# **Appendix III. Tools and Techniques**

**I. Reduced form regressions** . These studies typically use single-country time-series data or cross-country time-series data to test the statistical relationship between the following: (1) components of public spending and economic growth (this is roughly equivalent to _Y(B,K,O_ )); and (2) components of public spending and social indicators (this is roughly _B(Y,S,K,0_ )). The empirical evidence on the relative effectiveness of expenditure components has thus far been inconclusive. The robustness of the results is often sensitive to the empirical strategy employed and the countries and time period covered by the sample. The following gives a flavor of the techniques that have been used and their findings:

- _Cross-country growth regressions_ .<sup>33</sup> This typically uses panel data for a large cross-section of countries over time. A large empirical literature on economic growth has emerged since the early 1990s, due in part to developments in growth theory and the greater availability of cross-country macroeconomic data. Empirical studies of the link between expenditure policy and growth are reviewed in Baldacci, Clements, and Gupta (2003). Dollar and Kraay (2002) and Foster and Szekely (2001) (see Table 2) use aggregate cross-country data and cross-country micro data, respectively, to estimate the link between public spending and poverty. Both find a statistically significant effect of overall government consumption on poverty. Some studies use simple bivariate regressions using cross-country to examine the link between spending and poverty.<sup>34</sup>

- _Time-series studies_ . Country studies using time-series data for fairly long time periods provide important evidence on the causal impact of intervention on economic growth. A recent study of the impact of education on growth in India over the 19661996 period, for example, suggests that it is primary education that ha a strong causal impact on growth, compared to other education levels (Self and Grabowski 2004). Researchers have also employed vector auto-regression (VAR) techniques to explore the time-series properties of public investment and growth, accounting for potential dynamic interrelationships without imposing a causal structure. A series of studies by Pereira (2000, 2001, 2002) demonstrate the dynamic relationship between public investment in infrastructure and private output (private sector GDP, employment,

> 33 Devarajan, Swaroop, and Zou (1995) using data on 29 countries over the 1970-1990 period suggest that public capital spending has a negative but insignificant effect on growth while current spending has a positive and significant impact on growth. They also find that transport and communication have a negative and significant impact on growth while health and education have a negative but insignificant impact on growth. In contrast, a study of 39 low-income countries over the 1990-2001 period finds that higher public expenditure on capital raises economic growth (Gupta, Clements, Baldacci, and Mulas-Granados 2003). A recent study finds that investment in education is the only expenditure outlay significantly associated with growth in a sample of 30 developing countries (Bose, Haque, and Osborn 2003).

> 34 Mosley and Hudson (2001) estimate the correlation between expenditure components and poverty using cross-country data. Based on this exercise, they calculate a “pro-poor expenditure” (PPE) index or the ratio to GDP of spending components associated with poverty. In addition, results from recent cross-country regressions have been used to quantify the poverty elasticity of growth.

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