Wednesday, August 16, 2017

The New Zealand Election and Monetary Policy

The Labour Party wants the RBNZ to target unemployment in addition to inflation. No one likes unemployment, but this does not make it a good policy change. It has been used before and failed everywhere.

Most probably, the policy meant to target the unemployment rate in the short-run (over the business cycle or maybe over the election cycle).[1] A reduction of the short-run unemployment rate below the Natural Rate of Unemployment (NRU) requires the RBNZ to generate higher inflation. According to the Phillips curve, there is a negative relationship between wage inflation and unemployment. The celebrated Phillips curve was first introduced by a Kiwi, Bill Phillips in 1958

Politicians liked that relationship because it meant that they could trade-off more inflation for lower unemployment. Unfortunately, it is not that simple. It is a textbook economics that the Phillips curve shifts up and down every time the expectations about the inflation rate change. And, what matters for work is the real wage rate – the wage adjusted for inflation – not the nominal wage; in other words, the purchasing power of the money wage.   

The unemployment rate changes in the short run: (1) because firms hire labour as long as the real wage paid to workers is below labour productivity (the marginal productivity of labour); stop hiring when the real wage rate is  equal to labour productivity; and layoff labour when real wages increase above labour productivity. And, (2) because workers accept job offers when the real wage rate is higher than the reservation wage, and don’t when it is lower (here). The reservation wage depends on the generosity of unemployment and other benefits.  

The RBNZ comes into the picture because it can affect real wages via expected inflation. Workers and employers take this expected inflation rate to calculate the purchasing power of the offer (i.e., the real wage).

This policy will not work according to the traditional Phillips curve because the relationship between inflation (money wages) and unemployment is unstable, especially in New Zealand because the RBNZ has successfully removed the trend from inflation (stationary).  The plot (RBNZ data) shows no negative relationship to be exploited.

The relationship between inflation and unemployment changes overtime. It has been observed to be positive, or even zero. Therefore, there is no guarantee that the RBNZ can reduce unemployment in the short run by increasing inflation; quite the opposite could happen. The U.S. and most of the industrial nations followed such policy before and those of who remember the 1970s and the 1980s must remember the double digit inflation rate and the devastation that followed.

So how will Labour design the new PTA policy?
There will be no inflation target as we know it today because they have to inflate the economy to reduce unemployment.

First, how? They either reduce the interest rate, perhaps making it negative as other central banks have done. Or, use quantitative easing, i.e., pump more money into the system by buying securities. Here is the recent IMF view on the effect of negative interest rate on the banking system and financial stability.

I would add that inflating the economy would impact the housing market quite considerably. More money, more inflation, more spending and more demand for housing. That would make the job of the new governor very difficult because the RBNZ also regulates the financial system and formally cares about stabilizing housing prices.

Second, by how much they have to increase inflation, and could they? It will have to be a lot given that monetary conditions defined by the current exchange rate and credit restrictions seem tight.

That said, it might be worse if Labour is thinking about the long-run unemployment. In this case they have an old and a dead idea. The government must come up with an estimate of something called the Non-Accelerating Inflation Rate of Unemployment (NAIRU) to replace the current inflation target. It means a particular inflation rate (not a range and not any number) that does not accelerate and consistent with the long-run unemployment rate. This is not economics, but rather alchemy. If there is no trade-off between inflation and unemployment in the long run, i.e., a vertical Phillips curve, then there is no such an inflation rate because the vertical Phillips curve implies that any inflation rate is consistent with long-run unemployment. The NRU is not the same thing as the NAIRU. The former is determined in the labour market, not by monetary conditions while the latter is about an unstable relationship between inflation and the unemployment. Most people agree that there is no trade-off in the long run. If the policy is indeed about the long-run, not about the short run then we know it won’t work.

The NRU is unknown and the estimates are highly uncertain. So whatever you want to call the long-run unemployment rate, what is that number? A few weeks ago the chief economist of the RBNZ John McDermott gave a speech, where he said that they are looking for the starts, among them I assume is U* - the long-run unemployment rate. No one knows for sure what that number is.

Also, the economic motivation behind this policy is unclear.  The Labour Party must have been advised that the relationship between inflation and unemployment is unstable, which is especially true in New Zealand. Also history shows that it never worked, because it generated persistent inflation with no reduction in unemployment, and took considerable efforts and costs to undo. In fact our labour indicators are reasonable OECD.   

Because the NRU is determined in the labour market and not by the RBNZ. Politicians must look at labor market policies that encourage work, reduce benefits, increase training and up-skilling, increase matching efficiency, help small businesses, and increase foreign direct investments etc. because monetary policy cannot deliver lower unemployment. In fact the proposed policy may increase volatility and generate higher inflation only.

[1] Economists know that monetary policy cannot change the Natural Rate of Unemployment. Milton Friedman explained 50 years ago. No new evidence suggests otherwise.

Tuesday, July 25, 2017

Politics and Public Finance in New Zealand’s Election Year

Surely no one likes poverty. Politicians of different parties might share the same preferences, but they have different budget constraints. The recent Green’s stump speeches call for ending poverty by significantly increasing social benefits paid for by higher taxes on the “rich.” Rhetoric aside, a tax increase is not a good economic idea especially when our productivity is already sluggish. Social benefits do not end poverty.

For example, over the period 2001 to 2015, the New Zealand government’s average tax revenue as a percent of GDP (29 percent on average) is higher than Australia (23 percent on average); it is significantly higher than Singapore (13 percent on average), Germany (11 percent on average), and Switzerland (9.5 percent).[1] For New Zealand, the tax revenues are mostly from taxing consumption, labor income and company profits. Australia’s average tax revenue is less than New Zealand’s even though it taxes capital gains and New Zealand doesn’t.[2] The figure below plots the World Bank Development Indicators data (July 2017).

So why does New Zealand, which is the least populated among all these countries, has such high tax revenue/GDP ratio? More spending! Typically, government’s major expenditures are on health, education, social services, and the military. Our military spending is trivial, about 1 percent of GDP so I will ignore it.[3] However, on average over the period from 2001 to 2015, New Zealand spent 9.45 percent of GDP on health (about 26 billion dollars), more than Australia (8.79 percent), only slightly less than Germany (10.7 percent) and Switzerland (10.9 percent) and significantly less than Singapore (3.75 percent).[4] See the figure below.

On education, New Zealand’s spending is the highest, nearly 6.5 percent of GDP (about 16 billion dollars). Australia (5 percent), Germany (4.7 percent) and Switzerland (5 percent) spend less than New Zealand. Singapore spends the least, 3.25 percent only.

The Labour party says (here) that it will “address chronic under-funding of health, education…” even though the data suggest that there is no such under-funding! New Zealand spends more than Australia on health, just as much as Germany and Switzerland, and three times as much as Singapore. On education, spending surpassed all of them.

More public spending on services does not necessarily mean better outcomes. There is a strong empirical evidence for that. In education, our students do not do as well as Singaporeans. Performances in standardized tests such as PISA, for example, indicate that Singapore, which spends much less public money on education, is always on the top of the world.[5]

Also, Singapore’s average annual labor quality growth, although small, 0.9 percent (see the Conference Board data), still the highest comparably.[6] Germany and Switzerland have an average annual growth of labor quality of 0.1 percent only. New Zealand’s average annual labor quality growth is 0.6 percent, still slower than Singapore, but much higher than both Germany and Switzerland. The average annual growth rate of labor quality in Australia is half that of New Zealand (source: Conference Board).

The same is true for public health. Although it is difficult to measure output, there is no credible empirical evidence that more spending on national public health systems improves outcomes.

Government spending is a function of the size and the scope of market failure. The question is how much market failure is there in education and health to justify more public spending. I do not think we answered this question in New Zealand.
Here is the big spending item. OECD data show that average net total social expenditure as a percent of GDP for New Zealand is 16.5 percent (43 billion dollars). New Zealand is not alone: it is 19 percent in Australia, 25.4 percent in Germany, and nearly 22 percent in Switzerland.[7] And here is the difference: Singapore spends only 3.5 percent of its GDP on social programs!  

Nevertheless, some expenditures on social benefits could be justified. However, benefits raise the reservation wage and increase unemployment. Singapore, which spends the least on social benefits has the lowest unemployment rate, 1.7 percent in 2015.[8]    

Before getting excited about increasing social spending, note that taxes reduce labor productivity.[9] The plot below shows that Switzerland, Germany, and Singapore, which tax the least, have higher GDP per capita than us.

My advice to the politicians who advocate more taxes is to consider alternative policies to help the poor without taxing potential productivity of the whole country.  
New Zealand already has the highest tax revenue as a percent of GDP. They could reallocate expenditures, e.g., increase X and reduce Y. There must some waste in the public sector; cut it. Better, think about firm productivity-indexed wage subsidy (See the Nobel Laureate Edmund Phelps).      

[1] I chose Singapore because the data are available. Korea and Hong Kong would be just as good examples to use for comparisons regarding tax and spending issues. I chose Switzerland because there has been some public interest in this country as a model that New Zealand should emulate. 

[2] Since 2009 Germany levies a flat rate tax on private income from capital and capital gains. The tax rate is 25 percent plus 5.5 percent solidarity surcharge. The tax is levied at German sources as capital yields tax. There is a tax refund for personal income tax rate below percent.

[3] Australia spends about 1.8 percent, Germany 1.2, and tiny Singapore spends 3.5 percent of its GDP on defense.

[4] The IMF World Economic Outlook data (2017) estimated GDP at current prices in 2016 to be 261 billion New Zealand dollar.

[6] “Measure of the changes in the composition of the workforce. This indicator is based on underlying data on employment and wages by educational attainment, which are estimated econometrically in some cases.”

[7] Data for Switzerland correspond to 2013 which the last data published on the OECD stats.

[8] The average unemployment rate for the period 1970-2015 (a proxy for the natural rate) is 3.78 percent. Unemployment in Germany, Switzerland and Australia are similar to New Zealand on average.

[9] For international evidence see for example, Razzak and Belkacem (2016), Taxes, Natural Resource Endowments, and the Supply of Labor: New Evidence, in Handbook of Research on Public Finance in Europe and the MENA Region, IGI Global Research Publishing, USA, (eds.,) M. Mustafa Erdoğdu and Bryan Christiansen, Chapter 23, PP 520-544, May 2016.

Friday, July 14, 2017

Marginally Attached Workers in New Zealand

Discouraged workers are those who have actively searched for jobs in the past 12 months but believe that there are no jobs for them. OECD publishes data on what they call marginally attached workers. They are defined Marginally attached are persons aged 15 and over, neither employed, nor actively looking for work, but are willing/desire to work and are available for taking a job during the survey reference week. Additionally, when this applies, they have looked for work during the past 12 months. This measure is broader in scope than the discouraged worker data-set and may be used to produce alternative measures of labor underutilization.” 

The figure below plots New Zealand, Australia (shorter sample), and the United States OECD data as a percent of working age population (15-64) from 2000. New Zealand and Australia are on the primary axis and the U.S. is on the secondary axis. The picture is rather dramatic and alarming. The trend is rising in New Zealand and falling in Australia and the United States. We have nearly 300,000 marginally attached workers in New Zealand. Given the relatively smaller size of New Zealand labor market, that amount to more than 10 percent of the working age population. The number of workers in the U.S. is really a small relative to the size of the working age population. On average, Australia’s rate of marginally attached workers is more than 10 times as large as that of the U.S. New Zealand’s is more than three times as large as Australia’s.[1]

These workers could have been unemployed for a long time; or do not have the updated skills needed to get a job; or have suffered from sort of discrimination. These people would take a job if it were offered. Usually the number of such workers falls during the recovery as in the case of the U.S. and Australia in the figure below. Surprisingly, the number increased in New Zealand. Discouraged workers do not include those who have dropped out of the labor force because they went back to school, disables, and women who are on maternity leave.

New Zealand's labor statistics appear healthy, except for this strange statistic. The upward trend is alarming because there might be thousands of highly skilled and productive workers who are discounted for other reasons. I did not find any papers on the subject in New Zealand Ministry of Business, Innovation, and Employment.


[1] New Zealand sample is 2000-2014, Australia and the U.S. 2007-2014.

Sunday, July 2, 2017

The Missing Productivity Conundrum

Why has productivity been falling everywhere?

The latest issue of the Journal of Economic Perspectives has two papers on this problem.[1]The first is by Martin Feldstein, who focuses on the mismeasurement of real GDP, hence the mismeasurement hypothesis.[2] He says, “After studying the methods used by the US government statistical agencies as well as the extensive previous academic literature on this subject, I have concluded that, despite the various improvements to statistical methods that have been made through the years, the official data understate the changes of real output and productivity.” He emphasizes the inability to measure the “improvements in the quality of goods and services,” and especially for new goods and services.  

The second paper disputes the mismeasurement hypothesis.[3] Chad Syverson focuses on ICT. He shows evidence that the slowdown in productivity has occurred in many countries and not only in the U.S. and perhaps long before 2004. (See (Haldane) for the same in the U.K.) He also shows that the surplus created by the new goods and services (i.e., ICT) is far less than the missing output, which caused the slowdown in productivity. He also makes other points worth examining.

My argument is that mismeasurement explains a lot of the missing productivity in OECD countries.

First, labor productivity, measured by the ratio output/input, is pertinent to profit-maximizing firms. It makes no sense to talk about productivity, and to invest in non-profit maximizing firms and sectors.  

Second, GDP data include both profit-maximizing firms (and sectors) output and non-profit maximizing public sectors and government agencies’ output. Inability to measure the quality of goods and services notwithstanding, services output in particular is very difficult to measure, especially in non-profit maximizing sectors. Imagine the difficulty of measuring output in the public sector and government administration and services. It might be easier to measure the output of a private school or a private hospital services by inferring output from the profit function. The data, however, do not allow for such classification. Therefore, I argue that aggregate output (total real GDP) is not the right measure to use for calculating labor productivity because it includes a lot of “hard to measure outputs.”  

Third, the higher the share of services in GDP the lower measured labor productivity.

To illustrate, for New Zealand, I measure labor productivity using the sectoral data ANZSIC06 published by Stat NZ and employment by sectors (data for hours by industry are unavailable). The data are available from 2004 to 2017. Then I modify this labor productivity by subtracting the following “services” sectors data from total industry output and employment: Professional, Scientific, Technical, Administrative and Support Services, Public Administration and Safety, Education and Training, Health Care and Social Assistance, Arts, Recreation and Other Services, rental and real estate activity, and a column labeled “Not Specified.” I did not remove: Financial and Insurance Services because I think that this sector’s output could be measured or inferred from the firm’s profit function.[4]

I convert the two productivity measures to indexes (2004=100). Here is the plot.

It is clear that labor productivity (less services) is significantly higher than the conventional measure which includes public sector services.

Caveat: there must be a number of firms and agencies within the services sectors, which I removed, that are profit-maximizing. For example, in the public service sectors there are private schools, private hospitals, private arts and recreation etc. I cannot separate these from the data because I do not have enough information.

The average share of these services in GDP over the sample is 52 percent. It has been relatively stable.

This graph plots the growth rate of NZ labor productivity.

New Zealand missing growth is nearly 1 percentage point on average, which is quite a significant mismeasurement.

Labor productivity will continue to be understated until measurements of services are addressed by statistical agencies.

[1] Journal of Economic Perspectives Vol. 31, Issue 2, Spring 2017.
[2] Underestimating the Real Growth of GDP, Personal Income, and Productivity.
[3] Challenges to Mismeasurement Explanations for the US Productivity Slowdown.
[4] The Conference Board data KLEMS report figures for they call Market Activity whereby these sectors are subtracted from the total industry data.

Friday, May 12, 2017

The evidence-based New Zealand housing price policies

Last week I reported some estimates of the long-run magnitudes of the effects of demand and supply measures on the New Zealand house price index. The question is what to do with this evidence.
It seems obvious from the data that the most effective way to dampen the house price increase is to increase the supply of housing.

Demand management is less effective and could be even damaging. It is rather silly to argue that a recession, caused by tight monetary policy (i.e., higher interest rate) or contractionary fiscal policy (i.e., new taxes), is needed to reduce housing prices and resolve the affordability problem.

Specific taxes may reduce speculative excesses. The Treasury knows all about the size, the efficacy, etc. but this is an election year and politicians would be reluctant to introduce new taxes.

Higher real house prices (i.e., adjusted for inflation) imply higher wealth for Kiwis. Wealth is not a bad thing, or is it? Recall that the main concerns about high house prices are about affordability and financial instability. Just as many Kiwis will benefit from having affordable housing many middle class Kiwis will lose from lower housing prices. Policy errors could send the whole economy into a deep recession because most of the assets held by New Zealanders are housing’s.

Also on the demand management side, restricting immigration has a small effect on housing prices as the data show, unless we have a huge reduction or shutting down the immigration program completely. In addition to immigration, there could be corporations, banks, or even nonresidents in additional to people buying houses in New Zealand. 

The price and the quantity (the number) of housing are determined (simultaneously) by the intersection of demand and supply. These curves shift up and down because of shocks. In absence of interventions, restrictions and regulations both the price and the quantity adjust until they settle at a new equilibrium.

That is not the case in reality. We have two independent regulations affecting the quantity and the price of housing.

First, we know that there have been increasing building regulations, requirements, and land restrictions among other regulations that affect the quantity of housing. These regulations (interventions) increased the cost, and essentially altered the slope of the supply curve of housing. When the demand for housing increases because of the increase in income, population, immigration etc. most of the adjustment to the shock, under such regulations, has to be in the price, naturally, because the quantity is restricted by regulations. Thus, more demand means higher prices.

Second, the RBNZ intervenes with different regulations to deal with the house price increase under the new, widely followed, international arrangements of maintaining financial stability.[1]  Central banks regulate credit flow. They essentially try to manage demand for housing.  However, the problem is initially on the supply side really.

So what we have is two different regulators regulating the price and the quantity in the housing market, independently and fully discretionary, making it impossible for prices and quantities to adjust to shocks. The natural mechanism of adjustment in the housing market is impaired and it has no chance of working under these sorts of regulations.

To resolve the housing problem we have to allow the quantity of housing to vary by increasing the supply of housing (i.e., relaxing restrictions on the quantity of housing). This will bring the housing price down. One hundred thousand new building consents reduce the price house index by about 7000 index points. However, we should also allow the price of housing to vary (i.e., by relaxing the restrictions on credit and the banking system). Otherwise, we will continue to have adjustment problems in the housing market.

New Zealand banking system is good by international standards. Credit rating is Aaa. The global financial crisis did not affect us because the banking standards are high. Banks know best how to lend, to whom, and at what price, under the RBNZ’s banking supervision guidelines. These guidelines have been in place for some time; they are well tested. Therefore, to bring the housing market back to normal by removing restrictions on the supply side, discretionary intervention in the credit market needs reexamination. 

[1] Macro-prudential policy is a discretionary regulatory intervention aims, in this case, at restricting credit in housing market when the central bank thinks there are “bubbles” or “speculative excesses” in order to prevent “systemic risk” and financial instability. Read Lars P. Hansen “Challenges in identifying and measuring systemic risk,” CEMFI working paper No. 1305 I have written about this before (see older blogs) and I still believe that it is quite murky area in economic policy. 

Sunday, April 30, 2017

New Zealand House Price, Bubbles and Immigration

Many people seem to link high housing prices with bubbles and immigration. This is an election year so more will be said as the date of the election draws closer. Unfortunately, I have not seen any economic analysis and measurements except from some graphs and some stories, here and there.

I think that bivariate graphs are fine, but uninformative of a multivariate problem like this one. It is quite common to eyeball a strong graphical correlation, which disappears in regression analysis. Some stories, however, are reasonable. For example, expansionary (i.e., loose) monetary policy usually results in credit expansion, which leads to a higher demand for housing, and higher prices. I showed in a blog before that excess supply of money is also correlated with higher housing prices at a reasonable lag. Some dismiss money and talk about low interest rate instead. Fine too, because a lower interest rate increases the demand for money and credit and leads to higher asset prices. There is a general agreement that looser monetary policy also stimulates output (GDP) in the short run. It ends up opening a positive output gap whereby GDP exceeds its long-run potential over the business cycle. That must have an effect on the demand for housing, hence high housing price. These stories are fairly grounded in economic theory.

 Housing supply issues are also widely acknowledged. Finally, the story that an increase in immigration could add a demand pressure on housing, why not? But where is the test? The graph shows cyclical variations in the housing price index and net immigration. There is a weak correlation (0.30), where immigration leads, but totally breaks down in 2008.

Bubbles mean that the increase in housing prices is not justified by the economic fundamentals. I think that there is no reliable statistical test for bubbles and I have not seen a credible and convincing proof for bubbles. However, when the supply of housing is fixed for whatever reason then shocks that shift up the demand for housing (imagine a downward slopping curve intersecting the vertical supply curve) will only result in ever higher housing prices. This is quite easy to understand. 

Here are some simple measurements. I estimate a simple equation for housing prices in New Zealand, whereby the level of the house price index (base=1000) depends on aggregate income (GDP), which reflects money and credit markets, interest rate, fiscal policy factors….etc. (think about the goods and money markets).[1] It also depends on a measure of the supply of housings, which I approximate by the ratio of the value of new building consents / the value of the existing stock of housing. Finally, we have net immigration as an addition explanatory.[2]

Looking at the estimated long-run equilibrium of the model and the actual housing price index shows no bubbles whatsoever. The housing price fluctuates around the equilibrium closely. It was above equilibrium from 1996 to 2000; slightly overshot its equilibrium value in 2015; and less than or equal to equilibrium during the rest of the period.

The coefficient of net immigration, conditional on demand and supply factors, is 0.015 (with a standard deviation of 0.0055). So an increase (decrease) of 50,000 people leads to an increase (decrease) in the housing price index by 750 points, e.g., from 1000 to 1750. That is a growth of 75 percent. GDP has a larger effect on housing prices. The coefficients is 0.059 (with standard error 0.0050). The supply factors have a coefficient of -0.07 (standard error 0.0550). An increase in new building consents relative to the stock of housing lowers the housing price index. This is a very large coefficient. An increase of 100,000 new building consents lowers the housing price index by 7000 points.  

Unfortunately I do not have data for Auckland and other regional housing markets so my analysis is restricted to New Zealand as a whole. I hope that this is a useful analysis. Someone may want to take it further, which could shed more light on the matter. Panel data analysis could be done if regional data are available. 

[1] We cannot have credit in the model because it is highly correlated with GDP. The same is true for money.

[2] The equation is estimated over the period Mar 1996 to Dec 2016 using two-sided Dynamic-OLS (Phillips-Loreatn, 1991). This is a most efficient estimator. It is equivalent to a system of equations and accounts for endogeneity and serial correlation. Housing price index is regressed on a constant term and the levels of GDP, the ratio of new building consents / housing stock ratio, and net immigration; the growth rates of these three variables; one lag and one lead of the growth rates (i.e. the two-sidedness); and an error correction term. It assumes that the levels are integrated of order 1, and cointegrated. Standard errors are consistent Newey-West. The data are from the RBNZ website. Monthly new building consents are from Stat NZ. It includes residential buildings, houses, apartments, townhouses, and retirement village units. I ignored public building and constructions.     

Friday, January 20, 2017

The U.S. Presidents and the Economy?

The U.S. has a new president. Hopes are for a better economy. The U.S. presidents could affect the lives of millions, some for better and some for worse. The question is how do they affect the average. The affect the macro-economy primarily via their tax policies because taxes, whether on labor income or capital, affect productivity, labor supply and capital accumulation. Most economists agree that taxes have real effects. Other policies such as the spending on infrastructure, defense, the environment, health, education, R&D and social policies can also have some effect on growth and income per capita. However, there is no consensus about the empirical size of the average effects of these policies. The president's policy effects on growth may take a much longer time than the time he spends in office. These policies also change, and expected to change, from one president to another. A tax reduction today may well mean a tax increase tomorrow.  

Here are the average growth rates of real GDP and real GDP per capita for the periods of the presidencies of Carter, Reagan, Bush I, Clinton, Bush II, and Obama. GDP may not be a perfect measure of well being, but that is the only measure available today. These growth rates did not vary across presidents significantly, except for the Obama presidency. For him, the growth rates are relatively lower than the rest and lower than the overall averages from 1977 to 2015. However, the Obama presidency coincided with the global financial crisis and the Great Recession from October 2007 to April 2009. The U.S. economy also experienced recessions during Bush I and Bush II presidencies. For Bush I, there was a recession from October 1989 to January 1991. For Bush II, there was a short recession from January 2001 to July 2001. In these three presidencies, the growth rates were relatively lower. However, there is no evidence that presidents Bush I, II, and Obama caused these recessions just as there is no evidence that Carter, Reagan, and Clinton caused the expansions.The people were equally better off during the Reagan and Clinton eras even though they are two different presidents with very different policies. 

Average of Annual Real GDP and Real GDP per Capita Growth Rates (%)

GDP per Capita
Bush I
Bush II
Std. is the standard deviation. These are almost the same for both growth rates.
Asterisk: data are up to2015.
Red color denotes growth below the overall average and periods included recessions.
The data source is the Federal Reserve Bank of St. Louis.