Bio-based and Applied Economics 13(2): 147-160, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13482 Bio-based and Applied Economics BAE Copyright: © 2024 Pagliacci, F., & Zavalloni, M. Open access, article published by Firenze University Press under CC-BY-4.0 License. Firenze University Press | www.fupress.com/bae Citation: Pagliacci, F., & Zavalloni, M. (2024). The political economy deter- minants of agri-environmental funds in the European Rural Development Programmes. Bio-based and Applied Economics 13(2): 147-160. doi: 10.36253/ bae-13482 Received: August 30, 2022 Accepted: September 29, 2023 Published: July 25, 2024 Data Availability Statement: All rel- evant data are within the paper and its Supporting Information files. Competing Interests: The Author(s) declare(s) no conflict of interest. Editor: Fabio Santeramo; Silvia Cod- eroni ORCID FP: 0000-0002-3667-7115 MZ: 0000-0002-6291-7653 The political economy determinants of agri- environmental funds in the European Rural Development Programmes Francesco Pagliacci1, Matteo Zavalloni2,* 1 Dipartimento Territorio e Sistemi Agro-Forestali (TESAF), Università di Padova, Italy 2 Dipartimento di Economia, Società, Politica (DESP), Università degli Studi di Urbino Carlo Bo, Italy *Corresponding author. E-mail: matteo.zavalloni@uniurb.it Abstract. In recent years, agricultural policies have expanded their scope to include funding for the promotion of environmental sustainability in agriculture. However, these policies have been often overlooked in the political economy literature. This article aims to investigate the factors influencing the allocation of funds towards envi- ronmental goals in the Rural Development Programmes of the European Union Com- mon Agricultural Policy. The main findings of this study indicate a positive correlation between GDP per capita and the allocation of the environmental budget. Conversely, delegating the management of these programmes to sub-national polities has a nega- tive impact on the budget allocation. Therefore, it seems that maintaining some central control over the budget allocation might favour the environmental sustainability of the agricultural sector. Keywords: EU Rural Development Policy, political economy, agri-environmental schemes, environmental federalism. JEL Codes: D72, O13, Q18. 1. INTRODUCTION Agriculture has been historically the subject of pervasive policy inter- ventions, even though their nature has been extensively developed over time. The general pattern is that, with economic development, interventions tend to switch from dis-incentivization toward subsidization of agricultural activi- ties (Anderson et al., 2013). Even within high income economies the sup- port to agriculture has substantially evolved over time, from price support, toward coupled and ultimately non-coupled subsidies (Anderson et al., 2013). Especially in high income economies, since the 1980s, the scope of govern- ment interventions has broadened from a support to production to larger shares of funds allocated to e.g. R&D (Swinnen et al., 2000), infrastructures development (OECD, 2020) and the environmental goals (Baylis et al., 2008). For example, in the European Union since the 2000s, funds of the Common Agricultural Policy (CAP) have been allocated, through the Rural Devel- https://doi.org/10.36253/bae-13482 http://www.fupress.com/bae https://doi.org/10.36253/bae-13482 https://doi.org/10.36253/bae-13482 https://orcid.org/0000-0002-3667-7115 https://orcid.org/0000-0002-6291-7653 mailto:matteo.zavalloni@uniurb.it 148 Bio-based and Applied Economics 13(2): 147-160, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13482 Francesco Pagliacci, Matteo Zavalloni opment Programmes (RDPs), to agri-environmental schemes, aimed at incentivizing the provision of envi- ronmental public goods (Matthews, 2013). To explain the existence and persistence of agri- cultural policies, the literature has relied on the lens of political economy (Swinnen, 1994). A number of deter- minants have been empirically analysed, among the others: electoral incentives (Fałkowski and Olper, 2014), personal preferences of the legislators (Bellemare and Carnes, 2015), lobbying and institutional settings (Olper et al., 2014). However, the great bulk of the literature has focused on the determinants of the extensive margins of agricultural policies, i.e., to what extent the agricultural sector is affected by government interventions (Ander- son et al., 2013). Surprisingly little has been said on in the intensive margins of agricultural policies, i.e. what determines the allocation of funds, within agricultural policies, for objectives that are beyond production or maintenance of agriculture. The objective of this article is to assess the political economy determinants of the allocation of agricultural policy funds toward environmental goals. Our focus is on the European RDPs. The decisions on RDP fund allocations are set within a common, EU-level, frame- work (e.g., common priorities), but are eventually del- egated to national or subnational authorities, according to the principle of vertical subsidiarity. Thus, they pro- vide an interesting example for the issue here at stake. We address five main sets of explicatory variables: the societal demand for a greater environmental quality; the importance of the agricultural sector in the economy, which reflects into its bargaining power; the political characteristics –the ideology of the government coali- tions in charge; the agri-environmental conditions of the area; and whether the RDP is managed at the national or subnational level (i.e., issue of decentralization). Using a fractional regression model, we find that the most robust determinants of environmental budget allocations are GDP per capita (positively correlated), population den- sity and management decentralization (both negatively correlated). The main value of the article is to complement the literature on the political economy of agricultural poli- cies by unveiling the determinants of funds for agri- environmental goals, a topic largely ignored so far (Fre- driksson and Svensson, 2003), even though on the rise (Mamun et al., 2021). Indeed, several articles focus on the determinants of expenditures on the agri-environ- mental schemes of the European RDPs (Bertoni and Olper, 2012; Camaioni et al., 2019, 2016, 2013; Glebe and Salhofer, 2007; Zasada et al., 2018), or of similar meas- ures (Hackl et al., 2007). While expenditures and budg- ets are obviously connected, looking at the former adds the noise of the specific design of the measures and of the farmers uptake, and cannot be fully interpreted as a government choice (Glebe and Salhofer, 2007). At the same time, this article also speaks to the more general literature on the relationship between institutions and environmental quality, which has not deepened the topic on agricultural policies (Dasgupta and De Cian, 2018). One of the few exceptions is the analysis by Fredriksson and Svensson (2003), who inves- tigate the link between political instability and the strin- gency of environmental regulation (hence, not subsidy) faced by the agricultural sector. Finally, we also contribute to the literature on effect of environmental policies decentralization (Droste et al., 2018; Fredriksson and Wollscheid, 2014; Sigman, 2014). The framework of the RDP implementations, that are managed by both national and subnational authorities, enables to give insights also on the consequence of poli- cy decentralization, an issue that has been seldom inves- tigated with respect to agricultural policies (Bareille and Zavalloni, 2020). The results provide several policy implications. Despite the paucity of the literature on the issues, the environmental impact of the agricultural sector is a major concern (Crippa et al., 2021), and understanding the drivers of policies addressing it seems of paramount importance. Finally, decentralization of agricultural policies is often debated for the CAP reforms and our results can feed the debate revolving on it (COM(2018) 392 final, 2018). The remainder of the paper is structured as follows. Section 2 provides a policy background focus- ing on the environmental goals in agriculture and on the EU 2014-2020 programming period of the CAP. Sec- tion 3 describes selected data and implemented methods. Section 4 shows and discusses the main results. Section 5 concludes and provides some policy recommendations. 2. BACKGROUND: ENVIRONMENTAL GOALS IN AGRICULTURAL POLICIES AND IN THE EU RURAL DEVELOPMENT PROGRAMMES Environmental goals attached to agricultural sub- sidies are a longstanding, albeit minor, presence. In the USA, a first example is the 1936 Soil Conservation Act, aimed at incentivizing soil conservation practices (Cain and Lovejoy, 2004). Only since the 1980s, however, in OECD countries the share of budget linked to environ- mentally friendly practices has substantially increased (Guerrero, 2021). Indeed in 1985 environmental protec- tion became the main (nominal) rationale for the imple- 149The political economy determinants of agri-environmental funds in the European Rural Development Programmes Bio-based and Applied Economics 13(2): 147-160, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13482 mentation of the USA Conservation Reserve Programme, subsidising practices aimed at e.g. improving environ- mental quality or providing wildlife habitat (Hellerstein, 2017). Similarly, in 1985 an EU regulation allowed mem- ber states to design incentives for farmers implement- ing environmentally friendly practices, even though the uptake of this possibility was rather limited (Mat- thews, 2013). For a set of countries (OECD and others), Figure 1 shows that most of the budget toward environ- mental goals is linked to general support to agriculture conditional on some forms of input constraint -manda- tory input constraints, in Figure 1. Voluntary measures – voluntary environmental input constraints, in Figure 1 – such as the agri-environmental schemes have also increased over time, even though they remain limited to about 6-7% of the total support (Guerrero, 2021). In the EU, voluntary agri-environmental measures are currently implemented within the RDPs. RDPs represent the so-called Pillar 2 of the CAP. They were first formu- lated in the Agenda 2000 reform, as part of a strategy to move away from coupled support and broaden the scope of the CAP (Matthews et al., 2017) and they are currently supported by the European Agricultural Fund for Rural Development (EAFRD) of the EU. Since the Agenda 2000 reform, four programming periods have taken place: 2000- 2006, 2007-2013, 2014-2020, 2021-2027. A comprehensive overview of the CAP and its environmental goals is out of the scope of this paper, and we refer to e.g. Matthews (2013) for a detailed description of the topic. The current version of the Rural Development Pol- icy is the 2021-2027 one, which in fact has only started in 2023, i.e., with a two-year delay. It followed exten- sive negotiations between the European Parliament, the Council of the EU and the European Commission for the approval of the Multiannual Financial Framework of the EU (as a consequence of both Brexit process and the outbreak of the Covid-19 pandemics). Thus, due to the lack of data on the current programming period, our analysis focuses on the 2014-2020 programming period, when the RDPs were legislatively based on the Regulation (EU) No 1305/2013 of the European Parlia- ment and of the Council, which provided the guide- lines for their formulations and structure. Even though the general framework was set at the EU level and plans were approved by the EC, national authorities had some degree of freedom in implementing them (eventu- ally increased in the current 2021-2027 programming period). First, following the vertical subsidiarity prin- ciple, member states could delegate the management of the RDPs to subnational authorities (Beckmann et al., 2009). During the 2014-2020 programming period, 20 EU Member States maintained a nation-wide implemen- tation, while the remaining countries opted for a sub- national implementation. On the one hand, Germany, Belgium, Finland, Portugal, and the UK opted for the NUTS-1 level implementation (considering either single NUTS-1 regions, e.g., the Länder in Germany or groups of them, as in the case of the UK). On the other, France, Italy, and Spain opted for the NUTS-2 level implemen- tation (e.g., the Régions in France, the Regioni in Italy, and the Comunidades Autónomas in Spain). Second, the managing authorities – either at the national or the sub- national level – chose their own allocation of funds, with some constraints, prioritising specific goals among the existing ones. According to article 5 of the Regulation No 1305/2013, the RDP budgets, funded by the EAFRD, must be shared among, centrally determined, 6 priorities, or goals: (1) fostering knowledge transfer and innovation in agriculture, (2) enhancing farm viability and com- petitiveness, (3) promoting food chain organisation, (4) restoring, preserving and enhancing ecosystems related to agriculture and forestry, (5) promoting resource effi- ciency and supporting the shift towards a low carbon and climate resilient economy, (6) promoting social inclusion. At the same time, EAFRD budget was allocated to a set of measures, i.e., specific areas of interventions, aimed at achieving the aforementioned goals (Table 1). Within the current framework and according to the classification provided in Table 1, environmental meas- ures are granted a specific attention. According to article 59 of the Regulation No 1305/2013, at least 30 % of the total EAFRD contribution to each RDP shall be reserved 0 30% 60% 90% 1986 1991 1996 2001 2006 2011 2016 Mandatory input constraints Voluntary input constraints No input constraints Not Applicable Other input constraint and animal welfare (voluntary) Figure 1. Share of subsidy type on the total Producer Support Esti- mate for a set of countries (OECD and others). Own elaboration on data from OECD (2020), downloadable at https://www.oecd.org/ agriculture/topics/agricultural-policy-monitoring-and-evaluation/. For technical explanation of the variables, we refer to OECD (2016). https://www.oecd.org/agriculture/topics/agricultural-policy-monitoring-and-evaluation/ https://www.oecd.org/agriculture/topics/agricultural-policy-monitoring-and-evaluation/ 150 Bio-based and Applied Economics 13(2): 147-160, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13482 Francesco Pagliacci, Matteo Zavalloni for the following measures: M04 (only considering envi- ronment and climate related investments), M08, M10, M11, M12 (except for Water Framework Directive relat- ed payments), M13 and M15. This is to achieve specific environmental goals in the EU. 3. DATA AND METHODS 3.1. Empirical model and data The goal of this article is to assess the determinants behind the decision to allocate funds to environmental goals in the RDPs of the CAP. The shape and type of policies result from the interactions of several elements. Similarly to other analyses (e.g. Bertoni and Olper, 2012; Fredriksson and Svensson, 2003), we argue that the resulting share of budget allocated to environmental goals is determined by the interaction among five main factors: i) the societal demand for higher environmental quality, ii) the bargaining power of the agricultural sec- tor, iii) the political environment, iv) the environmental conditions of the area, v) the polity level that manages the funds. Our expectation is that higher demand for environmental quality will be translated into relatively larger budget for environmental goals. At the same time, low environmental quality will also call for larger budget for environmental goals. However, while the funds we are investigating are targeting agriculture, the sector might prefer support to investments and efficiency, rath- er than sustainability goals, and hence greater bargain- ing power would result in lower budget for environmen- tal goals. The political environment builds upon those two blocks. Party ideology and the composition of the government might filter the general preferences of the public. Moreover, decentralization of agri-environmental policies, while might result in better targeting of local public goods, could end up in free-riding behaviour due to spillover effects. In the next paragraph, we describe the depend- ent and the explanatory variables that we use to proxy the aforementioned elements. Given the structure of the RDP managing authorities, the analysis is grounded on a territorial basis. Indeed, our units of analysis are the polities covered by each RDP managing authority, either at national or sub-national level. For the current analysis, we consider 100 RDPs and the related polities, excluding from the full set: i) the French DOM (namely, Guadeloupe, Guyane, La Réunion, Martinique and May- otte) due to data availability, ii) the UK RDPs, for the difficulties to account for the functioning of the local (i.e., subnational) polities in that country, and iii) the national level RDPs, when the lower tiers are the main Table 1. Description of measures and related articles in the Regulation No 1305/2013. Articles Short description RDP codes 14 Knowledge transfer and information actions M01 15 Advisory services, farm management and farm relief services M02 16 Quality schemes for agricultural products, and foodstuffs M03 17 Investments in physical assets M04 18 Restoring agricultural production potential damaged by natural disasters and catastrophic events and introduction of appropriate prevention actions M05 19 Farm and business development M06 20 Basic services and village renewal in rural areas M07 21-26 Investments in forest area development and improvement of the viability of forests M08 27 Setting -up of producer groups and organisations M09 28 Agri-environment-climate M10 29 Organic farming M11 30 Natura 2000 and Water Framework Directive payments M12 31-32 Payments to areas facing natural or other specific constraints M13 33 Animal welfare M14 34 Forest-environmental and climate services and forest conservation M15 35 Co-operation M16 36-39 Risk management M17 40 Financing of complementary national direct payments for Croatia M18 42-44 Leader M19 151The political economy determinants of agri-environmental funds in the European Rural Development Programmes Bio-based and Applied Economics 13(2): 147-160, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13482 managing authorities (i.e., in the case of France, Italy, Spain). The dependent variable is represented by the share of the RDP budget allocated to environmental meas- ures in year 2014 (i.e., considering the first budget allo- cation). To operationalize the preferences for environ- mental goals we address the constraint set by article 59 of the Regulation No 1305/2013, in terms of both key measures and minimum budget allocation (see Section 2). We define our dependent variable, M-environment as the ratio between the RDP funds for environmen- tal goals (i.e., budget allocated to measure 4, measure 8, measure 10, measure 11, measure 12, measure 13, and measure 15) that go beyond the minimum level fixed by the EU Regulation and its complementary. For example, imagine the RDP budget is 100€, and budget allocated to environmental goals is 37€. Our dependent variable is given by 7/70. As robustness check, we also run two additional models. In the first one, we define the dependent vari- able as the share of the budget (year 2014) allocated to priorities (4) “restoring, preserving and enhanc- ing ecosystems related to agriculture and forestry” and (5) “promoting resource efficiency and supporting the shift towards a low carbon and climate resilient econo- my” (P-environment); in the second one, we define the dependent variable as the share of the budget (year 2014) allocated to agri-environmental schemes only, i.e. to measure 10 (M10). Figure 2 shows the rather uneven allocation of M-environment, P-environment, and M-10 at the pro- gramming level across the EU. Data on the RDP budget allocations have been collected from the European Com- mission website (https://cohesiondata.ec.europa.eu/) and in all cases we considered the total financing, i.e., including both the EU EAFRD funds and the national co-financing. In particular, Table 2 returns the main descriptive statistics for the alternative specifications of the dependent variables. We now turn to the set of explicatory variables. When considering them, the first dimension we address is the demand for environmental quality. Following pre- vious research (e.g. Franzen and Vogl, 2013), we take into account GDP per capita and population density as a proxy for the societal demand for environmental quality. The large literature on the environmental Kuznets curve indi- cates that, after a certain threshold, income is a key driv- er of environmental quality and policy implementation (Dasgupta et al., 2002; Dinda, 2004; López and and Mitra, 2000; Maddison, 2006). Moreover, we use population den- sity as a proxy for the degree or urbanization, which is also expected to be positively correlated to higher environmen- tal quality, and hence higher share of budget allocated to environmental goals (e.g. Franzen and Vogl, 2013). The second element is the economic relevance of the agricultural sector. A larger magnitude of the agricul- tural sector might turn into a larger bargaining power of the sector itself, which, we argue, eventually turn into a reduction of the support to environmental measures in the RDP (Fredriksson and Svensson, 2003). However, following Olson (1971), even the counterargument can be made: the larger the sector, the more is difficult to coordinate and hence the lower the bargaining power. To have proxies for the bargaining power of the agricul- tural sector, we rely on three indicators: share of utilised agricultural area with respect to the total area of the rel- evant polity, number of farmers per million inhabitants and share of Gross Value Added of agriculture out of the total Gross Value Added. As a third group of variables, politics aspects are considered. In terms of politics, first, we consider the ideology of the government in charge. Several papers find that ideology plays a role in the level of protection and support to agriculture (Klomp and Haan, 2013; Olp- (a) (b) (c) M environment (exceeding 30%) P environment (% of expend.) M 10 (% of expend.) 1st quartile 4th quartile Figure 2. Allocation of environmental budget across the EU in 2014: a) M-environment, b) p-environment, and c) M-10. Source: authors’ elaboration. https://cohesiondata.ec.europa.eu/ 152 Bio-based and Applied Economics 13(2): 147-160, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13482 Francesco Pagliacci, Matteo Zavalloni Table 2. List and description of the variables included in the models, by type. Name Meaning Year Specification Source Mean (Std. Dev.) Dependent variables M-environment Ratio of the share of the total RDP budget allocated to measure 4, measure 8, measure 10, measure 11, measure 12, measure 13, and measure 15 exceeding minimum (30%) over the total range. 2014 Share cohesiondata.ec.europa.eu 0.27 (0.18) P-environment Share of the total RDP budget allocated to priority 4, and priority 5 2014 Share cohesiondata.ec.europa.eu 0.52 (0.12) M10 Share of the total RDP bud- get allocated to measure 10 2014 Share cohesiondata.ec.europa.eu 0.15 (0.08) Environmental demand Density Population density (thou- sand inhab. per square km) avg. 2010-2014 continuous (1000 inhab.) Eurostat - Population density 0.17 (0.19) GDP Per capita income (in thou- sand €) avg. 2010-2014 continuous (1000€) Eurostat - GDP at current market prices by NUTS 2 regions 25.71 (7.86) Bargaining power of agriculture UAA_share Utilised Agricultural Area (UAA) out of total land area 2013 share Eurostat – Farm Structure Survey 0.41 (0.15) Farm per mill inhab Number of farms per mil- lion inhab. 2013 continuous Eurostat – Farm Structure Survey 19.92 (22.81) GVA_share % of Agricultural Gross Va- lue Added out of total Gross Value Added 2013 % ARDECO database 2.85 (1.95) Politics Parties Number of parties in the cabinet that was in charge at the date of approvation of the RDP - continuous Authors’ elaboration on Döring and Manow, (2020) Schakel and Massetti, (2018) 1.90 (1.00) Left_right Average position of the ca- binet in terms of its overall ideological stance (from left to right), by considering the position of each party in the coalitions (weighted by the number of their seats) - continuous (0 = Extreme left to 10 = Extre- me right) Authors’ elaboration on Döring and Manow (2020), Schakel and Massetti (2018), Polk et al. (2017) 4.30 (1.70) Agri-environmental conditions N_sur_kg_ha Average Nitrogen surplus (kg per ha), based on 16 Nitrogen surplus estimates avg. 2010-2014 continuous Batoo et al. (2022) 35.35 (18.15) Animals_ab Thousand cows and live swines per thousand inhab. avg. 2010-2014 continuous Eurostat - Animal popula- tions by NUTS 2 regions 0.57 (0.67) HNV Share of high nature value (HNV) farmland out of the total area 2012 % Authors’ elaboration on European Environment Agency (EEA) data on the basis of the Corine Land Cover (CLC) accounting layers 18.76 (14.06) (Continued) http://cohesiondata.ec.europa.eu http://cohesiondata.ec.europa.eu http://cohesiondata.ec.europa.eu 153The political economy determinants of agri-environmental funds in the European Rural Development Programmes Bio-based and Applied Economics 13(2): 147-160, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13482 er, 2007) as well as for the level of environmental pro- tection (Pacca et al., 2020). Following Klomp and Haan (2013), we address the ideology of the whole government cabinet (rather than simply the government head) by computing the average position of the cabinet in terms of its overall ideological stance (from left to right). Polk et al. (2017) computed ideological stance of EU par- ties, by assigning each of them a position on a scale from 0 (extreme left) to 10 (extreme right). Parties on the economic left wanted government to play an active role in the economy, while those on the economic right emphasized a reduced economic role for government: privatization, lower taxes, less regulation, less govern- ment spending, and a leaner welfare state. For the sake of our analysis, and as a reference point, we take the average score for the whole cabinets that were in charge of the relevant polity in the period up to the approval of the first RDP version, i.e., in most of the cases year 2014. Note that regional politics might be more complex than the national one, as regional parties are often a key player in local elections and hence governments and the local institutional architectures exhibit a great degree of heterogeneity across EU Member States (Schakel, 2013; Schakel and Massetti, 2018). Second, we also con- sider the number of parties that compose the govern- ment coalitions. This has been considered to affect state expenditures (Perotti and Kontopoulos, 2002) and pro- tection to agriculture (Beghin and Kherallah, 1994). The fourth element we address is the agri-environ- mental conditions of the relevant polities to which the RDPs refer. Agri-environmental measures are aimed at reorienting the sector toward more environmentally friendly practices, thus the lower the agri-environmental quality of the area, the higher the agri-environmental funds should be (Bertoni and Olper, 2012). As a proxy for environmental quality, we use four indicators: aver- age Nitrogen surplus, number of animals (cows and live swine) per thousand inhabitants, share of high nature value (HNV) farmland out of the total area, share of agricultural areas, forest and semi natural areas under moderate or severe level of erosion. All of them are expected to be negatively correlated to environmental quality, but the share of HNV farmland. Lastly, we address whether the RDP was managed at the national level, or if its implementation was delegat- ed to lower tiers. We consider such an element because it is a structural characteristic of (some) RDPs, which in fact has been usually disregarded by the political econo- my literature of agricultural policies (as they are mostly set at the national level). However, the variation in the polity level decision making, within the same policy framework, enables to explore the effect of decentraliza- tion on (agri-) environmental policies and hence to add results to the increasing literature on environmental pol- icy decentralization (Fredriksson and Wollscheid, 2014) and more in general on the environmental federalism (Shobe, 2020). In addition to the previous explanatory variables, in any of the selected models we also add two variables to control for population size and Eastern European Coun- tries (EEC). Population size is crucial to disentangle the effect of decentralization, holding the demographic size of the polity constant. The inclusion of a geographical dummy for EEC addresses the 20th-century historical differences across Europe. The list of the variables and their sources is listed in Table 2. Name Meaning Year Specification Source Mean (Std. Dev.) Erosion mode- rate-severe Share of agricultural areas, forest and semi natural are- as under moderate or severe level of erosion, out of the total agricultural areas, fo- rest and semi natural areas 2010 % Eurostat - Estimated soil erosion by water, by ero- sion level, land cover and NUTS 3 regions (source: JRC) 17.19 (15.88) NUTS Nuts RDP being managed at the sub-national level - Dummy authors’ elaboration Control variables Pop Total resident population avg. 2010-2014 Continuous (million inhab.) Eurostat - Population 4.33 (5.23) EEC RDP belonging to an Ea- stern Europe Country - Dummy authors’ elaboration Table 2. (Continued). 154 Bio-based and Applied Economics 13(2): 147-160, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13482 Francesco Pagliacci, Matteo Zavalloni 3.2. Econometric strategy In the framework of the CAP, different polities man- age different budget size. To control for it, we focus on the relative share of the total budget for environmental goals, rather than on its absolute value. However, frac- tional dependent variables – as the one under considera- tion here – pose some methodological challenges. The first challenge is related to the functional form of the model (Ramalho et al., 2011). Firstly, fractional dependent data (as in this case) are bounded only within the [0, 1] interval, whereas standard econometrics gener- ally assumes normally distributed dependent variables (Ronning, 1990). Secondly, a “negative bias” (Aitchison, 1986, p. 53) affects them, as fractional dependent vari- ables add up to one. Even in the case of more than two categories, there will be always at least one pair of nega- tively correlated shares. Due to these specific properties, conventional regression models – which simply ignore the bounded nature of the dependent variable and assume a linear conditional mean model for it – should be avoided. Some scholars opted for assuming the logistic relationship, preferring to estimate by least squares the log-odds ratio model. However, this empirical strategy has some impor- tant drawbacks (see Ramalho et al., 2011 for details). For the sake of this analysis, we adopt the fractional regression models, as originally modelled by Papke and Wooldridge (1996). Following their approach, the sim- plest solution for dealing with fractional response vari- ables only requires the assumption of a functional form for y that imposes the desired constraints on the con- ditional mean of the dependent variable, i.e. E(y|x) = G(xθ), where G(·) is a known nonlinear function satisfy- ing 0 ≤ G(·) ≤ 1. Papke and Wooldridge (1996) suggested as possible specifications for G(·) any cumulative distri- bution function. Among alternative choices, the logis- tic function is considered as an obvious choice, hence: E(y|x) =𝐸𝐸(𝑦𝑦|𝑥𝑥) = !!" "#!!" . . As suggested by Papke and Wooldridge (1996), this function may be consistently estimated by using the robust quasi-maximum likelihood (QML) method, which is based on the Bernoulli log-likelihood function (see Ramalho et al., 2011 for deeper details). With regard to the empirical strategy, we estimate – for each of the dependent variables, i.e., M-environment, P-environment and M10, – six alternative models, as it follows: Y = βdD + βaA + βpP + βeE + βrR + βcC + ε (1) Y = βdD + βcC + ε (2) Y = βaA + βcC + ε (3) Y = βpP + βcC + ε (4) Y = βeE + βcC + ε (5) Y = βrR + βcC + ε (6) Where: – Y is the (n × 1) vector, where n = 100, indicating the share of budget allocation devoted to the environ- mental issues, according to alternative specifications (M-environment, P-environment and M10). – D is the (n × 2) matrix of the proxies for the demand for environmental quality and βd is the (2 x 1) vector of respective unknown parameters. – A is the (n × 3) matrix of agricultural sector vari- ables and βa is the (3 × 1) vector of respective unknown parameters. – P is the (n × 2) matrix of politics and polity vari- ables and βp is the (2 × 1) vector of respective unknown parameters. – E is the (n × 4) matrix of environmental-quality variables and βe is the (4 × 1) vector of respective unknown parameters. – R is the (n × 1) vector of decentralization variable and βr is the respective unknown parameter, – C is the (n × 2) matrix of control variables and βc is the (2 × 1) vector of respective unknown parameters. – ε is the (n × 1) vector of error terms. The implementation of the fractional regression models was performed by using the software R (R Core Team, 2021). 4. RESULTS AND DISCUSSION Table 3 reports the results of all the models. Across model specifications, three are the most robust results. First, the results indicate that GDP is positively correlat- ed with the budget allocated to environmental goals (see section 3 for the description of the dependent variables). This result is in line with the large literature on the rela- tionship between economic development and environ- mental quality (Grossman and Krueger, 1995) and with previous results on the political economy determinants of the stringency of environmental regulations to agri- cultural activities (Fredriksson and Svensson, 2003). Note that even expenditures on agri-environmental meas- ures are found to be positively correlated to the GDP per capita of the area (e.g. Bertoni and Olper, 2012). The result is robust to the model specification being positive and significant also when GDP is isolated from the oth- er variables (model 2) and with different specification of the dependent variables (P-environment and M-10). The odd ratios (Table 4) indicate that an increase by €1000 in GDP per capita induces an increase by 3.2% in the budg- et allocated to M-environment. Second, DENSITY is neg- 155The political economy determinants of agri-environmental funds in the European Rural Development Programmes Bio-based and Applied Economics 13(2): 147-160, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13482 atively correlated to budget for environmental goals. This is in contrast with our expectations, i.e., on the intuition that more urbanized areas would have demanded for a higher allocation of funds to the environmental goals. One interpretation of this result might lie in the idea that, at the EU level, population density actually captures other dimensions than per capita income, both in the North and in the South of the continent. The odd ratios indicate that additional 1000 inhabitants per square kilo- metre translate in a large reduction for the environmen- tal budget (M-environment) (almost by 91%), an effect that is larger than the (positive) effect of GDP. Third, decentralization (NUTS) is negatively cor- related to the environmental budget. The dummy indi- cating a subnational polity is statistically significant and negatively correlated to the environmental budget share in any model specification. The literature on the topic is rather ambiguous and finds that the impact of decentralization on the allocation of funds to the envi- ronmental goals depends on the type of pollutants taken into account (Fredriksson and Wollscheid, 2014; Sigman, 2014, 2005). In our case, the result seems to indicate that decentralization would lead to a race to the bottom (Millimet, 2003) in allocating environmental budgets in the RDPs. While further analyses are required to under- stand the mechanisms behind it, such a result can also be interpreted in terms of governance scope (Schakel, 2009). For example, in Italy only some policy aspects are delegated to regional administration (health policies, for example), and hence, probably, a greater grip from lob- bying is on them. The odd ratios suggest that decentrali- zation has a strong effect: the delegation to lower gov- ernment tiers induce a reduction in the budget allocated to M-environment, P-environment and M-10 by respec- tively 61%, 45% and 36%. Turning to the politics aspect of our problem, the number of parties that compose a cabinet is negatively correlated to the different proxies for environmental budgets (and significant in most of the models’ specifi- cations). This might suggest that environmental public goods require greater political coherence, in order to be funded. However, ideology seems not to be linked to any preferences for environmental budget allocation, as the coefficient for LEFT_RIGHT is non-significant. Howev- er, the effect of politics on budget allocations deserves a more comprehensive analyses, where e.g. electoral incen- tives are explicitly accounted for (List and Sturm, 2006; Pacca et al., 2020). Moreover, we only consider the gov- ernment coalition in charge of the first version of the RDPs, to better address the effect of ideology it would be interesting to assess how changes in the government coalitions impact on the RDP budget allocations. Surprisingly, the proxies for the bargaining power of the agricultural sector are all non-significant in any mod- el specifications. To this regard, it is important to consid- er that we are analysing fund allocation among different goals but whose ultimate target is anyhow the agricultural sector. Probably, farmers preferences among the goals gets watered and no clear priority emerges. Note however that, when focusing on real expenditures rather than alloca- tions, Zasada et al. (2018) also find that the agricultural bargaining power (proxied by the share of agricultural area) have little explanatory power. Similarly, Bertoni and Olper (2012) find a complex relationship between share of population working in agriculture and expenditures devoted to agri-environmental schemes. Finally, a complex picture is drawn from the analysis of the agri-environmental conditions. The HNV and the nitrogen surplus are respectively negatively and positively correlated to the share of budget allocated to M10. When considering the other two dependent variables, the signs of the coefficients are reversed. This difference might be due to the different characteristics of each depend- ent variable under consideration. Actually, while meas- ure 10 only supports activities that are strictly linked to agri-environmental measures and that represent a cost from the farmers point of view, other dependent vari- ables encompass a broader set of interventions, including investments for higher resource efficiency. 5. CONCLUSIONS AND POLICY RECOMMENDATIONS In this work, we analyse the political economy determinants of the share of the budget allocated for environmental goals in the EU RDPs, by considering the 2014-2020 programming period. The main idea is that such a budget is the result of some main determi- nants: i) demand of environmental quality, ii) bargaining power of the agricultural sector, iii) characteristics of the politics of the RDPs managing authorities, iv) environ- mental quality of the area; and v) tier levels of the RDPs managing authorities (national vs subnational levels). While a substantial literature has addressed the political economy of the support to the agriculture, very little has been said on the determinants of policies targeting the sustainability of the agricultural sector. In comparison to previous articles – which mostly addressed the deter- minants of the ex-post expenditures on agri-environ- mental schemes – the focus on budget allocation allows us to put a greater emphasis on the determinants of the political decision process behind the choice of allocating funds to the environmental goals rather than to other goals (often competing with each other). Bio-based and Applied Economics 13(2): 147-160, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13482 Ta bl e 3. R es ul ts o f t he m od el s ( ro bu st st an da rd e rr or s i n pa re nt he se s) . M -e nv iro nm en t P- en vi ro nm en t M -1 0 (1 ) (2 ) (3 ) (4 ) (5 ) (6 ) (1 ) (2 ) (3 ) (4 ) (5 ) (6 ) (1 ) (2 ) (3 ) (4 ) (5 ) (6 ) (I nt er ce pt ) 0. 24 3 -1 .5 71 ** * -0 .5 58 * -0 .7 89 * -0 .8 35 ** -0 .1 08 0. 90 3 * -0 .2 92 ° 0. 26 8 ° 0. 32 2 * 0. 30 1 * 0. 61 3 ** * -2 .0 05 ** * -2 .6 29 ** * -1 .9 08 ** * -2 .2 07 ** * -1 .6 76 ** * -1 .3 25 ** * (0 .7 74 ) (0 .2 68 ) (0 .2 51 ) (0 .3 10 ) (0 .2 84 ) (0 .2 24 ) (0 .3 73 ) (0 .1 50 ) (0 .1 49 ) (0 .1 58 ) (0 .1 40 ) (0 .1 31 ) (0 .4 11 ) (0 .1 92 ) (0 .2 35 ) (0 .1 87 ) (0 .1 65 ) (0 .2 08 ) D en sit y -2 .4 01 * -2 .4 50 * -0 .6 99 * -0 .6 13 -0 .8 72 ** -0 .9 27 ** (1 .1 92 ) (1 .0 85 ) (0 .3 30 ) (0 .4 63 ) (0 .2 68 ) (0 .3 26 ) G D P 0. 03 2 ° 0. 03 5 ** * 0. 01 9 * 0. 01 9 ** 0. 01 7 ° 0. 03 5 ** * (0 .0 16 ) (0 .0 10 ) (0 .0 09 ) (0 .0 06 ) (0 .0 09 ) (0 .0 07 ) U A A _s ha re -0 .5 14 -0 .9 20 -0 .1 48 -0 .2 46 0. 20 4 0. 41 4 (0 .7 94 ) (0 .7 51 ) (0 .4 12 ) (0 .3 95 ) (0 .4 90 ) (0 .5 75 ) Fa rm p er m ill in ha b -0 .0 02 0. 00 3 0. 00 0 0. 00 0 -0 .0 03 -0 .0 07 (0 .0 05 ) (0 .0 05 ) (0 .0 02 ) (0 .0 02 ) (0 .0 03 ) (0 .0 04 ) G VA _s ha re -0 .0 20 -0 .0 32 -0 .0 09 -0 .0 27 0. 03 3 0. 00 5 (0 .0 57 ) (0 .0 64 ) (0 .0 29 ) (0 .0 31 ) (0 .0 37 ) (0 .0 51 ) Pa rt ie s -0 .2 70 ** * -0 .0 77 -0 .1 51 ** * -0 .0 77 ° 0. 02 2 0. 09 6 ° (0 .0 75 ) (0 .0 94 ) (0 .0 40 ) (0 .0 45 ) (0 .0 54 ) (0 .0 57 ) Le ft_ rig ht -0 .0 66 -0 .0 23 -0 .0 58 * -0 .0 26 0. 04 0 0. 04 5 (0 .0 53 ) (0 .0 47 ) (0 .0 25 ) (0 .0 24 ) (0 .0 34 ) (0 .0 36 ) N _s ur _k g_ ha 0. 00 4 -0 .0 03 -0 .0 01 -0 .0 03 0. 00 7 ** 0. 00 6 * (0 .0 05 ) (0 .0 06 ) (0 .0 03 ) (0 .0 03 ) (0 .0 02 ) (0 .0 03 ) A ni m al s_ ab -0 .2 25 -0 .1 02 -0 .0 72 -0 .0 41 -0 .1 07 ° -0 .0 29 (0 .1 72 ) (0 .1 85 ) (0 .0 69 ) (0 .0 93 ) (0 .0 57 ) (0 .0 71 ) H N V 0. 01 5 * 0. 01 5 * 0. 00 7 * 0. 00 6 ° -0 .0 10 * -0 .0 10 * (0 .0 06 ) (0 .0 07 ) (0 .0 03 ) (0 .0 03 ) (0 .0 05 ) (0 .0 05 ) Er os io n m od er at e- se ve re -0 .0 08 -0 .0 16 ** -0 .0 06 * -0 .0 10 ** * -0 .0 03 -0 .0 08 ° (0 .0 06 ) (0 .0 06 ) (0 .0 03 ) (0 .0 03 ) (0 .0 05 ) (0 .0 04 ) N U TS -0 .9 50 ** -0 .9 38 ** * -0 .5 93 ** * -0 .5 58 ** * -0 .4 42 * -0 .5 65 ** (0 .3 41 ) (0 .2 16 ) (0 .1 60 ) (0 .1 34 ) (0 .1 87 ) (0 .2 00 ) Po p 0. 00 7 0. 01 8 0. 00 2 0. 00 7 0. 00 6 -0 .0 15 -0 .0 01 0. 00 2 0. 00 0 0. 00 3 0. 00 3 -0 .0 07 0. 00 9 0. 02 5 0. 02 3 0. 02 3 0. 00 7 0. 01 5 (0 .0 25 ) (0 .0 22 ) (0 .0 19 ) (0 .0 19 ) (0 .0 20 ) (0 .0 20 ) (0 .0 10 ) (0 .0 10 ) (0 .0 09 ) (0 .0 09 ) (0 .0 10 ) (0 .0 08 ) (0 .0 15 ) (0 .0 15 )° (0 .0 16 ) (0 .0 15 ) (0 .0 13 ) (0 .0 14 ) EE C -0 .9 96 ** -0 .4 79 -0 .5 62 ° -0 .4 08 -0 .7 08 * -1 .2 08 ** * -0 .6 39 ** -0 .2 47 * -0 .3 28 * -0 .2 57 * -0 .4 85 ** -0 .7 93 ** * -0 .4 78 ° -0 .0 70 -0 .1 16 -0 .4 32 -0 .2 09 -0 .7 32 * (0 .3 80 ) (0 .2 99 ) (0 .3 17 ) (0 .2 86 ) (0 .3 24 ) (0 .8 24 ) (0 .1 98 ) (0 .1 26 ) (0 .1 35 ) (0 .1 26 ) (0 .1 48 ) (0 .1 57 ) (0 .2 87 ) (0 .2 55 ) (0 .2 64 ) (0 .2 71 ) (0 .2 52 ) (0 .2 85 ) O bs .d el et ed (m iss in g) 4 0 0 4 3 0 4 0 0 4 3 0 4 0 0 4 3 0 Ef ro n ps eu do R- sq ua re d 0. 40 2 0. 23 3 0. 05 5 0. 03 0 0. 09 1 0. 14 2 0. 38 9 0. 14 4 0. 06 6 0. 07 8 0. 13 1 0. 16 1 0. 38 4 0. 23 9 0. 08 9 0. 07 6 0. 20 4 0. 13 6 157The political economy determinants of agri-environmental funds in the European Rural Development Programmes Bio-based and Applied Economics 13(2): 147-160, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13482 Ta bl e 4. R es ul ts o f t he m od el s – o dd ra tio s. M -e nv iro nm en t P- en vi ro nm en t M -1 0 (1 ) (2 ) (3 ) (4 ) (5 ) (6 ) (1 ) (2 ) (3 ) (4 ) (5 ) (6 ) (1 ) (2 ) (3 ) (4 ) (5 ) (6 ) (I nt er ce pt ) 1. 27 5 0. 20 8 0. 57 2 0. 45 4 0. 43 4 0. 89 8 2. 46 6 0. 74 7 1. 30 7 1. 38 0 1. 35 2 1. 84 6 0. 13 5 0. 07 2 0. 14 8 0. 11 0 0. 18 7 0. 26 6 D en sit y 0. 09 1 0. 08 6 0. 49 7 0. 54 2 0. 41 8 0. 39 6 G D P 1. 03 2 1. 03 6 1. 01 9 1. 01 9 1. 01 7 1. 03 5 U A A _s ha re 0. 59 8 0. 39 8 0. 86 3 0. 78 2 1. 22 7 1. 51 3 Fa rm p er m ill in ha b 0. 99 8 1. 00 3 1. 00 0 1. 00 0 0. 99 7 0. 99 3 G VA _s ha re 0. 98 1 0. 96 9 0. 99 1 0. 97 3 1. 03 4 1. 00 5 Pa rt ie s 0. 76 4 0. 92 6 0. 86 0 0. 92 6 1. 02 2 1. 10 1 Le ft_ rig ht 0. 93 6 0. 97 7 0. 94 4 0. 97 4 1. 04 1 1. 04 7 N _s ur _k g_ ha 1. 00 4 0. 99 7 0. 99 9 0. 99 7 1. 00 7 1. 00 6 A ni m al s_ ab 0. 79 8 0. 90 3 0. 93 1 0. 96 0 0. 89 8 0. 97 2 H N V 1. 01 5 1. 01 5 1. 00 7 1. 00 6 0. 99 0 0. 99 0 Er os io n m od er at e- se ve re 0. 99 2 0. 98 4 0. 99 4 0. 99 0 0. 99 7 0. 99 2 N U TS 0. 38 7 0. 39 1 0. 55 3 0. 57 2 0. 64 3 0. 56 9 Po p 1. 00 7 1. 01 9 1. 00 2 1. 00 7 1. 00 6 0. 98 5 0. 99 9 1. 00 2 1. 00 0 1. 00 3 1. 00 3 0. 99 3 1. 00 9 1. 02 5 1. 02 3 1. 02 4 1. 00 7 1. 01 5 EE C 0. 36 9 0. 61 9 0. 57 0 0. 66 5 0. 49 3 0. 29 9 0. 52 8 0. 78 1 0. 72 0 0. 77 3 0. 61 6 0. 45 2 0. 62 0 0. 93 2 0. 89 0 0. 64 9 0. 81 2 0. 48 1 158 Bio-based and Applied Economics 13(2): 147-160, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13482 Francesco Pagliacci, Matteo Zavalloni The analysis shows that the determinants behind the allocation of the European Rural Development Pol- icy budget to environmental goals are similar to those found in the literature concerning environmental poli- cies in general. The results seem to show the critical role played by an increase in the average wealth (as prox- ied by GDP per capita) favouring a larger environmen- tal support. This result is not new – being in line with previous literature– but it is confirmed also for the EU RDP. Moreover, different proxies for the lobbying power of the agricultural sector (as proxied by the UAA, the number of farms, and the agricultural GVA) show no significance, hence the supposed competition between the agricultural support on the one hand and a broader support toward multifunctionality, and the environment in particular, on the other does not find strong support. Decentralization is linked to lower budgets allocated to environmental goals and display a strong effect. The combination of the effect of per capita income and of decentralization seems to suggest that delegating RDPs management to subnational authorities might be particularly problematic, given the high heterogeneity of development across European regions. The results seem to indicate that, if environmental issues are at stake, maintaining a relatively centralized grip on the envi- ronmental budget would be desirable. To this regard, the decision undertaken in the implementation of the current 2021-2027 RDPs can be considered as positive for the implementation of a policy more in favour of agri-environmental targets. Indeed, the Regulation No 2115/2021 sets that all new rural development actions will be incorporated into national-level CAP strategic plans, establishing specific rules on support for strategic plans to be drawn up by EU countries under the com- mon agricultural policy. The emerging results are insightful, despite the existence of some possible shortcomings in the work. 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