU32 RU33 RU34 RU35 RU36 RU37 RU38 RU39 RU41 RU42 RU43 RU44 RU45 RU48 RU4A RU4C RU4D SI01 SI02 SK01 SK02 SK03 SK04 UA001 UA002 UA003 UA006 UA007 UA008 UA009 UA00C UA00D UA00G UA00I UA00J UA00M UA00O UA00P UKC1 UKC2 UKD1 UKD2 UKD3 UKE1 UKE2 UKE3 UKF1 UKF3 UKG1 UKG2 UKG3 UKH1 UKH2 UKI UKJ1 UKJ3 UKJ4 UKK1 UKK2 UKK3 UKL1 UKL2 UKM2 UKM3
In the given case, an instrumental variable has to be a determinant of market access but also has to be exogenous to numeracy. Moreover, the variable should not be prone to influences of another underlying variable which may drive its values and affect both market access and numeracy. Thus, in line with [START_REF] Redding | The low-skill, low-quality trap[END_REF]Venables (2004), Breinlich (2006) and [START_REF] López-Rodríguez | Human Capital Accumulation and Geography: Empirical Evidence from the European Union[END_REF], we take the distance from Luxembourg as our first instrumental variable. This variable captures the advantages conferred by being close to the centre of Europe. Second, as proposed by the same authors, we use the (area) "size of a region's home country" (López-Rodríguez et al. 2007, p. 223), capturing the advantages that are created by big national markets for the market access of a region. 14 The use of a similar strategy as previous authors also enables us to put their results for today into a larger historical context, which is the aim of this paper. The results of our IV models are shown in column 5. The IV estimates for (logarithmic) market access are once again highly significant at the 1 % level. The sign of the coefficient does not change and the level of the coefficients is in between the ones in our other specifications. In other words, the coefficient of (logarithmic) market access was 0.08 in the OLS, 0.10 in the Tobit and now is at 0.09 in our IV models. In sum, the IV results confirm once more the importance of market access for numeracy.

However, one may wonder if our results are robust to the use of other human capital variables and other time periods in the past. Therefore, our alternative indicator for human capital in the past is literacy in 1930. For this reason, our results would need to be confirmed by the use of this alternative indicator. However, given the use of another dependent variable (i.e., another human capital proxy), the consideration of a later time period (i.e., 80 years later than our numeracy estimates), and another dataset for the calculation of market access (although compatible with the dataset for 1850), we clearly would not expect to obtain the same results, including the same level of coefficients. In particular, the scatterplot has shown that literacy rates are much more dispersed than numeracy rates. For this reason, we expect higher coefficients in our 1930 regressions.

Nevertheless, we expect to come to the same broad conclusions using this alternative specification.

Figure 5 Population potential in Europe in 1950

Note: Graphical representation using natural breaks (Jenks) with 32 classes. Values decrease from the highest to the lowest value in the following broad order of colours: white, pink, blue, green, yellow, orange and red.

Source: Own calculations, data on agglomerations provided by [START_REF] Moriconi-Ebrard | Geopolis, pour comparer les villes du monde[END_REF].

To achieve a maximum of comparability with our earlier results, we take the same approach as for numeracy in 1850. First, we find that the results of the population potential calculations appear to be quite similar around 1950 (see Figure 5). The 'core' of population potential is still located within the Golden Triangle, i.e. the industrial areas of England, Paris, Belgium and western Germany. The Iberian Peninsula, Scandinavia and eastern Europe makes still up the periphery. Some differences emerge, however. For example, there appeared to be only two significant city centres with a high population potential in eastern Europe, that is St. Petersburg and Moscow. Now, these two cities are joined by Donetsk. In contrast, the cities in Spain and Portugal are not so relevant outliers anymore.

The higher fertility rates and increasing urbanisation in eastern Europe over the previous century may explain these changes. Still, the overall pattern is quite robust to these rather minor changes. In addition, it appears to be still more closely related to current market access estimations by [START_REF] López-Rodríguez | Human Capital Accumulation and Geography: Empirical Evidence from the European Union[END_REF]. Similar to their data for the year 2000, Romanian Bucharest is now a positive outlier (accompanied by Bulgarian Sofia).

Major population potential levels are now extending until Polish Wroclaw and Italian

Milan, again quite similar to the recent data for 2000. These findings give additional validity and show the robustness of our estimations and may indicate of the long-term nature of regional market access levels.

Next, plotting market access and literacy shows that their positive correlation is also quite clear (see Figure 6). Note that there are no literacy data for several developed countries in 1930 such as the Scandinavian countries, Germany or the UK. [START_REF] Kirk | Europe's Population in the Interwar Years[END_REF] estimates that these countries had literacy rates between 95 and 100. In the following, we exclude the regions from these countries (as has been done in Figure 6). Alternatively, we can also take the hypothesis that these regions had a literacy rate of 100. In any case, there are no apparent outliers. The relationship between literacy and market access is even closer than for numeracy.

Figure 6

Literacy and market access, ca. 1930 The corresponding regression results are shown in Table 3 (always including country dummies). This time, we propose two different specifications. First, we exclude the developed countries without any official literacy data (column 1 to 3). Column 1 shows that log market access is again positive and highly significant at the 1 % level. That is, a 1 % increase in log market access increases literacy by 0.58 %. This is substantially more than the 0.08 % that we obtained for numeracy in 1850. As noted above, this higher level corresponds to our expectations. All coefficients are higher than in 1850 because literacy rates are more dispersed than numeracy rates. A similar reasoning applies to the higher negative and significant coefficient for log distance to Luxembourg (column 2). As we have excluded all estimated literacy data for developed countries, the remaining countries do not reach the upper bound of 100 % literacy. For this reason, we do not need to perform Tobit regressions. Even if we perform them, we get the same results (not shown).

AL AM AT11 AT12 AT13 AT21 AT22 AT31 AT32 AT33 AT34 AZ BA BE21 BE22 BE23 BE24 BE25 BE32 BE33 BE34 BE35 BG31 BG32 BG33 BG34 BG41 BG42 BY001 BY003 BY004 CZ01 CZ03 CZ04 CZ05 CZ06 CZ07 CZ08 EE00 ES11 ES12 ES13 ES21 ES22 ES23 ES24 ES30 ES41 ES42 ES43 ES51 ES52 ES53 ES61 ES62 FI13 FI18 FI19 FI1A FI20 FR10 FR21 FR22 FR23 FR24 FR25 FR26 FR30 FR41 FR42 FR43 FR51 FR52 FR53 FR61 FR62 FR63 FR71 FR72 FR81 FR82 FR83 GR11 GR12 GR14 GR21 GR22 GR24 GR25 GR41 GR42
Therefore, we proceed with the IV estimation, using the same strategy as in our numeracy regressions (column 3). The coefficient of log market access remains highly significant at the 1% level and largely stable, increasing only slightly from 0.58 to 0.61. This is the same tendency we have already observed in our numeracy sample.

Table 3

Market access and literacy, ca. 1930

31 (1) (2)
(3) (4) Second, we include the developed countries with their estimated literacy rates (column 4 to 8). Most of these countries have estimated literacy rates of 95-100 %.15 Thus, we assume that these countries had literacy rates of 100 %. As now a number of countries have reached the upper limit, we perform Tobit analyses in addition to OLS models. 16Although we prefer to exclude countries that have the same estimated literacy rate for each region, as we did in our previous specification, this alternative strategy may allow us to show the effects of including all of Europe. We start again with OLS models (column 1 and 2). While the significance levels are identical, the coefficient of log market access (column 4) decreases importantly from previously 0.58 to 0.32 due to the higher number of observations (with a high level of literacy). Log distance to Luxembourg (column 5) shows a similar move downwards. Moving to Tobit models, the coefficient of log market access increases from 0.32 (column 4) to 0.57 (column 6) by more than 50 %. However, the same appears for log distance to Luxembourg (column 7). In consequence, taking account of the upper limit increases the coefficient to the same level as when the countries with estimated high literacy rates were excluded (see the initial models in column 1 to 3). Finally, we perform an IV regression (column 8). The coefficient in the IV regression (0.29) is similar to the one in the initial OLS models without the estimated regions (0.32, column 4) and highly significant.

In sum, market access is a highly significant determinant of literacy in every model. The coefficients are higher, as expected, in our literacy regressions for 1930 than for the ABCC in 1850, and they show the robustness of our results over time.

All in all, our results show that space matters in education. We find a coreperiphery pattern in Europe similarly to the literature that analyses the EU today (see section 2). Market access has a significant influence on human capital, confirming the 'penalty of remoteness' hypothesis. Moreover, because we are referring to the rather distant past with our data, the current regional distribution of human capital and economic development appears to be rather stable in the longer run.

This gives important implications for regional policy. Remoteness equals backwardness -this statement is certainly an exaggeration, but remoteness makes backwardness definitely more likely and makes it more difficult to get out of it. However, remoteness does not imply that remote areas are necessarily trapped in a vicious circle caused by geographic location. While geographic location and market access play an important role in explaining regional human capital differences, our results also show that, for example, remote countries (in market access terms) such as Finland and Estonia have outperformed their expected human capital level. In consequence, other factors may lead to better human capital performance, such as strong institutions and cultural values directed towards education.

In this context, transport costs also matter. In fact, the concentration and agglomeration of firms and labour depends on the level of transport costs [START_REF] Combes | Le rôle des marchés locaux du travail dans la concentration spatiale des activités économiques[END_REF], see also [START_REF] Hippe | Human capital formation in Europe at the regional level -implications for economic growth[END_REF]. One can distinguish three development stages, assuming the existence of two regions A and B. First, both regions are in autarky as transport costs are at a high level. In consequence, trading and transferring capital or labour is still impossible at this stage between the regions. Decreasing transport costs allows the regions to begin trading, and real incomes rise in both regions. However, at a certain threshold of lowering trade costs, regions begin to diverge and regional inequalities to rise. More specifically, while real incomes in A increase with the formation of an agglomeration, they decrease in B, which loses consumers. On the other hand, if transport costs fall even further to another threshold, region A becomes again less attractive for capital and labour, which turn towards B. In the end, both regions find themselves at a higher level of real incomes and regional inequalities have again disappeared [START_REF] Combes | Le rôle des marchés locaux du travail dans la concentration spatiale des activités économiques[END_REF], see also [START_REF] Hippe | Human capital formation in Europe at the regional level -implications for economic growth[END_REF]. Thus, policies need to take into account the level of transport costs, as they are an important factor for regional inequalities.

Public policy can lower transport costs. As Faiña et al. (2016, p. 353) In consequence, while public policy cannot influence the geographic location of a region, it can act upon these other factors. Nevertheless, policy needs to acknowledge the role of remoteness, and adapt its policies according to the needs engendered by it. In other words, it has to develop policies which are appropriate, given the relative location and market access level of a region, to ensure that all regions are able to grow and that regional inequalities are not exacerbated. However, cohesion policy faces a number of risks, as has been outlined by Farole et al. (2011): first, markets and their functioning may be distorted by allowing inappropriate investments to be made at the regional level. Second, public subsidies may produce a crowding out effect of private investment. Third, they may protect regions from the markets, but in doing so create an environment less friendly towards adaptation to evolving conditions. Fourth, they may involuntarily make these regions dependent on public subsidies, so that they cannot survive without external support and cannot generate themselves the necessary environment leading to economic growth. Last, the elites that may (partly) be responsible for the lower development level of a region (in the historical period under study, these may be, e.g., local landlords (see also Galor et al. 2009, Baten and Hippe forthcoming), may have the power to use cohesion policy investments for their own rent extraction, thus promoting inefficient and growth-limiting local institutions.

For these reasons, it is important to consider the particular context of a region, the given institutional framework and the relevant stakeholders (see also [START_REF] Hippe | Human Capital in European Regions since the French Revolution: Lessons for Economic and Education Policies[END_REF].

Therefore, as Farole et al. (2011) and [START_REF] Hippe | Human capital formation in Europe at the regional level -implications for economic growth[END_REF] emphasise, a 'one size fits all' strategy may not work. In particular, Farole et al. (2011) provide some useful criteria how to advance policies, by differentiating various kinds of regions (from core-metropolitan regions to peripheral sparsely populated ones) and the goals that cohesion interventions would like to achieve. As for the latter, the authors outline the aim to 1) generate growth in the EU as such, 2) diffuse growth from the core to the periphery, 3) encourage growth in particular peripheral areas with potential, 4) support regions with less potential and, finally, 5) provide assistance to regions with least potential, while acknowledging that the latter may be in conflict with the aim to promote growth in the entire EU. In some circumstances, it may be best for cohesion policy to focus on national institutions, because the specific policies are effective on a national level (e.g., the legal framework). Yet in others, the involvement of regional institutions may be more appropriate, in particular when it comes to implementing policies.

But again, special care needs to be taken not to involuntarily support and promote inefficient structures in underdeveloped regions by giving responsibility into the wrong hands (see also e.g., Farole et al. 2011, Hanushek and[START_REF] Hanushek | The Knowledge Capital of Nations[END_REF]. A better understanding of the regional success stories coming from countries like South Korea may provide a useful broader perspective on this issue, showing that change into the right direction is possible (Farole et al. 2011), if the appropriate regionally tailored policy strategy is applied. Whatever strategy is chosen, it should be appropriately defined, assessed, conditioned on certain criteria and rigorously monitored, following a specified number of guidelines and mechanisms (Farole et al. 2011), to avoid any of the potential negative effects of external policy interventions.

Conclusions

This paper has analysed the importance of market access to explain the spatial distribution of human capital levels in the European regions in the long run. The central focus of the paper is whether remoteness was connected to backwardness in the past, as has been postulated by [START_REF] Redding | Distance, Skill Deepening and Development: Will Peripheral Countries Ever Get Rich?[END_REF] and tested by e.g., [START_REF] López-Rodríguez | Human Capital Accumulation and Geography: Empirical Evidence from the European Union[END_REF] for the European regions in the present.

In particular, we construct a new combined dataset using two different indicators of human capital, numeracy and literacy, to check the robustness of our results. More specifically, we employ, first, the age heaping method in order to approximate numeracy values for 1850. Second, literacy is a standard human capital proxy in Europe for the end of the 19 th and the first half of the 20 th century. Therefore, we use literacy in 1930 as our alternative specification. These two binary indicators also partly mirror the assumptions of the underlying theoretical model. Moreover, data on European cities have been used to proxy for market access. In this direction, the standard concept of population potential has been employed to generate average market access estimations for the European regions.

The results show that market access is highest in the regions of the Golden Triangle, i.e., England, northern France, Belgium and western Germany. This is an area where there is a concentration of population and economic prosperity (see e.g., [START_REF] López-Rodríguez | Human Capital Accumulation and Geography: Empirical Evidence from the European Union[END_REF]. In general, the farther one moves away from this centre, the lower is the level of market access. Thus, market access is lowest in the peripheral areas of periphery structure of education also in the past.

Moreover, OLS, Tobit and IV regressions of market access on numeracy highlight that numeracy is significantly higher in regions with higher market access. Thus, after the literature has in particular used educational attainment for the current period, our numeracy and literacy estimates show that the 'penalty of remoteness' hypothesis is not only valid for today but that its importance can be traced back even to the middle of the 19 th century.

This underlines once more that this penalty has existed for a long time in Europe.

Thus, it may continue to exist also in the future if not the right policy decisions are taken.

These future policies should take into account the particular specificities of the region under consideration, and through a variety of measures such as monitoring should ensure that policies do indeed support the aims set out by regional policy.

Literacy and numeracy in developing countries

Figure 2 Location

 2 Figure 2

  robustness with the literacy data in 1930. The calculated population potential values for 1850 are illustrated in Figure3. In the following we refer to countries and regions in their current boundaries. It is apparent that the highest population potentials are found in the areas of Paris, London and Manchester and the wider locus up to Belgium and the western parts of Germany. In the current literature, this area is often called Golden Triangle and has also been identified by market access studies such as[START_REF] López-Rodríguez | Human Capital Accumulation and Geography: Empirical Evidence from the European Union[END_REF] for 2000. The similarity of our historical market access estimates to their current estimates further shows the validity of our approach and of potentially existing long-term spatial configurations. Given the size of the aforementioned cities, in particular of Paris and

  lower are the potential population values. Going farther away from the centre, the highest estimated values are located in the regions of the UK, France, Germany, Switzerland, Italy, parts of Austria and Spain. Polish regions are already in the next level. Nevertheless, there are some outliers to the overall rule. Large cities create their own high local population potential which explains the different shading in the areas of e.g., Madrid, Hamburg, Berlin, Prague, Vienna, St. Petersburg and Moscow.

  Note: ***, **, * indicate significance at the 1, 5 and 10 percent level. Robust p-values in parentheses. ln(Literacy)

  Note: bo = Bolivia, br = Brazil, cl = Chile, co = Colombia, ec = Ecuador, in = India, ke = Kenya, mx = Mexico, pa = Panama, tz = Tanzania Source: Hippe (2012b).

  The service sector has become much more relevant, both in terms of GDP and employment, than agriculture in many European countries. In contrast, the simple structure of the model may even more closely mirror the development during the European industrialisation process. Most European countries only began to industrialise during the 19 th or even the 20

th century. For example,

[START_REF] Broadberry | Agriculture and structural change: lessons from the UK experience in an international context[END_REF]

's data show that agriculture had still a share of 50 % in agricultural employment in West Europe in 1870. The share increases to 57 % in South Europe and even 70 % in East Europe. Without taking major assumptions, it is evident that these shares would be even higher for 1850, illustrating the crucial stake of agriculture in Europe at that time. Ongoing industrialisation in West Europe increases the share of industry while decreasing agricultural employment to 32 % in 1929. Nevertheless, a third of overall employment is still a relevant share for agriculture, particularly if compared to only 5 % of employment in West Europe in 1992 (and certainly less today). However, industrialisation occurred later and slower in other, peripheral parts of Europe. Therefore, the same share only dropped to 53 % in South Europe and to 66 % in East Europe in 1929.

For this reason, the theoretical model's two sector model and the associated switch from unskilled to skilled workers appears related to the historical period under study

(i.e., 1850 and 1930)

. The higher market access in the core industrialising European countries would imply that skilled workers are rarer in the periphery. Can we find this theoretical result also in the data?

  for regional human capital in Europe in 1850. Numeracy is derived from the age heaping method. Age heaping as a method for calculating basic human capital values has been established by the recent literature (e.g.,[START_REF] A'hearn | Quantifying Quantitative Literacy: Age Heaping and the History of Human Capital[END_REF][START_REF] Crayen | Global Trends in Numeracy 1820-1949 and its Implications for Long-Run Growth[END_REF][START_REF] Hippe | Regional Inequality in Human Capital Formation in Europe, 1790 -1880[END_REF][START_REF] Hippe | How to measure human capital? The relationship between numeracy and literacy[END_REF]. In particular, we use the ABCC Index to measure numerical abilities. In fact, it measures the share of individuals that are able to calculate.

More specifically, historical census data, and in part even data for today's LDCs, show a clear pattern of rounding. Many people were not able to calculate their age. Therefore, they guessed their age to fulfil the census requirements set up by the state. Given that human biology serves as a first aid for calculations (e.g., five fingers on one hand, ten fingers in total), they rounded their ages on 0 and 5 (see also

[START_REF] Harper | A bioeconomic study of numeracy and economic calculation[END_REF]

. This specific rounding behaviour can be used to calculate numeracy estimates. In particular, an index can be constructed which measures the deviation of actually observed age statements to the one which would be expected. This is the so-called Whipple Index. However, this index is not very intuitive, as it takes values from 100 (the highest numeracy score) to 500 (the lowest score). For this reason, A'

Hearn et al. (2009)

have proposed a linear transformation of the index, so that its values range from 0 (lowest) to 100 (highest numeracy), i.e. the ABCC index. It has been shown that this rough proxy of numeracy is well correlated with other standard human capital proxies such as literacy

[START_REF] A'hearn | Quantifying Quantitative Literacy: Age Heaping and the History of Human Capital[END_REF][START_REF] Hippe | How to measure human capital? The relationship between numeracy and literacy[END_REF]

) and primary school enrolment

Table 1 Descriptive statistics for ABCC and market access, ca. 1850

 1 Literacy data are not available for a range of European regions for 1850, which is why the ABCC Index is the more suitable indicator for that period. However, literacy became a standard human capital indicator during the second half of the 19 th century and was used in many countries throughout the first half of the 20 th century. Many public debates focused on the eradication of illiteracy in a number of European countries. Still, this aim was sometimes not achieved until the second half of the 20 th century, so that it is a valuable indicator for 1930. Therefore, it can be used as the representative human capital proxy for 1930. Literacy is defined as

		obs
	Variable	.	mean	sd	min	max
	ABCC	298	90.79	12.29	26.38	100.00
						17165.4
	Market access	298 4472.78 2199.80	995.13	7
	Distance	298	14.16	11.37	0.61	51.50
	Second, literacy is our alternative human capital proxy and available for 1930.

Table 2 Market access and ABCC, ca. 1850

 2

	(1)	(2)	(3)	(4)	(5)

We follow[START_REF] López-Rodríguez | Human Capital Accumulation and Geography: Empirical Evidence from the European Union[END_REF] and limit ourselves to the supply side of the model. For a complete presentation, see e.g.,[START_REF] López-Rodríguez | New Economic Geography And Educational Attainment Levels In The European Union[END_REF].

Given the variable "Distance to Luxembourg", we have excluded Luxembourg in all our regressions and do not list it here.

These outliers are Ponte Delgada which is on the Azores Islands and far off the European continent. Moreover, we excluded Oral which is not located in the limits of today's definition of Europe.

Clearly, it would be preferable to use an even closer theory-based measure, including regional price and interregional trade flow data. Yet, as[START_REF] López-Rodríguez | Human Capital Accumulation and Geography: Empirical Evidence from the European Union[END_REF] already emphasise, this measure is not available for today. Without surprise, it is not available for the past either, so that we have to rely on our alternative but fairly good proxy estimates.

Other economic measures, such as regional GDP data, are not yet available for an important part of European regions, in particular in Eastern Europe.

An exception is Greater London, where we had to use the NUTS 1 level due to unavailability of more disaggregated data.

Note that we have opted for the presentation of the results with the logarithmic form of the ABCC. We have also done all regressions without this transformation and obtained the same results (only the value of the coefficients changed which is a logical consequence of the transformation).

Similar toLópez-Rodríguez and Faina (2007), we also explore the possibility of outliers that would have an effect on our results by computing Cook's distance. According to Cook's distance, there are no outliers (i.e. with a value > 1) in all the regressions.

Because we use the logarithmic form of the ABCC here, the upper limit (corresponding to 100) is approximately 4.6052.

Borders and countries in ca. 1850 are considered. Because we are interested in the domestic market and trade advantages, we consider Germany as being constituted by those countries that had joined the Zollverein (German Customs Union). Data on country sizes (in geographical square miles) come from Annuaire Statistique et Historique Belge (1857).

See Kirk (1946) for more information.

These countries are Denmark, Germany, Ireland, Netherlands, Norway, Sweden, Switzerland, United Kingdom and parts of Austria.

Directorate for Growth and Innovation, Human Capital and Employment Unit. The views expressed are purely those of the writers and may not in any circumstances be regarded as stating an official position of the European Commission.

Appendix Data

Regional data in 1850 include the following countries (in current borders):

Albania, Armenia, Austria, Azerbaijan, Bosnia-Herzegovina, Belgium, Bulgaria, Belarus, Switzerland, Czech Republic, Germany, Denmark, Estonia, Spain, France, Georgia, Greece, Croatia, Hungary, Ireland, Italy, Lithuania, Luxembourg, Latvia, Moldova, FYROM, Netherlands, Norway, Poland, Portugal, Romania, Russia, Slovenia, Slovakia, Ukraine, United Kingdom, Serbia, Montenegro.

Regional data in 1930, without estimated observations, include the following countries (in current borders): Albania, Armenia, Austria, Azerbaijan, Bosnia-Herzegovina, Belgium, Bulgaria, Belarus, Czech Republic, Estonia, Spain, France, Greece, Croatia, Hungary, Italy, Lithuania, Latvia, Moldova, FYROM, Poland, Portugal, Romania, Russia, Slovakia, Ukraine, Serbia, Montenegro.

Regional data in 1930, with estimated observations, include the following countries (in current borders): Albania, Armenia, Austria, Azerbaijan, Bosnia-Herzegovina, Belgium, Bulgaria, Belarus, Switzerland, Czech Republic, Germany, Denmark, Estonia, Spain, France, Greece, Croatia, Hungary, Ireland, Italy, Lithuania, Latvia, Moldova, FYROM, Netherlands, Norway, Poland, Portugal, Romania, Russia, Sweden, Slovakia, Ukraine, United Kingdom, Serbia, Montenegro. However, one would not always expect such an almost perfect pattern to emerge.

Numeracy as an indicator for human capital

Numeracy measures other human capital characteristics than literacy. Its method of calculation is different from literacy, as literacy is most often provided in historical censuses. In contrast, numeracy is derived from age distributions provided in such censuses but it is not directly stated in the censuses. In this way, it may in some cases have advantages to literacy, as the distribution of literacy is related to the distribution of linguistic (and thus ethnic) groups. The provision of linguistic skills in the predominant state language was often one important aspect of mass education in 19 th century Europe.

Census questions sometimes only included a lower number of languages to choose from as actually existed in a country. For example, in the Russian Empire it was not deemed possible (and wanted) to include all languages in the census questionnaire, as the number of different languages amounted to the hundreds. Likewise, imagine the case for countries such as France, where a high share of French citizens did not speak French in the middle of the 19 th century. The case is even more evident in countries such as Austria-Hungary.

While literacy is the standard indicator for the 19 th and parts of the 20 th century, it may be prone to some biases that numeracy is not. In this respect, the use of two alternative indicators allows us in this study to avoid any conclusions which are solely based on one of these proxies. This provides a higher level of certainty and robustness.

Note that the characteristic age heaping pattern is not a European cultural trait. It can be found similarly in other countries and world regions. For example, [START_REF] Hippe | How to measure human capital? The relationship between numeracy and literacy[END_REF] also considers microcensus data from developing countries in Africa, Asia and Latin America in the second part of the 20 th century. Comparing the derived numeracy values with literacy data, he finds similar tendencies as in historical Ireland, illustrating the close relationship between numeracy and literacy throughout time and independent of cultural characteristics in these considered continents: