Relevant and even prescient commentary on news, politics and the economy.

Shooting in Little Rock

I used to live in Little Rock,  so waking up this morning to the news of the shooting in Little Rock was a bit of a shock.  Fortunately, the expletive expletive who did the shooting was a bad shot and nobody got killed.

I don’t even know how to comment on this, though, so I’m going to just to put it up… This is a screenshot I just took from the night club’s website which shows the act that was performing last night. I guess what with the events of the last few hours it didn’t occur to anyone to take it down:
power lounge screen shot 1

 

Click to embiggen.

There’s a video floating around (look for it yourself) showing the shooting.   There were an awful lot of shots fired very, very quickly.  No innocent bystander with another gun could have stopped the shooter before he killed quite a few people; the only saving grace is that the shooter was incompetent.

I don’t have much to add except that there must be some happy medium, some better outcome for a country than where we are now.  We need to arrive at a point of where there are fewer guns that can shoot as quickly, or fewer such guns in the hands of people who would use them, or fewer people who would use them floating around.  I suspect all of the above is the best option, but I have no practical ideas how to get there.

 

 

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Some Examples of the Hiring Process

A just-released paper by the Behavioral Economics Team of the Australian Government (BETA) looks at hiring processes in the Australian Public Service Commission. Here’s the summary:

This study assessed whether women and minorities are discriminated against in the early stages of the recruitment process for senior positions in the APS, while also testing the impact of implementing a ‘blind’ or de-identified approach to reviewing candidates.

Over 2,100 public servants from 14 agencies participated in the trial. They completed an exercise in which they shortlisted applicants for a hypothetical senior role in their agency. Participants were randomly assigned to receive application materials for candidates in standard form or in de-identified form (with information about candidate gender, race and ethnicity removed).

We found that the public servants engaged in positive (not negative) discrimination towards female and minority candidates:

- Participants were 2.9% more likely to shortlist female candidates and 3.2% less likely to shortlist male applicants when they were identifiable, compared with when they were de-identified.
- Minority males were 5.8% more likely to be shortlisted and minority females were 8.6% more likely to be shortlisted when identifiable compared to when applications were de-identified.
- The positive discrimination was strongest for Indigenous female candidates who were 22.2% more likely to be shortlisted when identifiable compared to when the applications were de-identified.

Interestingly, male reviewers displayed markedly more positive discrimination in favour of minority candidates than did female counterparts, and reviewers aged 40+ displayed much stronger affirmative action in favour for both women and minorities than did younger ones.

Overall, the results indicate the need for caution when moving towards ’blind’ recruitment processes in the Australian Public Service, as de-identification may frustrate efforts aimed at promoting diversity.

Ignoring the authors’ failure to write in proper American, I can think of four very obvious reasons for the results described in the paragraph that begins with the word “Interestingly.” I wonder whether the people who did this study realized what was going on and decided to opt for discretion over valor.

On not-quite-the-same topic, here’s a 2010 paper by Ruffle and Shtudenter:

Job applicants in Europe and in Israel increasingly imbed a headshot of themselves in the top corner of their CVs. We sent 5312 CVs in pairs to 2656 advertised job openings. In each pair, one CV was without a picture while the second, otherwise almost identical CV contained a picture of either an attractive male/female or a plain-looking male/female. Employer callbacks to attractive men are significantly higher than to men with no picture and to plain-looking men, nearly doubling the latter group. Strikingly, attractive women do not enjoy the same beauty premium. In fact, women with no picture have a significantly higher rate of callbacks than attractive or plain-looking women. We explore a number of explanations and provide evidence that female jealousy of attractive women in the workplace is a primary reason for the punishment of attractive women.

So, who are the fiends discriminating against unattractive men and attractive women? Well, it turns out that they are the people staffing the HR department in various companies. And who staffs the HR department?

In light of the above, the jealousy explanation seems especially fitting when we consider that 93% of the respondents in our sample were female (as determined by their voice when they left a voicemail message, their name when they sent an email or by a discreet phone call to the company when there was any doubt as to the respondent’s sex). One may be concerned that the person calling back to invite the candidate for an interview may not be the same discriminating person who screened the CVs. Yet, human resource departments in Israel and indeed throughout the West are staffed predominantly by women. To verify this stereotype, we asked to speak with the person who screens candidates’ CV when conducting the post-experiment survey. In 24 of the 25 (96%) companies we interviewed that person is a female. Moreover, these woman are young (ranging in age from 23 to 34 with an average age of 29) and typically single (16/24 or 67%) – qualities more likely to be associated with a jealous response when confronted with a young, attractive competitor in the workplace.

I think the authors are on pretty safe ground when they note that this phenomenon is largely due to the gender of those typically staffing HR departments.  I am not as convinced that jealousy is the root cause of their behavior, though.

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Stadiums or Schools: An Analysis of Public Expenditures

Dan here…I don’t usually pass along a study that has a company attached to the article itself, but thought this one might be of interest for readers.

On government handouts sports, stadiums or schools is the political side of the issue.

Stadiums or Schools: An Analysis of Public Expenditures

What we found is that ten states have allocated public funds to fund new professional sports stadiums since 2008. This does not include state expenditures on collegiate or high-school sports facilities. While there are certainly debates we should have over, for example, how much a state spends on high school instruction versus a high school football stadium, because school (i.e. college and high-school) sports facilities are technically part of a school and have some (the size of which is, of course, debatable) educational benefit, we left them out. We therefore focused on public revenue used to finance professional sports stadiums for privately owned teams.

The ten states have allocated nearly six billion dollars for these facilities since 2008. What’s troubling is that six of those states–Florida, Georgia, Michigan, New York, Texas and Wisconsin–have, over the same period, cut their education budgets. Those six states have allocated over $4 billion to help finance privately owned sports stadiums while at the same time cutting their state education budgets. Most alarming, three of those states–Georgia, Texas and Wisconsin–rank in the top 12 among states that have cut education budgets since 2008.

Ultimately, the issue of using taxpayer dollars to fund privately owned sports stadiums raises larger ethical questions about public expenditures. These questions become particularly important when situated within the recent history of cuts to education budgets and rising college tuition costs in most states. Moreover, in an era of incessant government austerity, shouldn’t we be putting specific fiscal constraints on the lease agreements between professional sports teams and state governments? This seems especially prudent given the fact that virtually every analysis of the long term economic effects of stadiums find no evidence that cities receive anywhere near an attractive return on their investment. Cities, in fact, lose money on these investments. Most recently, a study done by the Federal Reserve Bank of St. Louis found that “86 percent of economists agreed that ‘local and state governments in the U.S. should eliminate subsidies to professional sports franchises.’”

Considering the antitrust exemption enjoyed by sports teams and the often billionaire net worths of their owners, maybe it is time to consider laws that require owners commit a sizeable majority percentage of funding for stadiums in their lease agreements before the public has to commit any funds, or prohibits state support altogether. Moreover, if we are going to continue to divert public monies to sports stadiums, maybe it is time for sports teams to commit more real economic development to their local communities. For instance,

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Arithmetic is Hard: Wage-Bracket Creep

There has been a lot of very good critique of the methodology of the University of Washington’s study of the minimum wage increase in Seattle. However, I want to repeat and emphasize a very simple point that jumps out.

A static low-wage cutoff point, whether it be $19 or $100, automatically reduces the size of the treatment group (Seattle) if wages in the treatment group are increasing faster than the wage of the control group.

This is not erudite statistical methodology. This is simple arithmetic. If the wages in the treatment group increase at a higher percentage rate than the wages in the control group, more workers are lifted above the $19 threshold in the treatment group than in the control group.  This is true if the treatment group and the control group are otherwise absolutely identical. This is what I call wage-bracket creep. The extremely simplified example below shows how this looks, the yellow cells represent workers whose jobs and hours would be “lost” (to the study) as they pass the $19 threshold:

See how much worse off the treatment group is than the control group? The yellow cell occupants haven’t lost their jobs, they have simply been excluded from their respective groups because their wage now exceeds the static cutoff amount.

Of course, I wondered if the study authors could be making such a simple arithmetic mistake. So I reached out to one of the authors, who generously replied but appeared to confirm that they relied on a static threshold. I say appeared because some of the replies were, shall we say, “ambiguous” but did not disclaim use of a static threshold when I sought explicit confirmation or denial.

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Five graphs for 2017: mid year update

Five graphs for 2017: mid year update

– by New Deal democrat

At the beginning of the year, I identified 5 trends that bore particular watching, primarily as potentially setting the stage for a recession next year.  Now that we are halfway through the year, let’s take another look at each of them.

#5 Gas Prices

One potential pressure point on the economy was gas prices, which appear to have made a long- bottom in January of 2016. As they began to rise, consumer inflation has increased from non-existent to almost 3%. So the issue was, will they rise even further and drive inflation even higher?
And the answer so far this yeear has been a resounding “No!”  Typically it has taken a 40% YoY increase in gas prices to shock the consumer.  Gas price increases did briefly approach that point early in the year, but since then they have retreated all the way to being negative YoY:

This has actually helped boost real wages, as we will see further below.

#4 The US$

Another potential pressure point on the economy was a big increase in the relative value of the US$, which was part of the shallow industrial recession of 2015.  The $ started to rise again after the November election.  Here too after an initial spike, the data has calmed down again:

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The Seattle Study: Increasing the Minimum Wage as a Way to Boost High Income Jobs

by Peter Dorman (originally published at Econospeak)

The Seattle Study: Increasing the Minimum Wage as a Way to Boost High Income Jobs

As labor market mavens all know by now, the University of Washington team chosen by the city of Seattle to evaluate its minimum wage law has issued a new report.  This one is particularly juicy since it covers the increase from $11 to $13 an hour, which moved Seattle into new territory, beyond what has been studied elsewhere.  The report makes much of its use of Washington State data which include not only numbers but also hours worked, allowing (in this respect) a more precise analysis of the effect of changes in the statutory minimum on employment.

The headline result is that the elasticity of hours worked to changes wages actually paid is in the vicinity of -300%.  The key paragraph is this:

Our preferred estimates suggest that the Seattle Minimum Wage Ordinance caused hours worked by low-skilled workers (i.e., those earning under $19 per hour) to fall by 9.4% during the three quarters when the minimum wage was $13 per hour, resulting in a loss of 3.5 million hours worked per calendar quarter. Alternative estimates show the number of low-wage jobs declined by 6.8%, which represents a loss of more than 5,000 jobs. These estimates are robust to cutoffs other than $19.  A 3.1% increase in wages in jobs that paid less than $19 coupled with a 9.4% loss in hours yields a labor demand elasticity of roughly -3.0, and this large elasticity estimate is robust to other cutoffs.

This has got the labor econ blogosphere quite excited: finally, after years of published studies that largely downplayed the labor demand disincentive effects of minimum wage laws, a report has been issued that finds immense negative effects—vastly larger in fact than those that have appeared in the past.
The backdrop to this, of course, is the economic performance of the city of Seattle itself, which has been about as strong as any city in the country.  During the period of the latest minimum wage increase Seattle has experienced essentially full employment, as reflected in an unemployment rate of about 3%.  Thus, any negative impact in one part of the city’s economy had to have been offset by positive impacts elsewhere.

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Explaining the Gender Wage Gap

From Thomas Edsall in the NY Times

At one end of the scale, men continue to dominate.

In 2016, 95.8 percent of Fortune 500 CEOs were male and so were 348 of the Forbes 400. Of the 260 people on the Forbes list described as “self-made,” 250 were men. Wealth — and the ability to generate more wealth — must still be considered a reliable proxy for power.

But at the other end of the scale, men of all races and ethnicities are dropping out of the work force, abusing opioids and falling behind women in both college attendance and graduation rates.

Edsall’s comments are very compatible with this by Deary et al:

There is uncertainty whether the sexes differ with respect to their mean levels and variabilities in mental ability test scores. Here we describe the cognitive ability distribution in 80,000+ children—almost everyone born in Scotland in 1921—tested at age 11 in 1932. There were no significant mean differences in cognitive test scores between boys and girls, but there was a highly significant difference in their standard deviations (P<.001). Boys were over-represented at the low and high extremes of cognitive ability. These findings, the first to be presented from a whole population, might in part explain such cognitive outcomes as the slight excess of men achieving first class university degrees, and the excess of males with learning difficulties.

It is also compatible with this by Lynn and Kanazawa:

This paper presents the results of a longitudinal study of sex differences in intelligence as a test of Lynn’s (1994) hypothesis that from the age of 16 years males develop higher average intelligence than females. The results show that at the ages of 7 and 11 years girls have an IQ advantage of approximately 1 IQ point, but at the age of 16 years this changes in the same boys and girls to an IQ advantage of 1.8 IQ points for boys.

Lynn and Kanazawa’s paper sample is for all kids born in Great Britain during one fine week in March of 1958. The abstract and the paper focuses on mean differences.  They seem to mean a lot to the two authors, and most especially Lynn, but to me the differences in mean are small and of lesser importance than other things they note.   To mangle a metaphor, the multiplier (when it comes to differences in population outcomes) is the standard deviation.  To see what I mean, here are a couple of tables from the Lynn and Kanazawa paper:

Lynn and Kanazawa tables

Notice the standard deviations are larger for males in every sub-sample. What they tell you is that even if the mean intelligence of men and women is the same, you expect far many more idiots and far many more geniuses among men than among women in most areas of human endeavor that require something identifiable as cognition or IQ.

But it’s not just at the tails; you expect to see more “pretty stupid” and “pretty smart” men than women. The female population simply displays less variance at all ends of the spectrum.

Neuroscientists are also finding that there is more variability in men’s brains than in women’s. Which is to say, patterns of variability in measures of cognition observed in the studies mentioned earlier are very likely to apply to other fields as well.

Now, consider making money. Everyone does it to some extent. But we should see more variation men’s earnings than women’s earnings.

Throw on one more detail: income distributions are truncated at the bottom. There is a minimum wage, after all. People simply don’t get paid less than that. But even in the absence of a minimum wage, people who don’t make enough to survive are very euphemistically removed from the distribution.  In fact, in most careers, there is a baseline and most people earn closer to that baseline than to the level that superstars in the field make.  People’s abilities may fall on a standard normal distribution, but incomes are described by something that more closely resembles a Chi-Squared distribution.

Which is to say, chunks are taken out from the bottom end of the female and male income distributions. However, a larger proportion of the low end of the male distribution is removed because, due to the larger male variance, more men fall below the floor.

That right there is the gender wage gap, as well as Edsall’s observation.

You disagree? Does today’s America’s differ from what you would expect in a world were men and women have similar intelligence, but men have a great deal more variance? If it does, tell me how.

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Seattle Minimum Wage

Words cannot describe the torment experienced by the data before they confessed what the University of Washington team got them to confess. I can only urge readers with an open mind to study Table 3 carefully. The average wage increase, from the second quarter of 2014 to the third quarter of 2016, for all employees of single site establishments was 18 percent. Eighteen percent! That is an annual increase of almost 8 percent. For two and a quarter years in a row. Not bad. And the number of hours worked of ALL employees of single site establishments? Up 18 percent in a little over two years. That too is an increase of almost 8 percent per annum.
Now multiply that wage by those hours and the total payroll for all employees rose 39.5 percent over the course of nine quarters. An annual rate of increase of 17.5 percent. These are BIG numbers. They are freaking HUGE numbers.
It must have taken a team of at least six academics to extract a 9.4 percent decline in hours from the 86,842 workers (out of a total of 336,517) earning under $19 dollars an hour at these single-site establishments. Look at the Table and weep.

 

Now, as I mentioned in a comment on Peter’s post, bracket creep alone could do away with at least 7 percent of the missing hours of workers earning under $19 and hour. That is unless we assume that everyone making between about $18 and $19 got approximately zero wage increases while the rest of the crew were getting 10 percent raises. Look at the God damned table. This isn’t rocket science.

Minimum Wage Increases, Wages, and Low Wage Employment: Evidence from Seattle

 

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