5. Further Robustness Checks
I next address a variety of potential concerns about the robustness of the benchmark estimates
of the multiplier presented in Table 4. Given the noisy and highly-imperfect data on government
spending and output in many of the developing countries that comprise my sample, a first concern is
that the results in Table 4 might be driven by a small number of influential observations. To investigate
this possibility more systematically, I use a procedure suggested by Hadi (1992) to identify influential
observations in the reduced-form and first-stage regressions (the ratio of the corresponding two slope
coefficients being the 2SLS estimate of the multiplier). I then re-estimate the OLS, first-stage, and 2SLS
regressions, excluding these influential observations. The results of this first robustness check are
reported in the first three columns of Table 6. The OLS estimates of the multiplier change very little
relative to the benchmark results. The 2SLS estimates of the multiplier are virtually unchanged once
influential observations are removed, ranging from 0.37 to 0.39, and moreover they are slightly more
precisely estimated than before. This is in part due to an even stronger first-stage relationship after
removing influential observations in the IDA and high-disbursement samples, in which the first-stage Fstatistics
jump to 41.2 and 30.3, respectively.