Tag Archives: Income

Historical Living Standards in International Comparison

On VoxEU, Peter Lindert summarizes his recent work on long-term international comparisons of living standards. Lindert compares nominal incomes per capita, deflated by historical prices (for staple goods). He makes five points:

The real income gap between Northwest Europe and the major Asian countries was greater since the 1500s than even Maddison had estimated.

Contrary to all previous estimates, Mughal India around 1600 was already far behind both Japan and Northwest Europe.

Within Europe, the new estimation procedure shows little bias in Maddison’s estimates.

Average incomes in North America were already higher than in Britain or France in the late 17th century, long before Maddison’s c.1900 catching up date for the US versus Britain. A similar ‘frontier advantage’ has now emerged from estimates for Australia in 1870.

Adding what is known about the ability to buy luxuries and capital goods raises the income of Western Europe relative to all other regions, except possibly resource-rich North America.

Who Voted for Brexit?

In a CEPR Discussion Paper, Sascha Becker, Thiemo Fetzer, and Dennis Novy argue that education and income mainly explain voting outcomes. In the abstract of their paper, the authors write:

We find that exposure to the EU in terms of immigration and trade provides relatively little explanatory power for the referendum vote. Instead, … fundamental characteristics of the voting population were key drivers of the Vote Leave share, in particular their education profiles, their historical dependence on manufacturing employment as well as low income and high unemployment. … within cities, we find that areas with deprivation in terms of education, income and employment were more likely to vote Leave.

Benjamin Todd’s “80,000 Hours”

80,000 hours, that’s how many hours we typically spent working over a lifetime, according to Benjamin Todd and the 80,000 hours team. They have published a book/ebook on how to make the best of it.

Their advice for a dream job: Look for

work you’re good at,

work that helps others,

supportive conditions: engaging work that lets you enter a state of flow; supportive colleagues; lack of major negatives like unfair pay; and work that fits your personal life.

The book discusses strategies to build a career plan, and a career. The main text closes with this summary:

Explore to find the best options, rather than “going with your gut” or narrowing down too early. Make this your key focus until you become more confident about the best options.

Take the best opportunities to invest in your career capital to become as badass as you can be. Especially look for career capital that’s flexible when you’re uncertain.

Help others by focusing on the most pressing social problems rather than those you stumble into – those that are big in scale, neglected and solvable. To make the largest contribution to those problems, consider earning to give, research and advocacy, as well as direct work.

Keep adapting your plan to find the best personal fit. Rather than expect to discover your “passion” right away, think like a scientist testing a hypothesis.

And work with a community.

In an appendix, the authors advise (potential) undergraduates to

aim for the most fundamental, quantitative option you can do i.e. one of these in the following order: mathematics, economics, computer science, physics, engineering, political science / chemistry / biology,

or otherwise,

focus on developing communication skills in philosophy, history or English.

The best choice is a combination. There is high demand for people who can understand quantitative topics and communicate clearly.

Appendix 8 contains useful career review summaries with “facts on fit” and “next steps” (see also this link for updates). For example, the authors advise that

[a]n economics PhD is one of the most attractive graduate programs: if you get through, you have a high chance of landing a good research job in academia or policy – promising areas for social impact – and you have backup options in the corporate sector since the skills you learn are in demand (unlike many PhD programs). You should especially consider an economics PhD if you want to go into research roles, are good at math (i.e. quantitative GRE score above 165) and have a proven interest in economics research.

But they warn that an Economics PhD takes a long time and

[d]oing highly open‐ended research provides little feedback which can be demotivating.

A final appendix discusses areas where people who want to help others possibly can have a large impact. As very promising areas, the authors identify

  • Biosecurity,
  • Climate change (extreme risks),
  • Factory farming,
  • Global priorities research,
  • Health in poor countries,
  • Land use reform,
  • Nuclear security,
  • Risks posed by artificial intelligence,
  • Smoking in the developing world, as well as,
  • Promoting effective altruism (the movement related to the book).

Dynamics of the World Income Distribution

In a Resolution Foundation report, Adam Corlett examines the “Elephant Curve.” The curve shows that between 1988 and 2008 income growth in the 70th to 95th percentile range of the world income distribution was much lower than for almost all other percentiles. Since the lower middle class of rich countries is situated around the 80th percentile of the distribution the Elephant curve has been interpreted as evidence for stagnating middle class incomes in the rich countries.

Corlett emphasizes that

  • the country composition in 1988 and 2008 is not the same. Holding it constant the Elephant curve is less pronounced.
  • “Population changes, rather than just income changes, have driven the income growth distribution in the elephant curve.” Holding the relative population size across countries constant the Elephant curve is less pronounced.
  • There is lots of variation across developed economies. “[T]he weak figures for the mature economies as a whole are driven by Japan (reflecting in part its two ‘lost decades’ of growth post-bubble, but primarily due to likely flawed data) and by Eastern European states (with large falls in incomes following the collapse of the Soviet Union after 1988). Looking only at the remaining mature economies, far from stagnation we find average real income growth of 52 per cent with strong growth across the distribution, though slightly higher at the top. [But] there are great differences between these nations. US growth of 41 per cent was notably unequally shared, with low (but not zero) growth for poorer deciles meaning that the US comes closest to matching the stagnation and inequality narrative – despite international trade being much less important on a national level there than elsewhere [my emphasis]. But most people in most other rich countries experienced stronger growth.”