africa

Yale Center for the Study of Globalization
tie changes in the structure of institutions to changes in the inclination to intervene
in markets.
12.6 Multivariate analysis
Emboldened by this reasoning, we return to the use of quantitative data, this time
making use of multivariate methods in order to identify the relationships among
institutional change, policy choice, and economic performance.
Throughout, we adopt a difference-in-difference specification. Given that the treatment,
institutional change, occurred at different times in different countries, our
model takes the form of a fixed-effects regression with individual year dummies:
where Y it is the growth rate of productivity in country i in year t, 0i are time-invariant
unobservable country effects, t
are year dummies, X is a vector of observed covariates,
and D it is a dummy equal to 1 for each country-year observation in which the
government in power is one that had been chosen in competitive elections. is the
coefficient of interest; it provides a measure of the relationship between the nature
of political institutions and—depending on the context—the growth of TFP in agriculture
or in the greater economy. 1i is a country-specific trend coefficient multiplying
the time trend t; it provides a test of the identifying assumption of common trends
implicit in difference-in-difference specifications.
We also employ a form of mediation analysis to determine whether the relationship
between institutional change and changes in the rate of growth of total factor productivity
runs through changes in public policies. In our analysis of the agricultural
sector, we do so by estimating three linear regressions (Imai and others, 2011):
where Y is the outcome variable (agricultural TFP growth), T is the treatment variable
(electoral competition), M is the mediating variable (RRA or BMP), and X is a
vector of control variables. When we turn to TFP growth in the economy as a whole,
we do so less formally and note whether the relationship between policy choice
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