TECHNOLOGY AT WORK

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74 Citi GPS: Global Perspectives & Solutions February 2015 Macro policy depends on whether actual GDP can keep up with potential GDP Everything else being equal, skill-biased technological change would increase potential GDP by making both capital and labour more productive in the production process. The question then is whether actual GDP can keep up with potential, absent policy intervention. There is some empirical evidence to suggest that if technological change occurs at the high end of the skill distribution, causing wage gains to be concentrated there, the answer is no. In that scenario, skill-biased technological change may lead to a persistent gap of potential GDP over actual GDP, leading to downward pressure on inflation and thereby to chronically depressed central bank policy rates. Saving and investment decisions drive the gap between potential and actual GDP The mechanism driving the growing gap between potential and actual GDP works through saving and investment decisions. If skill-biased technological change occurs at the upper end of the skill distribution, wage gains are likely to be concentrated where income levels are already relatively high. The academic literature, on balance, favours the idea that saving rates rise with income. 113 Hence, skill-biased technological change may result in more income flowing to those with relatively high saving rates, depressing aggregate consumption demand. Weak consumer spending may in turn negatively impact capex decisions at firms, which are a function of future consumer demand. The end result in this stylised example is that aggregate expenditure in the economy is unable to keep up with the economy’s potential, leading to subdued wage and inflation pressure and thereby to a persistently loose stance of monetary policy. 114 In addition, technological change could depress policy rates through a mechanism unrelated to the wage distribution, by resulting in a decline in the relative price of investment goods (think cheapening of personal computers). This process has already been underway for decades. 115 If new technology continues to make certain goods in the economy relatively cheap, that could result in downward pressure on overall price inflation, leading to a bias by central banks to favor lower policy rates. 113 For example, in Dynan, Skinner and Zeldes (2006), savings in the bottom income quintile are roughly zero compared to over 25% of income in the highest quintile. Consistent results hold for proxies of permanent income, such as levels of educational attainment and lagged and future earnings obtained from longitudinal surveys. 114 As mentioned, all of this is ‘everything else equal.’ In an open economy setting, capex decisions are not made solely on the basis of expected future domestic consumer demand; rising foreign demand could offset the weakness in investment spending brought on by high domestic saving patterns. Moreover, demographics complicate the picture greatly. Older people tend to spend more than younger people. Thus, aging demographics, which are occurring in a number of advanced economies, will likely put upward pressure on domestic consumer spending, thus leading to an upward bias on policy rates – see Goodhart and Erfurth (2014). Finally, borrowing constraints matter greatly. Steve Randy Waldman argues that so long as debt accumulation by lower-wage earners occurs alongside growing wage inequality, it will negate the consumptiondepressing effects of growing wage inequality – Waldman (2012). Moreover, Gauti Eggertsson and Neil Mehrotra describe an overlapping generations model where a within-generation rise in wage inequality leads to an excess of the supply of savings over the demand for savings, thereby depressing the real interest rate, provided that those demanding savings (those in the younger generation and those at the bottom of the wage distribution in the generation that saw an increase in wage inequality) are credit constrained – see Eggertsson and Mehrotra (2014). 115 In Buiter, Rahbari and Seydl (2014), we document that since 1970, the average price of a unit of investment for a broad range of advanced economies has fallen by roughly 17% relative to the price of a unit of GDP and by 20% relative to a unit of personal consumption (as measured by the PCE deflator). © 2015 Citigroup
February 2015 Citi GPS: Global Perspectives & Solutions 75 Large global output gaps are likely not a result of technological change Empirically, it is true that much of the advanced world is still grappling with large output gaps following the fallout of the 2008 global financial crisis (Figure 56). Is that a result of technological change and the robotics revolution? Probably not. That outcome probably has more to do with the long-lasting demand-side effects of the previous business cycle. Many advanced economies remain in debt-deleveraging mode from years of excessive debt accumulation (especially in the housing sector), and this has held back consumer spending and business investment. In fact, for all the talk of a ‘Second Machine Age,’ it has yet to show up in the productivity data; total factor productivity, a measure of an economy’s long-term technological dynamism, has not noticeably sped up on the back of the robotics revolution (Figure 57). In other words, the output gaps we are observing are not a result of accelerating potential GDP but of weakly growing actual GDP. 116 Figure 56. Output gap Figure 57. Total factor productivity growth (3-year rolling average) 6 4 2 0 -2 -4 -6 -8 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 Source: IMF % Potential of GDP Euro Area US Japan UK 3.5 YY% Change, 3Yr Moving Avg. 3.0 2.5 2.0 1.5 1.0 0.5 0.0 -0.5 Euro Area -1.0 UK -1.5 Japan US -2.0 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 Source: European Commission To be sure, tracking economy-wide productivity in real time is a difficult empirical endeavour; one in which economists don’t have a good track record. And there may be good reasons to think that the official productivity data are not tracking changes in the information economy quickly enough. 117 So what do the data on wages say? Are they accelerating at both ends of the skill distribution in line with the job polarisation story, or just at the high end? The answer depends on the country under consideration. The United States has seen wages accelerate at the high end of the skill distribution much more quickly than at the low end (Figure 58). The same is true for Canada, Germany and 116 If anything, the sluggish recovery since the global financial crisis has served to reduce the growth rate of potential GDP through ‘hysteresis effects’ (e.g., workers losing their skills from being out of the labour force for an extended period). See Summers (2014). 117 One concern is that the statistics on economic output do a poor job of tracking goods and services that, while expensive to design initially, can be copied at very low or zero marginal cost. See Mokyr (2014). In addition, Barry Eichengreen emphasises the importance of “range of adaptation” with regard to new technology, meaning that it often takes time for existing industries to reorient themselves around new technological breakthroughs. While robotics is disrupting production in a number of industries, it may take a while for the effects to fully show up in the economy-wide productivity statistics. See Eichengreen (2015). © 2015 Citigroup
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