{"id":11273,"date":"2021-04-09T13:01:32","date_gmt":"2021-04-09T12:01:32","guid":{"rendered":"http:\/\/www.economicsobservatory.com\/?post_type=question&p=11273"},"modified":"2021-04-12T11:24:09","modified_gmt":"2021-04-12T10:24:09","slug":"economic-evolution-fast-and-slow","status":"publish","type":"question","link":"https:\/\/www.economicsobservatory.com\/test\/economic-evolution-fast-and-slow","title":{"rendered":"Economic evolution: fast and slow"},"content":{"rendered":"\n

Newsletter from 9 April 2021<\/strong><\/p>\n\n\n\n

Economics is often seen as stuck in its ways, a discipline that clings to old ideas \u2013 perfect markets, for example \u2013 and the old models that embed these unrealistic assumptions. But economics and economies change, in the past 18 months perhaps more than ever. This week a key theme at the Economics Observatory is economic evolution: where it exists and where it doesn\u2019t.<\/p>\n\n\n\n

An important new piece<\/a> published this morning gets to the heart of a tension that all \u2018macro\u2019 economists \u2013 academics and policy-makers alike \u2013 come up against: the general versus the specific, the global versus the local, the big picture versus the individual\u2019s lived experience.<\/p>\n\n\n\n

On the one hand, we know that economy-wide or \u2018aggregate\u2019 values matter a great deal. An example: the aggregate fiscal position (the state of the government\u2019s finances, in other words) will influence a country\u2019s payments on its debt, its capacity to invest and offer generous social safety nets. Aggregate growth matters, since it influences unemployment rates (and the fiscal position).<\/p>\n\n\n\n

But when you interview people about economics, you find that aggregates matter very little: our local, personal experience is more important to us. A common critique naturally follows: how can macro-economics, which looks at the economy as a whole, say anything meaningful about our individual lives or help us to set policy?<\/p>\n\n\n\n

Ben Moll and Natalie Rickard show how, by tracking the evolution of macro models<\/a> \u2013 both long-term and in response to the Covid pandemic. Writing this from Bristol, where economics Nobel laureate Angus Deaton undertook a chunk of his research, I have to re-use a great quote that Ben and Natalie pulled from his 2016 Nobel lecture<\/a>:<\/p>\n\n\n\n

\u2018While we often must focus on aggregates for macroeconomic policy, it is impossible to think coherently about national well-being while ignoring inequality and poverty, neither of which is visible in aggregate data\u2019.<\/em><\/p>\n\n\n\n

The modern approach, as the authors explain, puts market imperfection and differences between people at its heart. The roots, in fact, are deep: Nicholas Kaldor wrote about the importance of distribution<\/a> in the mid-1950s, and a tranche of seminal papers between 1986<\/a> and 1998<\/a> laid out a new way of taking differences between individuals \u2013 by wealth, by income or by the types of economic shock they face \u2013 seriously.<\/p>\n\n\n\n

The biggest test of a model is not if it is perfect \u2013 that is not the aim \u2013 but whether it helps us to think about the economy and, most vitally, to clarify policy options. The latest breed of models are doing just that, with a great new visualisation of some of Ben\u2019s research (joint with Gianluca Violante<\/a> and Greg Kaplan<\/a>). It shows how switching on and off fiscal support \u2013such as stimulus payments or tax breaks \u2013 affects the trade-offs policy-makers face when dealing with a pandemic. As Ben and Natalie conclude, there is much to do but the world of macroeconomic modelling is evolving.<\/p>\n\n\n\n

When evolution fails<\/h3>\n\n\n\n

By contrast, some economic outcomes can get stuck or even go backwards, for decades or more. Ethnicity and wage inequality in the UK is one such example. A recent official commission \u2013 the Sewell Report<\/a> \u2013 pointed to a convergence of employment and pay across ethnicities in the UK. But does this optimistic picture stand up to the data? Alan Manning and Rebecca Rose took a closer look<\/a> in another new Observatory piece.<\/p>\n\n\n\n

Long-term trends are important, so Alan and Rebecca restrict themselves to ethnicities that statisticians have tracked all the way back to the mid-1990s in Britain: white, black, Indian, Pakistani, Bangladeshi and Chinese. They control for a range of factors including age, qualifications and marital status to help them make accurate comparisons.<\/p>\n\n\n\n

The authors\u2019 test is simple: does ethnicity still drive inequality in the UK labour market? A group of charts gives the answer: inequality, measured as the \u00a0difference in pay between a white worker and ethnic minority worker, has not narrowed. For many groups, it is stagnant and for some it has widened.<\/p>\n\n\n\n

There are also important differences by gender and ethnicity. Overall, inequality is lower between women of different ethnicities than for men, as the charts below show. For example, black men\u2019s earnings are over 15% below the average white male worker\u2019s (the authors use long run averages to smooth out noise, so the numbers in the chart below include data up to 2019). The Pakistani community \u2013 both men and women, totalling 485,000 workers (1.5% of the workforce) \u2013 is the worst off. Their pay is lowest of all and the gap is widening.<\/p>\n\n\n\n

Mean wage gap by ethnic minority group: Men (left) versus women (right)<\/h4>\n\n\n\n
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