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Is democracy good for the poor?

I ran across this paper by Michael Ross. Here’s the abstract:

Many scholars claim that democracy improves the welfare of the poor. This article uses data on infant and child mortality to challenge this claim. Cross-national studies tend to exclude from their samples nondemocratic states that have performed well; this leads to the mistaken inference that nondemocracies have worse records than democracies. Once these and other flaws are corrected, democracy has little or no effect on infant and child mortality rates. Democracies spend more money on education and health than nondemocracies, but these benefits seem to accrue to middle- and upper-income groups.

This is an interesting idea. One of their key points is the datasets that are usually analyzed have missing-data patterns that bias the results. I am sympathetic toward this reasoning. Another issue is controlling for systematic differences between countries, so that the analysis is looking at countries that have transitions to and from democracy. I’m thinking that it might make sense to have two separate models for the two different transitions. Also, I’m wondering whether it would make sense to look at longer time lags. I have no quick solutions here, but it seems like it would be a good problem for a student to look at, to reanalyze the data and see what turns up.

On a more substantive direction, the last part of the paper has some discussion of why democracy might not be so great for the poor. But since the results are all comparisons with non-democracies, I’d think there should be some disucssion of the choices made by non-democratic regimes. (Or maybe this is there, and I’m just unfamiliar with this research area.)

Other comments:

Figure 1 could look nice by removing the bars and instead putting in a think line for each country, then a thick line showing the averages (which will go where the bars do now). Also, ditch the horizontal lines, remove the overlabeling on the y-axis, and have the range of the axis go to the range of the data. (Sending the axis for log(mortality rate) to zero is meaningless since you’re on the log scale!)

The tables should be graphs (of course). But, actually, the tables provide a great illustration why it would be helpful to scale the variables by dividing by two standard deviations. For example, in Table 3, it’s hard to interpret the coefficients for income, HIV, pop density, growth, etc–they differ by orders of magnitude, but the x-variables are all on different scales. It would be easier to see quickly what’s going on if the variables were prescaled. (Table 6 has an even more striking example.)

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