Corruption and Growth?

Last week we considered economic growth, and noted the role that institutions play in fostering, or at least being correlated with, economic growth. We considered corruption when thinking about rent seeking: whether economic agents create value themselves, or seek to apportion the value created by others – for example, via piracy or theft.

We saw a version of the Corruption Perceptions Index, a measure of corruption amongst countries, noting which countries were deemed less corrupt, and which others were more corrupt. Today Bloomberg reports on the 2015 Corruption Perceptions Index, in which apparently Brazil and Turkey show the biggest falls. Related or otherwise, Reuters reports that recently the IMF downgraded its GDP growth forecast for Brazil to -2.5% from 1.1% in 2016, and the World Bank cut its growth forecasts for Turkey recently.

Economics Conversations: Does Manufacturing Matter?


In today’s second conversations session of term we’re looking at UK manufacturing. Since 1970 it’s fallen from 27% of the UK economy to 10% in 2013. The snapshot of a table from a government report into manufacturing, shown above, shows that this decline in relative importance is not confined to the UK.

Nonetheless, many worry about this de-industrialisation trend, both here and in America. Why do folk worry? Arguments range from there being something intrinsically healthy in making things, through to the impact on the trade balance, and the provision of jobs in particular for those who would struggle to earn as well in non-manufacturing industries. In the case of the UK, it is argued the ever increasing levels of inequality and regional imbalances all draw from the decline in the importance of manufacturing.

Evidence of the latter appears quite clear from the decline in many former industrial areas of the UK, but need it be the case? Why haven’t firms moved into these areas given that there are large amounts of unemployed labour that could be employed relatively cheaply? Where generally there’s land to be used which again is relatively cheap compared to the South East? Why haven’t such workers been re-trained to be equipped to work in other lines of work?

An Economist article makes a number of points against the arguments emphasising the importance of manufacturing; while the balance in goods might look bad, the US and UK are more than ample enough exporters of services to make up for that imbalance, and if manufacturing was providing high paid work disproportionately given the relative levels of productivity and hence value added (as suggested by the idea that it provided high wage jobs for those that wouldn’t get them elsewhere), then this might be symptomatic of why the industry has been in decline.

What are your thoughts? Come along at 1pm to the next Economics Conversations event!

Unemployment Down but a Rate Rise Unlikely

Two macroeconomic stories have adorned the main headlines on the BBC website in the last 24 hours.

First, Mark Carney, current Governor of the Bank of England, ruled out interest rate rises in the near future – highly unlikely in 2016, it seems. Then this morning the news is that unemployment, the number of people not in work but seeking work (hence counted as part of the labour force) is at its lowest level since 2005.

As with many economic variables, a change in any direction is not unambiguously a good thing; for interest rates, which have been low for many years now, this is good for borrowers (for example, people with mortgages to buy houses), but bad for savers. This is because the rate the Bank of England sets heavily influences the rates set by banks around the country, so borrowers will continue to have to pay back their loans on lower interest rates, but savers will continue to earn very little interest on their savings.

The reality that the Bank doesn’t think a rate rise is likely implies the Bank thinks the prospects for economic growth are not so great; this comes on the back of the Chancellor’s warning about a dangerous cocktail of factors affecting UK growth in the coming year. Prospects for growth have reduced.

This comes, though, in stark contrast to the good news from the labour market. That unemployment is falling is generally a good thing – it’s hard to think of particularly convincing reasons why it wouldn’t be. Sometimes people point out that the fall might be based only on workers going into short-term or zero-hours contracts, or part-time or self-employment, all of which are seen as less secure jobs for workers, and signs not of high confidence amongst employers.

It may also be that some are back to work, but the 5% that remain have been out of work for a long time and are starting to become discouraged. If people have stopped looking for work, they stop being classed as unemployed and hence the unemployment rate can fall due to this. It is quite standard that after recessions the number of long-term unemployed increases, and while this does not excuse the reality or make light of it, it does present a problem for the government in attempting to find policies to get such people back into the workforce.

Hate quants? But it’s awesome!

If you’re the average first year undergraduate student (yes, I know, nobody’s really the average, but anyhow) you’ll either really hate quants (econometrics), or you’ll feign dislike in order to avoid seeming to be a geek.

My hope is that as you learn more about economics, you’ll learn to enjoy and even love the subject more, but also realise that data, and hence econometrics, is utterly central to all of it. All of the theories we teach you in micro and macro need to be verified out there in the real world, and the only way to do that properly is to collect data about the real world. Testing theories properly also requires that we learn appropriately what the data can, and is, telling us. This bit is econometrics. It’s absolutely essential if we’re going to determine which economic theories are worth taking seriously, and which we can safely discard.

Data can be pretty awesome at times, too. For example, in this day and age betting is ubiquitous on all kinds of events – see if you want to get some sense of this. Data on the bets multiple bookmakers offer for events as diverse as the Premiership (Leicester City, really?!), and the next elimination on Strictly Come Dancing. These are predictions, or forecasts, about unknown as yet future events. Economic activity relies entirely on predictions about future events – how many sales will my company get with that new product, will that job be just right for me, should I take out insurance on my new phone, and so on…

If you’re concerned about more conventional data though, and the important messages we can learn from a proper and detailed look, here’s an example from yesterday on earnings. Hopefully it makes the point really clear: it’s vital for our good as a nation, and as a society, that we know about our statistics. Stagnant earnings growth that spawned the whole “cost of living crisis” (however real it felt for your dear lecturer over that period ;-)) may well have been bad statistics caused by a misleading calculation of the average that treats new entrants to the labour market, on low wages, equally to existing members of the workforce who are receiving more “normal” pay rises. Worth a read.

It makes the bigger point though: there’s an issue with how our statistics are calculated, and that needs to be investigated. Thankfully that is happening; I’m no fan of the Chancellor of the Exchequer, but this is one of his better moved by some distance: he has set up a review into how statistics like GDP are being calculated, particularly in this day and age of masses of data (think about how much data Tescos and Sainsburys must have on you). Dry stuff I’ll grant you, but this section is particularly relevant for the first week of term after Christmas:

The Review was prompted by the increasing difficulty of measuring output and productivity accurately in a modern, dynamic and increasingly technological economy. In addition, there was a perception that ONS were not making full use of the increasingly large volume of information that was becoming available about the evolution of the economy, often as a by-product of the activities of other agents in the public and private sectors. Finally, frequent revisions to past data, together with several recent instances where series have turned out to be deficient or misleading, have led to a perception by some users that official data are not as accurate and reliable as they could be.