Category Archives: Economic Policy

Congestion taxes can make society worse off

A new paper by Jeffrey Brinkman in the Journal of Urban Economics (working version here) analyzes two phenomena that are pervasive in urban economics—congestion costs and agglomeration economies. What’s interesting about this paper is that it formalizes the tradeoff that exists between the two. As stated in the abstract:

“Congestion costs in urban areas are significant and clearly represent a negative externality. Nonetheless, economists also recognize the production advantages of urban density in the form of positive agglomeration externalities.”

Agglomeration economies is a term used to describe the benefits that occur when firms and workers are in proximity to one another. This behavior results in firm clusters and cities. In regard to the existence of agglomeration economies, economist Ed Glaeser writes:

“The concentration of people and industries has long been seen by economists as evidence for the existence of agglomeration economies. After all, why would so many people suffer the inconvenience of crowding into the island of Manhattan if there weren’t also advantages from being close to so much economic activity?”

Since congestion is a result of the high population density that is also associated with agglomeration economies, there is tradeoff between the two. Decreasing congestion costs ultimately means spreading out people and firms so that both are more equally distributed across space. Using other modes of transportation such as buses, bikes and subways may alleviate some congestion without changing the location of firms, but the examples of London and New York City, which have robust public transportation systems and a large amount of congestion, show that such a strategy has its limits.

The typical congestion analysis correctly states that workers not only face a private cost from commuting into the city, but that they impose a cost on others in the form of more traffic that slows everyone down. Since they do not consider this cost when deciding whether or not to commute the result is too much traffic.

In economic jargon, the cost to society due to an additional commuter—the marginal social cost (MSC)—is greater than the private cost to the individual—the marginal private cost (MPC). The result is that too many people commute, traffic is too high and society experiences a deadweight loss (DWL). We can depict this analysis using the basic marginal benefit/cost framework.

congestion diagram 1

In this diagram the MSC is higher than the MPC line, and so the traffic that results from equating the driver’s marginal benefit (MB) to her MPC, CH, is too high. The result is the red deadweight loss triangle which reduces society’s welfare. The correct amount is C*, which is the amount that results when the MB intersects the MSC.

The economist’s solution to this problem is to levy a tax equal to the difference between the MSC and the MPC. This difference is sometimes referred to as the marginal damage cost (MDC) and it’s equal to the external cost imposed on society from an additional commuter. The tax aligns the MPC with the MSC and induces the correct amount of traffic, C*. London is one of the few cities that has a congestion charge intended to alleviate inner-city congestion.

But this analysis gets more complicated if an activity has external benefits along with external costs. In that case the diagram would look like this:

congestion diagram 2

Now there is a marginal social benefit associated with traffic—agglomeration economies—that causes the marginal benefit of traffic to diverge from the benefits to society. In this case the efficient amount of traffic is C**, which is where the MSC line intersects the MSB line. Imposing a congestion tax equal to the MDC still eliminates the red DWL, but it creates the smaller blue DWL since it reduces too much traffic. This occurs because the congestion tax does not take into account the positive effects of agglomeration economies.

One solution would be to impose a congestion tax equal to the MDC and then pay a subsidy equal to the distance between the MSB and the MB lines. This would align the private benefits and costs with the social benefits and costs and lead to C**. Alternatively, since in this example the cost gap is greater than the benefit gap, the government could levy a smaller tax. This is shown below.

congestion diagram 3

In this case the tax is decreased to the gap between the dotted red line and the MPC curve, and this tax leads to the correct amount of traffic since it raises the private cost just enough to get the traffic level down from CH to C**, which is the efficient amount (associated with the point where the MSB intersects the MSC).

If city officials ignore the positive effect of agglomeration economies on productivity when calculating their congestion taxes they may set the tax too high. Overall welfare may improve even if the tax is too high (it depends on the size of the DWL when no tax is implemented) but society will not be as well off as it would be if the positive agglomeration effects were taken into account. Alternatively, if the gap between the MSB and the MB is greater than the cost gap, any positive tax would reduce welfare since the correct policy would be a subsidy.

This paper reminds me that the world is complicated. While taxing activities that generate negative externalities and subsidizing activities that generate positive externalities is economically sound, calculating the appropriate tax or subsidy is often difficult in practice. And, as the preceding analysis demonstrated, sometimes both need to be calculated in order to implement the appropriate policy.

Does the New Markets Tax Credit Program work?

Location-based programs that provide tax credits to firms and investors that locate in particular areas are popular among politicians of both parties. Democrats tend to support them because they are meant to revitalize poorer or rural areas. In a recent speech about the economy, presumed Democratic nominee Hillary Clinton spoke favorably about two of them: the New Markets Tax Credit Program and Empowerment Zones.

Some Republicans also support such programs, which they view as being a pro-business way to help low-income communities. However, House Speaker Paul Ryan’s recent tax reform blueprint generally disapproves of tax credit programs.

Due to the volume of location-based programs and their relatively narrow objectives, many taxpayers are unfamiliar with their differences or unaware that they even exist. This is to be expected since most people are never directly affected by one. In this post I explain one that Hillary Clinton recently spoke about, the New Markets Tax Credit (NMTC) program.

The NMTC program was created in 2000 as part of the Community Renewal Tax Relief Act. It is managed by the Community Development Financial Institutions Fund, which is a division of the U.S. Treasury Department.

The NMTC program provides both new and established businesses with a tax credit that can be used to offset the costs of new capital investment or hiring new workers. The goal is to increase investment in low income communities (LIC) in order to improve the economic outcomes of residents.

Even though the program was started in 2000, no funds were issued to investors until 2003 (although some funds were allocated to the program in 2001 and 2002). Since 2001 over $43 billion has been allocated to the program. The figure below shows the allocations by year, amount issued to investors, and the total amount allocated from 2001 – 2014 (orange bar, uses right axis).

NMTC allocations

Figure 1

Practically all of the allocated funds from 2001 to 2012 have been issued to investors. A little over $250 million remains from 2013 and $1.3 billion from 2014. As the figure makes clear, this program controls a non-trivial amount of money.

The types of projects funded by the NMTC program can be seen in the figure below. The data for this figure comes from a 2013 Urban Institute report.

NMTC projects funded

Figure 2

So what have taxpayers gotten for their money? The program’s ‘fact sheet’ asserts that since 2003 the program has

“…created or retained an estimated 197,585 jobs. It has also supported the construction of 32.4 million square feet of manufacturing space, 74.8 million square feet of office space, and 57.5 million square feet of retail space.”

Like many government program administrators, those running the NMTC program seem to confuse outputs with outcomes. Presumably the goal of the NMTC program is not to build office space, which is a trivial achievement, but to improve the lives of the people living in low income communities. In fact, the program’s fact sheet also states that

“Investments made through the NMTC Program are used to finance businesses, breathing new life into neglected, underserved low-income communities.”

What really matters is whether the program has succeeded at “breathing new life” into LICs. To answer this more complicated question one needs to examine the actual economic outcomes in areas receiving the credits in order to determine whether they have improved relative to areas that haven’t received the credits. Such an exercise is not the same thing as simply reporting the amount of new office space.

That being said, even the simpler task of measuring new office space or counting new jobs is harder than it first appears. It’s important for program evaluators and the taxpayers who fund the program to be aware of the reasons that either result could be speciously assigned to the tax credit.

First, the office space or jobs might have been added regardless of the tax credit. Firms choose locations for a variety of reasons and it’s possible that a particular firm would locate in a particular low income community regardless of the availability of a tax credit. This could happen for economic reasons—the firm is attracted by the low price of space or the location is near an important supplier—or the location has sentimental value e.g. the firm owner is from the neighborhood.

A second reason is that the firms that locate or expand in the community might do so at the expense of other firms that would have located there absent the tax credit. For example, suppose the tax credit attracts a hotel owner who due to the credit finds it worthwhile to build a hotel in the neighborhood, and that this prevents a retail store owner from locating on the same plot of land, even though she would have done so without a credit.

The tax credit may also mistakenly appear to be beneficial if all it does is reallocate investment from one community to another. Not all communities are eligible for these tax credits. If a firm was going to locate in a neighboring community that wasn’t eligible but then switched to the eligible community upon finding out about the tax credit then no new investment was created in the city, it was simply shifted around. In this scenario one community benefits at the expense of another due to the availability of the tax credit.

A new study examines the NMTC program in order to determine whether it has resulted in new employment or new businesses in eligible communities. It uses census tract data from 2002 – 2006. In order to qualify for NMTCs, a census tract’s median family income must be 80% or less of its state’s median family income or the poverty rate of the tract must be over 20%. (There are two other population criteria that were added in 2004, but according to the study 98% qualify due to the income or poverty criterion.)

The authors use the median income ratio of 0.8 to separate census tracts into a qualifying and non-qualifying group, and then compare tracts that are close to and on either side of the 0.8 cutoff. The economic outcomes they examine are employment at new firms, number of new firms, and new employment at existing firms.

They find that there was less new employment at new firms in NMTC eligible tracts in the transportation and wholesale industries but more new employment in the retail industry. Figure 2 shows that retail received a relatively large portion of the tax credits. This result shows that the tax credits helped new retail firms add workers relative to firms in transportation and manufacturing in eligible census tracts.

The authors note that the magnitude of the effects are small—a 0.2% increase in new retail employment and a 0.12% and 0.41% decrease in new transportation and wholesale employment respectively. Thus the program had a limited impact during the 2002 – 2006 period according to this measure, despite the fact that nearly $8 billion was granted to investors from 2002 – 2005.

The authors find a similar result when examining new firms: Retail firms located in the NMTC eligible tracts while services and wholesale firms did not. Together these two results are evidence that the NMTC does not benefit firms in all industries equally since it causes firms in different industries to locate in different tracts. The latter result also supports the idea that firms that benefit most from the tax credit crowd out other types of firms, similar to the earlier hotel and retail store example.

Finally, the authors examined new employment at existing firms. This result is more favorable to the program—an 8.8% increase in new employment at existing manufacturing firms and a 10.4% increase at retail firms. Thus NMTCs appear to have been primarily used to expand existing operations.

But while there is evidence that the tax credit slightly increased employment, the authors note that due to the limitations of their data they are unable to conclude whether the gains in new employment or firms was due to a re-allocation of economic activity from non-eligible to eligible census tracts or to actual new economic activity that only occurred because of the program. Thus even the small effects identified by the authors cannot be conclusively considered net new economic activity generated by the NMTC program. Instead, the NMTC program may have just moved economic activity from one community to another.

The mixed results of this recent study combined with the inability to conclusively assign them to the NMTC program cast doubt on the programs overall effectiveness. Additionally, the size of the effects are quite small. Thus even if the effects are positive once crowding out and reallocation are taken into account, the benefits still may fall short of the $43.5 billion cost of the program (which doesn’t include the program’s administrative costs).

An alternative to location-based tax credit programs is to lower tax rates on businesses and investment across the board. This would remove the distortions that are inherent in location-based programs that favor some areas and businesses over others. It would also reduce the uncertainty that surrounds the renewal and management of the programs. Attempts to help specific places are often unsuccessful and give residents of such places false hope that community revitalization is right around the corner.

Tax credits, despite their good intentions, often fail to deliver the promised benefits. The alternative—low, stable tax rates that apply to all firms—helps create a business climate that is conducive to long-term planning and investment, which leads to better economic outcomes.

Washington DC is set to become the latest city to make it illegal for low-skill people to work

In the latest example of politics trumping economics, Washington DC’s city council voted to increase the city’s minimum wage to $15 per hour by 2020. The economic arguments against a minimum wage are well-known to most people so I won’t rehash them here, but if you want to read more about why the minimum wage is bad policy you can do so here, here, and here.

In a nutshell, the minimum wage prices lower-skill workers out of the market by setting the wage higher than the value they can produce for their employer; if a worker only produces $9 worth of value in an hour an employer can’t pay her $10 per hour and stay in business.

The minimum wage has the strongest impact on low-skill workers since they tend to produce the least amount of value for their employers. Two categories of such workers are teenagers, who lack experience and have yet to finish their education, and adults with less than a high school degree. The figures below depict the employment and unemployment rates for these two groups in the Washington DC metro area (MSA) and the city proper (District only) from 2009 to 2014 (most recent data available) using 5-year American Community Survey data from American FactFinder.

DC 16-19 employed

As shown in the figure only about 15% of DC’s 16 – 19 year olds were employed (orange) in 2014 compared to about 25% in the MSA as a whole. The percentage has fallen since 2009 and doesn’t appear to be recovering. Increasing the price of such workers certainly won’t help.

The next figure shows the percentage of people with less than a high school degree who were employed.

DC less HS employed

Again, the percentage has fallen in DC since 2009 and is far below the MSA as a whole. Less than half of adults with less than a high school degree are employed in DC compared to 67% in the Washington metro area. If employers relocate to other jurisdictions within the MSA once the minimum wage law takes effect it will make it more difficult for the less-educated adults of DC to find a job.

The next two figures show the unemployment rates for both groups in both areas. As shown, the unemployment rate is higher in DC than in the MSA for both groups and has been trending upward since 2009.

DC 16-19 unemp

DC less HS unemp

It’s outlandish to think that raising the minimum wage will improve things for the 35% of 16 – 19 year olds and 21% of high school dropouts who were looking for a job and couldn’t find one under the old minimum wage of only $9.50.

Politicians and voters are free to ignore economic reality and base their decision making on good intentions, but when doing so they should at least know the employment facts and be made aware of the futility of their intentions. I predict that we will see more automation in DC’s restaurants, hotels, and bars in the future as workers get relatively more expensive due to the higher minimum wage. This will only make it harder for DC’s teenagers and less-educated residents to find work, which as shown above is already a difficult task.

Northern Cities Need To Be Bold If They Want To Grow

Geography and climate have played a significant role in U.S. population growth since 1970 (see here, here, here, and here). The figure below shows the correlation between county-level natural amenities and county population growth from 1970 – 2013 controlling for other factors including the population of the county in 1970, the average wage of the county in 1970 (a measure of labor productivity), the proportion of adults in the county with a bachelor’s degree or higher in 1970 and region of the country. The county-level natural amenities index is from the U.S. Department of Agriculture and scores the counties in the continental U.S. according to their climate and geographic features. The county with the worst score is Red Lake, MN and the county with the best score is Ventura, CA.

1970-13 pop growth, amenities

As shown in the figure the slope of the best fit line is positive. The coefficient from the regression is also given at the bottom of the figure and is equal to 0.16, meaning a one point increase in the score increased population growth by 16 percentage points on average.

The effect of natural amenities on population growth is much larger than the effect of the proportion of adults with a bachelor’s degree or higher, which is another strong predictor of population growth at the metropolitan (MSA) and city level (see here, here, here, and here). The relationship between county population growth from 1970 – 2013 and human capital is depicted below.

1970-13 pop growth, bachelors or more

Again, the relationship is positive but the effect is smaller. The coefficient is 0.026 which means a 1 percentage point increase in the proportion of adults with a bachelor’s degree or higher in 1970 increased population growth by 2.6 percentage points on average.

An example using some specific counties can help us see the difference between the climate and education effects. In the table below the county where I grew up, Greene County, OH, is the baseline county. I also include five other urban counties from around the country: Charleston County, SC; Dallas County, TX; Eau Claire County, WI; San Diego County, CA; and Sedgwick County, TX.

1970-13 pop chg, amenities table

The first column lists the amenities score for each county. The highest score belongs to San Diego. The second column lists the difference between Green County’s score and the other counties, e.g. 9.78 – (-1.97) = 11.75 which is the difference between Greene County’s score and San Diego’s score. The third column is the difference column multiplied by the 0.16 coefficient from the natural amenity figure e.g. 11.75 x 0.16 = 188% in the San Diego row. What this means is that according to this model, if Greene County had San Diego’s climate and geography it would have grown by an additional 188 percentage points from 1970 – 2013 all else equal.

Finally, the last column is the actual population growth of the county from 1970 – 2013. As shown, San Diego County grew by 135% while Greene County only grew by 30% over this 43 year period. Improving Greene County’s climate to that of any of the other counties except for Eau Claire would have increased its population growth by a substantial yet realistic amount.

Table 2 below is similar to the natural amenities table above only it shows the different effects on Greene County’s population growth due to a change in the proportion of adults with a bachelor’s degree or higher.

1970-13 pop chg, bachelor's table

As shown in the first column, Greene County actually had the largest proportion of adults with bachelor’s degree or higher in 1970 – 14.7% – of the counties listed.

The third column shows how Greene County’s population growth would have changed if it had the same proportion of adults with a bachelor’s degree or higher as the other counties did in 1970. If Greene County had the proportion of Charleston (11.2%) instead of 14.7% in 1970, its population growth is predicted to have been 9 percentage points lower from 1970 – 2013, all else equal. All of the effects in the table are negative since all of the counties had a lower proportion than Greene and population education has a positive effect on population growth.

Several studies have demonstrated the positive impact of an educated population on overall city population growth – often through its impact on entrepreneurial activity – but as shown here the education effect tends to be swamped by geographic and climate features. What this means is that city officials in less desirable areas need to be bold in order to compensate for the poor geography and climate that are out of their control.

A highly educated population combined with a business environment that fosters innovation can create the conditions for city growth. Burdensome land-use regulations, lengthy, confusing permitting processes, and unpredictable rules coupled with inconsistent enforcement increase the costs of doing business and stifle entrepreneurship. When these harmful business-climate factors are coupled with a generally bad climate the result is something like Cleveland, OH.

The reality is that the tax and regulatory environments of declining manufacturing cities remain too similar to those of cities in the Sunbelt while their weather and geography differ dramatically, and not in a good way. Since only relative differences cause people and firms to relocate, the similarity across tax and regulatory environments ensures that weather and climate remain the primary drivers of population change.

To overcome the persistent disadvantage of geography and climate officials in cold-weather cities need to be aggressive in implementing reforms. Fiddling around the edges of tax and regulatory policy in a half-hearted attempt to attract educated people, entrepreneurs and large, high-skill employers is a waste of time and residents’ resources – Florida’s cities have nicer weather and they’re in a state with no income tax. Northern cities like Flint, Cleveland, and Milwaukee that simply match the tax and regulatory environment of Houston, San Diego, or Tampa have done nothing to differentiate themselves along those dimensions and still have far worse weather.

Location choices reveal that people are willing to put up with a lot of negatives to live in places with good weather. California has one of the worst tax and regulatory environments of any state in the country and terrible congestion problems yet its large cities continue to grow. A marginally better business environment is not going to overcome the allure of the sun and beaches.

While a better business environment that is attractive to high-skilled workers and encourages entrepreneurship is unlikely to completely close the gap between a place like San Diego and Dayton when it comes to being a nice place to live and work, it’s a start. And more importantly it’s the only option cities like Dayton, Buffalo, Cleveland, St. Louis and Detroit have.

Baltimore’s misguided move to raise its minimum wage will harm its most vulnerable

Baltimore’s city council, like others around the country, is considering raising the city’s minimum wage to $15 per hour. This is an ill-advised move that will make it harder for young people and the least skilled to find employment, which is already a difficult task in Baltimore.

The figure below shows the age 16 – 19 labor force participation (LFP) rate, employment rate, and unemployment rate in Baltimore City from 2009 to 2014 (most recent data available). The data are from the American Community Survey table S2301.

baltimore 16-19 emp stats

As shown in the figure, the LFP rate declined along with the employment rate, which has caused the unemployment rate to hold steady at approximately 40% (red line). So 40% of Baltimore’s unemployed teens were searching for a job but couldn’t find one and only 20% of all teens were actually employed, a decline of 4 percentage points (blue line). How is increasing the minimum wage to $15 per hour going to help the 40% who are looking for a job find one?

The minimum wage increase may help some people who are able to keep their job at the higher wage, but for the 40% who can’t find a job at the current minimum wage of $8.25, an increase to $15 is only going to make the task harder, if not impossible. Who is standing up for these people?

The data are just as gloomy when looking at workers with less than a high school degree, which is another group that is severely impacted by a higher minimum wage. As the figure below shows, the employment rate is falling while the unemployment rate is rising.

baltimore lt hs emp stats

In 2009 over 42% of people in this skill group were employed (blue line). In 2014 only 37% were, a decline of five percentage points. Meanwhile, the unemployment rate increased from about 19% to over 25% (red line). And all of this occurred while the economy was supposedly improving.

Again we should ask; how is a higher minimum wage going to help the 25% of high school dropouts in Baltimore who are unemployed find a job? It won’t. Unemployed workers do not become more attractive as employees simply because the city council mandates a higher wage.

What’s going to happen is that more people in this skill group will become discouraged and leave the labor market entirely. Then they will earn $0 per hour indefinitely and be forced to rely entirely on family, friends, and public assistance to live. A $15 minimum wage destroys their chances of finding meaningful employment and unduly deprives them of opportunities to better their lives.

This is the unseen effect of minimum wage hikes that $15 supporters rarely acknowledge. When faced with the higher cost, firms will hire workers who can justify a $15 wage and those who cannot will be unable to find employment. Additionally, firms will start using more technology and automation instead of workers. This happens because consumers want low prices and high quality, and as the minimum wage increases technology and capital become the best way to give consumers what they want. Over time workers in states with lower minimum wages may be forced out of the labor market as well as new technologies spread from high minimum wage areas to low minimum wage areas.

Another common argument put forth by minimum wage supporters is that taxpayers subsidize firms that pay low wages. But this is not true. Firms like Wal-Mart, McDonalds, and the countless other large and small business that employ low-skill workers are doing their part by giving people an opportunity. Firm owners did not unilaterally decide that all Americans should have a minimum standard of living and they should not be required to provide it on their own. Ultimately, advocates of a higher minimum wage who worry that they are subsidizing firms will likely be forced to contribute even more tax dollars to social programs since the wage for unemployed workers is $0.

Furthermore, why $15 and not $20? The argument is that $15/ hour is the minimum necessary to maintain a basic standard of living for working Americans but that argument is subjective. In fact, it can be extended to other areas. For example, should new hires be paid more than an entry-level salary so they can pay off college debt and maintain the standard of living of their parents?

To the extent that Americans deserve a particular lifestyle, providing it is a collective burden that should be shared by everyone. Politicians, clergy, union heads and other minimum wage supporters who want to push the entire burden onto firms are abandoning the moral obligation they claim we all share.

While minimum wage supporters mean well they appear to be blind to those who are harmed by wage controls. And those who are harmed are some of the most vulnerable members of the workforce – high school drop-outs, recent immigrants and urban youth. The minimum wage is a misguided policy that consigns these vulnerable members of the labor force to the basement of the economy and prevents any escape.

States with lower minimum wages will feel the impact of California’s experiment

California governor Jerry Brown recently signed a law raising California’s minimum wage to $15/hour by 2022. This ill-advised increase in the minimum wage will banish the least productive workers of California – teens, the less educated, the elderly – from the labor market. It will be especially destructive in the poorer areas of California that are already struggling.

And if punishing California’s low-skill workers by preventing them from negotiating their own wage with employers isn’t bad enough, there is reason to believe that a higher minimum wage in a large state like California will eventually affect the employment opportunities of low-skill workers in other areas of the country.

Profit-maximizing firms are always on the lookout for ways to reduce costs holding quality constant (or in the best case scenario to reduce costs and increase quality). Since there are many different ways to produce the same good, if the price of one factor of production, e.g. labor, increases, firms will have an incentive to use less of that factor and more of something else in their production process. For example, if the price of low-skill workers increases relative to the cost of a machine that can do the same job firms will have an incentive to switch to the machine.

To set the stage for this post, let’s think about a real life example; touch screen ordering. Some McDonald’s have touchscreens for ordering food and coffee and San Francisco restaurant eatsa is almost entirely automated (coincidence?). The choice facing a restaurant owner is whether to use a touch screen or cashier. If a restaurant is currently using a cashier and paying them a wage, they will only switch to the touch screen if the cost of switching and the future discounted costs of operating and maintaining the touch screen device are less than the future discounted costs of using workers and paying them a wage plus any benefits. We can write this as

D + K + I + R < W

Where D represents the development costs of creating and perfecting the device, K represents the costs of working out the kinks/the trial run/adjustment costs, I represents the installation costs, and R represents the net present value of the operating and maintenance costs. On the other side of the inequality W represents the net present value of the labor costs. (In math terms R and W are: R = [ ∑ (rk) / (1+i)^n from n=0 to N ] where r is the rental rate of a unit of capital, k is the number of units of capital, and i is the interest rate and W = [ ∑ (wl) /(1+i)^n from n=0 to N ] where w is the wage and l is the amount of labor hours. But if this looks messy and confusing don’t worry about it as it’s not crucial for the example.)

The owner of a restaurant will only switch to a touch screen device rather than a cashier if the left side of the above inequality is less than the right side, since in that case the owner’s costs will be lower and they will earn a larger profit (holding sales constant).

If the cashier is earning the minimum wage or something close to it and the minimum wage is increased, say from $9 to $15, the right side of the above inequality will increase while the left side will stay the same (the w part of W is going up).  If the increase in the wage is large enough to make the right side larger than the left side the firm will switch from a cashier to a touch screen. Suppose that an increase from $9 to $15 does induce a switch to touch screen devices in California McDonald’s restaurants. Can this impact McDonald’s restaurants in areas where the minimum wage doesn’t increase? In theory yes.

Once some McDonald’s restaurants make the switch the costs for other McDonald’s to switch will be lower. The reason for this is that the McDonald’s that switch later will not have to pay the D or K costs; the development or kinks/trial run/adjustment costs. Once the technology is developed and perfected the late-adopting McDonald’s can just copy what has already been done. So after the McDonald’s restaurants in high wage areas install and perfect touch screen devices for ordering, the other McDonald’s face the decision of

I + R < W

This means that it may make sense to adopt the technology once it has been developed and perfected even if the wage in the lower wage areas does not change. In this scenario the left side decreases as D and K go to 0 while the right side stays the same. In fact, one could argue that the R will decline for late-adopting restaurants as well since the maintenance costs will decline over time as more technicians are trained and the reliability and performance of the software and hardware increase.

What this means is that a higher minimum wage in a state like California can lead to a decline in low-skill employment opportunities in places like Greenville, SC and Dayton, OH as the technology employed to offset the higher labor costs in the high minimum wage area spread to lower wage areas.

Also, firm owners and operators live in the real world. They see other state and local governments raising their minimum wage and they start to think that it could happen in their area too. This also gives them an incentive to switch since in expectation labor costs are going up. If additional states make the same bad policy choice as California, firm owners around the country may start to think that resistance is futile and that it’s best to adapt in advance by preemptively switching to more capital.

And if you think that touch screen ordering machines aren’t a good example, here is a link to an article about an automated burger-making machine. The company that created it plans on starting a chain of restaurants that use the machine. Once all of the bugs are worked out how high does the minimum wage need to be before other companies license the technology or create their own by copying what has already been done?

This is one more way that a higher minimum wage negatively impacts low-skill workers; even if workers don’t live in an area that has a relatively high minimum wage, the spread of technology may eliminate their jobs as well.

A $15 minimum wage will excessively harm California’s poorest counties

Lawmakers in California are thinking about increasing the state minimum wage to $15 per hour by 2022. If it occurs it will be the latest in a series of increases in the minimum wage across the country, both at the city and state level.

Increases in the minimum wage make it difficult for low-skill workers to find employment since the mandated wage is often higher than the value many of these workers can provide to their employers. Companies won’t stay in business long if they are forced to pay a worker $15 per hour who only produces $12 worth of goods and services per hour. Statewide increases may harm the job prospects of low-skill workers more than citywide increases since they aren’t adjusted to local labor market conditions.

California is a huge state, covering nearly 164,000 square miles, and contains 58 counties and 482 municipalities. Each of these counties and cities has their own local labor market that is based on local conditions. A statewide minimum wage ignores these local conditions and imposes the same mandated price floor on employers and workers across the state. In areas with low wages in general, a $15 minimum wage may affect nearly every worker, while in areas with high wages the adverse effects of a $15 minimum wage will be moderated. As explained in the NY Times:

“San Francisco and San Jose, both high-wage cities that have benefited from the tech boom, are likely to weather the increase without so much as a ripple. The negative consequences of the minimum wage increase in Los Angeles and San Diego — large cities where wages are lower — are likely to be more pronounced, though they could remain modest on balance.

But in lower-wage, inland cities like Bakersfield and Fresno, the effects could play out in much less predictable ways. That’s because the rise of the minimum wage to $15 over the next six years would push the wage floor much closer to the expected pay for a worker in the middle of the wage scale, affecting a much higher proportion of employees and employers there than in high-wage cities.”

To put some numbers to this idea, I used BLS weekly wage data from Dec. of 2014 to create a ratio for each of California’s counties that consists of the weekly wage of a $15 per hour job (40 x $15 = $600) divided by the average weekly wage of each county. The three counties with the lowest ratio and the three counties with the highest ratio are in the table below, with the ratio depicted as a percentage in the 4th column.

CA county weekly min wage ratio

The counties with the lowest ratios are San Mateo, Santa Clara, and San Francisco County. These are all high-wage counties located on the coast and contain the cities of San Jose and San Francisco. As an example, a $600 weekly wage is equal to 27.7% of the average weekly wage in San Mateo County.

The three counties with the highest ratios are Trinity, Lake, and Mariposa County. These are more rural counties that are located inland. Trinity and Lake are north of San Francisco while Mariposa County is located to the east of San Francisco. In Mariposa County, a $600 weekly wage would be equal to 92.6% of the avg. weekly wage in that county as Dec. 2014. The data shown in the table reveal the vastly different local labor market conditions that exist in California.

The price of non-tradeable goods like restaurant meals, haircuts, automotive repair, etc. are largely based on local land and labor costs and the willingness to pay of the local population. For example, a nice restaurant in San Francisco can charge $95 for a steak because the residents of San Francisco have a high willingness to pay for such meals as a result of their high incomes.

Selling a luxury product like a high-quality steak also makes it relatively easier to absorb a cost increase that comes from a higher minimum wage; restaurant workers are already making relatively more in wealthier areas and passing along the cost increase in the form of higher prices will have a small effect on sales if consumers of steak aren’t very sensitive to price.

But in Mariposa County, where the avg. weekly wage is only $648, a restaurant would have a hard time attracting customers if they charged similar prices. A diner in Mariposa County that sells hamburgers is probably not paying its workers much more than the minimum wage, so an increase to $15 per hour is going to drastically affect the owner’s costs. Additionally, consumers of hamburgers may be more price-sensitive than consumers of steak, making it more difficult to pass along cost increases.

Yet despite these differences, both the 5-star steakhouse in San Francisco and the mom-and-pop diner in Mariposa County are going to be bound by the same minimum wage if California passes this law.

In the table below I calculate what the minimum wage would have to be in San Mateo, Santa Clara, and San Francisco County to be on par with a $15 minimum in Mariposa County.

CA comparable min wage

If the minimum wage was 92.6% of the average wage in San Mateo it would be equal to $50.14. Using the ratio from a more developed but still lower-wage area – Kern County, where Bakersfield is located – the minimum wage would need to be $37.20 in San Mateo. Does anyone really believe that a $50 or $37 minimum wage in San Mateo wouldn’t cause a drastic decline in employment or a large increase in prices in that county?

If California’s lawmakers insist on implementing a minimum wage increase they should adjust it so that it doesn’t disproportionately affect workers in poorer, rural areas. But of course this is unlikely to happen; I doubt that the voters of San Mateo, Santa Clara, and San Francisco County will be as accepting of a $37 + minimum wage as they are of a $15 minimum wage that won’t directly affect many of them.

A minimum wage of any amount is going to harm some workers by preventing them from getting a job. But a minimum wage that ignores local labor market conditions will cause relatively more damage in poorer areas that are already struggling, and policy makers who ignore this reality are excessively harming the workers in these areas.

Exit, voice, and loyalty in cities

Economist Albert Hirschman’s 1970 book Exit, Voice, and Loyalty: Responses to Decline in Firms, Organizations, and States presents a theory of how consumers express their dissatisfaction to firms and other organizations after a decline in product or service quality. In terms of interjurisdictional competition exit is demonstrated by migration: dissatisfied residents migrate to a community that better matches their preferences for local government services, externality mitigation, and fiscal policy. Voice, on the other hand, requires staying in place and is usually manifested through voting. Other methods such as protests, letters, and public comments directed at officials may also be effective ways to create change.

Loyalty plays a role in whether voice or exit is employed. Someone who is loyal to a city will be less likely to exit due to a given deterioration in quality. Hirschman argues that loyalty serves an important function by limiting the use of exit and activating voice. If exit is too easy, the quality-conscious people most capable of using voice to elicit change at the local level will tend to leave early, sparking a “brain drain” and generating cumulative deterioration. If some of the most quality-conscious residents are loyal they will remain in place, at least initially, and try to fix a city’s problems from within i.e. they will use some method of voice.

The presence of loyalty within a city’s population has implications for city population decline and growth. The diagram below, based on one from Hirschman’s book on p. 90, shows the relationship between city quality and population.

Exit and loyalty diagram

Quality deteriorates as one moves up the y-axis and population increases along the x-axis, which enables a depiction of the relationship between quality and population similar to that of a traditional demand curve.

The example begins at point A. If quality declines from Q1 to Q2, the population will decline from Pa to Pb. The relatively small population decline relative to the decline in quality is due to the presence of loyalty. Loyalty can be conscious, meaning that the loyal residents are aware of the quality decline and are staying to try to improve the situation, or it can be unconscious, meaning that some residents are unaware that quality is deteriorating. These unaware residents appear loyal to outsiders, but in reality they have just not perceived the decline in quality. Perhaps the decline has not impacted their particular neighborhood or is so gradual that many people don’t realize it is happening. Hirschman notes that unconscious loyalty will not spark voice since by definition the resident is unaware that decline is occurring.

As quality continues to decline from Q2 to Q3 it becomes more observable and even the most loyal residents accept the fact that voice will not save their city. Additionally, the unconscious “loyal” residents will finally notice the decline. Both groups of people will then exit the city in order to reside somewhere else. This leads to a larger drop in population and is shown in the diagram as a movement from Pb to Pc.

This pattern is repeated as a city recovers. An initial quality improvement from Q3 to Q2 induces a relatively small amount of migration back to the city (Pc to Pd), since most people will need confirmation that the city has actually started down a path of sustainable improvement before they will return. Further improvement from Q2 to Q1 will generate a larger increase in population, represented by a movement from point D to point A (Pd to Pa).

What is interesting about this theoretical analysis is that it generates two different populations for the same level of quality. At quality Q2 the city’s population will be relatively large (Pb) if the city is declining in quality and it will be relatively small (point Pd) if the city’s quality is improving. This means that a declining city such as Detroit, Baltimore, Cleveland, Buffalo, etc. will have to make substantial quality improvements before they will see a large influx of people. So even if a city such as Cleveland returns to its 1970 level of relative quality we shouldn’t expect a drastic increase in population, as this model predicts that its population will be less than its actual 1970 population since it will be on the returning curve (CDA) rather than the exiting curve (ABC).

A city that is consistently losing population over a long period of time faces a variety of problems such as increased crime, declining housing values, a decline in the quality of public services, and higher costs in the provision of public services. Fixing these problems is often expensive and this model implies that the costs required for increasing quality from Q1 to Q2 will not result in substantial population gain, which means per capita costs to taxpayers are unlikely to decline by much and may even increase as the city begins to improve. This model predicts that revitalizing America’s struggling cities is a more difficult task than many politicians and policy makers are acknowledging.

Economic freedom matters at the local level too

Since 1996 the Fraser Institute has published an annual economic freedom of the world index that ranks countries according to their level of economic freedom. They also publish an economic freedom of North America Index that ranks the US states, Canadian provinces, and Mexican provinces using similar data.

Both of these studies have been used to show that countries and states/provinces with relatively high levels of economic freedom tend to be better off in several ways, including higher GDP per capita, longer life expectancy, and greater economic growth. Countries with higher levels of economic freedom tend to have higher quality democracies as well.

A quick google search reveals that there has been a lot of other research that looks at the relationship between economic freedom and various outcomes at the country and state level. However, substantially less research has been done at the local level and there are two main reasons for this.

First, it’s hard to gather data at the local level. There are thousands of municipalities in the US and not all of them make their data easily available. This makes gathering data very costly in terms of time and resources. Second, a lot of policies that impact economic freedom are enacted at the federal and state level. Because of this many people probably don’t think about the considerable effects that local policy can have on local economies.

There has been one study that I know of that attempts to create an economic freedom index for metropolitan areas (MSAs). This study is by Dr. Dean Stansel of SMU, a coauthor of the economic freedom of North America index. The MSA economic freedom index runs from 0 (not free) to 10 (very free) and was created with 2002 data. I am currently working on a paper with Dean that uses this index, but I was recently inspired to use the index in a different way. I wanted to see if economic freedom at the MSA level impacted subsequent employment and population growth, so I gathered BEA data on employment and population and ran a few simple regressions. The dependent variables are at the top of each column in the table below and are private, non-farm employment growth from 2003 – 2014, proprietor employment growth from 2003 – 2014, and population growth from 2003 – 2014.

MSA econ freedom regressions

I also included a quality of life index independent variable from another study in order to control for the place-specific amenities of each MSA like weather and location. This variable measures how much people would be willing to pay to live in a particular MSA; a positive number means a person would pay to live in an area, while a negative number means a person would have to be paid to live in an area. Thus larger, positive numbers indicate more attractive areas. The index is constructed with 2000 data.

As shown in the table, economic freedom has a positive and significant effect on both measures of employment and population growth. The quality of life index is also positive and significant for private employment growth (column 1) and population growth (column 3, only at the 10% level). We can calculate the magnitude of the effects using the standard deviations from the table below.

MSA econ freedom sum stats

Using the standard deviation from column 1 (0.84) we can calculate that a one standard deviation increase in economic freedom would generate a 2 percentage point increase in private employment growth from 2003 – 2014 (0.84 x 0.024), a 4.5 percentage point increase in proprietor employment growth, and a 2.9 percentage point increase in population growth.  A one standard deviation change would be like increasing San Francisco’s level of economic freedom (6.70) to that of San Antonio’s (7.53).

Similarly, a one standard deviation increase in the quality of life index would lead to a 2.1 percentage point increase in private employment growth from 2003 – 2014 (0.000011 x 1912.86) and a 1.9 percentage point increase in population growth. A one standard deviation change would be like increasing the quality of life of Montgomery, AL (-21) to that of Myrtle Beach, SC (1643).

I think the most interesting finding is that quality of life does not affect proprietor employment while economic freedom’s largest effect is on proprietor employment (column 2). According to the BEA proprietor employment consists of the number of sole proprietorships and the number of general partners. Thus it can act as a proxy for the level of entrepreneurship in an MSA. This result implies that economic freedom is more important than things like weather and geographic location when it comes to promoting small business formation and entrepreneurship. This is a good sign for cities located in colder regions of the country like the Midwest and Northeast that can’t do much about their weather or location but can increase their level of economic freedom.

Of course, correlation does not mean causation and these simple regressions omit other factors that likely impact employment and population growth. But you have to start somewhere. And given what we know about the positive effects of economic freedom at the country and state level it seems reasonable to believe that it matters at the local level as well.