region concerning both context and selected outcomes. Section 4 then looks in more detail at Egypt starting with an overview of fuel pricing before turning to estimates of the costs of congestion and pollution. Section 5 is concerned with the water-energy nexus and the ways in which mispricing of energy affects water use and depletion with a specific emphasis on the Republic of Yemen. Section 6 provides a first, tentative estimate of the costs of pollution and congestion drawing on data contained in a recent World Bank study of Cairo. These measures are linked to a wider health/productivity indicator initially assembled by the World Health Organization, termed DALY. We provide a simple simulation of the impact of energy price increases (viz., reduction in subsidy) on these variables. Section 7 lays out a more extended modeling framework (relying on the UN ForFITS model) that we apply to measure the impact of energy pricing on pollution, congestion and CO2 emissions, as well as capturing the interactive effects relating to modes of transport and substitutions across modes. More specifically, this section represents a simulation exercise that links various scenarios (changes in international oil prices and reduction in domestic subsidies, both in medium and short term) to the overall CO2 emissions. The results from this exercise are then used to quantify the cumulative health effect of energy subsidies reduction.

# **2. Transport: External effects and the main channels**

Vehicular transport reliant on fossil fuels generates a range of effects that have economic costs. These can be grouped under a number of rubrics. The first relates to the direct effect of emissions. There is a substantial body of evidence that vehicle emissions can affect not only individuals’ health but also have a wider effect on climate change variables, the consequences of which may be both local and global, although these may not be easily quantifiable. Regarding the first channel, the usual way of thinking about this is to try to estimate what the emissions associated with vehicle usage do to a set of health indicators and, by implication, to productivity. A negative shock to health – whether through a fall in life expectancy or an increase in morbidity – will have a direct impact on productivity and potentially on growth. For simplicity, this can be termed the productivity channel where the link is from use of a particular set of transport technologies to emissions to health outcomes. In these instances, there may be a complex lag structure and some important non-linearities. For example, emissions may reach certain thresholds beyond which health outcomes deteriorate at an accelerated pace. But fossil-fuel transport use will also tend to affect productivity through further related channels. Notably, congestion – resulting from an excess of vehicles for a given stock of transport infrastructure – will affect the amount of time that people require for, inter alia, getting to and from work and hence act as an effective subtraction from working time. There may in addition be a host of other more intangible effects of a psychological nature that can have an impact on individuals’ behavior and productivity. Anecdotal data from a wide range of locations – not just in MENA – suggests that these effects can be large and highly deleterious.

To this point, the broad problem has been framed in terms of an impact of a broad technology on outcomes at the level of individuals with that impact then aggregating to impose some economy-wide impact. However, our problem in this paper is narrower. Specifically, we are interested in the marginal impact of an energy price subsidy – defined as the deviation from a 3