Happy Labor Day! 🛠️ As we celebrate the achievements of workers and the contributions they’ve made to society, what better way to delve into the world of labor and employment than with a fresh Tidy Tuesday dataset? This week’s data comes from the comprehensive “Union Membership, Coverage, and Earnings from the CPS” dataset, courtesy of Barry Hirsch from Georgia State University, David Macpherson from Trinity University, and William Even from Miami University.

This dataset is a rich source of information, shedding light on the intricate dynamics of union membership, coverage, and earnings. So, grab your coffee, get comfortable, and let’s embark on this Tidy Tuesday journey together, exploring the intricate tapestry of labor data!

Import data

Code

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from scipy import stats
import geopandas
from lets_plot.geo_data import *

from lets_plot import *
from lets_plot.mapping import as_discrete

LetsPlot.setup_html()

There are three distinct datasets to investigate: one focusing on demographic information, another centered on wage data, and the third associated with various states. Let’s read them all in.

Code

demographic_df = pd.read_csv(
    "https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-09-05/demographics.csv"
)
wages_df = pd.read_csv(
    "https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-09-05/wages.csv"
)
states_df = pd.read_csv(
    "https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-09-05/states.csv"
)

Data dictionaries

Here are the corresponding data dictionaries:

Demographics data: Data sources:

1973-1981: May Current Population Survey (CPS)
1982: No union questions available
1983-2022: CPS Outgoing Rotation Group (ORG) Earnings Files

The definition of union membership was expanded in 1977 to include “employee associations similar to a union”.

variable	class	description
year	double	When the data was collected.
sample_size	double	The number of wage and salary workers ages 16 and over who were surveyed.
employment	double	Wage and salary employment in thousands.
members	double	Employed workers who are union members in thousands.
covered	double	Workers covered by a collective bargaining agreement in thousands.
p_members	double	Percent of employed workers who are union members.
p_covered	double	Percent of employed workers who are covered by a collective bargaining agreement.
facet	character	The sector or demographic group contained in this row of data.

Wages data: Data sources:

1973-1981: May Current Population Survey (CPS)
1982: No union questions available
1983-2022: CPS Outgoing Rotation Group (ORG) Earnings Files

The definition of union membership was expanded in 1977 to include “employee associations similar to a union”.

variable	class	description
year	double	When the data was collected.
sample_size	double	The number of wage and salary workers ages 16 and over who were surveyed and provided earnings and hours worked information.
wage	double	Mean hourly earnings in nominal dollars.
at_cap	double	Percent of workers with weekly earnings at the top code of $999 through 1988, $1923 in 1989-97, and $2885 beginning in 1998, with individuals assigned mean earnings above the cap based on annual estimates of the gender-specific Pareto distribution.
union_wage	double	Mean wage among union members.
nonunion_wage	double	Mean wage among nonunion workers.
union_wage_premium_raw	double	The percentage difference between the union and nonunion wage.
union_wage_premium_adjusted	double	Estimated as exp(b)-1 where b is the regression coefficient on a union membership variable (equal to 1 if union and 0 otherwise) from a semi-logarithmic wage equation, with controls included for worker/job characteristics. Included in the all-worker wage equation are the control variables: years of schooling, potential years of experience [proxied by age minus years of schooling minus 6] and its square [both interacted with gender], and categorical variables for marital status, race and ethnicity, gender, part-time, large metropolitan area, state, public sector, broad industry, and broad occupation. Controls are omitted, as appropriate, for estimates within sectors or by demographic group [i.e., by class, gender, race, or industry sector]. Workers who do not report earnings but instead have them imputed [i.e., assigned] by the Census are removed from the estimation samples in all years, except 1994 and 1995 when imputed earners cannot be identified. Inclusion of imputed earners causes union wages to be understated, nonunion wages overstated, and union-nonunion wage differences understated. For 1994-95, the sample includes imputed earners and estimates in those years have been adjusted to remove the bias from imputation.
facet	character	The sector or demographic group contained in this row of data.

States data:

Data source: Current Population Survey (CPS) Outgoing Rotation Group (ORG) Earnings Files

variable	class	description
state_census_code	double	Census state code used in CPS
state	character	State name.
sector	character	Employment sector.
observations	double	CPS sample size.
employment	double	Wage and salary employment in thousands.
members	double	Employed workers who are union members in thousands.
covered	double	Workers covered by a collective bargaining agreement in thousands.
p_members	double	Percent of employed workers who are union members.
p_covered	double	Percent of employed workers who are covered by a collective bargaining agreement.
state_abbreviation	character	State abbreviation.
year	double	Year of the survey.

As in our previous Tidy Tuesday blog, I believe we can address the following questions using this dataset:

What are the overarching trends in the labor force, particularly regarding union memberships?
Do specific demographic groups or occupations display a higher likelihood of union membership?
Do union members experience any financial advantages or benefits compared to non-union workers?
Which states have the highest number of union members or affiliated unions?

What are the overarching trends in the labor force, particularly regarding union memberships?

The figure below illustrates a consistent upward trajectory in the workforce’s growth, whereas union membership and the number of individuals covered by unions have been on a declining trend over the decades.

Code

(
    demographic_df.query("facet.str.contains('all wage')")
    .drop(columns=["sample_size", "facet", "p_members", "p_covered"])
    .melt(id_vars="year", var_name="Description")
    .pipe(
        lambda df: ggplot(df)
        + geom_line(aes("year", "value", color="Description"), size=1.5)
        + labs(
            title="Evolution of Workforce Size and Union Membership Across the Years",
            subtitle="While employment numbers have been on the rise, the proportion of employed workers \nwho are union members and covered by unions has been steadily decreasing.",
            x="",
            y="Number of people in thousands",
            caption="© 2023 by Barry T. Hirsch, David A. Macpherson, and William E. Even.",
        )
        + theme(
            plot_subtitle=element_text(size=12, face="italic"),
            plot_title=element_text(size=15, face="bold"),
        )
    )
)

Figure 1: Evolution of Workforce Size and Union Membership Across the Years

Do specific demographic groups or occupations display a higher likelihood of union membership?

Next, our investigation will focus on identifying potential demographic disparities among workforce members and uncovering the professions with the highest likelihood of union membership. We’ll utilize the “p_members” column, which represents the percentage of employed workers who are union members, to delve into these aspects.

As evident in Figure 2, a substantial decrease in union membership is observable across nearly all demographic groups over the decades.

Code

(
    demographic_df.query("facet.str.contains('demographics')")
    .assign(facet=lambda df: df.facet.replace({"demographics:": ""}, regex=True))
    .pipe(
        lambda df: ggplot(df, aes("year", "p_members", color="facet"))
        + geom_line(
            size=1.5,
        )
        + labs(
            title="Demographic Trends in Union Membership as a Percentage of the Total Workforce Over Time",
            subtitle="Almost all demographic groups exhibit a significant decline in union membership over the decades.",
            x="",
            y="Percent of employed workers who are union members",
            caption="© 2023 by Barry T. Hirsch, David A. Macpherson, and William E. Even.",
        )
        + theme(
            plot_subtitle=element_text(size=12, face="italic"),
            plot_title=element_text(size=15, face="bold"),
        )
        + ggsize(800, 500)
    )
)

Figure 2: Evolution of Workforce Size and Union Membership Across the Years

While certain professions exhibit declining trends in union memberships, it’s noteworthy that some public sector occupations, such as postal service, police, and local government jobs, maintain the highest and most consistent levels of union participation (Figure 3).

Code

(
    demographic_df.query(
        "(~facet.str.contains('demographics')) and (~facet.str.contains('all'))"
    ).pipe(
        lambda df: ggplot(df, aes("year", "p_members", color="facet"))
        + geom_line(
            size=1.5,
        )
        + labs(
            title="Workforce Sector-Specific Trends in Union Membership",
            subtitle="Public sector occupations demonstrate higher union participation rates.",
            x="",
            y="Percent of employed workers who are union members",
            caption="© 2023 by Barry T. Hirsch, David A. Macpherson, and William E. Even.",
        )
        + theme(
            plot_subtitle=element_text(size=12, face="italic"),
            plot_title=element_text(size=15, face="bold"),
        )
        + ggsize(800, 500)
    )
)

Figure 3: Workforce Sector-Specific Trends in Union Membership

Do union members experience any financial advantages or benefits compared to non-union workers?

The next figure is both straightforward and highly impactful, providing a direct comparison of average wages for union members versus non-union members.

Code

(
    wages_df.query("facet == 'all wage and salary workers'")
    .drop(
        columns=[
            "wage",
            "sample_size",
            "at_cap",
            "union_wage_premium_raw",
            "union_wage_premium_adjusted",
            "facet",
        ]
    )
    .melt(id_vars="year", var_name="Description")
    .pipe(
        lambda df: ggplot(df, aes("year", "value", color="Description"))
        + geom_line(
            size=1.5,
        )
        + labs(
            title="Differences in Mean Hourly Earnings for Union and Non-Union Workers",
            subtitle="Union workers consistently display higher mean wages in comparison to non-union members.",
            x="",
            y="Mean hourly earnings in nominal dollars",
            caption="© 2023 by Barry T. Hirsch, David A. Macpherson, and William E. Even.",
        )
        + theme(
            plot_subtitle=element_text(size=12, face="italic"),
            plot_title=element_text(size=15, face="bold"),
        )
    )
)

Figure 4: Differences in Mean Hourly Earnings for Union and Non-Union Workers

As with our previous figures, we will now delve into demographic variations and job sector disparities. To unveil these potential distinctions, we will use the “union_wage_premium_raw” column, which indicates the percentage difference between union and non-union wages.

The most significant disparities in hourly wages (Figure 5) between unionized and non-unionized workers are observed within the construction sector. These disparities have fluctuated over the decades, ranging from below 40% to as high as 80%. It’s important to note that almost all sectors exhibit higher earnings for union members, except for federal, manufacturing, and wholesale/retail workers (particularly in recent times).

Code

(
    wages_df.query(
        "(~facet.str.contains('demographics')) and (~facet.str.contains('all'))"
    ).pipe(
        lambda df: ggplot(df, aes("year", "union_wage_premium_raw", color="facet"))
        + geom_line(
            size=1.5,
        )
        + labs(
            title="Sector-specific Differences in Mean Hourly Earnings for Union and Non-Union Workers",
            subtitle="The disparities between wages range from -20% to +80%.",
            x="",
            y="The percentage difference between the union and nonunion wage",
            caption="© 2023 by Barry T. Hirsch, David A. Macpherson, and William E. Even.",
        )
        + theme(
            plot_subtitle=element_text(size=12, face="italic"),
            plot_title=element_text(size=15, face="bold"),
        )
        + ggsize(800, 500)
        + geom_hline(yintercept=0, linetype=5, color="#6c6c6c", size=1)
    )
)

Figure 5: Sector-specific Differences in Mean Hourly Earnings for Union and Non-Union Workers

In addition to gender-specific differences, it’s intriguing to observe the relationship between education levels and wage disparities between unionized and non-unionized workers. Workers with education levels below college tend to benefit more from union membership, whereas, in general, individuals with higher education levels tend to have lower wages when affiliated with a union.

Code

(
    wages_df.query("facet.str.contains('demographics')")
    .assign(facet=lambda df: df.facet.replace({"demographics:": ""}, regex=True))
    .pipe(
        lambda df: ggplot(df, aes("year", "union_wage_premium_raw", color="facet"))
        + geom_line(
            size=1.5,
        )
        + labs(
            title="Demographics-specific Differences in Mean Hourly Earnings for Union and Non-Union Workers",
            subtitle="It appears that females tend to benefit more from union membership in comparison to males, \nparticularly in terms of the percentage difference between union and non-union wages.",
            x="",
            y="The percentage difference between the union and nonunion wage",
            caption="© 2023 by Barry T. Hirsch, David A. Macpherson, and William E. Even.",
        )
        + theme(
            plot_subtitle=element_text(size=12, face="italic"),
            plot_title=element_text(size=15, face="bold"),
        )
        + ggsize(800, 500)
        + geom_hline(yintercept=0, linetype=5, color="#6c6c6c", size=1)
    )
)

Figure 6: Demographics-specific Differences in Mean Hourly Earnings for Union and Non-Union Workers

Which states have the highest number of union members or affiliated unions?

To illustrate variations in union membership across states, we will leverage Lets Plot’s capabilities for visualizing spatial data. Given the extensive dataset, our focus will be on highlighting a subset of the data, specifically the most recent trends in 2022 across all sectors.

As evident in Figure 7, certain states like New York, Alaska, Hawaii, Washington, California, and Oregon have a significant portion of their workforce as union members. Conversely, states like Utah, South Dakota, and the Carolinas display lower percentages of union membership among their workforce.

Code

filtered_data = states_df.query("sector == 'Total' and year == 2022")
boundaries = (
    geocode_states(filtered_data["state"])
    .scope("USA")
    .inc_res(4)
    .get_boundaries(resolution=4)
)

(
    ggplot()
    + geom_livemap()
    + geom_polygon(
        aes(color="p_members", fill="p_members"),
        data=states_df.query("sector == 'Total' and year == 2022"),
        map=boundaries,
        alpha=0.9,
        map_join=[["state"], ["state"]],
        show_legend=False,
        size=0.01,
    )
    + theme(
        axis_title="blank", axis_text="blank", axis_ticks="blank", axis_line="blank"
    )
    + scale_fill_brewer(palette="Greens")
    + ggsize(800, 500)
)

Figure 7: Percentage of Union Members Across States

There you have it! I hope you found this week’s Tidy Tuesday analysis insightful. I would encourage everyone to dive deeper into this dataset as I’ve only scratched the surface; there’s a wealth of knowledge waiting to be uncovered here.

Happy coding, and until next time, see you in our next exploration!

--- title: 'Tidy Tuesday: Union Membership in the United States' author: Adam Cseresznye date: '2023-09-05' categories: - Tidy Tuesday jupyter: python3 toc: true format: html: code-fold: true code-tools: true --- ![Photo by Claudio Schwarz](https://images.unsplash.com/photo-1572371402638-c654219dd0f8?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=2070&q=80){fig-align="center" width=50%} Happy Labor Day! 🛠️ As we celebrate the achievements of workers and the contributions they've made to society, what better way to delve into the world of labor and employment than with a fresh [Tidy Tuesday dataset](https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-09-05/readme.md)? This week's data comes from the comprehensive "Union Membership, Coverage, and Earnings from the CPS" dataset, courtesy of Barry Hirsch from Georgia State University, David Macpherson from Trinity University, and William Even from Miami University. This dataset is a rich source of information, shedding light on the intricate dynamics of union membership, coverage, and earnings. So, grab your coffee, get comfortable, and let's embark on this Tidy Tuesday journey together, exploring the intricate tapestry of labor data! # Import data ```{python} #| tags: [] import pandas as pd import numpy as np import seaborn as sns import matplotlib.pyplot as plt from scipy import stats import geopandas from lets_plot.geo_data import * from lets_plot import * from lets_plot.mapping import as_discrete LetsPlot.setup_html() ``` There are three distinct datasets to investigate: one focusing on demographic information, another centered on wage data, and the third associated with various states. Let's read them all in. ```{python} #| tags: [] demographic_df = pd.read_csv( "https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-09-05/demographics.csv" ) wages_df = pd.read_csv( "https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-09-05/wages.csv" ) states_df = pd.read_csv( "https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-09-05/states.csv" ) ``` # Data dictionaries Here are the corresponding data dictionaries: **Demographics data:** Data sources: - 1973-1981: May Current Population Survey (CPS) - 1982: No union questions available - 1983-2022: CPS Outgoing Rotation Group (ORG) Earnings Files The definition of union membership was expanded in 1977 to include "employee associations similar to a union". |variable |class |description | |:-----------|:---------|:-----------| |year |double |When the data was collected. | |sample_size |double |The number of wage and salary workers ages 16 and over who were surveyed. | |employment |double |Wage and salary employment in thousands. | |members |double |Employed workers who are union members in thousands. | |covered |double |Workers covered by a collective bargaining agreement in thousands. | |p_members |double |Percent of employed workers who are union members. | |p_covered |double |Percent of employed workers who are covered by a collective bargaining agreement. | |facet |character |The sector or demographic group contained in this row of data. | **Wages data:** Data sources: - 1973-1981: May Current Population Survey (CPS) - 1982: No union questions available - 1983-2022: CPS Outgoing Rotation Group (ORG) Earnings Files The definition of union membership was expanded in 1977 to include "employee associations similar to a union". |variable |class |description | |:---------------------------|:---------|:---------------------------| |year |double |When the data was collected. | |sample_size |double |The number of wage and salary workers ages 16 and over who were surveyed and provided earnings and hours worked information. | |wage |double |Mean hourly earnings in nominal dollars. | |at_cap |double |Percent of workers with weekly earnings at the top code of \$999 through 1988, \$1923 in 1989-97, and \$2885 beginning in 1998, with individuals assigned mean earnings above the cap based on annual estimates of the gender-specific Pareto distribution. | |union_wage |double |Mean wage among union members. | |nonunion_wage |double |Mean wage among nonunion workers. | |union_wage_premium_raw |double |The percentage difference between the union and nonunion wage. | |union_wage_premium_adjusted |double |Estimated as exp(b)-1 where b is the regression coefficient on a union membership variable (equal to 1 if union and 0 otherwise) from a semi-logarithmic wage equation, with controls included for worker/job characteristics. Included in the all-worker wage equation are the control variables: years of schooling, potential years of experience [proxied by age minus years of schooling minus 6] and its square [both interacted with gender], and categorical variables for marital status, race and ethnicity, gender, part-time, large metropolitan area, state, public sector, broad industry, and broad occupation. Controls are omitted, as appropriate, for estimates within sectors or by demographic group [i.e., by class, gender, race, or industry sector]. Workers who do not report earnings but instead have them imputed [i.e., assigned] by the Census are removed from the estimation samples in all years, except 1994 and 1995 when imputed earners cannot be identified. Inclusion of imputed earners causes union wages to be understated, nonunion wages overstated, and union-nonunion wage differences understated. For 1994-95, the sample includes imputed earners and estimates in those years have been adjusted to remove the bias from imputation. | |facet |character |The sector or demographic group contained in this row of data. | **States data:** Data source: Current Population Survey (CPS) Outgoing Rotation Group (ORG) Earnings Files |variable |class |description | |:------------------|:---------|:------------------| |state_census_code |double |Census state code used in CPS | |state |character |State name. | |sector |character |Employment sector. | |observations |double |CPS sample size. | |employment |double |Wage and salary employment in thousands. | |members |double |Employed workers who are union members in thousands. | |covered |double |Workers covered by a collective bargaining agreement in thousands. | |p_members |double |Percent of employed workers who are union members. | |p_covered |double |Percent of employed workers who are covered by a collective bargaining agreement. | |state_abbreviation |character |State abbreviation. | |year |double |Year of the survey. | As in our previous Tidy Tuesday blog, I believe we can address the following questions using this dataset: * What are the overarching trends in the labor force, particularly regarding union memberships? * Do specific demographic groups or occupations display a higher likelihood of union membership? * Do union members experience any financial advantages or benefits compared to non-union workers? * Which states have the highest number of union members or affiliated unions? # What are the overarching trends in the labor force, particularly regarding union memberships? The figure below illustrates a consistent upward trajectory in the workforce's growth, whereas union membership and the number of individuals covered by unions have been on a declining trend over the decades. ```{python} #| fig-cap: Evolution of Workforce Size and Union Membership Across the Years #| label: fig-fig1 #| tags: [] ( demographic_df.query("facet.str.contains('all wage')") .drop(columns=["sample_size", "facet", "p_members", "p_covered"]) .melt(id_vars="year", var_name="Description") .pipe( lambda df: ggplot(df) + geom_line(aes("year", "value", color="Description"), size=1.5) + labs( title="Evolution of Workforce Size and Union Membership Across the Years", subtitle="While employment numbers have been on the rise, the proportion of employed workers \nwho are union members and covered by unions has been steadily decreasing.", x="", y="Number of people in thousands", caption="© 2023 by Barry T. Hirsch, David A. Macpherson, and William E. Even.", ) + theme( plot_subtitle=element_text(size=12, face="italic"), plot_title=element_text(size=15, face="bold"), ) ) ) ``` # Do specific demographic groups or occupations display a higher likelihood of union membership? Next, our investigation will focus on identifying potential demographic disparities among workforce members and uncovering the professions with the highest likelihood of union membership. We'll utilize the *"p_members"* column, which represents the percentage of employed workers who are union members, to delve into these aspects. As evident in @fig-fig2, a substantial decrease in union membership is observable across nearly all demographic groups over the decades. ```{python} #| fig-cap: Evolution of Workforce Size and Union Membership Across the Years #| label: fig-fig2 #| tags: [] ( demographic_df.query("facet.str.contains('demographics')") .assign(facet=lambda df: df.facet.replace({"demographics:": ""}, regex=True)) .pipe( lambda df: ggplot(df, aes("year", "p_members", color="facet")) + geom_line( size=1.5, ) + labs( title="Demographic Trends in Union Membership as a Percentage of the Total Workforce Over Time", subtitle="Almost all demographic groups exhibit a significant decline in union membership over the decades.", x="", y="Percent of employed workers who are union members", caption="© 2023 by Barry T. Hirsch, David A. Macpherson, and William E. Even.", ) + theme( plot_subtitle=element_text(size=12, face="italic"), plot_title=element_text(size=15, face="bold"), ) + ggsize(800, 500) ) ) ``` While certain professions exhibit declining trends in union memberships, it's noteworthy that some public sector occupations, such as postal service, police, and local government jobs, maintain the highest and most consistent levels of union participation (@fig-fig3). ```{python} #| fig-cap: Workforce Sector-Specific Trends in Union Membership #| label: fig-fig3 #| tags: [] ( demographic_df.query( "(~facet.str.contains('demographics')) and (~facet.str.contains('all'))" ).pipe( lambda df: ggplot(df, aes("year", "p_members", color="facet")) + geom_line( size=1.5, ) + labs( title="Workforce Sector-Specific Trends in Union Membership", subtitle="Public sector occupations demonstrate higher union participation rates.", x="", y="Percent of employed workers who are union members", caption="© 2023 by Barry T. Hirsch, David A. Macpherson, and William E. Even.", ) + theme( plot_subtitle=element_text(size=12, face="italic"), plot_title=element_text(size=15, face="bold"), ) + ggsize(800, 500) ) ) ``` # Do union members experience any financial advantages or benefits compared to non-union workers? The next figure is both straightforward and highly impactful, providing a direct comparison of average wages for union members versus non-union members. ```{python} #| fig-cap: Differences in Mean Hourly Earnings for Union and Non-Union Workers #| label: fig-fig4 #| tags: [] ( wages_df.query("facet == 'all wage and salary workers'") .drop( columns=[ "wage", "sample_size", "at_cap", "union_wage_premium_raw", "union_wage_premium_adjusted", "facet", ] ) .melt(id_vars="year", var_name="Description") .pipe( lambda df: ggplot(df, aes("year", "value", color="Description")) + geom_line( size=1.5, ) + labs( title="Differences in Mean Hourly Earnings for Union and Non-Union Workers", subtitle="Union workers consistently display higher mean wages in comparison to non-union members.", x="", y="Mean hourly earnings in nominal dollars", caption="© 2023 by Barry T. Hirsch, David A. Macpherson, and William E. Even.", ) + theme( plot_subtitle=element_text(size=12, face="italic"), plot_title=element_text(size=15, face="bold"), ) ) ) ``` As with our previous figures, we will now delve into demographic variations and job sector disparities. To unveil these potential distinctions, we will use the *"union_wage_premium_raw"* column, which indicates the percentage difference between union and non-union wages. The most significant disparities in hourly wages (@fig-fig5) between unionized and non-unionized workers are observed within the construction sector. These disparities have fluctuated over the decades, ranging from below 40% to as high as 80%. It's important to note that almost all sectors exhibit higher earnings for union members, except for federal, manufacturing, and wholesale/retail workers (particularly in recent times). ```{python} #| fig-cap: Sector-specific Differences in Mean Hourly Earnings for Union and Non-Union Workers #| label: fig-fig5 #| tags: [] ( wages_df.query( "(~facet.str.contains('demographics')) and (~facet.str.contains('all'))" ).pipe( lambda df: ggplot(df, aes("year", "union_wage_premium_raw", color="facet")) + geom_line( size=1.5, ) + labs( title="Sector-specific Differences in Mean Hourly Earnings for Union and Non-Union Workers", subtitle="The disparities between wages range from -20% to +80%.", x="", y="The percentage difference between the union and nonunion wage", caption="© 2023 by Barry T. Hirsch, David A. Macpherson, and William E. Even.", ) + theme( plot_subtitle=element_text(size=12, face="italic"), plot_title=element_text(size=15, face="bold"), ) + ggsize(800, 500) + geom_hline(yintercept=0, linetype=5, color="#6c6c6c", size=1) ) ) ``` In addition to gender-specific differences, it's intriguing to observe the relationship between education levels and wage disparities between unionized and non-unionized workers. Workers with education levels below college tend to benefit more from union membership, whereas, in general, individuals with higher education levels tend to have lower wages when affiliated with a union. ```{python} #| fig-cap: Demographics-specific Differences in Mean Hourly Earnings for Union and Non-Union Workers #| label: fig-fig6 #| tags: [] ( wages_df.query("facet.str.contains('demographics')") .assign(facet=lambda df: df.facet.replace({"demographics:": ""}, regex=True)) .pipe( lambda df: ggplot(df, aes("year", "union_wage_premium_raw", color="facet")) + geom_line( size=1.5, ) + labs( title="Demographics-specific Differences in Mean Hourly Earnings for Union and Non-Union Workers", subtitle="It appears that females tend to benefit more from union membership in comparison to males, \nparticularly in terms of the percentage difference between union and non-union wages.", x="", y="The percentage difference between the union and nonunion wage", caption="© 2023 by Barry T. Hirsch, David A. Macpherson, and William E. Even.", ) + theme( plot_subtitle=element_text(size=12, face="italic"), plot_title=element_text(size=15, face="bold"), ) + ggsize(800, 500) + geom_hline(yintercept=0, linetype=5, color="#6c6c6c", size=1) ) ) ``` # Which states have the highest number of union members or affiliated unions? To illustrate variations in union membership across states, we will leverage Lets Plot's capabilities for visualizing spatial data. Given the extensive dataset, our focus will be on highlighting a subset of the data, specifically the most recent trends in 2022 across all sectors. As evident in @fig-fig7, certain states like New York, Alaska, Hawaii, Washington, California, and Oregon have a significant portion of their workforce as union members. Conversely, states like Utah, South Dakota, and the Carolinas display lower percentages of union membership among their workforce. ```{python} #| fig-cap: Percentage of Union Members Across States #| label: fig-fig7 #| tags: [] filtered_data = states_df.query("sector == 'Total' and year == 2022") boundaries = ( geocode_states(filtered_data["state"]) .scope("USA") .inc_res(4) .get_boundaries(resolution=4) ) ( ggplot() + geom_livemap() + geom_polygon( aes(color="p_members", fill="p_members"), data=states_df.query("sector == 'Total' and year == 2022"), map=boundaries, alpha=0.9, map_join=[["state"], ["state"]], show_legend=False, size=0.01, ) + theme( axis_title="blank", axis_text="blank", axis_ticks="blank", axis_line="blank" ) + scale_fill_brewer(palette="Greens") + ggsize(800, 500) ) ``` There you have it! I hope you found this week's Tidy Tuesday analysis insightful. I would encourage everyone to dive deeper into this dataset as I've only scratched the surface; there's a wealth of knowledge waiting to be uncovered here. Happy coding, and until next time, see you in our next exploration!