nep-eff New Economics Papers
on Efficiency and Productivity
Issue of 2020‒10‒05
thirteen papers chosen by

  1. Technical efficiency and inefficiency: Reassurance of standard SFA models and a misspecification problem By Kumbhakar, Subal C.; Peresetsky, Anatoly; Shchetynin, Yevgenii; Zaytsev, Alexey
  2. Mechanization Services and Technical Efficiency in China’s Wheat Production By Huan, Meili; Dong, Fengxia
  3. Farm resource use as influenced by diverse cropping systems in the Australian Northern cropping zone By Kotir, Julius; Bell, Lindsay; Kirkegaard, John; Whish, Jeremy; Aikins, Kojo Atta
  4. Farm-level Innovation and Productivity: Evidence from Groundnut Farming in Malawi By Owusu, Eric S.; Bravo-Ureta, Boris E.
  5. Growing Like China: Firm Performance and Global Production Line Position By Davin Chor; Kalina B. Manova
  6. Do Farmers’ Organizations Impact Production Efficiency? Evidence from Bangladeshi Rice Farmers By Bairagi, Subir K.; Mishra, Ashok K.
  7. Technology, industrial dynamics and productivity: a critical survey By Mehmet Ugur; Marco Vivarelli
  8. The performance of microfinance institutions: An analysis of the local and legal constraints By Delaram Najmaei Lonbani; Bram De Rock
  9. Examining the effects of federal crop insurance premium subsidies on allocative and technical inefficiency in the U.S. cornbelt By Njuki, Eric
  10. The Economics and Productivity of U.S. Cow-Calf Production By Nehring, Richard; Gillespie, Jeffrey M.
  11. Drivers of Profit Inefficiency in Iowa Crop Production By Sawadgo, Wendiam PM; Plastina, Alejandro
  12. The Clean Water Act and CAFOs: Effects of Regulatory Avoidance on Productivity By Chen, Chen-Ti; Crespi, John M.
  13. Precision Agriculture Technology Usage and Adoption Patterns By Hanson, Erik; Roberts, David C.

  1. By: Kumbhakar, Subal C.; Peresetsky, Anatoly; Shchetynin, Yevgenii; Zaytsev, Alexey
    Abstract: This paper formally proves that if inefficiency ($u$) is modelled through the variance of $u$ which is a function of $z$ then marginal effects of $z$ on technical inefficiency ($TI$) and technical efficiency ($TE$) have opposite signs. This is true in the typical setup with normally distributed random error $v$ and exponentially or half-normally distributed $u$ for both conditional and unconditional $TI$ and $TE$. We also provide an example to show that signs of the marginal effects of $z$ on $TI$ and $TE$ may coincide for some ranges of $z$. If the real data comes from a bimodal distribution of $u$, and we estimate model with an exponential or half-normal distribution for $u$, the estimated efficiency and the marginal effect of $z$ on $TE$ would be wrong. Moreover, the rank correlations between the true and the estimated values of $TE$ could be small and even negative for some subsamples of data. This result is a warning that the interpretation of the results of applying standard models to real data should take into account this possible problem. The results are demonstrated by simulations.
    Keywords: Productivity and competitiveness, stochastic frontier analysis, model misspecification, efficiency, inefficiency
    JEL: C21 C51 D22 D24 M11
    Date: 2020–09
  2. By: Huan, Meili; Dong, Fengxia
    Keywords: Production Economics, Community/Rural/Urban Development, Productivity Analysis
    Date: 2020–07
  3. By: Kotir, Julius; Bell, Lindsay; Kirkegaard, John; Whish, Jeremy; Aikins, Kojo Atta
    Abstract: Many farming systems in Australia are underperforming. For example, a recent analysis showed that only about 29% of current crop sequences in the northern grains region of Australia are achieving 80% of their water-limited yield potential (Hochman et al., 2014). This is compounded by tight profit margins and changing climate and market conditions. Available evidence also suggests that between 2013 and 2018, the cost of consumable inputs, such as fertiliser, has increased by 5.7% (ABARES, 2018). Also, over the past five years, the cost of agricultural machinery in Australia has increased by 13.4% (ABS, 2018). However, several farming system component analyses and simulations have predominantly focused on the impact of biophysical processes on farming system performance, including soil quality, water use efficiency, dynamics of nitrogen, crop yields, and disease and nematodes effects of farming practices at the paddock scale. While biophysical optimisation of the farming system may be possible to improve the efficiency of most farming systems, key elements that are often ignored is how the intensity and diversity of different cropping systems impact on whole-farm factors, such as labour and machinery resources. Far from being obvious, these input resources are critical because they modify farm productivity and profitability in the short and long term. Moreover, a consideration of these factors is crucial because they can influence the adoption of farm innovations. The central objective of this study is to examine farm resource constraints with a focus on machinery, labour requirements and fuel requirements as influenced by diverse crop rotations in the northern grain-growing region of Australia. Our analysis is based on three steps. First, we simulated different crop rotations over 112 years (i.e., 1900-2012) of historical climate records using the Agricultural Production Simulator (APSIM). These crop rotations were identified following focus group meetings with leading farmers and advisors throughout the northern cropping zone of Australia. Second, we obtained information on machinery and labour parameters from existing literature, local technical guides and through a consultation process with farm advisers and growers (N = 26 farmers). Finally, we combined the APSIM generated outputs with the machinery and labour data to comprehensively determine how different crop rotations affect labour and machinery requirements within the farming system using analysis of variance. Results showed that the low-intensity systems required 46% less labour per ha than the higher-intensive systems, while the less diverse systems required about 33% less labour per ha than the more diverse systems. Planting and spraying operations respectively represent about 27% and 37% of total fieldwork requirements. Also, the labour required per ha is less in bigger farms compared to smaller farms, which may be explained by the larger machines used by these larger farms. For all sequences considered, peak labour periods fell in July, October to November, while non-peak period is August to September and December to January, corresponding with the periods in which most farm production activities occur. We conclude that Diversified crop rotation systems had significant effect on labour and machinery requirements and differed significantly among rotations (P < 0.05). Also, diverse rotations may create higher labour demand and peak periods that might, in some cases, limit the adoption of diversified crop rotations in some farm businesses, suggesting that labour efficiency can be an important consideration in farming systems research and analysis. These findings will be explored further as part of the on-going development of a bio-economic modelling to explore the trade-offs and synergies between system performance objectives and impacts of innovations options at the whole-farm level.
    Keywords: Farm Management
    Date: 2020–09–16
  4. By: Owusu, Eric S.; Bravo-Ureta, Boris E.
    Keywords: Productivity Analysis, Production Economics, Community/Rural/Urban Development
    Date: 2020–07
  5. By: Davin Chor; Kalina B. Manova
    Abstract: Global value chains have fundamentally transformed international trade and development in recent decades. We use matched firm-level customs and manufacturing survey data, together with Input-Output tables for China, to examine how Chinese firms position themselves in global production lines and how this evolves with productivity and performance over the firm lifecycle. We document a sharp rise in the upstreamness of imports, stable positioning of exports, and rapid expansion in production stages conducted in China over the 1992-2014 period, both in the aggregate and within firms over time. Firms span more stages as they grow more productive, bigger and more experienced. This is accompanied by a rise in input purchases, value added in production, and fixed cost levels and shares. It is also associated with higher pro fits though not with changing profit margins. We rationalize these patterns with a stylized model of the firm lifecycle with complementarity between the scale of production and the scope of stages performed.
    Keywords: global value chains, production line position, upstreamness, firm heterogeneity, firm lifecycle, China
    JEL: F10 F14 F23 L23 L24 L25
    Date: 2020
  6. By: Bairagi, Subir K.; Mishra, Ashok K.
    Keywords: Productivity Analysis, Production Economics, Agribusiness
    Date: 2020–07
  7. By: Mehmet Ugur (Institute of Political Economy, Governance, Finance and Accountability, University of Greenwich, United Kingdom); Marco Vivarelli (Dipartimento di Politica Economica, DISCE, Università Cattolica del Sacro Cuore – UNU-MERIT, Maastricht, The Netherlands – IZA, Bonn, Germany)
    Abstract: We review the theoretical underpinnings and the empirical findings of the literature that investigates the effects of innovation on firm survival and firm productivity, which constitute the two main channels through which innovation drives growth. We aim to contribute to the ongoing debate along three paths. First, we discuss the extent to which the theoretical perspectives that inform the empirical models allow for heterogeneity in the effects of R&D/innovation on firm survival and productivity. Secondly, we draw attention to recent modeling and estimation effort that reveals novel sources of heterogeneity, non-linearity and volatility in the gains from R&D/innovation, particularly in terms of its effects on firm survival and productivity. Our third contribution is to link our findings with those from prior reviews to demonstrate how the state of the art is evolving and with what implications for future research.
    Keywords: Innovation, R&D, Survival, Productivity
    JEL: O30 O33
    Date: 2020–09
  8. By: Delaram Najmaei Lonbani; Bram De Rock
    Keywords: Microfinance; Performance; Location and Legal status; Heterogeneity; DEA; Meta-frontier
    JEL: O16
    Date: 2020–09–17
  9. By: Njuki, Eric
    Keywords: Productivity Analysis, Production Economics, Agricultural and Food Policy
    Date: 2020–07
  10. By: Nehring, Richard; Gillespie, Jeffrey M.
    Keywords: Production Economics, Agribusiness, Resource/Energy Economics and Policy
    Date: 2020–07
  11. By: Sawadgo, Wendiam PM; Plastina, Alejandro
    Keywords: Agricultural Finance, Productivity Analysis, Production Economics
    Date: 2020–07
  12. By: Chen, Chen-Ti; Crespi, John M.
    Keywords: Resource/Energy Economics and Policy, Agricultural and Food Policy, Productivity Analysis
    Date: 2020–07
  13. By: Hanson, Erik; Roberts, David C.
    Keywords: Agricultural Finance, Productivity Analysis, Agribusiness
    Date: 2020–07

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