Today the Bureau of Labor Statistics released the July 2026 Consumer Price Index data for the Denver-Aurora-Lakewood metro area. Prices in the Denver area rose 3.9 percent over the year ending in July 2026. Nationally, prices rose 3.4 percent over the same period. Core inflation, which strips out food and energy, ran 3.5 percent in the Denver area against 2.5 percent for the US. The Denver headline number is down from 5.0 percent in the May release.

Denver area inflation is still slightly above the U.S. average but a bit closer than it was two months ago. As I will get more into below, a big part of increasing prices around Denver are rising gas prices. It’s difficult to say how long this will last, given the uncertainty of the war in Iran, but the recent slow down may be a sign for optimism. After a steady decline in inflation from its peak in 2022, Denver area inflation was creeping back up above 2.5% at the start of 2026. Hopefully, May 2026 was another peak and we are on the way back down toward lower inflation. We will have to wait until October to see if this continues.

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Where prices moved

By major category, the fastest-rising component over the past year was Energy at 17.2 percent, with gasoline up 25.6 percent, and the slowest was Shelter at 2.0 percent. Food rose 3.4 percent.

Notable moves

The Denver CPI covers 55 published item categories beyond the headline number. A few stand out this release as shown in the table below.

Gas prices are still up on the year, but have declined over the last two months. This should be a notable relief for many people after the shock of going from under $2.50/gallon earlier this year to over $4.00/gallon in just a few months. When I have filled up recently I am now paying closer to $3.50/gallon depending on where I go.

The year-over-year increase for the education and communication commodities category is a bit odd. Not only is it the second highest year-over-year increase in the table, but is substantially higher than the change nationally. Given the data I have, I cannot tell exactly what is contributing to this but it may be related to computer and software price increases. I wouldn’t put much into that ‘maybe’ though, could simply be noisy data as the BLS warns for small samples in these local item areas.

The shelter category (which includes rents, implied rents of owner-occupied homes, and things like hotel stays) shows prices are up on the year but lower when compared nationally and showed a decline compared to the May release. This is a trend I think many in the area would like to see continue, especially renters who have seen some relief in rent over the last year or so. But you may feel differently if you are trying to sell a condo right now.

Last week, Mayor Johnston gave his State of the City speech looking ahead to a possible second term. As Paul Teske wrote shortly after the speech, the major focus was on affordability in Denver. Within the bucket of affordability one of the highlights was childcare costs in Denver.

I recently wrote a post here on childcare costs in Colorado which covered what I see as three of the major causes of high childcare costs including cost disease, regulation, and the demand side. So, I was happy to see the Mayor putting a spotlight on this issue that parents all around the city are dealing with.

Mayor Johnston did not go into great detail in his speech but laid out a framework for a task force and policy changes intended to address childcare costs. Some of this sounds good but I don’t know how optimistic I am that it will bring about the desired changes. Part of this has to do with how the Mayor discussed childcare regulations.

The Mayor rightly points out that regulations are one of the things making it more difficult for providers to enter the market. But the way he describes this is “red tape” leading to providers drowning in paperwork. His exact quote was, “cut the red tape so passionate educators can open and run a quality childcare business without drowning in paperwork.” Some work has shown that the fixed costs of compliance are significant. But I’m skeptical that direct compliance costs are the most costly part of the relevant regulations.

Even if the process of opening a daycare was streamlined (applications made easier, dealing with a single agency, etc.) such that “red tape” was cut, this wouldn’t mean constraints on child to staff ratios or other regulations suddenly became more flexible. I think a more serious look at regulations that goes beyond simplifying processes is warranted here. But I also don’t want to discount that even cutting paperwork would be an improvement.

My hope is that the task force looks into building some real flexibility into existing regulations. However, I am not even sure what they can achieve given that many of these regulations come from the state government. So, even if the Mayor’s plan was aimed at dramatic cuts in regulations, it’s not clear much could be done. Where there is room to create some regulatory flexibility at the city level, it should be pursued. And if that flexibility creates more options and allows for more entry without significant safety or other concerns that could be a major win toward the Mayor’s goals and may even encourage changes at the state level.

Another point on the topic of childcare in this speech was an idea for universal childcare in Denver. One version of this amounts to a subsidy for childcare along the lines of the city’s pre-K program. A subsidy will probably help people but will not directly address the supply problem the Mayor also pointed out. There is a large gap between how many daycare slots are available and how many children could fill those slots. Increasing demand while keeping supply constrained can just continue to push prices higher. But without specifics on what this would look like in Denver, I’ll save any further comments for when this comes out. It’s entirely possible there is a version of universal childcare that is a major help for Denver parents without creating a whole new set of problems.

In 2020, Colorado voters passed Prop EE, increasing cigarette and tobacco taxes and joining the growing number of states that tax nicotine products commonly used as alternatives to tobacco and cigarettes. Although many states have implemented taxes specific to e-cigarettes, Colorado is (as far as I can tell) unique in that its nicotine products tax applies to “any product that contains nicotine derived from tobacco or created synthetically that is intended for human consumption,” except for tobacco and cigarettes, which are taxed separately, and products such as nicotine gum or patches, which are considered cessation products. Even with those exceptions, the tax covers a wide variety of products including e-cigarettes and increasingly popular nicotine pouches such as Zyn. These products are subject to a percentage tax of the manufacturer’s list price, for which wholesalers and distributors are liable, that started at 30% when the law was implemented and increases to 62% by July 2027.

I’m hopelessly addicted to nicotine, so I have some personal (financial) reasons why I don’t like this tax. And as much as I might like to, I am not going to spend this post railing against it. Instead I am going to use it as an example to talk a bit about tax incidence and how Colorado is in a unique situation because of TABOR. The first two parts of this post will explain the basic economics of tax incidence and revenue. The incidence part will focus on how theory predicts and empirical work supports that for addictive products like nicotine, taxes are almost entirely paid by consumers. For similar reasons, we can also expect that for something like the broad category of nicotine products, a lot of tax revenue can be raised. But where I think this gets interesting is where TABOR comes in.

Colorado’s TABOR (Taxpayer’s Bill of Rights) amendment to the state constitution is a complicated law, but one of its features is to set rules for how the state government must go about implementing new taxes. Among these rules is what happens when revenue from a new tax exceeds projections. The default is to refund the excess to taxpayers and lower the tax in proportion to the excess unless the question is put to voters who may allow the government to retain the excess revenue and keep the tax at the planned rate (note that this is different from the “TABOR refunds” on your state tax return). For the purposes of this post, whether that feature of TABOR is desirable or not in general is beside the point. The nicotine products tax is an interesting case of how TABOR can, in principle, create a situation where the legal taxpayers may receive a refund even if they did not bear the economic burden of the tax.

Revenues from the Prop EE taxes on nicotine products (along with increases in tobacco and cigarette taxes) were ultimately underestimated by more than $20 million. As I argue in the rest of the post, this meant the taxes could have been refunded to suppliers rather than the consumers who bore most of the burden of the tax. But the legislature was able to put the question up to the voters in 2023, who favored allowing the government to retain the excess revenue and keep taxes on their planned schedule.

The taxes discussed here are an extreme example with nearly no overlap between the legal taxpayer and who actually paid. But the TABOR rule that defaults to refund the legal taxpayer means that there are likely many cases where a refund may go to those who only face a portion of the true economic cost of the tax.

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Tax incidence and the nicotine products tax

The simplified way to understand the idea of economic incidence is to compare the elasticities of supply and demand. Whichever side of the market is relatively less elastic will bear more of the burden of a tax. All this means is that whoever is less sensitive to changes in prices will bear more of the tax, regardless of who sends the money to the government.

When economists think about tobacco taxes, they point to the addictive nature of nicotine making consumer demand fairly inelastic. When cigarette prices increase, smokers don’t cut back that much. But cigarette producers can shift their supply away from high-tax areas fairly easily. So, smokers tend to bear a much larger share of the burden of tobacco taxes than producers. And this isn’t just an Econ 101 model and some assumptions. There is solid empirical evidence that smokers do in fact bear most of the burden of cigarette taxes.

A 2012 paper from Ryan Sullivan and Donald Dutkowsky that estimates the effects of local and state excise taxes on cigarettes finds evidence of more than full pass-through, meaning a $1 increase in cigarette taxes raises the price of a pack of cigarettes by more than $1. In another paper from the same year by Matthew Harding, Ephraim Leibtag, and Michael Lovenheim using Nielsen Homescan data, the authors find cigarette taxes are less than fully passed through to pack prices (only about $0.85 for every $1 increase in taxes on average).

Although their results differ somewhat, the evidence supports the theoretical reasoning that smokers tend to bear a much larger share of the cigarette tax burden than producers.

I don’t have estimates of demand elasticity for broader categories like nicotine products, but when we think of consumer demand we can make some reasonable assumptions about when demand will be more or less elastic. Demand for Marlboro cigarettes will be more elastic than demand for cigarettes in general. Why? More substitutes generally means more elastic demand. Maybe there is some brand loyalty such that demand for Marlboro Reds would be less elastic than, say, demand for a particular brand of orange juice, but we should still expect that consumers will be more sensitive to price changes even for their favorite cigarette than for cigarettes in general.

One way to convince yourself of this is to consider how much pricing power Marlboro really has. Could they start charging $25 for a pack of cigarettes and expect to lose only a few customers? Probably not. So, if we accept that consumer demand is typically less elastic the fewer substitutes there are, we can extend this to think about what that means for that broad category of nicotine products being taxed in Colorado.

The nicotine products tax encompasses a large share of cigarette substitutes that consumers may turn to, including vapes and nicotine pouches. If we compare demand for tobacco and demand for nicotine products, the intuition is that tobacco demand is more elastic than nicotine products demand simply because there are more substitutes for tobacco than there are for the category of nicotine products. If only e-cigarettes were taxed, we might expect substitution to nicotine pouches, but if both are being taxed at the same time, substitution doesn’t allow a consumer to avoid the tax.

The nicotine products tax in Colorado was not alone either. Tobacco and cigarette taxes were to increase under the same law. And because the taxes on substitutes like tobacco and cigarettes were also going to increase, we shouldn’t expect a large amount of substitution away from things like e-cigarettes and pouches to tobacco products (but maybe some substitution to the exempted medical products like nicotine patches). An additional implication is that we should expect the users of nicotine products to bear an even larger share of these taxes than tobacco users bear of tobacco taxes, unless there is reason to believe that the producers of nicotine products have systematically less elastic supply than tobacco producers, which seems unlikely.

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Tax revenue and the nicotine products tax

How the difference in elasticities affects incidence is not all that matters here either. We can also take this understanding of differences in elasticity on the demand side to make predictions about how much revenue may be collected from a tax.

If we assume some fixed elasticity of supply, the less elastic demand is, the more revenue will be raised for a given tax. The reasoning for who bears the burden of the tax is similar to why we expect more revenue with less elastic demand. When consumers are not very responsive to price increases (meaning they don’t reduce their consumption all that much), there is little change in quantity demanded between the pre-tax world and the post-tax world, and the government captures a portion of receipts for each sale.

For the sake of a simple example, assume that an increase in cigarette and nicotine product taxes completely passes through to consumer prices such that a $1 increase in cigarette/nicotine product taxes increases the price of cigarettes/nicotine products by $1. We can use this as a starting point to see how small differences in elasticity create big differences in how much revenue we expect a tax to raise.

Let’s say there is no tax in the market for cigarettes and the price of a pack is $10. At this price, consumers purchase 1 million packs of cigarettes every month. The government introduces a $1 cigarette tax, increasing the price to $11 per pack.

If we want to make a prediction about revenue raised by this tax, we can use the elasticity of demand for cigarettes. Suppose we think the elasticity is -0.4, meaning that when prices increase by 10%, quantity demanded declines by 4%. So, under the $1 tax (equivalent to a 10% increase in price), quantity demanded falls from 1 million packs per month to 960 thousand packs per month, and the government collects $960,000 in tax revenue every month.

Now, we can do the same kind of example for nicotine products. The market price for nicotine products is $10 before any tax and consumers purchase 1 million units monthly. Then, the government implements a $1 tax on nicotine products, but now let’s say that the elasticity of demand for nicotine products is -0.2 (half that for cigarettes). The $1 tax in this market reduces quantity demanded by only 2%, from 1 million units per month to 980 thousand. The government is able to collect $980,000 in taxes in this market. The $20,000 monthly difference in revenue becomes nearly a quarter million over the course of a year.

The sections on incidence and revenue give us two things to keep in mind: 1) Consumers pay more of a tax the more inelastic demand is (relative to the elasticity of supply); 2) Revenue raised from a tax will be higher the more inelastic demand is.

TABOR and the nicotine products tax

Now that we know the basics of tax incidence and revenue, I can explain a weird possibility created by Colorado’s TABOR. I am not going to explain TABOR completely in this post (not sure I even could without reading much, much more about it), but one feature of the law that is relevant here is how new taxes are handled.

When the Colorado government implements a new tax and revenue collections exceed projections, the TABOR default is to refund excess revenue and reduce the tax rate unless voters approve a measure for the government to retain the excess and maintain the new tax rate. The legislature can introduce a ballot measure to seek voter approval on this.

Whether because no ballot measure is referred or because voters reject one, the excess revenue is refunded to whoever paid it to the government. But who pays the government tells us nothing about who bears the economic burden of the tax. So, a nicotine products tax levied on wholesalers and distributors but likely paid almost entirely by consumers could in principle result in revenue being refunded to the side of the market that didn’t bear the tax burden. That would be a windfall for the wholesalers and distributors.

Of course, this is only in principle, but voters were given this option a couple years after the Prop EE taxes went into effect. What happened? The Legislative Council Staff (LCS) is responsible for creating the revenue projections for new taxes in their fiscal impact statements. The fiscal impact statement for the Prop EE cigarette, tobacco, and nicotine products taxes lays out how the LCS went about estimating revenue from the new nicotine products tax based on the limited data they had access to. And one assumption they made in their projections was that demand for nicotine products is more elastic than demand for cigarettes or other tobacco products. If what I am saying about the elasticity of nicotine products is true, then assuming more elastic demand than for cigarettes or tobacco would lead to an underestimate of revenue raised from nicotine products.

By the time the question reached voters in 2023, revenue had already come in above projections. The fiscal impact statement from LCS for the Prop II ballot measure in 2023 showed excess revenue of more than $20 million and noted specifically that the “excess revenue was mostly due to tax revenue from nicotine products, such as e-cigarettes and vaping products.” Maybe the market for nicotine products grew more than expected because Zyn took off. Or maybe this was due to the data issues LCS notes in their initial fiscal impact statement. But I think the more likely story is that nicotine products were a less elastic market than LCS assumed and this led to more revenue being raised. It would be difficult to differentiate between unexpected market growth and the elasticity issue as contributors to the excess revenue. But the elasticity story makes more sense to me in part because the revenue estimate LCS used assumed 3-8% market growth, which is fairly large on the upper end.

To be fair, I am not arguing that the LCS made an obvious error. They were dealing with incomplete data on top of uncertainty about how the nicotine products market might evolve, among other things. This uncertainty is explicit in the fiscal impact statement, where LCS notes, “the market for nicotine and vaping products is expected to continue to evolve in coming years, and the pace of increasing consumption is expected to slow as federal, state, and local regulations on the industry tighten.” Their assumption about demand for nicotine products is not unreasonable either. If I had to guess, they were probably assuming that e-cigarettes make up the largest portion of this market, and I was able to find at least one empirical estimate of e-cigarette demand elasticity that is close to the number LCS used. But it’s hard to say for sure without knowing more about what went into their methodology. These are tough predictions to make, and it is easy in retrospect to point out one area that is a plausible culprit for a missed projection.

Ultimately, voters (by a strong margin) chose to allow the government to retain the excess revenue and keep the taxes in place. Wholesalers and distributors did not get their windfall. But that may not always happen. This TABOR rule doesn’t care about incidence.

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Over the last few weeks, I have not been posting as much because of a pretty big life change. My daughter was born at the end of May and most of my attention has been on her and my wife at home. We are fortunate enough to both have substantial time off from work to be at home with the baby but soon enough we will be facing the problem that most working parents face. How are we going to adjust to the looming childcare expense?

Working parents around the country, especially those without extended family to lend a helping hand, are faced with difficult financial choices when it comes to finding childcare. According to data from a report published by the Common Sense Institute, average monthly childcare costs in Denver are $1,574 and Boulder County tops the state’s most expensive counties with average monthly costs over $1,600. Compare these average costs to average wages in Denver and parents are spending 18% of their paycheck on childcare and even more in other Colorado counties.

The financial difficulties extend to providers as well. The Colorado Sun recently covered how providers like a daycare in Boulder operate on increasingly thin margins despite rising costs for parents. Leadership within the daycare quoted in the Colorado Sun article emphasizes that many of these difficulties stem from declining government support such as the freeze in the Colorado Child Care Assistance Program.

I think few would disagree that rising childcare costs pose a problem for working families but there is considerable debate over what is causing it and what can be done about it. What I want to focus on here are a few explanations for why these costs are so high in the first place. The first is the “Baumol effect” or “cost disease.” Second is how regulations, looking specifically at Colorado, can increase costs. And lastly I’ll offer a less often discussed factor on the demand side.

The Baumol effect

The Baumol effect or cost disease idea goes back to the economist William Baumol, who developed a straightforward theory to explain how differences in productivity growth across sectors of the economy could result in diverging costs and ultimately prices of outputs. I’ll give my simplified retelling of the theory.1 Start by assuming there are two sectors in the economy, the goods producing sector and the services sector. In the goods producing sector, labor productivity can be easily augmented with capital or technology and in the services sector, which is more labor-intensive, productivity remains relatively constant, largely unaffected by increasing capital and technology. Rising productivity in the goods producing sector pushes up wages there. But if workers in the service sector can move between sectors, they won’t just ignore the opportunity for higher wages. And this could mean fewer workers in the service sector to produce output if employers do nothing. What can employers in the service sector do to try and keep workers? They can offer higher wages to workers to keep them around, even though there has not been much rise in productivity. When input costs (like workers’ wages) rise without a corresponding increase in productivity, relative prices must rise and output may fall.

Childcare is a labor-intensive service, so Baumol’s theory might help explain its rising costs. You need people around to take care of all those kids and there’s really not much machines or technology can do to change that (yet?). We should then expect that for the childcare industry to keep workers it must pay them more. The theory does not predict that childcare workers are making great money, only that their wages are growing over time relative to their productivity growth. A simple comparison to see if we have evidence in favor of the Baumol effect for childcare is to look at growth in average wages in childcare compared to growth in average wages for all private industries as shown in the chart below for Denver County going back to 1990. Average wages in childcare have shown a more than 5x increase over the period while average wages in all private industry have gone up about 4x. Childcare costs are high and wages are growing, so Baumol’s theory is probably part of the story here. But that doesn’t mean this theory can completely explain why childcare costs are so high.

Childcare regulations

Another factor that contributes to high childcare costs is regulation. Childcare regulations include restrictions on daycare facilities like how many square feet of indoor space per child or fence heights for outdoor areas. They also include thresholds for child-to-staff ratios and licensing requirements for operators and staff. The Archbridge Institute has a state-level dataset that provides useful details on these sorts of regulations. Colorado is about average on the strictness of its childcare regulations.

Depending on their stringency, these regulations can restrict supply and put upward pressure on operating costs. How much this is contributing to the rising costs of childcare is up for debate. Alex Tabarrok has written that the Baumol effect likely dominates by comparing rising costs in the more heavily regulated childcare sector to those in the lightly regulated pet daycare sector. But some regulations specific to childcare can also interact with the Baumol effect. Take child-to-staff ratio regulations.

Colorado, like many other states, varies its child-to-staff ratios by age. The ratio is lowest for the youngest groups with 5 children per staff member from ages 0 through 2.5 years. That number jumps up to 8 for 2.5 to 3 year olds and then all the way up to 15 for 7 to 8 year olds. Suppose we have a daycare for children 0 to 2.5 years old that is about to open. The daycare has 10 children signed up for the coming month and must have at least 2 staff members to meet these regulatory thresholds. If the daycare is to sign up just one more child, it would have to hire another employee or risk being in violation of the regulation. Even at the state minimum wage of $15.16/hour, an additional full-time employee creates more than $2,400/month in additional labor costs. Either enrollment must be capped or the daycare charges enough to at least cover labor costs.

It is not that these child-to-staff ratios are unreasonable. I certainly wouldn’t want to send my daughter to a daycare where one employee is trying to take care of a dozen infants and toddlers by themselves. But these thresholds create hard cutoffs that reduce the flexibility of care centers. That lack of flexibility can increase operating costs and contribute to higher prices. But higher prices are not just a matter of operating costs, we need to look at the demand side as well.

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Demand for childcare

Like I said above, regulation or not, I wouldn’t send my daughter to a daycare that couldn’t give her the time and attention I thought was necessary for her to be happy and properly cared for. I am not alone there either. Most if not all parents feel similarly and I don’t think it stops there. Maybe part of the rising cost of childcare is not cost disease or regulation but a rise in quality that people are paying for.

It is not exactly fair to say that this demand story is totally unrelated to the cost disease. As Tyler Cowen points out in a 1996 comment on Baumol, there is an optimistic version of the cost disease argument in Baumol’s own work. Rising productivity in some parts of the economy increases the opportunity cost of production in the less productive parts (the arts in his case, childcare here), so prices rise and output may fall. But rising productivity also means rising incomes. As incomes rise, people are willing to pay for services like childcare even at higher prices, so output may not fall and could even increase. Cowen then goes further and argues against the cost disease in the arts altogether. The arts were never stagnant, he says. The arts kept improving, so comparing the cost of a performance today to one decades ago is comparing apples to oranges. Part of what looks like the same thing getting more expensive is people paying for a better thing. A version of that argument applies to childcare too.

Parents have gotten richer, and I believe that shows up in two ways here. They are willing to pay more for childcare, and they are willing to pay for a higher quality version of it. You can see the second one all over the Denver area. La Petite Academy, a national chain of childcare centers with several locations in Denver’s surrounding counties, streams live video of the classroom to parents, provides mathematics in their early preschool curriculum, and boasts dedicated adult attention for infants through an assigned primary caregiver. The Gardner School in Denver sends real-time updates through a parent app and emphasizes research-based S.T.E.A.M. activities. The Gardner School even offers enrichment programs for children as young as two with options like dance, karate, music, cooking, computers, and foreign language. If you want to start foreign language learning for your child even sooner, Denver International Schoolhouse in Aurora will start your infant on Spanish immersion. These are just a few examples from a quick search of additional services beyond basic childcare offered by many providers.

Much of this was not on the menu a generation ago or at least available to the extent it is now. So even if parents today buy about the same hours of care each month as parents did 30 years ago, comparing cost per hour over time is not comparing the same product. But this raises a question. If some of these quality improvements like the live streaming and apps are technology based, why would this make childcare more expensive? Notice that many of these quality upgrades still lean heavily on labor. The language immersion, the STEM classes, the enrichment programs all require people, and people whose time is not easily augmented with capital or technology. These kinds of quality improvements are expensive to provide and are not the same product as basic childcare. Even if the examples I listed capture only part of the market (the more expensive end), this will still be part of the rise in average costs. Although it is likely that basic childcare is getting pricier, parents are also choosing to buy a higher quality version.

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Conclusion

Childcare is expensive and likely will continue to be that way. Perhaps easing some of the more stringent regulations can help. One candidate would be licensing exemptions or lighter requirements for smaller daycares, say something like 10 children or fewer. Under current Colorado law, child care licenses are required for any regular care of more than four children or more than two children under the age of two. If you have two kids and want to hire a nanny, the nanny doesn’t need to be licensed. But if you and a couple of neighbors, each with two kids, wanted regular care provided by one parent who stays home or to split the cost of a couple nannies, then a license is necessary.

A broader exemption or lighter requirements (along the lines of this suggestion from Kelsey Piper), not necessarily completely unregulated, could open up the possibility for a daycare analog to homeschooling pods or “microschools” that became more popular during and since the pandemic. No one bats an eye at having your friends or neighbors watch your kids or hiring a nanny on your own, so why not create a more conducive environment for families of young children to create daycare pods or something similar?

A daycare pod doesn’t get around the issue of childcare being labor intensive, but it creates some flexibility not allowed by current regulations. And because pods are small and more individualized, they may be able to more easily meet the idiosyncratic demands of families in different communities. On the cost side, expanding supply should ease some of the upward pressure on prices. And it seems easier to work out alternative arrangements among families to share responsibilities or otherwise reduce the financial hit associated with traditional childcare.

A couple weeks ago, I posted a brief walkthrough of the Colorado economic indicators dashboard I host on my website. Doing that post and seeing similar dashboards (like this one from Alexander Salter) made me realize mine was missing inflation. Well, the dashboard now includes inflation data: the Denver metro area CPI compared against the U.S. city-average CPI, along with a table of Denver metro CPI components.

You can access the dashboard here. Below I will show a few screenshots of what the new inflation tab looks like and explain what is included.

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Chart Comparing Denver MSA CPI to U.S. City-Average CPI

The first thing you will see on the inflation tab is a chart where you can compare inflation and the price index for the Denver-Aurora-Lakewood MSA against the U.S. city average. There are three views you can switch between, matching the tabs on the chart including the 12-month change, the change from the prior published period, and the index level itself. The screenshots below show each of these views for the data from January 2019 to May 2026, but the visible range is adjustable if you want a longer or shorter window.

The 12-month change is the standard headline inflation measure, and right now it shows Denver-area prices up 5.0% over the year ending May 2026 compared to 4.2% for the U.S. city average.

One thing worth knowing when reading these charts is that the Denver MSA CPI is published every other month while the national series is monthly. That doesn’t matter much for the 12-month view, but in the prior-period view below, the Denver line shows two-month changes against one-month changes for the U.S., so the two lines are not directly comparable there (the chart notes this as well).

The index level view also lets you choose the base period, with every line re-indexed to equal 100 at whatever period you select. In the screenshot above, the base period is January 2019.

Denver-Aurora-Lakewood MSA CPI Components Table

Below the chart is an interactive table where you can add or remove the published components of the Denver metro CPI, modeled on the BLS news-release table. Not every component is published for the Denver MSA, but the big ones are there including food, energy, gasoline, shelter, and all items less food and energy among a few others. Columns show the latest index value, the 12-month and prior-period changes for the Denver area, and the 12-month change for the U.S. city average. The table doesn’t allow comparisons across different time periods but gives a quick look at how the most recent price changes in the Denver area compare to the average around the country.

A standout in the screenshot above is gasoline. Gas prices in the Denver area are up nearly 42% over the year ending May 2026, compared to 40.5% nationally. The table also makes it easy to see what is driving the recent local numbers which most recently is energy, not shelter. Shelter prices in the Denver area are up a modest 3.8% over the same period while energy prices are up more than 25%.

As before, I added the inflation tab with the help of Claude Code.

Last week, Governor Jared Polis vetoed the surveillance pricing bill passed by the Colorado legislature in May. Before the veto, I wrote a post here at Front Range Economics laying out an argument for why the bill should be vetoed. As much as I’d like to think tagging Governor Polis in my notes on the post got him to read it and influenced the decision, that probably didn’t happen (a new blog with fewer than 50 subscribers is not moving the needle on policy, yet).

The governor’s previous veto on a similar bill was a good indication of what he would do here. Not only did Polis make the right decision, in my opinion, but he also laid out reasoning that shows he has thought about this issue and came to a conclusion informed by the economics of the behaviors the bill targeted. His objection centered on the bill’s breadth, which he wrote captures “any technology that incidentally influences a price or wage amount” and, because of that broad sweep, “would punish differentially lower prices, not just higher prices.” That is the same overbreadth problem I focused on in my original post. His full veto letter can be read here.

Before and after the veto, sponsors and other proponents of the bill strongly disagreed with this line of reasoning:

Rep. Javier Mabrey - “Everybody is talking about how concerned they are with the rising cost of living, and this bill was about preventing companies from spying on us to price gouge us. I don’t know how anyone can credibly make the case that it’s a good thing for the biggest companies in the world to know everything about us in how they set our prices.”

Sen. Iman Jodeh - “We all lose when large corporations can set different prices for different people based on sensitive data they’ve collected.”

Rep. Jennifer Bacon - “Right now, Coloradans aren’t participating in a fair market, they’re playing against a supercomputer designed to extract as much as possible from them. When corporations use personal data to decide what you’ll pay or what you’ll earn, that’s not supply and demand, it’s exploitation.”

Lee Hepner of the American Economic Liberties Project - “[Polis] might have the most pathetic legacy of any outgoing Dem governor. His career will be over soon, and our work is just beginning.”

I am not entirely surprised at this reaction. But the more negative reactions I saw online about the veto, the clearer it became that this is another instance of zero-sum thinking underlying the arguments in favor of bills like those against surveillance pricing. In this post, I’ll argue that some of the basic insights from economics can help move us away from this way of seeing the world to better appreciate the benefits of exchange.

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Zero-sum thinking vs. the positive-sum nature of exchange

Let me first define what I mean by “zero-sum thinking.” This is a way of seeing the world, or at least some interactions (particularly those in markets or involving large corporations), where one party in an exchange can only gain at the expense of the other. There is some recent academic work by Sahil Chinoy, Nathan Nunn, Sandra Sequeira, and Stefanie Stantcheva measuring how pervasive this thinking is in the United States, where it comes from, and how it shapes people’s policy preferences. We also see this thinking in a variety of political issues from AI and data centers to big business and markets more generally.

In the context of the surveillance pricing bill, zero-sum thinking manifests in much of the framing of the problems the bill’s proponents are trying to tackle. For example, Representative Mabrey has been quoted as saying, “Coloradans don’t want to be spied on and scammed,” in reference to the bill. The argument implicit in this framing is something like the following. In a digital world where companies collect data on consumers from their internet activity, including purchase histories across websites, ads they click on social media, and even browsing habits, those companies have a leg up on individual consumers. With that data and now with the help of AI, these companies can then more easily exploit consumers and increae their profits. So, if companies engage in surveillance/individualized pricing and increase profits, it must be because they are screwing over consumers, in this case through higher prices.

The problem with this view is that it misunderstands the nature of voluntary exchange. At the simplest level, where we imagine a single buyer and a single seller, we can see how exchange (when voluntary) makes both parties better off. Suppose I am going through my closet to get rid of some old clothes. I have a nice jacket that I haven’t worn in a while and want to get rid of it, but not so much that I am willing to just donate it. I think, I won’t take anything less than $50. The jacket is still in good condition, and if I can’t get at least $50, I’ll keep it in my closet. So, I put it on Facebook Marketplace or eBay or something at $75. Maybe I don’t get $75, but there’s room for someone to low-ball me a little and I still get a price I’m willing to sell at. Out there somewhere there is someone looking for a nice jacket and he doesn’t want to pay $200 for a new one but would be willing to pay up to $100 for a used one in good condition. In this exchange, we have a bargaining range on price that sits between my minimum sell price and the buyer’s maximum purchase price (between $50 and $100). Any price agreed upon within that range will make us both better off.

So, the potential buyer comes across the listing for my jacket at $75. He thinks it looks pretty good and the price is decent but wants to negotiate a little. He sends me an offer of $65. The offer looks good enough to me and I accept. Where are we now? The jacket in my possession was worth $50 to me. I receive $65 and am made $15 better off. The buyer was willing to pay up to $100 for a similar jacket but only had to give up $65, so he got $100 worth of a jacket at the cost of $65. He is $35 better off than before the exchange. Although this is a simple hypothetical example, it gets at the core of how voluntary exchange works. People only accept the terms of trade if they expect to be made better off, and in turn surplus is created. And this is only the weak case of positive-sum trade. If we extended the example to account for production and specialization, the gains would be even larger. But for the sake of a simple example, let’s stick with pure exchange.

Now, I don’t want to dismiss the possibility that something like surveillance pricing can shift the distribution of the surplus created through exchange. Suppose I somehow knew that the potential buyer in the example above was really willing to pay up to $100. What might I do when I see the $65 offer? I would probably counter with something higher, maybe $90. Suppose the buyer accepts. Now, I am made $40 better off and the buyer is only made $10 better off. Notice that the total surplus of the exchange is the same ($50), but now I have a larger share of the surplus. Something like this is certainly possible in exchanges where large companies have better information on consumers. But what I want to point out is that the trade, though its terms are worse for the buyer than the original trade, still leaves both of us better off.

This, I think, is the strongest version of the proponents’ concern, and it deserves to be taken seriously. Senator Jodeh’s quote above is referring to this kind of shift in who captures the surplus. But notice what the jacket example shows: even when the seller knows the buyer’s maximum and captures more of the gains, we do not all lose. The trade still made both of us better off. And when economists have measured personalized pricing in the real world, the results don’t match the worst case either. In a field experiment by Jean-Pierre Dubé and Sanjog Misra, personalized pricing raised the firm’s profits and shifted some surplus its way, but over 60 percent of customers actually faced lower prices than they would have under a single uniform price. Some people may lose out, but the authors warn that it is not clear cut that policies restricting personalized pricing help consumers.

Some may look at this situation and still feel it represents an injustice. Why should large corporations be allowed to systematically rewrite the terms of trade and transfer more surplus to themselves? However, it is not clear to me how legislation can work against that injustice without also distorting or discouraging many other mutually beneficial trades that are not deemed unjust. That was the core of my argument in the original post, and it is the core of the governor’s veto letter too. The cost of that seems much higher than the potential surplus transfer that can occur in principle.

Conclusion

Zero-sum thinking can cloud what people view as useful or beneficial policy. But thinking a bit more about how exchange works can help clarify the risks well-intentioned policies pose for everyday exchange. The jacket example above is not entirely realistic. Most market exchanges we engage in today do not involve back-and-forth negotiation between buyer and seller. Even absent direct negotiation, big business’s access to large swaths of consumer data may enhance awareness of consumer responses to different pricing strategies. They may now know you are willing to pay more for your groceries than they are charging, but they also have a better idea about how sensitive their consumers are to price changes and may find it better for business to keep prices uniform. And that data may also help them price in a way that creates more mutually beneficial exchanges that otherwise would not occur.

To the extent that zero-sum thinking on these issues remains common, I believe we will continue to see attempts, like this bill, to tackle the problems implied by the zero-sum framing. That will be true whether politicians actually hold this thinking or not. The more politically popular these ideas are, the more opportunities there will be for politicians to capture votes by promising to pass legislation to address the problem, whether that be surveillance pricing or something similar (see the failed attempt this session to control prices in stadiums and other venues). The sponsors of this bill are not being subtle about it either. The day of the veto, Representative Mabrey promised “we’ll be back next session.” I believe him. And the next version of this bill will not land on the same desk.

Zero-sum thinking, however, is not entirely pervasive. Colorado’s own recent record shows lawmakers here can recognize positive-sum policy when the gains are easier to see. In 2020, the legislature created an occupational credential portability program making it easier for workers licensed in other states to work in Colorado, and it passed without a single no vote in either chamber. In 2024, lawmakers legalized accessory dwelling units in many metro areas around the state and opened up land near transit for denser housing, reforms built on the idea that more housing makes both builders and buyers better off. These reforms share a premise the surveillance pricing bill rejected. Exchange creates gains for both sides. The closer we come to seeing pricing the way people are starting to see housing and (some parts of) labor markets, the lower the risk of new laws that distort mutually beneficial exchange.

After a few hours of seeing how Codex was different than using a chatbot, I wanted to see how much it could really do. I had been thinking about creating a data dashboard for Colorado economic data but kept putting off learning how to do this. I know how to code OK in R and Stata but didn’t know the first thing about making a dashboard. I put an empty folder on my Desktop and put Codex to work with my idea to pull BEA and BLS data for an interactive dashboard on Colorado economic indicators, with a focus on the Denver-area counties.

The first version was crude and not what I wanted but over the course of a couple days and in total maybe a few hours of working with Codex, I had a pretty nice looking dashboard that fit what I had imagined before starting. I now host that dashboard on my website and it is linked on the homepage of Front Range Economics here on Substack. You can follow the link here to check it out for yourself.

The purpose of this post is to provide a short walk-through of the dashboard and explain why I think it is a useful tool for anyone interested in Colorado economics (and some self-promotion). I see this dashboard as easier to use than the BEA or BLS websites themselves, and much of the data is downloadable.

I’ll give a brief walk-through of the features on the dashboard and then give an example of what kind of questions you can answer using this data. Obviously, I won’t take credit for the data itself. None of this would be possible without government sources like BLS and BEA. I guess I can’t even take credit for the dashboard itself, I didn’t write a line of the code that went into this. I only guided Codex (and later made improvements with Claude Code) on what I wanted to include, how I wanted it to look, and other tweaks along the way. I am also interested in improving this any way I can, so please reach out if you have any suggestions or ideas for what should be added or changed.

The Dashboard Homepage

The first thing you will see when you open the dashboard in your browser is an interactive county map of Colorado. The screenshot below should give you an idea of what it looks like when the chosen metric is employment. There are five metrics you can choose from to populate the map including employment as shown in the screenshot, real GDP, per-capita personal income, unemployment rate, and average weekly wages. Again, these are sourced from the BEA and BLS.

When you choose a metric, you can hover over a county tile to see the most recent available value for that county. For example, looking at average weekly wages, we can see Jefferson County’s were $1,522 in Q3 2025. There is also a search bar you can use to find the county you want rather than hovering around the map.

If you click a county on the map, or search for a county and hit enter, you will select that county to be highlighted and populate a profile card to the right of the map. The example profile card below shows the most recent available values for four out of the five metrics. The metric at the top is whatever your selected metric is on the map (in this case employment) and then below are real GDP, per-capita personal income, and the unemployment rate. This gives a snapshot of economic conditions for that county.

The Data Explorer

Along the top of the page you will see tabs for other parts of the dashboard. The next one I’ll discuss is the Data Explorer. This allows you to compare any of the five metrics from the main page across counties, metro areas, Colorado as a whole, and the nation. The example below is comparing real GDP from 2008 to 2024 for Denver, Adams, Arapahoe, Boulder, and Broomfield counties. In this comparison we can see that Denver County has experienced substantially more growth in real GDP than some of the surrounding counties over the last 16 years. Geographies are chosen via a dropdown menu and can be found via search on the menu as well.

The Denver Region Tab

The final tab I will discuss is the Denver Region tab. This part of the dashboard focuses on data for the seven counties making up the Denver area. The Denver-Aurora-Centennial MSA is actually made up of 10 counties in the area, something I will address next time I update the dashboard. To me, the most interesting part of this tab on the dashboard is the look at output by major industry. At the top of this page you can choose a county, and then scroll down to the industry explorer and choose a year. On the left of the industry explorer there is a table as shown below listing several major industry categories with dollar output in the county, output share in the county, output share for the Denver region (defined by the aggregate of the seven counties included on the page), and share for the entire state. Note that for the Denver region and Colorado columns, the figure is not the selected county’s share of output, but the industry’s share of output at the region and state levels.

The example below shows the industry explorer for Denver County in 2024. We see the largest major industry by output in Denver is finance, insurance, real estate, rental, and leasing with over $30 billion in output making up just over a quarter of all output for the county. You can also click one of the major industries in the table to highlight it on the right side of the industry explorer. Here, manufacturing is chosen, and in the graph of share of output we can see the decline of manufacturing in Denver from just over 4% of output to just under 3%. There is also the option to make comparisons to the Denver region and Colorado in the graph, though not shown in the screenshot.

Industry Composition Across Three Counties

Now that you have an idea of how to use the dashboard and the data that is available, I want to give an example of the kind of descriptive question you could answer using it. Let’s do a comparison of industry composition across three counties: Denver, Arapahoe, and Adams. I’ll limit this to looking at the top five major industries for each county in 2024 and frame the question broadly. What are the main differences in sources of output across the three counties? Tables showing top five major industries for each county are shown in the screenshots below.

Looking at Denver first, we see in 2024, just over 45% of all output came from two major industries (finance and related businesses and professional and business services). The next three biggest contributors include government, transportation and warehousing, and information whose summed output share is still less than that from finance and related businesses. As far as top two major industries, Arapahoe is similar to Denver with around 42% of output coming from finance and related businesses and professional and business services. But Arapahoe looks a bit different when we look at the remaining top contributors which include information; education, health, and social assistance; and wholesale trade. Finally, Adams looks the most different among the three with the largest industry being government at over 17% of total output and finance and related businesses trailing closely behind. The remaining top contributors in Adams consist of wholesale trade, construction, and professional and business services. Finance and related businesses are clearly major contributors to output across all three counties, but there is substantial diversity as well, even when only looking at the top five.

You could use these same tables to make comparisons to the Denver region or Colorado as a whole, or choose a different set of counties to make additional comparisons. There’s a lot of data there and plenty to dig deeper into.

I’ll leave it at that. Take a look around the dashboard for yourself, and again reach out if you have any suggestions.

I like to think of myself as a seasonal runner. As the name implies, I do not run year-round. I wait until the weather warms up a bit and I have more free time, which is usually when the spring semester ends. Then, I am running 2-3 times per week in the mornings. My runs are one of the few activities where I enjoy listening to audiobooks. Before this, I always found it hard to enjoy or stay engaged with audiobooks. But once I was running more regularly, that time became the perfect time for me to really focus on an audiobook and now I listen to a handful every year.

Because I don’t always listen to audiobooks, it doesn’t make sense for me to always subscribe to a service like Audible. Sure, I could buy books individually through Audible, but often it is cheaper for me to subscribe a few months at a time and get a backlog of books that I’ll chip away at while I run. On top of this, I am generally a frugal person. If there is a way to get audiobooks for cheaper, I will find it. Amazon seems to have some sense of this based on my use and whatever other data they have on me and every few months I will get an offer in my inbox to come back to Audible for a few months at a steeply discounted price, typically $1/month for three months.

When that email hits my inbox, I sign up and use my credit for a book I have been wanting and set a reminder on my phone to cancel right before the price goes back up to the higher normal tier. I get three books over the course of this discounted window and Amazon gets a little money out of me. After I cancel and eventually finish all my cheap audiobooks, Amazon inevitably sends me another one of these emails months later. Rinse and repeat.

Amazon isn’t alone in this sort of user-targeted discount behavior. Many online retailers will send you a discount code for a product you leave in your cart for a while. For instance, I recently was thinking about buying a new backpack and found one online that looked like it would work for me but the current price was a little higher than I wanted to spend at the moment. I had put it in my cart on their website and thought I would just come back later. About a week later, I received an email encouraging me to revisit my cart and take advantage of 10% off my purchase. That discount put the backpack at a price that was more appealing and so I bought it. Here too, I got the product I wanted and paid a little less, and the company still got some money out of me.

I am sure that many people have experienced similar things with online shopping whether with big companies like Amazon or others. Why do I bring this up? The pricing or discounting strategies that I just described sound like a good deal, but under Colorado’s recently passed individualized pricing bill (HB26-1210), they would be heavily restricted. After being introduced in February of this year, the bill went through multiple revisions before being passed in early May. As the passed version of the bill is written (full text of the bill can be found here), Amazon and other companies would not be allowed to use consumer data on me to offer discounts for something like an Audible subscription or other products. If the bill became law it would restrict a lot of individualized discounting (pricing) that occurs. That much is not in dispute as it is the stated purpose of the bill. The problems with it, as will be detailed below, are more about the broad reach it has and whether it will actually help consumers.

What is in the bill?

Before I talk about any issues with the bill, let me go through a bit more about what the bill actually says because it is not as if the state legislators are simply trying to get rid of discounts. In November of last year, I had the opportunity to talk through some of these issues with one of the bill’s sponsors, Colorado State Senator Mike Weissman. We appeared together on a virtual panel hosted by MSU Denver’s College of Business for our MBA students. From that discussion, I gathered that Senator Weissman is worried about consumers unknowingly facing higher prices on a variety of goods and this bill is an attempt to address that problem. Many of the points I brought up in response will be made in this post now that the bill has passed.

From the perspective of Weissman and as stated by other proponents of the bill, its purpose is to curb companies’ ability to charge higher prices or set lower wages using algorithms. An earlier but related bill, HB25-1004, was aimed specifically at restricting landlords from setting rent using algorithms that many argued could allow for collusion among landlords (related to concern over RealPage). Governor Jared Polis vetoed this bill last year. This new bill is much broader in scope. Those in favor of this bill and others like it often frame the problem around examples such as customers facing higher prices for a flight to attend a funeral or ride-share companies like Uber and Lyft targeting individual drivers with unfairly low wages, but more broadly around large corporations using big data to squeeze consumers and workers. I am going to focus on the consumer prices portion of the bill. In my reading, the exceptions for individualized wage setting make the restrictions much narrower than the consumer prices side.

An “individualized” price is defined in the bill as:

“specific to or inferred about an individual or group, band, class, or tier of individuals with particular personal characteristics, online behaviors, or biometrics”

where personal characteristics are defined to broadly capture mutable and immutable qualities. The online behaviors part of the definition is itself defined broadly to include:

“an individual’s actions, habits, preferences, affiliations, associations, financial circumstances, or interests that are observable, measurable, or inferred through digital, online, or electronic observation or surveillance.”

Individualized prices cannot be determined using a “price or wage setting algorithm” if the algorithm is a substantial factor in that determination. What counts as a price or wage setting algorithm is also quite broad:

“Any technology, software, program, machine-based system, or computational process that: uses statistical modeling, data analytics, artificial intelligence, or other data processing techniques to analyze surveillance data…”

So if a company collects, purchases, or otherwise obtains data on its consumers and then analyzes that data with some software and offers different prices, it is engaging in a deceptive trade practice. But the bill makes some exceptions.

The bill carves out exceptions for financial decisions such as creditworthiness, mortgages and other loans, and insurance. Additional exceptions include publicly disclosed eligibility criteria such as volume purchases, joining a mailing list or loyalty program, and special pricing for groups such as teachers, members of the military, students, and senior citizens.

If the bill becomes law, violations of individualized pricing restrictions are considered deceptive trade practices under the Colorado Consumer Protection Act. The Act lets the attorney general and district attorneys bring actions and lets private parties sue and recover attorney fees, so the cost a company weighs is not just a possible fine but the prospect of having to defend the practice at all.

Issues with the bill

Despite the exceptions and the use of words such as “surveillance” (which seem intended to make the bill appear as if it targets only the most nefarious version of individualized pricing), the bill is so broad that it will likely discourage companies from offering individualized discounts (prices) for fear of facing costs if this becomes law.

Let’s go back to the Amazon example for a moment. How is Amazon offering me the lower price for the Audible subscription? I don’t know exactly (the bill sees this as a problem) but I do know they have data on my use of the service, my purchase history, how long I have stayed subscribed in the past, and probably how I’ve responded to the discount emails I have received in the past as well. That information is some input into whether I and other similar customers receive the discount email. This might be fully automated by internal software, or maybe it’s AI. Who knows? The discount does not fall into a regular promotion to all customers like a Prime Day discount nor is it a discount for me being a teacher. As far as I can tell, this sort of pricing falls into the sort of individualized pricing that would be banned. Even if not exactly, is it worth it for Amazon to keep trying this and risk penalties? Or, is it easier for Amazon to stop offering these pricing promotions to customers in Colorado? My guess is the latter.

You might be asking yourself, who cares that you can’t get your precious discounted Audible subscription? Surely, it is worth it that some minor discounts go away so that the completely unfair practice of charging people their max willingness to pay for everyday necessities can be stopped. I might agree with that if the law wasn’t so broad and if this problem of people being charged dramatically different prices for goods was as common as the bill and its proponents appear to assume. One of the sponsors of this bill argued that this is really a bill about tackling affordability. I’m skeptical taking away the ability to use individualized pricing will have much of an effect on affordability.

One reason for my skepticism is that there are already mechanisms to discourage companies from charging high and unpredictable prices. For one, people already hate the worst version of the type of pricing the bill is aimed at. Even a more minor version, such as Wendy’s proposed dynamic pricing plan in 2024 (which may not be restricted under the bill, though that is not entirely clear), was met with anger from consumers and quickly walked back. Similarly, Instacart was found to be experimenting with algorithmic pricing on grocery items with price differences across customers on a wide range of products. After a Consumer Reports article on the practice came out, and the obvious negative reaction from consumers, Instacart stopped the practice. It also is not even clear that Instacart was engaging in “surveillance” pricing or price discrimination as typically understood in economics.

Organized consumer backlash such as in the two examples above is not always easy to achieve but it need not be organized. Competitive pressures can achieve the same outcome. Suppose Instacart quietly began introducing algorithmic pricing again. I get charged $4 for a jar of pickles and my pickle-loving friend gets charged $7. This happens across products and stores. More people start paying higher prices for certain goods and don’t really understand why. Whether or not people understand why prices are higher, people make adjustments when prices change. And other companies can try to capture customers who are disillusioned with using Instacart. Maybe Walmart promises its Walmart+ customers that the prices you see for your delivery order are the same as in the store you’re ordering from (this is something they already actually do). People start making the switch and get their groceries delivered from Walmart rather than Instacart. How might Instacart respond? If they lose enough customers, they might do away with algorithmic pricing again, no organized consumer backlash needed.

My point is that we have seen pretty effective use of consumer backlash against companies engaging in the sort of practices this bill is aimed at restricting and the pressure of competition points in the same direction. My main issue with the bill is that it doesn’t just target Instacart using AI to charge you more for lemons than your neighbor. It is broad enough that it gets rid of things like selective discounts which are pervasive and often a benefit to consumers.

Related to this point, I think a major flaw in the thinking behind this bill is the assumption that consumer data and pricing algorithms are only ever used to raise prices for consumers. Raising prices is not always the most profitable thing for a company to do. Of course, if a company can get a higher price for its product it will try to do so. But only under the expectation that the increase in revenue from raising the price is not offset by the loss in revenue from losing customers no longer willing to purchase at the higher price. I am not denying that the “surveillance pricing” described here can mean higher prices for some consumers, but the assumption that this is a necessary feature of surveillance pricing is incorrect. Companies are also interested in capturing customers that would never purchase from them at the standard sticker price. Being able to figure out who those people are and offer them lower prices is a benefit to both the company and the consumer. This expands the market and new consumers benefit who were priced out before.

In addition to the misunderstanding that individualized pricing necessarily means higher prices for consumers, one of the sponsors of the bill, Representative Javier Mabrey, has emphasized that it is some of the biggest companies in the world behind much of the problem. I agree that bigger companies like Amazon are able to use individualized pricing at a much more sophisticated level than a small business. But if individualized prices are defined as any price that uses consumer data and the price is set using an algorithm which could be anything from using a spreadsheet on consumer data to look at trends to using something like ChatGPT to analyze customer data, there is no reason to think that many small businesses wouldn’t also find themselves in violation.

Many small businesses use services like Toast that help with consumer insights and offer help setting prices. Even if you sell something on Ebay here and there, the website has features that allow you to use their software in helping set prices and target customers who are viewing your product with different prices. Those are software programs that could be considered to be a substantial factor in setting price and now a locally owned retailer could face legal costs too. And if a small business might also be in violation, they will likely find it much harder to fight against complaints and legal costs made possible by this bill. Amazon has millions of other customers they can focus more on. Amazon or other large corporations are unlikely to take a major financial hit because of this bill, but small businesses might, depending on how strong enforcement is.

Two final issues I will raise are related to the uncertainty that the bill creates about what is and isn’t restricted.

First is the algorithm’s role in pricing. As the bill is written, it must be “a substantial factor in setting, offering, or determining a price.” What constitutes “substantial” here? According to the bill it “means more than a de minimis or incidental factor that informs the price…” What if a company uses an algorithm whose inputs are consumer data and spits out a range of prices but then a human reviews and chooses the final price that is offered? What if customers are randomly shown a range of prices as a company experiments? Is that restricted or not? Depending on how steep the penalties are, it might not be worth it for a company to try and test the limits of what is substantial.

Second, the exceptions to this bill include differential prices “justified by temporal differences, including fluctuations based on supply and demand.” This sounds like a reasonable exception. How else are companies supposed to determine demand for a product other than collecting consumer data and using analytical tools to land on the right price? The demand curve for a company’s product is not directly observable. Demand must be estimated and consumer data is a useful input. But what falls under the exception is not very clear.

Even something as simple as A/B testing prices is a way companies try to estimate demand and determine prices. That practice involves using what this bill defines as an algorithm to show different customers different prices as a way to find the profit-maximizing price. Is this going to be restricted too? Companies may also segment customers into different markets and charge prices accordingly. Is that differential pricing based on fluctuating demand, or restricted individualized pricing? When Uber uses surge pricing after a concert, presumably this is because demand increases in that location. But Uber must use location data from customers to determine who sees the surge price and who doesn’t. The bill’s exceptions to using location data do not include something like Uber using surge pricing in this way. But it may be fine if this is considered a fluctuation in demand. Again, I am not sure companies will take on the risk to find out and may just err on the side of caution.

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Conclusion

Overall, I think support for this bill comes from a misunderstanding of how differential pricing is used, the assumption that it necessarily (or at least typically) leads to consumer harm, and the fact that calling it surveillance pricing makes it sound scarier than it actually is.

I don’t know exactly where the line is to have a better version of this bill that does deal with at best annoying and at worst unjust pricing practices using consumer data. But this current version threatens the ability of companies to experiment with prices in ways that make more exchanges possible. I understand that companies do this to make more money and restricting this will mean they make less money. However, harming the profits of companies by taking this tool away does not imply consumers will be made better off.

If the goal really is to avoid only the truly unfair or deceptive uses of individualized pricing, I am not sure any version of this bill is necessary given the existing Colorado Consumer Protection Act. The Act already makes it a deceptive trade practice for any business to knowingly or recklessly engage in “any unfair, unconscionable, deceptive, deliberately misleading, false, or fraudulent act or practice” (see Section 6-1-105(1)(rrr)).

I would think that deceptively charging some customers unfairly higher prices based on factors they have no idea about or lowering some wages using similar practices falls under this provision. I am no legislator and I’m guessing there is good reason a bill was proposed to supplement the existing consumer protections. But if enforcing the existing law on a more case-by-case basis can avoid creating uncertainty, causing unintended limitations to pricing practices that help companies and consumers, and creating additional compliance costs, it should be done. Regardless of whether the goals can be achieved under existing law, Governor Polis should veto this bill as currently written.

Part 1 of this post on Denver’s restaurant industry looked at restaurant employment and establishments. I compared changes in both metrics relative to their 2019 levels using surrounding counties and a small group of “peer counties” to reach the following conclusions: 1) Denver full-service restaurants are in a clear slump, and 2) Denver limited-service restaurants are doing OK; employment and establishments are still above pre-pandemic levels. The first part of this conclusion confirms what much local reporting and other posts on this subject have found, but I have not seen much of anything discussing the limited-service side of the problem. If you haven’t read that post already, check it out before getting into Part 2 here.

The conclusions from Part 1 are surprising for a couple reasons. First is that Denver appears to be facing a distinct problem among its full-service restaurants, suggesting that there are factors specific to Denver causing this. I wouldn’t go as far as to say this is unique to Denver. But across similar counties and other counties in the metro area, Denver stands out. Second is that the problem is mostly limited to full-service. In terms of both employment and establishments, limited-service restaurants are not struggling in the same way.

On the question of why this is happening, I want to be able to explain what it is about Denver but also differences across restaurant type to have a better understanding of why Denver’s full-service restaurants specifically are struggling so much. When I first started doing the background research and drafting for this follow-up post, I was going to cover a few different factors including labor costs, regulatory issues, and a bit on the demand side.

But as I continued working on this, the labor costs side of the story seemed to be the most compelling and will be the focus of what follows. Labor costs are among the chief concerns raised by operators as detailed in the State of Denver Restaurants report linked in the previous post. This complaint made sense to me as a real problem but some critics of the report have viewed this more as owners vs. workers and framed labor costs as a scapegoat for other issues. Some critics of the report have even gone so far as to argue that there are simple conflict-of-interest reasons why the report focused on labor costs and the minimum wage. I believe the critics were too quick to dismiss labor costs as a major part of the story.

I will make the case that Denver’s minimum wage increases since 2020, particularly for the tipped minimum wage, can explain some of the full-service restaurant struggles. Then, I will propose a hypothesis related to this that can explain why limited-service restaurants in Denver are faring better. On the question of what can be done, I have more uncertainty. Given the evidence I show in this post, lowering Denver’s tipped minimum wage to the state level seems reasonable. What I am uncertain about is how much of an effect it would really have and how likely it is to happen as it seems like an unpopular idea.

I realize that this post is only looking at one among multiple causes of the decline in Denver’s restaurants and it would be silly to claim that there is a single cause that completely explains the decline. Too many things are happening at once that are impacting operating costs and the demand side for restaurants to really nail down what is happening in a single city. That doesn’t mean the minimum wage isn’t an important part of this story. For the reader who wants to look at evidence of other factors contributing to the general problem with Denver’s restaurants, I will link again to the State of Denver Restaurants report. That report isn’t the only source out there on this issue and was conducted on behalf of restaurant operators but does cover many different factors.

Real Wages in Full-Service Restaurants: Denver vs. Comparison Counties

Before getting into the minimum wage, I want to show a couple of comparisons along the lines of what I did for Part 1 but now looking at real wages for full-service restaurant employees. I will be using QCEW data again to make these comparisons and the same two groups of comparison counties (neighboring counties and the peer counties group). From the QCEW data, I have nominal weekly wages for full-service restaurant employees in each county through Q3 2025. Then following the BEA’s method for implicit regional price deflators, wages are deflated using the product of the relevant Regional Price Parity (RPP) and the national PCE price index.1 I will use the same indexing strategy as before where the wages for each county are set to 100 in Q1 2019. Then, I can compare how wages have evolved since 2019 across these counties to see whether restaurant workers in Denver have experienced wage growth that stands out from comparison counties. I am also separating this from the minimum wage discussion that follows because wage gains that appear in this series are not representative of wage gains directly related to increases in the minimum wage. The minimum wage will have an effect but not for every restaurant employee captured in this data.

Because we are looking at total weekly wages divided by employee head count, the average weekly wage series for each county is capturing full-time employees, part-time employees, salaried employees, tipped employees, non-tipped employees, you get the idea. The point is, using this data alone, I don’t know what the composition of employees looks like and cannot make clear claims about how much of any wage increase is due to the minimum wage specifically. So even if wages are rising during the period of minimum wage increases in Denver, some of that might be due to the minimum wage increase, but some of it could also reflect a data artifact such as a shift toward full-time workers pushing up weekly wages or other factors unrelated to the minimum wage. But I still want to know how wages have evolved in Denver compared to other places to understand how distinct the labor costs problem actually is.

The first comparison shown in the chart below is between Denver county and neighboring counties which include Adams, Arapahoe, Boulder, Broomfield, Douglas, and Jefferson. Despite some declines during the height of the pandemic, all counties have experienced real wage growth relative to their levels at the start of 2019. But Denver leads the pack with around a 30% increase in wages relative to 2019. Keep in mind this is adjusted for inflation, so I am not simply capturing wages going up with prices in general. So at least among nearby counties, Denver stands out in the growth of its labor costs.

Now, let’s turn to comparing Denver to several peer counties including Davidson, Hennepin, Multnomah, Salt Lake, and Travis shown in the next chart. Note that labels correspond to the largest city in each county. The comparison is broadly similar to what was shown for neighboring counties. All counties have experienced growth in restaurant workers’ wages, but Denver leads the pack.

The comparisons paint a clear picture. Denver’s restaurant workers have achieved substantial wage growth since 2019 and at a level that stands out among its neighbors and some comparable counties. Next is to try to understand to what extent this can be attributed to the minimum wage.

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Denver’s Minimum Wage

Before assessing to what extent the rise in real wages is associated with the minimum wage, I want to briefly discuss the basic economic theory of minimum wage policy and the surrounding empirical literature on these effects.

First, let’s go over the effects of a minimum wage using the textbook model of a competitive labor market. For those unfamiliar with the supply and demand framework, this is a labor market where labor demand (the employer side of the market) is downward sloping, labor supply is upward sloping, and their intersection determines the going wage rate in that labor market. Employers will hire more labor hours the lower wages are (labor demand is downward sloping). Employees will work more labor hours the higher wages are (supply is upward sloping).

Absent any minimum wage policy, the market wage is determined by supply and demand. A minimum wage is a price floor in this market, and when it is set at a level above the wage determined by the intersection of labor demand and supply, we have a situation where employers are willing to hire fewer employee hours at this wage than workers are willing to work. The basic prediction of this model is that wages go up but the total number of hours hired in the market goes down.

The negative impact on employment is a directional prediction of the model. We’d need to know about the demand and supply curves and the minimum wage to get a better idea about the size of the impact on employment. However, this is also not the only model used to predict effects of the minimum wage. In a model where employers have market power, a minimum wage can increase employment and wages. How we decide which model’s predictions are correct is an empirical question.

Over the last few decades a sizable and increasingly sophisticated empirical literature in economics has found a range of effects of minimum wages in different contexts. In some cases, there appears to be no short-run effect on employment as in the famous Card-Krueger study. Others find positive effects and still others have found negative effects.

Arin Dube and Ben Zipperer created a database calculating what they call the own-wage-elasticity (OWE) which represents the percentage change in employment divided by the percentage change in minimum wage to arrive at a summary number of the impact of the minimum wage (see, Dube’s post introducing the database).2 Even when looking only at estimates compiled on studies from the U.S. for retail or restaurant workers, estimates range from large and negative to large and positive. But because each study is dealing with a different context and different increases in the minimum wage it is difficult to make any general empirical claims about the effects of the minimum wage. That does not mean, however, that no claims can be made about the empirical effects of the minimum wage. I’ll turn now to Denver’s local minimum wage which has been higher than the Colorado minimum wage since 2020.

Denver’s increases in the minimum wage since 2020 are not moderate especially when compared to increases in Colorado’s minimum wage (both tipped and non-tipped) as shown in the table below. And even if we restricted our attention to the labor market for restaurant employees, a reasonable assumption is that the market is competitive. So, as an initial prediction given these two facts, I would say the minimum wage likely causes some decrease in employment hours.

The minimum wage table above also shows a substantial difference in the growth of Denver’s tipped minimum wage relative to the city’s standard minimum wage (about a 15 percentage point difference). This on its own is going to cut against full-service restaurants more than it would for limited-service restaurants. Another relevant factor to consider here is how the employer side of the market responds to minimum wage increases over time. It has now been six years since Denver increased its minimum wage from the Colorado state level. And as Brian Albrecht points out, because demand is more elastic in the long run, we should expect the size of the employer response to minimum wage increases to grow over time. That is, the longer a minimum wage increase is in effect, the larger any downward adjustment in labor use among employers should be.

Put it this way, if you are a restaurant employer and the minimum wage increases it is unlikely that you are going to drastically cut employee hours the next shift or even during the quarter the increase occurs. But the longer that higher wage is in effect, the more you will adjust. Maybe you hire fewer seasonal workers or don’t rehire when an employee leaves. Maybe only the best of your staff get put on the schedule full-time going forward. This long-run adjustment framing will matter for how I think about the divergence between full-service and limited-service later in the post.

Estimating Minimum Wage Effects in Denver

At this point, we know restaurant workers’ wages are high in Denver and have grown more than surrounding counties. The minimum wage has also increased substantially over the last several years. But how much of the increase in real wages is attributable to the increase in the minimum wage? To answer this question, I’ll refer to another recent Substack post from Arin Dube which gave me the idea. In his post, Dube looks at several red states that adopted higher minimum wages and compares them to other states which stayed at the federal level as a natural experiment. The setup is straightforward, he looked at the differences in wages and employment between the states that did and did not adopt higher minimum wages before and after the adoption year. Then, the gap after the minimum wage increase gives some idea of wage changes that can be attributed to the minimum wage and the same for employment.

Luckily for me, Colorado presents a similar scenario where I can use this approach. With the exception of Boulder, Denver is the only nearby county to adopt a minimum wage above the state level in 2020. Denver is my treated county and the surrounding counties Adams, Arapahoe, Broomfield, Douglas, and Jefferson serve as the controls who stayed at the Colorado minimum wage. Denver and the surrounding counties also share a state policy environment that might affect restaurant operators’ labor decisions, such as Colorado’s FAMLI (covering things like maternity and paternity leave) and the state’s sick-leave policy. All of these counties also experience similar inflation. Any gap in wages and employment found between Denver and its neighbors should reflect factors specific to Denver, with the local minimum wage being the most obvious candidate.

Then, we can look at the evolution of restaurant wages and employment in Denver relative to the counties that did not adopt a local minimum wage and see what the gap looks like. To the extent that there is a positive gap in wages we can be more sure that wages are rising because of the minimum wage. Similarly, to the extent that there is a negative gap in employment, we can be more sure that the minimum wage is causing a decline in employment.

A brief note on what this is not. I am not presenting a rigorously specified empirical strategy to recover a causal estimate. There are some clear issues with this setup that would threaten clean identification. Edgewater, one of the cities that does have a higher than Colorado minimum wage, is in Jefferson county but is being treated as a control. That poses the threat of downward bias on the estimated growth in wages due to the minimum wage in Denver. Also, the state minimum wage has not been fixed since 2020; it has just grown more slowly than Denver’s. The “control” counties are therefore not genuine controls. They did have increases in the minimum wage, just smaller increases than took place in Denver. Because of this, we should expect bias toward zero for any wage and employment effects estimated. However, this approach should be more informative than the naive comparisons earlier in the post in part because if there are substantial increases in wages and substantial decreases in employment relative to surrounding counties, we should expect that those would be more pronounced using a cleaner control set.

The chart below shows the side-by-side results for this approach on full-service restaurant wages and employment. Both are set to 0 in Q1 2019, and what we see for wages is that by late 2025, real wages for full-service restaurant workers in Denver had grown about 12 percentage points more than wages in surrounding counties relative to their 2019 levels. This gap is consistent with Denver’s minimum wage increases driving a meaningful share of Denver’s recent wage growth, though how much exactly is not clear. The right-hand panel shows substantial declines in restaurant employment at the same time wages are rising. By late 2025, full-service restaurant employment in Denver was more than 10 percentage points lower than employment in surrounding counties relative to 2019.

Again, I won’t pretend these are causal estimates. We have some evidence that Denver’s restaurant workers’ wages increased as the minimum wage increased and more so than neighboring counties that stayed on the lower state minimum wage. We also have evidence that restaurant employment declined over the same period and substantially more than in neighboring counties. This pushes me closer to believing that the minimum wage increases in Denver can explain some of the decline in the restaurant industry.

From the perspective of restaurant owners, this means they could face a few dollars an hour difference in labor costs per employee when determining to open in, say, the city of Wheat Ridge in Jefferson County or in Denver. That doesn’t imply that most operators are going to choose Wheat Ridge just to save on labor costs. Foot traffic differs across these locations and the hit to the consumer base may be more than enough to offset labor cost savings, but this will matter on the margin. Operators have a clear cost incentive to open restaurants in surrounding counties and opt out of Denver.

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What About Limited-Service Restaurants?

The remaining piece of the puzzle though is why limited-service restaurants are not doing as poorly. Higher labor costs can explain why Denver restaurants under-perform those in surrounding counties, but not the full-service vs. limited-service divergence. Now, I will get back to the long-run elasticity point to present a hypothesis. More adjustment on the labor-demand side doesn’t just mean that full-service restaurants use less labor and that some eventually exit because they can’t make it. It might also mean that additional adjustments are being made to have a more permanent reduction in labor use. And one way to do this is to adjust the business model of the restaurant before it opens.

I’ll start with the premise that full-service restaurants tend to be more labor-intensive businesses than limited-service restaurants. A full-service restaurant will have to hire wait staff, hosts, cooks, people to clear tables, dishwashers, etc. Maybe in the face of higher labor costs, operators begin to combine employee tasks. Wait staff handles clearing tables, and there are no busboys. There is no host position and wait staff handles customers as they come in. Cooks double as dishwashers. But even with some expansion of job duties to cut down on staff, we should expect limited-service restaurants to have fewer employees on average.

Limited-service restaurants still have cooks and people to handle orders at the cash register but there is no wait staff, no need for hosts, and combining tasks is relatively easier. Cashiers can collect trays and clear off tables when there are lulls in the line (customers might even handle some chunk of this). Shifts can overlap, so cashiers handle other tasks toward the end of their shift and the employee coming in takes over at the register. Luckily, the QCEW data I have been using across these two posts does allow me to at least present a proxy for the sort of labor intensity I am describing here.

The proxy measure I have in mind is simply average employees per establishment by restaurant type. That is, the employee headcount divided by the number of establishments for both full-service and limited-service restaurants. Admittedly, this measure is not perfect. Restaurants of both types will vary in how many employees they have, and headcount might overstate labor intensity. For example, a restaurant with more part-time employees may appear more labor-intensive than one with fewer (but more full-time) employees putting in the same total hours. But as shown in the chart below, there is a clear and persistent gap in the average number of employees per restaurant across types.

Prior to the pandemic, this gap was much larger. Full-service restaurants had around 9 more employees per restaurant than limited-service restaurants on average. Since the pandemic, the gap remains but has shrunk. Full-service restaurants now have something closer to 5 more employees per restaurant than limited-service restaurants. The gap shrinking is consistent with the idea that full-service restaurants have had to figure out how to combine tasks or adjust to operating with fewer employees. However, it is also consistent with larger restaurants exiting and leaving behind more full-service restaurants that hire fewer employees, even without any task combination. The persistent gap across restaurant types is consistent with the starting premise that operating a full-service restaurant tends to be more labor-intensive than operating a limited-service one, even if task adjustments or some other factor have occurred to shrink the gap. And if full-service is more labor-intensive than limited-service, then a minimum wage increase raises full-service operating costs by more than limited-service costs in percentage terms.

With this difference in labor intensity across restaurant types in mind, let’s imagine someone looking to open a restaurant. Our restaurateur plans to open a full-service restaurant and she starts thinking about hiring. She will need to hire wait staff, cooks, a manager, and maybe hosts. Each of these employees must be paid at least the minimum wage or the tipped minimum wage depending on their role. After doing some back-of-the-envelope calculations, she sees how much revenue she’d have to bring in to break even with these costs and realizes it just won’t work. The restaurant would have to be a massive success just to cover costs.

At this point, our new restaurant owner may just give up but that is not the only option. She could tweak her plan a bit and open as a limited-service restaurant. Why might this be a solution? The restaurant with counter-service can be a way to permanently lower labor costs. The first cut would be to the wait staff. A few people to staff the cash registers, who may also perform other tasks, means fewer people adding to the total wage bill. She still needs cooks and a manager, but overall the total number of employees and working hours can easily be slashed before the restaurant even opens. And in this scenario she still gets to open a version of her restaurant.

The more labor costs rise and the more permanent these changes are, the more likely we should expect this decision to be made. That is one channel through which the long-run adjustment to the minimum wage increase can take place.

The difficulty with this hypothesis is that it’s not clear how or where it would show up in the data. It is unlikely that there are many full-service restaurants making the switch to limited-service. But if that were happening, I am not aware of a dataset that would allow me to see something like reclassification from full- to limited-service. Similarly, I am not aware of any dataset at the county level with detailed industry classification for gross flows of establishments. And what I have in mind is at the stage of planning or getting ready to open a new restaurant, some operators are opting to go with the limited-service model. This isn’t something that would be captured in any data that I am aware of.

The best I can do with the data I have is to do the same “natural experiment” comparison done for full-service this time for limited-service and see the wage and employment gaps for limited service in Denver compared to surrounding counties that adhere to the state minimum wage. This evidence will not tell me whether more operators are choosing limited-service. What it does do is allow me to see whether wages for limited-service workers have risen relative to surrounding counties more or less than was the case for full-service restaurants.

If the relative wage increases are less, then limited-service restaurants are not only less labor-intensive but also less costly to operate in Denver relative to surrounding counties, making the monetary incentive for adjustment clearer. The minimum wage might also produce similar increases in wages for limited-service employees as it did for full-service employees. Even in this case, however, there is still a difference in cost hit. As discussed above, there is a persistent gap in labor intensity across restaurant types.

If my hypothesis is correct, I would also expect any declines in employment in the limited-service sector to be weaker than those found for full-service. But if both wage gains and employment declines are close to the same for limited-service, it’s unlikely this entry-adjustment idea is correct. At best, this is evidence of the shift in incentives between operating a full-service and a limited-service restaurant, not the outcomes predicted by my hypothesis.

The chart below shows the side-by-side comparison of wages and employment for limited-service restaurants in Denver relative to the neighboring counties. The left panel suggests that wages for limited-service restaurant workers have risen similarly to those in full-service, 10 percentage points for limited-service compared to 12 percentage points for full-service. These numbers are close enough to suggest that the minimum wage has likely affected wages in both sectors equally. The changes in employment are not the same, however. Although in the most recent periods there is a negative employment gap, it is much smaller than the one for full-service restaurants. Limited-service restaurant employment is around 4 percentage points lower in Denver than in surrounding counties as of late 2025. The same figure for full-service employment was more than 10 percentage points lower.

The wage finding shows the minimum wage affected per-worker pay in full-service and limited-service about equally. But because full-service restaurants employ more workers per establishment (even as employment has declined for full-service), the total cost per restaurant is larger for full-service. The cost advantage of limited-service has only widened as Denver’s minimum wage has risen, consistent with the hypothesis that the divergence across restaurant sectors can also be partly explained by the minimum wage. If someone is looking to open a restaurant in Denver, the incentives are pointing toward a limited-service business model.

What to Do?

Where does this leave us? Even without a clean causal estimate, the evidence is consistent with Denver’s minimum wage increases lifting restaurant wages above what surrounding counties saw and weighing on restaurant employment. There is also evidence that this is not affecting limited-service restaurants to the same extent. Together, the evidence suggests that both the distinct problem of Denver’s restaurants and the divergence across sectors of the restaurant industry can be partly explained by Denver’s minimum wage.

There is a limit to what this means for policy though. Even if I think that the broader market for low-wage labor in Denver is competitive and the recent increases in the minimum wage have been large enough to cause employment declines more generally, the evidence shown here is strictly on restaurants and even within that sector there is variation. I wouldn’t confidently say that Denver should revert the minimum wage to the state level because the story may not be the same in different areas.

But the Colorado state legislature passed a law last year which would allow cities like Denver to bring their tipped minimum wage closer to the state level. Governor Polis explicitly called on Denver and other cities in Colorado with higher minimum wages to address the tip credit when this law passed. Given what I have shown here this is a reasonable policy. Denver’s tipped minimum wage being lowered to the state level will probably provide some relief to full-service restaurants. And if we wanted restaurants to be doing better, the seemingly obvious response would be to do this. The problem, however, is that this seems politically unpopular. As far as I know, there hasn’t been any formal discussion of this issue in the Denver City Council yet but advocacy groups and over 50 restaurants have already urged the city not to reduce the tipped minimum wage. Maybe there is some support and this will happen but I’m not optimistic.

Despite the seeming unpopularity, I want to make the case that lowering Denver’s tipped minimum wage is limited in scope and worth taking a chance on. Tipped workers are concentrated in the restaurant industry and lowering the tipped minimum wage limits the scope of impact. It also doesn’t necessarily lead to an outcome where restaurant workers, especially those who are typically tipped, go through a decline in wages. The tip credit already operates so that employers must make up the difference if a tipped worker’s base wage plus tips falls below the non-tipped minimum wage. So, the minimum wage floor is still there. What changes is who pays for the floor, employers or customers. Restaurant operators can also choose to pool tips or eliminate tipping. Some will do this and can pay their wait staff the higher non-tipped minimum wage. Other operators will choose the lower tipped minimum wage and allow tips. Workers can then figure out which is the better deal, and job switching will occur over time.

If Denver does revert to the Colorado tipped minimum wage, I would not expect a rapid recovery in full-service restaurants. The downward adjustment took time and so would any recovery. Maybe this new equilibrium with fewer full-service restaurants is fine, but I wouldn’t mind seeing more restaurants popping up. Denver is an underrated food city, and making it easier for operators to experiment and provide consumers with more options would be great to see.

Sadly though, we arrived in Denver in what appears to be a period of struggle for the city’s restaurant industry. In the few years we have been here, many places we enjoyed eating at have closed, some after not even a year of being open. One example is EatYa Pizza, which used to be on the 16th Street Mall and was only open for 9 months. EatYa was excellent. Roman style pizza by the slice with unique toppings at a decent price. I could walk over there from my office for lunch to grab a couple slices and have a better lunch than the sandwich I usually bring from home. It was somewhere I took people when they came into town to visit or just to grab dinner to-go and eat at home.

EatYa was not a one-off closure it seemed. Even just along the 16th Street Mall since living here other restaurants have come and gone including a great Asian spot (YumCha). Local reporting over the last few years has brought up again and again and again that both consumers and operators are frustrated and feeling like the restaurant industry has gotten more difficult. These problems have become severe enough that Denver Economic Development & Opportunity, Visit Denver, and inKind put together the State of Denver’s Restaurants report which included surveys with operators. You can find the report here. This report is informative, showing restaurant employment is down and costs are up. Although the report covers a lot, the peer-comparisons it makes are mostly about wage policy. Those comparison cities are helpful and I want to extend this to see whether patterns in restaurant employment and establishments are shared across counties in Colorado as well as some peer counties. Then, the minimum wage question and other issues related to cost can be taken up in Part 2. My goal here with the comparisons is to try and get a better sense of how distinct the problem is to Denver and where it might be concentrated within the industry.

We can imagine two possible worlds, in both of which some portion of restaurant owners would rightly feel that surviving in the industry has become more difficult. The first is a genuine slump which could be measured by a decline in the number of establishments. Restaurants are closing and there aren’t new restaurants replacing the ones that close. But the other possibility is that the restaurant industry has a lot of churn. Many restaurants might be closing, but there are new restaurants opening to replace the ones closing. This is not to say that restaurant owners have an easy job. Maybe there are ways the business could be made easier, but the point is that there is a difference between how difficult it is to successfully run a restaurant and how healthy we might consider the industry to be.

In the world where churn is high, turnover isn’t necessarily a bad thing. That is a sign of competition and consumers voting with their dollar on who cuts it for meeting their culinary desires in the city. Not every consumer will be happy in this world, I certainly was disappointed when EatYa Pizza shuttered, but this would be a sign that the Denver restaurant industry is competitive which comes with some harsh realities especially given rising costs. I will also differentiate between full-service and limited-service restaurants to see whether this is an across-the-board problem or specific to one sector of the industry.

As a follow up to this post, I will focus more on the costs side of this issue and policy responses that would make sense for Denver. Over the last couple years there have already been considerations to alter the tipped minimum wage, changes to licensing to make it somewhat easier to open new restaurants, and even the Mayor’s off-the-cuff idea to add a surcharge to restaurant bills and use the revenues to subsidize the businesses. I am sympathetic to some of these (no, not the Mayor’s idea), but I think it is important first that there is a clear sense of the problem before the costs and policy sides are considered.

Data on Denver’s Restaurant Industry

The main source of data I will rely on for this post is the Bureau of Labor Statistics Quarterly Census of Employment and Wages (QCEW). This is some of the same data used in the State of Denver’s Restaurants report linked above. This data is great because it is primarily based on state unemployment tax records rather than surveys which avoids some issues with response rate. My focus will be on looking at restaurant employment and establishments in Denver county and several other counties for comparison. But the QCEW data will also allow me to look at different types of restaurants according to their NAICS classification such as full-service vs. limited-service restaurants (think sit-down service vs. counter service).

The data presented in charts below will not be the raw numbers but values that are indexed to their Q1 2019 value. The purpose here is to establish an easy to interpret pre-pandemic baseline for employment and establishments. For example, if a county’s index reaches 110, employment is 10% higher than its Q1 2019 level and if it fell to 90, then employment is 10% lower. Presenting the data in this way frames the changes in employment and establishments relative to 2019 which is a bit conservative. For instance, if there was employment growth leading up to 2019 and then a decline during the pandemic, some might not consider there to be true recovery unless employment returns back to pre-2019 trends or might see Q1 as seasonally low employment. As presented here, however, I will consider a return to the 2019 level as recovery which will likely understate the problem to a degree. All the charts below show quarterly data going back to the first quarter of 2014 through the third quarter of 2025 (the latest available data from QCEW).

Restaurant Employment in Denver and Neighboring Counties

The first indicator we will look at is restaurant employment. The chart below plots employment in full-service restaurants for Denver and several surrounding counties. Relative to 2019, all counties show a similar pattern after 2019. The pandemic hits and restaurant employment craters, then begins to increase rapidly back toward 2019 levels over a short period. But for many of these counties, employment never returns to its pre-pandemic level. For Denver county specifically, employment stayed well below its 2019 levels and then went through another decline between 2022 and 2024 ending around 15% below 2019 employment levels. And when we compare this performance to each county’s performance against its own 2019 baseline, we see that the only other county that has fared as poorly as Denver is Broomfield, the smallest of the surrounding counties.1 Although most counties never recovered to their own 2019 employment levels, none are quite as bad as Denver and Broomfield. Douglas county is a standout that has seen full recovery and then some since 2022.

Employment may be down, but maybe restaurants have gotten better at serving the same number of meals with fewer employees. With things like QR code menus, pay with your phone, and limiting what full-service looks like, this is entirely possible. As take-out, delivery, and counter-service options have become more popular, it might also be the case there has been a shift in the composition of the type of restaurants in Denver toward more limited-service which could partially explain declining employment in the full-service sector.

This next chart compares employment in Denver county across two different types of restaurants and restaurant employment in total. Looking first at total restaurant employment, there is still no full recovery to the early 2019 level, but when we look at employment at limited-service restaurants the changes after 2020 look much different. From its low point during the height of the pandemic, limited-service restaurant employment increased back toward its 2019 level and eventually peaked at more than 10% above that 2019 baseline in 2023. Between 2024 and mid-2025, limited-service employment began declining back toward the 2019 level but as of the third quarter of 2025 was still around 5% above the 2019 baseline.

The divergence in employment trends across restaurant types is the first piece of evidence to suggest that not all of Denver’s restaurants are facing the same problem. Things could be better in the limited-service sector but there is no clear slump at least when looking at employment. The slump is more clearly limited to full-service restaurants. With that in mind, we can turn to data on restaurant establishments.

Number of Restaurants in Denver and Neighboring Counties

The establishments data shows the total number of restaurants in whatever county we are looking at. These numbers count multiple locations of the same restaurant individually. Each Illegal Pete’s counts as 1, for example. One thing to note, however, is that because this is total establishments it is not perfect for determining churn or slump. For that, I would need the number of openings and closings each quarter. What I can say with this data is that if the number of total establishments is declining and falling below the pre-pandemic baseline that is clear evidence of a slump. The main limitation is that if there is limited change in the total number of restaurants this would be consistent with either a high churn environment where openings and closings roughly cancel out or where few new restaurants are opening but there are also not many closures. I cannot determine which is the case looking only at totals.

The establishments data pairs well with employment data in helping determine whether restaurants are figuring out how to stay open with fewer employees or whether there is a decline in both employment and the number of restaurants on net. As done with employment, I will start with a chart showing full-service restaurant establishments in Denver and surrounding counties. Right away the story looks a little different. In Denver, despite initial declines early on in the pandemic, the number of full-service establishments didn’t fall below the 2019 level until 2024. Take out and delivery were still options during the height of the pandemic which meant restaurants could stay open even if with much leaner staff. This suggests that despite employment never recovering, Denver was able to add new restaurants for a few years until the decline we see in 2024.

The steep decline continues through 2025 where the latest data show that the number of full-service establishments sits below the 2019 number. Boulder county also saw a decline in establishments and sits just below the 2019 number of restaurants (a decline of 1 establishment). And Broomfield once again appears to be doing poorly, with the number of restaurants 7% below the 2019 figure. Douglas, Arapahoe, Adams, and Jefferson counties have all seen gains in the number of full-service restaurants since 2019. Those counties appear to have figured out how to deal with declines in restaurant employment while still opening new restaurants, the same can’t be said for Denver and Broomfield. Employment and establishments are down in Denver county. It is safe to say there is a slump for full-service restaurants in Denver. The Broomfield case is interesting and may be worth looking more into, but my focus is going to stay on Denver county.

It will be instructive to look at any potential differences in changes in the number of establishments across restaurant types. We observed differential changes in employment across full-service and limited-service restaurants and may see the same thing here. The next chart shows this data for Denver county alone. Again, using 2019 as our 100 value, we can see changes in types of restaurant establishments relative to the first quarter of 2019, pre-pandemic. The change in the number of establishments shows a slight decline during the pandemic across both restaurant types but never below the 2019 baseline. Then, there is an increase in establishments and that is where we begin to see a divergence between full-service and limited-service establishments. At their peak, the number of limited-service restaurants was around 13% higher than in 2019 while full-service restaurants peaked at only around 8% above their 2019 total.

Starting around the same time as the sharp drop in full-service restaurant employment, the number of full-service restaurants begins to decline and this continues through 2025 where the number of restaurants sits around 3% below the 2019 level. The number of limited-service restaurants also declines toward its 2019 level over these quarters but never falls below 100, the most recent data point shows the number of limited-service restaurants is around 3% above the first quarter of 2019. Splitting the restaurant industry into the two sectors shows again that there is not a uniform problem for Denver restaurants. Full-service restaurants are struggling. Employment is dropping and more restaurants are closing than are opening. Limited-service restaurants, on the other hand, are doing better. Employment and establishments are both still above pre-pandemic levels.

At this point it seems clear that Denver’s restaurant industry is facing challenges distinct from those in surrounding counties. The exception is Broomfield which is doing about the same as Denver in terms of full-service employment and even worse in terms of the number of full-service restaurants. But we can also move outside Colorado to make comparisons with similar urban counties around the country to see whether these issues are shared with others outside Colorado.

Restaurant Employment and Numbers in Denver and “Peer” Counties

For another set of comparisons, I chose five additional “peer counties” to compare against Denver on restaurant employment and establishments. These counties (largest cities in parentheses) include Davidson (Nashville, TN), Travis (Austin, TX), Hennepin (Minneapolis, MN), Multnomah (Portland, OR), and Salt Lake (Salt Lake City, UT). Denver is somewhat unusual in this comparison because it’s a consolidated city-county while most others are not, Davidson being the exception. So, these are not perfect comparisons, some counties are much larger than Denver and capture more of the surrounding suburbs but do share some characteristics such as an urban downtown core. The counties also vary in their regulatory and minimum wage environment, some more progressive in policy with higher local minimum wage, others not as much. I am not trying to replicate Denver but choose counties that are more plausible comparisons than say comparing Denver to Los Angeles or New York. These are also counties that are home to cities used in the minimum wage comparison in the State of Denver’s Restaurants report across multiple tiers, some with federal minimum wages and others with higher minimum wages. For this section, I will keep it to two charts.

The first chart brings us back to full-service restaurant employment but now comparing Denver county to the peer counties. We already know what has happened in Denver. What is interesting here is that Denver remains among the two worst performers when it comes to employment. The only county to remain well below its 2019 baseline is Portland which is even further below its 2019 baseline. All other comparison counties are within a few percent of their 2019 baseline, suggesting something closer to stability than marked gains or declines. Not great performance, but notably better than Denver and Portland. Denver and Portland are the two poor performance standouts in this set of peer counties. They also share some features of their policy environment, including higher minimum wages, which is one possible explanation among others. Minneapolis also has a high minimum wage but is doing much better than Denver and Portland, minimum wage is only part of the story. I will address this more in Part 2.

What about establishments? Basically the same story. Denver is only doing ever so slightly better than Portland but this time all other counties are above their 2019 baseline. So, Denver is not unique in facing challenges across full-service restaurants. Portland is doing worse. But many other counties are not seeing this problem. Some people point to national declines in people eating out or shifting to take-out and delivery, yet we don’t see declines in full-servicce establishments across the board in this comparison set. So there is more to the story than national trends.

Conclusion

So, what is going on with Denver’s restaurants? Two things really. Full-service restaurants are in a genuine slump. Fewer people are being employed and more restaurants are closing. This is a good indication that many restaurant operators cannot hire as many workers as they used to and many are choosing to exit the market. But limited-service restaurants are faring better, they continue to open new restaurants and hire more people. And this looks somewhat distinct to Denver. Many of the surrounding counties are not seeing the same declines in employment or establishments. Even when looking outside Colorado, Denver county is doing worse than other plausible comparisons except Portland which is doing even worse. The remaining questions are why is this happening and what can be done about it on the policy side. This post will be followed up with another looking more at the costs and policy sides of Denver’s restaurant woes.

Part 2 of this post can be read here.

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