Agricultural rent in land-use models: comparison of frequently used proxies

ABSTRACT

Agricultural rent in land-use models: comparison of frequently used proxies. Spatial Economic Analysis. This paper compares the performance of econometric land-use models based on three proxies for agricultural land rent: farmers’ revenues, land prices and shadow land prices derived from a mathematical programming model. We consider different land-use classes (agriculture, pasture, forest, urban and other), different determinants (economic, physical and demographic) of land-use shares and different spatial econometric specifications. It is found that the inclusion of spatial components significantly improves the quality of predictions. In terms of economic interpretation, the shadow land prices provide the most stable and intuitive results.

摘要

土地使用模型中的农业地租：经常使用的代理之比较。Spatial Economic Analysis. 本文根据农业用地地租的三大代理—农夫税收，土地价格，以及从数学程式化模型推导出的影子土地价格，比较计量经济土地使用模型的表现。我们考量不同的土地使用类别（农业，放牧，森林，城市以及其他），不同的土地使用比率之决定因素（经济，物理环境与人口），以及不同的空间计量经济规范。本研究发现，纳入空间元素将能显着改善预测的质量。在经济诠释方面，影子土地价格提供了最稳定且直观的结果。

RÉSUMÉ

Loyers agricoles dans des modèles d’utilisation des terrains: comparaison entre les proxys fréquemment adoptés. Spatial Economic Analysis. Nous comparons des modèles économétriques d’usage des sols avec trois proxys de la rente agricole: revenu agricole, prix des terres et prix implicite (shadow-price) issu d’un modèle de programmation mathématique. Nous considérons cinq usages des sols (agriculture, pâturage, forêt, urbain et autre) expliqués par des variables économiques, physiques et démographiques. Les résultats montrent que: les trois proxys donnent des résultats similaires en termes de qualité de prédiction, la prise en compte des effets spatiaux améliore significativement la qualité des prédictions et le prix implicite fournit les résultats les plus stables et intuitifs de point de vue de l’interprétation économique.

RESUMEN

Alquiler agrícola en los modelos del uso del suelo: comparación de los indicadores utilizados con más frecuencia. Spatial Economic Analysis. En este artículo comparamos el rendimiento de los modelos econométricos del uso del suelo basados en tres indicadores para el alquiler de tierras agrícolas: los sueldos de los agricultores, los precios del suelo y los precios sombra del suelo derivados de un modelo de programación matemática. Consideramos diferentes clases del uso del suelo (agrícola, pastoreo, bosque, urbano y otros), diferentes determinantes (económicos, físicos y demográficos) de la participación en el uso del suelo y distintas especificaciones econométricas. Observamos que la inclusión de los componentes espaciales mejora en gran medida la calidad de las predicciones. En términos de interpretación económica, los precios sombra del suelo proporcionan los resultados más estables e intuitivos.

KEYWORDS:

• land-use-share model
• spatial dependence
• land rent
• prediction

关键词:

• 土地使用比率模型
• 空间依赖
• 地租
• 预测

MOTS-CLÉS:

• modèle de partage de l’utilisation des terrains
• dépendance spatiale
• loyer des terrains
• prédiction

PALABRAS CLAVES:

• modelo de la participación en el uso del suelo
• dependencia espacial
• alquiler del suelo
• predicción

JEL:

• C21
• Q15
• R12

ACKNOWLEDGEMENTS

The authors thank Professor Paul Elhorst for his very helpful advice as well as the anonymous reviewers for valuable comments and suggestions which greatly improved the manuscript. The authors thank Pierre-Alain Jayet (Economie Publique, INRA) for providing the data on land shadow prices and Antonello Lobianco (LEF, INRA) for providing data on forest rents. Anna Lungarska expresses her gratitude to the INRA US ODR team for their hospitality. The usual disclaimers apply.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

SUPPLEMENTAL DATA

Supplemental data for Appendix D for this article can be accessed at 10.1080/17421772.2017.1273542.

Notes

1 See Randall and Castle (1985) for a detailed presentation on the concept of land rent.

2 The economic supply-side model AROPAj (for detailed description, see Jayet et al., 2015) is based on the Farm Accountancy Data Network (FADN).

3 For more information on CLC, see http://land.copernicus.eu/pan-european/corine-land-cover/.

4 De Pinto and Nelson (2007) enumerate three types of ad-hoc corrections for spatial effects which are available in the land-use literature: spatial sampling, latitude and longitude as exogenous variables, and spatially lagged geophysical variables.

5 Logistic share models are preferred for three main reasons: (1) they ensure that the predicted share functions lie (strictly) in the interior of the 0–1 interval; (2) they are parsimonious in their parameters; and (3) they are empirically tractable thanks to the so-called log–linear transformation.

6 LeSage and Pace (2009) provide motivations for regression models that include spatial autoregressive processes.

7 For our estimations of the different spatial specifications we use the R package spdep (Bivand, Hauke, & Kossowski, 2013; Bivand & Piras, 2015).

8 Areal phenomena’s minimal mapping unit is of 25 ha and for linear phenomena the scale is 1 ha.

9 FranceAgriMer, www.franceagrimer.fr/.

10 A small agricultural region is a French territorial division and is a subdivision of the administrative regions. Their territory varies from some 1000 ha to more than 400,000 ha.

11 Agricultural rent is the remuneration of land as a factor of production. The equality between the LM associated with the total land constraint and the agricultural rent results from application of the duality theorem to the profit maximization problem. Following this approach the profit maximization problem is equivalent to the cost minimization problem. For a general description, see McFadden (1978).

12 French Rural Code, Article L411-11. In some regions this regulation is circumvented and new tenants are often obliged to pay under-the-counter former ones in order to obtain rights on the land.

13 For instance, manure could be used as a fertilizer on crops, while some biomass produced could be destined for animal feeding.

14 Inflation estimates for the period were obtained from the World Bank, http://data.worldbank.org/indicator/NY.GDP.DEFL.KD.ZG/.

15 For instance, an obligatory set-aside clause increases demand for low-quality land and consequently its rent.

16 We do not directly account for inter-equation correlation. However, as shown in Chakir and Le Gallo (2013), since the same explanatory variables are used in each equation, we do not expect this to affect our results significantly.

17 Actual values of the test are provided in Tables D2, D10 and D18 in Appendix D in the supplemental data online.

18 The complete results of the tests are provided in Tables D44 and D45 in Appendix D in the supplemental data online.

19 Full details on estimates are provided in Tables D2–D25 in Appendix D in the supplemental data online.

20 This and the following figures are available in Tables D2–D25 in Appendix D in the supplemental data online.

 

Additional information

Funding

The authors acknowledge funding from the Agence Nationale de la Recherche (ANR) under the ORACLE project [grant number ANR-10-CEPL-011] and the ModULand project [grant number ANR-11-BSH1-005].