On the contribution of information and communication technology to productivity growth in Australia

Accurately identifying productivity cycles is a daunting task. Estimation results have been found to be quite sensitive even to the minor variations in the period selected (Parham et al. 2001). Moreover, time series econometrics is highly data intensive and may not produce very useful results for any one particular cycle. A longer sample is therefore preferable. Of course, the longer the sample, the greater the chance of significant temporal structural breaks in parameter values and model specificity.

From an econometric point-of-view, the use of a larger sample is always preferable than using a shorter sample as estimation is more efficient, provided any structural changes are properly allowed for in the estimated model. For that reason, using data back to the 1960s allows one to cover various cycles of economic activity. As reported in Parham (2004b), Australia experienced a relatively better performance in output growth, labour and MFP—contributed to by strong growth in capital services and hours worked—in the 1960s as compared to the 1970s and 1980s. Subsequently, labour productivity and MFP boosted again in the 1990s, followed once again by an apparent slowdown in the 2000s. Modelling labour productivity from the 1960s, therefore, allows a counterbalancing of these ups and downs and provides a better understanding of the role of the various contributing factors to productivity from a longer term perspective.

Market sector in Australia consists of 16 out of 19 industrial sectors. It includes all industries except for Public Administration and Safety; Education and Training; Health Care and Social Assistance; and Ownership of Dwellings.

The Information, Media and Telecommunication sector includes units engaged in (1) creating, enhancing and storing information products in media, (2) transmitting information products using analogue and digital signals, and (3) providing transmission services and/or operating the infrastructure to enable the transmission and storage of information and information products.

These may happen through other contextual factors. As for example, Christopoulos and McAdam (2013) found that expenditures on ICT bolsters the positive impact openness on technical efficiency among the OECD manufacturing sector.

For brevity, we focus on Australian literature on ICT and productivity. Please see Shahiduzzaman and Alam (2014a) for a review of ICT-economic growth nexus for Australia as well as OECD countries. Draca et al. (2006), Kretschmer (2012) provides a comprehensive review of international literature on ICT-productivity paradox.

One of the reviewers pointed to the possible role of complementary factors in the relationship between ICT and productivity. However, these complementary variables work through the channel of MFP (Mc Morrow et al. 2010), a shift parameter in the models. In addition, in our model, non-ICT capital consists of R&D and other forms of capital. Nonetheless, it is possible to disaggregate different forms of capital and show their impact on MFP. Given the length and scope of the paper, we intend to cover this very important issue in our future research. We present results for quality-adjusted hours worked of labour for a shorter sample in the panel data analysis.

The market sector accounted for about 83 % of Gross Value Added at basic prices (in chain value measure) for all industries in 2012–13 (ABS 2014a).

While the ABS has expanded its market sector to cover the 16 industries listed earlier, insufficient disaggregated data are available for the extra four industries to be included in the industry level analysis.

The critical F values provided by Narayan (2005) are preferred over the values provided by Pesaran et al. (2001) as the former is suited for a sample size of 30–80 as compared to 500–1000 observations for the latter.

Detailed diagnostic test results can be obtained upon request.

The results on the selection of optimal lags and Johansen cointegration are not reported here, but will be made available from the corresponding author upon request.

It should be noted that this result of unidirectional causality is quite sensitive to the inclusion of data for 2013. In fact, bi-directionality is supported when the model is estimated for the period of 1965–2012.