Tagged: economics

0

Brian Tamanaha’s Straw Men (Part 2): Who’s Cherry Picking?

(Reposted from Brian Leiter’s Law School Reports)

BT Claim 2:  Using more years of data would reduce the earnings premium

BT Quote: There is no doubt that including 1992 to 1995 in their study would measurabley reduce the ‘earnings premium.’” 

Response:  Using more years of historical data is as likely to increase the earnings premium as to reduce it

We have doubts about the effect of more data, even if Professor Tamanaha does not.

Without seeing data that would enable us to calculate earnings premiums, we can’t know for sure if introducing more years of comparable data would increase our estimates of the earnings premium or reduce it.

The issue is not simply the state of the legal market or entry level legal hiring—we must also consider how our control group of bachelor’s degree holders (who appear to be similar to the law degree holders but for the law degree) were doing.   To measure the value of a law degree, we must measure earnings premiums, not absolute earnings levels.

As a commenter on Tamanaha’s blog helpfully points out:

“I think you make far too much of the exclusion of the period from 1992-1995. Entry-level employment was similar to 1995-98 (as indicated by table 2 on page 9).

But this does not necessarily mean that the earnings premium was the same or lower. One cannot form conclusions about all JD holders based solely on entry-level employment numbers. As S&M’s data suggests, the earnings premium tends to be larger during recessions and their immediate aftermath and the U.S. economy only began an economic recovery in late 1992.

Lastly, even if you are right about the earnings premium from 1992-1995, what about 1987-91 when the legal economy appeared to be quite strong (as illustrated by the same chart referenced above)? Your suggestion to look at a twenty year period excludes this time frame even though it might offset the diminution in the earnings premium that would allegedly occur if S&M considered 1992-95.”

There is nothing magical about 1992.  If good quality data were available, why not go back to the 1980s or beyond?   Stephen Diamond and others make this point.

The 1980s are generally believed to be a boom time in the legal market.  Assuming for the sake of the argument that law degree earnings premiums are pro-cyclical (we are not sure if they are), inclusion of more historical data going back past 1992 is just as likely to increase our earnings premium as to reduce it.  Older data might suggest an upward trend in education earnings premiums, which could mean that our assumption of flat earnigns premiums may be too conservative. Leaving aside the data quality and continuity issues we discussed before (which led us to pick 1996 as our start year), there is no objective reason to stop in the early 1990s instead of going back further to the 1980s.

Our sample from 1996 to 2011 includes both good times and bad for law graduates and for the overall economy, and in every part of the cycle, law graduates appear to earn substantially more than similar individuals with only bachelor’s degrees.

 

Cycles

 

This might be as good a place as any to affirm that we certainly did not pick 1996 for any nefarious purpose.  Having worked with the SIPP before and being aware of the change in design, we chose 1996 purely because of the benefits we described here.  Once again, should Professor Tamanaha or any other group wish to use the publicly available SIPP data to extend the series farther back, we’ll be interested to see the results.

0

Brian Tamanaha’s Straw Men (Part 1): Why we used SIPP data from 1996 to 2011

(Reposted from Brian Leiter’s Law School Reports)

 

BT Claim:  We could have used more historical data without introducing continuity and other methodological problems

BT quote:  “Although SIPP was redesigned in 1996, there are surveys for 1993 and 1992, which allow continuity . . .”

Response:  Using more historical data from SIPP would likely have introduced continuity and other methodological problems

SIPP does indeed go back farther than 1996.  We chose that date because it was the beginning of an updated and revitalized SIPP that continues to this day.  SIPP was substantially redesigned in 1996 to increase sample size and improve data quality.  Combining different versions of SIPP could have introduced methodological problems.  That doesn’t mean one could not do it in the future, but it might raise as many questions as it would answer.

Had we used earlier data, it could be difficult to know to what extent changes to our earnings premiums estimates were caused by changes in the real world, and to what extent they were artifacts caused by changes to the SIPP methodology.

Because SIPP has developed and improved over time, the more recent data is more reliable than older historical data.  All else being equal, a larger sample size and more years of data are preferable.  However, data quality issues suggest focusing on more recent data.

If older data were included, it probably would have been appropriate to weight more recent and higher quality data more heavily than older and lower quality data.  We would likely also have had to make adjustments for differences that might have been caused by changes in survey methodology.  Such adjustments would inevitably have been controversial.

Because the sample size increased dramatically after 1996, including a few years of pre 1996 data would not provide as much new data or have the potential to change our estimates by nearly as much as Professor Tamanaha believes.  There are also gaps in SIPP data from the 1980s because of insufficient funding.

These issues and the 1996 changes are explained at length in the Survey of Income and Program Participation User’s Guide.

Changes to the new 1996 version of SIPP include:

Roughly doubling the sample size

This improves the precision of estimates and shrinks standard errors

Lengthening the panels from 3 years to 4 years

This reduces the severity of the regression to the median problem

Introducing computer assisted interviewing to improve data collection and reduce errors or the need to impute for missing data

Introducing oversampling of low income neighborhoods
This mitigates response bias issues we previously discussed, which are most likely to affect the bottom of the distribution
New income topcoding procedures were instituted with the 1996 Panel
This will affect both means and various points in the distribution
Topcoding is done on a monthly or quarterly basis, and can therefore undercount end of year bonuses, even for those who are not extremely high income year-round

Most government surveys topcode income data—that is, there is a maximum income that they will report.  This is done to protect the privacy of high-income individuals who could more easily be identified from ostensibly confidential survey data if their incomes were revealed.

Because law graduates tend to have higher incomes than bachelor’s, topcoding introduces downward bias to earnings premiums estimates. Midstream changes to topcoding procedures can change this bias and create problems with respect to consistency and continuity.

Without going into more detail, the topcoding procedure that began in 1996 appears to be an improvement over the earlier topcoding procedure.

These are only a subset of the problems extending the SIPP data back past 1996 would have introduced.  For us, the costs of backfilling data appear to outweigh the benefits.  If other parties wish to pursue that course, we’ll be interested in what they find, just as we hope others were interested in our findings.

0

Brian Tamanaha’s Straw Men (Overview)

(Cross posted from Brian Leiter’s Law School Reports)

Brian Tamanaha previously told Inside Higher Education that our research only looked at average earnings premiums and did not consider the low end of the distribution.  Dylan Matthews at the Washington Post reported that Professor Tamanaha’s description of our research was “false”. 

In his latest post, Professor Tamanaha combines interesting critiques with some not very interesting errors and claims that are not supported by data.   Responding to his blog post is a little tricky as his ongoing edits rendered it something of a moving target.  While we’re happy with improvements, a PDF of the version to which we are responding is available here just so we all know what page we’re on.

Stephen Diamond explains why Tamanaha apparently changed his post: Ted Seto and Eric Rasmusen expressed concerns about Tamanaha’s use of ad hominem attacks.

Some of Tamanaha’s new errors are surprising, because they come after an email exchange with him in which we addressed them.  For example, Tamanaha’s description of our approach to ability sorting constitutes a gross misreading of our research.  Tamanaha also references the wrong chart for earnings premium trends and misinterprets confidence intervals.  And his description of our present value calculations is way off the mark.

Here are some quick bullet point responses, with details below in subsequent posts:

  • Forecasting and Backfilling
    • Using more historical data from SIPP would likely have introduced continuity and other methodological problems
    • Using more years of data is as likely to increase the historical earnings premium as to reduce it
    • If pre-1996 historical data finds lower earnings premiums, that may suggest a long term upward trend and could mean that our estimates of flat future earnings premiums are too conservative and the premium estimates should be higher
    • The earnings premium in the future is just as likely to be higher as it is to be lower than it was in 1996-2011
    • In the future, the earnings premium would have to be lower by **85 percent** for an investment in law school to destroy economic value at the median
  • Data sufficiency
    • 16 years of data is more than is used in similar studies to establish a baseline.  This includes studies Tamanaha cited and praised in his book.
    • Our data includes both peaks and troughs in the cycle.  Across the cycle, law graduates earn substantially more than bachelor’s.
  • Tamanaha’s errors and misreading
    • We control for ability sorting and selection using extensive controls for socio-economic, academic, and demographic characteristics
    • This substantially reduces our earnings premium estimates
    • Any lingering ability sorting and selection is likely offset by response bias in SIPP, topcoding, and other problems that cut in the opposite direction
    • Tamanaha references the wrong chart for earnings premium trends and misinterprets confidence intervals
    • Tamanaha is confused about present value, opportunity cost, and discounting
    • Our in-school earnings are based on data, but, in any event, “correcting” to zero would not meaningfully change our conclusions
  • Tamanaha’s best line
    • “Let me also confirm that [Simkovic & McIntyre’s] study is far more sophisticated than my admittedly crude efforts.”
0

Personality Types, Creativity, and Same-Sex Marriage

Co-authored with June Carbone 

UCLA’s Williams Institute has just issued two studies on the economic effects of gay marriage. The first study, on the relationship between a state’s approach to marriage equality and population migration – documents that members of the “creative class” –  people who “create’ as their job – who are in same-sex relationships were much more likely to move to Massachusetts following the Goodridge decision and the legalization of same-sex marriage. The study’s author suggests that this could improve help the state’s economy in the long-term. A second study shows that same-sex weddings have added over $100 million to the Massachusetts economy  (although this is not even a drop in the bucket in the $300 billion spent in Massachusetts in, for example 2004). Serendipitously, David Brooks wrote an op ed in the New York Times today, “In Praise of Dullness,” discussing  a different study that found the ideal C.E.O. is ” humble, diffident, relentless and a bit unidimensional,” in short, “not the most exciting people to be around.”  This study complements the work of journalists and political scientists, such as Bill Bishop and Andrew Gelman,  who increasingly find that the high tech centers of the country (including the Boston corridor) attract that same creative class open to new ideas and approving of same sex marriage, while the conscientious, more religious, and conventional family oriented types are drawn to other regions – regions that tend to oppose same-sex marriage.

Do these divisions suggest that opposition to same-sex marriage is in our genes – or at least our personality types? The CEOs and the creative class of the new economy may not belong to different tribes, but they tend to see the world through different lenses that color  their  perceptions.      Read More