Introduction

The Impact of Minimum Wage Raises on Cost of Living.

The topic we decided to research was the effect that a higher minimum wage has on the cost of living in its respective city. We are both from Los Angeles which has a very high minimum wage at above $17, and both of us, having grown up there, have seen the extremely high cost of living in the city, we have seen how wages and living expenses interact first hand, and decided to explore the topic more.

This made us think whether raising the wage helps uplift those who live off it or perpetuates the problem by involuntarily increasing the overall cost of living in the city by more than the wage increases. To do this, we will analyze the minimum wage increases and determine how they might perpetuate economic hardships that people may be going through. This topic is extremely important because it directly affects everyone who lives in any area with a minimum wage, whether it be the Federal Government’s rate or a specific city’s. It relates to current economic discussions because the constant raising of minimum wages can be viewed as a constant threat to inflation, especially within cities like Los Angeles. This in turn affects everyone, those who earn minimum wage and those who make more than it, because everyone will always need to have a home to live in and food to eat, along with whatever is required to live comfortably. Our approach to answering this question involves looking at specific cities, we want to select cities that are at a similar level of development comparable populations. We will analyze cities with high wages like Los Angeles, San Francisco, and Seattle. We also look at cities with low minimum wages, like Dallas and Miami. Our sources of data will consist of informational sites that provide raw data, like Zillow, when trying to find average rent costs in specific areas and average home prices. The US Bureau of Labor Statistics and Local Area Unemployment Statistics to find out how wage increase have affected minimum wage workers and their employment, as the cost of their work rises. The US Department of Labor to find the individual minimum wages of the cities we will look into. We will also use several other data sources to help pool together what the actual cost of living is like in each city, like CPI. Our project is in the format of a traditional research paper. We think this format is the best fit for our topic because it allows us to present a structured, detailed analysis of our topic and supporting evidence. Also, while using credible sources and data we have found, a traditional research paper is much more professional and better overall to explain our main points compared to a blog post format, which is more casual. Our goal is to assess whether raising the minimum wage helps uplift workers, but on the other hand, it can lead to a higher cost of living. With this, a research paper will be best for us to present a well-supported argument.

Data Section, what is the data?

This analysis draws from multiple trusted datasets, including historical minimum wage data, Consumer Price Index (CPI) levels sourced from the Bureau of Labor Statistics, county-level unemployment rates, Zillow rent price data, and estimated living wage figures from the MIT Living Wage Calculator. To build a comprehensive and consistent dataset, we aligned all sources by city and year, merging them into a unified panel that enabled cross-sectional and time-series comparisons. Key variables such as “Minimum Wage,” “Rent,” “CPI_U,” “Unemployment Rate,” and “Estimated Living Wage”, were standardized to facilitate regression analysis and data visualization. The study focuses on major metropolitan areas, including San Francisco, Los Angeles, Seattle, Dallas, and Miami, each selected for their economic diversity and levels of economic development. This integrated approach allowed us to empirically test how changes in minimum wage levels relate to broader cost-of-living pressures, particularly housing and inflation, across different local economies.

The minimum wage data used in this project is sourced from the U.S. Department of Labor's Wage and Hour Division (WHD) records, supplemented by state government releases for metro-specific minimum wage rates when available. This dataset tracks the legally mandated minimum hourly wage across different cities over time, focusing on the same metropolitan areas as the rent and CPI analysis: Los Angeles, San Francisco, Seattle, Dallas, and Miami. The data covers the period from January 2019 to April 2024. Because minimum wage laws often change at the beginning of calendar years (or mid-year in some cases), particular attention was paid to tracking the effective dates of changes to accurately align wage data with monthly economic indicators.

Minimum wages are a critical factor influencing cost of living, wage inflation, and the overall price level of services, particularly in low-income sectors like food service, retail, and healthcare. For this reason, including minimum wage trends helps provide a more complete picture of urban economic conditions and complements the analysis of consumer prices and housing costs. Python libraries such as pandas were used to process and merge minimum wage timelines with the broader economic datasets. Analyses include constructing time series plots showing minimum wage changes over time and calculating the percentage change between wage adjustments. This allows us to explore the relationship between policy-driven wage floors and broader inflation patterns across U.S. metro areas.

Rent

The rental data used in this project was sourced from Zillow's publicly available housing market datasets. Specifically, we use the "Zillow Observed Rent Index" (ZORI), which tracks median rental prices at the metropolitan level over time. To better account for variations in property size and to create a more standardized measure of housing costs, we focused on rental prices per square foot rather than absolute average rental prices. This allows for more accurate comparisons across regions with different housing markets. The data spans the period from January 2015 to April 2024 and covers all the key metro areas we are using as samples, such as Los Angeles, San Francisco, Seattle, Dallas, and Miami.

Given that rent is a major component of household living expenses and is heavily weighted in the overall cost of living, analyzing rent trends is essential for understanding regional differences in cost-of-living and inflation pressures. We could have reviewed median house prices as well, but as it relates to our topic, we assume that the vast majority of minimum wage earners cannot afford to own a house and rent instead. Using Python libraries such as pandas and matplotlib, we clean, process, and visualize the rental data. Our analysis includes creating time series plots of rent per square foot across cities, calculating descriptive statistics (mean, standard deviation, minimum, and maximum), and examining relative growth rates over time. These analyses help us assess how housing costs contribute to broader inflation trends in different urban areas.

CPI

This data pulls together CPI data for each respective county. As it relates to minimum wage, we are using historical CPI data as a measure of inflation. When we run our regression comparing historical wage increases to historical CPI data, we will look for a clear relationship between minimum wage increases and the rising cost of living across various U.S. cities. Cities like San Francisco, Seattle, and Miami, which have higher minimum wages, also have higher CPI levels compared to places like Dallas. Over the past year, Los Angeles and Miami, both with higher wage floors, experienced stronger CPI growth, while Dallas saw the slowest increase. In the short term, recent CPI changes also show that cities with higher wages, like San Francisco and Miami, are experiencing faster price increases, while Dallas even recorded a slight decline. This suggests that although higher minimum wages aim to uplift workers, they often contribute to higher living costs, which can offset the benefits of wage growth. Overall, the data suggests that minimum wage increases can help workers in the short run, but also risk driving up living costs and potentially straining local economies over time. This is shown in the regressions later in the paper. CPI data is crucial to the study of urban inflation, as it directly measures changes in the cost of living faced by households. Analyzing city-level CPI allows for comparisons of how inflationary pressures differ across regions, driven by factors like housing, transportation, and food costs. The data was retrieved using Python tools (requests and pandas) and processed to create a unified timeline across cities. Analyses include descriptive statistics of CPI movements and the construction of time series line charts that show inflation trends for each metro area. This visualization allows for easy comparison of inflation trajectories and highlights periods where specific cities experienced higher or lower inflation relative to others.

Liveable wage

The livable wage data used in this project was sourced from the MIT Living Wage Calculator, which estimates the minimum income necessary for individuals and families to meet basic needs without public assistance, adjusted by region. The dataset provides annual estimates of required living wages for metropolitan areas based on local housing costs, food, healthcare, transportation, and other essentials. Livable wage data complements the analysis of minimum wage and rent trends by providing a benchmark for what workers need to earn in different cities to achieve a modest standard of living. While minimum wage laws set legal wage floors, livable wages reflect actual cost-of-living realities and help highlight disparities between what workers are paid and what they require to afford basic expenses. We wanted to include this information because it displays the earning gap between the required living wage and minimum wage. Closing this gap is a very complex thing to do, and not in the scope of our research; however, it is relevant as it most likely influenced the implementation of drastically increased minimum wage policies seen in cities like San Francisco and Los Angeles by their respective policymakers.

Unemployment

The full set of graphs shows a clear pattern: cities that have aggressively raised the minimum wage, such as Seattle, San Francisco, and Los Angeles, also show consistent increases in both the cost of living and the living wage required to meet basic expenses. The time series of minimum wage growth highlights Seattle and San Francisco pushing past $17–$18/hour by 2024, while Dallas remains flat at the federal wage of $7.25. We need this historical data as it is the most central piece of data to our topic. We used the Department of Labor to find these values. Correspondingly, the average living wage in Seattle and San Francisco climbs above $28 and $31, respectively, while Dallas lags far behind. CPI data reinforces this link: cities with faster wage growth tend to experience faster price inflation, as seen in San Francisco and Miami’s recent spikes. Meanwhile, the year-over-year changes in both living and minimum wages reveal that although wage increases appear to uplift earnings, they often contribute to parallel increases in living costs, potentially canceling out real gains. Notably, Dallas, which made no major adjustments to its minimum wage, has experienced slower growth in both CPI and living wage demands, suggesting a more stable economic environment. Taken together, this data suggests that while higher minimum wages can offer short-term financial relief, they also carry the risk of fueling inflation and raising the overall cost of living, which could impact both affordability and employment over time.

The unemployment data used in this project was sourced from the U.S. Bureau of Labor Statistics (BLS) Local Area Unemployment Statistics (LAUS) program, which provides monthly estimates of unemployment rates for metropolitan areas. This dataset tracks the percentage of the labor force that is unemployed but actively seeking work. For consistency with the other variables analyzed, unemployment data was collected for the metro areas of Los Angeles, San Francisco, Seattle, Dallas, and Miami, spanning the period from January 2019 through April 2024. Where available, seasonally adjusted unemployment rates were used to account for predictable fluctuations related to seasonal employment patterns.

Unemployment is a critical factor influencing local economic conditions, consumer spending power, and regional inflation dynamics. High unemployment can suppress wage growth and demand, while low unemployment can fuel higher wages and price pressures. Therefore, analyzing unemployment trends helps contextualize movements in the CPI, rent prices, minimum wages, and livable wages. Python libraries such as pandas and matplotlib were used to clean and visualize the unemployment data. Analyses include constructing time series plots of unemployment rates across cities and calculating summary statistics to compare the labor market performance of different regions over time.

Regressions

Log Log Regression

Regression interpretation

The regression results reveal a statistically significant and positive relationship between minimum wage and key cost-of-living indicators such as CPI and rent, supporting the hypothesis that higher minimum wages are associated with higher consumer prices and housing costs. These findings suggest a pass-through effect, where wage increases do contribute to inflationary pressures as firms adjust prices to compensate for rising labor costs, and consumers nominally have more purchasing power. Log-log regressions confirm that these relationships are proportional, indicating meaningful elasticity between wages and living expenses. However, the regression between minimum wage and unemployment was not statistically significant, implying that wage increases did not lead to clear short-term changes in employment levels. Overall, the empirical evidence indicates that raising the minimum wage is closely tied to increases in the cost of living, particularly in the high-cost urban areas that we have used as samples.

Empirical Analysis/ Conclusion

This empirical analysis explores the relationship between historical minimum wages and key economic indicators across major U.S. cities. Using real-world data, we ran multiple linear regressions with living wage as the explanatory variable and found strong, positive associations with CPI levels, average rent, and minimum wage levels, indicating that cities with higher living costs also maintain higher wage floors and consumer prices. The analysis also included a log-log regression, reinforcing the rent elasticity in response to wage changes. Each regression was visualized with labeled scatter plots to highlight differences by city, allowing for clearer interpretation of localized economic pressures. These models collectively suggest that while higher wages may support affordability, they also track closely with increased costs of living, underscoring the complexity of wage policy outcomes in urban economies. We ran regressions to calculate for the elasticity coefficients and found that although increasing minimum wage did increase overall costs of good and rent (cost of living) we found that the actual coefficient for both CPI and rent regressions was less than one on average, signifying that when you do increase minimum wage by 1% these costs increase by a value of less than 1%. What can be seen in the regression charts is that in certain cities, such as Los Angeles and San Francisco, their elasticity coefficient is higher than a value of one as their data point lies above the regression line, meaning that in real terms increasing the minimum wage corresponds to a greater than 1% increase in rent and CPI, so these cities should stop increasing wage rates. This analysis overall does not explicitly state that things such as rent and CPI are solely dependent on wage policies, but rather that the increase in these values is most likely a byproduct of increased wage earnings.