The distribution of wealth has long been a concern of social scientists and economic thinkers. Typically, the distribution of wealth has been studied by looking at the differences in wealth between men and women or between ethnic groups. This paper focuses on analyzing the wealth distribution within ethnic groups as well as within countries. Using multiple regression analysis, we find that there is substantial difference in the distributions of wealth for whites, Asians, and Indians. The differences in wealth appear to be caused by different rates of accumulation and sharing of wealth.
Our estimates of national wealth distributions are based on the existing survey sample. The data for these distributions come from the Current Family Study, a nationally-representative sample of households in the United States. Each sample yields a different result, but the standard errors of our estimates of national wealth distributions are in general small. The results of simulations conducted on this data confirm that the majority of the variance in wealth distributions is due to additive factors, such as age and marriage. There is also some unexplained variation due to proportions and preferences for private consumption goods.
Our analysis indicates that although there is significant variation across ethnic groups, there is some degree of equality across nations. Specifically, the largest gaps in wealth distribution are seen between Asian and American whites, and between blacks and American Indians. The largest gaps in wealth distribution are also seen between Hispanics and Americans. However, there is a large degree of regional variation in the wealth inequality across U.S. states, counties, and cities.
On the other hand, the wealth distribution rule of thumb for this paper is: a high mean wealth and a low mean income produces a high rate of wealth inequality. Specifically, we use logistic regression to estimate the probability that an individual in the top wealth quintile will earn at least twice the income of someone in the bottom quintile. Combining these estimates of the potential incomes of different wealth quintiles, we can approximately estimate the likely proportion of wealth owned by the richest 10%. Estimates for the rest of the population are not presented because the range for each group is so wide that any correlation would be extremely misleading.
To examine the Nordic countries in terms of their treatment of wealth, we perform a Po estimation in which we use national wealth inequality as a function of both household income and household wealth. Again, a logistic regression is used to estimate the probability that a wealthy family in one country will have the same wealth as a similar wealthy family in another country. Here too, the United States stands out as having a significantly greater national average wealth gap (a mean income of more than $75,000) than the European countries studied, but the U.S. has the greatest wealth inequality (median household wealth: $3.1 million). The Nordic countries do not fare well when it comes to household income-to-wealth ratios. The mean income of the median household is less than half the Nordic countries’ median household income, and wealth are distributed very unevenly, with much more wealth owned by the richest 10% of the population than by the remainder.
Does Sweden’s steady-state economy reflects a true wealth distribution? Using macroeconomic indicators from the International Monetary Fund and the World Bank, as well as data on Sweden’s wealth as reflected in its gross domestic product, we can analyze the behavior of the wealth distribution. The results are not surprising. Sweden’s steady-state economic growth over the past quarter century has been among the highest in the developed world. As long as wealth continues to become increasingly unevenly distributed through the population, it is likely that the U.S. will experience a period of increased wealth polarization between the haves and the have-nots. Only through careful policy changes can we prevent this process from becoming undercapitalized.
