Federal subsidies have long been a part of the US political and budgetary system, and they undoubtedly have a cumulative effect on US fiscal policy. Based on data from the US Bureau of Economic Analysis (BEA), federal subsidies have grown right alongside GDP.
As expected, the bulk of subsidy payments have been allocated to agriculture and housing, but other sectors of the economy have also benefited.
While many of these subsidies may be well-intentioned, I have been skeptical of the efficacy of subsidies ever since my Econ 101 class in college almost 2 decades ago. The notion of the deadweight loss to the economy that is necessarily the outcome of a subsidy has always stuck with me. In an overly simplified nutshell, the cost of the subsidy to the government is x; the recipient of the subsidy is paid y; and the difference between the two (x-y) is simply lost. Despite this loss, subsidies continue to flourish, partly because the benefit is concentrated while the cost is spread out among all taxpayers. In other words, an particular interest group has a clear incentive to push for a subsidy, while the cost to the individual taxpayer is so small that it’s not worth the fight.
But if we stand back and look at the effect on the economy as a whole, do subsidies in the aggregate help or hurt the economy? While each individual subsidies carries with it a deadweight loss, do the potential positive knock-on effects of the subsidy (increased employment, higher consumption, perhaps) eventually offset this loss? Those are the questions that I sought to answer by sifting through the data.
SPOILER ALERT: My overall conclusion is that by examining the period from 1960-2015, the rise of subsidies has had a negative effect on US economic growth. While subsidies buttressed economic growth in the early years of the period under review, the effect turned negative in the 1980s following an expansion of subsidies during that time. My complete analysis is detailed below and my data and calculations can be found here.
My primary areas of interests for this analysis were subsidies and economic growth. The BEA provided data on the level of subsidies each year since 1960. The dataset included data on subsidies overall and also provided data on the constituent parts of the overall subsidy level: agricultural, housing, maritime, aircraft carriers, and other.
To represent economic growth, I obtained BEA data on nominal GDP, nominal GDP growth, real GDP, and real GDP growth over the same period.
As I built out my analysis, I realized that in order to more accurately explain the relationship between subsidies and growth, I also needed a few additional macroeconomic indicators. I therefore added unemployment, labor productivity and inflation data to the mix. Unemployment and labor productivity data were obtained from the US Bureau of Labor Statistics (BLS), and I sourced inflation data from the World Bank.
My original hypothesis was that while subsidies were great for the individual recipients, they represented a raw deal for the US taxpayer overall. To test this theory, I started very simply by considering the direct relationship between growth (nominal and real GDP and nominal and real GDP growth) and subsidies. I first began with the broad measure of subsidies, and then looked into its constituent parts
Given that housing and agricultural subsidies provided the bulk of subsidy outlays for much of the period since 1960, when analyzing the individual types of subsidies, I focused on those two forms and disregarded maritime, aircraft, and others. Just by glancing at the correlation (or rather lack thereof) among just these factors alone shows that there is more to the story than just GDP and subsidies.
Looking at the interplay of Real/Nominal GDP Growth and the various subsidies metrics, the scatter plots are all over the place. There is no obvious correlation. This is also reflected in an OLS regression of the same inputs. Both of the following regressions use subsidies as the independent variables, but the first sets nominal GDP growth as the dependent variable while the second employs real GDP growth. In the case of nominal GDP growth, none of the independent variables are statistically significant (as noted by a P<0.05). For real GDP growth, on the other hand, agricultural subsidies alone met the threshold of statistical significance, with a positive sign. However, that regression had an R-squared value of just 0.20, leaving four-fifths of the change in real GDP growth unexplained.
I next added unemployment data to the mix. I wanted to test whether there was a relationship between unemployment (as the dependent variable) and subsidies (as the independent variables). Again agricultural subsidies was the only variable to show any significance, but this time the effect was negative. (This was true when I included nominal GDP growth, but not with real GDP growth.) The R-squared in this case was just 0.18, so I clearly had much more work to do.
With such low measurements of R-squared, I searched for additional variables that could help better explain the relationship between growth and subsidies. Looking again at the graph of Nominal GDP and subsidies over time, the two factors seem to rise in concert.
This relationship is even better illustrated by the following scattergram, which shows a near perfect correlation of 0.97.
I began to wonder how much of this relationship was just the effect of inflation. So I added inflation to my list of independent variables so that its effect could be explicitly considered. Likewise, I felt that it was likely that a sizeable portion of the real gains in GDP were due to increased labor productivity as businesses have invested in technology over the period under consideration. Similarly, I included population growth to account for simple demographic shifts, both in the labor force and in the number of consumers.
As you can see in the table below, when taking all of the abovementioned factors into consideration, subsidies has a negative coefficient that is statistically significant. This suggests that a rise in subsidies is associated with a decrease in economic growth.
Labor Productivity, Unemployment and Inflation were also significant and all had the signs that one would expect: positive, negative, and positive, respectively. Population Growth, however, while significant, had the opposite sign from what I had expected. Not only was the coefficient negative, but at -4.15, it had a much greater effect on Nominal GDP Growth than the other independent variables combined.
The above graph of Nominal GDP and Population Growth over time, further illustrates the tendency of nominal GDP growth to weaken as the population grows at a faster rate. I expected that an expanding demography would have a positive effect on economic growth, but the data suggests otherwise.
Taking a closer look at subsidies over time, one sees that the level of subsidies in the US began to accelerate in the early 1980s.
I therefore segmented the data to examine the effect of subsidies prior to 1980 and from 1980 onward. First, prior to 1980, when subsidies were on a lower growth trajectory, Subsidies actually had a positive effect on Nominal GDP Growth, suggesting that higher subsidy levels was associated with higher levels of economic growth from the period of 1960 through 1979.
While the reason for the increase of subsidies is beyond the scope of this analysis, one possible narrative that could be worth exploring is the contrast from one period to the next. It is very possible that policy makers in the early 1980s began to more heavily rely on subsidies due to the economic accretive effect of subsidies in the 1960s and 1970s. Perhaps low-level subsidies are actually good for the economy, but at some point in the 1980s, the US reached a tipping point at which the economic gains from subsidies were replaced by economic headwinds.
In conclusion, a close examination of the data suggests that the US economy would be better off without subsidies, or at least with a much lower level of subsidies. The deadweight loss from subsidies that economic theorists claim appears to be supported by reality. While further study would be necessary to determine the optimal level of subsidies for the US economy, this analysis suggests that a lower subsidy levels would be expected to have a positive effect on economic growth. The question remains, however, how low can subsidies be cut before the pendulum begins to swing in the other direction?
In the segmented data, the Population Growth variable has been removed due to its statistical insignificance in the early years (P-value of 0.259) and it just barely reaching the level of significance in the latter year (P-value of 0.045). Additionally, the inclusion of Population Growth in the analysis of the segmented periods greatly reduced the significance of the other independent variables in the analysis during both periods.
Unemployment and Labor Productivity Data: US Bureau of Labor Statistics
GDP Data: Federal Reserve Economic Data (FRED)
Subsidy Data: US Bureau of Economic Analysis
Inflation and Population Data: World Bank World Development Indicators