[Paper Review] Political influence and corporate profits: a study of Hungarian firms
The paper assesses political rent seeking in Hungary during the 2010s by comparing profit shares of the 1,000 largest Hungarian firms across 2008–2012 and 2019–2023, identifying sectors with increased rents and contrasting results with Czech firms.
This paper investigates the extent of political rent seeking in Hungary in the 2010s. Political capitalism--where powerful private interests influence public policy for private gain--creates opportunities for rent seeking that vary across sectors. The analysis is based on a theoretical model assuming rent seeking occurs in a three-stage process: changes in economic institutions granting regulatory privileges, which are enhanced by political-business networks; this leads to scarcities, and increased market power in certain markets; which then generates rents. To quantify this, the study evaluates Hungarian political capitalism by examining the impact of political decisions on firms' rents, analysing the profit trends of the 1,000 largest Hungarian firms (selected annually by net sales) and comparing their mean profit share (earnings before tax) across two periods: 2008-2012 and 2019-2023. A significant increase in a sector's mean profit share was assumed to indicate increased rent seeking. Using Welch's two-sample t-tests, three sectors were identified as potentially experiencing increased rent seeking: agriculture, construction, and financial and insurance activities. Quantitative findings include a 320% increase in mean agricultural profit share (70% in mean ROA), a more than fivefold increase in construction mean profit share (mean ROA from 3.3% to 10.1%), and a more than 6.5 times increase in financial sector mean profit share. Furthermore, a similar Czech analysis showed no significant increases in any sector's profit share, suggesting that the detected rises in Hungarian sectors are linked to domestic activities rather than external factors, which strengthens the findings.
Motivation & Objective
- Investigate the extent of political rent seeking in Hungary during the 2010s.
- Model how political decisions, regulatory privileges, and business networks translate into rents.
- Quantify sectoral changes in firm profits to infer Rent-Seeking dynamics.
- Compare Hungarian results with Czech data to distinguish domestic versus external drivers.
Proposed method
- Propose a three-stage theoretical model: regulatory changes, political-business networks, leading to scarcity and market power, then rents.
- Analyze profit trends of the 1,000 largest Hungarian firms (by net sales) across two periods: 2008–2012 vs 2019–2023.
- Use mean profit share (earnings before tax) as a proxy for rents and compare across sectors.
- Apply Welch’s two-sample t-tests to identify sectors with significant increases in mean profit share.
- Contrast Hungarian sector results with a Czech analysis to assess domestic versus external factors.
Experimental results
Research questions
- RQ1Does political capitalism explain increases in sectoral firm profits in Hungary between 2008–2012 and 2019–2023?
- RQ2Which sectors show evidence of increased rent seeking according to changes in mean profit shares?
- RQ3Is the observed rise in Hungarian sector profits driven by domestic factors, or external (Czech) influences?
- RQ4How do regulatory privileges and political-business networks translate into observable profit gains for large Hungarian firms?
Key findings
- Agriculture shows a 320% increase in mean profit share (70% mean ROA).
- Construction shows more than a fivefold increase in mean profit share, with mean ROA rising from 3.3% to 10.1%.
- Financial and insurance activities show more than a 6.5 times increase in mean profit share.
- A Czech analysis shows no significant increases in any sector’s profit share, suggesting domestic factors drive the Hungarian results.
Better researchstarts right now
From reading papers to final review, dramatically reduce your research time.
No credit card · Free plan available
This review was created by AI and reviewed by human editors.