Last week, my colleague Rob Moore wrote about relative poverty measures and how we might benefit from changing the way we calculate poverty. At Scioto Analysis, we calculate our Ohio Poverty Measure, a version of a relative poverty measure focused on the state of Ohio. We conduct this as a part of our social bottom line work, updating it when we can find time between client projects.
Our project was based on work done in other places such as Wisconsin, New York City, and California. The main difference between these reports and the Supplemental Poverty Measure is that these measures use American Community Survey data rather than Current Population Survey data.
The American Community Survey is much larger than the Current Population Survey, but it doesn’t have as much detail. This means that researchers get the additional geographic resolution needed in order to construct these more local poverty measures, but they need to create models to estimate some of the information that is not included in the American Community Survey.
Recently, the updated data for the California Poverty Measure was released. I wanted to highlight some of the takeaways from this report to help demonstrate why it’s important to have such local poverty data.
In 2023, 9.8% of working Californians between ages 25 and 64 (about 1.5 million people) were living in poverty. The California Poverty Measure poverty threshold for that year averaged $43,990 for a family of four, but varied widely depending on location.
About half of all working adults in poverty were employed full-time for the whole year, and their poverty rate was 6.7%. For those working part-time all year, the rate shot up to 23.6%, and for those working only part of the year, 18.4%. The expiration of pandemic-era supports contributed to a 1.1 percentage point increase in the worker poverty rate from 2022.
An additional 2.3 million people were near poverty, with incomes between 100% and 150% of the poverty line. Certain industries and occupations had especially high poverty rates. Agriculture and service sectors like administrative services, leisure and hospitality, and other services all saw rates above 18%. Among occupations, fishing, farming, forestry, and building maintenance topped the list, with one in four workers in poverty.
Geography matters, too. Southern coastal California had some of the highest poverty rates for working adults, with Los Angeles County at 12% and Orange County at 11.3%. Regions like the Sacramento area and Northern California had much lower rates, around 6.6%. In high-cost regions, even full-time employment was not always enough to escape poverty, pointing to the interplay of wages, expenses, and safety net accessibility. These geographic insights are unique among these types of poverty measures.
Family structure also shapes poverty risks. Most poor working adults live with other adults, often with children. Poverty is particularly acute for single parents without other adults in the household, nearly one in four are in poverty. For single adults without children, the rate is lower but still significant.
The California Poverty Measure shows that earnings account for about 80% of poor workers’ resources. These are supplemented by programs like the Earned Income Tax Credit and CalFresh. Without these supports, another 4% of workers would fall into poverty.
For policymakers, these findings demonstrate that higher wages, more hours, and career advancement are important but not sufficient on their own. Access to child and elder care, healthcare, and education and training all play a role in whether work leads to economic security.
We plan on updating our own Ohio Poverty Measure sometime in the near future. Hopefully we will one day have the capacity to make regular updates so we can get these types of insights in real time for Ohio.