Is the US Presidential Election a Random Walk?

 

Today is (finally) the US Presidential Election. Can the results be predicted ahead of time? The Economist Magazine has a prediction model (here)  but most experts seem to be saying that the race is too close to call. I'll make a prediction because I have a little different approach than other forecasters. My results won't be very comforting.

First, I've created an Index of Political Support (see the graphic above, click to enlarge, the solid red line is the toss-up election line). A positive standard score indicates support for the GOP; a negative value indicates support for the Democratic party (this isn't a value judgment, just a way to present data).  Elections happened every four years in the US, but the support index is continuous and nonlinearly interpolated between elections. Support has cycled back and forth between parties over time with the mid-1970s to mid-1990s being GOP high points (except for the Carter years).

Then, I tested four models: (1) A Random Walk (RW), (2) A BAU (Business-As-Usual), (3) a US Economy Index model and (4) A World System Index model. The Index models are similar to the Atlanta Fed GDPNow Model except that the Political Support Index is substituted for GDP. If we accept the results of opinion polling (here), the US Economy should be the major determinant of Political Support with Foreign Policy (the World System Index) being the fourth most important. The BAU model would essentially mean that people cast their votes traditionally, possibly based on family upbringing. But, what would the RW model mean?

A Random Walk model, Support(t) = Support(t-1) + E(t-1), indicates that Political Support today is a function of yesterday's Political Support plus Random Error, E. It is sometimes called the Drunkard's Walk which assumes that randomness rules our lives, "...history being one damned thing after another." It would mean that Political Support is essentially unpredictable.

It turns out the the RW and BAU are the best models using the AIC Criterion. The RW or BAU result feel pretty appropriate for the current Presidential Election in the US. The voters seem to be very conflicted and acting randomly. The GOP nominee, a former single-term president, has an unsavory political and criminal record. The Democratic nominee, the current Vice President, is fighting deep-seated racial and misogynistic undercurrents in American history. The US has had only one Black president and has never had a female president.

What does this mean for our political system? David Easton's model of the Political System (here) might need to be modified. Support might be a poor method of controlling the System if it is simply a Random Walk. We have to wait for some Shock such as the Vietnam War or the 1973 Oil Crisis or the Great Inflation the Dot-Com Bubble or the Subprime Mortgage Crisis or the COVID-19 Pandemic (did I miss anything) to force the country out of random voting. Even then, it seems that the voters have poor memories (the botched handling of the COVID-19 Pandemic was during the Trump Administration and the Inflation Reduction Act did not cause the COVID-19 supply-chain inflation).

There is plenty of blame to go around. Can the Two-Party System be expected to field reasonably competent candidates who will run the country effectively? Can the Legal System hold political elites accountable for criminal activity? Can Congress impeach presidents who are incompetent? Can our Educational Systems produce citizens who can process economic and political information and make decisions?

Maybe my most depressing question is whether Democracy is compatible with Capitalism? Every day we go to work in organizations that are Authoritarian. They are not democracies. Our only experience with Representative Democracy is periodic elections which seem unable to sort out competent from incompetent candidates. Competent candidates seem to want nothing to do with political offices.

One positive trend is the Ballot Initiative (there should be more of these and some States do not allow them). Some of the big questions of the day (here) should be put to the voters. The political parties should make recommendations for each initiative and give their reasons. Only minor issues should be put to incompetent political representatives.

Data and Sources

Are Migrants Stealing Your Jobs?

The US Presidential Election is tomorrow. In a prior post (here) I presented poll results from the Pew Research Center showing that the Economy, Health Care,Violent Crime and Immigration were the major issues, of concern to more the 60% of all voters. What the polling doesn't tell me is whether these issues are interrelated. I expect they are but in no straight forward way (that's why they are political issues).

There is a mountain of research results on each issue; none of it is very easy for the average voter to wade through. Here's what the Artificial Intelligence (AI) System ChatGPT produces after surveying the results:

In summary, while there can be local variations, the data does not support a general link between higher immigration and increases in either unemployment or crime.

Interestingly, AI does not agree with Political Intelligence here. For example, the Heritage Foundation, a Right-Wing Think Tank,  argues that "...2/3 of Federal Arrests involve non-citizens". Of course, both ChatGPT and the Heritage Foundation have biases because they base their conclusions on very limited data and biased mental models. So, the assertions and arguments don't satisfy me.

In the causal diagram above (click to enlarge), I try to make some links that are missing from the literature which concentrates almost entirely on presenting numbers for Net Migration, Employment, Unemployment, Crime and Healthcare. My working hypothesis is that all these variables are being driven by Shocks and internal dynamics within the US Economy and the World-System. Causal links with "?" indicate that the direction of causation is unclear, at least to me.

First, let me summarize my results before getting into details. The models I will use are similar to the Atlanta Federal Reserve's Economy Now model but I've expanded the variables of interest to include Crime, Net Migration and Healthcare (the EconomyNow app, which you can download on your cell phone, covers GDP, Wages, Employment and Consumer Prices). It's the same model I have used in prior post (here and here). To summarize my findings:

• Crime Crime rates have been declining since 1974 with 1981 being the peak for violent crime rates. In the short run, crime rates are a random walk or driven by US Economic performance. In the long run, crime rates are driven by the World-System, particularly events in Latin America (this result should appeal to the Right-Wing). But, it is not driven by immigration.
• Government Healthcare Expenditure In the short-run, healthcare expenditure is also a random walk and driven by events in the World-System. It also is not driven by immigration.
• Unemployment In the short run, and the long run, unemployment is driven by events in the US Economy and not by immigration.
• Net Migration For net migration (more people entering than leaving the country), I don't get a very clear picture (this is probably why it is a perfect issue for wild political distortions). The best model is a Business-As-Usual (BAU) model, likely the result of US immigration restrictions. However, it does not support the assertion that the US has Open Borders.

If you are still interested, here are the details.

Crime

The graphic above (click to enlarge) shows violent crime over time across administrations (left panel) and violent crime after a NETM shock. Crime rates have been declining since a high in the late 1970s, bottomed out during the Obama Administration, began to climb again during the Trump administration and leveled off during the Biden Administration. A shock to NETM (right panel) decreased crime rates but not by a lot (the y-axis of both graphs is measured in standard score units).

The shocks to CRIME from World-System events are displayed in the graphic above. The two major shocks were the September 11 Attacks in 2001 during the Bush Administration and the COVID-19 shock during the Trump administration.

Government Healthcare Expenditure

The graphic above (click to enlarge) shows Federal Government Healthcare expenditures across administrations (left panel) and shocks to Health Care expenditure from NETM in the right panel. You can clearly see that Obama Care (the Affordable Health Care Act of 2010) "bent the curve" on US Healthcare expenditures. It is interesting that the time path of GHEALHX is best described as being driven by World-System events, specifically the state of the economy in Latin America. To my knowledge, this link has never been demonstrated before and should be considered controversial. However, shocks from NETM, although increasing GHEALHX, only have minor effects. And, the COVID-19 shock during the Trump Administration can be clearly seen. It would be fair to conclude from the graph above that abolishing Obama Care (a plank of the Trump Administration and Project 2025) would "unbend" the GHEALTHX curve.

Unemployment

The graphic above shows Unemployment (LU) by administration (in the left pane, driven by the US Economy) and shocks to LU from NETM in the right pane. You can clearly see the LU shocks at the beginning of the Reagan Administration, the beginning of the Clinton Administration, the beginning of the Obama Administration and the COVID-19 LU shock during the Trump Administration. Shocks to NETM (right pane) actually decrease unemployment but the effects are not very large.

Net Migration

The graphic above (click to enlarge) shows Net Migration (NETM) being driven by the World-System (left pane) and a shock to NETM from LU. NETM was particularly high in the Bush I and Clinton administrations as a result of World-System events. Shocks to LU decreased NETM, meaning that when Unemployment was high in the US, NETM was reduced (no jobs for migrants).

Summary

I have already summarized the results above, but I hope that the displays of Crime rates, Government Healthcare Expenditure, Unemployment, and Migration (GDP and Inflation here and here,  respectively) might help an interested voter make decisions about the upcoming Presidential Election.

One point that I hope is also made by the graphics is that "cherry-picking" isolated numbers and percentages (as seems to be the case in political rhetoric and Media presentations) mean very little without historical analysis. All the data presented above (and a lot more) are available from the World Bank Development indicators (here) and is freely available to you if you have other topics of interest.

If you would like to see some other series presented as an Economy Now model, please contact me. I am happy to post about other topics (otherwise, I'll just keep pursing my interests).

US Inflation by Administration

 

We are now within a week of the 2024 Presidential Election. There seems to be a lot of confusion in the American electorate about the state of the Economy and the role (if any) that has been played by prior administrations. In a earlier post (here), I looked at GDP across eight Administrations starting in 1974 (I also explain how to read the graphic above). In this post, I will look at Inflation, specifically the Consumer Price Index (CPI).

There were two major episodes of Inflation since 1974:  (1) The Great Inflation of the late-1970s and early 1980s and (2) the COVID-19 Pandemic Inflation of 2020. Both these historical periods provide fascinating Economic History and provide clues about how the Economic System really works (particularly the Nixon Price Controls and the current debate). Commentators with an axe to grind will argue that inflation is caused by Government Expenditure. For example, the Inflation Reduction Act of 2022 is erroneously argued to have caused the Inflation of 2020, but causation doesn't run backwards in time. 

Without becoming sidetracked in the causes of Inflation (I'll address that elsewhere), the major causes of Inflation in the Modern period are economic Shocks (the first row of the figure above):  

Since 1975, we've had (1) the Vietnam War, (2) the Great Society Programs, (3) the Arab Oil Embargo, and (4) the COVID-19 Pandemic. Everyone reading this lived through the COVID-19 Pandemic and experienced  supply chain disruptions (remember hoarding toilet paper). Any supply shocks will increase prices--that's simple ECON 101. Aggregate Demand shocks can also increase inflation, but the effects pale by comparison. 

It's inevitable that the current administration (Carter in the late-1970s and Trump in 2020) gets blamed for everything that happens on their watch. Except for botched or nonexistent attempt at Price Controls (they were used effectively during WWII), the shocks that hit the Economic System and resulted in Inflation had external causes. It is really the Political System's response or poor memory of cause-and-effect that has led to a bad taste in Voter's mouths.

In any event, the COVID-19 Pandemic Inflation is over, the  Inflation Reduction Act of 2022 has not created permanent increases in the CPI and the Atlanta Fed Economy Now App (you can get it on your cell phone and the forecasting approach is similar to the one I use) also predicts declining Inflation.

My take on the current Inflation episode is that the Political System missed an opportunity to implement a reasonable, possibly temporary, regime of Price Controls. For example, the Kroger-Albertson's Supermarket Merger  which was blocked by the Federal Trade Commission (FTC) could have been used to exert some control over Food Prices. As a condition of the merger, the new company would have been required to accept Price Gouging laws. Food Price Inflation has been major complaint of consumers. Capping price increases at Kroger-Albertson's would have been a major stimulus to their business. Blindly following Neoliberal Free-Market principles has prevented the US from responding to Inflation. Voters should be concerned about failures in the Political System.

US Economic Performance by Administration

 

We are about a week away from the US 2024 Presidential Election. Polling seems to show that likely voters are very concerned about the economy and hold each succeeding administration responsible for economic performance (see below). 

The graphic above shows Gross Domestic Product (GDP) performance since 1975 by the various presidential administration. The black line is actual GDP, the dashed red line is the GDP Attractor Path and the dotted and dashed blue and green lines are the lower- and upper-98% prediction intervals, respectively. The model used for prediction is my version of the Atlanta Fed GDP Now model.

The model shows that for most of the period (except briefly in the Reagan administration) the economy performed better than might have been expected from the Attractor Path. The Clinton and Bush II administrations even briefly reached the upper-98% prediction interval. The Trump administration inherited a solid economy from the Obama administration but was clearly affected by the COVID Pandemic. The Biden administration, so far, has almost returned to the upper-98% prediction interval, meaning the economy has performed extraordinarily well.

The best prediction for the future is that the economy will return to the GDP Attractor Path which means that whichever party wins the White House, the administration will face downward pressures on economy growth.

Polling on Economic Performance

A recent CBS poll (here) shows that respondents rated the prior Trump Administration (Jan 2017 to Jan 2021) as better than the current Biden Administration on economic performance. The polling does not seem to reflect the actual data (see above) but the COVID Pandemic hit during the Trump administration and respondents seem to discount the resulting economic shocks.

Update

Current BEA estimates (here) show GDP increasing within the upper-98% prediction interval. The economy continues to perform quite well inspite of pessimistic polling data.

Was It a Mistake for Britain to Enter the EU?

I have written an earlier post (here) showing the effect on Gross Domestic Product (GDP) of Britain leaving the EU and pursuing a Go-It-Alone Scenario. The effect, based on the UK20 model run with no inputs, was that GDP would peak in 2020. The pro-BRitain EXIT (Brexit) model makes the argument that Britain should have never joined the EU. I will investigate that counterfactual in the following post.

The way to construct a BrNoEU (Britain No EU) counterfactual would be to estimated a statistical model using only data prior to the assumed entry point. Then the model prediction for some variable, such as GDP, would be forecast into the future and compared with what actually happened. If entering the EU was good for GDP, the fictitious future should have shown slower growth than actually happened.

The history of Britain's entry into the European Union (EU) is somewhat complex (here), but let's for the sake of simplicity use the signing of the Maastricht Treaty on Nov 1, 1993 as the starting point. For a number of reasons to be discussed next, I decided to start the model in the year 2000 and do an attractor-path simulation with the UK2000 model model starting in 1960. The best UK2000 model has no inputs meaning that the best way to think about Britain in the late 20th century was as a Go-It-Alone nation.

To understand the counterfactual, look at the time plot above. The dark solid line is the actual path of GDP displayed here from 1980 to 2012. The dashed red line is the attractor path constructed by simulating the UK2000 model from 1960 to 2040. The dashed green and blues lines are the 98% upper and lower prediction intervals. The prediction intervals are relatively narrow for the period where we have data and start to get wider apart in the future as our confidence in the prediction decreases.

Looking at the actual path for GDP (dark solid line), prior to the Maastricht Treaty, Britain was having some trouble staying on the attractor path. The economic bubble that started in the mid-1980s had popped by the point the Maastricht treaty was signed. At the time, it must have looked as if Britain needed some help maintaining economic growth and joining the EU offered that hope. Indeed, from the 1990s till the Financial Crisis of 2007-2008, it looked as if joining the EU had been a success. The hope that Britain would keep growing (red arrow) above the attractor path, however, was an unrealistic expectation. Currently, the British economy is on the attractor path looking as if there was not much benefit from entering the EU.

How do we understand this counterfactual? If the pro-Brexit position is that the British economy would return to the growth rates of the 1993-2007 period by leaving the EU, the model shows that joining the EU would most likely lead to the departure from the attractor path and the 2007-2008 Financial Crisis put an end to the boom. The pro-Brexit position confuses membership in the EU with the effects of the 2007-2008 Financial Crisis. Leaving the EU will not increase GDP growth and will likely take the British economy back to the boom-and-bust period of the 1980s.

If you look back at my earlier post (here), you will also see that whatever Britain does, attractor path growth for the economy is slowing. The economy is maturing and Brexit will not change that and will probably only make matters worse. This, of course, would not be an easy argument to sell to the British public.

Can every country have the US standard of living?

The field of Development Economics is based on the idea of Convergence: Because underdeveloped economies have faster growth rates than developed economies, all economies will eventually converge in terms of per capita income (taken as a proxy for the standard of living). Convergence holds out hope to developing economies: adopt Western economic models, open your economies to global trade and eventually your citizens will enjoy the same high standard of living as the US. 

Unfortunately, the Convergence model is based on three faulty assumptions: (1) Every economy has basically the same underlying economic model differing only in parameter values, (2) We only need to consider economic variables e.g., Gross Domestic Product (GDP) and (3) There is no such thing as a world-system, we only have isolated countries that can interact independently through global trade.

The first two assumptions can be summarized with the Neoclassical Economic Growth Model (the Solow-Swan Model with a Cobb-Douglas production function).

The causal directed graph (path diagram) for the model is displayed above. One portion of the population (N) is employed as labor (L). Labor and exogenous technological change (T) drive output (Q). Capital stock (K) and Energy Consumption (E) are endogenous variables, that is, produced through economic activity. The final output is Consumption (C).  If the model is estimated from data, there can also be error terms and shocks (V2 and V3). Sometimes land (NR, natural resources) is included as an input, but often resources are ignored.

Every country is assumed to have the same basic economic models (see for example the William Nordhaus DICE and RICE models) differing only in parameter values (rates of population growth, rates of technological change, labor productivity, rates of investment, etc.). If you accept the model, it is easy to reason that rapid population growth and rapid technological change will lead to higher capital investment, higher consumption (but not necessarily consumption per capita) and higher energy use. Since there is typically higher population growth in less developed economies and since technology (knowledge) is a public good, then the predicted catch-up or convergence follows directly from the model. Needless to say, not all economists agree with the model or agree that it is supported by data, but enough do so that it contains the dominant thinking on economic growth. 

The model is myopic; it ends with consumption and energy use but does not consider the environmental impacts. The assumed counterfactual is that all countries can reach the US standard of living without environmental impacts. We can include a measure of environmental impact by adding the Ecological Footprint (EF) to the model (but see the WARNING note below). The EF measures the human demand on nature. It compares human consumption of environmental resources (demand) to biological capacity (BioCap in the directed graph above, environmental supply). Biocapacity is the biologically productive area within the country, a measure that is different from total land area because some land is unproductive (e.g, the majority of land underneath major metropolitan areas or in deserts). The ratio of consumption per capita to biocapacity per capita measures the EF or carrying capacity of the physical environment. If consumption exceeds biocapacity, the level of consumption is not sustainable unless supplemented by trade or unless technological change increase biocapacity. Obviously, not all countries can exceed biocapacity and make it up through trade. Carrying capacity without trade can be exceeded in the short run but is eventually unsustainable because the environment continues to loose biocapacity (the self-loop in the directed graph), that is, looses the ability to meet the demands placed on it.

The EF for the world system is displayed in the first time series plot at the start of this post. Somewhere around the 1990s, the world system supposedly exceed it's carrying capacity (EF > 1.0). The world system did not collapse in the 1990s but, by this measure, we have been degrading our environmental support systems since then.

We can run some simple counterfactuals with EF data. For example, the graph above assigns US consumption levels to every individual in the world population but assumes no improvement of biocapacity. By 2020 (forecasting using the WL20 model), we would need the current biocapacity of almost five Earths to meet consumption demand.

If we were to assume that biocapacity of the entire world system reached the current biocapacity of the US, we would top out at around 2.5 Earths by 2500. It seems unlikely that biocapacity will reach US levels throughout the world, especially in arid countries. A reasonable prediction might be somewhere between these two forecasts.

The conclusion from this exercise is that convergence between all the economies in the world-system is seems unlikely. The US lifestyle is, in this sense, unsustainable. Either the US (and a few other Northern countries) will have to reduce its standard of living, be forced to reduce its standard of living (the ecological collapse after 2050 in the first forecast) or it will always have dominant economies. Even if the US would gladly reduce its standard of living to some low level (it's unlikely that any economy would) what would that level be and how many people in the world system could share it without degrading environmental systems? And, what will happen when the less developed world realizes that there is no hope of sharing Western standards of living? And, what might the world look like after an ecological collapse? More importantly, since the future really cannot be known, what do less developed countries do in the short run? I'll address that question in future posts.

WARNING: The Ecological Footprint (EF) is a measure which has been widely criticized and, at one extreme, called scientifically useless. From a statistical perspective, these critiques describe construct validity: does the EF construct measure what it claims to measure. There are many arbitrary assumptions in the construction of the EF (that is, the one supplied by the Global Footprint Network and used above) and the EF has become overburdened with sustainability interpretations that make it hard to know what is "supposed" to be measured. But there are other types of validity: face validity (does the measure superficially look right), content validity (are the right indicators being included in the measure) and criterion validity (is the measure useful in models and is it related to other measures in a reasonable way). My interest has been in the criterion validity of the EF. As can be seen above, it is useful in models and can be predicted (the dashed blue and green lines are the 98% bootstrap prediction intervals). Does it really mean that we might use five times our current biocapacity at some time in the future? No! If we give up the idea that this must be an absolutely correct measure, we can still ask relative questions that are interesting: how does it change over time and in different countries? How is it related to other measures of economic development? Can we construct alternative EF measures and how do they correlate to the one provided by the Global Footprint Network. I'll present some of this analysis in future posts.

No, Q3-2017 3% GDP growth does not support Big Tax Cuts!

The Washington Post recently published an article titled Third quarter's strong economic growth could boost GOP tax effort. The article predicts the Trump administration will make the case that if you want economic growth to keep increasing to 4% (President Trump's goal), we need tax cuts. The Financial Forecast Center (here) predicts GDP Growth Rates out to the end of 2017 (graphic above). The forecast graph does not even have room for 3%, let alone 4%, GDP growth this year. Who is right?

The GOP tax cut plan is based on a long chain of reasoning. Tax cuts are supposed to increase investment and consumption (I = iY - T and C = cY - T). Investment and consumption are supposed to increase National income (as does Government expenditure and the Balance of Payments, Y = I + C + G + BOP). On the other hand, tax cuts increase the deficit D = G - T which is supposed to crowd out investment through the interest rate effect (increased interest rates discourage borrowing). Any one of these effects could fail to materialized or provide only a short term boost to the economy when the Deficit chickens come home to roost.

Let's ask a more systemic question: What kind of growth rate is the US economy capable of sustaining? Another way to state the question is to ask what is the attractor path for GDP growth? The  attractor path for the annualized growth of US GDP (based on quarterly data, here) is displayed above. Out to 2030, the attractor path (the dashed red line based on the state of the US economy from the USL20 model*) is stable at around 2%. The 98% bootstrap prediction intervals (the dashed green and blue lines) suggest that numbers from 1.5% to 2.5% are probable. Growth rates above 3.5% or even negative are possible but the economy will return over time to around 2%. This is the growth rate that the US Economy can reasonably sustain given the current physical structure and economic organization. 

The real growth of the US economy, Q = f(K,L,Tech), is based on how capital, labor and technology are combined. Changes in who has money may or may not make a difference to the physical structure and economic organization of the US economy. The excess money available from tax cuts (especially if directed at the wealthy) can be spent on luxury goods or stock market speculation, neither of which will build economic capacity through investment and employment. 

If the fractured GOP is able to legislate tax cuts, time will tell how the US economy is affected. The most likely prediction is that high growth rates, if they happen at all, would be very temporary.

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* The best model (determined by the AIC criterion) is ag(GDPQ)(t) =  (F)ag(GDPQ)(t-1) + G(S)(t-1) + Qe(t), where ag() is annualized growth, F, G and Q are coefficient matrices, S is the state vector from the USL20 model and e(t) is random error. The current upsurge in US GDPQ is driven by e(t).