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The Macroeconomic Dimensions of Credit: A Comprehensive Analysis of Finance, Inequality and Growth
(2024)
Schumpeter's monetary growth theory is particularly influential for the modern understanding of the macroeconomic role of banks and credit. Based on this theory, this dissertation examines the macroeconomic role of the financial system, especially credit, in (1) generating economic growth, (2) directing economic resources and (3) distributing wealth.
Chapter 3 first shows empirically that 1) there is a positive correlation between the growth of credit and economic growth, even for developed countries, 2) no empirical correlation between household saving and economic growth can be established, and 3) there are both positive, negative and insignificant effects of credit on economic growth at country-specific level. Thus, there is broad empirical support for Schumpeter's monetary hypotheses.
A particularly interesting application of Schumpeter's growth theory can be seen in China. The results of the empirical study suggest that there is generally a positive correlation between credit and economic growth in China, that is, however, not linear in terms of regions, time and size of the financial system. Furthermore, the results in Chapter 4 suggest that credit-financed industrial policy in China may have contributed to more investment and GDP growth, although there are non-linearities between individual industries and types of companies.
Finally, Chapter 5 raises the question of the role of the financial system in the distribution of wealth. While credit to households and companies, together with indicators of working and saving behavior and the age structure of the population, are the most important determinants of wealth inequality, there are also various non-linearities in the relationship between credit and wealth inequality, including in relation to the level of development of financial systems and home ownership ratios.
This study describes the Chinese growth model over the past 40 years. We show that China's growth model, with its dominant role of the banking system and "the banker", is a perfect illustration of the necessity and power of Schumpeter's "monetary analysis". This approach has allowed us to elaborate theoretically and empirically the uniqueness of the Chinese model. In our empirical analysis, we use a new dataset of Chinese provincial data to analyze the impact of the financial system, especially banks, on Chinese economic development. We also empirically assess the role of the financial system in Chinese industrial policy and provide case studies of the effects of industrial policy in specific sectors. Finally, we also discuss macroeconomic dimensions of the Chinese growth process and lessons that can be drawn from the Chinese experience for other countries.
This dissertation contributes to the empirical analysis of economic development. The continuing poverty in many Sub-Saharan-African countries as well as the declining trend in growth in the advanced economies that was initiated around the turn of the millennium raises a number of new questions which have received little attention in recent empirical studies. Is culture a decisive factor for economic development? Do larger financial markets trigger positive stimuli with regard to incomes, or is the recent increase in their size in advanced economies detrimental to economic growth? What causes secular stagnation, i.e. the reduction in growth rates of the advanced economies observable over the past 20 years? What is the role of inequality in the growth process, and how do governmental attempts to equalize the income distribution affect economic development? And finally: Is the process of democratization accompanied by an increase in living standards? These are the central questions of this doctoral thesis.
To facilitate the empirical analysis of the determinants of economic growth, this dissertation introduces a new method to compute classifications in the field of social sciences. The approach is based on mathematical algorithms of machine learning and pattern recognition. Whereas the construction of indices typically relies on arbitrary assumptions regarding the aggregation strategy of the underlying attributes, utilization of Support Vector Machines transfers the question of how to aggregate the individual components into a non-linear optimization problem.
Following a brief overview of the theoretical models of economic growth provided in the first chapter, the second chapter illustrates the importance of culture in explaining the differences in incomes across the globe. In particular, if inhabitants have a lower average degree of risk-aversion, the implementation of new technology proceeds much faster compared with countries with a lower tendency towards risk. However, this effect depends on the legal and political framework of the countries, their average level of education, and their stage of development.
The initial wealth of individuals is often not sufficient to cover the cost of investments in both education and new technologies. By providing loans, a developed financial sector may help to overcome this shortage. However, the investigations in the third chapter show that this mechanism is dependent on the development levels of the economies. In poor countries, growth of the financial sector leads to better education and higher investment levels. This effect diminishes along the development process, as intermediary activity is increasingly replaced by speculative transactions. Particularly in times of low technological innovation, an increasing financial sector has a negative impact on economic development. In fact, the world economy is currently in a phase of this kind. Since the turn of the millennium, growth rates in the advanced economies have experienced a multi-national decline, leading to an intense debate about "secular stagnation" initiated at the beginning of 2015. The fourth chapter deals with this phenomenon and shows that the growth potentials of new technologies have been gradually declining since the beginning of the 2000s.
If incomes are unequally distributed, some individuals can invest less in education and technological innovations, which is why the fifth chapter identifies an overall negative effect of inequality on growth. This influence, however, depends on the development level of countries. While the negative effect is strongly pronounced in poor economies with a low degree of equality of opportunity, this influence disappears during the development process. Accordingly, redistributive polices of governments exert a growth-promoting effect in developing countries, while in advanced economies, the fostering of equal opportunities is much more decisive.
The sixth chapter analyzes the growth effect of the political environment and shows that the ambiguity of earlier studies is mainly due to unsophisticated measurement of the degree of democratization. To solve this problem, the chapter introduces a new method based on mathematical algorithms of machine learning and pattern recognition. While the approach can be used for various classification problems in the field of social sciences, in this dissertation it is applied for the problem of democracy measurement. Based on different country examples, the chapter shows that the resulting SVMDI is superior to other indices in modeling the level of democracy. The subsequent empirical analysis emphasizes a significantly positive growth effect of democracy measured via SVMDI.