Recent literature shows that sectoral shocks can cause significant impacts on macroeconomic growth because of their propagation through complex input-output (IO) linkages. However, the empirical literature is rare and limited to only few developed economies. This article quantifies the impact of sectoral multi-factor productivity (MFP) shocks on sectoral value-added growth (SVAG) and identifies sectors which have more influence on economic growth for Australia. We use IO tables covering data for 114 industries across 19 Australian and New Zealand Standard Industrial Classification (ANZSIC) divisions from 2006 to 2019. Additionally, we propose to use network measures of centrality and local density to rank sectors according to their low or high influences. Importantly, we proposed a new empirical strategy used to capture the heterogeneity in the effects of sectoral productivity shocks between low-influence and high-influence sectors on SVAG. Empirical results from this new model delivers several important findings. First, idiosyncratic sectoral MFP shocks have significant impacts on sectoral economic growth for Australia during the surveyed period. Second, using the Leontief multiplier table to capture the marginal effect of productivity shocks on SVAG across sectors obscures heterogeneity by network position. Third, network measures could provide a better way to classify sectors into low- and high-influence, which helps with policy planning.
Connell et al. (2026) studied this question.