Productivity and Profitability of US Agriculture: Evidence from a Panel of States

Ball, V. Eldon; Färe, Rolf; Grosskopf, Shawna; Margaritis, Dimitri

doi:10.1007/978-1-4419-6385-7_7

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…Our DEA results are in line with other empirical studies that analyze the TFP growth in the U.S. agricultural sector using the same data source. Zofío and Lovell (2001), Ball et al (2010), O'Donnell (2012b and Ball et al (2016) also find substantial TFP growth 16 . It is driven by technical progress rather than efficiency change in line with Zofío and Lovell (2001) and Ball et al (2016).…”

Section: Empirical Application: Us Agriculturementioning

confidence: 85%

“…We compare across all 48 states when computing the necessary distance functions and thus assume that all states have access to a similar production technology. This is also the approach of Zofío and Lovell (2001) and Ball et al (2010). Alternatively, we could compare states within the same agricultural region (see Table 2).…”

Section: Empirical Application: Us Agriculturementioning

confidence: 99%

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Decomposing the Luenberger–Hicks–Moorsteen Total Factor Productivity indicator: An application to U.S. agriculture

Ang

Kerstens

2017

European Journal of Operational Research

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This paper introduces a decomposition of the additively complete Luenberger-Hicks-Moorsteen Total Factor Productivity indicator into the usual components: technical change, technical inefficiency change and scale inefficiency change. Our approach is general in that it does not require differentiability or convexity of the production technology. Using a nonparametric framework, the empirical application focuses on the agricultural sector at the state-level in the U.S. over the period 1960 − 2004. The results show that Luenberger-Hicks-Moorsteen productivity increased substantially in the considered period. This productivity growth is due to output growth rather than input decline, although the extent depends on the convexity assumption of the technology. Technical change is the main driver, while the role of technical inefficiency change and scale inefficiency change also depends on the convexity assumption of the technology.

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Section: Empirical Application: Us Agriculturementioning

confidence: 85%

Section: Empirical Application: Us Agriculturementioning

confidence: 99%

Decomposing the Luenberger–Hicks–Moorsteen Total Factor Productivity indicator: An application to U.S. agriculture

Ang

Kerstens

2017

European Journal of Operational Research

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“…The 'total' difference is the simple sum of the sub-aggregates, which makes them very useful when summarizing panel data as we have here. 4 Another advantage of using differences rather than ratios is that they circumvent problems arising from the presence of zeroes in the data. 5 Use of differences is also a more convenient tool to analyze the sources 3 This indicator is as also known as the Bennet-Bowley productivity indicator based on the work of Bennet (1920) …”

Section: Indicatorsmentioning

confidence: 99%

The role of energy productivity in U.S. agriculture

et al. 2015

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This paper investigates the role of energy on U.S. agricultural productivity using panel data at the state level for the period 1960-2004. We first provide a historical account of energy use in U.S. agriculture. To do this we rely on the Bennet cost indicator to study how the price and volume components of energy costs have developed over time. We then proceed to analyze the contribution of energy to productivity in U.S. agriculture employing the IntroductionThe purpose of this study is to investigate the role that energy plays in the U.S.agricultural sector, both in terms of its role as a factor of production and its role as a contributor to productivity growth. Our analysis employs a unique data series compiled by the U.S. Department of Agriculture's Economic Research Service (ERS).The data consist of a state-by-year panel, which will allow us to assess the impact of technological advances over the study period as well as the effect of volatile energy prices. Of particular interest are the effects of major energy market shocks (e.g. the oil price shocks of the 1970s) on energy productivity and the profitability of the U.S.agriculture. The data set includes three outputs and six inputs; the latter include direct energy use in agriculture as well as indirect energy use as, for example, consumption of agricultural chemicals. 1 Both price and quantity data are available. A more detailed description of the data set is given in Section 3 below.Our first objective is to give an historical accounting of energy consumption in U.S.agriculture. While direct energy consumption in the agricultural sector represents only a very small fraction of the total U.S. energy use, changes in the energy market can have a large impact on costs and, therefore, on profitability of the sector as well as on food prices. 2 The effects of energy costs on profitability may also be greatly exacerbated by changes in fertilizer and pesticide costs, both of which are significant energy users. Here we rely on a Bennet (1920) indicator decomposition of profit into price and volume indicators, which can also be decomposed into changes over time and space. This decomposition is possible due to the additive structure of the Bennet indicator. This is not possible with the more familiar Fisher and Törnqvist indexes.Thus our work will provide an additional tool for the analysis of the role of energy in agriculture.1 Energy inputs feature in every stage of agricultural production, from making and applying chemicals to fueling farm machinery used in tillage and harvesting of crops, and to electricity for livestock housing facilities. Such reliance on energy consumption has left farmers vulnerable to high energy costs and volatile energy market fluctuations, thereby highlighting the importance of efficient use of energy for farm profitability and for more sustainable agricultural practices (see Levine, 2012). 2 For example, Wang and McPhail (2012) report that in addition to global food demand, energy shocks also play an important role in explaini...

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“…Because agricultural practices vary significantly in different regions of the US (e.g., Corn Belt vis-à-vis the Appalachians) due to geographical and climatic conditions (seeBall et al, 2010), it is important to account for regional differentials in mix efficiency in our analysis. The data show that the different regions in the US produce different agricultural outputs and use different input mixes.…”

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confidence: 99%

Estimating input-mix efficiency in a parametric framework: application to state-level agricultural data for the United States

Ahmad

2020

Applied Economics

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This paper contributes to the productivity literature by demonstrating novel econometric methods to estimate input-mix efficiency (IME) in a parametric framework. Input-mix efficiency is defined as the potential improvement in productivity with change in input mix. Any change in input-mix (e.g., land to labor ratio) will result in change in productivity. We minimize a nonlinear input-aggregator function (e.g., Constant Elasticity of Substitution) to derive an expression for input-mix efficiency. We estimate a Bayesian stochastic frontier for obtaining mix efficiency using US state-level agricultural data for the period 1960-2004. We note significant variation in input-mix efficiency across the states and regions, attributable to diverse topographic, geographic and infrastructure conditions. Furthermore, comparisons of allocative and mix efficiencies provide insightful policy implications. For example, the production incentives such as taxes and subsidies could help farmers in adjusting their input mix in response to changes in input prices, which can affect the US agricultural productivity significantly. We provide a simple way of estimating mix efficiency in an aggregate-input, aggregate-output framework. This framework can be extended by i) using flexible functional forms; ii) introducing various time-and region-varying input aggregators; and iii) defining more sophisticated weights for input aggregators.

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Productivity and Profitability of US Agriculture: Evidence from a Panel of States

Cited by 5 publications

References 11 publications

Decomposing the Luenberger–Hicks–Moorsteen Total Factor Productivity indicator: An application to U.S. agriculture

Decomposing the Luenberger–Hicks–Moorsteen Total Factor Productivity indicator: An application to U.S. agriculture

The role of energy productivity in U.S. agriculture

Estimating input-mix efficiency in a parametric framework: application to state-level agricultural data for the United States

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