Working poverty is a pervasive issue facing Californians.

The state’s social safety net programs are key mechanisms for addressing working poverty, yet the stigmatization of program usage persists and causes hesitancy among its potential beneficiaries.

Unemployment programs raise questions about work ethic; food subsidies draw attention to beneficiaries’ spending habits; and work leave programs call into question the prioritization of work and family. 

Recent research has begun to destigmatize program usage by analyzing program enrollees and their work outcomes. A 2015 analysis of Medicaid, the Children’s Health Insurance Program (CHIP), and Temporary Assistance for Needy Families (TANF) found that 50 percent of California’s expenditures on the programs went to working families.

Researchers examined California’s first state-level paid family leave program to assess whether higher weekly benefit amounts (WBA) increased the duration of leave or led to unfavorable future participation in the labor market. The authors found that women who earned near the maximum benefit from the program did not take longer leaves nor did they reduce future participation in the labor force; to the contrary, women in the program had an increased likelihood of returning to their pre-leave job.

Finally, ongoing analyses of piloted Universal Basic Income programs are showing promising results with regard to labor market participation and overall ability to find better work. In Stockton, for example, an analysis of a 2019 pilot program found that around 43 percent of participants in the program had a full or part-time job, and only 2 percent had suspended their search for a job.

Transitioning into dissimilar jobs may be a reasonable expectation for early-career workers. For others, such as older workers, these transitions become more difficult and less likely to succeed.

Supplementation of the labor force, however, may become necessary in the near future due to the aging of California’s population. Previous research has documented that an aging population can drive polarization because states with aging populations tend to reduce their support for K-12 and higher education.

Projections from the state’s Department of Finance suggest that the population aged 65 and up will increase from 6.41 million in 2020 to 9.72 million in 2040 and 11.66 million by 2060. In contrast, the corresponding projections for the number aged 0 to 19 are 10.23 million, 9.27 million, and 9.03 million.

In other words, while today there are 60 percent more Californians aged 0 to 19 than 65 and up, by 2060 there will be 23 percent fewer. Labor force participation data illustrates the near-term impact of an aging demographic: California’s labor force participation rate began declining in the early 2000s and was at just 62 percent in 2020.

Another commonly-cited reason for increasing polarization is an overall reduction in collective bargaining power of California workers. Union membership in California steadily declined from 40 percent in the 1950s to 24 percent in 1980 to 15 percent in 2018 (Glass 2015; CFWC 2021). Declining union membership is linked to polarization in a number of ways. The probability of low wage employment, for example, is reduced by 39 percent with union membership, compared to a 33 percent reduction among college degree holders (CFWC 2021).

Aside from negotiating higher pay and better benefits, unions also play a critical role in retraining workers when economic cycles or technological change interrupt the normal operation of a given occupation. Likewise, unions have driven changes to pregnancy and parental leave regulations, working hours and conditions, unemployment insurance, and wrongful termination laws, each of which supports workers’ ability to transition into and out of jobs with less risk to meeting basic needs. 

The decline in California union membership represents a departure from the state’s deep history of labor organizing— César Chávez and Dolores Huerta’s work to establish the National Farm Workers Association, for example, is a critical part of state and national history. Such efforts resulted in California being an international leader in cementing workers’ rights into policies and legislation. Despite the decline in union membership in previous decades, California is showing signs of a labor organizing revival.

Recent developments in the technology industry, for example, demonstrate Californians’ willingness to re-engage their collective bargaining power. The formation of the Alphabet Workers Union, a group of Google engineers and other workers who organized to gain some control over the company’s global influence, suggests that the re-emergence of unions may not be relegated to blue-collar labor (Conger 2021). Similarly, the 2020 vote on Proposition 22 prompted discussion about the need for collective action among workers in gig economy contract positions (Hiltzik 2021).

Union membership data from the past two years add support to the anecdotal examples listed above. California’s union membership increased for the first time in many years in both 2019 and 2020, rising by 99,000 in 2019 and 139,000 in 2020 to a rate of 16.5 percent of the labor force (BLS 2021c). While still well off of the membership numbers from decades past, these numbers give reason to pay close attention to labor organizing and its potential impact on polarization in the near future.

The aging population in California illustrates a, perhaps, more concerning trend—one that has been exacerbated during the COVID-19 pandemic. At least seven million Californians live in Health Professional Shortage Areas, wherein counties do not have adequate levels of primary, dental, or mental health care professionals.

The California Future Health Workforce Commission—created in 2017—found that these shortages are most severe in some of California’s largest and fastest-growing regions, including the Inland Empire, Los Angeles, and San Joaquin Valley, as well as most rural areas.

Attracting healthcare workers to these areas is just one component of the shortage. Moreover, these shortfalls predominantly affect Latino, African American, and Indigenous communities.

As such, the commission identified the need for the state to fix the growing mismatch between its existing workforce and its increasingly diverse population. Specifically, although California’s population will be majority people of color by 2030, these communities remain severely underrepresented in the health workforce.

This industry offers good career pathways, but has also exposed wide inequalities in wages and work-life balance for professionals in the field, particularly for home healthcare workers and nursing aides as compared to advanced degree holders in the healthcare field.

Social and labor trends toward polarization and inequality may be exacerbated by technological trends that place low-wage California workers in a precarious position. Low-wage jobs are a frequent target for vendors of automation, largely due to the perceived simplicity and physically-taxing qualities of repetitive, manual work.

Industrial robots are one contemporary example of this phenomenon. The industrial robotics market had surged into a nearly $50 billion industry by 2017, with 9.4 percent of U.S. plants already adopting robots and 28.3 percent of all manufacturing workers being exposed to robots by 2018.

Ongoing research suggests that these robot uptake numbers will increase in California elsewhere, particularly as robot capability improves in industries like manufacturing, logistics and warehousing.

The Covid-19 pandemic introduced additional pressures on companies to automate low-wage jobs. At first glance, the impetus comes from the ongoing worker shortage: In July 2021, the U.S. Labor Department reported the highest number of job openings in the 20 years it has been collecting data.

Whether or not the shortage is due to expanded emergency unemployment benefits—enabling would-be workers to remain home and search for better jobs—is a subject of debate, with some initial research suggesting that enhanced benefits are not the cause.

No matter the reason, executives in low-wage labor-intense industries reported renewed demand for robotic systems during the pandemic, specifically “plug-and-play” systems that are easier to integrate into existing processes than the most advanced systems available.

The future of manufacturing jobs is unclear. On the one hand, automation in manufacturing, in particular, is likely to increase in the years to come because of the growth of the industry paired with an aging demographic, population stagnation, and organizational interest in augmenting the workforce with new technologies.

Nevertheless, California is number one in the nation for factory jobs and its GDP from manufacturing increased 13 percent over the past five years to $316 billion in 2020. This increase outpaces all other states, with Texas seeing 9 percent growth over the same period. 

Robotics uptake demonstrates the increasing automation of “blue collar” jobs. But other types of work are also increasingly susceptible to automation, primarily via artificial intelligence and machine learning.

Recent research suggests that the impact of these cutting-edge technologies could exacerbate polarization and, by extension, inequality in California. Such conditions can further narrow skill requirements of the remaining jobs, thereby increasing the practical distance between entry-level and more advanced positions. 

Specifically, entry-level “white collar” jobs (e.g., legal clerking, data cleaning) are a persistent target of machine learning implementations. Automating these jobs, though, could remove a viable pathway to higher-paying professions: In contrast to workers in deskilled blue-collar jobs, entry-level white-collar jobs can offer on-the-job learning that is applicable in jobs farther up the skill and wage ladder. Entry-level workers would instead handle small, rare exceptions in their work, a characteristic of both old and new forms of automation and that limits transferable skill-building.

Without appropriate intervention, the rapid adoption and implementation of advanced technologies such as machine learning would solidify the link between technological change and deepening income inequality in California.

Some regions of California are more exposed to the deployment of machine learning based on the composition of jobs in the area. The Suitability for Machine Learning Rubric, developed by Brynjolfsson, Mitchell, and Rock (2017), could enable an early assessment of the likelihood of each region of California to be affected by machine learning at a large scale.

While all regions fall in a moderate suitability range based on currently available data—between 3.0 and 3.1 on a scale of 5—the scores for individual occupations vary much more. Two of California’s most common jobs—cashiers and personal care aides—score at 3.4 and 2.795, respectively. Policymakers and stakeholders might consider using such tools to evaluate economic development and technology regulation policies in each California region.

< Back

Next >