inequality

Jordan Peterson’s remarks on UBI

Published by Anonymous (not verified) on Fri, 22/09/2017 - 4:00pm in

Jordan Peterson, cultural critic, psychologist, and member of the Self Authoring online service, gave his remarks on Universal Basic Income. His concerns seem to be largely drawn from a similar issue critics have with the idea, primarily in the face of leisure time: will people become lazy and unmotivated? Can people handle a life with none of the traditional burdens

The post Jordan Peterson’s remarks on UBI appeared first on BIEN.

Inequality Is Probably Costing You a Lot of Money

Published by Anonymous (not verified) on Thu, 21/09/2017 - 1:47am in

This post originally appeared at Talk Poverty.

When political scientists Jacob Hacker and Paul Pierson released Winner-Take-All Politics in March 2011, it made headlines. The book’s vivid descriptions of how moneyed interests had come to dominate the Washington political scene captured media attention and helped shape conversations around public policies affecting economic inequality.

On Winner-Take-All Politics

January 13, 2012

But while Winner-Take-All Politics got a lot of attention, the media missed a crucial part of the book: Rising inequality comes at a high cost to individual workers. In the book, Hacker and Pierson presented calculations showing that if inequality had stayed constant from 1979 to 2006, the bottom 90 percent of Americans would make up to 36 percent more per year than they currently do.

Half a decade later, inequality is still growing. It also still isn’t getting the media attention it deserves, even though it’s making a massive impact on Americans’ lives. It’s like climate change: There is nothing “new” about growing inequality, so it gets pushed out of the news in favor of White House scandals and presidential tweets. But just like global warming, economic inequality is slowly but surely destroying the livelihoods of many Americans.

You can see this quite clearly when you look at how the distribution of household income has changed over the past 50 years. I extended Hacker and Pierson’s original calculations to include incomes from 1968 to 2015, giving us about two decades’ worth of additional data beyond other recent calculations. The wider timeframe shows an even deeper decline in income than the authors originally reported.

The table below breaks this down by income bracket. The second column shows what each group’s average household income was in 2015; the third column shows what the group’s average income would have been if inequality had stayed the same between 1968 and 2015.

 Author’s calculations based on 2016 data from the US Census Bureau.

Source: Author’s calculations based on 2016 data from the US Census Bureau.

 
If it weren’t for the increase in inequality, the bottom 40 percent of households would be making more than 35 percent more today.

The ‘winners’ from increased inequality are really a small group of incredibly rich Americans.

The gains, of course, have gone to the very wealthiest Americans — especially those in the top 5 percent. Due to the rise in inequality, higher-income — those in the top 20 percent of the income distribution but not in the top 5 percent — have seen a 9 percent increase in their annual incomes. But incomes for households in the top 5 percent are 26 percent higher — an increase nearly three times as great. This reveals something important about the nature of rising inequality: The “winners” from increased inequality are really a small group of incredibly rich Americans, who are taking increasingly large shares of the total national income.

The findings are pretty difficult to refute. Conservatives have long argued that household income statistics are unreliable because they fail to account for differences in household size. But the increase in inequality appears just as real even when we look at “equivalence-adjusted income shares,” which control for differences in household size and composition.

In fact, the figure below shows that households in the bottom 40 percent of the income distribution have actually seen their share of national income decline more when we use the equivalence-adjusted household income that addresses conservatives’ concerns.

The poorest fifth of households saw their share of national income decline from 4.2 percent in 1968 to 3.1 percent in 2015, a drop of 1.1 percentage points. However, if we instead look at equivalence-adjusted income, their share of the national income dropped more than twice as much (from 5.8 percent to 3.4 percent, a drop of 2.4 percentage points). Conservatives are right to say that normal household income statistics can be misleading; but that’s because the normal statistics under state the rise in inequality, not because they overstate it.

The rise in inequality is no statistical mirage. It is undoubtedly real — and its effects have been pernicious. Our country’s poorest households lose more than $4,000 every year as a result of the growth in inequality; lower-income families lose more than $11,000; and middle-class families lose around $13,000. That money could pay for real things that families have to do without, whether it’s better food or new shoes, a trip to the doctor or a great summer camp.

If the rise of economic inequality is going to be the great untold story of our time, then reducing inequality should be the greatest progressive objective of the 21st century.

The post Inequality Is Probably Costing You a Lot of Money appeared first on BillMoyers.com.

The Myths Of Recovery: Why American Households Aren’t Better Off

Published by Anonymous (not verified) on Thu, 21/09/2017 - 1:00am in

Above Photo: Workers pack and ship customer orders at the 750,000-square-foot Amazon fulfillment center on August 1, 2017 in Romeoville, Illinois. The stagnation of real incomes from 1999 through today is structural, not cyclical Off the top, the figures published by the U.S. Census Bureau on Tuesday are encouraging: • Median household income rose to $59,039, the second straight gain; • The percentage of people in poverty fell to 12.7%, returning to around pre-recession levels; • The supplementary poverty measure also fell, to 13.9%; • The percentage of people without health insurance coverage fell to 8.8%. The excitement of some analysts reporting these as a major breakthrough along the trend is understandable, as notionally, 2016 U.S. median household income has finally surpassed the previous peak, recorded in 1999. Back then, median household income (adjusted for official inflation) stood at $58,665 and at the end of 2016 it registered $59,039. Opinion: It turns out that Obama got a bum rap on the economy As this chart clearly illustrates, notionally, we are in the “new historical peak” territory. Alas, notional is not the same as tangible. And here are the reason why the tangible matters probably more than the notional: 1) Consider the following simple timing observation: real incomes took 17 years to recover from the 2000-2012 collapse. And the Great Recession, officially, accounted for only $4,031 in total decline of the total peak-to-trough drop of $5,334. Which puts things into a different framework altogether: the stagnation of real incomes from 1999 through today is structural, not cyclical. The “good news” are really of little consolation for people who endured almost two decades of zero growth in real incomes: their life-cycle incomes, pensions, wealth are permanently damaged and cannot be repaired within their lifetimes. 2) The Census Bureau data shows that bulk of the gains in real income in 2016 has been down to one factor: higher employment. In other words, hours worked rose, but wages did not. American median householders are working harder at more jobs to earn an increase in wages. Which would be OK, were it not down to the fact that working harder means higher expenditure on income-related necessities, such as commuting costs, child-care costs, costs for caring for the dependents, etc. In other words, to earn that extra income, households today have to spend more money than they did back in the 1990s. Now, I don’t know about you, but for my household, if we have to spend more money to earn more money, I would be looking at net increases from that spending, not gross. Census Bureau does not adjust for this. There is an added caveat to this: caring for children and dependents has become excruciatingly more expensive over the years, since 1999. Inflation figures reflect that, but the real income deflator takes the average/median basket of consumers in calculating inflation adjustment. However, households gaining new additional jobs are not average/median households to begin with — and most certainly not in 2016, when labor markets were tight. In other words, the median household today is more impacted by higher inflation costs pertaining to necessary non-discretionary expenditures than the median household in 1999. Without adjusting for this, notional Census Bureau figures misstate (to the upside) current income gains. 3) In 1999, the Census Bureau data on household incomes used a different methodology than it does today. The methodology changed in 2013, at which point in time, the Census Bureau estimated that 2013 median income was about $1,700 higher based on new methodology than under pre-2013 methodology. Since then, we had no updates on this adjustment, so the gap could have actually increased. Tuesday’s numbers show that median household income at the end of 2016 was only $374 higher than in 1999. In other words, it was most likely around $1,330 or so lower, not higher, under the pre-2013 methodology. Taking a very simplistic (most likely inaccurate, but somewhat indicative) adjustment for 2013-pre-post differences in methodologies, the current 2016 reading is roughly 1.6% lower than the 2007 local peak, and roughly 2.3% lower than the 1999-2000 level. 4) Costs and taxes do matter, but they do not figure in the Census Bureau statistic. Quite frankly, it is idiotic to assume that gross median income matters to anyone. What matters is after-tax income net of the cost of necessities required to earn that income. Now, consider a simple fact: in 1999, a majority of jobs in the U.S. were normal working-hours contracts. Today, a huge number are zero-hours and gig-economy jobs. The former implied regular and often subsidized demand for transport, childcare, food associated with work etc. The latter implies irregular (including peak hours) transport, childcare, food and other services demand. The former was cheaper. The latter is costlier. To earn the same dollar in traditional employment is not the same as to earn a dollar in the gig economy. Worse, taxes are asymmetric across two types of jobs too. The gig economy adds to this problem yet another dimension. Many gig-economy earners (e.g. Uber drivers, delivery & messenger services workers, or AirBnB hosts) use income to purchase assets they use in generating income. These are not reflected in the Census Bureau earnings, as the official figures do not net out cost of employment. 5) Finally, related to the above, there is higher degree of volatility in job-related earnings today than in 1999. And there are longer duration of unemployment spells in today’s economy than in the 1990s. Which means that the risk-adjusted dollar earned today requires more unadjusted dollars earned than in 1999. Guess what: Census Bureau statistics show not-risk-adjusted earnings. You might think of this as an academic argument, but we routinely accept (require) risk-adjusted returns in analyzing investment prospects. Why do we ignore tangible risk costs in labor income? The key point here is that any direct comparison between 1999 and 2016 in terms of median incomes is problematic at best. It is problematic in technical terms (methodological changes and CPI deflator changes), and it is problematic in incidence...

Cartoon of the day

Published by Anonymous (not verified) on Wed, 20/09/2017 - 10:00pm in

Income and geographic distribution of low-income renters in Toronto

Published by Anonymous (not verified) on Wed, 20/09/2017 - 5:16pm in

In this second of a series of housing-related posts I analyze the income and geographic distribution of renter-occupied households in the City of Toronto. My first post focussed on affordability and inequality trends by analyzing time series (2001-16) data for Ontario by household income quintiles. As a complement, this blog studies the income and geographic distribution of low-income and other renter households in Toronto based on census-tract (CT) data for 1996 and 2006. I expect to update and expand on this analysis after 2016 data is released later this year. This Toronto-specific analysis confirms the earlier provincial-level findings with respect to the broader structure and dynamics of the rental market. Based on this more disaggragate basis, I find that increased between-CT household income inequality is being driven by increases in inequality in owner households. The data shows significant income sorting by geography, so that higher (lower) income renters and owners tend to live in the same higher (lower) income CTs. Lower-income renters are concentrated in lower average income CTs, pay lower rents, but face a much higher rent burden. In subsequent posts I will update this analysis and discuss the policy implications and initiatives of these and other findings.

Census-tract rental data for Toronto

The Neighbourhood Change Research Partnership (NCRP) has been undertaking research on socio-spatial polarization trends in Canadian metropolitan areas for more than a decade. As part of this ongoing work, the NCRP purchased custom tabulations from Statistics Canada of census data at the CT level for a number of census metropolitan areas (CMA) and census years. The NCRP has kindly made the 1996 and 2006 tabulations available to me, including for the City of Toronto. The data includes over 520 CTs, which averaged about 1,725 and 1,865 households per CT in 1996 and 2006 (from 900,000 to 975,000 households in total), for an increase of just over 8% over the ten-year period. The number of renter households declined from 475,000 to 445,000 while owner household increased from 425,000 to 530,000 over the same period. Hence the proportion of renter households decreased from about 52% to 46% from 1996 to 2006.

The NCRP data tabulation is relatively detailed and includes average income for a a number of households per CT. However, the tabulation does not include quintile-specific income data. However, it does include disaggregate data for renters with a household income below 50% the median household income for the Toronto CMA (this measure is known as the Low Income Measure (LIM)). The number of LIM renter households was constant at around 200,000 over the period, which accounted for about 22% and 20% of all households and about 42% and 45% of all renters, respectively. For purposes of linking the current work to the quintile-based analysis of the first blog, I consider such LIM renters as approximating first quintile renters (in general, the LIM threshold is somewhat lower than the upper limit of the first quintile income group, but this is offset in this tabulation by renters not fully making up (70%) the first quintile or all households). Those “Other” renters with incomes above the LIM therefore approximate the renters in the second to fifth income quintiles. The number of Other renter households declined from 275,000 to 245,000 over the ten-yer period.

 

Renter Income Distribution

Table 1 includes average household income for renters and owners separately and for all households combined (in constant 2006 dollars) as well as the corresponding Gini coefficients. The table confirms that renter incomes are about half those of owners and that most average income gains over the 1996-2006 period accrued to owners. Between-CT income inequality increased over the period as well, as the corresponding Gini coefficient increased from 0.216 to 0.293. Table 1 shows that while between-CT owner income inequality increased (from 0.190 to 0.291), between-CT renter inequality decreased slightly (from 0.186 to 0.168), indicating that the overall between-CT increase in inequality was driven primarily by increases in between-CT owner inequality.

Figure 1 shows average household income for owners (green) and renters (blue) in each CT for 1996 and 2006, graphed against CT average household income (in constant 2006 dollars). The 1996 and 2006 trendlines for owners have very high R2, which indicates that there is a very strong correlation between owner and total income in each CT (this is expected at higher income CTs, given the generally very high proportion of owners in the CT). The shape and slope of the owner trendlines is very similar, suggesting that this correlation is relatively stable over time. The trendlines for renter households have relatively high R2, also suggesting a strong correlation. As a whole, Figure 1 shows that lower (higher) income renters tend to live in the same CTs as lower (higher) income owners. This shows that the well-known phenomenon of income sorting by geography by owner households is also applicable to renter households.

 

Renter Geographic Distribution

Figure 2 shows the percent of all renter (blue) and LIM renters (green) in each CT for 1996 and 2006, graphed against CT average household income, in constant 2006 dollars. For all renters and LIM renters the trendlines for both years show that the proportion of renter households decrease with average household income. As expected, the trendlines for LIM renters are below those of the the all renters, meaning that the former are more concentrated in lower-income CTs.

 

Table 2 shows the distribution of renters by CT income quintile for 1996 and 2006. An equal distribution would be 20% in each income quintile. However, Table 2 shows a considerable concentration in the lower quintile CTs, for example showing that 32% of all renters lived in the the first income quintile of CTs in 1996, increasing to 33% in 2006. However, the proportion of renters in the second quintile decreased from 24% to 22%, therefore lowering the concentration in that series of lower-income CTs. LIM Renters are even more heavily concentrated in the lower-income CTs, with 66% and 62% living on the first and second quintiles in 1996 and 2006, respectively. That decrease suggests lower concentration in lower-income CTs.

Table 3 provides the respective average and Gini coefficients for the the proportion of all and LIM renters and confirms that, overall, renters were indeed more unevenly distributed in 2006 compared to 1996 because the respective Gini coefficients increased from 0.275 to 0.304. As expected, the Gini for LIM renters declined somewhat from 1996 to 2006, indicating that they were less unevenly distributed.

 

Rent Expenditures and Rent Burden

Figure 3 shows the average rent paid by LIM renter households in each CT for 1996 and 2006, graphed against CT average household income (in constant 2006 dollars). In real terms, average LIM rents increased about 5% to about $775 per month. Figure 3 shows that rents generally increased with average CT income. Rents for Other renters (not shown) increased by about 1% to about $1,055 and thus tend to be about 40% higher than those for LIM renters. In my first post I noted that income cut-off data available for this analysis (such as quintile limits and LIM thresholds) does not adjust for household size and hence that there is an over-representation of smaller households in first quintile and LIM data. It is in this context that a significant proportion of the difference in rents paid by LIM versus Other renters may be explained by quantity differences (i.e. Other renters with an average of 2.45 persons/household, renting larger units than LIM renters with 1.90 an average of persons/household), with the residual rent difference being due to quality differences. I will explore this quantity/quality aspect of rent differences between LIM and Other renters in a subsequent blog.

 

Figure 4 presents LIM rents as a percent of household income for 1996 and 2006, graphed against CT average household income, in constant 2006 dollars. Other renters (not shown) paid a relatively steady average of about 19% of their income of rent for 1996 and 2006, suggesting these households geographically sort themselves by average CT income. On the other hand, LIM renters are generally struggling with rent, paying an average of about 57% of their income in 1996 and about 55% in 2006. This modest decrease is due to average real incomes increasing more (9%) than average rent (5%) from 1996 to 2006. This decrease is probably one of the main reasons that LIM renters become somewhat less concentrated in lower-income CTs over the period.

 

Concluding Thoughts

My first housing-related post presented provincial-level time-series data to conclude that over the 2001-2016 period rent expenditures for Ontario first quintile renter households increased faster than for other renters and exceeded income increases so that these households had to expend an increasing share of their income on rent. The current CT-level analysis for Toronto shows increased between-CT household income inequality is being driven by increases in inequality in owner households and significant income sorting by geography, so that higher (lower) income renters and owners tend to live in the same higher (lower) income CTs. Similarly, the proportion of LIM renters within CTs decreases as average renter incomes increase. The current work found that over the 1996-2006 period LIM rents also increased faster than for other renters, but that the average rent burden decreased slightly over the period because average incomes increased at a slightly faster rate. This slightly lower rent burden was one of the main reasons for slightly lower concentration of lower-income renters in low-income CTs. Statistics Canada released the income-related data from the 2016 Census last week, which suggests that the custom tabulation that corresponds to the current analysis may be available later this year. I look forward to being able to update this post with that data and discuss the policy implications and initiatives of these and other findings.

Income and geographic distribution of low-income renters in Toronto

Published by Anonymous (not verified) on Wed, 20/09/2017 - 5:16pm in

In this second of a series of housing-related posts I analyze the income and geographic distribution of renter-occupied households in the City of Toronto. My first post focussed on affordability and inequality trends by analyzing time series (2001-16) data for Ontario by household income quintiles. As a complement, this blog studies the income and geographic distribution of low-income and other renter households in Toronto based on census-tract (CT) data for 1996 and 2006. I expect to update and expand on this analysis after 2016 data is released later this year. This Toronto-specific analysis confirms the earlier provincial-level findings with respect to the broader structure and dynamics of the rental market. Based on this more disaggragate basis, I find that increased between-CT household income inequality is being driven by increases in inequality in owner households. The data shows significant income sorting by geography, so that higher (lower) income renters and owners tend to live in the same higher (lower) income CTs. Lower-income renters are concentrated in lower average income CTs, pay lower rents, but face a much higher rent burden. In subsequent posts I will update this analysis and discuss the policy implications and initiatives of these and other findings.

Census-tract rental data for Toronto

The Neighbourhood Change Research Partnership (NCRP) has been undertaking research on socio-spatial polarization trends in Canadian metropolitan areas for more than a decade. As part of this ongoing work, the NCRP purchased custom tabulations from Statistics Canada of census data at the CT level for a number of census metropolitan areas (CMA) and census years. The NCRP has kindly made the 1996 and 2006 tabulations available to me, including for the City of Toronto. The data includes over 520 CTs, which averaged about 1,725 and 1,865 households per CT in 1996 and 2006 (from 900,000 to 975,000 households in total), for an increase of just over 8% over the ten-year period. The number of renter households declined from 475,000 to 445,000 while owner household increased from 425,000 to 530,000 over the same period. Hence the proportion of renter households decreased from about 52% to 46% from 1996 to 2006.

The NCRP data tabulation is relatively detailed and includes average income for a a number of households per CT. However, the tabulation does not include quintile-specific income data. However, it does include disaggregate data for renters with a household income below 50% the median household income for the Toronto CMA (this measure is known as the Low Income Measure (LIM)). The number of LIM renter households was constant at around 200,000 over the period, which accounted for about 22% and 20% of all households and about 42% and 45% of all renters, respectively. For purposes of linking the current work to the quintile-based analysis of the first blog, I consider such LIM renters as approximating first quintile renters (in general, the LIM threshold is somewhat lower than the upper limit of the first quintile income group, but this is offset in this tabulation by renters not fully making up (70%) the first quintile or all households). Those “Other” renters with incomes above the LIM therefore approximate the renters in the second to fifth income quintiles. The number of Other renter households declined from 275,000 to 245,000 over the ten-yer period.

 

Renter Income Distribution

Table 1 includes average household income for renters and owners separately and for all households combined (in constant 2006 dollars) as well as the corresponding Gini coefficients. The table confirms that renter incomes are about half those of owners and that most average income gains over the 1996-2006 period accrued to owners. Between-CT income inequality increased over the period as well, as the corresponding Gini coefficient increased from 0.216 to 0.293. Table 1 shows that while between-CT owner income inequality increased (from 0.190 to 0.291), between-CT renter inequality decreased slightly (from 0.186 to 0.168), indicating that the overall between-CT increase in inequality was driven primarily by increases in between-CT owner inequality.

Figure 1 shows average household income for owners (green) and renters (blue) in each CT for 1996 and 2006, graphed against CT average household income (in constant 2006 dollars). The 1996 and 2006 trendlines for owners have very high R2, which indicates that there is a very strong correlation between owner and total income in each CT (this is expected at higher income CTs, given the generally very high proportion of owners in the CT). The shape and slope of the owner trendlines is very similar, suggesting that this correlation is relatively stable over time. The trendlines for renter households have relatively high R2, also suggesting a strong correlation. As a whole, Figure 1 shows that lower (higher) income renters tend to live in the same CTs as lower (higher) income owners. This shows that the well-known phenomenon of income sorting by geography by owner households is also applicable to renter households.

 

Renter Geographic Distribution

Figure 2 shows the percent of all renter (blue) and LIM renters (green) in each CT for 1996 and 2006, graphed against CT average household income, in constant 2006 dollars. For all renters and LIM renters the trendlines for both years show that the proportion of renter households decrease with average household income. As expected, the trendlines for LIM renters are below those of the the all renters, meaning that the former are more concentrated in lower-income CTs.

 

Table 2 shows the distribution of renters by CT income quintile for 1996 and 2006. An equal distribution would be 20% in each income quintile. However, Table 2 shows a considerable concentration in the lower quintile CTs, for example showing that 32% of all renters lived in the the first income quintile of CTs in 1996, increasing to 33% in 2006. However, the proportion of renters in the second quintile decreased from 24% to 22%, therefore lowering the concentration in that series of lower-income CTs. LIM Renters are even more heavily concentrated in the lower-income CTs, with 66% and 62% living on the first and second quintiles in 1996 and 2006, respectively. That decrease suggests lower concentration in lower-income CTs.

Table 3 provides the respective average and Gini coefficients for the the proportion of all and LIM renters and confirms that, overall, renters were indeed more unevenly distributed in 2006 compared to 1996 because the respective Gini coefficients increased from 0.275 to 0.304. As expected, the Gini for LIM renters declined somewhat from 1996 to 2006, indicating that they were less unevenly distributed.

 

Rent Expenditures and Rent Burden

Figure 3 shows the average rent paid by LIM renter households in each CT for 1996 and 2006, graphed against CT average household income (in constant 2006 dollars). In real terms, average LIM rents increased about 5% to about $775 per month. Figure 3 shows that rents generally increased with average CT income. Rents for Other renters (not shown) increased by about 1% to about $1,055 and thus tend to be about 40% higher than those for LIM renters. In my first post I noted that income cut-off data available for this analysis (such as quintile limits and LIM thresholds) does not adjust for household size and hence that there is an over-representation of smaller households in first quintile and LIM data. It is in this context that a significant proportion of the difference in rents paid by LIM versus Other renters may be explained by quantity differences (i.e. Other renters with an average of 2.45 persons/household, renting larger units than LIM renters with 1.90 an average of persons/household), with the residual rent difference being due to quality differences. I will explore this quantity/quality aspect of rent differences between LIM and Other renters in a subsequent blog.

 

Figure 4 presents LIM rents as a percent of household income for 1996 and 2006, graphed against CT average household income, in constant 2006 dollars. Other renters (not shown) paid a relatively steady average of about 19% of their income of rent for 1996 and 2006, suggesting these households geographically sort themselves by average CT income. On the other hand, LIM renters are generally struggling with rent, paying an average of about 57% of their income in 1996 and about 55% in 2006. This modest decrease is due to average real incomes increasing more (9%) than average rent (5%) from 1996 to 2006. This decrease is probably one of the main reasons that LIM renters become somewhat less concentrated in lower-income CTs over the period.

 

Concluding Thoughts

My first housing-related post presented provincial-level time-series data to conclude that over the 2001-2016 period rent expenditures for Ontario first quintile renter households increased faster than for other renters and exceeded income increases so that these households had to expend an increasing share of their income on rent. The current CT-level analysis for Toronto shows increased between-CT household income inequality is being driven by increases in inequality in owner households and significant income sorting by geography, so that higher (lower) income renters and owners tend to live in the same higher (lower) income CTs. Similarly, the proportion of LIM renters within CTs decreases as average renter incomes increase. The current work found that over the 1996-2006 period LIM rents also increased faster than for other renters, but that the average rent burden decreased slightly over the period because average incomes increased at a slightly faster rate. This slightly lower rent burden was one of the main reasons for slightly lower concentration of lower-income renters in low-income CTs. Statistics Canada released the income-related data from the 2016 Census last week, which suggests that the custom tabulation that corresponds to the current analysis may be available later this year. I look forward to being able to update this post with that data and discuss the policy implications and initiatives of these and other findings.

Cartoon of the day

Published by Anonymous (not verified) on Tue, 19/09/2017 - 10:00pm in

Book Review: Marx, Capital and the Madness of Economic Reason by David Harvey

Published by Anonymous (not verified) on Tue, 19/09/2017 - 8:44pm in

In Marx, Capital and the Madness of Economic Reason, David Harvey provides a new systemisation of Karl Marx’s work in order to uncover, explore and explain the ‘madness of economic reason’ in the twenty-first century. This is an impressively wide-ranging work that draws upon Marx as a toolbox for contending with the crises of capital today, but Joshua Smeltzer is left questioning whether this is the appropriate conceptual apparatus to achieve this. 

If you are interested in this book review, you may also like to listen to/watch David Harvey’s LSE lecture, ‘Marx, Capital and the Madness of Economic Reason’, recorded 18 September 2017. 

Marx, Capital and the Madness of Economic Reason. David Harvey. Profile Books. 2017.

Find this book: amazon-logo

David Harvey, the author of The Companion to Marx’s Capital series and numerous other books on Marx and Marxism, has returned once more to the German philosopher and political economist, this time in order to provide a systematisation of Marx’s work that could explain and unearth the symptoms of a pervasive ‘madness of economic reason’ in the twenty-first century.

Part of Harvey’s drive to present an updated version of Marx relevant to the twenty-first century is directed against two intellectual sparring partners: on the one hand, recent biographies of Marx by Jonathan Sperber and Gareth Stedman Jones that, while ‘invaluable’, ‘both […] forget that the object of Marx’s study in Capital was capital and not nineteenth-century life’ (xiii); and on the other, ‘a supposedly scientific, highly mathematized and data driven field’ of orthodox economics (xiv). While Harvey’s engagement with the latter runs through the text, he largely avoids engaging with a historical reading of Marx’s work, preferring instead to present Marx as providing the answers to contemporary economic crises.

As a result, Harvey uses Marx’s work as a toolbox from which he updates and applies diverse concepts to illuminate the contemporary contradictions of capital. Harvey’s analysis is impressively wide-ranging, covering topics as varied as global natural resource consumption and Chinese economic policy (178-84), the Greek debt crisis (83, 205) and proposals for new trade agreements such as TPP and TTIP (160-64). As Harvey is at pains to illustrate, across the world ‘daily life is held hostage to the madness of money’ (172), generating a state of seemingly perpetual crisis. Against this, he suggests that Marx’s work is an ‘open door through which we could progress to ever higher understandings of the underlying problems that inform our current reality’ (209). Indeed, Harvey’s new book invites the reader to enter into the conceptual world of Marxism and encourages a critical distance from the language of economic necessity.

Image Credit: (Marco Gomes CC BY 2.0)

Meant as a ‘guide,’ Harvey places particular emphasis in this book on the clarity of language and accessibility for a general reader. Particularly in the first chapter, Harvey seeks to explain key concepts in the vocabulary of contemporary Marxism through basic examples, such as the exchange of shirts and shoes in the market (4). Indeed, Harvey wants to show Marx as a thinker deeply relevant for the present, demonstrating, for instance, why the 15 dollar minimum wage proposals of both Bernie Sanders and Black Lives Matter would ‘amount to naught if hedge funds buy up foreclosed houses and pharmaceutical patents and raise prices […] to line their own pockets out of the increased effective demand exercised by the population’ (47). To safeguard against this, Harvey argues that we need ‘strict regulatory intervention to control these living expenses, to limit the vast amount of wealth appropriation occurring at the point of realisation’ (47). Perhaps following Marx’s famous dictum, Harvey provides both an explanation for contemporary crises as well as a means of changing them.

And yet, at crucial moments, Harvey seems to forget the general audience for whom the book is intended. For example, Figures Two and Three on ‘Visualizing Capital as Value in Motion’ (6) and ‘The Three Circuits of Capital’ (151) attempt to make Marx ‘no more difficult to understand than the standard visualization of the hydrological cycle’ (7) – certainly a worthy endeavour. However, the text provides no key for the dizzying array of colour-coded arrows, leaving the reader to guess the significance of using a dotted black line to connect ‘reproduction of Labour power’ to ‘Labour power’ versus using a solid black line to connect ‘commodities’ to ‘Labour power’. Likewise, ‘Money Capital’ is the only term to be highlighted in black and surrounded by a grey box, but the significance of this formatting is left without explanation. For someone who hasn’t spent half a century interpreting Marx, an interpretative key would have been welcome.

Likewise, Harvey states in the opening chapter that ‘the only way to be true to my mission is to tell the story of capital in Marx’s own language’ (4). And yet the rest of the chapter is surprisingly light on citations of Marx’s work – there are only three, and all are to Grundrisse – let alone Marx’s language. For example, Harvey tells us that ‘at worst, Marx tends to concede […] that the rate of profit will tend over time to equalise between industrial capital and the other distributive forms’ (20), but this statement is not followed by any direct reference to Marx’s work.  Moreover, Harvey readily jettisons the idea of using Marx’s language when, on the subject of environmental protection and renewable energy, he notes that ‘Marx did not consider questions of this sort, but the visualisation here constructed, based on his thinking, is easily adapted to take such questions into account’ (22).

It seems then that we are confronted not with Marx’s language or even Marx’s thought, but rather Harvey’s revision and systematisation of it. This is particularly noticeable in Harvey’s discussion of Capital Vol. 2, in which he faults Marx for not conforming to his own expectations, noting that Marx ‘ignores the facts of distribution’, which Harvey finds ‘particularly annoying’, or that ‘oddest of all […] is the assumption that all commodities trade at their value’ (29).

Harvey ends his book with an apocalyptic warning:

to pretend [capital] has nothing to do with our current ailments and that we do not need a cogent, as opposed to fetishistic and apologetic representation of how it works, how it circulates and accumulates among us, is an offence against humanity that human history, if it manages to survive that long, will judge severely (210).

Finding a solution to the manifold crises of capital is certainly an imperative, but it remains a question if Harvey’s conjuring of the Ghost of Marxism Past will ultimately provide the appropriate conceptual apparatus to do so.

Joshua Smeltzer is a doctoral student at the University of Cambridge pursuing a PhD in Politics and International Studies, with a focus on twentieth-century German Political Thought.

Note: This review gives the views of the author, and not the position of the LSE Review of Books blog, or of the London School of Economics. 


Insanely Concentrated Wealth Is Strangling Our Prosperity

Published by Anonymous (not verified) on Tue, 19/09/2017 - 6:19am in

Tags 

inequality

By Steve Roth

Remember Smaug the dragon, in The Hobbit? He hoarded up a vast pile of wealth, and then he just hung out in his cave, sitting on it (with occasional forays to further pillage and immolate the local populace).

That’s what you should think of when you consider the mind-boggling hoards of wealth that the very rich have amassed in America over the last forty years. The picture at right only shows the very tippy-top of the scale. In 1976 the richest people had $35 million each (in 2014 dollars). In 2014 they had $420 million each — a twelvefold increase. You can be sure it’s gotten even more extreme since then.


Bottom (visible) pink line is the top 10%.

These people could spend $20 million every year and they’d still just keep getting richer, forever, even if they did absolutely nothing except choose some index funds, watch their balances grow, and shop for a new yacht for their eight-year-old.

If you’re thinking that they “deserve” all that wealth, and all that income just for owning stuff, because they’re “makers,” think again: between 50% and 70% of U.S. household wealth is “earned” the old-fashioned way (cue John Houseman voice): it’s inherited.

The bottom 90% of Americans aren’t even visible on this chart — and it’s a very tall chart. The scale of wealth inequality in America today makes our crazy levels of income inequality (which have also expanded vastly) look like a Marxist utopia.

American households’ total wealth is about $95 trillion. That’s more than three-quarters of a million dollars for every American household. But roughly 50% of households have zero or negative wealth.

Now of course you don’t expect 20-year-olds to have much or any wealth; there will always be households with none. But still, the environment for young households trying to build a comfortable and secure nest egg — the American dream? — has gotten wildly competitive and hostile over recent decades. (If we had a sovereign wealth fund, everyone would have a wealth share from birth.)

But here’s what’s even more egregious: that concentrated wealth is strangling our economy, our economic growth, our national prosperity. Wealth concentration drives a vicious, downward cycle, throttling the very engine of wealth creation itself.

Because: people with lots of money don’t spend it. They just sit on it, like Smaug in his cave. The more money you have, the less of it you spend every year. If you have $10,000, you might spend it this year. If you have $10 million, you’re not gonna. If you have $1,000, you’re at least somewhat likely to spend it this month.

Here’s one picture of what that looks like (data sources):

These broad quintile averages obviously don’t put across the realities of the very poor and the very rich; each quintile spans 25 million households. But the picture is clear. The bottom quintiles turn over 40% or 50% of their wealth every year. The richest quintile turns over 5%. For a given amount of wealth, wider wealth dispersion means more spending. It’s arithmetic.

Now go back to those top-.01% households. They have about $5 trillion between them. Imagine that they had half that much instead (the suffering), and the rest was spread out among all American households — about $20,000 each.

Assume that all those lower-quintile households spend about 40% of their wealth every year. They each get to spend an extra $8,000, and enjoy the results. Sounds nice. And it’s spending that simply won’t happen with concentrated wealth. The money will just sit there.

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Now obviously just transferring $2.5 trillion dollars, one time, is not going to achieve this imagined nirvana. Nor is it bloody well likely to happen. That example is just to illustrate the arithmetic. Absent some serious changes in our wildly skewed income distribution (plus capital gains, the overwhelmingly dominant mechanism of wealth accumulation, which don’t count as “income”), that wealth would just get sucked back up to the very rich, like it has, increasingly, for the past forty years — and really, the past several thousand years.

If wealth is consistently more widely dispersed — like it was after WW II — the extra spending that results causes more production. (Why, exactly, do you think producers produce things?) And production produces a surplus — value in, more value out. It’s the ultimate engine of wealth creation. In this little example, we’re talking a trillion dollars a year in additional spending and production. GDP would be 5.5% higher.

If you want to claim that the extra spending would just raise prices, consider the last 20 years. Or the last three decades, in Japan. And if you think concentrated wealth causes better investment and greater wealth accumulation, ask yourself: what economic theory says that $95 trillion in concentrated wealth will result in more or better investment than $95 trillion in broadly dispersed wealth? Our financial system is supposed to intermediate all that, right?

Or ask yourself: would Apple be as successful as it is if its business model was based on selling eight-million-dollar diamond-encrusted iPhones? Broad prosperity is what made Apple, Apple. Concentrated wealth distorts producers’ incentives, so they produce, for instance, a million-dollar Maserati instead of forty (40) $25,000 Toyotas — because that’s what the people with the money are buying. Which delivers more prosperity and well-being?

This little envelope calc is describing a far more prosperous, comfortable, and secure society — far richer and and one hopes far more peaceful than the one we’re facing under wildly concentrated wealth. With the possible exception of a few very rich multi-generational dynasties, everyone’s grandchildren will be far better off 50 years from now if wealth is more widely dispersed. And over that half century, hundreds of millions, even billions of people will live far richer, better lives.

Why wouldn’t we want that? Why wouldn’t we do that? (We know the answer: rich people hate the idea — even those who aren’t all that rich but foolishly buy into the whole trickle-down fantasy. And the rich people…have the power.)

By contrast to that possibility, here’s what things look like over the last seven decades:

Here are the results — growth in inflation-adjusted GDP per capita:

The last time economic growth broke 5% was in 1984. And the decline continues.

So how do we get there, given that we’ve mostly failed to do so for millennia? Start with a tax system that actually is progressive, like we had, briefly, during the postwar heyday of rampant and widespread American growth and prosperity. And greatly expand the social platform and springboard that gives tens of millions more Americans a place to stand, where they can move the world.

All of this dweebish arithmetic, of course, doesn’t put across the real crux of the thing: power. Money is power. So it is, so it has been, and so it shall be in our lifetimes and our children’s lifetimes (world without end, amen). This is especially true for minorities, who have been so thoroughly screwed by our recent Great Whatever. Money is the power to walk away from a shitty job. To hire fancy lawyers and lobbyists, maybe even buy yourself a politician or two. If we want minorities to have power, they need to have money.

Add to that dignity, and respect, which is deserved by every child born: sadly but truly, they are delivered to those who have money. You can bemoan that reality, but in the meantime, let’s concentrate on the money.

If you want to create a workers’ utopia, a better world for all, seize the wealth and income.

2017 September 18

=================

Data Sources

The data for the tall chart is from Gabriel Zucman, PSZ2016AppendixTablesII(Distrib).xlsx Table TE3. Google sheet with data and chart here.

Average wealth by quintile is from the Federal Reserve’s Survey of Consumer Finance (SCF), scf2013_tables_internal_nominal.xls, Table 4. (Top 20% wealth in the table above is an average of the means for 80-90% and 90-100%.) The most recent triannual SCF release, covering 1989-2013, determined the year chosen for the table. The next release, through 2016, should be out imminently.

Spending by quintile is from the BLS Consumer Expenditure Survey (CEX; earlier years here), Table 1101 (adjusted; see below): https://www.bls.gov/cex/2013/combined/quintile.xlsx. All annual expenditure-by-quintile tables 1984-2016 in one spreadsheet here.

Note: Measuring expenditures is very difficult, especially the spending of the very rich. They’re not keen to answer lengthy surveys like the CEX, given that they don’t even want their housekeepers to know that they paid $6 for a loaf of bread. As a result, CEX — which breaks out spending by quintile — misses about 40% (xlsx) of the spending tallied in the BEA’s Personal Consumption Expenditures (PCE) — which doesn’t. As a rough corrective for that discrepancy, the spending-by-quintile figures in the table above are CEX measures multiplied by 1.66. This “PCE correction” results in far more plausible spending figures, especially for the top 20%: Average $165,000 in 2103 annual spending versus CEX’s $100,000.

 

The post Insanely Concentrated Wealth Is Strangling Our Prosperity appeared first on Evonomics.

Book Review: After Piketty: The Agenda for Economics and Inequality edited by Heather Boushey, J. Bradford DeLong and Marshall Steinbaum

Published by Anonymous (not verified) on Mon, 18/09/2017 - 9:32pm in

In After Piketty: The Agenda for Economics and Inequality, editors Heather Boushey, J. Bradford DeLong and Marshall Steinbaum bring together contributors to reflect on the influence of Thomas Piketty’s Capital in the Twenty-First Century and to draw attention to topics less explored in Piketty’s analysis. While this is a work of serious scholarship that is suited primarily to an academic audience, these reflections on inequality as an economic as well as moral, social and political issue are of significance for all, finds Asad Abbasi. 

After Piketty: The Agenda for Economics and Inequality. Heather Boushey, J. Bradford DeLong and Marshall Steinbaum (eds). Harvard University Press. 2017.

Find this book: amazon-logo

Life expectancy for people living around Canary Wharf is 89 years. For people at Canada Water, the next stop on the Jubilee line, life expectancy is 78 years. The life expectancy gap of eleven years between these two stations is equal to that between Switzerland and Bangladesh or between British women born in 2011 and British women born in the 1950s.

What explains such a dramatic change in life expectancy within a two-minute tube journey? One probable answer is that London is an unequal city. The richest ten per cent in London own 62.8 per cent of the city’s total wealth. This disparity pervades other forms of inequality such as education, political voice and even the ‘basic unit of inequality’, the life expectancy rate. But how can we account for this unequal distribution?

In 2013, Thomas Piketty’s Capital in the Twenty-First Century provided a sophisticated explanation for inequality in the western world. Unequal wealth, Piketty posited, has less to do with productivity or efficiency than with ‘the process by which wealth is accumulated and distributed’ which ‘contains powerful forces pushing towards divergence, or at any rate towards an extremely high level of inequality’ (2013, 27). Analysing this, Piketty found that historically the return on capital (r) consistently floated above the growth rate (g): r > g. In other words, wealth grows faster than economic output. This implies that the tiny fraction of people with capital will continually receive a larger share of the total wealth of the economy resulting in unequal wealth distribution, such as the one we see in London. Only a shock that increases growth, such as education or technology, or one that decreases capital, such as wars, will lessen wealth inequality.

After Piketty: The Agenda for Economics and Inequality, edited by Heather Boushey, J. Bradford De Long and Marshall Steinbaum, further explores the ‘process by which wealth is accumulated’ and the ‘powerful forces’ that shape the divergence. After Piketty starts with a neat, formal summary of Piketty’s Capital, serving as a solid foundation for anyone not familiar with this work.

Image Credit: (jmettraux CC BY 2.0)

The thrust of After Piketty is not that Piketty got everything wrong in his analysis but that he missed a few important points, which this book highlights. After Piketty is split into five parts. The first discusses reception of Piketty’s Capital, and in the last section Piketty is given an opportunity to respond to the ideas discussed in the volume. The middle three sections, which form the core of the text, are ‘Conceptions of Capital’, ‘Dimensions of Inequality’ and ‘Political Economy of Capital and Capitalism’. The editors have done an astute job assembling chapters of such variety under these categories.

Though George Orwell is referenced in this book, After Piketty is no Animal Farm. Readers without a background in economics will find some chapters daunting, terminology-wise. Each chapter introduces a niche aspect of inequality. Yet, the book binds together at least four common themes. First is praise of Piketty’s work and its transformative influence on academia, policy and legislation. The second articulates the need for better wealth data. The third common theme is the book’s principal focus on the US. Almost all the chapters deal with inequality in the US, except a few in which Europe, Britain and the Global South are discussed. The fourth theme, and the one which I will discuss in this review, is about the ‘process’ of wealth accumulation and the ‘powerful forces’ causing wealth divergence.

What Explains the Divergence?

Image Credit: (Amanda Slater CC BY SA 2.0)

Chapters Two and Three by Robert Solow and Paul Krugman – originally published as reviews for Piketty’s Capital in New Republic (April 2014) and New York Review of Books (May 2014) respectively – lead the discussion of Piketty’s analysis. For Krugman, not wealth but the ‘compensation and income’ (67) of the top tier, at least in the US, are the source of the divergence. Solow, however, agrees with Piketty that r > g causes divergence, but suggests that this equation is ‘not rooted in any failure of economic institution’ but ‘on the ability of the economy to absorb increasing amounts of capital without substantial fall in rate of return’. The absorption of capital, Solow explains, ‘may be good for the economy […] but […] not for equity within the economy’ (55). But is it an inherent quality of economy to absorb capital without a fall in rate of return? Not really.

For Suresh Naidu (Chapter Five), the process of wealth accumulation is not ‘guaranteed, but instead must be maintained via government administration and the legal systems’ (115). Naidu dissects Piketty into ‘Domestic Piketty’, in which politics plays a passive role, and ‘Wild Piketty’, where politics, and in particular institutions, form an important framework for understanding capital. For Elisabeth Jacobs (Chapter 21), the role of the state in Piketty’s work ‘is remarkably sanitized of any question of power dynamics’ (517). Power is ‘everywhere and nowhere’ in Piketty’s history (512). Jacobs uses Albert Hirschman’s categories of voice, exit and loyalty to show the politics behind the ‘powerful forces’.

For Laura Tyson and Michael Spence (Chapter Eight), the ‘powerful force’ causing inequality is digital technology. Digital technology enables two things. First, technology enables capital to move towards cheap labour. Second, it substitutes low-cost workers with machines. For David Weil (Chapter Nine), it is the ‘outsourcing’ of jobs, which ‘allows redistribution of gains upwards’ (224). Through outsourcing, large firms create a ‘competition between service providers’, which results in lower wages for those working for them.

For David Grewal (Chapter Nineteen), ‘legal foundations’ form the powerful force which enables the ‘persistent dominance of capital over the rest of the economy’ (472). Markets are not something ‘in the abstract’ but a type of ‘socioeconomic regime’ (478). And it is the ‘higher order constitutional protection for property’ (485), which is difficult to change, that provides the persistent high rate of return throughout history for the propertied class – the elites.

The elites, Boushey notes in Chapter Fifteen, ‘are increasingly marrying each other’ (374), affecting present income and future bequests (375), thereby resembling the marriage markets of the nineteenth and twentieth centuries. However, elites of our times are unique because they are, according to Gareth Jones (Chapter Twelve), ‘Non-Doms’: mobile and living in several countries at one time. Jacobs makes a similar argument: ‘Global elites can essentially shop for the destination that will treat their resources more favourably’ (537).

Just like the elites, the capital of the twenty-first century is also different from earlier eras. The rate of return is maintained by forming ‘extra legal spaces’, such as tax havens and other offshore jurisdictions which blur legal controls and capital information (290). The City of London, Jones suggests, doesn’t pay its accountants £2 billion per year for accurate information (292).

Even if, as described by Piketty, the process of accumulation and the forces of power that render wealth inequality prove correct  – that is, r > g –  even then, for Branko Milanovic, high inequality is avoidable. The famous ‘Elephant curve’, which appears in Christoph Lakner’s chapter on global inequality, shows that the low-income earners in the west, the blue collar workers, gained zilch from globalisation. Think Brexit. The remedy, Milanovic proposes, is ‘wider ownership of capital’ (256).

For Daina Ramey Berry (Chapter Six), it is important to analyse the initial divergence of income between the rich and the poor. Contrary to Piketty’s ‘anodyne model of capital accumulation’, Ramay writes that the ‘colonial and antebellum 1 percent became rich by exploiting enslaved people’s labour’. For Berry, Piketty ignores ‘the fact that the slave trading and slave labour were at the foundations of western economies from the fifteenth century through nineteenth century’ (129).

In response, if not outright defence, Piketty admits that his book didn’t ‘devote sufficient attention’ to slavery. He does point out that ‘slave value reported’ in his work attempts the ‘first explicit computation’ of a slave economy (549), but also that these are ‘based upon total number of slaves recorded in census, whether they are owned by private individuals, corporation, or municipal governments, so I am not sure they are as strongly underestimated as suggested by Daina Ramey Berry’ (658, n15).

In the final chapter, Piketty explains, defends and elaborates upon Capital. Capital, Piketty notes, embodies multidimensional history, rooted as much in politics as in economics. Capital serves as an ‘introduction’ to this history (548-53). ‘Had I believed’, Piketty quips, ‘in the one dimensional neoclassical model of capital accumulation […] then my book would have been 30 pages long rather than 800 pages’.

Piketty argues that capitalism contains an inherent capacity to produce unequal societies. In order to rein this tendency, he suggests implementing a global wealth tax. More importantly, Piketty hopes that his work provokes discussion on wealth and inequality. After Piketty not only generates such debate, but also deepens it by highlighting the gaps missed by Piketty. For this reason, After Piketty ticks the box as being as much an ‘homage’ to, as a critique of, Piketty’s Capital.

After Piketty is not your typical holiday read. It is work of serious scholarship. The academic language of some chapters pinpoints its intended audience: scholars, students, policymakers and politicians. Yet, the topics discussed in the book affect all citizens. High inequality should concern everyone because it is a moral, social and political issue.

As the United Kingdom negotiates exit terms with EU officials, it seems that ‘decades of inequality’ in Britain, and the EU’s commitment towards the ‘profit making interests of a tiny elite’, resulted in Brexit. In The Age of Uncertainty, John Galbraith warned against the tumultuous effects of unequal wealth: ‘When reforms from the top became impossible, the revolution from the bottom became inevitable’.

Asad Abbasi has a Masters degree in Political Economy of Late Development from the London School of Economics. Currently, he is researching conceptual frameworks of development.

Note: This review gives the views of the author, and not the position of the LSE Review of Books blog, or of the London School of Economics. 


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