What Is an Exogenous Variable? (With Classification Tips)
Updated July 14, 2023
A woman holding a piece of paper by her side. There are also two different lists titled "Endogenous" and " "Exogenous" and these definitions:
A variable in an economic model that exists within the model or system under analysis
A variable in an economic model that exists outside the model or system of analysis
Economists and statisticians encounter several types of variables when considering economic models. Exogenous and endogenous variables are two types of economic values that you can classify based on their relationship with other variables within the model. Understanding the difference between these variables and how to classify them can help you evaluate economic models and advance your career in economics or statistics. In this article, we define exogenous and endogenous variables, describe the importance of understanding these types, explore examples of each, and provide tips for variable classification.
What is an exogenous variable?
An exogenous variable is a type of variable in an economic model that's not affected by other variables in the system. A variable that's exogenous exists outside the economic model, which illustrates the economic process using variables and their relationships. These types of variables are only affected by factors outside the model, meaning they are similar to independent variables. As these variables don't exist in the model, economists can't use the model to predict the value of these variables. Here are some characteristics of these types of variables:
The model cannot explain or determine the variables.
These variables influence endogenous variables within the model.
Economists consider these variables as fixed when they enter the model.
The independent variables are exogenous in experiments involving a double-blind or controlled method. In these types of studies, only factors outside the system can affect the variables. For example, a researcher's bias or interpretation of the results may affect these variables, as the researcher is external to the study. It's important to perform controlled experiments to reduce the possibility of external effects on these types of variables.
Exogenous variable examples
Applying this concept to real-world scenarios can make them easier to understand. Here are some example situations to consider:
External factors in agriculture
Consider the following example of exogenous variables:
A farmer is modelling their farm's production of corn. They consider the variables of soil type, fertilizer, pests, and weather. Within this model, the farmer wants to determine which variables can affect the crops, but the crops cannot affect them. In this model, the farmer determines the soil type and fertilizer may influence each other. Only the external factors of weather and pests can cause more or fewer crops to grow, but the crops and other variables cannot affect these factors in return. The farmer determines that weather and pests are the exogenous factors in the model.
Below is another example of an exogenous variable:
A sole proprietor made a net income of $200,000 in the previous year. The calculation of net income depends on a variety of factors, including tax rate and interest payments. As no other factor in the model can affect the tax rate, the business owner determines the tax rate to be exogenous.
What is an endogenous variable?
An endogenous variable is one in an economic model that has a value determined by the model. It's similar to the dependent variable, meaning its value correlates to the other variables within the system being studied. As opposed to exogenous ones, you can use the economic model to predict endogenous variables. In experiments, it's possible to manipulate endogenous variables to produce a specific result. Here are some characteristics of this variable type:
The model can explain or determine the variables.
Changes in other variables influence the value of endogenous variables within the model.
Economists don't consider these variables as fixed when they enter the model.
Endogenous variables are an important part of econometrics, economic modelling, and linear regression. Understanding endogenous variables is an essential skill for economists, as it helps demonstrate the effect one variable has on another. Many fields consider endogenous variables, including sociology, psychology, political science, statistics, and meteorology. For example, social scientists may research the factors that affect the endogenous variable of education in developing countries. Identifying the factors that influence education in these countries can help researchers better understand the limitations and issues with accessing education in these areas.
Endogenous variable examples
Here are some examples of real-world situations involving endogenous variables:
Cookies sold by a bakery
Consider the following example of an exogenous factor:
A bakery is modelling its cookie production. The manager considers several variables in the model, including the number of employees working and the price of cookie ingredients to determine the number of cookies sold. The endogenous variable is the number of cookies sold, as it depends on the other variables in the model. When more employees are working and the price of ingredients is low, the cookie sales increase. An exogenous factor the bakery manager might consider is holidays, as that may affect the cookies sold, but the number of cookies sold doesn't affect the instance of the holiday.
Lamps produced by a manufacturer
Here's an example of an exogenous factor in production:
A lighting manufacturer is considering the number of lamps it produces. The number produced depends on other variables in the model, including the price of lamp parts, making the endogenous variable the number of lamps produced. As the number of lamps produced doesn't affect the price of lamp parts, the manufacturer considers the part price as the exogenous factor in this scenario.
Why is it important to understand these variables?
Exogenous and endogenous variables are both important in economics, statistics, and business. It's important for economists and financial analysts to understand both types of economic variables and how they can influence economic models and the business cycle. Here are some real-world applications of these variables:
Agriculture: In farming, the number of crops produced annually is endogenous, as it often depends on several other variables, such as pests, crop disease, and weather. These external variables that affect the crops are exogenous, as they exist outside of the model.
Interest rates: In economics, the interest rate is endogenous, as it may depend on a variety of exogenous variables, like economic shifts. Further, investment returns are endogenous, as the exogenous government tax rates affect the returns.
Supply and demand: Many factors can affect supply and demand. Exogenous factors include changes in the economy and consumer attitudes because income, investment returns, and government spending can explain the changes.
Income: Typically, you can consider income to be the endogenous variable, as it depends on exogenous factors, such as changes in consumer behaviours and government tax rates. Sometimes, such as in discussions of consumer spending, you might classify income as an exogenous factor, as it has a direct effect on the spending levels of individuals.
Education: The level and quality of an individual's education is an endogenous variable. When evaluating the education levels of a population, you may find a relationship between factors like location, socioeconomic status, and income.
Tips for variable classification
Besides knowing the definitions of exogenous and endogenous variables, it's important to understand how to classify these variables. While it may be straightforward to identify independent and dependent variables in an experiment, you might find it challenging to identify those that are exogenous and endogenous. As one factor may be exogenous in one example and endogenous in another, it may be helpful to consider these tips for classifying variables:
Determine whether the variable depends on other variables
You might consider whether the variable depends on other variables to help with classification, remembering that exogenous factors are independent and endogenous factors are dependent. If the variable doesn't depend on variables within the model, you can classify the variable as exogenous. Conversely, if the variable depends on other variables within the model, it's an endogenous variable. For example, in a salt mine, the amount of salt produced is the exogenous variable, as it depends on other variables, including equipment speed, the miners' experience, and humidity levels.
Consider whether your economic model can predict the variable
To classify variables, you can consider whether the economic model you're using can predict the value of the variable. If the variable is endogenous, the model can predict its value, as these variables exist within the model. For example, you can predict that the fuel consumption of commuters increases as the commute time also increases. Conversely, a model can't predict the value of the variable if it's exogenous, as these variables exist outside of the model and have no direct or formulaic relationship. For example, your favourite colour has no direct influence on your level of education.
Use mathematical equations
You may use mathematical techniques and equations to help classify variables. Simultaneous equations can help with variable classification, as equations can't explain variables if they are exogenous. Conversely, mathematical equations can explain endogenous variables.
For example, a landscaper may use an equation with the variables of weather, cost of materials, and number of employees working to determine their expected weekly project load. In this instance, the landscaper can consider the number of employees working as an endogenous variable, as other factors in the equation can influence the number, such as rainy days. The weather and cost of materials are exogenous, as other variables in the model cannot affect these amounts.
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