Income Diversity

Percent of households earning more than $100,000 in units

This indicator measures the percent of households earning more than $100,000.Explore the data
percent
Current
30percent
Dec 2020 Target
Goal Period ended December 2020
Show chart
    Added on 21 de enero de 2019, after the goal ended.

            What is this indicator?

            Income disparity is an important indicator of the economic strength of the community. This indicator measures the percent of the population earning more than $100,000 per year.

            Why is it important?

            Income disparity in the city is a geographic as well as an economic issue. The presumption is that equal numbers of rich and poor living in a community foster an economic and social equilibrium. Such would theoretically be the case if this ratio is "1.0", a state of income parity. Large income disparity, however, can cause inequalities in access to city and social services, resources and well-being. It also threatens the long-term stability of the economy and the community.

            How are we doing?

            As of 2010, 38% of the population was earning more than $100,000 per year. This percentage has been rising steadily since 1990, which is an indication of gentrification in the community. The current housing stock may not meet the housing needs of those residents earning less than $100,000. In addition, the jobs paying more than $100,000 may not be located in the city. Therefore those people working in the city of Santa may be commuting from other communities and those living in Santa Monica may be commuting to other areas for work. This leads to an increase in the job/housing balance, increased vehicle miles traveled, and increased congestion.

            In looking at the indicator for income disparity, it is important to consider poverty. The 2013 Census Report finds that the percent of residents living below the poverty level in Santa Monica is 11.2%. This is compared with 15.9% in California and 22% in Los Angeles.

      Data Governance

      describes the quality of the data itself. Governance issues generally indicate that the data source is considered incomplete or unreliable.

      Model Health

      describes the quality of the predictive model. If the model health is poor, the trend prediction should not be trusted.

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