arXiv subjects

Paul Hünermund

Publications and source records attributed to Paul Hünermund.

4 recordsLinked to original sources

arXiv2025

Inventor Mobility After the Fall of the Berlin Wall

This study examines the inter-organizational and spatial mobility patterns of East German inventors following the fall of the Berlin Wall. Existing research often overlooks the role of informal institutions in the mobility decisions of inventors, particularly regarding access to and transfer of knowledge. To address this gap, we investigate the unique circumstances surrounding the dissolution of the German Democratic Republic, which caused a significant shock to establishment closures and prompted many inventors to change their jobs and locations. Our sample comprises over 25,000 East German inventors, whose patenting careers in reunified Germany post-1990 are traced using a novel disambiguation and matching procedure. Our findings reveal that East German inventors in technological fields where access to Western knowledge was facilitated by industrial espionage were more likely to pursue inter-organizational mobility and continue their inventive activities in reunified Germany. Additionally, inventors from communities with strong political support for the ruling socialist party encountered difficulties in sourcing knowledge through weak ties, resulting in a lower likelihood of continuing to patent. However, those who overcame these obstacles and continued to produce inventions were more likely to relocate to West Germany, leaving their original social contexts behind.

econ.GN↗

arXiv2024

On the Nuisance of Control Variables in Regression Analysis

Control variables are included in regression analyses to estimate the causal effect of a treatment on an outcome. In this paper, we argue that the estimated effect sizes of controls are unlikely to have a causal interpretation themselves, though. This is because even valid controls are possibly endogenous and represent a combination of several different causal mechanisms operating jointly on the outcome, which is hard to interpret theoretically. Therefore, we recommend refraining from interpreting marginal effects of controls and focusing on the main variables of interest, for which a plausible identification argument can be established. To prevent erroneous managerial or policy implications, coefficients of control variables should be clearly marked as not having a causal interpretation or omitted from regression tables altogether. Moreover, we advise against using control variable estimates for subsequent theory building and meta-analyses.

econ.EM↗

arXiv2023

Double Machine Learning and Automated Confounder Selection -- A Cautionary Tale

Double machine learning (DML) has become an increasingly popular tool for automated variable selection in high-dimensional settings. Even though the ability to deal with a large number of potential covariates can render selection-on-observables assumptions more plausible, there is at the same time a growing risk that endogenous variables are included, which would lead to the violation of conditional independence. This paper demonstrates that DML is very sensitive to the inclusion of only a few "bad controls" in the covariate space. The resulting bias varies with the nature of the theoretical causal model, which raises concerns about the feasibility of selecting control variables in a data-driven way.

econ.EM↗

arXiv2023

Causal Inference and Data Fusion in Econometrics

Learning about cause and effect is arguably the main goal in applied econometrics. In practice, the validity of these causal inferences is contingent on a number of critical assumptions regarding the type of data that has been collected and the substantive knowledge that is available. For instance, unobserved confounding factors threaten the internal validity of estimates, data availability is often limited to non-random, selection-biased samples, causal effects need to be learned from surrogate experiments with imperfect compliance, and causal knowledge has to be extrapolated across structurally heterogeneous populations. A powerful causal inference framework is required to tackle these challenges, which plague most data analysis to varying degrees. Building on the structural approach to causality introduced by Haavelmo (1943) and the graph-theoretic framework proposed by Pearl (1995), the artificial intelligence (AI) literature has developed a wide array of techniques for causal learning that allow to leverage information from various imperfect, heterogeneous, and biased data sources (Bareinboim and Pearl, 2016). In this paper, we discuss recent advances in this literature that have the potential to contribute to econometric methodology along three dimensions. First, they provide a unified and comprehensive framework for causal inference, in which the aforementioned problems can be addressed in full generality. Second, due to their origin in AI, they come together with sound, efficient, and complete algorithmic criteria for automatization of the corresponding identification task. And third, because of the nonparametric description of structural models that graph-theoretic approaches build on, they combine the strengths of both structural econometrics as well as the potential outcomes framework, and thus offer an effective middle ground between these two literature streams.

econ.EM↗