Agricultural and Rural Labour Markets in the EU Candidate Countries of Croatia, Former Yugoslav Republic of Macedonia and Turkey. Factor Markets Working Paper No. 6, September 2011

This paper provides an overview and comparison of labour markets in agricultural and rural areas in the three candidate countries for the EU membership: Croatia, the Former Yugoslav Republic of Macedonia and Turkey. We analyse and compare the labour market structures and the factors driving them. The analyses are based on the available cross-section and time-series data on agricultural labour structures and living conditions in rural areas. Considerable differences are found among the candidate countries in the importance of the agricultural labour force, between rural and urban labour, and in poverty and living conditions in rural areas. Agricultural and rural labour market structures are the result of demographic and education processes, in addition to labour flows between agricultural and non-agricultural activities, from rural areas to urban ones and migration flows abroad. Declines in the agricultural labour force and rural population are foreseen for each of the candidate countries, but with significant variations between them. Showing different patterns over time, labour market developments in the sector and rural areas have been shaped by the overall labour market institutions, conditions and other factors in each country, such as the legal basis, educational attainment and migration flows, as well as the presence of non-agricultural activities in rural areas.

The Valuation of Agricultural Land and the Influence of Government Payments. Factor Markets Working Paper No. 10, December 2011

This study gives an overview of the theoretical foundations, empirical procedures and derived results of the literature identifying determinants of land prices. Special attention is given to the effects of different government support policies on land prices. Since almost all empirical studies on the determination of land prices refer either to the net present value method or the hedonic pricing approach as a theoretical basis, a short review of these models is provided. While the two approaches have different theoretical bases, their empirical implementation converges. Empirical studies use a broad range of variables to explain land values and we systematise those into six categories. In order to investigate the influence of different measures of government support on land prices, a meta-regression analysis is carried out. Our results reveal a significantly higher rate of capitalisation for decoupled direct payments and a significantly lower rate of capitalisation for agri-environmental payments, as compared to the rest of government support. Furthermore, the results show that taking theoretically consistent land rents (returns to land) and including non-agricultural variables like urban pressure in the regression implies lower elasticities of capitalisation. In addition, we find a significant influence of the land type, the data type and estimation techniques on the capitalisation rate.

A Spatial Analysis of Agricultural Land Prices in Bavaria. Factor Markets Working Document No. 50, June 2013

This paper empirically analyses a dataset of more than 7,300 agricultural land sales transactions from 2001 and 2007 to identify the factors influencing agricultural land prices in Bavaria. We use a general spatial model, which combines a spatial lag and a spatial error model, and in addition account for endogeneity introduced by the spatially lagged dependent variable as well as other explanatory variables. Our findings confirm the strong influence of agricultural factors such as land productivity, of variables describing the regional land market structure, and of non-agricultural factors such as urban pressure on agricultural land prices. Moreover, the involvement of public authorities as a seller or buyer increases sales prices in Bavaria. We find a significant capitalisation of government support payments into agricultural land, where a decrease of direct payments by 1% would decrease land prices in 2007 and 2001 by 0.27% and 0.06%, respectively. In addition, we confirm strong spatial relationships in our dataset. Neglecting this leads to biased estimates, especially if aggregated data is used. We find that the price of a specific plot increases by 0.24% when sales prices in surrounding areas increase by 1%.