Growth in France and the US: The Bottom 90% Have Done Better in France

France vs US, 1980-2012, GDP per capita overall and of bottom 90%

A.  Introduction

Conservative media and conservative politicians in the US have looked down on France over the last decade (particularly after France refused to join the US in the Iraq war, and then turned out to be right), arguing that France is a stagnant, socialist state, with an economy being left behind by a dynamic US.  They have pointed to faster overall growth in the US over the last several decades, and average incomes that were higher in the US to start and then became proportionately even higher as time went on.

GDP per capita has indeed grown faster in the US than it has in France over the last several decades.  Over the period of 1980 to 2007 (the most recent cyclical peak, before the economic collapse in the last year of the Bush administration from which neither the US nor France has as yet fully recovered), GDP per capita grew at an annual average rate of 2.0% in the US and only 1.5% in France.

But GDP per capita reflects an average covering everyone.  As has been discussed in this blog (see here and here), the distribution of income became markedly worse in the US since around 1980, when Reagan was elected and began to implement the “Reagan Revolution”.  The rich in the US have done extremely well since 1980, while the not-so-rich have not.  Thus while overall GDP per capita has grown by more in the US than in France, one does not know from just this whether that has also been the case for the bulk of the population.

In fact it turns out not to be the case.  The bottom 90%, which includes everyone from the poor up through the middle classes to at least the bottom end of the upper middle classes, have done better in France than in the US.

B.  Growth in GDP per Capita in France vs. the US:  Overall and the Bottom 90%

The graph at the top of this post shows GDP per capita from 1980 to 2012 for both the US and France.  The figures come from the Total Economy Database (TED database) of the Conference Board, and are expressed in terms of 2012 constant prices, in dollars, with the conversion from French currency to US dollars done in terms of Purchasing Power Parity (PPP) of 2005.  PPP exchange rates provide conversions based on the prices in two respective countries of some basket of goods.  They provide a measure of real living standards.  Conversions based on market exchange rates can be misleading as those rates will vary moment to moment based on financial market conditions, and also do not take into account the prices of goods which are not traded internationally.

Real GDP per capita (for the entire population) rose for both the US and France over this period, and by proportionately somewhat more in the US than in France.  These incomes are shown in the top two lines in the graph above, with the US in black and France in blue.  GDP per capita in France was 83% of the US value in 1980, and fell to 72% of the US by 2012.

But the story is quite different if one instead focuses on the bottom 90%.  The GDP per person of those in the bottom 90% of the US and in France are presented in the lower two lines of the graph above.  The figures were calculated using the distribution data provided in the World Top Incomes Database, assembled by Thomas Piketty, Emmanuel Saez, and others, applied to the GDP and population figures from the TED database.  The US distribution data extends to 2012, but the French data only reaches 2009 in what is available currently.

The Piketty – Saez distribution data is drawn from information provided in national income tax returns, and hence is based on incomes as defined for tax purposes in the respective countries.  Thus they are not strictly comparable across countries.  Nor is taxable income the same as GDP, even though GDP (sometimes referred to as National Income) reflects a broad concept of what constitutes income at a national level.  But for the moment (the direction of some adjustments will be discussed below), distributing GDP according to income shares of taxable income is a good starting point.

Based on this, incomes (as measured as a share of GDP, and then per person in the group) of the bottom 90% in France were 88% of the US level in 1980.  But this then grew to 98% of the US level by 2007, before backing off some in the downturn.  That is, the real income of the bottom 90%, expressed purely in GDP per person, rose in France over this period from substantially less than that for the US in 1980, to very close to the average US income of that group by 2007.  And since one is talking about 90% of the population, that is all those other than the well-off and rich, this is not an insignificant group.

C.  Most of the US Income Growth Went to the Top 10%

Figures on the growth of the different groups, and their distributional shares, show what happened:

France US
GDP per Capita, Rate of Growth, 1980-2007
  Overall 1.5% 2.0%
  Bottom 90% 1.4% 1.0%
Share of GDP, 1980
  Top 10% 31% 35%
  Bottom 90% 69% 65%
Share of GDP, 2007
  Top 10% 33% 50%
  Bottom 90% 67% 50%
Share of Increment of GDP Growth, 1980-2007
  Top 10% 36% 62%
  Bottom 90% 64% 38%

As noted before, overall GDP per capita grew at a faster average rate in the US than in France over this period:  2.0% annually in the US vs. 1.5% in France.  But for the bottom 90%, GDP per capita (for the group) grew at a rate of only 1.0% in the US while in France it grew at a rate of 1.4% per year.  The French rate for the bottom 90% was almost the same as the overall average rate for everyone there, while in the US the rate of income growth for the bottom 90% was only half as much as for the overall average.

Following from this, income shares did not vary much over the 1980 to 2007 period in France.  That is, all groups shared similarly in growth in France.  In contrast, the top 10% in the US enjoyed a disproportionate share of the income growth, leaving the bottom 90% behind.

In 1980 in France, the top 10% received 31% of the income generated in the economy and the bottom 90% received 69%.  With perfect equality, the top 10% would have had 10% and the bottom 90% would have had 90%, but there is no perfect equality.  The US distribution in 1980 was somewhat more unequal than in France, but not by much.  In 1980, the top 10% received 35% of national income, while the bottom 90% received 65%.

This then changed markedly after 1980.  Of the increment in GDP from growth over the 1980 to 2007 period, the top 10% received 36% in France (somewhat above their initial 31% share, but not by that much), while the bottom 90% received 64%.  The pattern in the US was almost exactly the reverse:  The top 10% in the US received fully 62% of the increment in GDP, while the bottom 90% received only 38%.  As a result of this disproportionate share of income growth, the top 10% in the US increased their overall share of national income from 35% in 1980 to 50% in 2007.  Distribution became far more unequal in the US over this period, while in France it did not.

The data continue to 2012 for the US, but the results are the same within roundoff.  That is, the top 10% received 62% again of the increment of GDP between 1980 and 2012 while the bottom 90% only received 38%.  For France the data continue to 2009, but again the results are the same as for 1980 to 2007, within roundoff.

With this deterioration in distribution, the bottom 90% in the US saw their income grow at only half the rate for the economy as a whole.  The top 10% received most (62%) of the growth in GDP over this period.  In France, in contrast, the bottom 90% received close to a proportionate share of the income growth.  For those who make up the first 90%, economic performance and improvement in outcomes were better in France than in the US.  Only the top 10% fared better in the US.

D.  Other Factors Affecting Living Standards:  Social Services and Leisure Time

In absolute terms, even with the faster growth of real incomes of the bottom 90% in France relative to the US over this period, the bottom 90% in France came close to but were still a bit below US income levels in 2007.  They reached 98% of US income levels in that year, and then fell back some (in relative terms) with the start of the 2008 downturn.

But the calculations discussed above were based on applying distributional shares from tax return data to GDP figures.  For income earning comparisons, this is reasonable.  But living standards includes more than cash earnings.  In particular, one should take into account the impact on living standards of social services and leisure time.

Social services include services provided by or through the government, which are distributed to the population either equally or with a higher share going to the poorer elements in society.  An example of a service distributed equally would be health care services.  In France government supported health care services (largely provided via private providers such as doctors and hospitals) are made available to the entire population.  Since individual health care needs are largely similar for all, one would expect that the bottom 90% would receive approximately 90% of the benefit from such services, while the top 10% would receive about 10%.  If anything, the poor might receive a higher share, as their health conditions will on average likely be worse (and might account for why they are poor).  For other social services, such as housing allowances or unemployment compensation, more than 90% will likely accrue to the bottom 90%.

Taking such services into account, the bottom 90% in France will be receiving more than the 67% share of income (in 2007) seen in tax return data.  How much more I cannot calculate as I do not have the data.  The direction of change would be the same in the US.  However, one would expect a much lower impact in the US than in France because social services provided by or through the government are much more limited in the US than in France.  While Medicare provides similar health care as one finds in France, Medicare in the US is limited to those over 65, while government supported health care in France goes to the entire population.  And the social safety net, focussed on the poor and middle classes, is much more limited in the US than in France.

In addition, economists recognize that GDP per capita is a only crude measure of living standards as it does not take into account how many hours each individual must work to obtain that income.  Your living standard is higher if you can earn the same income but work fewer hours as someone else to receive that income, as the remaining time can be spent on leisure.  And there is nothing irrational to choose to work 10% fewer hours a year, say, even though your annual income would then be 10% less.  The work / leisure tradeoff is a choice to be made.

GDP per capita may often be the best measure available due to lack of data on working hours, but for the US and France such data are available (and are provided in the TED database referred to previously).  One can then calculate GDP per hour of work instead of GDP per capita, both overall and (using the same distributional data as above) for the bottom 90%.  The resulting graph for 1980 to 2012 is as follows:

France vs US, 1980-2012, GDP per hour overall and of bottom 90% (Autosaved)

By this measure, overall GDP per hour of work in France was similar to that of the US in the 1990s, but somewhat less before and after.  Overall GDP per capita was always higher in the US over this full period (the top graph in this post), and by a substantial 20% (in 1980) to 38% (in 2012).  Yet GDP per hour worked never varied by so much, and indeed in some years was slightly higher in France than in the US.

But for the bottom 90%, income received per hour of work has been far better in France than in the US since 1983.  By 2007, GDP per hour worked was 30% higher in France than in the US for the bottom 90%.  This is not a small difference.  French workers are productive, and take part of their higher productivity per hour in more annual leisure time than their US counterparts do.

E.  Summary and Conclusions

The French economic record has been much criticized by conservative media and politicians in the US, with France seen as a stagnant, socialist, state.  Overall GDP per capita has indeed grown faster in recent decades in the US than in France, averaging 2.0% per annum in the US vs. a rate of 1.5% in France.  While such a difference in rates might appear to be small, it compounds over time.

But the picture is quite different if one focusses on the bottom 90%.  This is not a small segment of the population, but rather everyone from the poor up to all but the quite well off.  Growth in average real income of this group was substantially faster in France than in the US since 1980.  While overall growth was faster in the US than in France, most of this income growth went to the top 10% in the US, while the gains were shared more equally in France.

Furthermore, when one takes into account social services, which are more equally distributed than taxable income and which are much more important in France than in the US, as well as leisure time, the real living standards of the bottom 90% have not only grown faster in France, but have substantially surpassed that of the US.

For those other than those fortunate enough to be in the top 10%, living standards are now higher, and have improved by more in recent decades, in France than in the US.

Contribution to GDP Growth of the Change in Inventories: Econ 101

This is the first post in a series that I will label “Econ 101”.  Their purpose will be to explain some economic concept that might generally not be clear to many, yet often appears (and often incorrectly) in news reports or other items that readers of this blog might see.  This first Econ 101 post is on how changes in private inventories enter into the National Income and Product (GDP) accounts, where there is often confusion on the contribution of rising or falling inventories to the growth of GDP.

In the most recent (December 22) release by the government of the GDP accounts in the third quarter of 2011, growth in overall GDP was an estimated (and disappointing) 1.8%. But many news reports stated that private inventories fell, and that had these inventories not changed, GDP growth would have been 1.4% points higher, or a more respectable 3.2%.  Yet when one looks at the underlying GDP figures issued by the BEA (the Bureau of Economic Analysis, US Department of Commerce), one sees that the change in private inventories was essentially zero (and in fact was slightly positive).  If inventories did not fall, why did many commentators state that a fall in inventories reduced GDP growth in the quarter?

The confusion arises because while the GDP (Gross Domestic Product) accounts measure the flow of production (how much was produced during some period of time), and the flow of how much was then sold (e.g. for consumption or investment), inventories are a stock, and it is the change in the stock of inventories that enters into the GDP accounts.  GDP is the flow of goods and services produced in the economy, and these goods and services are then sold for various purposes, including private consumption, private fixed investment, government consumption and investment, and exports, with imports also a supply of goods that can be sold.  But goods produced in some period will not necessarily match goods sold in that same period.  The difference is accounted for by either a rise or a fall in inventories.  Hence the change in the stock of inventories, when added to final sales (with imports entering as a negative), will equal total goods and services produced, which is GDP.

From one period to the next, we are normally interested in how much GDP rose or fell in that period compared to the previous one.  And we are interested in seeing how much of that growth in GDP will match up with and can be accounted for by growth of consumption, investment, and other elements of final sales.  These demand components are important, particularly in the economy as it is now.  With high unemployment and production well less than capacity, production of goods and services is driven by the demand for them.  Hence one is looking at the change in consumption or fixed investment or government expenditures from one period to the next.  And as the balancing item between GDP production and final sales, one would now be looking at the change in the change in inventories.

The term “the change in the change in inventories”  is a mouthful, and not often seen in news reports (indeed, I have never seen it used).  But that is what then leads to the confusion.  In the third quarter of 2011 (in the estimates released by the BEA on December 22), the change in private inventories was essentially zero, as noted above.  But there had been some positive growth in private inventories in the second quarter of 2011. Hence, the change in the change in inventories, going from something positive to essentially zero, was negative.  That is, if inventories had continued to increase in the third quarter of 2011 as much as they had in the second quarter, GDP growth would not have been 1.8% but rather would have been 3.2%.  The change in the change in inventories meant GDP growth was 1.4% points less than what it otherwise would have been.

The point can perhaps best be illustrated by some simple numerical examples.  Suppose for some fictitious economy, that GDP (the production of goods and services) is initially 1000 (in, say, billions of dollars), while the total of final sales (for consumption, fixed investment, and so on) is 950.  With production of 1000 and sales of 950, inventories will increase by 50.  Assume the stock of inventories at the start of the period is 500, so the stock will total 550 (50 more) by the end of the period.  The figures are as in this table:

Period 1 Period 2 Change % Change
GDP 1000 1050 +50 5%
  Change in Inventories  50  80 +30 3.0% points
  Final Sales 950 970 +20 2.0% points
Stock of Inventories:
    Start 500 550
    End 550 630
In the second period, suppose that production (GDP) increases by 50, or 5%, to 1050, while final sales only grow by 20, to 970.  The difference between production and sales must accumulate in inventories, so the change in inventories will now be 80.  Therefore, the change in the change in inventories will be 30 ( =80-50), and the contributions to the 5% growth in GDP will be 2.0% points from the change in final sales, and 3.0% points from the change in the change in inventories.  It is also worth noting that the stock of inventories has now grown to 630 by the end of the second period, which is substantially higher as a share of GDP or of final sales than it was at the start of period 1.  Hence, there is reason to assume that producers will likely scale back production (GDP) in the near future as long as final sales growth remains so sluggish, as there is likely little reason to accumulate even more unsold inventories on the shelves.
The second example will illustrate the case where inventories continue to rise, but at a slower pace than in the first period:
Period 1 Period 2 Change % Change
GDP 1000 990 -10 -1%
  Change in Inventories  50  20 -30 -3.0% points
  Final Sales 950 970 +20 +2.0% points
Stock of Inventories:
    Start 500 550
    End 550 570
In this example, final sales still grows by 20 to 970.  But producers here have scaled back production to just 990, or 1% below what it had been, with inventories now growing by just 20 rather than the 80 of the first example.  The change in inventories is still positive (at +20), but the change in the change in inventories is now negative, at -30.  The contributions to the -1% growth in GDP growth is made up of +2.0% points from final sales, and -3.0% points from the change in the change in private inventories.
As a final example, we will look at a case where the change in private inventories is negative.
Period 1 Period 2 Change % Change
GDP 1000 1050 +50 5%
  Change in Inventories -50 -20 +30 +3.0% points
  Final Sales 1050 1070 +20 +2.0% points
Stock of Inventories:
    Start 500 450
    End 450 430
Final sales once again grows by 20, although now from 1050 to 1070.  Sales is greater than production in each period, and inventories are drawn down by 50 in the first period and by 20 in the second period.  But while the change in inventories is negative in each period, that change is less negative in the second period than it is in the first.  That is, the change in the change in inventories is a positive 30, and this accounts for 3.0% points of the 5% growth in GDP.  It is also valuable to note that with inventories falling in each period, the total stock of inventories by the end of the second period is getting fairly low, so it is reasonable to expect that producers will aim to replenish inventories in future periods, with this then acting as a spur to growth.
Such swings in inventories are often important when economic growth is turning around, as at the start of a recovery from a downturn, or at the start of a downturn following a boom.  An example is seen at the end of the most recent recession, in the middle of 2009. The economy was in a state of collapse in 2008, the last year of the Bush Administration, and this fall carried over into the first half of 2009.  This downturn was then halted and reversed as a result of the policies implemented at the start of the Obama Administration. GDP was falling at a huge 8.9% annual rate in the last quarter of 2008, and at a still very high 6.7% rate in the first quarter of 2009.  Growth was then still negative, but at only a 0.7% rate, in the second quarter of 2009, and then started to grow at a 1.7% rate in the third quarter, and at a 3.8% rate in the fourth quarter.
The change in private inventories was negative in each quarter throughout this period. Specifically, private inventories fell by $200.5 billion in the second quarter of 2009, fell again by $197.1 billion in the third quarter, and fell again by a further $66.1 billion in the fourth quarter.  But the change in the change in private inventories was positive in the third and fourth quarters (while negative in each, they were becoming less negative), and this then accounted for a positive 0.2% points of the 1.7% growth in GDP in the third quarter, and a strong 3.9% points of the 3.8% growth in the fourth quarter (when final sales in fact declined slightly, accounting for a -0.1% contribution to growth in that period).
To summarize:  As everyone knows from their first Econ 101 class in Macroeconomics, GDP is equal to Consumption + Investment + Government Spending + Net Exports (Exports minus Imports), where total Investment is equal to Fixed Investment plus the Change in Inventories.  The change in GDP will therefore equal the change in Consumption + the change in Investment + the change in Government Spending + the change in Net Exports, where the change in Investment will equal the change in Fixed Investment plus the change in the Change in Inventories.

The Worsening Distribution of Income: The Top 1% vs. the Other 99%

The Occupy Wall Street and related groups have brought to the fore concerns about the distribution of income in the US.  And there is validity to these concerns, as the distribution of income has deteriorated markedly in recent decades, starting with the Reagan period in the 1980s.  The share of the top 1% has well more than doubled, to a level not seen since the 1920’s just prior to the Great Depression, with this coming out of a declining share of the bottom 90%.

The graph above is taken from material assembled by Professor Emmanuel Saez and his colleagues and co-authors, and is available through his web site (link here). The specific data here is from a July 2010 update of material originally published by Professor Saez with Professor Thomas Piketty in 2003, with data now through 2008.  Professor Saez and colleagues from around the world have assembled an amazing set of data for a large number of countries, using tax return data to produce long time series of family (tax unit) income levels.  Such data can go much farther back in time than available income surveys will allow, to produce historical data otherwise unavailable.  While there are drawbacks (for example, tax units are not always the same as the household units one would prefer), nothing else can span such periods of time.

The US data show that there was a previous boom in the 1920s in the income share of the top 1%, peaking at 23.9% of national income in 1928.  But the share then fell in the Great Depression and during World War II, and with the reforms and structural changes implemented during that period, eventually came to about a 10% share in the 1950s and 1960s, and to about 9% in the 1970s.  But it then started to grow, and reached a 23.5% share in 2007.  It fell back in 2008, with the onset of the financial collapse in the last year of the Bush Administration.  But as noted in this blog posting of November 26 on this site, profits rebounded in 2009 to now, so it is likely the top 1% share has also rebounded.

It is also interesting that this increase in income concentration is essentially only at the very top:  the top 1% is seeing almost all of the increase.  Those in the 90 to 95% percentile, and in the 95 to 99% percentile, have seen some trend rise in their income shares in recent decades, but they have been close to flat.  Rather, the higher share of the top 1% has come out of a falling share of the bottom 90% (the shares of all groups together must sum to 100%).  The share of the bottom 90% has dipped to about 50%, from a level of about 65% (plus of minus a percentage point or two) from 1942 to 1982.  This is a remarkable change after four decades of such stability.

Another interesting calculation, done by Professor Saez, found that 52% of the increase in real national income between 1993 and 2008 accrued to the top 1% of families, even though this top 1% only had a 14% share of national income at the start of this period (and 21% at the end).  That is, more than half of income growth over this 15 year period went to just the top 1% of the population, and where this group accounted for only a 14% share of income at the beginning.  Had the growth been balanced, the top 1% would have obtained 14% of the income growth, not 52% of it.

The data here does not explain why there has been this increasing concentration of income in the US.  One has also seen increasing concentration in other countries around the world in recent decades (the data is available here), which suggests some basic global structural changes are part of the cause.  But the global pattern has not been as extreme as in the US, suggesting that policy changes in the US begun under Reagan and continued since, are also part of the cause.