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Top1. Introduction
Strategic management decisions in the regulation of any sector of the economy require an integrated methodology for assessing its performance. The main factors of productivity growth in agriculture include improvement of agricultural practices and ensuring optimal input intensities. Productivity analysis is closely related to the problematique of productivity measures and data. Especially, oftentimes multiple factors characterize a particular activity and aggregation is needed to capture the available information.
The indices and indicators are the key tools for measurement of the productivity growth. The analysis of indices was initiated in the middle of the nineteenth century. The indices, in general, seek to show the overall development of prices and volumes over a certain period. Price and quantity indices rely on various methods of calculation, and it is necessary to have a good knowledge of their features. In the context of productivity growth, a number of researchers relied on the Malmquist productivity index as a measure for productivity growth (Ait-Sidhoum et al., 2021; Kijek et al., 2016). The latter index allows decomposing the productivity growth into technical efficiency change and technical change. It is important to emphasize that technical efficiency (growth) is only one component of the total factor productivity (growth). Still, further decomposition of the Malmquist and other measures is possible.
Total factor productivity (TFP) is often defined as the ratio of aggregate output to aggregate input, where quantity indices are used for the aggregation. To measure the components of productivity growth, one must first have an accurate definition of productivity and then a procedure for calculation of the relevant productivity indices (or indicators) that meet this definition. Even though the Malmquist index is one of the most commonly used methods for measuring changes in productivity over time, it has been criticized for being unable to completely explain productivity growth in the sense of changes in the aggregate input and output (O’Donnell, 2012). This property makes the difference from the TFP measures. In general, the frontier-based TFP measures are popular for measurement of agricultural productivity growth as they require no data on prices that are usually inaccurate or missing.
There is no consensus on the use of various indices and indicators for productivity measurement. The aggregation-based measures (e.g., Fisher index, Tornqvist index, Bennet indicator) can be used when the price data are available. Otherwise, when input and output prices are missing, the Malmquist, Hick-Moorsteen, Fare-Primont indices, and Luenberger indicator can be used. For more details on the measures of the (total factor) productivity, one may refer to Galonopoulos et al. (2011) and Grifell-Tatjé and Lovell (2021).
The measures of the TFP can be used for policy analysis without being interesting themselves. Researchers emphasize the importance of linking productivity measures to research and development (R&D) activities in each country. Andersen (2015), Alston and Pardey (2014), and Wang et al. (2012) noted that in thinking about future productivity growth in agriculture, the agricultural R&D must be taken into account. Thus, it is important to assess the TFP growth in agriculture and provide more insights on the methodology of the TFP measurement in the context of agriculture.
Much of the earlier literature has discussed the applications of the productivity measures. However, little attention has been paid for the sources of information and comparison of the resulting productivity measures. Therefore, this paper addresses the literature gap on the information sources for measurement of the agricultural (total factor) productivity and provides a comparative analysis of the several key databases for the input, output, and productivity data relevant for the agricultural sector.