贫困,再分配和中产阶级:通过概率分布进行再分配与通过线性所得税系统进行再分配
1Department of Sociology, New York University, New York, NY, United States.
Frontiers in sociology
|February 19, 2024
概括
线性所得税制度可以通过防止减贫政策伤害中产阶级来保护中产阶级. 该系统确保公平,并防止对社会稳定的负面影响.
科学领域:
- 经济学 经济学 经济学
- 公共财政是公共财政.
- 社会政策 社会政策 社会政策
背景情况:
- 两次交叉的收入分配创造了转移群体,使最贫穷和最富有的人受益,同时损害了中产阶级.
- 线性所得税制度维护了税收正义的原则,确保税后收入,税金和税率与税前收入的增加.
研究的目的:
- 将收入分配动态与线性所得税原则联系起来.
- 提出线性所得税作为一种保护中产阶级免受潜在有害的减贫政策的机制.
- 通过注入资源和估计公平的税收参数,探索维持税收公平性的方法.
主要方法:
- 用初始和替代分布来说明收入分配动态.
- 分析最低,中位数,平均值以下比例和不平等的变化.
- 制定战略,以保护线性所得税制度免受公平违规,并估计公平的参数.
主要成果:
- 证明了交叉收入分配如何产生具有不同影响的"转移组".
- 展示了线性所得税制度与税收正义原则的结合.
- 提供了将线性所得税适应资源注入和人口感知公平性的方法.
结论:
- 线性所得税制度提供了一个强大的框架,以减轻某些减贫战略对中产阶级的负面影响.
- 这种方法通过保护中产阶级来支持社会稳定,中产阶级被认为对社会凝聚力至关重要.
- 对公平税收参数的实证估计是可行的,确保公众对税收制度的接受.
相关概念视频
Probability Distributions
7.0K
The probability of a random variable x is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
7.0K
Outliers and Influential Points
4.0K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
4.0K
Probability Histograms
11.6K
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
11.6K
Test for Homogeneity
2.0K
The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can...
2.0K
Distributions to Estimate Population Parameter
4.1K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.1K
Choosing Between z and t Distribution
2.8K
The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...
2.8K


