Do economic models need to take account of the ‘special’ nature of ICT?
Print this pageFive papers deal with measurement, methodological and theoretical issues related to the estimation of the impact of ICT on productivity.
The first paper, The role of ICT in Australia’s economic performance, by Diewert and Lawrence, estimates productivity growth at the sectoral level. Advanced quantitative economic techniques are used to explore whether the assumptions used in modelling might bias econometric estimates for a rapidly developing input such as ICT. They found that traditional growth accounting is likely to understate the MFP benefit from ICT use. For sectors with sufficiently robust data they found that ICT investment is worth more to producers than the market price, after adjusting for quality. They suggest that underestimations might occur, because, in the long run users benefit more from ICT capital than its cost. However, their findings were not conclusive because of inconsistencies in the Australian Bureau of Statistics (ABS) productivity database at the sectoral level.
The second companion report, Estimating aggregate productivity growth for Australia; the role of ICT, deals with productivity growth at the aggregate national level. In order to avoid the difficulties with sectoral data, Diewert and Lawrence and the ABS built a productivity database for the ‘extended’ market sector. This includes all national production except for government administration and defence. Using this database and an econometric estimation model they found that:
- Australia’s productivity performance over the last 40 years has been significantly underestimated;
- ICT inputs are worth around 40 per cent more to producers than what they pay for them (this finding is further discussed in section 6); and
- 85 to 90 per cent of Australia’s MFP growth is accounted for by technical progress rather than increasing returns to scale in production.
A paper by Carlaw, The role of ICT in Australia’s economic performance (chapter 4 in the collection of papers on methodologies and measurement), used a different approach, which investigated the implications of general purpose technology (GPT) theory for productivity growth. In GPT models, technological change is directly quantified, and is independent of economic performance. The MFP calculations in this model enable the testing of the impact on productivity growth of factors such as time lags, returns to knowledge and returns to scale in production.
Carlaw builds formal three and four sector GPT simulation models that capture the key stylised facts known from the growth literature. He finds that the Australian productivity bonus does not occur immediately with investment in ICT, but occurs later as learning and innovation maximise the productivity of ICT.
A study by Carlaw and Lipsey, General purpose technologies and the information economy: an evolutionary approach to macroeconomic modelling uses a formal two-sector GPT model calibrated against Australia’s productivity data. The model shows that Australia’s pattern of productivity growth is consistent with the unusual characteristics of a GPT, in that productivity falls when the GPT is rapidly diffusing, and rises as the GPT matures. The research also confirms that technologically driven patterns of economic change cannot be determined from observations on aggregate statistics. This is because of the complex interaction effects between several GPTs operating at the same time.
Economic models of technological change
A number of theoretical models have been developed to explore technological change. With the exception of evolutionary economics, these models are based on economic equilibrium concepts. This implies that the quantity of demand and supply in each market is assumed to be equalised at the market clearing price.
The neo-classical growth model takes technological progress as external to the system. Some key assumptions in this model, which may not reflect reality, include perfect competition and constant return to scale in production. The neo-classical model provides the theory behind MFP calculations.
Endogenous growth theories recognise that there are market imperfections and these are essential for providing incentives for innovation. Despite the presence of market imperfections, these models are still based on economic equilibrium concepts.
Evolutionary economics, in contrast, emphasises the non-equilibrium characteristics of the innovation process and the fact that innovation is linked to diversity and selection. Examples of non-equilibrium market conditions include information deficiencies and sales price below the average cost of production.
General purpose technology (GPT) theory postulates that a small number of major technological breakthroughs and their gradual diffusion combined with widening applications are the principal drivers of long-term productivity growth. ICT is considered a prominent recent GPT, as is biotechnology. GPT modelling combines features from evolutionary economics and endogenous growth theory.
Carlaw and Lipsey suggest that a major challenge for the economic analysis of technological change is to establish the ‘evolutionary economics’ framework as a valid complement to models based on equilibrium concepts (such as neo-classical and endogenous growth theories) and ensure that the insights of evolutionary economics are taken into account in innovation policy.
The DCITA study, Reviewing the evidence (chapter 5 in the collection of papers on methodologies and measurement), found that the high average MFP growth experienced in the late 1990s may have been associated with cyclical effects, and it may not be a robust indicator of a change in Australia’s productivity growth trend. This finding does not support the hypothesis that the acceleration of MFP growth in the 1990s was mainly driven by microeconomic reform. This study adds weight to other evidence suggesting that ICT played a significant role in lifting productivity growth in the 1990s in conjunction with micro-economic reform.
This is chapter 3 in the collection of papers titled ‘ICT and Australian Productivity: Methodologies and Measurement’.
- Document ID: 68024 |
- Last modified: 5 February 2008, 9:03am