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Evidence on the Persistence of Farm Profit in the Long-Run

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Evidence on the Persistence of Farm Profit in the Long-Run Do Profit Rates Converge? Evidence on the Persistence of Farm Profit in the Long-run Aderajew Tamirat†, Andres Trujillo-Barrera‡ and Joost M.E. Pennings†, ‡ Selected Paper prepared for presentation at the Agricultural & Applied Economics Association 2018 AAEA Annual Meeting, Washington, D.C. August 5-7, 2018. Copyright 2018 by Aderajew Tamirat, Andres Trujillo-Barrera and Joost M.E. Pennings. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies. † Maastricht University, Department of Finance ‡ Wageningen University and Research, Marketing and Consumer Behavior Group †,‡ Maastricht University, Department of Finance and Department of Marketing Abstract Ensuring farm survival and competitiveness requires a better understanding of why some farms consistently perform better (worse) than others. This article investigates the drivers of farm profitability, the degree of abnormal profit persistence, and its determinants based on a unique longitudinal data set collected from a panel of 2000 Dutch farms between 2001 and 2015. We apply a quantile regression approach to examine the drivers of farm profitability, and a dynamic panel System- GMM estimation to estimate the persistence of abnormal farm profit. The results of the quantile regression show that working capital, labor productivity, leverage, capital intensity, and investment are important determinants of profitability. The findings suggest that working capital is important for farms’ flexibility and their capacity of adapting to changing circumstances in environments where they don’t receive regular income from agricultural products. Estimates using the dynamic panel model provide evidence on abnormal profit persistence. Profit persistence is responsive to farm risk exposure, investment, capital intensity, leverage, working capital and diversification. Thus, long-run farm profit can be achieved and sustained by reducing costs through economies of scale, ensuring adequate working capital to cope with the cash flow mismatch, enhancing labor productivity, and minimizing the farm’s capital intensity levels. Key words: Profit persistence, System-GMM estimation, quantile regression, and dynamic panel model. 1. Introduction Why some firms persistently earn higher profits that deviate from the average earned in the industry? Are differences in profitability due to random events or driven by resources and capabilities? Most of the literature supports the existence of firms that exhibit abnormal profits that persist in the long run (Hirsch, 2017)). While most studies consider the manufacturing and service sectors, articles focused on the agricultural sector at the farm level are scarce. Exceptions include Schumacher and Boland (2005), Hirsch and Gschwandtner (2013), and Hirsch and Hartmann (2014), that examine profit persistence in the food industry. Unlike other manufacturing and service sectors, agribusiness is characterized by specific circumstances, including seasonality of production, different legal forms, whereby sole proprietorship is the predominant legal form, smaller decision-making units, and limited access to equity markets. These unique characteristics increase the likelihood of cash flow mismatches, heterogeneous risk profiles, and create different patterns of decision making, providing a relevant context for extending the study of profit persistence into the agricultural sector. Farm profitability has been in the spotlight in recent years because farm income has exhibited higher volatility compared to other industries (Burns et al., 2015; García‐Germán et al., 2016) and because of major socioeconomic changes, including new land use demand, 1 globalization, agricultural policy reforms, and a decline in the number of farms3 in the developed world. In addition to these trends, we observe a different level of farm profitability; some farms consistently perform better (worse) than others (European Commission, 2017). Such variations even after accounting for farm size and types raise several questions: are these differences in profit level and persistence systematic or random? what are the determinants of these differences? and can these determinants be influenced by farmers themselves and by policy instruments? From a policy perspective, these questions are relevant since most agricultural policy instruments aim to increase farm competitiveness. More importantly, it is critical for farms to benchmark their operations in the same sector and relative to other sectors, so they can address potential weaknesses and capitalize on strengths. This paper contributes to the literature in four ways: first, we extend the understanding of profit persistence and its drivers to the agriculture sector, which helps farms and public policy makers aiming to ensure the economic viability and sustainability of farms. Second, examining the determinants of long-run farm profitability provides insights about the distinct farm resources and capabilities that help farms gain and sustain competitive advantages. Third, contrary to previous studies, which focused on a single farm type, we use panel data to consider four farm types: dairy, livestock, field crop, and horticulture farms. This allows for the analysis of a larger part of the farm sector in the Netherlands, while the panel data enables us to provide insight into the evolution of farm profit over time and its variation across farm types. Finally, this study is based on a dynamic panel model of Blundell and Bond’s (1998) System-GMM estimator. This approach is crucial as the dynamics of farm profits can be too complex to be captured by the simple AR (1) model often used in the literature, and the modified dynamic panel model helps to solve misspecification and potential endogeneity problems common to static models. This article is structured as follows. The next section provides a theoretical background and conceptual framework. This is followed by a description of the variables, data used, and methodology applied. Next, the results of the empirical analyses are presented and discussed. The final section sets out the conclusion and limitations and provides suggestions for further 3 In 2005, the European Union (excluding Croatia) had an estimated 14.5 million farmers. By 2013, this number declined to 10.8 million, i.e. a 34.2% decline, equivalent to an average decline of 3.8% per year (Eurostat, 2015). The Dutch farm sector was no exception. Between 2000 and 2014, the number of agricultural holdings significantly decreased from 101,550 to 65,500 farms, representing a 35.5% decline and exceeding the average EU decline (Berkhout and Van Bruchem, 2015). 2 research. 2. Theoretical background The classic perfect competition model considers the deviation of firm profit from the industry’s average (norm) a short-run phenomenon as competition will cause profit to converge to the norm in the long run. However, a substantial number of firms earn profits above the norm for sustained periods of time (Gschwandtner and Hauser, 2016), constituting an abnormal profit persistence that the classic model fails to explain (Hirsch and Hartmann, 2014). Several models have been developed to explain this phenomenon where the Structure Conduct Performance (SCP), the Market-Based View (MBV) and the Resource-Based View (RBV) are predominant. The central premise of SCP is that abnormal profit is determined by the competitive dynamics and structure of the industry. That can be assessed by analyzing elements such as barriers to entry and exit, product differentiation, the number of competitors, and the number of buyers and sellers, which help to determine how firms behave and perform, i.e., gain and sustain abnormal profits (Porter, 2008). The MBV is an extension of the SCP and argues superior performance is determined not only by industry structure, but also by market-positioning strategies, e.g. low-cost/product differentiation, employed in the target market segments that determine superior performance and its sustainability (Wiggins and Ruefli, 2002). The MBV stresses that firms design their strategies based on market analysis of the industry Recently, the focus in the literature has shifted from industry structure to firm’s internal resources and capabilities (Furrer et al., 2008). This shift has made the Resource Based View (RBV) the leading model for analyzing sustained superior performance. A central premise of the RBV is that firms compete and choose strategies based on their material, financial and human resources and their capabilities (Grant, 1991). Competitive advantage can be achieved and sustained by developing and owning resources that are scarce, valuable, non-substitutable and inimitable (Barney, 2001). Resources that sustain competitive advantage will also generate superior economic performance that will persist over time. The SCP, MBV, and RBV are similar in that they all assume that a firm’s ultimate goal is to achieve and sustain a competitive advantage and that above-normal profit is possible. They differ as to the sources of competitive advantage and the unit of analysis. Despite the relevance of each of these views in explaining abnormal profit, the RBV provides an 3 established analytical tool for examining the relationship between farm resources, profit and sustained abnormal profit. Hence, our conceptual framework is based on the RBV. 2.1 Conceptual framework Profit persistence is widely studied in the manufacturing and service
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