The Unassuming Economist

From a new working paper by David Ratner and Jae Sim:

“Is the Phillips curve dead? If so, who killed it? Conventional wisdom has it that the sound monetary policy since the 1980s not only conquered the Great Inflation, but also buried the Phillips curve itself. This paper provides an alternative explanation: labor market policies that have eroded worker bargaining power might have been the source of the demise of the Phillips curve. We develop what we call the “Kaleckian Phillips curve”, the slope of which is determined by the bargaining power of trade unions. We show that a nearly 90 percent reduction in inflation volatility is possible even without any changes in monetary policy when the economy transitions from equal shares of power between workers and firms to a new balance in which firms dominate. In addition, we show that the decline of trade union power reduces the share of monopoly rents appropriated by workers, and thus helps explain the secular decline of labor share, and the rise of profit share. We provide time series and cross sectional evidence.”

From a paper by Giulia Giupponi, Camille Landais, and Alice Lapeyre in the Journal of Economic Perspectives:

“What is the most efficient way to respond to recessions in the labor market? To this question, policymakers on the two sides of the pond gave diametrically opposed answers during the COVID-19 crisis. In the United States, the focus was on insuring workers by increasing the generosity of unemployment insurance. In Europe, instead, policies were concentrated on saving jobs, with the expansion of short-time work programs to subsidize labor hoarding. Who got it right? In this article, we show that far from being substitutes, unemployment insurance and short-time work exhibit strong complementarities. They provide insurance to different types of workers and against different types of shocks. Short-time work can be effective at reducing socially costly layoffs against large temporary shocks, but it is less effective against more persistent shocks that require reallocation across firms and sectors. We conclude that short-time work is an important addition to the labor market policy-toolkit during recessions, to be used alongside unemployment insurance.”

From VoxEU post by Leandro Prados de la Escosura:

“GDP per capita is a commonly used, but imperfect, proxy for human wellbeing. This column analyses the relationship between life expectancy at birth and per capita income over the past 150 years. It shows that life expectancy and per capita income growth behaved differently in terms of trends and distribution over the period. The relationship was particularly weak during the period 1914 to 1950. Separately, medical improvements and the diffusion of medical knowledge have been crucial drivers of life expectancy improvements across the world.

Human wellbeing is increasingly viewed as a multidimensional phenomenon, of which income is only one facet (Stiglitz et al. 2009, OECD 2011, Proto and Rustichini 2014). However, economists continue to rely on GDP to gauge wellbeing (Oulton 2012). A way to assess GDP as a comprehensive measure of wellbeing is by looking beyond per capita income. In a recent paper, I focus on life expectancy at birth – a synthetic measure of health – and its relationship with per capita income over the past 150 years (Prados de la Escosura 2022).

An important caveat is that, when assessing life expectancy over time and across countries, we need to bear in mind that original values of life expectancy are bounded and that life quality improves with the quantity of years lived (Prados de la Escosura 2021). A solution is provided by Kakwani’s (1993) non-linear transformation in which an increase in life expectancy at birth at a higher level implies a greater achievement than would have been the case had it occurred at a lower level.

Trends in life expectancy and per capita income

Life expectancy (expressed as a Kakwani index) exhibits slightly faster long-run growth than per capita GDP. A closer look, however, reveals an apparent development puzzle: economic growth and life expectancy gains do not match each other (Table 1). During the globalisation backlash between 1914 and 1950, real per capita GDP growth slowed down as world commodity and factor markets disintegrated, while life expectancy experienced major gains across the board. But, from 1950 onwards, life expectancy achieved, on average, smaller gains to GDP per head. Thus, world average life expectancy exhibited a major advance across the board before 1950, earlier than usually presumed and at odds with the view that that global health only improved after WWII, when new drugs from the West reached the rest of the world (Acemoglu and Johnson 2007, Klasing and Milionis 2020).”

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From a VoxEU post by Pascal Michaillat and Emmanuel Saez:

“Empirically, the unemployment rate is inversely related to the vacancy rate. Furthermore, servicing a job opening costs about as much as one job in terms of resources. This column shows that the labour market minimises waste when the unemployment rate equals the vacancy rate. It is too slack when the unemployment rate is higher and too tight when it is lower. Consequently, the efficient unemployment rate is simply given by the geometric average of the current unemployment and vacancy rates. At the beginning of 2022, the US labour market is excessively tight, and tighter than at any point since 1951.

Knowing whether an economy is too slack or at risk of overheating is crucial for macroeconomic policy. Economists generally look at price inflation, GDP level relative to potential, and the unemployment rate to assess, this but each measure has issues as can be seen when looking at the current US economy coming out of the Covid-19 crisis.¹ An increase in inflation, as experienced in 2021, can be a marker of an overheating economy, but inflation can also increase due to temporary disruptions such as supply chain issues. Assessing whether GDP is below or above potential is challenging as a crisis like Covid-19 also affects the productive potential of the economy. The unemployment rate is 3.6% as of March 2022, not yet lower than just before Covid-19 when the economy did not show signs of overheating. 

In this column, we propose a very simple rule to assess whether the economy, or more precisely the labour market, is too tight or too slack: are there more job openings than there are unemployed workers? This simple rule has intuitive appeal. If somehow job seekers were to be matched to job openings, would there be excess job openings, suggesting an economy with a shortage of willing workers (i.e. an overly tight labour market), or would there be excess job seekers left, suggesting an economy with too few jobs (i.e. an overly slack labour market)? It turns out that this simple intuitive rule can also be justified using the modern matching model that economists use.² This reconciles economic theory with the widely scrutinised job-seeker-per-job-opening statistic.³

The Beveridge curve

William Beveridge first noted in 1944 that the number of job openings and the number of job seekers in the UK move in opposite directions: When the economy is depressed, there are lots of job seekers and few job openings. Conversely, when the economy is booming, there are few job seekers and many job openings. This relationship has therefore been dubbed the ‘Beveridge curve’ and holds remarkably well in the US as well.⁴ Figure 1 depicts the time series of the unemployment rate u (all job seekers divided by the labour force which includes all workers and job seekers) and the vacancy rate v (all job openings divided by the same labour force) since 1951. The figure shows clearly that u and v move in opposite directions.”

Figure 1 The US unemployment and vacancy rates since 1951

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From the Conversable Economist:

“The wealth of a society is so much more than the value of houses, or the stock market, or retirement accounts. Wealth broadly understood should also include endowments of nature, ranging from wilderness to oil wells, as well as the human capital embodied in the education and skills of its people. Every few years, the World Bank takes on the task of measuring the world’s wealth in these broader ways. The most recent set of estimates appear in The Changing Wealth of Nations 2021 : Managing Assets for the Future.

Just to be clear, wealth represents an accumulation over time. This is different from GDP, which is the amount produced in a given year. Thus, world GDP in 2018 was about $86 trillion, but world wealth as estimated in this report was 13 times bigger at $1,152 trillion. Here are some estimates from “Chapter 3: Global and Regional Trends in
Wealth, 1995–2018,” by Glenn-Marie Lange, Diego Herrera, and Esther Naikal.

Here is how wealth was distributed around the world between countries of different income levels (I have left out some intermediate years in the table):”