Tag Archives: MSA

New York’s Buffalo Billion initiative has been underwhelming

New York’s Buffalo Billion plan has come under fire amidst an ongoing corruption probe looking into whether some contracts were inappropriately awarded to political donors. The investigation has led to funding delays and there are reports of some contractors and companies rethinking their investments. But even without these legal problems, it is unlikely that the Buffalo Billion initiative will remake Buffalo’s economy.

Buffalo, NY has been one of America’s struggling cities since the 1950s, but before then it had a long history of growth. After it became the terminal point of the Erie Canal in 1825 it grew rapidly; over the next 100 years the city’s population went from just under 9,000 to over 570,000. Growth slowed down from 1930 to 1950, and between 1950 and 1960 the city lost nearly 50,000 people. It has been losing population ever since. The Metropolitan Area (MSA), which is the economic city, continued to grow until the 1970s as people left the central city for the surrounding suburbs, but it has also been losing population since then. (click to enlarge figure)

buffalo-population

Buffalo’s population decline has not escaped the notice of local, state and federal officials, and billions of dollars in government aid have been given to the area in an effort to halt or reverse its population and economic slide. The newest attempt is Governor Andrew Cuomo’s Buffalo Billion, which promises to give $1 billion of state funds to the region. The investment began in 2013 and as of January 2016, $870.5 million worth of projects have been announced. The table below lists some of the projects, the amount of the investment, and the number of jobs each investment is supposed to create, retain, or induce (includes indirect jobs due to construction and jobs created by subsequent private investment). This information is from the Buffalo Billion Process and Implementation plan (henceforth Buffalo Billion Plan).

buffalo-billion-projects

The projects listed have been awarded $727 million in direct investment, $150 million in tax breaks and $250 million in other state funds. The total number of jobs related to these investments is 9,900 according to the documentation, for an average cost of $113,859 per job (last column).

However, these jobs numbers are projections, not actual counts. This is one of the main criticisms of investment efforts like Buffalo Billion—a lot of money is spent and a lot of jobs are promised, but rarely does anyone follow up to see if the jobs were actually created. In this case it remains to be seen whether reality will match the promises, but the early signs are not encouraging.

Executives of the first project, SolarCity, which received $750 million of benefits and promised 5,000 jobs in western New York, appear to have already scaled back their promise. One company official recently said that 1,460 jobs will be created in Buffalo, including 500 manufacturing jobs. This is down from 2,000 in the Buffalo Billion Plan, a 27% decrease.

The SolarCity factory is not scheduled to open until June 2017 so there is still time for hiring plans to change. But even if the company eventually creates 5,000 jobs in the area, it is hard to see how that will drastically improve the economy of an MSA of over 1.1 million people. Moreover, page eight of the Buffalo Billion Plan reports that the entire $1 billion is only projected to create 14,000 jobs over the course of 5 years, which is again a relatively small amount for such a large area.

Contrary to the local anecdotes that say otherwise, so far there is little evidence that Buffalo Billion has significantly impacted the local economy. Since the recession, employment in Buffalo and its MSA has barely improved, as shown below (data are from the BLS). There has also been little improvement since 2013 when the Buffalo Billion development plan was released. (City data plotted on the right axis, MSA on the left axis.)

buffalo-employment

Real wages in both Erie and Niagara County, the two counties that make up the Buffalo MSA, have also been fairly stagnant since the recession, though there is evidence of some improvement since 2013, particularly in Erie County (data are from the BLS). Still, it is hard to separate these small increases in employment and wages from the general recovery that typically occurs after a deep recession.

buffalo-county-wages

The goal of the Buffalo Billion is to create a “Big Push” that leads to new industry clusters, such as a green energy cluster anchored by SolarCity and an advanced manufacturing cluster. Unfortunately, grandiose plans to artificially create clusters in older manufacturing cities rarely succeed.

As economist Enrico Moretti notes in his book, The New Geography of Jobs, in order for Big Push policies to succeed they need to attract both workers and firms at the same time. This is hard to do since either workers or firms need to be convinced that the other group will eventually arrive if they make the first move.

If firms relocate but high-skill workers stay away, then the firm has spent scarce resources locating in an area that doesn’t have the workforce it needs. If workers move but firms stay away, then the high-skill workers are left with few employment opportunities. Neither situation is sustainable in the long-run.

The use of targeted incentives to attract firms, as in the aforementioned SolarCity project, has been shown to be an ineffective way to grow a regional economy. While such incentives often help some firms at the expense of others, they do not provide broader benefits to the economy as a whole. The mobile firms attracted by such incentives, called footloose firms, are also likely to leave once the incentives expire, meaning that even if there is a short term boost it will be expensive to maintain since the incentives will have to be renewed.

Also, in order for any business to succeed state and local policies need to support, rather than inhibit, economic growth. New York has one of the worst economic environments according to several different measures: It’s 50th in overall state freedom, 50th in economic freedom, and 49th in state business tax climate. New York does well on some other measures, such as Kauffman’s entrepreneurship rankings, but such results are usually driven by the New York City area, which is an economically vibrant area largely due to historical path dependencies and agglomeration economies. Buffalo, and western New York in general, lacks the same innate and historical advantages and thus has a harder time overcoming the burdensome tax and regulatory policies of state government, which are particularly harmful to the local economies located near state borders.

Buffalo officials can control some things at the local level that will improve their economic environment, such as zoning, business licensing, and local taxes, but in order to achieve robust economic growth the city will likely need better cooperation from state officials.

State and local policy makers often refuse to acknowledge the harm that relatively high-tax, high-regulation environments have on economic growth, and this prevents them from making policy changes that would foster more economic activity. Instead, politicians invest billions of dollars of taxpayer money, often in the form of ineffective targeted incentives to favored firms or industries, with the hope that this time will be different.

Discovering an areas comparative advantage and creating a sustainable industry cluster or clusters requires experimentation, which will likely result in some failures. Local and state governments should create an environment that encourages entrepreneurs to experiment with new products and services in their region, but they shouldn’t be risking taxpayer money picking winners and losers. Creating a low-tax, low-regulation environment that treats all businesses—established and start-up, large and small—the same is a better way to grow an economy than government subsidies to favored firms. Unfortunately the Buffalo Billion project looks like another example of the latter futile strategy.

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.

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.

Berkeley, CA and the $15 – oops – $19 living wage

Berkeley, CA’s labor commission – in what should be an unsurprising move at this point in Berkeley’s history – has proposed raising the city’s minimum wage to an astounding $19 per hour by 2020! The labor commission’s argument in a nutshell is that Berkeley is an expensive place to live so worker’s need more money. And while Berkeley may be an expensive place to live, mandating that employers pay a certain wage doesn’t necessarily mean that the workers will get that money. As one Berkeley restaurant owner noted:

“We can raise our prices. But you can’t charge $25 for a sandwich,” said Dorothee Mitrani, who owns La Note. “A lot of mom-and-pop delis and cafes may disappear.”

The article states that Ms. Mitrani’s

…. full-service restaurant now subsidizes her take-out shop, which she said is running in the red as a result of the increases already in place. If the minimum rose to $19, she expects she would have to shut it down.

Of course, there are some politicians – and unfortunately some economists – who insist that raising the minimum wage doesn’t have adverse effects on employment, despite sound theoretical reasoning and empirical evidence to the contrary. My Mercatus center colleague Don Boudreaux has compiled an extensive collection of blog posts at Café Hayek debunking and refuting every pro-minimum wage argument out there, and I encourage interested readers to check them out.

The minimum wage most adversely effects low-skill, inexperienced workers, such as those without a high school degree, below the poverty level, between the ages of 16 – 19, and with some type of disability. So how do the people who fit into those categories currently fare in Berkeley’s labor market?

The table below shows the labor force rate and percentage employed for people 16 and over in each of those categories in the city of Berkeley in 2013 and 2014. The data is from the ACS 1-year survey. (American FactFinder table S2301)

berkely min wage employment 2013-14

As the table shows the labor force rate and the employment rate for each of those categories is already low compared to the overall labor force rate in Berkeley of 67% and employment rate of 62%. From 2013 to 2014 both the labor force rate and the employment rate declined for people without a high school degree, while the employment rate increased in the other categories. Nothing in this table leads me to believe that it would be a good idea to make the workers in these categories more expensive to hire, as it seems it is already difficult for them to find employment and it’s getting more difficult for some.

The table below compares Berkeley to the surrounding San Francisco MSA using only 2014 data.

berkeley min wage emp vs SF MSA

This table reveals that compared to the surrounding area, workers in these categories fare worse in Berkeley. The percentage of people with less than a high school degree who are employed was 11 percentage points lower in Berkeley, while the percentage with a disability was 0.8 points lower and the percentage below the poverty level was 1.5 points lower. Out of the four categories only 16 – 19 year olds had a better chance of being employed in Berkeley than in the surrounding MSA.

Hopefully Berkeley’s city council reviews the labor market reality in their city and thinks about actual consequences vs. intentions before deciding to increase the price that low-skill workers are allowed to charge for their labor. It’s already difficult for low-skill, inexperienced workers to find a job in Berkeley and making it harder won’t help them.