Jean-Pierre Huiban
Research Director, INRA
UMR CESAER
26 Bd Dr Petitjean, BP 87999, 21079 Dijon Cedex
Tel : 03 80 77 26 90 - Fax : 03 80 77 25 71
E-mail : jean-pierre.huiban@enesad.inra.fr
and
Associate Professor and Researcher at ERUDITE
Faculté de Sciences Economiques et de Gestion
Université Paris XII
61 Avenue du Général de Gaulle
94010 Créteil
Tel : 33 01 41 78 46 66
Research fields
- Firms and Industry Spatial Location:
- Firm Labour demand
- Firms and Industries Demography
- R&D, Innovation and Firms Performances
(With the team ERUDITE, Université Paris 12)
- Determinants of innovation
- Innovation and Firms performances
Recent Publications
Articles published
- Huiban J. P. , Aubert F. , Dussol A. M. (2006), "La démographie
des établissements industriels: une différenciation entre
espaces urbains, périurbains et ruraux", Revue
d'Economie Régionale et Urbaine, 5, pp.751-779
- Blanchard P. , Huiban J. P. , Sevestre P. (2006), "R&D
and Productivity in corporate groups: an empirical investigation using
a panel of French Firms", Annales d'Economie et de
Statistique, [79-80]
- Huiban J. P., Detang-Dessendre C. , Aubert F. (2004), "Employment
and Technology: Does Space Matter ? Urban versus Rural Firms",
Environment and Planning A, 36, pp. 2033-2045
- Huiban J. P. , Aubert F. , Mariettaz J. (2002), "De l'urbain
vers le rural : les transferts d'établissements de l'agro-alimentaire",
Revue d'Economie Régionale et Urbaine, 3, pp. 423-448
- Huiban J. P. (2000), "Localisation spatiale et efficacité
de la firme agroalimentaire", Revue d'Economie Régionale
et Urbaine, 3, pp. 443-55
Articles in révision
-
Huiban J. P., (2009), “The spatial demography of new plants: urban creations and rural survivals”, in revision for Small Business Economics
Abstract
This paper studies the survival rate of new plants, according to their spatial location. The empirical material is drawn from a panel data set composed of more than 6 millions French plants, observed between 1993 and 2002, which provides samples of more than 300,000 new plants created each year. According to its location, each plant can be defined as either rural, periurban or urban one. A survival model is developed, introducing the location variable alongside the usual survival determinants as size, industry, and period. Estimation results clearly show the positive effect of the rural location on any kind of survival indicator. It appears to be easier for a firm to start an activity in urban areas but less difficult to survive in rural ones. While agglomeration forces easily explain the first result, the introduction of a local competition variable allows understanding the second. A negative relationship first occurs between this variable and the survival probability of new plants. But the true relationship is a U-inverted one. In rural areas, the low level of local competition favours the survival of existing firms, at least during the first years, while, in urban areas, the very high intensity of competition reduces significantly the young firm probability to survive.
- Huiban J. P., (2009), “Urban versus Rural Firms: Does Location
affects Labour Demand?”, in revision for Growth and Change
Abstract
A dynamic labor demand model is developed and estimated on 1,719 French firms in the food industries, observed over the period 1990-1997. Both descriptive statistics and estimation results (including GMM estimators) show that labor demand and its determinants vary according to firm location. Rural areas are characterized by a low adjustment speed and great sensitivity of labor demand to the labor cost. Periurban areas benefit from important economies of scale effects and from technological spillovers. Urban firms are faced with a decline in employment levels, which is mostly due to a faster adjustment of employment to the level of activity. The trade-off between agglomeration and congestion forces may explain the respective situations of both urban and periurban areas. However, the relative inertia which appears in rural areas may be analyzed in a different way, by considering the smaller number of potential opportunities which exist in these areas.
-
Huiban J. P., Musolesi A. (2009), “Innovation and Productivity in Knowledge Intensive Business Services”, in revision for Journal of Productivity Analysis
Abstract
This paper studies empirically the relationship between innovation and productivity in Knowledge Intensive Business Services using French micro data and sheds some new light about the production of knowledge. Both an innovation function and a production function augmented with dummy endogenous innovation are estimated. Three estimators which control for endogeneity of the dummy innovation are employed: the first is a maximum likelihood estimator of the equations' system while the other two are built in the instrumental variables framework. These estimators give useful complementary information because of the usual efficiency-robustness tradeoff comparing system-equations and single-equation estimators. We find that innovation is frequent in Knowledge Intensive Business Services and has a strong and positive effect on productivity. As in manufacturing, the main determinant of innovation is formal knowledge resulting from R&D or from acquisitions of equipment, patents or licenses. Finally, the use of a dynamic model provides some results on innovation persistence.
Works in progress
-
Gaigné C . , Huiban J. P. , Schmitt B . (2009), “Industry Location: Labour Costs versus vertical linkages”
-
Blanchard P., Huiban J. P., Musolesi A., Sevestre P., (2009), “Firms and innovation : to will or not to will?”
-
Blanchard P., Huiban J. P., Musolesi A., Sevestre P., (2009), “ No response and sample selection in Community Innovation Survey ”.
-
Blanchard P., Huiban J. P., Mathieu C., (2009), “Sunk Costs, Productivity Performances across Firms and Exit in the French Industries”.
Teachings
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Applied Econometrics
M2 Recherche, Université Paris 12 Créteil, 24h. -
Data panel Econometrics
M2 Pro MASERATI, Université Paris 12 Créteil, 24h,
Ecole Doctorale EGEE, Université Paris12 Créteil, 24h.