New Study of the "Gang of Ten" Energy Bill's Section 199 Repeal
We released my latest study today, which explores the tax burden and economic impact of the proposed “Gang of Ten” energy bill currently working its way through Congress.
The energy bill proposes roughly $84 billion in new federal spending on conservation and alternative energy programs, to be funded partly by corporate income tax changes affecting the oil and gas industry. My study explores the tax side of the proposal, estimating how the tax burden will be split between labor and capital, and how these burdens will impact the broader U.S. economy.
You can download a draft of the study here. Here’s the abstract:
The recently released “Gang of Ten” energy proposal includes revenue offsets that would exclude domestic oil and gas companies from the Section 199 deduction for domestic production activity. Using a simple input-output model we estimate the state-by-state impact of this proposal on tax burdens, employment, household earnings and economic output. We estimate the proposal will increase corporate tax burdens by approximately $13.57 billion over ten years, 44 percent of which will fall on households in the petroleum manufacturing states of Texas, California and Louisiana. Using RIMS II multipliers we estimate the proposal will reduce U.S. employment by roughly 637,000 jobs over ten years, reduce household earnings by $34.97 billion, and reduce total U.S. economic output by $185.95 billion.
A draft of the full study is available here.
(Note: For the official published version of the study from the Institute for Energy Research see here.)
Posted by Andrew on Wednesday September 10, 2008 | Feedback?
Estimating Tax Pyramiding Using Input-Output Tables: a How-To Guide
(Note: The equations in this article are hard to read in HTML. For a PDF version click here.)
When I sat down to write a paper on gross receipts taxes two years ago, there was surprisingly little written on them. Since then, there’s been an outpouring of scholarship as states like Illinois and Texas have considered replacing corporate income taxes with them. But a big drawback of these studies—including mine—is that few estimate the most important economic effect of gross receipt taxes: so-called “tax pyramiding.”
Gross receipts taxes work like a sales tax, except they apply to inputs as well as final goods. For a baker selling loaves of bread, the flour, electricity and packaging are all taxed first, then the loaf itself is taxed when sold to consumers. These extra layers of taxation get quietly built into the final selling price—something economists call “tax pyramiding.”
In this post I’ll explain how to quantify tax pyramiding using a simple input-output model. We’ll walk through an example for Washington State’s “Business & Occupation” (B&O) tax, which is one of the nation’s oldest gross receipts tax. At the end, I’ll hand over an input-output model I built to estimate pyramiding from the B&O tax using new state-level input-output tables released this year.
The last published estimate of pyramiding from the B&O tax was a 2002 study from the Washington State Tax Structure Study Committee. In that study, economists from the state Department of Revenue developed a method to estimate pyramiding using state-level input-output tables. Using 1998 data they found the tax pyramids 2.5 times on average, ranging from 1.4 times on computer services to a whopping 6.7 times on manufactured food.
In this example, we’ll use their method on the new 2002 input-output tables for Washington State. These data were just released in May. That means our estimate will not only be the first one in six years, but will also be most current one available anywhere.
Theory of Input-Output
The first step is to start with theory. Imagine we divide the economy into n industries, with each one buying materials, adding value to them, and selling the results to consumers. This is what’s known as an “input-output” view of the economy.
In this world, there are two basic identities that should always be true. First, the total value of an industry’s output should equal how much they spent on inputs, plus the amount of value they add in the production process. Think of a baker making bread: the value of the final loaf is equal to the value of the inputs plus the baker’s profit.
For industry number one, we can summarize this relationship like this:
(1) 
Where:
= Total output from industry one;
= Intermediate inputs purchased by industry one from industries one through n; and
= The value added by industry one.
The second identity that’s always true is that an industry’s total output will be equal to the amount other industries buy from them, plus the amount they sell to consumers as final products. In the example of the baker, his total output must equal the number of loaves he sells to restaurants and other firms, plus the number he sells directly to consumers.
Again for industry number one, we can write this second relationship like this:
(2) 
Where:
= Total output from industry one;
= The amount of output from industry one purchased as intermediate inputs by industries one through n; and 
= The amount of industry one’s output sold to consumers as final demand.
We can summarize these relationships in a table known as an input-output table. Here’s what it looks like. Summing down the columns gives equation one, while summing across the rows gives equation two:
(3) 
So that’s the theory of the input-output table. To see what an actual I-O table looks like in practice, see the table I’ve posted here.
For now, we’re only going to focus on the first relationship summarized in equation (1) and the columns of the table. Look at the first column. It says the sum of inputs purchased by industry one plus their value added is equal to their total output. Put differently,
(4) 
Now, what we want to do is find a way to modify equation (4) so that we get rid of the x’s, and find a way to express them in terms of Y’s instead. You’ll see why we want to do this in a minute—it makes the math a lot easier.
To do this, let’s define a new number 
. It’s equal to the share of industry j’s total output that’s purchased as an intermediate input by industry i. That is,
(5) 
This means a will be a fraction somewhere between zero and one. Think of it like this: industry j’s total output is a pie. The number 
is the slice of that pie industry i buys from them as inputs. So for example, if 
, then industry one buys 20 percent of the “pie” of output from industry two.
Now we can re-write equation (4) like this, substituting in a from equation (5):
(6) 
At this point, we’ve got something we can work with. Let’s translate equation (6) into linear algebra using matrices.
First, let’s put all the 
coefficients into one giant matrix. We’ll call this A. The way we’ll calculate A is by dividing every x in the input-output table (3) by its row sum. This makes sense—dividing 
by the sum of the first row is simply dividing industry two’s purchases from industry one by the total output from industry one. That is, it’s the share of industry one’s “pie” that’s purchased by industry two.
Here’s what the matrix A looks like:
(7) A = 
Now, let’s re-write equation (6) in matrix notation, using the new matrix A we just created:
(8) 
If you look carefully at equation (8), you’ll see that the n x n matrix of a’s on the right-hand side is just the transpose of the A matrix we defined in equation (7). We’ll dente this transposed matrix using a single quotation mark as A’.
Now, if we label the matrix of Y’s on the left Y, and the matrix of V’s on the far right V, we can re-write this jungle of matrix notation more simply as:
(9) 
Now, let’s solve this equation for Y, like this:
In the above, the matrix labeled I is an n x n identity matrix, which works just like the number one in regular algebra.
Equation (10) is a pretty famous one in input-output models. It’s the basic equation relating the output of the economy to the input coefficients matrix A and the value-added of industries in the economy. This is sometimes called a “Leontief model” of the economy, named after economist Wassily Leontief.
Now that we’ve got a model of the economy without any taxes, we need to create a model with a gross receipts tax added in. This will let us see the difference between the two, which is basically the impact of the tax. This comparison of side-by-side models—one with taxes and one without—is how we’ll measure tax pyramiding.
To create a model with a gross receipts tax, recall equation (6), which says the output of industry one is equal to its input purchases plus value added.
(6) 
A gross receipts tax applies to both intermediate inputs and final sales. So adding on a gross receipts tax simply multiplies the right-hand side of equation (6) by 
, where 
is the effective gross receipts tax rate on industry j. So equation (6) becomes,
(11) 
In the above, note that we’ve re-labeled industry output Y as 
to denote that it’s total output including the built-in gross receipts tax. To finish our model with taxes, we’ve got to define one more matrix. Let’s call it T, which is an n x n matrix with (1 + industry effective gross receipts tax rates) down the diagonal and zeros elsewhere:
(12) T = 
Now, we’re ready to add taxes into our model. Using the T matrix defined above, we can add in a gross receipts tax and re-write equation (8) like this:
(13) 
Using our simpler notation from equation (9), we can re-write this mess as the following:
(14) 
The last step is to solve this equation for 
. At that point, we’ll have our model that includes gross receipts taxes. Solve it like this:
And that’s our input-output model that includes a gross receipts tax. Now, what we can do is compare the value of output for each industry in our two models—one without the tax, and one with the tax. Here are the two models:
(10) No Tax: 
(15) With Tax: 
The difference between these is the tax burden for each industry caused by the gross receipts tax. What we’ll do is take this tax burden for each industry, express it as a percentage of that industry’s value added, and divide the result by the effective gross receipts tax rate for the industry.
If the result is one, there’s no tax pyramiding. In that case, the industry tax burden would simply be equal to the gross receipts tax rate, which means the tax was only paid once. If the result is greater than one, then the gross receipts tax is causing a pyramided tax burden greater than the actual gross receipts tax rate. Whatever this number is, we’ll call it the “degree of pyramiding” of the tax. That’s the number we’re after here.
Putting Theory into Practice
Now that we’ve got the theory worked out, let’s go find some data. In this example, we’ll calculate tax pyramiding for the Washington State B&O tax.
First, we need some input-output tables. Washington State has some of the best regional input-output tables in the nation, and new 2002 tables were just released in May 2008. You’ll find the table here:
http://www.ofm.wa.gov/economy/io/2002/default.asp
Open up this table in Excel, and use it to create the matrices that go into our two models in equations (10) and (15). Create one tab with the full input-output table. You’ll have to create a row with total value added and label it “V”, and label the column with total industry output “Y”. In separate tabs, create the matrices A and I following the directions in the theory section above.
The last step is to create the T matrix. To do this, you’ll need data on industry effective B&O tax rates. You’ll find that at the following link to the Washington State Department of Revenue’s website. Look for Table 5 in the “Quarterly Business Review” section, which has B&O tax collections and bases by industry. The effective tax rate is just the tax collections divided by the base for each industry:
That’s everything you’ll need. All that’s left is to plug in equations (10) and (15) into two 51 x 1 column vectors in Excel, compare the differences for each industry, and calculate the degree tax pyramiding.
Once you’re done, your model should look something like this. Here’s my own Excel model estimating tax pyramiding of the B&O tax using 2007 tax collections and bases. The tax pyramids on average around three times, ranging from 1.6 on computer services to a sky-high 16.7 on petroleum products manufacturing:
Washington I-O Tax Pyramiding Model.xls
Posted by Andrew on Saturday August 16, 2008 | Feedback?
Creating an Input-Output Table from BEA Data: A How-to Guide
One of the hard parts about building Leontief input-output models is that the source data are hard to use.
Instead of producing a real input-output table, the Bureau of Economic Analysis (BEA) produces what they call “use” and “make” tables. The make table shows products produced by each industry, while the use table shows how products get used by industries, consumers and government. However, what we need for Leontief models is a table that shows only the industry-by-industry relationships.
In this post I’ll explain how to create an input-output table from BEA’s make and use tables. At the bottom, I’ve posted a spreadsheet with an I-O table I developed from the new 2002 BEA Benchmark Input-Output Data.
Building the I-O Table
The first step is to download the BEA’s use and make tables. The easiest ones to work with are the “Standard Make and Use Tables at the summary level,” which you’ll find at http://www.bea.gov/industry/xls/2002summary_makeuse.xls. These have 133 industry groups, which is enough detail for most research.
Once you’ve downloaded them, open the use table. Delete the two rows labeled “Noncomparable Imports” and “ROW adjustment”. There’s no domestic industry that produces these things, so you won’t need them in your industry-by-industry table. Next, label the intermediate-uses portion of the Use table “U”—you can label ranges in Excel by typing names in the upper-left box in the toolbar.
Next, open the make table. This is a 133×134 table showing industries down the rows and the products they make across the columns. Below the make table, create a new 133×134 matrix where each element is equal to the corresponding make table element divided by the column total. Label this matrix “M”.
Now we’re ready to build the I-O table. The classic table has three sections, which we’ll build one at a time: intermediate uses, final demands, and industry value added.
First, let’s build the intermediate-uses section. Call this “S”. You’ll calculate the “S” matrix by multiplying M*U, which we’ve defined above. This will create a 133×133 matrix that’s the intermediate-uses part of the table. Set this aside for now.
Next let’s calculate the final demands part of the table. That’s the right-hand section showing how much output from each industry is used by consumers, government and the rest of the world. In the use table, label the final demands portion of the table “D”. Create a new matrix labeled “Di” by multiplying M*D. This gives us a 133×13 matrix of final demands for the I-O table. This Di matrix will sit to the right of the intermediate portion of the input-output table (“S”) we calculated above. As a final step, add a column on the far right of the table that sums the intermediate uses and final demand for each row. This is total output for each industry.
Finally, let’s fill in the value-added section at the bottom of the table. To do this, first copy the total industry output we added to the far right column and transpose it into the bottom row of the table. This forces the row output of each industry to equal their column output, which is a fundamental equality in I-O tables.
Once you’ve pasted in the total output line, you’ll need to use the entries in the “Gross Operating Surplus” row in the table as balancing items to get the column totals for each industry to equal the row totals.
Once the row and column totals are equal—that is, output used for intermediate uses + final demands = inputs purchased + value added for all 133 industries—we’re done with our I-O table.
Bottom Line: 2002 Input-Output Table for the U.S.
Following the process above, you should end up with an I-O table that looks something like this. Here’s my own table built from the 2002 BEA Benchmark tables, which you’re free to use:
Once you’ve got an I-O table like this, it’s pretty easy to turn it into a Leontief model. This will let you model the distributional impact of carbon taxes, measure tax pyramiding of gross receipts taxes, and more.
In a future post, I’ll share a couple of these models I’ve put together over the last year or so.
Posted by Andrew on Saturday August 9, 2008 | Feedback?
Estimating Price Elasticity of Gasoline: A How-To Guide
Like most concepts in economics, price elasticity is easy to talk about but hard to measure. Most people understand the basic idea—some things have lots of substitutes while others we can’t do without. But few learn how to actually measure elasticity in the real world.
In this post, I’ll show you a simple way economists actually measure elasticities. We’ll develop a simple theory, write it down mathematically, find some real-world data, and crunch the numbers in Excel. At the end, I’ll hand over a spreadsheet with my own elasticity estimates for retail gasoline that replicate the numbers from a well-known recent econometric study.
Start with the Theory
Our goal here is to estimate the price elasticity of demand for retail gasoline. The first step is to start with a theory about the demand for gas.
The simplest theory is that we know gasoline—like everything else—should have a demand curve. What should it look like? In the simplest case, it should be driven by two things: the price of gas, and how much income people have. If gas prices rise consumption should fall; conversely, if income goes up gas consumption should rise also.
So there’s our theory. Now, let’s write it down mathematically. If gas demand is a function of prices and income, one way we can write it is like this:
G = a*P + b*Y
Where:
G = Gallons of gas demanded per year
P = The price of gas
Y = Average income in the economy
a, b = Coefficients for the magnitude of the impact of prices and income on gas demand. (Note: According ot our theory, the “a” coefficient on prices should be a negative number and the “b” coefficient on income should be positive.)
Now that we’ve got a theory written down, the next step is to translate it into a form that we can estimate in the real world. Think of the theory as an architect’s drawing—it’s a guide, but our goal is to actually to build the darn thing with hammer and nails.
To do this, think about real-world factors that might complicate our simple theory. For one, we should probably control for population by using per capita figures. Next, we should control for inflation by inflation-adjusting everything. Finally, we should control for seasonal variation somehow, since gas demand always peaks in summer and slows in winter.
Taking these messy details into account, here’s how we translate our theory into a relationship we can actually estimate. Economists call this “specifying the model”:
Gij = A + a*Pij + b*Yij + ei + eij
Where:
G = Per capita gas demand in month i and year j
A = The y-intercept term in our linear demand curve
Pij = The inflation-adjusted gas price in month i and year j
Yij = Real per capita disposable personal income in month i and year j
a, b = Coefficients on price and income
ei = A dummy variable for the month of the year to control for seasonal variation (there are actually eleven of these, one for each month January through November; they’re one if it’s the month in question and zero otherwise); this is called “seasonal fixed effects”
eij = A mean-zero random error term for month i and year j.
This way of specifying our model is called “linear”. This isn’t the only way to do it—at the end I’ll mention another way called “double log” that has some advantages. But for now, we’re ready to collect some data and run a regression.
Go Find the Data
The best source for data is always official government sources. Here’s the data we’ll use for this:
1. Gallons of gas demanded: We’ll use data from the U.S. Energy Information Administration. It’s called “product supplied”. The numbers are in barrels, so you’ll have to multiply them by 42 to convert them to gallons:
http://tonto.eia.doe.gov/dnav/pet/hist/mgfupus1m.htm
2. Gas prices: We’ll use numbers from the U.S. Bureau of Labor Statistics here. It’s from their “average price data” series, and it’s the monthly retail price of gas:
http://www.bls.gov/cpi/home.htm
3. Income: We’ll use data from the U.S. Bureau of Economic Analysis for this one. It’s called “disposable personal income”, and it comes from line 26 on Table 2.1 from the National Income and Product Account (NIPA) tables:
http://www.bea.gov/national/nipaweb/SelectTable.asp?Selected=N#S2
4. Something to inflation-adjust prices and income: For this one, we’ll use the implicit price deflator for GDP from the Bureau of Economic Analysis. It’s on line 1 of NIPA Table 1.1.9:
http://www.bea.gov/national/nipaweb/SelectTable.asp?Selected=N#S1
5. Population figures to turn gallons and income into per capita figures: This is a hard one, because we need monthly figures and the U.S. Census Bureau only produces annual figures. Also, it’s hard to piece together a consistent series from before and after each decennial census. Thankfully I’ve done the hard work for you—the Excel files below include a monthly population series I put together myself.
Once you’ve compiled these data in columns in an Excel sheet, you’re ready to run your regression. When you do, you should find something like this:
For 2000-2007, the coefficient on gas prices should be about -1.04, and the coefficient on income should be about 0.001. Using the formula for price elasticity of E = (Average price over the period/Average quantity over the period)*(price coefficient), that implies a price elasticity of demand of about -0.048 and an income elasticity of about 0.49.
And that’s about what we’d expect. We know short-run demand for gas is inelastic, and has a negative relationship with prices. And we know that income should be positively related to gas demand, which it is.
By the way, the above method is based on a well-known 2006 study from Hughes, Knittel and Sperling which you can read at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=930730. If you’d like to try the regression yourself, here are the files you’ll need:
Average U.S. Gas Prices 1978-2007
Gasoline Demand 1945-2007
GDP Price Deflators 1947-2007
Personal Income Data 1947-2007
US Population 1900-2007
For those who want to see how everything should look in a final Excel file, here are my own estimates. They’re basically similar to what Hughes, Knittel and Sperling found. The first file uses the linear specification above. The second one uses a “double log” specification, which basically takes the log of the data. The big advantage of the latter is that the regression coefficients are also the price and income elasticities, which is handy:
Price elasticity of demand for gasoline: Linear model.
Price elasticity of demand for gasoline: Double log model.
Posted by Andrew on Friday August 1, 2008 | Feedback?
New report on tax burdens and government spending by age groups
Released a new report today exploring the distribution of tax burdens and government expenditures by age group in 2004. It’s a spin-off project from the fiscal incidence model we built back in March. Here are some key findings:
• As the Baby Boom generation prepares to retire, lawmakers should be aware of the distribution of taxes and government spending across age groups.
• America’s youngest households aged 25 and under received $2.32 in government spending for each dollar of taxes paid in 2004. Middle-aged households aged 45 to 54 received $0.73 per tax dollar, and America’s oldest households aged 75 and over received $4.93 per dollar of taxes paid;
• As a group, households aged 35 to 64 pay more in taxes than they receive in government spending, while households under age 35 and over age 64 receive more government spending than they pay in taxes. Overall between $376 billion and $872 billion per year is fiscally transferred from middle-aged groups to the youngest and oldest Americans each year through government taxes and spending;
• Over a lifetime, government spending follows a U-shaped pattern, with large education and welfare spending in youth and large Social Security and Medicare payments in old age. But even within each age group, there are large differences in taxes and government spending across households at different income levels.
Posted by Andrew on Monday June 4, 2007 | Feedback?
New York Sun on fiscal incidence study
Our fiscal incidence study got a nice write up in this morning’s New York Sun. The author compares our findings to an influential new study of tax progressivity from Profs. Thomas Piketty and Emmanuel Saez:
Messrs. Chamberlain and Prante took an entirely different approach [from Piketty and Saez]. Rather than focusing solely on federal individual taxes, they analyzed the distribution of all taxes paid — federal, state, and local. They looked at fifths of the population and didn’t produce data for fractions of the top percent. They analyzed the period between 1991 and 2004, thereby avoiding the pitfalls of comparing incomes before and after 1988.
And they added another dimension to the analysis. They analyzed government spending and which income groups it benefits — all government spending for goods and services, and also transfer payments to individuals, such as unemployment benefits and Social Security.
Households in the two lowest quintiles — a quintile is one-fifth of the whole — received 51% of all government spending because they received more transfer payments. Messrs. Chamberlain and Prante concluded that “both taxes and spending appear to have large distributional effects on households,” and that our tax system is very progressive. The share of total taxes paid by the top quintile rose to 49% in 2004 from 46% in 1991, after peaking at 51% in 2000.
Households in the lowest fifth of incomes received about $8 in federal, state, and local spending for every tax dollar they paid, whereas households in the top fifth of earners received only 41 cents. This shows a tax system with substantial progressiveness.
Looking at the burden of taxes paid in light of benefits received makes far more sense than looking at these taxes in isolation, so Messrs. Chamberlain and Prante are more persuasive.
Posted by Andrew on Friday April 20, 2007 | Feedback?
Director's law in tennessee
I’ve got a new analysis of a proposal in Tennessee to fund a package of education, smoking cessasion and farm program spending with a 40-cent increase in the state’s cigarette tax. Unfortunately there’s an unanticipated side effect:
While the Governor’s plan to boost education spending may be well intended, funding the Schools First initiative through tobacco taxes rather than general sales taxes will make low-income households in Tennessee much worse off that they could otherwise be. Because the plan relies almost entirely on cigarette tax revenue, at least three components of the plan have the perverse effect of redistributing millions of dollars from low-income Tennesseans to the highest-income households in the state.
Sounds like a case of Director’s Law if you ask me. Read the full piece here.
P.S.—Bonus points for locating the Tax Foundation reference in the famous Stigler piece above.
Posted by Andrew on Wednesday April 18, 2007 | Feedback?
Podcast interview on fiscal incidence study
I’ve got a new podcast interview out today. The subject: our recent study of combined U.S. tax and spending distributions. As always, there’s a transcript.
Posted by Andrew on Tuesday April 17, 2007 | Feedback?
New survey on U.S. tax opinions
Released a new report this week on our 2007 Annual Survey of U.S. Attitudes on Taxes and Wealth. This is the third year we’ve done the survey, so we’ve got a decent time series going on some of the questions. Here’s the executive summary:
Executive Summary
While foreign policy continues to dominate politics in Washington, the 2007 Annual Survey of U.S. Attitudes on Taxes and Wealth shows that the domestic issues of tax complexity, fairness and burdens continue to weigh heavily on the minds of the American people. For the third consecutive year, we find that a majority of U.S. adults say the federal income taxes they pay are “too high,” that the federal tax code is complex, and that the U.S. tax system is in need of major changes or a complete overhaul.
This report summarizes the findings of our third annual survey of U.S. opinions on taxes. All results are based on a Harris Interactive® survey conducted on behalf of the Tax Foundation between March 5 and 12, 2007. The survey covers a nationwide cross section of 2,012 U.S. adults aged 18 or older. All data from this and previous years’ surveys are available for download free of charge at www.taxfoundation.org under “Public Opinion Surveys on Taxes.”
Posted by Andrew on Thursday April 12, 2007 | Feedback?
New fiscal incidence working paper
Finally released a new working paper today on something economists call “fiscal incidence”—the combined distribution of tax burdens and government spending across income groups in the U.S. Here’s the abstract:
Abstract
While the U.S. tax system is progressive, the distribution of government spending makes the overall fiscal system more progressive than is apparent from tax distributions alone. Using a microdata model we estimate the distribution of federal, state and local taxes and spending between 1991 and 2004. We find households in the lowest quintile of income received roughly $8.21 in federal, state and local government spending for every dollar of taxes paid in 2004, while households in the middle quintile received $1.30, and households in the top quintile received $0.41. Overall, tax payments exceeded government spending received for the top two quintiles of income, resulting in a net fiscal transfer of between $1.031 trillion and $1.527 trillion between quintiles. Both taxes and spending appear to have large distributional effects on households, and these effects have grown since 1991. The results suggest tax distributions alone are an inadequate measure of progressivity, and policymakers should examine both tax and spending distributions when judging the overall fairness of policy toward income groups.
Full study is here. Here’s the original Tax Foundation study on the subject from 1967.
Posted by Andrew on Thursday March 22, 2007 | Feedback?
New paper on federal taxes by city, county and U.S. congressional district
I’ve got a new paper out today that’s a fun stroll through the geographic landscape of federal tax burdens throughout the nation. Here’s the summary:
Executive Summary
In 2004 the federal government in Washington spent $2.18 trillion, roughly one-fifth of the U.S. economy. To finance that spending, it collected $1.91 trillion from taxpayers across the United States. However, the burden of those federal taxes did not fall equally on the cities, counties and states that comprise the diverse geographic landscape of the United States.
Some areas of the nation bear a heavy tax burden, while others pay comparatively little. Many previous Tax Foundation studies have estimated federal tax burdens at the state level, but none has provided detailed estimates down to the narrow geographic areas that taxpayers most closely identify themselves with, such as counties, cities and congressional voting districts.
This report presents the Tax Foundation’s most detailed portrait of the geographic spread of the federal tax burden to date. It provides estimates of all federal taxes—individual income taxes, corporate income taxes, payroll taxes, estate taxes and all federal excise taxes—by major city area, county, congressional district and state, illustrating the striking diversity of impact that federal tax policies established by Congress have on communities across the United States.
Full paper is here. For those who want the technical methodology behind the numbers, the working paper is here.
Posted by Andrew on Thursday March 22, 2007 | Feedback?
Working paper on modeling federal tax burdens by narrow geographic areas
Got a new technical working paper out today that grew out of this post. Here’s the abstract:
Abstract
The burden of federal taxes does not fall equally on the cities, counties and congressional districts that comprise the geographic landscape of the United States. Because tax collections figures provide little information about the true economic burden of taxes, researchers must employ various statistical methods to estimate the economic incidence of federal taxes across geographic areas. We outline a detailed methodology for modeling the burden of each federal tax—individual income, corporate income, payroll, estate and gift, and all excises—by narrow geographic areas. Using this model, we provide estimates of federal tax burdens by three geographic areas for Calendar Year 2004: major city area, county and U.S. congressional district.
Posted by Andrew on Thursday March 22, 2007 | Feedback?
problems with illinois' gross receipts tax plan
Got a few mentions today in a story exploring the questionable economics behind Illinois Gov. Rod Blagojevich’s plan to enact a Depression-era gross receipts tax in Illinois:
The gross receipts tax targets every transaction or sale that comes in the door and would replace the current corporate income tax that targets profits and allows for deductions.
The states that resurrected the tax in recent years had one major problem — pyramiding. Every transaction that takes place to transform raw material into a final product had been taxed at different amounts as different industries became involved in the process, said Andrew Chamberlain, an economist with the Tax Foundation in Washington, D.C.
“Even something as simple as a loaf of bread shows pyramiding,” Chamberlain said.
The mill has to buy the grains to make flour, so that transaction is taxed. Then that flour is sold to a bakery, and that is taxed. The bakery makes the flour into bread and sells it to a distributor, and that’s taxed. Finally, that bread is sold to the consumer and it’s taxed again, he said.
“With the auto industry, it’s even more complex and has more layers of pyramiding,” Chamberlain said…
Because different tax programs will hurt some businesses and help others, it’s difficult to say what impact a gross receipts tax would have on Illinois businesses and the economy, said Chamberlain. He was one of the sources of research as Illinois leaders were shaping their tax plan.
“I talked with them (Illinois officials) extensively and told them the problems with a gross receipts tax, and to be fair told them some of the advantages,” he said. “The next thing I heard was Illinois was considering the tax. It’s such a mistake.”
Posted by Andrew on Wednesday March 7, 2007 | Feedback?
An idea for estimating payroll taxes by narrow geographic areas
(Note: The post below later became this working paper on estimating federal taxes by geography, and this report summarizing the results.)
Let’s say we want to know how much payroll taxes are paid by residents of some U.S. county. Seems easy. Just go to the Bureau of Economic Analysis (BEA) website, and look it up the Regional Economic Accounts, right?
Unfortunately, that won’t do. BEA accounts give total payroll taxes remitted by employers in each county, and that counts both residents and nonresidents who commute in from other areas. Turns out official figures for counties are hard to come by. Same for cities, congressional districts or ZIP codes.
So that’s our problem. Here’s a rough-and-ready solution another economist and I worked out recently. It uses a simple fact about the U.S. income distribution—that it approximately follows what statisticians call an exponential distribution—to generate decent estimates of payroll taxes for counties, cities and other narrow geographic areas.
Some Basic Facts
Here’s how payroll taxes work. Earnings are subject to 15.3 percent payroll taxes. Of that, 2.9 percent is for Medicare, and 12.4 percent is for Social Security. But the Social Security portion applies only to about the first $90,000 of income—$87,900 in 2004.
This "capped" structure becomes a huge pain in the neck when estimating payroll taxes by geography. Some areas are richer than others, which means they have more earners above the cap. So to estimate payroll taxes by area, you’ve got to have knowledge of the full income distribution for each area. And that’s not easy to find.
So here’s what we do. First, go to the BEA’s website and gather two pieces of data for each county you want to examine: county population, and the earnings of county residents. Note that to find resident earnings, you’ll have to manually sum "earnings by place of work" and "adjustment for residence" from published BEA tables.
Take those earnings and divide by population. That’s the average earnings for the county. Let’s call this 
.
Some Math You’ll Need
Most U.S. incomes can be approximately described by what’s called an exponential probability density function. Here’s what it looks like:
(1) 
.
In equation (1), is the number for mean county earnings we calculated above. The next step is to turn equation (1) into a cumulative probability distribution that lets us plug in values for some income level, and find the percentage of people earning less than that amount.
Let’s call this new function F(x). Here’s what it looks like:
(2) 
.
Equations (1) and (2) look complex, but there’s an elegant feature of them. They’ve got just one parameter: . And we already know that from above. That means we can use them to estimate the income distributions of counties by knowing only one fact about each one—mean county income.
Here’s how it works.
Step One: How Many People Earn Above and Below the Payroll Cap?
Using equation (2), we calculate the percentage of residents earning above and below the payroll tax cap. The 2004 cap was $87,900. Plug this in for x in equation (2), with 
equal to average county income. That gives us the percentage of people earning below the cap. Then subtract that figure from one. That’s the percentage of people who earn above the cap. Multiplying each of these by county population, and set the results aside. We’ll need those in a minute.
Step Two: How Much Was Earned Above the Cap?
Taxable earnings for those above the cap are simple to calculate. Take the number of people earning above the cap from step one, and multiply by the 2004 cap of $87,900. That’s the total taxable income of folks earning above the cap. Sit that number aside until the end.
Step Three: How Much Was Earned Below the Cap?
Finding taxable earnings for people below the cap is harder. Every penny of earnings of that group are subject to payroll taxes. That means we have to derive the full earnings of folks below the cap analytically from the exponential distribution above.
The simplest way to do that is to calculate the probability-weighted average earnings of everybody below the cap. Then we’ll just multiply that by the number of people below the cap.
To do this, we put on our math hat and write the following integral for the probability-weighted average earnings of people below the cap,
(3) 
,
where k is the 2004 payroll cap of $87,900. Think of equation (3) like this. If we take every possible earnings level from zero to k, and multiply each by the probability that somebody will earn that amount, we’ll end up with a probability-weighted average wage for everybody together. That’s what equation (3) gives you.
So let’s solve it. Thinking hard back to high-school calculus, we integrate this by parts. Here’s what we get:
(4) 
.
With a little more algebra, this monster simplifies to,
(5) 
,
where W is the probability-weighted average earnings of folks below the cap. That’s the number we need.
Step Four: Put It All Together
Now we’re home free. All that’s left is to take the W we calculated in equation (5), and multiply it by the population earning below the payroll tax cap, which we calculated back in step one. That gives us the total income earned by everybody below the cap. Then we add that to the taxable income of everybody above the payroll cap, which we found in step two.
Putting those two together and multiplying by the 12.4 percent payroll tax rate, we’ve got total Social Security payroll taxes for the area. And we’re done.
Table 2 shows some estimates using this method for the five sample counties from earlier. These estimates can then be used to allocate nationwide payroll tax aggregates to those counties.
Table 2. Here's what some results of this method look like for a few sample counties in 2004.
County | State | Population | Mean Resident Earnings | Percentage Residents Below Cap | Probability-Weighted Mean Earnings Below Cap | Social Security Payroll Tax Per Capita | Medicare Payroll Tax Per Capita | Effective Payroll Tax Rate |
Los Angeles | California | 9,917,331 | $26,224 | 96.5% | $22,228 | $3,041 | $761 | 14.5% |
King (Seattle) | Washington | 1,777,746 | $39,818 | 89.0% | $25,773 | $4,043 | $1,155 | 13.1% |
St. Louis | Missouri | 1,007,723 | $33,334 | 92.8% | $24,656 | $3,619 | $967 | 13.8% |
Denver | Colorado | 555,991 | $38,571 | 89.8% | $25,621 | $3,968 | $1,119 | 13.2% |
Washington | D.C. | 554,239 | $48,065 | 83.9% | $26,228 | $4,480 | $1,394 | 12.2% |
As far as I can tell, the above works as a rough estimate for cities and counties. But be careful drilling down to really small areas. The math probably falls apart if you plug in Hyannis, Nebraska, population 287. Otherwise, enjoy.
Further Reading
Banerjeea, Anand et al. 2006. "A Study of the Personal Income Distribution in Australia." Physica A 370: 54–9.
Borges, Ernesto P. 2003. "Empirical Nonextensive Laws for the County Distribution of Total Personal Income and Gross Domestic Product." Physica A 334: 255-66.
Bureau of Economic Analysis. 2005. "Local Area Personal Income." (Available at http://bea.gov/regional/pdf/overview/Regional_LAPI.pdf.) Washington, D.C.: U.S. Commerce Department.
Dragulescu, A. and V.M. Yakovenko. 2000. "Evidence for the Exponential Distribution of Income in the U.S.A." The European Physical Journal B 20: 585-9.
National Institute of Standards and Technology. 2007. Engineering Statistics Handbook. (Available at http://www.itl.nist.gov/div898/handbook/). Washington, D.C.: U.S. Commerce Department, Chapter 1.3.6.6.7.
Silva, A. C., and V. M. Yakovenko. 2005. "Temporal Evolution of the ‘Thermal’ and ‘Superthermal’ Income Classes in the U.S.A. During 1983-2001." Europhysics Letters 69: 304-10.
Posted by Andrew on Monday February 12, 2007 | Feedback?
Michigan Public Radio on tax credits for film production crews
Landed a quote in a story by Michigan’s NPR affiliate on the use of tax credits to lure Hollywood production crews to local areas:
But not everyone agrees film incentives are healthy in the long term. Andrew Chamberlain is an economist with the Tax Foundation in Washington D.C. He’s researched the impact of state and local film incentives.
“There’s surprisingly little evidence that film tax incentives create the kind of long term stable family types of jobs that most people have in mind,” said Chamberlain.
Chambelain says on top of that, there’s been a rapid increase in competition between states to lure the film industry.
“And so when everyone’s doing it, everyone can’t win, because film credits are really a beggar-thy-neighbor tax policy. Not everyone can benefit at the same time,” said Chamberlain. (Full piece here.)
There’s some audio here also.
Posted by Andrew on Thursday January 11, 2007 | Feedback?
Piece on the alternative minimum tax by congressional district
I’ve got a new short piece estimating the impact of the alternative minimum tax (AMT) by all 435 congressional districts and the District of Columbia for 2004. Here’s the intro:
As the 110th Congress convenes this month a key issue facing lawmakers is whether to reform—or possibly repeal—the Alternative Minimum Tax (AMT). Although the growing AMT has caused anxiety throughout Congress, not all lawmakers’ congressional districts are equally affected. An analysis of recently released IRS data reveals that some congressional districts are much more heavily affected by AMT expansion than others—suggesting some federal lawmakers have a much stronger incentive to reform the AMT than others. (Full piece here.)
FYI, we also ran the numbers by state, by county, and by MSA. And we’ve also got it by ZIP code if anybody wants to email me for it.
Posted by Andrew on Tuesday January 9, 2007 | Feedback?
Paper on the economics of gross receipts taxes
I’ve got a new paper out today on a growing trend in state and local taxation: the return of Depression-era gross receipts taxes. Here’s the executive summary:
Executive Summary
State governments have traditionally raised revenue from business by taxing corporate income. But in recent years the growing difficulty of administering state corporate income taxes has prompted a search for alternative ways of taxing companies. This search for new business taxes has ironically sparked a resurgence in one of the world’s oldest broad-based tax structures: the gross receipts tax, also known as the “turnover tax.”
Gross receipts taxes have a simple structure, taxing all business sales with few or no deductions. Because they tax transactions, they are often compared to retail sales taxes. However, they differ in a critical way. While well designed sales taxes apply only to final sales to consumers, gross receipts taxes tax all transactions, including intermediate business-to-business purchases of supplies, raw materials and equipment. As a result, gross receipts taxes create an extra layer of taxation at each stage of production that sales and other taxes do not—something economists call “tax pyramiding.”
Advocates of gross receipts taxes generally defend them on two grounds. First, it is argued that their simple structure makes them easy for states to administer and for companies to comply with, in contrast to notoriously complex state corporate income taxes. Second, because they tax an expansive base of all transactions in the economy, they are able to raise a given amount of revenue at lower rates than any other tax, making them politically attractive to lawmakers.
But while gross receipts taxes appear to be a simple alternative to complex corporate income taxes, this simplicity comes at a great cost. Gross receipts taxes suffer from severe flaws that are well documented in the economic literature, and rank among the most economically harmful tax structures available to lawmakers. The economic problems with gross receipts taxes are not the result of poor implementation by lawmakers. The flaws are inherent in their design. State lawmakers searching for alternatives to complex state corporate income taxes should be wary of gross receipts taxes, and should instead seek more economically neutral ways of taxing business.
Posted by Andrew on Monday December 4, 2006 | Feedback?
Op-ed on cigarette tax regressivity and social costs of smoking
I’ve got an op-ed in this morning’s Los Angeles Times on California’s Prop 86, which may lead to unintended consequences worse than the public health problem of smoking itself:
The Proposition 86 Poor Tax
In California, a state famous for its progressive politics, a proposition on the Nov. 7 ballot includes a shockingly regressive tax on the state’s poorest residents. Unfortunately, that’s the reality behind Proposition 86—an initiative that aims to cut smoking through a dramatic hike in the state’s cigarette tax, from 87 cents to $3.47 a pack, the nation’s highest rate.
It’s nice to pretend that cigarette taxes come out of the hides of Big Tobacco. But it’s mostly low-income groups who take it on the chin when cigarette taxes rise.
Low-income Californians are much more likely to be smokers, and as a group they spend a lot more on cigarettes than the wealthy as a percentage of their income. One recent analysis of U.S. Census data found that tobacco taxes take a 50-times-larger share of income from those earning less than $20,000 than those earning more than $200,000. That makes cigarette taxes the most regressive way of funding state government programs.
The general sales tax is routinely derided as unfairly regressive, but it’s like a millionaire’s tax compared with the tax on cigarettes.
Most people who support progressive taxes—that is, taxes that fall most heavily on the wealthy—would consider such a regressive tax outrageously unfair. So if Californians plan to raise taxes on smokers, who are disproportionately also among the state’s poorest, they’d better have a good reason.
What might be a good reason? One would be if smokers imposed costly damages on nonsmokers in society. Do smokers impose “spillover” burdens on society, justifying a special tax on them?
It turns out they don’t. Over the last 15 years, evidence has accumulated showing smokers hardly cost society more than anyone else. Dozens of peer-reviewed studies throughout the 1990s from economists such as Harvard’s Kip Viscusi and Willard Manning Jr. from the University of Chicago demonstrate conclusively that nearly all the costs of smoking — healthcare, higher insurance premiums, lower productivity at work—are borne by smokers themselves.
Over their lifetimes, smokers cost taxpayers only trivially more than nonsmokers—about 32 cents a pack, according to most studies. That’s far below the current tax of 87 cents a pack and a fraction of the $3.47-a-pack tax supplied by Proposition 86.
Once we realize smokers are mostly hurting themselves and not others in society, we’re left with an ugly reality: The only justification for a $3.47 cigarette tax is straightforward paternalism. The rich have always turned up their noses at uncouth behaviors of the poor. Proposition 86 just burnishes that condescension into state law, raising some revenue in the process.
But the worst aspect of such condescension? It doesn’t work very well. Punitive approaches such as higher cigarette taxes don’t make smokers quit. They cause smokers to buy tax-free cigarettes on military bases, Indian reservations and over the Internet. Even with today’s 87-cent tax, the California Board of Equalization says about 300 million untaxed packs of cigarettes are sold in the state each year—a figure that will boom if Proposition 86 passes.
The programs funded by Proposition 86 have broad public appeal. Health insurance for low-income children, funding for emergency hospital services and public education about the health risks of tobacco are supported by most Californians. So why not fund them with broad-based taxes on everyone?
Raising cigarette taxes isn’t just bad for the poor. It’s bad for lawmakers’ credibility. Who really believes California politicians are “anti-smoking” when they’re hooked on tobacco-tax cash themselves?
Andrew Chamberlain and Patrick Fleenor are economists at the Tax Foundation in Washington.
Posted by Andrew on Saturday October 28, 2006 | Feedback?
Tax exporting and rental car taxes
Another few quotes on problems with funding local government with rental car taxes on outsiders, from Business Travel Executive magazine:
“Funding programs by taxing outsiders from other states or cities is what economists call ‘tax exporting.’ This type of taxation makes lawmakers less accountable to their constituencies and encourages overspending on projects that may or may not make economic sense,” states Andrew Chamberlain, staff economist for the Tax Foundation, a non-partisan educational organization in Washington, DC. “If a specific project is really needed, economists believe it should be funded through broad-based taxes spread fairly and evenly among those who will benefit from the project.” He says that economists believe that overall, car rental excise taxes are unreliable, regressive and retaliatory. (Full piece.)
Also, there’s another article in Auto Rental News with a bunch of quotes from me here.
Posted by Andrew on Wednesday September 27, 2006 | Feedback?
Minneapolis Fed on tax incentives for film companies
I landed a few quotes in a story on the practice of state and local lawmakers handing out tax incentives to lure filmmakers in the September issue of fedgazette, from the Federal Reserve Bank of Minneapolis. Some clips from the piece:
...[J]ust how much film incentives cost and how much is gained remain a mystery in most programs. The Tax Foundation’s Chamberlain noted that there are no studies by economists that determine the financial benefits or costs of film incentives because very few governments track their costs and tax benefits.
It may be a compelling argument from state and local officials about getting more than you had before, Chamberlain said. “But it’s really a wash or loss.” He added, however, that “you’ll never convince [politicians] that it’s not a good idea—the payoff is too big.” Chamberlain said he once heard another economist describe the incentive issue this way: Incentives were not designed to create jobs but to create job announcements. This may be a little harsh, but “the literature speaks with one voice,” Chamberlain said. “At the national level, this does nothing to spur more activity.”
From an economist’s viewpoint, these are terrible policies, Chamberlain said. In the long run, incentives will erode the tax base because they favor certain (often new) businesses over others, and the tax burden falls disproportionately on existing businesses. The notion of incentives as an investment leaves something to be desired as well. To be considered an investment, incentives should return the original capital plus some profit—in other words, after all the adding and subtracting, incentives should lead to higher total tax revenue…
Chamberlain, from the Tax Foundation, acknowledged that states face a classic prisoner’s dilemma. Here, two crime suspects interviewed separately are offered reduced sentences if each rats the other out; if both stay mum, they’ll go free. But because they are separated, neither trusts the other to keep quiet—so each rats on the other in order to secure a lesser sentence, and ultimately both are worse off.
Incentives work the same way, Chamberlain said. If all states eliminated incentives, they would all be better off; films would still get made, and they would go to the most optimal locations, while states could focus scarce tax dollars on traditional public goods rather than on film incentives. But they’re unable to do so because they can’t trust other states to do the same, and doing nothing is even worse, because states lose economic activity to others offering incentives.
Michigan’s Lockwood believes the incentive game has gotten out of hand, with each state upping the ante a little more. Chamberlain and others suggest that federal legislation may offer the only resolution, essentially legislating a cease-fire to the incentive arms race.
Posted by Andrew on Sunday September 10, 2006 | Feedback?
