Story Idea

We want to write a story examining minimum wage laws in the United States in order to assess and report on how populations who depend on minimum wage have fared over time. We plan on examining Massachusetts and Texas, since Texas has always pegged their minimum wage to the Federal minimum wage, whereas Massachusetts has increased theirs. Why are these wages so different across the country, time and jurisdiction for the same jobs?

Audience Relevance

This story would be relevant to policy makers who are voting on/drafting minimum wage laws, academics studying minimum wage laws, and citizens who want to become more informed about the effects of increasing the minimum wage. Most importantly, the target audience is college students and young workers who are finding themselves making too little off of minimum wage to pay off loans.

Interviewing the Data

Load the dataset

library(tidyverse) # Be sure to load any additional libraries you may need here
## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --
## v ggplot2 3.3.3     v purrr   0.3.4
## v tibble  3.1.0     v dplyr   1.0.5
## v tidyr   1.1.3     v stringr 1.4.0
## v readr   1.4.0     v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
# Load your data here by using a function like read_csv()
library(readxl)
min_wage <- read_xlsx("min_wage_data.xlsx")

Preview the dataset

Here’s what the data look like:

# Use the head() function (or some other function) to quickly show what your data look like.
head(min_wage)
Question #1

Is the frequency of the data conducive to further analysis or should it be aggregated/subet in any way?

yearly <- min_wage %>% 
  separate(Date, into = c("Year", "Month", "Day"), sep = "-") %>% 
  group_by(Entity, Year) %>% 
  summarise(yearly_average = mean(`Minimum Wage`)) %>% 
  filter(Year >= 1972)
## `summarise()` has grouped output by 'Entity'. You can override using the `.groups` argument.

This data is currently a monthly basis. This is not ideal, as minimum wage laws are unlikely to change on a monthly basis. We will start by finding the yearly average for each entitiy. Also, we only have data for all three entities after 1972, so we will subset the data to only look after 1972.

Question #2

How do MA, TX, and Federal Minimum Wages compare over time?

yearly %>% 
  ggplot(aes(Year, yearly_average, group = Entity))+
  geom_line(aes(color = Entity))+
  theme_minimal()+
  labs(y = "Minimum Wage (Dollars)")+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

Texas has consistently been lower than or equal to the federal minium wage. The minimum wage in Massachusetts tracked very closely with the Federal minimum wage, but then began to diverge upwards in 1999

Question #3

In which years were the Massachusetts and Federal minimum wage equal?

MA_fed_same <- yearly %>% 
  filter(Entity != "Texas") %>% 
  arrange(Year) %>% 
  group_by(Year) %>% 
  mutate(diff = yearly_average - lag(yearly_average, default = yearly_average[1])) %>% 
  filter(Entity == "Massachusetts", diff == 0)
  
MA_fed_same <- MA_fed_same$Year

Massachusetts and the Federal Government had the same minimum wage in the following years: 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1992, 1993, 1994, 1995.

Question #4

In which years were the Texas and Federal minimum wage equal?

TX_fed_same <- yearly %>% 
  filter(Entity != "Massachusetts") %>% 
  arrange(Year) %>% 
  group_by(Year) %>% 
  mutate(diff = yearly_average - lag(yearly_average, default = yearly_average[1])) %>% 
  filter(Entity == "Texas", diff == 0)
  
TX_fed_same <- TX_fed_same$Year

Texas and the Federal Government had the same minimum wage in the following years: 1988, 1989, 2002, 2003, 2004, 2005, 2006, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021.

Question #5

How many times did MA increase minimum wage? How many times did Texas? And the Federal minumum?

MA_increase <- yearly %>% 
  filter(Entity == "Massachusetts") %>% 
  arrange(Year) %>% 
  mutate(diff = yearly_average - lag(yearly_average, default = yearly_average[1])) %>% 
  filter(diff > 0)

TX_increase <- yearly %>% 
  filter(Entity == "Texas") %>% 
  arrange(Year) %>% 
  mutate(diff = yearly_average - lag(yearly_average, default = yearly_average[1])) %>% 
  filter(diff > 0)

Fed_increase <- yearly %>% 
  filter(Entity == "Federal") %>% 
  arrange(Year) %>% 
  mutate(diff = yearly_average - lag(yearly_average, default = yearly_average[1])) %>% 
  filter(diff > 0)

Massachusetts increased the minimum wage 19 times. Texas increased the minimum wage 5 times. The Federal government increased the minimum wage 17 times.

Reflection and Next Steps

How has your story idea changed after interviewing your dataset, if at all? If your story idea is changing after asking these questions, take note of that here. If it has not, what about your analysis to date gives you greater confidence in your idea?

After interviewing our dataset, we have generated a list of questions about the legislative side of this story that the data do not tell. Specifically, after looking at the wages of Texas over time in comparison, we wonder what defines the lag where the state minimum wage falls below the federal minimum wage. Are there rules defining when legislation can be updated? What obligation do the states have to update their minimum wage laws? Is this obligation defined by relative dates (relative to federal policy) or concrete dates (i.e. 5 years after federal updates, states must update). It also made us wonder how it is that Texas spent so long paying it’s workers less than the federal minumum wage. Why is Texas’ minimum wages so much lower than Massachusetts, and especially federal minimum wages? What is the process for creating and updating minumum wage legislation?

What else would you have to do in order to fully report out this story? For example, who might be some good human sources that could be integrated into this story? What other datasets or documents could aid you in reporting this story?

We need to collect information that will control for inflation, population, labor market inventory, cost of living, other wage legislation, other legislative bodies in order to draw conclusions about comparisons made between states. This might include historical CPI-U data, Census data, Bureau of Labor Statistics data, and minimum wage legislative documents for each geography.

In terms of interviewees, it could be very useful to add anecdotal evidence about those who are minimum wage workers, and from those who have a hand in designing and updating legislation. These human sources might look like:

  • A 2015-2020 college grad who earned money during college through a minimum wage job/worked a minumum wage job in general
  • A 1965-1979 college grad who earned money during college through a minimum wage job/worked or works a minimum wage job in general
  • A professor or legislator with expertise on the design and process of minimum wage legislation