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My dissertation focuses on economics of higher education. Specifically, I study how scientists’ social network gives indications on their later career (Chapter 1), the universities’ research performance (Chapter2), and the overall production of research outputs (Chapter3). Building on the current surge of social network analysis, all the papers are built upon networks of co-authors. This thesis contributes to the study of social networks. It documents the prevalence of research collaborations and how they impact the production of science, making a case for taking this phenomenon into account when designing funding mechanisms. Chapter 1 looks at how a researcher’s professional network influences her career path, and I specifically consider the career of young economists on the American academic market. I exploit an original dataset building from the researchers’ individual vitae and their publication records. I investigate the impact of social network on career path by looking at the correlation between early career network metrics and the quality of the institutional affiliation of the researcher. I find that the number of social ties a researcher has as well as her relative position in the research network matters for explaining career mobility and success, even when controlling for publications. Having more co-authors boost the early career, while a higher quality of publications matters on the long run. In Chapter 2, I look at the impact of inter-university partnerships on the production of research outputs.Using an original data set of scientific publications and universities’ budgets, I analyze the network of research in Spain based on the network of Spanish co- authors. I show how the growth in research productivity of Spanish institutions before the crisis was linked to the increase in universities’ budgets and in inter- university collaborations. The results show that the size of the university is the key factor to understand universities productivity. The network multiplier is significant and positive, indicating that collaboration has a positive effect. Finally, in the context of the current crisis, I am able to identify the universities that are the least productive, taking into account their own characteristics and the indirect effects of the collaborations. This analysis has clear policy implications,as the least productive universities could be targeted to minimize the impact of further budgets cuts.Finally, Chapter 3 focuses on the link between the composition of the scientists’ workforce and the amount of research produced. Using Chapter 2’s dataset enriched by a list of the applicants to the two most prestigious postdoctoral grants in Spain, I am able to identify the young researchers in the co-authorship network. I study the link between the number of young researchers and the total research output. All three chapters show how important collaborations are in the production of science. The first chapter shows how some network metrics correlate with career outcomes, giving indication on how much to engage in collaborative work. The second paper shows how network analysis can be used to produce performance rankings of universities taking into account the partnerships. Finally, the third chapter makes a case for the importance of policies targeting young scientists. Further research can be done to understand the link between competition for students and resources and the co-authorship network, or the endogenous process of career changes and changes in the network. / Doctorat en Sciences économiques et de gestion / info:eu-repo/semantics/nonPublished

Geographic clusters of firms have been extensively studied in different bodies of literature, but little attention has been paid to the process of cluster formation and its determinants. While focusing on the effects of clusters on innovations and on the productivity of firms, the literature has neglected the agency of entrepreneurs in cluster emergence. This thesis aims to contribute to the literature on clusters by studying the role of personal networks and firm networks in three aspects of the emergence of clusters: 1) the early stages of formation; 2) the creation of business relations between firms; and 3) the creation of knowledge among clustered firms. The analysis was conducted using the biotechnology sector in four geographic regions of Chile as a case study. Data on firms was collected using in-depth interviews and a survey. The analysis of the data was carried out using content analysis, multilevel estimations and econometric analysis. The results reveal three main findings. First, the personal and business relations of entrepreneurs can determine the location decisions of firms. Second, personal relations are positively associated with the emergence of formal business relations between firms. Third, the number of personal and business connections a firm has positively affects its production of knowledge, measured as patent applications and scientific journals. These findings suggest that social networks within a cluster shape its emergence and development. The results also show that the different types of networks coexisting in a cluster - personal networks, business networks and research networks, among others - affect one another and determine the development of clusters. The implications of this research may be helpful for policy-makers, professional associations and cluster managers. Activities to foster personal interaction between members of a cluster and other key actors - universities, incubators, venture capital firms, government agencies, etc. - may generate collaborations between firms that would not otherwise emerge.

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Why do we observe such dramatic differences in labour productivity across countries in the macro data? This thesis argues that the growth empirics literature oversimplifies the complexity of the production process across countries and neglects data cross-section and time-series properties, leading to bias in the empirical estimates. Chapter 1 presents two general empirical frameworks for cross-country productivity analysis and demonstrates that they encompass the growth empirics literature of the past decades. We introduce our central argument of cross-country heterogeneity in the impact of observables and unobservables on output and develop this against the background of the pertinent time-series and cross-section properties of macro panel data. Chapter 2 uses data from 48 countries to estimate manufacturing production functions. We discuss standard and novel estimators, focusing on their treatment of parameter heterogeneity and data time-series and cross-section properties. We develop the Augmented Mean Group (AMG) estimator and show its similarity to the Pesaran (2006) Common Correlated Effects (CCE) approach. Our results confirm parameter heterogeneity across countries in the impact of observable inputs on output. We check the robustness of this finding and highlight its implications for empirical measures of TFP. Chapter 3 investigates the heterogeneity of agricultural production technology using data for 128 countries. We develop an extension to the CCE estimators which allows us to suggest that TFP is structured such that countries with similar agro-climatic environment are influenced by the same unobserved factors. This finding offers a possible explanation for the failure of technology-transfer from advanced countries of the temperate 'North' to developing countries of the arid/equatorial 'South'. Our Monte Carlo simulations in Chapter 4 investigate the performance of the AMG, CCE and standard (micro-)panel estimators. Failure to account for cross-section dependence is shown to result in serious distortion of the empirical estimates. We highlight scenarios in which the AMG is biased and offer simple remedies.

This thesis consists of four separate chapters which address different aspects of inequality and income determination. The first three chapters are country-level studies which examine (1) how incomes are shaped by spatial price differences, (2) the factor income composition, and (3) enterprise size. The final chapter analyses how income inequality changed at the global level. The first chapter investigates the implications of regional price differences for earnings differentials and inequality in Germany. I combine a district-level price index with administrative earnings data from social security records. Prices have a strong equalising effect on district average wages in West Germany, but a weaker effect in East Germany and at the national level. The change in overall inequality as a result of regional price differences is small (although significant in many cases), because inequality is mostly explained by differences within rather than between districts. The second chapter is motivated by the rapid increase in top income shares in the United States since the 1980s. Using data derived from tax filings, I show that this pattern is very similar after controlling for changes in tax unit size. Over the same period as top income shares increased, the composition of these incomes changed dramatically, with the labour share rising. Using a non-parametric copula framework, I show that incomes from labour and capital have become more closely associated at the top. This association is asymmetric such that top wage earners are more likely to also receive high capital incomes, compared with top capital income recipients receiving high wages. In the third chapter, I investigate the positive cross-sectional relationship between enterprise size and earnings using panel data from Ghana. I find evidence for a significant firm size effect in matched firm-worker data and a labour force panel, even after controlling for individual fixed effects. The size effect in self-employment is stronger in the cross-section, but it is driven by individual time-invariant characteristics. The final chapter studies the global interpersonal income distribution using a newly constructed and improved database of national household surveys between 1988 and 2008. The chapter finds that the global Gini remains high and approximately unchanged at around 0.7. However, this hides a substantial change in the global distribution from a twin-peaked distribution in 1988 into a single-peaked one now. Furthermore, the regional composition of the global distribution changed, as China graduated from the bottom ranks. As a result of the growth in Asia, the poorest quantiles of the global distribution are now largely from Sub-Saharan Africa. By exploiting the panel dimension of the dataset, the analysis shows which decile-groups within countries have benefitted most over this 20-year period. In addition, the chapter presents a preliminary assessment of how estimates of global inequality are affected by the likely underreporting of top incomes in surveys.

Cette thèse a pour objectif d’étudier la façon dont les économistes ont théorisé la relation entre science, technologie et croissance économique des années cinquante à aujourd’hui. Nous identifions deux approches des liens entre science, technologie et croissance : une approche « néoclassique » et une approche « évolutionniste ». L’approche « néoclassique » considère les progrès scientifiques et technologiques comme exogènes aux processus économiques et analyse les processus de croissance comme étant soumis à des rendements constants. L’approche « évolutionniste » défend quant à elle une représentation interactionniste des liens entre science et technologie, considère les progrès technologiques et scientifiques comme étant endogènes aux processus économiques et analyse les processus de croissance comme étant soumis à des rendements croissants. Nous analysons l’émergence de ces deux approches dans les années cinquante et soixante et expliquons leur opposition avec une domination de l’approche « néoclassique » jusque dans les années quatre-vingt (Partie 1). Nous montrons ensuite que l’approche « évolutionniste » devient dominante à partir des années quatre-vingt (Partie 2). / The aim of this thesis is to examine the way economists theorized the links between science, technology, and economic growth in the post-war era. We identify two approaches of the links between science, technology, and economic growth : a “neoclassical” approach and an “evolutionary” approach. The “neoclassical” approach considers scientific and technological progress as exogenous to economic processes and makes the hypothesis of constant returns to scale. The “evolutionary” approach defends an interactive representation of the links between science and technology, considers scientific and technological progress as endogenous to economic processes, and makes the hypothesis of increasing returns to scale. We study the development of the two approaches in the fifties and sixties, and explain their opposition and the dominance of the “neoclassical” approach up to the eighties (Part 1). We then show that the “evolutionary” approach has been the most influential since the eighties (Part 2).