The long-run impact of human capital on innovation and economic growth in the regions of Europe

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The notion of human capital

This thesis focuses on human capital formation. However, what is human capital? Nobel laureate Gary S. Becker defines human capital as “the knowledge, information, ideas, skills, and health of individuals” (Becker 2002, p. 3). Alternatively, the OECD takes a more economic approach, defining this form of capital as “the knowledge, skills, competencies and other attributes embodied in individuals that are relevant to economic activity” (OECD 1998, p. 25). Thus, the most common proxies for human capital are educational variables, which are also employed in this thesis. According to Becker (2002), human capital is the most decisive type of capital in contemporary economies. He refers to studies showing that human capital accounts for over 70% of total capital accumulation in the US, representing more than a fifth of total GDP. Consequently, “[t]echnology may be the driver of a modern economy, especially of its high-tech sector, but human capital is certainly the fuel” (Becker 2002, p. 3).
Before characterising human capital in greater detail, the term itself and its history should be explained and briefly portrayed. According to one of the early proponents of the human capital concept, Nobel laureate Theodore W. Schultz, human capital “is a form of capital because it is the source of future earnings, or of future satisfactions, or of both of them. It is human because it is an integral part of man“ (Schultz 1972, p. 5). What then is so special about human capital? “The most critical attribute of human capital arises from the fact that the person and his human capital are inseparable. The person must always be present wherever the services of his human capital are being rendered” (Schultz 1972, p. 8).
Nevertheless, it must be stressed that the concept of human capital has a much longer history. The notion of a specific capital invested in human beings appears in the works of Adam Smith. For example, he makes explicit reference to the acquired abilities of individuals: “[t]he acquisition of such talents, by the maintenance of the acquirer during his education, study, or apprenticeship, always costs a real expense, which is a capital fixed and realized, as it were, in his person. Those talents, as they make a part of his fortune, so do they likewise that of the society to which he belongs. The improved dexterity of a workman may be considered in the same light as a machine or instrument of trade which facilitates and abridges labor, and which, though it costs a certain expense, repays that expense with a profit” (Smith 1776/1976, p. 265-266; Folloni 2010).
Nevertheless, Smith was not the first author to reflect on the notion of capital that is invested in human beings (see Folloni 2010). Petty had already considered factors in addition to land and population to explain the wealth of nations in the 17th century. Therefore, the value of labour should be taken into account (Petty 1690/1899). Subsequently, Cantillon also referred to the value of labour but more in the sense of the cost entailed in the maintenance of slaves and their children (Cantillon 1755/1952, see also Hofflander 1966). Smith was less interested in measuring this value than in understanding the differences in remuneration across occupations (Folloni 2010).

Attributes and effects of human capital

Human capital can be categorised according to different criteria. For example, one may refer to its attributes and effects, as emphasised by Kwon (2009). He shows that, on the one hand, Crawford (1991) distinguishes between different attributes or characteristics of human capital. These can be classified into two categories. The first category describes human capital as being self-generating and expandable. In fact, knowledge is boosted when it is used, differentiating it from other resources such as raw materials. The expansion of human capital may be due to exogenous reasons and endogenous causes. Thus, knowledge may be enlarged by the interplay of external factors such as external knowledge or information and endogenous skills or experiences.
1 One may mention the fact that human capital was chosen as the ‘non-word’ of the year by German linguists in 2004 (FAZ 2005).
Second, it is also possible to diffuse this knowledge to other agents. This shows that human capital is sharable. Finally, the last characteristic of human capital is that it is transportable because it is an integral part of its holder.
In sum, one may say that the first two characteristics (expandable, self-generating) increase human capital’s volume, while the latter two (sharable, transportable) increase its range (Crawford 1991).
On the other hand, Kwon (2009) states that human capital may have effects from the very micro level up to the macro level. In other words, it has repercussions on individuals but also on organisations and society as a whole.
He highlights this point by considering the labour market. In the internal labour market, human capital may have a positive effect on individuals’ income by increasing their productivity (e.g., Schultz 1961, Becker 1964, Schultz 1971, Lucas 1988). Evidently, employers prefer highly productive employees because they maximise the profitability of a company. They are also more mobile in the labour market than other employees. These facts allow such employees to move upwards in the hierarchical structure (Sicherman and Galor 1990). In the external labour market, the employment possibilities of an unemployed person are also affected by his human capital (e.g., Vinokur et al. 2000). For example, it is easier for individuals with a high level of human capital to access information and obtain job opportunities.

A more recent indicator: numeracy and the age heaping method

The understanding of concepts of natural numbers is unique to humans (Hauser and Spelke 2004). However, research on numeracy has only been increasing during the last decades. This is both illustrated by the recent important number of publications on the subject and by the history of the term itself. In fact, the term ‘numeracy’ only appeared at the end of the 1950s in the so-called Crowther report. This report was presented to the Ministry of Education of the United Kingdom in 1959. Its aim was to give a “mirror image of literacy” (Central Advisory Council for Education 1959, p. 269) because the term ‘literacy’ had already been well-established at that time.
Until today, policy actions often focus more on literacy than on numeracy skills (UNESCO 2005). This may be surprising because we are surrounded by numbers in our everyday life and every form of organised society has been based on numbers throughout history (Cohen 2005). Accordingly, “[h]istory records the ever-increasing need for ordinary men and women to be able to count and to do simple arithmetic” (Cohen 2005, p. 18). Therefore, numeracy is an important aspect of human capital. In consequence, it is also on today’s policy agenda. For example, it is seen to be “contributing to the empowerment, effective functioning, economic status, and well being of citizens and their communities” (Gal 2000, p. IX).
However, it is important to specify what is meant by numeracy, as the meaning of the term has been increasingly enlarged. Today, it describes the “ability to add, subtract, multiply and divide” (UNESCO 2005, p. 421). But it may also be defined as the “ability to process, interpret and communicate numerical, quantitative, spatial, statistical and even mathematical information in ways that are appropriate for a variety of contexts” (UNESCO 2005, p. 150). These different possibilities of defining numeracy reflect the non-existence of a standard and universal definition (O’Donoghue 2002).
Historical numeracy can be measured usng the age heaping method. Age heaping as such is nothing new for researchers in fields such as demography. Nevertheless, while it has been a major problem for these disciplines because it creates a bias to the true age distribution of a population, it enables researchers of human capital to approximate historical numeracy levels.
The basic idea is that there is a general phenomenon in historical censuses (and still in some censuses of current developing countries). In fact, there are more individuals who state that their age ends on the numbers 0 and 5 than on others. It is possible to show that this age heaping is primarily the result of lacking age awareness. In other words, there is an important part of individuals that did not know their own age and were not able to calculate it. This is why age heaping gives an indication of the numeracy of the population. Given the very large availability of historical censuses, measuring numeracy by applying the age heaping method allows to trace back human capital over the long run and at small territorial units. This is a major advantage with regard to other human capital proxies which are more limited in these space and time dimensions.
Why is the heaping phenomenon on zero and five by far the most important one? What is so special about these numbers? Sheets-Johnstone (1990) gives a hint in indicating that our biology predetermines us to employ our bodies for the communication with other individuals. This circumstance can partly explain the fact that counting words are related to hands and fingers in an important number of worldwide languages. For instance, in the English language the number ‘five’ comes indirectly from the ancient term for ‘fist’ which was derived from the ancestor of the Proto-Indo-European languages, the so-called Nostratic (Winter 1992, Manaster Ramer et al. 1998, Harper 2008). The biological preconditions that humans have ten fingers and ten toes may thus explain this phenomenon.


Theories of human capital, economic growth and regional development

The idea that human capital may be considered as a determinant of economic growth has a long history (see Demeulemeester and Diebolt 2011), and its key importance has increasingly been considered over the last decades. It has already been indicated that Smith and Marshall had incorporated the notion of something akin to human capital in their thinking. However, only after World War II a proper theory of human capital was developed. In particular, Becker was an important founder of this theory (e.g., Becker 1981). Initially, human capital theory was designed for microeconomics, relating incomes to human capital, but has subsequently been adapted to macroeconomics.
Later on, the relationship between human capital and growth was more directly examined. Human capital was deemed to explain whether the differences in human capital in the work force were able to give an explanation for the ‘residual’ total factor productivity (TFP) after having taken account of inputs from labour and capital (e.g., Denison 1967, Jorgenson and Griliches 1967; Schütt 2003). Nevertheless, the real surge in contributions in this issue only emerged thanks to the ‘New Growth Theory’. This new interest manifested itself by a bulk of convergence regressions in cross-country settings and the intention to reveal the fundamental causes of different growth patterns among countries. To this end, many variables have been introduced but one in particular stood out from the others: human capital (Schütt 2003).
Therefore, first, the following subsections give a very brief but not exhaustive introduction to some of the most important economic growth models that include human capital implicitly or explicitly: exogenous, endogenous and, in particular, unified growth theory.3 Then, the spatial dimension of economic growth is presented by sketching some characteristics of New Economic Geography.

Human-capital augmented Solow model

In contrast, the human-capital augmented Solow model extends the original model by introducing human capital H as an input in the aggregate production function. This function is a Cobb-Douglas production function with labour-augmenting technological progress. According to the important contribution by Mankiw et al. (1992), it can be represented in the following form: (2.2).
with Y representing output, K representing capital, H representing the human capital stock, A representing the technological level and L representing labour. Output elasticity is measured by to the input K, to H and 1- – to AL. Thus, the aggregate production function exhibits, first, constant returns to scale and, second, diminishing returns to the reproducible factors of production due to the assumption that + < 1. This model has in common with the original Solow model that both population growth and technological progress are exogenously given and that capital depreciates (Schütt 2003).
One of the main disadvantages of these exogenous growth models is that human capital is exogenously given and not endogenously determined in the model. In contrast, this is the major advancement of the endogenous growth models.

Endogenous growth models

Endogenous growth models have been particularly influential over the last decades. Romer’s work in 1986 (Romer 1986) can be named as the first important contribution in this field. These ‘new growth models’ aim at endogenising the different sources that lead to growth. In this way, the growth rate it is not exogenously given anymore but it is established within the endogenous growth model itself.
The overall category of endogenous growth models can be divided into two main approaches (Aghion and Howitt 1998; Schütt 2003). The first line of thought focuses on human capital accumulation as the main driver of growth. The second approach underlines the importance of technological change for the creation of economic growth. Both approaches will now be briefly presented.

Human capital accumulation

Lucas (1988) initiated this line of research which highlights the effects of human capital accumulation on economic growth. In his model, he assumes that the economy is constituted by identical individuals who maximise their life-time utility. These individuals, or agents, can control their degree of consumption and the time which is allocated between the acquisition of skills and work. Thus, physical capital is accumulated by the level of consumption and the future productivity is determined by the agent’s allocation of time. The corresponding production function is (2.3). with Y representing output, K representing capital, L representing labour and A representing technology. Moreover, u is the share of time that an individual allocates to work (and thus 1-u is the fraction that he allocates to the accumulation of human capital) and ha represents the average human capital that exists in this economy. Note that Lucas assumes that A is constant and population growth is exogenous. However, perhaps the most fundamental assumption concerns the relationship between 1-u and the rate at which human capital grows, i.e., . He assumes that this relationship is linear, that is (2.4).
where denotes the maximum level of growth that h can achieve. This is also called the “productivity of schooling” (Aghion and Howitt 1998, p. 330). In consequence, the level of human capital does not determine its growth rate. In simple terms, independent of the amount of already accumulated human capital, a certain effort always leads to an identical growth rate of human capital. More intuitively, one may explain this result by evoking the fact that already acquired skills make it easier to learn (Romer 2001). Acquiring skills is not subject to diminishing returns so that growth in human capital is unlimited in this model. This effect endogenously generates growth, growth being dependent on and 1-u (Schütt 2003).

Table of contents :

1. Introduction
1.1 Human capital, economic growth and regional analysis
1.2 Aim and contribution of the thesis
1.3 Outline of the thesis
2. Methodological background
2.1 Three-dimensional approach
2.2 Definition and measurement of human capital
2.2.1 The notion of human capital
2.2.2 Attributes and effects of human capital
2.2.3 Proxies of human capital
2.3 Theories of human capital, economic growth and regional development
2.3.1 Origins
2.3.2 Exogenous growth models
2.3.3 Endogenous growth models
2.3.4 Unified Growth Theory
2.3.5 New Economic Geography
2.4 Definition of the regional level in Europe
2.5 Appendix
2.5.1 Tables
2.5.2 Figures
3. Are you NUTS? The factors of production and their long-run evolution in Europe from a regional perspective
3.1 Introduction
3.2 Brief overview of economic growth models
3.3 Methodology and data
3.4 Results
3.4.1 Land
3.4.2 Capital and labour
3.4.3 Technological progress
3.4.4 Human capital
3.5 Conclusion
3.6 Appendix
3.6.1 Figures
4. How to measure human capital? The relationship between numeracy and literacy
4.1 Introduction
4.2 Literature
4.2.1 Literacy
4.2.2 Literacy and numeracy
4.2.3 Numeracy
4.2.4 Some economic and social implications
4.3 Data
4.4 Methodology
4.5 Results
4.6 Conclusion
4.7 Appendix
4.7.1 Data
4.7.2 Tables
4.7.3 Figures
5. Regional inequality in human capital formation in Europe, 1790 – 1880
5.1 Introduction
5.2 Economic differences between European countries in the 19th century
5.3 Human capital and education in 19th century Europe
5.4 Deriving age heaping from historical censuses
5.5 Data
5.6 Results
5.6.1 The development of human capital in the European countries .
5.6.2 Taking a closer look at the educational differences in Europe .
5.6.3 Regional differences at the European level
5.6.4 Inequalities of regional human capital distribution
5.7 Conclusion
5.8 Appendix
5.8.1 Data
5.8.2 Tables
5.8.3 Figures
6. ‘Keep them ignorant.’ Did inequality in land distribution delay regional numeracy development?
6.1 Introduction
6.2 Literature review
6.2.1 Economic growth and inequality
6.2.2 The economic and social effects of large farms: the example of England
6.3 Data
6.3.1 Overview
6.3.2 Regional land distribution in Europe
6.4 Results
6.4.1 OLS models
6.4.2 Instrumental Variable Models
6.4.3 Comparison of our results with other data
6.5 Conclusion
6.6 Appendix
6.6.1 Data
6.6.2 Tables
6.6.3 Figures
7. Spatial clustering of human capital in the European regions .
7.1 Introduction
7.2 Evolution of basic education in the European regions
7.3 Data
7.4 Exploratory Spatial Data Analysis
7.5 Results
7.5.1 Global spatial autocorrelation
7.5.2 Moran scatter plots
7.5.3 Moran significance maps
7.5.4 Robustness checks
7.6 Conclusion
7.7 Appendix
7.7.1 Data
7.7.2 Tables
7.7.3 Figures
8. Remoteness equals backwardness? Human capital and market access in the European regions: insights from the long run
8.1 Introduction
8.2 Regional human capital formation in Europe, today and in the past
8.3 NEG and the economic geography of Europe
8.4 Theoretical model
8.5 Data and methodology
8.6 Results
8.7 Conclusions
8.8 Appendix
8.8.1 Tables
8.8.2 Figures
9. Regional human capital formation in Europe in the long run, 1850 – 2010
9.1 Introduction
9.2 Human capital formation in Europe in the (very) long run
9.3 Methodology and data
9.4 Results
9.4.1 Evolution of human capital in the European regions, 1850-2010
9.4.2 Evolution of intranational inequality
9.5 Conclusion
9.6 Appendix
9.6.1 Data
9.6.2 Tables
9.6.3 Figures
10. The long-run impact of human capital on innovation and economic growth in the regions of Europe
10.1 Introduction
10.2 Literature
10.3 Methodology and data
10.4 Results
10.4.1 Regional economic development, innovation and human capital in the European regions today
10.4.2 The influence of historical human capital on regional economic development and innovation today in the European regions
10.5 Conclusion
10.6 Appendix
10.6.1 Data
10.6.2 Tables
10.6.3 Figures
11. Summary, policy recommendations and directions for future research
12. References 


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