Contribution of the Common Agricultural Policy to agricultural productivity of EU regions during 2004–2012 period


The aim of this paper is to investigate the influence of Common Agricultural Policy (CAP) subsidies on agricultural (labour and total factor) productivity growth of EU regions during the period 2004–2012. The objective is to assess the impact of this policy on agricultural growth and competitiveness of regions, first in the aftermath of the fundamental reforms of the decoupling policy and second during the historic eastward enlargement of the EU, which deepened asymmetric spatial patterns and may have led to the CAP having a different spatial impact. The analysis uses an econometric approach based on an augmented Cobb-Douglas production function. The impact is proved to be mixed; positive when the change of subsidies with a 1-year lag is considered, which is related to farm strategies, and negative when the level of subsidies, which is based on reference data, is considered. In the case of the new member states, the effect is negative, confirming the CAP’s incompatibility with the agricultural structures of the area.

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  1. 1.

    In the 1980s CAP, spending was mainly on price support through market mechanisms (intervention and export subsidies), and due to this, agricultural surpluses were increased. This policy benefited large holdings (20% of agricultural holdings were taking 80% of the funds) and certain production specialisations (mainly field crops and grazing livestock farms), which led to low productivity levels and fail to exploit comparative advantages. Thus, the 1992 (MacSharry) CAP reform reduced the market price support and replaced it with producer support in the form of direct payments. Agenda 2000 introduced rural development policy as a second pillar. In the 2003, reform most direct payments were decoupled from current production as they were based on the farmer’s historical receipts, while rural development expenditure continued to increase. The 2008 Health Check continued along the path of CAP reform, and further reduced market support (European Commission 2016a; Ezcurra et al. 2010).

  2. 2.

    The DEA and SFA methods decompose total factor productivity changes into the contributions of technical change, technical efficiency change, allocative efficiency change, and scale efficiency change.

  3. 3.

    The variable was deflated to real values in 2005 prices using the index for goods and services currently consumed in agriculture (Petrick and Kloss 2012). All deflators are provided by Eurostat (2018).

  4. 4.

    Augmented with the inclusion of human capital, following Mankiw et al. (1990).

  5. 5.

    The econometric model was based on Eq. (3), so the physical capital stock sk was represented by the variable of investments (INV), the human capital stock sh due to the difficulty of measurement of investments in human capital was substituted for (following Kosfeld et al. 2006) by the indicator of the level of human capital (HUMAN), the parameters of the labour and technology growth (n+g) and of the depreciation rate δ were represented by the exogenous quantities (EXQU), the time variable(s) ηi were represented by the time dummy variable of crisis (CRISIS), and the set of conditional variables μi consists of the variables of subsidies (SUB, SUBCH) and liabilities (LIAB, LIABCH).

  6. 6.

    All deflators are region-specific (by country) and are for agricultural sector (they are not commodity-specific). Moreover, the indexes are not bilateral and thus do not allow comparisons between countries (Ball et al. 2001).

  7. 7.

    Technological heterogeneity also exists between sectors but the present analysis focuses on its spatial dimension.

  8. 8.

    It = Kt+1-(1-δ)Kt, where K is the total fixed capital stock, t is the time period, and δ is the depreciation rate (Rizov et al. 2013).

  9. 9.

    An equally common proxy of human capital used is the average years of schooling of employees (Ciccone et al. 2004).

  10. 10.

    The overall validity of instruments was tested by the Hansen test of over identifying restrictions that failed to reject the null hypothesis at the 5% confidence level for all the cases and by the difference-in-Hansen test of exogeneity of instrument subsets that verified the exogeneity and thus the appropriateness of the instruments used in the econometric model. The Arellano-Bond test also failed to reject the null hypothesis of no autocorrelation.

  11. 11.

    The hypothesis of biased and non-biased technological change has also been tested by running a simple specification model for all the cases (Ezcurra et al. 2010; Kazukauskas and Newman 2010), with explanatory variables being the level and change of subsidies, in order to be investigated whether the existence of a technologically improving environment leads to differentiated results.

  12. 12.

    The inclusion of other variables that might capture spatial asymmetries and heterogeneities such as the changes in the utilised area, economic size, volume of agricultural exports, or highly educated employment did not yield any statistically significant results in the econometric model. A more spatially disaggregated level (than FADN regions) would provide a more insightful picture.


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The authors are extremely grateful to the editor and the two anonymous referees for their constructive comments on the earlier version of the manuscript.

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Duquenne, MN., Tsiapa, M. & Tsiakos, V. Contribution of the Common Agricultural Policy to agricultural productivity of EU regions during 2004–2012 period. Rev Agric Food Environ Stud 100, 47–68 (2019).

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  • Common Agricultural Policy
  • Subsidies
  • Agricultural productivity growth
  • EU regions