Work in Progress
Navigating Complex Written Instructions: Physician-Patient Communication and Health Outcomes
This paper investigates how the complexity of written hospital discharge instructions affects patient outcomes. Complexity is captured with the SMOG index, which gauges the years of education required to comprehend the text. I link SMOG-based measures to key post-discharge indicators, including 28-day readmissions, emergency department visits, and mortality, controlling for individual characteristics, clinical diagnoses, and discharge locations, with patient-level clustered standard errors. Findings indicate that more complex instructions significantly increase 28-day mortality: requiring one additional year of schooling corresponds to a 3.1% rise in mortality. This effect emerges around three days post-discharge and remains elevated over the subsequent month. For readmissions, complexity follows a U-shaped pattern: moderate levels near the sample mean result in higher 28-day unplanned readmissions than either simpler or more complex instructions. This pattern aligns with scenarios where low-literacy patients struggle with moderately difficult instructions, while higher-literacy patients can navigate more complex material. Socioeconomic status, a proxy for health literacy, suggests that complexity burdens disadvantaged patients to a greater extent, though interaction terms are not statistically significant. Overall, aligning discharge instructions with patients’ comprehension abilities may improve adherence, reduce preventable readmissions, and enhance patient welfare.
Long-term Effects of Growing Up with a Disabled Sibling (with Maria Hofbauer Pérez and Felicia Stokke)
Growing up with a disabled sibling presents unique challenges and opportunities for non-disabled siblings. While these siblings may experience reduced parental attention, they may also develop enhanced cognitive empathy and independence — skills valuable in adulthood. This paper investigates the long-term effects of growing up with a disabled sibling on labor market outcomes using comprehensive Swedish administrative data. We employ two identification strategies: a matched difference-in-differences analysis comparing families with and without a disabled child, and a within-family comparison that controls for unobservable time-invariant family characteristics. Our findings reveal that although individuals with disabled siblings perform worse in school — evidenced by lower grades in ninth-grade exit exams — their earnings at age 35 do not significantly differ from those without disabled siblings. To reconcile these findings, we explore three potential channels: parental time investment, financial resources, and the formation of social preferences. Notably, we find that individuals with disabled siblings are more likely to pursue careers in social sectors, indicating a shift in occupational choice rather than a lasting deficit in human capital accumulation. This study contributes to the literature by providing the first empirical evidence on the long-term labor market impacts of growing up with a disabled sibling. Our results suggest that living with a disabled sibling during formative years may influence career trajectories, supporting the hypothesis that personal experiences shape occupational preferences.
Testing identification in mediation and dynamic treatment models (with Martin Huber and Lukas Laffers) - arXiv Preprint
We propose a test for the identification of causal effects in mediation and dynamic treatment models that is based on two sets of observed variables, namely covariates to be controlled for and suspected instruments, building on the test by Huber and Kueck (2022) for single treatment models. We consider models with a sequential assignment of a treatment and a mediator to assess the direct treatment effect (net of the mediator), the indirect treatment effect (via the mediator), or the joint effect of both treatment and mediator. We establish testable conditions for identifying such effects in observational data. These conditions jointly imply (1) the exogeneity of the treatment and the mediator conditional on covariates and (2) the validity of distinct instruments for the treatment and the mediator, meaning that the instruments do not directly affect the outcome (other than through the treatment or mediator) and are unconfounded given the covariates. Our framework extends to post-treatment sample selection or attrition problems when replacing the mediator by a selection indicator for observing the outcome, enabling joint testing of the selectivity of treatment and attrition. We propose a machine learning-based test to control for covariates in a data-driven manner and analyze its finite sample performance in a simulation study. Additionally, we apply our method to Slovak labor market data and find that our testable implications are not rejected for a sequence of training programs typically considered in dynamic treatment evaluations.
Household Labor Supply Elasticities: Evidence from Cross-Border Workers (with Sarah Lein, Andreas Peichl, Kurt Schmidheiny, and Peter Zorn)
After the Swiss National Bank unexpectedly abandoned a minimum exchange rate policy in 2015, the Swiss franc appreciated by more than 10 percent against the Euro. The appreciation implied a sudden increase in real wage incomes for over 40,000 Germancross-border commuters into Switzerland because these workers consume in Euros with a nominal wage in Swiss Francs. We use this exchange rate shock to estimate the own-wage and cross-spouse labor supply elasticities from administrative tax returns data and find a 5% drop in taxable income in CHF for cross-border workers and a 3% reduction in taxable income for cross-border worker spouses. We provide evidence for intensive margin adjustments in hours worked consistent with these estimates. This suggests that the uncompensated labor supply elasticity is small but negative and that joint household decisions matter for labor supply.