is easier to tie aid. Nevertheless we attempt to correct for any possible endogeneity bias empirically using instrumental variables estimation.

In table 5 we treat aid share as endogenous in estimating its effects on tied aid using 2SLS. Exogenous instruments for aid share include (1) geographic distance between the capital cities of the donor and recipients, (2) a dummy for whether or not the recipient is a former colony of the donor country, and (3) share of aid from multilateral donors, including the EC. Data on distance were obtained for 1946 observations from Gleditsch (2011), and supplemented with our own research for 13 other observations belonging to Tonga and Kiribati. The colonial ties variable was the product of our own research, and is defined with respect to the final colonial power for recipients where there was more than one. Data on multilateral aid shares are from AidData.

Colonial ties have been shown in various studies to be associated with higher aid from a donor (e.g. Alesina and Dollar, 2000). Donors also tend to focus more of their aid on countries within their geographic region, for example Japan and Australia in the East Asia and Pacific region. This likely reflects enhanced geo-political interests, as well as lower costs in providing aid (such as shorter flights for donor missions). Colonial ties and distance vary across observations both by donors and by recipients. The third instrument, share of aid from multilaterals, is a recipient-level variable. In recipients with higher shares of multilateral aid, aid shares for each bilateral donors should be lower (unless there are fewer of them present). The share of aid from multilaterals should have no direct effect on tying status of bilateral aid, although we cannot rule out the possibility that the decisions of some bilaterals may be influenced one way or the other by greater multilateral involvement in a country. For colonial ties and geographic distance, it is conceivable they could affect aid tying directly. If donors have more of a stake in development outcomes in recipients that are ex-colonies or geographically closer, they might not only provide more aid to them but also provide it in ways that make it more productive. However, our instruments easily pass the relevant tests of over-identification in our 2SLS tests, reported in table 5. Depending on which of the three alternative tied aid measures are used, p values for the Hansen’s J test statistic range from .29 to .38, so the null hypothesis that the excluded instruments are correctly excluded cannot be rejected.

Equation 1 in table 5 reports the first stage regression for our 2SLS tests.<sup>13</sup> As in tables 3 and 4, standard errors are adjusted for donor-clustered observations. Distance, colonial ties, and multilateral aid share are all significant at the .01 level. Coefficients for distance and multilateral share are negative as expected, and positive for colonial ties as expected. An F test confirms the strength of these excluded instruments: the F statistic is 6.7, significant at the .0024 level.

Equation 2 reports the second-stage results for tied aid, based on definition 1. The aid share coefficient is negative as in the OLS regressions, and is somewhat larger in absolute value. It is significant only at the .10 level however. Results for other variables, including the LDC dummy, are very similar to their OLS counterparts. Equation 3 reports second-stage results for tied aid based on definition 2 (i.e. treating partially tied aid as

> 13Estimations were performed using STATA’s XTIVREG2 procedure.

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