, and be ready to detect those shifts. it should be obvious that technological change, by increasing productivity, often reduces process lags. changes in rates of obsolescence or physical depreciation can change stock – flow lags. and as technology makes more information available more frequently, we might expect that learning lags could speed up. this particular implication is not guaranteed, though. sometimes more information just adds noise. i shall talk more about that in a moment. some lags, especially decision lags, depend on legal and social processes. so they can change, if those responsible for those processes desire it. a good example here is the streamlining of building approval processes in some jurisdictions over recent years. decision processes can also slow down, if more and more rigorous governance is being demanded, whether by external parties or the decision - makers themselves. whatever the reason for it, a change in the length of lags can have huge economic implications. for the hog cycle examples, price dynamics can change noticeably. and where a shorter lag comes from increased productivity and therefore lower cost, we can see marked changes in relative prices. 10 of 12 on lags there can also be big social implications from a change in lags. probably the best example is the enormous decline in the cost of recording and transmitting information over the centuries. ideas, good and bad, can now reach the whole world in seconds. in past centuries, each new copy of a text was done by hand and could take months. the result is more information to more people, not all of it better information. what it means for monetary policy i ’ ve already referenced the long and variable lags of monetary policy. i would like to draw out three more general implications of lags for monetary policy making. first, most forecasts leverage known process lags. for example, we use building approvals to help forecast residential construction. stock – flow lags are also important, but in my view these are often underappreciated in many forecasting models. second, the lags from monetary policy to macroeconomic effects are primarily learning lags. that is why they are variable, as well as long. people take time to realise that demand has increased enough that it ’ s worth hiring a new employee. likewise people take time to decide that demand has increased enough to be worth changing their prices. the time taken will depend on many factors. for example, the lags to changing prices are determined by things such as

people to borrow more and spend. lower interest rates also reduce the servicing burden on existing debt, which can free up some cash to spend more. overall, though, we would ordinarily expect debt to rise when monetary policy is boosting growth. when household debt is high, it poses some risks. in particular, it could make household spending and well - being less resilient to shocks, including individual - specific outcomes. so the question then arises : what is the appropriate balance between the risk of higher debt and the benefits of the hoped - for faster decline in unemployment? in part, this depends on whether a particular level of interest rates induces households to keep increasing debt indefinitely, or whether debt instead accumulates up to some point, or some relativity to income, and no further. the experience of recent decades, and the arithmetic of debt - servicing burdens, suggests the latter. 2 but it is hard to be certain. another question to ask when weighing up this balance is whether the question of how low unemployment can go depends on how fast you are getting there. it ’ s usually assumed that there is a level of unemployment, below which wages growth starts to pick up. this is sometimes called the ‘ full employment ’ rate of unemployment. a more technically correct, but less attractive, name is the ‘ nonaccelerating inflation rate of unemployment ’, or nairu. 3 in some other countries, unemployment has fallen well below previous estimates of the nairu before wages growth actually started to pick up. as a result, more recent nairu estimates have tended to be revised down. that brings me to two examples of a learning lag that i consider to be particularly relevant to the current situation. the first is the simple one that it might just take a while before firms start to realise how hard it is to find suitable labour, and decide to offer higher wages. so it could be that it takes a little while for wages to pick up once unemployment has fallen below the nairu. if so, it would mean that nairu hasn ’ t really moved in these countries, and the revisions we see are just an artefact of the way this unobservable variable is measured. if that is what is going on, then this mechanism would also be relevant in australia. as the bank has explained on several occasions recently, although there appears to still be a deal of spare capacity in the labour market, many firms are reporting that they are finding it hard to recruit suit