It is a well known fact in economics that statistical correlation does not imply causation. If in the data two variable increase or decrease approximately at the same time then correlation is positive and if they go in the opposite directions approximately at the same, correlation is negative.
Probably the most common example of significant correlation that does not say anything about causality is positive correlation between wage and education in the data. One person can say that people get higher wages because they know a lot and it benefits them when they are hired. Another person can say that higher wages allow people to get better education. And both would be right. However, there can be a third person who would look even deeper into the problem and say that there is another factor that influences both previous ones such as IQ or inborn smartness. It is easier for a person who is smart to learn (for this reason we might see higher education levels). At the same time employers might not care about education per se, but about how smart you are and how much you can contribute to the company. See the graph below (please forgive my drawing skills).
OK, so where is the problem? The problem is that the data on how smart people are is not available. Moreover, it is very hard to measure and involves measurement errors that would equally screw the results. This is what the largest oval is about – we observe what is inside it but we do not observe what is outside it, in this example – data on IQ. The part of the graph outside the largest oval is part of the theory behind the relationship between wage and education. In fact, the third guy’s statement is the most difficult to come up with. It is always easier to think in terms of variables that you already have in the data, but IQ data is not a part of it. Definitely, the theory between wage and education without IQ would not be complete and it is possible to end up with a wrong result.
Great, but how is it related to housing problems? In one of my previous posts I posted two pictures: interstate mobility declines over 2005-2006 and drops by approximately 35% (pic). At the same time volatility of unemployment increases a lot in 2008 (another pic from the same post). Here is a new picture that depicts correlation between unemployment rates across states and number of houses that are “underwater” (Value of your house is less than you owe for it in loans). Sources are BLS (for unemployment rates) and CoreLogic (for equity reports).
Nevada has 62.6% houses underwater in the first quarter of 2011 and 12.9% unemployment rate…So it means that if we delay foreclosures on those houses we increase unemployment in this state relative to others by for example reducing labor mobility? Right? Former governor of CA got an answer ( youtube video with sound). Not quite, even if there was a real decline in labor mobility in the states with a lot of people underwater that won’t mean that one caused the other. It might not be foreclosure delays per se that lock people in their house and thus they should not be implemented. Moreover, the HARP intervention was poorly designed and resulted in high redefault rates. But this information is not in the data, it is “the third guy’s logic”.
Everyone is worried about whether a double dip recession is on the way. Google search for “double dip recession” gives millions of results with fresh articles and posts in most major media websites : MSNBC, NY Times, USA today, WSJ and others. Many media are being very pessimistic about it, claiming that the economy has already entered another recession, other caution that it is possible. For these reasons, I decided to review several recession indicators used by economists. These are not the same than those usually mentioned in the media. t. Quick answer to the question whether we are in another recession is : nobody knows and simply cannot say for sure. Why? Read on.
First of all what is a recession? According to NBER, the organization who actually determines whether we are in the recession or not:
A recession is a period between a peak and a trough, and an expansion is a period between a trough and a peak. During a recession, a significant decline in economic activity spreads across the economy and can last from a few months to more than a year. Similarly, during an expansion, economic activity rises substantially, spreads across the economy, and usually lasts for several years.
Keywords in this definition are “significant” and “decline”. In other words, during recession economic activity should be declining, not just stay on the same level. Also, this decline should be significant according to NBER committee. So why cannot we say whether we are heading into a recession or not now?
- New monthly data calculated by NBER and used to determine whether the economy is in the recession is very noisy. Numbers often get revised later. For example, some economists who use econometric methods to forecast future values of GDP found that using the last observation when it becomes available makes the prediction for the future periods worse. For this reason NBER usually waits 6 to 21 months. For example, the committee determined that there was the peak in 2007 11 months after it had occurred.
- Only if many indicators reach a peak, NBER declares that this was a peak. Same happens with troughs. Looking on one indicator, such as GDP or the unemployment rate, is not enough.
OK, that is good, but what if somebody does not want to wait a year or more for NBER finally announcing that there actually was a recession today? NBER uses the following list of indicators (xls source for this information with some data):
- GDP, NIPA table 1.1.6
- GDI, NIPA Table 1.7.6 (“I” stands for income)
Source for this data is here
- Macro Advisers historical monthly real GDP (xls source)
- New Stock-Watson index of monthly GDP (source)
- New Stock-Watson index of monthly GDI (source)
- Real manufacturing and trade sales (SIC befor ’97, NAIC after ’97. NIPA Detailed Tables 2AU and 2BU)
- Index of industrial production (source)
- Real personal income less transfers (NIPA Table 2.6, see source for quarterly data)
- Aggregate weekly hours index in total private industries (source)
- Payroll survey employment (source)
- Household survey employment (source)
Interpretation of all these indicators is straight-forward: if it goes down, the economy is not doing well and if it goes up things are great. All of the source websites present you with the graphs with adjustable time periods. Comparing todays values with the values during past recessions one can see what kind of contraction/expansion in each variable NBER considers significant and makes corresponding conclusion
So what do we see in this data? Seems like nothing bad is going on for now and there is no reasons to panic. Yay! However, if one looks at the growth rates of these variables, you can measure how well the economy recovers. The general answer is that the recovery has slowed down. While there is still no reason to panic, because it might be just noise or it will accelerate soon, it is a warning sign .
But what about other indicators often used by media, such as unemployment rate? Aren’t they indicators of economic activity? Yes and no. They have their own peculiarities and have to be used with caution. For example, quoting NBER, unemployment rate
…often rises before the peak of economic activity, when activity is still rising but below its normal trend rate of increase. Thus, the unemployment rate is often a leading indicator of the business-cycle peak. … On the other hand, the unemployment rate often continues to rise after activity has reached its trough. In this respect, the unemployment rate is a lagging indicator.
And finally, for the dessert, some very interesting high-frequency indicators (because they are not so well-known one has to create graphs him-/herself ) that are available to anyone. The description is available in this post by Rebecca Wilder and the graphs with the recent data is available in this post which I am using here as well. These indicators are
- Weekly initial unemployment claims (These series are not used by NBER due to a lot of noise in it. Rebecca Wilder suggests using 4-week moving average of seasonally adjusted initial claims or its growth rate). For now these series look like that:
Rebecca’s comments about this graph:
Claims are elevated but ticked up last week. If claims do not fall back in coming weeks, the unemployment rate will rise again. This could indicate the outset of a contracting economy.
- The US Energy Information Administration’s weekly estimates of distillate fuel oil supplied to the end user in thousands of barrels per day (real) (This data is not seasonally adjusted. It works as an indicator for domestic demand for goods that are transported across the country). For now it looks like this data shows an increase.
- And the last Rebecca’s indicator is daily Treasury tax receipts that are slowing but growth remains positive
There are other series that are used in media but do not serve as indicators of recession for the US economy. These series include government debt (high levels of government debt do not mean we are in a recession, as long as people believe that it will be repaid. Demand for US bonds is not decreasing!) , inflation (Currently the Fed does not want prices to decrease, not increase! Rising prices for cotton probably mean that the demand for cotton went up. Moreover, inflation would erode government debt which is also a good thing for the US) .
To sum up, it does not seem like we are heading into another recession for now, but there are several signs that the recovery has slowed down.
In any case, in the current situation government has to do something to get the economy from the terrible situation in which we are now. Of course, if aliens do not suddenly attack the United States.