Human capital and rural development policy: Evidence from European FADN regions

Maietta, Ornella Wanda; De Devitiis, Biagia; Destefanis, Sergio; Suppa, Domenico Article Human capital and rural development policy: Evidence from European FADN regions Bio-based and Applied Economics (BAE) Provided in Cooperation with: Firenze University Press Suggested Citation: Maietta, Ornella Wanda; De Devitiis, Biagia; Destefanis, Sergio; Suppa, Domenico (2019) : Human capital and rural development policy: Evidence from European FADN regions, Biobased and Applied Economics (BAE), ISSN 2280-6172, Firenze University Press, Florence, Vol. 8, Iss. 3, pp. 239-259, https://doi.org/10.13128/bae-8888 This Version is available at: https://hdl.handle.net/10419/321740 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Bio-based and Applied Economics 8(3): 239-259, 2019 ISSN 2280-6180 (print) © Firenze University Press ISSN 2280-6172 (online) www.fupress.com/bae Full Research Article DOI: 10.13128/bae-8888 Human capital and rural development policy: evidence from European FADN regions Ornella Wanda Maietta1, Biagia de devitiis2, sergiO destefanis3,*, dOMenicO suppa4 1 Università di Napoli Federico II 2 Università di Foggia 3 Università di Salerno 4 Università della Campania “L. Vanvitelli” Abstract. This paper analyses the evolution and policy drivers of the productivity of farmers’ human capital in EU agriculture from 1986 to 2010. The empirical analysis employs farm data sourced from the Farm Accountancy Data Network Standard Results as well as Eurostat’s information on farm holders’ educational-attainment levels. Productivities of human capital are measured by the shadow prices for three levels of educational attainment of farm family labour, computed using Data Envelopment Analysis with variable returns to scale, and related to a Malmquist index of total factor productivity and to selected policy variables. The results indicate that productivities of farmers’ human capital trend upwards and are positively associated with rural development payments. Keywords. productivity of human capital, shadow prices, technical efficiency, productivity growth, specific education, agricultural change. JEL Codes. 047, 015, D24, E24, C43. 1. Introduction Human capital requires investment in learning new skills, both through traditional schooling and postschool job training. It also represents a crucial source of productivity gains and long-term economic growth. According to the neoclassical approach (Mankiw et al. 1992), human capital is a fundamental input into the aggregate production function, and its accumulation explains the process of economic growth. On the other hand, the Schumpeterian approach holds that growth results from the initial endowment of human capital, which influences a country’s or region’s capability to innovate and catch up with the technology of the leader area (Nelson and Phelps 1966; Benhabib and Spiegel 1994). *Corresponding author: [email protected]t. The authors gratefully acknowledge comments from three anonymous referees on a previous version of the paper. The usual disclaimer applies. 240 Ornella Wanda Maietta et al. At any rate, few economists would dispute that for most of the world’s agriculture, immaterial inputs – including human capital – are now crucial for total factor productivity (TFP) growth. This growth is no longer a resource-based process driven by material input accumulation, but a productivity-based process driven mainly by immaterial input accumulation (Fuglie 2015; Ball et al. 2016). Knowledge-intensive work environments are increasingly common, creating a situation in which human capital relates to entrepreneurial outcomes more than ever before (Unger et al. 2011). It has also long been known that education in agriculture enables farmers to allocate inputs more efficiently (Welch 1970) and to optimise their information searches (Ram 1980). The educational system imparts the ability to summarise information from various sources and to engage in nonroutine problem solving (Swaim 1995; Gasson 1998). Technical education favours participation in agri-environmental schemes (Dupraz et al. 2002), improves eco-efficiency (Van Passel et al. 2009; PicazoTadeo et al. 2011) and increases the value added per annual working unit (Carillo et al. 2013). In this context, how is European agriculture responding to these challenges? In principle, education offers higher returns for individuals working in any sector experiencing technological progress (Blundell et al. 1999). Hence, the returns on farmers’ education are linked to a changing agricultural technology and production structure. If these conditions do not change, farmers’ incentive to acquire education dwindles (Huffman 2001). Now, there is little doubt about the existence of an ongoing demand for new skills in European agriculture (European Commission 2014). Crucial to the present analysis, public policy also plays a role in incentivising the accumulation of human capital. Since 2005, as a result of the Fischler Reform and subsequently the CAP Reform 2014– 2020, direct support (Pillar I subsidies) and structural policies (Pillar II payments) have pursued a more entrepreneurial approach to agricultural business management through increased market orientation and competitiveness (Clark, 2009). Corporate efficiency and environmental safeguarding became key issues. In terms of direct-support policies, farm aid has largely been decoupled and subject to cross-compliance. As for structural interventions, rural development policy has been strengthened with funds and policy instruments aimed at facilitating the provision of environmental goods. In addition, activities have been diversified in a more targeted and locally tailored manner. It is expected that the stronger market orientation of direct support will foster aggregate productivity gains for the sector as a whole. This prediction rests partly on the assumption that only high-performing farms will survive due to their ability to thrive in an environment that promotes continuous learning and problem solving (Henke et al. 2011) and partly on the belief that the transition from a traditional agricultural policy to a rural one may improve the policy communities and networks relevant to farmers (Keating and Stevenson 2006) or the farmers’ business strategies (Clark 2009; Severini and Tantara 2013). Productivity-enhancing effects may result also from the rural development plans and human-capital transfers carried out within the CAP. However – and this is the central focus of this paper – these reforms could also have increased the productivity of higher-order cognitive skills, an issue that has wide-ranging policy relevance because higher returns for human capital may attract this input into the sector (Olper et al. 2014; Garrone et al. 2019). Although these arguments suggest the existence of a link between the CAP reforms and human-capital productivity in agriculture, this relationship has yet to be investigated empiri- 241 Human capital and rural development policy cally.1 There is a simple way of testing the hypothesis that the greater CAP market orientation has enhanced the productivity of human capital in agriculture: determining whether the relative shadow price of the human capital embodied in European farmers has increased after the CAP reforms. Therefore, this paper primarily aims to measure the relative shadow price of farm family labour for three levels of educational attainment from 1986 to 2010. These relative shadow prices are computed by applying the data envelopment analysis with variable returns to scale (DEA-VRS) for all EU-27 Farm Accountancy Data Network (FADN) regions, for all years for which information on farm holders’ trainings is available. DEA has been widely used in growth accounting studies because it does not impose restrictive functional forms on the production frontier and is much more directly interpretable than other approaches in terms of production theory (Arcelus and Arocena 2000; Filippetti and Peyrache 2013). Due to data availability, we focus on three levels of educational attainment: low, medium and high (further details are given in the research materials that are available online). Because TFP growth influences the productivity of human capital (and vice versa), a second and complementary task of this paper is to measure the growth in TFP by computing a Malmquist TFP index (which is possible only for a balanced panel of EU-12 FADN regions). To the best of the authors’ knowledge, no other study has measured a TFP index for European agriculture at the regional level over so long a period. It should be emphasised that some of the utilised data are not readily available from public sources, as explained in greater detail in section 4 and the research materials. The analytical framework proposed in this paper may be replicated to evaluate the productivity of human capital for similar situations in other sectors, particularly when labour is mainly self-employed and lacks a market price. The analysis could also be extended to provide absolute (as opposed to relative) shadow prices for human-capital services, which could be used in a DEA-based cost–benefit analysis (see, e.g., Kortelainen and Kuosmanen 2006). The remainder of the paper adheres to the following structure. Section 2 reports on the history of the CAP and provides some descriptive statistics. Sections 3 and 4 focus on the methodology and data used, respectively. Section 5 describes and comments on the empirical results, and the paper offers concluding remarks in section 6. The paper also includes a research materials section that describes the empirical framework further and reports some robustness checks. 2. The evolution of human capital in EU agriculture and the CAP Table 1 reports the percentage of farmers with full agricultural training, our proxy for high human capital, as calculated from Farm Structure Survey (FSS) data, as well as the percentage of the population aged 15 to 64 years with tertiary education, as calculated from Eurostat data. Educational attainment is a poor indicator of the extent to which individuals possess the cognitive skills and technical knowledge required to carry out more demanding and better-paid jobs; nonetheless, the table highlights the well-known gap between rural and urban educational levels (Swaim 1995). Whereas the percentage of the population with tertiary qualifications, 1 There is, however, an empirical literature on the relationships between CAP reforms and TFP. We comment on this literature, whose results are rather diverse, when discussing our evidence in section 5. 242 Ornella Wanda Maietta et al. measured in either 2010 or 2018, is not appreciably sensitive to the EU aggregate considered (if anything, it increases at each EU enlargement), the percentage of farmers with full agricultural Table 1. Human capital in EU agriculture and economy, 2010 and latest available years. Areas % farmers with full agricultural training, 2010 % population from 15 to 64 years with tertiary education, 2010 % farmers with full agricultural training, 2016 % population from 15 to 64 years with tertiary education, 2018 EU-6 14.9 21.8 16.1 26.2 EU-9 14.4 23.9 16.4 29.1 EU-10 13.6 23.8 14.3 29.0 EU-12 12.0 24.1 12.1 29.4 EU-15 12.3 24.1 12.5 29.7 9 EU-25 12.6 23.2 13.8 29.1 EU-27 11.3 22.7 11.5 28.5 NB: EU-6, EU-9, EU-10, EU-12, EU-15, EU-25, EU-27 to be defined in the research materials. Source: Own elaborations on FSS and Eurostat Regional statistics. Figure 1. Farm holders with full agricultural training (%), 2010. Source: Own elaborations on FSS and Eurostat Regional statistics. 243 Human capital and rural development policy training (either in 2010, available from our dataset, or in 2016, the latest year for which we can retrieve some aggregate information) tends to decrease with each EU enlargement. The cross-sectional distribution of the percentage of farm holders with full agricultural training across FADN regions is further depicted in Fig. 1. The percentage ranges from 0.2% in Ipiros-Peloponissos-Nissi Ioniou to 45.9% in Latvia and Luxembourg. Generally, the most rural regions exhibit the lowest percentage of farmers with full agricultural training. The significant differences observed in human capital across EU regions may be explained by divergent agricultural education systems, agricultural structures and farm-size distributions. Yet, human capital has improved over time. According to FSS data, in 1990, the percentage of farm holders with medium and high human capital in EU12 was 12% and 7%, respectively. In 2010, these figures were 20% and 12%. It could be asked whether policies, by affecting the incentives for human-capital accumulation, have favoured or hampered this improvement. Before dealing with this question in the following sections, we proceed to give a detailed account of the most relevant changes of the CAP in this sphere. The CAP has undergone several changes since the 1980s, including production limits to reduce surpluses (milk quotas were first applied in 1984); during this time, much emphasis has been placed on environmentally sound farming. The first fundamental reform occurred in 1992 with the MacSharry Reform, followed by “Agenda 2000” in 1999, the Fischler Reform in 2003 and the CAP Reform 2014–2020 in 2013. In 1992, the MacSharry Reform caused a shift from market to producer support. Cereal, oilseeds and livestock intervention prices were scaled down. Computed on the basis of average regional yield levels, per-hectare compensatory payments were also introduced, along with compulsory set-aside requirements attached to these payments. In 1999, the “Agenda 2000” Reform further cut intervention prices, bringing them closer to world market levels while aligning cereal, oilseed and livestock payments in order to promote the competitiveness of European agriculture. “Agenda 2000” also initiated the Rural Development Policy, a wider structural strategy of decentralised spatial management for rural territories in Member States (MSs). This policy sought to encourage sustainable development by valorising both agricultural and nonagricultural activities. In 2003, the Fischler Reform was introduced, promoting sustainability and cohesion. Farmers received a single payment calculated by dividing the total payments received over a historical period by the number of hectares on the farm. Previously related to the number of animals or the milk quota size, premiums were largely added to the flat-rate compensation per hectare. Single farm payments favour the use of land relative to other inputs in agricultural production and reduce the yields of many commodities; their total output response is less than the price support (Sckokai and Anton 2005). Furthermore, these payments severed the link between production and farm income support. This decoupling sought to orient farmers towards the market while still providing them with a degree of income stability. Farmers were free to produce what they judged most profitable, so long as the land was used for agriculture. Income stability was intended to serve as compensation for higher production standards with regard to consumer protection, animal welfare and environmental conservation (compared to many non-European countries). Anyone failing to fulfil this ‘cross-compliance’ condition risked a reduction in their direct income payments (Moro and Sckokai 2013). The reform was in place from 2005 onwards, but decoupled payments fully replaced direct aid only in 2007. 244 Ornella Wanda Maietta et al. The Fischler Reform has also strengthened the role of services in fostering agricultural human capital and competitiveness. Each MS must set up an advisory system aimed at farms in order to satisfy compliance requirements. The Programme for Rural Development (2007–2013) provided funding for the supply of advisory services and other actions2 aimed at human-capital transfer (Contó et al. 2012). Yet, the background of both advisory services operators and private business consultants was often agronomic and not business management, resulting in outdated or incomplete professional skills (Clark 2009). Indeed, the fact that the returns for professional and technical training are lower than those for managerial training (Blundell et al. 1999) prompts the need to promote entrepreneurship through education. In 2013, CAP contents were again redesigned over the programming period 2014–2020. In particular, single farm payment has been unpacked into different payments targeting different goals and partly tailored to farm-specific characteristics. According to European Regulations, only some of these payments (base payment, greening payment and payment for young farmers) are mandatory for MSs, unlike other kinds of payment (coupled, for less favoured areas, for small farms). The introduction of the greening payment, conditional on compliance with certain “agricultural practices beneficial for the climate and the environment”, reflects the EU legislators’ intention to provide a more consistent justification for CAP instruments, emphasising their role in pursuing environmental sustainability (European Commission 2010 a, b; Matthews 2013; Cimino et al. 2015; Erjavec and Erjavec 2015). The key role of services has also been strengthened during the period 2014–2020. In particular, the Programme for Rural Development pays greater attention to knowledge transfer and information actions, including vocational training and skills acquisition by farmers (or SMEs operating in rural areas),3 and to advisory services, farm management and farm support.4 3. The empirical methodology The shadow price associated with an input indicates how much more output could be obtained by increasing the amount of that input by one unit. It is a measure of the opportunity cost of that input and reflects its marginal productivity. In the field of productivity measurement, shadow prices are estimated when market prices are inapplicable, unknown or inappropriate. They can also be used as appropriate indicators of input productivities. Carrying out intercountry comparisons of agricultural productivity, Coelli and Prasada Rao (2005) and Nin-Pratt and Yu (2010) estimated shadow input prices in order to obtain input cost shares as market prices are distorted due to government intervention. Ten Raa and Mohnen (2002) used shadow input prices as a valuation of input productivities unaffected by market power, disequilibrium in factor holding, suboptimal capacity utilisation and returns to scale. 2 Examples include vocational training for consultants (Measure 111) and support for cooperation in the development of new products, processes and technologies (Measure 124). 3 Examples include training courses, workshops and coaching, as well as short-term agricultural exchanges and visits to farms (Article 14 of Council Regulation (EU) No 1305/2013) 4 These include three types of measures: supporting farmers and related operators in the use of advisory services to improve economic and environmental performance and resilience to climate change, encouraging the establishment of farm management and promoting the training of advisers. 245 Human capital and rural development policy Shadow prices may be estimated through nonparametric linear programming or through parametric regression analysis. Examples of the nonparametric approach include the study of industrial wastes (Reig-Martínez et al. 2000), volunteer work (Destefanis and Maietta 2009), hospital outputs (O’Donnell and Nguyen 2013), biodiversity (Sipilainen and Huhtala 2013), undesirable outputs (Leleu 2013), and water and wind resources (Ilak et al. 2015). DEAbased shadow prices have also been used in cost–benefit analyses of environmental services (Kortelainen and Kuosmanen 2006). Within a nonparametric framework, shadow prices are determined as the solution to multiplier or dual linear programming problems. They are the multipliers revealed by individual producers in an effort to maximise their relative efficiency (Fried et al. 2008). In this paper, in order to determine the shadow prices of inputs, we rely on the DEA-VRS technique,5 implemented through the solution of the multiplier (dual) problem BccDI proposed by Banker et al. (1984): ii ii i i ii i ii I Dii i ,, i ii BCC ( ): max 0 0, 0 , w w + = − +≤ ≥≥ μ νω xy μy νx 1 μ y νx μν (1) where x is the input vector and y is the output vector, ni and mi are the shadow prices or multipliers of inputs and outputs, respectively, and wi is an indicator of returns to scale. Note that whereas ni and mi must be greater than or equal to zero, wi may be positive, negative or zero, which makes it possible to use the optimal value of this variable to identify the nature of returns to scale. This input-oriented problem is solved by finding values for ni and mi that maximise output “values” miyi + wi, subject to a normalising constraint on input “values” (which avoids the occurrence of infinite solutions to the problem) and to the constraint that efficient output “values” must be smaller than or equal to input “values”. As a consequence of these constraints, shadow prices computed from different frontiers are not directly comparable (Kuosmanen and Kortelainen 2006). However, it is possible to compare the ratio between the shadow prices of two inputs, which is the marginal rate of technical substitution between these inputs (Ouellette and Vigeant 2016). For this reason, our analysis always considers the shadow prices of family-labour categories as ratios calculated vis-àvis the shadow price of paid labour. The computation of shadow prices may provide values equal to zero for some outputs or inputs. Input shadow prices are zero in cases of slack in the primal envelopment form. It is also possible to have a zero value in the case of multiple optimal solutions (Olesen and Petersen 2015). Indeed, the estimated DEA frontier is not smooth. Its kinks in primal space correspond to flats in dual space that fail to yield unique shadow prices for strongly efficient units, that is, observations with zero slacks in the primal envelopment form (Chambers and Färe 2008). In order to solve this problem, Olesen and Petersen (2015) proposed a “facet 5 When the data are expressed on “an average per farm” basis (as in this paper), it is sensible to stick to a variablereturns-to-scale technology (Coelli and Prasada Rao 2005). 246 Ornella Wanda Maietta et al. analysis” of the convex hull, making it possible to identify well-defined shadow prices for strongly efficient units as well. DEA-VRS can also be used in order to compute Malmquist indexes for TFP growth. This index, explained in detail in the research materials, is one of the most widely used tools for measuring TFP growth of firms, industries and countries (Mizobuchi 2017). It enables decomposition of TFP growth into movements towards or away from the production frontier, technical progress and scale-related factors. We chose a nonparametric approach for the computation of both shadow prices and the Malmquist index, because, unlike econometric estimation, this approach does not rely on any assumption about the functional form of input–output relationships or of stochastic disturbances. 4. The data and the empirical specifications The bulk of data for this study were obtained from FADN and refer to a representative farm at the regional level, commonly used in sector models based on linear programming (Jonasson and Apland 1997) and for intercountry productivity analysis (Rizov et al. 2013). We are aware that reliance on these data may lead to the neglect of some interesting heterogeneities characterising the phenomenon under scrutiny. However, microdata across FADN regions are unavailable across a time span sufficiently long to allow investigation of the CAP reforms. Furthermore, the literature contains few aggregate analyses concerning the role of entrepreneurial human capital in local development (Marvel et al. 2016). More generally, it has been stressed recently that the use of microdata in policy evaluation may lead to biased results, because analyses based on them neglect the presence of spillover effects (see, e.g., instance Deaton 2019). Data on representative farms at the regional level can be downloaded from the Standard Results section of the FADN database. For the purpose of this study, version A1 of FADN Standard Results was downloaded,6 because it refers to a representative farm at the regional level for the period 1989–2012. Meanwhile, Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria (CREA; formerly Istituto nazionale di economia agraria, or INEA) provided version A1, with 34 variables for the period 1986–1988 (RICA RI/CC/882 rev. 3, described in Dell’Acqua 1995). We also relied on the Eurostat FSS (for the period 1986–2010), which is the only harmonised source for human-capital data in EU agriculture; it periodically measures the percentage of farm holders with practical, basic and full agricultural training. Unfortunately, no such information is available for paid labour. Note that the territorial location of the FSS corresponds to NUTS2 regions, which are not necessarily the same as FADN regions (see the research materials for an explanation of the matching procedure across these territorial definitions and for other information about the data). Table 2 reports the descriptive statistics of the variables. On average, the commercial farm employed 1.28 family work units, the farmer plus another (part-time) family member, and 0.64 paid work units in the time period under consideration. Family labour is much more likely to have lowor medium-level educational attainment (which corresponds to the information available from European Commission 2014). 6 http://ec.europa.eu/agriculture/ricaprod/database/consult_std_reports_en.cfm 253 Human capital and rural development policy associated with the marginal rates (and CMI). Human-capital transfer payments and decoupled payments are positively associated only with the marginal rates for medium levels of human capital. For human-capital transfer payments, these findings align with previous evidence indicating that the relationship between entrepreneurship outcomes and entrepreneurship education and training programmes is lower for training-focused educational interventions than for academic-focused educational interventions (Martin et al. 2013). On the other hand, decoupled payments are different from zero only in the last two years. A longer time span of application might have yielded a more significant correlation for this policy. In any case, according to our evidence, only rural development payments are associated with the marginal rates across all categories of human capital. On the whole, our evidence points to a favourable assessment of CAP reforms. They are associated with a higher productivity of family labour, and the apparently ineffective compensatory payments were replaced by more relevant policies. The results, however, suggest that the association between higher productivity and rural development payments is more robust than that for decoupled payments, and support previous evidence on the ineffectiveTable 7. Kendall’s coefficients. Human capital categories Kendall’s simple rank correlation coefficient) between CAP variables and marginal rates of substitution for family labour by human capital categories. Kendall’s partial rank correlation coefficient) between CAP variables and marginal rates of substitution for family labour by human capital categories. CMI as confounder. Kendall’s coefficients of concordance among CAP variables, marginal rates of substitution for family labour by human capital categories and CMI. Compensatory payments/ Gross farm income High HK 0.05* 0.05* 0.31 Medium HK -0.01 -0.01 0.30 Low HK -0.02 -0.01 0.31 Decoupled subsidies/ Gross farm income High HK -0.01 0.00 0.30 Medium HK 0.10*** 0.10** 0.33 Low HK 0.02 0.02 0.33 Human capital transfer payments/ Gross farm income High HK -0.01 -0.01 0.27 Medium HK 0.04 0.13** 0.30 Low HK -0.02 -0.02 0.29 Rural development payments/ Gross farm income High HK 0.00 0.01 0.35* Medium HK -0.02 0.04* 0.37** Low HK 0.05* 0.05* 0.39*** NB: CMI is the cumulative Malmquist index. All variables are netted out of region and year effects (computed through median polish). Stars denote coefficient significances: * means a p-value < 0.1; ** a p-value < 0.05; *** a p-value < 0.01. Source: Own elaborations on FADN and FSS data. 254 Ornella Wanda Maietta et al. ness of training-focused educational interventions. This finding implies that the CAP reforms of the past decades, which have gradually increased the budget for rural development and promoted the economic self-sufficiency of communities through investment in local partnership, have favourably influenced the productivity of farmers’ human capital. It is interesting to compare these results with those from the empirical literature on the CAP reforms and various measures of productivity, mainly TFP (Mary 2013; Rizov et al. 2013; Kazukauskas et al. 2014; Boulanger and Philippidis 2015; Smit et al. 2015; Latruffe and Desjeux 2016; Dudu and Kristkova 2017). It is difficult to summarise the very diverse results obtained in these papers. The gist of their evidence, however, is that the impact of CAP instruments on productivity depends very much on the type of instruments. Decoupling seems to have on the whole a positive impact on productivity. Moreover, an important channel for productivity improvement is the increased specialisation in more productive farming activities. In particular, several studies (Latruffe and Desjeux 2016; Boulanger and Philippidis 2015; Smit et al. 2015; Dudu and Kristkova 2017) have argued that there may be heterogeneous effects across different types of rural development payments (such as lessfavoured-areas payments, agri-environmental measures and investments in human capital and physical capital). Therefore, future research on the productivity of human capital should consider in greater detail the impact of different types of rural development subsidies and analyse its evolution for various types of agricultural production. 6. Concluding remarks This paper provides evidence about the evolution of the productivity of family labour endowed with different levels of human capital across the EU FADN regions and about the association of this evolution with TFP and changes in the CAP. The issue is relevant for agricultural growth because TFP growth in today’s agriculture is driven largely by human capital and other immaterial inputs. We find in section 5 that low human-capital accumulation may constrain TFP growth across EU regions. It has also been noted in section 2 that there are still significant differences in farmers’ human-capital endowment across EU regions. We then ask whether the CAP, by affecting the incentives for human-capital accumulation in agriculture, has favoured the attraction of this input into the agricultural sectors of EU regions. We measure the productivity of farm family labour for different levels of educational attainment (low, medium and high) using the relative shadow prices obtained by applying DEA-VRS to data sourced from the Standard Results of the FADN. Subsequently, these shadow prices are associated with indicators of CAP measures and a Malmquist TFP index. Our evidence points to an increasing trend for the shadow prices of all categories of family labour, but in particular for those with medium and high educational attainment. In relation to policy, we find a robust association between productivity growth, shadow prices of human capital and rural development payments. Decoupled subsidies and training transfers are also associated with higher productivity in the case of low and medium levels of human capital, but this evidence is less pervasive. The policy implication we draw from these results is that rural development payments are more relevant than other kinds of payments in enhancing TFP growth and human-capital productivity. The findings of our study have wide-ranging policy relevance, because higher returns for human capital may reduce the outflow of labour from agriculture. Adverse economic condi- 255 Human capital and rural development policy tions caused by the global economic crisis have reinforced the arguments for job creation in agriculture. For example, the European Commission’s recent “Communication on the Future of the Common Agricultural Policy (CAP)” identified fostering jobs in rural areas and attracting new people into the agricultural sector as key policy priorities (European Commission 2017). Looking ahead to the post-2020 CAP, the ongoing shift to rural development seems to be the right direction to pursue. Yet, as explained at the end of the previous section, further investigation in this field is required. A related issue concerns the greater attention paid to knowledge transfer and information actions in the CAP Reform 2014–2020. In fact, it remains to be seen whether these policies can overcome the strictures of previous trainingfocused educational interventions (Martin et al. 2013). Future research on these fields will of course take advantage of greater variation in the data across time. Finally, in this study, shadow prices have been used to evaluate the services of an input lacking a market price, that is, human capital. In future research, this analysis could be extended to provide absolute (as opposite to relative) shadow prices for human-capital services, which could be used in a DEA-based cost–benefit analysis. References Arcelus, F.J. and Arocena, P. (2000). Convergence and productive efficiency in fourteen OECD countries: A non-parametric frontier approach, International Journal of Production Economics 66: 105-117. Arnade, C. (1994). Using Data Envelopment Analysis to Measure International Agricultural Efficiency and Productivity. Technical Bulletin No. 1831 Economic Research Service. Washington: U.S. Department of Agriculture. Ball, E., Wang, S.L., Nehring, R. and Mosheim, R. (2016). Productivity and Economic Growth in U.S. Agriculture: A New Look. Applied Economic Perspective and Policy 38: 30-49. Banker, R.D., Charnes, A. and Cooper, W.W. (1984). Some models for estimating technical and scale inefficiencies in Data Envelopment Analysis. Management Science 30: 1078-1092. Benhabib, J. and Spiegel, M.M. (1994). The role of human capital in economic development, evidence from aggregate cross-country data. Journal of Monetary Economics 34: 143-173 Berde, C. and Piros, M. (2006). A Survey of Human Resource Management and Qualification Levels in Hungarian Agriculture. Journal of Agricultural Education and Extension 12: 301-315. Bernini Carri, C. (1995). Sources of output growth and agricultural performance in EU countries – 1960 to 1980. European Review of Agricultural Economics 22: 231-248. Blundell, R., Dearden, L., Meghir, C. and Sianesi, B. (1999). Human Capital Investment: The Returns from Education and Training to the Individual, the Firm and the Economy. Fiscal Studies 20: 1–23. Boulanger, P. and Philippidis, G. (2015). The EU budget battle: Assessing the trade and welfare impacts of CAP budgetary reform. Food Policy 51: 119–130. Burgess, P.J. and Morris, J. (2009). Agriculture technology and land use futures: The UK case. Land Use Policy 26S: S222-S229. 256 Ornella Wanda Maietta et al. Carillo, F., Carillo, M.R., Venittelli, T. and Zazzaro, A. (2013). Aging and succession on Italian farms. PAGRI 1: 39-55. Caves, D.W., Christensen, L.R. and Diewert, W.E. (1982). The economic theory of index numbers and the measurement of input, output and productivity. Econometrica 50: 1393-1414. Chambers, R.G. and Färe, R. (2008). A “calculus” for data envelopment analysis. Journal of Productivity Analysis 30: 169–175. Cimino, O., Henke, R. and Vanni, F. (2015). The effects of CAP greening on specialised arable farms in Italy. New Medit 14 (2): 22–31. Clark, J. (2009). Entrepreneurship and diversification on English farms: Identifying business enterprise characteristics and change processes. Entrepreneurship & Regional Development 21: 213-236. Coelli, T. and Prasada Rao, D.S. (2005). Total factor productivity growth in agriculture: a Malmquist index analysis of 93 countries, 1980–2000. Agricultural Economics 32: 115–134. Contò, F., Fiore, M., La Sala, P. and Papapietro, P. (2012). The role of education, knowledge and human resources for the agricultural development in the perspective of new CAP, an hypothesis of change in Basilicata. Applied Studies in Agribusiness and Commerce 6: 123-130. Costantini, M. and Destefanis, S. (2009). Cointegration analysis for cross-sectionally dependent panels: The case of regional production functions. Economic Modelling 26: 320-327. Deaton, A. (2019). Randomization in the tropics revisited: a theme and eleven variations. Woodrow Wilson School, Princeton University. Mimeo written for F. Bédécarrats, I. Guérin and F. Roubaud, Randomized controlled trials in the field of development: a critical perspective, Oxford University Press. Dell’Acqua, S. (1995). La banca dati comunitaria presso l’INEA. INEA Internal Report. Rome: INEA. Destefanis, S. and Maietta, O.W. (2009). The Productivity of Volunteer Labour: DEAbased Evidence from Italy. In Paid and Unpaid Labour in the Social Economy editd by Destefanis, S. and M. Musella. Heidelberg: Physica Verlag. Destefanis, S. and Sena, V. (2005). Public capital and total factor productivity: New evidence from the Italian regions, 1970-98. Regional Studies 39: 603-617. Dudu, H. and Kristkova, Z. (2017). Impact of CAP Pillar II payments on agricultural productivity. EUR 28589 EN, Luxembourg: Publications Office of the European Union. Dupraz, P., Vanslembrouck, I., Bonnieux, F. and Van Huylenbroeck, G. (2002). “Farmers’ Participation in European Agri-Environmental Policies”. Paper presented at the Xth EAAE Congress, Zaragoza, August 28-31. Erjavec, K. and Erjavec, E. (2015). ‘Greening the CAP’–Just a fashionable justification? A discourse analysis of the 2014–2020 CAP reform documents. Food Policy 51, 53–62. European Commission (2008). Community Committee for the Farm Accounting Data Network, RI/CC 1256 rev. 5. Brussels: European Commission. European Commission (2010a). Public debate on CAP post 2013. http://ec.europa.eu/agriculture/cap-post-2013/debate/index_en.htm. 257 Human capital and rural development policy European Commission (2010b). Commission communication on the CAP towards 2020. http://ec.europa.eu/agriculture/cap-post-2013/index_en.htm. European Commission (2014). Prospects for skilled agricultural workers. EU Skills Panorama 2014. Brussels: European Commission. European Commission (2017). The Future of Food and Farming. COM(2017) 713 final. European Commission, Brussels. Färe, R., Grosskopf, G., Lindgren, B. and Zhang, Z. (1994). Productivity growth and technical efficiency change in industrial countries. American Economic Review 84: 66-83. Filippetti, A. and Peyrache, A. (2013). Is the Convergence Party Over? Labour Productivity and the Technology Gap in Europe, Journal of Common Market Studies, 51(6): 1006-1022. Fried, H.O., Lovell, C.A.K. and Schmidt, S.S. (2008). Efficiency and Productivity. In The Measurement of Productive Efficiency and Productivity Growth, edited by Fried, H.O., C.A.K. Lovell, and S.S, Schmidt. Oxford: Oxford University Press. Fuglie, K. (2015). Accounting for Growth in Global Agriculture. Bio-based and Applied Economics 4: 201-234. Garrone, M., Emmers, D., Olper, A., and Swinnen, J. (2019). Impact of the Agri-Environmental Scheme of the Common Agricultural Policy on Agricultural Employment (No 289831, 172nd EAAE Seminar, May 28-29). Gasson, R. (1998). Educational Qualifications of UK farmers: a Review. Journal of Rural Studies 14: 487-498. Gitto, S. and Mancuso, P. (2012). Bootstrapping the Malmquist indexes for Italian airports, International Journal of Production Economics 135: 403-411. Ilak, P., Rajšl, I., Krajcar, S. and Delimar, M. (2015). The impact of a wind variable generation on the hydro generation water shadow price. Applied Energy 154: 197-208. INEA. various years. L’agricoltura italiana conta, Rome: INEA. Jonasson, L. and Apland, J. (1997). Programming sector models: frontier technology and inefficiency. European Review of Agricultural Economics 24: 109-131. Han, U., Asmild, M. and Kunc, M. (2014). Regional R&D Efficiency in Korea from Static and Dynamic Perspectives. Regional Studies 50: 1170-1184. Henke, R., Severini, S. and Sorrentino, A. (2011). From the Fischler reform to the future CAP. In The Common Agricultural Policy after The Fischler reform edited by Sorrentino, A., R. Henke, and S. Severini. London: Routledge. Hoaglin, D.C., Mosteller, F. and Tukey, J.W. (1983). Understanding Robust and Exploratory Data Analysis. New York: Wiley. Huffmann, W.E. (2001). Human capital: education and agriculture. In Handbook of Agricultural Economics edited by Gardner, B.L., and G.C. Rausser. Amsterdam: Elsevier Science. Kazukauskas, A., Newman, C. and Sauer, J. (2014). The impact of decoupled subsides on productivity in agriculture: a cross-country analysis using microdata. Agricultural Economics 45: 327-336. Keating, M., and Stevenson, L. (2006). Rural Policy in Scotland after Devolution. Regional Studies 40: 397-407. Kuosmanen, T., and Kortelainen, M. (2006). Valuing Environmental Factors in Cost-Benefit Analysis Using Data Envelopment Analysis. The Fondazione Eni Enrico Mattei. Note di Lavoro Series No 96. 258 Ornella Wanda Maietta et al. Latruffe, L. and Desjeux, Y. (2016). Common Agricultural Policy support, technical efficiency and productivity change in French agriculture. Review of Agricultural, Food and Environmental Studies, 97(1): 15-28. Leleu, H. (2013). Shadow pricing of undesirable outputs in nonparametric analysis. European Journal of Operational Research 231: 474-480. Malmquist, S. (1953). Index number and indifference surface. Trabajos de Estatistica 4: 409-242. Mankiw, N. G., Romer, D. and Weil, D.N. (1992). A contribution to the empirics of economic growth. The Quarterly Journal of Economics 57: 407-437. Martin B.C., McNally J.J. and Kay, M.J. (2013). Examining the formation of human capital in entrepreneurship: A meta-analysis of entrepreneurship education outcomes. Journal of Business Venturing 28: 211-224. Marvel, M.R., Davis, J.L. and Sproul, C.R. (2016). Human Capital and Entrepreneurship Research: A Critical Review and Future Directions. Entrepreneurship Theory and Practice 40: 599-614. Mary, S. (2013). Assessing the Impacts of Pillar 1 and 2 Subsidies on TFP in French Crop Farms. Journal of Agricultural Economics 64 (1): 133-44. Matthews, A. (2013). Greening agricultural payments in the EU’s Common Agricultural Policy. Bio-based and Applied Economics 2 (1): 1-27 Maudos, J., Pastor, J.M. and Serrano, L. (2000). Efficiency and Productive Specialization: An Application to the Spanish Regions. Regional Studies 34: 829-842. Mizobuchi, H. (2017). Productivity indexes under Hicks neutral technical change, Journal of Productivity Analysis 48 (1): 63-68. Moro, D., and Sckokai, P. (2013). The impact of decoupled payments on farm choices: conceptual and methodological challenges. Food Policy 41: 28-38. Munroe, D. (2001). Economic efficiency in Polish peasant farming: an international perspective. Regional Studies 35: 461-471. Nelson, R.R., and Phelps, E.S. (1966). Investment in humans, technological diffusion and economic growth. American Economic Review 56: 69-75. Nilsson, P. (2017). Productivity effects of CAP investment support: Evidence from Sweden using matched panel data. Land Use Policy 66: 172-182. Nin-Pratt, A. and Yu, B. (2010). Getting implicit shadow prices right for the estimation of the Malmquist index: The case of agricultural total factor productivity in developing countries. Agricultural Economics 41: 349-60. O’Donnell, C.J. and Nguyen, K. (2013). An econometric approach to estimating support prices and measures of productivity change in public hospitals. Journal of Productivity Analysis 40: 323-335. Olesen, O.B. and Petersen, N.C. (2015). Facet Analysis in Data Envelopment Analysis. In Data Envelopment Analysis. A Handbook of Models and Methods, edited by J. Zhu, Boston: Springer. Olper, A., Raimondi, V., Cavicchioli, D. and Vigani, M. (2014). Do CAP payments reduce farm labour migration? A panel data analysis across EU regions. European Review of Agricultural Economics 41: 843-873. Ouellette, P. and Vigeant, S. (2016). From partial derivatives of DEA frontiers to marginal products, marginal rates of substitution, and returns to scale, European Journal of Operational Research, 253: 880-887. 259 Human capital and rural development policy Picazo-Tadeo, A.J. Gómez-Limón J.A. and Reig-Martínez, E. (2011). Assessing farming eco-efficiency: A Data Envelopment Analysis approach. Journal of Environmental Management 92: 1154-1164. Ram, R. (1980). Role of Education in Production: A Slightly New Approach. The Quarterly Journal of Economics 95: 365-373. Reig-Martínez, E., Picazo-Tadeo, A., and Hernández-Sancho, F. (2001). The calculation of shadow prices for industrial wastes using distance functions: An analysis for Spanish ceramic pavements firms, International Journal of Production Economics 69: 277-285. Rizov, M., Pokrivcak, J. and Ciaian, P. (2013). CAP Subsidies and Productivity of the EU Farms. Journal of Agricultural Economics 64: 537-557. Sckokai, P. and Antón, J. (2005). The Degree of Decoupling of Area Payments for Arable Crops in the European Union. American Journal of Agricultural Economics 87: 12201228. Sckokai, P. and Moro, D. (2009). Modelling the impact of the CAP Single Farm Payment on farm investment and output. European Review of Agricultural Economics 36: 395423. Severini, S. and Tantari, A. (2013). The effect of the EU direct payments policy and its recent reform on farm income inequality, Journal of Policy Modeling 35 (2): 212-227. Simar, L. and Wilson, P.W. (1998). Productivity Growth in Industrialized Countries, Discussion Papers 1998036. Centre for Operations Research and Econometrics. CORE. Université Catholique de Louvain. Simar, L. and Wilson, P.W. (1999). Estimating and bootstrapping Malmquist indices. European Journal of Operational Research 115: 459-471. Sipiläinen, T. and Huhtala, A. (2013). Opportunity costs of providing crop diversity in organic and conventional farming: would targeted environmental policies make economic sense? European Review of Agricultural Economics 40: 441-462. Smit, M.J., Van Leeuwen, E.S., Florax, R.J.G.M. and De Groot, H.L.F. (2015). Rural development funding and agricultural labour productivity: A spatial analysis of the European Union at the NUTS2 level. Ecological Indicators 59: 6-18. Swaim, P.L. (1995). Job training lags for rural workers. Rural Development Perspectives 10: 53-60. Ten Raa, T., and Mohnen, P. (2002). Neoclassical Growth Accounting and Frontier Analysis: A Synthesis. Journal of Productivity Analysis 18: 111-128. Tone, K., and Tsutsui, M. (2017). Malmquist productivity index models. In Advances in DEA Theory and Applications: With Extensions to Forecasting Models edited by Tone, K. Wiley, First Edition. Unger, J.M., Rauch, A., Frese, M. and Rosenbusch, N. (2011). Human capital and entrepreneurial success: A meta-analytical review. Journal of Business Venturing 26: 341358. Van Passel, S., Van Huylenbroeck, G., Lauwers, L. and Mathijs, E. (2009). Sustainable value assessment of farms using frontier efficiency benchmarks. Journal of Environmental Management 90: 3057-3069. Welch, F. (1970). Education in production. Journal of Political Economy 78: 35-59.