|
on Efficiency and Productivity |
|
Issue of 2020‒10‒05
thirteen papers chosen by |
| 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 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:102797&r=all |
| By: | Huan, Meili; Dong, Fengxia |
| Keywords: | Production Economics, Community/Rural/Urban Development, Productivity Analysis |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea20:304403&r=all |
| 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 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aare20:305243&r=all |
| By: | Owusu, Eric S.; Bravo-Ureta, Boris E. |
| Keywords: | Productivity Analysis, Production Economics, Community/Rural/Urban Development |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea20:304243&r=all |
| 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 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_8536&r=all |
| By: | Bairagi, Subir K.; Mishra, Ashok K. |
| Keywords: | Productivity Analysis, Production Economics, Agribusiness |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea20:304179&r=all |
| 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 |
| URL: | http://d.repec.org/n?u=RePEc:ctc:serie5:dipe0011&r=all |
| By: | Delaram Najmaei Lonbani; Bram De Rock |
| Keywords: | Microfinance; Performance; Location and Legal status; Heterogeneity; DEA; Meta-frontier |
| JEL: | O16 |
| Date: | 2020–09–17 |
| URL: | http://d.repec.org/n?u=RePEc:sol:wpaper:2013/312665&r=all |
| By: | Njuki, Eric |
| Keywords: | Productivity Analysis, Production Economics, Agricultural and Food Policy |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea20:304285&r=all |
| By: | Nehring, Richard; Gillespie, Jeffrey M. |
| Keywords: | Production Economics, Agribusiness, Resource/Energy Economics and Policy |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea20:304363&r=all |
| By: | Sawadgo, Wendiam PM; Plastina, Alejandro |
| Keywords: | Agricultural Finance, Productivity Analysis, Production Economics |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea20:304356&r=all |
| By: | Chen, Chen-Ti; Crespi, John M. |
| Keywords: | Resource/Energy Economics and Policy, Agricultural and Food Policy, Productivity Analysis |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea20:304472&r=all |
| By: | Hanson, Erik; Roberts, David C. |
| Keywords: | Agricultural Finance, Productivity Analysis, Agribusiness |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea20:304265&r=all |