A complete guide to panel data and its professional uses

By Indeed Editorial Team

Published 5 June 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

If you wish to become an economist, you may benefit from understanding how to identify and classify panel data. As financial professionals often use this data to build long-term economic forecasts, it's beneficial to learn how to effectively interpret its trends. By following this guide, you can learn how to apply this data type to professional tasks. In this article, we define the term panel data, provide examples of this concept, detail the advantages of collecting this data type and explain how it differs from pooled data.

What is panel data?

Panel data is statistical information gathered by monitoring multiple actors over a long period. Professionals use it to learn how a set factor changes over time. They can then contrast their findings against an underlying trend or external influences to determine the root causes of these statistical shifts. Although this type of data set may contain many actors, they're usually connected by a common identifying factor, such as age, industry or profession.

Given its long-term focus, this data type has many economic applications. For example, you can use this data to compare an organisation's stock price to those of its competitors' over a long period to see if it performed favourably. If not, you could then use your findings to discover the root causes of this failure, such as limited investment or productive inefficiencies. Conversely, you may compare rising inflation rates to the availability of trained hauliers to discover potential links between these phenomena. If rising inflation mirrors a decline in these professionals' availability, you can assume that a link exists.

Related: How to analyse data: definition, steps, benefits and skills

What are the practical uses of this data type?

The below section offers three examples stating how economists can use this data type to complete practical tasks:

Managing clients' assets

One practical use of panel data is predicting the future performance of financial assets to ensure that clients earn a good return on their investment. In this situation, you can contrast the historical price or dividend rate of a financial asset against general economic indicators, such as interest rates or inflation. You can then track how an asset performed when this indicator changed in the past to judge if it's worth buying.

Example: If you're assessing a bond, you may compare its historical dividend payments to interest rates from that point in time. If the dividend payment typically increased at a much higher rate than interest rates, you may advise clients to avoid this product. Though they could earn a good return, the bond's volatility means that it's more likely that its issuer could default on their loan repayments.

Related: Careers in asset management (with responsibilities)

Predicting business growth

If you specialise in business planning, you can also use this data type to direct future investment decisions by creating long-term forecasts. In this situation, you may pick two variables from past business records, before extracting values from different years or months within a historical period. You can then compare these variables to how the business performed as economic conditions changed to predict how it may perform in similar conditions in the future.

Example: If the economy experiences rising inflation, you could use past records to determine if the firm's profits rose in similar periods in the past. If these variables often rose in unison, you can assume that rising inflation may deliver rising profits. If not, you could conduct research into the past causes of falling profits, such as rising supply or labour costs.

Related: What is business forecasting (with definition and methods)?

Predicting exchange rates

You may also use this data type to predict future exchange rates to judge if it makes financial sense for a firm to export products abroad. In this context, you may pick average monthly exchange rates for currencies often used in international trade, such as the Pound Sterling and the US Dollar. You can then cross-reference these values to determine which currency has historically out-valued the other. If the Sterling usually out-values the Dollar, you can assume that this trend may continue in the future. As strong currencies can make exports less competitive, you may decide against expanding the organisation.

What are the advantages of using this data type?

The section below defines three advantages to using this data type to complete professional tasks, each with a contextual example to guide you:

Distinguish between variables

One advantage of using this data type is that you can better distinguish between fixed variables and random variables affecting data. Fixed variables are consistent for a single actor, remaining constant or changing at a set rate between data samples. Conversely, random variables rely on random events, making them harder to predict. By sampling data from several sources and from several points in time, you can contrast factors that remain consistent and those that change unpredictably.

Example: When assessing an organisation's share price over a seven-day period, you could use this data type to differentiate between fixed and random variables. In this situation, it's clear that the firm's name and the date are fixed variables, as they remain consistent during the sample period. Conversely, stock price is a random variable, as its value can change starkly between sample days depending on external variables, such as market speculation.

Related: 10 common types of variables in research and statistics

Identifying correlations within data

Another benefit of using this data type is that you may better identify correlations between two data sources over an extended period. Here you may select two pieces of data that you wish to compare, before choosing samples from set moments to judge if a shift in one sample causes a similar or opposite shift in the other. If they shift in the same direction, the samples have a positive relationship. If they move in opposite directions, they have a negative relationship. If neither pattern occurs, there is no intrinsic relationship between the two samples.

Example: If you work for an oil producer, you can compare rising share prices to fuel prices to judge if they've also risen in recent weeks. As rising shares suggest that traders view the firm as a secure investment, you may assume that this confidence stems from rising fuel prices. In this context, you may extract data about recent share and fuel price changes to assess if both prices rose by similar margins over a set period, such as the last 30 days. If so, you can consider these two variables to have a positive relationship.

Predicting economic performance

Another benefit of using this data type is that you may more easily predict the future performance of the UK economy. In this situation, you may use trends detected from data covering several past years or decades to predict how specific industries may perform in the future, assuming similar economic conditions. You might then feed this data into an economic model, using these projections to influence a firm's business plans.

Example: If you're predicting the UK energy market's likely performance over the next few years, you could use past trends to aid your analysis. In this situation, you may review consumers' average annual energy bill rises during the period 1985-2020. You can then use the average inflation rate for each year in that period to judge if rising inflation may cause energy prices to rise significantly in the present day. If rising inflation historically caused energy prices to rise quickly, you could assume this may also happen today. If not, you can discount any supposed relationship.

How does pooled data differ from panel data?

Pooled data is statistical data derived from several unrelated sources, being combined for a certain purpose. Although you could use both data types to create economic forecasts, pooled data provides a more detailed picture of a market or economy's performance. Two key differences between these types of data are:

Subject focus

One difference between these types of data concerns their subject focus. Pooled data offers a glimpse at the performance of unrelated economic actors at set points in time, whilst panel data focuses on related actors. In this context, you could use the former data type to develop more complex economic forecasts, including a greater quantity of financial data. In contrast, the latter data type covers single topics, although you may feed this data into a more general economic model.

Cross-sectional differences

Another difference between these two data types involves cross-sectioning pieces of data across set points in time. Although you may use both data types to observe relationships between two economic variables at set points in time, the specific way to do so differs. Using pooled data, you can cross-section data from varied periods, though the units of data that you use could differ between certain moments. Contrastingly, the initial data type uses the same two data units, whatever point in time you gather data from.

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