Panel Data 101: What Is Panel Data And Why Is It Important?

Panel data (also called longitudinal data) is a type of data that involves tracking the same entities over time. This allows for repeated measures, which can be used to observe changes in behavior, productivity, and even health outcomes. Panel data is important because it can help us understand how different factors change over time and how they might be related to one another.

For example, let’s say we wanted to study the relationship between smoking and lung cancer rates. We could use panel data to track a group of people over time to see how their smoking habits change and whether or not they develop lung cancer. Additionally, this would give us information about how different individuals respond to changes in their smoking habits (for example, quitting smoking altogether or reducing the number of cigarettes smoked daily). This provides much more data than if we just looked at two snapshots in time (for example, by studying smokers and nonsmokers at two different points).

There are many potential uses for panel data beyond studying the effects of individual changes in health. Panel data can be used to explore relationships between firms and consumers, changes in economic productivity over time, or even crime rates across different neighborhoods. The possibilities are endless — panel data is an extremely useful tool for researchers looking for trends and patterns over long periods.

Another advantage of panel data is that it can help us control for other variables that might affect our outcome of interest (in this case, lung cancer). For instance, we might want to control for age since older people are more likely than younger people to get lung cancer regardless of smoking habits. Using panel data, we can follow the same individuals over time so that age isn’t a confounder in our analysis.

This article will discuss the basics of panel data and where you can find secondary sources of information to develop your own data sets. Feel free to use the table of contents to skip around to find specific information.


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What Is Panel Data?

What Is Panel Data?

Panel data generally refers to data sets that involve measurements collected over a specific period on a group of units. Panel data is also sometimes called longitudinal data, meaning that it involves observations made over a period of time on the same unit. This type of data differs from cross-sectional data, which contains information about only a single point in time, or from time series data, which tracks changes over time for a single unit.

Panel datasets are powerful because they allow researchers to control individual-specific characteristics like age or gender and time-invariant factors such as location). This means that panel datasets can be used to answer causal questions such as “Do older workers have higher wages?” or “Does moving to a new city decrease happiness?”

There are many different ways to collect panel data. One common method is repeated surveys: researchers interview the same individuals multiple times and ask them about their experiences and opinions on various topics. Another option is administrative records: government agencies and other organizations keep track of people’s characteristics and outcomes over time as part of their normal operations (e.g., birth certificates and tax returns).

Panels can also be created by following groups of people who share certain characteristics (e.g., all employees at a company) into the future and tracking what happens to them. This method is sometimes called cohort analysis.

Examples of Panel Data

Examples of Panel Data

You can find examples of panel data in many different fields, such as economics, social sciences, medicine and epidemiology, finance, and the physical sciences. In microeconomics, panel data might be used to track GDP across multiple countries or unemployment rates across different states. Income dynamics studies and international current account balances are other possible examples.

Panel data might be used in finance to look at stock prices by firm or market volatilities by country or firm. Public health is another area that can take advantage of panel data. For example, public health insurance and disease survival rate data can provide valuable insights into child development and well-being.

Panel datasets may come in various formats. A simple data panel table can stack observations of each variable from all groups across all time periods into one column. In other words, the table would denote a group, a time period, and a notation (or value for that group and given period). This is sometimes called long format data. A panel data table can also store observations for a single variable across separate groups and in separate columns. In other words, it could have multiple notations. This is sometimes referred to as a wide data format.

Here is a simple example of a panel data set:

Person Year Monthly Rent Age Sex
1 2013 $2,000 23 F
1 2014 $2,100 24 F
1 2015 $2,200 25 F
2 2013 $3,500 27 M
2 2014 $3,500 27 M
2 2015 $3,400 29 M

The table above contains data collected over several years. The characteristics studied were monthly rent, age, and sex for two people across three years. This is an example of a balanced panel because each person is observed for defined characteristics per year. If, for example, data does not exist for each person every year, then the data would be unbalanced.

So basically:

  • A balanced panel dataset contains for all groups the same number of observations.
  • An unbalanced panel dataset does not have uniform information. Occasionally, there are missing values for some of the groups.

Take note that specific panel data models are valid only for balanced datasets. For unbalanced panel datasets, they first need to be condensed to include only the consecutive periods for which there are observations for all individuals in the cross-section. To do so, include only consecutive periods with full observational records available for every individual in your cross-section (i.e., every unit of measurement).

Say we want to study five people over 10 years:

  • Person A has complete observational records from Years 1 through 10
  • Person B has complete observational records starting from Year 2
  • Person C has complete observational records starting from Year 3
  • Person D has complete observational Records starting from Year 4
  • Person E has observational records available only from Year 5

You need to create four separate panels:

  • Panel 1 will include Persons A through D and cover Years 1 through 4
  • Panel 2 will also include Persons A through D but cover Years 2 through 5 (since this is when person E joins the sample)
  • Panel 3 covers Years 3 through 6 and includes Persons B through E
  • Panel 4 finally covers Years 4 through 10 and also includes Persons B through E

Why Use Panel Data?

Why Use Panel Data?

Panel data is a type of quantitative, numerical data collected over time. This data can predict future trends, discover correlations, and conduct various other forms of analysis. If you’re interested in collecting panel data, understanding its best uses and advantages is crucial.

There are several advantages to panel data:

  • Captures both the common and individual characteristics of groups
  • Richer in information, variability, and efficiency than either time series or cross-sectional data
  • Detects and measures statistical effects the other two types cannot
  • Has less estimation bias when researchers aggregate groups into a single time series

Panel data can help correct various biases that might exist in other types of data sets. Additionally, because panel data includes repeated measurements at the individual level, we can better understand the order of events and make sounder causal conclusions and policy recommendations.

Collecting panel data can be useful for many professionals, depending on what they’re studying, because of the following characteristics:

Panel data is great for strong correlations

Panel data is great for strong correlations because it allows researchers to study how two or more variables change over time in the same individuals. Suppose a study collected annual data on the same 200 individuals’ levels of education and household incomes over 20 years. The researchers could then use panel data to make a positive correlation between education and income. This would be difficult to do with cross-sectional data because there would be too much variability between different groups of people (e.g., those born into wealthy families vs. those who were not).

Panel data can be used for many studies, not just ones involving correlations between variables. For instance, panel data can also be used to examine changes in behavior over time or to compare outcomes across groups of people (such as men vs. women or young adults vs. older adults). Researchers have even used panel data to track disease outbreaks by following the same individuals over time and recording any new symptoms they develop.

In general, panel data provides a valuable tool for researchers who want to understand how something changes over time within the same group of people.

Panel data allows for better prediction of future trends

Panel data is often used in economics and econometrics to predict future trends. By tracking how variables have behaved in the past, panel data can give researchers a good sense of how they will interact in the future. In many cases, panel data is more accurate than other prediction methods, such as regression analysis, because it captures more information about how variables have changed over time.

For example, if a researcher wanted to study housing prices in different cities over time, they could use panel data on city-specific prices from the past 30 years to predict prices over the next five. This would give them a much better idea of future trends than if they had just looked at housing prices in one city over time without panel data.

Overall, panel data is a very useful tool for anyone interested in predicting future trends based on historical information.

Panel data provides information for future analysis

Speaking of future trends, the panel data you collect now may provide insight for future studies related to variables included in your data. Most people understand that data can be used to study past trends and predict future ones. However, few realize just how powerful data can be in this regard. For instance, let’s say you’re a researcher studying employment in the 1920s. You could use panel data collected during that decade to help optimize your analysis. The 1920s data may include information like job types, average wages, and unemployment rates, all of which can help current researchers better comprehend past employment trends. This is just one example of how panel data can provide insight into future studies.

In general, panel data refers to a dataset that contains repeated measures for the same individuals over time (e.g., monthly salary records for all employees of a company). This type of dataset is particularly useful for studying changes or transitions over time — such as those related to employment status, wages, family composition, etc.

There are many ways to collect panel data, but surveys are perhaps the most common method (like the Panel Study of Income Dynamics in the U.S.). However, panel datasets can also be created using administrative records (such as tax returns or payroll records) or through special censuses designed specifically for research purposes (like the National Health Interview Survey in Canada).

Whatever their source, all panel datasets have certain features in common: they contain observations on multiple variables over time for each unit and often involve some form of linkage between units across waves of observation.

How To Use Panel Data

How To Use Panel Data

Remember our example table above?

Person Year Monthly Rent Age Sex
1 2013 $2,000 23 F
1 2014 $2,100 24 F
1 2015 $2,200 25 F
2 2013 $3,500 27 M
2 2014 $3,500 27 M
2 2015 $3,400 29 M

Cross-sectional time series data provides information about two different components. The cross-sectional component reflects the differences observed between individual subjects or entities, while the time series component shows how one subject changes over time. For example, researchers could focus on the differences in data between each person in a panel study and/or look at how rent changes for one person in the table above over the course of the study.

But it is the regression methods used in panel data that give economists the ability to derive valuable insights from various sets of information. Though this flexibility can make panel data analysis complex, it’s why panel data sets have the advantage over conventional cross-sectional or time series data in economic research. Panel data gives researchers a great deal of freedom to explore different types of relationships and phenomena thanks to a large number of unique data points.

Panel data may be preferred because it allows for within-group comparisons. For example, if you were studying the effect of different teaching methods on student performance, you could use panel data to compare students who were taught using the new method with those using the old one. This would give you information on how effective the new teaching method is compared to the old one.

Another reason panel data may be preferred is that it can help control for confounding variables. For example, imagine you want to study how income affects health outcomes. However, many other factors affect health outcomes (such as education level, genetic predisposition, etc.), so simply looking at income might not give you an accurate picture of what’s really going on. But if you had panel data with information on all these different factors, you could more accurately determine whether income truly has an effect on health outcomes.

For these and other interrelated reasons, professionals often use panel data for statistical, economic, and financial research. In any field of study, panel data may be analyzed to reach specific conclusions or allow other researchers access to the information for their own studies. Below are some of the most common uses of panel data:


Panel data can be extremely useful for statistical analysis and research, as it allows for the examination of trends over time. For example, panel data could be used to examine changes in health care outcomes, human development indicators, or education and housing metrics. Additionally, panel data can be used for more complex studies comparing cities or regions based on various factors such as graduation rates, post-graduation employment rates, grades, and standardized test scores. Panel data provides a wealth of information that can be used to better understand things like college graduation rates within one area and general trends in educational systems worldwide.

Additionally, this type of data opens up new possibilities for comparative research across different geographical areas or periods.


Microeconomic panel data provides a valuable tool for understanding complex economic phenomena on a smaller scale. By collecting data on a specific area or region, economists can paint a more accurate picture of the underlying trends and forces at work. This data has been used to study everything from unemployment rates to income and housing values, like in the ” Income and Poverty in the United States” study that examined the average income of people living in different cities throughout the U.S. The researchers could identify which areas had higher or lower income levels using microeconomic panel data collected from various sources. Without this information, it would have been much more difficult to make such detailed comparisons between income levels in different cities across the country.

Overall, microeconomic panel data provides valuable insight into the small-scale economic activity that can be difficult to observe using other methods. As our economy continues to evolve and change, this type of analysis will only become more important in years to come.


Macroeconomics is the study of large-scale economic phenomena, such as aggregate production, employment, inflation, and international trade. Macroeconomists use data from a variety of sources to study these phenomena, including panel data. This type of data allows economists to examine how economic variables change over time within a given unit (i.e., country) and how they vary across units at a given point.

Suppose we want to study the purchasing power of different currencies relative to each other over the past decade. We could use panel data from that period to learn how specific currency values changed during that time frame.


When making investment decisions, financial analysts must consider a multitude of factors, including stock prices, market volatility, and individual wealth. Let’s say that stock prices are the most important out of these three factors because they directly impact an investor’s potential profits. To predict future stock price movements with any degree of accuracy, analysts often turn to panel data. In the context of stocks, this means tracking how specific stocks have performed at regular intervals over a set period. This information is extremely valuable to analysts because it allows them to identify trends and patterns in the market that can be used to make investment decisions.

For example: an analyst wants to find the best-performing stocks over 20 years. By gathering panel data for various stocks during this time frame, the analyst could identify which ones outperformed the others by seeing how their prices changed over time. This information would then be used to make investment choices based on expected future performance.

A note about heterogeneity in panel data

Modeling data that is observed over time typically addresses the dependence that is likely to exist across observations within the same group. The primary difference between models for panel data and time series models resides in the fact that panel data models allow for heterogeneity across groups and introduce individual-specific effects.

Take a panel data set with GDP information for five countries — the United States, France, Canada, Greece, and Australia.

  • If there’s an economic recession globally, it will affect all five countries and cause changes in GDP for all of them.
  • However, if there’s only an election in Australia, that is likely to impact only the GDP of Australia.
  • A change in trade policy in North America may regionally impact just the United States and Canada.
  • Changes in euro exchange rates will most directly impact Greece and France.

Panel data models can address heterogeneities across individuals, while pure cross-sectional methods or pure time series models may not be valid when this heterogeneity is present.

Collecting Information for Panel Data

Collecting Information for Panel Data

Building a panel data set requires a lot of money and people, which poses a big challenge for smaller teams of researchers trying to keep the data representative and consistent over time. For example, if we wanted to build a panel data set of people’s voting habits over time, we would need to track the same individuals as they move through time and measure their voting behavior at different points in their lives. This can be challenging for researchers because it requires following subjects over a long period, which is often not feasible. Panel data sets can also be expensive to build because they require collecting information from a large number of people repeatedly over time.

Collecting primary data (information researchers collect themselves, like through surveys) is often not an option for those without large teams or budgets. Their challenge is finding information via secondary sources to use in panel data sets in a tedious, time-consuming process.

Ensuring reliable sources of information

Many researchers often use secondary data (data you did not acquire through your own methods that originates from somewhere else). If this is the case for you, ensure the information comes from a reliable source and that the data collected is in the same time period. By doing so, putting together different points of information from various sources should not impair the reliability of your data set.

It’s true that the collection methodology from different sources will often differ. However, if the object of the data collection has passed peer or industry review, it should be acceptable. At that point, what’s important is the quality of the information and, by extension, the reliability of the source and collection method. It’s no longer about who collected the information.

Note, however, that before combining data from for-profit and nonprofit organizations, it is important to check and double-check the accuracy of the information. The original motive for collecting the data may not have been purely scientific. This data should be treated with caution if the sources were tainted with financial interests or policy agendas.

There are many data sources on the internet and many organizations with lists of data sources online. Here are a couple of economic data sources available online:

U.S. government data sources (U.S.-specific information):

  • Statistical Abstract of the United States: A great starting point for both macro and micro data, containing over a thousand tables of key U.S. and some international data. Additionally, it provides definitions and sources for more detailed information. Copies are available in the reference section of most libraries.
  • The Economic Report to the President: Contains an abundance of macroeconomic time series data for the United States. You can find hard copies of it in the reference section at your local library.
  • National Bureau of Economic Research: Has a vast collection of public economic, demographic, and enterprise data that has been gathered over the years to fulfill the specific requests of researchers affiliated with NBER affiliated for particular projects.
  • Bureau of Economic Analysis: Publishes Survey of Current Business and other data.
  • Bureau of Labor Statistics: Collects and provides key data on employment, unemployment, and earnings.
  • Bureau of Census: Provides information from many surveys, including the information-packed Decennial Census.
  • National Center for Health Statistics: Lots of health data sets, including NHIS and Health United States.

And some sources for macrodata:

All of the above is just a sampling of freely accessible information you can use to develop data sets for your panel data. There are analogous or comparable sources of information for nearly every modern economy in the world. You can also find a wealth of information on an international scale from global organizations like the UN.

Leveraging Web Scraping

Gathering the required data for panel analysis, especially from external sources, is often challenging, particularly for small to mid-sized businesses. This is where proxies and web scraping become invaluable.

When most people think of web scraping, they think of extracting data from websites. However, web scraping can also extract data from sources not intended to be accessed or used by humans, such as APIs. There are a few different ways to scrape data from a website. One way is to use a web scraper, a program that automatically retrieves specific data from a website. Another way is to use manual techniques, such as copy and paste — obviously highly inefficient and impractical.

Web scraping can be used for a variety of purposes, such as lead generation, price comparison, market research, and even competitive intelligence gathering. Of course, collecting panel data is also one such use case.

Using proxies

Proxy web scraping is an automated web scraping method that uses a proxy server to collect data from websites. A proxy server acts as an intermediary between your computer and the internet. When you make a request to a website, the proxy server will forward the request to the website and then send you the response. There are several reasons why you might want to use a proxy server when web scraping.

We know that manually copy-pasting information from each web page is a huge waste of time and resources, especially when there are web scrapers that can cache data and requests. However, the efficiency of a scraper is not lost on website servers. They can easily detect automated scrapers sending requests dozens, if not hundreds, of times every second. Once they catch on to that, they will block your IP address to prevent further action. Using a proxy server for automated web scraping can help you to circumvent this restriction and access the website.

Websites generally block scrapers as their reckless use can overload servers. Worse, attackers can use similar technologies to perform automated, malicious actions on the site. One such attack is called a distributed denial of service (DDoS) that uses crawler-like bots that infect machines to coordinate large-scale attrition attacks on targets to crash their servers. Unfortunately, the measures sites take to defend their servers also flag innocent web scraper users.

Proxies can also hide your identity when scraping data from websites. The proxy server makes the request on your behalf, so the website will not know your IP address. This can help you keep your personal data safe.

DIY proxy web scraping

Unless you can fund the establishment and maintenance of a research team as a primary source, most of the information for your panel data will come from secondary sources. And unless you want to spend hours upon hours manually copying and pasting data, your best bet is automated web scrapers.

Proxies will allow your scraper to continue pulling data at a steady pace. As your web scraping becomes more complex (or as you add more scrapers), you need more powerful proxies to support the effort. This adds one more layer to DIY proxy web scraping: a reliable proxy provider that can either offer the advanced proxies you require or provide rotating proxies to scale with your needs — or both.

This is why proxy providers are the only viable option for many organizations when it comes to web scraping on a large scale. It would be very resource-intensive (and expensive) to try and establish and maintain proxy servers manually or with a limited number of resources, the same way it’s impractical to do manual data transfer instead of web scraping. By using proxy providers, you can increase your web scraping efforts flexibly and quickly without investing a lot of money or time.

Overall, proxies offer a great way to increase the efficiency and effectiveness of your automated web scraping activities while minimizing the cost and effort required. They should definitely be part of your tool kit if you want to collect data on a large scale — which is usually the case for panel data.

Ethical Proxy Use for Scraping Panel Data

Ethical Proxy Use for Scraping Panel Data

Rayobyte is the perfect proxy provider for anyone who needs to scrape the web or support their panel data collection needs. We offer residential, Internet Service Provider (ISP), and data center proxies, so you’re sure to find the right type of proxy for your needs. Plus, we’re a professional and ethical company that you can trust.

When it comes to web scraping, procuring residential proxies is often your best course of action. This is because they provide you with IP addresses assigned to actual people by their internet service providers. This not only means that the IP addresses are valid but also that they’re constantly changing, which lets your web scrapers do their job without setting off any alarms. Plus, we carefully source our residential proxies and work hard to keep downtime to a minimum.

Data center proxies are a great option if you’re looking for faster speeds. These proxies route traffic through data centers, which results in quicker connections. The trade-off is that you’ll have fewer unique and nonresidential IP addresses, but they’re more affordable overall. If you’re planning on doing any web scraping, data center proxies can be an effective solution.

ISP proxies lie somewhere in between the previous two types, offering a mix of speed and anonymity. These proxies are associated with an ISP but housed in a data center, so you get the speed of data centers and the authority that comes with using an ISP.


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Final Thoughts

Final Thoughts


This primer on panel data may not delve into all the complex models and analyses possible, but it’s a starting point to show you what panel data is and why it can be important for your organization. Leveraging data panels is key to unlocking highly valuable insights from various sources of information. While primary data sources are preferable, there simply is no way for smaller teams to build, let alone maintain, the infrastructure to support the effort. It’s easier to manage secondary sources of data, but the collection of information from external data sources can often be challenging.

But if you can find the right sources, it’s often just a matter of successfully scraping the information to reap the benefits of panel data. If you’re keen on collecting invaluable secondary data for panel data needs on your own, try Scraping Robot to automate much of your workload. Explore our available proxy options now.

The information contained within this article, including information posted by official staff, guest-submitted material, message board postings, or other third-party material is presented solely for the purposes of education and furtherance of the knowledge of the reader. All trademarks used in this publication are hereby acknowledged as the property of their respective owners.

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