When preparing to measure for horizontal inequality, in addition to asking the three main questions above, you need to think of how you collect and compare the data. What group identity markers you select to measure for is key, as a factor that might appear at first to be salient might not be. You might also try measuring for salient factors across different countries.
One place to start is with survey and census data. There's an initiative known as the Demographic Health Survey
(DHS) program that conducts comprehensive surveys in many countries, and in some they measure for ethnicity or other variables. You can take data like this, plug in different factors and compare groups to see if inequalities emerge.
There are also local datasets in different countries that you can use. But there's always the issue that you're depending on the people who designed the survey, and if that organization failed to measure for something salient then their data likely won't reflect it either.
You can also try to figure out if there's a proxy for salience, like political relevance, representation in parliament, officially recognized language status and the like. These might still leave out relevant group divisions, but it could be a good place to start.
There are also NGOs or international research programs, like Minorities at Risk
, that also look at social as well as political relevance.
When looking for political factors, you also have to be aware of nuances like tokenism
. Is a group actually represented in parliament, for example, or are there merely a few powerless MPs that give the impression of representation? You can also look at representation as compared to group size or general demographics.
Some proxies for socioeconomic development and inequalities could be education rates, health or infant mortality. Economic proxies could look for whether people have water in their homes, or TVs and other kinds of assets as compared to other members of that society. Then, of course, there's GDP per capita within specific groups.
Measuring for political inequality has produced some solid data and correlations, as well as for economic factors relating to deprivation (but not as much for privilege, which has generated more mixed evidence). Social indicators are still something new and there haven't been enough (or diverse enough) studies to create solid recommendations just yet.
Group identifiers with a growing amount of evidence include geographical region, migrant status, age and gender. While these are promising developments, the precise nature of these links is still being analyzed, as are their links with structural conditions.
Inequality within groups is also a new factor that's currently being studied, but so far there are only mixed results.
And while links are being shown in certain contexts, it's another thing to prove causality. In other words: is conflict causing the inequality, or is the inequality causing the conflict? Sometimes it goes both ways and it's hard trying to pin down the dynamics exactly.
When it comes to making policy implications from this kind of data, there's the temptation to say "reduce inequality and we get less conflict!" But it's more complicated than that. You might get less violence from underprivileged and deprived groups, but maybe privileged groups might take up arms to protect their resources.
That, and when you address inequality and try to even things out in one way, you might trigger another problem somewhere else. So it's complicated. And, even if you do find some great evidence and make solid links, all you'll be able to bring to the table is a probability model: if you make a concrete change in society, then the probability of a conflict goes down. Nothing's for certain.
And people like certainties, especially politicians who make decisions. But that is not, Solveig reminds us, how social sciences work. We make do with what we have, test our models, look for the missing pieces and try, in the end, for a less-incomplete picture of the world.