Tag Archives: weather

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.

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.

Where’s the growth?

In a famous Wendy’s commercial from 1984, three elderly women are examining a hamburger with a rather large bun when one of them asks “Where’s the beef?” in order to express her disappointment that the burger is all bun and no meat. When it comes to the economy growth is like the beef of a burger – without it all you’re left with is fluff and filler.

For the last 8 years the US economy has been mostly fluff and filler. Sure unemployment is down, but that is largely due to a lower labor force participation rate. Wage growth has been anemic and total GDP growth remains below the pre-recession long-run average of 3%.  GDP per capita growth is weak too.

Within a country as large as the US different regions are going to have different levels of GDP per capita and different growth rates for a variety of reasons including labor force characteristics, industry composition, weather, and geography. In order to examine the differences across the US, the graph below depicts the natural log of real GDP per capita in 2009 dollars for the 9 census divisions from 2001 to 2014. Because the natural log is on the y-axis the slope of the line corresponds to the growth rate between years. The black line is the US Metropolitan Area average and does not include rural areas.

ln real per cap gdp by cen div 2001-14

I created the census division average by generating a population weighted average of the real per capita GDP of the Metropolitan Statistical Areas located in each division. The weights are adjusted for each year in the data. Also, since the averages discussed in this post do not include rural areas one can think of them as the urban average in each census division. The population data for the weights and the real GDP per capita data are from the BEA.

As shown in the graph, the highest average real GDP per capita is in the New England division (orange) while the lowest is in the East South Central (purple), although as of 2014 the Mountain is not far ahead.

The slopes of the lines are steeper on average prior to the recession, indicating that the regions were growing faster during the pre-recession period. This is particularly noticeable in the Mountain and South Atlantic division, where real GDP per capita growth has essentially been zero (flat line) since 2009. Growth has also slowed considerably in the Pacific division (dark blue). Only in the East North Central (yellow) and West South Central (brown) does it appear that growth has reached or eclipsed its pre-recession rate.

The next graph below shows the average real per capita GDP by census division in three separate years – 2001, 2007, and 2014. This makes it easier to see the changes in levels over time.

real per cap gdp by cen div 2001,07,14

Real GDP per capita was higher in 2014 than in 2007 (year prior to the recession) in only three divisions – the Mid Atlantic, West North Central, and West South Central. The rest of the country has experienced either no gain or a decrease in the case of the South Atlantic and Mountain divisions. Together these graphs are hardly evidence of a strong economy.

High per capita GDP is not a perfect measure of economic prosperity but it is strongly correlated with many of the other things people care about. Countries with a higher level of per capita GDP are healthier, freer, and happier. The data presented here show that the US economy is struggling when it comes to growth, especially in the South Atlantic and Mountain divisions where people have become worse off on average. Whoever the next president is, he or she needs to come up with an answer to the question – Where’s the growth?

 

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.

City population dynamics since 1850

The reason why some cities grow and some cities shrink is a heavily debated topic in economics, sociology, urban planning, and public administration. In truth, there is no single reason why a city declines. Often exogenous factors – new modes of transportation, increased globalization, institutional changes, and federal policies – initiate the decline while subsequent poor political management can exacerbate it. This post focuses on the population trends of America’s largest cities since 1850 and how changes in these factors affected the distribution of people within the US.

When water transportation, water power, and proximity to natural resources such as coal were the most important factors driving industrial productivity, businesses and people congregated in locations near major waterways for power and shipping purposes. The graph below shows the top 10 cities* by population in 1850 and follows them until 1900. The rank of the city is on the left axis.

top cities 1850-1900

 

* The 9th, 11th, and 12th ranked cities in 1850 were all incorporated into Philadelphia by 1860. Pittsburgh was the next highest ranked city (13th) that was not incorporated so I used it in the graph instead.

All of the largest cities were located on heavily traveled rivers (New Orleans, Cincinnati, Pittsburgh, and St. Louis) or on the coast and had busy ports (New York, Boston, Philadelphia, Brooklyn, and Baltimore). Albany, NY may seem like an outlier but it was the starting point of the Erie Canal.

As economist Ed Glaeser (2005) notes “…almost every large northern city in the US as of 1860 became an industrial powerhouse over the next 60 years as factories started in central locations where they could save transport costs and make use of large urban labor forces.”

Along with waterways, railroads were an important mode of transportation from 1850 – 1900 and many of these cities had important railroads running through them, such as the B&O through Balitmore and the Erie Railroad in New York. The increasing importance of railroads impacted the list of top 10 cities in 1900 as shown below.

top cities 1900-1950

A similar but not identical set of cities dominated the urban landscape over the next 50 years. By 1900, New Orleans, Brooklyn (merged with New York) Albany, and Pittsburgh were replaced by Chicago, Cleveland, Buffalo, and San Francisco. Chicago, Cleveland, and Buffalo are all located on the Great Lakes and thus had water access, but it was the increasing importance of railroad shipping and travel that helped their populations grow. Buffalo was on the B&O railroad and was also the terminal point of the Erie Canal. San Francisco became much more accessible after the completion of the Pacific Railroad in 1869, but the California Gold Rush in the late 1840s got its population growth started.

As rail and eventually automobile/truck transportation became more important during the early 1900s, cities that relied on strategic river locations began to decline. New Orleans was already out of the top 10 by 1900 (falling from 5th to 12th) and Cincinnati went from 10th in 1900 to 18th by 1950. Buffalo also fell out of the top 10 during this time period, declining from 8th to 15th. But despite some changes in the rankings, there was only one warm-weather city in the top 10 as late as 1950 (Los Angeles). However, as the next graphs shows there was a surge in the populations of warm-weather cities during the period from 1950 to 2010 that caused many of the older Midwestern cities to fall out of the rankings.

top cities 1950-2010

The largest shakeup in the population rankings occurred during this period. Out of the top 10 cities in 1950, only 4 (Philadelphia, Los Angeles, Chicago, and New York) were still in the top 10 in 2010 (All were in the top 5, with Houston – 4th in 2010 – being the only city not already ranked in the top 10 in 1950, when it was 14th). The cities ranked 6 – 10 fell out of the top 20 while Detroit declined from 5th to 18th. The large change in the rankings during this time period is striking when compared to the relative stability of the earlier time periods.

Economic changes due to globalization and the prevalence of right-to-work laws in the southern states, combined with preferences for warm weather and other factors have resulted in both population and economic decline in many major Midwestern and Northeastern cities. All of the new cities in the top ten in 2010 have relatively warm weather: Phoenix, San Antonio, San Diego, Dallas, and San Jose. Some large cities missing from the 2010 list – particularly San Francisco and perhaps Washington D.C. and Boston as well – would probably be ranked higher if not for restrictive land-use regulations that artificially increase housing prices and limit population growth. In those cities and other smaller cities – primarily located in Southern California – low population growth is a goal rather than a result of outside forces.

The only cold-weather cities that were in the top 15 in 2014 that were not in the top 5 in 1950 were Indianapolis, IN (14th) and Columbus, OH (15th). These two cities not only avoided the fate of nearby Detroit and Cleveland, they thrived. From 1950 to 2014 Columbus’ population grew by 122% and Indianapolis’ grew by 99%. This is striking compared to the 57% decline in Cleveland and the 63% decline in Detroit during the same time period.

So why have Columbus and Indianapolis grown since 1950 while every other large city in the Midwest has declined? There isn’t an obvious answer. One thing among many that both Columbus and Indianapolis have in common is that they are both state capitals. State spending as a percentage of Gross State Product (GSP) has been increasing since 1970 across the country as shown in the graph below.

OH, IN state spending as per GSP

In Ohio state spending growth as a percentage of GSP has outpaced the nation since 1970. It is possible that increased state spending in Ohio and Indiana is crowding out private investment in other parts of those states. And since much of the money collected by the state ends up being spent in the capital via government wages, both Columbus and Indianapolis grow relative to other cities in their respective states.

There has also been an increase in state level regulation over time. As state governments become larger players in the economy business leaders will find it more and more beneficial to be near state legislators and governors in order to lobby for regulations that help their company or for exemptions from rules that harm it. Company executives who fail to get a seat at the table when regulations are being drafted may find that their competitors have helped draft rules that put them at a competitive disadvantage. The decline of manufacturing in the Midwest may have created an urban reset that presented firms and workers with an opportunity to migrate to areas that have a relative abundance of an increasingly important factor of production – government.

Fixing municipal finances in Pennsylvania

Last week I was a panelist at the Keystone Conference on Business and Policy. The panel was titled Fixing Municipal Finances and myself and the other panelists explained the current state of municipal finances in Pennsylvania, how the municipalities got into their present situation, and what they can do to turn things around. I think it was a productive discussion. To get a sense of what was discussed my opening remarks are below.

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Pennsylvania is the 6th most populous state in the US – just behind IL and in front of OH – and its population is growing.

PA population

But though Pennsylvania is growing, southern and western states are growing faster. According to the US census, from 2013 to 2014 seven of the ten fastest growing states were west of the Mississippi, and two of the remaining three were in the South (FL and SC). Only Washington D.C. at #5 was in the Northeast quadrant. Every state with the largest numeric increase was also in the west or the south. This is the latest evidence that the US population is shifting westward and southward, which has been a long term trend.

Urbanization is slowing down in the US as well. In 1950 only about 60% of the population lived in an urban area. In 2010 a little over 80% did. The 1 to 4 ratio appears to be close to the equilibrium, which means that city growth can no longer come at the expense of rural areas like it did throughout most of the 20th century.

urban, rural proportion

2012 census projections predict only 0.66% annual population growth for the US until 2043. The birth rate among white Americans is already below the replacement rate. Without immigration and the higher birth rates among recent immigrants the US population would be growing even slower, if not shrinking. This means that Pennsylvania cities that are losing population – Erie, Scranton, Altoona, Harrisburg and others – are going to have to attract residents from other cities in order to achieve any meaningful level of growth.

PA city populations

Fixing municipal finances ultimately means aligning costs with revenue. Thus a city that consistently runs a deficit has two options:

  1. Increase revenue
  2. Decrease costs

Municipalities must be vigilant in monitoring their costs since the revenue side is more difficult to control, much like with firms in the private sector. A city’s revenue base – taxpayers – is mobile. Taxpayers can leave if they feel like they are not getting value for their tax dollars, an issue that is largely endogenous to the city itself, or they can leave if another jurisdiction becomes relatively more attractive, which may be exogenous and out of the city’s control (e.g. air conditioning and the South, state policy, the decline of U.S. manufacturing/the economic growth of China, Japan, India, etc.). The aforementioned low natural population growth in the US precludes cities from increasing their tax base without significant levels of intercity migration.

What are the factors that affect location choice? Economist Ed Glaeser has stated that:

“In a service economy where transport costs are small and natural productive resources nearly irrelevant, weather and government stand as the features which should increasingly determine the location of people.” (Glaeser and Kohlhase (2004) p. 212.)

Pennsylvania’s weather is not the worst in the US, but it I don’t think anyone would argue that it’s the best either. The continued migration of people to the south and west reveal that many Americans like sunnier climates. And since PA municipalities cannot alter their weather, they will have to create an attractive fiscal and business environment in order to induce firms and residents to locate within their borders. Comparatively good government is a necessity for Pennsylvania municipalities that want to increase – or simply stabilize – their tax base. Local governments must also strictly monitor their costs, since mobile residents and firms who perceive that a government is being careless with their money can and will leave for greener – and sunnier – pastures.

Fixing municipal finances in Pennsylvania will involve more than just pension reform. Act 47 was passed by the general assembly in 1987 and created a framework for assisting distressed municipalities. Unfortunately, its effectiveness is questionable. Since 1987, 29 municipalities have been placed under Act 47, but only 10 have recovered and each took an average of 9.3 years to do so. Currently 19 municipalities are designated as distressed under Act 47 and 13 of the 19 are cities. Only one city has recovered in the history of Act 47 – the city of Nanticoke. The average duration of the municipalities currently under Act 47 is 16.5 years. The city of Aliquippa has been an Act 47 city since 1987 and is on its 6th recovery plan.

Act 47 bar graphAct 47 under pie chartAct 47 recovered pie chart

The majority of municipalities that have recovered from Act 47 status have been smaller boroughs (8 of 10). The average population of the recovered communities using the most recent data is 5,569 while the average population of the currently-under communities is 37,106. The population distribution for the under municipalities is skewed due to the presence of Pittsburgh, but even the median of the under cities is nearly double that of the recovered at 9,317 compared to 4,669.

Act 47 avg, med. population

This raises the question of whether Act 47 is an effective tool for dealing with larger municipalities that have comparatively larger problems and perhaps a more difficult time reaching a political/community consensus concerning what to do.

To attract new residents and increase revenue, local governments must give taxpayers/voters/residents a reason for choosing their city over the alternatives available. Economist Richard Wagner argues that governments are a lot like businesses. He states:

“In order to attract investors [residents, voters], politicians develop new programs and revise old programs in a continuing search to meet the competition, just as ordinary businesspeople do in ordinary commercial activity.” (American Federalism – How well does it support liberty? (2014))

Ultimately, local governments in Pennsylvania must provide exceptional long-term value for residents in order to make up for the place-specific amenities they lack. This is easier said than done, but I think it’s necessary to ensure the long-run solvency of Pennsylvania’s municipalities.

More reasons why intergovernmental grants are harmful

In a recent blog post I explained how intergovernmental grants subsidize some businesses at the expense of others. But that is just one of several negative features of intergovernmental grants. They also make local governments less accountable for their fiscal decisions by allowing them to increase spending without increasing taxes. The Community Development Blog Grant (CDBG) money that local governments spend on city services or use to subsidize private businesses is provided by taxpayers from all over the country. Unlike locally raised money, when cities spend CDBG money they don’t have to first convince local voters to provide them with the funds. This lack of accountability often results in wasteful spending.

These grants also erode fiscal competition between cities and reduce the incentive to pursue policies that create economic growth. If local governments can receive funds for projects meant to bolster their tax base regardless of their fiscal policies, they have less of an incentive to create a fiscal environment that is conducive to economic growth. The feedback loop between growth promoting policies and actual economic growth is impaired when revenue can be generated independently of such policies e.g. by successfully applying for intergovernmental grants.

Some of the largest recipients of CDBG money are cities that have been declining since the 1950s. The graph below shows the total amount of CDBG dollars given to nine cities that were in the top 15 of the largest cities in the US by population in 1950. (Click on graphs to enlarge. Data used in the graphs are here.)

CDBGs 9 cities 1950

None of these cities were in the top 15 cities in 2014 and most of them have lost a substantial amount of people since 1950. In Detroit, Cleveland, St. Louis, and Buffalo the CDBG money has not reversed or even slowed their decline and yet the federal government continues to give these cities millions of dollars each year. The purpose of these grants is to create sustainable economic development in the recipient cities but it is difficult to argue that such development has occurred.

Contrast the amount of money given to the cities above with that of the cities below:

CDBGs 9 cities 2014

By 2014 the nine cities in the second graph had replaced the other cities in the top 15 largest US cities by population. Out of the nine cities in the second graph only one, San Antonio, has received $1 billion or more in CDBG funds. In comparison, every city in the first graph has received at least that much.

While there are a lot of factors that contribute to the decline of some cities and the rise of others (such as the general movement of the population towards warmer weather), these graphs are evidence that the CDBG program is incapable of saving Detroit, Buffalo, St. Louis, Cleveland, etc. from population and economic decline. Detroit alone has received nearly $3 billion in CDBG grants over the last 40 years yet still had to declare bankruptcy in 2013. St. Louis, Cleveland, Baltimore, Buffalo, and Milwaukee are other examples of cities that have received a relatively large amount of CDBG funding yet are still struggling with population decline and budget issues. Place-based, redistributive policies like the CDBG program misallocate resources from growing cities to declining cities and reduce the incentive for local governments to implement policies that encourage economic growth.

Moreover, if place-based subsidies, such as the CDBG program, do create some temporary local economic growth, there is evidence that this growth is merely shifted from other areas. In a study on the Tennessee Valley Authority, perhaps the most ambitious place-based program in the country’s history, economists Patrick Kline and Enrico Moretti (2014) found that the economic gains that accrued to the area covered by the TVA were completely offset by losses in other parts of the country. As they state, “Thus, we estimate that the spillovers in the TVA region were fully offset by the losses in the rest of the country…Notably, this finding casts doubt on the traditional big push rationale for spatially progressive subsidies.” This study is further evidence for what other economists have been saying for a long time: Subsidized economic growth in one area, if it occurs, comes at the expense of growth in other areas and does not grow the US economy as a whole.

Don’t make us drive these cattle over the cliff

First a brief note: I am now blogging at the American Spectator on economic issues. I invite you to visit the inaugural posts. Last week, I covered the fiscal cliff. Like many others, I also marvel at the audacity of the pork contained therein.

Lately the headlines have given me a flashback to 1990 and those first undergrad economics classes. And not just econ but also U.S. history and the American experience with price floors and ceilings. In this post I’ll discuss the floors.

As I note at The Spectacle one of the matters settled by the American Taxpayer Relief Act is the extension of dairy price supports from the 2008 farm bill. Now, Congress won’t be “forced to charge $8 gallon for milk.” To me, nothing screams government price-fixing more than this threat aimed to scare small children and the parents who buy their food.

Chris Edwards explains how America’s dairy subsidy programs work in Milk Madness. Since the 1930’s the federal government  has set the minimum price to be charged for dairy. A misguided idea from the start, the point of the program was to ensure that dairy farmers weren’t hurt by falling prices during the Great Depression. When market prices fall below the government set price the government agrees to buy up any excess butter, dry milk or cheese that is produced. Thusly, dairy prices are kept artificially high which stimulates more demand.

According to Edwards’ study, the OECD found that U.S. dairy policies create a 26 percent “implicit tax” on milk, a regressive tax that affects low-income families in particular. Taxpayers pay to keep food prices artificially high, generate waste, and prevent local farmers from entering a caretlized market.

Now for the cows. The recession revealed that the nation has an oversupply of them. The New York Times reports that rapid expansion in the U.S. dairy market driven by increased global demand for milk products came to a sudden halt in 2008. Farmers were left with cows that needed to be milked regardless of the slump in world prices. The excess dry milk was then sold to the government but only at a price that was set above what the market demanded.

In other words, in a world without price supports, farmers could have sold the milk for less at market and consumers would have enjoyed cheaper butter, cheese and baby formula. Instead, the government stepped in, bought $91 million in milk powder so the farmer could get an above-market price and keep supporting an excess of milk cows. Rather than downsize the dairy based on market signals (and sell part of the herd to other dairy farmers, or the butcher) farmers take the subsidy and keep one too many cows pumping out more milk than is demanded.

It turns out auctioning a herd is not something all farmers are anxious to do. Some may look for additional governmental assistance to keep their cattle fed in spite of dropping prices, increased feed costs, and bad weather. To be sure eliminating farm subsidies would produce a temporary shock (a windfall for farmers and sticker shock for consumers), but in the long run as markets adjust everyone benefits.Dairy cows in the sale ring at the Warragul cattle sales, Victoria, [2]

New Zealand did it. Thirty years later and costs are lower for consumers, farmers are thrivingenvironmental practices have improved, and organic farming is growing. While politicians and the farm lobby may continue pushing for inefficient agricultural policy in spite of the nation’s fiscal path,as Robert Samuelson at Real Clear Politics writes, “If we can’t kill farm subsidies, what can we kill?”

 

More money for FEMA does not guarantee improved results

Before Congress passed $9.7 billion in Hurricane Sandy relief spending today, Governor Christie made headlines for his angry response to the House GOP’s delay in approving relief funds. The new spending will provide FEMA with money to pay out claims to those holding federal flood insurance. While the Hurricane Sandy relief effort gives political immediacy to FEMA funding, the Center for American Progress proposes a longer term strategy for dealing with natural disasters:

There must  be a dedicated source of revenue to fund predisaster mitigation programs that is not susceptible to budget cuts or political manipulation. Since the frequency and/or severity of extreme weather events will be exacerbated by climate change, it makes sense to raise revenue for resiliency from the fossil fuels whose combustion emits carbon pollution responsible for climate change.

The perspective that disaster recovery is a core responsibility of the federal government is widely shared, and voices as diverse as Governor Christie to the Center for American Progress express this opinion. However, the Mercatus Center’s Gulf Coast Recovery Project conducted in the wake of Hurricane Katrina demonstrates that funneling federal dollars toward disaster relief does not guarantee positive results for disaster victims. While the FEMA response to Hurricane Sandy went more smoothly than the Hurricane Katrina response, the federal government simply doesn’t have the capability to respond quickly and efficiently to individuals’ needs following a disaster, and channeling more resources to FEMA from any revenue source will not change the this fact.

As Pete Leeson and Russ Sobel write in a 2007 paper (pdf):

Following a natural disaster, on the one side there are “relief demanders”—individuals who desperately need disaster-relief supplies, including evacuation, food, shelter, medical attention, and so forth. On the other side, there are “relief suppliers”—individuals ready and willing to bring their supplies and expertise to bear in meeting the relief demanders’ needs. On both sides of this “market,” information is decentralized, local, and often inarticulate. Relief demanders know when relief is needed, what they need, and in what quantities, but they do not necessarily know who has the relief supplies they require or how to obtain them. Similarly, relief suppliers know what relief supplies they have and how they can help, but they may be largely unaware of whether relief is required and, if it is, what is needed, by whom, and in what locations and quantities.

[. . .]

Government’s informational deficit in the disaster-relief context is an unavoidable outcome of the centralization of disaster relief management when relief is provided by the state. Disaster-relief reforms that leave government as the primary manager of natural disasters are thus bound to fail. Correcting government’s information failure in the context of disaster relief requires eliminating its root cause: government involvement itself.

Researchers on the Gulf Coast Recovery Project found that non-profits, civic organizationsprivate firms, and individuals were more successful at providing the goods and services needed for recovery than the federal government.

Aside from the inherent challenges facing federal disaster response, funneling federal tax dollars to coastal areas prone to flooding leads to moral hazard. Because residents of flood-prone areas purchase federal insurance, taxpayers subsidize those who choose to live in these high-risk areas. Eli Lehrer of the R Street Institute explains this aspect of the Hurricane Sandy Relief Bill to Climate Wire:

“The mitigation piece of it is problematic,” said Eli Lehrer, president of the R Street Institute, a conservative organization that works with environmentalists and insurers to reduce subsidies in public insurance programs. “I think the bill should be drastically scaled back overall.”

He suggests that the disaster supplemental package could be cut in half. That would save taxpayer money, he says, now and in the future — by reducing incentives to develop coastlines. Lehrer also proposes cutting the federal share of post-disaster rebuilding costs to 50 percent. Currently, the government pays for 75 percent of recovery efforts, and Obama is asking Congress to increase that to 90 percent for Sandy survivors.

Politicians and activists who support a large role for the federal government in responding to disasters may have the best of intentions, but these intentions cannot circumvent the knowledge problems that government faces in disaster relief. By reducing the cost of developing in flood plains, greater reliance on the federal government for disaster mitigation and relief will be a costly effort unlikely to provide an adequate response when the next disaster strikes.

Twenty states face bill for Unemployment Benefits

The Center on Budget and Policy Priorities has a new analysis highlighting the $35 billion bill that 20 states owe to the federal government for covering benefit extensions. The report points to one of the design problems with the current program. The joint federal-state unemployment insurance program (UI) is financed via a payroll tax. States have kept the tax too low and thus did not build up enough reserves in the UI fund to weather the recession. This isn’t the first time UI has run into this problem, in fact it’s a perennial issue. Alan Krueger of Princeton provides a summary of some of the structural weakness in UI, a program unchanged since the New Deal.

While it is widely recognized that UI is structurally broken, solutions vary considerably. In a paper for The Brookings Institute, Rosen and Kletzer suggest “strengthening the federal role” in UI that would require states to harmonize eligibility criteria and benefit levels, increased eligibility and benefits financed by a higher FUTA tax. In addition Rosen and Kletzer propose a wageloss insurance program for those who become employed at a lower wage than their previous job; and lastly private accounts for the self-employed.

The Tax Foundation proposes another set of fixes. These include loosening up restrictions in the program to allow states to experiment with alternative programs, as well as the establishment of individual accounts.

In September the Obama Administration proposed a ‘sweeping reform’ of the current program. Included was the wageloss subsidy for the employed. In last week’s SOTU the president stressed transforming UI into an employment program via job training services. But these new appendages avoid the problem that UI was created to address: how to smooth private consumption during times of temporary and involuntary unemployment?

What about a private insurance model? Trooper Sanders makes the case at The Huffington Post.