Income inequality for households: a long biased history of Gini ratio. 2021 revision

The Census Bureau measures incomes and reports the estimates. One of the main questions is income inequality – personal and households. We published a post in 2012 on the bias in the household Gini ratio. Here we revise the previous study with new data.

In 2012, our first point was that the Gini ratio for personal incomes reported by the Census Bureau from the very same data set (CPS ASEC conducted every March) does not change much since 1994. The upper panel in Figure 1 reproduces the Gini ratio from the previous post, which varies from 0.494 to 0.512 – a relatively narrow window. In the lower panel, the dataset is extended to 2019 and the rise from 0.494 in 2007 to 0.524 in 2013 is a challenge for an economic explanation. This is a catastrophic and unexpected change in inequality. The years after the Great Recession (say, after 2010 with G=0.503) were not characterized by some outstanding economic processes or events. These were the years of President Obama.

In another post on January 17, 2021, we reported an unprecedented fall in the share of “compensation of employees” in the total personal income (PI) as reported by the Bureau of Economic Analysis. Figure 2 presents the corresponding curve, which demonstrates the accelerated decrease from 0.660 in 2006Q3 to 0.614 in 2016Q3. The 0.046 drop in the share of income from jobs reported by BEA is synchronized with the personal Gini rise by 0.03. The trough in 2020 related to the COVID-19 pandemic may be an interesting economic experiment for income distribution. Personal income in 2020 does not change much (even increase in Q2 and Q3 against pre-crisis expectations) despite the drop in compensation of employees. The question is where we will find the government social benefits to persons (+2.5 trillion in Q2 and +1 trillion in Q3 compared to the previous year). My current guess – stock market.

Figure 1.  Personal incomes:  Upper panel: Gini ratio evolution between 1994 and 2010 as presented in this post. Lower panel. Gini ratio evolution between 1994 and 2019. Between 2007 and 2013 the Gini ratio raised from 0.494 to 0.524, i.e. by 0.03.

Figure 2.  Ratio of compensation of employees and Personal Income (BEA. Table 2.1. Personal Income and Its Disposition). Quarterly data.  The fall 0.655 in the third quarter of 2006 to 0.614 in 2016Q3.

The upper panel in Figure 3 is borrowed from the previous post and shows the history of Gini ratio for households between 1967 and 2010. (The lower panel extends the period to 2019). We normalized the ratio to its maximum value (0.477 in 2011) in order to show that this inequality measure had risen by 20% since 1967. This dramatic increase was interpreted as harm for the US. In my view, this is just a misunderstanding of the income measurement procedures. Unlike personal incomes, the household income data are collected for entities that can evolve in size in all directions. There are two limit cases: 1) all households may have just one person and then the household Gini is fully equivalent to the personal Gini, which is higher as we can learn from Figure 1; 2) all people represent one household and then the Gini is 0 because there is no inequality for 1 object. For a given personal income distribution, any other combination of people gathering in households should give the Gini between 0 and the personal Gini. Reconfiguring the households’ sizes and personal content for the same population one may change the Gini for the household incomes without changing personal incomes. Therefore, the split of the population into households defines the Gini for a given population and time point. The distribution of the increasing number of people among households, i.e. the distribution of household sizes, and the personal income distribution are changing in time, and the household Gini is evolving in sync with these changes. The Census Bureau’s approach is straightforward – they measure the distribution of the household incomes and calculate the Gini ratio. This ratio is incompatible with the previous years since the distribution of household sizes is changing. Moreover, it is changing in the direction of the split of bigger households into smaller pieces, eventually into the single-person-households. Hence, the household income distribution approaches the personal income distribution and this must be accompanied by an artificial increase in the Gini ratio. This increase is reported as a big problem of American households. This is a definitional problem, however, and has no relevance to real changes in income distribution illustrated in the lower panel in Figure 1.   

The Census Bureau does not explicitly report the distribution of household sizes (in persons) and one has to make an own estimate, which is easy, however. Figure 4 presents (old and new) the total household population (different from the civil population or residential population) and the number of households reported by the CB.  Figure 5 depicts (old and new) the evolution of the average household size since 1967. Actually, it was quite spectacular: from 3.2 in 1967 to 2.49 in 2011. Between 2010 and 2019, the mean household size hovered around the 2.5 level. This constant mean size could be interpreted as the constant household size distribution between 2000 and 2019.

Does it matter for the household income inequality?  As we discussed above, the Gini ratio depends on the size distribution of objects if these are not indivisible persons. Intuitively, more low-income (e.g. one person) households result in a higher Gini ratio. The fall in average size indicates that one gets more and more small households over time and … the Gini ratio increases accordingly. The link between the average household size and the Gini ratio is not linear (as we discussed before, many household size distributions have the same average size) but Figure 6 shows the (old and new) product of the normalized Gini curve for households (see Figure 3) and the curve in Figure 5. This product is an approximation of the constant 1967 household size distribution as if all people in every year after 1967 were distributed in the same household size structure as in 1967. This product compensates the size distribution change but does not compensate the income change in the households, i.e. we do not compensate the process of income gain or loss in the households with time, and we do know that the income distribution for a given household size has been changing with time (see these posts). In Figure 6, we see a corrected (and likely closer to reality) Gini history.  This corrected normalized Gini is not fully compensated for the household size changeover time but tells a different story.

The original Gini ratio for households corrected to the change in the household size distribution is depicted in Figure 7. In 2019, the level is the same as in 1967 – 0.397. The positive shift from 1992 (0.358) to 1993 (0.378) is completely artificial. In 1993, there was a revision to income definition and all time series were subject to dramatic changes. Therefore, the current level is below that in 1967 if to use the 1967 household income definition.

Overall, the Gini ratio for households has not been changing as the CB estimate says because these estimates do not take into account the change in the household size distribution.

As we wrote in 2012, this is a methodological error.  The same logic must be applied to family income distribution.  Another sufferer is the mean income.  Since the size of households has been decreasing the number of households has been growing faster than the total household population.  The mean household income must also be corrected for the changing size.  Figure 8 shows the actual evolution of the mean income.  There was a period of constant mean income between 1996 and 2013 with no significant change in the average household size. Since 2014, the mean income curve has been demonstrating tangible growth.

Figure 3. The evolution of normalized Gini ratio for households. Old and new versions

Figure 4. The evolution of the total household population and the number of households (both in thousands)

Figure 5. The evolution of average household size.

Figure 6. Corrected normalized (see Figure 3) Gini ratio.

Figure 7. Original (Census Bureau) Gini ratio corrected to the change in household size distribution. In 2019, the level is the same as in 1967 – 0.397. The positive shift in 1992 (0.358) to 1993 (0.378) is completely artificial. Therefore, the current level is below that in 1967.

 

Figure 8. The growth of normalized (household) mean income and that corrected for the fall in the household average size.

Why post-Soviet countries oriented to West fail economically?

The Maddison Project Database and the Total Economy Database of the Conference Board both provide estimates of real GDP per capita, GDPpc, in almost all countries worldwide. The GDPpc is the key parameter to describe the level of economic development in a given country. We described the process of the transition from socialism to capitalism in the post-Soviets courtiers (see this post) using the GDPpc published by the Total Economy Database and concluded that the transition finished around 2003 to 2005 with the following growth along the capitalist path. The prediction was that the former socialist countries have to grow at a rate of 5% to 10% per year depending on the initial level of GDPpc. We compared many times the evolution of GDPpc in Russia and Ukraine and reported that Russia has been following the predicted growth trajectory while Ukraine is still below its peak level in 1989. The main problem is that this gap cannot be closed and will last for decades. One can conclude that, in economic terms, Ukraine is a failed economy.

Since Ukraine was not able to reach a stable growth trajectory it is interesting to revisit other post-Soviet countries with tight political links to the West: Georgia and Moldova. Figure 1 shows that these two economies also failed: Georgia is still below the 1988 level and Moldova reached it in 2019. There are no signs that these two countries and Ukraine will be able to grow in the future at a rate corresponding to the level of GDPpc. One of the reasons is that they gave up all competitive advantages.

Kyrgyz Republic is an example of a failed economy as well. The political turmoil can never assist economic growth and there is a large probability that the political conditions will prohibit economic growth in the future.  

Armenia is an example of successful growth after the deep fall in the mid-90s. In 2019, it more than doubled the peak level in 1989. Azerbaijan has been also growing along a steep trajectory between 2003 and 2008, but the Great Recession and oil price fall effectively stopped the growth at the level of $15,000 per head as observed between 2009 and 2019.

There are two most positive examples of the countries with multi-vector foreign policies – Kazakhstan and Belorussia. Both countries retained their industrial and human capital and have been growing along the projected trajectories of economic growth. Belorussia stopped growing due to internal and external political problems but it has full potential to restart the growth process.

Economic problems have very specific character – one cannot regain the lost time – economic losses are irreversible. These losses most negative for the poor part of the population since social guarantees and benefits are not funded appropriately. I would say – do no mess with West – if the failed economies had economic and political hardship with the Western Economic Powers. But Georgia, Moldova and Ukraine consider themselves as allies of the West.

So, better to say – do not make friends with West.

Estonia, Lithuania and Latvia are not examples of economic success for the other post-Soviet countries. They are fully sponsored by the EU and no economic problems of the other countries can disturb them. Moreover, a larger part of the economically active population from these three countris has moved to the west.

   

Watching blockbuster "Biden"

In the absence of blockbusters, I am going to watch Biden following his promises

1. COVID-19. 100 million by 100 days (my estimate of success probability 30%). Reason - there are forces interested in failure. Thrilling

2. Taxes. Increasing taxes will likely suppress the US economy, which is not in a good shape already. China fills the US markets.

3. Iran deal. I have no clue what may happen - all actors have positions different from the original.

4. 75,000,000 for-Trump voters, which are real persons with families. I expect that some of Biden supporters will try to punish them. It will be a fantastic show. The whole world prepares popcorn: Rocky Balboa vs Ivan Drago.  

5. Police and army. These people are mainly conservative and it is not clear why they have to shot their own legs

6. The US demonstrated the clear weakness of institutes and the recovery can be only kind of overshooting, i.e. a mid-size war. Hopefully, Russia is not the goal

Berlin authorities placed children with pedophiles for 30 years

 

Deutsche Welle: Berlin authorities placed children with pedophiles for 30 years

Crimea. The difference between the OECD and TED/MPD political approaches to economic statistics.

 Following our previous post comparing Russia and Ukraine, we have checked the Russian population estimates from the Total Economic Database of the Conference Board and the OECD.  Comprehensive data comparison includes population counting because it is used in the per capita estimates, which is an economic variable best describing the real economic performance of a given economy. As we reported, the TED likely includes the population of Crimea in the total Russian population. Figure 1 shows the evolution of the total population in Russia since 1960. There is a significant step in the total population between 2013 and 2014, i.e. the year of Crimea reunification with the Russian Federation.

Since we compare three major sources of economic information (TED, Maddison Project Database, and the OECD) it is instructive to present the OECD estimates. As mentioned before, the MPD and TED population estimates for Russia are identical.  In Figure 1,   includes the OECD population estimates for Russia and demonstrates the deviation from the TED in 2014. There was no reunification according to the OECD. It is another example of a political approach to economic data. As more independent economic sources, the TED and MPD demonstrate an economic approach to economic parameters. The OECD is driven by the policies of the state parties of the organization. At the same time, the MPD and TED do not exclude Crimea from the Ukrainian economy. The OECD does not publish data for Ukraine but one can suggest that the Crimean economy is somehow included in the Ukrainian economic statistics. Such an approach would introduce significant bias in the Ukrainian economic statistics due to the large differences in economic performance. Figure 2 presents the annual population increment and highlight the spike in 2014 in the TED data.

 

Figure 1. Total population in Russia since 1960. There is a step from 2013 to 2014 in the TED estimate.

 

Figure 2. Annual population increment in Russia as reported by the TED and OECD.

Real GDP and population in Russia and Ukraine as reported by the Maddison Project Database and the Total Economy Database. Crimea

 In one of the previous posts, we compared the evolution of real GDP per head in Russia and Ukraine since 1980. Data obtained from the Maddison Project Database (MPD) were used. After the detailed comparison of the data from the MPD, Total Economy Database, and the OECD, we found that the GDP per capita estimates can differ dramatically between these sources. In order to get a less biased comparison, we have to revisit our estimates and extend the set of data sources. Since Ukraine is not included in the OECD database, we limit comparison in this post to the TED and MPD.

Figure 1 compares the real GDP per capita evolution in Russia and Ukraine as reported by the TED and MPD. The reference years are different, and thus, the levels also differ between these two sources. The normalization to the corresponding levels in 1980 allows a comparison of the relative growth. Figure 2 displays the normalized curves. The relative performance of Russia and Ukraine is quite different for the MPD (large difference) and the TED (much smaller difference). Figure 3 displays the ratio of GDP per capita in Russia and Ukraine for these two sources. The TED gives the total difference of 1.4 in 2018 and the MPD estimates the same difference as 1.74. Such discrepancy is commonplace in the MPD and TED data. It is difficult to judge which estimate is more accurate: the TED usually gives larger growth to the USA and the MPD more generous to European countries. Both could be biased.

The Maddison Project Database does not explicitly provide the real GDP estimate. The TED includes such an economic series among many others. Figure 4 depicts the real GDP in Russia and Ukraine between 1970 and 2019 (in 2019 $). The ratio of the GDPs reaches 7.44 in 2019, but evolution is also important for the relative performance comparison.  Figure 5 presents the same curves as in Figure 4, but normalized to their respective levels in 1970. One can see that the current real GDP in Ukraine is just 61% of the peak level in 1989. This low value is partly explained by the falling population in Ukraine. Figure 6 shows the evolution of the population in Russia and Ukraine between 1970 and 2019 as reported by the TED. Both time series are normalized to the 1970 levels. There is a significant increase in the Russian population from 2013 to 2014 and a similar but much smaller decrease in the Ukrainian population. The increment is 2,594,000 and the fall in Ukraine lasts a few years with a total of ~1,000,000 between 2013 and 2016. It looks like the TED added the Crimean population to Russia but did not subtract it from Ukraine. The MPD population estimates are identical to the TED.   

Figure 1. Comparison of real GDP per capita evolution in Russia and Ukraine as reported by the TED and MPD. The reference years are different and thus the levels also differ between these two sources.

Figure 2. The relative performance of Russia and Ukraine as represented the GDP pc curves normalized to their levels in 1980. 

Figure 3. The ratio of GDP per capita in Russia and Ukraine for the TED  and MPD data

Figure 4. Real GDP in Russia and Ukraine between 1970 and 2019 as reported by the TED.

Figure 5. Same as Figure 4, but normalized to the 1970 levels. 

Figure 6. The evolution of the population in Russia and Ukraine between 1970 and 2019 as reported by the TED.