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The Restructuring of the European Pharmaceutical Industry between 2000 and 2018

100%
Nagy B.
Comparative Economic Research. Central and Eastern Europe
|
2021
|
vol. 24
|
issue 2
103-118
PL
Aby sprostać wymaganiom globalnej konkurencji, Unia Europejska (UE) kładzie szczególny nacisk na rozwój opartych na wiedzy, innowacyjnych branż. Przemysł farmaceutyczny, jako dział produkcji zaawansowanych technologii, ma w Europie długą tradycję. Jednak rozkład zatrudnienia i wartości dodanej w przemyśle farmaceutycznym nie jest równomierny w obrębie Unii, a jego rozwój w czasie jest również zróżnicowany. W niniejszym artykule dokonano analizy zmian struktury przemysłu farmaceutycznego w Unii Europejskiej w oparciu o grupy państw. Porównano rozwój zatrudnienia w branży farmaceutycznej w latach 2000–2018 w trzech grupach państw. Użyto prostej metody dekompozycji, aby oddzielić wpływ wzrostu sektora i zmiany wydajności pracy na zmiany zatrudnienia w przemyśle farmaceutycznym, aby dowiedzieć się, do jakiego stopnia podobnie ewoluowała ta branża w różnych grupach państw. Z analizy wynika, że o ile w 12 państwach będących członkami UE przed 2004 r. (Core EU) zatrudnienie nieznacznie wzrosło jednocześnie ze znacznym wzrostem wartości dodanej, to dziewięć państw postsocjalistycznych (PS9) osiągnęło łącznie nieco większy wzrost wartości dodanej przy znacznym wzroście zatrudnienia. W międzyczasie cztery państwa Grupy Wyszehradzkiej (V4) osiągnęły wzrost wartości dodanej podobny do PS9, ale jeszcze większy wzrost zatrudnienia. Wskazuje to na koncentrację części przemysłu farmaceutycznego o wyższej produktywności pracy w państwach Core UE, podczas gdy w słabiej rozwiniętych państwach postsocjalistycznych rozwija się część przemysłu farmaceutycznego o niższej wydajności pracy.
EN
To meet the requirements of global competition, the European Union (EU) places particular emphasis on the development of knowledge‑intensive, innovative industries. The pharmaceutical industry, as a high‑tech manufacturing subsection, has a long tradition in Europe. However, the distribution of pharmaceutical industry employment and value added is not even within the Union, and its temporal dynamics is also different. In the present paper, I examine the change of the structure of the pharmaceutical industry within the Union using country groups. I compare the development of pharmaceutical industry employment in the period between 2000 and 2018 in three country groups. I use a simple decomposition method to separate the effects of sector growth and labor productivity change on the change of pharmaceutical employment to find out how similarly this industry evolved in the different country groups. The analysis shows that while in the 12 original, i.e., pre–2004, member states (Core EU), employment slightly increased alongside a considerable increase in value added, the nine post‑socialist countries (PS9) achieved slightly greater value added expansion combined with substantial employment growth. Meanwhile, the four Visegrád countries (V4) achieved a value added growth similar to the PS9, but an even greater employment growth. This indicates that the part of the pharmaceutical industry operating with higher labor productivity is concentrating in the Core EU countries, while in the less developed post‑socialist countries, the part of the pharmaceutical industry with lower labor productivity is developing.
2

Hyperbolic Discounting and Economic Policy

100%
Nagy B.
Review of Economic Perspectives
|
2010
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vol. 10
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issue 3
71-86
EN
Economic policy-making often entails comparison between immediate costs and flows of future benefits or immediate benefits and series of future costs. Economics has a tool to handle such comparisons: the present- and future value calculations and the net present value rule. Experimental economics, however, has strongly criticised the method of exponential discounting applied in such calculations. Based on experiments for the sake of more psychological realism, they propose alternative methods to the exponential model: hyperbolic and quasi-hyperbolic discounting models.The present paper has a twofold objective: first, to review these different models and the relationships between them to show how the different models will yield different results when calculating and comparing present values of a single future payment, but even more if we compare present values of flows of future payments. The literature has not yet employed the hyperbolic and quasi-hyperbolic models for such calculations. Second, I point out why it is important to heed the findings of experimental economics especially in the field of economic policy-making.
3
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Google and Apple mobility data as predictors for European tourism during the COVID-19 pandemic: A neural network approach

39%
Nagy B., Gabor M. R., Bacoș I. B., Kabil M., Zhu K., Dávid L. D.
Equilibrium. Quarterly Journal of Economics and Economic Policy
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2023
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vol. 18
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issue 2
419-459
EN
Research background: The COVID-19 pandemic has caused unprecedented disruptions to the global tourism industry, resulting in significant impacts on both human and economic activities. Travel restrictions, border closures, and quarantine measures have led to a sharp decline in tourism demand, causing businesses to shut down, jobs to be lost, and economies to suffer. Purpose of the article: This study aims to examine the correlation and causal relationship between real-time mobility data and statistical data on tourism, specifically tourism overnights, across eleven European countries during the first 14 months of the pandemic. We analyzed the short longitudinal connections between two dimensions of tourism and related activities. Methods: Our method is to use Google and Apple's observational data to link with tourism statistical data, enabling the development of early predictive models and econometric models for tourism overnights (or other tourism indices). This approach leverages the more timely and more reliable mobility data from Google and Apple, which is published with less delay than tourism statistical data. Findings & value added: Our findings indicate statistically significant correlations between specific mobility dimensions, such as recreation and retail, parks, and tourism statistical data, but poor or insignificant relations with workplace and transit dimensions. We have identified that leisure and recreation have a much stronger influence on tourism than the domestic and routine-named dimensions. Additionally, our neural network analysis revealed that Google Mobility Parks and Google Mobility Retail & Recreation are the best predictors for tourism, while Apple Driving and Apple Walking also show significant correlations with tourism data. The main added value of our research is that it combines observational data with statistical data, demonstrates that Google and Apple location data can be used to model tourism phenomena, and identifies specific methods to determine the extent, direction, and intensity of the relationship between mobility and tourism flows.
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