Democracy and Struggle for Definition

Common definitions happen to be useful in research. Instead of arguing what “democracy” means, economists agree on the same definition and move on to important things, for example, the relationships between democracy and economic growth:

Acemoglu et al., "Democracy Does Cause Growth."
Acemoglu et al., “Democracy Does Cause Growth.”

So, you study relationships between specific “democracy” (usually Polity IV) and specific “growth” (real GDP per capita). No problem in studying another democracy and another growth. But until economists conquer the world and turn everyone into an economist, very few heads are working on the issue right now. And these heads have to focus on very specific definitions, like the Polity IV components of “democracy”:

Competitiveness of Executive Recruitment
Openness of Executive Recruitment
Constraint on Chief Executive
Competitiveness of Political Participation

This makes communication with the public difficult, though. For one reason, the public understands “democracy” differently. The great source for making a representative public definition of “democracy” is the World Values Survey (WVS). The survey reaches thousands of respondents in 52 countries, and since 1995, asks questions about democracy. Specifically, it asks to “tell me for each of the following things how essential you think it is as a characteristic of democracy,” and offers nine scales. Summary stats of these scales (min 1 — not an essential characteristic; max 10 — an essential characteristic):

Definition of democracy given by the WVS respondents
Definition of democracy given by the WVS respondents

The first row is the answer to the question how good democracy is for the respondent’s political system (min 1 — “Very good”; max 4 — “Very bad”). The rest of the variables are the components of democracy as defined by 74,000 people from 52 countries. You can think of the respective means (third column) as weights each variable has in the public definition of democracy. The standard deviations aren’t huge, which implies some consensus across many people.

These components seemingly have little in common with the Polity IV’s definition. To make sure, let’s compare the Polity IV index of political regimes with question V141 from the WVS, which asks “how democratically is this country being governed today.” A scale ‘from 1 to 10, where 1 means that it is “not at all democratic” and 10 means that it is “completely democratic.”’ Hence, I compare two things: the respondent’s opinion about the state of democracy (as she understands the concept) in her country and Polity IV’s expert’s opinion about the state of democracy (as defined by Polity IV) in the same country.

Democracies described by the public in the WVS and by experts in the Polity IV dataset have almost nothing in common:


Though the regression line is not horizontal and the relationship is significant at 0.01, R² is just 14%. Not what you may expect from two things with the same name.

This nerdy fact is important because results in the social sciences are relevant only for concepts as they’ve been defined in the social sciences. If your definition X is different from my definition X, then my investigations of X are useless to you. If someone thinks that “democracy” is when “the army takes over when government is incompetent” (row six in the table) then this democracy does not cause economic growth that the first figure shows. In fact, the authors of the figure describe in detail how they constructed the measure of democracy that is relevant for growth.

Of course, when it comes to elections and policy making, economists become as humble as dentists. That is, not even dentists, but also physicians know how to fix the economy—contrary to what economists think on the same matter. In addition, there’s no organized communication between people and researchers. When the public is so poorly informed about actual research, the difference in definitions—of which “democracy” is just one example—seem unimportant. It is, to an extent. But whenever serious research makes it into the media, it’s better to doublecheck the words.

Macroeconomics Models and Force of Habit

The public rightly questioned macroeconomics and academic finance after the 2008 burst. Record housing prices and debt, both relative to income, look a plausible cause for concern and they are. Why, then no one prevented it?
The design of the markets discourages companies from being overly cautious. Banks didn’t quit inflated housing markets because these markets were still inflating. Profits reinforce participation.
The designers of the markets had got obvious signals too late to avoid consequences. And very few wanted to be the person who bursts balloons with a needle at a birthday party anyway. Governments and central banks waited for problems to come first.
Many more versions exist. But none of them can explain the bubble with lack of knowledge alone. People in finance see housing prices every day, and high ratios are quite telling, apart from answering the question, “When will this trend end?”
Designers and players played by the rules, and they certainly had selfish incentives. Academia was relatively free of these rules and incentives. Did macroeconomists have selfish incentives to find a bubble, instead?
Yes and no. You will barely find a major university economist who likes forecasting. Because sometimes the predictions come true. Thus, sometimes they don’t. Economists prefer discussing things that have happened already. And they do it unhurriedly. Operative policy interventions are unlikely in the environment where even publishing an academic paper takes up to several years.
More so, it’s difficult to find a serious academic paper that includes policy recommendations. Scholars explain things that have occurred. Policymakers can use these insights to forecast. By 2008, policymakers had models. Were these models good? They happened to have specific limitations. But even bankers had incentives to use the best models they might get to quit the housing market in time.
There’re no obstacles to adopting models with better predicting power. Then, maybe policymakers did use the best models they had? Rather, they used the most reliable equations: the ones that they understand and used for years. And DSGE models won over various alternatives, including those by heterodox economists, who offered equations that predicted the crisis.
A theory that predicts one-in-fifty-years events is not trusted because it can hardly earn a reputation of a reliable one. No, the theory itself may be predictive and great, but it lacks an empirical base to show its fitness. That makes this theory and underlying models an unlikely candidate for widespread use.
Macroeconomics is responsible for not knowing enough in the sense of biologists who don’t know how to cure cancer. There’re wrong turns and no malicious incentives. Right turns require outstanding efforts and time. Including time for gathering unique data, like the data that came from the terrible Great Recession. Bad theories still can be the best, until we have more evidences. Economics works when we recognize limitations of previous theories and try to build better ones.