an empirical analysis of the determinants of the rural development policy spending

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An empirical analysis of the determinants of the Rural Development

policy spending for Human Capital

Beatrice Camaioni1, Valentina Cristiana Materia2

1. DEAR, Università degli Studi della Tuscia, Viterbo, Italy2.Department of Economics, Università Politecnica delle Marche, Ancona, Italy

122nd European Association of Agricultural Economists Seminar

Evidence-Based Agricultural and Rural Policy MakingMethodological and Empirical Challenges of Policy Evaluation

February 17th – 18th, 2011, Ancona (Italy)

associazioneAlessandroBartola studi e ricerche di economia e di politica agraria

Centro Studi Sulle Politiche Economiche, Rurali e AmbientaliUniversità Politecnica delle Marche

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122nd EAAE Seminar, February 17th – 18th , 2011, Ancona (Italy)

Outline

A. The aim of the paperB. The Human Capital (HC) policy

overview in Rural Development (RD) plans regional analysis of HC expenditure

C. Empirical analysis D. Concluding remarks

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A. The aim of the paper

Analyse the distribution of the Rural Development (RD) expenditure for specific measures related to Human Capital across EU

Investigate which factors weigh more in determining the expenditure for the Human Capital policy of the EU regions (Nuts 2 level)

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B. The Human Capital (HC) policy EU 2020 strategy:

– smart growth (education, knowledge and innovation)– sustainable growth (a resource-efficient, greener and more

competitive economy)– inclusive growth (high employment and economic, social

and territorial cohesion)

RD policy framework:Generational change, training and education, and advisory services are associated with the enhancement of human capital in order to pursue the objective of competitiveness (Axis 1)

» Vocational training and information actions (111)» Setting up of young farmers (112)» Early retirement (113)» Use of advisory services (114)» Setting up of management, relief and advisory services (115)

Human capital and knowledge transfer

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Overview in RD plans (1)

Programming period 2007-2013– 96.1 billion euro EAFRD available for RD

policy

• 44.5% to Axis 2 – Agro-environment• 33.6% to Axis 1 – Competitiveness• 13.3% to Axis 3 – Diversification, • 5.9% to Axis 4 – Leader • 2% to Technical assistance

– HC: 7.8% of the entire budget for RD policy

71% Physical Capital and Innovation 23% HC and Knowledge transfer2% Food&Processing modernisation,

Innovation&Quality4% other Axis 1 measures

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Overview in RD plans (2)

0 2 4 6 8 10 12 14 16 18

Germany

Slovakia

Luxembourg

Estonia

Austria

United Kingdom

Latvia

Czech Republic

Romania

Malta

Finland

Hungary

Ireland

the Netherlands

Portugal

Bulgaria

Sweeden

Denmark

Slovenia

Italy

Total EU-27 (EAFRD)

Cyprus

Spain

Greece

Belgium

Lithuania

France

Poland

EU-27: 7,8%

Relative importance of HC budget on total RD policy

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Overview in RD plans (3)Member States allocation for HC measures

0% 20% 40% 60% 80% 100%

BelgiumBulgaria

Czech RepublicDenmarkGermany

EstoniaIrelandGreece

SpainFrance

ItalyCyprusLatvia

LithuaniaLuxembourg

HungaryMalta

the NetherlandsAustriaPoland

PortugalRomaniaSloveniaSlovakiaFinland

SweedenUnited Kingdom

Total EU-27 (EAFRD)

Vocational training and information actions Setting up of young farmersEarly retirement Use of advisory servicesSetting up of management, relief & adv. services

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Regional analysis of HC expenditure Divergences btw MS may reflect:

– Difficulties in terms of capacity of spending?– “Administrative” consequence? – Legitimate political choice?

The emerging picture for EU-15:

– The Continental regions show the highest capacity of spending and the highest value of HC expenditure/holdings

– The Northern regions show the highest value of HC expenditure/AWU

– The Southern regions show lagging value for both the indicators (but NOT Spain and Italy)

AT, BE, DE, FR, LU, NL

DK, FI, IE, SE, UK

GR, PT

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HC expenditure/holdings

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HC expenditure/AWU

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C. The empirical analysisWhich factors might determine the differences btw regions in

terms of spending for HC? Do they really explain the emerging distribution of expenditure?

A set of relevant socio-economic (baseline and impact) indicators selected from CMEF:

Dependent variable: HC expenditure (thousand euro) Year: 2007-2008 Several estimation attempts (OLS)

INDEPENDENT VARIABLE DESCRIPTION CMEF INDICATORS

GDP_PPS_PC GDP per capita in Purchasing Power Standards (PPS) (EU-27 = 100)

Economic development GVA_AGR

Gross Value Added in primary sector (millions of euro)

MANGER_EDU_AGR Percentage of managers with basic or full agricultural training

Training and Education

AGE_RATIO_35_55 Ratio between the number of farmers under 35 and the number of farmers over 55 (percentage)

Age structure

LAB_PROD Labour productivity (GVA/AWU) Labour productivity AWU Labour force Annual working units

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Some interesting findings...

First attempt of estimation: – we use the only CMEF indicators… but:

• Significant: Age ratio (+) and % managers with a basic or full agricultural training (-)

• Not significant: GDP and GVA/AWU

Second attempt:– we use a “proxy” for lab. Productivity... but:

• Significant: GDP, AWU, age ratio, % managers with a basic or full agricultural training

• Not significant: GVA

At regional level, are there other variables, in any way related to CMEF, significant and influent

as it seems?

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Results of the last estimationVARIABLES

COEFFICIENT (STD ERROR)

P>|z|

GDP_PPS_PC 45,81 ** 0,004

(15,63)

GVA_AGR -2,023 ** 0,004

(0,702)

AWU 0,158 ** 0,000

(0,026)

AGE_RATIO_35_55 134,51 ** 0,000

(36,31)

UAA 0,002 ** 0,001

(0,000)

FARMS -0,076 ** 0,001

(0,021)

RURAL -18,55 0,983

(887,5)

CONVERG 337,43 0,780

(1205,9)

CONS_ -6873,2 ** 0,002

(2212,2)

Number of observations: 212

R2: 0,4588

Adj R2: 0,4375

The age structure is the main factor of influence (+)

The fact that a region is Rural or Converg. seems not significant

AWU (+), UAA (+) GVA is not

significant (-)

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D. Concluding remarks (1)

Although the relevance of the HC issue in light of the EU 2020 challenges, the budget dedicated to this policy is relative low (7.8%) with respect to the entire budget for the RD policy (2007-

2013)

No homogeneity btw the EU countries in terms of spatial distribution of the spending for HC: Member States with a lower budget profile on HC, tend to invest in more complex and time consuming measures (vocational training), while countries allocating more funds to the HC policy invest more in generational turnover measures ( “premium” measures: early retirement and setting up of young farmers)

The empirical estimations demonstrate that at regional level the variable strictly associated to HC as suggested by the CMEF are not relevant

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D. Concluding remarks (2)

Rather, other variables, in any way related to agriculture, are relevant in the decision of spending:

... age structure and AWU are obviously relevant, in fact, they reflect the target of the beneficiaries the measures analysed are addressed to

... but also the UAA, as indicator of the importance of agriculture in the regions, and the number of holdings have a great impact

TO DO...

– extend this analysis to a longer series of data covering several years

– repeat the analysis distinguishing by measures– apply an estimation by GWR techniques, in order to test the

spatial effects

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Thank you for your attention

[email protected] [email protected]

an empirical analysis of the determinants of the rural development policy spending

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