Technical Efficiency of the Food Manufacturing : A Stochastic Frontier Analysis of European Firms

The European Union accounts for the world’s largest food and beverage manufacturing industry. It contributes to more than one-third of global food exports. The disruption of the supply chains of energy and fuel due to political unrest in the continent has worsened the efficiency outlook of the region’s food industry which was just starting to recover from COVID-19 shock. In this study, a stochastic frontier estimation using the translog production function was employed to assess the technical efficiency of the European Union food industry. Cross-sectional data from 1,516 food manufacturing firms of 27 European Union countries corresponding to the period 2020-2021 was used for empirical analysis. The inefficiency effect model included the factors identified in the blueprint developed by the European Commission to make the transition pathways of industrial ecosystems possible. The study estimated the input elasticities with respect to capital and labor were 0.24 and 0.47, respectively. The overall technical efficiency of the European Union food industry was estimated at 0.81. The firm size, firm age, custom and trade regulations, tax rates, bonuses related to performance and political instability emerged as statistically significant drivers of inefficiency. Expanding firm size and regulating tax rates and custom regulations are revealed to be the most influential factors to improve efficiency. Country variation is significant and a common policy independent from politics to improve micro-environment of food manufacturing firms is likely to reduce country variation according to the model results.

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Publication Details

Journal
Tropical Agricultural Research
Published
2025-04-01
DOI
https://doi.org/10.4038/tar.v36i2.8930
Primary Topic
Firm Innovation and Growth
Type
article
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article

Technical Efficiency of the Food Manufacturing : A Stochastic Frontier Analysis of European Firms

D. V. P. Prasada
Tropical Agricultural Research
Firm Innovation and Growth
article

Technical Efficiency of the Food Manufacturing : A Stochastic Frontier Analysis of European Firms

D. V. P. Prasada
article en

Abstract

The European Union accounts for the world’s largest food and beverage manufacturing industry. It contributes to more than one-third of global food exports. The disruption of the supply chains of energy and fuel due to political unrest in the continent has worsened the efficiency outlook of the region’s food industry which was just starting to recover from COVID-19 shock. In this study, a stochastic frontier estimation using the translog production function was employed to assess the technical efficiency of the European Union food industry. Cross-sectional data from 1,516 food manufacturing firms of 27 European Union countries corresponding to the period 2020-2021 was used for empirical analysis. The inefficiency effect model included the factors identified in the blueprint developed by the European Commission to make the transition pathways of industrial ecosystems possible. The study estimated the input elasticities with respect to capital and labor were 0.24 and 0.47, respectively. The overall technical efficiency of the European Union food industry was estimated at 0.81. The firm size, firm age, custom and trade regulations, tax rates, bonuses related to performance and political instability emerged as statistically significant drivers of inefficiency. Expanding firm size and regulating tax rates and custom regulations are revealed to be the most influential factors to improve efficiency. Country variation is significant and a common policy independent from politics to improve micro-environment of food manufacturing firms is likely to reduce country variation according to the model results.

Tropical Agricultural ResearchVol. 36(2)
Openalex Percentile: Top 91%
Firm Innovation and Growth
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Technical Efficiency of the Food Manufacturing : A Stochastic Frontier Analysis of European Firms — D. V. P. Prasada · Tropical Agricultural Research (2025) | TGRS Research Map | TGRS