Hello, AP Comparative Government and Politics Students! I’m Mr. Tesch, and welcome to Global Chat, where we explore the ins and outs of the field of study known as “Comparative Politics.”

The world today has around one hundred and ninety five countries. Each of these countries is made up of people speaking some of the thousands of different languages spoken today, and practicing nearly as many different religions. Some live in places where political power is highly concentrated, like North Korea. Others live in places where political power is shared, like the United States. All people, whether they know it or not, are part of a struggle for power that we call “politics.”

Political science is the study of this ongoing struggle for power around the world. It is a big field, and political scientists will specialize in different areas. These include international relations, political theory, political economy, public policy, and others. All of them draw upon expertise from the fields of economics, philosophy, history, geography, and sociology.

Our subject is Comparative Politics, and its focus is on the different ways in which people organize themselves politically, comparing the political systems that people create in different countries. Studying this subject helps us answer some of the world’s big questions. Why do some states centralize political power, whereas others democratize? What roles do governments play in shaping the economy? How and why do people organize themselves into political groups? What causes political violence? Why are some states so wealthy, while others remain poor?

To begin to answer these questions, we should look at how political scientists go about comparing different countries, and we should also talk about exactly what it is they are comparing. That means we need to talk about “methods.”

First and foremost, we should make clear that political scientists compare states on the basis of hard facts and evidence, or what we can call “empirical” reasoning. They do not merely restate their own opinions, which is something we would call a “normative” statement. As a person, for example, I might say that I think democracy is a good thing. To a political scientist, though, that doesn’t really mean anything at all. Good for who? Good in what way? Why is it good? What is the alternative? What does good even mean? Now that doesn’t mean that opinions aren’t important — obviously, they are. But in terms of science, just sharing opinions doesn’t really get us anywhere in answering our big questions.

To avoid these kinds of “normative” statements, Comparative Politics relies both on quantitative and qualitative data. This “data” is what we use to make comparisons between and inferences about the countries that we study.

When we think about quantitative data, we think about numbers. In Comparative Politics, this often means looking at different kinds of economic statistics. The first of these statistics that you’ll hear a lot about is Gross Domestic Product, or GDP, which measures the total amount of wealth produced in a state each year. Looking at a state’s GDP allows political scientists and economists to make generalizations about the wealth and productivity of a country.

For example: in 2018, the total GDP of the United States was around eighteen trillion dollars. That takes into account not only the value of all of the things that are made in the USA such as houses, cars, computers, or buildings, but it also includes the value of services rendered such as medical treatments, loans issued, legal services provided, and so on.

GDP is a very important measure that gives us a good idea about a country’s wealth, but it does not tell us everything. Let’s say we want to know how well-off the populations of different countries are. If we look only at GDP, then we might say that China and the United States are almost equally well off, because China has a GDP of nearly thirteen trillion dollars and is second only to the United States in terms of total economic output. That would not be quite right, though, because what we didn’t do was consider the relative sizes of each country’s population. China has a population of almost one billion three hundred million people, while the United States only has a population of about 330 million people. (In terms of the potential to produce manufactured goods, this also happens to be why we say that China has a significant comparative advantage.)

To compare the United States and China, then, we need to divide GDP by each country’s population, which gives us something called “GDP per capita,” or the amount of wealth produced in a country per year and per person on average. If we do this, we find that the United States has a GDP per capita of around 59,000 dollars — a much larger figure than China’s roughly 9,000 dollars. With these figures, a political scientist might reasonably conclude that the people of the United States produce more wealth than those of China, and that this is very likely because of their different political-economic systems.

As you can see, GDP per capita is a very useful measure of wealth, but even it has its limitations. It does not tell us anything about how much wealth or income people in a particular country have, or how the wealth that is produced in a country is distributed. For that, we need something called the Gini coefficient.

The Gini coefficient measures how equally or unequally the wealth of a population is distributed. It is a statistic that is given as a value between 0 and 1, where 0 indicates a population that is perfectly equal in terms of wealth, and 1 indicates a population that is perfectly unequal. To wrap your head around this concept, we can use an imperfect but useful metaphor. Picture a room full of children and their teacher. In this room, the teacher is the only person who has any income at all, meaning that the inequality of this population would be given by a Gini coefficient of 1. If the teacher were to leave the room, however, the children would be perfectly equal, and would be represented by a Gini coefficient of 0.

When applied to an entire country, the Gini coefficient gives us an idea of how equally the income of a population is distributed. In a country with a higher Gini coefficient, a smaller number of people receive a larger share of that country’s wealth. This is even true in countries that are similar in terms of productivity. The United States and the Netherlands have a similar GDP per capita, for example, but while the Netherlands have relatively low inequality with a Gini coefficient value of 28.6, the United States has a higher Gini coefficient value of 41.5.

When it comes to economics, then, quantitative data is obviously useful. But what if we wanted to look at other things, such as how much freedom a country offers its citizens, or how well a country keeps its citizens and their property safe and secure? Wouldn’t it be nice if we could also describe these kinds of things with numbers?

As it turns out, political scientists have tried their hand at this as well. There is something called the Freedom House Index that ranks countries by how much political and economic freedom their people have; the United Nations ranks countries on how happy, secure, healthy, and wealthy people are with something called the Human Development Index; and the Failed States index ranks countries on their ability to keep their people safe and secure. There are a number of useful indexes out there that we can explore, and it’s pretty clear that quantitative data will be useful to us as we set out to compare countries and their political systems.

Nevertheless, numbers can only tell us part of the whole story. What if we wanted to know about the languages that people speak, what they believe, what their values are, what their cultures are like, and how their political systems actually operate? Here, we must turn to other types of information, something we refer to as qualitative data.

Qualitative data comes in many forms. To keep things simple, we can define qualitative data as any data that is not numerical. For political scientists, qualitative data provides the information that quantitative data leaves out. It can include things like national constitutions, maps, speeches, political cartoons, charts, and even artworks.

In order to go about studying Comparative Politics, we will need to use both kinds of these kinds of data. The most important question, though, is HOW we will use this data. And for that, we need to talk about the difference between causation and correlation.

One type of question that political scientists attempt to answer involves causation. We will tackle many of these questions ourselves. What caused the people of Iran to revolt in 1979? What causes some states to be wealthy like the United Kingdom, while others like Nigeria are relatively poor? Why do the people of some states choose leaders with authoritarian traits, like the people of Russia?

There are many answers to these kinds of questions, but often it can be difficult to pin down which is the best answer. For example, is Nigeria a poor country because the oil industry is controlled by the state? Or because enough foreign companies don’t want to do business there? Or because Nigeria does not have a very good education system? Or because it has a great deal of ethnic conflict? Or because it’s political system is ripe with corruption? Or is it all of the above?

In Comparative Politics, it can be difficult to determine causation with certainty. As we just saw, there are a lot of variables that might influence political policies, regime stability, or a state’s level of economic development, but no real way for us to isolate and demonstrate which is actually producing the change that we observe. Countries are not laboratories, and we can’t simply conduct experiments in them.

Instead, we will sometimes have to be satisfied with saying that there is a correlation or connection between two or more variables. Going back to our earlier question, when we say that states like Nigeria that have a large amount of oil tend to be poorer and less developed, we are stating a correlation between two variables. In recent years, political scientists have shown repeatedly that countries that have a large amount of natural resources, especially oil, tend to be poorer and less developed. This is a very interesting correlation known as the resource curse, and is one we will be studying later on in our course.

In this video, we’ve talked about the differences between quantitative and qualitative information, normative and empirical statements, and causation and correlation. Throughout the AP Comparative Government and Politics course, we will be making use of these basic tools. For more information and additional resources, please check in the description below this video. And remember to subscribe to catch more of our videos. Thank you for watching, and see you next time!