Trend growth after the crisis
The post-crisis exercise is more interesting, though more perilous in terms of measurement, because we need to project the evolution of fundamentals – investment, labour inputs and total factor productivity. We run several scenarios for each of the three input factors in order to span a wide range of scenarios, and provide a framework for assessing:
Long-lasting damages from the crisis.
The additional effect of ageing.
Some benchmarks for the capacity of economic policy to lift growth.
We also assume several things about capital and productivity:
Regarding physical capital, we assume that the investment growth rate returns to its pre-crisis level by 2020.
A downside risk is that investment growth does not recover fully (for example, because banks fail to provide the necessary funding). In this case, we assume investment growth is only half what it was before the onset of economic turmoil. An upside risk to our estimates would be if investment were to bounce back by 2020 to the level that would have prevailed had the financial crisis not occurred.
Regarding human capital, we use the UN’s working-age population projections for taking account of demographics and run two scenarios for the unemployment rate.
One where we assume strong hysteresis and that the working-age levels stay at the European Commission’s 2014 estimates (i.e. permanently higher than before the crisis), and another where unemployment rate steadily declines (using IMF projections) on the back of the ongoing, albeit slow, recovery.
We assume that productivity was likely over-estimated pre-crisis but collapsed to an unusually low level during the crisis.
As a result, we filter total factor productivity over the whole historical period (1995-14) and use this value for the projection period (2015-20). We also estimate productivity through a convergence equation, which would slightly lift productivity in peripheral countries in the future. In that case, we use the framework of a standard convergence equation with a large sample of countries, controlling for country-specific effects, which allows speed of catch-up to vary with the distance to the technology frontier and the initial level of human capital. To this effect, we estimate total factor productivity through a Nelson-Phelps technology diffusion model similar to Foure et al. (2010).
This exercise suggests that in the absence of policy reforms, trend growth will have been damaged significantly, by at least one percentage point, post-crisis, compared with pre-crisis levels, although our range of estimates is quite large depending on the set of assumptions being used. However, under the most favourable set of assumptions which would assume significant policy reforms (investment recovers to pre-crisis growth levels, rapid decline in the unemployment, rapid catch-up with the technology leader), trend growth would be in line with pre-crisis levels and could even be higher in Italy and Germany which had the lowest trend growth prior to the crisis.
Our central scenario requires the most agnostic assumptions: permanent loss in the level of capital, but the growth rate recovers; unemployment improves in line with previous recoveries; productivity growth remains in line with historical average.
Under that scenario, trend growth for the four main Eurozone countries lies between little less than 1% and slightly less than 2%, post-crisis, with trend growth highest in Spain and France; and the lowest for Italy and Germany. Ageing explains a large part of this variation (see Table 3). Lower productivity and employment are the main reasons for the drop in trend growth compared to pre-crisis levels. The evolution of investment is the more sensitive assumption in determining trend growth in our set of scenarios, though the impact is not uniform across countries.
Trend growth after the crisis
The post-crisis exercise is more interesting, though more perilous in terms of measurement, because we need to project the evolution of fundamentals – investment, labour inputs and total factor productivity. We run several scenarios for each of the three input factors in order to span a wide range of scenarios, and provide a framework for assessing:
Long-lasting damages from the crisis.
The additional effect of ageing.
Some benchmarks for the capacity of economic policy to lift growth.
We also assume several things about capital and productivity:
Regarding physical capital, we assume that the investment growth rate returns to its pre-crisis level by 2020.
A downside risk is that investment growth does not recover fully (for example, because banks fail to provide the necessary funding). In this case, we assume investment growth is only half what it was before the onset of economic turmoil. An upside risk to our estimates would be if investment were to bounce back by 2020 to the level that would have prevailed had the financial crisis not occurred.
Regarding human capital, we use the UN’s working-age population projections for taking account of demographics and run two scenarios for the unemployment rate.
One where we assume strong hysteresis and that the working-age levels stay at the European Commission’s 2014 estimates (i.e. permanently higher than before the crisis), and another where unemployment rate steadily declines (using IMF projections) on the back of the ongoing, albeit slow, recovery.
We assume that productivity was likely over-estimated pre-crisis but collapsed to an unusually low level during the crisis.
As a result, we filter total factor productivity over the whole historical period (1995-14) and use this value for the projection period (2015-20). We also estimate productivity through a convergence equation, which would slightly lift productivity in peripheral countries in the future. In that case, we use the framework of a standard convergence equation with a large sample of countries, controlling for country-specific effects, which allows speed of catch-up to vary with the distance to the technology frontier and the initial level of human capital. To this effect, we estimate total factor productivity through a Nelson-Phelps technology diffusion model similar to Foure et al. (2010).
This exercise suggests that in the absence of policy reforms, trend growth will have been damaged significantly, by at least one percentage point, post-crisis, compared with pre-crisis levels, although our range of estimates is quite large depending on the set of assumptions being used. However, under the most favourable set of assumptions which would assume significant policy reforms (investment recovers to pre-crisis growth levels, rapid decline in the unemployment, rapid catch-up with the technology leader), trend growth would be in line with pre-crisis levels and could even be higher in Italy and Germany which had the lowest trend growth prior to the crisis.
Our central scenario requires the most agnostic assumptions: permanent loss in the level of capital, but the growth rate recovers; unemployment improves in line with previous recoveries; productivity growth remains in line with historical average.
Under that scenario, trend growth for the four main Eurozone countries lies between little less than 1% and slightly less than 2%, post-crisis, with trend growth highest in Spain and France; and the lowest for Italy and Germany. Ageing explains a large part of this variation (see Table 3). Lower productivity and employment are the main reasons for the drop in trend growth compared to pre-crisis levels. The evolution of investment is the more sensitive assumption in determining trend growth in our set of scenarios, though the impact is not uniform across countries.
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