“For whoever has will be given more”? Land rental decisions and technical efficiency in Ukraine

Kvartiuk, Vasyl; Bukin, Eduard; Herzfeld, Thomas Article — Published Version “For whoever has will be given more”? Land rental decisions and technical efficiency in Ukraine Land Use Policy Provided in Cooperation with: Leibniz Institute of Agricultural Development in Transition Economies (IAMO), Halle (Saale) Suggested Citation: Kvartiuk, Vasyl; Bukin, Eduard; Herzfeld, Thomas (2024) : “For whoever has will be given more”? Land rental decisions and technical efficiency in Ukraine, Land Use Policy, ISSN 1873-5754, Elsevier, Amsterdam [u.a.], Vol. 146, https://doi.org/10.1016/j.landusepol.2024.107336 , https://www.sciencedirect.com/science/article/pii/S0264837724002898 This Version is available at: https://hdl.handle.net/10419/302221 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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Land rental decisions and technical efficiency in Ukraine Vasyl Kvartiuk a,* , Eduard Bukin b , Thomas Herzfeld a,c a Leibniz Institute of Agricultural Development in Transition Economies (IAMO), Theodor-Lieser-Str. 2, Halle (Saale) 06120, Germany b Justus-Liebig-Universit¨ at Giessen, Institute of Agricultural Policy and Market Research, Senckenbergstraße 3, Gießen 35390, Germany c Martin-Luther-Universit¨ at Halle-Wittenberg, Universit¨ atsplatz 10, Halle (Saale) 06108, Germany ARTICLE INFO Keywords: Land rental market Efficiency Land concentration Land reform Ukraine ABSTRACT Land rental markets had played a critical role in providing farms access to Ukraine’s agricultural land before the ban on land sales was lifted in 2021. This paper examines whether rental-based land relations can promote land use by more productive farms in the context of imperfect institutions. In particular, we examine whether Ukrainian farms’ decisions to rent land are linked to their agricultural ability. Utilizing a rich panel of more than 16,000 Ukrainian commercial agricultural producers for 2005–2015, we analyze demand-side determinants of participation in the land rental market. The evidence suggests that farms’ total factor productivity is disconnected from their decisions to rent land. Land accumulation appears to be driven by other context-related factors, including existing local land concentration and orientation toward cash crop production. Results call for launching a land sales market and improving rental market infrastructure because these measures can align land rental prices with the value of the marginal product of land. 1. Introduction Although optimal agricultural organization and respective land policies have been puzzling scholars for more than three decades and still present an actively debated issue, relatively little attention has been paid to a post-socialist transition context. Literature has extensively considered land markets as a mechanism for creating opportunities for land transfers from less to more productive farms and as a povertyreducing tool (de Janvry et al., 2001; Deininger, 2003). Although land ownership is widely considered to induce investments and improve productivity (Besley, 1995; Koirala et al., 2016; Place, 2009), land sales markets are often difficult to implement with imperfect institutions. Because many transition economies are known for high transaction costs in land purchases, thin land sales markets, and credit constraints (Lerman et al., 2004), renting land is often the primary way of transferring land-use rights. In these environments, land rental markets may play an important role in enhancing agricultural efficiency because they may enable land to flow towards more efficient producers (Sadoulet et al., 2001) and provide access to land for the poor (Deininger and Binswanger, 2001). The idea is that land rental markets may minimize transaction costs of land exchange and are, thus, more efficient than other forms of land relations (Deininger et al., 2009; Deininger and Binswanger, 2001; Vranken and Swinnen, 2006). However, very little research has been conducted on how rental markets actually work in settings with imperfect institutions. Rental markets may be thin, local elites may be able to exert market power, depressing prices and accumulating land, and landowners’ lack of negotiating capacity may disrupt land flow towards more capable producers. This study explores whether land rental markets contribute to an effective land exchange in a post-Soviet setting with imperfect institutions. Existing literature on the relationship between rental decisions and agricultural ability almost exclusively focuses on farm households where non-separability and the existence of multiple income sources might facilitate access to land (Huy and Nguyen, 2019; Rahman, 2010; Tan et al., 2018). Regionally, existing studies have mainly focused on Southeast Asia and Africa. Former Soviet states differ substantially in institutional setup and land ownership patterns. In Russia, Ukraine, and Kazakhstan, former workers of collective farms received land plots as part of the de-collectivization during the 1990s. Most rent these plots out to large commercial farms instead of starting a family farm. Naturally, land rental decisions in commercial farms are expected to differ from household decisions, as there are no additional income sources, and new challenges arise with hired labor supervision and remuneration. It is unclear whether land rental markets can facilitate * Corresponding author. E-mail address: [email protected] (V. Kvartiuk). Contents lists available at ScienceDirect Land Use Policy journal homepage: www.elsevier.com/locate/landusepol https://doi.org/10.1016/j.landusepol.2024.107336 Received 11 August 2020; Received in revised form 2 July 2024; Accepted 22 August 2024 Land Use Policy 146 (2024) 107336 Available online 4 September 2024 0264-8377/© 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ). productivity-enhancing land distribution in these circumstances. We address these research gaps by examining the link between farms’ ability and their propensity to rent land. Ukraine represents an interesting case because land rental has been an almost exclusive way to access agricultural land. The reason is that by adopting the new Land Code in 2002, the Ukrainian government introduced private property rights on land, followed by an immediate moratorium forbidding land sales. Land-intensive agricultural production with large commercialized producers puts Ukraine in one group with countries with similar natural and environmental conditions as well as comparable reform paths: Russia and Kazakhstan. The Ukrainian land rental market had been in place for over two decades, leading to substantial land use concentration, giving rise to large farms operating on several hundreds of thousands of ha (Keyzer et al., 2017; Mamonova, 2015). One explanation for the emergence of large-scale farms and local land concentration could be the persistence of economies of scale in crop production and a higher return to land in large production units. However, Deininger et al. (2018) have not been able to prove that larger farms are more productive in Ukraine. This raises the question of how rental decisions are made and whether production efficiency plays a role. The main objective of this paper is to analyze whether the Ukrainian land rental market facilitates the conveyance of agricultural land to more efficient agricultural producers. First, we review the literature on the factors affecting farmer’s rental decisions. The theory suggests that the value of the marginal product of land should motivate the decision to rent land, implying that more productive farms should operate larger areas. However, initial land concentration stemming from the times of collective farming may distort this relationship. Second, we use unique farm-level panel data from Ukraine to trace the developments of the land rental market over 11 years between 2005 and 2015. Then, we estimate the determinants of participation in the land rental market for commercial agricultural producers, analyzing the connection between farms’ agricultural ability and the likelihood of renting land. In doing so, we explicitly consider the initial land concentration and how large farms may exploit their advantageous position. Based on the results, we discuss the implications of the current land relations for the growth of the Ukrainian agricultural sector. Our results do not support the hypothesis that more productive farms rent more land. Other context-related factors appear to be the drivers of rental decisions. In particular, initial rayon-level land concentration appears to generate farm size polarization with further growth of large farms and further shrinkage of their smaller counterparts. Moreover, the availability of cheap land may generate incentives for land-intensive business models focusing on large-scale cultivation of cash crops with a low added value per hectare. Finally, the growth of the agricultural sector appears to have been brought about not by the size expansion but by the entry of more productive and the exit of less productive farms. The rest of this paper is organized in the following fashion. Section 2 articulates our literature review focusing on the efficient use of land as a production factor and initial land concentration. Section 3 provides a brief excurse of the institutional context of Ukrainian land relations. Data and methods are presented in Section 4, whereas we present the results in Section 5. Finally, Section 6 concludes and discusses the implications of our findings. 2. Conceptual framework Our research questions are embedded in an old debate about the farm size-productivity relationship because land rental is by far the most important way of accessing land in Ukraine. An observation that large farms tend to be less productive has turned into a stylized fact with a coined term – “inverse farm size – productivity relationship” (Chayanov, 1926; Eastwood et al., 2010; Eswaran and Kotwal, 1986; Lipton, 2009). Well-known principal-agent problems of farm owners’ supervision of the hired labor have been the primary explanation for the inverse size – productivity relationship (Barrett et al., 2010; Feder, 1985). However, modern technologies (e.g., precision farming) and management approaches may attenuate the effect of labor supervision challenges (Deininger and Byerlee, 2012; Rada and Fuglie, 2019). In this case, labor constraints may be relaxed, unleashing potential drivers of land accumulation if other non-land factor markets are unhindered. Because land rental markets represent a flexible way of accessing land (Sadoulet et al., 2001), they could allow new farmers to grow or contribute to land accumulation by large farms. Understanding a farmer’s decision to rent land will provide a theoretical foundation for testing whether more efficient farmers rent in more land. 2.1. Land rental decisions The aggregate productivity of an agricultural sector may be improved via an effective land exchange mechanism. A functional land rental market can generate a flow of land from less to more able farmers (Sadoulet et al., 2001). Underutilized land imposes opportunity costs on more able farmers and generates incentives for land exchange, putting land to the most efficient use. As a result, land rental should facilitate efficient factor allocation, improving the aggregate efficiency of land cultivation. Basic microeconomic theory informs a farm’s decision to rent land. The departure point is that a profit-maximizing farm will add units of a production factor until the factor’s price equals the value of its marginal product (Dasgupta et al., 1999; Lackman, 1977). Because the value of the marginal product of land depends on the production processes, its value will be higher for the farms that use all production factors more efficiently. In other words, the decision to rent in an additional land plot depends on the farmer’s ability to use it in the production process effectively. Thus, a farmer with low agricultural ability will not be able to compete in the rental market, facing high opportunity costs of holding land, which generates incentives to reduce the portfolio of rented land. Consequently, we expect farms with superior agricultural ability to rent more land. This forms a basis for our test, with the central hypothesis stipulating a positive correlation between a farm’s agricultural ability and its activeness on the land rental market. Farmer’s renting decisions may be affected by other incentives if rental markets or other factor markets show imperfections. For instance, imperfect factor markets may incentivize the self-selection of business models focusing on certain crops. On the one hand, household farms facing challenges renting land and lacking alternative off-farm employment opportunities may overuse labor because they cannot adjust their farm size (Sen, 1966). Conversely, commercial farms may be in a better position to overcome land rental transaction costs, making it easier for them to expand their sizes. In this case, the marginal product of land will exceed the rental price and lead to land overutilization (Deininger et al., 2018). Relaxed constraints for larger commercial farms may incentivize less intensive cultivation. For instance, in the Chinese context, less labor-intensive grain production was found to be associated with less constrained access to land (Min et al., 2017; Qiu et al., 2020). The lower demand for labor per unit of land in grain production compared to, for instance, livestock or vegetable production allows to deal with labor shortages and/ or high supervision costs. At the same time, more mechanized production systems are expected to benefit from larger cultivated areas (Luo, 2018; Pingali, 2007). Thus, in the Ukrainian context, land-intensive business models oriented towards export crops (corn, wheat, and sunflower) should be associated with a higher land rental activity. 2.2. Land concentration via rental markets Initial distribution of land on a local level may affect the transaction costs of land exchange, impeding the achievement of efficiencyenhancing distribution of land. In particular, locations with substantial amounts of land controlled by large farms may be subject to prohibitive V. Kvartiuk et al. Land Use Policy 146 (2024) 107336 2 transaction costs of land access for smaller farms or new entrants. Furthermore, renting large amounts of land may require substantial administrative capacities for managing the contracts. Thus, larger farms are typically better positioned and will have an advantage in negotiating with local landowners. As a result, new or smaller farmers may face additional costs of land access and may be disadvantaged in the competition for land access compared to larger counterparts. Additionally, farmers cultivating small areas may be excluded from local political processes (Acemoglu et al., 2004; Binswanger and Deininger, 1997). This, in turn, may limit their political influence on the decision-making of local authorities with respect to land distribution. For instance, influential farms may have an advantage in renting state-owned agricultural land 1 due to their superior bargaining power relative to their small counterparts. Large farms may also have incentives to cement their dominant position and expand their rented land if the land prices are lower than the value of the plots’ marginal product. The logic is that several dominant land users may be able to deter potential entrants (Balmann et al., 2021; Martinelli, 2014). A situation when a few farms control a substantial share of land may discourage smaller farms from renting in land within this particular location because of limited rental options. For instance, dominant farms may strategically rent specific plots, increasing the costs of land consolidation for potential entrants (Hartvigsen, 2015) or play a role of price leaders (Graubner et al., 2021), making renting land within a given region unattractive. As a result, competition for land based on efficiency considerations may be hindered, generating misallocation of land and labor. Small farms may be stuck with insufficient land rental possibilities, whereas large counterparts may use their political and strategic position to expand cultivated areas further. Because Ukrainian large farms emerged on the basis of the former collective farms (Swinnen and Vranken, 2007), their dominant position may had been attained exogenously, giving rise to initial inequality in rented land distribution. If average rental prices are far below the marginal product of land, 2 we expect all types of farms to compete for land. However, because of the lower transaction costs, we expect farms with a dominant position in the local rental market to reinforce their position and rent in more land. On the other hand, smaller farms failing to compete with larger counterparts may shrink or exit agricultural production, contributing to farm size polarization. With increasing land concentration, we expect a self-selection process where each remaining farm’s market and political power is expected to increase, and thus farms might rent in more land. Thus, we hypothesize that farms located in regions with relatively unequally distributed land should rent in more land. 3. Land relations in Ukraine After the collapse of the Soviet Union, Ukraine set the course for liberal land reforms in the early 1990s but then got trapped in the transition period. Fig. 1 demonstrates the evolution of the major legislative initiatives. The first important milestone that signified the launch of the land reform was the 1995 Presidential Decree 3 which launched the distribution of the so-called “conditional land shares” (CLS) – stakes in the former collective farms. Employees of collective farms and certain groups of the rural population were eligible to receive CLS. Later on, the 1999 Presidential Decree 4 gave a chance to the CSL holders to convert their shares into physical plots (Lerman et al., 2007), subsequently creating ca. 7 million land owners, who owned 27.6 million ha. However, the institutions necessary for establishing a market-based exchange of ownership titles were not developed then. The 2002 Land Code that paved the way for new land relations in Ukraine could be considered a major breakthrough because it clearly defined property rights related to agricultural land. However, the subsequently adopted Moratorium on land sales took away the basic right of the landowners to sell land. Initially planned as a temporary measure, the Moratorium has been prolonged ten times since its adoption. Only in 2020, the Ukrainian parliament adopted a law stipulating the launch of a restricted sales market in 2021. In the meantime, access to land for agricultural producers was almost exclusively based on land rental. Land rental prices were formed based on a so-called ‘normative monetary value’ of land, a reference value artificially set based on soil quality and expected revenues. Several observers claim that the ‘normative monetary value’ of land was set way below the value of the marginal product of land and that the gap increased over the years (Kvartiuk and Herzfeld, 2019; Nivyevskyy, 2019; Nivyevskyy and Nizalov, 2016). Apart from being low, Kuns (2017) suggests that land prices were often paid in the form of agricultural produce. Ukrainian landowners were disadvantaged because of their low bargaining power and, thus, their ability to exert upward pressure on prices. Rental prices below the value of the marginal product of land incentivize land accumulation and lead to overutilization of this production factor (Deininger et al., 2018). In addition, we observe anecdotal evidence of illegal expropriation of land and hostile overtakes. The combination of low prices and tenure uncertainties generates the incentives for business models of export-oriented annual crop cultivation on large areas (Kvartiuk and Herzfeld, 2019). These institutional settings have contributed to the emergence of Ukraine as one of the world’s leading producers and exporters of grains and oilseeds (Keyzer et al., 2017). Within these institutional settings, three major types of agricultural producers emerged in Ukraine: agricultural enterprises (including state enterprises), individual farms, and households. Fig. 2 breaks down land use by each type of agricultural producer and by land-use type (owned vs. rented). We see that commercial farming on owned land is rare. A light grey color represents the small amount of land owned by individual farms and agricultural enterprises that managed to acquire small areas before the 2002 sales ban. Importantly, households predominantly farm on owned land and account for ca. 4.3 million small agricultural producers that cultivated about 15.7 million ha (43.1 % of the total agricultural land) in 2016. They mainly produce for subsistence consumption, orienting excess to the local markets. The State Statistics Service of Ukraine (SSSU) estimated that households accounted for 38.7 % of the value of agricultural crops produced in 2016. Despite the households’ significant role in Ukrainian agriculture, this study focuses on commercial agricultural producers: individual farms and agricultural enterprises. Family farming has been promoted in Ukraine to facilitate the commercialization of the households that rented land in addition to what they owned. For this purpose, individual farms were defined by the law in 2003 5 and have been granted preferential conditions on establishment, taxation, and access to land. Individual farms have been gaining importance and reached 4.4 million ha in total land use in 2016 (12 % of the total agricultural land), represented by about 32,000 producers. They farm predominantly on rented land (89 % of the total land used by this category). 1 Roughly a quarter of all agricultural land was owned by the Ukrainian state as of 2016. 2 We explain why that is the case in the Ukrainian context in Section 3. 3 Decree of the President of Ukraine No. 720/95 from 08.08.1995 “On the order of distribution of lands transferred to a collective ownership of agricultural enterprises and organizations”. 4 Decree of the President of Ukraine No. 1529/99 from 03.12.1999 “On urgent measures for accelerating the reforms in agricultural sector of the economy”. 5 Law No. 973 from 19.06.2003 “On individual farming”. V. Kvartiuk et al. Land Use Policy 146 (2024) 107336 3 Agricultural enterprises represent legal entities typically run by hired managers and often owned by individuals not directly involved in agriculture or by the state. Many of these enterprises were created on the basis of former collective farms but almost exclusively operated on land rented from the CLS holders: they owned only 1.5 % (200,000 ha) of the total land in 2015. The share of land operated by enterprises declined after restructuring but stabilized in the mid-2000s. As of 2016, they account for roughly 16.3 million ha or 47 % of the total agricultural land while being represented by 9796 legal entities. A distinctive feature of Ukrainian agriculture is a relatively large operational scale in comparison to the rest of the world. Ukraine hosts some of the largest agricultural enterprises in the world. The average utilized land per farm amounts to 460 ha but the largest farms operate up to 500,000 ha (Deininger et al., 2018). A substantial share of land is used by agricultural enterprises between 500 ha and 4000 ha in size, which likely represent enterprises organized around former collective farms. In addition, many farms are affiliated with a central holding forming multibranch farms – so-called ‘agriholdings’ (Deininger et al., 2018; Graubner et al., 2021). Apart from better managerial and administrative capacities, these farms typically enjoy better access to credit and other forms of outside capital, contributing to substantial land concentration with total land holdings spanning up to half a million ha. Enterprises operating more than 10,000 ha account for 17.7 % of the cultivated land (SSSU, 2017). Because the majority of these farms inherited CLS-land from collective farms (Lerman et al., 2007), land concentration was a given condition across Ukraine in the early 2000s. Thus, the unequal initial distribution of land, which generated political and market power for large farms, in combination with low rental prices may have contributed to extreme land concentration outcomes. 4. Data and methods 4.1. Data To test our hypotheses outlined above, we use a unique farm-level panel dataset covering the period from 2005 to 2015. Data has been collected annually by the State Statistics Service of Ukraine (SSSU). Observations are sampled from farms registered as legal entities, irrespective of farm type, and exceeding one out of five size thresholds defined by SSSU. 6 Approximately 8000 farms are covered by the sample each year accounting for 17 million ha of agricultural land (48 % of all agricultural land). Overall, our sample covers 16,950 agricultural enterprises and individual farms distributed across 625 rayons 7 (districts) of Ukraine, which produce about half of the domestic agricultural output. This makes it a unique data source for mediumand large-scale enterprises in Ukraine. 8 However, it is important to note that this dataset Fig. 1. Timeline of the major milestones in the Ukrainian land reform. Fig. 2. Distribution of land use by farm types. Source: SSSU (2017). 6 Survey participation thresholds remained stable between 2005 and 2015. In general, any legal-entity producing agricultural commodities has been sampled if it were qualifying for any of the following thresholds: size above 200 ha; number of full-time workers above 20; number of livestock above 50 heads of cattle, or swine or sheep or 500 for poultry; or annual revenue above 150 thousand UAH per year (ca. 6000 USD in 2020) 7 SSSU’s standard reporting form “50SG” covers from 85 % in 2005 to 95 % in 2015 of land operated by all agricultural enterprises (state and privately owned) as well as 79–60 % of land operated by individual farms registered as legal entities (based on data from (SSSU, 2017)). 8 For the years 2014–2015, no data is available for the territories occupied by the Russian Federation: Crimea and part of territories of the Donbass region. V. Kvartiuk et al. Land Use Policy 146 (2024) 107336 4 does not include small producers that do not pass the minimum criteria for being included in the sampling population set by SSSU. Furthermore, it is an unbalanced panel with relatively high attrition where only 24 % of farms are present each year (see Appendix A for further details). Available variables include standardized farm management information and performance statistics. It contains data on farms’ expenditures on inputs, produced crops, and livestock as well as volumes and values of sales in each calendar year. Specifically, the input data distinguishes between land area, labor in full-time equivalent (FTE), and expenditures on land rent, hired labor, seeds, fertilizers, petrol, services, machinery, and other items. 9 We deflated all monetary data to the 2018 price level using the Consumer Price Index (CPI) and then converted the values into US dollars. To ensure data consistency and to narrow the scope of the analysis, we restrict the dataset in a number of ways. First, we focus on farms that specialize in crop production, restricting the sample to the farms that meet both of the following criteria in each year a farm is present in the sample: 1) a share of costs related to crop production in their total costs is greater than 25 % and 2) operational size is above 5 ha. 10 Moreover, as we are interested in changes in rented land, we exclude all the farms that appeared in the panel only once (those farms that existed for one year or were qualified to be covered by the survey only once). Table 1 presents the descriptive statistics. The first observation is that between 2005 and 2015, the average amount of owned land decreased and rented land increased for all types of producers except for the state farms that continued relying on the cultivation of state-owned land. Importantly, the average farm size increased for all categories of producers. Secondly, we observe considerable growth in land concentration. Observing the Herfindahl-Hirschman Index (HHI) of the land used within a given rayon, 11 we see a moderate country-wide increase from 0.22 to 0.31 throughout our panel. Fig. 3 highlights the spatial differences in land concentration and their changes over the decade. Main regions of grain production stretching from the south through the center and to the northeast of Ukraine experienced moderate levels of land concentration. On the other hand, agricultural land appears to be more concentrated in the north-western and western regions. These patterns largely coincide with the operation domains of large farms. Moreover, we observe signs of the business models’ re-orientation towards large-scale grain production. First, enterprises appear to have substantially increased the shares of corn, soybeans, and oilseeds at the expense of barley, sugar beets, and oats. These grains have been among the major export commodities during 2005–2015. Because the SSSU collects the data on a calendar year basis, volumes of crops sold in each specific year are rarely the same as the production levels. Consequently, the amount of output sold may be higher or lower compared to the actual production. In addition, there may be a potential bias due to the transfer pricing of crop output within vertically integrated crop-livestock farms. We follow Deininger et al. (2018) in dealing with these challenges and calculate the values of the produced crops in 2018 USD for each farm based on the median crop prices at the rayon level and individual farms’ production quantities. Examining the data on farms’ performance reveals clear differences between the three types of agricultural producers. Although farms’ output per ha increased considerably on average across all farm types, these gains are less pronounced for state enterprises. Their level of output per ha was only half of the respective value for agricultural enterprises in 2015 although it also grew roughly 50 % during the decade of 2005–2015. These differences are also traceable in the cost structure, indicating that state-owned agricultural enterprises are fundamentally different from other types of producers. Total production costs went up 2.7 times for all farm types, whereas state farms experienced only a 1.4fold increase. This may reflect the fact that the managers of state farms may utilize outdated technologies due to a lack of investments. For instance, state farms spent three times less on fertilizer per ha in 2015, and petrol costs almost did not change and were only 50 % of the level of other farm types. Importantly, although the land rental prices almost tripled for all farm types, state farms, on average, paid only 19 % of the average rental prices in 2005 and 12 % in 2015. This illustrates their competitive advantage in accessing cheaper state-owned land. 4.2. Methodology Following our theoretical framework, we aim to explain agricultural producers’ participation in the Ukrainian land rental markets. In particular, our goal is to test whether producers with better agricultural ability tend to rent in more land. This will help understanding whether current land relations stimulate a flow of land toward more efficient agricultural producers. First, we explain the amount of land rented by each farm i Rental finali in the last period where it is observed in the sample. Our key explanatory variable is a measure of the farm i’s initial agricultural ability α initi at the time of its first appearance in the sample. In line with our first hypothesis, we expect it to be positive should more productive farms rent more land. In addition, we include the initial area of land owned (Owned initi), the level of initial land concentration in rayon j HHI initij at the time when the farm entered our panel, and other farmspecific characteristics as a vector of control variables Xi: Rental finali=β0+β1 α initi+β2Owned initi+β3HHI initj+β4Xi+ ε i (1) As the rented area at the end of the observation period for each farm falls in an interval between zero and infinity, we utilize Tobit regressions. 12 We minimize the risk for endogeneity between the rented land and the farms’ economic performance because we explain the final land rental with the initial agricultural ability estimated at the time of the farms’ entry into the sample. This ensures that in most cases, we have large lags. The same logic is applied to our proxies for land concentration and owned land. Second, we explain the changes in land use over the observed period between 2005 and 2015. 13 To achieve that, we construct a new dependent variable for the model represented by Eq. 1: the difference in rented land area between the last and the first farm-specific observation following the year of the production function estimation ΔAreai= Area finali−Area initiali. Note that the difference in rented land excludes the year used to calculate agricultural ability to minimize endogeneity. The variable ΔAreai captures only land use changes achieved through rental markets, excluding land sales, which was prohibited at the time. In addition, we estimate this model separately for several subsamples: farms with decreased and increased utilized land as well as for farms that stayed during the whole period, exited, and entered the land market. We expect that in the sample of the farms with decreasing land use more productive farms demonstrate a smaller decline in their land 9 As there is no information on capital and machinery per se, we assume machinery costs being approximated by the annual amortization expenditures. Land costs are only recorded for the crops production, while the livestock production contains records of the feed costs. 10 Records on farms smaller than 5 ha may be biased because of measurement errors considering SSSU’s sampling strategy outlined above. 11 We follow the literature and define HHI as the sum of the squared shares of the land users in a given rayon. As a result, it ranges from zero to one. Because owned agricultural land represents only roughly one tenth of the land used, we focus on the concentration of the land used. 12 In the Ukrainian context, farms typically do not rent out land as they have miniscule amounts of owned land according to the data of State Service for Geodesy, Cartography, and Cadaster. Thus, we cannot observe renting out patterns which represents left-censoring of our dependent variable at zero. 13 We use the last period for which data is available for those farms that exited the panel. V. Kvartiuk et al. Land Use Policy 146 (2024) 107336 5 Table 1 Descriptive statistics. All farm types Agricultural enterprises Individual farms State farms 2005 2015 2005 2015 2005 2015 2005 2015 Output per ha at rayon med. prices* (2018 USD) 242.8 (212) 623.4 (488) 245.8 (211.4) 650.8 (504.1) 230.3 (170.3) 483.4 (320.8) 193.1 (255.5) 326.5 (257.4) Total utilized land, ha 2156.2 (2432.8) 2230.8 (4758.7) 2148.6 (2484.2) 2231.1 (5021.7) 1949.5 (1561.7) 2084.5 (1657.4) 2672.3 (2084.4) 2890.7 (4578.6) Owned land, ha 176 (823.4) 94.5 (877.3) 93.3 (598.5) 28.7 (313) 49.4 (208.8) 31.5 (160.8) 2532.4 (2067.3) 2740.6 (4600.8) Rented land, ha 1980.2 (2395.3) 2136.3 (4714.4) 2055.3 (2450.3) 2202.4 (5021) 1900 (1562.7) 2052.9 (1651) 139.9 (539.1) 150.1 (467.1) Labor, average number of full-time workers 101.3 (116.8) 47.4 (112.9) 100.6 (116.5) 48.7 (119.2) 73.1 (64.8) 30.7 (31.9) 162.9 (157.6) 78.9 (108.9) Land concentration at rayon (HHI) 0.221 (0.238) 0.305 (0.297) - - - - - - Total costs*, 2018 USD per ha 175.9 (417.3) 486.6 (1783) 176.7 (433.2) 503.1 (1909.4) 170.5 (124.2) 425.6 (437.8) 166.7 (258.2) 229.3 (180.3) Labor costs*, 2018 USD per ha 27.3 (127.4) 33.8 (172.9) 26.6 (130.6) 34.9 (185.1) 24 (23.2) 24.2 (37.7) 46.7 (125.9) 33.2 (59) Petrol costs*, 2018 USD per ha 30.9 (23.3) 57.5 (381.4) 30.9 (23.4) 58.4 (409.8) 33.2 (20.1) 57.7 (31.6) 28.9 (25.6) 32.6 (25.7) Seed costs*, 2018 USD per ha 20.4 (22.4) 57 (181.4) 20.6 (22.4) 59 (183.8) 21.1 (16.3) 52.1 (187.3) 16.1 (27) 19.9 (18) Fertilizer costs*, 2018 USD per ha 23.8 (39.9) 91.8 (113.4) 24.3 (41.2) 94 (116.5) 23.8 (24.5) 92.2 (93.7) 13.3 (19.7) 27.1 (42.2) Machinery/capital costs*, 2018 USD per ha 22.7 (55.7) 57.4 (129.3) 22.3 (57.3) 58.2 (134.5) 24.8 (27.8) 61.6 (98) 27.8 (43.5) 22.6 (30.2) Service costs*, 2018 USD per ha 18.9 (56.8) 92.3 (513.1) 19.1 (58.4) 96.2 (550.4) 13 (41.7) 64.2 (78.8) 20.6 (31.8) 75.7 (87.8) Other costs**, 2018 USD per ha 31.9 (153.6) 96.8 (638.5) 32.8 (160.9) 102.5 (685.8) 30.6 (22.6) 73.5 (57.4) 13.2 (34.5) 18.2 (28.7) Land rental price, 2018 USD per ha 22.2 (63.6) 58.2 (363.2) 22.5 (58.3) 60.9 (390.1) 20.7 (138.5) 50.6 (27.6) 4.3 (17.8) 6.8 (24.3) Wheat share in harvested area, % 37 (15.1) 31.5 (16.8) 36.9 (15.1) 31.1 (16.9) 35.6 (14) 33.1 (15.2) 40.1 (16.1) 35.3 (15.8) Oilseeds share in harvested area, % 18.2 (14.5) 26.9 (17.2) 18.2 (14.5) 26.4 (17.2) 20.5 (14.8) 32 (15.7) 15.8 (13) 26.3 (18.5) Corn share in harvested area, % 6.2 (10) 17.9 (19.9) 6.4 (10.3) 18.9 (20.5) 6.1 (7.7) 12.4 (13.5) 3.3 (4.2) 8.7 (14.1) Soybean share in harvested area, % 2.2 (6.1) 10.2 (15.9) 2.2 (6.2) 10.6 (16.1) 1.7 (4.1) 7.5 (13.8) 2.1 (5.6) 7.3 (15.7) Barley share in harvested area, % 19.1 (10.9) 8.2 (9.6) 19 (11) 7.6 (9.5) 19.8 (9.9) 11.3 (9.8) 20.9 (11.2) 12.4 (9.8) Sugar share in harvested area, % 3.7 (6.8) 1.3 (4.9) 3.8 (6.9) 1.4 (5.2) 3.6 (6.3) 0.5 (2) 2 (4.3) 0.6 (2.8) Rye share in harvested area, % 0 (0) 0.6 (4) 0 (0) 0.6 (4.1) 0 (0) 0.3 (1.9) 0 (0) 1.6 (6) Oat share in harvested area, % 2.9 (6.8) 0.6 (2.8) 2.9 (6.8) 0.5 (2.8) 2.2 (5.6) 0.4 (1.5) 3.4 (7.3) 2.6 (5.2) Number of observations 6136 7034 5594 6088 328 776 214 170 Notes: Standard deviations are given in brackets. For all variables (except for “Land concentration at rayon, (HHI)”) means and standard deviations are weighted by the farm size. * variables are denominated per hectare of land use (owned and rented). ** Variable “Other costs” includes the costs of access to land. Source: Authors’ calculations based on the SSSU data. Fig. 3. Spatial distribution of land concentration (HHI index) at the rayon level. V. Kvartiuk et al. Land Use Policy 146 (2024) 107336 6 holdings (i.e., positive sign of β 1 because the dependent variable is negative for this sub-sample). On the other hand, more able farms should also grow more in the sample with farms that experienced an increase in farm size (i.e., positive sign of β 1 ). Moreover, exit-entry analysis should provide clues about the factors influencing the decisions to enter, exit, or stay on the rental market. Naturally, the key to this analysis is to estimate unobservable agricultural ability α i. We employ three different approaches to estimate abilities. First, we follow Schmidt and Sickles (1984) in calculating a farm-specific fixed effect (within transformation) using panel regression (FE). Second, we calculate technical efficiency scores based on the Stochastic Frontier Analysis (SFA) with time-invariant efficiency scores distributed according to the truncated normal distribution following (Battese and Coelli, 1992). Third, we derive Total Factor Productivity (TFP) from the Solow residual of the pooled-OLS production function (Deininger et al., 2009; Deininger and Jin, 2005; Schmidt and Sickles, 1984). To calculate each of the above, we estimate a Cobb-Douglas production function of the following general form: Yit =γ+βXimt +δZint + α i+ ε it (2) where Yit −is the log of the value of crop output of the farm i in time t; Ximt −is the vector of logs of m input costs used by farm i in time t; Zint − is the vector of the n control variables such as reverse dummy variables for zero input use following (Battese, 1997), linear trend, and crop-year specific dummy variables indicating that a given crop was produced on the farm. With the FE or SFA estimators, we derive α i which is a time-invariant, farm-specific fixed effects or inefficiencies, respectively. Then we transform SFA inefficiencies into technical efficiencies (e− α i), which we use as a first proxy for agricultural ability. FE estimates represent another proxy for the ability. Finally, in the specification with pooled cross-sections, farm-specific TFP is calculated as TFPi =∑t ε it/ni, where ni is the number of years that the farm is present in the sample, and is used as an alternative proxy for agricultural ability. All these parameters are estimated along with the coefficients γ,β,and δ. 5. Main results 5.1. Estimating agricultural ability Before estimating the model with the determinants of land rental, it is informative to examine the results of the model with the production function estimations (Table 2). Models (1) to (3) in Table 2 estimate the abilities on a time-invariant subsample of farms pooled from their first year of their appearance in the panel to avoid endogeneity issues in our main estimations below. Models (4)-(5) exploit the whole variation of the panel and estimate the production functions based on the whole sample. Because we deal with an unbalanced panel with 38.6 % of the farms present during the whole sample (see Appendix A for details on panel structure), it is important to consider the farms entering and exiting during our period. Thus, we estimate the efficiencies for the “entrants” at the time of their first appearance in the sample. To ensure the robustness of our results, we compare our estimates with those obtained from a balanced panel (see Appendix B) which appear to be very similar. All the coefficients of the input costs show the expected signs and are statistically significantly different from zero, predicting the output with relatively high explanatory power. We find land to be the most important production factor with an elasticity ranging from 20.1 % for the OLS to 23.1 % for FE estimation. These figures are way above the average share of land rental expenditures in total costs (12.6 %). Models (4)-(6) suggest that these figures increased further over time. This indicates an uncompetitive allocation of land as we would observe a closer match Table 2 Production function estimations. First year Whole sample (1) (2) (3) (4) (5) (6) Variables OLS FE SFA Pooled OLS FE SFA Utilized land (ha) 0.226*** (0.021) 0.231*** (0.022) 0.233*** (0.038) 0.232*** (0.004) 0.343*** (0.006) 0.268*** (0.009) Labor costs 0.043*** (0.010) 0.041*** (0.010) 0.051** (0.018) 0.067*** (0.002) 0.064*** (0.003) 0.09*** (0.003) Seeds costs 0.186*** (0.013) 0.187*** (0.014) 0.192*** (0.019) 0.162*** (0.003) 0.142*** (0.003) 0.156*** (0.019) Fertilizers costs 0.134*** (0.007) 0.134*** (0.008) 0.132*** (0.014) 0.129*** (0.002) 0.100*** (0.002) 0.116*** (0.003) Petrol costs 0.154*** (0.012) 0.148*** (0.013) 0.141*** (0.019) 0.113*** (0.003) 0.080*** (0.003) 0.086*** (0.014) Machinery/capital costs 0.053*** (0.007) 0.053*** (0.007) 0.056*** (0.014) 0.050*** (0.002) 0.034*** (0.002) 0.048*** (0.003) Services costs 0.091*** (0.005) 0.092*** (0.006) 0.094*** (0.010) 0.100*** (0.002) 0.089*** (0.002) 0.107*** (0.002) Other costs 0.100*** (0.011) 0.100*** (0.011) 0.099*** (0.018) 0.139*** (0.002) 0.109*** (0.003) 0.127*** (0.003) Linear trend 0.102*** (0.002) 0.082*** (0.002) 0.086*** (0.002) Constant 1.267 (0.862) 2.313*** (0.360) 0.310*** (0.025) 0.732*** (0.055) Scale elasticity 0.987*** (0.0097) 0.987** (0.0101) 0.999** (0.0234) 0.993*** (0.0023) 0.961*** (0.0051) 0.997*** (0.0053) N 11,710 11,710 11,710 80,245 80,245 80,245 Adj. R 2 / % total variance due to inefficiency (SFA) 0.842 0.798 57.3 % 0.877 0.571 68.2 % Mean Technical Efficiency (st. dev.) 0.632 (0.148) 0.7066 (0.165) Note: p-values are * <0.05; ** <0.01; *** <0.001. Clustered, heteroscedasticity, and autocorrelation robust standard errors (Arellano, 1987) are presented in the parenthesis for pooled OLS and FE specifications, while regular standard errors are reported for the SFA. All continuous dependent and independent variables are in logarithmic form; input costs are expressed in thousands of constant 2018 USD; reverse dummy variables are used for compensating zero input use following Battese (1997). Multiple crop-specific dummy variables are introduced to control for the crop composition at the farm level each year. Model (2) utilizes rayon fixed effects because each farm is observed only once. Source: Authors’ calculations based on the SSSU data. V. Kvartiuk et al. Land Use Policy 146 (2024) 107336 7 between these values otherwise (Dobbelaere and Mairesse, 2013). Interestingly, labor has a very low contribution to the output in all three specifications, which is reflected by the share of labor in the cost structure. This suggests that business models focusing on cash crops’ cultivation heavily rely on non-labor production factors where land represents the most important production factor. Finally, various material inputs have a substantial contribution to output. The models (1)-(3) estimated on the first year are similar to the models (4)-(6) estimated on the whole panel sample, suggesting robustness of the results. Obtained agricultural abilities across the three approaches are also close to each other (correlation coefficients: 0.90–0.97), suggesting a high robustness of the estimations. Estimation results suggest that returns to scale are close to constant. Estimated scale elasticities vary between 0.988 and 1.002. Although in most of the models, we cannot reject the hypothesis that the scale elasticity is different from one. Scale elasticities in models (4) and (5) are significantly different from one suggesting minimal diminishing returns to scale. To explore the relationship between farm size and the three measures of a farm’s ability further, we use Loess smoothing depicted in Fig. 4. 14 Across the plots based on different efficiency proxies, we see a slight increase in the efficiency up to the size of 1000–1500 ha with a consequent slight decrease as we move along the x-axis. For farms larger than 10,000 ha, the variance is too large to draw any meaningful conclusions about the farm size vs. efficiency relationship. Because the SFA model assumes a stochastic nature of the frontier analysis, the confidence intervals of the green line plotted based on SFA estimations are narrower compared to the lines based on FE and pooled OLS estimates. Therefore, we rely on the SFA measures of technical efficiency as an approximation of a farm’s ability in our further analysis. We observe substantial spatial variation in our agricultural ability proxies. The average technical efficiency is 70.7 % of the possible production level (standard deviation - 16.5 %). Fig. 5 presents the spatial distribution of the efficiency scores on the rayon level at the beginning and at the end of our sample (the choropleth map is based on deciles of respective distributions for comparability reasons). We observe a cluster of high efficiency in the eastern parts which were hindered by the Russian military invasion in 2014. Although we see some efficiency improvements in the southern regions, the central parts with the most intensive agricultural production do not show signs of improvement. 5.2. Determinants of land rental Table 3 presents the estimations of the determinants of land rental. Most importantly, we find the coefficients of the initial agricultural ability to be negative and significant across all the specifications. This implies that more efficient farms tend to rent in less land in the last observed period, and conversely, those farms that rent in more land appear to be less efficient. Quantitatively, this means that a farm with a 10 % higher agricultural ability throughout our period was likely to end up with 141.1 ha less rented land on average. Our finding is precisely the opposite of what we would expect in a well-functioning land rental market where more able farms should rent in more land. In particular, rental-based land relations in Ukraine up until 2015 appear to have facilitated a flow of land toward less efficient agricultural producers. Testing our hypothesis related to land concentration, we find that initial regional land distribution matters for further farming modes. Thus, farms that were operating in a rayon with a high degree of land concentration in 2005 were more likely to have rented more land at the end of the observation period. The effect appears to be relatively large, as a 10 % increase in the HHI index with respect to a reference rayon is associated with 132.6 ha more rented land in the last observed year. We also find that the coefficient of the interaction term between rayon land concentration and agricultural ability does not differ from zero in a statistical sense. Initially owned land appears to be positively associated with the land rented at the end of the observation period, contrary to our expectations and the results of similar studies (e.g., Deininger and Jin, 2005; Vranken and Swinnen, 2006). Owned land was most typically held in the form of the CLS, representing negligible amounts compared to the rented areas. As the farm grows, it may be better positioned to attract CLS from the individuals in its vicinity. As a result, we may observe a complementarity between the CLS and rented land. A farm’s legal form appears to play a significant role in its activity in the land rental market. We find the coefficients of the dummy for an individual farm to be positive and statistically significant, suggesting that this type of producer was more likely to accumulate land at the end of the observation period compared to the agricultural enterprises, which are accounted for in the intercept. We observe an opposite picture with the dummy for state farms, which appears to exert a persistent negative effect throughout the specifications. This implies that state enterprises tend to rent less land than their private counterparts Our evidence suggests that farm size expansion is closely associated with the focus on cash crops cultivation. Most of the coefficients of the variables reflecting the percentage of area cultivated with these crops are positively and significantly related to the dependent variable. For instance, an additional 10 % of land initially allocated for corn was associated with ca. 365.1 ha more rented land at the end of the period. Similarly, 10 % more land allocated for wheat in 2005 is associated with ca. 60.6 ha of additionally rented land in 2015. This means that agricultural enterprises cultivating crops that are suitable for exports were more likely to rent in more land. This is in line with our hypothesis about annual export-oriented crop cultivation driving farm expansion via land rental markets. 5.3. Determinants of changes in land rental We now examine the factors that may impact the changes in land rental explicitly considering the types of farms during the observed period. In particular, we divide the sample into subsamples according to the following criteria: farms that stayed over the whole period, exited, and entered the land market (Table 4). We use the same explanatory variables as in Table 3 above. As three interaction terms are present in the model, we also estimate marginal effects for each regressor at the mean of the corresponding interaction terms. Our estimations do not reveal convincing evidence of rental markets facilitating the flow of land towards more efficient farmers as the coefficients of the agricultural ability are insignificant in most of the specifications. Among the farms that operated during the whole period of 2005–2015 (stayers), more able ones were more likely to rent in more land. Moreover, those stayers that cultivated corn and sugar beets appear to have been typical export-oriented farms operating large areas as the coefficients of the shares of respective crops are positive, significant, and large in magnitude. These farms, often restructured from collective farms, inherited substantial land rental contracts. In contrast, more efficient entrants rented less land on average, suggesting higher production intensity or difficulties in accessing land. Notably, for the entrants, we find the coefficient of the interaction term between the ability and land concentration to be negative and significant implying that they were more productive in the areas with higher land concentration. If farms’ efficiency drives their land rental decisions, we should observe this in the subsamples with shrinking and growing farms in a more pronounced way. We thus re-estimated our models on the subsamples with farms that grew and shrank by at least 5 % and 25 % (Table 5). However, marginal effects of agricultural ability do not demonstrate significant effects. In contrast, initial land concentration appears to exert a polarizing effect: farms with decreasing land holding 14 All the smoothers are based on full-sample estimations with the exception of the initial technical efficiency. V. Kvartiuk et al. Land Use Policy 146 (2024) 107336 8 Note: Technical efficiency distributions are standardized with the help of the a standard normal distribution with a zero mean and standard deviation of 1. This allows compatibility of these distributions as they are estimated based on different samples. Appendix D. Rayon-level estimations of the shares of land freed by "exiters" Share of land under exiters Coefficients Agricultural ability (rayon-level mean) −1.164*** (0.345) Initial land concentration in a rayon (HHI) −0.763* (0.310) Agricultural ability (rayon-level mean) * Initial land concentration in a rayon (HHI) 0.718 (0.489) Intercept 1.066*** (0.221) Marginal effects Agricultural ability (rayon-level mean) −0.978*** (0.251) Land concentration in a rayon in 2005 (HHI) −0.309*** (0.058) Number of independent variables 4 88 Number of observations 605 Notes: *p<0.1; **p<0.05; ***p<0.01. Standard Errors are present in parenthesis. 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