This dissertation studies the role of occupation-specific human capital in explaining the long-run decline in labor market dynamics observed in the United States for the past four decades. Chapter 1 presents empirical facts on labor market outcomes by required occupation-specific training. This is to provide evidence that (i) required length of occupation-specific training is a proxy for the specificity of human capital to perform the occupation and that (ii) increasing occupation specificity has led to the decline in labor market dynamics. First, I find from the Dictionary of Occupational Titles and O*NET that for the past four decades, within occupations, there has been an increase the amount of time needed to become trained in the occupation. I then find from the Survey of Income and Program Participation that the average wage loss experienced by occupation switchers after unemployment increases when their occupation held before unemployment has faced over time an increase in occupation-specific training. I take this as evidence that the observed increase in occupation-specific training over time has made human capital less transferable across occupations. I then proceed to use the Monthly Current Population Survey, combined with the required length of occupation-specific training by occupation from the Dictionary of Occupational Titles and O*NET, to do a shift-share decomposition of the decline in labor market outcomes. The decline in the aggregate job separation rate and the increase in unemployment duration is accounted for mostly by the increase in specific training within occupations. Motivated by my empirical analysis, in Chapter 2, I then build a search-and-matching model to learn how the increase in specificity within occupations explains the decline in the aggregate job separation rate. The main ingredients are endogenous job separations and occupation-specific human capital that workers acquire during employment and lose when they switch occupations. My model has two occupation specificity parameters: (i) the average duration of occupation-specific training and (ii) the output gap by which nontrained workers are less productive because they have not yet acquired the occupation-specific capital. To ask my model how much of a decline it predicts in the aggregate job separation rate when occupations become more specific, the occupation specificity parameters in the model are increased to match the increase in occupation specificity in the data. The increase in the average duration of occupation-specific training matches the required length of occupation-specific training from the Dictionary of Occupational Titles and O*NET. The increase in the output gap is informed by the estimated increase in the wage penalty faced by occupation switchers (relative to non-occupation switchers) when their previously held occupation requires more occupation-specific training, obtained from the Survey of Income and Program Participation. The model predicts 60% of the decline in the aggregate job separation rate. Chapter 3 relaxes the assumption that occupation switching is exogenous in Chapter 2, endogenizing occupation switching in addition to job separations. The model predicts a greater increase in the average unemployment duration in line with the data. In the model, the longer unemployment spells are due to the unemployed trained workers, whose human capital has become more specific to their previous occupation, choosing not to switch occupations. If they switch occupations, they could quickly end their unemployment spell. This would however come at the cost of larger wage cuts because their human capital has become less transferable to a different occupation. Occupation switchers would also have to earn these lower wages for a longer period of time until they become trained in their new occupation. Hence, despite a low probability of getting reemployed in the same occupation as before, previously trained workers increasingly choose not to switch occupations, which increases the average unemployment duration.

本文在分析不同改革運動中的成敗關鍵，文中分析日本明治維新、中國大躍進、文化大革命及改革開放中不同經濟因素之相互影響。文中從歷史中歸納出兩種對工業化及經濟發展最重要的因素：農業生產力及教育水平。日本明治維新及中國文化大革命時農業生產力及教育水平不斷上升，導至社會工業化及經濟發展加速；大躍進時則只有教育水平上升，農業生產力提高，故此工業化最後失敗及經濟水平低下。而把這模型套在改革開放亦同樣有效： 在改革開放時，農業生產力、教育水平、工業化及經濟發展四個指標同時上升。 / Inspired by the Meiji Restoration in Japan and Cultural Revolution in China, we constructed a theoretical model that explained economic performance of both historical episodes. We argued that the two elements: agricultural productivity and human capital are vital for industrialization and hence important for economic growth. In Meiji Restoration and Cultural Revolution, agricultural productivity and human capital both increase. The employment share moving from agricultural sector to non-agricultural sector and positive economic growth were recorded during those two periods of time. The model can also be used to explain the post-reform economic performance in China. We also find qualitative evidence that the lack of one element - agricultural productivity - will not contribute to a successful industrialization and may have adverse effect in economic growth. / Detailed summary in vernacular field only. / Cheung, Ting Yuen Terry. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 40-45). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts also in Chinese. / English Abstract --- p.i / Chinese Abstract --- p.ii / Acknowledgement --- p.iii / Chapter I. --- Introduction --- p.1 / Chapter II. --- Historical Facts --- p.7 / Chapter A. --- Two Historical Episodes with Both Elements --- p.7 / Meiji Restoration --- p.7 / Cultural Revolution --- p.11 / Chapter B. --- A Historical Episode Lack of one Element --- p.14 / Chapter C. --- Summary Note and Application --- p.17 / Chapter III. --- Model --- p.19 / Chapter A. --- Model without Capital --- p.19 / The Environment --- p.19 / Optimization --- p.21 / Comparative Statics --- p.25 / Chapter B. --- Model with Capital --- p.28 / Closed Economy --- p.28 / Small Open Economy --- p.34 / Chapter IV. --- Conclusion and Further Research --- p.35 / Remark for Further Research --- p.37 / Reference --- p.39 / Appendix --- p.45

Using 1980 - 2012 annual data, the study empirically investigates the dynamic relationship between financial development and economic growth in three developing countries (South Africa, Brazil and Kenya) and three developed countries (United States of America, United Kingdom and Australia). The study was motivated by the current debate regarding the role of financial development in the economic growth process, and their causal relationship. The debate centres on whether financial development impacts positively or negatively on economic growth and whether it Granger-causes economic growth or vice versa. To this end, two models have been used. In Model 1 the impact of bank- and market-based financial development on economic growth is examined, while in Model 2 it is the causality between the two that is explored. Using the autoregressive distributed lag (ARDL) bounds testing approach to cointegration and error-correction based causality test, the results were found to differ from country to country and over time. These results were also found to be sensitive to the financial development proxy used. Based on Model 1, the study found that the impact of bank-based financial development on economic growth is positive in South Africa and the USA, but negative in the U.K – and neither positive nor negative in Kenya. Elsewhere the results were inconclusive. Market-based financial development was found to impact positively in Kenya, USA and the UK but not in the remaining countries. Based on Model 2, the study found that bank-based financial development Granger-causes economic growth in the UK, while in Brazil they Granger-cause each other. However, in South Africa, Kenya and USA no causal relationship was found. In Australia the results were inconclusive. The study also found that in the short run, market-based financial development Granger-causes economic growth in the USA but that in South Africa and Brazil, the reverse applies. On the other hand bidirectional causality was found to prevail in Kenya in the same period. / Economics / DCOM (Economics)

Using 1980 - 2012 annual data, the study empirically investigates the dynamic relationship between financial development and economic growth in three developing countries (South Africa, Brazil and Kenya) and three developed countries (United States of America, United Kingdom and Australia). The study was motivated by the current debate regarding the role of financial development in the economic growth process, and their causal relationship. The debate centres on whether financial development impacts positively or negatively on economic growth and whether it Granger-causes economic growth or vice versa. To this end, two models have been used. In Model 1 the impact of bank- and market-based financial development on economic growth is examined, while in Model 2 it is the causality between the two that is explored. Using the autoregressive distributed lag (ARDL) bounds testing approach to cointegration and error-correction based causality test, the results were found to differ from country to country and over time. These results were also found to be sensitive to the financial development proxy used. Based on Model 1, the study found that the impact of bank-based financial development on economic growth is positive in South Africa and the USA, but negative in the U.K – and neither positive nor negative in Kenya. Elsewhere the results were inconclusive. Market-based financial development was found to impact positively in Kenya, USA and the UK but not in the remaining countries. Based on Model 2, the study found that bank-based financial development Granger-causes economic growth in the UK, while in Brazil they Granger-cause each other. However, in South Africa, Kenya and USA no causal relationship was found. In Australia the results were inconclusive. The study also found that in the short run, market-based financial development Granger-causes economic growth in the USA but that in South Africa and Brazil, the reverse applies. On the other hand bidirectional causality was found to prevail in Kenya in the same period. / Economics / D. Com. (Economics)

Chapter 1: The Determinants of the Production of Research by US Universities<p>Chapter 2: Investment in Vocational and General Human Capital: A Theoretical Approach<p>Chapter 3: Urban Migrations and the Labor Market<p>Chapter 4: Local social capital and geographical mobility<p>Chapter 5: Social Supervision and Electoral Stability on the Geographical Scale in Belgium / Doctorat en Sciences économiques et de gestion / info:eu-repo/semantics/nonPublished

EXECUTIVE SUMMARY<p><p>This thesis aims at analysing some aspects of Venture Capital (VC) and high-tech entrepreneurship. The focus is both at the macroeconomic level, comparing venture capital from an international point of view and Technology-Based Small Firms (TBSF) at company and founder’s level in Belgium. The approach is mainly empirical.<p>This work is divided into two parts. The first part focuses on venture capital. First of all, we test the impact of VC on productivity. We then identify the determinants of VC and we test their impact on the relative level of VC for a panel of countries.<p>The second part concerns the technology-based small firms in Belgium. The objective is twofold. It first aims at creating a database on Belgian TBSF to better understand the importance of entrepreneurship. In order to do this, a national survey was developed and the statistical results were analysed. Secondly, it provides an analysis of the role of universities in the employment performance of TBSF.<p>A broad summary of each chapter is presented below.<p><p>PART 1: VENTURE CAPITAL<p><p>The Economic Impact of Venture Capital<p><p>The objective of this chapter is to perform an evaluation of the macroeconomic impact of venture capital. The main assumption is that VC can be considered as being similar in several respects to business R&D performed by large firms. We test whether VC contributes to economic growth through two main channels. The first one is innovation, characterized by the introduction of new products, processes or services on the market. The second one is the development of an absorptive capacity. These hypotheses are tested quantitatively with a production function model for a panel data set of 16 OECD countries from 1990 to 2001. The results show that the accumulation of VC is a significant factor contributing directly to Multi-Factor Productivity (MFP) growth. The social rate of return to VC is significantly higher than the social rate of return to business or public R&D. VC has also an indirect impact on MFP in the sense that it improves the output elasticity of R&D. An increased VC intensity makes it easier to absorb the knowledge generated by universities and firms, and therefore improves aggregate economic performance.<p><p>Technological Opportunity, Entrepreneurial Environment and Venture Capital Development<p><p>The objective of this chapter is to identify the main determinants of venture capital. We develop a theoretical model where three main types of factors affect the demand and supply of VC: macroeconomic conditions, technological opportunity, and the entrepreneurial environment. The model is evaluated with a panel dataset of 16 OECD countries over the period 1990-2000. The estimates show that VC intensity is pro-cyclical - it reacts positively and significantly to GDP growth. Interest rates affect the VC intensity mainly because the entrepreneurs create a demand for this type of funding. Indicators of technological opportunity such as the stock of knowledge and the number of triadic patents affect positively and significantly the relative level of VC. Labour market rigidities reduce the impact of the GDP growth rate and of the stock of knowledge, whereas a minimum level of entrepreneurship is required in order to have a positive effect of the available stock of knowledge on VC intensity.<p><p>PART 2: TECHNOLOGY-BASED SMALL FIRMS<p><p>Survey in Belgium<p><p>The first purpose of this chapter is to present the existing literature on the performance of companies. In order to get a quantitative insight into the entrepreneurial growth process, an original survey of TBSF in Belgium was launched in 2002. The second purpose is to describe the methodology of our national TBSF survey. This survey has two main merits. The first one lies in the quality of the information. Indeed, most of national and international surveys have been developed at firm-level. There exist only a few surveys at founder-level. In the TBSF database, information both at firm and at entrepreneur-level will be found.<p>The second merit is about the subject covered. TBSF survey tackles the financing of firms (availability of public funds, role of venture capitalists, availability of business angels,…), the framework conditions (e.g. the quality and availability of infrastructures and communication channels, the level of academic and public research, the patenting process,…) and, finally, the socio-cultural factors associated with the entrepreneurs and their environment (e.g. level of education, their parents’education, gender,…).<p><p>Statistical Evidence<p><p>The main characteristics of companies in our sample are that employment and profits net of taxation do not follow the same trend. Indeed, employment may decrease while results after taxes may stay constant. Only a few companies enjoy a growth in both employment and results after taxes between 1998 and 2003.<p>On the financing front, our findings suggest that internal finance in the form of personal funds, as well as the funds of family and friends are the primary source of capital to start-up a high-tech company in Belgium. Entrepreneurs rely on their own personal savings in 84 percent of the cases. Commercial bank loans are the secondary source of finance. This part of external financing (debt-finance) exceeds the combined angel funds and venture capital funds (equity-finance).<p>On the entrepreneur front, the preliminary results show that 80 percent of entrepreneurs in this study have a university degree while 42 percent hold postgraduate degrees (i.e. master’s, and doctorate). In term of research activities, 88 percent of the entrepreneurs holding a Ph.D. or a post-doctorate collaborate with Belgian higher education institutes. Moreover, more than 90 percent of these entrepreneurs are working in a university spin-off.<p><p>The Contribution of Universities to Employment Growth<p><p>The objective of this chapter is to test whether universities play a role amongst the determinants of employment growth in Belgian TBSF. The empirical model is based on our original survey of 87 Belgian TBSF. The results suggest that both academic spin-offs and TBSF created on the basis of an idea originating from business R&D activities are associated with an above than average growth in employees. As most ‘high-tech’ entrepreneurs are at least graduated from universities, there is no significant impact of the level of education. Nevertheless, these results must be taken with caution, as they are highly sensitive to the presence of outliers. Young high-tech firms are by definition highly volatile, and might be therefore difficult to understand.<p><p>CONCLUSION<p><p>In this last chapter, recommendations for policy-makers are drawn from the results of the thesis. The possible interventions of governments are classified according to whether they influence the demand or the supply of entrepreneurship and/or VC. We present some possible actions such as direct intervention in the VC funds, interventions of public sector through labour market rigidities, pension system, patent and research policy, level of entrepreneurial activities, bankruptcy legislation, entrepreneurial education, development of university spin-offs, and creation of a national database of TBSF.<p> / Doctorat en Sciences économiques et de gestion / info:eu-repo/semantics/nonPublished