I already have two articles queued up:

“Workers Take Risks, Too!” – Coming August 22^nd

“The Fake Nihilism of the Right” – Coming September 5^th

And even more I’m currently writing:

“But What Does the Data Say? Taxes”

“The Uphill Battle of the Left”

“Book Review: Cultish by Amanda Montell”

“Book Review: Poverty, By America by Matthew Desmond.”

There’s too much going on in the world and I have way too many ideas in my head to quit now. Let’s get to 1000! No, let’s get to 100,000!

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Methodology

As I explained in my “Hypothesis Testing” article, I’m using a p-value of 0.10 or less for determining if a relationship is statistically significant. This is because the IMF only considers 37 countries considered advanced economies and I want to avoid false negatives. The sample size is lower for some of my charts due to missing data. For example, Andorra and San Marino didn’t have healthcare rankings so the charts involving that metric compared the 35 countries with available data. The amount for Current Healthcare Expenditures per capita is a 20-year average of nominal PPP international dollars. While they’re not adjusted for inflation, all thirty-seven countries are measured over the same period. The rankings and median line still apply. Percentage of GDP is inflation-neutral, which is one of the reasons I posted it alongside the nominal expenditures. Some of the charts I provide show a p-value of zero. While this is mathematically possible, it’s more likely my decision to round the numbers to three decimal places is what’s causing them to show up as “0.000.” My primary goal was not to establish causation, but to determine if relationships existed between two variables frequently compared by critics. Correlation alone is not enough to establish causation, but a lack of correlation is evidence against causation. For example: the lack of correlation between total healthcare spending and economic freedom shown in one of the charts is evidence against healthcare spending making the US less free.

Without further delay, on to the data!

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Claim: Universal healthcare would be too expensive

The claim I’ve heard the most from critics of universal healthcare is that we can’t afford it. This is false because we’re already spending the money. In fact, the US spends more money than any other nation on healthcare, both nominally and as a percentage of its GDP!

As shown in the graph above, the US spent an average of $8,704.80 or 16.02% of its GDP per year. This is well above the median for all the advanced economies, and no other country comes close in nominal healthcare spending or as a percentage of GDP. Again, we’re the only country without universal healthcare. While this doesn’t prove universal healthcare would be cheaper, no other country paying as much as we do is a good argument in favor of that. In my opinion, the US wouldn’t rank #1 on both the above charts if universal healthcare was more expensive. However, this wouldn’t be a data article if I just relied on my opinion. I verified by examining the data.

The plot shows no correlation (r = -0.030) between healthcare spending (% GDP) and GDP per capita, meaning there’s no data supporting the claim that universal healthcare is too expensive. As shown by the data, the other advanced economies can afford it despite most of them having smaller GDPs per capita than us (we rank 7/37 using 20-year averages). There’s no reason to believe we can’t.

I can even take it step further by separating government spending and private spending.

As shown above, a little over half of American healthcare spending is private. Even countries with a higher percentage of private spending spending (like Switzerland) still spend less than we do! Despite only having the fourth highest percentage of private healthcare spending, we still spend the most as a percentage of our GDP and second most in nominal dollars. Critics can’t argue the US spends more because we’re the largest advanced economy because “per capita” controls for population.

Some of the critics will counter this by mentioning that most of these countries have much smaller populations than America. The question I have for them is this: Okay, and? The problem with the population argument is there’s no explanation for why the population matters. Why does America having a higher population than all the other advanced economies mean we can’t implement universal healthcare? If the full argument is “our higher population means universal healthcare would be too expensive,” then the graphs above on healthcare spending and healthcare spending’s lack of correlation with GDP per Capita refute this claim. Countries with Universal Healthcare spend less and have GDPs per Capita both lower and higher than us.

Why do we spend more? Health insurance is based on risk. The people who are lower risk subsidize people who are higher risk. This is known as risk pooling (Blumberg & Giovannelli, 2026). Administering all this risk costs money. So does managing which hospitals, treatments, and medicines an insurance company is willing to cover. In fact, healthcare administration alone costs Americans billions of dollars (Gee & Spiro, 2019). In addition to covering medical treatment and high administrative costs, health insurance companies also need to make a profit. People are not just paying for their medical treatment, risk pools, and administrative costs, they’re also paying to fill the coffers of insurance executives.

Universal healthcare functions the same way, but the government is absorbing some or all the risk. This can be in the form of paying for treatment directly and/or by leveraging its power as a monopsony to negotiate lower prices with insurance companies, pharmaceutical companies, and hospitals. The result is people paying much less even in universal systems that still include private insurance. The citizens pay for it in the form of taxes, which brings me to the next criticism of universal healthcare.

What does the data say about universal healthcare being too expensive? False!

Claim: Universal healthcare will vastly increase our taxes

“Vastly increase our taxes” is a weasel phrase. It means different things to different people and most people can’t/won’t specify what it means to them when I ask. One of the few people who answered my question pointed to countries like Norway and Sweden but refused to provide any more details than that. The reason so many critics rely on weasel phrases like “vastly increase our taxes” is probably because they don’t know what they’re talking about. Most Americans don’t understand how taxes work (Callaway, 2024).

When the people who do understand taxes claim universal healthcare will increase our taxes, they’re usually talking about marginal tax rates. In progressive tax systems, marginal tax rates are brackets set up to tax the higher portions of their income more. For example, let’s assume a country has only two brackets, with the first one topping out at $50K. Any income above $50K is in the second bracket. Someone who makes $100K/year is going to be in a different marginal tax bracket than someone who makes $50K/year, but only for the money they make exceeding $50K. All money they make $50K and below will be taxed under the first bracket. To bring this back to reality, the more tax-savvy critics of universal healthcare here in America argue passing universal healthcare will either increase the tax rates in one or more of our tax brackets or require the creation of new tax brackets. There’s just one big problem: marginal tax rates are a distraction.

As I said in my “Laffer Curve” article, marginal tax rates don’t matter all that much for generating revenue. If we want to know how much people pay, we need to look at effective tax rates (see heatmap below).

As shown above, there’s positive, statistically significant correlation between effective tax rates and both total (r = 0.516) and central government (r = 0.737) tax revenue. As I stated in my “Correlation vs. Causation” article, causation requires correlation, sequence of events, and no plausible alternative explanation. Both correlation and the earning of revenue coming after taxing make a very strong argument for higher effective tax rates generating more government revenue. Additionally, the heatmap shows statistically significant moderate correlation between effective tax rates and government spending on healthcare (r = 0.348). Critics will argue this must mean the government needs higher taxes to spend more on healthcare, but this relationship lacks sequence. The correlation coefficient can’t tell us if a country’s desire to spend more government funds on healthcare makes it collect more taxes or if collecting more taxes gives it more government money to spend on healthcare. But for the sake of argument let’s assume the critics are right.

If it’s true that countries have higher effective tax rates so they can spend more money on healthcare, that still doesn’t mean the US’s taxes will vastly increase. In fact, it doesn’t mean our taxes will increase at all! To show what I mean, here’s a bar chart showing how our effective tax rates compare to other advanced economies.

America is in the middle. Every nation below and above us has universal healthcare. If nations with lower tax rates than us can still afford universal healthcare, it means having universal healthcare doesn’t automatically mean we will have higher taxes.

Realistically, however, the US probably will raise taxes to pay for universal healthcare. But what would that really look like? As shown in the graphs above, universal healthcare will likely greatly reduce both public and private spending. Even with the increase in taxes, Americans making $75K a year will save $9000 in medical fees (Flannery, 2026). According to National Nurses United (2024), “even with the tax increase proposed by legislators, 95% of Americans will pay less than they do now. The taxes would replace premiums, co-pays, and deductibles.” As a reminder, over half of our healthcare spending is private. This doesn’t even factor in the extra money gained from stronger bargaining power and fewer cases of job lock. (I go into more detail about all of this in my “Benefits of Universal Healthcare” article.) So even with increased taxes, we’ll still save money.

What does the data say about universal healthcare vastly increasing our taxes? False!

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Claim: Universal healthcare slows economic growth

As someone with an MBA, I really don’t understand the point of this criticism. There are quite a few events that can slow a country’s economic growth. Some of these events are bad, but other events are good. Ironically, acquiring a larger GDP/GDP per capita is correlated with economic growth. Known as the convergence hypothesis, poorer countries grow faster than richer ones to “catch up” with the rest of the world (Ravikumar et al., 2024). And many of the other events disappear when economists analyze them over a long enough timeline, like the twenty-year averages I’ve been using. Over that twenty-year period, we’ve had two large recessions, but both are smoothed out by averaging the shrinking in those years with the growth of the others. Yet despite all that, this argument remains one critics continue to bring up.

Let’s start by just looking at the economic growth rates.

Of the advanced economies, only one country has experienced negative growth: San Marino. The US ranks 15/37. All the countries ranked above and below it have universal healthcare. That’s evidence against the existence of universal healthcare harming economic growth, but how does it look on a heatmap compared to other healthcare spending metrics?

With almost no correlation (r = 0.034) and a very high p-value (p = 0.840), there’s no evidence of a statistically significant relationship between the presence of universal healthcare and economic growth. While I could’ve inferred this from the bar chart, this is about the data and not my inferences. Additionally, universal healthcare has weak positive correlation with government spending on healthcare and weak negative correlation with private spending on healthcare. This makes sense because most universal healthcare programs have more government spending than private spending. Government spending and private spending are inversely proportional to each other, which is why their correlation coefficient is perfectly negative (r = -1.000, p = 0.000). The most interesting part of this heatmap, however, is universal healthcare’s moderately negative correlation with total healthcare spending, both per capita (r = -0.539, p = 0.001) and as a percentage of a country’s GDP (r = -0.591, p = 0.000). As I showed earlier in this article, the US spends the most on healthcare (for inferior results, more on this later) despite not having universal healthcare. The statistically significant relationships shown by the data are a good first step towards arguing universal healthcare is cheaper than the American system.

But this section isn’t about how cheap universal healthcare is, this is about economic growth. I’ve already shown there’s no relationship between universal healthcare and economic growth, but what about how a country spends on healthcare? Critics often argue healthcare impacts economic growth because of the increased government spending on healthcare. The heatmap above shows there’s no statistically significant correlation between government healthcare spending (r = -0.238, p = 0.155), private healthcare spending (r = 0.237, p = 0.157), and economic growth. This is evidence against the increased percentage of government spending associated with universal healthcare slowing economic growth. However, there is a form of spending that is correlated with economic growth: total healthcare spending. Whether the spending is government or private doesn’t matter, but the quantity of it does. Both per capita spending (r = -0.455, p = 0.005) and spending as a percentage of GDP (r = -0.459, p = 0.004) show moderate negative correlation with economic growth. Since America has the highest healthcare spending in both categories, this is the start of a good argument for our current healthcare system being detrimental to economic growth!

What does the data say about universal healthcare slowing economic growth? False!

Claim: Universal healthcare weakens economic freedom

“Economic freedom” is another weasel phrase. When you ask someone what defines “economic freedom,” they will usually say something like “lower taxes” or “less government.” Unfortunately, both are also weasel phrases. Believe it or not, there exist a definition of “economic freedom” widely accepted by economists—two, actually. They are Economic Freedom Index by the Heritage Foundation and the Economic Freedom of the World by the Fraser Institute. Yes, the “Project 2025” people are considered an authority on economic freedom. That’s capitalism for you!

America isn’t first on either of them. Again, having countries above or below us with universal healthcare means having universal healthcare doesn’t automatically lead to less economic freedom. That addresses the generalization, but what does the relationship look like?

According to the heatmap above, the only three statistically significant relationships between universal healthcare and the six other metrics are government healthcare expenditure (% health expenditure), private healthcare expenditure (% health expenditure), and total healthcare expenditure (% GDP). There is no statistically significant relationship between the existence of universal healthcare and either measurement of economic freedom. In other words, there is no statistical evidence universal healthcare weakens economic freedom.

That said, there are a couple of other interesting parts of this graph. The first is the only statistically significant relationship the Economic Freedom Index has among the healthcare metric shown is with the Economic Freedom of the World. This means not only does the existence of universal healthcare have no impact on economic freedom (as the Heritage Foundation measures it), but healthcare spending doesn’t either. Not even government spending on healthcare has a statistically significant relationship with economic freedom. Despite this, Heritage Foundation often cites economic freedom as justification for opposing universal healthcare. Even using the preferred metric of universal healthcare’s harshest critics, the data shows absolutely no correlation between universal healthcare and reduced economic freedom.

In addition to having statistically significant correlation with the Economic Freedom Index, the Economic Freedom of the World has statistically significant correlation with government healthcare expenditure (% health expenditure) and private healthcare expenditure (% health expenditure). The former has negative correlation and the latter has positive correlation, but correlation is not equal to causality. Just because a relationship exists doesn’t mean private healthcare spending is good for Fraser’s definition of economic freedom and government healthcare having a negative relationship doesn’t mean it’s bad for economic freedom. Regardless, there’s no statistically significant correlation between the Economic Freedom of the World and a country having universal healthcare.

What does the data say about universal healthcare weakening economic freedom? False!

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Claim: Universal healthcare lower the quality of our care

Even though I hear this criticism for universal healthcare almost as much as I hear it’s too expensive, I decided to save it for last because it’s the easiest to refute. It also shows just how flawed America’s existing health system is. There are multiple quality metrics associated with healthcare: life expectancy, infant mortality, maternal mortality, and the global ranking of the healthcare system.

Some critics might ask, why aren’t you using the Human Development Index (HDI)? The HDI combines life expectancy with two other measurements not related to healthcare. If a country’s life expectancy is correlated with universal healthcare, it’s likely its HDI will be too. This makes the HDI redundant, but for the sake of being thorough, I will still provide a bar chart showing how America compares.

The United States ranks in the upper half on the HDI. While the fifteen countries that surpass it all have universal healthcare, only one metric of the HDI is related to healthcare (life expectancy). That’s why it’s more important to look at the healthcare metrics independently. Before I get to the heatmap, remember the US spends the most on healthcare, but is ranked one of the lowest of all the advanced economies.

I modified the chart a bit to show the actual numbers related to each country because the Healthcare Ranks are global, but we’re only looking at the advanced economies. Andorra and San Marino (it uses Italy’s system) are missing from the Global Healthcare Rankings chart because they weren’t ranked by the original source, U.S. News & World Report. While the US isn’t the lowest ranking healthcare system among the advanced economies, we’re getting a bad deal for what we’re spending. Again, I can give my opinions all day, but that’s not what this article is about.

Plenty of numbers to point out in this one! I’ve already covered most of the spending numbers of above, but both infant (r = -0.384, p = 0.019) and maternal (r = -0.285, p = 0.088) mortality are moderately negatively correlated with universal healthcare. While this doesn’t prove universal healthcare causes lower infant and maternal mortality rates, the lack of positive correlation is evidence against universal healthcare being detrimental to these metrics.

There’s no correlation between life expectancy and universal healthcare. While critics of universal healthcare may argue this is a win for them, what it really means is no argument can be made for America’s strictly for-profit system allowing us to live longer.

Before I get to healthcare rankings, I want to take another quick look at healthcare spending. Total healthcare spending is positively correlated (r = 0.413, p = 0.011) with life expectancy. This is true despite America spending the most on healthcare and having one of the lowest life expectancies of the advanced economies. This makes a great argument for America’s for-profit healthcare system being the exception (in a bad way) and not the rule. Spending more money on healthcare could lead to longer lifespans if the healthcare system isn’t guided by profits. There’s another metric correlated (r = -0.470, p = 0.004) with total healthcare spending: healthcare system rankings.

For Healthcare Rankings, a lower rank is better (e.g. #1 is better than #25) so the correlation coefficients for good and bad relationships will be reversed. For example, correlation coefficient for Total healthcare spending and healthcare ranking being negative means there’s evidence of a relationship between more total spending on healthcare and higher quality treatment. Neither the proportion of government nor private healthcare spending have any impact on where a healthcare system ranks, showing it’s more about how the money is spent vs. who spends the money. Infant (r = 0.609, p = 0.000) and maternal (r = 0.525, p = 0.001) mortality are both positively correlated with healthcare rankings. While it shocked me at first, I remembered lower ranking systems have larger numbers. Life expectancy is the opposite. The number increasing as the healthcare rank decreases is good. With the exception of government and private spending, healthcare rank and life expectancy (r = -0.849, p = 0.000) have the strongest statistically significant relationship on the entire chart!

We can talk about costs, taxes, growth, economic freedom all day long, but at the end of the day healthcare is about saving lives and improving the quality of life. Compared to most other advanced economies, America fails in that regard. Even among the countries our healthcare system technically outranks, we still lose to most of them in maternal and infant mortality.

What does the data say about universal healthcare lowering the quality of our care? False!

Conclusion:

When comparing the advanced economies, the data doesn’t support any of the most common criticisms of universal healthcare. Some people will push back on this by trying to say, “But you said correlation doesn’t equal causality so how does this mean anything?” Yes, correlation doesn’t equal causality, but I haven’t been trying to establish causality. In fact, quite the opposite. As I said at the beginning of this article, correlation is a necessary first step towards causality. The lack of statistically significant correlation supporting these criticisms of universal healthcare is evidence against them being true. If critics want to continue to argue universal healthcare would be bad for America, they’re either going to need to find better data or better arguments.

This article addressed the most common criticisms of universal healthcare using hard data. To find out how universal healthcare would benefit America from a socioeconomic perspective, check out my other article, “The Benefits of Universal Healthcare.”

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Sources:

Blumberg, L. J., & Giovannelli, J. (2026, March 11). What health insurance risk pooling is, and why it’s key to maintaining affordability. The Commonwealth Fund. https://www.commonwealthfund.org/publications/explainer/2026/mar/health-insurance-risk-pooling-key-maintaining-affordability

Callaway, Z. (2024, May 29). National tax literacy poll: Understanding the tax code. Tax Foundation. https://taxfoundation.org/blog/national-tax-poll-understanding-taxes/

Flannery, M. E. (2026, May 12). The education case for single-payer healthcare systems. NEA. https://www.nea.org/nea-today/all-news-articles/education-case-single-payer-healthcare-systems

Gee, E., & Spiro, T. (2019, April 8). Excess administrative costs burden the U.S. Health Care System. Center for American Progress. https://www.americanprogress.org/article/excess-administrative-costs-burden-u-s-health-care-system/

National Nurses United. (2024, September 9). Flyers. Medicare For All. https://medicare4all.org/flyers-2/

Ravikumar, B., Chinagorom-Abiakalam, D., & Smaldone, A. (2024, August 16). Convergence or divergence? A look at GDP growth across richer and poorer countries. Federal Reserve Bank of St. Louis. https://www.stlouisfed.org/on-the-economy/2024/aug/convergence-divergence-gdp-growth-richer-poorer-countries

The sources for the data are provided on the charts/graphs themselves.

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Taxing above or below that rate means the government isn’t maximizing its potential revenue. Although Laffer applied his curve universally to all forms of taxation, its primary political application has historically been leveraged to justify cutting top marginal income tax rates (Fieldhouse, 2013). The most famous example was Ronald Reagan justifying cutting the top marginal tax rate to 28% using the Laffer Curve in the 1980s. Since Reagan, conservative commentators, politicians, and economists have continued to focus on top marginal tax rates and have also used it to argue against most other tax increases. While it makes sense on the surface, the usefulness of this economic theory is questionable at best.

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How do we know it’s parabolic?

Proponents of the Laffer Curve claim the reason the Laffer Curve is parabolic because taxing too much causes people to lose their incentive to work. For example, taxing people’s income at a flat rate of 100% would make them stop working since they won’t be able to keep any of their money, leading to the government gaining no revenue. The image I made above is typically how the Laffer Curve is imagined. While it could be argued that people won’t stop working, for the sake of discussion I will assume a 100% tax rate will result in zero revenue collection. The problem is even if we assume government revenue returns to $0 at 100% taxation, that doesn’t prove the Laffer Curve is parabolic!

What do I mean? There are multiple ways to design the Laffer Curve while keeping government revenue at $0 for 0% and 100% taxation.

As shown above, the curve could be bimodal. If that’s the case, government revenue would peak at a lower tax rate, taper off in the middle, and peak again at a higher rate. One could argue the lower peak is due to there being one tax rate optimized for motivation and another optimized for revenue. Since the middle of the curve is optimized for neither, it generates less revenue.

Or it could be a plateau. It’s possible there’s a wide range of tax rates where government revenue is the highest before declining as it approaches 100%. The extra revenue the government collects from increasing the tax rate could cancel out its citizens’ declining motivation to work for those extra dollars before the latter overtakes the former. Another approach based on the Laffer Curve being flatter is spreading the tax burden out over a wider range. If there is a large plateau, then the goal becomes making sure more people pay the existing taxes as opposed to raising tax rates. While conducting research for this article, I found out the Bipartisan Policy Center advocates for exactly this (Lautz, 2026). While I don’t fully agree with their conclusion, it was nice to learn some right-leaning organizations are criticizing the Laffer Curve.

Alternatively, it could be completely asymmetric. It’s possible government revenue steadily rises with the tax rate before sharply dropping off near 100%, like a cliff. Based on what I learned in business school, there are two different types of motivation: extrinsic and intrinsic. Extrinsic motivators are rewards like money or gift cards. To quote Alfie Kohn (2018), “Do this, and you’ll get that!” These rewards are limited based on what a company is able to provide. Intrinsic motivators are more abstract. Self-fulfillment, worker autonomy, and belief in the “mission” of the company are examples of intrinsic motivators. They’re potentially infinite (Lefton & Buzzota, 2004).

Extrinsic motivators are limited in their effectiveness. Not everyone is motivated by money. In fact, money becomes even less of a motivator as people’s financial needs are met (Kohn, 2018). One could argue if the government uses the revenue it collects to meet people’s needs, it could keep increasing tax rates before people’s motivation to work harder starts to disappear. If I were to accept a version of the Laffer Curve, it would be one like the model presented above.

I want to emphasize the three different Laffer Curves above are all purely speculative. That said, even a parabolic relationship could be skewed to the left or the right, which brings me to my next point…

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Where’s the peak? Nobody knows!

The real problem with the Laffer Curve is not what happens at the beginning or end of the curve, it’s that no one can agree on what happens in the middle!

In the first graph I provided, and multiple other versions available on the internet, the optimal tax rate is labeled as “t*.” Why doesn’t it have a number associated with it? Because no one knows what it is!

Anti-tax politicians love to cite the Laffer Curve as justification for lowering taxes. They claim the current top marginal tax rate is too high, and we could make more money by giving people back more of the money they make. The logic behind it is the same logic behind Reaganomics: “Trickle-Down.” Conservative/Neoliberal economists argued lowering taxes meant companies could afford to give people more jobs, which would provide the government with a larger pool of workers to tax. What we saw instead was the concentration of wealth at the top.

Like I said in my “Correlation vs. Causation” article, correlation isn’t causation. However, the correlation between lowering the marginal tax rate with rising income inequality and the increasing federal deficit makes an argument against the belief that lowering taxes generates more revenue. If the Laffer Curve is real, one could argue tax cuts of the 1970s, and definitely the 1980s, put us on the left side of the peak.

The Laffer Curve Isn’t Universal

Unlike what many proponents of lower taxes claim, there isn’t just one Laffer Curve. Different Laffer Curves apply to different types of taxes and in some cases differently based on who is paying that tax. For example, the Laffer Curve would be different for value-added taxes on luxury goods like yachts or certain cars than one for value-added taxes on items like food. People who can afford to buy yachts or fancy cars can afford to avoid taxes by purchasing them overseas and either just paying any import costs or simply only using them when they travel to other countries. For food, most people purchase it nearby and are rarely willing to travel to buy it just to avoid taxes. Depending on where the person lives, the commute to where the taxes are lower costs them more than just paying the taxes!

The Laffer Curve is ultimately a misdirection

I’m about to say something that might be confusing given the graphs above: marginal tax rates don’t matter that much. Before I explain why, let’s be clear about what the Laffer Curve was meant to do.

Laffer created the Laffer Curve to support the following hypothesis:

• The plot of a tax rate of a country vs. the total revenue the government earns from that tax is an upside-down “U” with a peak.

• A country’s workers would lose their motivation to produce more as their total tax burden approached 100%.

• The government will lose money despite increasing taxes if enough people refuse to work, which is why the revenue decreases once the tax rate reaches a certain percentage.

• Therefore, it’s important to make sure tax rates stay as close to the peak as possible.

Many conservative proponents of the Laffer Curve have added to Laffer’s hypothesis:

• The current marginal tax rate is still too high.

• High marginal tax rates negatively impact economic growth.

• While it’s most important to reduce central/federal government marginal taxes for the wealthiest members of country, taxes at every level (federal, state, and local) negatively impact economic growth.

• Therefore, the goal is to cut taxes as much as possible and keep them low to promote economic growth.

The conservative focus mostly on marginal tax rates is misleading. What really matters is how much revenue the government can collect. For example, the wealthiest Americans faced a top marginal tax rate of over 90% in the 1950s, but their effective tax rate was only 42% (Greenberg, 2017; Steinbaum, 2017). (To be fair, that’s still much higher than it is now.) Tax loopholes and deductions allowed people to avoid the higher tax rate. One of the ways the owner class avoided these taxes was putting most of their revenue back into their companies and employees as opposed to keeping it as profits. To drive this point home, let’s look at a heatmap.

Because of the missing country data for the “Effective Tax Rate” category (see note in above heatmap), I’m restricting the dataset to the 31 advanced economies with complete records across all categories of data. As stated in my “Hypothesis Testing” article, I will be evaluating statistical significance against a threshold of alpha = 0.10 (p <= 0.10) to avoid false negatives.

Based on the heatmap above, there’s no statistically significant correlation (p > 0.10) between the top marginal tax rate (Top Marginal Tax Rate in the heatmap) and total tax revenue (Total Govt. Tax Rev (% GDP) in the heatmap) or central/federal government tax revenue (Central Govt. Tax Rev (% GDP) in the heatmap), challenging the conservative add-ons to Laffer’s hypothesis. However, there’s strong correlation between the effective tax rate and central government tax revenue (r = 0.737; p < 0.10). Many conservatives focus on top marginal tax rates, but it’s the effective tax rates that have a relationship to central/federal government revenue. The same can be said for total tax revenue (r = 0.516; p < 0.10) It’s not the maximum tax rate that matters, it’s how much money the government collects! This is further reinforced by the lack of correlation between the top marginal and effective tax rates (r = 0.087), and the lack of a statistically significant relationship between top marginal tax rates and the central and total government tax revenues. In other words, there’s no data supporting the Laffer curve accurately describing how top marginal tax rates affect government tax revenue.

I’m not saying the Laffer Curve doesn’t have any truth to it or can’t be modified to become more robust. What I’m saying is the existing Laffer Curve is useless for arguing lower taxes increase government tax revenue.

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But what about the other additions to Laffer’s hypothesis?

As stated above, many conservative proponents of the Laffer Curve use it to argue against high marginal tax rates and for additional tax cuts for the wealthy. They believe high (as they define it) marginal tax rates harm economic growth, but is this true? Well…

Per the pair of charts above, the ordinary least squares (OLS) regression shows strong, negative, and statistically significant correlation between top marginal tax rates and GDP per capita growth over the last twenty years. While correlation doesn’t equal causality, this does show 47.8% of the movement in GDP per capita growth can be explained by movement in the top marginal tax rates. So that’s it? The conservatives were right and marginal tax rates do harm economic growth. Not so fast, look at the other chart.

Taking a weighted least squares (WLS) regression using aggregate GDP, the statistical significance of the relationship disappears (p > 0.10). Even though the correlation coefficient is the same, the relationship between top marginal tax rates and economic growth becomes statistical noise when weighed against each country’s GDP.

Why is WLS more important? OLS assumes the thirty-one advanced economies I sampled for this article are completely independent of each other. While this is fine for metrics like how much tax revenue a country collects or how much a country spends on healthcare, a nation’s economic growth is affected by other countries. Advanced economies are given their name partially because of the variety of their export and “degree of integration into the global financial system” (International Monetary Fund, 2026). The interdependence of the advanced economies means they can’t be treated like isolated entities. Larger economies like the US and Japan are going to have more of an impact on worldwide economic growth than smaller economies like New Zealand and Luxembourg. This is why the WLS provides a more accurate picture than OLS.

I’ve established that the marginal tax rate is a misdirection due to having no statistically significant relationship with central/federal government revenue and almost nonexistent correlation with total tax rates (federal, state, and local), and effective tax rates. I’ve shown how effective tax rates have a stronger relationship with government revenue. But is there a relationship between the effective tax rate and GDP per capita growth?

As shown by the correlation coefficient (r = -0.148) and p-value (p = 0.426) there’s no statistically significant relationship between a country’s effective tax rate and how much its GDP per capita grows.

While that answers the questions about tax rates, what about tax revenues? As shown in the heatmap, there’s strong negative correlation between the amount of revenue the government collects (as a % of GDP) and GDP per capita growth. Taking this fact by itself, it refutes the Laffer Curve because a strong downward trend is not parabolic, but does it mean collecting more taxes ultimately hurt economic growth? No. As I said, correlation isn’t causality and there is one more strong relationship to consider:

As shown above, GDP per capita is negatively correlated with GDP per capita growth. This is a well-known phenomenon known as economic convergence. As a country gets richer, its growth starts to slow. Additionally, countries with more economic output can collect more revenue from taxes. This explains the relationship between government revenue and economic growth rates.

With the refutation of both Laffer’s original hypothesis and the claims frequently added by conservative commentators and politicians, there is one last question left to ask…

Is the Laffer Curve even a curve?

Proponents of the Laffer curve will argue everything I’ve said here is invalid because linear regression can’t accurately determine correlation for a curve. This is a fair point. If the Laffer Curve is really a curve (parabolic or otherwise), linear regression will deliver inaccurate r and p values. The solution? Use nonlinear regression to see if a curve actually exists.

The above nonlinear regression returns two large p-values (p = 0.312; p = 0.424). Therefore, there’s no evidence supporting the Laffer Curve being a curve. The linear regression models hold. Despite all this, maybe the conservatives are on to something. Maybe taxes are bad. If so, what can we do about it?

If ending taxation is the real goal…

Taxes are an inevitable part of living in modern society. While roads, safe medicines (or safer, at least), building codes, and radio frequency bands may seem boring or mundane, they’re important parts of the infrastructure we all need to live, work, and operate in society. The government needs money to pay for building and maintaining that infrastructure…under capitalism.

The reason we’re forced to debate worthless models like the Laffer Curve in the first place is because capitalism inherently relies on extracting wealth from the working class to function. The best way to manage a broken system is to get rid of it entirely. That means abolishing capitalism and, eventually, money in general!

Capitalism requires money because money is a necessary component of class. Socialism and communism don’t require class to operate and therefore don’t require money. In fact, communism requires a moneyless society to exist. The way to stop worrying about the Laffer Curve, or taxes in general, is to abolish money. That will require getting rid of the economic system that depends on it!

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Sources:

Fieldhouse, A. (2013, April 2). A review of the economic research on the effects of raising ordinary income tax rates: Higher revenue, unchanged growth, and uncertain but potentially large reductions in the growth of inequality. Economic Policy Institute. https://www.epi.org/publication/raising-income-taxes/

Greenberg, S. (2017, August 4). Taxes on the Rich Were Not That Much Higher in the 1950s. Tax Foundation. https://taxfoundation.org/data/all/federal/taxes-on-the-rich-1950s-not-high/

International Monetary Fund. (2026). World Economic Outlook (WEO) - Frequently Asked Questions. World Economic Outlook (WEO). https://www.imf.org/en/publications/weo/frequently-asked-questions#4q2

Kohn, A. (2018). Punished by Rewards: The Trouble with Gold Stars, Incentive Plans, A’s, Praise, and Other Bribes. Houghton Mifflin Company.

Lautz, A. (2026, March 6). What a new Laffer curve paper tells us about raising taxes. Bipartisan Policy Center. https://bipartisanpolicy.org/issue-brief/what-a-new-laffer-curve-paper-tells-us-about-raising-taxes/

Lefton, R. E., & Buzzotta, V. R. (2004). Leadership Through People Skills. McGraw-Hill.

Steinbaum, M. (2017, August 8). Effective Progressive Tax Rates in the 1950s. Roosevelt Institute. https://rooseveltinstitute.org/blog/effective-progressive-tax-rates-in-the-1950s/

Graph data sources provided on the graphs themselves (if applicable).

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I know this will make me sound like a nerd, but hypothesis testing is one of my favorite parts of data analysis/statistics! Not only is it a key part of the scientific method, but it’s frequently used to determine the validity of a mathematical relationship. Hypothesis testing between variables helps determine if their relationship is statistically significant, but what is it and how is it done?

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What is a hypothesis?

A hypothesis is an educated guess. The scientific method starts with a question. A hypothesis is logical answer to that question. To reuse the Lean Six Sigma example from the previous article (also reposted above), the question would be, “Why is there a bottleneck in the production line?” The hypothesis is the industrial engineer saying, “I think the order we process the widgets is causing the bottleneck.”

How is hypothesis testing done?

It depends on the goal and the type of available data. Is the data from a randomized, controlled experiment (like the kind from the Lean Six Sigma Example) or is it observational data (like my advanced economies data)?

Some steps are the same for both types of data. All hypothesis testing follows the same pattern. The tester forms two hypotheses: the null hypothesis (H₀) and the alternate hypothesis (H_A). The H₀ is the “default” hypothesis. It states the change the tester made or the relationship they observed is not statistically significant. H_A is the opposite, the change made or relationship observed is statistically significant. The goal of a hypothesis test is not to “prove” H_A, but to show we can confidently reject H₀ because our gathered data is unlikely (low p-value) to happen by chance. Since inductive reasoning doesn’t deal in proofs, it’s impossible to “accept” the H_A. Because hypothesis testing can’t prove H_A, it’s subject to errors.

There are two types of errors: type 1 errors and type 2 errors. A type 1 error is a false positive. In other words, the tester wrongfully rejects the null hypothesis. A type 2 error is a false negative. The tester maintains the null hypothesis when they should’ve rejected it. These mistakes can happen when there are errors in the experiment, like an insufficient sample size, for example. While z-testing is good for experiments with thirty samples or more, smaller samples require t-testing. T-testing is the most common form of hypothesis testing. It’s similar to z-testing, but accounts for the extra noise associated with small sample sizes. If the testers accidentally used z-testing instead of t-testing, they could reach an erroneous conclusion.

With experimental data, the tester has complete control over the environment. Because they can control for outside variables, a significant p-value directly hints at a causal link. A/B Testing is a common form of gathering experimental data. A company tests two versions (A & B) of a website or advertisement in a randomized controlled experiment. Usually, A is the original version of the website and B is a version with a change. The company tests a new website feature on a random group of users. If the test yields a low p-value, they know the feature caused the change (good or bad) in user behavior.

The gold standard for experimental data is a p-value of 0.05, meaning there’s a less than 5% chance the results of the experiment were statistical noise.

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Observational data is more complicated. As the name suggests, observational data is data acquired through observing events in nature, also known as observational studies or natural experiments. While hypothesis testing here is still important, it can’t be used to establish causation. What it can do is show an observed relationship between the independent and dependent variables is not due to statistical noise/random chance. While this doesn’t show the independent variable causes the dependent one, it provides statistically significant evidence a relationship exists.

However, this doesn’t mean observational data can’t be used as the first step for establishing causation. If the observational data provides evidence of a statistically significant relationship, we can go back to the three criteria for causation I mentioned in my other article:

• The events are correlated.

• The second variable comes after the first.

• There are no plausible alternative explanations for the correlation.

Statistically significant evidence of a relationship meets the first criterion. If the observers are also able to establish a sequential relationship and rule out any other plausible alternatives, then there is statistically significant evidence for causation.

Because observational data is often limited (e.g. the number of countries with advanced economies is currently thirty-seven), it’s difficult to achieve a lower p-value without a strong relationship (a high r² in regression analysis). Small samples make it harder to achieve statistical significance because the data is “noisier.” I will be using a 90% confidence level (p-value = 0.10 or lower) for my “What Does the Data Say?” series to account for this. While this means my hypothesis tests will have a lower, more lenient threshold for significance, it will allow me to avoid more type 2 errors with such a small sample size.

I’m concerned about avoiding type 2 errors because the main purpose of the “What Does the Data Say?” series is to look at the relationships between multiple economic metrics that describe the prosperity and quality-of-life among the advanced economies. Since one of the themes of the series is pointing out the lack of causality between two metrics by finding no correlation, I want to make sure I’m not writing off any potential relationships just to get a “win.”

After choosing a confidence level, the important part is sticking with it. This is where many people make mistakes. Humans are biased toward beliefs they want to be true. This causes people to move the goalpost after the testing is done so their hypotheses support what they already believe. Going back to the Lean Six Sigma example, if the industrial engineer really wanted their hypothesis to be correct, they might be tempted to accept a calculated p-value of 0.07 because it’s “close enough.” While this may make the engineer feel good, it does the company a disservice by adding type I errors (false positives) to their improvement process.

So why bother?

To bring this back to my “What Does the Data Say?” series, hypothesis testing allows me to determine if there’s statistically significant evidence of a relationship between two variables. In the case of linear relationships, like the ones I deal with in my data sets, the establishment of a relationship sets the stage for further experimentation and/or observation. Indeed, multiple economists have won Nobel prizes by doing exactly this. Dr. David Card “proved” using observational data that immigrants don’t depress wages (Magesan, 2021). Another example is the economists, Dr. David Card (again), Dr. Joshua Angrist, and Dr. Guido Imbens, who “proved” minimum wage increases don’t cost jobs by observing natural experiments between multiple sets of towns where the main difference was one state having a higher minimum wage than the other (Nobel Prize Outreach 2026, 2026). Again, “proved” is in quotes because what they actually did was achieve statistical significance, which is as close to definitive proof as empirical analysis allows. All three won the 2021 Nobel Prize for their work.

Conversely, hypothesis testing allows me to determine there is no statistically significant evidence of a relationship between two variables, and this is where it gets fun. I used hypothesis testing in the above chart to determine there was no statistically significant evidence of a relationship between government spending on healthcare (as a percentage of all healthcare expenditure) and the Economic Freedom Index. This means politicians claiming the increased government expenditure associated with universal healthcare would destroy economic freedom have no evidence to back up their claims!

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Sources:

Magesan, A. (2021, October 26). Nobel Winner David Card Shows Immigrants Don’t Reduce the Wages of Native-Born Workers. University of Calgary. https://ucalgary.ca/news/nobel-winner-david-card-shows-immigrants-dont-reduce-wages-native-born-workers

Munro, R. A., Ramu, G., & Zrymiak, D. J. (2022). ASQ Certified Six Sigma Green Belt Handbook (3rd Edition). American Society for Quality.

Nobel Prize Outreach 2026. (2026). The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2021. NobelPrize.org. https://www.nobelprize.org/prizes/economic-sciences/2021/summary/

Wheelan, C. J. (2014). Naked Statistics: Stripping the Dread from the Data. W.W. Norton & Company.

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Most reading this have likely seen a graph like the one above. Two obviously unrelated events moving together over a set period of time. These kinds of graphs are made to teach an important lesson: correlation isn’t causation. While this simple takeaway is a good heuristic, the math is more complicated than that!

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What is correlation?

Correlation is the measure of how well two variables move together over a certain period of time. Data analysts, economists, mathematicians, etc. measure correlation on a scale from -1 to 1. This is a decimal known as the correlation coefficient (r). Positive values mean the variables move in the same direction. Negative values mean they move in opposite directions. Values close to zero mean they don’t move together at all. In other words, no correlation. In the example above, the two variables, Parks & Recreation Degrees and Tummy Ache Searches, are shown to have strong positive correlation because they move together in the same direction. The graph lists r = 0.980. This is almost perfect positive correlation. If one variable moves in a certain direction, there’s a very high chance the other variable will move in the same direction.

Why is this useful?

As an MBA, I learned a bit about managing long-term portfolios. One way to create a balanced stock portfolio is to have some stocks with negative correlation. When two stocks are negatively correlated, one is likely to go down as the other rises. An example of this is stocks of seasonal businesses. A stock for a company selling ice cream will likely be negatively correlated with a stock for a company that sells winter coats. While it may sound counterintuitive to have a portfolio where some stocks fall when others rise, this allows for an overall balanced portfolio. The same goes for having a mix of stocks and bonds, which are often negatively correlated. (NOTE: I’m providing this example for academic purposes only. This is NOT financial advice.)

Additionally, knowing the correlation coefficient provides more insight into the relationship between the two variables. The coefficient of determination (r^2) describes how much the first variable explains variation in the second variable. It is a decimal with a range from 0 to 1. Unlike the correlation coefficient, the coefficient of determination is always positive. It’s mainly used in regression analysis.

Regression analysis is one of the most powerful features of statistics. Regression analysis can take one or more independent variables and determine their effect on one or more dependent variables. In other words, it can determine whether a relationship exists. To bring it back to the graph above, r^2 = 0.960 means there’s a strong relationship between the two variables. To be specific, 96% of the variance in the number of searches for “tummy aches” is explained by the variation in the number of people with Parks and Recreation degrees. The p-value being below 0.01 means there’s a less than 1% chance this relationship is just statistical noise/random chance.

Then that’s a wrap, right? More people getting degrees in Parks & Recreations is the reason more people are searching online for answers about tummy aches. Should we call the Health and Human Services, the Center for Disease Control, and the Department of Education (or what’s left of them, anyway) right now and stop people from getting Parks & Recreation degrees due to their obvious health hazards? Not exactly. As most reading may have predicted, something isn’t right here.

While there exists a strong relationship between getting a Parks & Recreation degrees and searching for information about tummy aches online, it doesn’t mean one causes the other. As powerful as correlation and regression analysis are, they’re not enough to determine causation.

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What is causation?

Causation is a relationship between two variables where the first variable (the independent variable) causes the second (the dependent variable). Establishing a causal relationship requires at least three factors:

• The events are correlated.

• The second variable comes after the first.

• There are no plausible alternative explanations for the correlation.

While the events in the graph above are strongly correlated, it fails to meet the other two requirements. There’s no evidence searching for tummy aches comes after getting a Parks & Recreation degree. A plausible alternative explanation for the correlation is the growth of the US population over the last ten years, which explains why there are more people getting Parks & Recreation Degrees and why there are more people searching for tummy aches.

An obvious example of causation is a person placing their hand on a hot stove and being burned. There is strong correlation: every time that person puts their hand on the hot stove, they get burned. There is a sequence of events: they put their hand on the hot stove before they were burned. There is no plausible alternative explanation (e.g. their hand was already burned from an injury suffered prior to touching the hot stove).

How do we determine causation?

This one is a bit more complicated. Math alone is not enough to determine causation. There needs to be an experiment where the first variable is as isolated as possible from any other variable that could impact the experiment. The best way to do this is a controlled experiment.

Lean Six Sigma experts do this all the time:

1. There’s a bottleneck in the production line.

2. An industrial engineer speculates changing the order of a process will eliminate the bottleneck.

3. The company conducts an experiment where the only change made to the process is the one the engineer suggested.

4. They compare the result of the change to the results before the change (a.k.a. the control).

However, there’s not always a chance to do a controlled experiment. Some experiments would be unethical to conduct, like making people smoke cigarettes to determine if they cause cancer or not. Instead, we have to rely on naturally occurring experiments. Even though the number has greatly decreased, enough people smoke in America to gather data on them. This is known as observational data. There are enough smokers that we can control for factors like age, weight, prior medical history, and income. After doing this, we can look at the data and determine if smoking causes cancer.

There’s one caveat. In both cases, the experiment doesn’t “prove” the first variable causes the second. Testing and experimentation are forms of inductive reasoning and therefore can’t prove anything. What they do instead is provide a predetermined level of confidence (e.g. 90%) for safely rejecting the null hypothesis (there is no causal relationship). I will expand more on this in my upcoming article about hypothesis testing.

One last point:

While correlation can’t determine if the first variable causes the second, it can make a good argument for the opposite. As stated above, in a linear relationship, correlation is a requirement for causation. If there’s no correlation, there’s no causation. To bring this back to my “But What Does the Data Say?” series, I found while running the numbers that the correlation between government spending on healthcare and national scores on the Economic Freedom Index is mildly negative (r = -0.139; p-value = .413).

Some politicians/pundits will quickly jump on this to argue government spending on healthcare is detrimental to economic freedom, but look at the high p-value. While it looks like government increasing their portion of all healthcare spending has slightly negative relationship to economic freedom, the p-value shows it’s just statistical noise. In other words, there’s no relationship between government spending on healthcare and economic freedom! I’ll explain why the p-value matters in my next entry in the “Logic Lessons” series: Hypothesis Testing.

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Sources:

Munro, R. A., Ramu, G., & Zrymiak, D. J. (2022). ASQ certified six sigma green belt handbook (3rd edition). American Society for Quality.

Vigen, T. (2024, January). Associates degrees awarded in Parks & Recreation correlates with Google searches for “tummy ache” (R=0.98). Tylervigen.com. https://www.tylervigen.com/spurious/correlation/1472_associates-degrees-awarded-in-parks-recreation-leisure-fitness-and-kinesiology_correlates-with_google-searches-for-tummy-ache

Wheelan, C. J. (2014). Naked Statistics: Stripping the Dread from the Data. W.W. Norton & Company.

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America is 250 years old today, and we’re far from exceptional. We have a dark history of colonialism and imperialism. We’re currently funding Israel’s genocide of the Palestinians, starving Cuba with a military blockade, and our President is a rapist (Pengelly, 2023). Despite all that, I still love my country. That’s why I make these posts and engage in other forms political activism: to improve the United States (US).

Before we can start improving, we need to know where America stands.

All these graphs will focus on the thirty-seven countries classified by the International Monetary Fund (IMF) to be advanced economies (International Monetary Fund, 2023, 2026). They’re also developed countries by the United Nation’s (UN’s) Human Development Index (HDI). I omitted Puerto Rico, Hong Kong, Macau, and Taiwan. While they’re classified as advanced economies, they’re part of other countries (the US and China, respectively).

I chose these countries to try to be as fair as possible. It’s easy to make any developed country look good when comparing it to a developing one, but how does America rank among its peers?

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I will only be providing national rankings and benchmarks for this article. My goal is simply to show where America stands, not to perform a deep-dive bivariate analysis. The US will be red, every other country will be blue. The black dotted line going through each graph represents the median of all the measured countries. Without further delay, on to the data!

Gross Domestic Product (GDP) per capita is the “universal” benchmark for a country. GDP per capita measures the economic output per citizen by dividing a nation’s GDP by its population, thus eliminating the latter as a variable. Large countries like the US, India, and China can be compared to small countries like Andorra, Singapore, and San Marino. Economists, sociologists, statisticians, and health professionals consider this a best-all-around indicator of a country’s prosperity. That said, it’s not a perfect measurement. The GDP doesn’t account for economic inequality within a country or give a complete picture of the quality of life (Ball, 2023). For example, all other things being equal, a country where people work twelve hours a day will have a higher GDP per capita than a country where people work eight hours a day, but most would argue the people working only eight hours have a higher quality of life than those working twelve (Mankiw, 2024).

The HDI addresses some of the shortfalls with GDP per capita. As stated above, the HDI is a UN metric that takes education, life expectancy, and Gross National Income (GNI) and scales it to a number between 0 and 1 (United Nations Development Programme, 2026). The higher the number, the more developed a country.

Instead of looking purely at economic output, the HDI factors in quality-of-life measurements like education and life expectancy. It uses GNI instead of GDP because that focuses only on the revenue a country generates from its citizens/residents and not the money brought in from people using that nation as a tax haven. Despite these improvements, the HDI fails to address income inequality. That’s where the Gini coefficient comes in.

The Gini coefficient measures income equality within a country on a decimal scale from 0 to 1. The lower the number, the more equal the country (Hasell, 2023).

As shown in the chart, America is the most unequal of the advanced economies.

The Gini has its own set of limitations. It doesn’t consider a country’s development level, and it can’t explain why inequality exists. Two nations could have the same Gini coefficient with different levels of prosperity. That’s why it’s important to examine more than one metric when determining a country’s global stance!

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One thing I’ve always heard growing up is taxes are bad. High taxes lead to lower economic output, a lower quality of life, and less economic freedom. Our low taxes were described as the reason why we had the “best economy in the world” and why we “far surpassed Europe” in quality of life. As shown by the GDP and HDI graphs above, those claims are questionable at best. But at least we have lower taxes than everyone else! Well, almost…

One of the pitfalls of the “low taxes” argument is people fixate on marginal tax rates. I include myself in this because until I started doing the research for this series, I was one of them! In reality, marginal tax rates don’t tell us how much a country is collecting. For example, America’s maximum tax rate was above 90% in the 1950s, but very few people actually paid that amount due to loopholes and deductions. That’s why tax revenue as a percentage of GDP is important: it explains how much money the government collects from taxes. As shown above, America is towards the bottom. As I will explain in my upcoming article on taxation, that’s not inherently a bad thing.

This is the part where people on the right will say, “Exactly! America’s low tax rate is why we have the highest annual income in the world!” But do we really?

Ouch! But at least everyone has home. Wait, don’t tell me…

America is in the bottom third for homeownership. This one surprised me a little. As I will cover more in depth in a later article in the series, America has the cheapest homes of any advanced economy. This provides another example of why looking at more than one metric is important. Looking solely at GDP per capita, America is one of the richest nations in the world. I plan to dive deeper into it in another article, but this shows America doesn’t have a wealth generation problem. We have a wealth distribution problem, which is exactly the kind of problem socialists like me wish to solve!

So far we’ve learned America isn’t the richest, isn’t the most developed, isn’t the most equal, isn’t the country with the lowest taxes, isn’t the highest annual income per capita, and isn’t the country with the highest percentage of homeownership. But we still have the most economic freedom, right? Right? I’m sorry…

This one is probably going to get me in trouble. As shown above, the Heritage Foundation created the Economic Freedom Index. Yes, the reactionaries who invented Project 2025 are considered a global authority on economic freedom. (Yay, capitalism!) They based their assessment on twelve different measurements grouped into four categories: rule of law, government size, regulatory efficiency, and market openness (The Heritage Foundation, 2026). As shown in the chart above, we’re not even in the top ten among advanced economies.

Before I get to the last part of this article, I want to address something I hinted at in the second paragraph. I’m not using this article to make any moral judgements about America’s actions. The First Principle of Unitarian Universalism is respecting “the inherent worth and dignity of every person” (Unitarian Universalist Association, 2026). I believe America’s actions, both internationally and domestically, are in violation of this principle. While I have moral disagreements with the way the country is being/has been run, this article isn’t meant to justify that position. Conversely, some will argue that America is the greatest country in the world because of [insert moral value(s) here]. They’re entitled to their opinions, but this is about the data. The data states, based on multiple objectives measures, America is not the best country in the world. Again, some people may argue the metrics I picked are irrelevant, but the burden of proof would be on them to explain why they believe those metrics are useless.

Finally, as some might have guessed, each graph I posted has a twist. The US is red and all the other countries are blue. While that makes where America stands more obvious, that’s not the only reason I chose to make the US a different color. In every graph, the US is the only country that doesn’t have universal healthcare. (San Marino has full access to Italy’s system.) My next article will explain, using data and bivariate analysis, how not having universal healthcare negatively impacts our rankings.

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Sources:

Ball, J. A. (2023). The Myth and Propaganda of Black Buying Power: Media, Race, Economics. Palgrave Macmillan, an imprint of Springer.

Hasell, J. (2023, June 30). Measuring inequality: What is the Gini coefficient?. Our World in Data. https://ourworldindata.org/what-is-the-gini-coefficient

The Heritage Foundation. (2026, February). Index of Economic Freedom: The 12 Economic Freedoms. Index of Economic Freedom. https://economicfreedom.heritage.org/pages/report#indexTwelveFreedoms

International Monetary Fund. (2023, April). World Economic Outlook Database - Groups and Aggregate Information. World Economic Outlook Database. https://www.imf.org/en/publications/weo/weo-database/2023/april/groups-and-aggregates

International Monetary Fund. (2026). World Economic Outlook (WEO) - Frequently Asked Questions. World Economic Outlook (WEO). https://www.imf.org/en/publications/weo/frequently-asked-questions#4q2

Mankiw, N. G. (2024). Principles of Economics. Cengage.

Pengelly, M. (2023, August 7). Judge says E Jean Carroll allegation Trump raped her is ‘substantially true’ in court dismissal. The Guardian. https://www.theguardian.com/us-news/2023/aug/07/donald-trump-rape-language-e-jean-carroll

Unitarian Universalist Association. (2026). Seven UU Principles. Unitarian Universalist Association. https://www.uua.org/beliefs/what-we-believe/principles

United Nations Development Programme. (2026). Human development index (HDI). UNDP. https://hdr.undp.org/data-center/human-development-index

Chart data sources are listed on the charts themselves.

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It’s always nice to read a book that ends up confirming what I already suspected to be true (like Nomadland), but this time I got more than I bargained for. They say the truth hurts and this book stung!

Don’t Talk About Politics by Dr. Sarah Stein Lubrano is about changing minds. But it’s not a traditional book about how to persuade people. It’s a book about how humans process information, handle cognitive dissonance, and how the traditional ways of persuading people to change their political views are broken. She discusses what doesn’t work, what does work, and provides suggestions for how we can reorganize society to make it easier for people to adopt new ideas. It’s a short read—just under 250 pages—and packed with many hard truths, but it also leaves a few unanswered questions.

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Lubrano’s first two chapters talk about why the “marketplace of ideas” and debate are nonsense.

People who’ve come here from my Facebook know I love to argue/debate. Some people watch sports, I argue with people. I don’t debate subjects like whether gay people have the right to exist (they do). That’s an automatic block because I don’t platform bigots, but I will debate economics and certain other social issues with people all day long. Whether I win or lose, it’s fun for me. This book says I shouldn’t be doing that—and it’s right!

While now I only do it for fun, I used to be a true believer in marketplace of ideas and debate. I believed over time good (more factual) ideas would win out over bad (less factual) ones because people would choose the better ideas voluntarily. I believed if I only argued well enough, people would adopt my ideas because they would see they were closer to truth. Then I got older.

Without going into too many details, I learned very quickly having “good” ideas doesn’t matter because the viability of an idea depends more on what people want to be true and/or confirms what they already believe vs. how true that idea is. “Winning” an argument didn’t mean much because most people (myself including, I’m mature enough to admit) hold on to their beliefs for mostly irrational reasons. In other words, feelings mattered more than facts when it came to accepting new ideas. Multiple books I’ve read over the years confirmed that, and Lubrano took it one step further. Even without our inherent biases, there are multiple obstacles an idea must overcome before it even reaches our ears! I would go into more detail, but I don’t want to spoil what I believe to be one of the best parts of the book!

Lubrano then goes on to argue protests aren’t effective method of political change. Most politicians don’t care about them, and they don’t change the minds of the people who don’t agree with them (most of the time). My first instinct was to argue that the Black Lives Matter protests of 2020 were evidence she was wrong. She anticipated that. Her book showed that while Black Lives Matter protests were very effective at changing people’s opinions, it was the exception and not the rule. As someone who has participated in many protests and advocates for protesting as a means of affecting change, this was a hard lesson for me to learn.

Does that mean protests are pointless? No. Lubrano argued protests are very effective at changing the minds of the people who participate in them. Many of the people who participated in the Black Lives Matter protests in 2020 went on to move into other forms of political activism. Their participation in one event paved the way for them to do other social/racial justice work. So while protests themselves are not effective for changing minds, they help create the next generation of activists who will. This brings me to one of my favorite points Lubrano made: people act first and think later.

Humans hate cognitive dissonance, especially when it comes to something as deeply part of their identity as their political beliefs. What do we do to avoid this cognitive dissonance? Usually change our beliefs to reflect our actions. For example, let’s assume a guy, Bob, is against helping the poor. For one reason or another, he visits a mutual aid station or soup kitchen. His actions cause him to soften his beliefs about helping the poor. It doesn’t matter if he’s a volunteer or a user. He will do whatever it takes to avoid cognitive dissonance. The takeaway? To change someone’s mind, don’t try to argue with them, bring them to protest or a mutual aid station.

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Another good point Lubrano made was humans think with their friends. The story of the lone philosopher sitting on top of a mountain and coming back with wisdom is usually just that: a story. While small doses of isolation can help people deal with information overload, that’s not how humans do most of their thinking. Humans are social creatures designed to cooperate. This includes hunting, gathering, working, living, and thinking. We often change our ideas to reflect the groups we are with, and groups frequently evaluate new ideas brought to that group together. Even in the case of the isolated philosopher, they will eventually share their beliefs with other people and collaboratively revise them. That’s because who you live with affects how you think.

People are affected by who they see in their daily lives. Even if they don’t directly interact with those people, simply seeing them around raises their tolerance for them. People who live diverse areas with people of multiple races, religions, beliefs, and sexual orientations are more likely to be tolerant of those people and have beliefs reflecting that. This is another effect of our desire to avoid cognitive dissonance. It’s hard to keep believing Muslims are dangerous if the woman you buy apples from at the farmer’s market every couple of weeks is a Muslim. This is why many of the most racist people are ones who live in communities with only members of their same race. Lubrano connects this to one another one of her points: isolation is dangerous.

It’s no secret that capitalism is isolating us from each other. Most of us are overworked, underpaid, leaving us too exhausted to do anything when we get home. The few of us who still want to go out find ourselves with fewer third spaces available to meet and interact with other people. Parks, libraries, and civic centers have been replaced with bars, clubs, and other places we need to pay to access. Lubrano argues this is dangerous.

According to Lubrano’s research, isolation is horrible for humans. Not only does it make us more miserable (at first), it makes us more sensitive. The enhanced sensitivity makes us worry about and dread social interactions and causes us to take events personally when they may have nothing to do with us. It increases our paranoia and makes us distrust other human beings, which leads directly to adopting reactionary views. Even worse, prolonged isolation makes us no longer feel like we need other people so we stop trying. Despite no longer feeling the pain of loneliness, isolation will still continue to damage us. Isolation literally shrinks our brains, specifically the parts use for social interaction. This, in my opinion and hers, is what’s most dangerous about isolation.

Two topics I felt Lubrano could’ve done a better job covering were the spectrum of allies and deep canvassing.

The spectrum of allies is the idea that everyone you meet exists on a scale from “strongly aligned” to “strongly opposed.” Lubrano asserted the people you have the best chance of convincing just by exposing them to ideas are “weakly aligned” or “neutral” to your position. As someone who has learned this through almost thirty years of experience, I would have liked to hear more about the political theories and research surrounding this idea.

Deep canvassing was the other hard truth for me. It’s the way you reach people on the opposite end of the spectrum of allies. Deep canvassing is when you knock on someone’s door and instead of handing out fliers or talking briefly about a political candidate, you sit them for a long time (at least thirty minutes) and have a conversation about a political issue. To summarize an example Lubrano used in the book, two people visit the house of someone who has bigoted views against trans people. The two people sit down and have a conversation about why the person is hostile to trans people and towards the end of the conversation, one person, who happens to be trans, asks, “Well, I’m trans. Do you still feel that way about trans people?” Sounds ridiculous, right? Well it works! Most of the time this causes people to soften their stance. Why is this a hard truth? Because this is what Daryl Davis does with KKK members, and those who have read my other article know how I feel about him. Deep canvassing being effective means Daryl Davis’s methods are more effective than I would like to believe.

Does that mean we all need to start talking to KKK members? No! Lubrano stated although deep canvassing was effective at the individual level, it was time-consuming and people were still studying its long-term impact. Indeed, at least one of the people Daryl Davis “converted” was arrested for shooting at counterprotesters at the “Unite the Right” rally in Charlottesville. While I understand deep canvassing was still being researched, I would’ve liked to learn about its second and third order consequences.

While I understand the main point of Lubrano’s book was trying to win people over with arguments is inefficient at best, I still would’ve liked to learn more about the above concepts.

My main disappointment with her book was Lubrano’s failure to mention how racism played a big role in the disappearance of public third spaces—especially in the South. While I understand this wasn’t the case for most other democratic nations, the focus of her book was the US. In the US the main reasons third spaces started disappearing was because white people refused to integrate. That’s the reason public pools disappeared from the South, for example. In the north this took the form of Black people being confined to neighborhood with low-quality housing and very little funding for public spaces like parks. Lubrano correctly points out the reduction in both third spaces and associations like bowling clubs happened because of the economic crash in the 1970s and the subsequent election of Ronald Reagan. Reagan forced Neoliberalism on the United States, which resulted in the privatization of many public spaces. What Lubrano failed to point out is how Reagan won his election by campaigning on racism (e.g. “welfare queens”).

Last but not least, I want to commend Lubrano for going out of her way to condemn Israel’s genocide of Palestine. During her section about identity, she mentions she is Jewish but she’s also a leftist. She talks about her willingness to speak out against what Israel is doing to the Palestinians creates friction between her and some of the Jewish people in her community. Overall point of the segment was to discuss how identity isn’t best way to form associations that are conducive to adopting new ideas. In my opinion, she could’ve done this perfectly without talking about what Israel did to Palestine. There were multiple other examples she could’ve used, but she chose that one. When I read this book in the spring of 2026, public opinion had thankfully shifted in favor of Palestine. She couldn’t have known that when she published the book in mid-2024. She stood up for what she believed was right even though it meant taking a risk. I respect that!

I give Don’t Talk About Politics by Dr. Sarah Stein Lubrano an 8.5/10. The book is a great read, and I’m not just saying that because the first few chapters confirmed what I already believed. It delivers many well-researched hard truths and Lubrano both cites her sources and provides a list of material for “further reading.” The book loses a point for not discussing how racism led to the erosion of the third space—and could very well again if we’re not careful. The other half-point lost is because I felt like there were some good topics she just left hanging. At less than 250 pages, this is still a must read for those who wish to understand why changing minds is about building better systems instead of making better arguments.

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Treating coding as both a hobby and tool for data analysis, I use Python for statistical modeling and data visualization. Not long after launching this publication, I began engaging with readers across the political spectrum. Being a socialist, most of the criticism came from the right. While the conversations frequently devolved into superficial insults and logical fallacies, I occasionally entered good-faith discussions with people who were interested in evidence.

To ground those debates in reality, I began pulling macro-level statistics directly from primary global databases like the World Bank, the OECD, and the IMF. While the data told one story in dense, sprawling spreadsheet form, plotting the variables in Python told a completely different one. That’s when I realized what originally started as a tool for personal use could benefit everyone.

That structural story is what I plan to share.

“But What Does the Data Say?” will apply rigorous data analytics to our most common, high-stakes policy debates. We will cut through the partisan talking points and simply let the regressions point the way. Let’s go where the data takes us!

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Below will be a list of each entry in the series.

1. But What Does the Data Say? Where America Stands

Universal Healthcare has been a popular idea in the US for over a decade. I remember reading during the 2016 election cycle that over half of the country supported universal healthcare. As of late 2025, it is up to 64% (Daniller, 2025). While not as high as its peak in 2006, support for universal healthcare has been steadily rising for the past decade (Daniller, 2025). “Universal healthcare” can mean different things to different people. It could mean guaranteed health insurance for all Americans, or it could be a single-payer system like the UK, Canada, or Taiwan. For the rest of this article, I will be using “universal healthcare” to mean a single-payer system like Medicare-for-All.

What is universal healthcare/Medicare-for-All?

Universal healthcare/Medicare-For-All is “a single-payer, government-run health care program in which all Americans are covered and which replaces almost all other existing public and private plans” (Sarlin & Kimelman, 2019). Single-payer is when “a single public or quasi-public agency takes responsibility for financing healthcare for all residents” (Sylvie, 2016). In other words, government pays the hospital bill instead of the patient. While each country implements it a little differently, the above remains the same. Most of the hospitals in the UK are government owned. The proposed single payer model in the US would keep hospitals private, but they would bill the government instead of the patient and/or the patient’s insurance. No more copays, no more worrying about whether treatment is covered by insurance, no more fighting with insurance companies to get them to pay what their policy claims they’ll pay. Best of all, no more medical debt! Easy and cheap!

But just because universal healthcare is easy and cheap doesn’t mean it’s good. What are the benefits of universal healthcare and why is it better than our current system?

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Lower Costs

Universal Healthcare is cheaper. Between 2023 and 2033, Americans are projected to spend $42.9T ($55T in 2025 dollars) on healthcare (Federal Reserve Bank of Minneapolis, 2026; Public Citizen, 2025). That includes both private a federal spending. According to Public Citizen (2025), “Medicare for All spending would be approximately $37.8 trillion between 2017 and 2026.” Converting that to 2025 dollars, that’s $49.6T (Federal Reserve Bank of Minneapolis, 2026). Still less than what we’re projected to spend on the current system.

Why the decrease? Private expenditures mostly disappear with universal healthcare. People no longer have to worry about the copays, insurance premiums, and/or out-of-network expenses they currently pay. Most American families will save thousands of dollars a year. A family making $75,000/year could save over $9000 (Flannery, 2026)! That would be like getting a 12% raise! This is consistent with another study stating universal healthcare “likely to lead to a 13% savings in national healthcare expenditure” (Galvani et al., 2021).

The right-wing, Koch-funded Mercatus Center stated in 2018 that single-payer healthcare would cost us $32.6T between 2022 and 2031(Balhous, 2018). That’s around $42T in 2025 dollars (Federal Reserve Bank of Minneapolis, 2026). So even the most conservative (pun intended) estimates show universal healthcare saving us money!

But what about taxes?

One of the main criticisms of universal healthcare is it will increase our taxes. Some economists argue it would cut taxes for most Americans, but for the sake of discussion let’s assume it will raise them (Saez & Zucman, 2019). Senator Bernie Sanders has stated multiple times universal healthcare will increase our taxes (Thomas & Regan, 2016). Indeed, his proposed bill does contain a tax increase, but an increase in taxes doesn’t mean Americans will pay more. According to National Nurses United (2024), “even with the tax increase proposed by legislators, 95% of Americans will pay less than they do now. The taxes would replace premiums, co-pays, and deductibles.” So even with higher taxes, universal healthcare will be cheaper for almost all Americans than the current model.

Increased Bargaining Power

Over 65% of Americans are dependent on private health insurance to cover the costs of care (Keisler-Starkey & Bunch, 2024). That insurance is usually provided through employers, which reduces the cost of the insurance for the employees. While this may sound like a good deal for employees, it’s actually a good deal for employers. Employers get tax breaks for providing their employees with insurance and it gives them an important bargaining chip against unions (Delaney, 2020; Desmond, 2023).

Even with Obamacare, most people can’t afford the quality of health insurance they receive without their employers. Corporations know this and use health insurance when negotiating with unions. This makes it harder for unions to secure better pay, improved working conditions, and better benefits for their employees. Universal healthcare would shift power away from corporations (Delaney, 2020).

With universal healthcare, employees would no longer depend on their employers for health insurance. Employees will have more leverage at the negotiating table both as individuals and as unions. It also improves the economy by allowing for greater employee mobility. This brings me to another problem caused by our existing healthcare system: job lock.

Universal Healthcare Reduces Job Lock

Job lock is when people choose to stay in a job because they’re afraid of losing their health insurance. While it other benefits can also cause job lock, it’s usually health insurance.

When employers know their employees won’t relocate due to health insurance, it allows them to make cuts in other areas. Employees receive lower pay and worse working conditions. It also leads to lower earning potential because the employee is stuck at a job that refuses to give them raise when they could be moving to new jobs that offer better pay and/or better fit their skills.

According to Teirlinck & Stoilova (n.d.), job lock hurts the overall economy by lowering productivity and stifling innovation. It’s exactly what I learned in business school. Labor mobility to essential for increasing economic growth (raising GDP). People need to be able to go where they’re needed so they can find positions that better use their skills and increase their earning potential. People being unable to change jobs stifles economic growth. Slower economic growth contributes to a lower quality of life for a country.

Reduced Poverty

The cost of medical treatment and the impact of medical debt on US households have been topics of discussion for decades. Even people who don’t support universal healthcare understand how expensive healthcare is. Despite having insurance, most Americans have had to pay hundreds—or even thousands—of dollars for a medical expense (myself included). According to GoFundMe’s CEO, a third ($650M) of all the website’s payouts go to healthcare costs (Martinez, 2023). Even with that, 40% of Americans still have some form of medical debt (Sparks et al., 2026). That debt has pushed some of them into poverty. Universal healthcare would reduce poverty by preventing further medical debt. In my opinion, any universal healthcare bill should come with medical debt forgiveness.

While the cost of medical debt is a problem, another issue not frequently discussed is how sickness impacts income. Sick people earn less money and are less able to gain and maintain employment. It’s not because they’re “lazy” or “weak,” it’s because many of them don’t have sufficient access to the medicine and/or treatment they need. Worse, if people go without treatment long enough, manageable conditions can turn into more serious ones. For example, Type II Diabetes can lead to kidney failure or limb amputations if it goes untreated (Desmond, 2023). Universal healthcare allows people with certain health conditions to gain the treatment they need and remain healthy enough to maintain employment. Access to medical treatment is one of the reasons why poverty rates in countries with universal healthcare like the Canada and the UK are lower than they are here in the US.

Before I move on, I want to go on record saying I don’t think people who are sick—or anyone, really—should be forced to work. I don’t believe one of the advantages of universal healthcare is being able to push people with chronic conditions into the workforce “because now they can afford treatment.” This is one of the many reasons why I support Universal Basic Income. The only reason I’m discussing this at all is because there are many people who want to work but can’t because their inability to get treatment for their medical conditions prevents them from doing so. Finally, I would be remiss if I didn’t mention the biggest obstacle people with medical conditions face is employment discrimination (Harris et al., 2019). No amount of medicine is going to cure bigotry. We need to do better as a society on that front!

Better Quality of Care

Most importantly, universal healthcare improves the quality of care. The Centers for Medicare and Medicare (2025) define quality of outcome as “the degree to which health services for individuals and populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge.” A healthcare outcome “is defined as the results or consequences of treatment or healthcare” (Klose, 2016). Putting these two together, the quality of care is measured by positive health outcomes.

The US is the only developed country without universal healthcare and has the worst care. Compared to ten other countries, including Canada and the UK, the US ranked last in overall healthcare and specifically in access to care and healthcare outcomes (Blumental et al., 2024). Most notably, our life expectancy is lower than those of the other countries and this is despite spending more on healthcare than they do (see above chart). Our infant mortality rate is also the highest in the developed world and is especially bad for Black children (Jang & Lee, 2022; Petrullo, 2023).

Not having universal healthcare significantly hindered our ability to response to COVID-19. “If the U.S. had had a single-payer universal health care system in 2020, nearly 212,000 American lives would have been saved that year, according to a new study” (Blair, 2022). It also would’ve been cheaper. With universal healthcare, “the country would have saved $105 billion in COVID-19 hospitalization expenses alone” (Blair, 2022). Even worse, the US saw a larger dip in life expectancy because of the pandemic vs. the UK and Canada (World Bank Group, 2026).

Additionally, the US has fewer doctors per 1000 people than many other developed nations (Telesford et al., 2025). This shortage of doctors has hit rural areas in the US extra hard because populations are smaller and people are poorer (Horstman et al., 2025; Kettles, 2025). Fewer hospitals can afford to operate in these areas, leading to monopolies. Even metropolitan areas face increased healthcare costs due to monopolization (AHIP, 2026). On top of this, many US doctors are moving to countries like Canada. While Trump was the reason many doctors were fleeing north in 2025, this has been a problem since before Trump won in 2016 (Glauser, 2014; Kelman, 2025). Another reason they’re moving north is Canada pays their doctors better than America does. “Increased pay is the main driver of this interest. Philpott said family physicians, pediatricians and psychiatrists can make $100000 more annually in Canada, on average, compared to the US” (Glauser, 2014). With private healthcare, profits determine how much a medical facility can pay their physicians. A public system creates opportunities for them to be paid more since profit is no longer the goal. This contradicts the claim made by critics that universal healthcare will lead to doctor shortages in the US. If the other countries are an indicator, universal healthcare will increase the number of doctors available in the US!

One problem not discussed often is how healthcare costs and medical debt make our health worse. This is mainly due to people delaying treatment because it’s too expensive. Even people with insurance have this problem:

Many insured adults said they or a family member had delayed or skipped needed health care or prescription drugs because they couldn’t afford it in the past 12 months: 29 percent of those with employer coverage, 37 percent covered by marketplace or individual-market plans, 39 percent enrolled in Medicaid, and 42 percent with Medicare (Collins et al., 2023).

As mentioned in the previous section, not being able to afford treatment can make health problems worse. And as health problems get worse, they get more expensive to treat. Buying medications for Type II Diabetes is less expensive than dialysis for kidney failure due to diabetic nephropathy. Medicare-4-All would remove the costs associated with healthcare and allow people to get diagnosed with and treated for diseases while they’re easier and less expensive to manage. To quote Tay Zonday, “Poverty charges interest.”

But what about “wait times?”

Critics of universal healthcare love to bring up “wait times” as a reason to not to implement it. There are multiple different problems with this criticism, but I’m only going to focus on three.

First, “wait times” is a weasel phrase. It has different meanings depending on who you ask, but it’s treated as if it has only one. This is also known as an equivocation fallacy. What specific wait times are critics discussing? Wait times for elective surgery? Wait times to see their primary care physicians or other forms of preventative medicine? Wait times for to see a specialist? Wait times for emergency room visits? It could mean waiting for any number of different parts of the medical system, some of which people may care about less than others. So when people talk about wait times, they need to specify which wait time they’re discussing. Then they need to provide evidence supporting that wait time and why that wait time means the US shouldn’t adopt universal healthcare.

Second, there’s one wait time critics never discuss: the wait times of people who can’t afford medical treatment. Regardless of what “wait time” is being discussed, their wait is infinite! While the Affordable Care Act (Obamacare) has reduced this number of people, there are still millions of Americans per year who go without treatment because they can’t afford the costs of medical treatment (USAFacts Team, 2026). Over 68,000 of those Americans will die (Office of U.S. Sen. Bernie Sanders, 2023).

Finally, depending on which wait times we’re discussing, they could be shorter under universal healthcare! Under Medicare only 21% of seniors wait more than 4 weeks to see a specialist (National Nurses United, 2020). Many European countries have shorter wait times to see a primary care physician/general practitioner. “According to a recent study by the Consumer Choice Center, the average wait for a GP appointment in the United States in 2023 was around three weeks, two to ten times longer than in Europe” (Fleck & Richter, 2024).

Down with Private Healthcare!

In conclusion, our current healthcare system is garbage. Not only is it more expensive than universal healthcare, but it provides worse service too. Critics of universal healthcare like to mock the Canadian system, but our doctors are lining up to work there! While Canada’s system is far from perfect, it still provides more benefits at lower costs than our system. They live longer than we do, for example. Additionally, they don’t have to worry about losing their healthcare when their boss lays them off to do stock buybacks. America faces multiple problems due to economic inequality. In addition to improving people’s health, universal healthcare would be a great first step in combatting them!

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(P.S. This article covered the benefits of universal healthcare. For deep, data-driven dive into the criticisms of universal healthcare and why the data doesn’t match them, check out my latest article: “But What Does the Data Say? Healthcare.”)

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Balhous, C. (2018, July 30). The costs of a national single-payer healthcare system. Mercatus Center. https://www.mercatus.org/research/working-papers/costs-national-single-payer-healthcare-system

Blair, J. (2022, June 20). Study: More than 335,000 lives could have been saved during pandemic if U.S. had Universal Health Care. Yale School of Public Health. https://ysph.yale.edu/news-article/yale-study-more-than-335000-lives-could-have-been-saved-during-pandemic-if-us-had-universal-health-care/

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Delaney, J. (2020, February 14). Medicare for all helps unions by taking health care off the bargaining table [truthout]. Health Care For Us. https://hc4us.org/medicare-for-all-helps-unions-by-taking-health-care-off-the-bargaining-table-truthout/

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Galvani, A. P., Parpia, A. S., Foster, E. M., Singer, B. H., & Fitzpatrick, M. C. (2020). Improving the prognosis of health care in the USA. The Lancet, 395(10223), 524–533. https://doi.org/10.1016/s0140-6736(19)33019-3

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Saez, E., & Zucman, G. (2019, October 25). Make no mistake: Medicare for all would cut taxes for most Americans | Emmanuel Saez and Gabriel Zucman. The Guardian. https://www.theguardian.com/commentisfree/2019/oct/25/medicare-for-all-taxes-saez-zucman

Sarlin, B., & Kimelman, J. (2019, June 20). What is “medicare for all” and how would it work? NBCNews.com. https://www.nbcnews.com/politics/elections/what-medicare-all-how-would-it-work-n1014256

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Sylvie, A. (2016, June 27). Single Payer Healthcare: Pluses, minuses, and what it means for you. Harvard Health. https://www.health.harvard.edu/blog/single-payer-healthcare-pluses-minuses-means-201606279835

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Telesford, I., Wagner, E., & Cox, C. (2025, October 6). How does the quality of the U.S. health system compare to other countries?. Peterson-KFF Health System Tracker. https://www.healthsystemtracker.org/chart-collection/quality-u-s-healthcare-system-compare-countries/#health-system-capacity

Thomas, K., & Regan, M. D. (2016, January 18). Sanders pitches tax hike to pay for Universal Health Care Plan. PBS. https://www.pbs.org/newshour/politics/sanders-pitches-tax-hike-to-pay-for-universal-health-care-plan

USAFacts Team. (2026, May 18). How many people in America skip medical treatment due to healthcare costs?. USAFacts. https://usafacts.org/articles/how-many-people-skip-medical-treatment-due-to-healthcare-costs/

World Bank Group. (2026). Life expectancy at birth, total (years) - United States, United Kingdom, Canada. World Bank Open Data. https://data.worldbank.org/indicator/SP.DYN.LE00.IN?end=2024&locations=US-GB-CA&start=2000

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Yes, you read that right: house everyone!

The United States (US) is currently in a housing crisis. As of 2024, median home prices have more than doubled in America since 1965—and that’s after adjusting for inflation (Winters, 2024)! Meanwhile, median household income has only risen by about 22% over that same period (United States Census Bureau, 2021; Federal Reserve Bank of St. Louis, 2025). The results are longer commutes (people having to buy homes further away), skyrocketing rental prices (because no one can afford a home near where they work), increased private debt (mortgages, credit cards to cover income gaps), and a record number of homeless people! While there are many possible ways to mitigate these issues, there’s only one way to solve them completely: give people homes.

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When I say “give people homes” I don’t mean give them a low-interest loan, a grant for a down payment, or some sort of split-payment program (the individual pays X, the government pays Y), I mean give people homes. The government should supply every adult with a place to live free of charge. We could call it something catchy like “Universal Basic Housing.” Universal Basic Income (UBI) would give everyone a check to cover their basic needs, Universal Basic Housing (UBH) would give everyone basic lodging.

I want to emphasize “basic.” I’m not saying Uncle Sam should give everyone a mansion, I’m saying every adult should be guaranteed at least a one-bedroom, one bathroom apartment. The size could be adjusted to accommodate single people with kids, married couples/families, or adults caring for other adults, but they still wouldn’t be luxury accommodations. This would give everyone a starting point from which to (if they choose) launch their careers and/or save enough to buy better homes!

Why everyone and not only homeless people? Because it works better that way. One of the reasons public housing has declined in quality here in the US is because it wasn’t offered to everyone. Since only homeless people and the very poor, people who are underrepresented in politics, had access to it, there were few people to speak out against funding cuts to Housing and Urban Development (HUD). The other reason was people who wanted to cut HUD’s funding spread propaganda racializing public housing (the same way they did/are doing with welfare) knowing white people would oppose it if they thought only Black people were benefitting from it (Chow, 2018; Stanford University, 2018). When everyone has access to it, more people are invested in maintaining it. This is why Alaska’s Permanent Fund Dividend remains popular across the state (Widerquist, 2024). More importantly providing everyone would homes would be more beneficial to all working-class Americans.

It would lower the costs of both homes and apartments. If everyone is guaranteed housing, paying rent/having a mortgage becomes a want instead of a need. This will lower the demand for housing, which will reduce prices. People who want more than just the basics will be better able to afford it. A side benefit of this will be lower interest rates. With fewer people needing loans, banks will need to lower interest rates to incentivize people to take them. The same goes for credit cards. Cheaper homes mean less debt (Zimmerman & Hoffman, 2024)!

UBH would improve the quality of non-government homes and apartments. Public options create choice. Free options mean anyone looking to sell homes or rent out a place to live will have to either improve their services or raise the quality of their living spaces to attract customers. Why would someone pay to live in a rundown apartment with no hot water and questionable electrical wiring when they can get a decent apartment for free? Why would someone buy a rat-infested home with foundation issues when they can have a clean, safe place to live for free?

What if the owners can’t afford to fix those properties? My first instinct is to ask why they’re trying to charge people for subpar living spaces, but I understand for some people it may be their only option. The government could purchase these properties from the owners and either repair them or bulldoze them to build better housing (discussed more later).

Guaranteed housing raises wages. Safety nets give people options. It’s easier for people to advocate for higher wages when they don’t have to worry about being homeless if their boss retaliates. This same logic can be applied to improving working conditions and combatting interpersonal issues such as sexual harassment. Like with UBI, some people will take advantage of stable housing to go back to school, take care of their children, or quit work to start their own business or project (Widerquist, 2024). With fewer people working jobs they hate just to make rent, companies will need to make those jobs more desirable. The quickest way to do this would be to raise wages.

Everyone is just safer this way. Stable housing lowers crime and helps people beat addiction—which lowers crimes even further (National Low Income Housing Coalition, 2021). In the case of homeless people, it will save us money. Giving them homes is cheaper than letting them sleep on the streets (Miles, 2014; Yglesias, 2019). It will also remedy situations where people stay in unhappy and/or abusive relationships because they can’t afford to live on their own. Additionally, housing security reduces domestic violence and is gives survivors a safe space to heal (Clough et al., 2013; Austin et al., 2022; Jaleel, 2025).

How would do we do it? There are multiple ways, but I will focus on three: universal housing vouchers, buying up existing properties, and new construction.

Matthew Desmond proposed universal housing vouchers as a method of giving everyone a place to live in his book Evicted. Instead of giving people a place to live, the government would provide them with a voucher for X amount of money. For example, a voucher for $1000/month would mean the government would pay for rent up to that amount. In the book, Wisconsin provided this service to people who are qualified for the program. One advantage is it lowered the cost of housing (Desmond, 2016). Landlords would lower the price of rent to match the amount on the voucher. Most of them would rather have a guaranteed $1000 from Uncle Sam than $1200 from a renter who may not pay (Desmond, 2016). It would also require the least amount of change. Management of the houses/apartments would remain the job of the landlords (or managers) just like it now. The only difference would be the government covering rent instead of the tenant. A disadvantage is it would require money for monitoring/inspection to make sure the apartments people are living in are up to code/meet the minimum requirements established by the government. Another problem is the voucher would be dependent on how well the government is able to keep up with inflation. Giving people $1000/month for rent may work for the first couple of years, but eventually that’s not going to be enough (Minimum wage, anyone?). One way to address this is to have the voucher amount automatically adjusted for inflation. Additional adjustments in price would need to be made for areas with higher costs of living (e.g. San Francisco).

Instead of giving tenants a check and inspecting local apartments, the government could buy and maintain the apartments themselves. While this has a much higher upfront cost, it could save the government money in the long run. Private landlords still need to make a profit, meaning housing vouchers would need to be adjusted for both inflation and opportunity costs. The government doesn’t have that constraint. The government could operate public housing at a “loss” if it wanted to, providing housing at no cost and paying the necessary maintenance fees. Additionally, the government owning the apartments/housing cuts out the middle person. Instead of pressuring landlords to do repairs or upgrade housing units, the government can do it themselves. Unfortunately, this approach is far from perfect. When buying apartments and other forms of housing, the government has to work with what’s already there. As Desmond described in Evicted, and Mamdani has shown in recent videos of New York City, many of the places available are subpar. They’re moldy, have non-functions systems (e.g. no heat), or are falling apart (sometimes literally). It may be more economical for the government to bulldoze them and start over. Finally, the most obvious problem is landlords may not be willing to sell. The government can’t buy what isn’t for sale.

The government could build new. The government could purchase land and build modern living spaces designed for durability and longevity. Like buying existing housing, there is a higher upfront cost, but it has more advantages. Because the government is starting from scratch, they could design living spaces based on what’s best for the communities. This could mean designing living spaces that encourage people to meet their neighbors, like including courtyards and parks. Adding commercial spaces into these areas like miniature grocery stores to combat food deserts or dry-cleaning places for professionals requiring uniforms and suits would also be an option. Hired architects could design buildings to reduce mold accumulation and use materials less susceptible to rust and corrosion. In places known for extreme weather, they could factor this into construction (e.g. tornado shelters). Like with buying apartments, the government doesn’t have to worry about making a profit. The main disadvantage to this would be much higher upfront costs. Building something from scratch costs much more than buying something that’s already been constructed, but it might be worth it!

Personally, I believe the best solution would be mix of all three. The government should start with housing vouchers since it’s the fastest way to address the problem of housing. While providing housing vouchers, the government should focus on buying and building housing.

What about the people who already own homes? Since 1965 homeownership in America has remained between 60 – 70% (Federal Reserve Bank of St. Louis, 2026). These people are the least likely to benefit from UBH because they already have better homes than what the program will offer. Additionally, it’s likely they will suffer from declining home values due to decreased demand—like what happened in 2008. What can be done about this? One solution is providing homeowners with a check to compensate them for the loss in value. They could either invest this money or apply it towards their mortgages. Homeowners could get a chance to refinance their homes based on the decreased interest rates and a reduced loan amount based on the decrease in value of their homes. The government could compensate banks for their losses in the form of tax write-offs. Even though they’re not getting housing, the government can still ensure existing homeowners benefit from UBH!

Last but not least, I want to address an argument that keeps coming up in discussions about homelessness: what about the people who choose to be homeless? Okay, what about them? This demographic only represents less than 20% of homeless people, and most of the people who would benefit from UBH are not homeless anyway (Bowers, 2019). Depending on the reasons they want to be homeless, alternative arrangements could be made for them. Maybe they want to be part of a camp on a piece of land, for example. We can’t force them to be housed if they don’t want to, but we can still provide housing for everyone else in the country!

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Sources:

Austin, A. E., Durrance, C. P., Runyan, C. W., Runyan, D. K., Martin, S. L., Mercer, J., & Shanahan, M. E. (2022). Affordable housing through the low-income housing tax credit program and intimate partner violence-related homicide. Preventive Medicine, 155, 106950. https://doi.org/10.1016/j.ypmed.2021.106950

Bowers, C. (2019, November 17). Myth #2: People choose to be homeless and most decline services when offered. United To End Homelessness. https://unitedtoendhomelessness.org/blog/myth-people-choose-to-be-homeless-and-most-decline-services-when-offered/

Chow, K. (2018, June 8). Why more white Americans are opposing government welfare programs. NPR. https://www.npr.org/sections/codeswitch/2018/06/08/616684259/why-more-white-americans-are-opposing-government-welfare-programs

Clough, A., Draughon, J. E., Njie-Carr, V., Rollins, C., & Glass, N. (2013). ‘having housing made everything else possible’: Affordable, safe and stable housing for women survivors of violence. Qualitative Social Work, 13(5), 671–688. https://doi.org/10.1177/1473325013503003

Desmond, M. (2016). Evicted. Crown/Archetype.

Federal Reserve Bank of St. Louis. (2025, September 9). Real Median Household Income in the United States. FRED. https://fred.stlouisfed.org/series/MEHOINUSA672N

Federal Reserve Bank of St. Louis. (2026, April 28). Homeownership Rate in the United States. FRED. https://fred.stlouisfed.org/series/RHORUSQ156N

Jaleel, S. (2025, October 24). Turning housing into freedom: How affordable housing can empower survivors of domestic violence. Housing Up. https://housingup.org/2025/10/24/turning-housing-into-freedom-how-affordable-housing-can-empower-survivors-of-domestic-violence/

Miles, K. (2014, March 25). Housing the homeless not only saves lives - it’s actually cheaper than doing nothing. NOEMA. https://www.noemamag.com/housing-the-homeless-not-only-saves-lives-its-actually-cheaper-than-doing-nothing/

National Low Income Housing Coalition. (2021, July 12). New Study Finds that Providing People Experiencing Homelessness with Housing has Positive Impacts on Health, Crime, and Employment. National Low Income Housing Coalition. https://nlihc.org/resource/new-study-finds-providing-people-experiencing-homelessness-housing-has-positive-impacts

Stanford University. (2018, May 31). Perceived threat to racial status leads to opposition of welfare among white Americans, Stanford sociologist finds. Stanford Report. https://news.stanford.edu/stories/2018/05/welfare-opposition-linked-threats-racial-standing

United States Census Bureau. (2021, October 8). Income in 1965 of families and persons in the United States. Census.gov. https://www.census.gov/library/publications/1967/demo/p60-051.html

Widerquist, K. (2024). Universal Basic Income. The MIT Press.

Winters, M. (2024, May 13). Here’s how much housing prices have skyrocketed over the last 50 years. CNBC. https://www.cnbc.com/2024/05/13/how-much-more-expensive-homes-are-since-1965.html

Yglesias, M. (2019, February 20). The most cost-effective way to help the homeless is to give them homes. Vox. https://www.vox.com/2014/5/30/5764096/homeless-shelter-housing-help-solutions

Zimmerman, R., & Hoffman, L. (2024, July 24). Increasingly debt-strapped consumers concerned about rising housing-related costs. Fannie Mae. https://www.fanniemae.com/research-and-insights/increasingly-debt-strapped-consumers-concerned-about-rising-housing-related-costs

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With income inequality continuing to grow and the election of Mayor Mamdani in NYC, we’re hearing more people talk about capitalism, socialism, and communism [1][2]. Unfortunately for us, however, few people talking about these economic systems seem to understand what they are. To provide some clear definitions, I will be writing an article about each one. The articles will describe what they are, what they’re not, how I feel about them. My previous articles covered capitalism and socialism. This article will be about communism.

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What is communism?

Communism is a little different from the previous two words I discussed. First, it’s not just an economic system. Communism is form of social organization. Second, its definition changes whether you’re talking about “communism (little ‘c’)” as a system or “Communism (big ‘C’)” as a form of government. This is important because they have two different, and opposing, definitions (more on this later). Let’s start with communism. According to the dictionary, communism is “a theory or system of social organization based on the holding of all property in common, actual ownership being ascribed to the community as a whole or to the state” [3]. To expand on this further, communism is a moneyless, classless and stateless society [4][5]. When Marx and Engels wrote about communism, this is what they meant. When left-wingers talk about “communism,” this is what they mean.

Okay, then what is Communism?

According to the dictionary, Communism is a form of government where “(often initial capital letter) a system of social organization in which all economic and social activity is controlled by a totalitarian state dominated by a single and self-perpetuating political party” [3]. This is the exact opposite of a moneyless, class, and stateless society. Communist (big “C”) are forms of state capitalism (the means of production are privately controlled by the state) with the intention of transitioning to socialism and eventually communism. The idea is a strong state is needed to protect against the violence and exploitation of “free market” capitalism and Western Imperialism. Once these forces had been defeated worldwide, the world could start to transition toward communism. Examples of Communist countries are the former USSR, China, and Cuba. All of these countries currently have autocratic states that privately control the means of production. While these countries are nominally Communist, they’re really totalitarian governments using capitalism as their economic system. Ostensibly, they are directing capitalism in a way that benefits most citizens as opposed to a select few, but its effectiveness at that is debatable. Just to remove any ambiguity, these countries are neither socialist nor communist. When right-wingers talk about “communism,” what they’re actually thinking of is “Communism” (a.k.a. state capitalism).

For the rest of this article, I will be discussing communism.

What isn’t communism?

State Capitalism. As stated above and in my previous article, state capitalism is when the means of production are privately controlled by the state. Communism requires socialism, which is mutually exclusive to capitalism. While countries like China and Cuba wish to be communist and are actively working towards that goal, they are not there yet. They currently function under state capitalism.

Any form of government or hierarchical society. Communism is stateless. There’s no such thing as a communist country because countries are a type of state. It’s also classless so there can’t by a tiered system of people. There are no “owners” and “workers” in communism. There are also no “rich” and “poor” because money doesn’t exist and all private property is collectively owned.

“Not having your own house, car, toothbrush, etc.” These are all forms of personal property. Communism distinguishes between personal and private property. These are personal property because you use them. You live in your house, you drive your car, you use your toothbrush to clean your teeth. Private property would be land, extra houses, factories, farms, etc. used to generate profits. While communism allows people to have personal property, it doesn’t allow people to have private property. Ownership of private property is designated and enforced by the state. If there’s no state, who is going to enforce your claim on a piece of land that doesn’t contain your house? Why would you need private property in a moneyless society, anyway?

“No iPhone!” The implication here is that without free market capitalism, the iPhone (or any other piece of technology) wouldn’t exist. In other words, no one would’ve been inspired to invent the smartphone without the profit motive present in the “free market.” This is ridiculous for two reasons:

First, the USSR and China have proved free market capitalism isn’t necessary for technological advances. There was a point in time where the USSR was more advanced than the US technologically (Remember Sputnik?). At the time of the Russian Revolution, it was an agrarian society behind most of Western Europe. Currently, Chinese electric vehicles (EVs) outperform US ones and were 70% of all EVs manufactured in 2024 [6]. The reason you’ve probably never heard of them is the US government put high tariffs on them—so much for the “free market!”

Second, most of the technologies used in the iPhone were invented in government labs. One of these technologies was the multiple-touch touchscreen. When touchscreens were first invented, they could only register one touch at a time. It was impossible to do mundane things like zoom in and out on photos using your fingers, because that required the screen being able to reach multiple touches at the same time. Universities figured out how to do it through federal-funded research [7]. Apple just used the technology in their iPhone. In other words, without taxpayer-funded research, there would be no iPhone [8].

Socialism (kind of). This one is a bit complicated. All forms of communism are socialist, but not all forms of socialism are communist. Socialism is when workers control the means of production. A moneyless, classless, and stateless society would have to be socialist by default. If certain people have control over the means of production and others don’t, you are back to having two classes: owners and workers. However, not all socialist societies have to be moneyless and stateless. Communism requires all three.

How do I feel about it?

It’s complicated. I like idea of communism, but I don’t think it’s achievable. I believe the US could become socialist in a generation. I even believe we could build a moneyless and classless society within 100 – 200 years. What I’m skeptical about is statelessness.

The reason countries like the former USSR, China, Cuba had/have autocratic states with limited civil liberties was Western Imperialism. The argument was if they allowed free speech, Western nations would exploit that freedom to inundate the people with in anti-state propaganda or plant the seeds of revolution. The United States especially has a long history of causing and funding coups in South and Central America [9]. Not even Europe was safe from US interventions. Cuba has been subject to many atrocities at the hand of the US. The US tried multiple times to assassinate Fidel Castro, has a decades-old embargo against Cuba, and is currently starving Cuba with a military blockade. Why did the US do this to all these countries? Because they are/were “communist.”

For communism to work, all countries in the world would have to be working towards communism and be willing to dissolve their states. If half the countries on the planet decide to become communist and dissolve their state governments, there will be nothing to protect them from the capitalist half of the world. They will see the communist half of the planet as easy targets for exploitation. Some will point to how Vietnam was able to successfully defeat the United States, but Vietnam had a functioning state government capable of organizing military resistance. That would not be the case under communism.

While I don’t want to completely rule it out, I don’t see a situation where enough of the world embraces communism that capitalism peacefully dissolves. If the violent history of capitalism is any indicator, a state of some kind would still be needed to organize defenses against it. And if there’s state, it’s not communism.

Full Disclosure

I’ve had this article finished for over month, but it’s taken me so long to publish because I don’t like how I ended it. I don’t like to talk about problems without offering possible solutions, but I don’t have a possible solution. I can’t think of a way for communism to peacefully defeat capitalism. So I’m asking for your help. Let me know in the comments if there’s a way to get enough of the world to embrace communism that capitalism peacefully dissolves.

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Sources:

[1] https://www.epi.org/multimedia/unequal-states-of-america/

[2] https://ourworldindata.org/economic-inequality

[3] https://www.dictionary.com/browse/communism

[4] https://en.wikipedia.org/wiki/Communist_society

[5] https://www.britannica.com/topic/classless-society

[6] https://www.aljazeera.com/news/2025/11/20/chinas-evs-dominate-the-world-why-not-in-the-us-and

[7] https://www1.udel.edu/udaily/2015/oct/university-research-100814.html

[8] https://thebreakthrough.org/issues/energy/the-iphone-and-the-invisible-hand-of-government

[9] https://www.npr.org/2026/01/02/nx-s1-5652133/us-venezuela-interventionism-caribbean-latin-america-history-trump

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All property is force. I’m not saying that’s good or bad, I’m saying there’s no such thing as a fully voluntary property arrangement. What do I mean? Property is a restriction. More than it says what the owner can do, it says what non-owners can’t do. Take land, for example. If Person A owns a piece of land, Person A can bar anyone else from entering or occupying that land. Their right to decide who can occupy that land exists regardless of whether everyone else agrees with them. This is not a voluntary arrangement because everyone else is forced to abide by whatever rules Person A sets up for the land. Since it’s not voluntary, some person or entity is going to need to enforce this restriction. An entity like the government.

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In the US, the government determines property rights. When someone buys a house, they’re issued a deed. That deed is a legal document describing who owns that home. If someone else tries to claim they own that house, they have no standing. If both parties claim to have a deed, the government decides which one of their claims is valid. Property rights exist because the government can universally resolve disputes and has the authority to enforce that resolution. If the losing party disagrees with the ruling, they either have to suck it up or find some other way to resolve the dispute. Even in the case of the latter, the government will usually still need to be involved.

Even though the government represents a universal force, this force allows for more peaceful resolutions. Property disputes can happen in courts where conflicts are resolved with words and evidence instead of bloodshed. Without the government or some other third party, who has the biggest guns defines property rights. In a capitalist society, that’s usually the person with the most money. They have the resources to afford the best weapons and to hire people to use them. If people don’t give up their resources willingly, the wealthy can deploy their private armies to take them by force. This is why anarcho-capitalism (an oxymoron, but that’s another conversation) and other forms of government based solely on “voluntary exchange” ultimately result in dictatorships of capital.

While allowing a third party to determine property rights has its advantages, it’s not a perfect system. That third party is vulnerable to corruption. Take the US, for example. The owner class in the US amassed so much wealth it was able to buy the government. What was supposed to be a neutral third party now rules in ways that favor its benefactors. One example of this was the Citizens United v. FEC (2010) ruling, which removed limits on corporate spending in politics under the guise of “free speech.” The five Supreme Court justices who ruled in favor removing the limits on corporate spending were appointed by presidents who favored the interest of the owner class—the same owner class that contributed to their campaigns. This happened again last month with the Supreme Court ruling against the Voting Rights Act—a ruling made by Justices appointed by a President favoring the owner class.

So, what can be done? Some people have proposed, and campaigned on, a constitutional amendment to reverse Citizens United v. FEC [1]. Others have argued states can circumvent the ruling completely by redefining what corporations are [2]. In my opinion, these are good for treating the symptoms but not curing the disease. The disease is corporations making too much money. If they had lower profits, they wouldn’t be able to spend money on SuperPACs to bribe politicians. How do we lower their profits? The best way would be to force them to pay their workers more. Unionization is the most accessible method to people now. In my opinion, the best way to reduce corporate money in politics is to democratize the workplace. Let the workers decide what’s done with companies’ profits. I’m sure bribing politicians will be the last item on the list!

Sources:

[1] https://www.npr.org/2016/02/14/466668949/presidential-candidates-pledge-to-undo-citizens-united-but-can-they

[2] https://www.americanprogress.org/article/the-corporate-power-reset-that-makes-citizens-united-irrelevant/

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I couldn’t end Black History Month without talking about the Montgomery Riverfront Brawl!

It goes without saying, but I’m saying it anyway: I do not condone violence! I am recounting these events for strictly comedic purposes and am not saying this is how people should behave when facing racism.

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You can read all the boring details in the article linked below [1]. In summary, a racist white family illegally parked their boat in a dock meant for steamboats, preventing one of the boats coming in from docking. With people stranded in the water, one of the dock workers (who was Black) politely asked the family to move and they refused. The family attacked the worker, leading to a brawl where family got into a fight with multiple other Black workers from the company, culminating in one of the family members being hit with a folding chair. The family was arrested, leading to some hilarious mugshots.

Unfortunately for the Black workers who were attacked, the police didn’t charge any of the family members with a hate crime. It did lead to bunch of great memes, though.

Over 100 years ago, Black inventor Nathaniel Alexander patented the folding chair [2]. Though he’s no longer alive today, I’m sure he would smile at the thought of his invention being used in the defense of his people!

Sources:

[1] https://en.wikipedia.org/wiki/Montgomery_Riverfront_brawl

[2] https://hyperallergic.com/the-black-history-of-the-montgomery-brawl-folding-chair/

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Our ability to think far surpasses those of any other creature. Starting with the control of fire, it has allowed us to invent tools to overcome most of our natural disadvantages. We built machines to make up for our lack of strength and invented medicines to extend our lives, to name a couple of examples. Doing this required pattern recognition, data analysis, judgement, and the ability to share this information with others. That said, our skills aren’t perfect.

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When we are in a crisis or reacting to events around us, our brains often use shortcuts to make decisions quickly. While this was/is necessary to ensure our survival, it can often cause us to make mistakes. This is especially true if we use the same shortcuts when trying to make rational decisions or persuade others. These mistakes are known as logical fallacies.

What are logical fallacies? According to Purdue University, logical fallacies “are common errors in reasoning that will undermine the logic of your argument” [1]. In other words, arguments containing logical fallacies are usually invalid and therefore not worth any further consideration. While they are often committed accidentally, some people intentionally use logical fallacies to deceive, persuade, or scam others. Advertisements are notorious for using logical fallacies like appealing to popularity (discussed below) to get people to buy their products. Blatantly lying to people in advertisements is illegal, but using bad arguments is not. This is just one of many reasons why it’s important to be able to understand and recognize logical fallacies. There are dozens of logical fallacies out there, but here are six of the most common ones:

Ad Hominem

Attacking the person instead of the argument and then claiming the person’s argument is invalid because of it. I encountered this fallacy quite often back in my hardcore atheist/anti-theist days (for the record, I am now an agnostic Unitarian Universalist). Conservative Christians would often claim my arguments against their religion were invalid because I was a “christophobe” or a “God-hater.” Whether or not that was true (for the record, it wasn’t) that by itself didn’t mean the arguments I made were invalid. Another version of this is attacking a person’s motivations instead of their argument. Someone’s motivation for making an argument doesn’t mean that argument is invalid.

Affirmation of the Consequent

In my deductive reasoning article, I explained principles (p) and consequences (q). If “p” implies “q,” then the presence of “p” means “q” is also present. The affirmation of the consequent fallacy is when someone argues that the presence of “q” means “p” is also present. This is commonly used as justification for tone-policing/concern trolling. A Black person says, “Black Lives Matter” and the concern troll responds with, “This is why Trump won!” (Another common version of concern-trolling argument is the one in the image above.) While Trump did win in 2024 (q), there’s no evidence it was because people were saying “Black Lives Matter” (p). In fact, it could very well be true Trump won because people didn’t say “Black Lives Matter” enough!

Anecdotal Fallacy

I’m sure we’ve all seen this fallacy at least once. Person A shares scientific evidence supporting point XYZ and Person B responds by saying their personal experience refutes Person A’s evidence. This is fallacy because one incident doesn’t negate statistical significance. Another reason is there’s no way to verify the accuracy of Person B’s statement. I’m not calling them a liar (though they could be), I’m saying they could be confusing correlation with causation or misremembering the details. That said, there are times when anecdotes are fine to use. If someone claims your lived experiences are wrong because it contradicts their unsubstantiated opinions, no one will fault you for trusting your experiences over the other person’s opinions.

Burden of Proof Reversal

“Prove me wrong!” “Change my mind!” “You can’t prove that I don’t!” All of these are examples of burden of proof reversal. Someone makes a strong positive or negative claim and then demands you prove them wrong. This is a fallacy because the burden of proof is on the person making the claim. If they can’t provide evidence to support their claims, you have no obligation to provide evidence refuting them.

Divine Fallacy

“It’s true because I said so/want it be so!” Some alternate versions of this are people claiming statements are “self-evident,” “common sense,” or “reality.” Claiming something doesn’t need evidence doesn’t make it so. You can dismantle this fallacy by simply asking, “What makes it self-evident?” If you’re feeling feisty, another way to dismantle this argument is to make the opposite claim, state it’s also self-evident, and then ask them why their claim is more valid than yours. You’d think it would be obvious to most adults that saying something is true doesn’t make it so, but here we are.

Naked Assertion/Unsubstantiated Claim

This is when someone makes a claim without proof. The easy answer to this is to ask them for a source, proof, or evidence of their claim. If they refuse, you can tell them, “Claims without evidence can be dismissed without evidence.” The one caveat to this is some claims are very difficult, if not impossible, to prove. For example, certain personal experiences. A person may claim a helmet saved their life as a kid because they ran into something and the collision sheared off a piece of their helmet instead of their head. Unless they still have that helmet, there’s no way to “prove” that story is true. People will either believe them or they won’t. While this is technically still a naked assertion, the reason behind why the person is telling the story likely makes it a better fit for a different logical fallacy. (For the record, I strongly believe helmets save lives.)

BONUS: The Fallacy Fallacy

This is a controversial one. The fallacy fallacy is when someone claims a person’s conclusion is wrong because the argument they made to support it contains a logical fallacy. The reason this fallacy is controversial is because many people misunderstand what logical fallacies are. While it’s true arguments containing logical fallacies are invalid, that doesn’t mean the conclusion of said arguments are wrong. It means the arguments don’t support the conclusion and better arguments are needed. Otherwise, it’s an unsubstantiated claim.

These are only six (Plus a bonus one!) of the most common fallacies. There are dozens of logical fallacies out there. I plan to cover more of the common ones in a later article.

Finally, some people will get upset with you for pointing out the logical fallacies in their statements. Let them. Their anger doesn’t make you wrong. In fact, that’s another logical fallacy: appeal to emotion.

Sources:

[1] https://owl.purdue.edu/owl/general_writing/academic_writing/logic_in_argumentative_writing/fallacies.html

The rest comes from the knowledge I acquired through the logic classes I took for my engineering degree, the ethics classes I took for my MBA, and decades of talking politics.

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Being an artist doesn’t make racism okay. There are ways to draw Black people without turning them into racist caricatures. There are ways to cosplay Black characters without adopting Black hairstyles (e.g. cornrows) or engaging in Blackface.

Additionally, you can’t use the excuse “I’m just trying to get in character/trying to be authentic” for dressing up in a racist way or drawing Black people in a racist way. There are plenty of ways to portray racism against Black people in art without creating racist art!

Image Artist, Michelle Olens:

https://community.wacom.com/en-us/drawing-while-black-2023/

https://www.artstation.com/m-olens

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Let’s make something clear: Black Republicans are practically nonexistent. They’re a fraction of a fraction of a fraction.

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What do I mean? Black people make up about 14% of eligible voters [1]. Only 64% of eligible voters, regardless of race, showed up in 2024 (this is high for America, unfortunately) [2]. That comes out to 155M people [2]. For the sake of simplicity, I’m going to assume all races voted evenly. That means about 9% (14% x 0.64) people who voted in 2024 were Black (it was actually less). So about 14M people. Of that 9%, only 15% of them voted for Trump (83% voted for Harris). That gives us 2.1M Black Republicans, if we assume that every Black person who voted for Trump identified as Republican. There are about 34.5M Black eligible voters [1]. This means only 6% of Black voters are Republicans.

If Black Republicans such a small percentage, why do they get so much attention? Because Republicans love to use them as shields! Republicans point to the Black people, especially the high-profile ones like Clarence Thomas and Thomas Sowell, in their party and say, “See? We aren’t racist! If we were racist, why would Clarence Thomas be in our party? Why would Thomas Sowell be a popular conservative author?” It’s pretty much the “I can’t be racist because I have Black friends!” defense, but for the whole party. That’s why the Republican Party continues to elevate Black conservatives.

[1] https://www.pewresearch.org/short-reads/2024/01/10/key-facts-about-black-eligible-voters-in-2024/

[2] https://goodauthority.org/news/2024-brought-high-voter-turnout-but-a-growing-racial-gap/

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For those who can’t read the small text, the guy in the first panel is asking, “Why are there Black people in *insert fantasy that doesn’t exist in real life*?”

There’s zero reason there can’t be Black elves, Black mermaids, or Black vampires. It’s FANTASY. It’s MADE-UP. It’s NOT REAL. How do you know there couldn’t be elves who come from areas where they needed more melanin in their skin? I didn’t realize there was a Grand Council of Fantasy that met periodically and decided what an elf was supposed to look like.

There’s nothing wrong with Black characters in fantasy. You’re just racist.

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Before I proceed, I want to mention what others have already stated: only a small percentage of people Tourette’s Syndrome uncontrollably shout obscenities. People trying to defend this guy by saying this happened because of his Tourette’s Syndrome are arguably being ableist by painting everyone with Tourette’s with a broad brush. Okay, on with the post!

Being disabled isn’t an excuse for racism. Even if we assume the man had the form of Tourette’s that made him shout obscenities uncontrollably, it doesn’t make his actions okay. It also doesn’t make it okay to invalidate the feelings of people who were hurt by him using the N-word. Whether we like it or not, words have power. The N-word has centuries of racism, violence, degradation, and humiliation behind it. That’s not going to go away just because the man who shouted it had a disability.

This is a case of intent vs. impact. Under the most “innocent until proven guilty” assumption, this was an unfortunate accident. While the man didn’t intend to shout the N-word at a couple of Black people, he still did it. That means there is now a problem that must be addressed. If you accidentally stepped on someone’s foot, you still caused an injury that needs to be addressed. You should at least apologize to the person you hurt. Likewise, the man should apologize for using the N-word.

And while I’m talking about the N-word, there’s no such thing as an “N-word Pass.” One Black person giving you permission to use the N-word (doesn’t matter if it’s soft-a or hard-r) around them doesn’t stop it from being racial slur. It doesn’t prevent you from being racist, either. Even if every single Black person you know gives you permission to use it, there’s the fact that you STILL WANT TO USE IT despite knowing what the word means and how hurtful it is. Maybe you should unpack that.

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I’ve been politically active for over twenty-five years and one of the recurring themes with the Right is lying about being offended. When a right-winger claims they became right-wing because “liberals hurt their feelings,” many people will scramble to find out how they can make their message less “offensive.” My question is this: how do we know they were telling the truth?

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I frequently encounter this behavior from the Right. How do I know? Because they will fight tooth and nail against me over something like universal healthcare only for me to find them agreeing a day earlier with a post about universal healthcare made by a wh1te person. Unless they had a change of heart overnight, they weren’t fighting me because of honest disagreement. This is why I don’t believe most of the things right-wingers tell me.

Even though it happens to me often, I don’t believe it’s a race issue. I’ve seen the Right direct their fake outrage at anyone who espouses a view to the left of Ronald Reagan. I’ve seen them do it women, LGBTQ people, immigrants, disabled people, poor people, and even cishet wh1te men. No one is safe! The Right will specifically seek out left-leaning people and try to weaponize their crocodile tears against them.

It’s the oldest trick in the book. Humans are social creatures built for cooperation. This means most people don’t like offending other people. People on the Right take advantage of this by acting offended when someone speaks about an issue that’s important to them. This often leads to the person dialing back what they said and/or being more hesitant to speak up again. That’s what the Right really wants: to silence their opposition.

So, what can we do? Show a bit of skepticism. Make them work for it. If someone on the Right claims saying, “Black Lives Matter” is deeply racist, ask them to prove it. Or, if you’re feeling frisky, ask them why you should care about what they think. If they try to claim you’re just pushing them further to the right, ask them why you should care. Seriously. Are there lasting effects of one person moving further to the right? Probably not!

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Adding to what’s said in the meme, there are three other reasons why this phrase is ridiculous:

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1. This phrase is only used on the victims of hate and not the perpetrators. No one is telling racists they shouldn’t fight what (they perceive as) hate with hate. Why aren’t these people telling Klansmen they should combat the Great Replacement with love and that burning crosses is “not a productive way to express yourself?” Because that would be ridiculous, and they know it’s ridiculous. The reason they say this kind of stuff to marginalized people is they hope we’re gullible/foolish enough to fall for it. This is especially true of the phrase’s close cousin, “Hate only breeds hate!”

2. What makes pushing back against racism “hateful?” Why should we listen to someone who considers pushing back against racism “hateful?” In my opinion, them calling your righteous anger at being discriminated against “hate” says way more about them than it does about you.

3. Most of the people who use this phrase will quote MLK Jr. out of context when they receive pushback. MLK Jr.’s definition of “love” included holding people accountable for their actions, especially their racism. In fact, he would consider being silent about their bigotry an act of hate. To MLK Jr. “fighting hate with hate” would be staying silent while those around you face discrimination. You can read more about this in his book, “Where Do We Go from Here: Chaos or Community.”

Bonus for the spirited contrarian: Why not fight hate with hate? Let’s say for the sake of argument that you are fighting hate with hate and these people are telling you stop. Why should you? What proof do they have that fighting hate with hate is a bad strategy? If it’s working, what reason do you have to stop doing it? The burden of proof is on the person telling you not to do it!

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