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Unions are not only good for workers, they’re good for communities and for democracy : High unionization levels are associated with positive outcomes across multiple indicators of economic, personal, and democratic well-being

Report • By Asha Banerjee , Margaret Poydock , Celine McNicholas , Ihna Mangundayao , and Ali Sait • December 15, 2021

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We know that unions promote economic equality and build worker power, helping workers to win increases in pay, better benefits, and safer working conditions.

But that’s not all unions do. Unions also have powerful effects on workers’ lives outside of work.

In this report, we document the correlation between higher levels of unionization in states and a range of economic, personal, and democratic well-being measures. In the same way unions give workers a voice at work, with a direct impact on wages and working conditions, the data suggest that unions also give workers a voice in shaping their communities. Where workers have this power, states have more equitable economic structures, social structures, and democracies.

Income and economic protections

We find that, on average, the 17 U.S. states with the highest union densities:

• have state minimum wages that are on average 19% higher than the national average and 40% higher than those in low-union-density states
• have median annual incomes $6,000 higher than the national average
• have higher-than-average unemployment insurance recipiency rates (that is, a higher share of those who are unemployed actually receive unemployment insurance)

Health and personal well-being

We find that the states with the highest union densities:

• have an uninsured (without health insurance) population 4.5 percentage points lower, on average, than that of low-union-density states
• have all elected to expand Medicaid under the Affordable Care Act, protecting their residents from falling into the “coverage gap”
• are more likely to have passed paid sick leave laws and paid family and medical leave laws than states with lower union densities

We find that:

• Significantly fewer restrictive voting laws have been passed in the 17 highest-union-density states than in the middle 17 states (including D.C.) and the 17 lowest-union-density states.
• Over 70% of low-union-density states passed at least one voter suppression law between 2011 and 2019.

A wealth of scholarship documents the positive effects unions have for workers, both those who are unionized and those who are not. We summarize these below.

Higher wages and decreased income inequality. On average, a worker covered by a union contract earns 10.2% more in wages than a peer with similar education, occupation, and experience in a nonunionized workplace in the same industry (EPI 2021e). This wage advantage is known as the “union wage premium.” But unions don’t just help union workers—they help all workers (Bivens et al. 2017). When union density is high, nonunion workers benefit, too, because unions effectively set broader standards—including higher wages—which nonunion employers must meet to attract and retain the workers they need (Rosenfeld, Denice, and Laird 2016; Mishel 2021). The combination of the direct wage effect for union members and this “spillover” effect for nonunion workers means unions are crucial to raising wages for working people and reducing income inequality (Card 1996, 2001; Card, Lemieux, and Riddell 2018).

Reduced wage gaps. Unions also help to reduce gender and racial/ethnic wage gaps. Hourly wages for women represented by a union are 4.7% higher on average than for nonunionized women with comparable characteristics (EPI 2021d), and research looking at specific cases suggests that unions reduce gender wage gaps for similar jobs within a given workplace (Gould and McNicholas 2017). For example, Biasi and Sarsons (2020) show that the expiration of teacher collective bargaining agreements led to an increase in the wage gap between men and women with similar credentials, implying that the terms of the collective bargaining agreement had previously helped to minimize such wage gaps. Unions have also historically helped and continue to help close wage gaps for Black and Hispanic workers (Farber et al. 2021). Black workers represented by a union are paid 13.1% more than their nonunionized Black peers, and Hispanic workers represented by a union are paid 18.8% more than their nonunionized Hispanic peers (EPI 2021d).

Increased government revenue and decreased government spending. Unionization has a range of positive economic impacts in addition to decreasing wage inequality and closing gender and race wage gaps. Sojourner and Pacas (2018) find that union membership yields a positive “net fiscal impact”—or, to put it simply, unionized workers have more income and therefore pay more taxes. Unions pave the way for more income and wealth-building for workers and therefore more revenue for the government.

Sojourner and Pacas (2018) also find that unionized workers use fewer public benefits. Higher incomes allow workers and their families to be less dependent on government benefits, and unions also help workers win benefits such as health insurance from their employers.

Employer-sponsored benefits including health insurance, retirement, and paid leave. Union workers are far more likely than nonunion workers to be covered by employer-provided health insurance. More than nine in 10 unionized workers have access to employer-sponsored health benefits, compared with just 68% of nonunion workers, and union employers contribute more to their employees’ health care benefits (EPI 2021d). Furthermore, union employers are more likely to offer retirement plans and to contribute more toward those plans than comparable nonunion employers. Union workers are also more likely to have paid sick days, vacation and holidays, more input into the number of hours they work, and more predictable schedules (EPI 2021d).

Strengthened health and safety. Unions also improve the health and safety of workplaces by providing health insurance and paid sick time, requiring safety equipment, and empowering workers to report unsafe conditions without fear of retaliation (Zoorob 2018; Amick et al. 2015). So-called right-to-work legislation that weakens unions has been associated with a roughly 14% increase in the rate of occupational fatalities (Zoorob 2018).

Increased civic engagement and broader community benefits. Beyond wages, benefits, and safety, recent scholarship shows the indirect effect unions have on people’s political and personal attitudes and on the broader community and economy as a whole. Frymer and Grumbach (2021) find that union membership reduces white racial resentment. Feigenbaum, Hertel-Fernandez, and Williamson (2019) analyze the relationship between unions and political advocacy, specifically on policies related to worker empowerment and economic justice. They find that weakening unions (through the enactment of “right-to-work” laws) has significant long-term political and economic effects, such as lower voter turnout, lowered organized labor contributions, less voter mobilization, fewer working-class candidates serving in state legislatures and Congress, and more conservative state policy. These political consequences undoubtedly affect not only the communities in which they take place, but also the broader economy, as the chosen candidates enact economic policies.

——

Our analysis in this report supports this existing scholarship on unions. The strong relationship between union density and a range of economic, personal well-being, and democratic outcomes is consistent with the idea that unions focus the political power of workers and result in the advancement and defense of policies that benefit the broad interests of workers, their families, and their communities.

The data we analyze across a wide range of indicators support the notion that through advocating for higher wages and better benefits for members and, more generally, by mobilizing and building grassroots coalitions and acting as one of the main countervailing forces against rising corporate concentration, unions act as a channel for producing and cementing positive economic, health, and democratic outcomes in the communities in which they are active.

Methodology: How we measure union density

First, a brief note on how we measure union density at the state level. In this report, we categorize union density as the share of workers in a state who are members of a union or covered by a collective bargaining agreement. Union density data are averaged across states from 2015 to 2019 to give a more accurate estimate of state unionization rates and avoiding temporary single-year changes. We end at 2019 to avoid any potential distortions related to the 2020–2021 COVID-19 pandemic and ensuing recession. We draw our data from the Economic Policy Institute extracts 1 of the Current Population Survey (CPS) Outgoing Rotation Group (ORG), a nationally representative monthly survey of U.S. households conducted by the Census Bureau on behalf of the Bureau of Labor Statistics.

For the purposes of this analysis, we divide the 50 U.S. states, plus the District of Columbia, into three groups based on their level of union density. These groupings are shown in Table 1 : The 17 states with the highest union densities are “high-union-density” states (with 13.5%–24.7% of workers covered); the next 17 states (including D.C.) are “medium-union-density” states (with 8.3%–13.3% of workers covered); and the remaining 17 are “low-union-density” states (with 3.2%–7.7% of workers covered).

Figure A depicts the data from Table 1 in map form. While there are some regional clusters within the density groups, unionization rates vary nationwide.

Share of workers represented by a union ranges from 3.2% in South Carolina to 24.7% in New York : Union density of the 50 U.S. states plus D.C., in descending order and grouped into high-, medium-, and low-union-density states

Notes: Union density  is defined as the share of workers in the state who are  represented by a union,  including  union members   and other workers who are   covered by  a union  contract,  based  on the variable “union”  from EPI extracts of CPS-ORG microdata.

Union density  is defined as the share of workers in the state who are  represented by a union,  including  union members   and other workers who are   covered by  a union  contract,  based  on the variable “union”  from EPI extracts of CPS-ORG microdata. We average union density data across 2015 to 2019 for each state to give a more accurate estimate of states’ typical unionization rates over time. We do not include data beyond 2019 in our averages, to avoid any potential distortions related to the 2020–2021 COVID-19 pandemic and ensuing recession.

Source: EPI analysis of 2015–2019 Current Population Survey Outgoing Rotation Group (CPS-ORG) microdata for all workers ages 16 and older.

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Unionization varies widely by state : Union density by state, 2015–2019 average

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Notes: Union density is defined as the share of workers in the state who are represented by a union, including union members and other workers who are covered by a union contract, based on the variable “union” from EPI extracts of CPS-ORG microdata.

Union density is defined as the share of workers in the state who are represented by a union, including union members and other workers who are covered by a union contract, based on the variable “union” from EPI extracts of CPS-ORG microdata. We average union density data across 2015 to 2019 for each state to give a more accurate estimate of states’ typical unionization rates over time. We do not include data beyond 2019 in our averages, to avoid any potential distortions related to the 2020–2021 COVID-19 pandemic and ensuing recession.

Source:  EPI analysis of 2015–2019 Current Population Survey Outgoing Rotation Group (CPS-ORG) data for all workers ages 16 and older.

Economic well-being

The first category of well-being we examine is economic well-being, broadly defined as working people having the means to support themselves. We look at the relationship between union density and three indicators related to economic security and stability: the minimum wage, median income, and the unemployment insurance (UI) recipiency rate. We find that states with higher union density have consistently higher minimum wages, incomes, and UI recipiency rates than states with lower union density.

Minimum wage

For most people, salary and wages are the main source of income used to cover cost of living. State minimum wages establish a wage floor for the minimum hourly wage employers must pay workers in that state. More than half of U.S. states have passed laws raising their minimum wage above the federal minimum (EPI 2021b). Laws that increase a state’s minimum wage directly boost the pay of the lowest-paid workers—who too often have little bargaining power—by effectively shifting the wage negotiation from one between an employer and an individual worker to one between employers and the broader community. Since relative pay plays an important role in wage setting, minimum wages also have an indirect effect on wages above the minimum, by raising the base of comparison for higher earners (Spriggs and Klein 1994). The “ripple effect,” in which employers give nonmandated raises to maintain a similar wage structure after a change in the wage floor (through minimum wage legislation, for example), also has an outsized impact on workers (Wicks-Lim 2006).

While worker productivity has risen over the years, growth of real wages and incomes has been slow or stagnant for most working people for most of the last four decades (EPI 2021c). In the four decades through 2019, low-wage workers (those at the 10th percentile of the wage distribution, that is, those who make less than 90% of all workers) saw their wages rise only 3.3% in inflation-adjusted terms, compared with a rise of 15.1% for the median worker (right in the middle of all wage earners) and 63.2% for high-wage workers (those at the 90th percentile, who make more than 90% of all workers) (Gould 2020a). Workers being paid the federal minimum wage, currently $7.25 per hour, have actually seen a 30% fall in their inflation-adjusted earnings over the last 50 years (Shierholz 2021). Figure B depicts the geographic variation of union density and 2021 state minimum wages.

Union density and minimum wages by state

Sources: EPI analysis of 2015–2019 Current Population Survey Outgoing Rotation Group (CPS-ORG) microdata for all workers ages 16 and older; EPI Minimum Wage Tracker , updated August 2021.

Unions have played a central role in organizing and mobilizing campaigns to increase state and local minimum wages. The Service Employees International Union (SEIU), for example, has had a crucial role in the successful national Fight for $15 campaign (Greenhouse 2015). A recent report by the National Employment Law Project estimated that since 2012, Fight for $15 has helped raise wages for nearly 26 million workers, winning over $150 billion in additional income (Lathrop, Lester, and Wilson 2021).

As Figure C shows, the high-union-density states (listed in Table 1) have a higher average state minimum wage ($11.40) than the medium- and low-union-density states (at $9.22 and $8.10, respectively). The average minimum wage in the high-union-density states is $3.31 higher—or more than 40% higher—than in the low-union-density states.

Average state minimum wage is 40% higher in high-union-density states than in low-union-density states : Average minimum wages in high-, medium-, and low-union-density states

Notes: Minimum wage data are current as of 2021. Union density is defined as the share of workers in the state who are represented by a union, including union members and other workers who are covered by a union contract, based on the variable “union” from EPI extracts of CPS-ORG microdata. Low-union-density states are the 17 states with the lowest average union densities from 2015–2019 (all less than 8%). Medium-union-density states are the 17 states (including D.C.) in the middle of the union-density rankings (with union densities ranging from 8.3% to 13.3%). High-union-density states are the 17 states with the highest average union densities from 2015–2019 (greater than or equal to 13.5%). See Table 1 for more detail about these groupings.

Minimum wage data are current as of 2021. Union density is defined as the share of workers in the state who are represented by a union, including union members and other workers who are covered by a union contract, based on the variable “union” from EPI extracts of CPS-ORG microdata. Low-union-density states are the 17 states with the lowest average union densities from 2015–2019 (all less than 8%). Medium-union-density states are the 17 states (including D.C.) in the middle of the union-density rankings (with union densities ranging from 8.3% to 13.3%). High-union-density states are the 17 states with the highest average union densities from 2015–2019 (greater than or equal to 13.5%). See Table 1 for more detail about these groupings. We average union density data across 2015 to 2019 for each state to give a more accurate estimate of states’ typical unionization rates over time. We do not include data beyond 2019 in our averages, to avoid any potential distortions related to the 2020–2021 COVID-19 pandemic and ensuing recession.

Sources: EPI analysis of 2015–2019 Current Population Survey Outgoing Rotation Group (CPS-ORG) microdata for all workers ages 16 and older; EPI Minimum Wage Tracker , updated August 2021.

Black, Hispanic, and Asian American/Pacific Islander women—along with Black and brown workers as a whole, who have long been overrepresented in low-wage service sectors—have benefited disproportionately from these efforts (Banks 2019). Historically, expansions of the minimum wage have also contributed to closing racial wage gaps (Derenoncourt and Montialoux 2021). In the present, minimum wage campaigns, which are strongly backed by unions, have played a significant role in raising incomes and lowering wage disparities for workers of color (Lathrop, Lester, and Wilson 2021).

Median household income

Another key related economic well-being indicator is annual median household income. Income is useful to examine alongside minimum wages because it gives us a picture of what the typical working household actually earns over the course of a year, not just the minimum employers are required to pay an individual worker.

We observe a clear, positive relationship between unionization and median household income across states. The national median income was $69,720 in 2019. As Figure D shows, high-union-density states had an average median income about $6,000 higher than the national average. In comparison, the low-union-density states had an average median income about $6,500 lower than the national average. It is also worth noting that the high-union-density states had an average annual median income over $10,000 higher than low-union-density states. These results are consistent with the evidence that unions directly raise wages of union workers and, when union density is high enough, also raise the wages of nonunion workers. These wage increases then translate into higher annual incomes.

Median household incomes in high-union-density states are more than $12,000 higher, on average, than median incomes in low-union-density states : Average median household income (2019) in high-, medium-, and low-union-density states

Notes: Union density is defined as the share of workers in the state who are represented by a union, including union members and other workers who are covered by a union contract, based on the variable “union” from EPI extracts of CPS-ORG microdata. Low-union-density states are the 17 states with the lowest average union densities from 2015–2019 (all less than 8%). Medium-union-density states are the 17 states (including D.C.) in the middle of the union-density rankings (with union densities ranging from 8.3% to 13.3%). High-union-density states are the 17 states with the highest average union densities from 2015–2019 (greater than or equal to 13.5%). See Table 1 for more detail about these groupings. Median incomes are by household. 

Union density is defined as the share of workers in the state who are represented by a union, including union members and other workers who are covered by a union contract, based on the variable “union” from EPI extracts of CPS-ORG microdata. Low-union-density states are the 17 states with the lowest average union densities from 2015–2019 (all less than 8%). Medium-union-density states are the 17 states (including D.C.) in the middle of the union-density rankings (with union densities ranging from 8.3% to 13.3%). High-union-density states are the 17 states with the highest average union densities from 2015–2019 (greater than or equal to 13.5%). See Table 1 for more detail about these groupings. We average union density data across 2015 to 2019 for each state to give a more accurate estimate of states’ typical unionization rates over time. We do not include data beyond 2019 in our averages, to avoid any potential distortions related to the 2020–2021 COVID-19 pandemic and ensuing recession. For each group, we calculate the average of the 17 states’ median incomes. Median income here refers to income from all sources, including nonwork income sources, however, for the large majority of people surveyed, most of their income comes from labor earnings.

Sources: EPI analysis of 2015–2019 Current Population Survey Outgoing Rotation Group (CPS-ORG) microdata for all workers ages 16 and older; U.S. Census Bureau, “ Historical Income Tables: Households ,” Table H-8, accessed August 2021.

Unemployment insurance

The final measure of economic well-being we examine is the unemployment insurance (UI) recipiency rate. When a worker is laid off from a job and their household income falls, unemployment insurance helps the worker support themselves and their family until they find another job. The UI recipiency rate, reported by the Department of Labor, represents the “insured unemployed” as a percentage of the total unemployed—i.e., the share of unemployed workers who actually receive UI benefits. (Note that we are intentionally analyzing only 2019 data to avoid distortions due to the pandemic economy and legislative changes, most notablythe creation of several temporary, pandemic-related federal enhancements to the UI system. The 2020 federal emergency programs plugged several key gaps in the state systems and minimized, though did not eliminate, drastic state differences in benefits. The 2019 data represent UI benefits from the states’ regular UI programs only.)

Figure E highlights the positive relationship between unionization and UI recipiency rates. The high-union-density states had an average UI recipiency rate of 37% in 2019, roughly double the average UI recipiency rate of the lowest-density third of states (18%). In low-union-density states, fewer than one in five workers who applied for UI benefits received them, compared with nearly two in five in high-density states.

Unemployed workers are twice as likely to receive unemployment benefits if they live in high-union-density states than if they live in low-union-density states : Average UI recipiency rate (2019) in high-, medium-, and low-union-density states

Notes: The UI recipiency rate in each state is the share of unemployed workers who are receiving unemployment insurance benefits through the state’s regular UI program (i.e., not through federal emergency programs such as Pandemic Unemployment Assistance). We use a simple average of UI recipiency rates within the high-, medium-, and low-union-density state groupings. Union density is defined as the share of workers in the state who are represented by a union, including union members and other workers who are covered by a union contract, based on the variable “union” from EPI extracts of CPS-ORG microdata. Low-union-density states are the 17 states with the lowest average union densities from 2015–2019 (all less than 8%). Medium-union-density states are the 17 states (including D.C.) in the middle of the union-density rankings (with union densities ranging from 8.3% to 13.3%). High-union-density states are the 17 states with the highest average union densities from 2015–2019 (greater than or equal to 13.5%). See Table 1 for more detail about these groupings.

The UI recipiency rate in each state is the share of unemployed workers who are receiving unemployment insurance benefits through the state’s regular UI program (i.e., not through federal emergency programs such as Pandemic Unemployment Assistance). We use a simple average of UI recipiency rates within the high-, medium-, and low-union-density state groupings. Union density is defined as the share of workers in the state who are represented by a union, including union members and other workers who are covered by a union contract, based on the variable “union” from EPI extracts of CPS-ORG microdata. Low-union-density states are the 17 states with the lowest average union densities from 2015–2019 (all less than 8%). Medium-union-density states are the 17 states (including D.C.) in the middle of the union-density rankings (with union densities ranging from 8.3% to 13.3%). High-union-density states are the 17 states with the highest average union densities from 2015–2019 (greater than or equal to 13.5%). See Table 1 for more detail about these groupings. We average union density data across 2015 to 2019 for each state to give a more accurate estimate of states’ typical unionization rates over time. We do not include data beyond 2019 in our averages, to avoid any potential distortions related to the 2020–2021 COVID-19 pandemic and ensuing recession.

Sources:  EPI analysis of 2015–2019 Current Population Survey Outgoing Rotation Group (CPS-ORG) microdata for all workers ages 16 and older; Department of Labor Employment & Training Administration, Unemployment Insurance Data Chartbook .

The large differences in the UI recipiency rates across states are not based on any particular economic logic. These differences largely reflect the wide scope states have to determine specific features of eligibility criteria, the application process, benefit amounts, and duration of benefits. Many states have made the application and eligibility process difficult in order to cut costs by reducing the number of successful claims (Badger and Parlapiano 2020; Stewart 2020; Wamsley 2020).

Unions use their influence to increase UI eligibility, ease of access, benefit levels, and benefit duration, to the benefit of all workers, union and nonunion (Hertel-Fernandez and Gould-Werth 2020). In their role as intermediary institutions that can act as a complement to legislated benefits and protections, unions also inform and educate the workers they represent about the unemployment insurance system and guide them through the application process (Walters and Mishel 2003). Recent research has found that unionized workers are 19 percentage points more likely to apply for and receive benefits than nonunionized workers after accounting for worker, job, and state characteristics (Hertel-Fernandez and Gould-Werth 2020). The same research finds that unions can help close large racial gaps in accessing UI.

Summary of economic well-being indicators

Across the states, unionization is consistently associated with higher levels of economic well-being as measured by the level of a state’s minimum wage, median annual income, and access to unemployment insurance benefits. In each case, there are clear channels through which unions can—and do—influence these critical economic outcomes.

Personal well-being

The second category of well-being we examine is personal well-being, which we define as an individual’s physical and mental health. Within the category of personal well-being, we look at three indicators: health care coverage, Medicaid expansion, and access to paid family and sick leave. As we demonstrate below, states with higher union density are more likely to have higher levels of all three personal well-being metrics.

Access to health insurance

The first marker of personal well-being we examine is access to health insurance. Almost all union workers (94%) have access to health insurance, compared with just 67% of nonunion workers (Gould 2020b). Economic research has documented that unionized workers also face lower direct costs for health care coverage than their nonunionized peers (Buchmueller and DiNardo 2001). Unions are an important channel workers use to secure high-quality health insurance, and when union members gain these benefits, nonunion employers are more likely to offer better compensation, including health care benefits, in order to remain competitive (Mishel 2021).

There are a variety of ways people can access health insurance, whether privately through employer-based programs or direct purchase; through a public program such as Medicaid, Medicare, and the Veterans Administration; or some combination. In 2019 in the U.S., about two-thirds of the population had health insurance coverage through private sources, including employer-based (Keisler-Starkey and Bunch 2020, Table HIC-4_ACS). Unfortunately, many people fall through the cracks and are unable to access any private or public health insurance programs, either because their employer does not offer a health care plan or because they earn too much or too little to qualify for one of the public programs. This “coverage gap” ensnares over 2 million people each year (Lukens and Sharer 2021). In this section, we examine how the uninsured rate—i.e., the share of a state’s residents who fall into this coverage gap—intersects with union density levels.

Figure F shows a clear negative relationship between state unionization rates and the uninsured rate: States with high union densities have an uninsured population 4.5 percentage points lower, on average, than that of low-union-density states. Put another way, people in low-union-density states are nearly 1.7 times as likely to lack any form of health insurance as their peers in high-union-density states. Unions directly impact health care coverage by bargaining for employer-sponsored health care coverage for employees and their dependents. Having not only access to health insurance, but also some or all of the costs covered by the employer—which unions have successfully negotiated for—greatly improves job quality.

Residents of high-union-density states are more likely to have health insurance : Average uninsured rate (2019) in high-, medium-, and low-union-density states, 2019

Notes: Union density is defined as the share of workers in the state who are represented by a union, including union members and other workers who are covered by a union contract, based on the variable “union” from EPI extracts of CPS-ORG microdata. We average union density data across 2015 to 2019 for each state to give a more accurate estimate of states’ typical unionization rates over time. Low-union-density states are the 17 states with the lowest average union densities from 2015–2019 (all less than 8%). Medium-union-density states are the 17 states (including D.C.) in the middle of the union-density rankings (with union densities ranging from 8.3% to 13.3%). High-union-density states are the 17 states with the highest average union densities from 2015–2019 (greater than or equal to 13.5%). See Table 1 for more detail about these groupings.

Sources:  EPI analysis of 2015–2019 Current Population Survey Outgoing Rotation Group (CPS-ORG) microdata for all workers ages 16 and older; Keisler-Starkey and Bunch, “ Health Insurance Coverage in the United States ,” U.S. Census Bureau, September 2020.

Unionization and health and economic outcomes are deeply intertwined. Access to high-quality health insurance, including health care for dependents, improves household and individual financial security. Investment in health care and health care access has also been linked to other positive economic outcomes, such as boosted income (Raghupathi and Raghupathi 2020). Access to health care also leads to clear positive health outcomes, including a lower rate of chronic illness and a higher likelihood of receiving preventative care for critical health conditions (Tolbert, Orgera, and Damico 2020; Washington 2001). In our analysis, we see the same pattern for personal well-being as for economic well-being: States with high unionization not only have higher minimum wages and higher median household incomes, but they also have higher health insurance coverage rates than states with low unionization.

Medicaid expansion

To fully understand how unions intersect with health care coverage, we examine—alongside the uninsured rate—whether a state has expanded Medicaid under the Affordable Care Act (ACA) to broaden eligibility to more low-income people. The Medicaid public insurance program provides critical health coverage to families and individuals with low incomes. As with the unemployment insurance system, the Medicaid program has broad federal guidelines but is administered by states, with a 2012 Supreme Court decision ruling that Medicaid eligibility and expansion could be decided by states. As a result, eligibility, benefits, and coverage differ greatly by state.

In the states that did not expand their Medicaid programs, eligibility is limited to an annual income of just under $9,000 for a family of three (41% of the poverty line). By contrast, states that have adopted Medicaid expansion have extended eligibility to people with incomes up to $17,609 for an individual (138% of the poverty line) (Garfield, Orgera, and Damico 2021). Expanding Medicaid would help to close the “coverage gap” and provide health insurance to the estimated 2 million people who do not qualify for Medicaid and who do not earn enough to be eligible for other subsidized plans under the ACA (Simmons-Duffin 2021). Research has found that Medicaid expansion has improved access to health care; improved health outcomes, including fewer premature deaths; lowered uncompensated costs; bolstered financial security; boosted economic mobility; and more (CBPP 2020).

As Figure G shows, there is a clear difference between high- and low-union-density states in terms of Medicaid expansion. By 2019, all 17 high-union-density states had expanded Medicaid, compared with just five of the low-union-density states. Over 70% of the low-union-density states have not expanded Medicaid. (The map in Figure H shows specifically which states had and had not expanded Medicaid by 2019, along with each state’s union density.)

The 17 highest-union-density states have all adopted the Affordable Care Act’s Medicaid expansion : Number of high-, medium-, and low-union-density states that had adopted the Medicaid expansion as of 2019

Notes: Union density is defined as the share of workers in the state who are represented by a union, including union members and other workers who are covered by a union contract, based on the variable “union” from EPI extracts of CPS-ORG microdata. Low-union-density states are the 17 states with the lowest average union densities from 2015–2019 (all less than 8%). Medium-union-density states are the 17 states (including D.C.) in the middle of the union-density rankings (with union densities ranging from 8.3% to 13.3%). High-union-density states are the 17 states with the highest average union densities from 2015–2019 (greater than or equal to 13.5%). See Table 1 for more detail about these groupings.

Union density is defined as the share of workers in the state who are represented by a union, including union members and other workers who are covered by a union contract, based on the variable “union” from EPI extracts of CPS-ORG microdata. Low-union-density states are the 17 states with the lowest average union densities from 2015–2019 (all less than 8%). Medium-union-density states are the 17 states (including D.C.) in the middle of the union-density rankings (with union densities ranging from 8.3% to 13.3%). High-union-density states are the 17 states with the highest average union densities from 2015–2019 (greater than or equal to 13.5%). See Table 1 for more detail about these groupings. We average union density data across 2015 to 2019 for each state to give a more accurate estimate of states’ typical unionization rates over time. We do not include data beyond 2019 in our averages, to avoid any potential distortions related to the 2020–2021 COVID-19 pandemic and ensuing recession. To maintain consistency with avoiding 2020–2021 COVID-19 distortions, we examine Medicaid expansion status as of 2019. Therefore Idaho, Kentucky, Maine, and Montana, which expanded Medicaid in 2019, are grouped in the “Expanded Medicaid” category, while Arkansas, Missouri, Nebraska, Oklahoma, and Utah, which expanded Medicaid in 2020 or 2021, are listed in the “Did Not Expand Medicaid” category. Missouri and Nebraska are medium-union-density states while Arkansas, Oklahoma, and Utah are low-union-density states.

Sources:  EPI analysis of 2015–2019 Current Population Survey Outgoing Rotation Group (CPS-ORG) microdata for all workers ages 16 and older; Kaiser Family Fund, “ Status of State Medicaid Expansion Decisions ,” September 8, 2021.

Union density and Medicaid expansion by state

Notes: Idaho, Kentucky, Maine, and Montana expanded Medicaid in 2019 and so are listed under the “Expanded Medicaid” category. Arkansas, Missouri, Nebraska, Oklahoma, and Utah expanded Medicaid in 2020 or 2021 and so they are listed in the “Did Not Expand Medicaid” category to be consistent with the fact that we report union density data from 2019 and earlier. Union density is defined as the share of workers in the state who are represented by a union, including union members and other workers who are covered by a union contract, based on the variable “union” from EPI extracts of CPS-ORG microdata.

Idaho, Kentucky, Maine, and Montana expanded Medicaid in 2019 and so are listed under the “Expanded Medicaid” category. Arkansas, Missouri, Nebraska, Oklahoma, and Utah expanded Medicaid in 2020 or 2021 and so they are listed in the “Did Not Expand Medicaid” category to be consistent with the fact that we report union density data from 2019 and earlier. Union density is defined as the share of workers in the state who are represented by a union, including union members and other workers who are covered by a union contract, based on the variable “union” from EPI extracts of CPS-ORG microdata. We average union density data across 2015 to 2019 for each state to give a more accurate estimate of states’ typical unionization rates over time. We do not include data beyond 2019 in our averages, to avoid any potential distortions related to the 2020–2021 COVID-19 pandemic and ensuing recession.

Sources: EPI analysis of 2015–2019 Current Population Survey Outgoing Rotation Group (CPS-ORG) microdata for all workers ages 16 and older; Kaiser Family Fund, “ Status of State Medicaid Expansion Decisions: Interactive Map ,” September 8, 2021.

Unions have been long been central in supporting grassroots campaigns and coalitions to expand Medicaid. Broad union political support for Medicaid can pave the way for more and more states—even low-union-density states—to follow suit with successful Medicaid ballot initiatives. For example, the Fairness Project, funded by the SEIU United Healthcare Workers West in California, was instrumental in getting Medicaid expansion initiatives on ballots in Idaho, Utah, and Nebraska in 2018; all three initiatives passed (Kodjak 2018). Recently, union-backed ballot initiatives have also met with success in other low- and medium-union-density states such as Kentucky, Missouri, Arkansas, and Oklahoma (Kliff 2020; Levey 2018).

It is worth noting that the data underlying Figures G and H also underscore an important connection between unionization rates, health outcomes, economic security, and racial disparities: Many of the low- and medium-density-states that have the highest shares of uninsured residents, and many of the states that have not expanded Medicaid, also have relatively high concentrations of Black workers.

For example, out of the 17 low-union-density states we categorize, 10, or about 59%, had a share of Black workers in the labor force higher than the national average in 2020 (BLS 2021). This relative concentration persists within the subcategories we examine as well; for example, of the 12 low-union-density states that had not expanded Medicaid as of 2019, about two-thirds have a Black working population above the national average in 2020 (the national Black labor force share was about 12.6%). Given the ugly racist origins of “right-to-work” anti-union laws, it is not surprising that these states with relatively large Black populations have continued to suppress unions and worker collective action (Ros n.d.).

Conversely, the surprising victory of Medicaid expansion in low-union-density, high-Black-population states such as Louisiana and Virginia suggests that when unions advocate for popular issues across the nation, the momentum can spread, and further victories can contribute to lowering racial and economic disparities despite the odds and even in states with a low union presence.

Union-supported Medicaid expansions are one more example of a “spillover effect” and channel through which unionization benefits not only union members, but also members of the broader community.

Paid sick and family leave

The final personal well-being metric we examine is access to paid sick leave and paid family and medical leave. Specifically, we look at which states have passed laws to ensure that workers have access to these important benefits. Paid sick leave allows workers to take time off when they are sick without worry over loss of income, while paid family and medical leave allows workers to take extended time off to address a serious health condition, to care for a new child, or to care for an ill family member (Gould 2018). Paid sick and family leave is necessary not only for physical health, but for mental health as well—mitigating the stress of illness or family change by providing economic and job security during extended time off work. Significant research has shown, for example, that paid parental leave significantly improves maternal mental health by allowing recovery and adjustment time (Romig and Bryant 2021).

Thanks to unions’ collective bargaining successes, union workers have greater access to paid sick days than nonunion workers: More than nine in 10 workers—93%—covered by a union contract have access to paid sick days, compared with only 75% of nonunion workers (BLS 2020). This means that 25% of nonunion workers do not have access to even a single day of paid sick leave. Moreover, access to paid sick time is vastly unequal—94% of the highest-wage workers have access to paid sick days while only 31% of the lowest-paid workers do. Many states and localities are beginning to address this inequity by passing laws requiring employers to provide paid sick leave.

The situation is even bleaker for workers who need family and medical leave—whether unionized or not. Only 20% of all workers have access to paid family and medical leave, meaning that nearly 80% of workers do not (BLS 2020 Table 31). This presents workers with a choice between their careers and their caregiving responsibilities precisely when they need their paychecks the most, such as following the birth of a child or when they or a loved one falls ill. To ensure workers do not have to make that difficult choice, we need a national paid family leave program. However, in the absence of such a program, several states have enacted paid family leave laws.

Unions have played an integral role in coalition campaigns to pass paid sick day and family leave laws at the state and local levels, making paid leave a key policy plank alongside higher wages (Brown 2013; MacGillvary and Jacobs 2018; Salsberg 2018; Thoet 2016).

Figure I shows that nearly half of high-union-density states have passed both paid sick and family leave; in contrast, none of the low-union-density states have. While the majority of U.S. states still, unfortunately, do not have either paid sick or family leave laws, the tangible impact that unions have had in their advocacy for such laws is evident in the pattern of progress so far.

High-union-density states are more likely to have passed paid leave laws : Number of high-, medium-, and low-union-density states with paid leave laws, 2019

Notes : Union density is defined as the share of workers in the state who are represented by a union, including union members and other workers who are covered by a union contract, based on the variable “union” from EPI extracts of CPS-ORG microdata. We average union density data across 2015 to 2019 for each state to give a more accurate estimate of states’ typical unionization rates over time. Low-union-density states are the 17 states with the lowest average union densities from 2015–2019 (all less than 8%). Medium-union-density states are the 17 states (including D.C.) in the middle of the union-density rankings (with union densities ranging from 8.3% to 13.3%). High-union-density states are the 17 states with the highest average union densities from 2015–2019 (greater than or equal to 13.5%). See Table 1 for more detail about these groupings.

Union density is defined as the share of workers in the state who are represented by a union, including union members and other workers who are covered by a union contract, based on the variable “union” from EPI extracts of CPS-ORG microdata. We average union density data across 2015 to 2019 for each state to give a more accurate estimate of states’ typical unionization rates over time. Low-union-density states are the 17 states with the lowest average union densities from 2015–2019 (all less than 8%). Medium-union-density states are the 17 states (including D.C.) in the middle of the union-density rankings (with union densities ranging from 8.3% to 13.3%). High-union-density states are the 17 states with the highest average union densities from 2015–2019 (greater than or equal to 13.5%). See Table 1 for more detail about these groupings. We average union density data across 2015 to 2019 for each state to give a more accurate estimate of states’ typical unionization rates over time. We do not include data beyond 2019 in our averages, to avoid any potential distortions related to the 2020–2021 COVID-19 pandemic and ensuing recession.

Sources:  EPI analysis of 2015–2019 Current Population Survey Outgoing Rotation Group (CPS-ORG) microdata for all workers ages 16 and older; Bipartisan Policy Center, State Paid Family Leave Laws Across the U.S. , February 10, 2020; A Better Balance, Overview of Paid Sick Time Laws in the United States , updated October 18, 2021.

Democratic well-being

The final category we examine is democratic well-being, specifically the right to vote. Recent sustained attacks on this fundamental right threaten to undermine democratic stability. We look at legislation restricting voting and find that there is a strong correlation between voting restrictions and low union density.

The right to vote is a core tenet of our democracy, won and enshrined after years of sustained protest and activism. Yet many states continue to take actions that disproportionately disenfranchise people of color (Johnson and Feldman 2020). The number of voter suppression laws enacted across the country is high and rising, and the voting rights of all citizens—especially the voting rights of people of color—are under attack. States have passed restrictions on when and where early voting can take place, when and how people can register to vote, who can vote by mail, and what types of identification are required to vote, among other forms of voter suppression. These barriers to voting have disproportionately impacted racial minorities, poor people, and young and old voters, all of whom are less likely to have the accepted voter ID (Brennan Center n.d.).

Figure J shows the relationship between union density and voter suppression legislation in the 50 states. (Note that D.C.—which is part of the medium-union-density group in other charts—is not included in the voter restrictions data.) A majority of low- and medium-union-density states have passed at least one voter restriction bill, while the vast majority of high-union-density states have passed none. Among high-union-density states, 13 out of 17 did not pass any voter restrictions between 2011 and 2019, while only seven of the medium-union-density states and five of the low-union-density states can claim this distinction. Nine medium-union-density states and 12 low-union-density states passed voting restrictions. A total of 26 voter suppression laws were passed among the 17 low-union-density states between 2011 and 2019.

Voter restriction bills are more likely to pass in low-union-density states than in high-union-density states : Number of high-, medium-, and low-union-density states that have passed restrictive voting laws, 2011–2019

Notes: Union density is defined as the share of workers in the state who are represented by a union, including union members and other workers who are covered by a union contract, based on the variable “union” from EPI extracts of CPS-ORG microdata. Low-union-density states are the 17 states with the lowest average union densities from 2015–2019 (all less than 8%). Medium-union-density states are the 16 states (D.C. is not included in this chart) in the middle of the union-density rankings (with union densities ranging from 8.3% to 13.3%). High-union-density states are the 17 states with the highest average union densities from 2015–2019 (greater than or equal to 13.5%). See Table 1 for more detail about these groupings.

Union density is defined as the share of workers in the state who are represented by a union, including union members and other workers who are covered by a union contract, based on the variable “union” from EPI extracts of CPS-ORG microdata. Low-union-density states are the 17 states with the lowest average union densities from 2015–2019 (all less than 8%). Medium-union-density states are the 16 states (D.C. is not included in this chart) in the middle of the union-density rankings (with union densities ranging from 8.3% to 13.3%). High-union-density states are the 17 states with the highest average union densities from 2015–2019 (greater than or equal to 13.5%). See Table 1 for more detail about these groupings. We average union density data across 2015 to 2019 for each state to give a more accurate estimate of states’ typical unionization rates over time. We do not include data beyond 2019 in our averages, to avoid any potential distortions related to the 2020–2021 COVID-19 pandemic and ensuing recession. This analysis looks at restrictions passed by states from 2011 to 2019. We include as examples of voting restrictions laws that require voter ID or proof of citizenship, limit absentee and mail-in collection, limit registration, or limit participation by those previously incarcerated.

Sources:  EPI analysis of 2015–2019 Current Population Survey Outgoing Rotation Group (CPS-ORG) microdata for all workers ages 16 and older; Brennan Center for Justice, “ State Voting Bills Tracker 2021 ,” last updated May 28, 2021.

Unions have, both historically and in the present, been central to the fight to protect voting rights. Historically, labor and workers’ rights were a central focus of the civil rights movement and went hand in hand with voting rights (Moore 2021). A. Philip Randolph, one of the leaders of the March on Washington in 1963, was also a leader of the Brotherhood of Sleeping Car Porters (BSCP), one of the most influential all-Black labor unions. In the 1960s, the AFL-CIO lobbied and offered testimony for the Civil Rights Act of 1964 and the Voting Rights Act of 1965. In March 2021, after Georgia passed voter suppression bill SB 202, AFL-CIO President Richard Trumka wrote that voter suppression laws were “aimed at silencing working people of color across the state” (AFL-CIO 2021).

Through long-standing advocacy and work to protect the vote, unions have linked voting rights to workers’ rights. To this day, unions continue to mobilize workers to vote. Union voter mobilization has broad effects, including helping to determine which political leaders are elected and what occupational backgrounds they come from. Sojourner (2012) finds that unions promote election of people from working- and middle-class jobs into political leadership.

In this report, we have sought to demonstrate that the benefits of unionization extend far beyond the workplace. High union density is consistently associated with a much broader set of positive spillover effects across multiple dimensions. Not only do unions directly benefit the workers they represent, but their political advocacy helps to drive an array of strongly positive outcomes more broadly, especially in states where unions represent a sizeable share of the workforce. These positive outcomes include wage increases, better health benefits, easier access to unemployment insurance, access to paid sick leave, access to paid family and medical leave, and unrestricted voting opportunities.

However, union density levels across the country are not as high as they could be. While nearly half of all nonunion workers say they want a union in their workplace, only 12% of all workers are covered by a union contract (Brenan 2021; EPI 2021d). Current law places too many obstacles in the way of workers trying to organize and gives employers too much power to interfere with workers’ free choice (Mishel, Rhinehart, and Windham 2020). It is therefore critical that policymakers enact reforms that restore a meaningful right to organize and collectively bargain. One simple way to help accomplish this would be to pass the Protecting the Right to Organize (PRO) Act, which will help restore the right to organize and give workers access to a union and the well-being it promotes.

Unionization can be a key driving force as we continue to build an equitable recovery and invest in the creation of jobs with good wages and access to benefits (Hersh 2021). Building union density is not just a worker or workplace issue, but it is also a mechanism to uplift families and communities. The relationship we have demonstrated between high union density and higher household incomes, access to health care and paid leave, and fewer voting restrictions highlights the importance of protecting the right of workers to organize. This right could be a fundamental component in strengthening economic security, quality of life, civil and voting rights, and racial justice in our communities.

Acknowledgments

The authors thank William E. Spriggs, whose research inspired this report; John Schmitt for his thoughtful input; and Daniel Perez for his research assistance.

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Labor unions and health: A literature review of pathways and outcomes in the workplace

J. paul leigh.

a Department of Public Health Sciences, University of California, Davis, Davis, CA, United States

b Center for Poverty and Inequality Research, University of California, Davis, Davis, CA, United States

c Center for Healthcare Policy and Research, University of California, Davis, Davis, CA, United States

Bozhidar Chakalov

Associated data.

• • Unique literature review links economic and epidemiologic studies on unions.
• • Unions raise wages, decrease inequality, and thereby likely improve health.
• • Unions decrease discrimination and affect other determinants of health.
• • Unions improve workplace safety and health and decrease job-related fatalities.
• • Unions are an underappreciated social determinant of health.

Extensive economic research demonstrates correlations between unions with wages, income inequality, health insurance, discrimination, and other factors. Corresponding epidemiologic literature demonstrates correlations between income, income inequality, insurance, discrimination, and other factors with health. The first purpose of this narrative review is to link these literatures and identify 28 possible pathways whereby labor unions might affect the health of workers. This review is restricted to effects within workplaces; we do not consider unions' political activities. This review covers studies from the US, Europe, and Canada from 1980 through April 1, 2021. Pathways are grouped within five domains informed by the CDC 5-domain model of social determinants of health and the traditional 3-domain model of occupational medicine. Linked pathways include wages, inequality, excessive overtime, job satisfaction, employer-provided health insurance (EPHI), and discrimination. Second, we identify studies analyzing correlations between unions directly with health outcomes that do not require links. Outcomes include occupational injuries, sickness absence, and drug overdose deaths. Third, we offer judgments on the strength of pathways and outcomes --- labeled “consensus,” “likely,” “disputed” or “unknown” --- based on literature summaries. In our view, whereas there are four “consensus” pathways and outcomes and 16 “likely” pathways and outcomes for unions improving health, there are no “consensus” or “likely” pathways for harming health. The strongest “consensus” pathways and outcomes with salubrious associations include EPHI, OSHA inspections, dangerous working conditions, and injury deaths. Fourth, we identify research gaps and suggest methods for future studies. Unions are an underappreciated social determinant of health.

1. Introduction

Economic research is extensive pertaining to correlations between unions on the one hand and lower income inequality, higher wages, less overtime, less discrimination, more employer-provided health insurance and additional factors on the other. Epidemiologic research is extensive on correlations between health with the same or similar factors. It is somewhat surprising how separate these two literatures are. Only a few economic studies address how unions directly affect health, but these are limited to two outcomes: sickness absence and occupational injury. And whereas epidemiology has an entire subfield for occupational epidemiology, we found only a handful of studies (discussed below) addressing the direct associations between unionization and health. The authoritative text on occupational epidemiology no longer contains a chapter on unions ( Levy et al., 2017 ). The authoritative text on social epidemiology ( Berkman et al., 2014 ) does not mention unions in its 39-page index. The first purpose of this narrative review is to link the economic and epidemiologic literatures. Second, we review the few studies on direct correlations between unions and health outcomes. Third, we offer judgments on whether the links and correlations represent effects of unions on health. Fourth, we make suggestions for future research.

Unions have been at the forefront of recent debates surrounding income inequality, stagnant wages, “deaths of despair,” and protection and adequate pay for essential workers during the covid-19 pandemic. Income inequality in the US is now at historic levels not seen since the beginning of the Great Depression ( Keshner, 2019 ). Inflation-adjusted US wages have been stagnant or falling for middle- and low-wage workers for over 40 years ( Mishel et al., 2015 ). Epidemiologic studies find income inequality harms the population’s health ( Kawachi and Kennedy, 2002 ). Kristol and Cohen (2017) estimate that 44% of the increase in wage inequality in US private sector jobs from 1988 to 2012 was attributed to the decline in unionization. “Deaths of despair” refer to the 20-year increase in deaths due to drug overdoses, alcoholism, and suicides primarily among middle-aged Americans ( Case and Deaton, 2020 ). These deaths have been cited as causes of the stunning recent annual drops in US life expectancy even before covid ( Devitt, 2018 ). The last time there were drops in US life expectancy was in 1918 during the Spanish Flu pandemic. “Deaths of despair” have been partially attributed to decades-long erosion of the numbers of unionized, well-paying, blue-collar jobs in the US ( Case and Deaton, 2020 ). A similar phenomenon is threatening the UK and the decline of unions is again being cited as one cause ( Joyce and Xu, 2019 ). Deaths, 2021 , Blanchflower, 2019 suggest that “deaths of despair” and falling wages since the Great Recession contributed to the election of Donald Trump and votes for Brexit. Many researchers and commentators view unions as playing pivotal roles in reducing income inequality, wage stagnation, “deaths of despair”, and especially in improving treatment of essential workers ( Case and Deaton, 2020 , Kapos, 2020 , Mishel et al., 2015 , Nunn et al., 2019 ).

Many people view the 1950s as a time when America was “Great” for the American worker. The economy was growing rapidly, wages were increasing at all economic levels, and unemployment was low. It was also a time that private-sector labor unions were at their zenith of power in the US. Approximately 33% of the workforce was unionized in the 1950s and the vast majority was in private, not public unions ( Mayer, 2014 ). Private-sector unionization began a steady decline in the 1960s to arrive at the 2019 level of 6%, matching that from the 1910s in the US ( Bureau of Labor Statistics, 2020 ).

It is beyond the scope of this review to discuss the numerous factors causing these sharp declines in private-sector unions ----declines in some manufacturing industries such as autos and steel with historically high unionization rates likely played roles--- but one factor for which there is near unanimous agreement deserves a brief mention: politics. Countries such as France, Denmark, Sweden, Canada and Germany, among others, also experienced declines in manufacturing but experienced no or only modest declines in the percent of the workforce covered by union contracts; the political climate in these countries is cited as largely responsible ( Rosenfeld, 2014 ). There are unique examples from the US. The 1947 Taft-Hartley Act allowed states to pass so-called “right-to-work” (RTW) laws that permitted workers in unionized workplaces to opt-out of joining and paying dues, i.e. to free ride. RTW laws have had negative effects on unions’ abilities to organize ( Ellwood and Fine, 1987 ). By 2017, 27 states had adopted these laws. As another example, President Reagan fired all striking air traffic controllers in 1981 thereby signaling that his administration did not support unions. Beginning in the 1980s, organized business began aggressively opposing unions ( Rosenfeld, 2014 ). But this 40-year decline is poised for change. Public approval for unions, 64% in 2019, is near a 50-year high, with the highest approval among people < 35 years old; and politicians have noticed ( Jones, 2019 ).

Public-sector unions followed a different path. In 1959, Wisconsin became the first state to allow state workers to unionize; most other states subsequently followed. By the late 1970s, the percent of all public workers in unions rose to approximately 33%. Unlike private-sector unionization, public-sector unionization did not decline but rather has stabilized around 33–34% since the 1980s in the US. In 2019, approximately 7.1 million workers were in public-sector and 7.5 in private-sector unions ( Bureau of Labor Statistics, 2020 ). Private- and public-sector unionization combined was 10.3% of the workforce in 2019.

In 2019, for both private and public-sector unions combined, African Americans (11.2%) had higher union membership rates than white non-Hispanics (10.3%), Hispanics (9.1%) or Asians (8.9%) ( Bureau of Labor Statistics, 2020 ) in the US. People in the 45–64 age bracket had the highest rates of all age brackets (12.7%) ( Bureau of Labor Statistics, 2020 ). The rate for full-time workers (11.2%) was roughly double that for part-time workers (5.5%). Among all 16.4 million workers represented by unions, 14.6 million were members and 1.8 million were not members but were nevertheless covered by union contracts ( Bureau of Labor Statistics, 2020 ). In 2017, women comprised approximately 47% of private-sector union members and 58% of public-sector members ( Wolf and Schmitt, 2018 ). Although we are not aware of statistics on occupations by gender by sector, data on educational attainment suggests that female union members are more likely to hold higher disproportionate shares of both low- and high-status occupations in the public than private sector. Approximately 14% of public-sector union members have less than a high school degree and 34% have more than a college degree; the percentages for private-sector union members are 9% and 11% ( Wolf and Schmitt, 2018 ). In part, this reflects public schools in which cafeteria workers and janitors are not required to have high school degrees, but teachers are required college degrees.

We identify 28 possible pathways involving linked economic and epidemiologic literatures and seven health outcomes involving studies on the direct effects of unions on health. Each of these 28 economic and epidemiologic sets of studies as well as those for health outcomes studies merit their own literature review. This study, however, will not conduct 63 separate literature reviews (28 economic, 28 epidemiologic, and 7 direct). This review is restricted to effects within workplaces. We do not consider unions’ political activities involving, for example, minimum wages, universal basic income, or universal health insurance.

Unique literature search strategies were followed. Three leading labor economics texts (and references therein) formed the primary sources for the 28 economic pathways ( Ehrenberg and Smith, 2015 , Kaufman and Hotchkiss, 2003 , McConnell et al., 2017 ). Google scholar searches identified the prominent epidemiologic and public health studies for the 28 epidemiologic pathways as well as four of the seven direct effects (self-rated health, drug overdoses, mortality, food insecurity). Donado’s (2015) literature review and subsequent studies citing Donado were used as the basis for our search involving the fifth and sixth direct effects: fatal and non-fatal occupational injuries. Brown and Sessions’ (1996) literature review and subsequent studies citing Brown and Sessions formed the basis of our search involving the final direct effect: work absences. The appendix provides greater detail for the search strategies for injuries and absences. For all strategies, papers were excluded if they had no direct data-based estimate of effects of unions on pathways or pathways on health. Additional exclusions were for editorials, news stories, blogs, testimony, legal briefs, undergraduate papers, and thought pieces. All searches included 1980 through April 1, 2021. Most studies were from the US, but some were from Europe and Canada. No studies were drawn from economically developing nations. Whereas the subjects of this review ---workers--- are sometimes viewed only through the lens of occupational medicine, the effects of unions are far-reaching and best viewed also through the lens of public health as shown in the model below.

This review follows a straightforward format. First, we present a model for analyzing direct effects of unions on the health of workers covered by collective bargaining contracts. Second, we review the economics literature pertaining to possible health pathways that are prevalent in unionized versus non-unionized workplaces. Third, we review the epidemiological literature pertaining to whether these likely pathways have health or behavior associations in working and other populations regardless of union status. Fourth, we consider the relatively few (mostly economic) studies on direct associations between union status and health outcomes. The paper closes with a summary, limitations, and suggestions for future research.

2. Model of effects of unions on health

Table 1 presents a model for union pathways with domains on the left side and pathways on the right. The model is informed by the literature on the Social Determinants of Health (SDoH) and Occupational Medicine. Healthy People 2020 posits five domains for the SDoH: 1) Economic Stability, 2) Education, 3) Health and Health Care, 4) Neighborhood and Built Environment, and 5) Social and Community Context ( Centers for Disease Control, 2020 ). The leading textbook on Occupational Medicine posits three domains: Physical and Mechanical, Biological and Chemical, and Psychosocial ( Levy et al., 2017 ). For relevance in the union and workplace context, a subcategory of Neighborhood and Built Environment---pollution--- can be combined with Physical, Mechanical, Biological and Chemical to form what may be labeled Environment and Work Organization. There is overlap between portions of Social and Community Context with Psychosocial which may be labeled simply Psychosocial. The pathways are gleaned from three leading labor economics texts as well as economics literature on differences between union and non-union workplaces for which there are corresponding epidemiologic studies. The first author has been teaching classes and researching subjects in labor economics and social epidemiology since 1980. Pathways are more fully described in the next section.

Model for Effects of Unions on Health of Workers.

The arrangement of pathways in the five domains is straightforward but some require comment. Excessive overtime, shift and graveyard work, workers and unemployment compensation, workplace flexibility for individual workers, non-standard and “gig” working conditions, piece-rate pay, and vacation leave are characteristics of the work environment and hence placed into Environment, but they also have economic and psychosocial dimensions. For example, excessive overtime can increase earnings and therefore be considered in Economic Stability. Shift and graveyard work can affect circadian rhythms which, in turn, can affect psychosocial health. In addition, whereas sub-categories for all three Occupational Medicine domains are well represented in the pathways, some subcategories for SDoH are not. Quality of housing and level of violence in the neighborhood are subcategories within Neighborhood and Built Environment. It is unlikely that workers within their workplaces are associated with housing or neighborhood violence in the same way that they are associated with, for example, wages or working conditions. The SDoH domains and subcategories are used as a guide, not the ultimate arbiter of which pathway to include in which domain.

The pathways included in Table 1 are more numerous than any other list appearing in the literature of which we are aware. Future researchers will no doubt imagine others. It is likely that other imagined pathways will fall into one of the five domains, however, thereby underscoring the utility of the model.

Table 1 describes pathways for workers at the workplace. This review will not extend to effects of unions on society at-large; they are simply too numerous. For example, teachers’ unions might promote health classes for their students; police unions might support members who kill African Americans; national unions might support environmental laws; effects of labor unions on national levels of employment or housing availability --- two important determinants of health--- are largely unknown. Nevertheless, two broad effects will be addressed: economy-wide wage levels and income inequality. The first part of Table 2 , labeled I, applies to union versus non-union workplaces; part II applies to society at-large, combining union with non-union workplaces.

Possible health pathways.

3. Associations between unions and possible pathways; Columns 1–3 Table 2 .

The first column in Table 2 identifies pathways. Columns 2 and 3 pertain to predominantly economic literature on unions; columns 4 and 5 pertain to predominately epidemiologic literature regardless of union status. Column 2 provides references for the judgments. Column 3 provides our judgments regarding the findings in the literature. For some pathways, there is consensus. For example, all studies with which we are aware find unionized establishments have more OSHA inspections (pathway #11). For other pathways, such as “job security,” (pathway #4) findings are disputed. We created a category, “likely”, which indicates that judgment leans to one side. Finally, we use “unknown” for pathways for which there are two or fewer studies. We use these words to describe our judgments of findings, not the findings themselves. The reader should have high confidence that when we describe findings as “disputed” that there are at least two (probably many more) studies with inconsistent findings. Our “likely” judgment means that while there may be inconsistent findings, we believe the evidence leans in a consistent direction. In general, readers should have more confidence in “consensus”, “disputed” and “unknown” judgments than “likely” ones. We sought to make Table 1 self-explanatory. Nevertheless, several pathways require additional comments.

The lion’s share of economic research has addressed private, not public unions. It is likely that all conclusions and judgments drawn in Table 1 also apply to public unions albeit to a lesser degree. For example, considering pathway #1, whereas private unions generate a 20% wage advantage over private non-union workplaces, public unions generate a 10% advantage ( McConnell et al., 2017 ). When the same conclusions and judgments cannot be drawn for public unions, we will so indicate.

Wages and within-firm wage inequality are the first and second pathways and there is consensus: unions increase wages --- especially for low-wage workers--- and reduce within-firm inequality compared to non-union workplaces. A leading labor economics textbook estimates a 15% union wage for private-sector and government-sector unions combined ( McConnell et al., 2017 ). This wage advantage represents a transfer from capital (business owners) to labor ( Mishel, 2012 ). Mishel (2012) estimates that unionized workers are 53.9 % more likely to have employer-provided pensions (pathway #3).

Pathway #5 involves discrimination for which there are two forms: employment and wages. Historians disagree on the extent of union discrimination against Blacks for the first 60 years of the twentieth century. Some unions and affiliates (e.g. AFL) explicitly excluded Blacks but others (e.g. CIO) welcomed them ( Hill, 1996 ). In addition, some unions, such as the Brotherhood of Sleeping Car Porters, were exclusively Black. A recent analysis finds that in the two decades following World War II, Blacks were over-represented in unions and enjoyed a greater union wage advantage than whites ( Farber et al., 2020 ). Explicit discrimination against other minorities, particularly Asians, and women also occurred within some unions in these 60 years but, again, there is no consensus on the extent of that discrimination ( Hill, 1996 ).

Beginning in the 1970s, most studies find less discrimination against either women or minorities than in the labor market at-large with respect to employment ( Leonard, 1985 ). In 2016, 65% of persons covered by either private or public-sector union contracts were either women or minorities ( Bivens et al., 2017 ). Beginning in the 1970s, while there is no consensus, numerous studies indicate less wage discrimination in either private- or public-sector unions ( Bivens et al. 2017 ). Within the public sector, unions raise wages for women more than for men ( Freeman and Leonard, 1987 ). For both public- and private-sector unions combined, Mishel (2012) estimates union wage premiums are higher for Blacks (17%) and Hispanics (23%) than whites (11%).

Even though formal education (pathway #7) is a powerful SDoH, research on the effects of unions on educational attainment is sparse and ambiguous ( Blanchflower, 2006 , Ewer, 2000 ). For example, Blanchflower (2016) finds educational attainment is negatively correlated with private sector membership but positively correlated with public sector membership.

There is consensus that unionized workplaces are more likely to have employer-provided health insurance (EPHI) in the US (pathway #8). One estimate is that unionized workplaces have insurance coverage rates that are 18.3 percentage points higher than those for non-unionized workplaces ( Bivens et al., 2017 ). Buchmueller et al. (2002) find that de-unionization explains about a third of the decline in EPHI coverage between 1983 and 1997 in the US. Mishel (2012) estimates that unionized workers are 3.4% more likely to have paid sick leave (pathway #9).

We distinguish between exposure to dangerous working conditions (pathway #12) versus health and injury outcomes resulting from exposures ( Table 3 ). Exposure can be assessed, for example, with questions to workers such as “does your job ever expose you to….” followed by possibilities including, for example, dangerous chemicals, viruses, bacteria, radiation, fire, electricity, or air pollution ( Leigh, 1982 ). The consensus is that unionized workplaces are more hazardous than non-unionized ones. But there is a question regarding assessing blame: do unions create hazards or vice versa? A leading labor economics text suggests unions are more likely to form in hazardous workplaces and once formed, hazardous conditions are reduced, at least in the private sector; few analyses have addressed the public sector ( Kaufman and Hotchkiss, 2003 ). A thorough discussion of this issue appears in the analysis of Table 3 .

Direct associations between unions and health outcomes.

The consensus is that unions increase the likelihood of OSHA inspections (pathway #13) and, given workers experience injuries or unemployment, unions increase the likelihood of receipt of workers compensation and unemployment compensation benefits (pathway #14). Union members may feel less threatened than non-union members by possible employer retaliation resulting from contacting OSHA or filing workers’ compensation claims ( Weil, 1991 , Hirsch et al., 1997 ).

Both pathways #17 and #18 include shiftwork. Pathway #17 applies to the total amount of shiftwork while #18 applies to worker control over whether to engage in shiftwork. Individual worker-controlled flexibility (#18) includes, for example, the ability to: work at home; have compressed workweeks part of the year; temporarily change start and quit times; alter the pace of work; choose shifts; require predictable hours ( Cotti et al., 2013 , Duncan and Stafford, 1980 , Kaufman and Hotchkiss, 2003 , Keune, 2013 ). Flexibility has implications for family health as it would allow workers more time to take care of sick family members. Whereas unions may decrease the availability of working from home or ability to alter the pace of work, they enlarge the capacity to choose which shift to work and to require predictable work hours, particularly for members with seniority. The effects of unions on individual worker-controlled flexibility are therefore disputed.

Jobs with non-standard work arrangements have also been referred to as alternative, precarious, contingent, gig, freelance, or independent contract (#19). There is no agreed-upon definition, but these jobs are typically temporary, do not have an explicit or implicit contract for on-going employment, and shift some of the risk of business onto workers ( Howard, 2017 ). The Bureau of Labor Statistics estimates that nearly 14% of the workforce held contingent or alternative jobs in 2017 ( Bureau of Labor Statistics, 2018 ). These jobs are disproportionately non-union ( OECD, 2019 ). Unions have been at the forefront of keeping standard jobs from becoming gig jobs as well as helping to change the legal classification of gig work from independent contractor to employee. ( Tronsor, 2018 , CBS News, 2021 ). European unions might have success in their attempts to include gig work in their sectoral bargaining arrangements ( Doherty and Franca, 2020 ).

A systematic review with meta -analysis finds unions correlate with low job satisfaction (pathway #22) but does not find unions cause low satisfaction ( Laroche, 2016 ). One explanation is that union members are encouraged to “speak up”, to express any displeasure with working conditions to management; another is that dissatisfied workers are more likely to join unions. Further evidence for this “speaking up” hypothesis pertains to quit rates which can be viewed as the strongest expression of job dissatisfaction. Most studies find unionization lowers quit rates ( McConnell et al., 2017 ).

Pathway #23 pertains to “job strain” and/or “job control” ( Schnall et al., 1994 ). We are unaware of economic studies comparing union and non-union workers on these dimensions and only one epidemiologic study ( Gillen et al. 2002 ) which, incidentally, finds no union/non-union differences.

Social support at work includes mentoring, cooperative spirit, willingness to share resources, companionship, emotional support, and other factors among co-workers and, when appropriate, supervisors ( Park et al., 2004 ). We are unaware of empirical studies addressing union/non-union differences for social support at work (#24). Nevertheless, it is likely that unions promote this pathway given unions encourage members to attend meetings, voice grievances with one another, and solidarity ( Hagedorn et al., 2016 )

Fairness and justice (#25) encompass many dimensions and these can be in conflict ( Fuller and Hester, 2001 ). For example, does fairness dictate that workers be promoted based on seniority or productivity or some combination of the two? Unions give considerable weight to seniority ( McConnell et al., 2017 ). On the other hand, unions generally have grievance and arbitration procedures that encourage workers to voice complaints and sometimes change work practices. A (dated) survey finds that 83% of American workers believe unions protect them from “unfair practices” by management ( Kochan, 1979 ).

Self-esteem, respect, and stigma (#26) receive enormous attention in the organizational psychology literature but we are unaware of studies exploring union/non-union differences ( Pierce and Gardner, 2004 ). One survey of steelworkers finds union membership satisfies socio-emotional needs such as “approval, self-esteem, affiliation, and respect” ( Fuller and Hester, 2001 ). On the other hand, it could be that unions carry a stigma given some historical connections to organized crime ( Gibney et al., 2018 ).

Pathways #27 and #28 pertain to society at-large. Unions may have effects on wages, benefits, and unemployment throughout the economy depending on effects on non-union workplaces (#27). There are conflicting theories ( McConnell et al., 2017 ). One theory holds that unions raise wages “too high”, forcing employers to cut their unionized workforces. These laid-off workers will flood the labor market in non-union sectors, leading to declines in wages and benefits and increasing unemployment in those sectors. But there are also threat effects according to a different theory: non-union firms might increase wages and benefits to discourage any threat posed by their workers possibly deciding to unionize ( Fortin et al., 2021 ). Finally, there is consensus that unions reduce economy-wide wage and income inequality (#28). Western and Rosenfeld (2011) find de-unionization from 1973 to 2007 explains from 20% to 33% of rising wage inequality in the US. Raphael (2011) finds unionization and inequality associations across 21 OECD countries.

4. Associations between pathways and health regardless of union status; Columns 1, 4, 5, Table 2 .

Epidemiologists and some economists have investigated the pathways in Table 2 independent of union status. Column 4 provides prominent studies and texts and column 5, our judgments. Again, many entries in Table 2 are self-explanatory and do not require additional comment.

Several hypotheses surround higher wages (pathway #1). First, higher wages can improve access to health care as workers are more able to afford it. Women with low incomes will feed and clothe their children before spending on themselves ( Elliot et al., 2015 ). On the other hand, higher wages might allow workers to buy more cigarettes, drugs, or alcohol ( Leigh et al., 2019 ). We are unaware of studies on the effects of pensions on health (#3). Studies find Social Security Supplemental Income benefits (theoretically like pensions) decrease disability in the elderly ( Arno et al., 2011 ). Substantial literature indicates increases in income, especially for low-income people, improves health ( Glymour et al., 2014 ). For example, Davis et al. (2018) query cross-sections of Britons to determine minimum levels of income required to meet basic material needs for food, clothing, shelter, and so on; Gibson et al. (2020) review 27 studies on interventions similar to Universal Basic Incomes and find some health benefits. Discrimination (#5) harms psychological health and increases unhealthy behaviors (e.g. smoking) of women and especially minorities; effects on physiological health are “inconsistent and weak” ( Krieger, 2014 ). Epidemiologic studies typically do not separate employment from wage discrimination.

When compared to “no insurance”, EPHI (pathway #8), improves health of workers and their families ( O’Brien, 2003 ). If the US moves to universal coverage, however, this comparison may become moot. We are not aware of studies that compare health outcomes among similar workers with EPHI versus, for example, Medicaid or individual private insurance. But for the foreseeable future, many employed people in the US will likely not have insurance. Regarding other fringe benefits, Asfaw et al. (2017) find sick leave (pathway #9) reduces flu-related absences because the flu is less likely to spread and Rossin (2011) finds maternity leave (pathway #10) improves child health.

Studies have identified disproportionate shares of workplace hazards and injuries within non-standard jobs (#19) ( Howard, 2017 ). Apouey and Stabile (2019) find that non-standard employment is associated with good mental health due to the job control and flexibility. Piece work and incentive pay (#20) likely harm health ( DeVaro and Heywood, 2017 ). The first economist to suggest harm was Adam Smith: “Workmen …. when they are liberally paid by the piece, are very apt to overwork themselves, and to ruin their health and constitution in a few years” ( DeVaro and Heywood, 2017 ).

Economy-wide increases in either wage or income inequality (#28) likely harm population health. We are not aware of epidemiologic studies addressing only wage inequality (as there are in economics). A plethora of epidemiologic studies, however, have addressed income inequality. Ross et al.(2000) find inequality increases mortality in the US but has no effect in Canada. But Kawachi et al., 2014 , Pickett and Wilkinson, 2009 find reduced inequality improves health across states and nations. Moreover, there is a wealth of epidemiologic literature addressing health effects of income reaching back decades ( Kitagawa and Hauser, 1973 )

5. Direct associations between unions and health outcomes

Table 3 presents pathways, judgments, and studies on the direct associations of unions with various measures of health. There are far fewer of these studies than appear in Table 2 . These studies differ from those in Table 2 in that the dependent variable measures health and the key independent variable measures unions; no links are required between economic and epidemiologic studies. The first two dependent variables in the first two rows summarize literature reviews in the Appendix. All studies recognize the possibility of reverse causality: workplace hazards might result in more unions because unions might be more likely to form in workplaces that have significant hazards. Some studies attempt to remove reverse causality with instrumental variables and/or longitudinal data ( Donado, 2015 ) so that researchers can test whether unions reduce the number of injuries from existing high levels. An additional complicating factor is that unions likely help workers apply for and receive workers’ compensation benefits ( Hirsch et al., 1997 ). Our assessment of the literature in the Appendix is that unions decrease fatal injuries (pathway #29) but findings for non-fatal injuries (#30) are disputed.

There is consensus for the second dependent variable: unions increase reported sickness absence (#31). There is no consensus, however, for explaining the correlation. Most researchers suggest that rather than indicating unions cause sickness, unions encourage workers to take more sick days when they are truly sick. Union workers may not feel as threatened with employer retaliation as non-union workers for taking days off. Finally, seven unique studies in the bottom rows of Table 3 pertain to unions improving self-rated physiological and psychological health, drug overdoses, mortality, and food insecurity.

Table 4 provides a summary of findings. We created five categories for assessments. For a pathway to qualify within either the “Consensus unions improve health” or “Consensus unions harm health” there must have been consensus either in both columns 2 and 4 of Table 2 or within Table 3 . For the two “likely” categories, qualification was broader: either the pathways in both columns 2 and 4 of Table 2 were “likely” or one was “likely” and the other was “consensus”; or the pathway in Table 3 is “likely.” If any pathway garnered a “disputed” or “unknown” judgment in either column 2 or 4 of Table 2 or Table 3 , that pathway was classified as “disputed and unknown” in summary Table 4 .

Summary of findings on pathways and outcomes.

*Note: Reported absence is not an outcome; only actual sickness or injury leading to absence is an outcome.

Findings in Table 4 reveal that whereas there are four consensus pathways and outcomes and 16 likely pathways and outcomes for unions improving health, there are no consensus or likely pathways for harming health. We cannot conclude, however, that unions improve health overall because there are 15 disputed and unknown pathways and outcomes and any of these may have powerful harmful effects.

7. Limitations and methodological issues for future research

This study has limitations. First, readers may not agree with our judgments regarding summaries of findings, but they at least have a place to begin to form their own or construct studies to test these pathways. Yet setting aside judgments, we identify 35 pathways and outcomes and cite relevant studies for each. Virtually all “union and health” studies with which we are aware have identified no more than three pathways. Malinowski et al., 2015 , Hagedorn et al., 2016 identify more but they do not link their pathways to the economics literature, nor do they identify as many as in this study. Second, with the exception of effects on economy-wide wages and inequality, we do not include any other possible economy-wide effects such as possible effects on the quality of products (e.g. unionized nurses providing better cardiovascular care ( Ash and Seago, 2015 ) or union support for political public health initiatives such as Obamacare or effects on broader Social Determinants of Health such as housing. With four exceptions ---employer-provided health insurance (EPHI), paid family leave, individual worker-controlled flexibility and wages--- we do not address effects on families. Finally, this review primarily focuses on private-sector unions because, apart from wages, there is little research on possible pathways for public-sector unions.

There are lessons from studies in Table 2 , Table 3 for future research. First, most research has been conducted within the private sector. When public- and private-sector sector unions are analyzed, they are frequently separated. Second, comparison groups must be constructed with an eye to the structure of the relevant labor market. For example, within the private-sector, blue-collar rather than white-collar workers are much more likely to be unionized. The comparison group for private-sector unions, therefore, should be non-unionized blue-collar, not white-collar, workers. Third, there are major gaps in research pertaining to, for example, union/non-union differences in education, job strain, and justice, and effects of pensions on health. Fourth, whatever the health dependent variable might be there is the possibility of reverse causality e.g. hazardous conditions may lead workers to form unions. To address this possibility researchers might use longitudinal data containing people who have joined or left unions over time or instrumental variables or propensity scores.

8. Conclusion

We first link predominately economic with predominately epidemiologic literatures to identify 28 job-related pathways whereby unions might influence the health of workers. Pathways include wages, wage inequality, and discrimination. Second, we report on studies with direct associations between unions and health including occupational injuries and absence from work. We cannot conclude that there is consensus that unions improve or harm overall worker health; we nevertheless find considerably more salubrious than harmful pathways and outcomes. Unions can also have effects outside workplaces; for example, they can help galvanize political support for public health legislation or minimum wages. But such effects are myriad and beyond the scope of this review. Unions are underappreciated institutions for affecting not only worker health, but the health of workers’ families and the public at-large.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.pmedr.2021.101502 .

Appendix A. Supplementary data

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The Impact of Trade Unions: What Do Economists Say?

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• Zafiris Tzannatos  

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The effects of trade unions, and of collective bargaining more broadly, on the specific sectors in which they operate or the economy at large vary depending on what unions, employers and governments do or want to do and whether the economy is competitive and open to trade. Various combinations of unions, firms, governments and types of economies can give rise to different economic outcomes of an otherwise identical configuration of organized labour. Even for the same economy, and union characteristics, effects can be different at different points in a country’s history. And even if the rypology of unions and collective bargaining were clear, there is a multitude of indicators for economic performance: this chapter focuses on more than 30 such indicators (Table 8.1) and this only because it presents a summary of more than one thousand studies on the subject, perhaps omitting as many others.

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The labor market effects of trade unions in an open economy: Layard meets Melitz

Do unions increase labor shares evidence from us industry-level data.

Hayek, Hutt and the Trade Unions

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There are effects in other areas that are not examined in this chapters such as wage dispersion, returns to schooling, voluntary turnover, profitability levels and growth differential, speed of implementation of new technology and implementation of physical investments, research and development and so on. These are coved in Aidt and Tzannatos (2002) and, more recently, in Aidt and Tzannatos (2006) ‘Unions and Microeconomic Performance: A Look at What Matters for Hard Core Economists (and Employers)’, International Economic Review , 125(4), 257–78.

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Tzannatos, Z. (2008). The Impact of Trade Unions: What Do Economists Say?. In: Berg, J., Kucera, D. (eds) In Defence of Labour Market Institutions. The International Labour Organization. Palgrave Macmillan, London. https://doi.org/10.1057/9780230584204_8

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Annual Review of Political Science

Volume 20, 2017, review article, labor unions, political representation, and economic inequality.

• John S. Ahlquist 1
• View Affiliations Hide Affiliations Affiliations: School of Global Policy and Strategy, University of California San Diego, La Jolla, California 92093; email: [email protected]
• Vol. 20:409-432 (Volume publication date May 2017) https://doi.org/10.1146/annurev-polisci-051215-023225
• First published as a Review in Advance on March 09, 2017
• © Annual Reviews

Decades of research across several disciplines have produced substantial evidence that labor unions, on balance, reduce economic disparities. But unions are complicated, multifaceted organizations straddling markets and politics. Much of their equality-promoting influence occurs through their ability to reduce class-based inequity in politics and public policy. Declining unionization across much of the developed world is eroding workers’ bargaining power. Reduced economic leverage puts pressure on union solidarity and weakens labor-based political movements. Important research design problems and significant heterogeneity across unions, regions, countries, and time imply a continued need for more work.

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"Exploring the role of trade unions in the representation of worker interests"

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Premised on the fact that there are different globalizations going on today, this comprehensive study successfully integrates structural and symbolic analyses of communications and media policy in the conflicted spaces of the nation-state, trans-nation, and sub-nation. Chakravartty & Sarikakis’s remarkably systematic approach to media policy, technology, content, and civil society formation, fills in crucial details left behind by grand theory, including progressive postcolonial theory of global communication. In doing so, the book re-energizes the hackneyed field of international media studies and transforms it. John Nguyet Erni, City University of Hong Kong Media Policy and Globalization combines careful scholarship with a clear, accessible style that creatively integrates some of the best elements of critical theory. The book marks an important step in the development of media policy scholarship because it skilfully integrates political economic and cultural studies perspectives. It does an especially good job of placing research on state and gender theory into the centre of policy analysis. Vincent Mosco, Queen’s University, author of The Digital Sublime Media Policy and Globalization serves up an ambitious, readable, and concise synthesis of how the messy world-system of communication policy is described and pondered in the communications and media studies discipline. Global Media and Communication In addition to its well-structured analyses, the book is written in an easy, accessible manner and offers rich empirical material and useful case studies for teaching purposes. Cees J. Hamelink, Amsterdam/Brisbane, Publizistik This book presents many rich clues for us to look further at on-going policy debates. Those clues point us toward inclusion of a variety of national, non-national, international, regional, and civil players as well as their organic connections. For any researcher, graduate student, or upper-division undergraduate student interested in global media debate today, this book provides not only the most up-to-date references, but also a fresh way to look at multiple-level analytical levels of analysis. Atsushi Tajima, SUNY , Global Media Journal The ideas and explanation in this book are a very welcome antidote to the dominant discourse of the virtues of the market, new technologies and competition. The proponents of technological determinism have for the past 10 years asserted that greater audiovisual delivery capacity will automatically deliver diversity and pluralism and have sought to roll back virtually all audiovisual regulation. The authors describe well the valid political, social, economic and particularly cultural questions which demand an answer if the public interest is to be served in communications policy and the regulation which should flow from it. The authors rightly underline that the screen, large or small, is central to our democratic, creative, cultural and social life and that policy makers should give greater space to the views of civil society and parliamentarians interested in advancing the public interest. Rare is the attention paid to the realities of the digital divide as played out across the globe which provides important information for campaigners for greater technological redistribution and cultural diversity worldwide. Carole Tongue, Visiting Professor, University of the Arts, London, Former MEP spokesperson on public service broadcasting

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Post-Neoliberal Globalization: International Trade Rules for Global Prosperity

This paper analyzes frameworks for the design of the rules for international trading, assuming that it is possible to have some rule of law. In the Arrow-Debreu benchmark, where there is no economic power and political power is seemingly irrelevant, there is no need for trade agreements – free trade is the optimal policy for each country. But under even minimal deviations from that benchmark, trade agreements matter. We focus on environments in which there are market failures, technology is endogenous, and there is political power. Power dynamics play, for instance, a critical role in the design, implementation, and enforcement of agreements, with the latter being a critical difference between international agreements and domestic contracts and a key determinant of the feasibility and consequences of agreements. With endogenous technology, trade rules proscribing industrial policies may lead to lower growth and greater cross-country inequalities. Finally, we develop a framework which may be useful in the design and implementation of trade rules.

The authors would like to acknowledge the very thoughtful and constructive comments from David Vines, Simon Cowan, Giovanni Dosi, participants of seminars of the Oxford Review of Economic Policy, the University of Sao Paulo (Brazil), and The Foundations of Complex Evolving Economies Conference at Sant’ Anna School of Advanced Studies (Pisa, Italy), the excellent editorial work of Meaghan Winter, and the financial support of the Sloan and Hewlett Foundations. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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Martin Guzman & Joseph E Stiglitz, 2024. " Post-neoliberal globalization: international trade rules for global prosperity, " Oxford Review of Economic Policy, vol 40(2), pages 282-306.

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Integrating trade-in strategies for optimal pre-positioning decisions in relief supply-chain systems.

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Ju, Y.; Hou, H.; Yang, J.; Ren, Y.; Yang, J. Integrating Trade-In Strategies for Optimal Pre-Positioning Decisions in Relief Supply-Chain Systems. Systems 2024 , 12 , 216. https://doi.org/10.3390/systems12060216

Ju Y, Hou H, Yang J, Ren Y, Yang J. Integrating Trade-In Strategies for Optimal Pre-Positioning Decisions in Relief Supply-Chain Systems. Systems . 2024; 12(6):216. https://doi.org/10.3390/systems12060216

Ju, Yingjie, Hanping Hou, Jianliang Yang, Yuheng Ren, and Jimei Yang. 2024. "Integrating Trade-In Strategies for Optimal Pre-Positioning Decisions in Relief Supply-Chain Systems" Systems 12, no. 6: 216. https://doi.org/10.3390/systems12060216

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Abortion United Evangelicals and Republicans. Now That Alliance Is Fraying.

The southern baptist convention, long a bellwether for american evangelicalism, voted to oppose the use of in vitro fertilization..

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The Southern Baptist Convention, the largest denomination of Protestant Christians in the United States, voted at an annual gathering last week to oppose the use of in vitro fertilization.

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Together Against Trafficking in Human Beings

Trafficking in human beings is a crime that should have no place in today’s society. It destroys individuals’ lives by depriving people of their dignity, freedom and fundamental rights. It is often a violent crime committed by organised crime networks.

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37% of the victims of trafficking in the EU are EU citizens, and a significant number of them are trafficked within their own country. However, non-EU victims have increased in recent years and they now outnumber victims with an EU citizenship. The majority of victims in the EU are women and girls who are mainly trafficked for sexual exploitation. The ratio of male victims has more than doubled in the last years.

Around 15% of victims of trafficking in the EU are children.

The most common forms of trafficking in the EU is sexual exploitation and labour exploitation . Both forms of exploitation amount to an equal share of victims. Most traffickers in the EU are EU citizens and often of the same nationality as their victims. More than three quarters of perpetrators are men.

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This crime brings high profits to criminals and carries with it enormous human, social and economic costs. Trafficking in human beings is often linked with other forms of organised crime such as migrant smuggling, drug trafficking, extortion, money laundering, document fraud, payment card fraud, property crimes, cybercrime and other.

This complex criminal phenomenon continues to be systematically addressed in a wide range of EU policy areas and initiatives from security to migration, justice, equality, fundamental rights, research, development and cooperation, external action and employment to name a few.

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The EU Anti-Trafficking Coordinator is responsible for improving coordination and coherence among EU institutions, EU agencies, Member States and international actors, and for developing existing and new EU policies to address Trafficking in Human Beings.

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