The Unassuming Economist

From a new VoxChina post on China’s rising income inequality:

“China has witnessed persistent increases in economic inequality since the early 1990s when the urban labor market began its transformation — from centrally-controlled to market-driven. Using the Urban Household Survey data, this paper (Feng and Tang, 2018) documents the trends in income inequality over the period of 1992-2009 and decomposes changes in income inequality by considering three main factors: the labor market, human capital, and marriage market. We find that labor market factors account for approximately three-quarters of the overall increases in income inequality while the falling marriage rate has contributed the other quarter. Changes in human capital levels and marital assortativeness have not contributed to the rising inequality.”

“Our results suggest that labor market factors are most important to the increases in family income inequality during the 1992-2009 period. Figure 1 shows that if allthree of the factors are held unchanged at the levels of 1992/93, the Gini coefficient would only increase from 0.266 to 0.298, instead of to the actual 0.407. Therefore, labor market factors collectively contribute more than three-quarters of the total increase in inequality. Further, labor market factors are more important in affecting the upper part of the income distribution as they account for almost all of the changes in P90/P50, the ratio of the 90th percentile to median family incomes.

A more careful examination suggests that each labor market factor plays an important role. Changes in employment and returns to education both contribute about 16 percent of the change in inequality over the study period, while within-education-group inequality accounts for around 37 percent of the overall increase in inequality. Therefore, within-education-group inequalities are not only crucial in changes to individual income inequality, as many previous researchers have noted, but are also important in the rise in regards to family income inequality.”

From a new Social Europe post:

“Traditionally, higher productivity has benefited workers through reductions in working time, among other things. When the TUC was founded in 1868, average working hours were 62 per week. Now (including those who work part-time), average weekly hours have almost halved, to around 32.”

“That change didn’t happen by accident, but was the result of union campaigning. Robert Owen, the socialist pioneer who had popularised the demand for an eight-hour day at the beginning of the 19th century, coined the slogan: Eight hours’ labour, Eight hours’ recreation, Eight hours’ rest.

Following trade-union agitation, the Factory Acts of 1874 were the first kind of legislation to put clear limits on the working day—though back then, at ten hours, they fell short of union demands. The May Day demonstration of 1890, which drew hundreds of thousands of people to Hyde Park in London, was centred around the demand for an eight-hour day—following a call from the international trade-union movement for ‘a great international demonstration’ so that in all countries and all cities, on the appointed day, ‘the toiling masses shall demand of the State authorities the legal reduction of the working day to eight hours’.

And in 1919, when the International Labour Organisation (ILO) met for the first time in the wake of World War I, its initial convention was on working time. The preamble explicitly adopted ‘the principle of the 8 hours day or of the 48 hours week’ (though there was a long list of carve-outs—including the entirety of India).

So trade unions have an eye on history when they see predictions about the potential for extra wealth to be generated from robotics and artificial intelligence. The consultancy firm PWC has estimated that UK gross domestic product will be up to 10 per cent higher in 2030 as a result of artificial intelligence, equivalent to a boost to GDP of more than £20 billion, or extra spending power of up to £2,300 a year per household. One way that wealth could potentially be shared more equitably with workers is through a reduction in working time—as we saw in the last century.”

From a new VOX post by David Autor:

“Labour markets in US cities today are vastly more educated and skill-intensive than they were 50 years ago, but urban non-college workers now perform much less skilled work than they did. This column shows that automation and international trade have eliminated many of the mid-skilled non-college jobs that were disproportionately based in cities. This has contributed to a secular fall in real non-college wages.”

“Figure 2 depicts the aggregate relationship between population density and occupational structure at the level of 722 commuting zones (CZs) covering the contiguous US states between 1970 and 2015.

The three panels of this figure report the CZ-level share of employment among working-age adults into the three broad occupational categories described above: traditionally low-education, low-wage services, transportation, labourer, and construction workers; traditionally mid-education, middle-wage occupations made up of clerical, administrative support, sales, and production workers; and high-education, high-wage, professional, technical, and managerial workers.

The horizontal axis is the natural log of population density (the number of residents divided by CZ land area). For consistency, I used each zone’s population density in 1970 throughout, and the data are weighted by the count of working-age adults in each CZ. Each plotted point in the bin-scatter represents approximately 5% of all workers in each year.

The rising set of upward-sloping curves in the right panel show that while denser CZs have traditionally been more intensive in high-skill work, the level and slope of this relationship between density and skill-intensity has risen consistently over multiple decades. The overlapping downward-sloping curves in the left panel show that the fraction of workers engaged in low-skill occupations has historically been much smaller in high-density CZs, and this pattern has changed little over decades. In the middle panel, the fan-shaped set of curves show that the denser CZs were exceptional in the 1970s for having far more middle-skill work than suburban and rural CZs. But this exceptional feature attenuated. By 2015, the densest CZs had less middle-skill work than the suburbs or rural areas.”

From a new IMF working paper by Reda Cherif and Fuad Hasanov:

“Industrial policy is tainted with bad reputation among policymakers and academics and is often viewed as the road to perdition for developing economies. Yet the success of the Asian Miracles with industrial policy stands as an uncomfortable story that many ignore or claim it cannot be replicated. Using a theory and empirical evidence, we argue that one can learn more from miracles than failures. We suggest three key principles behind their success: (i) the support of domestic producers in sophisticated industries, beyond the initial comparative advantage; (ii) export orientation; and (iii) the pursuit of fierce competition with strict accountability.”

From a new IMF working paper by Dilyana Dimova:

“The labor share in Europe has been on a downward trend. This paper finds that the decline is concentrated in manufacture and among low- to mid-skilled workers. The shifting nature of employment away from full-time jobs and a rollback of employment protection, unemployment benefits and unemployment benefits have been the main contributors. Technology and globalization hurt sectors where jobs are routinizable but helped others that require specialized skills. High-skilled professionals gained labor share driven by productivity aided by flexible work environments, while low- and mid-skilled workers lost labor share owing to globalization and the erosion of labor market safety nets.

The value-added share accrued to labor commonly known as the labor share—the ratio of labor compensation (wages and benefits) to national income—has been on a downward trend in the EU in the last couple of decades (Figure 1). This trend is observed both in recession-hit Advanced Economies (AE) like Ireland, Portugal and Spain as well as in economically prosperous Germany and the Netherlands (Figure 1, upper panels), and began around 2012–13 after the Great Recession (GR). In New Member States (NMS), Estonia, Hungary, Latvia and Lithuania experienced a decline in 2009–15 and are on the rebound (Figure 1, lower panels). Other NMS economies such as Croatia, Poland and Romania have yet to return to their 2002 levels. The positive exception is Bulgaria whose labor share has been on an upward trend due to an economic deepening from relatively low levels.

This paper looks at the evolution of the labor share by industry and by skill level and considers the effect of various structural factors on the EU-wide stagnation and erosion of the labor share. Following Dao et al. (2017), first a shift-share analysis is used to demonstrate the extent to which the downward trend in the labor share is driven by within-sector/skill category declines or by changes across sectors/skill category. The analysis establishes that within-sector/skill category changes account for the majority of labor share fluctuations and provides justification for the structural factor analysis. Then the paper quantifies the extent to which structural drivers track changes in the labor share in 28 EU-member countries, representing both advanced economies and transitional economies, in a cross-country panel study that uses disaggregated data for twelve industry sectors and three skill categories.”