An East West Germany Comparison

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1 The Role of Urbanization and Agglomeration Economies in Regional Convergence

An East West Germany Comparison

M.Sc. International Economics and Business – University of Groningen Master Thesis

Michiel Buijsman S1908901

m.c.buijsman@student.rug.nl

Supervisor: R. Ortega-Argilés

Co-assessor: A. A. Erumban

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2 Abstract

This thesis examines conditional beta-convergence, using the calculated averages, between former East and West Germany on a regional level, meaning city districts and counties, from 1996 till 2012.

Specific attention will be given to the role of urbanized and metropolitan regions, which are subject to agglomeration economies and spillover effects. Evidence was found for conditional beta- convergence between Eastern and Western regions, however, urban regions performed worse relative to rural regions. Furthermore, this research discovered that the traditional East West division is turning into a North South division, where the South performs better. Additionally, regressions to discover what drives the convergence are performed and the importance of the younger labor force and the quality of human capital is confirmed.

Key words: regional conditional beta-convergence, East and West Germany, urbanization,

agglomeration effects, externalities.

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3 Abstract ... 2

1. Introduction ... 4

2. Literature Background ... 6

2.1 Economic Growth and Convergence ... 6

2.2 Determinants of Economic Convergence ... 7

2.2.1 Agglomeration Effects ... 7

2.2.2 Intangible Drivers ... 12

2.2.3 Accessibility and Openness ... 16

2.2.4 Financial Resources and Investment ... 18

3. Additional Background Information ... 21

3.1 Stylized Facts about Germany ... 21

3.2 Regional Classifications ... 22

4. Hypotheses ... 25

5. Methodology and Model ... 27

5.1 Data ... 27

5.2 Variables ... 28

5.3 The Model ... 30

6. Results ... 35

7. Discussion and Conclusion ... 41

8. References ... 43

9. Appendix ... 51

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4 1. Introduction

Twenty-six years ago, on the 9

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of November 1989, the fall of the Berlin Wall was celebrated, and on 1 July 1990, former East and West Germany were economically united again with a common currency. Till the unification both regions were governed under different economic systems, which left significant differences. Recently, a research by Ragnitz et al. (2009) calculated that since the economic unification and the introduction of the solidarity surcharge in 1991, and up till 2005 net- transfers worth €1.600 billion were made to Eastern Germany and this continues yearly with transfers of about €100 billion. These structural transfers were implemented to level the economies of former East and West Germany, or at least regions in Eastern Germany should converge to the Western regions. The German case is an interesting case due to historical events and during the existence of East and West Germany different economic systems were present. Economic development continued but along a different path and after the reunification the challenge was substantial, namely converging regions which have been separated for a long time. First, it would be interesting to confirm existing research on the convergence of former East and West Germany (Niebuhr, 2000; Görzig et al., 2005; Kosfeld et al., 2006; Funke and Strulik, 2000; Juessen, 2005 and Demary and Röhl, 2009) and additionally which determinants of economic growth were important.

However, there are also those who predict that Eastern Germany will not or very slowly catch-up (Smolny, 2003; Ragnitz, 2000; Klodt, 2000).

This thesis looks at the convergence rate of Eastern and Western German regions at the administrative units of city districts and counties, which are almost comparable to NUTS 3-level classification. The model by Guastella and Timpano (2015) looks at the regional growth rate of the European Union (EU) from 1995 to 2007 at NUTS1 and 2 employing a cross-regional regression model, this model will be followed as a guideline. Their work looks at the prominent sources of externalities, namely the accumulation of knowledge, human capital and agglomeration, and its influence on economic growth. Guastella and Timpano (2015) found that the effect of knowledge is more important in developed regions, while there is the potential for innovation and agglomeration in less developed regions. Earlier work by Cuadrado-Roura (2001) also studied convergence at the European region level from 1977 to 1994 and found that the convergence progress almost halted, but additionally some examples of well-performing regions are given and which factors in those regions caused higher growth rates.

This research will make use of a basic Solow economic growth model (Solow, 1956) with several

extensions. The first extension stems from the literature, which starts with the critique of Romer

(1986), who states that knowledge, an important driver of economic growth, was omitted in the

original model. Lucas (1988) agreed with Romer (1986) and both underline the importance of

knowledge as an important factor of production. Besides the traditional factors of labor and capital,

knowledge is added, but this is an endogenous variable and is the result of externalities and

spillovers (Audretsch and Keilbach, 2004). However, as Audretsch and Keilbach (2004) continue, it is

stated that the neoclassical production function omitted another source of economic growth namely

entrepreneurship capital, which is “the capacity for economic agents to generate new firms”, and

that entrepreneurial activity may account for a significant part of the unexplained growth differences

in traditional production function models (Baumol, 2002). Therefore, one could think about the

effects of entrepreneurial capital on economic growth and its effect on regional convergence.

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5 The previously mentioned effects are to some extent related with externalities and spillover effects.

Besides an East West comparison this thesis examines to which extent agglomeration economies and urbanized regions have influenced convergence. As Brakman et al. (2001) put it, analysis of agglomeration of firms and people in cities or metropolitan areas is heavily related with “the economics of agglomeration, a term which refers to the decline in average costs as more production occurs within a specified geographical area” (Anas, et al., 1998: 1427). Thus, variables which are characteristic for agglomeration economies are included in the analysis discovering which are able to explain regional economic growth.

Thus, the investigation of convergence between former East and West Germany will be focused on.

The key elements which will be focused on are considering the agglomeration economies and the urbanized regions, whether these performed differently. Therefore, firstly the question if there was significant convergence between former East and West Germany. Additionally, convergence is reviewed per individual state and instead of an East-West comparison, a North-South comparison will be made as well. Furthermore, did the urbanized regions perform better than the rural regions and were agglomeration effects present, and if so, were these positively related to regional GDP per capita growth rate. This will be done by looking at regions classified as urban and as being part of a metropolitan region.

This will be done in the following order. Chapter 2 provides the literature background, explaining the concepts of convergence and agglomeration and creates four different categories of interest;

agglomeration effects, intangible drivers, accessibility and openness and lastly investments and

financial resources. Chapter 3 gives some additional background information with some stylized facts

about Germany and the different geographical classifications considered. Chapter 4 lists all the

hypothesis of this work, whereas chapter 5 explains the data, methods, all variables and the baseline

model and all other models derived from the baseline model. Chapter 6 gives the results of the

equations and chapter 7 is considered with the discussion, conclusion and the limitations and

possibilities for future research. Lastly, chapter 8 is the reference section and chapter 9 is the

appendix.

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6 2. Literature Background

2.1 Economic Growth and Convergence

In a very general sense convergence means the coming together of two or more distinct and separate factors. Specifically, economic convergence according to the Cambridge Dictionary means:

“a process in which economies of different countries become more similar to each other”. However, besides countries, convergence could also take place on a regional scale. An economically and politically important question is whether poorer countries or regions are able to catch-up with richer countries or regions. Barro (2012) found that according to the “iron law of convergence” countries would narrow the real GDP per capita rate around 2% per year.

The concept of convergence is closely connected to the issue of economic growth, which aims to study those factors influencing economic growth and explaining differences (Taylor and Woodford, 1999). Convergence studies (e.g. Barro and Sala-i-Martín, 1991; 1992; Barro, 2012; Caselli, et al.

1996) are all based on the basic economic growth model and the convergence hypothesis by Solow (1956). In Solow’s work economies will converge toward a certain steady-state per capita income at a declining growth rate, however, under the simplified assumption of decreasing returns to production factors. However, over time important changes have been made due to the development of New Growth Theory (NGT) (Lucas 1988; Romer 1986) and the New Economic Geography theory (NEG) (Fujita et al. 2001; Krugman and Venables 1996), who added endogenous growth factors, which are important explaining regional development (Guastella and Timpano, 2015).

The key elements originating from the NGT are human capital and innovation. The NEG adds agglomeration economies, and together with human capital and innovation are important factors in explaining regional growth (Guastella and Timpano, 2015). In the extended Solow-Swan model Barro (2012) models various variables differently; the distinction between human and physical capital is included and technology is endogenized. Technology is concerned with the effects of R&D and innovation, which are very important externalities and above all sources of economic growth. What becomes obvious is that research on regional economic growth has developed and was substantially extended. However, as Guastella and Timpano (2015) argue, often growth and convergence models lack simultaneous inputs from these different streams of literature. Therefore, it is important to include variables for the accumulation of knowledge, human capital and agglomeration as sources of externalities in a cross-region regression. Indicator variables regarding knowledge, human capital and agglomeration will be explained into more detail in chapter 5.

In the literature different types of convergence are depicted, each explaining convergence in a

different manner, therefore a brief explanation on economic convergence is given. Economic

convergence can be divided into beta- (β) and sigma- (σ) convergence, where beta-convergence

refers to poor countries or regions growing at a faster rate than rich ones (Barro and Sala-i-Martin,

1991). Additionally, beta-convergence can be conditional, which means: “if the growth rate of real

per capita GDP is negatively related to the starting level of real per capita GDP, after holding fixed

some other variables, such as initial levels of human capital, measures of government policies, the

propensities to save and have children, and so on” (Barro and Sala-i-Martin, 1995). In other words,

economies grow faster if in the current situation it is further away from its own steady-state of

capital per worker and its own long-run equilibrium (Vogel, 2003).

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7 Or as stated comprehensively by Sala-i-Martín (1996) if there is a negative relationship between the growth rate of income per capita and the initial level income there is beta-convergence, this means that poor economies tend to grow faster than wealthy ones. Conditional beta-convergence will be focused on, because it is suitable for cross-country or regional comparison, which implies that if socio-economic or political variables are sufficiently homogeneous there will be convergence in income or production levels in per capita growth rates (Vogel, 2003).

The second type of convergence is sigma-convergence; this refers to a decline in regional disparities of per capita income or product over time (Barro and Sala-i-Martin, 1991). Those two types of convergence are connected in the sense that, for sigma-convergence to happen it requires beta- convergence, however, this is not a sufficient condition (Young et al. 2008), and this process is offset by new disturbances which tend to diverge the income division (Barro and Sala-i-Martin, 1995). Thus, by studying sigma-convergence it is possible to determine whether regional differences became smaller, conditioned on various determinants. However, sigma-convergence is out of the scope of this research and could be used in future work.

2.2 Determinants of Economic Convergence

The basic growth function used in convergence analysis always includes indicators on physical capital, human capital and the level of technology and additionally all other added indicators. This work proxies per capita income by Gross Domestic Product (GDP) per capita. In a later stadium Gross Value Added (GVA) will be used as a robustness check. Testing for convergence relies on a cross- regional regression of per capita income growth over a certain period, however, always based on the initial level of per capita income (Guastello and Timpano, 2015).

Expecting convergence requires the initial income level, in this case the GDP per capita in 1996, to be negative and significant. The model used in this work will be extended by other indicators grounded in the literature and subsequently hypothesis will be formed. The following sections are divided in agglomeration effects, accessibility and openness, intangible drivers and financial resources and investments.

2.2.1 Agglomeration Effects

Agglomeration economics describes the decrease in average costs as more productions takes place

within a specified geographical area (Anas, Arnott and Small, 1998: 1427), this is often used to

explain the agglomeration of firms and people in cities and metropolitan areas. Or put differently, it

relies on increasing returns to scale (Brakman et al., 2001). Furthermore, it is possible to distinguish

between internal and external economies of scale, in which the former means decreasing average

costs through production increases and the latter means decreasing average costs through output

increase industry-wide (Scitovsky, 1954). Scitovsky (1954) distinguishes between pure and pecuniary

external economies; the former recalls that an increase in industry output changes the technological

relationship, due to increasing stocks of knowledge through positive information spillovers, and

hence decreases average costs, and the latter functions through price effects, and is due to a large

local market for specialized inputs and labor market pooling (Brakman et al., 2001). Thus, in

agglomerated areas there is either a large skilled labor force present or production can be more

easily sold.

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8 External economies of scale are thus dependent on spillovers or externalities, and whenever discussing about spillovers or externalities external economies of scale are meant. Lastly, two types of externalities can be distinguished, Marshall-Arrow-Romer (MAR) externalities, which are called

“localization economic” and are sector-specific spillovers, and Jacobs externalities, called

“urbanization economics” and are city-specific spillovers, however, both have in common that those are regional economic externalities or spillovers, thus in order to benefit one has to be closely located to one another (Brakman et al., 2001). Fujita and Thisse (1996, 2002) discuss theories of agglomerations, which also include urban economics, and found that increasing returns, theoretically similar as above, and spatial competition increased agglomeration. Anas, Arnott and Small (1998) added two additional factors, the existence of non-homogeneous space, which means agglomerations occur due to natural physical factor, e.g. a natural harbor, and internal economies of scale. Quoting Ottaviano and Thisse (2004, p. 2576), who listed five important points considering location and agglomeration:

1. The economic space is the outcome of a trade-off between various forms of increasing returns and different types of mobility costs;

2. Price competition, high transport costs and land use foster the dispersion of production and consumption; therefore

3. Firms are likely to cluster within large metropolitan areas when they sell differentiated products and transport costs are low;

4. Cities provide a wide array of final goods and specialized labor markets that make them attractive to consumer/workers; and

5. Agglomerations are the outcome of cumulative processes involving both the supply and demand sides.

On the other side there are diseconomies of scale or so-called negative externalities. Congestion is discussed by Brakman et al. (2001), which is referred to as a collective term associated with urban agglomeration and are for instance limited physical space, traffic congestion, higher land and housing prices, etc. Costs associated with negative externalities can be quite substantial and will only be accepted up till a certain level, and exceeding this triggers a relocation process (Brakman et al., 2001). Furthermore, unemployment is associated with a negative agglomeration force, where Blanchflower and Oswald (1994, 1996) developed ‘their empirical law of economics’ which explains that: “doubling the unemployment rate of some regions will drive down the regional wage level by roughly 10%”, which has a negative effect on regional economies.

The agglomeration effects are divided into three subsections, (1) employment and unemployment, (2) business activity and entrepreneurship and (3) densities and other agglomeration effects.

Employment and unemployment

Traditionally economic literature proxied labor market performance by using the unemployment

rate, however, this changed and turned towards looking at the employment and labor force

participation rates (Perugini and Signorelli, 2004). Perugini and Signorelli (2004) state that using

employment rates are better, because the unemployment rate depends on the participation rate,

which is the labor supply, which in turn depends on the employment rate. They argue that job

opportunities and employment rates reflect the sustainability of the welfare state better.

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9 The effect of the labor force participation rate researched by Blomström, Lipsey and Zejan (1996) found that the labor force participation rate has a positive significant effect on the economic growth of a country or region. The use of the labor force participation rate is useful since it also takes into account demographic changes, such as birth rates and the relative share of dependent population to the working population (Blomström et al., 1996). Additionally, Vogel (2003) shows that the labor participation and the employment share in the electronic sector positively influence regional growth and that the employment in the agricultural sector should have a negative influence on economic growth, as explained in the theory, however for this proof is lacking.

As indicated before Lucas (1988) added indicators of human capital to economic growth analysis and specifically human capital accumulation and externalities created during the cause of work or so- called learning by doing. This is taken into practice by Mankiw, Romer and Weil (1990) who add the human capital to the cross-regional growth regression and found that human capital is indeed an important factor in explaining cross-regional differences. Considering human capital one can look at the quantity or the quality of the labor force. The quality of the labor force is more complex to measure and has a more intangible character and it will be explained in a later section.

Regarding the supply of human capital, one can look at the total employment in a country or at the total labor force participation rate. The difference between the two is, is that the former divides the employed persons on the inhabitants aged between 15 and 65, and the latter looks at all inhabitants who can work, thus all employed and unemployed inhabitants, but it excludes students and pupils.

The labor market in Germany has a special feature, because at the economic reunification the wages, as well as insurances and pensions of former East and West Germany were leveled causing difficulties for Eastern firms, because of higher wages, but relatively low labor productivity, therefore they were unable to compete with Western firms and pushed the Eastern regions into a depression with subsequent unemployment (Krüger and Pischke, 1995). Furthermore, in a study by Eckey, Kosfeldt and Türck (2007) based on labor market regions and local variables, between 1995 and 2002, it was found that each region had a different speed of convergence and that this speed is substantially lower for the manufacturing sector than the service sector. Regarding regional differences it is found that the South has a long half-life time and the North a short, meaning that in 2000, the Southern regions with high labor productivity and a low unemployment rate will be the most prosperous (Eckey et al., 2007). Additionally, Klodt (2000) explains that another wave of painful structural adjustment is required when public subsidies will gradually be reduced and thus the firms and sectors depending on subsidies will become vulnerable and should adjust.

Therefore, it is expected that the influence of unemployment has a negative effect on the economic growth and subsequently on the convergence between Eastern and Western regions. It is possible to look at unemployment into further detail and distinguish certain groups, female unemployment and youth unemployment. However, precise measures of unemployment will be explained in chapter 5.

As explained by Esteve-Volart (2009) the exclusion of females from the labor force or partly exclusion

from managerial position has a negative effect on the economic growth because females are

excluded from the talent pool, which leads to a decrease of average talent and aggregate

productivity. The same conclusion is reached by Cuberes and Teignier (2012), who found that an

increased inequality in access to managerial positions reduces output per worker and the gender gap

in labor force participation would also decrease per capita income. Furthermore, due to wage

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10 inequality per capita income will decrease the larger the gender gap is (Cavalcanti and Tavares, 2008). See Cuberes and Teignier (2014) for an extensive explanation considering gender inequality and economic growth. Thus, regarding female employment and unemployment different conclusions are present. Due to wage inequality it could be possible that when more females are employed it could still have a negative effect on economic growth. On the other hand, as explained above, the increase in female labor force should have a positive effect on economic growth, however, from the present data it is not possible to distinguish between genders in certain sectors.

Regarding youth employment and unemployment, O’Higgins (2001) work is very extensive in explaining and defining this. O’Higgings (2001) argues that these measures are important, because the youth labor market reflects the opportunities, but also how the transition from education to labor market is affected. Additionally youth employment is important in the sense of experience and development, thus it is expected that youth unemployment has a negative effect on the economic growth and employment a positive effect.

Funke and Strulik (1999) found evidence for conditional beta-convergence for Western German federal states, but better technology, the accumulation of human capital and agglomeration externalities caused divergence patterns or uneven growth patterns. Moreover, Suedekum (2005) found that, considering core and periphery regions, workers and production will agglomerate in core regions and exhibit lower unemployment rates as compared to more rural and sparsely populated, peripheral regions. Additionally, Suedekum (2005) shows for EU-15 that unemployment rates follow a transnational core-periphery structure, which actually resemble the spatial configuration of GDP per capita. Lastly, Suedekum (2005) is able to explain that labor migration to densely populated and rich regions, which receive most migrants, initially increases labor supply competition, but afterwards also increases labor demand and due to the better exploitation of scale economies in core regions a stronger regional polarization regarding real wages and unemployment rates. Therefore, unemployment is seen as negative externality and employment as a positive externality, which intuitively also linked with the following subsection of business activity and entrepreneurship.

Business activity and entrepreneurship

Another important part of economies are certain business activities and entrepreneurship.

Cuadrado-Roura (2001) shows that the presence of small and medium sized enterprises (SMEs) are always a positive factor in his analysis of successful regions in Europe and explains that the entrepreneurial spirit is an important condition which pushes economic growth. More in general showed by Brakman et al. (2001) firm presence, especially in urbanized regions, is a positive externality. Firm presence is of course related to employment characteristics, which are explained in the previous section; hence it should have a positive effect.

In work by Fritsch and Wyrwich (2014) about the level of entrepreneurship in Germany from 1925 till

2005, it is shown that self-employment and the formation of new business have a long lasting effect,

even in the case of abrupt and drastic changes in its surrounding environment. In their work it is

argued that regions with a relatively high level of entrepreneurship and start-up activities today will

likely perform better in the future, either due to regional specific determinant of entrepreneurship

which remain stable over time or a certain regional entrepreneurship culture is present which

survives external pressures (Fritsch and Wyrwich, 2014). Their work is based on previous work from

Audretsch and Keilbach (2004) where entrepreneurial capital is introduced and measured by the

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11 number of patents, the number of startups; in total and the share of high technical manufacturing or ICT relative to the total start-ups, and a significant positive relationship between entrepreneurial capital and economic growth was found for Germany. The regional-specific determinants of entrepreneurship consist of the regional entrepreneurial culture and a supportive infrastructure, particularly the availability of consulting and investing financial institutions, which have an evident positive effect on the level of entrepreneurial activity (Fritsch and Wyrwich, 2014).

Due to the long timeframe of their work Fritsch and Wyrwich (2014) could analyze the entrepreneurial culture of Eastern Germany in a socialistic system, in these forty years supportive infrastructure was destroyed, however, the entrepreneurial culture did survive, this indicates the long-term characteristics. Furthermore, Eastern Germany experienced a substantial growth in its manufacturing sector and the significant growth of industrial clusters in different regions (Demary and Röhl, 2009), which are possibly able to explain regional economic growth, but these clusters could also subject to agglomeration externalities.

Densities

The last subsection considering agglomeration effects focuses on several measures of densities.

Agglomeration indicates automatically higher density; the different measures are population density, employment density, population-employment density and car density. With higher densities come congestion costs or crowding costs, which could negatively influence regional economies.

Employment density could be seen as positive effect considering the labor market pooling effect and positive spillover effects, on the other hand it could also be negative by a crowding labor market, and perhaps it could be at the expense lower skilled groups. Additionally, population density could be positive in the sense of the previously mentioned spillovers, and contact potential, but on the other hand negative, because it could involve crowding costs at the housing market. Additionally, instead of looking at employment density or population density, the BBSR provides a different measure as well, the population-employment density, which adds the inhabitants and employees at the place of work instead of place of living. Therefore it is an indicator for maximum space requirements in the daily routine (BBSR, 2015).

Congestion, specifically traffic congestion, is a negative agglomeration effect and could decrease economic performance. One measure of looking at traffic congestion is car density, which is the amount of motor vehicles per 1,000 people. As shown by Brakman et al. (2001) increases in urban agglomeration consequently increases the car density between 1980 and 2003 in a selection of European countries.

Connected with density and higher rates of agglomeration is the land price. An increase in density

means more people in the same area, which accordingly have to share the limited amount of land,

which in turn increases the price of land. Prices of housing and land have been added by Thomas

(1996) and Hanson (2005), who argue that higher prices are a negative externality, even though

agglomerated areas have a wage premium. If at a certain moment transportation costs decrease

under a certain level, the advantage of market proximity falls as well and could initiate a relocation

process (Brakman et al., 2001).

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12 Lastly, the regional population potential and as put by the BBSR: “a measure of the possibility of spatial interactions. The more people can be reached in the vicinity of a place and the smaller the travel distances the higher its contact potential”. This gives some indication of space and could indicate the effect regional externalities can have on regional economic growth.

Thus, these density measures give above all an indication of agglomeration and agglomeration externalities, some are considered to postive and others appear to have a negative effect.

Furthermore, considering these agglomeration effects in the light of convergence, it could increase regional economic performance of agglomerated regions and cause divergence between regions classified as urban and rural. Connected with agglomeration effects are intangible drivers, as explained by Glaeser et al. (1991) agglomerated areas are often characterized by a greater stock of skilled labor, human capital and research institutions, which in turn are encouraging superior economic performance.

2.2.2 Intangible Drivers

Moreover, quantity of human capital does not suffice in explaining economic growth, the quality of human capital is just as, or maybe even more important. Roth and Thum (2010) argue that it is widely recognized that nowadays knowledge and intellectual capital are major determinants of innovation and consequently influence growth, employment and competitiveness. Similarly, Guastella and Timpano (2015) summarize that formal models of endogenous growth focus on the contribution of knowledge in the production process, this has two effects; firstly, knowledge embedded in educated people increases productivity, and secondly, knowledge could be codified, formalized and materialized and employed for product and process innovations. But it is important to keep proximity in mind, as Funke and Niebuhr (2005) show for West Germany that knowledge spillovers exist across functional regional boundaries, however significant spillovers are primarily found in geographically close.

As described above, the inclusion of human capital into growth models gained popularity due to the influence of NGT (Romer , 1986; Lucas, 1988) and NEG theory (Fujita et al., 2001; Krugman and Venables, 1996). Guastella and Timpano (2015) point out that the inclusion of endogenous growth variables could imply that convergence is not possible or that divergence is actually possible, where the latter is proven in a cross-country study of Mankiw et al. (1992) where equilibrium per capita income levels vary and are explained by human capital endowments. Other work on the relationship between human capital and economic growth find a positive and significant relationship as well;

Badinger and Tondl (2003) additionally found that higher levels of human capital make a

technological catch-up possible, López-Bazo et al. (2004) and Ertur and Koch (2006) found that

human and physical capital accumulation generate positive externalities, which contribute to

regional economic growth. Considering urban agglomeration economies it is shown that from the

long-standing human capital theory, spillovers between people are an important determinant for

urban growth, because a city with more human capital will grow faster, with additional higher wages

and income levels (Brakman et al., 2001). Additionally, Berry and Glaeser (2005) found that next to

higher wages and income levels in cities, that divergence is visible for those cities with initially higher

human capital stocks and subsequently show higher growth rates of human capital.

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13 Thus, the empirical evidence suggests that there is a positive relationship between human capital and economic growth. Focusing on the intangible drivers of human capital and knowledge externalities requires a distinction between on the one hand education and skills and on the other employment in knowledge intensive sectors. Education and skills to some extent explain something about the qualification, human capital potential and to the contribution of knowledge in production, which assumes that knowledge is translated into increased productivity (Adams, 1990). Employment in knowledge intensive sectors explains something about either the generation of knowledge embedded in employees on the job or the economic outcome of their work.

Education and skills

Thus it is clear that human capital and the quality of human capital have an important role in explaining economic growth. Bartelsman et al. (2014) explain the Germany education system as follows: “In Germany, the education system is characterized by a well-established, successful dual education system—combining general, transferable skills and structured learning on the job—

supportive for providing high-quality technical skills and for creating a high degree of specialization of skilled employees”.

Considering education, Vandenbussche et al. (2006) show that the higher amount of university- educated workers, the larger the increase in productivity will be, however this depends on the closeness to the technological frontier. The technological frontier is concerned with the advancedness of technology and innovations, the closer a country is to this frontier, the higher the quality. One important finding was that the growth-enhancing margin within the OECD countries is that of skilled human capital instead of total human capital and that the closer to the technological frontier a country is the stronger the effect on growth (Vandenbussche et al., 2006). Bartelsman et al.

(2014) show that in Germany the average innovation performance is higher in most industries, and that German firms close to the technological frontier benefit from higher returns from human capital. Madsen (2014) find similar positive effects on productivity growth of education attainment and the interaction between education and distance to the technological frontier. An empirical study by Aghion and Cohen (2004) shows whenever a country is closer to the technology frontier higher education is increasingly important, and additionally education has two effects on economic growth, namely higher educated persons are more productive and it positively influences the adoption, diffusion and development of new technologies.

Pointed out by Hanushek and Kim (1995) and Hanushek and Kimko (2000) is the fact that higher human capital accumulation and more fundamental, meaning university research, generates higher economic growth levels. Also, showed by Hanushek and Woessman (2012), better test scores lead to higher growth, however due to the unavailability of such test scores on this regional level education enrollment levels and early primary education leavers without any degree are take into account.

Furthermore, the amount of apprenticeships will be looked at shortly. Apprenticeships add to

economic growth and stability (Black, 2007 and Nilsson, 2010) and in comparison with the British

system Grugulis (2003) found that the German system, combining academic and practical technical

skills, provide a robuster foundation for the labor force and influence regional economies positively.

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14 Furthermore, according to the European Commission continuing education and training are considered to play a major role preventing the widening of socioeconomic inequalities within the EU (Dieckhoff, 2007). The European Commission (2000) states that “the development of human resources, in particular skill upgrading and extending life-long learning, is crucial in a knowledge- based economy”. Hence, a variable explaining offered adult education courses at so-called

‘Volkshochschule’, which are community colleges, will be included and expected to positively influence economic growth. However, large regional differences could contribute to different growth rates and complicate the convergence process.

Focusing on the qualification of the labor force Klodt (1990) found that formal training levels in former East Germany exceeded the corresponding levels in former West Germany and Bellman and Brussing (1998) found that the informal qualification levels are somewhat equal to Western levels.

Thus, the qualification in itself could not fully explain the gap between Eastern and Western Germany.

Thus, enrollment in tertiary education institutions will be included, because this could give an image about the quality of and the size of potential labor force and could include positive knowledge spillovers. However, variables considering the possession of a tertiary degree will be looked as well, since not every city or town has a university or university of applied sciences and after students graduate they will move and life somewhere else and therefore the diffusion of high qualified inhabitants is a suitable measure. Higher qualifications and enrollment rates are expected to have a positive influence on economic growth and early leaver a negative. Additionally, apprenticeships and adult education are expected to be positively related and provide a more long-term foundation for a higher qualified labor force and subsequently economic growth.

High-skilled employment

As explained in the previous section, spillover effects and positive externalities are present due to increased quantity and quality of the human capital, likewise when considering employment in knowledge-intensive sectors. Knowledge-intensive sectors contain employment in Research and Development (e.g. scientists and engineers), knowledge-intensive business services and services industries, additional supporting industries such as finance, insurance and other finance related employment and employment in the creative sector (e.g. music, art and film industry).

First the creative sector, Brakman et al. (2001) state that cities in former times competed to attract industries in an industry-based economy, but this shifted to a service-based economy and that cities which succeeded in attracting creative people and firms, which is key to urban growth. Florida (2002) goes beyond this point and argues that people as workers and consumers are just as important as firms in explaining economic growth and innovation. However, Glaeser (2004) argues that the term

‘creative class’ is just the same as human capital and that larger cities have more city-specific

amenities which in turn attracts creative people (Glaeser et al., 2001). On the other side, work by

Marlet et al. (2007) provide evidence that creative employment in the Netherlands explains

employment growth better than education, but of course creativity and human capital, in their case

education, are highly correlated, and thus creative employment and human capital both have a

positive effect on urban growth. Similarly, Piergiovanni et al. (2012) find in an empirical study

considering Italian provinces, that an increase in firms in the creative sector positively influences

employment growth and regional growth.

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15 Secondly, as explained above, knowledge can be translated into product and process innovations, which are key in endogenous growth theory, and the long-run distribution of income will be determined by the innovative capacity of the region (Guastella and Timpano, 2015). Fagerberg and Verspagen (1996) and Fagerberg et al. (1997) find that R&D related indicators are able to explain differences in the income distribution. Rodríguez-Pose and Crescenzi (2008) find a positive relationship between regional growth and innovation in a study of the EU25. Sterlacchini (2008), studying the EU15, found the same positive relationship between innovation and growth, but only in more developed regions, however human capital remained positive, irrespective of the development level. Thus, innovation has a positive effect on regional economic growth, however regional differences can be explained by looking at the initial stock, the regional knowledge based. As Dosi (1988) explains, a larger regional knowledge base means more experience in innovative activities and better opportunities to innovate and therefore result into higher rates of economic growth.

Moreover, the previous researches mostly focus on innovation and production, however in a research by Inklaar et al. (2008) there is no evidence that productivity externalities for university- educated employees in leading market services industries are present. Hipp et al. (2013) use the following definition of knowledge-intensive business services (KIBS) as: “a set of activities that, through their use as intermediary inputs, affect the quality and efficiency of the production activities by complementing or substituting in-house service functions” (Muller and Doloreux, 2009; Kox and Rubalcaba, 2007). The work of Hipp et al. (2013) demonstrates the relatively high innovative and cooperative profile of KIBSs, but caution for heterogeneity across KIBS industries. Muller and Zenker (2001) actually show that KIBS are considered as ‘industry brains’ and those regions where KIBSs are represented and with easy access to them have a competitive advantage and better economic development, and stimulate the generation and diffusion of knowledge within innovation systems.

Furthermore, as explained by Klodt (2000) a large productivity gap is still present, even after large financial transfers and equal qualifications of the labor force, it is argued that a part of the gap is explained by the composition of industries and excessive reliance on subsidies and focus on heavy manufacturing and or industries instead of knowledge intensive industries. Partially related is a research by Kronenberg and Volgmann (2014), looking at the German Rhine-Ruhr area and show that knowledge-intensive employment is very sector-specific and a spatial pattern of employment dynamics is present. Thus, location plays an important role, especially when considering the different performances between municipalities within or outside metropolitan areas (Kronenberg and Volgmann, 2014).

Thus, to conclude this section on intangible drivers, it is clear that education, skills and employment in knowledge-intensive sectors positively influence innovation, productivity and economic growth.

However, it is difficult to determine what the direct effect of each of the factors is on economic growth due to its intangible characteristics. Education and knowledge positively influence productivity and innovation, but the adoption and diffusion of innovations as well. There are also some sign of spillover effects, however, these are geographically limited and could be dependent on the initial level of education and knowledge. Urban agglomerations attract higher-educated people and the so-called ‘creative people’, and therefore create a reinforcing spillover effect. Thus, literature agrees that these variables influence economic growth positively in one way or another, but possibly due to differing initial performance levels it could cause regional divergence instead of convergence.

The importance of location is mentioned several times, therefore accessibility indicators are next.

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16

2.2.3 Accessibility and Openness

Looking at economic growth and economic convergence requires one to take a spatial component in to account. In this case accessibility and openness are spatial indicators related to agglomerations and its potential externalities.

Accessibility

Cuadrado-Roura (2001) explains that the accessibility of the region represents a key factor related to better performance in his research considering successful regions in Europe. Accessibility is explained as more than just the physical perspective, because accessibility is also related to speed of diffusion of innovations and technologic developments and investments and political decision making (Cuadrado-Roura, 2001).

Accessibility according to ESPON is related to the quality of transport and communication infrastructure of a location, depending on the capacity, connectivity, speed and other similar characteristics (ESPON, 2009). According to ESPON (2009) accessibility indicators, based on transport infrastructure, are usually measures for regional competitiveness and economic potential of regions.

Considering Germany, there are some core-periphery differences visible, with poorer values for Eastern regions, regions between major airports show better values, and, most German regions take partial advantage of its favorable location in the European mainland, however, many regions do not utilize their full location potential (ESPON, 2009).

Vickerman, Spiekermann and Wegener (1999) explain that in its most straightforward form it is implied that better infrastructure leads to lower transport costs or to a wider range of choice and more competition, which actually corresponds with the idea of the NEG-theory. Improved access to input materials and markets in a region leads to, ceteris paribus; higher productivity and competition and therefore those regions perform better than regions with poorer accessibility (Vickerman et al., 1999). The results of their work (Vickerman et al., 1999) considering the high-speed rail trans- European Networks (TENs), indicate that the current developments may enhance the accessibility gap between core and peripheral regions. Similar effects, diverging states of accessibility, considering roads were found by Gutiérrez and Urbano (1996). Additionally Vickerman et al. (1999) argue that most developed regions develop new infrastructure earlier compared to less-developed and thus maintain higher levels of accessibility, however, one should take into account that public funds invested in new infrastructure could slow down economic development. Aschauer (1989) showed with a macroeconomic model that the enhanced productivity and growth due to infrastructure investments outweigh crowding effects of these investments. However, important to take into account is the fact that accessibility and infrastructure investment on a regional scale are subject to complexities, such as the smaller a region is the larger the net benefit for the non-resident users or the greater the costs for the residents, or a smaller local multiplier effect (Vickerman et al., 1999).

Additionally, and not unimportantly, one should bear in mind that the causality between increased

accessibility in the form of improved infrastructure and/or larger infrastructure investments and

economic performance is difficult to determine and does not have a decisive answer (Vickerman,

1995).

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17 Due to developments in the literature and the increased attention of the NEG-theory (Krugman, 1991; Fujita et al., 1999) the relation between accessibility and economic development is viewed differently as well. Venables (2007) explains that in a context where firms benefit from external increasing returns to scale, improvements in transportation infrastructure generate wider economic benefits, thus include spill-over effects for neighboring regions. Matas, Raymond and Roig (2015) explain that the new theories stress the connection between transport costs and economies of scale, implying that infrastructure is an important element since it affects transport costs and the accessibility of regions. However, transportation investments could imply changes in the accessibility of firms to markets may increase productivity by affecting agglomeration economies (Matas et al., 2015), where agglomeration economies are defined as “external increasing returns to scale derived from the spatial concentration of firms and households that positively affect the productivity of firms”. The origination of agglomeration economies is influenced by physical proximity, which influences the effective employment size or density from which are able to profit from agglomeration benefits (Gibbons and Overman, 2009). Research considering accessibility agree that accessibility could also been seen as market potential (Matas et al.,2015), however different measures are used as proxies, Euclidean distance which could be considered truly exogenous, where travel times are subject to location decisions which lead to a higher demand for transport (Graham, 2006). However, as suggested by Lall et al. (2004) time-based measures of accessibility take into account the quality of the network and therefore have an advantage over looking at only distances.

Thus, accessibility is one way of looking the regional potential and is a source of spill-over effects, besides the direct effect of infrastructure investments on regional economies. Different measures, from different sources, of accessibility will be looked at. The shorter the travel time, thus higher accessibility, the larger the potential and agglomeration effects could be. Intuitively, the higher the travel time the higher the negative effect on regional economies.

Openness

Openness in this case is not regarded as the openness of economies relative to each other, but is concerned with the regional area use. Areas can different uses, areas can be urban (e.g. for houses or businesses), recreational (e.g. parks or wildlife) or the extraction of natural resources (i.e. agriculture, forestry and fishing). Two measures concerned with area use which are considered are the recreational areas and open space.

Green (2001) explains that recreational areas, areas dedicated to sport or camping or parks and green areas, are amenities. “Amenities provide benefits (or in economic terms, utility) to people through the direct consumption of specific aspects of land, natural resources and human activity”

(OECD, 1994). Or as defined by Power (1988: 142): “amenities are non-marketed qualities of a locality that make it an attractive place to live and work”. Green (2001) continues by explaining that these benefits are regional specific, immobile and that amenities are valuable, especially in the case of recreational areas from which individuals benefit from direct physical use. Several studies argued that quality of life plays an increasingly important role in economic growth (Dissart and Deller, 2000;

Halstead and Deller, 1997; Rudzitis, 1999) and additionally argued by Gottlieb (1994) is that the

argument for using amenity attributes as instruments for economic growth appears to be powerful,

even if it does not create additional jobs and income growth.

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18 Deller, Tsai, Marcouiller and English (2001) found that rural areas in the United States characterized by high levels of key natural resource amenity endowments and overall quality of life show higher overall levels of growth. Five amenity attributes were included; climate, developed recreation infrastructure, land, water and winter, all were significant and positively related to economic growth (Deller et al., 2001). Thus, recreational areas are amenities, which are expected to have a positive influence on economic growth, albeit not direct or immediately visisble.

Open space, using the measure from the BBSR, is defined as the total area minus the built/developed area. In this case unbuilt/undeveloped areas include front- and back gardens, playgrounds and parking lots, green areas, courtyards, storage areas, etc. This measure gives some indication as to how much of the area is actually filled with houses, buildings, factories, etc. and thus the openness of the region. As Anderson and West (2006) review the decentralization in the United States it is argued that although urban development satisfies a growing population’s need for additional housing and commercial space, open areas provide several benefits, including recreation amenities, pleasing views, or simply do not generate any negative externalities associated with development (Irwin, 2002). Fausold and Lilieholm (1999) attempt to give an economic value to open space considering positive and negative effects, however this is difficult since it includes intangible values as well, it differs between urban and rural areas, and open areas have benefits for citizens as well as economic impacts on local economies. However trade-off effects are recurring, undeveloped versus developed, but in the end increasing urbanization increases open land space values and the intangible value citizens attach to open space in urbanized regions (Fausold and Lilieholm, 1999).

Thus, therefore it is difficult to say whether the amount of open space has a negative or positive influence on economic growth. Development is positive for economic growth and the amount of open space present could represent area available for potential development for example. However, intangible values, such as the absence of negative externalities, attached to open space are difficult to measure.

2.2.4 Financial Resources and Investment

This section will shortly elaborate on financial resources and investments. Recapture from the introduction, Ragnitz et al. (2009) calculated that substantial money transfers have been made to Eastern Germany. This was necessary in order to counter the large scale migration from Eastern to Western Germany and these investments consisted among others of reconstruction, infrastructure, regional economic support funds, unemployment benefits, and much more (Kullas, 2011). All these different funds aimed to level the Eastern and Western German economies and create convergence among regions. Thus, investments should policy-wise have a positive effect on regional economies and due to exceeding investments in Eastern Germany these regions should be able to catch-up.

One of the measures the German government took to level regional economies was the so-called

“Finanzausgleich”, which literally translated is financial equalization, and is a mechanism in which richer states add more financial resources than they receive and the poorer states receive more than they invested. The goal of the “Finanzausgleich” is to provide states with lower tax incomes with sufficient financial resources. Providers of income are Western states, especially Bavaria and Hesse.

Receivers of transfers are a few Western and all Eastern states. The goals of this concept of

“Finanzausgleich” is to balance the financial resources of all states and improve economic conditions

in order to create economic growth and ultimately regional convergence (Ragnitz, 2014).

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19 Therefore, one measure of this research, “Finanzausgleich”, will be included and expected to have a positive effect on the regional growth and subsequent convergence.

Looking at investments, transport infrastructure investments are often focused on, since it is something more concrete and one is able to actually see it. Investments in transport infrastructure have different effects; the direct effects of government spending which adds to regional income, lowering the transport costs and it influences regional accessibility and relocation/agglomeration process (Lopez-Bazo et al., 1999; Dall’erba and Gallo, 2008; Vickerman et al., 1999). However, Dall’erba and Gallo (2004) found that these investments affect industry relocation process and agglomeration, but clearly favor the existing richer regions. Similarly, Vickerman et al. (1999) found that it favors central locations compared to more peripheral locations and therefore incapable of decreasing regional disparities. In a research focusing on European regions Dall’erba and Hewings (2003) found on the one hand that lower transport costs, due to improved inter-regional infrastructure, resulted in aggregate country growth however causing regional divergence, but on the other hand, intra-regional infrastructure investments positively correlated with regional growth in depressed areas, however did not have a strong effect on country growth. Kullas (2011) explains the effects of the infrastructure project German Unification (“Verkehrsprojekt deutsche Einheit”), where improvements in infrastructure, e.g. roads, railways and waterways, between Western and Eastern federal states were made, which could be seen as inter-regional investments as determined by Dall’erba and Hewings (2003). Kullas (2011) analyzing this in a NEG-model, found that reduced transport costs led to a steeper labor demand curve, which increased real wages and consequently the migration from Eastern to Western Germany. Furthermore, Rodriguez-Pose and Fratesi (2004) focused on other investments funds devoted to infrastructure of business support, but did not find a significant impact of those funds on regional economies.

In Funke and Strulik (2000) it is argued that private and public capital accumulation is important during the developing process and positively influences economic growth, and especially infrastructure per capita investment in lagging regions should be higher than in well-performing regions to increase the speed of convergence. They concluded that Eastern Germany will reach 80%

of Western Germany’s GDP per capita 20 years after the unification. However, this measure is based on the assumption and advice that future governments conduct an active infrastructure expenditure policy and continue to put effort into infrastructure accumulation in Eastern Germany (Funke and Strulik, 2000).

Additionally, recall that human capital, knowledge, innovation and R&D have a positive effect on

economic growth. The quality of human capital is important, therefore education receives a

substantial amount of attention in this debate and consequently spending on universities is regarded

as a positive effect. Guastella and Timpano (2015) summarize that investments in the innovative

capacity of the region is beneficial for regional growth when the technology gap is large or it causes

uncertainty in leading innovative regions. Aghion and Howitt (1992) provide evidence on the

mediating effect of investment in innovation on growth. Somewhat similar results have been found

by Dall’erba and Gallo (2008), who discovered that investments in R&D yield high social returns and

explain that empirical studies confirmed a positive relationship between growth and R&D

expenditures on a macroeconomic level. Moreover, Fagerberg et al. (1997) show that investments in

innovation are required and are also a prerequisite for successful knowledge diffusion, however, it is

also explained that the existence of a large technology gap causes knowledge spillovers to be slow or

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20 not present. Likewise, Venables and Gasiorek (1999), Vickerman et al. (1999) and Dall’erba (2004), studying the effect of European structural funds on long-run regional growth, found that these funds do not systematically add value in the long-run but that due to externalities the impact of regional funds is harder to interpret.

Thus, investments in transportation infrastructure are related to accessibility and agglomeration through transport costs, and are possibly responsible for core-periphery divisions and increasing regional disparities. Direct effects of transportation infrastructure investments are difficult to determine, but if there are any effects, these will probably be small and in close proximity. The evidence of investments in knowledge, innovation and R&D is mostly positive, but depends on the initial conditions.

Structural funds in general are difficult to interpret, they could have a positive effect, but this might

not be systematically. Several separate variables considering financial resources are included and

lastly an aggregate of all these resources in amount per inhabitant over the years. Finally, despite the

positive influence of knowledge intensive sectors, a measure of investments in manufacturing,

mining and quarrying is included to check whether this is still of significant importance.

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21 3. Additional Background Information 3.1 Stylized Facts about Germany

After the fall of the Berlin Wall the German people were able to move freely again, one country was created and on 1 July 1990 Eastern Germany could pay with the Deutsche Mark as well. However, after many years of separation, one can imagine that the economic conditions were quite different due to capitalistic and communistic systems. In order to rebuild Eastern Germany the so-called Solidary Surcharge was implemented in 1991. Every German taxpayer pays 5.5% tax on his income, which will be invested in Eastern Germany to rebuild and level the economies. The German government has stated that it will continue to tax fully till 2020 and after it will be decreased step- wise. About €1,600 billion is already invested and about €100 billion flows into Eastern Germany each year. However, to what extent were the Eastern regions able to catch-up? Before the analysis of convergence some stylized facts about Germany are presented as stated in the work by Uhlig (2007):



There is persistent migration from East to West, in particular between the age of 18 and 29.



Unemployment in East Germany is higher than in West Germany.



Wages are lower in East Germany.



Average labor productivity is lower in East Germany.



The welfare system provides comparable benefits in East and West Germany to short- and long-term unemployed workers.



There have been and will continue to be sizeable fiscal transfers from West to East Germany.



East and West Germany operate subject to the same federal law. Regional differences in the legal system and regulations are minor.



Regional differences in the educational system are minor.



Real estate is cheaper in East Germany.

Lastly, in Eastern Germany there is a net loss of population, lower fertility rates, GDP per capita only recently surpassed two-thirds of the Western level and the unemployment rate is twice as high (Demary and Röhl, 2009).

Indicator 1991 East relative to West German level (at 1991 prices)

GDP per capita 31.3

Equipment investment per person 63.6

Construction investment per person 67.2

Private consumption 50

Gross income per employee 46.7

Net wage and salary per employee 54.7

Unit labor costs 150.6

Table 1 – Indicators East/West comparison 1991. Source: Kiel Discussion Papers(1999).

Indicator 1991 Gross Value Added, East Germany, in %

Agriculture, Forestry and Fishery 3.3

Manufacturing 36.1

Trade and Transport 14.6

Service Sector 21.4

Governments, households and non-profit organizations 24.6

Table 2 – Indicators Gross Value Added for East Germany in %. Source: Kiel Discussion Papers (1999).

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22 3.2 Regional Classifications

Germany has 16 federal states in total; ten former West German and six former East German. Of those 16 states, there are three city states; Berlin, Hamburg and Bremen. However, Berlin is a special case since it is the capital and was divided into two parts until the reunification.

The data, which will be explained into more detail later, was mostly collected at the so-called “Stadt- und Landkreise”, which are city districts and counties. These regions form the basic spatial building blocks, created by either aggregation or by using spatial references from other administrative and non-administrative territorial units (INKAR); therefore the spatial units used are not directly comparable to the EUs NUTS classification.

Over the years spatial units were subject to several reforms, obviously this caused some difficulties measuring indicators over time. The BBSR developed a conversion key and I was therefore able to recalculate older data at the current spatial units and over 90% of the basic data give precise and correct results. The current division is from 31 December 2012:

An East West Germany Comparison

1

The Role of Urbanization and Agglomeration Economies in Regional Convergence