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The Highly Skewed Growth of Incomes Since 1980: Only the Top 0.5% Have Done Better Than Before

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Piketty - Saez 1947 to 2014, June 2015, log scale

A)  Introduction

The distribution of the gains from growth have become terribly skewed since around 1980, as the chart above shows and as has been discussed in a number of posts on this blog (see, for example, here, here, here, here, and here).  From 1980 to 2014, the bottom 90% of households have seen their real income fall by 3%, while the top 0.01% have enjoyed growth of 386%.  This was not the case in the post-war years up to 1980:  Over those decades the different income groups saw similar increases in their real per household incomes:  By 87% for average income for the period between 1947 and 1980. But that ended around 1980.

The data underlying these figures were recently updated to include estimates for 2014, and this may be an opportune time to look at them again and more closely.   Specifically, most analyses (as well as the chart above) focus on the incomes of the top 10%, the top 1%, and so on, even though these are overlapping groups.  The top 10% includes the top 1%, and an open question is the extent to which the gains of the top 10% reflects gains primarily in the top 1% or also gains of those in the 90 to 99% income range.  This will be examined below.  Finally, the post will look at the question of what share of the growth in overall incomes over the full 1980 to 2014 period went to the various groups.  As one would expect, the gains were highly concentrated for the rich.  What one might find surprising is how concentrated it was.

B)  Real Income Growth (or Decline) Between 1980 and 2014 

As seen in the chart above, the rich got far richer in the period since 1980, while not just the poor but even those making up fully 90% of the population, got poorer.

The data in the chart come from Professor Emmanuel Saez of UC Berkeley, who has for some years been providing the figures from which the incomes of the very rich can be calculated, often in collaboration with the now better known Thomas Piketty.  In late June, Saez released data updated through 2014:  See his June 29 post at the Washington Center for Equitable Growth website, with links there to an Excel spreadsheet from which the data used here was downloaded.  The basic data are now also available at the World Top Incomes Database, of Facundo Alvaredo, Tony Atkinson, Thomas Piketty, and Emmanuel Saez.  Note that this data for 2014 reflects initial estimates.  They may change as more detailed figures are released by the IRS (the ultimate source for the data).

The chart covers the 1947 to 2014 period, indexed to 1980 = 100 for each of the groups. A logarithmic scale is used as equal proportional changes will then show as equal distances on the vertical axis.  That is, the distance between index values of 50 and 100, between 100 and 200, and between 200 and 400, will all be equal as all represent a doubling on income.  This makes it easier to see and track relative changes in values across different periods, for example between 1947 and 1980 in comparison to 1980 to 2014.

The data through 2014 confirm the trends discussed before in this blog, with a sharp increase in inequality since 1980 but also with large year to year fluctuations.  The year to year fluctuations are especially large for the very richest.  The incomes reported here come from anonymized tax return data, and hence reflect incomes by tax reporting units (generally households) with incomes as defined for tax purposes.  The income figures include income from realized capital gains, and hence one sees peaks (especially among the very rich) around 2000 (due to the dotcom bubble) and again in the middle of the first decade of the 2000s (coinciding with the housing as well as stock market bubbles of those years). The fluctuations between 2012 and 2014 can also be explained, at least in part, from tax law changes.  The Bush tax cuts were allowed to expire for the very rich in 2013 (they were made permanent for everyone else), which created an incentive for the rich to bring forward their taxable incomes into 2012.  This increased reported income in 2012 (when their tax rates were lower) while reducing it in 2013.  There was then a return to more normal levels in 2014.

Average household incomes rose only by 27% over 1980 to 2014, a sharp slowdown from the 87% growth achieved on average between 1947 and 1980 (with one less year as well in that period, compared to 1980 to 2014).  An earlier post on this blog discussed the immediate factors that led to this sharp deceleration in growth for average incomes (at least for wages).  But I want to focus here on the growth in incomes of the higher income groups, where there was no such slowdown.

Between 1980 and 2014, the top 10% saw their average incomes rise by 82%.  This was far better than the 27% growth in overall average household income in the period, and even more so than the 3% fall in incomes for the bottom 90%.  But it is actually similar to the growth seen for most income groups between 1947 and 1980, when average incomes rose by 87%.  One could reasonably argue that the top 10% did not do especially well over this period, but rather only saw a continuation for them of the previous trend growth.

The ones who undisputedly did especially well post 1980 were the top 1%, top 0.1%, and especially the top 0.01%.  The richer you were, the greater the increase enjoyed in the post-1980 economy.  Note there is no necessity in this:  The households are stratified by their rank in income in each year, but the growth in incomes over the period could be greater for the top 10%, say, than the top 1%.  Indeed, this was the case over the 1947 to 1980 period.  But between 1980 and 2014, the higher your income, the higher your growth in income:  The average income of the top 1% rose by 169% between 1980 and 2014, by 281% for the top 0.1%, and by 386% for the top 0.01%.

It should not be surprising that the extreme rich are pleased with how their incomes have grown since 1980, which many have not unreasonably attributed to the election that year of Ronald Reagan.  But you have not done well if you are in the bottom 90% of the population – your real income has stagnated over this period of more than a third of a century, and indeed even fell slightly.

C)  Real Income Growth of Non-Overlapping Groups

As noted above, there is a potential issue when figures are provided for the top 10%, top 1%, top 0.1%, and top 0.01%.  Even though commonly done, the figures for the top 10% include the incomes of the top 1% (and the top 0.1% and top 0.01%).  That is, these are overlapping groups, and one cannot determine just from figures presented in such a way whether the share of the top 10% increased because of higher incomes for most of those in the group, or because those in the top 1% saw an especially sharp increase.  Similarly for the top 1% and top 0.1%.  Since the very richest enjoyed such a sharp increase in their incomes, one cannot say with certainty from just these figures whether the widening distribution reflected higher incomes for most of those with higher incomes, or just for the extremely rich.

Thus it is of interest to break down the population categories into non-overlapping groups:

Piketty - Saez 1947 to 2014, by exclusive categories, log scale, June 2015The bottom 90% is as in the chart at the top of this post (decline of 3% in their real per household incomes between 1980 and 2014), and growth was 27% for average household incomes over this period.

But then it is of interest to note that those with incomes in the 90 to 99% range of households saw real income growth over this period of just 47%.  While better than the overall average of 27%, it is worse than the average growth achieved of 87% in the third of a century before 1980.  It would be difficult to argue that they have done especially well in the period since 1980.  They did worse than what average growth for everyone was before.

The group in the 99 to 99.5% percentile of income (in red in the graph) saw their incomes over the 1980 to 2014 period as a whole rise by 89%.  This was almost exactly what their growth in incomes would have been had they grown at the same rate as average incomes grew between 1947 and 1980.  Thus they did not do worse post-1980, but also not better than what the average for everyone was before.

The groups that did do better post-1980 were those in the top 0.5% of the distribution. Those whose income put them in the top 99.5 to 99.9% of the population saw income growth of 127% over this period; those in the top 99.9 to 99.99% of the population saw income growth of 219%; while those in the top 0.01% enjoyed income growth of 386%. These groups did extremely well in the post-1980 economy.

Thus the slogans about the top 1% should perhaps be refined.  It is really the top 0.5%.

D)  The Share of Growth Going to the Rich

Finally, one can calculate what share of the growth in the economy over this period accrued to the different income groups.  The measure used here is the one the Professor Saez has used in his work, and has applied to various periods (although not to the 1980 to 2014 period).  It shows what share of the growth in the overall economy was captured, in per household terms, by the group identified:

Bottom 90%

Top 10%

Top 1%

Top 0.5%

Share of income in 1980

65%

35%

10%

7%

Share of 1980-2014 growth

-7%

107%

63%

54%

Share of income in 2014

50%

50%

21%

17%

Difference in Share 1980 – 2014

-15%

15%

11%

10%

The top 10% of households accounted for a little over a third (35%) of overall household incomes in 1980.  But between 1980 and 2014, they captured 107% of the gains in overall growth, raising their share of overall incomes to 50%.  The bottom 90%, in contrast, saw their per household real incomes fall.  They “gained” a negative share of the income growth, of -7% (the mirror image of the top 10%).  Their share of overall incomes fell from almost two-thirds (65%) in 1980 to just one-half (50%) by 2014.  These are huge changes in national income shares over such a period.

Breaking this down further, it is the top 1% and even more the top 0.5% that accounted for the bulk of this worsening in distribution.  The top 1% captured 63% of the gain in overall incomes between 1980 and 2014 (in per household terms), and saw their share of overall income more than double to 21% in 2014 from “just” 10% in 1980.  But the top 0.5% captured 54% of the gains, and saw their share rise from 7% to 17% over this period, or an increase of 10% points.  That is, the increased share of the top 0.5% accounted for, by itself, fully two-thirds of the 15% point increase for the top 10%.  Yet there are only one-twentieth (1/20) as many households in the top 0.5% as the 10%.

The distribution of the gains from growth have become extremely concentrated.  Just the top 0.5% (five-thousandths of the population) captured more than half of income growth generated by the economy over this 34 year period.

E)  Conclusion

The gains from growth have accrued overwhelmingly to the very rich since 1980.  And it is not really the top 10% who have done so well, nor even the top 1%, but rather the top 0.5% .  At the same time, the bottom 90% have seen their real incomes fall.

Something changed around 1980.  Growth before then (in the period since 1947) had been much more evenly distributed, with the rich as well as the bottom 90% doing similarly well, with growth of 1.9% per annum in average household incomes (a cumulative 87%).  To be fair, one cannot say with certainty that the turning point was in 1980 rather than a few years before or a few years after.  Incomes in any given year will depend a good deal on whether the economy is growing strongly or is in recession, and (especially for the rich) whether the stock market and other asset prices are booming or in a bust. The economy was also already struggling in the 1970s.  It is therefore difficult to mark when there has been a change in trend as opposed to fluctuations caused by year to year factors.  But something happened to the economy in either 1980 or in the years surrounding it.

Ronald Reagan was of course elected president in 1980.  He launched a broad set of policies that conservatives like to praise as the “Reagan Revolution”.  There is no doubt that a deterioration in distribution resulted from many of the policies that Reagan won (large tax cuts focussed on the rich, attacks on labor unions, a focus of macro policy on inflation rather than unemployment, deregulation of financial markets and other sectors, changing wage norms which led to giant compensation packages for CEOs and others at the top, and so on).  But to be fair, one should add that other structural changes in the economy in recent decades have also had an impact on distribution, such as changes in technology, from globalization, and following from these, an increasing number of “winner-take-all” markets.

But whether due to policy or structural changes or (almost certainly) a combination of both, it is clear that policy did not counteract the resulting extreme concentration in the benefits of growth accruing to very rich.  And that is a challenge that needs to be addressed.

Why Wages Have Stagnated While GDP Has Grown: The Proximate Factors

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Real GDP per Capita & Median Weekly Earnings, 1980-2013

A.  Introduction

A healthy debate appears to be developing in the run up to the 2016 elections, with politicians of all parties raising the issue of stagnant wages.  Republicans have charged that this is a recent development, and the fault of Obama, but that is certainly not the case.  As the diagram above shows, real median wages have been stagnant since at least 1980, despite real GDP per capita which is 78% higher now than then.  Real median wages are only 5% higher (and in fact unchanged from 1979).  In a normally developing economy, one would expect real GDP per capita and real wages to move together, growing at similar rates and certainly not diverging.  But that has not been the case in the US since at least the early 1980s.

Why has such a large wedge opened up between worker earnings and GDP per capita?  This blog post will look at the immediate factors that lead from one curve to the other.  This will all be data and arithmetic, but will allow one to decompose the separation into several key underlying factors.  A future blog post will look at policies that would address those factors.

B.  Moving from Growth in GDP per Capita to Stagnant Real Wages

The progression from GDP per capita to real wages, with intermediate steps shown, looks as follows:

Going from GDP per Capita to Median Wage, 1947 to 2013:14

The chart here goes back further, to 1947, to show the divergence in recent decades in a longer term perspective.  The data come from the Bureau of Economic Analysis (BEA) or the Bureau of Labor Statistics (BLS).  As one sees, the curves moved together until around the mid-1970s, after which they began to diverge.

1)  Real GDP per Capita

Starting at the top, real GDP per capita (the curve in blue) measures the progression, in real terms, of GDP per person in the US.  GDP captures the value of all goods and services produced in the economy.  Its price index, the GDP deflator, is a price index for all those goods and services.  Although there have been temporary dips with periodic recessions, real GDP per capita has in fact grown at a remarkably stable long term rate of about 1.9% per annum going back all the way to 1870.  The growth rate was in fact a bit higher, at 2.0%, from 1947 to 2014, as the 1947 starting point was somewhat below the long term trend.  With this growth, real GDP per person was 3.75 times higher in 2014 than what it was in 1947.

2)  Real GDP per FTE Worker

But wages are paid to individual workers, and the share of workers in the population can change over time.  The share has in fact grown significantly over the post-war period, and in particular since about the mid-1960s, principally due to women entering the labor force.  There will also be demographic effects leading to changes in the shares of the very young and of retirees.

With a growing share of the population in the labor force, real GDP per full time equivalent (FTE) worker (the measure of the labor force used by the BEA) will grow by less than it will per person in the population.  The path of real GDP per FTE worker (the curve in green in the chart above), will rise more slowly than the path for real GDP per capita.  The curves start to diverge in the mid-1960s, when large numbers of women began to enter the labor force.

It should also be noted that the divergence in the two paths will not necessarily continue forever.  Indeed, the paths have in fact grown broadly in parallel from around 1997 until 2008 (when GDP per capita dipped in the downturn that began in the last year of the Bush administration).  The number of women entering the labor force reached a peak as a share of the labor force around 1997, and a decade later the first of the baby boomers started to retire.

Thus while such demographic factors and labor force participation decisions led to a significant divergence in the two paths (between GDP per capita and GDP per FTE worker) from the mid-1960s to the late-1990s, the impact since then has been broadly neutral, and might in fact go the other way going forward.

3)  Average Real Wages using the GDP Deflator

Next, workers are paid wages, not units of GDP.  Wages and salaries made up roughly half of GDP in 1947, with most of the rest accounted for by profits to capital.  And it stayed in the narrow range of 49 to 51% of GDP continuously until 1974.  The share then fell to 48%, where it held until 1981, and then began to deteriorate much more sharply, to just 42% as of 2013 (the most recent year with this data).

If the share of wages in GDP had remained constant, then the growth of wages per FTE worker would have exactly matched the growth of GDP per FTE worker.  But with a declining share of wages in GDP (with a growing share of profits as the mirror image), the curve (shown in brown in the chart above) of wages per FTE worker will rise by less than the curve of real GDP per FTE worker.

4)  Average Real Wages using the Consumer Price Index

The curves so far have been measured in real terms based on the GDP deflator.  The GDP deflator is a price index that takes into account all goods and services produced in the economy, and the weights in the price index will be in accordance with the shares of each of the goods or services in the overall economy.  But to an individual, what matters is the prices of goods and services that he or she buys.  This is measured by the consumer price index (cpi), where the weights used are in accordance with the expenditures shares of households on each of the items.  These weights can be significantly different than the weights of the items in GDP, as GDP includes more than simply what households consume.

The curve in orange in the chart above is then the average real wage but with the cpi rather than the GDP deflator used to account for inflation.  From 1978 onwards, the average real wage based on the cpi grew by significantly less than the average real wage measured in terms of the GDP deflator.  That is, inflation as measured by the items that make up the cpi grew at a faster rate, from 1978 onwards, than inflation as measured by the items (and their weights) that go into the GDP deflator.  Up until 1978, the cpi and the GDP deflator grew at remarkably similar rates, so the two curves (brown and orange in the chart) follow each other closely up to that year.

What happened after 1978?  The prices of several items whose weight in the cpi is greater than their weight in the GDP deflator began to rise more rapidly than other prices.  Especially important was the rise in medical costs in recent decades, but also important was the rise in housing costs as well as energy (with energy increases already from 1974).

Thus wages expressed in terms of what households buy (the cpi) rose by less, from 1978 onwards, than when expressed in terms of what the economy produces overall (the GDP deflator).

5)  Median Real Wages using the Consumer Price Index

The final step is to note that average wages can be misleading when the distribution of wages becomes more skewed.  If the wages of a few relatively well off wage earners (lawyers, say) rise sharply, the average wage can go up even though the median wage (the wage at which 50% of the workers are earning more and 50% are earning less) has been flat.  And that median wage is what is shown as the red curve in the chart.

[Technical Note:  The median wage series used here is the median weekly earnings of full time workers, adjusted for inflation using the cpi.  The series unfortunately only starts in 1979, but is the only series on the median, as opposed to average, wage I could find that the BLS publishes which goes back even as far as that.  The source comes from the Current Population Survey, which is the same survey of households used to estimate the nation’s unemployment rate, among other statistics.]

Since 1980 (and indeed since 1979, when the series starts), the median real wage has been flat.  This is not a new phenomenon, that only began recently.  But it is a problem nonetheless, and more so because it has persisted over decades.

C.  The Astounding Deterioration in the Distribution of Income Since 1980

Aside from demographic effects (including the impact of women entering the labor force), and the differential impact of certain price increases (medical costs, as well as others), the reason median real wages have been flat since around 1980 despite an increase of real GDP per capita of close to 80% over this period, is distributional.  The share of wages in GDP has been reduced while the share of profits has increased, and the distribution within wages has favored the better off compared to the less well off (leading to a rise in the average wage even though the median wage has been flat).

That is, the US has a distribution problem.  Wages have lost relative to profits (and profits largely accrue to the rich and wealthy), and the wages of lower paid workers have fallen even while the wages of higher paid workers have risen.

There are therefore two reasons for the distribution of income at the household level to have deteriorated since 1980.  And one sees this in the data:

Piketty - Saez 1945 to 2012, Feb 2015

This is an update of a chart presented in an earlier post, with data now available through 2012, and with the period from 1945 to 1980 included on the same chart as well.  The data came from the World Top Incomes Database (now part of the World Inequality Database), which is maintained by Thomas Piketty, Emmanuel Saez, and others.  The data is drawn from individual income tax return filings, and thus the distribution is formally by tax reporting unit (which will normally be households).  The incomes reported are total taxable incomes, whether from wages or from capital.

Over the 33 years from 1947 to 1980, average reported taxable incomes rose in real terms (using the cpi price index to adjust for inflation) by 87%.  The incomes of the bottom 90%, the top 10%, and the top 0.01%, rose by almost exactly the same amount, while the incomes of the top 1% and top 0.1% also rose substantially (by 57% and 63% respectively).  It is amazing how close together all these figures are.

This changed dramatically from 1980.  As the chart above shows, the curves then started to diverge sharply.  Furthermore, the average reported income rose only by 24% over 1980 to 2012, even though real GDP per capita rose by 73% over this period.  The 24% average increase can be compared to the 28% increase over the same years in the average real wage (based on the cpi).  While from two totally different sources of data (income tax returns vs. the national income accounts of the BEA) and measuring somewhat different concepts, these are surprisingly close.

But while average real incomes per household rose by 24%, the bottom 90% saw their real incomes fall by 6%.  Instead, the rich gained tremendously:  by 80% for the top 10%, by 178% for the top 1%, by 312% for the top 0.1%, and by an astounding 431% for the top 0.01%.

The US really does have a distribution problem, and this deterioration in distribution largely explains why real median wages have stagnated since 1980, while real GDP per capita grew at a similar rate to what it had before.

D.  Summary

To summarize, in the post-war period from 1947 to about the mid-1970s, measures of real income per person grew substantially and at similar rates.  Since then, real GDP per capita continued to grow at about the same pace as it had before, but others fell back.  The median real wage has been stagnant.

One can attribute this to four effects, each of which has been broadly similar in terms of the magnitude of the impact:

a)  Real GDP per worker has grown by less than real GDP per capita, as the share of those working the population (primarily women) has grown, with this becoming important from around the mid-1960s.  However, there has been no further impact from this since around 1997 (i.e. the curves then moved in parallel).  It may be close to neutral going forward, but was an important factor in explaining the divergence in the period from the mid-1960s to the late-1990s.

b)  The average real wage (in terms of the GDP deflator) has grown by less than real GDP per worker, as the share of GDP going to wages has gone down while the share going to profits (the mirror image) has gone up, especially since about 1982.

c)  The average real wage measured in terms of the cpi has grown by less than the average real wage measured in terms of the GDP deflator, because of the rising relative price since 1978 of items important in the household consumption basket, including in particular medical costs, but also housing and energy.

d)  The median real wage has grown by less than the average real wage (and indeed has not grown at all since the data series began in 1979), because of increasing dispersion in wage earnings between the relatively highly paid and the rest.

The implication of all this is that if one wants to attack the problem of stagnant wages, one needs to address the sharp deterioration in distribution that has been observed since 1980, and secondly address issues like medical costs.  Medical costs have in fact stabilized under Obama, as was discussed in a recent post on this blog.  But while several of the measures passed as part of the Affordable Care Act (aka ObamaCare) have served to hold down costs, it is too early to say that the previous relentless upward pressure of medical costs has ended.  More needs to be done.

Future blog posts will discuss what policy measures could be taken to address the problem of stagnant real wages and the deterioration in the distribution of income, as well as what can be done to address medical costs.

Rising Income Inequality: Full Employment Would Have Kept the Bottom 20% From Falling Behind

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Real Income Growth of Bottom 20% vs Unemployment Rate, 1968-2012

A.  Introduction

President Obama highlighted in this year’s State of the Union address, as well as in other recent speeches and events, the importance of and concerns about the worsening distribution of income in the US.  As this blog noted in a post two years ago, income distribution has worsened markedly in the US since about 1980, when Reagan was elected.  This deterioration since 1980 is in sharp contrast to the period from the end of World War II until 1980, when incomes of all groups in the US moved upward together.  The paths then diverged sharply after 1980, with large increases in the incomes of the rich (and in particular the extremely rich:  the top 1%, top 0.1%, and especially the top 0.01%), while the real incomes of the bottom 90% were flat or even falling.

An important question, of course, is what to do to achieve more equitable growth, and in particular more rapid growth in the real incomes of those in the lower strata of the population.  Much of the discussion has focussed on measures such as improving our educational and training systems, to prepare workers for better paying jobs.  There is no doubt that such measures are important, and need to be done.  Their impact will, however, only be over the long term – in a generation for measures such as improvements in the educational system.

This blog post will focus on a more immediate action that can be taken:  returning the economy to full employment and keeping it there.  We will find that based on historic patterns, slack in the labor market due to less than full employment has been negatively associated with growth in the real incomes of the bottom 20% of households.  Furthermore, based on statistical regression parameters estimated from the historical data, the greater degree of slack in the US labor market since 1980 compared to that in the thirty years before 1980, largely suffices in itself to account for the relative deterioration of real incomes since 1980 of the bottom 20% of households compared to the top 20%.

This is an important result.  Note that the claim is not that greater slack in the labor market (on average) in the decades since 1980 was the sole cause of the deterioration of relative incomes of the poorest 20% vs. the richest 20%.  There were undoubtedly numerous reasons for this.  But what the finding does indicate is that had the unemployment rate after 1980 matched what it had been in the three decades before 1980, this would have largely sufficed in itself to offset the other factors, and would have led to a rate of growth in the real incomes of the bottom 20% close to what it was for the top 20%.

B.  The Relationship Between Real Income Growth of the Bottom 20% and the Unemployment Rate

The scatter diagram at the top of this post shows the relationship between the annual real income growth of the bottom 20% of households since 1968, and the average rate of unemployment in the same year.  The income data for the bottom 20% comes from the series produced by the US Census Bureau, and measures household cash income before tax and from all cash sources (so it will include Social Security, for example, but not payments under Medicare).  The series starts in 1967 (so 1968 is the first year for which one can compute the growth), and goes to 2012.  The unemployment rate comes from the standard series produced by the US Bureau of Labor Statistics, where the annual rate is the simple average of the monthly rates over the year.

The scatter diagram suggests there is a relationship between slack in the labor market (a higher unemployment rate) and the annual change in the real incomes of the bottom 20% of households, but that it is by no means a tight one.  Other factors matter as well.  But a simple ordinary least squares regression of the annual change in the real incomes of the bottom 20% against the average unemployment rate in that year, does suggest that the unemployment rate is an important and statistically significant factor.

The regression fitted line slopes downward with a coefficient of -0.8228, indicating that on average, a 1% point increase in the unemployment rate in the year will be associated with a 0.8228% point fall in the growth rate that year of the real incomes of the bottom 20%.  The t-statistic on the 0.8228 slope coefficient is 3.3, where any t-statistic greater than about 2.0 is generally seen as statistically significant (with a greater than 95% degree of confidence).  That is, with a greater than 95% degree of confidence, the results suggest that the coefficient is significantly different from zero (where zero would indicate no relationship).

The R-squared of the regression (an indication of correlation) is relatively modest at just 0.1982.  It can vary from zero to one.  This indicates that there is more than just the unemployment rate that accounts for the annual change in the real incomes of the bottom 20%.  But this does not mean that the unemployment rate does not matter.  The t-statistic for it is highly significant.  Rather, the modest R-squared indicates there are other factors as well which have not been identified here.

Similar regressions were run for the changes in the real incomes of the other quintiles of the household income groups.  The estimated coefficients became progressively closer to zero, from -0.82 for the bottom 20%, to -0.62 for the second 20%, to -0.52 for the middle 20%, to -0.47 for the fourth 20%, and then dropping sharply to -0.25 for the top 20%.  This suggests that the link to unemployment as a factor explaining the growth in the real incomes of the group became progressively less important for the richer groups.  And the t-statistic for the coefficient for the top 20% was only 1.0, indicating the estimated coefficient (of -0.25) was statistically not significantly different from zero (and hence that one cannot reject the hypothesis that no relationship is there).  The R-squareds for the regressions similarly fell steadily, from 0.1982 for the bottom 20%, to 0.19 for the second 20%, to 0.16 for the middle 20%, to 0.14 for the fourth 20%, and then dropping sharply to an extremely low 0.02 for the top 20%.

The results suggest that slackness in the labor market, as measured by the unemployment rate, was a significant factor in explaining the annual growth in the real incomes of the bottom 20% (with more unemployment leading to lower or indeed negative growth).  The results also suggest that higher unemployment did not have a statistically significant impact on the growth in real incomes of the top 20%.

C.  The Impact of Less Slack in the Labor Market

From 1950 to 1979, when growth was similar for all income groups (see this earlier blog post), the monthly unemployment rate averaged 5.17% in the US.  But from 1980 to 2012, the monthly rate averaged 6.44%, or 1.27% points higher.  The index of real incomes of the bottom 20% of households (in the US Census data cited above) had risen from 100.0 in 1967 (the earliest year with such data) to an index value of 118.9 in 1980.  But since then it has risen hardly at all, reaching only 119.5 in 2012.  The 1980 to 2012 growth rate was only 0.015% per year (note not 1.5% per year, but rather only one-hundreth of that).

Suppose the labor markets over 1980 to 2012 had been as close to full employment as they had been over the period 1950 to 1979.  Applying the estimated regression coefficient of -0.8228 to the 1.27% point difference in the average unemployment rates, the annual growth rate of the real incomes of the bottom 20% would have been 1.045% points higher (equal to 0.8228 x 1.27% points), and hence would have reached a growth rate of 1.06% a year (equal to 1.045% + 0.015%).  With such a growth rate, the real incomes of the bottom 20% would have reached an index value of 166.5 in 2012  This would have been close to the index value of the real incomes of the top 20% in that year of 169.8 (with 1967 set equal to 100.0).  Relative incomes would have grown similarly since 1967, and inequality (for the bottom 20% compared to the top 20%) would not have grown.

This is an interesting result.  It suggests that the higher unemployment rates we have on average suffered from since 1980 can account both for the stagnation of the real incomes of the bottom 20%, and the increasing inequality when comparing this group to the top 20%.  Note it does not offset all of the increasing inequality seen since Reagan was elected.  The real incomes of the top 1%, top 0.1%, and especially the top 0.01% have grown by far more than the incomes of the top 20%.  But keeping up with the top 20% would still be a major accomplishment.

A return to the economic performance that the US enjoyed in the three decades before Reagan would not be impossible.  To keep the average unemployment rate at the 5.17% rate achieved between 1950 and 1979 would not mean that all recessions need be avoided.  There were a number of recessions in the three decades before 1980.  But the recessions since 1980 (dating from January 1980 at the end of the Carter Administration, from July 1981 at the beginning of Reagan, from July 1990 during Bush I, from March 2001 at the beginning of Bush II and December 2007 at the end of Bush II) have been especially severe.  Avoiding those high peak rates of unemployment would have brought down the average.  Specifically, the average unemployment rate (based on the monthly figures) over 1980 to 2012 would have matched the 1950 to 1979 average if one would have been able to avoid those months since 1980 when the unemployment rate reached 6.4% or more.

D.  Conclusion

There is increasing recognition that the rise in inequality in the decades since 1980, and the stagnation since then in the real incomes of those in the lower strata of the population, cannot go on.  But the solutions commonly proposed, such as better education and training, will take decades to have an impact.

The analysis in this post indicates that the more immediate action of bringing the economy back to full employment and then keeping it close to full employment, would have a major positive impact on the real incomes of those in the bottom 20% of households, and would lead to a more equitable distribution.  The analysis suggests that had the unemployment rate over 1980 to 2012 been at the level achieved over 1950 to 1979, then the rate of income growth of the bottom 20% since 1980 would have been similar to that of the top 20%.  The higher rate of unemployment since 1980, on average, may well explain why growth was broadly equal among income groups in the three decades before 1980, but not in the three decades since.

While there are many factors that underlie income growth and distributional changes, particularly for those at the very top of the income distribution (the top 1% and higher), the results suggest that getting the economy back to full employment should be seen as critically important and valuable.  And there is no mystery in how to do this:  As earlier posts on this blog have noted, the fiscal drag from government cutbacks since 2009 can fully explain why full employment has yet to be achieved in this recovery.  Had government been spending been allowed to grow simply at its historical average rate, the economy would already have returned to full employment by now.  Had government spending been allowed to grow at the higher rate it had under Reagan, the US would likely have been back at full employment in 2011 or early 2012.

Unemployment matters.  Not only is it a direct and personal tragedy for those who have lost a job because of the macro mismanagement of the economy, it is also a waste of resources for the economy.  The evidence reviewed in this post suggests further that the greater degree of slack in the US labor market since 1980 may well explain the stagnation of real incomes of the poorer strata of the population, and the widening degree of inequality of recent decades for those other than in the extreme upper strata.