Four dummies were added to ensure the error term is normally distributed. Each can be considered a legitimate explanatory variable:<sup>44</sup>

- **Ghana 2008:** The coincidence of an election year and rapidly escalating food and fuel prices created strong pressure to spend above the approved budget.  The 2004 elections also created spending pressures but without additional commodity price movements.

- **India 2008/09:** Revenues fell below target due to the global recession while expenditures where sharply expanded within-year for stimulus purposes via a supplemental budget.

- **Pakistan 2007/08:** The budget was adversely affected by a combination of slower than expected real GDP growth, increased subsidies to partially insulate consumers from rising fuel and food prices, and a loss of fiscal discipline during a year of major political tumult.

- **Sierra Leone 2007** :   In 2007, donors withheld almost half the expected level of grant financing due to fiduciary concerns.  This led to the imposition of daily cash rationing in the short run and helped motivate successful, deeper investments in PFM capacity in the medium term.

As an additional test of robustness, all outlier observations were investigated to see if any acted as influential points. Several variables were affected by moderate changes in their slopes but only one, _Z(PCT_INT)∙rpos_ , was made insignificant by two influential points (see Table 10). There are no obvious reasons to remove those two points.

# **<u>TABLE 10: INFLUENTIAL POINTS FOR AGGREGATE EXPENDITURE DEVIATIONS</u>**

||**Outliers 1/**|**Influential Points 2/**|
|---|---|---|
|_Z(PCT_INT)∙rpos_<br>_H02 DRO_|Solomon Islands (2004)<br>Norway (2004 - 2006)|Jamaica (2003/04), Yemen (2005)|
|_H02 DRY_<br>_H03 r∙Z(PCT_CSE)_|Uganda (2005/06 – 2007/08)<br>Sierra Leone (2007), Solomon Islands (2004)||
|_H04 POLITY_<br>_H09 lnPOP_<br>_H09 r∙Z(lnPOP)_|Bhutan (2005/06 - 2007/08)<br>India (2005/06 - 2007/08)<br>Solomon Islands(2004)||

1. Observations outside of the mean plus or minus 2 standard deviations or, for highly skewed distributions, the most extreme observations.

2. Observations that, when excluded, make a coefficient insignificant at the 10 percent level.

The final equation is not perfect but it is sufficient to allow simple inference testing of the possibility that the hypothesized correlations in Section IV above are or are not significantly different from zero. The Jarque-Bera test fails to reject the null hypothesis of a normally distributed error term. White’s test fails to reject the null hypothesis of no heteroskedasticity. The Ramsey RESET test fails to reject the null of no mis-specification using squared fitted values of the dependent variable. The adjusted r-square is 0.57 with dummies and 0.41 without them. As before, even though this is very encouraging, some caution must be maintained since it is already known from the discussion in Section IV above that there _is_ at least one missing explanatory variable: unexpected shocks to external grants and net financing.

The results provide mixed support for the hypotheses advanced in section IV above. The constant is not statistically different from zero in equation 10b. This is borne out by Figure 1 which shows that _e_ is distributed symmetrically around zero. The interactive terms define the capacity to

> 44 Three of the four dummies overlap with an acceleration of the global price of petroleum an done overlaps with the global depression.  The author tried adding variables for petroleum price inflation and OECD growth, on the (admittedly weak) supposition that policy makers expect next year’s outcome to be the same as the previous year’s outcome.  These variables proved to be statistically insignificant with and without the dummies.  A better test, if data were available, would use each government’s own forecast versus the actual outcome.

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