arXiv subjects

Nadja van 't Hoff

Publications and source records attributed to Nadja van 't Hoff.

4 recordsLinked to original sources

arXiv2026

Gender Differences in Healthcare Utilisation: Causal Evidence from Unexpected Adverse Health Shocks

Women live longer than men yet report worse health. One common reading of this male-female health-survival paradox is that women also engage more with healthcare. We challenge that reading with causal evidence from the plausibly random timing of first-time non-fatal heart attacks and strokes in Danish administrative data. After such a shock, men increase their statin use and their general-practitioner visits substantially more than women. We find no evidence that the gap reflects a lower willingness among women to seek or take up care: men and women fill the same number of prescriptions, but women receive lower doses per fill, and the gap widens across drug classes where physicians have more discretion over what to prescribe. This points to provider behaviour rather than patient demand. Despite the additional treatment, men fare no better than women on mortality or morbidity, or on the labour-market outcomes we can measure. Given women's general survival advantage, this suggests women might have fared even better had they been treated as intensively as men. The gap arises even within a universal healthcare system and a country with comparatively low gender inequality, so removing barriers to access is not by itself sufficient to close it.

econ.GN↗

arXiv2026

Group-Level Treatment Effect Heterogeneity in Difference-in-Differences: A Balanced Approach

Understanding how treatment effects vary across groups is central to policy evaluation. In Difference-in-Differences designs, heterogeneity is often studied using subgroup or triple-difference analyses, which can suffer from conservative inference, reliance on parametric interaction structures, and sensitivity to differences in covariate distributions across groups. We propose the Balanced Group Average Treatment Effect on the Treated (BGATT), a new estimand that isolates heterogeneity in treatment responses from differences in covariate composition and is identified under standard conditional parallel-trends assumptions. BGATT provides a transparent target for comparing group-specific treatment effects. We derive an influence-function representation and develop estimators that are $\sqrt{n}$-consistent and asymptotically normal under flexible machine-learning estimation of high-dimensional nuisance components, enabling valid inference on both group-specific effects and differences across groups. Simulation evidence shows favorable finite-sample performance.

econ.EM↗

arXiv2024

Nudging Nutrition: Lessons from the Danish "Fat Tax"

In October 2011, Denmark introduced the world's first and, to date, only tax targeting saturated fat. However, this tax was subsequently abolished in January 2013. Leveraging exogenous variation from untaxed Northern-German consumers, we employ a difference-in-differences approach to estimate the causal effects of both the implementation and repeal of the tax on consumption and expenditure behavior across eight product categories targeted by the tax. Our findings reveal significant heterogeneity in the tax's impact across these products. During the taxed period, there was a notable decline in consumption of bacon, liver sausage, and cheese, particularly among low-income households. In contrast, expenditure on butter, cream, and margarine increased as prices rose. Interestingly, we do not observe any difference in expenditure increases between high and low-income households, suggesting that the latter were disproportionately affected by the tax. After the repeal of the tax, we do not observe any significant decline in consumption. On the contrary, there was an overall increase in consumption for certain products, prompting concerns about unintended consequences resulting from the brief implementation of the tax. Finally, we find strong evidence on an overall increase purchases of butter abroad for households living less than 50 km from the German boarder but we do not find strong evidence of spatial heterogeneous effects of the tax.

econ.GN↗

arXiv2024

Identifying Causal Effects of Discrete, Ordered and ContinuousTreatments using Multiple Instrumental Variables

Inferring causal relationships from observational data is often challenging due to endogeneity. This paper provides new identification results for causal effects of discrete, ordered and continuous treatments using multiple binary instruments. The key contribution is the identification of a new causal parameter that has a straightforward interpretation with a positive weighting scheme and is applicable in many settings due to a mild monotonicity assumption. This paper further leverages recent advances in causal machine learning for both estimation and the detection of local violations of the underlying monotonicity assumption. The methodology is applied to estimate the returns to education and assess the impact of having an additional child on female labor market outcomes.

econ.EM↗