The impact of the LEADER programme on the rural development
Name: Jeroen Bergschneider Student number: 10728678 Supervisor: P. Foldvari Date: 26-06-2018
Statement of originality
This document is written by Student Jeroen Bergschneider who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Abstract
This thesis contains an analysis of the LEADER programme for rural development. Subsidies are not always used properly, so this thesis analyses the effect of the LEADER subsidy on economic growth in rural areas. Goal of the thesis is to conclude whether the subsidy has a significant effect on rural development. The main research question is: Does the EU LEADER programme increase the rural economic growth? The hypothesis states that the subsidy has a significant effect on rural development. A multiple regression is done to answer the research question. In the regression, the growth rate of gross value added is the dependent variable which is explained by control variables and the subsidy. From the regression results it can be concluded that the subsidy has a significant positive effect on the growth rate of gross value added. With this outcome it can be concluded that the LEADER programme has a positive effect on rural development.
Table of contents
Introduction... 1
Literature review ... 2
Rural development and LEADER ... 2
Methodology ... 5
General model ... 6
Control variables ... 6
Regression model ... 8
Results... 9
Regression ...11
Conclusion ... 12
References ... 14
Appendix ... 16
Regression 1 ...16
Regression 2 ...16
Regression 3 ...17
Introduction
The last decades, European countries have developed in an economic point of view. People went to live in the cities, because demand for labour in the agricultural sector changed to the service sector. The economic growth in the rural areas was less than in the urban areas. To promote rural economic growth, the European Union (EU) started a Rural Development Programme.
Since 1991, the EU has a programme which has the goal to develop the rural area of the EU member states. This subsidy is called the LEADER program. LEADER is an acronym for the French sentence "Liaison Entre Actions de Développement de l'Économie Rurale", meaning 'Links between the rural economy and development actions' (European Commission, 2018). The way this subsidy is regulated, is by Local Action Groups (LAG’s). These groups represent the rural population. The LAG’s could apply for the LEADER subsidy by making a business plan with their ideas how they would develop the rural area (Ray C. , 2002). The last two programming periods lasted for seven years each. The most recent period is from 2014-2020, the one ahead is from 2007-2013. In the 2007-2013 period the total LEADER expenditures were 546 million euro (European Network for Rural Development, 2014) whereas the expenditures on the 2014-2020 period were almost twenty times higher: 9,5 billion euro (European Network for Rural Development, 2018). With this programme the EU wanted to develop the rural areas, but did the expenditures of respectively 546 million and 9,5 billion euro really had an effect on the rural development?
In some cases corruption can take place (Warner, 2003). Corruption can affect the effectiveness of the subsidy. Organized crime is more pervasive in the areas where the LEADER subsidy is meant for (Baronea & Narciso, 2015). This might be one of the reasons why the subsidy for rural development is not being used properly. Another reason might be that the people who receive the money from the development programme do not know how to invest the money properly. They could make the wrong decisions regarding economic risk and environment (van den Brink & den Ouden, 2012).
The goal of this thesis is to analyse the effectiveness of the EU LEADER subsidy. The effect of the subsidy on the growth rate of gross value added will be tested. However, questions have been raised about the effectiveness of the LEADER programme. The subsidy can be used improper by the people in the rural areas who receive the subsidy. This can result
in an ineffective way of developing the rural area. When the EU is aware of the ineffectiveness, they can make modifications to their programme to make it more effective.
To analyse whether the goal of the subsidy is met, the following question will be answered: Does the EU LEADER programme increase the rural economic growth? With the conclusion of this thesis the EU can consider whether their subsidy has a positive effect on the development of the rural area. If this outcome is negative, they could consider making modifications to their subsidy programme. To answer the research question, data from Eurostat is used. The question will be answered by doing a regression with the growth of Gross Value Added (GVA) as dependent variable. The LEADER subsidy is the main explanatory variable. On top of that, there are other control variables.
The remainder of this thesis will be structured as follows. In the following section the literature review will be discussed. In this section an overview of the characteristics of the rural area are described. Also information about the LEADER programme is presented. In the subsequent section the methodology is presented. This section contains information on the regression used to answer the research question. Also, information about the chosen variables is discussed. The results of the analysis are presented in the fourth section. The last section contains the conclusion and discussion.
Literature review
In this section the characteristics of rural areas are discussed. Also features of the LEADER programme are discussed in more detail and the findings of other papers. Finally, the hypothesis is stated.
Rural development and LEADER
In developing countries, a significant part of the population lives in rural area. In the rural area social services like pension plans, unemployment insurances and organized old age security does not exist. People in agriculture are often poor and face high poverty risk. This poverty risk is also weather dependent. When the harvest fails due to bad weather, the farmer has no income. This also accounts for the labourers who work in agriculture, but do not own agricultural land. When the harvest fails, these labourers do not have income. So, development of the agricultural process is a fundamental fact of life that plays a key role in the development of the rural area (Ray D. , 1998). The EU plays a role in the development of
the rural area. They have a programme for the development of the rural area. This programme is called LEADER (European Commission, 2018).
In 1991 the LEADER programme was launched by the European communities as an initiative for rural development. This programme is the key determining factor of rural development policy. The approach of the LEADER programme can be defined as follows: (a) geographically bordered, agglomeration region-minded approach; (b) bottom-up approach, essential local participation in development process; (c) formulation of new development needs and their thematic linking with all socio-economic subsystems; (d) founding a Local Action Group (LAG); (e) organising a networking system at national and community level; (f) supporting cooperation within the community or with a third country that helps the development of the LEADER region; (g) independent financial management (Ruszkai & Kovács, 2013).
In September 2005, the European Agricultural Fund for Rural Development (EAFRD), which LEADER is part of, started. They have three axes in which the objectives of this fund are divided, namely: Axis 1: Improving the competitiveness of the agricultural and forestry sector; Axis 2: Improving the environment and the countryside; Axis3: Rural development.
Axis 1 is divided in four areas:
1) Improve the potential of rural inhabitants;
2) Measures to develop physical potential of the rural inhabitants; 3) Measures to improve product and production quality;
4) Support for transitional measures in the new Member States. Axis 2 is divided in three areas:
1) Compensatory allowances for farmers living in the mountains or on other places with a handicap for agriculture;
2) Payments for agri-environmental and animal welfare measures;
3) Payments for non-productive investments which improve the ecological and social value.
The measures of axis 3 are meant for the development of the quality of life in rural areas and are divided in four axis:
1) Measures for diversification into non-agricultural activities to provide alternative income;
3) Vocational training;
4) Training on preparation and implementation of local development strategies.
The goal of agricultural policy is to ensure rural inhabitants of income, but that must not be the only objective. Such policy should also ensure that this happens in a manner by which as many people as possible benefits from the policy, and that it provides employment for lasting economic development (Reichert, 2006). To make a policy function, special groups are required for implementing the policy. In the case of LEADER these are LAG’s. They can apply for the subsidy by making a business plan. In this plan they can indicate how they will develop the rural area that they are responsible for. The LAG’s have knowledge about how to develop the rural area. The persistence of the LAG’s results in five advantages.
1) First of all, the local partnership is developed in three sectors: civil, public and private. 2) Second, the concept of subsidiarity acquires practical value.
3) Also, despite of the minimal resources that are available for the LAG’s, some significant developments are realised by the persistence of the LAG’s.
4) On top of that, the local value is grounded.
5) The fifth advantage of the LAG’s is that they can define the joint development together.
There are also opinions which state that money should not be spent on professionals of the LAG, but instead should be invested in the development. But they admit that without the professionals of the LAG, the future of the LEADER programme is in question (Ruszkai & Kovács, 2013).
Santos, Neto and Serrano (2015) state that public policies play a vital role in European vulnerable economies, such as rural areas. They state that the environment in which the rural businesses operates, is crucial for the development of the firm. Part of the business environment is easy access to finance. With an effective public policy, private investments, productivity and employment can be increased in low R&D areas. This is crucial for the LEADER programme to be effective. The effectiveness of the LEADER programme is also endorsed by Stanef (2013). She states that LEADER can help with the development of the poorest rural areas of Europe. With the programme, these areas have a real change to build capacity and well-being.
The LEADER programme was not successful in every region. In a pilot version of the programme in the north of Hungary there was little noticeable impact, before the country’s
entry to the EU. The impact was little noticeable because of the lack in organization of area selection and management. There was also a lack in the availability of scarce resources. The payments to the projects had delays, so the farmers already made an investment, but they did not receive the LEADER money in time. The delay of the payments was due to the change of government in 2002. The impact was also little noticeable because the strategies for the rural development had not added real value to the development (Ruszkai & Kovács, 2013).
As is mentioned above in this section, the LEADER programme is meant for the development of the rural area. This means cities do not qualify for this programme. That is why Elisa, Pecci and Pontarollo (2010) use NUTS2 data. They do research to the impact of regional development policies on farm structures in the EU. NUTS2 is data at regional level, so they are able to cut out agglomerations in their research. But in their case it is not possible to find data of all the EU member states at NUTS2 level. The reason why they use NUTS2 data, is because of the disparities between the regions. The relationships between the rural development and the factors at regional level are different amongst the regions of the EU member states (Elisa, Pecci, & Pontarollo, 2010). So, with the data at regional level they can analyse every region apart from eachother without any generalisation. This generalisation is though made in the 2007-2013 LEADER community planning documents. Here the regions are reffered to as homogeneous with reference to economic, geographic and social aspects (Ruszkai & Kovács, 2013).
The papers discussed in this literature review show that the LEADER programme can have a positive impact on the rural development. Because the only case in which the impact was little noticeable was a pilot programme, this thesis will follow conclusions of the papers that found positive impact. The hypothesis for this thesis states: The LEADER programme has a positive effect on the development of the rural area.
Methodology
In this section the model that is used for the analysis of the LEADER programme, is discussed first. After the description of the model, the regions of interest for this analysis and the way the data is collected are discussed. Subsequently, the control variables that are used in this analysis are explained. This thesis use cross sectional data analysis, where the growth rate of GVA is the dependent variable which is explained by the control variables.
General model
Research is done on economic growth by academics. One of the studies on economic growth is done by Durlauf, Kourtellos and Minkin (2001). To measure economic growth, they take five parameters into account: (1) g, the change in the log of income per capita; (2) log (n+0.05) , average growth rate of the working age population (defined as population between the ages of 15 and 64); (3) log(ss), average proportion of real investments (including government) to real GDP; (4) log(sp), average percentage of working age population that is in secondary school; (5) log(y ), initial per capita income (Durlauf, Kourtellos, & Minkin, 2001). These parameters are the basis for the parameters that are used in this thesis.
In the case of this thesis the multiple regression model is used. This model extends the single variable regression model to include additional variables as regressors. With the multiple regression model the effect of changing the LEADER subsidy can be measured, holding the other variables constant that are beyond the control of the LEADER programme (Stock & Watson, 2015). A multiple regression in general looks like:
𝑌" = 𝛽%+ 𝛽'𝑋'"+ 𝛽)𝑋)"+. . +𝛽+𝑋+" + 𝑢", 𝑖 = 1, … . , 𝑛
(Stock & Watson, 2015) Control variables
This thesis include three multiple regressions. The first regression has six control variables besides the LEADER programme variable. Regression 2 and 3 both have seven control variables besides the LEADER programme variable. These variables are not the same as in the paper of Durlauf, Kourtellosand Minkin (2001). The data of the variables they use is not available for the regions of interest in this thesis. In this thesis, 80 regions in total are analysed. These are regions from the following EU countries: Belgium, Finland, France, Italy, Portugal and Spain. The reason why is chosen for these countries and corresponding regions, is because the EU provides only regional data about the LEADER subsidy from these countries (European Network for Rural Development, 2018). The data corresponding to these countries comes from the website of the statistical institute from the EU, Eurostat. They have a database with regional specific data at NUTS2 and NUTS3 level (Eurostat, 2018).
From this database, eight variables are chosen to explain economic growth. The eight variables to explain the growth rate of GVA are: access to internet, robberies, deaths per
100.000 inhabitants, education rate, household income, poverty rate, Research & Development (R&D) expenditures and unemployment rate.
GDP is often considered the best measure of the national performance of the economy. It is the total expenditure on the economy’s output of goods and services (Mankiw, 2013). Gross value added (GVA) is calculated as GDP plus taxes on products minus subsidies on products (Eurostat, 2018). The growth rate of GVA is used because of the availability in the Eurostat database. The growth rate of GVA is measured as percentage change on the previous period. The next eight variables are control variables to explain this dependent variable.
Access to internet is measured as a percentage of households that have access to internet. This variable is used because internet could help with the stimulation of economic development. Besides internet, IT can improve productivity and economic growth (Wallsten, 2005). Research is also done about the impact of telecommunication infrastructure on economic development. It is found that when the penetration rate increases with 10 percent, economic growth raises with 1.5 percent (Aker & Mbiti, 2010).
Robberies are measured as number of robberies recorded by the police. When a region is developing, there are people who profit more from the development than others. This can raise the number of robberies committed, which can be explained by income inequality (Soares, 2004). An increase in robberies can thus indicate economic growth.
Preston (1975) state that death rate is negatively related with economic growth. So when the number of deaths are decreasing in the database, this can mean that the regional economy is growing. For this thesis all causes of deaths per 100.000 inhabitants are measured.
As measurement for education is chosen for the percentage of the working population from 25-64 years old, who have attended upper secondary, post-secondary non-tertiary and tertiary education. Education has an positive relation with economic growth. Knowledge and learning have positive impact on economic development (Shaw & Allison, 1999).
For the household income is chosen to use the purchasing power standard based on final consumption, because by using the purchasing power the household incomes of the regions can be compared. All the primary incomes are taken into account for the household income. When household income rises, households can spent more, this increases the growth of GVA.
Poverty risk is the risk people face to fall into poverty or the risk of social exclusion, in percentage. A study in India confirms that economic growth tends to reduce poverty. The
analysis is done with time series which went back to 1960. Poverty risk is also reduced by: higher average farm yields, higher public spending on development, higher (urban and rural) non-farm output and lower inflation. Also Ranis and Steward (2000) state that poverty reduces by economic growth.
All different types of R&D play a role in economic growth. Every type of R&D expenditures can have non equal impacts on economic growth (Goel, Payne, & Ram, 2008). That is why in this thesis is chosen for all the R&D expenditures in all sectors in the region.
According to Okun’s law, the unemployment rate and GDP have a negative relationship. For every percentage point unemployment rises, real GDP growth falls by two percent (Mankiw, 2013). This variable uses the unemployment rate of both men and women from 15-74 years old.
To control for the maintenance of urban areas, the variable population density is added. With this variable the regions with a high population density can be excluded from the regression and only the rural areas remain.
Regression model
The first regression model for the analysis in this thesis will look as follows:
Growth GVA" = 𝛽%+ 𝛽'"∙ 𝑆𝑢𝑏" + 𝛽)"∙ 𝐻𝐻𝐼"+ 𝛽@"∙ 𝑅𝐷𝐸"+ 𝛽D"∙ 𝑅𝑜𝑏" + 𝛽F"∙ 𝐴𝐼" + 𝛽H" ∙ 𝐸𝑑𝑢"+ 𝛽J"∙ 𝑈𝑅" + 𝑢"
In which Growth rate GVA stands for growth rate of Gross Value Added, Sub stands for the LEADER subsidy , HHI stands for HouseHold Income, RDE stands for Research & Development Expenditures, Rob stands for Robberies, AI stands for Access to Internet, Edu stands for Education rate, UR stands for unemployment rate and u stands for the error term.
In the results section, there are two extended regressions presented in table 1. These regressions are both extended with one control variable. The formula for model 2 regression looks as follows:
𝐺𝑟𝑜𝑤𝑡ℎ 𝐺𝑉𝐴" = 𝛽%+ 𝛽'"∙ 𝑆𝑢𝑏" + 𝛽)"∙ 𝐻𝐻𝐼" + 𝛽@"∙ 𝑅𝐷𝐸"+ 𝛽D"∙ 𝑅𝑜𝑏" + 𝛽F"∙ 𝐴𝐼" + 𝛽H" ∙ 𝐸𝑑𝑢" + 𝛽J"∙ 𝑈𝑅" + 𝛽R"∙ 𝐷𝑒𝑎𝑡ℎ"+ 𝑢"
In which Death stands for the logarithmic variable of the deaths per 100.000 inhabitants. The formula for model 3 regression looks as follows:
𝐺𝑟𝑜𝑤𝑡ℎ 𝐺𝑉𝐴" = 𝛽%+ 𝛽'"∙ 𝑆𝑢𝑏" + 𝛽)"∙ 𝐻𝐻𝐼" + 𝛽@"∙ 𝑅𝐷𝐸"+ 𝛽D"∙ 𝑅𝑜𝑏" + 𝛽F"∙ 𝐴𝐼" + 𝛽H" ∙ 𝐸𝑑𝑢" + 𝛽J"∙ 𝑈𝑅" + 𝛽R"∙ 𝑃𝑜𝑣"+ 𝑢"
In which Pov stands for the poverty rate.
The data gathered from the Eurostat website is at yearly basis. The data from 2007 until 2016 is used. The LEADER subsidy analysed in this thesis is divided in two periods seven years each. The first period is from 2007-2013 and the second period is from 2014-2020. Because only LEADER data about the period as a whole is available it can not be matched with the yearly data of the control variables. To solve this problem, the average of the variables are used over the periods 2007-2013 and 2014-2016. The second period only considers data until 2016, due to limited data availability. With the average data and the amount of subsidy, a cross sectional regression will be performed.
Results
This section contains the analysis of the regression done with the variables described in the methodology paragraph. First, a regression is presented with seven control variables. Subsequently, two regressions are discussed with eight control variables. The goal of this section is to analyse the LEADER programme. The regressions are done to analyse whether the LEADER variable has a significant effect on growth rate or not. The appendix will show the full output of the regressions.
The regressions in this section represents the 2007-2013 period of the LEADER programme. For the second period (2014-2020) is not enough data available to do a regression. For some variables, all the data is missing in the second period and others have a maximum of 3 years of data points. This would make the dataset, and thus the regression, unreliable. Also, only the data of regions with a population density which is lower than 900 is used to exclude the following urban regions: Hamburg, Berlin and Paris. Table 1 shows the results of the multiple regressions.
Table 1
(1) (2) (3)
VARIABLES Model 1 Model 2 Model 3
lnSubsidymillion 0.170** 0.162* 0.161* (0.0668) (0.0827) (0.0873) lnHouseholdincomethousand 0.184 -0.584 0.00495 (0.699) (1.228) (1.126) lnRDexpenditures 0.182 0.261 0.123 (0.215) (0.304) (0.374) lnrob -0.00488 -0.0130 -0.0251 (0.0432) (0.0463) (0.0559) Accesstointernet 0.0757*** 0.0741*** 0.0604* (0.0176) (0.0216) (0.0294) Education -0.0121 -0.00855 0.00395 (0.0131) (0.0182) (0.0308) Unemploymentrate 0.0376 0.0162 0.0604 (0.0286) (0.0441) (0.0374) lndeath -0.614 (0.801) Povertyrisk -0.0186 (0.0168) Constant -6.701*** -0.545 -5.532 (2.417) (7.838) (4.114) Observations 46 42 31 R-squared 0.684 0.689 0.562
Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
Regression
The table shows that the variable representing the LEADER subsidy, has an P-value <0.05. Therefore, this variable has an significant effect on the growth rate of GVA. Table 1 shows that when the subsidy increases with 1%, the growth rate increase with 0.01× 0.1696= 0.001696. Beta 1 is multiplied by 0.01 because the subsidy is measured as natural logarithm and the growth rate is measured as nominal number (Stock & Watson, 2015). The 95% confidence interval, which can be seen in the appendix, shows that all the values the coefficient can take are positive. Regarding the outcomes of this regression, it can be concluded that this analysis supports the hypothesis stated in the literature review namely, the LEADER programme has a positive effect on the development of the rural area. This conclusion accounts for regions in the six countries that are analysed as described in the methodology section.
When the logarithmic variable of the deaths per 100.000 is added, the subsidy variable is not significant at a 5% significance level anymore as can be seen in model 2. But it is still significant with an alpha of 10%. The same accounts for when adding the variable poverty risk as can be seen in model 3.
Besides the subsidy variable, there is one more variable that is constant in all three of the regression, access to internet. In the first two regressions this variable is significant with an alpha< 0.01. In the third regression it is significant with an alpha< 0.1. This means that access to internet has a significant effect on the growth rate of GVA. All the coefficients are positive so the relation between the dependent and independent variable is positive. Regarding the first regression, when access to internet increases with 1 percentage point, the growth rate will increase with 0.0757 percentage point. Therefore, it can be concluded that access to internet is a key determinant for economic growth. An improvement in the access to internet leads to an improvement in rural development.
However, not all control variables are significant. As shown in table 1, none of the control variables are significant in all of the three regressions. None of the insignificant variables has a 95% confidence interval which fully contains purely positive or negative values. So about none of the variables can be said for sure that they have a particular effect on economic growth. All insignificant independent variables can turn positive or negative.
Household income for instance, has a positive coefficient in the first regression and a slightly positive coefficient in the third but a negative coefficient in the second one. Also when regarding the 95% confidence interval of household income, it can be seen that the interval includes both positive as negative values. With this information, no conclusions can be made about the impact of household income on economic growth because of the insignificance.
Besides household income, also R&D expenditures are insignificant. Also for this variable applies that in all of the three regressions the 95% confidence interval contains both positive as negative values. Even in regressions 1, with the lowest robust standard error of the three, the 95% confidence interval is between -.2537 and +.617. For R&D expenditures applies too that no conclusions can be made about the impact on economic growth.
Further can be seen that unemployment rate has a positive coefficient in all of the three regressions. This seems like a contra-intuitive outcome. But when looking at the robust standard error and the 95% confidence interval, it appears that these positive coefficients can turn into negative which seems more intuitive regarding the dependent growth rate. When more people are working, more value is added so the growth rate would then increase.
Conclusion
First the conclusion about the effect of the LEADER programme on economic growth is stated. Subsequently, options for future research are mentioned.
To analyse the effect of the LEADER programme, this thesis made use of NUTS2 level data from the statistical institution of the EU, Eurostat, for the data of the control variables. With this data and the amount of subsidy a regression is done. As dependent variable the growth rate of gross value added is used. The outcome of the regression varied with the control variables that are used in the regression. With seven control variables as in regression 1 in the results section, the LEADER programme has an significant effect on the growth rate of gross value added with an alpha of 5%. In regression 2 and 3 an additional control variable is added. With this additional control variable, the subsidy still have a significant effect with an alpha of 10%. These outcomes are in line with the hypothesis stated in this thesis and the conclusion can be made that the LEADER programme increases economic growth. Concerning the significant positive effect of the LEADER programme on economic growth gives the EU no reason to reconsider their programme whether it is effective or not. They can assume that their programme is carried out in an effective way in the six countries researched in this thesis.
There are options for future research to this programme. First of all, a greater dataset would have a positive effect on the reliability of the regression. In the case of this thesis, not all the data went back to the beginning of the programme in 1991. In the coming years more data will be available for the 2014-2020 period. Because the LEADER programme has a duration of seven years, the average of all the variables is calculated. This means that instead of seven data points, now there is one data point per variable per region. This has a negative effect on the reliability of the regression. Also other variables can be chosen as control variables to explain the growth rate.
To make the conclusion stated in this thesis applicable for the whole of the EU, the number of regions can be increased. In this thesis only six countries are analysed. When extending the dataset with more regions, the regression outcome is more reliable for the whole EU.
Also research to the micro level of the programme can be done. For instance, if the economic situation of the farmer has increased over the years due to the LEADER programme.
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