regressive tax

Examples of regressive tax in the following topics:

• Comparing Marginal and Average Tax Rates

  • Taxes can be evaluated based on an average impact or a marginal impact and can be categorized as progressive, regressive, or proportional.
  • The opposite of a progressive tax is a regressive tax, where the relative tax rate or burden increases as an individual's ability to pay it decreases.
  • A regressive tax is a tax imposed in such a manner that the average tax rate decreases as the amount subject to taxation increases .
  • "Regressive" describes a distribution effect on income or expenditure, referring to the way the rate progresses from high to low, where the average tax rate exceeds the marginal tax rate.
  • Graph demonstrates a progressive tax distribution on income that becomes regressive for top earners.

• Taxes

  • Examples of an indirect tax include sales tax and VAT (value added tax).
  • Progressive Tax: The more a person earns, the higher the tax rate.
  • Regressive Tax:In a regressive tax system, poorer families pay a higher tax rate.
  • Although a regressive tax system is never explicitly used, some claim a sales tax is a type of regressive tax.
  • Categorize types of taxes into ad valorem taxes and excise taxes

• Trading off Equity and Efficiency

  • Income taxes are a laddered progressive tax where income tax rates are set in income bands or ranges.
  • At the highest income tax rate, income taxes can become regressive, since high earners are only subject to a constant albeit highest rate on their income.
  • These individuals and groups support a flat tax or proportional tax instead.
  • Income tax is a progressive tax that assumes a regressive nature at the highest tax rate.
  • Explain tax equity in relation to the progressive, proportional, and regressive nature of taxes.

• What Taxes Do

  • Taxes are the primary source of revenue for most governments.
  • Taxes are most readily understood from the perspective of income taxes or sales tax, although there are many other types of taxes levied on both individuals and firms.
  • Governments use different kinds of taxes and vary the tax rates.
  • Sales tax is a form of regressive taxation; the liability is based on the percentage of income consumed, which is higher for low income earners.
  • As a result, individuals earning a relatively lower income will pay a higher proportion of income in the form of sales tax, defining the regressive nature of the tax.

• Multiple Regression Models

  • Multiple regression is used to find an equation that best predicts the $Y$ variable as a linear function of the multiple $X$ variables.
  • You use multiple regression when you have three or more measurement variables.
  • One use of multiple regression is prediction or estimation of an unknown $Y$ value corresponding to a set of $X$ values.
  • Multiple regression is a statistical way to try to control for this; it can answer questions like, "If sand particle size (and every other measured variable) were the same, would the regression of beetle density on wave exposure be significant?
  • As you are doing a multiple regression, there is also a null hypothesis for each $X$ variable, meaning that adding that $X$ variable to the multiple regression does not improve the fit of the multiple regression equation any more than expected by chance.

• Polynomial Regression

  • For this reason, polynomial regression is considered to be a special case of multiple linear regression.
  • Although polynomial regression is technically a special case of multiple linear regression, the interpretation of a fitted polynomial regression model requires a somewhat different perspective.
  • This is similar to the goal of non-parametric regression, which aims to capture non-linear regression relationships.
  • Therefore, non-parametric regression approaches such as smoothing can be useful alternatives to polynomial regression.
  • An advantage of traditional polynomial regression is that the inferential framework of multiple regression can be used.

• How Income is Allocated

  • Policy reforms and regressive taxation have promoted disparity but are relatively minor contributors to existing inequality.
  • Wealthier people pay proportionally more of their income in taxes, which are then used to pay for services for the poor.

• Regression Analysis for Forecast Improvement

  • Regression Analysis is a causal / econometric forecasting method.
  • In regression analysis, it is also of interest to characterize the variation of the dependent variable around the regression function, which can be described by a probability distribution.
  • Familiar methods, such as linear regression and ordinary least squares regression, are parametric, in that the regression function is defined in terms of a finite number of unknown parameters that are estimated from the data.
  • Nonparametric regression refers to techniques that allow the regression function to lie in a specified set of functions, which may be infinite-dimensional.
  • The performance of regression analysis methods in practice depends on the form of the data generating process and how it relates to the regression approach being used.

• Estimating and Making Inferences About the Slope

  • You use multiple regression when you have three or more measurement variables.
  • When the purpose of multiple regression is prediction, the important result is an equation containing partial regression coefficients (slopes).
  • When the purpose of multiple regression is understanding functional relationships, the important result is an equation containing standard partial regression coefficients, like this:
  • Where $b'_1$ is the standard partial regression coefficient of $y$ on $X_1$.
  • A graphical representation of a best fit line for simple linear regression.

• Evaluating Model Utility

  • Multiple regression is beneficial in some respects, since it can show the relationships between more than just two variables; however, it should not always be taken at face value.
  • It is easy to throw a big data set at a multiple regression and get an impressive-looking output.
  • But many people are skeptical of the usefulness of multiple regression, especially for variable selection, and you should view the results with caution.
  • You should examine the linear regression of the dependent variable on each independent variable, one at a time, examine the linear regressions between each pair of independent variables, and consider what you know about the subject matter.
  • You should probably treat multiple regression as a way of suggesting patterns in your data, rather than rigorous hypothesis testing.