Analyzing the impact of investment spikes on dynamic productivity growth

Kapelko, Magdalena; Stefanou, Spiro E.

doi:10.1016/j.omega.2015.01.010

Cited by 40 publications

(45 citation statements)

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“…The decomposition of input‐ and investment‐specific dynamic productivity growth indicators in (a and b) show that these indicators provide more information on the sources of productivity growth than dynamic productivity growth measures accounting for all inputs simultaneously that dominate the existing literature (see for example Kapelko et al ., ,c, ; Oude Lansink et al ., ).…”

Section: Input‐specific Productivity Change In a Dynamic Settingmentioning

confidence: 99%

“…Silva and Stefanou () advance a non‐parametric approach to dynamic production analysis that distinguishes between the variable and the dynamic factors, and serves as a foundation for the measurement of dynamic productivity and efficiency measurement . Luenberger‐based approaches that have followed can decompose productivity change to identify the contributions of technical inefficiency change, scale inefficiency change and technical change (Kapelko et al ., ,c, ; Oude Lansink et al ., ). However, the existing dynamic approaches do not consider input‐specific contributions to dynamic productivity growth, which is demonstrated in this paper.…”

Section: Introductionmentioning

confidence: 99%

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Input‐Specific Dynamic Productivity Change: Measurement and Application to European Dairy Manufacturing Firms

Kapelko¹,

Stefanou²

2016

J Agricultural Economics

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We propose a new method for measuring and decomposing input‐specific productivity change in a dynamic context. The resulting input‐specific dynamic Luenberger productivity change indicator is decomposed to identify the contributions of input‐specific dynamic technical, technical inefficiency and scale inefficiency changes. The empirical application of the paper focuses on panel data of large firms in the European dairy processing industry over the period 2005–2012. The results show similar patterns for dynamic input‐specific productivity change and its components for labour across European regions (Eastern, Western and Southern), while differences between regions are found regarding materials and investments.

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Section: Input‐specific Productivity Change In a Dynamic Settingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Input‐Specific Dynamic Productivity Change: Measurement and Application to European Dairy Manufacturing Firms

Kapelko¹,

Stefanou²

2016

J Agricultural Economics

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“…Oude defined a dynamic Luenberger indicator that accounts for the dynamic nature of firms' production decisions through adjustment costs related to firms' investments. The dynamic Luenberger indicator was subsequently extended by Kapelko et al (2015a) and applied elsewhere (Kapelko et al 2015b(Kapelko et al , 2016(Kapelko et al , 2017a.…”

Section: Dynamic Luenberger Productivity Growth Indicator and Its Decmentioning

confidence: 99%

“…(meat and dairy processing and oils and fats industries), Kapelko et al (2015b) (meat processing industry), and Kapelko et al (2016) (meat processing and oils and fats industries); all of these studies found that average productivity is very close to zero, with its components telling a story of negative technical change, and positive technical inefficiency change and scale inefficiency change. Kapelko et al (2017b) reached similar conclusions regarding static productivity growth in the Spanish dairy processing industry.…”

Section: Introductionmentioning

confidence: 99%

Measuring productivity change accounting for adjustment costs: evidence from the food industry in the European Union

Kapelko

2017

Ann Oper Res

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This paper extends the measurement of dynamic productivity change over time to provide its full decomposition into economically meaningful components in the Data Envelopment Analysis framework. The dynamic approach accounts for dynamics of production decisions via adjustment costs and is visualized as a dynamic Luenberger productivity change indicator. The paper also estimates the dynamic productivity change and its components for a large dataset of European food companies from 2004 till 2012, grouped into Eastern, Southern, and Western regions. The study reveals three main results. First, the overall trend of dynamic technical regress and positive dynamic technical inefficiency change across almost all regions and sectors was found. Second, some differences for this general pattern were found for the bakery industry and for Eastern European firms. Thirdly, there are also some remarkable changes in indicators observed during the periods related to the financial crisis and the volatility of agricultural commodity prices.

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“…The sample is sufficiently large to conduct the inefficiency analysis and make statistical inferences about the impact of different factors on inefficiency (Golany and Roll 1989;Boussofiane et al 1991;Bowlin 1998;Friedman and Sinuany-Stern 1998;Dyson et al 2001). The data set was corrected for outliers using the approach outlined in Kapelko et al (2015b) 3 . 1 AU: Animal Unit is a standard unit used in calculating the relative grazing impact of different kinds and classes of livestock.…”

Section: Introductionmentioning

confidence: 99%

Improving the effectiveness of rural development policy in Chile

Leal¹

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In Chile, agriculture remains a key economic factor for rural development. Accordingly, the Chilean government, through the Agricultural Development Institute (INDAP), provides financial support for fostering entrepreneurship among small farmers to enable them to become more competitive in global markets. Despite this support, a declining number of farmers and an aging population are observed in rural areas. Most rural development programs focus on increasing competitiveness, whereas the stay-exit decision in agriculture is also affected by the characteristics and preferences of farmers and their families, and rural development policy. The overall objective of this dissertation was to provide technical and socioeconomic information for improving the effectiveness of rural development programs, which are seen as the tool for stopping rural migration. To accomplish this objective, first an analysis of the role of socioeconomic variables in the stay-exit decision in farming was made based on efficiency theory, exit barrier theory, and life-cycle theory. The association of potential factors with the stay-exit decision was identified using a probit model. This dissertation suggests that, besides the technical characteristics of the farming, which have been traditionally addressed in developing countries, rural development policies should focus on (i) farmer characteristics, i.e. age, gender, and expectations; (ii) farming system, i.e. multivariate production; and (iii) social aspect of rural society, i.e. membership of farmer associations. In addition, technical, economic, and social data was used to explore the scope for improving the technical efficiency of the sample farms and to identify the variables influencing technical efficiency. Two-stage models were performed to estimate (i) the technical efficiency and (ii) the input-specific technical inefficiency with which these farmers operate, and to identify the association of potential factors with either farm-level technical efficiency or input-specific technical inefficiency. Results for farm-level technical efficiency showed that farmers could reduce their input use by 30%. When measuring input-specific technical inefficiency results showed that the input use can be reduced by 26% (land use) to 37% (livestock use). This thesis also analyzed the effectiveness of existing rural development programs in Chile by comparing the opinions of experts regarding the effectiveness of these programs in achieving the main INDAP goals and the budget allocation among rural development programs. Results showed (i) a discrepancy between the allocation of the INDAP budget and the effectiveness of the rural development programs in achieving the overall INDAP goal; and (ii) that some INDAP programs are less effective in achieving INDAP goals. This suggested possibilities for reducing the current number of professional and technical advice programs and reallocating budget from less effective programs to programs that are more effective in the achievement of INDAP goals. Hence...

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Analyzing the impact of investment spikes on dynamic productivity growth

Cited by 40 publications

References 52 publications

Input‐Specific Dynamic Productivity Change: Measurement and Application to European Dairy Manufacturing Firms

Input‐Specific Dynamic Productivity Change: Measurement and Application to European Dairy Manufacturing Firms

Measuring productivity change accounting for adjustment costs: evidence from the food industry in the European Union

Improving the effectiveness of rural development policy in Chile

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