A while ago I blogged about “Is caring for the Environment a Luxury Good?”. It was motivated by the book Naked Economics: Undressing the Dismal Science written by Charles Wheelan (2012). Today I want to come back to his book and blog about the 9th chapter called “Keeping Score: Is my economy bigger than your economy?” The chapter’s goal is to take a closer look at how to measure modern economies in order to understand business cycles. Wheelan starts off with GDP as a measure for the well-being of an economy under the assumption that one uses (1) GDP per capita and (2) GDP figures adjusted for inflation. After an accessible discussion about the flaws of GDP as a measure for economic well-being, Wheelan concludes the chapter with a great overview on the vital signs of an economy which one should check alongside of GDP. His metaphor describes the holistic approach toward checking the economic and financial health of a country neatly. But what are the 10 items on the patient’s chart?
|1.||Economic well-being||Real GDP per capita|
|3.||Poverty||Poverty headcount ratio at national poverty lines (% of population)
Multidimensional poverty index (MPI)
|4.||Income inequality||Gini index|
|5.||Size of government||Ratio of all government spending to GDP|
|6.||Budget surplus/ deficit||Budget balance = revenues – spending|
|7.||Current account surplus/ deficit||Current account balance = Income earned from abroad – domestic income earned by foreigners|
|8.||National savings||National savings rate|
|9||Demographics||Age dependency ratio (retirees per worker)|
|10.||Total national happiness||?|
(Source: Wheelan, 2012, pp. 209-217; own work)
The table gives you a quick overview on the 10 indicators together with some possibilities how to measure them except for total national happiness for rather obvious reasons. One should care about all of them (and probably a whole bunch of other indicators) in order to get a good picture of an economy’s health.
Despite its flaws, real GDP per capita (which is consumption, investment, government expenditure and net exports divided by the total population) is still the most commonly used measure for economic prosperity. This is because – in the long-run – a country can only consume as much as it produces. Another important aspect is that while modest GDP growth does not necessarily benefit the neediest in the economy, a recession is likely to hit the poorest at the bottom of society the most. For example, businesses tend to lay off low-skilled workers first. Third, GDP is correlated with consumers’ and businesses’ perceptions about the economy’s outlook. Consumer confidence and business confidence impact GDP through their effect on retail sales and industrial production, respectively (Kuzmanović and Sanfey, 2012). A slowdown of GDP growth can therefore signal a deterioration of confidence about the future, which is of concern for policymakers. These three reasons endorse GDP for remaining an item on the patient’s chart.
The second vital sign policymakers care about is the unemployment rate, i.e. the ratio of unemployed to the total labour force. Deviations in the unemployment rate from its long-term trend, sometimes called the non-accelerating inflation rate of unemployment (NAIRU), tend to signal bad economic health. Unemployment below normal indicates an overheating of the economy while unemployment above normal indicates that the economy is running under capacity (underutilisation). Policymakers tend to care about staying close to the long-run trend in unemployment to smooth business cycles (inflationary and recessionary gaps).
The third vital sign is poverty. It results in a loss of human capital and may also point at systematic inequality of opportunity. Rising poverty (as well as inequality) undermine an economy’s social and political stability if not addressed by government and society. While there are many different methodologies to define and measure poverty, one of the easiest ways is to use a poverty headcount ratio which is the percentage of the population living below the national poverty lines (World Bank, 2016). Another common measure is the Multidimensional Poverty Index (MPI) which is published alongside with the Human Development Index (HDI). It is a more holistic approach by identifying multiple deprivations in the dimensions of health, education and the standard of living because poverty is far more complex than suffering from a lack of financial resources and living below an (arbitrary) monetary poverty line (OPHI, 2016).
Historically income inequality has been a minor concern. Many Economists used to focus on how to maximize the aggregate economic pie while neglecting how the pie is distributed among the population. This view was based on the notion that as long as the initial outcome was efficient it was then for the government to decide whether and how to redistribute resources. Furthermore, it was argued that a certain level of income inequality provided people with an incentive to work, innovate and become more productive. However, recent work by the OECD, for example, has shown that rising income inequality is correlated with a fall in economic growth. One of the reasons for this is that rising income inequality prevents the poor to accumulate human capital reducing social mobility and skills development in the economy which would spur productivity (OECD, 2014). Today, income inequality is a key concern and as said before in the paragraph on poverty, income inequality also has the potential to destabilize an economy. The most commonly used measure of income inequality is the Gini coefficient. It is derived from the Lorenz curve and lies within 0 and 100, where 0 implies perfect equality, i.e. everyone in the population would earn the same amount. For example, Norway (which leads the HDI world rankings currently) had an average Gini coefficient of 26.8 for years 2005 to 2013 and is one of the most equal countries in line with countries like Slovenia (24.9), Sweden (26.1) or Iceland (26.3). At the other end are countries like Chile (50.8) but also the United States (41.1; UNDP, 2015).
Another vital sign is the size of government relative to the size of the economy. While there are considerable differences in government spending as percentage of GDP across countries due to different perceptions about what government should and should not provide, one can use this measure to track changes in the indicator for one country over time to assess its health. During a recession government spending as percentage of GDP is expected to increase due to automatic stabilizers (unemployment insurance, transfers) as well as explicit stabilizers in a more deliberate Keynesian fashion (cut taxes, increase government spending on infrastructure). In booms government spending as a percentage of GDP will see a fall as the economy expands more and more rapidly. Hence this is why the indicator can track the patient’s health over time.
Both the budget balance and the current account balance are important vital signs for the financial health of an economy. In the long-run governments aim at balancing their budgets. Deviations, i.e. budget deficits and surpluses, are ‘expected’ to occur in the short-run. Governments tend to run a negative budget balance if the economy is in bad shape and surpluses if the government is said to be overheating. Overall there is evidence that budget deficits reduce domestic saving rates and push up interest rates (Gale and Orszag, 2004), making them undesirable even over shorter time periods. Likewise the current account should be balanced in the long-run but it can be positive (net lender to foreign countries) or negative (net borrower from abroad) in the short-run as well. While a negative current account balance (most often due to a negative trade balance) does not necessarily suggest bad financial health, it is clear that this cannot be sustainable in the very long-run due to issues around increasingly high debt servicing costs or credibility in the government’s ability to repay. Hence one should care about prolonged and severe imbalances regarding these two indicators.
The national savings rate is the eighth item on the patient’s chart. Neo-classical economic theory establishes a positive link between higher domestic savings and economic growth (Harrod-Domar model, Solow growth model) in the short or medium-run. In modern endogenous growth models higher domestic saving rates can even have a positive effect on growth in the long-run (Ciftcioglu and Begovic, 2010). Generally it is argued that higher domestic savings turn into more resources for the country to use for productive investments either domestically or abroad. For example, businesses can draw on domestic funds to finance their investments in new factories and other equipment and do not have to borrow from abroad, which would result in liabilities to foreigners and therefore an outflow of payments to foreign lenders in the future (USGAO, 2001). Also, in equilibrium the Savings-Investment (S-I) identity holds which requires that savings and investment are equal. Hence, a vital sign can be a relatively high domestic savings rate allowing countries to invest without the need to turn to foreign lenders.
Not unsurprisingly demographics are a key concern for policy makers today as more and more economies struggle over an ageing population. This is especially true for countries that run pay-as-you-go schemes like state pensions which are based on a pyramid-like population distribution, i.e. a large share of younger people paying into the scheme relative to the number of retirees receiving payments. However, many countries especially in Asia are ageing rapidly due to the last stage of their demographic transition with low birth and low death rates and long life expectancies (see The Life-Cycle Hypothesis and its Role in Household Saving for more). The health of an economy crucially depends on its productive workforce and with longer life expectancy and with an increasing ratio of retirees to workers current systems are likely to become unsustainable at some point and will need reforms in order to function. Already today governments’ budgets incur so-called implicit liabilities which are defined as future spending promises by government although they are not included in the conventional debt statistics (Krugman and Wells, 2009). Examples for the U.S. are Medicare, Medicaid and Social Security which represent large spending promises of the government.
Lastly Wheelan includes total national happiness as a vital sign for the health of an economy. While this is more symbolical, it does make an important point. Economic growth and development has its limits in terms of how much utility we can derive from more consumption. What is more, we derive utility from things which are not completely captured in GDP like health, education or political freedom. This is why some countries like France have tried to come up with new metrics of economic well-being more recently or the Kingdom of Bhutan has made it its goal is to maximise ‘gross national happiness’ instead of GDP, GNP or GNH (Fox, 2012).
In sum, one could argue that the patient’s chart has at least these ten items to check a country’s vital signs. GDP remains a measure of economic prosperity despite its flaws but has to be assessed alongside with other indicators like the unemployment rate, poverty or government spending and indebtedness.
I hope you enjoyed the rather theoretical post today. Many thanks for reading!
Ciftcioglu, S., and Begovic, N. (2010). Are domestic savings and economic growth correlated? Evidence from a sample of Central and East European countries. Problems and Perspectives in Management, 8(3), 30-35.
Fox, J. (2012). The Economics of Well-Being. Harvard Business Review, January-February 2012. Retrieved from: https://hbr.org/2012/01/the-economics-of-well-being#
Gale, W.G., and Orszag, P.R. (2004, 9-10 September). Budget Deficits, National Saving, and Interest Rates. Paper presented at Brookings Panel on Economic Activity. Retrieved from: http://www.brookings.edu/views/papers/20040910orszaggale.pdf
Krugman, P., & Wells, R. (2009). Macroeconomics (2nd ed.). New York, N.Y.: Worth Publishers.
Kuzmanovic, M., and Sanfey, P. (2012). Can consumer confidence data predict real variables? Evidence from Croatia (Working Paper No. 151). London: European Bank for Reconstruction and Development. Retrieved from: http://www.ebrd.com/downloads/research/economics/workingpapers/wp0151.pdf
OECD (2014). Does income inequality hurt economic growth? Focus on Inequality and Growth, December 2014, 1-4. Retrieved from: https://www.oecd.org/social/Focus-Inequality-and-Growth-2014.pdf
OPHI (2016). Oxford Poverty & Human Development Initiative: Policy – A Multidimensional Approach. Retrieved from: http://www.ophi.org.uk/policy/multidimensional-poverty-index/
UNDP (2015). Human Development Report 2015: Work For Human Development. New York, N.Y.: United Nations Development Programme. Retrieved from: http://report.hdr.undp.org/
USGAO (2001). National Saving: Answers to Key Questions. Washington, D.C.: United States General Accounting Office. Retrieved from: http://www.gao.gov/assets/210/201778.pdf
Wheelan, C. (2012). Naked economics: undressing the dismal science. New York, N.Y.: W.W. Norton & Company.
World Bank, 2016. Poverty headcount ratio at national poverty lines (% of population). Retrieved from: http://data.worldbank.org/indicator/SI.POV.NAHC