Influences of the Business Environment on Manufacturing Firms Technical Efficiencies: The Côte d’Ivoire Case

Roudaut, Nolwenn

doi:10.1007/s11123-006-7133-y

Cited by 8 publications

(8 citation statements)

References 13 publications

(9 reference statements)

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“…First, to the best of our knowledge, none of the previous analyses used such a rich dataset to simultaneously analyze the influence of numerous firm-specific and environmental factors on efficiency. Indeed, previous studies either focus on industry characteristics (e.g., Roudaut, 2006) or regional (e.g., Li and Hu, 2004), or size effects (e.g., Oczkowski and Sharma, 2005;Söderbom and Teal, 2004), aand thus provide only limited insight into the relative importance of a single influence. Second, we are not aware of any study using a representative sample of firms for the whole manufacturing sector of a national economy.…”

Section: Introductionmentioning

confidence: 99%

What Drives the Productive Efficiency of a Firm? The Importance of Industry, Location, R&D, and Size

Badunenko

Fritsch²,

Stephan

2008

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in Michael FritschFriedrich Schiller University Jena, Max Planck Institute of Economics Jena and German Institute for Economic Research (DIW Berlin), Germany (e-mail: m.fritsch@uni-jena.de) Andreas StephanJönköping International Business School, CESIS Stockholm and German Institute for Economic Research (DIW Berlin), Germany (e-mail: andreas.stephan@ihh.hj.se) AbstractThis paper investigates the factors that explain the level and dynamics of manufacturing firm productive efficiency. In our empirical analysis, we use a unique sample of about 39,000 firms in 256 industries from the German Cost Structure Census over the years 1992-2005. We estimate the efficiencies of the firms and relate them to firm-specific and environmental factors. We find that (1) about half the model's explanatory power is due to industry effects, (2) firm size accounts for another 20 percent, and (3) location of headquarters explains approximately 15 percent. Interestingly, most other firm characteristics, such as R&D intensity, outsourcing activities, or the number of owners, have extremely little explanatory power. Surprisingly, our findings suggest that higher R&D intensity is associated with being less efficient, though higher R&D spending increases a firm's efficiency over time.

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Section: Introductionmentioning

confidence: 99%

What Drives the Productive Efficiency of a Firm? The Importance of Industry, Location, R&D, and Size

Badunenko

Fritsch²,

Stephan

2008

SSRN Journal

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“…The CPI currently ranks 168 countries "on a scale from 100 (very clean) to 0 (highly corrupt)". Roudaut (2006) analyzed the influences of the business environment on manufacturing firms. Kato and Sato (2014) investigated the effects of corruption on the performance of the manufacturing sector at the state level in India.…”

Section: National Cost Factorsmentioning

confidence: 99%

Effects of Cost Factors on National Manufacturing Based on Global Perspectives

et al. 2017

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Currently, the real economy is the important basis for the development of a country, especially after the global financial crisis in 2008. Given that the manufacturing industry is the main part of the national real economy, many developed and developing countries have paid considerable attention to its significance. This study focused on cost factors given that they influence national manufacturing development. Initially, this study proposed two elements, namely, manufacturing development scale and manufacturing development level, to evaluate national manufacturing development from two aspects: quantity and quality. Subsequently, we extracted a series of cost factors on the bass of the theoretical framework and literature, including labor costs, financing costs, tax and rental costs, energy and raw materials, foreign trade exports and business environments. On the basis of the data of 13 main manufacturing countries around the world from 2000 to 2015, we tested the influence degree of each cost element index on the scale and level of national manufacturing industry development through a two-way fixed effects model and incorporated it with the development of China's manufacturing industry as a case study. Finally, we deduced the future development trend of the manufacturing industry by specifically analyzing the cost factors affecting the development of this industry and provided policy suggestions. The main innovation and contribution of this study including: to comprehensively evaluate the national manufacturing development from two aspects, namely, "quantity" and "quality"; to identify the impact of national cost of the six elements; to demonstrate and determine the extent of its impact on the development trend of manufacturing sector and carry out pre-judgment through empirical research on each indicator; to provide policy recommendations targeted for each of the indicators.

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“…First, Chapelle and Plane (2005) apply nonparametric DEA techniques and use a methodology in four steps to capture three effects: managerial, scale of production, and technical effect. Then Roudaut (2006) studies the impact of business environment on technical efficiency using a stochastic parametric method and interprets the effect of each business environment variable via the estimated parameters. Our strategy is a different from these two previous studies.…”

Section: Previous Empirical Workmentioning

confidence: 99%

“…Second, the survey was built to represent four sectors of production: agro-industries, textile, wood and metal working, and contrary to Chapelle and Plane (2005), we do not keep this segmentation and consider two subgroups of firms depending on the level of technology: Low technology sector (LT) sector and High Technology sector (HT). This classification, defined in Roudaut (2006), is motivated by two major concerns. First, as far as a production efficiency is estimated, the subsectors need to be as homogenous as possible in terms of technology.…”

Section: Previous Empirical Workmentioning

confidence: 99%

Explaining firms efficiency in the Ivorian manufacturing sector: a robust nonparametric approach

Roudaut

Vanhems

2011

J Prod Anal

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In this paper, we provide an analysis of Côte d'Ivoire firms performances and study the impact of qualitative external environmental factors on firm efficiencies. We adapt the one-step nonparametric robust methodology of Daraio and Simar 2005 to take in account qualitative environmental factors and we also compare the differences of behavior among two sub groups of firms characterized by different levels of technology. The sensitivity of our conclusions to environmental factors is analyzed using a bootstrapped test. We also check the robustness of our results upon time on two different years of observations.Keywords Firm efficiency Á Nonparametric estimation Á Conditional expected frontier of order m JEL Classification C13 Á C14 Á D20

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Influences of the Business Environment on Manufacturing Firms Technical Efficiencies: The Côte d’Ivoire Case

Abstract: C13, D20, L60, Stochastic frontiers, Business environment, Efficiency measurement, Manufacturing sector,

Cited by 8 publications

References 13 publications

What Drives the Productive Efficiency of a Firm? The Importance of Industry, Location, R&D, and Size

What Drives the Productive Efficiency of a Firm? The Importance of Industry, Location, R&D, and Size

Effects of Cost Factors on National Manufacturing Based on Global Perspectives

Explaining firms efficiency in the Ivorian manufacturing sector: a robust nonparametric approach

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