Mitchell's Musings

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  • 21 Dec 2016 6:52 PM | Daniel Mitchell (Administrator)

    Mitchell’s Musings 12-26-16: Wrap Up for 2016


    Daniel J.B. Mitchell


    This musing will be the last for 2016. As we did last year, we will discontinue musing during the coming winter quarter due to teaching obligations. Unless something that requires a special musing occurs, we will resume in April 2017. So here is some wrapping up for the year related to the presidential election.


    First, in last week’s musing (of December 19, 2016), we asked who might play the role that George Shultz – with his academic/research background - did during the Nixon administration. Nixon had fantasies about cabals that were tinkering with official statistics to make him look bad. He also wanted to manipulate data in his favor. But Shultz played a calming role.[1] We know that President-elect Donald Trump doesn’t take kindly to anything that contradicts his views of how things are or should be. But, as of last week, there were no academics in major roles that might protect official data from harm when the figures do not display supportive results. We did note that the one academic who prominently supported “Trumponomics” was Peter Navarro, a professor at UC-Irvine, but that he had not been appointed to any official position. According to the latest news, however, he has been appointed to oversee a new trade office in the White House.[2] So let’s hope that Navarro serves as a voice for honest official data production.


    Second, in a musing of three weeks ago, we suggested that when it came to explaining Trump’s narrow win Electoral College win, any simple unicausal story – e.g., it was all angry displaced white males – was inadequate. So let’s elaborate a little more on that notion. Suppose you want to tell a story that is some variant of “it’s the economy, stupid.” Economic models of election outcomes tend to focus on where the economy was moving before the election. In Appendix A to this musing, we show the percent change in nonfarm employment by state, divided up between “Clintonland” – the states which Hillary Clinton won – and “Trumpland” – the states that went to Donald Trump. Was it the case the Clintonland was prosperous and Trumpland was depressed?


    There was in fact little difference between Trumpland and Clintonland. According to the latest (preliminary) data, employment in the former rose by 1.5% over November 2015-November 2016. In Clintonland, the rate over that period was 1.6%. Moreover, both areas showed a mix of results with some states in each doing notably better or worse than others. Trumpland did contain some states adversely affected by low energy prices, states that experienced employment declines. (West Virginia – with its much-discussed depressed coal industry – actually grew in jobs at a 1.4% rate.)


    The three states that went for Trump that weren’t “supposed to,” and in which Green Party candidate Jill Stein pushed for recounts, showed mixed results. Michigan actually outpaced the national average. Pennsylvania was very sluggish (but not negative); Wisconsin had slower than average job growth. The Ray Fair economic model predicted early on that Trump would win – but that model referred to the popular vote (not the electoral vote) so it was wrong.[3] However, we can simply take that prediction to mean simply that a generic Republican should have beaten (in popular votes) a generic Democrat. Trump didn’t win the popular vote and squeaked by narrowly in certain swing states – presumably because of his high negatives. Clinton did better than “expected” in popular votes, but apparently did not overcome her own high negatives in states that mattered.


    The chart below has some further information that is part of the 2016 story. It shows November-to-November annual changes in nonfarm employment going back to 1940. (The November month selection matches our Appendix’s state-level data and, in any case, an official estimate for December 2016 is not available at this writing.) When you look at the chart, you should focus on the line at 2.5% growth. All recessions until 2000, regardless of their cause, showed significant “snap backs” of rapid growth (above 2.5%) at some point in the post-recession expansions. But the two recessions after 2000 never attained 2.5%. So there were two downturns and then an extended period of sluggishness, even though the unemployment rate came down to something like a full employment number eventually.

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    Given the macro picture, a generic Republican was predicted to win by a purely economic model rather handily against a non-incumbent in the popular vote. And that win didn’t happen. Moreover, lots of unusual events occurred: an FBI announcement, Russian hacking, recordings of nasty language, a hostile takeover of a major political party, a more extended primary season than expected for the other party, etc. Each event tilted a few votes one way or another. That is the story of the 2016 presidential election.


    By April 2017, when these musings resume, we’ll know more about the consequences.

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    Appendix A: Employment Change in Clintonland and Trumpland


    Clintonland

    % Nonfarm Employment change

    Nov 2015-Nov 2016

    Clinton States

    California

    2.3

    Colorado

    2.1

    Connecticut

    0.1

    Delaware

    1.2

    District of Columbia

    1.9

    Hawaii

    2.4

    Illinois

    0.7

    Maine

    0.4

    Maryland

    1.1

    Massachusetts

    2.0

    Minnesota

    1.1

    Nevada

    2.7

    New Hampshire

    2.1

    New Jersey

    0.5

    New Mexico

    -0.3

    New York

    1.1

    Oregon

    2.9

    Rhode Island

    1.2

    Vermont

    0.0

    Virginia

    1.0

    Washington

    3.1

    Total

    1.6

    ----


    ------------------------------

    [1] http://employmentpolicy.org/page-1775968/4462237#sthash.RU4m6oJi.dpbs. See also the musing of two weeks ago (December 12, 2016): http://employmentpolicy.org/page-1775968/4451466#sthash.wMLdjWoS.dpbs.  

    [2] http://www.cnbc.com/2016/12/21/trump-picks-china-hawk-peter-navarro-for-new-trade-post.html.  

    [3] Fair comments on his error: Why such a large error? While this is not possible to test, most people would probably say that it is due to Trump's personality. Had the Republicans nominated a more main stream candidate, they may have done much better---much closer to what the equation was predicting. The prediction from the equation from the beginning in November 2014 was that the Republicans were heavily favored. The election was theirs to lose because of the economy and the duration effect, and they almost lost it! See https://fairmodel.econ.yale.edu/vote2016/index2.htm.  

  • 15 Dec 2016 4:31 PM | Daniel Mitchell (Administrator)

    Mitchell’s Musings 12-19-16: Who Will Play George Shultz?


    Daniel J.B. Mitchell


    Last week’s musing dealt with economic data and possible dangers to the integrity of the federal data collection and distribution system in the event of a rogue president. In that musing we noted the episode in which former President Richard Nixon believed that a conspiracy among federal data analysts was presenting economic news unfavorable to him.


    Although we tend to think of newsworthy economic data as a fairly narrow group of series - inflation numbers from the CPI and other indexes, the unemployment rate, and real GDP growth – the federal data agencies turn out a very large array of numbers regularly. Even if these figures don’t make the news, they do influence the perceptions of forecasters and others who then interpret them in ways which filter into the larger public understanding of how well or poorly the economy is progressing.


    There are various aspects of economic data that are involved. Many series – such as the Big 3 mentioned earlier (inflation, unemployment, real growth) – are represented by specific series that are linked to concepts that have to be defined for measurement. For example, while inflation is generally defined as broad-based price increases, in practice there are detailed methodologies of data collection and data combination that are meant to reflect that concept as a specific index. Changes in methodology can produce variation in the series. There are in fact several versions of the CPI, to take an example, which differ in the “headline” number they produce due to variations in weighting and other factors.


    Another important element is the timing of data releases. It is the practice of the major statistical agencies to release data on an announced-in-advance schedule.[1] This practice has been in use at least since the 1960s. The notion is that with pre-announced timing, there won’t be charges that particular numbers were either withheld for a time (perhaps bad news for an incumbent) or rushed out (good news) to influence elections or other political decisions.


    In fact, during the Nixon administration – even apart from the President’s conspiracy views – there was controversy about how fast the numbers should be released. Should the numbers just appear when ready? Should statistical officials say something about them once released, i.e., provide some interpretation? Or should commentary be left to political appointees and outside analysts?[2] That issue had also arisen in the Lyndon Johnson administration after an episode in which the President had commented on what-he-viewed-as favorable statistical news before the official release date.[3]


    There are often important policy decisions that are linked to economic data. The Federal Reserve, for example, looks at the general macro condition of the economy before making its monetary decisions. Social Security payments are indexed to the CPI. It is inevitable, therefore, that key data series will be in danger of politicization and ultimately their protection will depend on key high officials.


    In the Nixon case, the protector role was played by George Shultz who was first Secretary of Labor and then Secretary of the Treasury.[4] Back in a Mitchell’s Musing of October 22, 2012, I noted Shultz’s role as a stabilizer of Nixon when the latter wanted to do something that he shouldn’t.[5] In the case described in that Musing, Nixon wanted to leak in advance certain Department of Commerce figures which he thought were favorable.[6] Shultz, however, after a phone conversation with Nixon, effectively ensured that the numbers were released appropriately.


    Shultz had a background as a professional economist and academic researcher. I suspect that most persons with that background would have the same inclinations as Shultz regarding data integrity. We have only limited information at this time as to who will be Trump’s key economic advisors. Gary Cohn, Trump’s designee as chair of the National Economic Council, is President and Chief Operating Officer of Goldman Sachs. Trump’s Secretary of the Treasury designee is Steve Mnuchin, also a person with a Goldman Sachs background (and other financial ventures). His Secretary of Labor designee is Andrew Puzder, a fast-food chief executive. (Within Labor is the Bureau of Labor Statistics.) And his Secretary of Commerce is Wilbur Ross, an investor who is said to be a turn-around specialist. (Within Commerce is the Bureau of Economic Analysis – producer of the national income accounts – and the Bureau of the Census.)


    The only person with an academic/research background connected with Trump is Professor Peter Navarro of the University of California, Irvine. However, it is unclear what that connection is – Navarro seemed to have attached himself to Trump during the election campaign initially rather than having been first invited. In any case, Navarro has so far not been named to any position in the Trump administration. Lawrence Kudlow, a former Reagan appointee with a background in the financial world and CNBC, is reported to be in line for chair of the President’s Council of Economic Advisors (CEA).[7] The CEA has traditionally been a location of academic researchers, but it will apparently not be this time, at least at the top.


    In short, there does not seem to be anyone at this point who could play the role in the forthcoming Trump administration that Shultz did with Nixon. There could thus be trouble ahead, particularly if any official data series shows an unfavorable result.

    ---

    [1] For example, the release data in 2017 for the U.S. Bureau of Labor Statistics are listed at https://www.bls.gov/schedule/2017/home.htm

    [2] Joseph P. Goldberg and William T. Moye, The First Hundred Years of the Bureau of Labor Statistics, 1985, pp. 221-226. Available at http://digitalcommons.ilr.cornell.edu/cgi/viewcontent.cgi?article=1699&context=key_workplace.

    [3] Ibid, p. 188.

    [4] It’s also important to note that Shultz in 1968 was president of LERA (the host site of EPRN and thus of this Musing); LERA was then known as the Industrial Relations Research Association.

    [5] http://employmentpolicy.org/Resources/Documents/MitchellMusings%2010-22-12_0.pdf

    [6] https://www.youtube.com/watch?v=1qV84JmgwVE

    [7] http://www.adweek.com/tvnewser/larry-kudlow-to-chair-trumps-council-of-economic-advisers/314496.  

  • 10 Dec 2016 10:37 AM | Daniel Mitchell (Administrator)

    Mitchell’s Musings 12-12-16: Looking Ahead


    Daniel J.B. Mitchell


    I attended the December 2016 conference of the quarterly UCLA Anderson Forecast last Tuesday. The Forecast had originally been predicated on an assumption of a Hillary Clinton presidency. (In fairness, the UCLA event is an exercise in economic forecasting, not a political prediction.) So assumed macroeconomic policies were based on the Clinton-as-president assumption. When the win went to Trump, the Forecast – which had originally been slated to focus on real estate developments – was hastily redone to look at the economic outlook under Trump presidency.


    The UCLA forecasters did not present their original Clinton prediction with a comparison to the Trump version. But the difference can be loosely seen by comparing the Forecast’s predictions made last September with the December edition. Of course, some of the difference between September and December is due to an addition three months of economic data, not to a change in the political outlook. But since drastic changes in the U.S. economy did not occur over that three-month period, the comparison is good enough to obtain a general picture of the Clinton vs. Trump impact – at least as the forecasters saw it. The table below summarizes the September-vs.-December differences.


                                    2017         2018  

    Forecast of Sept/Dec 2016     Sept/Dec     Sept/Dec

    ------------------------------------------------------------

    Real GDP Growth                   2.4%/2.4%      2.3%/3.0%

    Core CPI                          2.5%/2.3%      2.4%/2.5%

    90-Day T-Bill Yield               0.9%/1.2%      1.7%/2.6%

    30-Year T-Bond Yield              3.3%/3.3%      3.8%/4.4%

    Total Compensation Change         3.8%/3.9%      4.1%/4.2%

    Unemployment Rate                 4.8%/4.7%      5.1%/4.5%

    Federal Budget Deficit

     FY, % of GDP                   -3.5%/-4.6%    -3.9%/-6.0%

    Real Net Exports

     $ Billion (2009$)             -$640.9/-$634  -$688.0/-$758

    -----------------------------------------------------------


    So what was the message of the revision? Essentially, the new forecast assumes that Congress goes along with a fiscal stimulus through a tax decrease, but not necessarily through spending on infrastructure. However, the economy already appears to be close to full employment so the Fed reacts to the fiscal stimulus by a partial offset through monetary policy - a rise in interest rates. Inflation (wage and price) rises a bit – net – but the main effect by 2018 is a faster rate of real GDP growth. The tax cut worsens the federal budget deficit and, despite Trump’s concerns about the trade deficit, worsens it, too, as consumption demand spills over into more imports.


    Note that this forecast revision is conventional and linear. Just add some tax cuts to a semi-Keynesian forecasting model and let the computer grind out the predicted adjustment. Whatever the model, you will get something along the lines of what UCLA predicted. The conference forecasters did say that if the U.S. winds up in a trade war with China, their estimates would need to be further revised. Of course, trade wars are not the kind of events that economic forecasting models even purport to predict.


    And that is the problem. As noted in a musing two weeks ago, there could easily be other such consequences such as an oddball monetary policy if oddball candidates are appointed to the Fed. If China chose to retaliate against U.S. policy, instead of a trade war, it could dump its large holdings of U.S. dollar reserves. Such a step would in principle depreciate the dollar, something China doesn’t want to happen, taken by itself. But if China wanted to send a message, can such a response be ruled out? And if that response by China occurred, the actual short-term financial reaction could be very unsettling.


    Other world events – some kind of blow-up in the Middle East (on top of what is already occurring) -could also be destabilizing. No one knows what the Trump policy on Syria, for example, is going to be. Domestic immigration policy could also change dramatically. The current wisdom – based on little hard evidence – is that in reality mass deportations are not likely, despite earlier campaign rhetoric. But does anyone really know?


    Although the UCLA forecasters were aware of the kind of unpredictable events that might occur in a Trump presidency – and acknowledged that such events could upset their predictions - there was one possibility that they did not consider (or, at least, did not discuss at the conference event).


    It was implicitly assumed that whatever happens, there will be honest official statistics reporting the consequences of whatever economic and other policies that might ensue. But economic statistics don’t always deliver messages that presidents want to hear. And we do know from the tweeting and other campaign tactics of the president-elect that he doesn’t enjoy criticism from anyone. He seems unlikely to enjoy unfavorable news from his own government. So is the U.S. statistical apparatus immune from pressure to avoid presenting bad news (such as the rise in the international trade deficit and the federal budget deficit that the UCLA forecast currently projects)?


    The idea that a president might decide that unfavorable news from official federal economic statistics was the result of some kind of cabal – and then decide to do something about it – is not a fantasy. It occurred during the Nixon administration and, in fact, was caught on tape. From the Washington Post:


    As first recounted by Bob Woodward and Carl Bernstein in their 1976 book "Final Days," the frequently paranoid president — who had a history of anti-Semitic outbursts — became obsessed with the idea that a "Jewish cabal" at BLS was undermining him by issuing negative labor numbers. Nixon ordered his subordinates to tally up the number of Democrats and Jews in the agency.


    "There's a Jewish cabal, you know, running through this," Nixon fumed in July 1971 to his chief of staff, H.R. "Bob" Haldeman, according to White House tapes. "...And they all — they all only talk to Jews. Now, but there it is. But there's the BLS staff. Now how the hell do you ever expect us to get anything from that staff, the raw data, let alone what the poor guys have to say [inaudible] that isn't gonna be loaded against us? You understand?"


    According to journalistic accounts and documents, the task fell to Nixon aide Fred Malek, who first counted high-ranking Democrats at BLS using voter registration lists and then identified employees with "Jewish-sounding" names. He reported the resulting statistics to Nixon in a 1971 letter that became known as the "Jew-counting" memo, identifying 25 Democrats and 13 employees who "fit the other demographic criterion that was discussed."[1]


    The UCLA forecasters didn’t discuss the possibility that future data on which they rely might be subject to political pressure from the White House. They probably didn’t consider it. But unlike the hypothetical trade war with China they did mention (and which we have never had), it’s hard to deny that pressure on statistical agencies couldn’t happen with the advent of a vindictive president. It once did.

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    [1] Dan Eggan, “There was one president who tried to manipulate BLS,” Washington Post, October 5, 2012. Available at https://www.washingtonpost.com/news/post-politics/wp/2012/10/05/there-was-one-president-who-tried-to-manipulate-bls/.  See also https://www.youtube.com/watch?v=pQjiSQKIpS8. In this excerpt (starting at minute 4:28), Nixon and his aide Charles Colson discuss a phantom conspiracy in the U.S. Bureau of Labor Statistics (BLS) to adjust unemployment data in ways unfavorable to Nixon. Supposedly, the incoming Secretary of Labor Peter Brennan was going to fix the BLS problem.

  • 02 Dec 2016 1:26 PM | Daniel Mitchell (Administrator)

    Mitchell’s Musings 12-4-16: Everything Mattered (Somewhat)


    Daniel J.B. Mitchell


    Given the poor performance of political polls in the recent presidential election, one is reluctant to cite poll data to analyze what voters were trying to say. But here goes. The Kaiser Family Foundation did a post-election poll asking voters what motivated them in their choice of candidate. The results suggest that those analysts who are theorizing and explaining really don’t have a good answer, particularly if they are trying to find some single cause. It isn’t just a case of Midwestern manufacturing workers disgruntled about international trade. That issue was clearly present, of course, and back in the day those on the left would have seized on it as the “real” issue (since economics was supposed to be the underlying cause of all things).


    Nor is it just loss of “white privilege” due to changing demographics, although that factor is there, too, for at least some voters. But blaming the outcome on racism – which seems to be the trend among some in academia – isn’t THE story, either. The problem is that there is no “THE” story.

    ---


    ---

    Voters, according to the Kaiser poll, were upset about “the direction the country is headed,” – see the chart above - but no one factor in that nebulous concept seemed to dominate what it meant. Separating out the results by those who voted for Hillary Clinton vs. those who voted for Donald Trump doesn’t reveal some mysterious key to the election’s outcome, as the chart below shows.

    ---


    ---

    Separation of the results shows that Hillary voters were more concerned about the personality of Donald Trump than about the direction the country was seen to be headed. Trump voters were more concerned about the direction than about the personality of Hillary Clinton. (The fact that 31% said the main reason they voted for Hillary Clinton was that she wasn’t Trump suggests she wasn’t a strong candidate in her own right.) Note that “immigration” might be seen as a racism proxy. But few Trump or Clinton voters saw it as the major factor in their choices.


    The fact that the election was close and revolved around margins of relatively few votes in a limited number of “swing” states suggests that a search for a grand cause is misplaced. Right now, the Democrats are doing what the Republicans were supposed to do after the projected Clinton win, i.e., falling into factions and finger pointing. But in a close election, a marginally better candidate on the Democratic side could have reversed the actual result.


    Finally, the focus on those in the workforce with less than a college degree needs some elaboration. The standard tale seems to be that THE story was that the jobs for those less than a college degree are drying up due to technology. So it might be worth looking at a new survey released by the U.S. Bureau of Labor Statistics which characterizes jobs by minimum educational requirements. (The survey excludes jobs in the federal government and self-employment.) 

    ---


    ---

    Over 30% of jobs require less than a high school diploma, as can be seen on the chart above.[1] Another 45% require a high school diploma. So that’s about 3 out of 4 jobs. Another 4% require an associate’s (community college) degree. So we are now up to about 4 out of 5 jobs that don’t require a college degree. Of course, there are some folks in those jobs with more education than is required. And there are other forms of training that may be required apart from formal education. But still, we have not run out of less-than-college jobs. There is more to be said on that issue, of course, but – again – the search for THE cause of the 2016 election result is likely to be fruitless. Everything mattered somewhat. And almost anything that might have occurred, but didn’t, to favor Clinton might have reversed the outcome.

    ---

    [1] http://www.bls.gov/news.release/pdf/ors.pdf. The numbers underlying the chart can be found at http://www.bls.gov/ors/#data and searching for educational requirements.

  • 25 Nov 2016 1:27 PM | Daniel Mitchell (Administrator)

    Mitchell’s Musings 11-28-16: What Could Possibly Go Wrong?


    Daniel J.B. Mitchell


    There has been much commentary and controversy about various picks of the incoming Trump administration for top cabinet and other positions. No, I’m not going to offer more comments on those. Nor am I going to rehearse the numerous after-the-fact explanations and criticisms related to the election, polling (mis-)predictions, news media coverage, etc.


    There are some things an incoming administration – whatever its ideological leanings – can change quickly and others which take time. Things like Supreme Court appointments take time to go through the Senate approval process. Supreme Court appointments, even when justices are finally seated, take time to have an effect; it takes a while before cases come to them that can alter past judicial directions.


    But there is one area in which there can be important impacts quickly and that is at the Federal Reserve. Once approved (there is a Senate vetting process), appointees can quickly change monetary policy (or decide not to change it). Fiscal policy, in contrast, requires legislative approval which generally is an involved process. Thus, in the 2008 financial crisis and its aftermath, the Fed became the prime mover. Congress meanwhile debated whether fiscal stimulus should be applied through tax cuts or through spending (or how much of each) and which taxes or spending projects should be considered. When it came to spending, moreover, it turned out – as it generally does – that the “shovel-ready” projects put forward really weren’t shovel ready but could only gradually (too late?) came into force.


    The Fed, as the nation’s central bank, has long been a target of conspiracy theories both of the left and the right. It is, after all, a “bank,” right? Actually, although central banks such as the Bank of England emerged gradually out of commercial banks, in their modern guise, “bank” is a misleading term for central banks.[1] They can’t be appraised on a commercial basis, i.e., are they at risk of bankruptcy? Modern central banks create money. Repeat, modern central banks create money. (See the chart below showing the Fed’s response to the 2008 crisis.) So – absent some artificial legal constraint – they can’t be at risk. The cries to “audit the Fed,” therefore, are at best ambiguous. Audit it for what?




    People have trouble wrapping their heads around the idea that the Fed creates money out of nothing. Isn’t money based on something “real”? (No.) Are you saying it’s just a social convention? (Yes.) But don’t people work hard to get it, sometimes cheat or steal or even murder to get it? (Yes.) So how can it not be “real”? (Sorry, but is an “inch” real? It, like the “dollar” is a social convention.)

    Put bluntly, there are a lot of wacko ideas about the Fed and putting someone with wacko ideas in positions of authority at the Fed could have very bad consequences. Right now, there are two vacant positions at the Fed and more to come in a year or so including the Fed chair. The wrong folks could do a lot of damage in relatively short order.[2]


    It’s true that there are other presidential decisions – particularly in the foreign policy/military area – that could do a lot of damage quickly. But if you are making a list of things that could possibly go wrong, think about putting appointments to the Fed up towards the top.

    ---

    [1] Note that the central banks in countries which adopted the euro are no longer central banks in any meaningful sense of that word. They are at best legacy monetary institutions.

    [2] http://www.bloomberg.com/politics/articles/2016-11-22/trump-looks-to-put-stamp-on-fed-in-first-months-of-presidency.  


  • 18 Nov 2016 5:15 PM | Daniel Mitchell (Administrator)

    Mitchell’s Musings 11-21-16: One Out of Five - What Might Have Been


    Daniel J.B. Mitchell


    In early January 2013, shortly before President Obama was about to take the Oath of Office for his second term, I spoke at a LERA meeting in which the panelists gave their advice to the new president. Of course, he wasn’t there to hear the wisdom of the session. So the fact that I presented five

    recommendations and only one of them actually was implemented shouldn’t be a disappointment. However, it is of interest to ponder what might have happened if the various recommendations that were not followed instead had been.


    By the way, dear reader, the LERA presentation of advice from 2013 – along with all of the informative and engaging PowerPoint slides – is available for viewing and listening on the web! Just click and on:

    https://archive.org/details/MusingOnAdviceForTheNewAdministrationFiveSuggestions

    and use the player on that link. It will take about 21 minutes to hear and see the full presentation.


    The first piece of advice presented was tactical and was based on collective bargaining, an institution with which LERA members are naturally familiar. I pointed out that collective bargaining is a repeat game and therefore any single negotiation will influence subsequent negotiations. In particular, if you get rolled in one negotiation (if you were too soft), you will have a hard time convincing your opposite number in the future that you will hang tough. You will have to invest in teaching the other side that there is a new you.


    At the time – in my view – President Obama had been rolled in his dealings with Congress on government budgets (and shutdowns), debt ceilings, etc. Changing that stance would be difficult because of that past history, but it was worth an investment, I suggested. You can decide if that investment was made. My sense is that it wasn’t and that the deficiency showed up in other areas, too. Remember the line in the sand on chemical weapons use in Syria that was ignored when it was crossed? Remember Putin coming to the rescue by producing a face-saving deal with the Syrian government? Enough said.


    Recommendation number 2 was to avoid over-reliance on opinion polling. Given the political polling meltdown in the recent presidential election, my observation about the fallibility of polling doesn’t seem – in hindsight – to be at all off base. Perhaps less reliance on pollsters and more information from the field would have suggested that the Hillary Clinton candidacy was a more tenuous proposition than the White House evidently thought. Had that information arrived, actions might have been undertaken that could have produced a different election result. Clinton’s loss is also Obama’s, since his legacy “Obamacare” program is likely to be dismembered.


    Recommendation number 3 was to re-examine the entire federal economic data-gathering apparatus. My sense was that too many resources were going into theoretical manipulation of key indicators such as real GDP and the Consumer Price Index (CPI). No such re-examination occurred, however. Perhaps if it had occurred, better indicators might have been developed that would have highlighted the economic distress that had something to do with the Trump vote. For example, if the product quality improvement adjustments being built into the CPI and, thus, into “real” wages are excessive – maybe displaced workers don’t enjoy improvements in computers quite as much as someone thinks – perhaps the rustbelt’s labor market problems might have been better understood.


    The fourth recommendation – the only one that clearly was followed (not due to my LERA presentation, of course!) - dealt with the Federal Reserve. To avoid being an issue in the 2012 election, Fed Chair Ben Bernanke announced that he did not want to be reappointed when his term expired. My recommendation was to find a pragmatist in the Bernanke mold and avoid appointing a monetarist. (Monetarists were convinced that the Fed’s responses to the earlier Great Recession were about to cause a roaring inflation – something that was implausible in 2012-2013 and never occurred during Obama’s second term.) The President subsequently appointed Janet Yellen, a pragmatist in the Bernanke mold.


    Finally, the fifth observation dealt with the slow recovery from the Great Recession. I noted that while it is true empirically that recoveries from financial crises have tended to be slow and prolonged, that fact had built into it how economic policies responded to such crises in the past. Put another way, the past did not have to be prologue.


    However, the idea that the slow recovery was just the way the natural law of the universe worked seemed to become the general opinion in Washington. Given Congressional gridlock, conventional fiscal stimulus was not on the table. My suggestion was, therefore, that dealing with the U.S. international trade imbalance would - as a byproduct - produce far more stimulus than the official stimulus program provided. And I provided a mechanism for bringing the trade balance to zero. I don’t have to tell you that nothing of the sort happened.


    In view of the last election’s result, it is worth looking into the trade issue with political hindsight. When you look at the sectors of the economy that tend to employ blue collar workers – mining, construction, manufacturing, transportation, and utilities – you can see on the chart below that employment in these sectors has declined over a long period as a fraction of the total workforce.[1] It is also true that within these sectors, the decline occurred largely in the two sectors most exposed to international trade (mining and manufacturing).

    ---


    ---

    As I have pointed out in prior musings, given the level of the trade imbalance and the now relatively small job base provided by the blue collar trade sector, a return to balanced trade would not produce a return to anything like the roughly one third of the workforce seen on the chart in the early 1970s. If that is someone’s standard of when America was great, we are not going back there. Maybe you might get employment in the blue collar sectors up to 20% (from the current 17%).


    Such an increase in jobs isn’t nothing. But Trump supporters thinking that more could occur from renegotiated “great” trade deals are likely to be disappointed. Still, the chart also makes it obvious why trade is associated with rustbelt problems in the minds of those who now have less opportunity than they had in the past. The correlation is there, even if a one-to-one causation is not.

    On the other hand, the fact is that even though dealing with the trade issue would not restore some Golden Age of blue collar employment, nothing of significance was being done to address trade directly. In particular, nothing of significance was being done about currency issues and the related Japanese and later Chinese mercantilism.


    The 2016 election was close and the loser in fact got more popular votes than the winner. But might the results have been enough different if the trade issue in particular had been targeted as suggested? It would have provided more stimulus than the official stimulus program did and produced a faster recovery. And it would have responded to concerns about trade in “swing” states that in fact went unaddressed.


    For all sad words of tongue and pen, the saddest are these, 'It might have been.’”

    ---

    [1] Data are for the month of October of each year, seasonally adjusted. 


  • 12 Nov 2016 11:02 AM | Daniel Mitchell (Administrator)

    Note: Somehow, this item from Oct. 10, 2016 was either not posted or unposted. Here it is, out of sequence:


    Mitchell’s Musings 10-10-16: Makes Sense to Me


    Daniel J.B. Mitchell


    I came across an article in the Los Angeles Times business section recently with the print-version headline “Trade is seen as harmful.”[1] The article noted that a Pew Research Center poll found that “eight out of ten adults regarded outsourcing of jobs overseas and the growth of imports of foreign-made goods as harmful to U.S. workers. By comparison only half of the people surveyed saw automation as hurtful – even though many economists believe that new technologies and the mechanization of work have led to as many job losses as imbalanced trade.” The article went on to relate the trade concerns to the current presidential election campaign.


    Above are the actual poll results from the underlying Pew study.[2] Although the LA Times article seems to imply that respondents have misdiagnosed the relative importance of trade vs. automation, there may be an explanation. Unfortunately, Pew does not provide the precise wording of the questions asked. So exactly how respondents interpreted words such as “outsourcing” and “automation” is not clear. Were they given definitions? The hurt vs. help contrast also raises some questions. Do the words mean destroy jobs vs. create jobs? Or do the terms suggest making jobs more difficult to do vs. assisting workers to do their jobs? Or do the terms suggest some kind of good jobs vs. bad jobs distinction?[3] The LA Times article implicitly assumes the destruction/creation interpretation.


    Despite the ambiguities, let’s limit the interpretation to destruction vs. creation when thinking about the trade issue and how survey respondents reacted. Other things equal, the large trade imbalance (deficit) that has characterized the U.S. for decades has to be a source of net destruction. There are caveats, of course. Important among them is the fact that workers whose jobs are lost may end up in the non-trade sector (such as retail).[4] That is, imbalanced trade may shift the mix of jobs towards the non-trade sector without changing the total number of jobs.


    What about automation as a concern? Note that the LA Times article seems to use automation and technology interchangeably. If that is also how respondents reacted, at least some of them may have been thinking about the way technology provides an assist to workers in doing jobs. Thanks to computer technology, for example, it is easier to access information needed on the job than it used to be. Medical records are now available readily without going through paper files. There are many such examples.


    Finally, what about immigration? Respondents are roughly split on whether immigration hurts or helps American workers. The LA Times article notes that ten years ago, the result was much more anti-immigrant than it is now. Although the pace of illegal immigration is hard to measure precisely, another report from Pew suggests that during the past decade, net illegal immigration has halted.[5] That is, the absolute of number of illegal immigrants residing within the U.S. has stayed about constant, as shown on the chart below. Presumably, the Great Recession and its aftermath had an impact in discouraging a net inflow; up until the Great Recession occurred, the number had been rising. That shift to a net of zero may explain the attitudinal change.[6]


    In short, the Pew survey results do not seem to be counter-intuitive. They seem to go with the election narrative. Reflected in the outcome, you have the followers of Bernie Sanders who think trade is a problem but not immigration vs. the followers of Donald Trump who think both are a problem.  You have automation-technology seen as good and bad, perhaps because the question is posed ambiguously. It makes sense to me.

    ---

    [1] The online version of the article had a different headline focused on immigration, not trade: “Americans are feeling better about immigrants' economic effect — but Republicans aren't, survey shows.” See http://www.latimes.com/business/la-fi-pew-study-jobs-20161006-snap-story.html.  

    [2] The full report is at http://assets.pewresearch.org/wp-content/uploads/sites/3/2016/10/ST_2016.10.06_Future-of-Work_FINAL4.pdf. A summary is at http://www.pewsocialtrends.org/2016/10/06/the-state-of-american-jobs/.  

    [3] The fact that decline of unions is seen as more hurtful than helpful in the survey suggests that respondents may have been thinking – at least in part – about job characteristics such as pay and benefits.

    [4] Exports and imports may differ in their labor-using characteristics. At least in theory, however, U.S. imports should be more labor-intensive than exports which would intensify the net destruction effect. That is, even with balanced trade, there might be net displacement.

    [5] http://www.pewresearch.org/fact-tank/2016/09/20/5-facts-about-illegal-immigration-in-the-u-s/. Even when the stock of illegal immigrants is constant, there may be gross flows into and out of the U.S. But the inflows and outflows must be balanced, i.e., a net of zero, for the total stock to stay the same.

    [6] Immigration has had a U-shape in terms of skill with a concentration of unskilled immigrants and a lesser concentration of highly-skilled immigrants. Since this pattern is not a replica of the existing U.S. workforce, there may be both competition among substitutes (e.g., low-skilled natives vs. low-skilled immigrants) as well as complements, e.g., natives for whom demand for their labor is enhanced by the presence of immigrants. For example, native supervisors in southern poultry packing plants may benefit from the influx of low-skilled production workers. It seems unlikely, however, that this nuanced view of the impact of immigration across different groups within the workforce is driving the survey results.

  • 11 Nov 2016 12:57 PM | Daniel Mitchell (Administrator)

    Mitchell’s Musings 11-14-16: Do We Really Have All Our Marbles?


    Daniel J.B. Mitchell


    When I was in high school a long time ago, a teacher wanted to demonstrate the laws of probability and the concept of sampling. So he filled a large jar with marbles of four colors. Since he had filled the jar himself, he knew the proportions of each color which he told the class. He then chose various students to come up and – without looking in the jar – randomly pick out a couple of marbles. The idea was to show that as the sample increased in size, it would come to reflect the actual proportions in the jar.


    Now this demonstration took place in Stuyvesant High School in New York City, a special school – you had to take an SAT-type test to get in - which at the time was male-only. And the teacher was a patsy. So the nerdy teenage boys in the class managed to sabotage the demonstration. I won’t go into more details on what happened, but let’s suppose that the demonstration had proceeded as the teacher had planned. Indeed, as the sample size grew larger, the proportions in the sample would have come to resemble the actual proportions in the entire jar.


    I thought of the marbles-in-the-jar story given the polling fiasco that occurred in the presidential election last week. Basically, pollsters (the polling industry) had a Dewey-Beats-Truman event in 2016, whatever rationalizations and defenses they offer.  And there will be rationalizations offered in the weeks to come. Examples: Clinton won the popular vote so really those who projected a Clinton win were “right.” Voters changed their minds at the last minute. Etc., etc. You can just hear the self-justifying stories now.


    But there is a problem. Pollsters usually qualify their results by including a “margin of error.” Journalists take this margin to mean that there is zero probability of an error outside that range. But presumably “margin of error” actually has some relationship (what relationship exactly?) to the statistical concept of a confidence interval linked to sample size. A confidence interval, however it is defined, does not mean that there is zero probability of a result outside its boundaries.


    That issue is a comparatively minor part of the larger story. So let’s go back to the marble story. There is no such thing as a “likely marble.” If you took the marble out of the jar, its status is clear; it was previously a marble in the jar.  But there is the concept of a “likely voter.” When you poll people, you have to decide what filters to apply in order to count people who will actually vote. Whatever you do about that issue, you may be wrong. The fact that you may be wrong potentially adds to the actual “margin of error,” but not to the reported one; you can’t treat your sample as if it were marbles in a jar.


    Marbles in a jar don’t change their colors. No matter how you ask the question about what proportions are in the jar, the marbles and their colors are unaltered. But we know from years of research that the way a question is framed when people – not marbles – are surveyed, can influence the answer you obtain. So pollsters (hopefully) try to take account of that issue. But the way in which you take account potentially increases the “margin of error.” You might be wrong.


    The same is true with regard to response bias. You don’t have to worry about marbles refusing to come out of the jar. But no one has to answer the phone. No one who does answer the phone has to volunteer to be questioned. And there may be systemic links between responding to a pollster and political inclinations. Like the problem of question framing, you can add methodological steps to try and account for response bias. But you might be wrong and thus potentially add to your “margin of error.”


    One way to try to deal with the issue of a wide margin of error in any particular poll is to average a group of polls. If you took several separate samples out of the marble jar, in effect you would have produced a larger sample and a smaller confidence interval as a result. But pollsters asking about voting behavior using different filters and different questions are not the same as repeat samples of marbles from the jar. Pollsters may influence each other. If you see your poll is an outlier, you may modify your methodology to make it more like the consensus. So the unintended biases in one poll may induce biases in another.


    Anyway, exactly how do you average different polls? Do you weight different polls differently? What weights? Even using a simple (unweighted) average is a de facto decision about weights. The bottom line here is that polling is not the same as pulling marbles from a jar; “margins of error” calculated as if it were the same are generally going to be too small.


    What about using non-poll models based on economic variables? Note that the sample size of relevant elections is small; we have presidential elections only once every four years. And economic models that correctly predict the popular vote can be wrong about the Electoral College outcome, as in Bush vs. Gore in 2000 or Trump vs. Clinton in 2016. (If your economic model of the popular vote predicted Trump, you were wrong, even though you were right.)


    Generally, economic models tend to be estimated after some methodological massaging has occurred to find the best “fit” to past election data. Confidence in results has to go down as you keep revising your methodology to improve “fit.” Ultimately, a crooked enough line can fit any constellation of points but may not be good at predicting the next point in the series. Even if you buy the notion that “it’s the economy, stupid,” that view doesn’t tell you how to measure “the economy.” Real GDP? Unemployment? Trends vs. absolute values? There is nothing in the general idea that “the economy” is key to voter behavior that tells you exactly what the economy is.


    If you are really an economic determinist, of course, you face a paradox. Why should the party which the model predicts will lose put up a candidate at all? Why go through the expense of a campaign? Presumably, the answer is that there is some probability (“margin of error”) that the model’s forecast is wrong. So there must be possible things the disadvantaged campaign can do to reverse the predicted result. And if there are such things, surely some of them are non-economic. Maybe the observation – promoted by Democrats - that Dewey looked like the man on a wedding cake had some effect!


    Bottom line: When Dewey didn’t beat Truman in 1948, folks remembered that lesson for a while. But people also forget over time. And it is always possible to assert that nowadays we have new sophisticated high-tech methodology that didn’t exist back in the 1940s. (“This time it’s different” is not an idea confined to financial bubbles.) In fact, new technology does not always guarantee better results. Think about people switching from landlines to cellphones, for example, and what that may do to sampling. Do we even know?


    Be skeptical.

  • 04 Nov 2016 10:32 AM | Daniel Mitchell (Administrator)

    Mitchell’s Musings 11-7-16: Disunion


    Daniel J.B. Mitchell


    Much has been said about the base of support for the Trump candidacy, usually depicted as disaffected white males with less than a college degree. We have noted in past musings that there are plenty of Trump supporters who don’t fit the stereotype. The latest Field Poll data for California – a decidedly “blue” state – show among “likely voters” that 28% of those with a college degree are Trump supporters as are 22% among those with postgraduate work (more than a college degree). Twenty-four percent of Latinos are supporting Trump, despite the notion that he alienated that group with anti-immigrant rhetoric. Among those 18-39 years old, he has 17% support, despite the idea that the young are inherently liberal. So the Trump story is not exclusively one of support among the stereotyped base by any means.[1] The base alone would not produce the kind of polling numbers Trump has been receiving.


    Nonetheless, it is worth looking at the stereotyped group. And particularly for LERA readers, it is worth asking whether the decline in unionization has something to do with the fact that this group is looking for someone to give it political representation. In a sense, you can view the declining-union idea as a “bowling alone” story.[2] Unions once served the group as a form of representation – not only at the workplace, but more generally in the American polity. Now, after a long period of union decline within the group’s primary employment sectors, unions represent only a small fraction of the group.


    Neither political party, it can be argued, has been energetic in finding actual economic remedies for the group. But Democrats have largely continued to do so indirectly via their historical connection to unions, unions that have less and less contact within the group. Republicans have made direct appeals – not with economic remedies, but with “social” issues. So what has occurred is the Obama “guns and God” story combined with bowling alone.[3] It’s true that in the contemporary era, folks can represent themselves and interact via internet social networks. But individual self-expression is not effective group representation.


    Unions never represented a majority of the U.S. workforce or even close to it. But in, say, the 1950s, something like a third of nonfarm workers were union members.[4] The representation rate was uneven, more in some regions and industries than others. But if you were a blue collar, white male, there was a good chance you were a union member.


    We don’t have detailed demographics from the 1950s “golden age” of unionization. But the U.S. Bureau of Labor Statistics (BLS) did do a study, based on Current Population survey data, for 1970 (so a decade or more into the decline).[5] Nowadays, the stereotypical union worker is in the public sector, even though there are still more private than public union members.[6] (In 2015, there were 7.2 million public sector union members vs. 7.6 million private members.)


    In 1970, 1.1 million union members were in “public administration” out of a total of 17.2 million members in total.[7] (Note that the absolute number of union members was larger in 1970 than today despite substantial growth since 1970 in the workforce.) Moreover, in 1970 (with private unionization rates already in decline but public rates increasing), the average union membership rate in each of the two sectors was about the same. Nowadays, the overall unionization membership rate in the public sector is 35.2% versus a mere 6.7% in the private sector.


    Male Union Members as Percent of Male Wage and Salary Workers: 1970

                      Total            White         “Negro & Other”

    ------------------------------------------------------------------

    All               27.8%            27.6%              29.0%

    Agriculture        2.7              2.9                2.2

    Mining            38.9             38.7                  *

    Construction      41.3             42.2               33.7

    Manufacturing     38.4             37.8               43.7

    Transportation

     & Public

     Utilities        49.4             50.1               43.6

    Wholesale &

     Retail           12.9             12.8               13.9

    Services &

     Finance          11.7             11.2               15.6

    Public

     Administration   27.8             27.0               33.4

    ------------------------------------------------------------------

    White Collar      12.5             12.0               20.7

    Blue Collar       42.1             42.8               36.7

    Service           20.1             20.2               19.9

    ------------------------------------------------------------------

    *Base too small for an estimate.


    The table above summarizes the BLS estimates of unionization membership rates for males in 1970. To anyone familiar with contemporary data on unionization, the figures on the table are jarringly different from what prevails today. We can debate the long-term causes of union decline. And you don’t have to be a romantic about the role of unions in society and the economy, as some in academia are. But the simple fact is that over time, a sizable group in the U.S. population that was once represented in society has lost a major channel of voice, both in the workplace and in the broader sphere of economic policy. A vacuum was created and politics abhors a vacuum. So someone moved to fill the void and now there are consequences.

    ---

    [1] http://www.field.com/fieldpollonline/subscribers/Rls2554.pdf

    [2] “Bowling alone” refers to the Robert Putnam idea that social and community institutions (including unions) have been in decline.

    [3] https://www.washingtonpost.com/news/the-fix/wp/2015/12/21/obama-dusts-off-his-cling-to-guns-or-religion-idea-for-donald-trump/

    [4] Gerald Mayer, “Union Membership Trends in the United States,” Congressional Research Service, 2004. Available at http://digitalcommons.ilr.cornell.edu/cgi/viewcontent.cgi?article=1176&context=key_workplace.

    [5] U.S. Bureau of Labor Statistics, “Selected Earnings and Demographic Characteristics of Union Members, 1970,” U.S. GPO, 1972.

    [6] http://www.bls.gov/news.release/union2.t03.htm

    [7] Public administration does not cover the entire public sector. Some government-run employment sources (transit enterprises, etc.) are lumped in with other private workers. 

  • 29 Oct 2016 2:14 PM | Daniel Mitchell (Administrator)

    Mitchell’s Musings 10-31-16: Sitting Out the Election


    Daniel J.B. Mitchell


    The Federal Reserve, on and off during the 2016 presidential election campaign, has been accused of following a policy aimed at helping Hillary Clinton by keeping the economy artificially afloat. So what has been the recent history of Fed policy, particularly during the election cycle? It’s actually not all that hard to interpret the available data.




    The chart above shows the “adjusted monetary base,” essentially the credit injected by the Fed into the broader economy.[1] What we observe is a remarkable break from the past trend in the midst of the Great Recession and then various surges and pauses thereafter. Particularly in a period in which short-term interest rates were stuck near nominal zero, it makes more sense to judge monetary policy by what happened to the base than by changes in interest rates.


    There are – as can be readily seen – distinct periods of monetary policy depicted on the chart. It is, of course, interesting to analyze the details of who said what at meetings of the Open Market Committee, and what outside observers were citing as important developments at those times. But the chart provides us with an after-the-fact general overview that cuts through the noise. Below is a listing of what we can see, combined with labor market conditions as measured by unemployment:

    • Sept. 2008 – Jan. 2009: Surge. Unemployment rate rises from 6.1% to 7.8%, seasonally adjusted. This period contains the 2008 election and a general collapse of the financial sector which was rescued through various “bailout” policies.
    • Jan. 2009 – Aug. 2009: Pause: Unemployment rate rises from 7.8% to 9.6%. New president takes office. The Fed seems to want to see what the impact of its previous surge would be. But the economy continues to weaken. The surge prevented financial collapse but did not avert a severe downturn.
    • Aug. 2009 – Mar. 2010: Surge: Unemployment rate rises from 9.6% to 9.9%. The Fed – having observed the slide and the general poor economic outlook - tries more stimulus.
    • Mar. 2010 – Jan. 2011: Pause: Unemployment stabilizes (drop from 9.9% to 9.1%), but the labor market remains very soft. The previous surge delivered less than might have been hoped for.
    • Jan. 2011 – July 2011: Surge: Unemployment continues at its high level (9.1% to 9.0%).
    • July 2011 – Dec. 2013: Pause for a year and a half to see impact of prior surge: Unemployment drops from 9.0% to 6.7% as Fed essentially sits out the 2012 presidential election.
    • Dec. 2013 – Sept. 2014: Surge. The Fed concludes that – since inflation remains low – that the unemployment rate could be pushed down further. Unemployment drops from 6.7% to 6.0%.
    • Sept. 2014 – present: Pause. Unemployment drops from 6.0% to 5.0%. Fed implements a token slight rise in interest rates. Beyond that step, it essentially sits out the election. Inflation remains low.

    It is hard to look at this Fed policy history from 30,000 feet and conclude there is some active manipulation of monetary policy around election time. In the 2008 case, with the financial sector and the economy collapsing, of course the Fed became active. But it essentially sat out the 2012 election and has repeated that behavior in 2016.

    ---

    [1] The adjustments referred to in the adjusted monetary base involve changes in reserve requirements. None occurred during the period shown on the chart. The monetary base is currency in circulation plus deposits at the Fed by banks. 

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