1504.0 - Methodological News, Dec 2019  
ARCHIVED ISSUE Released at 11:30 AM (CANBERRA TIME) 12/12/2019   
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Firm-level Capital Stock and Multifactor Productivity Calculation

The Methodology Division of the Australian Bureau of Statistics, in partnership with the Centre for Applied Economics Research of the University of New South Wales, has undertaken research to develop experimental estimates of firm-level capital stock and to apply a method for the estimation of firm-level multifactor productivity (MFP) using integrated microdata from the Business Longitudinal Analysis Data Environment (BLADE).

The study tested a number of methods to derive estimates of firm-level capital stock using the BLADE data. Amongst the methods tested, the Perpetual Inventory Model (PIM) stood out as the most feasible to apply. PIM made use of data from the Business Activity Statement (BAS) and Business Income Tax (BIT) to derive a flow of firm capital stock. PIM is highly dependent on estimates of depreciation rate and initial capital stock. PIM applications usually differ in the assumptions they make for these two components.

The study made use of the capital stock estimates to calculate firm-level MFP. In calculating firm-level MFP, academics and researchers from national statistical organisations use various methods, which can be grouped into parametric modelling (e.g. stochastic frontier analysis, regression analysis) or non-parametric methods (e.g. index number, data envelopment analysis). In the study, the Tornqvist index is utilised to produce the experimental measure of firm-level MFP.

Preliminary results demonstrate that BLADE can be a useful statistical asset for calculating and analysing firm-level capital stock and firm-level productivity. Though exploratory and experimental in nature, these firm-level measures can help facilitate analysis of the micro drivers of productivity growth as well as provide useful insights on many productivity-related dynamics.

For more information or to provide feedback, please contact Franklin Soriano Methodology@abs.gov.au

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