Many studies have used tax data to measure the U.S. income distribution, but their results vary widely. For example, in 2014 the top 1 percent share of income is 21.5 percent in Piketty and Saez (2003 and updates), 16.7 percent in the Congressional Budget Office (2018), and 13.1 percent in our analysis. What accounts for such large differences? We provide a step-by-step analysis of how methodological differences affect the results and address issues raised in Piketty, Saez, and Zucman (2018, 2019). Important differences include accounting for declining marriage rates, including social insurance and employer benefits, accounting for tax reforms, and including income missing from tax returns.
Tax return data are increasingly the standard for tracking income statistics in the United States. However, these data have traditionally been limited by their inability to capture non-filers and to identify members of separate tax units living in the same household. We overcome these obstacles and create household records directly in the tax data using mailing address information included on tax forms. We then present the first set of tax-based household income and inequality measures for the entire income distribution. When comparing household income inequality results in the tax data to those using the March CPS, we confirm previous findings that the March CPS understates the inequality of household income. However, we also find that the previous approach of using tax units in the IRS data to proxy for households leads to an overstatement of household income inequality. Finally, using households in the IRS tax records, we illustrate how focusing on tax units rather than households alters the observed distribution of tax programs such as the Earned Income Tax Credit.JEL Codes: D31, H24
Fiscal stress pressures state legislators to either raise taxes or cut spending, but public pensions provide a vehicle to postpone tax increases and maintain current spending. I estimate that states cut their pension contributions at seven times the rate of other spending in response to fiscal stress. The cumulative impact of state undercontributions due to fiscal stress explains about 4% of mid-2008 actuarial underfunding. States not paying actuarially required contributions for reasons other than fiscal stress explains an additional quarter of underfunding. As investment returns explain little underfunding, much underfunding appears due to insufficient employee and actuarially required government contributions to keep up with growing pension liabilities.
The classical capacity planning problem considers the determination of the initial capacity for a particular network of processes and the timing and size of the future expansions. The data used for such a model are the forecasted demands and prices of raw material and products, as well as the utility costs. This paper expands the problem to also consider plant location, transportation of raw materials, and transportation of product to consumer markets. We also add budgeting constraints, which follow the cash flow through the life of the project and allow the project to finance the expansions. Finally, we add considerations about the price of the product in different markets. To illustrate the technique, we consider the case of ethyl lactate, a green solvent. The model was made stochastic, and financial risk is managed.
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