December Employment Slightly Off Expectations

By Thomas Cooley, Ben Griffy and Peter Rupert

Happy New Year! Today’s employment report from the BLS revealed that establishments increased employment by 156,000 in December.  In addition, over-the-month revisions decreased October employment by 7,000 while adding 26,000 to November’s job gain. There were 144,000 more private sector jobs, 12,000 of those in goods producing and 132,000 in service sector jobs. 2016 saw an increase of 2.2 million new jobs, lower than the 2.7 million jobs added in 2015.

empchgm-2017-01-06

Health care and social assistance led the way with a 63,300 employment gain. Durable goods manufacturing employment increased 15,000. On the downside, temporary help services shed 15,500 jobs; construction down 3,000 and mining and logging down 2,000.

While employment gains were less than many anticipated (somewhere in the 180,000 range) hours of work were also a bit disappointing, remaining at 34.3 after a downward revision to November from 34.4. to 34.3. Most of 2014 and 2015 saw the workweek in the 34.5 to 34.6 range while 2016 started off with a 34.6 reading but has declined over the year. avghours-2017-01-06

Real earnings of all private workers has been trending up, finally showing signs of wage growth. In real terms, however, the CPI has eaten away some of the gains.

ahecpi-2017-01-06

From the household survey the labor force increased 184,000, causing the participation rate to climb slightly to 62.7, while the number of employed increased 63,000, so that the unemployment rate increased from 4.65% to 4.72%.

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The overall picture for 2016 shows a labor market that continues to expand, but lethargically, although at a pace higher than the recovery from the 2001 recession.

emp-rec-rec-trough2017-01-06

Looking at the 12 month moving average there is some evidence of a slowing down in employment growth. However, given the recent election and the mostly positive effects on confidence measures as well as the stock market, perhaps signals a brighter future for the labor market.

payems_monthly_change-2017-01-06

 

Mr. Trump will Inherit a Robust Economy

By Thomas Cooley, Ben Griffy, and Peter Rupert

The BEA presented an early gift to the incoming administration with the final estimate of Q3 GDP growth, revised from 3.2% to 3.5%, the highest growth rate since Q3 of 2014. The strong GDP growth combined with an unemployment rate of 4.4% justifies the Fed’s December interest rate boost.

gdprealchgm-2016-12-22

The increase in real GDP in the third quarter was led by contributions from PCE (contributing 2.0 percentage point, see Table 2 in the link above), exports (1.6p.p.), private inventory investment (0.49p.p.), nonresidential structures (0.3p.p.), and federal government spending (0.16p.p.). Residential fixed investment was a drag on growth, falling for the second consecutive quarter, down 7.7% in Q2 and down 4.1% in Q3.

pcerealchgm-2016-12-22

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The U.S economy will continue to grow through the fourth quarter although it is unclear what impact the Trump election will have on Fourth Quarter results. Wholesale changes in trade policies, or merely expectations of changes in trade policy could begin impacting GDP as early as the next report.

gdp-cyc-trough2016-12-22There is some reason for pessimism on this score because Mr. Trump’s election has already pushed the dollar to new highs – currently nearly at par with the Euro.  This will hurt U.S. exports in the long run. And it may run counter to what Mr. Trump promised if the U.S. loses jobs in the Export industries.  Markets are pricing in a substantial gain in GDP and corporate profits on the basis of what is know so far and the yield curve has steepened significantly. But housing remains weak in the Fourth quarter so far and higher interest rates are not going to help that. The unfortunate fact is that the smoke really hasn’t cleared on Trump’s goals. It remains to be seen whether this optimism is warranted just as it remains to be seen what Mr. Trump’s policies actually turn out to be.

Electronic Voting Machines and the Election

By Thomas Cooley, Ben Griffy, and Peter Rupert

Three states are facing or currently undergoing a recount of votes cast, after a number of computer scientists reported some evidence of problems with the electronic voting. This finding was heavily disputed in the media, and seemingly little evidence was produced to support the conclusion that there was malfeasance in counties with electronic voting. Indeed, following the initial media response, the lead computer scientist backed away from initial reports, saying that there are flaws in electronic voting that could be easily exploited, and that an audit is important, but there isn’t direct evidence. We use our data to explore the claim that counties with electronic voting exhibited different voting patterns than their paper peers. What we find is definitely troubling: in some of the swing states, and specifically in states that were projected to vote Democratic at the top of the ticket, those with electronic voting had a decrease in the percent of the total vote going for the Clinton-Kaine campaign, and an increase for the Trump-Pence campaign. We try to determine if this is spurious by checking for patterns in other places with electronic voting, as well as during the 2012 election. We only find this correlation for swing states during the 2016 election.

Data:

We use the American Community Survey (5-year) for demographics (race, age, gender, education), data from the BLS on unemployment (October 2016 preliminary estimate), data from the BEA on personal income (2015 estimates; more recent estimates include many fewer counties. We use data from Verified Voting for voting machine type (here), which lists type, make, and model of voting machine by county for all states. Finally, we use voting data from Politico for the 2012 and 2016 elections, as well as data from CNN for the 2008 election. We have updated our data slightly since our last post, and the updated file is available here.

Results:

First, we graphically explore areas where various attributes (i.e. race, gender, education, income, unemployment, population size) do a good job explaining election outcomes, and areas where they do a worse job explaining the outcome. We started this in a previous post, and continue along those same lines. We find how much of the shift in voting patterns can be explained by these attributes by running a regression (including state fixed effects). We then use these predictions to assess how far each county is from their predicted outcome. Graphically, these differences are as follows:

resid.png

A “blue” county is one in which the Clinton campaign outperformed what would be predicted by the county’s demographic and economic characteristics, while a “red” county is one in which the campaign underperformed. The set of attributes do a good job explaining the election outcomes, with more than 90 percent of the counties falling less than 3 percentage points above or below our prediction. There do appear geographic patterns, however, in the over or under performance. Now, here’s the map of counties with electronic voting machines:

voting_machines.png

Green counties signify counties that exclusively employ paper balloting methods, while yellow counties are ones that employed either a mix of paper and electronic voting, or electronic voting exclusively. It’s worth noting that only 76 counties in the entire country use only electronic voting machines, with nearly all of these located in Pennsylvania. Now, as a visual explanation of what we will do, compare the two above maps. If you focus on the swing states (Wisconsin, Pennsylvania, North Carolina, and Florida), what you see is a pattern emerging in which our model underpredicts Democratic support in counties where paper ballot methods are prevalent, and overpredicts Democratic support in counties where electronic voting methods are prevalent. In other words, counties with electronic voting machines are (visually) less likely to vote for Clinton than we would expect given their demographic makeup. Importantly, this pattern does not appear to be  visually present in states that were never considered swing states, i.e. Texas, California, Washington, Illinois, where there is visually no correlation between voting methods and support. Focusing on Wisconsin, Pennsylvania, North Carolina, and Florida, we see

FL_NC_PA_WI_Diffs.png

Here, we remove all counties with only paper voting, and focus on four key states that employ a mix of electronic and paper voting. Yellow counties are those with electronic voting who disproportionately voted for the Republican ticket when compared to their county demographics. Key areas, specifically population centers in each state appear to have voted less frequently for the Democratic ticket than would be predicted by their characteristics. But of course, visual inspection can be deceiving, so we now turn to more robust analysis.

To assess whether there were inconsistencies in swing states for counties with electronic voting, we use the same specification as above, but include an indicator variable for whether a county is in one of Florida, North Carolina, Pennsylvania, or Wisconsin, as well as an indicator employs electronic voting machines (EVM in the table below).

test_2016.PNG

The coefficient of interest is the last one: This says that being in a swing state and having electronic voting in a county was associated with a 0.8 percentage point decrease in support for the Clinton campaign relative to support for the Obama campaign in 2012, after controlling for the attributes. This result is statistically significant, meaning that electronic voting machines in a county, or things that might be correlated with electronic voting machines in a county, are able to explain some of the results in these states. Ok, sorry, but here is a little “techy” stuff, we include state fixed effects (i.e., we account for how the overall state changed its vote during the election), employ clustered standard errors, and weight the counties by their population. This result is not limited to these four swing states (it is a larger effect if you include states that were considered swing states, but went Democratic, like Colorado). Our code and data are available here: code, data for those who wish to explore this result. We look at these four states because they were predicted to go Democratic before the election, and because exit polling the night of the election also put them squarely in the Democratic column:

pres_state_exit_pct.png

If we expand our group of states to include other “swing states,” these results continue to hold as well. One notable exception is Ohio, whose counties exhibited a positive association between electronic voting and difference in voting patterns. For Ohio, it’s important to note that a large number of votes (over 20%) were cast by mail prior to the election, and that polls as early as October 28th were suggesting that the state would move to the Republican column. This may not be entirely satisfactory, but we wouldn’t necessarily expect to detect an effect if large numbers of ballots were cast in advance. Our exit poll data was obtained from TDMS Research, and are “unadjusted (night of)” exit polls; Edison Research alters their exit polls after the election to better reflect the electorate that they believe voted. It’s worth noting that these unadjusted exit polls have been shown to be unreliable in the past.

Of course, what we find could simply be spurious correlation, or simply a correlation between the placement of electronic voting machines and some underlying factor that was correlated with additional support for the Republican Ticket. We can’t directly discount these explanations, but we can explore the variation in voting patterns among states that were never considered swing states. If these “non swing states” exhibit the same type of pattern, i.e. electronic voting machines implied fewer votes for the Democratic ticket, then we would think that electronic voting machines are more common in places that changed their votes in the election for some other reason. We first explore this for four strongly Republican states, Arkansas, Missouri, West Virginia, and Kansas. The counties in these states exhibited approximately the same average change in support for the Democratic ticket when compared with the swing states, -6.6% on average for counties in swing states, and -7.4% for counties in the strongly Republican States. They also have about the same prevalence of electronic voting machines, with 53% of swing counties having electronic voting, and 50% of strongly conservative counties having electronic voting. The results are as follows:

placebo_2.PNG

Unlike before, there is no correlation between electronic voting and a change in support for either party. Note that we can include larger strongly conservative states like Texas, and the results still hold. Now, is there any pattern in strongly Democratic-leaning states, like California, Illinois, Washington, and Virginia?

placebo_1.PNG

Again, we find no correlation. Note that we use Virginia because it contains variation in electronic voting, though it is arguably still a swing state.

This is pretty strong evidence (we believe) that counties in swing states with electronic voting are different in some important way that isn’t captured by some underlying correlation across the country. If we thought that there was some non-random placement of electronic voting machines across the country, we would expect the pattern from the swing states to hold up nationwide. It does not, which suggests that these differences are limited to places that were expected to be close during the election.

Finally, we repeat the same exercise for swing states during the 2012 election. Data on electronic voting for the 2012 election is also available from Verified Voting, and is included in our data for analysis. For this, we choose Florida, North Carolina, Virginia, and Ohio, states that were expected to be close during the 2012 election and also contain counties with and without electronic voting. What we find is the following:

test_2012.PNG

For the 2012 election, no correlation arises between electronic voting and states that were expected to swing the election. This again suggests that our results for the 2016 election are not simply spurious correlations.

It’s also worth noting that even if we assigned all counties in the country paper voting, the size of the effect is not large enough to change the election:

pres_state_pct_no_electronic.png

But, it’s hard to tell what the real size of the effect would be without more detailed data.

It’s tough to draw precise conclusions as to what these correlations mean. It’s still possible that there are other factors driving our results, other than electronic voting. But, what we do know is that results in key swing states differ in counties with electronic voting. Further, the patterns in these counties are not exhibited by other similar but not electorally important counties across the country. Additionally, electronic voting had no impact in swing states during the 2012 election. Taken together, it seems tough to dismiss the correlations that we have found in the data. While we don’t know how to interpret the findings practically, it certainly lends credence to the efforts to initiate recounts in several of the swing states.

Links:

uncleaned data: link

cleaned data: link

Stata code: link

github code (note, some of this code is mildly out of date; will update soon): link

Interactive maps:

Unexplained Variation map: link

Voting Machines map: link

Exit Polls map: link

Outcome with no Electronic Voting map: link

 

 

November Employment: so-so

By Thomas Cooley, Ben Griffy, and Peter Rupert

Today the BLS announced that November payroll employment increased 178,000. This was in line with expectations and consistent with recent months. Several of the headline numbers indicate a very strong jobs report: unemployment declined to its lowest level since August 2007; but these numbers mask the continued truncation in the labor force, as much of this decline was driven by a decline in the labor force participation rate. The establishment survey contained positive results for the employed.

empchgm-2016-12-02

Of the increase in employment, 156,000 were private sector jobs, up from 135,000 in October. The single largest category was the services sectors, providing 139,000 new jobs, which was more than the 128,000 created in October. About half of this came from professional services, while most of the rest was composed of education and health services. Government employment (local, state, and federal) increased by 22,000, up from 7,000 in October. Average weekly hours held constant at 34.4, having changed little over the past year:

avghours-2016-12-02.png

Hourly earnings showed a small decline, moving from $25.92 to $25.89 per hour, and breaking a year a positive growth, though the decline was small and year over year, the growth rate was still positive:ahe-2016-12-02.png

As with last month’s employment report, the household survey again contained some less positive results for the US labor market. Unemployment continued to trend down, declining to 4.6 percent from 4.9 percent in October, it’s lowest since before the recession:

 

More inclusive measures (U6) also exhibited this downward trend. The rate for adult men declined from 4.6 to 4.3, and the rate for women declined from 4.3 to 4.2. Superficially, all of these statistics are very positive. However, much of the decline was driven not by new jobs, but by unemployed leaving the labor market, which contributed about a third of the decline in the unemployment rate:

uu6rate-2016-12-03

lfp-2016-12-02.png

Accounting for this decline were a large decline in reentrants and new entrants to the labor market, which combined to account for 144,000 of the overall 387,000 decline in unemployment levels.unemp-composition-2016-12-02.png

Both of these statistics suggest that unemployment is a very persistent state for some workers, leading to discouragement among workers. Indeed, the household survey also reports a large uptick in marginally attached workers, from 1,700,000 to 1,932,000, with about half of this increase coming from discouraged workers.

The only real take-away is that indicators for the labor market are mixed at this point. For those who are attached to the labor market, there are positive signs about employment opportunities. The continuing concern is the decline in labor force participation. However, this report was sufficiently strong and should not deter the Fed from making its expected move on interest rates at he next meeting.

 

Download Our Election Data

In order to facilitate broader discussion of the election, we have written a set of python scripts to download and organize data relating to the election. There is still a fairly high barrier to obtaining election results, so we wanted to make a clean source available for those interested. In addition to the series discussed in our previous post (here), we have included data on voting machines for those who wish to explore questions related to the recount.The code will download election results, graph them, and merge them into a .csv for statistical analysis.

We have made them available through the following sources:

  1. github: here
  2. dropbox: here

To run it, install Python (we suggest Anaconda), open a terminal and run the “Main.py” program from the file in which it was downloaded after editing options. It is likely that with a fresh installation of Python, additional modules will be necessary. This can be done by opening a terminal and typing “pip install <module name>” without quotes, and the required module substituted for <module name>.

Series available (County-level):

  1. 2016 Election (President, House, Senate, Governor)
  2. 2012 Election (President, House, Senate, Governor)
  3. 2008 Election (President, House, Senate, Governor)
  4. 2004 Election (President, House, Senate, Governor)
  5. Economic Statistics (unemployment, income, establishments, industries)
  6. Demographics (race, age, gender, education)

Series provided but not merged (County-level):

  1. 2002 industry composition
  2. Voting Rights Act coverage
  3. Voting machine type (paper, electronic, etc.)

The available options are explained and edited in the “Main.py” file. We will be gradually updating our code to include options for more series, as well as merging the “extra” series currently not merged.

Any coding contributions or comments are much appreciated.