Tracking the Impact of Population Growth on Unemployment: How Maryland’s Demographic Changes Affected the Labor Market from 2000–2020

Introduction

When population increases in a state or county, it is often linked to positive economic indicators such as job creation and overall economic growth. However, can rapid population growth also strain local job markets, leading to increases in unemployment if the growth in the labor force outpaces job availability? This analysis investigates whether population growth has impacted unemployment rates across various counties in Maryland between 2000 and 2020. By understanding these dynamics, policymakers can make informed decisions about economic planning and workforce development.

The Research Question and Stakeholders

The key question explored in this study is: How will population growth affected unemployment rates across Maryland’s counties from 2000 to 2020? This question is relevant to state and local policymakers, who require insights into how demographic shifts impact local labor markets. By understanding the correlation between population growth and unemployment, policymakers can develop targeted job creation programs, allocate resources more effectively, and implement proactive measures to support local economies.

Other Possible Stakeholders:

• Urban Planners: As counties experience population booms, planners must ensure that employment opportunities grow at a proportional rate to avoid unemployment spikes.
• Economic Development Agencies: These agencies need data to plan economic initiatives that address unemployment in rapidly growing counties.
• Public Health and Social Service Providers: High unemployment can lead to increased demand for social services, requiring strategic resource planning.

Data Sources

For this study, the data was mainly collected from two primary sources:

1. Maryland Department of Planning: The population data for Maryland’s various counties from 2000 to 2020 was obtained from the Maryland Department of Planning: https://msa.maryland.gov/msa/mdmanual/01glance/html/pop.html

• U.S. Department of Agriculture

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Data Collection Methods

• The population data was obtained from the Maryland Department of Planning website, while the economic and unemployment data was sourced from the USDA Economic Research Service. These datasets were provided in PDF and CSV formats, which were downloaded and converted into a suitable format for analysis.

Data Cleaning

To focus the dataset on Maryland’s counties and unemployment-related variables, I performed the following cleaning steps:

1. Filter for Maryland counties: The dataset included data for all U.S. states and counties. I filtered the data to include only Maryland counties by using the state column to select relevant rows for Maryland.
2. Select relevant columns: I focused on unemployment-related variables, specifically the columns representing the civilian labor force, employed, and unemployed population for each year from 2000 to 2020. This also included unemployment rates for each corresponding year. The unnecessary columns, such as data on other states or unrelated economic factors, were removed.
3. Handle missing data: In some instances, there were missing data points for certain counties or years. To address this, I applied interpolation techniques to estimate missing values, ensuring the data was continuous and complete for analysis.
4. Data formatting and consistency: I ensured that county names and the year columns were formatted correctly to avoid mismatches during the analysis. Any inconsistent or redundant data entries were corrected to maintain uniformity across the dataset.

Code Availability: The full code for cleaning and processing the data is available in my GitHub repository, where you can explore the steps and scripts used to transform the raw data into a usable format for analysis

Finding and Visualization

The analysis of population growth and unemployment change rates across Maryland’s counties from 2000 to 2020 reveals a weak positive correlation of approximately 0.19 between the two variables.

A linear regression model was applied to further explore this relationship, resulting in the following equation:

Unemployment Change Rate = 6.54 x Population Growth Rate + 0.53

This means that for every 1% increase in population growth rate, there is an expected 6.54% increase in unemployment change rate, with a baseline unemployment change rate of 0.53% when population growth is zero. However, the weak correlation indicates that other factors besides population growth likely play a more significant role in determining unemployment changes across counties.

The visualization of the regression analysis (see figure 1) supports these findings, with the red regression line illustrating the predicted unemployment change rates based on population growth. While the regression line generally trends upward, the scatter of blue data points indicates a large degree of variability, highlighting that population growth alone does not fully explain changes in unemployment rates.

This suggests that policymakers and economic planners should consider additional variables when examining the impact of demographic shifts on local labor markets.

Fig 1. Relationship Between Population Growth and Unemployment Change in Maryland (2000–2020)

Code Availability: The full code for my analysis can be found in my GitHub repository GitHub repository. The repository includes the scripts used for data cleaning, exploratory analysis, and the creation of visualizations, as well as the linear regression model applied to examine the relationship between population growth and unemployment rates.

Limitations

• Data Granularity: County-level data may overlook smaller, localized trends. A more detailed analysis would require city or neighborhood-level data.
• Limited Variables: The analysis focused on population growth and unemployment, excluding factors like industry trends and policies. Including these could provide a more complete picture.
• External Factors: Events like the 2008 financial crisis and COVID-19 likely impacted unemployment but were not isolated in this analysis, potentially skewing the trends.
• Census Data Accuracy: Population data is based on the decennial census, meaning estimates for years in between may be less accurate due to interpolation or extrapolation.

Future Work

1. Incorporate More Variables: Future studies could include factors like job creation rates, industry trends, and migration patterns to better understand the impact of demographic changes on unemployment.

2. Regional or Sectoral Focus: Analyzing specific counties or industries may provide localized insights, helping shape more targeted economic policies.

3. Time Series Analysis: A deeper time series analysis could reveal how economic cycles affect the link between population growth and unemployment, aiding future predictions.