example variables in research

Scale: Nominal and ordinal.

The confounding variable could be the   and compels people to drink a lot of water and a cold drink to reduce heat and thirst caused due to the heat.

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Frequently Asked Questions

What are the 10 types of variables in research.

The 10 types of variables in research are:

• Independent
• Confounding
• Categorical
• Extraneous.

What is an independent variable?

An independent variable, often termed the predictor or explanatory variable, is the variable manipulated or categorized in an experiment to observe its effect on another variable, called the dependent variable. It’s the presumed cause in a cause-and-effect relationship, determining if changes in it produce changes in the observed outcome.

What is a variable?

In research, a variable is any attribute, quantity, or characteristic that can be measured or counted. It can take on various values, making it “variable.” Variables can be classified as independent (manipulated), dependent (observed outcome), or control (kept constant). They form the foundation for hypotheses, observations, and data analysis in studies.

What is a dependent variable?

A dependent variable is the outcome or response being studied in an experiment or investigation. It’s what researchers measure to determine the effect of changes in the independent variable. In a cause-and-effect relationship, the dependent variable is presumed to be influenced or caused by the independent variable.

What is a variable in programming?

In programming, a variable is a symbolic name for a storage location that holds data or values. It allows data storage and retrieval for computational operations. Variables have types, like integer or string, determining the nature of data they can hold. They’re fundamental in manipulating and processing information in software.

What is a control variable?

A control variable in research is a factor that’s kept constant to ensure that it doesn’t influence the outcome. By controlling these variables, researchers can isolate the effects of the independent variable on the dependent variable, ensuring that other factors don’t skew the results or introduce bias into the experiment.

What is a controlled variable in science?

In science, a controlled variable is a factor that remains constant throughout an experiment. It ensures that any observed changes in the dependent variable are solely due to the independent variable, not other factors. By keeping controlled variables consistent, researchers can maintain experiment validity and accurately assess cause-and-effect relationships.

How many independent variables should an investigation have?

Ideally, an investigation should have one independent variable to clearly establish cause-and-effect relationships. Manipulating multiple independent variables simultaneously can complicate data interpretation.

However, in advanced research, experiments with multiple independent variables (factorial designs) are used, but they require careful planning to understand interactions between variables.

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Examples of Independent and Dependent Variables

What Are Independent and Dependent Variables?

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Both the independent variable and dependent variable are examined in an experiment using the scientific method , so it's important to know what they are and how to use them.

In a scientific experiment, you'll ultimately be changing or controlling the independent variable and measuring the effect on the dependent variable. This distinction is critical in evaluating and proving hypotheses.

Below you'll find more about these two types of variables, along with examples of each in sample science experiments, and an explanation of how to graph them to help visualize your data.

What Is an Independent Variable?

An independent variable is the condition that you change in an experiment. In other words, it is the variable you control. It is called independent because its value does not depend on and is not affected by the state of any other variable in the experiment. Sometimes you may hear this variable called the "controlled variable" because it is the one that is changed. Do not confuse it with a control variable , which is a variable that is purposely held constant so that it can't affect the outcome of the experiment.

• What Is a Dependent Variable?

The dependent variable is the condition that you measure in an experiment. You are assessing how it responds to a change in the independent variable, so you can think of it as depending on the independent variable. Sometimes the dependent variable is called the "responding variable."

Independent and Dependent Variable Examples

• In a study to determine whether the amount of time a student sleeps affects test scores, the independent variable is the amount of time spent sleeping while the dependent variable is the test score.
• You want to compare brands of paper towels to see which holds the most liquid. The independent variable in your experiment would be the brand of paper towels. The dependent variable would be the amount of liquid absorbed by the paper towel.
• In an experiment to determine how far people can see into the infrared part of the spectrum, the wavelength of light is the independent variable and whether the light is observed (the response) is the dependent variable.
• If you want to know whether caffeine affects your appetite, the presence or absence of a given amount of caffeine would be the independent variable. How hungry you are would be the dependent variable.
• You want to determine whether a chemical is essential for rat nutrition, so you design an experiment. The presence or absence of the chemical is the independent variable. The health of the rat (whether it lives and can reproduce) is the dependent variable. If you determine the substance is necessary for proper nutrition, a follow-up experiment might determine how much of the chemical is needed. Here, the amount of the chemical would be the independent variable, and the rat's health would be the dependent variable.

How Do You Tell Independent and Dependent Variables Apart?

If you are having a hard time identifying which variable is the independent variable and which is the dependent variable, remember the dependent variable is the one affected by a change in the independent variable. If you write out the variables in a sentence that shows cause and effect, the independent variable causes the effect on the dependent variable. If you have the variables in the wrong order, the sentence won't make sense.

Independent variable causes an effect on the dependent variable.

Example : How long you sleep (independent variable) affects your test score (dependent variable).

This makes sense, but:

Example : Your test score affects how long you sleep.

This doesn't really make sense (unless you can't sleep because you are worried you failed a test, but that would be a different experiment).

How to Plot Variables on a Graph

There is a standard method for graphing independent and dependent variables. The x-axis is the independent variable, while the y-axis is the dependent variable. You can use the DRY MIX acronym to help remember how to graph variables:

D  = dependent variable R  = responding variable Y  = graph on the vertical or y-axis

M  = manipulated variable I  = independent variable X  = graph on the horizontal or x-axis

Test your understanding with the scientific method quiz .

Key Takeaways

• In scientific experiments, the independent variable is manipulated while the dependent variable is measured.
• The independent variable, controlled by the experimenter, influences the dependent variable, which responds to changes. This dynamic forms the basis of cause-and-effect relationships.
• Graphing independent and dependent variables follows a standard method in which the independent variable is plotted on the x-axis and the dependent variable on the y-axis.
• Difference Between Independent and Dependent Variables
• The Difference Between Control Group and Experimental Group
• How to Write a Lab Report
• What Is an Experiment? Definition and Design
• How To Design a Science Fair Experiment
• Boiling Points of Ethanol, Methanol, and Isopropyl Alcohol
• Understanding Experimental Groups
• 10 Examples of Heterogeneous and Homogeneous Mixtures
• The Difference Between Homogeneous and Heterogeneous Mixtures
• The Difference Between Intensive and Extensive Properties
• Chemical Properties of Matter
• What Is a Molecule?
• Examples of Physical Changes
• Commensalism Definition, Examples, and Relationships
• Acidic Solution Definition

15 Independent and Dependent Variable Examples

Dave Cornell (PhD)

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An independent variable (IV) is what is manipulated in a scientific experiment to determine its effect on the dependent variable (DV).

By varying the level of the independent variable and observing associated changes in the dependent variable, a researcher can conclude whether the independent variable affects the dependent variable or not.

This can provide very valuable information when studying just about any subject.

Because the researcher controls the level of the independent variable, it can be determined if the independent variable has a causal effect on the dependent variable.

The term causation is vitally important. Scientists want to know what causes changes in the dependent variable. The only way to do that is to manipulate the independent variable and observe any changes in the dependent variable.

Definition of Independent and Dependent Variables

The independent variable and dependent variable are used in a very specific type of scientific study called the experiment .

Although there are many variations of the experiment, generally speaking, it involves either the presence or absence of the independent variable and the observation of what happens to the dependent variable.

The research participants are randomly assigned to either receive the independent variable (called the treatment condition), or not receive the independent variable (called the control condition).

Other variations of an experiment might include having multiple levels of the independent variable.

If the independent variable affects the dependent variable, then it should be possible to observe changes in the dependent variable based on the presence or absence of the independent variable.  

Of course, there are a lot of issues to consider when conducting an experiment, but these are the basic principles.

These concepts should not be confused with predictor and outcome variables .

Examples of Independent and Dependent Variables

1. gatorade and improved athletic performance.

A sports medicine researcher has been hired by Gatorade to test the effects of its sports drink on athletic performance. The company wants to claim that when an athlete drinks Gatorade, their performance will improve.

If they can back up that claim with hard scientific data, that would be great for sales.

So, the researcher goes to a nearby university and randomly selects both male and female athletes from several sports: track and field, volleyball, basketball, and football. Each athlete will run on a treadmill for one hour while their heart rate is tracked.

All of the athletes are given the exact same amount of liquid to consume 30-minutes before and during their run. Half are given Gatorade, and the other half are given water, but no one knows what they are given because both liquids have been colored.

In this example, the independent variable is Gatorade, and the dependent variable is heart rate.  

2. Chemotherapy and Cancer

A hospital is investigating the effectiveness of a new type of chemotherapy on cancer. The researchers identified 120 patients with relatively similar types of cancerous tumors in both size and stage of progression.

The patients are randomly assigned to one of three groups: one group receives no chemotherapy, one group receives a low dose of chemotherapy, and one group receives a high dose of chemotherapy.

Each group receives chemotherapy treatment three times a week for two months, except for the no-treatment group. At the end of two months, the doctors measure the size of each patient’s tumor.

In this study, despite the ethical issues (remember this is just a hypothetical example), the independent variable is chemotherapy, and the dependent variable is tumor size.

3. Interior Design Color and Eating Rate

A well-known fast-food corporation wants to know if the color of the interior of their restaurants will affect how fast people eat. Of course, they would prefer that consumers enter and exit quickly to increase sales volume and profit.

So, they rent space in a large shopping mall and create three different simulated restaurant interiors of different colors. One room is painted mostly white with red trim and seats; one room is painted mostly white with blue trim and seats; and one room is painted mostly white with off-white trim and seats.

Next, they randomly select shoppers on Saturdays and Sundays to eat for free in one of the three rooms. Each shopper is given a box of the same food and drink items and sent to one of the rooms. The researchers record how much time elapses from the moment they enter the room to the moment they leave.

The independent variable is the color of the room, and the dependent variable is the amount of time spent in the room eating.

4. Hair Color and Attraction

A large multinational cosmetics company wants to know if the color of a woman’s hair affects the level of perceived attractiveness in males. So, they use Photoshop to manipulate the same image of a female by altering the color of her hair: blonde, brunette, red, and brown.

Next, they randomly select university males to enter their testing facilities. Each participant sits in front of a computer screen and responds to questions on a survey. At the end of the survey, the screen shows one of the photos of the female.

At the same time, software on the computer that utilizes the computer’s camera is measuring each male’s pupil dilation. The researchers believe that larger dilation indicates greater perceived attractiveness.

The independent variable is hair color, and the dependent variable is pupil dilation.

5. Mozart and Math

After many claims that listening to Mozart will make you smarter, a group of education specialists decides to put it to the test. So, first, they go to a nearby school in a middle-class neighborhood.

During the first three months of the academic year, they randomly select some 5th-grade classrooms to listen to Mozart during their lessons and exams. Other 5 th grade classrooms will not listen to any music during their lessons and exams.

The researchers then compare the scores of the exams between the two groups of classrooms.

Although there are a lot of obvious limitations to this hypothetical, it is the first step.

The independent variable is Mozart, and the dependent variable is exam scores.

6. Essential Oils and Sleep

A company that specializes in essential oils wants to examine the effects of lavender on sleep quality. They hire a sleep research lab to conduct the study. The researchers at the lab have their usual test volunteers sleep in individual rooms every night for one week.

The conditions of each room are all exactly the same, except that half of the rooms have lavender released into the rooms and half do not. While the study participants are sleeping, their heart rates and amount of time spent in deep sleep are recorded with high-tech equipment.

At the end of the study, the researchers compare the total amount of time spent in deep sleep of the lavender-room participants with the no lavender-room participants.

The independent variable in this sleep study is lavender, and the dependent variable is the total amount of time spent in deep sleep.

7. Teaching Style and Learning

A group of teachers is interested in which teaching method will work best for developing critical thinking skills.

So, they train a group of teachers in three different teaching styles : teacher-centered, where the teacher tells the students all about critical thinking; student-centered, where the students practice critical thinking and receive teacher feedback; and AI-assisted teaching, where the teacher uses a special software program to teach critical thinking.

At the end of three months, all the students take the same test that assesses critical thinking skills. The teachers then compare the scores of each of the three groups of students.

The independent variable is the teaching method, and the dependent variable is performance on the critical thinking test.

8. Concrete Mix and Bridge Strength

A chemicals company has developed three different versions of their concrete mix. Each version contains a different blend of specially developed chemicals. The company wants to know which version is the strongest.

So, they create three bridge molds that are identical in every way. They fill each mold with one of the different concrete mixtures. Next, they test the strength of each bridge by placing progressively more weight on its center until the bridge collapses.

In this study, the independent variable is the concrete mixture, and the dependent variable is the amount of weight at collapse.

9. Recipe and Consumer Preferences

People in the pizza business know that the crust is key. Many companies, large and small, will keep their recipe a top secret. Before rolling out a new type of crust, the company decides to conduct some research on consumer preferences.

The company has prepared three versions of their crust that vary in crunchiness, they are: a little crunchy, very crunchy, and super crunchy. They already have a pool of consumers that fit their customer profile and they often use them for testing.

Each participant sits in a booth and takes a bite of one version of the crust. They then indicate how much they liked it by pressing one of 5 buttons: didn’t like at all, liked, somewhat liked, liked very much, loved it.

The independent variable is the level of crust crunchiness, and the dependent variable is how much it was liked.

10. Protein Supplements and Muscle Mass

A large food company is considering entering the health and nutrition sector. Their R&D food scientists have developed a protein supplement that is designed to help build muscle mass for people that work out regularly.

The company approaches several gyms near its headquarters. They enlist the cooperation of over 120 gym rats that work out 5 days a week. Their muscle mass is measured, and only those with a lower level are selected for the study, leaving a total of 80 study participants.

They randomly assign half of the participants to take the recommended dosage of their supplement every day for three months after each workout. The other half takes the same amount of something that looks the same but actually does nothing to the body.

At the end of three months, the muscle mass of all participants is measured.

The independent variable is the supplement, and the dependent variable is muscle mass.  

11. Air Bags and Skull Fractures

In the early days of airbags , automobile companies conducted a great deal of testing. At first, many people in the industry didn’t think airbags would be effective at all. Fortunately, there was a way to test this theory objectively.

In a representative example: Several crash cars were outfitted with an airbag, and an equal number were not. All crash cars were of the same make, year, and model. Then the crash experts rammed each car into a crash wall at the same speed. Sensors on the crash dummy skulls allowed for a scientific analysis of how much damage a human skull would incur.

The amount of skull damage of dummies in cars with airbags was then compared with those without airbags.

The independent variable was the airbag and the dependent variable was the amount of skull damage.

12. Vitamins and Health

Some people take vitamins every day. A group of health scientists decides to conduct a study to determine if taking vitamins improves health.

They randomly select 1,000 people that are relatively similar in terms of their physical health. The key word here is “similar.”

Because the scientists have an unlimited budget (and because this is a hypothetical example, all of the participants have the same meals delivered to their homes (breakfast, lunch, and dinner), every day for one year.

In addition, the scientists randomly assign half of the participants to take a set of vitamins, supplied by the researchers every day for 1 year. The other half do not take the vitamins.

At the end of one year, the health of all participants is assessed, using blood pressure and cholesterol level as the key measurements.

In this highly unrealistic study, the independent variable is vitamins, and the dependent variable is health, as measured by blood pressure and cholesterol levels.

13. Meditation and Stress

Does practicing meditation reduce stress? If you have ever wondered if this is true or not, then you are in luck because there is a way to know one way or the other.

All we have to do is find 90 people that are similar in age, stress levels, diet and exercise, and as many other factors as we can think of.

Next, we randomly assign each person to either practice meditation every day, three days a week, or not at all. After three months, we measure the stress levels of each person and compare the groups.

How should we measure stress? Well, there are a lot of ways. We could measure blood pressure, or the amount of the stress hormone cortisol in their blood, or by using a paper and pencil measure such as a questionnaire that asks them how much stress they feel.

In this study, the independent variable is meditation and the dependent variable is the amount of stress (however it is measured).

14. Video Games and Aggression

When video games started to become increasingly graphic, it was a huge concern in many countries in the world. Educators, social scientists, and parents were shocked at how graphic games were becoming.

Since then, there have been hundreds of studies conducted by psychologists and other researchers. A lot of those studies used an experimental design that involved males of various ages randomly assigned to play a graphic or non-graphic video game.

Afterward, their level of aggression was measured via a wide range of methods, including direct observations of their behavior, their actions when given the opportunity to be aggressive, or a variety of other measures.

So many studies have used so many different ways of measuring aggression.

In these experimental studies, the independent variable was graphic video games, and the dependent variable was observed level of aggression.

15. Vehicle Exhaust and Cognitive Performance

Car pollution is a concern for a lot of reasons. In addition to being bad for the environment, car exhaust may cause damage to the brain and impair cognitive performance.

One way to examine this possibility would be to conduct an animal study. The research would look something like this: laboratory rats would be raised in three different rooms that varied in the degree of car exhaust circulating in the room: no exhaust, little exhaust, or a lot of exhaust.

After a certain period of time, perhaps several months, the effects on cognitive performance could be measured.

One common way of assessing cognitive performance in laboratory rats is by measuring the amount of time it takes to run a maze successfully. It would also be possible to examine the physical effects of car exhaust on the brain by conducting an autopsy.

In this animal study, the independent variable would be car exhaust and the dependent variable would be amount of time to run a maze.

Read Next: Extraneous Variables Examples

The experiment is an incredibly valuable way to answer scientific questions regarding the cause and effect of certain variables. By manipulating the level of an independent variable and observing corresponding changes in a dependent variable, scientists can gain an understanding of many phenomena.

For example, scientists can learn if graphic video games make people more aggressive, if mediation reduces stress, if Gatorade improves athletic performance, and even if certain medical treatments can cure cancer.

The determination of causality is the key benefit of manipulating the independent variable and them observing changes in the dependent variable. Other research methodologies can reveal factors that are related to the dependent variable or associated with the dependent variable, but only when the independent variable is controlled by the researcher can causality be determined.

Ferguson, C. J. (2010). Blazing Angels or Resident Evil? Can graphic video games be a force for good? Review of General Psychology, 14 (2), 68-81. https://doi.org/10.1037/a0018941

Flannelly, L. T., Flannelly, K. J., & Jankowski, K. R. (2014). Independent, dependent, and other variables in healthcare and chaplaincy research. Journal of Health Care Chaplaincy , 20 (4), 161–170. https://doi.org/10.1080/08854726.2014.959374

Manocha, R., Black, D., Sarris, J., & Stough, C.(2011). A randomized, controlled trial of meditation for work stress, anxiety and depressed mood in full-time workers. Evidence-Based Complementary and Alternative Medicine , vol. 2011, Article ID 960583. https://doi.org/10.1155/2011/960583

Rumrill, P. D., Jr. (2004). Non-manipulation quantitative designs. Work (Reading, Mass.) , 22 (3), 255–260.

Taylor, J. M., & Rowe, B. J. (2012). The “Mozart Effect” and the mathematical connection, Journal of College Reading and Learning, 42 (2), 51-66.  https://doi.org/10.1080/10790195.2012.10850354

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Organizing Your Social Sciences Research Paper

• Independent and Dependent Variables
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Definitions

Dependent Variable The variable that depends on other factors that are measured. These variables are expected to change as a result of an experimental manipulation of the independent variable or variables. It is the presumed effect.

Independent Variable The variable that is stable and unaffected by the other variables you are trying to measure. It refers to the condition of an experiment that is systematically manipulated by the investigator. It is the presumed cause.

Cramer, Duncan and Dennis Howitt. The SAGE Dictionary of Statistics . London: SAGE, 2004; Penslar, Robin Levin and Joan P. Porter. Institutional Review Board Guidebook: Introduction . Washington, DC: United States Department of Health and Human Services, 2010; "What are Dependent and Independent Variables?" Graphic Tutorial.

Identifying Dependent and Independent Variables

Don't feel bad if you are confused about what is the dependent variable and what is the independent variable in social and behavioral sciences research . However, it's important that you learn the difference because framing a study using these variables is a common approach to organizing the elements of a social sciences research study in order to discover relevant and meaningful results. Specifically, it is important for these two reasons:

• You need to understand and be able to evaluate their application in other people's research.
• You need to apply them correctly in your own research.

A variable in research simply refers to a person, place, thing, or phenomenon that you are trying to measure in some way. The best way to understand the difference between a dependent and independent variable is that the meaning of each is implied by what the words tell us about the variable you are using. You can do this with a simple exercise from the website, Graphic Tutorial. Take the sentence, "The [independent variable] causes a change in [dependent variable] and it is not possible that [dependent variable] could cause a change in [independent variable]." Insert the names of variables you are using in the sentence in the way that makes the most sense. This will help you identify each type of variable. If you're still not sure, consult with your professor before you begin to write.

Fan, Shihe. "Independent Variable." In Encyclopedia of Research Design. Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 592-594; "What are Dependent and Independent Variables?" Graphic Tutorial; Salkind, Neil J. "Dependent Variable." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 348-349;

Structure and Writing Style

The process of examining a research problem in the social and behavioral sciences is often framed around methods of analysis that compare, contrast, correlate, average, or integrate relationships between or among variables . Techniques include associations, sampling, random selection, and blind selection. Designation of the dependent and independent variable involves unpacking the research problem in a way that identifies a general cause and effect and classifying these variables as either independent or dependent.

The variables should be outlined in the introduction of your paper and explained in more detail in the methods section . There are no rules about the structure and style for writing about independent or dependent variables but, as with any academic writing, clarity and being succinct is most important.

After you have described the research problem and its significance in relation to prior research, explain why you have chosen to examine the problem using a method of analysis that investigates the relationships between or among independent and dependent variables . State what it is about the research problem that lends itself to this type of analysis. For example, if you are investigating the relationship between corporate environmental sustainability efforts [the independent variable] and dependent variables associated with measuring employee satisfaction at work using a survey instrument, you would first identify each variable and then provide background information about the variables. What is meant by "environmental sustainability"? Are you looking at a particular company [e.g., General Motors] or are you investigating an industry [e.g., the meat packing industry]? Why is employee satisfaction in the workplace important? How does a company make their employees aware of sustainability efforts and why would a company even care that its employees know about these efforts?

Identify each variable for the reader and define each . In the introduction, this information can be presented in a paragraph or two when you describe how you are going to study the research problem. In the methods section, you build on the literature review of prior studies about the research problem to describe in detail background about each variable, breaking each down for measurement and analysis. For example, what activities do you examine that reflect a company's commitment to environmental sustainability? Levels of employee satisfaction can be measured by a survey that asks about things like volunteerism or a desire to stay at the company for a long time.

The structure and writing style of describing the variables and their application to analyzing the research problem should be stated and unpacked in such a way that the reader obtains a clear understanding of the relationships between the variables and why they are important. This is also important so that the study can be replicated in the future using the same variables but applied in a different way.

Fan, Shihe. "Independent Variable." In Encyclopedia of Research Design. Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 592-594; "What are Dependent and Independent Variables?" Graphic Tutorial; “Case Example for Independent and Dependent Variables.” ORI Curriculum Examples. U.S. Department of Health and Human Services, Office of Research Integrity; Salkind, Neil J. "Dependent Variable." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 348-349; “Independent Variables and Dependent Variables.” Karl L. Wuensch, Department of Psychology, East Carolina University [posted email exchange]; “Variables.” Elements of Research. Dr. Camille Nebeker, San Diego State University.

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Types of Variables, Descriptive Statistics, and Sample Size

Feroze kaliyadan.

Department of Dermatology, King Faisal University, Al Hofuf, Saudi Arabia

Vinay Kulkarni

1 Department of Dermatology, Prayas Amrita Clinic, Pune, Maharashtra, India

This short “snippet” covers three important aspects related to statistics – the concept of variables , the importance, and practical aspects related to descriptive statistics and issues related to sampling – types of sampling and sample size estimation.

What is a variable?[ 1 , 2 ] To put it in very simple terms, a variable is an entity whose value varies. A variable is an essential component of any statistical data. It is a feature of a member of a given sample or population, which is unique, and can differ in quantity or quantity from another member of the same sample or population. Variables either are the primary quantities of interest or act as practical substitutes for the same. The importance of variables is that they help in operationalization of concepts for data collection. For example, if you want to do an experiment based on the severity of urticaria, one option would be to measure the severity using a scale to grade severity of itching. This becomes an operational variable. For a variable to be “good,” it needs to have some properties such as good reliability and validity, low bias, feasibility/practicality, low cost, objectivity, clarity, and acceptance. Variables can be classified into various ways as discussed below.

Quantitative vs qualitative

A variable can collect either qualitative or quantitative data. A variable differing in quantity is called a quantitative variable (e.g., weight of a group of patients), whereas a variable differing in quality is called a qualitative variable (e.g., the Fitzpatrick skin type)

A simple test which can be used to differentiate between qualitative and quantitative variables is the subtraction test. If you can subtract the value of one variable from the other to get a meaningful result, then you are dealing with a quantitative variable (this of course will not apply to rating scales/ranks).

Quantitative variables can be either discrete or continuous

Discrete variables are variables in which no values may be assumed between the two given values (e.g., number of lesions in each patient in a sample of patients with urticaria).

Continuous variables, on the other hand, can take any value in between the two given values (e.g., duration for which the weals last in the same sample of patients with urticaria). One way of differentiating between continuous and discrete variables is to use the “mid-way” test. If, for every pair of values of a variable, a value exactly mid-way between them is meaningful, the variable is continuous. For example, two values for the time taken for a weal to subside can be 10 and 13 min. The mid-way value would be 11.5 min which makes sense. However, for a number of weals, suppose you have a pair of values – 5 and 8 – the midway value would be 6.5 weals, which does not make sense.

Under the umbrella of qualitative variables, you can have nominal/categorical variables and ordinal variables

Nominal/categorical variables are, as the name suggests, variables which can be slotted into different categories (e.g., gender or type of psoriasis).

Ordinal variables or ranked variables are similar to categorical, but can be put into an order (e.g., a scale for severity of itching).

Dependent and independent variables

In the context of an experimental study, the dependent variable (also called outcome variable) is directly linked to the primary outcome of the study. For example, in a clinical trial on psoriasis, the PASI (psoriasis area severity index) would possibly be one dependent variable. The independent variable (sometime also called explanatory variable) is something which is not affected by the experiment itself but which can be manipulated to affect the dependent variable. Other terms sometimes used synonymously include blocking variable, covariate, or predictor variable. Confounding variables are extra variables, which can have an effect on the experiment. They are linked with dependent and independent variables and can cause spurious association. For example, in a clinical trial for a topical treatment in psoriasis, the concomitant use of moisturizers might be a confounding variable. A control variable is a variable that must be kept constant during the course of an experiment.

Descriptive Statistics

Statistics can be broadly divided into descriptive statistics and inferential statistics.[ 3 , 4 ] Descriptive statistics give a summary about the sample being studied without drawing any inferences based on probability theory. Even if the primary aim of a study involves inferential statistics, descriptive statistics are still used to give a general summary. When we describe the population using tools such as frequency distribution tables, percentages, and other measures of central tendency like the mean, for example, we are talking about descriptive statistics. When we use a specific statistical test (e.g., Mann–Whitney U-test) to compare the mean scores and express it in terms of statistical significance, we are talking about inferential statistics. Descriptive statistics can help in summarizing data in the form of simple quantitative measures such as percentages or means or in the form of visual summaries such as histograms and box plots.

Descriptive statistics can be used to describe a single variable (univariate analysis) or more than one variable (bivariate/multivariate analysis). In the case of more than one variable, descriptive statistics can help summarize relationships between variables using tools such as scatter plots.

Descriptive statistics can be broadly put under two categories:

• Sorting/grouping and illustration/visual displays
• Summary statistics.

Sorting and grouping

Sorting and grouping is most commonly done using frequency distribution tables. For continuous variables, it is generally better to use groups in the frequency table. Ideally, group sizes should be equal (except in extreme ends where open groups are used; e.g., age “greater than” or “less than”).

Another form of presenting frequency distributions is the “stem and leaf” diagram, which is considered to be a more accurate form of description.

Suppose the weight in kilograms of a group of 10 patients is as follows:

56, 34, 48, 43, 87, 78, 54, 62, 61, 59

The “stem” records the value of the “ten's” place (or higher) and the “leaf” records the value in the “one's” place [ Table 1 ].

Stem and leaf plot

Illustration/visual display of data

The most common tools used for visual display include frequency diagrams, bar charts (for noncontinuous variables) and histograms (for continuous variables). Composite bar charts can be used to compare variables. For example, the frequency distribution in a sample population of males and females can be illustrated as given in Figure 1 .

Composite bar chart

A pie chart helps show how a total quantity is divided among its constituent variables. Scatter diagrams can be used to illustrate the relationship between two variables. For example, global scores given for improvement in a condition like acne by the patient and the doctor [ Figure 2 ].

Scatter diagram

Summary statistics

The main tools used for summary statistics are broadly grouped into measures of central tendency (such as mean, median, and mode) and measures of dispersion or variation (such as range, standard deviation, and variance).

Imagine that the data below represent the weights of a sample of 15 pediatric patients arranged in ascending order:

30, 35, 37, 38, 38, 38, 42, 42, 44, 46, 47, 48, 51, 53, 86

Just having the raw data does not mean much to us, so we try to express it in terms of some values, which give a summary of the data.

The mean is basically the sum of all the values divided by the total number. In this case, we get a value of 45.

The problem is that some extreme values (outliers), like “'86,” in this case can skew the value of the mean. In this case, we consider other values like the median, which is the point that divides the distribution into two equal halves. It is also referred to as the 50 th percentile (50% of the values are above it and 50% are below it). In our previous example, since we have already arranged the values in ascending order we find that the point which divides it into two equal halves is the 8 th value – 42. In case of a total number of values being even, we choose the two middle points and take an average to reach the median.

The mode is the most common data point. In our example, this would be 38. The mode as in our case may not necessarily be in the center of the distribution.

The median is the best measure of central tendency from among the mean, median, and mode. In a “symmetric” distribution, all three are the same, whereas in skewed data the median and mean are not the same; lie more toward the skew, with the mean lying further to the skew compared with the median. For example, in Figure 3 , a right skewed distribution is seen (direction of skew is based on the tail); data values' distribution is longer on the right-hand (positive) side than on the left-hand side. The mean is typically greater than the median in such cases.

Location of mode, median, and mean

Measures of dispersion

The range gives the spread between the lowest and highest values. In our previous example, this will be 86-30 = 56.

A more valuable measure is the interquartile range. A quartile is one of the values which break the distribution into four equal parts. The 25 th percentile is the data point which divides the group between the first one-fourth and the last three-fourth of the data. The first one-fourth will form the first quartile. The 75 th percentile is the data point which divides the distribution into a first three-fourth and last one-fourth (the last one-fourth being the fourth quartile). The range between the 25 th percentile and 75 th percentile is called the interquartile range.

Variance is also a measure of dispersion. The larger the variance, the further the individual units are from the mean. Let us consider the same example we used for calculating the mean. The mean was 45.

For the first value (30), the deviation from the mean will be 15; for the last value (86), the deviation will be 41. Similarly we can calculate the deviations for all values in a sample. Adding these deviations and averaging will give a clue to the total dispersion, but the problem is that since the deviations are a mix of negative and positive values, the final total becomes zero. To calculate the variance, this problem is overcome by adding squares of the deviations. So variance would be the sum of squares of the variation divided by the total number in the population (for a sample we use “n − 1”). To get a more realistic value of the average dispersion, we take the square root of the variance, which is called the “standard deviation.”

The box plot

The box plot is a composite representation that portrays the mean, median, range, and the outliers [ Figure 4 ].

The concept of skewness and kurtosis

Skewness is a measure of the symmetry of distribution. Basically if the distribution curve is symmetric, it looks the same on either side of the central point. When this is not the case, it is said to be skewed. Kurtosis is a representation of outliers. Distributions with high kurtosis tend to have “heavy tails” indicating a larger number of outliers, whereas distributions with low kurtosis have light tails, indicating lesser outliers. There are formulas to calculate both skewness and kurtosis [Figures ​ [Figures5 5 – 8 ].

Positive skew

High kurtosis (positive kurtosis – also called leptokurtic)

Negative skew

Low kurtosis (negative kurtosis – also called “Platykurtic”)

Sample Size

In an ideal study, we should be able to include all units of a particular population under study, something that is referred to as a census.[ 5 , 6 ] This would remove the chances of sampling error (difference between the outcome characteristics in a random sample when compared with the true population values – something that is virtually unavoidable when you take a random sample). However, it is obvious that this would not be feasible in most situations. Hence, we have to study a subset of the population to reach to our conclusions. This representative subset is a sample and we need to have sufficient numbers in this sample to make meaningful and accurate conclusions and reduce the effect of sampling error.

We also need to know that broadly sampling can be divided into two types – probability sampling and nonprobability sampling. Examples of probability sampling include methods such as simple random sampling (each member in a population has an equal chance of being selected), stratified random sampling (in nonhomogeneous populations, the population is divided into subgroups – followed be random sampling in each subgroup), systematic (sampling is based on a systematic technique – e.g., every third person is selected for a survey), and cluster sampling (similar to stratified sampling except that the clusters here are preexisting clusters unlike stratified sampling where the researcher decides on the stratification criteria), whereas nonprobability sampling, where every unit in the population does not have an equal chance of inclusion into the sample, includes methods such as convenience sampling (e.g., sample selected based on ease of access) and purposive sampling (where only people who meet specific criteria are included in the sample).

An accurate calculation of sample size is an essential aspect of good study design. It is important to calculate the sample size much in advance, rather than have to go for post hoc analysis. A sample size that is too less may make the study underpowered, whereas a sample size which is more than necessary might lead to a wastage of resources.

We will first go through the sample size calculation for a hypothesis-based design (like a randomized control trial).

The important factors to consider for sample size calculation include study design, type of statistical test, level of significance, power and effect size, variance (standard deviation for quantitative data), and expected proportions in the case of qualitative data. This is based on previous data, either based on previous studies or based on the clinicians' experience. In case the study is something being conducted for the first time, a pilot study might be conducted which helps generate these data for further studies based on a larger sample size). It is also important to know whether the data follow a normal distribution or not.

Two essential aspects we must understand are the concept of Type I and Type II errors. In a study that compares two groups, a null hypothesis assumes that there is no significant difference between the two groups, and any observed difference being due to sampling or experimental error. When we reject a null hypothesis, when it is true, we label it as a Type I error (also denoted as “alpha,” correlating with significance levels). In a Type II error (also denoted as “beta”), we fail to reject a null hypothesis, when the alternate hypothesis is actually true. Type II errors are usually expressed as “1- β,” correlating with the power of the test. While there are no absolute rules, the minimal levels accepted are 0.05 for α (corresponding to a significance level of 5%) and 0.20 for β (corresponding to a minimum recommended power of “1 − 0.20,” or 80%).

Effect size and minimal clinically relevant difference

For a clinical trial, the investigator will have to decide in advance what clinically detectable change is significant (for numerical data, this is could be the anticipated outcome means in the two groups, whereas for categorical data, it could correlate with the proportions of successful outcomes in two groups.). While we will not go into details of the formula for sample size calculation, some important points are as follows:

In the context where effect size is involved, the sample size is inversely proportional to the square of the effect size. What this means in effect is that reducing the effect size will lead to an increase in the required sample size.

Reducing the level of significance (alpha) or increasing power (1-β) will lead to an increase in the calculated sample size.

An increase in variance of the outcome leads to an increase in the calculated sample size.

A note is that for estimation type of studies/surveys, sample size calculation needs to consider some other factors too. This includes an idea about total population size (this generally does not make a major difference when population size is above 20,000, so in situations where population size is not known we can assume a population of 20,000 or more). The other factor is the “margin of error” – the amount of deviation which the investigators find acceptable in terms of percentages. Regarding confidence levels, ideally, a 95% confidence level is the minimum recommended for surveys too. Finally, we need an idea of the expected/crude prevalence – either based on previous studies or based on estimates.

Sample size calculation also needs to add corrections for patient drop-outs/lost-to-follow-up patients and missing records. An important point is that in some studies dealing with rare diseases, it may be difficult to achieve desired sample size. In these cases, the investigators might have to rework outcomes or maybe pool data from multiple centers. Although post hoc power can be analyzed, a better approach suggested is to calculate 95% confidence intervals for the outcome and interpret the study results based on this.

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What are Examples of Variables in Research?

Table of contents, introduction.

In writing your thesis, one of the first terms that you encounter is the word variable. Failure to understand the meaning and the usefulness of variables in your study will prevent you from doing excellent research. What are variables, and how do you use variables in your research?

I explain this key research concept below with lots of examples of variables commonly used in a study.

You may find it challenging to understand just what variables are in research, especially those that deal with quantitative data analysis. This initial difficulty about variables becomes much more confusing when you encounter the phrases “dependent variable” and “independent variable” as you go deeper in studying this vital concept of research, as well as statistics.

Therefore, it is a must that you should be able to grasp thoroughly the meaning of variables and ways on how to measure them. Yes, the variables should be measurable so that you will use your data for statistical analysis.

I will strengthen your understanding by providing examples of phenomena and their corresponding variables below.

Definition of Variable

Variables are those simplified portions of the complex phenomena that you intend to study. The word variable is derived from the root word “vary,” meaning, changing in amount, volume, number, form, nature, or type. These variables should be measurable, i.e., they can be counted or subjected to a scale.

Examples of Variables in Research: 6 Phenomena

The following are examples of phenomena from a global to a local perspective. The corresponding list of variables is given to illustrate how complex phenomena can be broken down into manageable pieces for better understanding and to subject the phenomena to research.

Phenomenon 1: Climate change

Examples of variables related to climate change :

Phenomenon 2: Crime and violence in the streets

Phenomenon 3: poor performance of students in college entrance exams.

Examples of variables related to poor academic performance :

Phenomenon 4: Fish kill

Examples of variables related to fish kill :

Phenomenon 5: Poor crop growth

Examples of variables related to poor crop growth :

Phenomenon 6:  How Content Goes Viral

Notice in the above variable examples that all the factors listed under the phenomena can be counted or measured using an ordinal, ratio, or interval scale, except for the last one. The factors that influence how content goes viral are essentially subjective.

Thus, the variables in the last phenomenon represent the  nominal scale of measuring variables .

The expected values derived from these variables will be in terms of numbers, amount, category, or type. Quantified variables allow statistical analysis . Variable descriptions, correlations, or differences are then determined.

Difference Between Independent and Dependent Variables

Independent variables.

For example, in the second phenomenon, i.e., crime and violence in the streets, the independent variables are the number of law enforcers. If there are more law enforcers, it is expected that it will reduce the following:

The five variables listed under crime and violence in the streets as the theme of a study are all dependent variables.

Dependent Variables

For example, in the first phenomenon on climate change, temperature as the independent variable influences sea level rise, the dependent variable. Increased temperature will cause the expansion of water in the sea. Thus, sea-level rise on a global scale will occur.

I will leave the classification of the other variables to you. Find out whether those are independent or dependent variables. Note, however, that some variables can be both independent or dependent variables, as the context of the study dictates.

Finding the relationship between variables

How will you know that one variable may cause the other to behave in a certain way?

Finding the relationship between variables requires a thorough  review of the literature . Through a review of the relevant and reliable literature, you will find out which variables influence the other variable. You do not just guess relationships between variables. The entire process is the essence of research.

At this point, I believe that the concept of the variable is now clear to you. Share this information with your peers, who may have difficulty in understanding what the variables are in research.

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Your question is unclear to me Biyaminu. What do you mean? If you want to cite this, see the citation box after the article.

I salute your work, before I was have no enough knowledge about variable I think I was claimed from my lecturers, but the real meaning I was in the mid night. thanks

thanks for the explanation a bout variables. keep on posting information a bout reseach on my email.

You can see in the last part of the above article an explanation about dependent and independent variables.

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Dear Alhaji, just be clear about what you want to do. Your research question must be clearly stated before you build your conceptual framework.

Can you please give me what are the possible variables in terms of installation of street lights along barangay roads of calauan, laguna: an assessment?

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Types of Variables in Research – Definition & Examples

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A fundamental component in statistical investigations is the methodology you employ in selecting your research variables. The careful selection of appropriate variable types can significantly enhance the robustness of your experimental design . This piece explores the diverse array of variable classifications within the field of statistical research. Additionally, understanding the different types of variables in research can greatly aid in shaping your experimental hypotheses and outcomes.

Inhaltsverzeichnis

• 1 Types of Variables in Research – In a Nutshell
• 2 Definition: Types of variables in research
• 3 Types of variables in research – Quantitative vs. Categorical
• 4 Types of variables in research – Independent vs. Dependent
• 5 Other useful types of variables in research

Types of Variables in Research – In a Nutshell

• A variable is an attribute of an item of analysis in research.
• The types of variables in research can be categorized into: independent vs. dependent , or categorical vs. quantitative .
• The types of variables in research (correlational) can be classified into predictor or outcome variables.
• Other types of variables in research are confounding variables , latent variables , and composite variables.

Definition: Types of variables in research

A variable is a trait of an item of analysis in research. Types of variables in research are imperative, as they describe and measure places, people, ideas , or other research objects . There are many types of variables in research. Therefore, you must choose the right types of variables in research for your study.

Note that the correct variable will help with your research design , test selection, and result interpretation.

In a study testing whether some genders are more stress-tolerant than others, variables you can include are the level of stressors in the study setting, male and female subjects, and productivity levels in the presence of stressors.

Also, before choosing which types of variables in research to use, you should know how the various types work and the ideal statistical tests and result interpretations you will use for your study. The key is to determine the type of data the variable contains and the part of the experiment the variable represents.

Types of variables in research – Quantitative vs. Categorical

Data is the precise extent of a variable in statistical research that you record in a data sheet. It is generally divided into quantitative and categorical classes.

Quantitative or numerical data represents amounts, while categorical data represents collections or groupings.

The type of data contained in your variable will determine the types of variables in research. For instance, variables consisting of quantitative data are called quantitative variables, while those containing categorical data are called categorical variables. The section below explains these two types of variables in research better.

Quantitative variables

The scores you record when collecting quantitative data usually represent real values you can add, divide , subtract , or multiply . There are two types of quantitative variables: discrete variables and continuous variables .

The table below explains the elements that set apart discrete and continuous types of variables in research:

Categorical variables

Categorical variables contain data representing groupings. Additionally, the data in categorical variables is sometimes recorded as numbers . However, the numbers represent categories instead of real amounts.

There are three categorical types of variables in research: nominal variables, ordinal variables , and binary variables . Here is a tabular summary.

It is worth mentioning that some categorical variables can function as multiple types. For example, in some studies, you can use ordinal variables as quantitative variables if the scales are numerical and not discrete.

Data sheet of quantitative and categorical variables

A data sheet is where you record the data on the variables in your experiment.

In a study of the salt-tolerance levels of various plant species, you can record the data on salt addition and how the plant responds in your datasheet.

The key is to gather the information and draw a conclusion over a specific period and filling out a data sheet along the process.

Below is an example of a data sheet containing binary, nominal, continuous , and ordinal types of variables in research.

Types of variables in research – Independent vs. Dependent

The purpose of experiments is to determine how the variables affect each other. As stated in our experiment above, the study aims to find out how the quantity of salt introduce in the water affects the plant’s growth and survival.

Therefore, the researcher manipulates the independent variables and measures the dependent variables . Additionally, you may have control variables that you hold constant.

The table below summarizes independent variables, dependent variables , and control variables .

Data sheet of independent and dependent variables

In salt-tolerance research, there is one independent variable (salt amount) and three independent variables. All other variables are neither dependent nor independent.

Below is a data sheet based on our experiment:

Types of variables in correlational research

The types of variables in research may differ depending on the study.

In correlational research , dependent and independent variables do not apply because the study objective is not to determine the cause-and-effect link between variables.

However, in correlational research, one variable may precede the other, as illness leads to death, and not vice versa. In such an instance, the preceding variable, like illness, is the predictor variable, while the other one is the outcome variable.

Other useful types of variables in research

The key to conducting effective research is to define your types of variables as independent and dependent. Next, you must determine if they are categorical or numerical types of variables in research so you can choose the proper statistical tests for your study.

Below are other types of variables in research worth understanding.

What is the definition for independent and dependent variables?

An autonomous or independent variable is the one you believe is the origin of the outcome, while the dependent variable is the one you believe affects the outcome of your study.

What are quantitative and categorical variables?

Knowing the types of variables in research that you can work with will help you choose the best statistical tests and result representation techniques. It will also help you with your study design.

Discrete and continuous variables: What is their difference?

Discrete variables are types of variables in research that represent counts, like the quantities of objects. In contrast, continuous variables are types of variables in research that represent measurable quantities like age, volume, and weight.

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Democracy and Foreign Direct Investment in BRICS-TM Countries for Sustainable Development

• Open access
• Published: 05 September 2024

Cite this article

You have full access to this open access article

• Ibrahim Cutcu   ORCID: orcid.org/0000-0002-8655-1553 1 &
• Ahmet Keser   ORCID: orcid.org/0000-0002-1064-7807 2  

The study aims to examine the long-term cointegration between the democracy index and foreign direct investment (FDI). The sample group chosen for this investigation comprises BRICS-TM (Brazil, Russia, India, China, South Africa, Turkey [Türkiye], and Mexico) countries due to their increasing strategic importance and potential growth in the global economy. Data from 1994 to 2018 were analyzed, with panel data analysis techniques employed to accommodate potential structural breaks. The level of democracy serves as the independent variable in the model, while FDI is the dependent variable. Inflation and income per capita are considered control variables due to their impact on FDI. The analysis revealed a long-term relationship with structural breaks among the model’s variables. Democratic progress and FDI demonstrate a correlated, balanced relationship over time in these countries. Therefore, governments and policymakers in emerging economies aiming to attract FDI should account for structural breaks and the correlation between democracy and FDI. Furthermore, the Kónya causality tests revealed a causality from democracy to FDI at a 1% significance level in Mexico, 5% in China, and 10% in Russia. From FDI to democracy (DEMOC), there is causality at a 5% significance level in Mexico and a 10% significance level in Russia. Thus, the findings suggest that supporting democratic development with macroeconomic indicators in BRICS-TM countries will positively impact foreign direct capital inflows.

Graphical Abstract

Avoid common mistakes on your manuscript.

Introduction

Economies and governments require capital infusion to augment their production and employment levels. Underdeveloped and developing nations, despite having an abundance of land and labor, grapple with capital deficiencies. Consequently, these countries often seek foreign direct investment (FDI) to address this capital shortfall. Even emerging market economies are not immune to this phenomenon, with challenges intensifying globally post-COVID-19 pandemic. Khan et al. ( 2023 ) highlighted the pivotal role of institutional quality and good governance in attracting FDI. The need for FDI has grown exponentially in an increasingly globalized world characterized by interdependence among states. Democracy and the democratic status of states emerge as critical indicators of institutional quality. Kilci and Yilanci ( 2022 ) posit that the prolonged pandemic triggered the third most significant recession since the Great Depression of 1929 and the Global Financial Crisis of 2008–2009. Consequently, the demand for FDI has surged, positioning foreign investment as the foremost resource for fostering sustainable economic development. In light of the provided frame, this study addresses the following research questions:

What factors attract foreign direct investment to a country?

Which factors positively impact FDI?

Reviewing the existing literature reveals that scholars from diverse disciplines address similar questions using political variables like political stability and democracy levels or economic variables such as economic stability and natural resources . However, the impact of democracy on FDI is often overlooked . For example, studies by Baghestani et al. ( 2019 ) and Gür ( 2020 ) investigated variables like oil prices, exchange rates, exports, imports, and the global innovation index but seldom considered democracy’s role in attracting FDI . Similarly, studies examining the relationship between democracy and FDI, like those by Yusuf et al. ( 2020 ) and Ahmed et al. ( 2021 ), generally excluded data from BRICS-TM countries.

Li and Resnick ( 2003 ) assert that the two paramount features of modern international political economy are the proliferation of democracy and increased economic globalization . It has become apparent that FDI inflow is a manifestation of high-level globalization and the diffusion of democracy. According to the United Nations Conferences on Trade and Development (UNCTAD), 2002 data between 1990 and 2000, three-quarters of the total international foreign direct capital was directed toward democratic and developed countries (Busse, 2003 ).

The conceptualization of democracy, within both theoretical and historical frameworks, has been marked by inherent challenges (Suny, 2017 ). Aliefendioğlu ( 2005 ) defines democracy as the amalgamation of the ancient Greek terms “Demos” and “Kratos,” centered on the principle of self-governance by the people. In essence, democracy encompasses the utilization of popular sovereignty by and for the citizenry (Keser et al., 2023 ). Haydaroğlu and Gülşah ( 2016 ) contend that the contemporary manifestation of democracies is rooted in representative democracy, wherein individuals exercise their sovereignty by selecting representatives to act on their behalf. The spread of liberal or representative democracy is believed to be a driving force behind this shift in economic structures. The relational intersection between FDI flow and democratic mechanisms needs to be investigated. At this point, Voicu and Peral ( 2014 ) argue that economic development and modernization operate as background factors that affect the development of support for democracy. Therefore, an opinion emerges that there is an inevitable intersection between FDI flow and democratic mechanism.

Despite the sustained attention from academia and the public, the detailed understanding of democracy’s effect on FDI remains limited (Li & Resnick, 2003 ). There is a noticeable gap in the literature concerning studies investigating the impact of democracy on FDI, specifically in BRICS-TM countries , which are emerging markets that attract significant FDI. Moreover, the absence of structural break panel cointegration tests in previous analyses accentuates these gaps, forming the primary motivation for this research . The study aims to fill these voids by empirically examining the relationship between democracy and FDI using data from the emerging markets of BRICS-TM countries. These countries require substantial foreign capital and are crucial for the stable development of the global economy since they are expected to become pivotal centers in the multipolar world system. The study differs from other publications, employing unique methods, such as structural break panel cointegration tests, to address these objectives.

Reducing costs, increasing employment-oriented production, and enhancing export capacity are paramount in global competition. If a country cannot achieve these advancements with its existing potential and dynamics, attracting foreign capital becomes imperative, necessitating the creation of multiple attraction points to entice foreign direct investments. Consequently, attracting foreign capital is significant in today’s globalized world. This study provides insights into this pressing issue in the contemporary global competitive landscape by analyzing the long-term relationship between democracy and foreign direct investment. Considering their prominence in the world economy due to recent economic growth and competitive structures, the selection of BRICS-TM countries as a sample group underscores the study’s importance. The study acknowledges the strategic importance and increasing power of BRICS-TM countries, especially China and India, which have consistently attracted significant foreign capital in recent years. Using panel data analysis techniques that incorporate structural breaks addresses a crucial gap in the literature, offering a more accurate analysis of the democracy-foreign direct investment relationship in the BRICS-TM sample group. However, data constraints related to model variables alongside the limitations of evaluating results within the framework of the chosen sample group are acknowledged later in the “ Discussion ” section.

Lastly, there appears to be a gap in the existing literature concerning studies that investigate the impact of democracy on FDI flow in BRICS-TM countries . The countries that attract more FDI than others raise the question of whether their democracy level empirically influences the amount of FDI. Moreover, upon examining the limited studies exploring the relationship between democracy and FDI, it is evident that none applied the structural break panel cointegration test in their analyses. These gaps collectively serve as the primary motivation for this research. Thus, the study aims to address these gaps in the existing literature and scrutinizes whether there is cointegration between the level of democracy and FDI in a country by utilizing sample group data from emerging markets of BRICS-TM countries. This selection is significant as these countries are among emerging economies with considerable developmental potential. In essence, this study aims to empirically unveil the relationship between democracy and FDI , a crucial requirement for developing economies striving to attract more foreign capital for sustainable development . Additionally, this study employs distinctive methods, such as the structural break panel cointegration test, to investigate the subject, further elaborated in the “ Research Method and Econometric Analysis ” section.

In global competition, the imperative to reduce costs, increase employment-oriented production, and enhance export capacity is paramount. Given a country’s potential and dynamics, if these enhancements prove elusive, the necessity arises to attract foreign capital and establish various attraction points to incentivize foreign direct investments. Therefore, attracting foreign direct investment (FDI) to a country holds tremendous significance in today’s globalized world. Before investing, foreign capital rigorously assesses the potential profit opportunities and scrutinizes various socio-economic indicators, especially democracy. For these reasons, by analyzing the long-term relationship between democracy and foreign direct investment in the BRICS-TM sample, this study incorporates analyses and inferences regarding this crucial challenge in today’s globally competitive environment.

Furthermore, it is anticipated that the strategic importance and influence of BRICS-TM countries will continue to escalate in the upcoming years. Notably, countries in the sample group, particularly China and India, have consistently attracted substantial foreign capital, and their economies exhibit ongoing growth. As evident from the graphical analysis in the study, China stands out as the world leader in attracting foreign direct investment. Considering the economic size of Russia and Brazil, the geo-strategic location of Türkiye, and the natural resource wealth of China, India, and Mexico, it is apparent that these countries are central attractions for foreign direct capital. Events with significant consequences on the global stage, such as economic crises, wars, earthquakes, and elections, can induce substantial fluctuations and structural breaks in national economies. Hence, using panel data analysis techniques that allow for structural breaks in the study fills a critical gap in the literature. This approach provides a more accurate analysis of the democracy-foreign direct investment relationship in the BRICS-TM sample group. The primary limitation in the study’s analysis is the constraint arising from the variables included in the model. Additionally, selecting the BRICS-TM sample group as the focus on developing countries can be considered another limitation, restricting the evaluation of results within this specific sample framework. The study anticipates that the policy recommendations derived from the analysis findings will guide policymakers, market players, and new researchers.

The article is organized into the following sections: (1) “ Introduction ” section: This section initially furnishes broad information concerning the subject matter, elucidating the lacunae in the existing literature and delineating the limitations of the study. (2) “ Theoretical Frame and Literature Review ” section: Subsequently, the second section delves into the examination of the theoretical framework, scrutinizing the prevailing status of the literature. (3) “ Research Method and Econometric Analysis ” section: The third segment comprehensively addresses the research methodology employed and expounds upon the econometric analysis conducted. (4) “ Results ” section: The ensuing fourth chapter presents the study’s findings and results. (5) “ Discussion ” section: These results and findings are then systematically expounded upon in the fifth chapter within the context of the current literature. (6) “ Conclusion ” section: Culminating the study is a concluding section encapsulating the critical insights derived, followed by policy recommendations.

Theoretical Frame and Literature Review

As previously indicated, scarce studies have delved into the correlation between democracy and foreign direct investment (FDI). A comprehensive examination of the existing literature reveals a notable dearth of research focused on BRICS-TM countries, with most of them overlooking “democracy” as a variable and/or the connection between “democracy and FDI.” Conversely, researchers investigating FDI predominantly explore its associations with other variables, such as “exports and imports.”

The Status of the Literature on BRICS-TM Countries and Democracy and Foreign Direct Investment

The following two tables summarize the status of the current literature on the issue and its findings. In Table  1 , the literature on BRICS and/or BRIC + S + T + M countries, as well as its variables, methods, and findings, is given. Then, in Table  2 , the studies researching the relationship between democracy and FDI, their methodology, sample groups, and findings are summarized.

As can be seen in Table  1 , BRICS-TM countries were very rarely studied, and almost all of these studies neglected “democracy” as a variable and/or the relation between “democracy and FDI.” Alternatively, the studies that did examine FDI researched its relation with other variables such as export and import. Unique methods, such as structural break panel cointegration tests, were applied to investigate the issue, and this method comprises the novel part of the study. The details can be seen under the “ Research Method and Econometric Analysis ” section.

In summary, the literature review provided in Table  1 covers the relationship between democracy, foreign direct investment (FDI), and various other economic variables, focusing on BRICS-TM countries. Below is an analysis of the essential findings and gaps identified in the literature:

By applying AI (ChatGPT) to the information provided in Table  1 (studies on BRIC + S + T + M countries), key findings are double-checked and summarized below:

Limited focus on BRICS-TM countries: The literature review notes a scarcity of studies on BRICS-TM countries, with a lack of attention to the “democracy” variable in the context of FDI.

Variable relationships explored: Various studies investigate the relationships between different economic variables and FDI, such as oil prices, exchange rates, gross domestic product (GDP), international tourism, economic output, carbon emissions, exports, imports, and innovation.

Diverse methodologies: Researchers employ diverse methodologies, including directional analysis, panel ARDL cointegration, survey research, and panel cointegration, to analyze the relationships among variables.

Within this frame, a summary of the studies investigating the relationship between democracy and FDI or using similar variables is given in Table  2 .

As presented in Table  2 , none of the above studies analyzed the relationship among democracy, FDI, inflation , and GDP variables for BRICS-TM countries. In addition, none of the studies applied a structural break panel cointegration test in their analysis. All these gaps motivate the authors of this study to conduct such research.

Additionally, applying AI (ChatGPT) to the information provided in Table  2 , key findings from Table  2 are double-checked and summarized below (studies on the relationship between democracy and economics):

Limited studies on democracy and FDI in BRICS-TM: The literature highlights a gap in research, as none of the studies in Table  2 specifically analyze the relationship between democracy, FDI, inflation, and GDP variables in BRICS-TM countries.

Contradictory findings on democracy and economic growth: The studies in Table  2 present contradictory findings on the impact of democracy on economic growth. Some find a positive and significant effect, while others do not establish a significant relationship.

Methodological variety: Various methods, such as dynamic fixed effects, panel data regression analysis, panel cointegration, and causality analysis, are employed to explore the relationships between democracy, FDI, and economic growth.

Upon inspection of the limited studies, contradictory results emerge, even when employing data from diverse sample groups. An illustrative example is found in the work of Busse ( 2003 ), whose research can be summarized as follows:

Results from regression analysis between FDI and democracy reveal that analogous to studies by Rodrik ( 1996 ) and Harms and Ursprung ( 2002 ), multinational corporations (MNCs) exhibit a preference for countries where political rights and freedoms are legally and practically safeguarded.

Countries that enhance their democratic rights and freedoms tend to attract more FDI per capita than predicted (Busse, 2003 ).

Li and Resnick ( 2003 ) posited that investors typically favor regimes with advanced democracy and robust legal systems over states where their properties are at risk in dictatorial regimes. From this standpoint, one can infer that a significantly high level of democracy correlates with a markedly high level of FDI. In other words, property rights violations are diminished in developing countries with robust democracies, leading to increased FDI levels (Li & Resnick, 2003 ).

However, Haggard ( 1990 ) presents a contrary perspective, arguing that authoritarian regimes may appeal more to investors seeking to safeguard their economic assets and properties. An amalgamation of opposing views arises: investors from countries with underdeveloped democracies prefer collaboration with authoritarian regimes, whereas investors from developed nations lean toward familiar democratic regimes.

Despite the contradictory and complex findings from the limited number of studies on the potential relationship between democracy and FDI, it is contended that two influential factors contribute to investment flow toward countries with legally guaranteed and well-developed democratic rights. Firstly , as proposed by Spar ( 1999 ), a transition occurs from critical sectors like agriculture and raw materials to production and tertiary sectors in the flow and stock structure of FDI in developing countries. Secondly , there is a transformation in the interest and motivation of multinational enterprises toward developing countries based on sectoral development (Busse, 2003 ). This underscores the impact of democratic organizations established to secure democratic rights on FDI. In instances where poor democratic governance renders a country less appealing to foreign investors, the country faces a dilemma: choosing between the limited options of “loss of foreign capital” or “democratization” (Li & Resnick, 2003 ). Spar ( 1999 ) emphasizes that as the reliance on governments and their policies decreases, the need for a more democratic environment, a reliable and stable legal system, and appropriate market conditions becomes increasingly crucial for the overall well-being of the country’s economy.

Upon scrutinizing the most recent studies on the subject, a trend of contradictory findings becomes apparent. For instance, Yusuf et al. ( 2020 ) found that the democracy coefficient, as a variable signifying its impact on economic growth, lacks significance for West African countries in the short and long run. In contrast, Putra and Putri ( 2021 ) asserted that “democracy has a positive and significant effect on economic growth in 7 Asia Pacific countries.” Similar to Yusuf et al., in a panel data analysis encompassing the period from 1970 to 2014 and involving 115 developing countries, Lacroix et al. ( 2021 ) concluded that “democratic transitions do not affect foreign direct investment (FDI) inflows.”

A comprehensive review of existing empirical studies reveals a notable scarcity in the number of inquiries into the relationship between democracy and foreign direct investment (FDI) (Li & Resnick, 2003 ). Moreover, the available studies yield contradictory results on this matter. Addressing this issue, it is noteworthy that Oneal ( 1994 ) conducted one of the initial qualitative examinations on the impact of regime characteristics on FDI. Despite not identifying a statistically valid relationship between regime type and FDI flow, Oneal’s research is an early exploration of this intricate relationship.

Explorations into the connection between investor behavior and political regime characteristics, particularly in determining whether democratic or authoritarian features foster more foreign direct investment (FDI), have yielded divergent outcomes. Derbali et al. ( 2015 ) found a statistically significant relationship between FDI and democratic transformation. Through an econometric analysis encompassing a sample of 173 countries, with 44 undergoing democratic transformation between 1980 and 2010, the authors observed a substantial increase in FDI flow associated with democratic transitions.

Castro ( 2014 ) conducted a test examining the relationship between foreign direct investment (FDI) flow (the ratio of FDI flow to GDP) and indicators of “democracy” and “dictatorship” using a dynamic panel data model. Despite the analysis results failing to furnish evidence supporting a direct connection between FDI and democracy, the author emphasizes that this outcome does not negate the impact of political institutions on the flow of FDI. According to Mathur and Singh ( 2013 ), their study stands out as the inaugural examination focusing on the “importance given to economic freedom rather than political freedom” in the decision-making process of foreign investors. The authors concluded that contrary to conventional expectations, even democratic countries may attract less foreign direct investment (FDI) if they do not ensure guaranteed economic freedom. Malikane and Chitambara ( 2017 ) conducted a study exploring the relationship between democracy and foreign direct investment (FDI), employing data from eight South African countries from 1980–2014. The research findings indicate a direct and positive impact of FDI on economic growth due to the robust democratic institutions emerging as crucial catalysts in the respective sample countries.

Consequently, Malikane and Chitambara’s ( 2017 :92) study suggests that the influence of FDI on economic growth is contingent upon the level of democracy in the host country. Upon scrutinizing the studies above, a pattern of conflicting findings emerges concerning the relationship between the level of democracy and the influx of foreign direct investment (FDI) to a country . Studies commonly emphasize that the impact of democracy on FDI depends upon each country’s developmental stage. The prevalence of confusion, varying findings, and conflicting results underscores the significance of empirical analyses on this matter. A comprehensive examination of the overview identified gaps, and the need for new research is detailed under the subsequent subheading.

Overview of the Literature, Identified Gaps, and Requirements for New Research

After a detailed overview of the existing literature, the main features and gaps can be identified as follows:

Limited studies on democracy and FDI: The literature notes a scarcity of studies examining the relationship between democracy and FDI, and existing studies present conflicting results.

Context-dependent impact of democracy: Contradictory findings suggest that democracy’s impact on FDI may vary depending on a country’s development level.

Gap in BRICS-TM studies: The identified gap in the literature is the lack of research specifically addressing the relationship between democracy and FDI in BRICS-TM countries. The need for a structural break panel cointegration test is also emphasized.

Influence of political institutions: Some studies argue that solid democratic institutions positively influence FDI, while others suggest that economic freedom, rather than political freedom, may be more crucial for attracting FDI.

Requirements for new research: To fill the gap in the literature, new research should be conducted specifically targeting BRICS-TM countries.

Thus, when c onsidering the contradictory findings, future studies should explore the contextual factors influencing the relationship between democracy and FDI in different country settings. Conducting longitudinal analyses could provide insights into the dynamic relationship between democracy and FDI over time. Comparative studies between countries with different levels of democratic development can help in understanding the nuanced impact of democracy on FDI. Last but not least, given the emphasis on structural break panel cointegration tests, future research could incorporate these analytical tools for a more comprehensive understanding of the relationships under consideration.

Last but not least, Olorogun ( 2023 ) conducted research using data from sub-Saharan countries from 1978 to 2019 and found a “long-run covariance between sustainable economic development and foreign direct investment (FDI)” and a “significant level of causality between economic growth and financial development in the private sector, FDI, and export.” So, if a significant relationship can be found between democracy and foreign direct investment, the results may also provide a useful assessment for sustainable development.

In summary, while the literature review reveals valuable insights into the complex relationship between democracy, FDI, and economic variables, there is a clear need for more targeted research in the context of BRICS-TM countries by further exploration of the contextual factors influencing these relationships.

Research Method and Econometric Analysis

This section of the study delves into the analysis methods and interpretations of the relationship between democracy and foreign direct investment (FDI). The presentation encompasses the dataset and model specifications concerning the variables under scrutiny. Specifically, analyses were conducted utilizing econometric analysis programs, namely, EViews 12 , Gauss 23 , and StataMP 64 . The study culminated with interpreting findings and formulating policy recommendations based on the results obtained.

Data Set and Model

The study scrutinized the hypothesis to address the initial research inquiry, asserting a correlation between democracy and foreign direct investment (FDI). The research targeted BRICS-TM countries (Brazil, Russia, India, China, South Africa, Türkiye, Mexico) recognized for their increasing prominence in the global economy and anticipated growth in strategic significance. These seven emerging markets were chosen due to their demonstrated potential to attract FDI. The research covered annual data spanning 1994–2018 by employing panel data analysis techniques capable of accommodating structural breaks. Both democracy and foreign direct investments are susceptible to the influence of local and global dynamics, which can induce significant disruptions in the variables.

Consequently, the study utilized tests allowing for structural breaks to enhance the robustness of the analyses. The investigation aimed to uncover the long-term relationship between foreign direct investment and democracy , a critical indicator of economic development for emerging markets in recent years. The model developed for examining the relationship between democracy and foreign direct investment within the specified sample and data range is represented by Eq.  1 :

In the model, cross-section data is represented by i  = 1, 2, 3,…. N , while the time dimension is represented by t  = 1, 2, 3,….. T , and the error term is by ɛ.

The study’s model setup and variables were adapted from Yusuf et al. ( 2020 ), Putra and Putri ( 2021 ), and Lacroix et al. ( 2021 ) in the literature. Figure  1 shows the research design.

Research design

Table 3 shows the variables and data sources used in the model.

The study designated foreign direct investment (FDI), denoted as LNFDI, as the dependent variable. The independent variable was conceptualized as the democracy variable (DEMOC). To account for potential influencing factors, inflation (INF) and per capita income (PGDP) variables, known to impact FDI, were introduced into the model as control variables to draw upon insights from the existing literature. In the context of panel data analyses, selecting control variables involves consulting the literature to identify factors with substantial influence on the dependent variable. When examining factors impacting foreign direct investment (FDI), a frequently encountered category comprises various macroeconomic variables, among which inflation and per capita income are recurrently employed. Given the study’s sample composition—comprising the BRICS-TM countries—these two variables were incorporated into the model as control variables. This decision was motivated by their recurrent utilization in the literature and their direct relevance to foreign direct investments and production costs. Furthermore, the inclusion of these variables addressed a shared data constraint.

During the data collection phase, the study utilized indices reflecting “political rights” and “civil liberties,” which were acknowledged indicators of “democracy” in the literature. These indices, sourced from the Freedom House Database ( 2020 ), were incorporated into the analysis by calculating their means, which were then used as values for the democracy variable. This approach aligns with the practices of several researchers in the existing literature, such as Kebede and Takyi ( 2017 ), Doucoligaos and Ulubasoglu ( 2008 ), and Tavares and Wacziarg ( 2001 ), who have employed this index. The index operates on a scale from 1 to 7, where 1 represents the highest state of democracy and 7 corresponds to the lowest state. To facilitate analyses, calculations, and interpretation, the index values were scaled to ensure a range between 0 and 100.

Freedom House assesses the degree of democratic governance in 29 countries from Central Europe to Central Asia through its annual “Nations in Transit” report. The democracy score encompasses distinct ratings on various facets, including national and local governance, electoral processes, independent media, civil society, judicial framework and independence, and corruption. Most researchers (Dolunay et al., 2017 ; Martin et al., 2016 ; Osiewicz & Skrzypek, 2020 ; Steiner, 2016 ) frequently utilize the data provided by Freedom House in their studies. In addition to the independent variable of democracy (DEMOC), the model integrates control variables influencing FDI. Capitation (LNPGDP) and inflation (INF) variables were incorporated within this framework. A review of the existing literature reveals that factors affecting FDI, including inflation and per capita income, have been employed in models by researchers (Botric & Skuflic, 2005 ; Chakrabarti, 2001 ; Jadhav, 2012 ; Ranjan & Agraval, 2011 ; Vijayakumar et al., 2010 ).

In the literature, various variables such as “trade openness, level of human capital, unemployment rates, government supports, tax costs,” which are believed to influence foreign capital, are employed as control variables in models. On the other hand, in some research, the impact of institutional quality, such as democracy and governance, on environmental quality is studied. Within this frame, Shahbaz et al. ( 2023 ) found that “institutional quality variables impacted environmental quality differently. In this sense, it is detrimental for policymakers to consider concerted measures to decrease institutional vulnerabilities and reduce the level of the informal economy.” However, in this study, inflation and per capita income variables were chosen due to their prominence as the most frequently used variables in the literature (detailed in the “ Theoretical Frame and Literature Review ” section) and their comprehensive impact on foreign direct capital in terms of macroeconomics.

Furthermore, a shared data problem is evident in all variables from 1994 to 2018 for the BRICS-TM country sample group, particularly in variables other than the control variables in the model. Nevertheless, these issues have yet to be encountered as inflation and per capita income variables are comprehensive and fall within general macroeconomic data. Additionally, including many control variables in the model might obscure the significance of the effect on the dependent variable in hypothesis tests examining the relationship between democracy and foreign direct investment. Consequently, real GDP data, rather than nominal, were utilized in the analysis, and the logarithm of the data was represented as LNGDP.

As explored earlier, foreign investors prioritize economic freedom over political freedom when making investment decisions (Mathur & Singh, 2013 ). In this context, the assurance of economic liberty and the legal protection of property rights may be linked to the level of democracy, particularly in developed countries. This condition explains why the relevant variables should be incorporated into the model and tested. The logarithm of FDI (LNFDI) and per capita income (LNPGDP) variables were employed in the analyses. The rationale behind the logarithmic transformation lies in its capacity to facilitate the interpretation of analysis results and standardize variables on a specific scale. Additionally, taking logarithms of series does not result in information loss in data; it also aids in mitigating autocorrelation issues and allows the series to exhibit a normal distribution.

Econometric Method

The primary motivation behind the conducted study is to investigate the impact of the variable “democracy” on foreign direct investments through newly developed panel data analysis tests that allow for structural breaks, which are not commonly used in political science. In this regard, the study aims to be one of the pioneering works testing the relationship between variables related to political science and economics with an interdisciplinary perspective through innovative empirical studies. The methodological framework of this study, which analyzes the relationship between democracy and FDI through annual data from the 1994–2018 periods using panel data analysis and causality test, is outlined below:

Graphical representation of variables and analysis of descriptive statistics,

CD lm1 (Breusch & Pagan, 1980 ), CD lm1 , and LM adj tests (Pesaran et al., 2008 ) were used in the analysis to find the presence of cross-section dependence of variables.

Panel LM test (Im, Lee, & Tieslau, 2010 ) determined whether variables in the model have a unit root.

Delta test (Pesaran & Yamagata, 2008 ) was used to determine the homogeneity or heterogeneity of variables.

Cointegration test with multiple structural breaks (Westerlund & Edgerton, 2008 ) was conducted to determine the presence of cointegration between variables.

Kónya’s causality test (Kónya, 2006 ) was conducted to investigate the existence of causal relationships between variables.

In terms of methodology, the study aims to address a significant gap in the literature on democracy. Given the chosen sample group and the specified period, it becomes evident that structural changes must be considered in the analysis because the variables of democracy and foreign direct investment are particularly susceptible to global developments, leading to substantial shifts in the markets. A literature review indicates a preference for general country-based time series analyses over new-generation tests, with classical panel data analyses commonly employed for the selected country group. In summary, an examination of the literature reveals that studies on this issue predominantly rely on first- and second-generation linear panel data analysis techniques. Therefore, incorporating unit root and cointegration tests is crucial in significantly contributing to the literature, particularly by acknowledging and addressing structural breaks in the study. Additionally, it aligns with the theoretical framework that variables such as democracy and foreign direct capital investments, susceptible to the influence of global developments, are prone to structural changes. Consequently, employing panel data analysis techniques with structural breaks gains significance and enhances the motivation and scientific robustness of the study, mainly when a substantial data range is available.

The study focuses on the BRICS-TM countries: Brazil, Russia, India, China, South Africa, Türkiye Footnote 1 (Turkey), and Mexico . These nations have gained prominence in the global economy, and their strategic significance is anticipated to grow. The selection of this sample group is based on their demonstrated high performance and potential to attract substantial foreign direct investment globally. The study’s unique contribution lies in its examination of the impact of the democracy variable on foreign direct investments within this specific country group, employing innovative techniques not commonly found in the existing literature. Furthermore, the potential increase in foreign direct investment within these countries is expected to influence national and per capita incomes positively. The continuous enhancement of economic well-being and the rising accumulation of foreign direct investments could position these countries as new focal points of attraction in the medium and long term, fortifying their appealing characteristics.

Descriptive Statistics and Graphical Analysis of Variables

Graphical analyses provide valuable insights into the changes and fluctuations of variables over the years in econometric studies. The visual representation and interpretations of the study variables are presented in Fig.  2 .

Graphical representation of variables

The graphical analysis reveals the trend and volatility of FDI over the study period (1994–2018). Peaks and troughs may indicate significant events or economic shifts influencing FDI.

Democracy index: The graphical representation illustrates the changes in the democracy index across the selected countries. Distinct patterns or shifts may be observed, indicating periods of democratic development or regression.

Inflation (INF): The inflation variable is depicted graphically, highlighting its trajectory over the analyzed years. Fluctuations in inflation rates may correlate with economic events impacting FDI.

Per capita income (PGDP): The per capita income variable is visually presented, demonstrating its variations and trends. Per capita income changes can influence countries’ attractiveness for foreign investments.

These graphical analyses serve as a foundation for understanding the dynamics of the variables under investigation and provide a visual context for further econometric interpretations.

So Fig.  2 provides a comprehensive overview of the variables examined in the study. The following key observations can be made:

Foreign direct investment (FDI): China stands out as the leader in attracting the highest FDI among the BRICS-TM countries. South Africa exhibits the lowest FDI levels in the sample group.

Democracy index: China also holds the highest score in the democracy index, indicating its position as the most democratic among the selected countries. South Africa, on the other hand, has the lowest democracy index score.

Per capita income (PGDP): Russia demonstrates the highest per capita income among the countries, suggesting a relatively higher economic well-being. India, conversely, has the lowest per capita income in the sample group.

Inflation (INF): Russia and Türkiye experience the highest inflation rates, while other countries exhibit fluctuating patterns at lower and similar levels.

Table 4 provides a detailed overview of the descriptive statistics for the variables under consideration. The following key statistics offer insights into the central tendencies and variations within the sample group.

The analysis of the basic descriptive statistics in Table  4 yields several noteworthy findings:

Kurtosis values: The INF variable stands out with a kurtosis value exceeding 3, indicating a sharp peak and heavy tails in its distribution. All other variables exhibit kurtosis values below 3, suggesting relatively normal distributions without excessively heavy tails.

Skewness values: LNFDI and LNPGDP variables display negative skewness values, suggesting a longer left tail in their distributions. DEMOC and INF variables exhibit positive skewness values, indicating longer right tails in their distributions.

Jarque–Bera test: The Jarque–Bera test results indicate that the variables are statistically significant and deviate from a normal distribution. This departure from normality suggests that certain factors or events influence the distributions of the variables.

These findings provide insights into the shapes and characteristics of the variable distributions. As indicated by skewness and kurtosis values, the deviations from normality suggest that the variables may be subject to specific influences or events, contributing to their non-normal distributions. Researchers should consider these distributional characteristics when interpreting the results and drawing conclusions from the dataset.

Cross-section Dependence Test

The escalating interdependence among countries in global economies has rendered them susceptible to the impact of positive or negative developments in one nation affecting others. This phenomenon directly results from the deepening global integration associated with globalization. Consequently, econometric studies must incorporate cross-section dependence tests to gauge the extent of interaction between nations. Such tests aim to quantify how a shock in one country reverberates across borders, influencing other countries of the global economic landscape.

Studies addressing cross-section dependency (Andrews, 2005 ; Pesaran, 2006 ; Phillips & Sul, 2003 ) emphasize that failing to account for cross-section analysis may lead to biased and inconsistent results. Thus, all analyses should consider cross-sectional dependence in relevant studies (Breusch & Pagan, 1980 ; Pesaran, 2004 ).

The tests used to determine cross-section dependence were as follows:

When the time dimension is greater than the cross-section dimension ( T  >  N ), analyses were conducted using Breusch and Pagan’s ( 1980 ) CD lm1 test.

In cases when the time dimension is equal to the cross-section dimension ( T  =  N ), the CD lm2 test (Pesaran, 2004 ) was used to conduct analyses.

In cases when the time dimension was smaller than the cross-section dimension ( T  <  N ), analyses were conducted by CD lm test (Pesaran, 2004 ).

In cases when the time dimension is both smaller and greater than the cross-section dimension, analyses were conducted (LM adj ) test (Pesaran et al., 2008 ).

This study’s analysis focuses on the relationship between democracy and FDI across BRICS-TM countries, involving seven countries. With annual data spanning 1994–2018, the cross-section dimension is denoted by N  = 7 and the time dimension by T  = 25. Given that T  >  N , the study utilized the CD lm1 test (Breusch & Pagan, 1980 ) and CD lm1 and LM adj tests (Pesaran et al., 2008 ).

Given that T  >  N for the countries and time dimension, the decision-making is informed by the results of the CD lm1 and LM adj tests. Notably, LM adj test results were prioritized, considering the potential bias in cross-section dependency tests associated with the CD lm1 test. The findings of the cross-section dependence tests are presented in Table  5 .

Upon reviewing Table  5 , it is evident that the probability values for all variables are less than 0.01. Consequently, based on the LM adj test results, the null hypothesis stating “there is no dependence between sections” is rejected, while the alternative hypothesis suggesting “cross-section dependence between sections” is accepted.

The outcomes of the tests align with the characteristics of the contemporary global landscape, where any impactful event or development in one of the BRICS-TM countries has reverberations across others. Whether positive or negative, changes in one BRICS-TM nation can influence others, particularly in areas related to foreign direct investment (FDI) and democracy. As a result, policymakers in these countries should craft their future strategies with a keen awareness of this interconnectedness and the potential spillover effects on FDI and democracy. Indeed, the obtained result is consistent with theoretical expectations. The observed interdependence and influential power of the BRICS-TM country group align with the current dynamics of the globalized world. Their growing significance in the world economy and their strategic importance reinforces the decision that developments within these countries have substantial implications beyond their borders. This outcome urges the need for a nuanced approach to respond to the interconnected nature of these nations in the contemporary global landscape.

Panel Unit Root Test

In the initial phase of the econometric analysis, the stationarity of the variables in the models was determined through unit root analyses to address the spurious regression problem. Accurate results cannot be obtained when a unit root is present in a series of variables (Granger & Newbold, 1974 ). In panel data analysis, the primary consideration in stationarity tests is whether the countries are independent of each other or not. Unit root tests in panel data analysis comprise first- and second-generation tests, each with distinct characteristics. The first generation of unit root tests is further divided based on the homogeneity and heterogeneity assumptions of the countries. Some authors conducted tests under the homogeneity assumption (Breitung, 2005 ; Hadri, 2000 ; Levin et al., 2002 ), while some others pursued their analysis under the heterogeneity assumption (Choi, 2001 ; Im et al., 2003 ; Maddala & Wu, 1999 ).

Additionally, second-generation tests incorporate cross-section dependency into their analyses, whereas first-generation tests do not account for it. Given the dynamics of the global world, the use of second-generation tests in the literature is deemed more beneficial, as it is more realistic to assume that other countries will be affected by a shock experienced by one of the countries in the panel. Panel unit root tests have gained broader acceptance in time series analysis due to their ability to provide more meaningful results than standard stationarity tests. In recent years, there has been a preference for tests that allow for structural breaks, especially in series sensitive to economic variations such as foreign trade, exchange rates, and foreign capital. Hence, this study utilized panel unit root tests that consider structural breaks to assess the stationarity of variables susceptible to cyclical fluctuations, including democracy, inflation, per capita income, and FDI. Conducting stationarity tests without accounting for structural breaks can yield misleading results, making panel LM unit root tests with structural breaks the method of choice for this study.

The panel LM test (Im, Lee, & Tieslau, 2010 ) examines series in models with a level and trend, considering single and two breaks. In this study, analyses with a single break were preferred due to the shortness of the specified time interval and the events expected to cause breaks in the given period. The LM test statistics were employed to assess the hypothesis of “there is a unit root” (ϕ i  = 0). Compared to others, a distinctive feature of this test is its allowance for different breaking times for different countries. Moreover, it permits a structural break under both zero and alternative hypotheses, providing an additional advantage. The asymptotic distribution of the test follows the standard normal distribution, and it remains unaffected by the presence of a structural break. Table 6 presents the stationarity analysis results of the series for seven countries based on the model allowing breaks in level.

The analysis of Table  6  yields the following observations:

In unit root models allowing for a constant break, it is evident that all variables in the panel become stationary when their differences are calculated. In other words, since the series are stationary for the entire panel at the I(1) level, the necessary conditions for cointegration tests are met. The cointegration test indicates that global and local developments in countries cause structural breaks when considering these break dates.

On a country basis, the following conclusions can be drawn from Table  6 :

For the series whose differences are calculated, the FDI variable is stationary at the level value in Russia and India, while the same variable is stationary in India and Türkiye.

The per capita income variable is stationary at a level value only in Türkiye. However, the same variable is stationary in Brazil, India, and Türkiye for the series whose differences are computed.

The inflation variable is stationary at the level value in South Africa and Mexico. However, the same variable is stationary for the series whose differences are computed in Brazil, Russia, and China.

The democracy variable is stationary at the level value in Brazil, South Africa, and Türkiye. However, the variable is stationary in Brazil, Türkiye, and Mexico for the series whose differences are computed.

Table 7 shows the stationarity analysis results of seven countries based on the model that allows breaks in level and trend.

The results in Table  7 can be analyzed based on the following points:

General panel evaluation: Foreign direct investment (FDI) and per capita income variables are stationary at the level values when the panel is considered whole. Taking the difference of these variables increases the degree of stationarity. Inflation and democracy variables, among the other variables in the model, are stationary in the series when the difference is taken. However, they exhibit unit root characteristics at the level values. Overall, all series are stationary at the I(1) level with structural breaks for the entire panel. This suggests that the necessary conditions for the cointegration test are met. The dates of structural breaks indicate that social, political, and economic developments may have caused these breaks in the BRICS-TM countries included in the sample . These findings imply that significant events and changes in the socio-political and economic landscape of the BRICS-TM countries likely influence the structural breaks in the series.

Results from Table  7 can be interpreted on a country-specific basis as follows:

Brazil: FDI and per capita income are stationary at the level value. Inflation is stationary at the level, while democracy is stationary at the difference.

Russia: FDI and per capita income are stationary at the level value. Inflation is stationary at the level, while democracy is stationary at the difference.

India: FDI is stationary at the level value. Per capita income is stationary at the level, while inflation and democracy are stationary at the difference.

China: FDI is stationary at the difference. Per capita income is stationary at the level, while inflation and democracy are stationary at the difference.

South Africa: FDI is stationary at the level value. Per capita income is stationary at the level, while inflation and democracy are stationary at the difference.

Türkiye: FDI is stationary at the level value, per capita income is stationary at the level, and inflation and democracy are stationary at the difference.

Mexico: FDI is stationary at the difference. Per capita income is stationary at the level, while inflation and democracy are stationary at the difference.

These country-specific findings indicate variations in the stationarity characteristics of the variables, highlighting the importance of considering individual country dynamics in the analysis. The results of the panel unit root tests, both with and without structural breaks, provide insights into the stationarity of the variables. The interpretation suggests that a shock to one of the countries included in the model can lead to permanent effects that do not dissipate immediately. As confirmed by the tests, the non-stationarity of the series establishes the necessary condition for cointegration tests.

Moreover, when the same tests are conducted by taking the first-order differences of all series to achieve stationarity, it is observed that the variables become stationary at the I(1) level. This indicates that the variables are integrated in the first order, aligning with theoretical expectations. The I(1) characteristic implies that the variables exhibit a tendency to return to equilibrium after a shock, supporting the notion of long-run relationships among the variables.

Homogeneity Test of Cointegration Coefficients

The homogeneity of coefficients plays a crucial role in determining the relationship between variables in panel data studies. It helps organize subsequent tests used in the analysis. The homogeneity test examines whether the change in one country is affected at the same level by other countries. Coefficients are expected to be homogeneous in models for countries with similar economic structures, while they may be heterogeneous for countries with different economic structures. Pesaran and Yamagata ( 2008 ) developed the delta test based on Swamy ( 1970 ) to determine whether the slope parameters of cross-sections are homogeneous. The null hypothesis for this test is “slope coefficients are homogeneous.” Homogeneity, in the context of panel data analysis, implies that the coefficients of the slopes are the same for all units or entities within the panel. On the other hand, heterogeneity indicates that, at least in one of the entities, the slope coefficients differ from those in the rest of the panel. Testing for homogeneity helps assess whether the relationship between variables is consistent across all units or if there are significant variations.

As seen in Table  8 , the delta homogeneity test was performed to determine whether the slope coefficients of the model differ between units.

The delta test results indicate that the slope coefficients vary between units in the long term, given that the probability values for both test statistics are smaller than 0.05, as presented in Table  8 . This result suggests that the variables exhibit heterogeneity, implying that the relationships between variables are inconsistent across all units over the long term. The obtained result aligns with expectations and is consistent with the theory, indicating that the countries within the BRICS-TM sample exhibit different structures, and the coefficients are heterogeneous. This result suggests that the relationship between variables varies across these countries, emphasizing the sample group’s diverse economic characteristics and behaviors.

Panel Cointegration Test with Structural Break

Different methods are employed to determine the existence of long-term cointegration among the model’s variables. One set of methods is first-generation tests, which do not require cross-section dependence. The second set includes second-generation tests that consider cross-section dependence but do not incorporate structural breaks (Koç & Sarica, 2016 ). To obtain realistic and unbiased results, it is crucial to conduct tests that take structural breaks into account in cointegration analyses. Therefore, the panel cointegration test-PCWE (Westerlund & Edgerton, 2008 ) was employed, given that the series is stationary at the I(1) level.

PCWE was developed based on unit root tests that utilize Lagrange multiplier (LM) statistics, obtained from multiple repetitions (bootstrap). The merits of this test can be succinctly summarized as follows (Koç & Sarica, 2016 ; Göçer, 2013 ):

It takes into account cross-section dependency and structural breaks.

It accommodates heteroscedasticity and autocorrelation.

It identifies breaks at different dates for each country in terms of both constants and slopes.

Potential inherent problems in the model can be addressed with fully adjusted least squares estimators.

This test is effective in yielding reliable results even with small sample sizes.

This study opted for PCWE tests, given their robust characteristics. Additionally, considering the limited number of countries in the sample and the anticipation of few structural breaks in the specified period, the PCWE test was the preferred choice. As depicted in Table  9 , the determination of statistically significant cointegration between variables is made based on the significance levels of the probability values.

As indicated in Table  9 , cointegration is observed at a 5% significance level in the regime change model and a 1% significance level in the model without a break. The presence of cointegration suggests a long-term relationship between the variables of democracy and FDI in BRICS-TM. In simpler terms, democratic developments and FDI are correlated over the long run, indicating a balanced relationship between them. Future researchers may explore the direction of these variables across different samples. This study specifically tested the existence of a long-term relationship between FDI and democracy, and the inclusion of structural breaks was found to be significant. Governments and decision-makers, particularly in developing countries like BRICS-TM, should consider the relationship between democracy and FDI by taking structural breaks into account to attract foreign investment effectively. Therefore, it is emphasized that “any development related to democracy has the potential to influence FDI, and considering this factor is beneficial in the formulation and implementation of socio-economic policies.” No cointegration is observed in the “change at level” model. Indeed, the obtained results align with the study’s hypothesis. Considering the periods of structural breaks in the countries within the sample, it becomes evident that a long-term relationship exists between the variables incorporated into the model. This issue underscores the importance of considering not only the overall relationship between democracy and FDI but also the specific historical contexts and transitions in individual countries that might contribute to this relationship.

Regarding structural breaks in countries in the sample within the scope of cointegration in the regime change model, local and global developments, in general, cause breaks. The reasons for structural break dates in the sample countries are given in Table  10 .

The following items can be aligned with the breaking dates provided in Table  10 :

A recovery in macroeconomics and positive expectations toward agreements with the IMF became prominent after Russia’s transition economies in 1996.

2000 in Brazil is known as the period when the rapid growth trend started after passing the targeted inflation after the 1999 Russian Crisis.

Membership of China in the International Trade Union was evaluated as an essential development in the global economy in 2001.

Experiencing the biggest crisis in history in Türkiye in 2002 and starting a dominant single-party regime were remarkable developments.

The 2005 Election results in Mexico and the hurricane disasters, including an 8.7-magnitude earthquake, created significant socio-economic problems that year.

The ANC party’s coming to power alone in South Africa in 2009 was commented on as a consistent process for the national and regional economy; this situation also removed a series of uncertainties.

The devaluation experienced in India in 2016 has created a significant break.

Of course, the impact of such structural breaks should be considered. Toguç et al. ( 2023 ) argued that “differentiating these short-term and long-term effects has implications for risk management and policymaking.” Since structural break increases risks and uncertainty, foreign capital prefers to invest in other destinations.

Kónya’s Causality Test

This test (Kónya, 2006 ) investigates the existence of causality between variables using the seemingly unrelated regression (SUR) estimator (Zellner, 1962 ). One advantage of this test is that the causality test can be applied separately to the countries that make up the heterogeneous panel. Another important advantage is that it is unnecessary to apply unit root and cointegration tests, as country-specific critical values are produced. According to the test results, if the Wald statistics calculated for each country are greater than the critical values at the chosen significance level, the null hypothesis of “no causality between the variables” is rejected. In other words, a Wald statistic greater than the critical value indicates that there is causality between the variables.

The Kónya causality test results provided in Table  11 revealed a causality from democracy (DEMOC) to FDI at a 1% significance level in Mexico, 5% in China, and 10% in Russia. In addition, from FDI to democracy (DEMOC), there is causality at a 5% significance level in Mexico and a 10% significance level in Russia.

According to the results in Table  12 for the causality between foreign direct investment (FDI) and PGDP, the Kónya causality tests revealed a one-way causality from PGDP to FDI at a 10% significance level in Mexico.

According to the results provided in Table  13 for the causality between foreign direct investment (FDI) and inflation (INF), the results of the Kónya causality tests revealed a one-way causality from inflation to FDI at a 10% significance level in Türkiye and, conversely, a one-way causality from FDI to inflation at a 10% significance level in South Africa.

The study investigated the nexus between democracy and foreign direct investment (FDI) using annual data from a sample of seven countries within emerging markets from 1994–2019. According to cross-section dependence test results, all variables’ probability values were less than 0.01, indicating significant cross-section dependence. The rejection of the null hypothesis, stating “there is no dependence between sections” in favor of the alternative hypothesis suggesting “there is cross-section dependence between sections,” aligns with the contemporary global landscape. In today’s interconnected world, any impactful event or development in one of the BRICS-TM countries has reverberations across others, particularly in areas related to FDI and democracy. These findings underscore the imperative for governments and policymakers in these countries to craft future strategies with a keen awareness of this interconnectedness and the potential spillover effects on FDI and democracy.

Furthermore, the outcomes of the panel unit root test indicate that all variables in the panel become stationary at the I(1) level when their differences are calculated, meeting the necessary conditions for cointegration tests. This result suggests that global and local developments in countries cause structural breaks when considering these break dates. Variations in stationarity characteristics of variables were observed on a country basis, highlighting the importance of considering individual country dynamics in the analysis.

The delta homogeneity test results suggest that the variables exhibit heterogeneity, implying that the relationships between variables are inconsistent across all units over the long term. This aligns with expectations and emphasizes the diverse economic characteristics and behaviors within the sample group of BRICS-TM countries.

The Westerlund-Edgerton cointegration test results reveal significant cointegration between variables, observed at a 1% significance level in the model without a break and a 5% level in the regime change model. This result signifies a sustained relationship between FDI and democracy in BRICS-TM countries over the long term. Future researchers may explore the direction of these variables across different samples, while governments and decision-makers should consider this relationship, particularly in developing countries, to attract foreign investment effectively.

Kónya’s causality test results also provided significant causality between some of the variables in some countries within the sample group. Firstly, there is a causality from democracy (DEMOC) to FDI in Mexico (1% significance level), in China (5% significance level), and in Russia (10% significance level). Secondly, there is also a significant causality from FDI to democracy (DEMOC) in Mexico (5% significance level) and in Russia (10% significance level). Thirdly, a one-way causality could only be found from PGDP to FDI in Mexico (10% significance level). Fourthly, there is also a one-way causality from inflation to FDI in Türkiye (10% significance level) and a one-way causality from FDI to inflation in South Africa (10% significance level). Thus, Kónya’s causality test results supported the hypothesis of the research with significant results.

In conclusion, the empirical findings establish a statistically significant and robust relationship between the level of democracy and the flow of FDI in BRICS-TM countries. These findings underscore the intertwined nature of political and economic dynamics within these nations and highlight the importance of considering both aspects in policy formulation and decision-making processes.

The relationship between the democracy level and foreign direct investment (FDI) of BRICS-TM countries is an area that requires further exploration. Subsequently, comparing the findings of this study with those of previous research reveals its significance. While earlier studies predominantly concentrated on the preferences of host countries in attracting foreign investment, few delved into the factors influencing foreign investors’ choices. A notable exception is by Li and Resnick ( 2003 ), who highlighted the pivotal question of “Why do companies invest in foreign countries?” and proposed a theory positing that “democratic institutions impact FDI flow in both positive and negative ways” (Li & Resnick, 2003 :176). Their conclusions from data analysis of 53 developing countries spanning 1982–1995 align with the current study’s outcomes. Specifically, they found that (1) advancements in democracy lead to heightened property rights protection, fostering increased FDI inflows, and, (2) conversely, democratic improvements in underdeveloped nations result in diminished FDI flows. These findings correspond with our study, given that the sampled countries are a mix of developing and developed nations, mirroring the first scenario described by Li and Resnick.

Derbali et al. ( 2015 ) concluded in a similar vein in their study, examining a massive dataset spanning from 1980 to 2010 with 173 countries, 44 of which underwent democratic transformation. Their observation that “variables related to human development and individual freedom initiate the democratic transformation process, contrary to the social heterogeneity variable” aligns with the results of the present study when interpreted in reverse. This scenario prompts a chicken-and-egg question: Does the level of democracy positively influence the flow of FDI, or does FDI flow positively impact the level of democracy? The authors tackled this issue in the second stage of their analysis and determined that democratic transformation leads to a substantial increase in FDI inflows. Our findings corroborate this perspective with evidence from a different sample group of countries.

Malikane and Chitambara ( 2017 ) concluded in their study analyzing the relationship between FDI, democracy, and economic growth in eight South African countries from 1980 to 2014 that the FDI variable exhibits a direct and positive impact on economic development, explicitly implicating that strong democratic institutions serve as notable drivers of economic growth. Their findings suggest that the effect of FDI on economic growth is contingent on the level of democracy in the host country. In another study on developing countries, Khan et al. ( 2023 ) found that specific determinants of good governance, such as control of corruption, political stability, and voice and accountability, significantly attract FDI inflows. However, other determinants, including government effectiveness, regulatory quality, political system, and institutional quality, significantly reduce FDI inflows. On the contrary, they found that in Asian countries, all institutional quality indicators except control of corruption have a significant and positive effect on FDI inflows (Khan et al., 2023 ). The significant relationships identified between these phenomena across various indicators for developing and Asian countries align with the findings of our study.

Developed and developing nations actively engage in concerted efforts to attract foreign capital investments in the contemporary global economic landscape. Foreign direct investments (FDIs) stand out as a pivotal form of investment that significantly influences a country’s growth and development trajectory. The inflow of direct foreign capital brings multifaceted contributions to a nation’s economy, encompassing vital aspects such as capital infusion, technological advancement, elevated management standards, expanded foreign trade opportunities, employment generation, sectoral discipline, access to skilled labor, and risk mitigation.

In addition to all these, foreign direct investment (FDI) holds significant importance not only in the general context of sustainability but also specifically in sustainable development. To better understand this close relationship between sustainable development and FDI, first briefly examine the concept of sustainability. Simply put, sustainability entails maintaining a favorable condition through methods that cause no harm yet are supportable, legally and scientifically verifiable, defendable, and implementable (Ratiu, 2013 ). From a developmental perspective, it signifies maintaining continuity without losing control. According to Menger ( 2010 ), sustainability can be defined as the ability to grow and survive independently. The author emphasizes that the concept of sustainability is closely related to “creativity” and “cultural vitality,” as well as being an “internally growing” and “self-sustaining” trend with innovative effects that also attract different social strata.

Within the context of all these existing barriers and dilemmas, managing the process of reducing the negative aspects while increasing and offering the positives to people must be handled with care. This intricate process, termed sustainable development, is like the search for the cosmos in chaos as it aims to balance the economic, environmental, and social dimensions of both local urban areas and regional and national areas, and even the global sphere, especially with climate change becoming one of the main negative impacts on the environmental dimension. Gazibey et al. ( 2014 ) also noted that, while some problem areas, such as “poverty reduction” are mainly related to the economic and somewhat to the social dimensions of sustainability, other issues like “climate change” and “reduction of carbon footprint” are more related to the environmental dimension. An in-depth examination reveals that many problems, which may initially seem related to a single dimension, are intertwined with multiple dimensions. Thus, while attracting foreign direct investment to a country may seem primarily related to the economic dimension at first glance, it is closely linked to environmental and social dimensions.

In its most straightforward approach, meeting and satisfying the basic needs of individuals will subsequently prioritize higher-level needs. This, in turn, will support sustainable development in all three dimensions. Thus, while foreign capital invested in a country may initially support economic sustainability, its contribution to the socio-economic levels of individuals will lay the groundwork primarily for social and educational improvement in the medium and long term, secondarily for environmental enhancement to result in a more livable environment. For example, Xu et al. ( 2024 ) argued that “China is currently exploring a sustainable development mode of collaborative governance.” In a good level of governance, all social partners expected to be affected by the possible policies are included in the decision-making process. This process is related to and supports the participation dimension of democracy. So, as the pieces of a chain, a good level of democracy supports the level of governance, and governance supports the accumulation of FDI and economic performance. Consequently, these favorable conditions might pave the way for sustainable development. Another study (Olorogun, 2023 ) found a long-run relationship between financial development in the private sector and economic growth in sub-Saharan Africa, with the data spanning from 1978 to 2019. According to the results of the author’s research, there is a long-run covariance between sustainable economic development and foreign direct investment (FDI) and a significant level of causality between economic growth and financial development in the private sector, FDI, and export.

Indeed, sustainability resembles a ball resting on a three-legged stool: Any absence or imbalance in one of this tripod’s economic, social, or environmental legs will cause the ball to fall. In other words, sustainable development requires addressing all three dimensions in a balanced manner.

This idea brings us to the focus of this research: The level of democracy and the FDI variable and the relationship between these variables essentially concerns all three dimensions. In countries with a higher level of democracy, the possibility of developing policies that consider citizens’ demands and preferences is higher than in countries with lower levels of democracy. Conversely, in countries with lower levels of democracy , the likelihood of prioritizing the preferences and gains of specific individuals or groups over issues such as sustainability, environmental protection, and social welfare is higher. Consequently, this situation will negatively affect both the potential level of FDI attracted to the less developed country and, ultimately, the sustainable development momentum.

To sum up, numerous factors play a crucial role in shaping decisions related to foreign direct investments. Particularly in underdeveloped and developing countries, where domestic capital accumulation might be insufficient, the preference for attracting direct foreign capital investments emerges as a strategic choice over external borrowing. This strategic approach is driven by fostering economic development and sustainable growth while leveraging the benefits associated with foreign capital inflows.

The empirical evidence on the relationship between democracy and the level of foreign direct investment (FDI) often presents conflicting results, influenced by variations in study periods and sample compositions. Notably, these disparities can be traced back to the differing development levels of countries under scrutiny.

Reviewing previous studies reveals a recurring pattern wherein developed countries exhibit a positive and significant correlation between democracy and FDI. Conversely, in underdeveloped or developing nations, a negative relationship tends to prevail between these two variables. This disparity hinges on the distinct behavior of capital owners seeking to invest in already developed countries, where business transactions are grounded in established legal frameworks, property rights, and the rule of law. In contrast, underdeveloped and developing countries often witness capital owners engaging in potentially illicit and unethical business dealings with high risks and potential returns.

These arrangements are frequently based on different interests and assurances with individuals and groups in positions of power. In essence, the ease of resource acquisition, processing, and exportation in underdeveloped countries becomes contingent upon the presence of authoritarian regimes. Such relationships of interest with authoritarian regimes provide investment security for global investors. However, these regimes—keen on preserving these relationships—are disinclined to have their dealings exposed, which in turn leads to increased pressure on their citizens. The resulting mutualistic relationship transforms into a lucrative exploitation process.

When the outcomes of the panel data analysis incorporating structural breaks were examined, it was found that all variables demonstrated significance at the 1% level. The cross-sectional dependency analysis results indicated a significant cross-sectional relationship between the variables. In the panel unit root test, it was observed that the variables in the model exhibited unit roots at the level, but their differences rendered all variables stationary. The delta homogeneity test findings suggested that the variables lacked homogeneity. Furthermore, the results of the panel cointegration test with structural breaks affirmed a long-term relationship, with significance levels of 1% in the model without breaks and 5% in the regime change model. Lastly, the reached bidirectional and one-directional causality between FDI and democracy and other economic variables like inflation and PGDP in the sample group countries require policymakers to focus on each variable carefully especially on the level of democracy if they aim to reach a high level of FDI.

In conclusion, the findings of this study suggest the presence of a long-term relationship between democracy and FDI also supported by causality in some countries within the sample, as revealed through the analysis of data from BRICS-TM countries within emerging markets spanning the period 1994–2018. The significance of this relationship is particularly evident when considering the impact of structural breaks. It is emphasized that governments and policymakers in emerging markets (including those in BRICS-TM), which aim to bolster their economy’s resilience against various shocks, should not only consider structural breaks but also recognize the intricate connection between democracy and FDI. The study underscores that developments in democracy have the potential to influence FDI, emphasizing the importance of factoring this relationship into the formulation and execution of socio-economic policies. Lastly, using panel tests with a structural break, a method uncommonly employed in the empirical analysis of the democracy variable, may contribute as an additional dimension to the existing literature in this field.

In analyzing the relationship between democracy and foreign direct investment, the findings suggest a long-term relationship in all models except for the level change model. These results highlight the significance of democratic developments in the BRICS-TM countries influencing the inflow of foreign direct capital. Therefore, policymakers in emerging markets, particularly within BRICS-TM countries, are encouraged to prioritize democracy and foster democratic developments to attract foreign direct investments. Additionally, given the impact of global and local developments leading to structural breaks, it becomes crucial for these policymakers to closely monitor and interpret international and global events that may affect the resilience of their national economies, both negatively and positively. By doing so, emerging markets can enhance their resilience against various shocks, enabling policymakers to adeptly prepare their economies, private sectors, and stock markets for potential global risks.

Opting for direct foreign capital investments over external debt or short-term investments is a more rational approach for developing countries to accumulate capital for their overall development. As many countries seek to address the scarcity of capital, the understanding of the contributions of foreign capital to development improves, while global competition intensifies to attract foreign capital. Therefore, policymakers should focus on enhancing macroeconomic indicators such as inflation and national income and fostering democratic development, a fundamental trust factor for foreign capital. Demographic and institutional factors also affect the global or social fiscal pressure (Nuță & Nuță, 2020 ). Thus, as an institutional factor, positive developments at the level of democracy are fundamental in attracting foreign capital.

It is crucial for developing countries to prioritize and keep pace with indicators that foreign capital considers significant. Global companies prioritize countries they can trust, where investments can swiftly yield profits due to potential risks. The foundation of democracy in developing nations starts in the family and education realms. Proper education on the importance and necessity of democracy in the curriculum contributes to long-term awareness of democracy. Developing effective education policies within families can address intra-family democracy, fostering a culture of democracy throughout the country.

The reasons listed up to this point reiterate that attracting foreign direct investments to a country is of utmost critical importance for supporting sustainable development in all aspects of the nation. As discussed in the discussion section, while sustainability may appear to be solely related to the economic dimension at first glance, an increase in foreign direct investment toward a country has the potential to indirectly and positively impact the social and environmental dimensions of sustainability as well. When considering that the level of democracy also has a similar effect on the level of FDI, it should be expected that the level of democracy in a country is strongly correlated with the issue of sustainable development.

In conclusion, new researchers interested in this subject are recommended to conduct analyses on different country groups. Updating established models and testing hypotheses using various socio-economic indicators and analysis methods can further contribute to the literature.

Data Availability

The data set is uploaded to the system as a supplementary file and also uploaded to Figshare with the https://doi.org/10.6084/m9.figshare.21701966 .

Turkey’s name changed to Türkiye: According to the United Nations (UN)-Türkiye, the country’s name has been officially changed to Türkiye at the UN upon a letter received on June 1 from the Turkish Foreign Ministry (UN-Türkiye. (2022)). Turkey’s name changed to Türkiye, URL: https://turkiye.un.org/en/184798-turkeys-name-changed-turkiye , Accessed on: 02.07.2022.

Abbreviations

Brazil, Russia, India, China, South Africa, Türkiye, Mexico

The Democracy Index variable

Ecological footprint

• Foreign direct investment

Gross domestic product

Logarithm of foreign direct investment

Logarithm of per capita income

Multinational corporations

Per capita income

Political institutions

Regression coefficient value

World Development Indicators

Ahmed, Z., Ahmad, Z., Rjoub, H., Kalugina, O. A., & Hussain, N. (2021). Economic growth, renewable energy consumption, and ecological footprint: Exploring the role of environmental regulations and democracy in sustainable development. Sustainable Development, 30 (4), 595–605. https://doi.org/10.1002/sd.2251

Article   Google Scholar  

Aliefendioğlu, Y. (2005). Temsili demokrasinin ‘seçim’ ayağı (The election leg of the representative democracy. TBB Dergisi (TBB Journal), 60 (2005), 71–96.

Google Scholar  

Andrews, D. W. K. (2005). Cross-section regression with common shocks. Econometrica, 73 (5), 1551–1585. https://doi.org/10.1111/j.1468-0262.2005.00629.x

Baghestani, H., Chazi, A., & Khallaf, A. (2019). A directional analysis of oil prices and real exchange rates in BRIC countries. Research in International Business and Finance, 50 (C), 450–456. https://doi.org/10.1016/j.ribaf.2019.06.013

Banday, U. J., & Ismail, S. (2017). Does tourism development lead to a positive or negative impact on economic growth and environment in BRICS countries? A panel data analysis. Economics Bulletin, 37 (1), 553–567.

Botric, V., & Skuflic, L. (2005). Main determinants of foreign direct investment in the Southeast European countries. Transition Studies Review, 13 (2), 359–377. https://doi.org/10.1007/s11300-006-0110-3

Breitung, J. (2005). A parametric approach to the estimation of cointegrating vectors in panel data. Econometric Reviews, 24 (2), 151–173. https://doi.org/10.1081/ETC-200067895

Breusch, T. S., & Pagan, A. R. (1980). The Lagrange multiplier test and its applications to model specification in econometrics. Review of Economic Studies, 47 (1), 239–253. https://doi.org/10.2307/2297111

Busse, M. (2003). Democracy and FDI. HWWA Discussion Paper 220 . Hamburg Institute of International Economics (HWWA).

Castro, D. (2014). Foreign direct investment and democracy. The Honors Program Senior Capstone Project , Dissertation in Bryant University, May. pp.1–26. Retrieved June 10 2023, from https://digitalcommons.bryant.edu/honors_economics/18/ . Accessed 05.03.2024.

Chakrabarti, A. (2001). The determinants of foreign direct investment: Sensitivity analyses of cross-country regressions. Kyklos, 54 , 89–114. https://doi.org/10.1111/1467-6435.00142

Choi, I. (2001). Unit roots test for panel data. Journal of International Money and Finance , 20(2), 249–272. Retrieved June 10 2023, from https://doi.org/10.1016/S0261-5606(00)00048-6

Derbali, A., Trabelsi, L., & Zitouna, M. H. (2015). Democratic transition and FDI: Transition process matters. Munich Personal RePEc Archive MPRA Paper No. 77518 , posted 16 Mar 2017, 11 August, 1–38. Retrieved June 10 2023, from https://mpra.ub.uni-muenchen.de/id/eprint/77518 . Accessed 18 Jan 2024.

Dolunay, A., Kasap, F., & Keçeci, G. (2017). Freedom of mass communication in the digital age in the case of the Internet: Freedom house and the USA Example. Sustainability, 9 (10), 1739, 1–21. https://doi.org/10.3390/su9101739

Doucoligaos, H., & Ulubasoglu, M. A. (2008). Democracy and economic growth: A meta-analysis. American Journal of Political Science, 52 (1), 61–83. https://doi.org/10.1111/j.1540-5907.2007.00299.x

Erdoğan, S., Yıldırım, D. Ç., & Gedikli, A. (2019). Investigation of causality analysis between economic growth and CO2 emissions: The case of BRICS – T countries. International Journal of Energy Economics and Policy, 9 (6), 430–438. Retrieved June 10 2023, from https://doi.org/10.32479/ijeep.8546

Fernandes, G. W., de Oliveira Roque, F. O., Fernandes, S., de Viveiros Grelle, C. E., Ochoa-Quintero, J. M., Toma, T. S. P., Vilela, E. F., & Fearnside, P. M. (2023). Brazil’s democracy and sustainable agendas: A nexus in urgent need of strengthening. Perspectives in Ecology and Conservation, 21 (3), 197–199. https://doi.org/10.1016/j.pecon.2023.06.001

Freedom House. (2020). Democracy scores , Retrieved April 25 2022, from https://freedomhouse.org/report/freedom-world . Accessed 12.10. 2023.

Gazibey, Y., Keser, A., & Gökmen, Y. (2014). Türkiye’de illerin sürdürülebilirlik boyutlari açisindan değerlendirilmesi (The evaluation of the cities in Türkiye according to the dimensions of sustainability). Ankara University SBF Journal, 69 (3), 511–541.

Göçer, İ. (2013). Ar-Ge Harcamalarının Yüksek Teknolojili Ürün İhracatı, Dış Ticaret Dengesi ve Ekonomik Büyüme Üzerindeki Etkileri (Effects of RandD expenditures on high technology exports, balance of foreign trade and economic growth). Maliye Dergisi, 165 , 215–240. Retrieved June 10 2023, from https://www.researchgate.net/publication/296621402_Ar-Ge_Harcamalarinin_Yuksek_Teknolojili_Urun_Ihracati_Dis_Ticaret_Dengesi_ve_Ekonomik_Buyume_Uzerindeki_Etkileri

Granger, C. W. J., & Newbold, P. (1974). Spurious regressions in econometrics. Journal of Econometrics, 2 (2), 111–120. https://doi.org/10.1016/0304-4076(74)90034-7

Gür, B. (2020). The effect of foreign trade on innovation: The case of BRICS-T countries. Journal of Social, Humanities and Administrative Sciences, 6 (27), 819–830. Retrieved June 10 2023, from https://www.researchgate.net/publication/339847375_The_Effect_of_Foreign_Trade_on_Innovation_The_Case_of_BRICS-T_Countries . Accessed 25 Mar 2024.

Hadri, K. (2000). Testing for stationarity in heterogeneous panels. Econometrics Journal, 3 (2), 148–161. Retrieved June 10 2023, from https://doi.org/10.1111/1368-423X.00043

Haggard, S. (1990). Pathways from the periphery: The politics of growth in the newly industrializing countries . Cornell University Press.

Harms, P., & Ursprung, H. (2002). Do civil and political repression boost FDI? Economic Inquiry, 40 (4), 651–663. https://doi.org/10.1093/ei/40.4.651

Haydaroğlu, C., & Gülşah, Ç. (2016). Türkiye’de seçim sistemlerinin demokrasi ve ekonomi ilişkisi çerçevesinde incelenmesi. Uluslararası Politik Araştırmalar Dergisi, 2 (1), 51–63. https://doi.org/10.25272/j.2149-8539.2016.2.1.05

Im, K.S., Lee, J., & Tieslau, M. (2010). Panel LM unit root tests with trend shifts (March 1, 2010). FDIC Center for Financial Research Working Paper, No. 2010–1 . https://doi.org/10.2139/ssrn.1619918

Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115 (1), 53–74. https://doi.org/10.1016/S0304-4076(03)00092-7

Jadhav, P. (2012). Determinants of foreign direct investment in BRICS economies: Analysis of economic institutional and political factor. Social and Behavioral Sciences, 37 , 5–14. https://doi.org/10.1016/j.sbspro.2012.03.270

Kebede, J. G., & Takyi, P. O. (2017). Causality between institutional quality and economic growth: Evidence from sub-Saharan Africa. European Journal of Economic and Financial Research, 2 (1), 114–131. https://doi.org/10.5281/zenodo.438146

Keser, A., Kılıç, B., & Özbek, C.A. (2023). How the Demos [public] regulate the Kratos [administration] through repeated elections: Lessons learned from the elections in Türkiye for the government and opposition. İnsan Ve Toplum , 13(4), 66–93. https://doi.org/10.12658/M0704

Khan, H., Dong, Y., Bibi, R., & Khan, I. (2023). Institutional quality and foreign direct investment: Global evidence. Journal of the Knowledge Economy . https://doi.org/10.1007/s13132-023-01508-1

Kilci, E. N., & Yilanci, V. (2022). Impact of monetary aggregates on consumer behavior: A study on the policy response of the federal reserve against COVID-19. Asian Journal of Applied Economics, 29 (1), 100–122. Retrieved June 10 2023, from https://so01.tci-thaijo.org/index.php/AEJ/article/view/248476 . Accessed 21 Apr 2024.

Koç, A., & Sarica, D. (2016). Analysis on the relationship between the share of labour income and the level of union organization in selected OECD countries in the neoliberal era. Journal of Current Researches on Business and Economics, 6 (2), 29–56. Retrieved June 10 2023, from https://www.jocrebe.com/imagesbuyuk/0d2436-2-say%C4%B1%20tam%20dosyas%C4%B1.pdf . Accessed 14 Apr 2024.

Kónya, L. (2006). Exports and growth: Granger causality analysis on OECD countries with a panel approach. Economic Modelling, 23 (6), 978–992. https://doi.org/10.1016/j.econmod.2006.04.008

Lacroix, J., Meon, P. G., & Sekkat, K. (2021). Democratic transitions can attract foreign direct investment: Effect, trajectories, and the role of political risk. Journal of Comparative Economics, 49 (2), 340–357. https://doi.org/10.1016/j.jce.2020.09.003

Levin, A., Lin, C. F., & Chu, C. J. (2002). Unit root tests in panel data: Asymptotic and finite sample properties. Journal of Econometrics, 108 , 1–24. https://doi.org/10.1016/S0304-4076(01)00098-7

Li, Q., & Resnick, A. (2003). Reversal of fortunes: Democratic institutions and FDI inflows to developing countries. International Organization, 57 (1), 175–211. https://doi.org/10.1017/S0020818303571077

Maddala, G. S., & Wu, S. (1999). A comparative study of unit root tests with panel data and a new simple test. Oxford Bulletin of Economics and Statistics, 61 , 631–652. https://doi.org/10.1111/1468-0084.0610s1631

Magazzino, C. (2023). Ecological footprint, electricity consumption, and economic growth in China: Geopolitical risk and natural resources governance. Empirical Economics . https://doi.org/10.1007/s00181-023-02460-4

Magazzino, C., & Mele, M. (2022). Can a change in FDI accelerate GDP growth? Time-series and ANNs evidence on Malta. The Journal of Economic Asymmetries, 25 , e00243. https://doi.org/10.1016/j.jeca.2022.e00243

Magazzino, C., & Mele, M. (2022). A new machine learning algorithm to explore the CO2 emissions-energy use-economic growth trilemma. Annals of Operations Research . https://doi.org/10.1007/s10479-022-04787-0

Malikane, C., & Chitambara, P. (2017). FDI, democracy, and economic growth in Southern Africa. African Development Review, 29 (1), 92–102. https://doi.org/10.1111/1467-8268.12242

Martin, J. D., Abbas, D., & Martins, R. J. (2016). The validity of global press ratings. Journalism Practice, 10 (1), 93–108. https://doi.org/10.1080/17512786.2015.1010851

Mathur, A., & Singh, K. (2013). Foreign direct investment, corruption and democracy. Applied Economics, 45 (8), 991–1002. https://doi.org/10.1080/00036846.2011.613786

Menger, P.-M. (2010), Cultural policies in Europe from a state to a city-centered perspective on cultural generativity. GRIPS Discussion Paper No. 10–28 , GRIPS Policy Research Center,.1–9, Tokyo, Japan. Retrieved May 10 2022, from chromeextension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.grips.ac.jp/r-center/wpcontent/uploads/10-28.pdf

Muhammad, B., Khan, M. K., Khan, M. I., & Khan, S. (2022). Impact of foreign direct investment, natural resources, renewable energy consumption, and economic growth on environmental degradation: Evidence from BRICS, developing, developed and global countries. Environmental Science and Pollution Research, 28 , 21789–21798. https://doi.org/10.1007/s11356-021-16861-4

Nuță, A. C., & Nuță, F. M. (2020). Modelling the influences of economic, demographic, and institutional factors on fiscal pressure using OLS, PCSE, and FD-GMM approaches. Sustainability, 12 (4), 1681. https://doi.org/10.3390/su12041681

Ojekemi, O. S., Ağa, M., & Magazzino, C. (2023). Towards achieving sustainability in the BRICS economies: The role of renewable energy consumption and economic risk. Energies, 16 (14), 5287. https://doi.org/10.3390/en16145287

Olorogun, L. A. (2023). Modelling financial development in the private sector, FDI, and sustainable economic growth in sub-Saharan Africa: ARDL bound test-FMOLS, DOLS robust analysis. Journal of the Knowledge Economy . https://doi.org/10.1007/s13132-023-01224-w

Oneal, J. R. (1994). The affinity of foreign investors for authoritarian regimes. Political Research Quarterly, 47 (3), 565–588. https://doi.org/10.1177/106591299404700302

Osiewicz, P., & Skrzypek, M. (2020). Is Spain becoming a militant democracy? Empirical evidence from freedom house reports. Aportes-Revista de Historia Contemporanea, 35 (103), 7–33. Retrieved April 10 2022, from https://www.revistaaportes.com/index.php/aportes/article/view/526/296 . Accessed 28 Jan 2024.

Pesaran, M. H. (2004). General diagnostic tests for cross-section dependence in panels. IZA Discussion Paper No. 1240 . Bonn, Germany. Retrieved June 10 2023, from chromeextension://efaidnbmnnnibpcajpcglclefindmkaj/https://docs.iza.org/dp1240.pdf . Accessed 10 Jun 2024.

Pesaran, M. H. (2006). Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica, 74 (4), 967–1012. https://doi.org/10.1111/j.1468-0262.2006.00692.x

Pesaran, M. H., Ullah, A., & Yamagata, T. (2008). A bias-adjusted LM test of error cross-section independence. The Econometrics Journal, 11 (1), 105–127. https://doi.org/10.1111/j.1368-423X.2007.00227.x

Pesaran, M. H., & Yamagata, T. (2008). Testing slope homogeneity in large panels. Journal of Econometrics, 142 (1), 50–93. https://doi.org/10.1016/j.jeconom.2007.05.010

Phillips, P. C. B., & Sul, D. (2003). Dynamic panel estimation and homogeneity testing under cross-section dependence. The Econometrics Journal, 6 (1), 217–259. https://doi.org/10.1111/1368-423X.00108

Putra, R. F., & Putri, D. Z. (2021). The effect of corruption, democracy and foreign debt on economic growth in Asian Pacific countries. Jambura Equilibrium Journal, 3 (2), 66–71. https://doi.org/10.37479/jej.v3i2.10272

Raghutla, C., & Chittedi, K. R. (2021). Financial development, energy consumption, technology, urbanization, economic output and carbon emissions nexus in BRICS countries: An empirical analysis. Management of Environmental Quality, 32 (2), 290–307. https://doi.org/10.1108/MEQ-02-2020-0035

Rahalkar, H., Sheppard, A., Lopez Morales, C. A., Lobo, L., & Salek, S. (2021). Challenges faced by the biopharmaceutical industry in the development and marketing authorization of biosimilar medicines in BRICS TM countries: An exploratory study. Pharmaceutical Medicine, 35 , 235–251. https://doi.org/10.1007/s40290-021-00395-8

Ranjan, V., & Agraval, G. (2011). FDI inflow determinants in BRIC countries: A panel data analysis. International Business Research, 4 (4), 255–263. https://doi.org/10.5539/ibr.v4n4p255

Ratiu, D. E. (2013). Creative cities and/or sustainable cities: Discourses and practices. City, Culture and Society, 4 , 125–135. https://doi.org/10.1016/j.ccs.2013.04.002

Rodrik, D. (1996). Labor standards in international trade: Do they matter and what do we do about them? In R. Lawrence, D. Rodrik, & J. Whalley (Eds.), Emerging Agenda for Global Trade: High States for Developing Countries (pp. 35–79). Johns Hopkins University Press.

Shahbaz, M., Nuta, A. C., Mishra, P., & Ayad, H. (2023). The impact of informality and institutional quality on environmental footprint: The case of emerging economies in a comparative approach. Journal of Environmental Management, 348 , 119325. https://doi.org/10.1016/j.jenvman.2023.119325

Spar, D. (1999). Foreign investment and human rights. Challenge, 42 (1), 55–80. https://doi.org/10.1080/05775132.1999.11472078

Steiner, N. D. (2016). Comparing freedom house democracy scores to alternative indices and testing for political bias: Are US allies rated as more democratic by freedom house? Journal of Comparative Policy Analysis: Research and Practice, 18 (4), 329–349. https://doi.org/10.1080/13876988.2013.877676

Suny, R. G. (2017). The crisis of bourgeois democracy: The fate of an experiment in the age of nationalism, populism, and neo-liberalism. New Perspectives on Turkey, 57 , 115–141. https://doi.org/10.1017/npt.2017.32

Swamy, P. (1970). Efficient inference in a random coefficient regression model. Econometrica, 38 (2), 311–323. https://doi.org/10.2307/1913012

Tavares, J., & Wacziarg, R. (2001). How democracy affects growth. European Economic Review, 45 (2001), 1341–1373. https://doi.org/10.1016/S0014-2921(00)00093-3

Toguç, N., Kuşkaya, S., Magazzino, C., & Bilgili, F. (2023). The impact of natural disaster shocks on business confidence level and Istanbul stock exchange: A wavelet coherence approach. Geological Journal, 58 (12), 4610–4624. https://doi.org/10.1002/gj.4868

Vijayakumar, N., Sridharan, P., & Rao, K. C. S. (2010). Determinants of FDI in BRICS countries: A panel analysis. International Journal of Business Science & Applied Management (IJBSAM), 5 (3), 1–13.

Voicu, M., & Peral, E. B. (2014). Support for democracy and early socialization in a non-democratic country: Does the regime matter? Democratization, 21 (3), 554–573. https://doi.org/10.1080/13510347.2012.751974

Westerlund, J., & Edgerton, D. L. (2008). A simple test for cointegration in dependent panels with structural breaks. Oxford Bulletin of Economics and Statistics, 70 , 665–704. https://doi.org/10.1111/j.1468-0084.2008.00513.x

World Bank. (2020). World Development Indicators , Retrieved April 25 2020, from https://databank.worldbank.org/indicator/NE.EXP.GNFS.ZS/1ff4a498/Popular-Indicators# . Accessed 11 Nov 2023.

Xu, J., Wang, J., Yang, X., Jin, Z., & Liu, Y. (2024). Digital economy and sustainable development: Insight from synergistic pollution control and carbon reduction. Journal of the Knowledge Economy . https://doi.org/10.1007/s13132-024-01950-9

Yang, M., Magazzino, C., Abraham, A. A., & Abdulloev, N. (2024). Determinants of load capacity factor in BRICS countries: A panel data analysis. Natural Resources Forum, 48 (2), 525–548. https://doi.org/10.1111/1477-8947.12331

Yusuf, H. A., Shittu, W. O., Akanbi, S. B., Umar, H. M. B., & Abdulrahman, I. A. (2020). The role of foreign direct investment, financial development, democracy, and political (in) stability on economic growth in West Africa. International Trade, Politics and Development, 4 (1), 27–46. https://doi.org/10.1108/ITPD-01-2020-0002

Zellner, A. (1962). An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias. Journal of the American Statistical Association, 57 (298), 348–368. https://doi.org/10.1080/01621459.1962.10480664

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Practice Points/Highlights

1. From 1994 to 2018, there was significant cointegration between democracy and foreign direct investment (FDI) in BRICS-TM countries among the emerging markets.

2. Democratic developments and FDI move together in the long run and have a balanced relationship between them in Emerging Market Economies.

3. Policymakers in BRICS-TM countries need to develop democracy awareness and ensure democratic developments to attract foreign direct investment to secure a resilient economy in these emerging economies

4. Governments and decision-makers in emerging economies, such as BRICS-TM, who want to attract FDI need to consider the structural breaks and the relationship between democracy and FDI .

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Skin tone discrimination and birth control avoidance among women in Harris County, Texas: a cross-sectional study

• Kimberly Baker 1 ,
• Susan Tortolero Emery 1 ,
• Evelyn Spike 1 ,
• Jazmyne Sutton 2 &
• Eran Ben-Porath 2  

BMC Public Health volume  24 , Article number:  2375 ( 2024 ) Cite this article

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Introduction

Structural racism plays a major role in reproductive health inequities. Colorism, discrimination based on skin color, may profoundly impact reproductive health access and service delivery. However, quantitative research in this area is limited.

We administered an online survey of women ( n  = 1,299) aged 18–44 from Harris County, Texas to assess the relationship between skin color discrimination and reproductive health service avoidance. The survey included questions on demographics, self-reported skin tone, and dichotomous measures of previous discrimination experiences and avoidance of care because of perceived discrimination. Binary logistic regression was used to examine whether race/ethnicity, skin tone, and previous discrimination experiences were related to avoidance of contraceptive care because of perceived discrimination.

Approximately one-third (31.5%) of the sample classified themselves as non-Hispanic Whites (31.5%), 22.4% as Black, 27.4% as Hispanic and born within the US, and 7.6% as Hispanic born outside of the US. Approximately one-third of women classified themselves in the lightest skin tones, whereas almost one in five women classified themselves in the darkest skin tone palates. Darker skin tones had increasingly greater odds of reporting that they avoided seeking birth control out of a concern for discrimination compared to the lightest skin tone. After adjusting for race/ethnicity and sociodemographic variables (model 3), darker skin tones remained significantly associated with avoiding birth control.

This study demonstrates the role that skin color discrimination plays in negative reproductive health experiences. While this is not surprising given that those with racist ideologies developed the concept of these racial and ethnic categories, the apparent association with darker skin colors and avoidance of seeking birth control provides evidence that structural and individual racism continues to have far-reaching and insidious consequences.

Contraception is recognized for reducing maternal mortality, improving child health, increasing female empowerment, and decreasing poverty. However, not all women equally enjoy the benefits of access to contraception. Addressing colorism within reproductive healthcare has become critically important as the nation becomes increasingly diverse. Focusing on skin tone-based discrimination and its roots in anti-blackness expands our understanding beyond a Black–White binary traditionally applied when addressing racism in healthcare delivery.

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Racial and ethnic disparities in reproductive health access, services, and outcomes are prevalent [ 1 ]. These disparities are evidenced by the lower use of contraception among Hispanic and non-Hispanic Black women over the last decades, resulting in higher rates of unintended pregnancies and poorer maternal outcomes [ 1 , 2 , 3 ]. Barriers to hormonal contraceptive methods have been well described and include costs, proximity to affordable clinics, lack of over-the-counter access, affordable copays, and patients’ lack of awareness or misconceptions [ 4 , 5 ]. Other factors include healthcare providers’ attitudes, misconceptions, and limited training. For adolescent patients, consent and confidentiality are major barriers [ 4 , 5 ]. Mounting evidence suggests that structural racism may underlie many of these common barriers and extend to the interpersonal and internalized experiences of racism among women seeking care and the type of care provided to them [ 1 , 6 , 7 ].

While scientists have been describing racial and ethnic disparities in reproductive health outcomes, we are slow to acknowledge the underpinnings of these disparities. To understand the underpinnings, we must recognize that racial and ethnic classifications were created in the first place by scientists and others who had racist ideologies. As such, racial and ethnic classifications are complex social constructs with no biological basis and are deeply confounded with the stratification systems that perpetuate structural and individual racism and oppression. By understanding the origins and flaws of these classification systems, researchers can move past simply reporting reproductive health disparities based on race and further address the multiple levels of racism (structural, interpersonal, and internalized) that underlie reproductive health disparities.

One key factor increasingly associated with disparate outcomes in health, housing, and economic mobility is skin color discrimination, also known as colorism [ 8 , 9 ]. Colorism can be defined as discrimination based on the preference and value of people of lighter skin tones and Eurocentric features (straight hair, narrow facial features, e.g.) over darker skin tones, kinky hair, and more stereotypically Afrocentric facial features [ 10 ]. Colorism, an important form of racial discrimination, is garnering increased awareness due to its global prominence and impact on various health outcomes [ 8 , 11 , 12 , 13 , 14 ]. However, the effect colorism has on reproductive healthcare outcomes and contraception access has been overlooked.

Recent qualitative studies document women’s experience of racism and colorism during their healthcare encounters and over their reproductive life experiences. Specifically, women of darker skin tones felt subjugated to lesser treatment when accessing reproductive health services, surfacing long-standing experiences of phenotype discrimination that seldom gets documented in public health research [ 7 , 15 ]. Specifically, women described that racism impacted their ability to obtain timely healthcare services, their frequency of care, and their experiences with the healthcare system. Participants also reported that individual racism, as manifested through interactions with healthcare providers, negatively affected their use of reproductive healthcare services [ 7 ].

Another study suggested that colorism may impact access to prenatal care and delays in care. In this study, a quarter (24.8%) of women had delayed prenatal care, and daily experiences of racism were associated with delayed prenatal care. This association was moderated by self-reported maternal skin tone [ 16 ]. Elucidating the role of racism and colorism is essential in understanding the underlying causes of disparities in contraception use and the interventions that should be implemented to ameliorate these disparities. To determine the association between skin tone, perceived discrimination, and contraception care avoidance, we analyzed survey data collected from a representative sample of 1,299 women in a major southern US city.

The data for this analysis were collected through a cross-sectional survey of N  = 1299 women aged 18 to 44 reached online from February 10 through March 31, 2022. Respondents were recruited through a stratified random address-based sample (ABS) of Harris County, Texas ( n  = 777) and online non-probability-based opt-in panels ( n  = 522). Eligibility criteria included identifying as a woman or as currently able to become pregnant, between the ages of 18 and 44, and living in Harris County, Texas. Data collection was conducted by SSRS, a non-partisan survey research firm.

ABS recruitment involved two waves. The first wave received an initial survey invitation letter and a follow-up postcard a week later. The second wave was recruited four weeks after the follow-up mailing to wave one. The invitation letters included a study-specific URL, QR code, and a toll-free call-in phone number. The letter also listed a unique passcode that respondents needed to log into the survey online or provide to the telephone interviewer. The front side of the letter was in English, and the back was in Spanish. The letters had a one-dollar bill and a quarter included as a non-contingent incentive, while a $10 gift card was offered as contingent on completing the questionnaire. All mailing materials asked that a woman age 18 to 44 living in the household complete the survey. No within household selection method was used. The first wave resulted in 485 completed cases, and 292 respondents came from the second wave of data collection. Most ABS respondents completed the survey online ( n  = 777). Only n  = 33 ABS respondents completed the survey by phone. There were no statistically significant differences by age, race/ethnicity or educational attainment between those who completed the survey online and by phone.

Two third-party non-probability-based web panels, Torfac and Prodege, were utilized to reach additional respondents. Both panels recruit panelists through a variety of online platforms and require “double opt in” where respondents must confirm panel enrollment through a confirmation email after signing up on the panel website. Upon enrollment and through survey activity, demographic information such as age, gender and location information are collected from panelists. This information was used to send targeted email invitations and reminders to panelists likely to qualify for this survey. Panelists must have confirmed their age as under 44 and self-report being a woman living in Harris County, Texas.

Respondents from both the ABS and non-probability sample could complete the survey in English or Spanish. Survey items on discrimination and colorism were adapted from the Everyday Discrimination Scale and the New Immigrant Survey Skin Color Scale [ 17 , 18 ]. The primary outcome of this analysis is whether women avoided birth control because of perceived discrimination. The outcome variable was coded as yes if participants recorded that they experienced discrimination when going to a doctor or health clinic for birth control because of their race/ethnicity or skin tone. The survey also asked about demographic factors, had them rate their skin tone, and if they experienced discrimination because of their race/ethnicity, skin tone, parenthood, marital status, age, sex, or sexual orientation. Skin tone was only assessed for those who completed the online survey ( n  = 1299) and could choose one of 16 pictures of the skin tone that best described themself. The skin tone variable was then collapsed into five categories from lightest to darkest. Using a four-point Likert scale (very easy, somewhat easy, somewhat difficult, or very difficult), women were asked how difficult it was to find a doctor who treats them with dignity and respect when seeking birth control and reproductive healthcare. For the analyses, difficulty in finding a doctor was collapsed into very/somewhat easy compared to somewhat/very difficult. The questionnaire was tested by telephone with six respondents. The respondents completed the full survey. The questionnaire was modified based on their responses and points where they had difficulty answering.

Data management and analysis

The data was cleaned using a computer validation program to locate errors from incorrectly followed skip patterns, out-of-range values, and errors in data field locations. Quality checks were then performed on the final data. The following cases were flagged and reviewed: cases with more than 40% question non-response, cases with a time length less than one-quarter of the mean length by mode, and cases with more than 60% of the answer grids were similar (straight-lining questions). Three cases were removed after being flagged due to two or more issues.

The ABS data was weighted to account for differences in the probability of selection. Data was then weighted to balance the demographic profile of the sample to target parameters. Weighting of the ABS data was accomplished using SPSSINC RAKE, an SPSS extension module that simultaneously balances the distributions of all variables using the GENLOG procedure. The sample was weighted to match population estimates. The weighting parameters were race/ethnicity (Black, Hispanic, Else) by age (18–24, 25–34, 35–44), race/ethnicity by education (less than college, college+), detailed race/ethnicity (White, Black, Hispanic – US Born, Hispanic – Foreign Born, Other), and detailed education (high school or less, some college, college+). The benchmarks were derived from 2021 Current Population Survey (CPS) data [ 19 ]. Weights were trimmed to prevent individual interviews from having too much influence on the results.

Respondents reached through the opt-in panels were younger, with an average age of 30.5 years compared to 33.5 years among ABS respondents. Opt-in panel respondents also tended to have lower levels of educational attainment than those reached through ABS. 31% of opt-in respondents had a four-year college degree or more, compared to 58% of ABS respondents. To reduce selection bias while minimizing design effect within the non-probability sample, SSRS’s stepwise calibration methodology was used to determine a set of non-demographic internal benchmarks to weight the hybrid ABS and non-probability sample [ 20 ]. This calibration method is designed to ensure that estimates from the hybrid sample remain representative of the target population and has been tested across a wide range of healthcare and public opinion surveys. The combined ABS and non-probability samples were then weighted to the same demographic benchmarks used for the ABS sample as well as the internal benchmarks derived from the stepwise calibration.

The data was analyzed using the ‘survey’ package in R with base weights applied to account for the probability of selection. Binary logistic regression was used to examine whether race/ethnicity and skin tone were related to whether women avoided birth control because of perceived discrimination. Crude odds ratios were calculated for each variable. Adjusted odds ratios were calculated to examine whether demographic and other factors explained the relationship between race/ethnicity and the outcome variable (models 2 and 3) and whether these factors explained the relationship between skin tone and the outcome variable (models 3 and 4).

Table 1 displays the characteristics of the study sample, weighted and unweighted. Based on unweighted data, of the 1,299 women in the analysis, 41% were aged 30 and 39. Almost one-third of the sample classified themselves as non-Hispanic Whites (31.5%), 22.4% as Black, 27.4% as Hispanic and born within the US, and 7.6% as Hispanic born and outside of the US. Approximately one-third of women classified themselves in the lightest skin tones, whereas almost one in five women classified themselves in the darkest skin tone palates. Thirty-seven percent said they were single and never married, 14.6% were single and living with a partner, and 41.3% of women reported being married. Almost half (47.6%) reported being college educated. The majority (68.9%) of the sample reported being employed.

Table  2 displays the sample’s self-reported reproductive health experiences unweighted and weighted. Based on weighted data, overall, 14.9% of women aged 18–44 in Harris County said they avoided seeking birth control from a doctor or healthcare provider out of concern that they would be discriminated against or treated poorly because of their race or ethnicity, and 11.1% of women said they avoided seeking birth control out of concern that they would be discriminated against or treated poorly because of their skin tone. One in five women said they had previously experienced discrimination when going to a doctor or health clinic for birth control because of their race/ethnicity (21.1%), and 15.3% said they experienced discrimination when going to a doctor or health clinic for birth control because of their skin tone. One in five women said they had difficulty finding a doctor who treated them with dignity and respect when seeking birth control and reproductive healthcare (21.9%). 43% reported difficulty finding a doctor with a similar background and experiences when seeking birth control and reproductive healthcare.

Table  3 displays the bivariate associations between sociodemographic factors, previous experiences, and avoiding seeking birth control from a doctor or healthcare provider out of concern for discrimination. When compared to women who classified themselves as White, Black women were more than 13 times more likely to report avoiding seeking birth control because of discrimination concerns. Hispanic women born in the US were 8.6 times more likely, and Hispanic women born outside of the US were 14.6 times more likely to report avoiding seeking birth control from a doctor or other healthcare provider because of concern they would be discriminated against for their race, ethnicity, or skin tone. Compared to women classifying themselves in the lightest skin tone, all darker skin tones had increased odds of avoiding seeking birth control out of a concern for discrimination. Women with the two darkest shades of skin tones were 5.9 and 7.6 times more likely to avoid seeking birth control out of concern for discrimination. Those with lower income and those with less education had greater odds of avoiding seeking birth control out of a concern for discrimination than those with the highest income and education. Women who reported a previous experience of discrimination based on race/ethnicity were more than 30 times more likely, and women who reported prior discrimination based on skin tone were 20 times more likely to avoid seeking birth control out of concern they would be discriminated against.

Table  4 displays the multivariate associations between race/ethnicity, skin tone, and avoiding seeking birth control from a doctor or healthcare provider out of concern for discrimination after adjusting for sociodemographic factors. After adjusting for sociodemographic factors, Black women were 12.4 times more likely to avoid seeking birth control compared to non-Hispanic White women, Hispanic women born in the US were 6.5 times more likely to avoid seeking birth control, and Hispanic women born outside the US were 10.3 times more likely to avoid seeking birth control compared to non-Hispanic White women. Darker skin tones had increasingly greater odds of reporting that they avoided seeking birth control out of a concern for discrimination compared to the lightest skin tone. After adjusting for race/ethnicity and sociodemographic variables (model 3), darker skin tones remained significantly associated with avoiding birth control.

This study demonstrates the role that racial and ethnic categories and skin color play in negative reproductive health experiences. While this is not surprising given that the concept of these racial and ethnic categories was developed by those with racist ideologies, the clear association with darker skin colors and avoidance of seeking birth control provides further evidence that structural and individual racism continues to have far-reaching and insidious consequences.

Contraception is known as one of the greatest public health achievements of the 20th century and is recognized for improving the world’s health, reducing maternal mortality, improving child health, increasing female empowerment, and decreasing poverty [ 21 ]. However, not all women equally enjoy the benefits of access to contraception [ 21 ]. Documented disparities in contraception access and reproductive healthcare are multifactorial and complex and include availability and access to healthcare, transportation, health insurance, employment, and education [ 22 ]. These factors are confounded by centuries of structural racism and discrimination. For the past twenty years, studies have documented historical abuse and discrimination in healthcare settings stemming from bias and prejudice against minorities, greater clinical uncertainty when inter- with minority patients, and beliefs or stereotypes held by the provider about the behavior or health of minorities [ 23 ]. In 2020, the Kaiser Family Foundation reported that one in five Black and Hispanic adults said they were personally treated unfairly because of their race or ethnicity while getting healthcare in the past year [ 24 ].

Researchers must move past simply describing racial and ethnic differences in reproductive health and attributing these differences solely to social determinants such as poverty, education, and employment. Instead, colorism must be addressed as a global product of structural racism that impacts interpersonal and internalized experiences of discrimination that will require further study on solutions to address reproductive health inequities. Further, colorism in the American context is unique in that it is inextricably tied to the lasting vestiges of chattel slavery, Jim Crow segregation, and the subsequent policies that kept groups of people segregated and subjugated based on phenotype and ancestry [ 10 ]. We must be able to admit the role that racism rooted in anti-blackness has on reproductive health outcomes and how colorism functions as an agent of this phenomena [ 24 ].

Limitations

The study is conducted exclusively in a large urban southern city, potentially limiting the generalizability of the findings to rural or suburban areas, or even to other urban areas with different socio-economic or cultural contexts. The administration of the online survey might have excluded individuals without internet access or digital literacy.

Additionally, this study includes temporal limitations as polling captures opinions at a specific point in time, which may not reflect changes in public opinion over time. Events occurring after the data collection period can significantly alter public perceptions and attitudes.

By acknowledging these limitations, the study provides a transparent account of potential sources of bias and constraints on the findings, thereby offering a more nuanced interpretation of the results. Future research could aim to address these limitations by incorporating broader geographic samples, longitudinal designs, and methodological triangulation to enhance the robustness and generalizability of the findings.

Conclusions

This study provides colorism as a more specific focus in tackling racism in healthcare delivery now that calls for transforming the quality of care related to trust building and anti-racist practice are present [ 25 ]. Researchers need to test and disseminate strategies to ameliorate harm and ensure well-being for all. Lastly, addressing colorism within reproductive healthcare has become critically important as the nation becomes increasingly diverse. Focusing on skin tone-based discrimination and its roots in anti-blackness expands our understanding beyond a Black–White binary that is traditionally applied when addressing racism in healthcare delivery. Instead, these findings extend further awareness of the discriminatory practices among all people that contribute to a hierarchy based on skin color. We must intentionally develop, test, and disseminate strategies to ameliorate harm and ensure well-being for all.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

address-based sample

Current Population Survey

Sutton MY, Anachebe NF, Lee R, Skanes H. Racial and ethnic disparities in reproductive health services and outcomes, 2020. Obstet Gynecol. 2021;137(2):225–33. https://doi.org/10.1097/aog.0000000000004224 .

Article   PubMed   PubMed Central   Google Scholar  

Thiel de Bocanegra H, Braughton M, Bradsberry M, Howell M, Logan J, Schwarz EB. Racial and ethnic disparities in postpartum care and contraception in California’s Medicaid program. Am J Obstet Gynecol. 2017;217(1):47. https://doi.org/10.1016/j.ajog.2017.02.040 , e1-47.e7.

Article   Google Scholar  

Daniels KP, Valdez Z, Chae DH, Allen AM. Direct and vicarious racial discrimination at three life stages and preterm labor: results from the African American women’s Heart & Health Study. Matern Child Health J. 2020;24(11):1387–95. https://doi.org/10.1007/s10995-020-03003-4 .

Article   PubMed   Google Scholar  

Dennis A, Grossman D. Barriers to contraception and interest in over-the-counter access among low-income women: a qualitative study. Perspect Sex Reprod Health. 2012;44(2):84–91. https://doi.org/10.1363/4408412 .

Grindlay K, Grossman D. Prescription birth control access among U.S. women at risk of unintended pregnancy. J Women’s Health. 2016;25(3):249–54. https://doi.org/10.1089/jwh.2015.5312 .

Golden B, Asiodu IV, Franck LS, et al. Emerging approaches to redressing multi-level racism and reproductive health disparities. Npj Digit Med. 2022;5(1). https://doi.org/10.1038/s41746-022-00718-2 .

Thompson TM, Young Y-Y, Bass TM, et al. Racism runs through it: examining the sexual and reproductive health experience of black women in the South. Health Aff. 2022;41(2):195–202. https://doi.org/10.1377/hlthaff.2021.01422 .

Monk EP. Colorism and physical health: evidence from a national survey. J Health Soc Behav. 2021;62(1):37–52. https://doi.org/10.1177/0022146520979645 .

Monk EP. The unceasing significance of colorism: skin tone stratification in the United States. Daedalus. 2021;150(2):76–90. https://doi.org/10.1162/daed_a_01847 .

Russell-Cole K, Wilson M, Hall RE. The Color Complex: the politics of skin color in a New Millennium. Anchor Books; 2013.

Keyes L, Small E, Nikolova S. The complex relationship between colorism and poor health outcomes with African americans: a systematic review. Analyses Social Issues Public Policy. 2020;20(1):676–97. https://doi.org/10.1111/asap.12223 .

Laidley T, Domingue B, Sinsub P, Harris KM, Conley D. New evidence of skin color bias and health outcomes using sibling difference models: a research note. Demography. 2019;56(2):753–62. https://doi.org/10.1007/s13524-018-0756-6 .

Landor A, Barr A. Politics of respectability, colorism, and the terms of social exchange in Family Research. J Family Theory Rev. 2018;10(2):330–47. https://doi.org/10.1111/jftr.12264 .

Oh H, Lincoln K, Waldman K. Perceived colorism and lifetime psychiatric disorders among black American adults: findings from the National Survey of American Life. Soc Psychiatry Psychiatr Epidemiol. 2021;56(8):1509–12. https://doi.org/10.1007/s00127-021-02102-z .

Thomas SP. Street-race in Reproductive Health: a qualitative study of the pregnancy and birthing experiences among black and afro-latina women in south Florida. Matern Child Health J. 2021;26(4):700–7. https://doi.org/10.1007/s10995-021-03188-2 .

Slaughter-Acey JC, Sneed D, Parker L, Keith VM, Lee NL, Misra DP. Skin tone matters: racial microaggressions and delayed prenatal care. Am J Prev Med. 2019;57(3):321–9. https://doi.org/10.1016/j.amepre.2019.04.014 .

Williams DR, Yu Y, Jackson JS, Anderson NB. Racial differences in physical and mental health: socioeconomic status, stress, and discrimination. J Health Psychol. 1997;2(3):335–51. https://doi.org/10.1177/135910539700200305 .

Article   CAS   PubMed   Google Scholar  

Massey DS, Martin JA, Princeton University. The NIS Skin Color Scale. Office of Population Research, ; 2003. Accessed September 1, 2023. https://nis.princeton.edu/downloads/nis-skin-color-scale.pdf

Flood S, Warren JR, Ruggles S, King M, IPUMS, CPS. : Version 5.0. D030.V5.0 | IPUMS. Accessed September 1, 2023. https://doi.org/10.18128/D030.V5.0

SSRS. SSRS encipher hybrid: A validated methodology for blending probability and nonprobability samples. Accessed September 1. 2023. https://ssrs.com/wp-content/uploads/SSRS-Encipher-Hybrid-White-Paper.pdf

Boonstra H, Duran V, Northington Gamble V, Blumenthal P, Dominguez L, Pies C. The boom and bust phenomenon: the hopes, dreams, and broken promises of the Contraceptive Revolution. Contraception. 2000;61(1):9–25. https://doi.org/10.1016/s0010-7824(99)00121-3 .

Kumar N, Brown JD. Access barriers to long-acting reversible contraceptives for adolescents. J Adolesc Health. 2016;59(3):248–53. https://doi.org/10.1016/j.jadohealth.2016.03.039 .

Balsa AI, McGuire TG. Prejudice, clinical uncertainty and stereotyping as sources of health disparities. J Health Econ. 2003;22(1):89–116. https://doi.org/10.1016/s0167-6296(02)00098-x .

Brown HL, Small MJ, Clare CA, Hill WC. Black Women Health Inequity: the origin of Perinatal Health Disparity. J Natl Med Assoc. 2021;113(1):105–13. https://doi.org/10.1016/j.jnma.2020.11.008 .

Reed R, Osby O, Nelums M, Welchlin C, Konate R, Holt K. Contraceptive care experiences and preferences among Black women in Mississippi: a qualitative study. Contraception. 2022;114:18–25. https://doi.org/10.1016/j.contraception.2022.05.009 .

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The Houston Experiences in Reproductive Health Survey was made possible by grant supported through the Episcopal Health Foundation.

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KB contributed to the design of the survey and was a major contributor to the writing of the manuscript.STE contributed to the design of the survey, interpreted the statistical output, and contributed to the writing of the manuscript.ES contributed to the survey administration and to the writing of the manuscript.JS and EBP contributed to the data collection and analysis, as well as critical feedback and revisions of the manuscript.

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Correspondence to Kimberly Baker .

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Baker, K., Emery, S.T., Spike, E. et al. Skin tone discrimination and birth control avoidance among women in Harris County, Texas: a cross-sectional study. BMC Public Health 24 , 2375 (2024). https://doi.org/10.1186/s12889-024-19765-3

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Received : 15 December 2023

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DOI : https://doi.org/10.1186/s12889-024-19765-3

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