We start our first Zoom session on Monday 1 Feb (10.00am GMT) with introductions and administrative issues which should take no more than 20-30 minutes:
- course overview and syllabus
- textbook and online resources
- attendance and participation
- virtual office hours
- session formats
- assessment and grading
The remainder of Session 1 will introduce Gross Domestic Product – the key “go-to” number for summarising a country’s economic performance. GDP data are rarely out of the news. GDP data can win or lose elections, are central to key policy decisions and have enormous influence on private sector behaviour, not least global capital investment decisions.
I cannot over-emphasise how important data gathering, analysis and interpretation are. This course is all about macro models. Good models need the best data we can muster; data are always imperfect but we should work hard to ensure that foundations are as solid as they can be. Without that, it’s just garbage in, garbage out.
So no apologies for devoting early sessions to GDP data – both the practical issues and the conceptual pitfalls that we shall need to avoid. We shall look at the latest, Covid-infected, data from the US and UK to assess how 2020 has gone and what we can expect this year and next. We can only scratch the surface but the lessons learnt will put us in good shape for the rest of the course. We start with the easy stuff that you’ve probably come across already: Y = C+I+G+X-M. Accounting may seem tedious but it’s essential for cross-checking and ensuring data coherence. Be careful though. The circular flow and its related accounting identities give the impression that measuring GDP is pretty straightforward. I wish it were that simple.
On a practical level there are so many types of GDP measures out there: real, nominal, $-valued, index-based, per capita, seasonally adjusted, unadjusted.
Once you have chosen the “right” series you might want to calculate how quickly it has grown over any given period. How are you going to do that? It might sound easy but the potential for confusion is huge. There are many different ways of calculating growth rates.
Time will be taken to build technical skills by exploring the use of FRED, OECD and IMF data sources as well as increasingly popular “fast” data from Google, Twitter, etc . We shall also touch on the need for informative and professional visualisations.
Yet, armed with the “right” growth numbers, what do they actually tell you? Even mature and pandemic-free economies can demonstrate unnerving volatility over time. So, if a quarterly GDP growth number is weak, should we panic? Or do we just put it down to statistical noise? Data and information are not the same as knowledge and wisdom!
We also need to remember that GDP figures are built up from millions of individual households and companies. That means that the aggregate data may not be representative of “typical” firms or people. Higher productivity and new technology sound good-to-have but what if it means you, your friends, and your family are thrown out of work? “Expert” narratives welcoming modern development will no longer chime with a broad swathe of experience – a dangerous development that could seriously mislead readings of economic (let alone political) trends.
Moreover, data are not perfect. Even if there are no blatant lies, fresh information can come to light and new methods of putting the numbers together may be adopted. So, once data is published, the “truth” can change, sometimes dramatically.
Deeper conceptual problems also need to be addressed. GDP – dubbed one of the great inventions of the 20th century – started life in the 1930s as a way of measuring “stuff”. But advanced 21st century economies are now dominated by services rather than goods. This generates a lot of problems for bean counters with difficulties mounting in the wake of digital technology and intangible, sometimes “free”, services. Indeed, setting the production boundaries – what’s in, what’s out of GDP measurement – has been a perennial problem. Certainly US Presidential candidate Bobby Kennedy, speaking in 1968, had serious doubts about GDP as a metric.
To round up our selective intro to macro data we shall pose this question: does more GDP make you happier? I look forward to hearing your thoughts.
For class prep I recommend the BEA’s primer on national economic accounts – it contains a nice (and thankfully brief) overview of some key concepts you will need to master. Also have a look at this UK Statistics Office article featuring the dangers of misreading GDP in the Age of Covid.
If you have any questions or concerns, please email me as soon as possible. Again, the email link is available in NYU Classes, in the firewalled section of the website and in the syllabus already circulated to you.
27 Jan 2021