I'm a PhD candidate at the Vienna University for Economics and Business. My research interests lie in applied econometrics, environmental economics, and development economics.

I will be visiting the University of California, Berkeley from September until December. You can check out my CV , contact me via mail or phone , or find out more about my research , software , and teaching below.

Research and publications

I'm interested in practical econometric methods for investigating spillover effects, networks, and uncertainties, as well as issues related to deforestation and development. My ongoing work includes ...

• Kuschnig, N., Zens, G., and Crespo Cuaresma, J. (2023). Hidden in plain sight: Influential sets in linear regression. WP: .

  The sensitivity of econometric results is central to their credibility. In this paper, we investigate the sensitivity of regression-based inference to influential sets of observations and show how to reliably identify and interpret them. We explore three algorithmic approaches to analyze influential sets, and assess the sensitivity of a number of earlier studies in the field of development economics to them. Many results hinge on small influential sets, and inspecting them can provide crucial insights. The analysis of influential sets may reveal omitted variable bias, unobserved heterogeneity, lacking external validity, and informs about technical limitations of the methodological approach used.

• Kuschnig, N. (2023). Shrinkage in space — network effects in a Bayesian hierarchical model. WiP: , Slides: .

  In this paper, I present a modelling approach to jointly investigate connectivity between observations and its consequences — spillover effects. The approach is fully Bayesian, and uses hierarchical shrinkage priors to flexibly provide regularization where needed and let the data speak where it is possible. I make the prior information that is embodied in the restrictive assumptions of previous spatial models explicit, and loosen them by estimating connectivity parameters. For effective estimation, I develop efficient sampling procedures and a Gaussian process approximation to evaluate Jacobian determinants.

• Kuschnig, N. (2023). Leave-out leverage — an updating formula for the projection matrix. Report: , Code: .

  This report presents a tractable updating formula for elements of the projection matrix after one or more observations are removed. The formula is computationally cheap, and can be applied recursively to efficiently compute the leave-K-out leverage, and generalize various leave-one-out influence measures to higher orders. Implementations of the formula are made available.

• Kuschnig, N. and Vashold, L. (2023). The dynamics of cattle expansion and deforestation in the Brazilian Amazon.

Journal articles

• Kuschnig, N., Vashold, L., Soterroni, A., and Obersteiner, M. (2023). Eroding resilience of deforestation interventions — evidence from Brazil’s lost decade. Environmental Research Letters, 18(7):074039, DOI: 10.1088/1748-9326/acdfe7.

  Brazil once set the example for curtailing deforestation with command and control policies, but, in the last decade, these interventions have gone astray. Environmental research and policy today are largely informed by the earlier successes of deforestation interventions, but not their recent failures. Here, we investigate the resilience of deforestation interventions. We discuss how the recent trend reversal in Brazil came to be, and what its implications for the design of future policies are. We use newly compiled information on environmental fines in an econometric model to show that the enforcement of environmental policy has become ineffective in recent years. Our results add empirical evidence to earlier studies documenting the erosion of the institutions responsible for forest protection, and highlight the considerable deforestation impacts of this erosion. Future efforts for sustainable forest protection should be aimed at strengthening institutions, spreading responsibilities, and redistributing the common value of forests via incentive-based systems.

• Giljum, S., Wegner Maus, V., Kuschnig, N., Luckeneder, S., Tost, M., Sonter, L., and Bebbington, A. (2022). A pantropical assessment of deforestation caused by industrial mining. Proceedings of the National Academy of Sciences, 119(38):e2118273119, DOI: 10.1073/pnas.2118273119.

  Driven by rapidly increasing demand for mineral resources, both industrial mining and artisanal mining are intensifying across the tropical biome. A number of regional studies have analyzed mining-induced deforestation, but scope and patterns across all tropical countries have not yet been investigated. Focusing on industrial mining, we use geospatial data to quantify direct forest loss within mining sites in 26 countries. We also perform a statistical assessment to test whether industrial mining drives indirect deforestation in the mine surroundings. We show that direct deforestation concentrates only in a few countries, while industrial mining causes indirect deforestation in two-thirds of tropical countries. In order to preserve tropical forests, direct and indirect deforestation impacts of mining projects should be fully considered.

• Kuschnig, N. (2022). Bayesian spatial econometrics: A software architecture. Journal of Spatial Econometrics, 3(1):6–25, DOI: 10.1007/s43071-022-00023-w.

  Bayesian approaches play an important role in the development of new spatial econometric methods, but are uncommon in applied work. This is partly due to a lack of accessible, flexible software for the Bayesian estimation of spatial models. Established probabilistic software struggles with the specifics of spatial econometrics, while classical implementations do not harness the flexibility of Bayesian modelling. In this paper, I present a layered, objected-oriented software architecture that bridges this gap. An R implementation in the bsreg package allows quick and easy estimation of spatial econometric models, while remaining maintainable and extensible. I demonstrate the benefits of the Bayesian approach and using a well-known dataset on cigarette demand. First, I show that Bayesian posterior densities yield better insights into the uncertainty of non-linear models. Second, I find that earlier studies overestimate spillover effects for distance-based connectivities due to a scaling error, highlighting the need for tried and tested software.

• Kuschnig, N. and Vashold, L. (2021). BVAR: Bayesian vector autoregressions with hierarchical prior selection in R. Journal of Statistical Software, 100(14):1–27, DOI: 10.18637/jss.v100.i14.

  Vector autoregression (VAR) models are widely used for multivariate time series analysis in macroeconomics, finance, and related fields. Bayesian methods are often employed to deal with their dense parameterization, imposing structure on model coefficients via prior information. The optimal choice of the degree of informativeness implied by these priors is subject of much debate and can be approached via hierarchical modeling. This paper introduces BVAR, an R package dedicated to the estimation of Bayesian VAR models with hierarchical prior selection. It implements functionalities and options that permit addressing a wide range of research problems, while retaining an easy-to-use and transparent interface. Features include structural analysis of impulse responses, forecasts, the most commonly used conjugate priors, as well as a framework for defining custom dummy-observation priors. BVAR makes Bayesian VAR models user-friendly and provides an accessible reference implementation.

• Kuschnig, N., Crespo Cuaresma, J., Krisztin, T., and Giljum, S. (2021). Spillover effects in agriculture drive deforestation in Mato Grosso, Brazil. Scientific Reports, 11(1):1–9, DOI: 10.1038/s41598-021-00861-y.

  Deforestation of the Amazon rainforest is a threat to global climate, biodiversity, and many other ecosystem services. In order to address this threat, an understanding of the drivers of deforestation processes is required. Spillover effects and factors that differ across locations and over time play important roles in these processes. They are largely disregarded in applied research and thus in the design of evidence-based policies. In this study, we model connectivity between regions and consider heterogeneous effects to gain more accurate quantitative insights into the inherent complexity of deforestation. We investigate the impacts of agriculture in Mato Grosso, Brazil, for the period 2006–2017 considering spatial spillovers and varying impacts over time and space. Spillovers between municipalities that emanate from croplands in the Amazon appear as the major driver of deforestation, with no direct effects from agriculture in recent years. This suggests a moderate success of the Soy Moratorium and Cattle Agreements, but highlights their inability to address indirect effects. We find that the neglect of the spatial dimension and the assumption of homogeneous impacts lead to distorted inference. Researchers need to be aware of the complex and dynamic processes behind deforestation, in order to facilitate effective policy design.

• Kuschnig, N. (2021). Inadequate methods undermine a study of malaria, deforestation and trade. Nature Communications, 12(1):1–3, DOI: 10.1038/s41467-021-22514-4.

  In a recent study, Chaves et al. find international consumption and trade to be major drivers of ‘malaria risk’ via deforestation. Their analysis is based on a counterfactual ‘malaria risk’ footprint, defined as the number of malaria cases in absence of two malaria interventions, which is constructed using linear regression. In this letter, I argue that their study hinges on an obscured weighting scheme and suffers from methodological flaws, such as disregard for sources of bias. When addressed properly, these issues nullify results, overturning the significance and reversing the direction of the claimed relationship. Nonetheless, I see great potential in the mixed methods approach and conclude with recommendations for future studies.

• Bruckner, M., Wood, R., Moran, D., Kuschnig, N., Wieland, H., Maus, V., and Börner, J. (2019). FABIO — the construction of the food and agriculture biomass input-output model. Environmental Science & Technology, 53(19):11302–11312, DOI: 10.1021/acs.est.9b03554.

  Harvested biomass is linked to final consumption by networks of processes and actors that convert and distribute food and nonfood goods. Achieving a sustainable resource metabolism of the economy is an overarching challenge which manifests itself in a number of the UN Sustainable Development Goals. Modeling the physical dimensions of biomass conversion and distribution networks is essential to understanding the characteristics, drivers, and dynamics of the socio-economic biomass metabolism. In this paper, we present the Food and Agriculture Biomass Input–Output model (FABIO), a set of multiregional supply, use and input–output tables in physical units, that document the complex flows of agricultural and food products in the global economy. The model assembles FAOSTAT statistics reporting crop production, trade, and utilization in physical units, supplemented by data on technical and metabolic conversion efficiencies, into a consistent, balanced, input–output framework. FABIO covers 191 countries and 130 agriculture, food and forestry products from 1986 to 2013. The physical supply use tables offered by FABIO provide a comprehensive, transparent, and flexible structure for organizing data representing flows of materials within metabolic networks. They allow tracing of biomass flows and embodied environmental pressures along global supply chains at an unprecedented level of product and country detail and can help to answer a range of questions regarding environment, agriculture, and trade. Here we apply FABIO to the case of cropland footprints and show the evolution of consumption-based cropland demand in China, the E.U., and the U.S.A. for plant-based and livestock-based food and nonfood products.

Other publications

• Kuschnig, N. and Vashold, L. (2023). The economic impacts of malaria: past, present, future. Planetary Health, PDF: .

  Malaria places a great burden on the health and prosperity of many and occupies a great number of scientists and policymakers. The dynamics of the disease are tightly interwoven with economics — incidence is both tied to economic circumstances and impacts them. Economic research plays an important role in understanding and supporting the fight against malaria. The economic literature, however, features a number of peculiarities that can hamper accessibility and has been slow to approach interdisciplinary issues. In this chapter, we explain the economic perspective and summarise the literature on the economic impacts of malaria. Malaria has severe impacts on individual and aggregate economic outcomes, including mortality and morbidity, but also indirect burdens that materialise with a delay. The fight against malaria is not an economic policy per se, but may provide beneficial economic spillovers and can be vital in establishing an environment that allows for prosperity. Economic insights can make a difference in the design and implementation of effective and efficient eradication and control strategies. This is critical in the light of increasing disease (re-)exposure due to climate change and the emergence of resistant vectors and pathogens.

• Kramer, M., Kind-Rieper, T., Munayer, R., Giljum, S., Masselink, R., van Ackern, P., Maus, V., Luckeneder, S., Kuschnig, N., Costa, F., and Rüttinger, L. (2023). Extracted Forests: Unearthing the role of mining-related deforestation as a driver of global deforestation. WWF Report, PDF: .

Software

I'm passionate about free and open source software, and have written packages for Bayesian modelling, efficient computation, and data handling in R.

• Kuschnig, N. and Vashold, L. (2023). BVAR: hierarchical Bayesian vector autoregression. R package available on CRAN, paper at DOI: 10.18637/jss.v100.i14.
• Kuschnig, N. (2022). bsreg: Bayesian spatial regression models. R package on CRAN, paper at DOI: 10.1007/s43071-022-00023-w.
• Kuschnig, N. (2023). influence: Sensitivity to influential sets. R package on GitHub.
• Kuschnig, N. (2020). sanic: solving Ax = b nimbly in C++. R package on CRAN.
• Vashold, L. and Kuschnig, N. (2020). BVARverse: tidy Bayesian vector autoregression. R package on CRAN.

Teaching

I have taught courses at the Master's and Bachelor's level at WU, served as a teaching assistant at CEU, and organised two reading groups with various guests at WU.

• Econometrics 2 (WU Bachelor)

  Econometric methods, focused on causal inference, and supplemented with assignments that involve applied coding and prediction tasks. Topics include an introduction to statistical learning, causality, experiments, directed acyclic graphs, instrumental variables, non-linear models, maximum likelihood estimation, regularization, and methods for causal inference. Slides are available here .

• Bayesian Macroeconometrics (WU Master)
• Applied Econometrics (WU Bachelor)
• Econometrics 2 (CEU Master, TA)

Reading group

I organised and headed two reading groups at WU. The goal was for participants to discuss a range of economics- and research-related issues amongst themselves, and with experienced guests.

• Topics and guests included ...

  • evidence-based consulting with Gabriel Felbermayr,
  • the economics of automation with Klaus Prettner,
  • the Austrian micro data center with Harald Oberhofer,
  • uncertainty and growth with Jesús Crespo Cuaresma,
  • economics and politics of migration with Peter Brummund,
  • the Austrian national budget with Markus Marterbauer,
  • economic policy consulting with Christoph Badelt,
  • monetary policy in practice with Ewald Nowotny,
  • land use policy in Austria with Barbara Birli,
  • labour market experiments with Lukas Lehner & Anna Schwarz,
  • corporations and economic policy with Wilfried Altzinger,
  • new approaches to monetary policy with Lea Steininger,
  • green growth with Colleen Schneider & Thomas Neier,
  • incarceration and the racial divide with Fabian Siuda.