27 To create the fiscal space needed to finance these public sector contributions, the limitations of the existing public sector accounting frameworks must also be addressed in assessing possible trade-offs between the long-term payoffs of infrastructure expenditures and short-term financial stability. This assessment will be a challenge because there is not even an agreement among academics on the basic measures (for example, what is liquidity in the public sector?). In addition to a good framework, addressing the challenge will also require an investment in basic public sector data. Indeed, some of the basic fiscal debates still stems from the fact that the coverage of fiscal data is only tailored to meet a broad sense of fiscal status in relation to broad macroeconomic concerns but that it does not allow good assessment of sectoral policies.

For the poorest countries in which it is clear that the fiscal space is unlikely to be created any time soon, it seems reasonable to start considering the role of infrastructure grants—for access and sometimes for lifelines for a limited duration at least. The Swiss aid agency is a leader in this field and has a pilot program that needs to be analyzed much more carefully.<sup>31</sup> For many countries, the main challenge will actually be to come up with designs and choice of new financial instruments that will allow the public sector to leverage any fiscal space available. The need to connect infrastructure development with private financial markets as a way to leverage and mobilize more capital may be the most pressing and the most obvious. Bolivia’s capitalization experience and similar efforts in Eastern Europe have shown the possibilities as well as the challenges. Addressing these challenges requires better pooling of risks than in the past. This may require a willingness among donors to work together on the design of new and improved risk mitigation products and applications that allow national and subnational governments to pool risks across sectors and projects more effectively. It will also require, at the project level, improvements in risk allocation mechanisms to address currency and regulatory risks, which are among the greatest contributors to the high cost of capital in developing countries, particularly low-income countries.

This financing debate is taking place in an increasingly complex public sector organizational structure and the evidence reviewed suggests that there is a need to support the redesign of risk allocation mechanisms across government levels (and associated governance structures) to allow each level to optimize access to private financial markets for its infrastructure needs. In the process, the accomplishments and failures of almost 30 years of decentralization must be taken into account looking ahead and not only in terms of service levels but also in terms of cost-effectiveness, targeting, and associated corruption. When the institutional limitations are taken into account, there is reasonable evidence to believe that user fees are still the best way to finance decentralized public services, except when a large share of local users are poor. When intergovernmental transfers and grants take place, there is a need to more systematically assess the impact of corruption and political structures on the optimal financing scheme for infrastructure services across government levels. It is also crucial to be able to assess the relative importance of the urban and rural poor to ensure effective resource allocation. Similar needs assessment are also needed to improve the monitoring of infrastructure-specific community-driven programs. To be effective in internalizing these lessons both in the decentralization and the participation context, a good dose of realism needs to take over from some of the somewhat naïve idealism that continues to prevail in parts of the infrastructure development community.

More generally, from the strict viewpoint of the needs of the poor, because resources are limited and getting the biggest bang for the buck in terms of aid effectiveness is so crucial, getting 31 See Briceno et al. (2003) for more details