but growth would do nothing to prevent the current generation of the poor from losing out relative to future generations and to the non-poor. Similarly, we could add risk to the model; but again it is not obvious that this would change the basic conclusion. In fact, it might strengthen the findings: if households are taxed to pay current educational costs but are uncertain about how much their children will benefit, the net change in welfare for risk-averse poor households will be even lower.

# **3. An exacerbating factor:  Inefficiencies in education spending**

We have seen, then, that with a single change in assumptions – allowing imperfections in capital markets – the basic incidence model predicts that current generations of the poor may find that an ambitious program of educational quality improvement actually worsens their welfare. A second factor – inefficiencies in the government’s investment in public education – may further exacerbate this problem of intergenerational distribution of benefits. The model assumes that the additional taxation λ (or τ , in the earlier model) translates effectively into an increase ω in the future earnings of generations t+1 and following. Underlying the basic model is clearly an implicit assumption that ω is not only positive but also reasonably large relative to the additional resources devoted to education – that is, that the additional taxation λ translates without loss or leakage into ω . But is this a reasonable assumption?

In many cases, the answer may be no. Given the documented problems with efficiency of service delivery in many countries, there is a real danger that the additional taxation will yield few benefits for even future generations of poor households. The service delivery problems that are widespread in many education systems often afflict poor households even more than the wealthier and better-connected, as documented in detail by the _World Development Report 2004: Making Services Work for Poor People_ (World Bank, 2003). Consider the case of education, where there is strong evidence of inefficiencies in translating resources into results. First, revenues allocated for schools in poor areas often fail to reach their intended target because of leakages. In one famous example, in Uganda in 1991-95 only 13 percent of central-government capitation grants to schools for non-salary expenditures ever reached those schools (Reinikka & Svensson, 2004). Second, for political-economy reasons, additional resources may not go to the educational inputs with the highest marginal returns but to teacher salaries instead (Pritchett & Filmer, 1999). When the additional funds do ostensibly make it to the school level in the form of teacher salaries, teacher absenteeism or poor teaching skills may prevent money from translating into more rapid student learning. In India, a recent survey found that 25 percent of primary-school teachers were absent from school at any given time, and that the problem was most severe in poor states such as Bihar and Jharkhand, where absence rates reached 38 and 42 percent, respectively (Kremer, Muralidharan, Chaudhury, Hammer, & Rogers, 2005). And even when teachers are present and other resources reach the classroom, levels of learning are often abysmally low. In Chile, a 11