Comments on economics, mystery fiction, drama, and art.

Tuesday, October 04, 2016

High value-to-weight shipping

I was doing my econ history class tonight, and we were talking about the changes in ocean-going shipping in the early 19th century, in particular that one big item being carried was mail.  I pointed out that mail was a highly valuable thing to ship at the time, because it was about the only way to get information from the US to Europe and back.  And I sort of off-the-cuff asked how much the USPS charges to ship a ton of first class mail today.
32,000 ounces in a ton, times $0.46 per ounce...
I guess mail is still a high value-to-weight thing to ship...

Thursday, September 22, 2016

Employment, Unemployment, and Hours: A Comment on Farmer's "Unemployment and Hours Are Very Different Creatures"

Roger Farmer recently argued that, as the title of his blog post says, “Unemployment and Hours Are Very Different Creatures.”  I’m not so sure I agree. 

Using data for the 1965-2016 period, he charts the unemployment rate and the labor force participation rate in Chart 1.  The LFPR rises fairly steadily from 1965 until late 1999 and then begins to decline.  The UR, by contrast, rises in recessions and falls in recoveries, with no particular long-term trend.  He suggests—and this seems clear—that there is no relationship between the LFPR and the UR.

In Chart 2, he shows average weekly hours in the private sector (AveWH) an the unemployment rate.  For most of the 1965-2016 period AveWH are declining; again, there is no apparent relationship with changes in the UR.

The chart below indexes the employment-population ratio and (1 minus the unemployment rate—a 7% unemployment rate is therefore 93% here) using 2007 as the base period [IEPR and I(1-UR)] in order to  chart them along with an index, not of Average Weekly hours, but of Aggregate Weekly Hours (AggWH), which, on the BLS web site is presented with a base period of 2007 = 100.  (Data on AggWH are available beginning in March 2006).  All three of these are indexed to 2007, and, because I transformed the unemployment rate, we would expect the indexes to rise or fall together—if they are related at all.  I also included an index of Average Weekly Hours (AveWE) worked.


Well, they do appear to be related, and fairly closely—except for the index of AveWE.  Why do I find something different from Farmer?  His hours measure is Average Weekly Hours.  If, during downturns, employers respond by laying workers off and maintaining the average work week, we would see Aggregate Weekly hours to fall in recessions and rise in recoveries, even as Average Weekly Hours change little or not at all.  And, based on the 2006-2016 period, laying off workers and maintaining the work week is exactly what happened.  And we would expect the EPR to fall in recessions and rise in recovery, and (1 — UR) to fall in recessions (UR rises) and rise in recoveries (UR falls).

I only have one business cycle here (Farmer has 6 in his data series), so there’s no guarantee that what I have found will be replicated in subsequent cycles (or would appear in prior cycles if I had aggregate hours data).  But I’d be willing to bet on it.

Tuesday, September 06, 2016

A somewhat radical political proposal...

One problem we have in the US is that "off-year" (2010, 2014, 2018) election years generally have very low turnouts.  This is a problem, because it leads to very low turnouts for half of the elections of senators and congressional representatives, and for many state and local offices.  This is exacerbated by localities that have off-off year elections, as Indianapolis does--it elects its mayor and city council and other local officials the year before presidential elections--2011, 2015...(in Marion county 22.7% of registered voters voted in 2015, compared with 56% in 2012--which is itself shamefully low).

So what I propose is making all elected offices be for a term of 4 0r 8 years, and holding all elections in the same year as the presidential election.  (This will require a constitutional amendment, altering terms for representatives to 4 years, and senators to 8 years.)

Monday, August 22, 2016

You can't make this stuff up

"So to recap: not only did Donald Trump hand out Play-Doh to working class people in desperate need of food and water and money to rebuild their own modest homes, it turns out he didn’t even donate the Play-Doh he was handing out, and then he gave a six figure donation to a wealthy local hate monger. It’s increasingly beginning to look like Trump would have fared better if he’d taken the Louisiana Governor’s advice and just stayed away..."

Friday, August 05, 2016

More on Consumption Spending, GDP, and Inequality

On the Marginal Revolution blog (on August 4), Tyler Cowen posted a   link to a twitter feed, with this header:

The link shows personal consumption expenditures as a percent of GDP and a measure of income inequality (the Gini coefficient) from 1978.  Both PCE as a percent of GDP and the Gini coefficient have been rising—consumption has become a larger percentage of total spending in the US and inequality has been increasing.  Cowen’s “ahem” is, I suppose, an indication that we should be surprised that both of these have happened at the same time.

Leaving aside the issue I raised yesterday there is also the issue of what, exactly, has been happening to consumption, if we disaggregate households by income.  Fortunately, we can do this, using data from the Consumer Expenditure Survey.*  The CES presents measures of after-tax income and household spending by quintiles—the 20% of households with the lowest incomes, and so on.  (Which means each group has the same number of households in it).  So we can look at what has happened both to household (after-tax) income and to household consumption spending.  And the results are interesting.



% Change,
Q1 Income
Q2 Income
Q3 Income
Q4 Income
Q5 Income
Q1 Spending
Q2 Spending
Q3 Spending
Q4 Spending
Q5 Spending

Q refers to the income quintile, from lowest to highest.  The values have been
adjusted using the CPI, with 2011 used as the base year.  Spending can exceed
income when households receive transfer payments or use their savings to
pay for part of their spending.  Transfer payment receipts are the obvious
source of funds in Q1 and Q2.

Overall, income increased by about 39%...but spending increased only by 3.8%.  While about 60% of the total income increase went to the highest income quintile, more than 75% of the increase in consumer spending occurred in the highest-income households.  It’s worth noting that consumption spending actually fell in real terms in the lowest income quintile.
What this tells us is quite simple.  It’s possible for consumption spending to rise as a percentage of GDP and for income inequality to increase at the same time.  All that’s required is for the bulk of the consumption spending to occur in high income households.  And—that is exactly what happened.

Thursday, August 04, 2016

A "Gotcha" moment, not an analytical moment

On the Marginal Revolution blog today, Tyler Cowen posts a link, with this header
2. Consumption as a percentage of gdp has increased with inequality (ahem).
The link is not to a piece of analysis, but to a twitter feed. 

Well, that’s not a very interesting observation, now is it? Because GDP shares are constrained to sum to 100%, if one component of GDP is decreasing as a percentage of GDP, some other component has to be increasing. We might as well point out that government investment and consumption expenditures have been falling as a % of GDP as income inequality has been rising.
We might also point out that investment spending has been rising as a of GDP (with sharp downturns during recessions) since 1970–up from about 12.5% in 1970 to around 16.5% now (investment’s share of GDP is up by 30%–4%/12.5%–since 1970)–and that’s after investment spending has been depressed by two recessions in the past 16 years. Consumption’s share is up from 61.2% in 1970 o 69.3% in 2016–a 15% increase. The investment share of DDP has increased twice as fast (in percentage terms) as the consumption share.

So C+I as a % of GDP is up from 75% of GDP to 86% of GDP as inequality has been increasing–and would be even higher but for the failure of I to recover significantly. Does this tell us anything meaningful? If so, what? All this is, is a “gotcha” moment, not an analytical moment.

Monday, July 18, 2016

Rudy Giuliani:  "You know who you are, and we're coming to get you."

What does this even mean?  Apparently, "you" are radical Islamic terrorists.  But what about the "...we're coming to  get you..." part?  How does he propose to identify who is (and who is not) on his hit list?  How are we going to "[e] to get..." them?  Are we going to bomb?  Then how do we avoid killing, and maiming, people who are our friends?  And if we kill and maim our friends, how long will they continue to be our friends?  Are we going to send US military personnel into...where, exactly?  Iraq?  Syria?  Washington Heights?  With what mission, exactly?  Without, again killing people who only want to live in peace? 

This is cheap rhetoric.  It is designed, not to solve a problem, but to rouse a beast.  The problem with cheap rhetoric, though, is that all too often, the price we pay for it is obscenely high.