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Our paper focuses on methods to estimate causal effects in time series cross sectional data when the variables of interest exhibit a strong upward trend over time. We use Büthe and Milner (2008)'s analysis of the political effects of preferential trade agreements (PTAs) on foreign direct investment (FDI) flows as an example of the problems that trending variables create for time-series cross-section (TSCS) analysis. We argue unless the functional form of the trend in each country is known and correctly specified, detrending the variables of interest can bias the results and leaves out important information. We employ two alternative methods, matching and a fuzzy regression discontinuity design, to exploit the information that the trend offers in order to estimate the causal effect of PTAs on FDI flows. Our alternative analyses provides no support to Büthe and Milner (2008)'s conclusion that signing PTAs helps developing countries to attract FDI.
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