Konstantin M. Wacker’s research while affiliated with University of Groningen and other places

Purpose This paper aims at understanding the factors that are associated with disease outbreaks by investigating environmental and globalization risk factors. Design/methodology/approach As environmental factors we consider several dimensions of the Environmental Performance Index (EPI). To gauge globalization, we consider international trade flows and international flights, as well as two globalization indices accounting for economic and interpersonal globalization. Methodologically, we estimate a Poisson Random Effects model for panel data covering 195 countries over 25 years. Findings First, concerning the EPI dimensions, we find that improving sanitation and drinking water conditions are negatively and significantly correlated with the frequency of the outbreaks, whereas no robust relationship could be identified for changes in biodiversity habitat, ecosystem services, and waste management. Second, regarding the globalization factors, we find that an increase in imports and in the number of international flights are positively correlated with the number of outbreaks. Conversely, neither of the two globalization indices considered have a clear association with outbreaks. Originality The paper uses a new dataset of infectious disease outbreaks collected from the Disease Outbreak News produced by the World Health Organization (WHO) containing information on 1,462 events occurred between 1996 and 2019, covering 195 countries and 70 diseases. Practical implications In view of the results, local policy efforts in low-income countries should aim at improving sanitary conditions. This effort could be leveraged with development aid support. Once outbreaks have been detected in a location, international flight restrictions should be considered to reduce the international contagion rate. The main contribution of this paper is to provide empirical evidence for policymakers that can be used to minimize the probability of future pandemics.

Offering high-quality versions of a product provides countries with a competitive advantage. Economic measures of countries’ export quality, however, neglect that exported goods depend on foreign parts and components supplied by other countries. We provide a novel industry-level dataset of export quality that takes such global input-output linkages into account. We therefore link conventional export quality measures to input-output tables. This allows us to project out the imported intermediate input quality from overall export quality. The constructed dataset covers 76 countries and 21 industries. Our novel measure is positively correlated with countries’ income per capita and with industries’ growth of value added. Our data can be used to better understand the role of export quality for industries’ international competitiveness, macroeconomic developments, and to investigate how global supply chains matter in this context – an issue that has become apparent through Covid, war-related sanctions, and recent geopolitical restructuring of global supply chains.

Motivation angladesh's economic growth rate over the past three decades has been one of the highest in the world. This success is difficult to reconcile with a single macroeconomic explanation of development. Purpose Previous studies have investigated individual aspects that can explain Bangladesh's successful development. But a consistent account of how individual factors contributed to growth is currently missing. Can factors that correlate with growth in other countries explain the outstanding growth performance of Bangladesh? Or is the country's experience unique? Methods and approach We construct a panel data set for 149 countries since 1970 and combine growth regression techniques with a peer group comparison. Different specifications, subsamples, and estimation techniques are considered. Findings Factors correlating with growth in other countries can well‐explain the growth experience of Bangladesh. But two features are specific to the country. First, a combination of “good enough” policies led to considerable growth impulses from 1990 to 2005: the country's improvements in typical correlates of growth during this period were among the global top 5% for any 15‐year period investigated. Second, despite the absence of major reforms after 2005, Bangladesh defied the mean reversion in growth rates that most fast‐growing economies and peer countries experience. Policy implications Our results support the idea that a combination of “good enough” policies can jumpstart high growth in low‐income countries. Our findings further demonstrate that a stable macroeconomic and institutional environment helps to reap benefits of structural improvements in the long run.

Traditional poverty accounting decomposes changes in a country's poverty headcount ratio into changes in income and inequality. We argue that this approach is unsatisfactory from the perspective of policy analysis because it compares a country in two points of time without taking the country's initial situation, and hence its potential for poverty reduction, into account. We thus suggest comparing traditional poverty decompositions with a counterfactual situation. This counterfactual indicates what a country starting from its initial situation could be expected to achieve in terms of income, inequality, and, hence, poverty developments. We construct those counterfactuals by modeling income and inequality trends characterized by convergence and a “Kuznets” relationship between inequality and development. Parameters in those relationships are estimated using PovcalNet survey data from 144 countries and we construct our counterfactual poverty predictions for 71 developing countries. While there is overall a tight relationship between actual developments and counterfactuals, we identify several cases, where both deviate from each other and discuss the policy implications. We also check for commonalities in differently performing countries and find that those who fell particularly short of expectations often underwent political transition and state fragility.

This paper presents a new dataset of infectious disease outbreaks collected from the Disease Outbreak News and the Coronavirus Dashboard produced by the World Health Organization. The dataset contains information on 70 infectious diseases and 2227 public health events that occurred over the period from January 1996 to March 2022 in 233 countries and territories around the world. We illustrate the potential use of this dataset to the research community by analysing the spatial distribution of disease outbreaks. We find evidence of spatial clusters of high incidences (“hot spots”) in Africa, America, and Asia. This spatial analysis enables policymakers to identify the regions with the greatest likelihood of suffering from disease outbreaks and, taking into account their degree of preparedness and vulnerability, to develop policies that may help contain the spreading of future outbreaks. Further applications could focus on combining our data with other information sources to study, for instance, the link between environmental, globalization, and/or socioeconomic factors with disease outbreaks.

A common task in empirical economics is to estimate \emph{interaction effects} that measure how the effect of one variable X on another variable Y depends on a third variable H. This paper considers the estimation of interaction effects in linear panel models with a fixed number of time periods. There are at least two ways to estimate interaction effects in this setting, both common in applied work. Our theoretical results show that these two approaches are distinct, and only coincide under strong conditions on unobserved effect heterogeneity. Our empirical results show that the difference between the two approaches is large, leading to conflicting conclusions about the sign of the interaction effect. Taken together, our findings may guide the choice between the two approaches in empirical work.

In this paper, we empirically re‐assess the question which theoretical motives and empirical models are most suitable to explain global patterns of foreign direct investment (FDI). Compared to previous studies, we use bilateral FDI positions with a much more comprehensive coverage of emerging and developing economies, the IMF’s CDIS. We apply cross validation to assess the performance of the gravity model and the knowledge capital (KK) model and add cultural, institutional, and financial factors, as suggested by different theories on FDI determinants. We find the gravity model to achieve the best theory‐consistent out‐of‐sample prediction, particularly when parameter heterogeneity of South and North FDI is allowed for. Controlling for surrounding market potential is important to recover the horizontal effect of the gravity model. Our finding that the gravity model for FDI performs well but requires some degree of parameter heterogeneity and the inclusion of surrounding market potential provides a clear baseline for future empirical studies of FDI determinants. Inclusion of institutional, cultural, or financial factors seems less relevant and does not improve the model performance distinctly, although results for those variables are mostly in line with theoretical predictions.