Understanding human behavior in its entire complexity is an ambitious if not impossible challenge. It is however possible to study particular aspects of human behavior through experiments that allow us to isolate specific facets in the decision-making process, ultimately leading to a better understanding of human behavior as a whole. This thesis covers three experimental articles on unethical economic behavior and sheds light on the motives and circumstances that lead individuals to engage in these activities. Clearly, unethical behavior in all its different manifestations can pose great risk to society – both at the large (e.g. corporate tax evasion) and small (e.g. shoplifting) scale – making it a relevant topic to be studied in economic research. Trying to understand unethical behavior through the lenses of traditional economic theory is problematic.

The economy is a complex system because market participants do not act independently but adjust their behavior to other agents and to the outcome which emerges from their joint actions (Arthur, 2014). Dependencies among participants can impede policy makers capabilities to influence or steer the course of the economy. Kambhu et al. (2007) argue that to influence developments in financial markets, for instance to prevent crises from spreading, there are only “coarse or indirect options” available for policy makers. Similar to crises which propagate through a complex system, interventions might result in unintended side effects which can also disseminate through the system. Thus, in a complex system, unintended consequences of policy efforts may well be the rule. Policy makers try to ward off or mitigate negative consequences for the economy and society during periods of crisis. For instance, during the Covid crisis large scale support programs for firms in Western economies were set up to avoid bankruptcies. Similarly, during the sovereign debt crisis in the Eurozone, the European Central Bank (ECB) set up large scale asset purchase programs as well as additionally longer-term refinancing operations (LTRO) which provided immediate support to financial market participants’ liquidity positions and thereby prevented a melt-down of the financial system. During these periods, immediate and abundant liquidity supply is of utmost importance. Meanwhile, crisis measures, due to their massive scale and non-specific target group, may entail unknown or unintended side effects for instance on competition among market participants, firms’ investment behavior, or changes in lending strategies and risk taking behavior of banks. Likewise, new regulatory frameworks such as the introduction of new markets can have consequences previously not thought of. For policy makers it is important to know direct effects of policy interventions but also to be aware of the possibility and impact of indirect or unexpected side effects in order to evaluate measures taken and to learn for future design of regulation or intervention.

Finance has seen a spectacular growth in innovation and internationalization during the 20th century (Lane and Milesi-Ferretti, 2008; Shiller, 2013), resulting in a complex and highly interconnected network of financial entities spanning across jurisdictions (Allen and Gale, 1994; Popov and Udell, 2012). Meanwhile the challenges grow for policymakers to provide a framework which allows for the operation of a stable finan- cial system within their jurisdiction (Merton, 1995; Allen and Gale, 2000; Morrison and White, 2009; Ongena et al., 2013). The global financial crisis highlights that se- vere disruptions in the financial sector spread across jurisdictions (Wiggins and Met- rick, 2015) and can have large negative effects on the real economy (Chodorow-Reich, 2013). To contain systemic risk and contagion in the globalized financial system, sev- eral changes have been applied to the regulatory framework of international financial markets (G20, 2009). Regulatory efforts encompass among others the implementa- tion of macroprudential policies and the introduction of mandatory central clearing of derivatives. This thesis aims to contribute to the understanding of differential effects of financial policy reforms depending on the characteristics of the regulated financial entities and networks.

Labour market institutions are policy interventions aiming to improve labour market outcomes in terms of employment and wages. However, because labour markets are complex, it is not straight forward whether these policy interventions achieve their objectives. Instead, they may result in the opposite or induce side effects. In this dissertation, the impact of minimum wages and employment protection legislation on employment are studied. The first chapter discusses labour market institutions, focusing on the above mentioned polices. The second chapter analyses the effects of the German minimum-wage introduction on employment. The findings suggest that marginal employment decreased while regular employment increased. The third chapter studies the same policy with respect to a different aspect: The minimum wage attracts labour mobility from low-wage neighbouring countries (the Czech Republic and Poland) in the respective border regions. This inflow of foreign workers does not have a negative effect on native employment. The fourth chapter addresses a shortcoming of existing NK-SAM models that allow embedding employment protection legislation. A proposed extension improves the models’ empirical relevance under a restrictive assumption.

In complex systems, statistical dependencies between individual components are often considered one of the key mechanisms which drive the system dynamics observed on a macroscopic level. In this paper, we study cross-sectional time-lagged dependencies in financial markets, quantified by nonparametric measures from information theory, and estimate directed temporal dependency networks in financial markets. We examine the emergence of strongly connected feedback components in the estimated networks, and hypothesize that the existence of information feedback in financial networks induces strong spatiotemporal spillover effects and thus indicates systemic risk. We obtain empirical results by applying our methodology on stock market and real estate data, and demonstrate that the estimated networks exhibit strongly connected components around periods of high volatility in the markets. To further study this phenomenon, we construct a systemic risk indicator based on the proposed approach, and show that it can be used to predict future market distress. Results from both the stock market and real estate data suggest that our approach can be useful in obtaining early-warning signals for crashes in financial markets.

Urban economies are composed of diverse activities, embodied in labor occupations, which depend on one another to produce goods and services. Yet little is known about how the nature and intensity of these interdependences change as cities increase in population size and economic complexity. Understanding the relationship between occupational interdependencies and the number of occupations defining an urban economy is relevant because interdependence within a networked system has implications for system resilience and for how easily can the structure of the network be modified. Here, we represent the interdependencies among occupations in a city as a non-spatial information network, where the strengths of interdependence between pairs of occupations determine the strengths of the links in the network. Using those quantified link strengths we calculate a single metric of interdependence–or connectedness–which is equivalent to the density of a city’s weighted occupational network. We then examine urban systems in six industrialized countries, analyzing how the density of urban occupational networks changes with network size, measured as the number of unique occupations present in an urban workforce. We find that in all six countries, density, or economic interdependence, increases superlinearly with the number of distinct occupations. Because connections among occupations represent flows of information, we provide evidence that connectivity scales superlinearly with network size in information networks.

To safeguard financial stability and harmonise regulation, the European Commission substantially reformed banking supervision, resolution, and deposit insurance via EU directives. But most countries delay the transposition of these directives. We ask if transposition delays result from strategic considerations of governments conditional on the state of their financial, regulatory, and political systems? Supervisors might try to protect national banking systems and local politicians maybe reluctant to surrender national sovereignty to deal with failed banks. Alternatively, intricate financial regulation might require more implementation time in large and complex financial and political systems. We therefore collect data on the transposition delays of the three Banking Union directives and investigate observed delay variation across member states. Our correlation analyses suggest that existing regulatory and institutional frameworks, rather than banking market structure or political factors, matter for transposition delays.

“Is there some action a government of India could take that would lead the Indian economy to grow like Indonesia’s or Egypt’s? If so, what, exactly? If not, what is it about the ‘nature of India’ that makes it so? The consequences for human welfare involved in questions like these are simply staggering: Once one starts to think about them, it is hard to think about anything else. (Lucas 1988, 5)”

These words by the Nobel laureate Chicago economist Robert Lucas Jr. summarize why so many great scholars found it hard to “think about anything else” and spent their careers trying to understand the process of economic growth. Economies are complex systems resulting from the actions of many actors. This complexity makes it challenging, but also infinitely interesting, to understand the determinants of economic growth. What are the roles of human capital, fertility, ideas, basic science, and public policy for growth? These are just some of the important questions that were posed by many highly influential studies featured in the issues of the Journal of Political Economy over the years. Indeed, this journal has been the platform to diffuse many of the brilliant ideas and start important debates in the field of economic growth. In this short paper, my goal is to revisit some of those seminal papers, briefly describe some of the more recent contributions, and end with some thoughts about the future direction of the field. The reader should note in advance that the list of work covered here is by no means exhaustive and mostly targets work that has been featured in issues of the JPE.

Among the central tenets of globalization is the free migration of labor. Although much has been written about the benefits of globalization, little is known about its limitations and how antiglobalist sentiment can be strongly affected by high levels of immigration. Analyzing poll data from a group of EU countries affected by the recent migrant crisis, we find that over the last three years the percentage of right-wing (RW) populist voters in a given country depends on the prevalence of immigrants in this country’s population and the total immigration inflow into the entire EU. The latter is likely due to the perception that the EU functions as a supranational state in which a lack of inner borders means that “someone else’s problem” can easily become “my problem.” We find that the increase in the percentage of RW voters substantially surpasses the percentage of immigration inflow, implying that if this process continues, ongoing democratic processes will cause RW populism to prevail and globalization to rapidly decrease. We locate tipping points between the fraction of immigrants and the rise of RW populism, and we model our empirical findings using a complex network framework in which the success of globalization rests on a balance between immigration and immigrant integration.

In this paper we study the relationship between task complexity and the occupational wage- and employment structure. Complex tasks are defined as those requiring higher-order skills, such as the ability to abstract, solve problems, make decisions, or communicate effectively. We measure the task complexity of an occupation by performing Principal Component Analysis on a broad set of occupational descriptors in the Occupational Information Network (O*NET) data. We establish four main empirical facts for the U.S. over the 1980–2005 time period that are robust to the inclusion of a detailed set of controls, subsamples, and levels of aggregation: (1) There is a positive relationship across occupations between task complexity and wages and wage growth; (2) Conditional on task complexity, routine-intensity of an occupation is not a significant predictor of wage growth and wage levels; (3) Labor has reallocated from less complex to more complex occupations over time; (4) Within groups of occupations with similar task complexity labor has reallocated to non-routine occupations over time. We then formulate a model of Complex-Task Biased Technological Change with heterogeneous skills and show analytically that it can rationalize these facts. We conclude that workers in non-routine occupations with low ability of solving complex tasks are not shielded from the labor market effects of automatization.