Research

Territorial aspects have not been seriously considered in the policy design of the CAP nor Pillar 2 (Zasada et al. (2018)).

The impact of rural development support of CAP on the well-being of regions in Hungary has been demonstrated to be insignificant (Bakucs, Fertő, and Benedek (2019)).

Regions that are less rural tend to have higher Pillar 2 expenditure intensity compared to more rural regions (Camaioni et al. (2014)).

Distribution of funds from other regional policies have stronger association with regional disadvantage compared to Pillar 2 (Crescenzi, Filippis, and Pierangeli (2011)).

Expected correlations

Fostering the competitiveness of agriculture: small farming, agricultural employment and investments.

Ensuring the sustainable management of natural resources, and climate action: organic farming.

Achieving a balanced territorial development of rural economies and communities including the creation and maintenance of employment: GDP, unemployment.

Global indicator of spatial association

Moran’s I is defined as

\[I = \frac{n}{\sum_{i=1}^{n} \sum_{j=1}^{n} w_{i,j}} \frac{\sum_{i=1}^{n} \sum_{j=1}^{n} w_{i,j}(x_i - \bar{x})(x_j - \bar{x})}{\sum_{j=1}^{n}(x_i - \bar{x})^2}.\]

In case of queen contiguity \(n = {\sum_{i=1}^{n} \sum_{j=1}^{n} w_{i,j}}\), thus Moran’s I is \[I = \frac{\sum_{i=1}^{n} \sum_{j=1}^{n} w_{i,j}(x_i - \bar{x})(x_j - \bar{x})}{\sum_{j=1}^{n}(x_i - \bar{x})^2}.\]

Local indicator of spatial association (LISA)

First defined by Anselin (1995) as

\[I_i = \frac{(x_i - \bar{x})}{(x_i - \bar{x})^2/n}\sum_{j}w_{i,j}(x_j - \bar{x}).\]

Pearson’s correlation coefficient

The direction of causality is unclear and theoretically multidirectional.

\[r = \frac{\sum_{i=1}^{n}(x_i-\bar{x})(y_i-\bar{y})}{\sum_{i=1}^{n}(x_i-\bar{x})^2 \sum_{i=1}^{n}(y_i-\bar{y})^2}\]

Global spatial autocorrelation

##  [1] ""                                                              
##  [2] "\tMoran I test under randomisation"                            
##  [3] ""                                                              
##  [4] "data:  paySf$eafrd.cap  "                                      
##  [5] "weights: spatWts  n reduced by no-neighbour observations"      
##  [6] "  "                                                            
##  [7] ""                                                              
##  [8] "Moran I statistic standard deviate = 10.116, p-value < 2.2e-16"
##  [9] "alternative hypothesis: greater"                               
## [10] "sample estimates:"                                             
## [11] "Moran I statistic       Expectation          Variance "        
## [12] "      0.458045643      -0.004048583       0.002086784 "        
## [13] ""

Higher than the 0.201 estimated by Crescenzi, Filippis, and Pierangeli (2011).