The MacIver Institute has released new projections of the impact of the Tax Cuts and Jobs Act on Wisconsin:
…65,318 jobs would be created in 2018 if tax reform is enacted. The same study shows that Wisconsin workers could see their wages increased by nearly $2.5 billion.
This prediction from a commissioned study from John Dunham and Associates is documented here.
One choice passage from the documentation:
As with all models developed by JDA, the basic structure is a micro-economic model that takes into account the interactions between different types of taxpayers, and different industries. This differs from many of the other models being used by different organizations which are developed around macroeconomic forecasting platforms known as Computerized General Equilibrium (or CGE) models. We believe that this provides both a more flexible structure, particularly for forecasting state and industry level effects of a tax proposal. It also allows us to dispense with most of the ore [sic] controversial and complicated assumptions needed to run a CGE model, relying instead on the time tested national input-output model structure, which has been used since it was first developed to assist in industrial production planning during the Second World War.
The input-output model (associated with Wassily Leontiev) implies perfect complementarity of inputs, or alternatively, no substitutability. Hence, relative input prices have no effect on relative quantities of inputs used. This is a pretty odd assumption to incorporate into a model of taxation, where changes in relative prices would seem to be key to changing behavior.
With this structure, it might seem odd that there is an increase in GDP and employment relative to baseline implied by the model. The key assumption is apparently here:
… since all taxes are eventually passed through to people, the assumption that income would either rise or fall with changes in revenues across each tax incidence category is likely sound.
Well, sure, if Ricardian equivalence holds…
By the way, interesting predictions: 8.7 billion increase in state GDP in 2018 is about 3% of 2017Q2 GDP — i.e., state GDP will be 3% higher than baseline(!!!) in the year the tax cuts take effect; employment is about 2.2% higher than mid-2017 levels.
For more gems from MacIver, see here on minimum wage, on budgeting, tabulating employment (they spliced together seasonally adjusted and not-seasonally adjusted numbers!!!!).
no doubt these reports are from the top students of prof stryker at wossamotta university!
This is hilarious! Rectangular isoquants. Ugh. Leontiev production functions are useful if you’re looking at manufacturing cars over a very short run, but it makes no sense in a macro economy. It really doesn’t even make any sense in manufacturing when you look out over time.
A very long time ago I took a course on Soviet economics. As I recall the Soviets based their Five Year Plans on a Leontiev input-ouput model using something like sixty inputs with fixed prices. How’d that work out?
2slugbaits, you may want to look at the underproduction and inefficiency in the production possibilities frontier instead. Less regulation and optimism for businesses already boosted growth. Tax cuts to spur businesses starts next year.
http://money.cnn.com/2017/12/14/news/economy/economy-retail-sales-jobless-claims-tax-reform/index.html
PeakTrader There are two ways to measure inefficiency. The first (non-parametric) way is using something known as Data Envelopment Analysis (DEA). The second (parametric) way is a stochastic frontier analysis (SFA) approach. An SFA approach is much richer, but also more data intensive. You have to impose a production function. In SFA modeling it’s common to use a “translog” production function, which can be thought of as a Cobb-Douglas with interaction terms. Mathematically it’s a Taylor polynomial series. There are two types of inefficiencies. The first is “technical” inefficiency, which is the normal meaning of inefficiency. The second is “stochastic” inefficiency, which means observed inefficiencies that are not due to things that management controls, but are due to random shocks or innovations. An SFA approach decomposes the two kinds of inefficiencies. In other words, we observe a single distance from the production frontier, but we need to decompose that observation into two different kinds of inefficiencies. There are a lot of software packages for both DEA and SFA models. In my professional work I prefer to use William Greene’s LIMDEP package. In either the DEA or SFA approaches inefficiency is always scored against a “peer” firm, not some abstract notion of an efficient firm. If you’ve ever done any serious work along those lines, then you would know that it’s quite difficult to measure the extent to which tax cuts affect business efficiency scores. In other words, I’m suggesting that you might want to focus more on some serious econometrics and less on Club For Growth boosterism.
If you want to know more about how to calculate efficiency metrics, the Centre for Efficiency and Productivity Analysis at the University of Queensland is a good place to start.
http://www.uq.edu.au/economics/cepa/index.php
2slugbaits, there are many approaches. For example, we want to optimize energy efficiency in society with regulations and subsidies, rather than imposing trade-offs or opportunity costs that are too high or too low. Here’s one study:
https://e2e.haas.berkeley.edu/pdf/workingpapers/WP018.pdf
PeakTrader I think you’re conflating efficiency, optimization and pushing out the production frontier. Those are three very different concepts, but you seem to use them interchangeably. For example, a 3.6L engine that develops 305 horsepower will have a frontier curve that is further out than a 1.5L engine that develops 205 horsepower. But the 1.5L engine is more efficient because it generates more horsepower per cubic liter. But the 3.6L engine might be the optimal choice if you have to carry five passengers on the Autobahn. When you talk about optimization you always have to identify the constraints. When you solve a simple optimization problem you’re in the world of lagrangians and Hessian matrices. Those are not the tools you use when asking about efficiency
I Googled to see the credentials of JDA and here is what came up first:
http://wineamerica.org/john-dunham-associates
Wine America?
“John Dunham & Associates (JDA) is an economic research firm based in Brooklyn, New York. The firm excels in the area of policy economics, having conducted hundreds of studies on the impact of taxes and regulations for a wide range of industries and companies. In addition to New York, JDA has offices in Chicago and Washington, D.C.”
I live in Brooklyn and have never heard of them. More:
“Prior to starting his own firm, John was the senior U.S. economist with Philip Morris, producing research and information on key issues facing all of the company’s divisions. Before this, John was a senior economist for the New York City Mayor’s Office, the New York City Comptroller’s Office, and the Port Authority of New York and New Jersey where he conducted the economic impact analysis of the World Trade Center. John received his M.A. in economics from the New School for Social Research and his MBA from Columbia University.”
Philip Morris? Check. Nothing against Columbia University but an MBA?
Is this worse than the CEA and Treasury estimates for the tax reform? CEA uses discredited studies to estimate the effect of corporate tax cuts on wages (see the study by K. Clausing in the March 2013 issue of the NTJ). And Treasury’s one page “analysis” seems to conflate the revenue effects of the legislation with the effects of expected (or perhaps more accurately, hoped for) future growth. (“Treasury expects approximately half of this 0.7% increase in growth to come from changes to corporate taxation. We expect the other half to come from changes to pass-through taxation and individual tax reform, as well as from a combination of regulatory reform, infrastructure development, and welfare reform as proposed in the Administration’s Fiscal Year 2018 budget.”
I know some people in the Office of Tax Analysis and they are much better than this.
Leontiev? In the early 1990s, I had a conversation with Wassily Leontief and exchanged an email or two if I recall and I don’t remember spelling his family name that way. Just Leontief.
There is a Leontiev that wrote a book on centralized planning.
As for the rest, I-O models and the generated multipliers are the favourite rhetorical tools of special interest groups trying to get ahead.
Ironically enough, higher net subsidies (direct and upstream) lead to higher GDP multipliers. So if policy choices were actually based on multiplier magnitudes (they are not), it would favour providing more subsidies to activities that were already highly subsidized.
Erik Poole: My apologies for studying international economics in early 1980’s; I suspect it was a spelling more common in days earlier (so I’ll blame either Kindleberger’s textbook I used or Prof. Richard Cooper for my use of an alternative spelling; pretty sure with a “v” in Samuelson’s textbook). “Leontiev” is used for instance here.
By the way, sometimes people spell my name “Chen”. That is a perfectly appropriate transliteration of my family name in Pinyin. Of course, Wade Giles would do result in something different than “Chinn” and “Chen”. While I prefer “Chinn”, I don’t think I’d expect anybody to go into a long exegesis about what should be used, particularly if both had been used in citations.