nep-eff New Economics Papers
on Efficiency and Productivity
Issue of 2019‒12‒02
eight papers chosen by
Angelo Zago
Università degli Studi di Verona

  1. Technical Efficiency Analysis Of Indonesian Small And Micro Industries: A Stochastic Frontier Approach By Hery Purnomo Tunggal; Tati Suhartati Joesron
  2. The adoption of mechanization, labour productivity and household income: Evidence from rice production in Thailand By Srisompun, Orawan; Athipanyakul, Thanaporn; Isvilanonda, Somporn
  3. Production Efficiency in Small Agriculture: Do Migrant Remittances Matter?Evidence from Rural Nigeria. By ODOZI, JOHN CHIWUZULUM; Adeniyi, Oluwaosin; Yusuf, Sulaiman A.
  4. The Intellectual Spoils of War? Defense R&D, Productivity and International Spillovers By Moretti, Enrico; Steinwender, Claudia; Van Reenen, John
  5. Productivity trends and drivers in global agriculture: could the UK match up in a post Brexit world? By Revell, Brian
  6. Trade shocks, product mix adjustment and productivity growth in Italian manufacturing By Maria Gabriela Ladu; Andrea Linarello; Filippo Oropallo
  7. A Skeptical Note on the Role of Constant Elasticity of Substitution in Labor Income Share Dynamics By Paul, Saumik
  8. ‘Farms like Mine’: A Novel Method in Peer Matching for Agricultural Benchmarking By READER, Mark A; Wilson, Paul; Ramsden, Stephen; Hodge, Ian; Lang, Ben GA

  1. By: Hery Purnomo Tunggal (Master of Applied Economics, Padjadjaran University); Tati Suhartati Joesron (Master of Applied Economics, Padjadjaran University)
    Abstract: Indonesian small and micro industries (SMIs) grow rapidly, followed by the shifting of the agricultural sector to manufacturing sector. However, its low contribution to national economy indicates there are encountered problems of productivity and efficiency. The goal of this study is analyzing technical efficiency of Indonesian SMIs categorized by size and five subsectors classified by Indonesia Standard Industrial Classification (ISIC). This study examines crosssectional data from survey of Indonesian small and micro industries (VIMK) in 2014 estimated statistically using Stochastic Frontier Analysis (SFA). The results show that SMIs are labor intensive business, yet it faces diseconomies of scale. Hence, the role of capital increase should not be ignored. The key findings are mainly female ownership in the food processing industry positively contribute to efficiency improvement, the greater the sales the more efficient the business will function, younger entrepreneur is more efficient to manage several subsectors and access to financial sources positively contribute to efficiency improvement in clothing industry. Empowerment strategy to improve technical efficiency of SMIs should emphasis on intensively vocational/entrepreneurial training particularly for female and younger entrepreneurs, promotion for network building activity and deregulating microcredit scheme, especially for clothing industry.
    Keywords: small and micro industries, efficiency, stochastic frontier analysis
    JEL: L0
    Date: 2019–11
    URL: http://d.repec.org/n?u=RePEc:unp:wpaper:201903&r=all
  2. By: Srisompun, Orawan; Athipanyakul, Thanaporn; Isvilanonda, Somporn
    Abstract: The planning of mechanization requires the quantitative assessment of a mechanization index and the impact of this index on agricultural yield and economic factors. The purpose of this paper is to investigate the effect of the adoption of agricultural mechanization and scale production on labour productivity and the generation of income for farmers. Cross-sectional data for jasmine rice production by 569 households in 1,003 plots in the north eastern part of Thailand in 2017 were employed. The study found that the average rice planting workforce and labour productivity have an inverse relationship with planted area, while large farms have the highest ratio for machine labour to workforce. The rice yield, labour usage and labour productivity of the farmers varied by mechanization level (ML) and farm size while different levels of Machinery Owned labour (MO) have no effect on rice yield. Therefore, there are three main suggestions: 1) performing land consolidations, since applying a production strategy with large rice paddies may increase labour productivity and the net profit of rice famers; 2) improving the quality of machinery for use in rice production in Thailand, especially the performance of the machinery to prevent losses during harvest; and 3) increasing the mechanization level to 50-75%, which could also increase labour productivity and net returns.
    Keywords: Family labour, Farm size, Hired labour, Multivariate analysis-of-variance, Pillai's statistics, Production cost, Rice yield, Small farm
    JEL: Q12 Q16 Q18
    Date: 2019–11
    URL: http://d.repec.org/n?u=RePEc:tvs:wpaper:wp-016&r=all
  3. By: ODOZI, JOHN CHIWUZULUM; Adeniyi, Oluwaosin; Yusuf, Sulaiman A.
    Abstract: This paper investigates how remittances flow to Nigeria from household migrants correlate with farm production efficiency of the left behind in rural areas using the Living Standard Measurement Survey data set. We applied the production frontier model from which efficiency scores for two groups of farmers were recovered: migrant households and non-migrant households. We subjected the efficiency scores to Anova and stochastic dominance analyses. Mean production efficiency for migrant households was signifcantly higher at p<0.05. Across all percentiles, migrant households had higher technical efficiency level compared to households with out migrants. Thus rejecting the hypothesis of negative production efficiency effect of migrant remittances flow to farm households. While policy programmes should promote labour mobility and remittances, it supposes a complementary policy that promote labour saving farm technologies
    Date: 2018–10–06
    URL: http://d.repec.org/n?u=RePEc:osf:agrixi:jfvzn&r=all
  4. By: Moretti, Enrico (University of California, Berkeley); Steinwender, Claudia (MIT Sloan School of Management); Van Reenen, John (MIT Sloan School of Management)
    Abstract: In the US and many other OECD countries, expenditures for defense-related R&D represent a key policy channel through which governments shape innovation, and dwarf all other public subsidies for innovation. We examine the impact of government funding for R&D - and defense-related R&D in particular – on privately conducted R&D, and its ultimate effect on productivity growth. We estimate models that relate privately funded R&D to lagged government-funded R&D using industry-country level data from OECD countries and firm level data from France. To deal with the potentially endogenous allocation of government R&D funds we use changes in predicted defense R&D as an instrumental variable. In both datasets, we uncover evidence of "crowding in" rather than "crowding out," as increases in government-funded R&D for an industry or a firm result in significant increases in private sector R&D in that industry or firm. A 10% increase in government-financed R&D generates 4.3% additional privately funded R&D. An analysis of wages and employment suggests that the increase in private R&D expenditure reflects actual increases in R&D employment, not just higher labor costs. Our estimates imply that some of the existing cross-country differences in private R&D investment are due to cross-country differences in defense R&D expenditures. We also find evidence of international spillovers, as increases in government-funded R&D in a particular industry and country raise private R&D in the same industry in other countries. Finally, we find that increases in private R&D induced by increases in defense R&D result in significant productivity gains.
    Keywords: agglomeration, spillovers
    JEL: O30
    Date: 2019–11
    URL: http://d.repec.org/n?u=RePEc:iza:izadps:dp12769&r=all
  5. By: Revell, Brian
    Abstract: The analysis in the paper focuses on global trends in total factor productivity (TFP) growth and some of its key components and drivers. The relative performance of the UK in relation to many key countries with globally important agri-food sectors, either or both as exporters and or importers of agricultural products, and as potential targets of its future UK post-Brexit strategy are examined. Two approaches are explored in order to gain some insights into productivity growth and its measurement: the decomposition output growth through the contributions of growth in land, labour, capital, material inputs and TFP, and modelling output growth to identify the significant contributing variables. Finally, the challenges that the agricultural sector of the might face as a consequence of its proposed UK post Brexit agricultural policy (if and when it might happen) for its productivity are considered and some conclusions regarding the relevance to future agri-technology developments are outlined.
    Keywords: Agricultural and Food Policy, International Relations/Trade
    Date: 2019–10–21
    URL: http://d.repec.org/n?u=RePEc:ags:haaepa:296766&r=all
  6. By: Maria Gabriela Ladu (University of Sassari and ISTAT); Andrea Linarello (Bank of Italy); Filippo Oropallo (ISTAT)
    Abstract: In this paper we use firm-level data on the universe of Italian manufacturing multi-product exporters to test whether demand shocks in export markets lead multi-product exporters to increase their productivity. The main mechanism behind the documented productivity gains is the reallocation of resources across products within firms (Mayer et al., 2014 and 2016). Intuitively, the increased demand stemming from foreign markets will induce firms to adjust their product-mix by moving inputs from low to high productive/profitable uses. We find that these productivity gains are significant and account for about 30 per cent of aggregate productivity growth in the manufacturing sector.
    Keywords: Italian manufacturing sector, export, trade shocks, productivity
    JEL: D22 F14
    Date: 2019–10
    URL: http://d.repec.org/n?u=RePEc:bdi:opques:qef_513_19&r=all
  7. By: Paul, Saumik (Asian Development Bank Institute)
    Abstract: The constancy of the elasticity of factor substitution (σ) makes its role as a driver of the labor income share exogenous. The constant elasticity of substitution (CES) production function has predominantly been used to support this causal relationship. We argue that (i) capital-labor ratio determines the value of σ, and (ii) both capital-labor ratio and σ vary over time. We use a variable elasticity of substitution (VES) production framework that allows both labor income share and σ to change over time. Statistically significant empirical support is provided using the Japanese industrial productivity data. This suggests that the CES model may not be an ideal choice to examine the factor income share dynamics.
    Keywords: substitution elasticity; labor income share; production function parameters
    JEL: E21 E22 E25
    Date: 2019–04–22
    URL: http://d.repec.org/n?u=RePEc:ris:adbiwp:0944&r=all
  8. By: READER, Mark A (University of Cambridge); Wilson, Paul; Ramsden, Stephen; Hodge, Ian; Lang, Ben GA
    Abstract: To find opportunities to improve performance, comparisons between farms are often made using aggregates of standard typologies. Being aggregates, farm types in these typologies contain significant numbers of atypical enterprises and thus average figures do not reflect the farming situations of individual farmers wishing to compare their performance with farms of a ‘similar’ type. We present a novel method that matches a specific farm against all farms in a survey (drawing upon the Farm Business Survey sample) and then selects the nearest ‘bespoke farm group’ of matches based on distance (Z-score). We do this across 34 dimensions that capture a wide range of English farm characteristics, including tenure and geographic proximity. Means and other statistics are calculated specifically for that bespoke farm comparator group, or ‘peer set’. This generates a uniquely defined comparator for each individual farm that could substantially improve key-performance-indicators, such as unit costs of production, which can be used for benchmarking purposes. This methodology has potential to be applied across the full range of FBS farm types and in a wider range of benchmarking contexts.
    Date: 2019–07–15
    URL: http://d.repec.org/n?u=RePEc:osf:agrixi:5gjdp&r=all

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