Is U.S. Agricultural Productivity Growth Slowing?

Ball, Eldon; Schimmelpfennig, David; Wang, Sun Ling

doi:10.1093/aepp/ppt014

Cited by 41 publications

(27 citation statements)

References 29 publications

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“…Já Ball et al (2013) utilizaram dados estatís-ticos sobre a produtividade agrícola compilados pelo Departamento de Serviço de Pesquisa Econômica da Agricultura dos EUA para testar a hipótese de desaceleração da produtividade. Os índices de crescimento da produtividade abrangem os anos de 1948 a 2009.…”

Section: América Latina: Produtividade Agrícolaunclassified

“…Ainda segundo Pires e Garcia (2004), o Brasil experimentou uma alocação de recursos pesada na economia responsável pelo grande investimento em infraestrutura dentro do País na década de 70 do século XX. Já o estudo de Ball et al (2013) A última década do estudo está descrita na Tabela 8. Brasil, México e Argentina possuem, em média, as três maiores variações da PTF, 80,05%, 78,99% e 74,03%, respectivamente.…”

Section: Produtividade Total Dos Fatoresunclassified

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Produtividade Agrícola nos Países da América Latina

Ferreira¹,

Araujo

Tabosa

et al. 2016

Rev. Econ. Sociol. Rural

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Resumo: Este artigo estuda a produtividade total dos fatores (PTF) na agricultura de 19 países da América Latina no período de 1961 a 2010. Na análise do desempenho econômico dos países, utiliza-se a abordagem paramétrica da fronteira estocástica de produção. Verifica-se que todos os países apresentaram variação da PTF positiva para o período entre 1961 a 2010, ou seja, apresentaram crescimento do produto, assim como progresso técnico positivo. A maior variação da PTF corresponde ao Brasil, 55,73%; já a menor variação apresenta-se para Trinidad e Tobago, 42,06%. Contudo, a mudança na eficiência técnica mostrou-se decrescente para quase 50% da totalidade dos países. Tratando-se de eficiência de escala, apenas Argentina, Brasil e Uruguai apresentaram médias positivas para o período. Em relação à mudança na eficiência alocativa, todos os países mostraram-se decrescentes. Palavras-chaves:

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Section: América Latina: Produtividade Agrícolaunclassified

Section: Produtividade Total Dos Fatoresunclassified

Produtividade Agrícola nos Países da América Latina

Ferreira¹,

Araujo

Tabosa

et al. 2016

Rev. Econ. Sociol. Rural

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“…Breaks were found in TFP by Ball et al (2013), so a version of TFP was tested with a trend break in 1974 and an intercept shift (or drift) in 1985. Once these previously identified breaks were removed with dummy variables, TFP was found to be without further breaks (Table III).…”

Section: Datamentioning

confidence: 99%

“…Farm income can be related to differenced and stationary forms of the variables in other time-series-based models that exploit these time-series characteristics to avoid model specifications showing spurious relationships (Phillips, 1998). −14.8*** −13.1*** Exchange rates −33.6*** −33.6*** US gross domestic product (GDP) −74.5*** −43.3*** Farmland prices −67.9*** −34.1*** Stability of variable sub-groups with farm income a Input, output prices, and TFP −43.5*** Exchange and interest rates −14.9** GDP and land prices −23.1*** Notes: a Null hypothesis of stable coefficientsfull sample (in parenthesis) and 1964-2010 reduced sample; b Results from Ball et al (2013) used to create dummy variables for breaks in agricultural total factor productivity in 1974 and 1985 before qLL test was applied. *,**,***Significant at 10, 5, 1 percent level, respectively Source: ERS briefing room, Farm income and costs, available at: www.ers.usda.gov/briefing/ farmincome/ …”

Section: Unit-root Tests Without Structural Breaksmentioning

confidence: 99%

Determinants of farm income

Beckman

Schimmelpfennig

2015

Agricultural Finance Review

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Purpose -The recent fluctuations in farm income remind us of the boom-bust nature of the agricultural sector. To better understand these fluctuations in farm income, the purpose of this paper is to examine the relationship between farm income and influential factors from 1964 to 2010 allowing for structural breaks in the data. Design/methodology/approach -The authors estimate error-correction models for an overarching model and several sub-models at different scales based on their relationship with farm income: micro, meso, and macro. The authors then provide a series of impulse response functions (IRFs) that combine short-and long-run impacts in a rigorous framework indicating the response of farm income to shocks from any of the explanatory variables. Findings -Results indicate that prices paid (PP) and received by farmers, technological change, interest and exchange rates (ERs), gross domestic product (GDP) and land prices all influence farm income. Results using IRFs show how increases in farm income arise from shocks to prices received and GDP; while PP, interest rates, and land prices have a negative impact on farm income. Technological progress and ERs switch from having a negative short-run impact, to a positive long-run impact. Originality/value -This paper takes a fresh look at the single, overarching model for farm income determinants. The authors break this model into three separate levels, with results indicating that these sub-groups perform better than the one overarching model of all variables.

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“…13 Updates of the State-level statistics have been suspended in light of reduced USDA Economic Research Service (ERS) resources and the discontinuance of key sources of data series. While more upto-date data would have been desirable, this does not detract attention from the main interest of our analysis focussing on the 1970s major oil price shocks that resulted in a rapid and unexpected rise in energy prices and their aftermath linked to a slowdown in U.S. agricultural productivity growth (see Ball et al, 2013 …”

Section: Energy Costmentioning

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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Is U.S. Agricultural Productivity Growth Slowing?

Cited by 41 publications

References 29 publications

Produtividade Agrícola nos Países da América Latina

Produtividade Agrícola nos Países da América Latina

Determinants of farm income

The role of energy productivity in U.S. agriculture

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