Farm characteristics and exogenous factors influencing the choice to buy land in Italy

Russo, Silvia; Raggi, Meri; Bimbati, Barbara; Povellato, Andrea; Viaggi, Davide Article Farm characteristics and exogenous factors influencing the choice to buy land in Italy Bio-based and Applied Economics (BAE) Provided in Cooperation with: Firenze University Press Suggested Citation: Russo, Silvia; Raggi, Meri; Bimbati, Barbara; Povellato, Andrea; Viaggi, Davide (2025) : Farm characteristics and exogenous factors influencing the choice to buy land in Italy, Biobased and Applied Economics (BAE), ISSN 2280-6172, Firenze University Press, Florence, Vol. 14, Iss. 1, pp. 49-73, https://doi.org/10.36253/bae-15548 This Version is available at: https://hdl.handle.net/10419/321810 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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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 14(1): 49-73, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15548 © 2025 Author(s). Open access article published, except where otherwise noted, by Firenze University Press under CC-BY-4.0 License for content and CC0 1.0 Universal for metadata. Firenze University Press | www.fupress.com/bae Bio-based and Applied Economics BAE Citation: Russo, S., Raggi, M., Bimbati, B., Povellato, A. & Viaggi, D. (2025). Farm characteristics and exogenous factors influencing the choice to buy land in Italy. Bio-based and Applied Economics 14(1): 49-73. doi: 10.36253/ bae-15548 Received: December 20, 2023 Accepted: May 9, 2024 Published: June 13, 2025 Data Availability Statement: All relevant data are within the paper and its Supporting Information files. Competing Interests: The Author(s) declare(s) no conflict of interest. Associated Editor: Luca Salvatici, Valentina Raimondi, Francesco Pagliacci Editor in Chief: Fabio Bartolini ORCID MR: 0000-0001-6960-1099 BB: 0000-0002-4826-5003 AP: 0000-0002-6980-4862 DV: 0000-0001-9503-2977 Farm characteristics and exogenous factors influencing the choice to buy land in Italy Silvia Russo1,*, Meri Raggi2, Barbara Bimbati3, Andrea Povellato3, Davide Viaggi1 1 Department of Agricultural and Food Sciences, Alma Mater Studiorum, Università di Bologna, Viale Fanin 50, Bologna, 40127, Italy 2 Department of Statistical Sciences “P. Fortunati”, Alma Mater Studiorum, University of Bologna, Via Delle Belle Arti, 41, BO, Bologna, 40126, Italy 3 CREA – Research Centre for Agricultural Policies and Bioeconomy, Legnaro (PD), Italy *Corresponding author. E-mail: [email protected] Abstract. Access to land is one of the key factors of farm growth. However, related research is characterised by important gaps, in particular, facing the change over time in the nature and role of drivers of the land market. The objective of this paper is to identify the endogenous and exogenous factors that affect the decision to purchase land in Italy between 2013 and 2020. Five probit regression models were implemented to understand the role of a set of different determinants in land investment decision. The results show that factors related to capital in machinery and plant, energy production and the presence of a successor or young farmer are endogenous factors that positively influence the purchase decision. The ratio of rented land to utilised agricultural area and of family work units to total work units are endogenous factors that negatively affect the purchase decision. Exogenous factors related to the cost of capital and inflation rate affect the purchase of land in an opposite way, negatively and positively respectively. The role of Utilised Agricultural Area and Value Added per hectare varies depending on the specialisation considered. The research can support policymakers in designing policies to promote the survival and growth of farms, as well as to facilitate land investment by reducing barriers to land acquisition. Keywords: agricultural land market, land purchase, probit regression model, investment decision, purchase decision. JEL Codes: Q15, Q12. 1. INTRODUCTION Land represents a durable, fixed, heterogeneous, and non-reproducible resource and is one of the key productive factors of a farm. The purchase of land is one of the ways through which a farmer can access this fixed productive factor and represents a form of investment in a capital good. Compared to other forms of farm size growth, on the one hand, the purchase of land may require a major financial commitment and thus limits the investment in other productive assets (Jeong et al., 2022; Swinnen et al., 2016). 50 Bio-based and Applied Economics 14(1): 49-73, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15548 Silvia Russo et al. On the other hand, the full transfer of rights allows the new owner to use the land as a collateral asset in order to have greater access to credit (Binswanger et al., 1995; Bradfield et al., 2023; Swinnen et al., 2016). In comparison to investments in other types of on-farm assets, the purchase of land rarely takes place at the same time as it is planned because it is not certain that the farmer will find the supply on the local market meeting his/ her needs/capacity (Elhorst, 1993). For the farmer, the availability of land can be one of the main obstacles to the development and growth of the farm (Yanore et al., 2024). The land market is characterised by rigid supply and the purchase of land far from the farm centre would lead to increased costs and downtime (Cotteleer et al., 2008; Schimmenti et al., 2013). For all these reasons, the land market is generally defined as thin and local. The lack of data availability and the absence of wellstructured databases on land transactions, especially in Europe, has influenced and limited the research on the land market (De Noni et al., 2019). Over the years, research mainly focused on identifying the determinants of land value in specific local agricultural land markets or on how agricultural policy payments could influence land value (Baldoni et al., 2023; Czyzewski et al., 2017; Latruffe and Le Mouël, 2009; Michalek et al., 2014; Varacca et al., 2022). However, when analysing the literature relating to the investment decision, there appears to be little ex-post empirical research that takes into consideration the investment in land. The objective of this paper is to identify determinants that have influenced the farmer’s decision to purchase land in Italy between 2013 and 2020. The work is carried out using FADN data and factors are selected based on a literature review and data availability. The main novelty of the paper is that we use an original analytical framework and a conceptual model developed on the basis of the literature analysis using multiple streams of research, namely structural change in agriculture and the growth of farm size, the investment decision and the land market literature. The paper continues in Section 2 with the design of the framework. In Section 3 we proceed with the descriptive analysis of the available data and the presentation of the methodology. In Section 4, the results of the analysis are presented and will be discussed in Section 5. Section 6 is dedicated to the conclusions drawn from this study. In order to contextualize this research, a premise is needed. To the best of our knowledge, there are no studies that have identified the factors that may influence the decision of Italian farmers to invest in land. Consequently, this study and its results should be considered a preliminary exploratory attempt to identify and understand the effects of certain factors selected based on the original analytical framework. 2. THEORETICAL FRAMEWORK Land is a factor of production that is strongly connected to and not divisible from three other farm inputs such as machinery, (family) labour and buildings (Plogmann et al., 2022). Over the years, mechanisation and technological innovation have played an important role in improving farmers’ labour management and replacing the labour force leaving rural areas for better paid non-agricultural work. The adoption of machinery and technological innovation, especially when it is expensive and complex, have stimulated farmers to allocate their managerial skills, capital, and farm assets for the production of a few types of output and, thus, farm specialisation. These three factors have contributed to the development of both economies of scale and size. Although technological innovation is accessible to small and large farms, the latter seem to have more financial and managerial capacities, both internal and external, to invest in this factor. Thus, the growth in farm size induced by technological innovation seems to be stronger in large farms than in small ones. According to the theoretical literature, these dynamics generate pressures on small farms that might decide to exit the agricultural sector (Plogmann et al., 2022). In this regard, researchers have identified “off-farm income” as a factor that could play a dual role in the survival of small farms. On the one hand, the income generated by off-farm activities could represent the first step of the farm’s exit from the sector. On the other hand, this source of income could allow the farmer to remain within the agricultural sector because it could contribute to the stabilisation of the farmer’s income and facilitate access to credit, investment in farm assets, and stimulate the growth of farms managed by young farmers (Goddard et al., 1993; Hallam, 1991; Harrington and Reinsel, 1995; Key, 2020; Neuenfeldt et al., 2019; Weiss, 1999; Zimmermann et al., 2009). Human capital is one of the main factors that can influence a farmer’s investment decision. When talking about human capital, reference is made to demographic characteristics of the farmer and their family. In particular, the age of the farmer and the presence of a potential successor, and the level of education are among the main characteristics that can affect farm size and investment in land. As the age of the farmer increases, the farm enters the so-called maturity and/or decline phase and the farmer 51 Farm characteristics and exogenous factors influencing the choice to buy land in Italy Bio-based and Applied Economics 14(1): 49-73, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15548 may be more reluctant to increase the farm size (Bremmer and Oude Lansink, 2002). The presence of a potential successor could prevent the farm from entering the decline phase and thus positively affect the investment in a fixed input (Huber et al., 2015). Furthermore, the purchase of land could entail a major financial commitment and the application for a bank loan. In this regard, the presence of a young farmer or a successor could positively influence the time horizon of the investment and favour the purchase decision (Elhorst, 1993; Huber et al., 2015; Oskam et al., 2009; Oude Lansink et al., 2001). In addition to these factors, human capital also includes managerial skills that if not possessed by the farmer can be found in the external environment e.g. by turning to advisory and consultancy services (Boehlje, 1992). According to the literature, larger farms may have greater economic and financial capacities to access such services. The decision to invest in land is not only influenced by structural and socio-demographic characteristics of the farm, but also by exogenous factors such as the macroeconomic environment, land market regulations, and agricultural policies. The purchase of land represents an investment in a capital good that may require a major financial commitment. In this sense, the cost of capital and the financial position of the farmer could influence the decision and the level of investment. As the interest rate increases, the probability that the farmer is willing to invest and the level of investment decreases. Land is not only an important production asset of a farm but also a “safeheaven” asset (Schimmenti et al., 2013), attracting the interest of non-farmers who decide to invest in it to protect the capital value from inflation. An increase in the inflation rate leads to an increase in the price of land and vice versa (Elhorst, 1993; Lawley, 2021; Szymańska et al., 2021; Thijssen, 1996). Policymakers can use land regulation as an instrument to defend the farmers’ position and mitigate potential speculative force in farmland market. Each European Member State has full decision-making power over its own land regulation. In general, Western European countries have a more liberal land regulation than Eastern European countries. Among the Western countries, Italy is one of the European countries with the most liberal land regulation (Swinnen et al., 2016). With the aim of facilitating access to land for medium-sized farms with financial means, many European countries have provided for the right of pre-emption to be exercised either by local governments, as in France, Germany and the Netherlands, or by farmers, as in Italy (Galletto, 2018). In particular, the Italian government introduced this instrument to reduce the fragmentation of Italian farms, to improve the consolidation of the Italian agricultural sector and to facilitate the development of family farms. In Italy, Art. 8 Law n. 590/1965 and art. 7 Law n. 817/1971 establish that the Italian farmer may exercise the right of pre-emption of land if at least one of three cases occurs: a) he/she is the co-owner of the farm, b) he/she is a professional farmer who directly borders land for sale, c) if he/she has been renting the land for at least two years (Legge 590/1965; Legge 817/1971).The right of pre-emption has also been extended to agricultural partnerships (as a rule, simple partnerships, and general partnerships) if at least half of the partners are “owner-operator farmer”. Subsequently, between 2009 and 2016, the Italian State implemented tax concessions to improve the farmer’s position. The law states that: a) the Italian farmer with a family farm does not have to pay income tax or land use tax; b) the Italian farmer is exempt from paying income tax on the use of the land; c) in case of land purchase, when the buyer is a “owner-operator farmer” or professional agricultural entrepreneur, she/he will pay only 1% of the purchase price as tax, while any other buyer will pay 15%. In 2017, the European Parliament called on all Member States to review their land regulation in order to ensure fair access to land and to prevent it from being concentrated within a few large farms (European Parliament, 2017). In addition to preserving the farmer’s position, land regulation influences the capitalization of subsidies provided by agricultural policies within the value of land and rental rates. Stringent land regulation on the land market and land rental market would reduce the capitalisation of subsidies within the land price and rent. The literature presents both theoretical and empirical studies on whether and how much of the subsidies provided through policies are capitalised within the land price value. From a theoretical study, in a perfect market, decoupled direct payments, coupled direct payments, rural development programmes and environmental payments could be capitalised within the land price. However, empirical studies suggest that capitalisation in a real land market is lower than theorised and depends on many factors such as subsidy type, land supply elasticity and farm credit constraints. In addition to influencing land value, subsidies can also influence a farmer’s investment decision and level of investment. Subsidies were introduced with the main objective of supporting the farmer’s income and represent a form of income not affected by production risks. Consequently, subsidies could positively influence the investment decision and level especially in the presence of an imperfect market. The identified factors are not independent but interact and influence each other (Zimmermann et al., 2009). 52 Bio-based and Applied Economics 14(1): 49-73, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15548 Silvia Russo et al. In the literature, four empirical studies concerning the farm size growth were identified that adopted a regression model with the farm size as the dependent variable (Akimowicz et al., 2013; Bremmer and Oude Lansink, 2002; Brenes-Muñoz et al., 2016; Weiss, 1999). In the literature related to investment decision, two empirical researches were identified that also considered land as a form of investment (Elhorst, 1993; Oskam et al., 2009). In addition, Jeong et al. (2022) identified farm economic characteristics that could affect the decision to buy or lease land in Korea by adopting the machine learning algorithm “random forest”. Finally, Ziemer and White (1981) attempted to better estimate farmland demand in Georgia between 1970 and 1978 by accounting for the process underlying the decision to purchase. Based on the literature review, factors endogenous and exogenous to the farm that may have an influence have been identified and summarised in a conceptual model shown in Figure 1. Similar to the studies on farm size growth (Zimmermann et al., 2009), we do not assume that the identified exogenous and endogenous factors are independent of each other, but that they interact and condition each other. 3. MATERIALS AND METHODOLOGY 3.1. Data and descriptive analysis The analysis was conducted on Italian FADN data of Italian farms observed between 2013 and 2020. The data represent an unbalanced panel data consisting of 84610 observations representing 24212 farms. On average, the same farm remains in the sample for about 3 to 4 years. For each farm, there is information on the structural characteristics of the farm, data on the farm’s balance sheet, and data on the socio-demographic characteristics of the farms. Of the 24212 farms in the sample, 919 made at least one investment in land during the period in question, of these 176 farms made more than one investment (Table 1). Around 90% of the sample is characterised by specialised farms in cereals, arable crops, horticulture, fruit Economic environment Land market Policy: European agricultural policies and land regulation Purchase of Land Farmer’s sociodemographics characteristics Farm structural characteristics Endogenous factors Exogenous factors Figure 1. Conceptual model developed based on the literature review. 53 Farm characteristics and exogenous factors influencing the choice to buy land in Italy Bio-based and Applied Economics 14(1): 49-73, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15548 crops, olive growing, viticulture, dairy cattle, herbivores and granivores. The remaining 9.45% by non-specialised farms, of which 9.4% are mixed crop and livestock farms. Thirty-two percent of the sample is specialised in annual crops, 29.9% are permanent crops and 27.8% livestock farms (Table 2). Thirty-nine percent of the land purchases were conducted by farms specialising in permanent crops, followed by farms specialising in annual crops and livestock. In particular, 18% of the recorded transactions were conducted by farms specialising in fruit crops, 16.5% by vineyards, and 12% by farms specialising in arable crops (Table 2). In terms of average UAA, specialised livestock farms are the largest, followed by annual crops and permanent crops. Among all specialisations, farms specialised in viticulture have the smallest average farm size followed by those specialised in fruit crops and horticulture. There is an important difference in farm size between horticultural farms and those specialised in other annual crops. Farms specialised in permanent crops have lower “RENT/UAA” ratios than farms specialised in annual crops and livestock (Appendix 1). 3.2. Empirical Model Since the investment decision represents a discrete problem (Elhorst, 1993), to estimate the probability of participation decision we adopted a probit regression model. The empirical model implemented to conduct the quantitative analysis was developed based on the conceptual model in figure 1 and peculiarities of FADN data. In particular, the characteristics of our database did not allow us to conduct a dynamic analysis, which would be appropriate since investments in capital stock are not annual investments (Lefebvre et al., 2015) and generally do not occur at the same time as they are planned (Elhorst, 1993). The empirical probit model used is described by the following equation: Where: y*i is the binary dependent variable that assumes a value equal to 1 in the year in which the purchase occurs, 0 otherwise. εi is the composite error term. i represents the single observation, xki is the observed value of explanatory variables that described factors linked to farm characteristics, farmer Table 1 Descriptive analysis: Dimension of the unbalanced panel data. Full Sample Buyer % Number of observations 84610 1095 1.3 Number of farms 24212 919 3.8 Table 2. Descriptive statistics of the sample based on farm specialization. Specialization Sample % Total observation Buyers % Total observation N. Observations N. Observations No specialisation: 7997 9.45 91 8.3 Unclassifiable farms 11 0.013 0 0 Mixed crops and livestock farming 7986 9.4 91 8.3 Annual Crops 27796 32.9 312 28.5 Cereals 8812 10.4 102 9.3 Arable Crops 10292 12.2 133 12.15 Horticulture 8692 10.3 77 7.03 Permanent Crops 25305 29.9 432 39.45 Fruit Crops 10721 12.7 202 18.45 Olive growing 4034 4.8 47 4.3 Viticulture 10550 12.5 183 16.7 Livestock farms 23512 27.8 260 23.75 Dairy cattle 7339 8.7 102 9.3 Herbivores 12108 14.3 102 9.3 Granivores 4065 4.8 56 5.1 TOT 84610 100 1095 100 54 Bio-based and Applied Economics 14(1): 49-73, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15548 Silvia Russo et al. socio-demographic characteristics and exogenous variables. The effect of xi on is represented by . and are respectively the intercept and the errors for i. The equation is estimated using the ‘glm’ function in Rstudio of the ‘stats’ package. The explanatory variables (Table 3) introduced in the probit model are listed and defined below. 3.2.1. Description of the explanatory variables and expected relation Utilized agricultural area It is unclear what effect the initial size of the farm may have on the growth of farm size and on the investment decision. Given the nature and characteristic of the data of this variable, it was decided to introduce as an explanatory variable the “UAA SQ” which represents the squared value of the total initial UAA of the farm regardless of whether it is owned, leased, or free use. The use of the square variable is able to catch the non-linear effect of it. Assuming that farm size can also be a measure of the farm’s ability to generate income (Oude Lansink et al. 2001), we expect this variable to have a positive effect on the investment decision. Value added per hectares This variable was introduced as an explanatory variable representing the productivity of land. Through this variable, the aim is to understand whether the productivity per hectare derived from the farm’s activity affects the growth of the farm size through purchase. According to the literature, the farmer is encouraged to buy land when productivity is high (Ciaian et al., 2010). Therefore, it is assumed that, as productivity per hectare increases, the likelihood of the farmer investing in land increases. Value added per total work unit This explanatory variable represents the productivity of farm labour. It is defined as the ratio of value added to total work units. It is assumed that as productivity per labour unit increases, the probability of the farmer purchasing landalsoincreases. Production Specialisation When not focusing on a single specialization (e.g., the dairy sector), the researchers introduced a categorical variable related to farm specialisation (e.g Akimowicz et al., 2013) in order to understand whether the type of farm could influence the farm growth or investment decision. This is probably related to the fact that the type of assets needed by a farm varies according to their specialisation (Lefebvre et al., 2015). The data at our disposal include specialised and non-specialised farms. Specialisation is defined according to the technical-economic orientation of the FADN database (FADN, 2018). In contrast to this original classification, in this model farms classified as “mixed crop and livestock” are included in the “non-specialised farms”. Specialised farms fall into 9 categories: Cereal crops, arable crops, horticulture, fruit crops, olive crops, viticulture, dairy cattle, herbivores and granivores. Therefore, the explanatory variable was introduced into the model to account for the nine specialisation categories. “No specialisation” is used as the reference category since specialisation is one of the main drivers of the search for economies of scale and farm growth. Introducing this variable allows us to understand not only whether specialised farms invest more than non-specialised ones, but also whether the probability of buying land in Italy changes as specialisation changes. Utilised agricultural area *Specialisation The necessary assets of a farm and the “optimal size” vary depending on the type of farming (Lefebvre et al., 2015; Plogmann et al., 2022). In order to test whether the effect of farm size can vary according to the type of farming, it was decided to combine the two previous variables “UAAsq” and “Specialisation”., (Bremmer and Oude Lansink, 2002). Rent/Utilised agricultural area On the one hand, renting allows the farm more flexibility and the possibility to invest its liquidity in other productive assets (Swinnen et al., 2016). On the other hand, land managed as property allows the farmer to use it as collateral capital and thus to have greater access to credit (Swinnen et al., 2016). It was decided to introduce the ratio of the land managed under rent to the total utilised agricultural area of the farm as an inverse measure of the amount of collateral available (Benjamin and Phimister, 2002; Lefebvre et al., 2015). However, expectations on the direction of the effects of this variable are ambiguous. Machinery Plant Value Machinery and plant represent another form of collateral capital for a farm. It is assumed that high values of this variable correspond to a farm’s recent investment in such productive assets that vary proportionally to the farm area (Plogmann et al., 2022). Furthermore, there is a correlation between the intention to purchase land and investment in other farm assets (Lefebvre et al., 2015). 55 Farm characteristics and exogenous factors influencing the choice to buy land in Italy Bio-based and Applied Economics 14(1): 49-73, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15548 Therefore, it is hypothesised that the farm is inclined to purchase with the aim of maximising the productive capacity of the asset in which it has previously invested. Common Agricultural Policy The Common Agricultural Policy has been identified as an exogenous factor that can influence the land price, but also the decision and level of investment. Subsidies received and capital financing are not the same for every farm and for this reason it can be considered as an endogenous variable linked to structural characteristics of the farm. It was decided to introduce two continuous variables. the first, the ratio of income subsidies per hectare related to the first pillar of the Common Agricultural Policy and COM. The second, the value of the investment subsidies received by the farm between 2013 and 2020 and connected to the measures of the second pillar of the Common Agricultural Policy. Pre-purchase Investment in land is a planned, long-term investment (Elhorst, 1993; Oskam et al., 2009; Oude Lansink et al., 2001). The land market is thin and local, and it could be difficult for a farmer to find the amount of land he wants at one time. (Cotteleer et al., 2008; Elhorst, 1993). Therefore, it may happen that the farmer must make more than one purchase to reach the desired level of investment. The dummy variable “Pre_Purchase” assumes a value equal to one when the purchasing farm has already made a purchase previously between 2011 and 2020. Diversification activities In the literature reviewed, researchers have not considered the role that farm-related activities can have on farm growth and the investment decision. The related activities that can be stimulated by RDP measures allow for a diversification of the farm activity and represent a different form of income for the agricultural firm. Three dummy variables were introduced for three agricultural related activities: agrotourism, energy production and contracting. It is expected that conducting agricultural related activities increases the probability that the purchase will occur. Family work units Family labour can be considered as a fixed input of production within the farm (Elhorst, 1993) and Elhorst’s research showed that as family labour input increases, investment increases. Weiss (1999) and Oude Lansink et al. (2001) showed that the number of family members affects farm growth and the investment decision. The variable FWU/TWU was introduced into the model as a measure of how much the business depends on family labour. It is hypothesised that family farms have a greater interest in investing in the farm and farm growth and thus, as this ratio increases, the probability that the farm invests in land increases. Age of farmer and successor The age of the farmer and the presence of the successor can affect the growth of the farm and the investment decision. Since there may be several farmers and potential successors with different ages on the same farm, it was decided to create four dummy variables related to the holder and his/her age, and one related to the presence of the successor. In particular, four age ranges were identified to which dummy variables corresponded. Each dummy variable relating to the holder takes the value of one if there are no successors for that observation and if the holder or all the holders fall within the range defined by the dummy variable. If the observation corresponds to more than one holder falling in different age groups, all variables related to the holders will have value zero. The variable relating to the presence of a successor will take a value of 1 if there is at least one potential successor between the ages of 1 year and 40 years. A successor was the one who was classified within the dataset as the ‘son’ or ‘grandson’ of the farmer. Off-farm income In the literature, it is unclear whether the earning of an off-farm income can be a prelude to leaving the sector or represents a form of income that allows the farm to survive better and not leave the sector (Lefebvre et al., 2015; Plogmann et al., 2022). Based on the available data, a dummy variable was created which takes the value of 1 if the farmer or a member of his or her family who is employed part-time or full-time on the farm earns an off-farm income >2000 euros. 3.2.2. Exogenous factors As mentioned before, land is considered an asset that can be used as collateral and a safe investment option. The model introduces two external factors: inflation rate and interest rate. It is assumed that when inflation rates rise, the likelihood of purchasing land also increases. However, buying land may require a significant investment that the farm may need to finance through a bank loan. As interest rates go up, the probability of making such an investment decrease. The inflation rate values, Consumer Price Index-CPI, are obtained from the ISTAT website every December of the reference year, while the 56 Bio-based and Applied Economics 14(1): 49-73, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15548 Silvia Russo et al. interest rate is determined by the average annual yield of Italian BTPs (Multi-year Treasury Bonds), which can be found on the website of the Italian Treasury Ministry. 3.2.3. Descriptive analysis of explanatory variables Table 4 shows the descriptive analysis of the variables included in the model, in particular each variable has two values: one for all farms and one for “buyers” (farms investing in land during the reference period). The average initial area of the sample is 33.7 ha, which increases by about 5 ha if only buyers are taken into account. The value related to value added per hectare (VA/ha) exhibits significant variations among the farms. Nevertheless, these differences decrease considerably when only the buyers are considered. Furthermore, the average value of the variable “VA/ha” is lower for the buyers, whereas the median value for buyers is higher than the value within the entire sample. The average value of machinery and plant of the farms that invested in land is more than twice as high as the sample average. Table 3 Definition of the explanatory variables and expected effects on the decision to buy land. Variables Specification Type of variable Expected effect Farm structural characteristics UAAsq Utilised Agricultural Area square Continuous + Production specialisation Agricultural specialisations considered are: cereals, arable crops, horticulture, fruit crops, olive growing, viticulture, dairy cattle, herbivores, granivores. Categorical; Non-specialised farms as reference + VA/ha Ratio between Value added (excluding Income subsidies and COM subsidies) and UAA Continuous + VA/ TWU Ratio between Value Added and total work units Continuous + UAASQ *Production Specialisation Continuous*categorical; nonspecialised farms as reference + VA/ha*Specialisation Continuous*categorical; Non-Specialised farms as reference + VA/TWU*Specialisation Continuous*categorical; Non-Specialised farms as reference + RENT/UAA The ratio of the rented UAA to the UAA Continuous +/- Machinery_ Plant value Value of Machinery+ equipment + plant; It represents a proxy variable for level of innovation on farms Continuous + Income subsidies/ha Aid per hectare provided by First Pillar and COM Continuous + Investment subsidy Investment aid (Second Pillar) Continuous + Energy Production Farm produces renewable energy Dummy + Subcontracting activities Farm carries subcontracting activities Dummy + Agrotourism Farm carries out agrotourism activities Dummy + Pre_purchase Purchases made between 2010-2020 Dummy + FWU/TWU Ratio of family work units to total work units Continuous + Farmer sociodemographic characteristics FARMER_18_39 The farm manager is between 18 and 39 years old Dummy + FARMER_40_49 The farm manager is between 40 and 49 years old Dummy + FARMER_50_59 The farm manager is between 50 and 59 years old Dummy - FARMER_OVER60 The farm manager is aged 60 old or older Dummy - SUCC_1_39 There is a potential successor aged between 1 and 39 on the farm Dummy + OFFFARM_INCOME Farmer with non-agricultural income >2,000 euro; Children/ grandchildren, father-in-law, parent, wife employed part-time or regularly with non-agricultural income >2000 euro Dummy + Exogenous variables INTEREST RATE Interest rate recorded for each year on the Ministry of the Treasury website Continuous + INFLATION_ RATE Inflation rate taken for each year from the ISTAT website Continuous - 63 Farm characteristics and exogenous factors influencing the choice to buy land in Italy Bio-based and Applied Economics 14(1): 49-73, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15548 affect the investment decision. The results from the five models suggest that, in general, being between 18 and 50 years old has a positive effect on the decision to buy land. This probability is even higher if the farmer is aged 18-40. As was hypothesised, the variable on the perception of off-farm income has a positive influence on the purchase decision and is one of the most statistically significant variables (p<0.001). Consistent with the hypothesis, the variables relating to the external macroeconomic environment, i.e., the inflation and interest rates, influence the investment decision in opposite direction. As the inflation rate increases, the probability of investment decision increases. As the interest rate increases, the probability that the farmer will invest in land decreases. 5. DISCUSSION In comparison to the few empirical studies on the growth of farm size and investment decision including land, this research is conducted on the entire FADN dataset collected at the national level. The analyses are not based on a sample of farms specialising in one type of farming and/or located in a specific and limited geographical area. Our data are characterised by 90% observations of farms specialising in 9 different productions and differing in farm and socio-demographic characteristics. This heterogeneity of the analysed sample is due to intrinsic characteristics of the Italian agricultural sector. The average surface area of the farms in the sample is about 30 ha above the average UAA recorded in the last ISTAT 2010-2020 census (ISTAT, 2022). This is because the FADN sample is stratified and selects companies with a Standard Output above 8,000 Euro. In any case, the FADN data maintain a certain degree of representativeness of the agricultural sector and represent a useful resource in terms of the amount of data collected in Europe (Centre for European Policy Studies, 2008; Ciaian et al., 2010). Out of the total observations, only 1095 (1.29%) invested in land between 2013 and 2020. The high number of zero-observations can be attributed to the specific characteristics of the land factor and of the land market, as it is unlikely that farms invest in capital goods every year (Elhorst, 1993; Nilsen and Schiantarelli, 2003; Oskam et al., 2009). The high number of zero-observations and the complexity of ignoring the heterogeneity effect are some of the reasons why quantitative research using micro-data in the investment decision-making process is challenging (Elhorst, 1993). In the empirical studies on the farm size growth and the investment decision, the role of utilised agricultural area is unclear. The five models do not allow to clarify, but to better understand the role of this variable. The initial farm size influences the investment decision negatively but has a different effect depending on farm specialisation. This had already partly emerged in the study conducted by Bremmer and Oude Lansink (2002), which found that UAA had a positive influence on the size growth of arable crops farms and a negative influence on the growth of farms specialized in protected horticulture. In this research, the positive effect of the variable “UAA SQ” in the case of farms specialising in fruit crops, viticulture and horticulture can be linked to two different considerations. The first one is linked to characteristics of the FADN data. The mean and median value for permanent crops and horticulture farms is lower than for other crops. This could confirm the hypothesis that when a farm is very large it does not tend to invest in the land input (Lefebvre et al., 2015). The second one is related to the intrinsic characteristics of the type of farming. Variable Estimate Std.error Statistic p. value  FARMER_OVER60 -0.05757 0.08041 -0.71592 0.474042 SUCC_1_39 0.197505 0.083187 2.374226 0.017586 * OFF_FARM INCOME 0.116606 0.03276 3.559429 0.000372 *** Exogenous factors Inflation rate 0.102362 0.031866 3.212269 0.001317 ** Interest rate -0.12011 0.020178 -5.95262 2.64E-09 *** N. observations 84610 N. farms 24121 Pseudo R20.2 AIC    9440  Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1. Table 8. (Continued). 64 Bio-based and Applied Economics 14(1): 49-73, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15548 Silvia Russo et al. Table 9. Probit regression results based on the Model 5 specification. Variable Estimate Std.error Statistic p. value  (Intercept) -1.99498 0.115489 -17.2742 7.36E-67 *** Farm structural characteristics UAASq -2E-05 1.11E-05 -1.81586 0.069391 . VA/ha -7.9E-07 2.86E-06 -0.27699 0.781788 VA/TWU 1.32E-06 1.65E-06 0.799903 0.423767 No specialisation Cereals 0.07122 0.092882 0.766783 0.443211 Arable Crops 0.09771 0.082926 1.178278 0.238686 Horticulture -0.13821 0.097122 -1.42309 0.15471 Fruit Crops 0.023628 0.081487 0.28996 0.771847 Olive growing 0.051554 0.121524 0.424233 0.671396 Viticulture 0.057211 0.077299 0.740133 0.459219 Dairy cattle 0.082709 0.093701 0.88269 0.377404 Herbivores -0.07159 0.080316 -0.89129 0.372772 Granivores -0.03472 0.103522 -0.33543 0.737302 UAAsq*No specialisation UAAsq*Cereals 1.78E-05 1.13E-05 1.576344 0.114946 UAA sq*Arable Crops 1.49E-05 1.16E-05 1.281408 0.200051 UAA sq*Horticulture 1.92E-05 1.16E-05 1.644969 0.099976 . UAA sq*Fruit Crops 2.51E-05 1.15E-05 2.170636 0.029959 * UAA sq*Olive growing -4.5E-05 5.18E-05 -0.86149 0.388966 UAA sq*Viticulture 2.2E-05 1.15E-05 1.912193 0.055851 . UAA sq*Dairy cattle 1.7E-05 1.14E-05 1.488127 0.136717 UAA sq*Herbivores 1.94E-05 1.11E-05 1.747083 0.080623 . UAA sq*Granivores -1.1E-05 1.95E-05 -0.56654 0.571024 VA/ha*No specialisation VA/ha*Cereals -8.6E-05 5.8E-05 -1.48766 0.136839 VA/ha*Arable Crops -8E-06 8.82E-06 -0.9121 0.361714 VA/ha*Horticulture -3.7E-06 3.63E-06 -1.02881 0.303568 VA/ha*Fruit Crops 9.08E-06 4.61E-06 1.968187 0.049047 * VA/ha*Olive growing -4.3E-05 3.07E-05 -1.41899 0.155902 VA/ha*Viticulture 1.76E-06 4.38E-06 0.401473 0.688072 VA/ha*Dairy cattle 6.37E-06 8.45E-06 0.753606 0.451086 VA/ha*Herbivores -1.1E-05 1.02E-05 -1.0554 0.291243 VA/ha*Granivores -1.8E-06 3.54E-06 -0.50791 0.611519 AV/TWU*No specialisation AV/TWU*Cereals 2.36E-07 2.02E-06 0.116892 0.906946 AV/TWU*Arable Crops -1.7E-06 2.09E-06 -0.81922 0.412663 AV/TWU*Horticulture 7.66E-07 2.04E-06 0.375434 0.707337 AV/TWU*Fruit Crops -7.4E-07 2.09E-06 -0.35518 0.722453 AV/TWU*Olive growing 2.06E-06 3.96E-06 0.519424 0.603465 AV/TWU*Viticulture -5.5E-08 1.86E-06 -0.02988 0.976165 AV/TWU*Dairy cattle -2.7E-06 2.1E-06 -1.29293 0.196035 AV/TWU*Herbivores -5.1E-07 1.9E-06 -0.27174 0.785824 AV/TWU*Granivores 5.28E-07 1.79E-06 0.295245 0.767807 RENT/UAA -0.12308 0.034091 -3.6102 0.000306 *** FWU/TWU -0.35157 0.051736 -6.79551 1.08E-11 *** Machinary_ Plant Value 3.23E-07 7.85E-08 4.112943 3.91E-05 *** (Continued) 65 Farm characteristics and exogenous factors influencing the choice to buy land in Italy Bio-based and Applied Economics 14(1): 49-73, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15548 Unlike annual crops, farms producing permanent crops may prefer to establish new orchards on their own land. Obviously, permanent crops require a higher investment cost and return on investment time than annual crops. This could explain why farmers specializing in perennial crops might have an incentive to buy land because it grants them a property right that cannot be guaranteed by the rental contract. This aspect could be particularly relevant in a country like Italy where the law allows leases of less than 15 years. This consideration could explain the positive effect exerted by land productivity in the case of companies specialized in fruit crops. With regard to the effect of specialisation, the results showed that specialisation per se does not affect the probability of land purchase of the farms in the sample, contrary to what was assumed on the basis on the theoretical literature. The introduction of interactions of this categorical variable with the variables UAA, VA/ha, and VA/TWU has allowed for a better understanding of the behaviour of these factors. The results of the interactions suggest that the effect of firm size, initiation, and farm productivity may vary according to the specialization. Consequently, specialisation plays an important and crucial role in understanding and differentiating the effect of other factors on the probability of land investment. This would confirm what has emerged from the theoretical literature, namely that the factors that can determine farm growth are not independent but interact with each other. The effect of specialisation on farm growth and size had already emerged in the research conducted by Akimowicz et al. (2013) according to which specialisation influenced farm size, changes in farm size and growth intensity in the Midi-Pyrenees region between 2000 and 2007. Although theoretically it would be desirable for a farm to have a balance between owned and rented land, as the ratio of rented to total area (RENT/UAA) increases, the likelihood of farms increasing their share of owned land decreases. This result could be a confirmation of the findings of the last census of the Italian agricultural sector according to which the amount of land managed under lease has increased and this form of management is also becoming established in Italy (ISTAT, 2022). The descriptive analysis of the data in Annex 1 shows that the farms in the sample specialised in permanent crops have far lower “RENT/UAA” ratios than those specialised in annual crops and livestock farming. Understanding whether isolating this variable would have a different effect depending on the specialisations would be interesting. The results for the value of capital of machinery and plant confirm what the Bremmer et al. (2002), Lefebvre Variable Estimate Std.error Statistic p. value  Subsidies UE/SAU` 1.88E-05 1.72E-05 1.093637 0.274114 Capital Account 1.7E-06 1.11E-06 1.532366 0.125432 Energy production 0.147376 0.060086 2.452755 0.014177 * Subcontracting activities 0.213639 0.058772 3.635061 0.000278 *** Agrotourism -0.04862 0.062315 -0.78028 0.435227 Pre_PURCHASE 15.53225 38.49138 0.403525 0.686562 Farm socio-demographic characteristics FARMER_18_39 0.209433 0.081497 2.569824 0.010175 * FARMER_40_49 0.163576 0.079486 2.057915 0.039598 ** FARMER_50_59 0.088748 0.079589 1.115078 0.264817 FARMER_OVER60 -0.05863 0.080534 -0.72797 0.46663 SUCC_1_39 0.194356 0.083337 2.332157 0.019692 * OFF_FARM INCOME 0.113544 0.032812 3.460433 0.000539 *** Exogenous factors Inflation rate 0.102934 0.031912 3.225551 0.001257 ** Interest rate -0.12062 0.020214 -5.96728 2.41E-09 *** N. observations 84610 N. farms 24612 Pseudo R20.2 AIC    9450  Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1. Table 9. (Continued). 66 Bio-based and Applied Economics 14(1): 49-73, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15548 Silvia Russo et al. et al.(2015), and Jeong et al. (2022) research had already found. The former had shown how the degree of mechanisation influenced farm growth for arable crops and horticulture in the Netherlands. Lefebvre et al. (2015)’s study of farmers’ investment intentions in six European countries had shown that farmers are most likely to invest in one asset class after they have already invested in another. The correlation between the intention to invest in two types of assets was also shown between land and the purchase of machinery and machinery replacement. Furthermore, research by Jeong et al. (2022) had shown that the value of inventory and fixed assets were positively related to land acquisition. To the best of our knowledge, no research in the literature has included variables related to agricultural policies and activities in models to explain farm growth or land investment. The results on subsidies and funding related to European Agricultural Policies do not confirm the hypothesis that agricultural policies can directly influence the decision to invest in land. Rather, subsidies could be seen as a useful tool for the farmer to manage periods of market fluctuations rather than as a form of income to make a long-term investment. The RDP measures do not directly finance land purchases, but they encompass financing for investments in tangible farm assets, innovation, and farm diversification. The correlation analysis allows us to exclude the presence of a relationship between “Capital account” and the value of machinery, and of these two variables with the dummy variable relating to subcontracting and energy production. The results for the latter two variables and the forms of income derived from off-farm income lead to the conclusion that the investment in a capital good is supported by forms of income derived from a diversification of the activities carried out by the farmer. The results confirm the conclusions of previous research on the positive effect of the presence of a successor and a young farmer. Indeed, in line with the hypothesis, the presence of a young farmer or a farmer under 50 years of age positively influences the probability of purchase. This is probably due to the fact that the age of the holder has an impact on the time horizon of the investment. The results for the introduced exogenous variables confirm the hypothesis. The macroeconomic context influences the investment decision. The inflation rate was not included in the empirical literature analysed on farm size growth and investment decision, while the results regarding the cost of capital confirm what has already found by Elhorst (1993) and Oskam et al. (2009). It is worth noting that there were no significant changes in interest rates and inflation rates during the considered period. It would be necessary and useful to observe farms over a longer period to fully understand the impact of exogenous factors related to the macroeconomic context, such as those that have occurred in the last two years. The models explain 19% of the land investment decision, suggesting that there are other factors not considered that influence the decision to purchase land. The relative Pseudo R2 value is lower than that of other studies on structural change but more in line with studies on investment decision. As in other research (i.e. (Akimowicz et al., 2013), the available data and their quality have influenced the choice of explanatory variables and the type of analysis. It was not possible to conduct the analysis on balanced panel data and include explanatory variables related to the financial position of the farm, its local area, and national and municipal land regulation. Investments in capital goods could represent a significant investment that may even require a bank loan. These are rational decisions that the farmer makes after analysis of the internal and external business contest. Therefore, in order to study and understand this type of investment it would be appropriate to carry out the analysis on farms observed over a long period of time. When testing and implementing the model, we attempted to include the regional variable as a categorical variable. However, this variable reduced the statistical significance of other explanatory variables related to farm structure. The regional variable already contains information related to other variables such as specialisation, UAA, and RENT/UAA. This is because the Italian territory is highly heterogeneous regarding territorial structure, production, and farm management. For this reason, it was preferred not to include it. Furthermore, the land market is thin and local, and the absence of precise geolocation data for farms prevented the consideration of other external factors. Farmers tend to buy land near their activity to reduce and avoid downtime (Cotteleer et al., 2008). In this regard, the introduction of variables related to the right of pre-emption could be useful in understanding the Italian land market, given that such right is provided for within Italian legislation. Finally, in addition to data availability, the lack of literature has influenced the design of the theoretical framework for developing the conceptual model and the interpretation and discussion of the results. 6. CONCLUSIONS This research represents a first attempt at an ex-post study using microdata to identify the factors that have influenced the land investment decision in Italy by introducing variables related to structural and socio-demo- 67 Farm characteristics and exogenous factors influencing the choice to buy land in Italy Bio-based and Applied Economics 14(1): 49-73, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15548 graphic characteristics, economic performances, agriculture policies and the macroeconomic environment. The results showed that more than subsidies provided by agricultural policies, income-generating activities from other on-farm and off-farm activities positively influence land investment. In addition, specialisation appears to be an important factor not so much in the purchase decision, but in understanding and differentiating the effect of other farm structural factors on the likelihood of land investment. The variables RENT/UAA and Family Work units/Total Work Units are the main farm characteristics that negatively influence the probability of purchasing land in Italy. As expected, the presence and age of the successor have been confirmed as important sociodemographic characteristics for growth through acquisition. The research shows that the interest rate and inflation rate influence the probability of buying land. The five implemented models explain approximately 20% of the land investment decisions of the analysed farms. Therefore, other factors and the interaction between factors can influence farmers’ decisions. The lack of a well-structured database conditioned and limited this research as well as the empirical research analysed in the literature on farm size growth and land investment decisions. In particular, probit analysis on a balanced panel of farms observed over a long period of time was not possible with the available database. Investment in land is much less frequent than other types of investment. It is made following a farmer’s consideration of available farm assets, his/her own financial resources, the supply of land on the local land market, and macro-economic factors (i.e. interest rate and inflation rate). For this reason, the analysis of a balanced panel of farms observed for a long time could allow a more accurate analysis of the effect of determinants on the decision to purchase land. In addition, the database influenced the identification and selection of variables that could best capture the determinants that may influence the farmer’s decision and prevented the introduction of variables related to e.g. the financial situation of the farm and land regulation. In the future, the problem of the structured database could be solved by linking the databases available to different Italian institutions. 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Descriptive analysis of sample farm size based on farm specialization Specialisation Min VA/UL Mean VA/ TWU Median VA/ TWU sd VA/ TWU Max VA/ TWU UAA RENT/ VA/ha UAA RENT/ VA/ha UAA RENT/ VA/ha UAA RENT/ VA/ha UAA RENT/ VA/ha UAA UAA UAA UAA UAA No Specialised 0.01 0 -9964.8 -232589 32.33 0.38 4014.3 26437 15.77 0.21 1340.7 19547 51.39 0.41 29376.48 26155.84 920.1 1 1486980 574922 Annual crops 0.06 0 -71723 -153306 36.17 0.41 9380 34724 18 0.28 1370 24767 60.23 0.42 35042.68 36542.29 1754 1 1395764 1069950 Cereals 0.06 0 -13250 -153306 51.51 0.4 1039.6 38694 30 0.26 867.9 26348 67.6 0.41 1542 39848 1279 1 103921.6 509952 Arable crops 0.29 0 -20331 -111453 39.3 0.44 2719.2 32453 21.35 0.41 1083.4 23383 64.7 0.42 5877.1 38000 1754 1 120828.1 1069950 Horticulture 0.07 0 -71723 -107227 16.9 0.38 25721 33388 4.5 0.15 9282 25162 37.29 0.42 59108 30447 1101.75 1 1395764 617558 Permanent crops 0.2 0 -35024 -96116 14.73 0.27 6033 29864 7.81 0 3928 23011 25.13 0.39 7820.81 27845 526.87 1 248960 728767 Fruit crops 0.2 0 -5497 -29242 14.03 0.26 6514 29666 7.53 0 4472 23411 24.5 0.38 7182.5 24810 413 1 7182.5 416912 Olive crops 0.85 0 -3148 -23401 18.86 0.26 2903 23265 10.55 0 2199 19409 28.43 0.39 2953.2 18167 394 1 67758 205770 Viticulture 0.3 0 -35024 -96116 13.85 0.28 6741 32589 7.14 0 4185 24348 24.25 0.39 9299 32924 526.87 1 248960 728767 Livestock sector 0.05 0 -209342 -838045 51.74 0.46 6680 44475 28 0.44 1727 31113 73.45 0.41 44480 49666 1687.54 1 3792972 863036 Dairy cattle 0.2 0 -9222 -164891 49.13 0.48 4292 48736 27 0.49 2917 37914 65.66 0.4 5119 42163 770 1 90039 796655 Herbivores 0.1 0 -52896 -234751 61.15 0.47 2347.2 33066 36.26 0.44 853.1 24237 83.43 0.41 12450 36867 1687 1 403098 587929 Granivores 0.05 0 -209342 -838045 28.43 0.44 23898 70768 13.19 0.37 7439 52388 43.27 0.42 102830 76804 514.55 1 3792972 863036 71 Farm characteristics and exogenous factors influencing the choice to buy land in Italy Bio-based and Applied Economics 14(1): 49-73, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15548 Appendix 2 Analysis of relationships among independent variables Table A1. Results of Pearson correlation analysis (Part 1). Variable UAASq VA/ha VA/TWU Specialization RENT/UAA FWU/TWU Machinery_ Plant Value Subsidies UE/ SAU` Capital Account Energy production Subcontracting activities Agrotourism Pre_ PURCHASE Farm structural characteristics UAASq 1 -0.023 0.167 0.023 0.048 -0.140 0.206 0.000 0.020 0.043 0.027 0.027 0.007 VA/ha -0.023 1 0.123 0.028 -0.016 -0.120 0.015 0.068 0.008 0.019 -0.010 0.014 -0.001 VA/TWU 0.167 0.123 1 0.119 0.108 -0.152 0.272 0.105 0.038 0.151 0.045 0.004 0.023 Specialization 0.023 0.028 0.119 1 0.029 -0.028 0.037 -0.004 0.027 0.055 -0.069 0.003 -0.003 RENT/UAA 0.048 -0.016 0.108 0.029 1 0.023 0.045 0.017 0.025 0.047 0.065 -0.007 -0.005 FWU/TWU -0.140 -0.120 -0.152 -0.028 0.023 1 -0.184 -0.055 -0.064 -0.076 -0.001 -0.060 -0.024 Machinary_ Plant Value 0.206 0.015 0.272 0.037 0.045 -0.184 1 0.044 0.121 0.281 0.083 0.037 0.041 Subsidies UE/SAU` 0.000 0.068 0.105 -0.004 0.017 -0.055 0.044 1 0.029 0.015 -0.004 -0.015 0.000 Capital Account 0.020 0.008 0.038 0.027 0.025 -0.064 0.121 0.029 1 0.058 0.008 0.017 0.030 Energy production 0.043 0.019 0.151 0.055 0.047 -0.076 0.281 0.015 0.058 1 0.060 0.071 0.018 Subcontracting activities 0.027 -0.010 0.045 -0.069 0.065 -0.001 0.083 -0.004 0.008 0.060 1 0.00 0.01 Agrotourism 0.027 0.014 0.004 0.003 -0.007 -0.060 0.037 -0.015 0.017 0.071 -0.004 1 0.003 Pre_PURCHASE 0.007 -0.001 0.023 -0.003 -0.005 -0.024 0.041 0.000 0.030 0.018 0.010 0.003 1 Farm socio-demographic characteristics FARMER_18_39 2E-05 -0.010 0.004 0.042 0.168 0.000 0.033 -0.001 0.038 0.001 0.008 0.021 0.005 FARMER_4049 4E-03 0.014 0.046 0.040 0.099 -0.042 0.020 -0.003 0.013 0.011 0.019 0.013 0.009 FARMER_5059 -6E-03 0.004 0.015 -0.006 -0.008 -0.017 -0.014 -0.004 -0.017 0.002 0.007 -0.013 -0.002 FARMER_OVER60 -2E-02 -0.019 -0.083 -0.113 -0.183 0.026 -0.085 -0.008 -0.037 -0.049 -0.053 -0.049 -0.020 SUCC_1_39 2E-02 0.012 0.023 0.070 -0.025 0.033 0.064 0.002 0.017 0.046 0.023 0.047 0.012 OFF_FARM INCOME -3E-03 -0.018 -0.064 -0.015 -0.073 -0.016 0.005 -0.018 -0.003 0.001 0.002 0.037 0.019 Exogenous factors Inflation rate 5E-03 0.001151 0.008 0.001 -0.003 0.006 -0.001 -0.027 -0.008 -0.001 -0.001 -0.003 -0.003 Interest rate 6E-03 0.008741 -0.015 0.001 -0.032 -0.004 -0.001 -0.011 0.017 -0.020 -0.013 -0.016 -0.016 72 Bio-based and Applied Economics 14(1): 49-73, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15548 Silvia Russo et al. Table A1. Results of Pearson correlation analysis (Part 2). Variable FARMER_ 18_39 FARMER_ 4049 FARMER_ 5059 FARMER_ OVER60 SUCC_1_39 OFF_FARM INCOME Inflation rate Interest rate Farm structural characteristics UAASq 0.000 0.004 -0.006 -0.015 0.022 -0.003 0.005 0.006 VA/ha -0.010 0.014 0.004 -0.019 0.012 -0.018 0.001 0.009 VA/TWU 0.004 0.046 0.015 -0.083 0.023 -0.064 0.008 -0.015 Specialization 0.042 0.040 -0.006 -0.113 0.070 -0.015 0.001 0.001 RENT/UAA 0.168 0.099 -0.008 -0.183 -0.025 -0.073 -0.003 -0.032 FWU/TWU 0.000 -0.042 -0.017 0.026 0.033 -0.016 0.006 -0.004 Machinery_ Plant Value 0.033 0.020 -0.014 -0.085 0.064 0.005 -0.001 -0.001 Subsidies UE/SAU` -0.001 -0.003 -0.004 -0.008 0.002 -0.018 -0.027 -0.011 Capital Account 0.038 0.013 -0.017 -0.037 0.017 -0.003 -0.008 0.017 Energy production 0.001 0.011 0.002 -0.049 0.046 0.001 -0.001 -0.020 Subcontracting activities 0.01 0.02 0.01 -0.05 0.02 0.00 0.00 -0.01 Agrotourism 0.021 0.013 -0.013 -0.049 0.047 0.037 -0.003 -0.016 Pre_PURCHASE 0.005 0.009 -0.002 -0.020 0.012 0.019 -0.003 -0.016 Farm socio-demographic characteristics FARMER_18_39 1 -0.211 -0.222 -0.256 -0.134 -0.003 -0.010 0.005 FARMER_4049 -0.211 1 -0.283 -0.325 -0.170 0.021 0.002 0.014 FARMER_5059 -0.222 -0.283 1 -0.342 -0.179 0.019 0.002 -0.014 FARMER_OVER60 -0.256 -0.325 -0.342 1 -0.206 -0.119 0.001 -0.004 SUCC_1_39 -0.134 -0.170 -0.179 -0.206 1 0.103 0.006 -0.006 OFF_FARM INCOME -0.003 0.021 0.019 -0.119 0.103 1 -0.005 -0.014 Exogenous factors Inflation rate -0.010 0.002 0.002 0.001 0.006 -0.005 1 0.326 Interest rate 0.005 0.014 -0.014 -0.004 -0.006 -0.014 0.326 1