The Importance of Social and Government Learning in Ex Ante Policy Evaluation

We provide two methodological insights on \emph{ex ante} policy evaluation for macro models of economic development. First, we show that the problems of parameter instability and lack of behavioral constancy can be overcome by considering learning dynamics. Hence, instead of defining social constructs as fixed exogenous parameters, we represent them through stable functional relationships such as social norms. Second, we demonstrate how agent computing can be used for this purpose. By deploying a model of policy prioritization with endogenous government behavior, we estimate the performance of different policy regimes. We find that, while strictly adhering to policy recommendations increases efficiency, the nature of such recipes has a bigger effect. In other words, while it is true that lack of discipline is detrimental to prescription outcomes (a common defense of failed recommendations), it is more important that such prescriptions consider the systemic and adaptive nature of the policymaking process (something neglected by traditional technocratic advice)

Frictional Unemployment on Labor Flow Networks

We develop an alternative theory to the aggregate matching function in which workers search for jobs through a network of firms: the labor flow network. The lack of an edge between two companies indicates the impossibility of labor flows between them due to high frictions. In equilibrium, firms' hiring behavior correlates through the network, generating highly disaggregated local unemployment. Hence, aggregation depends on the topology of the network in non-trivial ways. This theory provides new micro-foundations for the Beveridge curve, wage dispersion, and the employer-size premium. We apply our model to employer-employee matched records and find that network topologies with Pareto-distributed connections cause disproportionately large changes on aggregate unemployment under high labor supply elasticity

Labor Flows and the Aggregate Matching Function: A Network-Based Test Using Employer-Employee Matched Records

The assumption of aggregate matching functions in labor markets is tested using a network configuration model for directed multigraphs. We use employer-employee matched records of the universe of employees and firms in Finland and find that aggregate matching functions, even at the level of submarkets, cannot explain the vast majority of the observed patterns of labor flows between firms. Our findings suggest the need for theoretical frameworks that take into account the structure of labor market frictions

Uncovering Vote Trading Through Networks and Computation

We develop a new methodological framework for the empirical study of legislative vote trading. Building on the concept of reciprocity in directed weighted networks, our method facilitates the measurement of vote trading on a large scale, while estimating the micro-structure of trades between individual legislators. In principle, it can be applied to a broad variety of voting data and refined for various specific contexts. It allows, for example, to study how vote trading in a specific legislative assembly varies over time. We validate our method with a computational model in which we control the level of vote trading. Finally, we demonstrate our framework in an analysis of four decades of roll call voting in the U.S. Congress

Synergistic Small Worlds that Drive Technological Sophistication

Advanced economies exhibit a high degree of sophistication in the creation of various products. While critical to such sophistication, the nature and underlying structure of the interactions taking place inside production processes remain opaque when studying large systems such as industries or entire economies. Using partial information decomposition, we quantify the nature of these interactions, allowing us to infer how much innovation stems form specific input interactions and how they are structured. These estimates yield a novel picture of the nuanced interactions underpinning technological sophistication. By analyzing networks of synergistic interactions, we find that more sophisticated industries tend to exhibit highly modular small-world topologies; with the tertiary sector as its central connective core. Countries and industries that have a well-established connective core and specialized modules exhibit higher economic complexity and output efficiency. Similar modular networks have been found to be responsible for maintaining a balance between integration and segregation of information in the human brain, suggesting a universal principle underlying the organization of sophisticated production processes

How does government expenditure impact sustainable development? Studying the multidimensional link between budgets and development gaps

Abstract We develop a bottom-up causal framework to study the impact of public spending on high-dimensional and interdependent policy spaces in the context of socioeconomic and environmental development. Using data across 140 countries, we estimate the indicator-country-specific development gaps that will remain open in 2030. We find large heterogeneity in development gaps, and non-linear responses to changes in the total amount of government expenditure. Importantly, our method identifies bounds to how much a gap can be reduced by 2030 through sheer increments in public spending. We show that these structural bottlenecks cannot be addressed through expenditure on the existing government programs, but require novel micro-policies intended to affect behaviors, technologies, and organizational practices. One particular set of bottlenecks that stands out relates to the environmental issues contained in the sustainable development goals 14 and 15

Revealing the Anatomy of Vote Trading

Cooperation in the form of vote trading, also known as logrolling,is central for law-making processes, shaping the development of democratic societies. Empirical evidence of logrolling is scarce and limited to highly specific situations because existing methods are not easily applicable to broader contexts. We have developed a general and scalable methodology for revealing a network of vote traders, allowing us to measure logrolling on a large scale. Analysis on more than 9 million votes spanning 40 years in the U.S. Congress reveals a higher logrolling prevalence in the Senate and an overall decreasing trend over recent congresses, coincidental with high levels of political polarization. Our method is applicable in multiple contexts, shedding light on many aspects of logrolling and opening new doors in the study of hidden cooperation