and recipient reports of aid in Uganda at least partially to the omission of TC from the budgets of recipient governments. Johnson and Martin (2005, p. 6) conclude that “HIPCs see direct payments by donors to foreign suppliers as highly problematic, as they are often not informed of the actual disbursements. This is especially true for technical assistance provided by expatriate experts, who are hired and paid by the donor”. Baser and Morgan (2001) find that TC is off-budget in the six African countries that they investigate. Drawing from the experiences of a much larger group of countries, OECD (2008, p. 59) notes that “technical co-operation expenditures are described as a particular problem in recording aid on budget”. Mokoro (2008), which is a detailed study of the role of aid in the budget process based on both an extensive literature review and case studies of ten Sub-Saharan African countries, identifies a clear hierarchy in the extent to which different aid modalities are disbursed via the treasuries of recipient governments and are captured in their accounts: most likely for general budget support and program aid, much less likely for project aid, and even less likely for technical assistance.

The summary statistics in Table 1 suggest that education aid is more than 70% TC, whereas approximately 40% of aid is TC in the health sector. This dominant role of TC in health aid and, especially, education aid is confirmed in the CRS directives (OECD, 2002, p. 26). The average SP aid is small and reflects that for many country-years, education and health SP aid are nearly zero. Particularly in the education sector, the variance in TC is large compared with that of the other sectoral aid modalities, which further reinforces the notion that the bias created by the failure to adequately address off-budget aid may be substantial (recall equation (3)).

The extent to which IP and ONM aid are reported in government budgets is more uncertain. Thus, the estimates of _βIP_ and _βONM_ are less informative for gauging the degree of fungibility.<sup>18</sup> However, using _SAIDSPit_ and _SAIDTCit_ as measures of on- and off-budget sectoral aid, respectively, it is possible to test the null hypothesis of no fungibility and the null of full fungibility in a manner consistent with the analysis in section I, as shown in Table 2.

Table 2: Null hypotheses for no and full fungibility with on- and off-budget aid

|**Theoretical null hypothesis:**|No fungibility|Full fungibility<br>|
|---|---|---|
|Aid on-budget (SP)|_βSP_ ⩾1|_βSP_ ⩽<sup>_∂SSPit_</sup><br>_∂Rit_<br>|
|Aid off-budget (TC)|_βTC_ ⩾0|_βTC_ ⩽<sup>_∂SSPit_</sup><br>_∂Rit_<br>_−_1|
|**Implemented null hypothesis:**|No fungibility|Full fungibility|
|Aid on-budget (SP)|_βSP_ ⩾1|_βSP_ ⩽0|
|Aid off-budget (TC)|_βTC_ ⩾0|_βTC_ ⩽_−_1|

The full fungibility tests require knowledge of the marginal effect of unconditional resources _R_ (typically measured as government expenditure net of aid), which may be obtained by following the two-stage procedure outlined in Devarajan et al. (2007).<sup>19</sup> Nevertheless, the data that I received from the IMF’s FAD do not contain total expenditures, revenue or borrowing. Because data availability for these variables in other databases is significantly more limited, a large fraction of the sample would be lost by following this procedure. Instead, I 13