
doi: 10.2139/ssrn.912735
This paper considers tax expenditures from two related perspectives. First, it analyzes how the incentives on Congress to use a tax expenditure change when principal agent problems are considered. For example, it considers whether tax expenditures can reduce moral hazard or adverse selection problems created by delegations to expert agencies. Second, it considers the condition under which tax expenditures should be expected to be redundant with direct expenditures, as many are. The two, principal agent problems and redundancy, are related because redundancy is often seen as a solution to the principal agent problem. The paper concludes that both principal agent concerns and redundancy might lead to an increase in the use of tax expenditures, although the circumstances in which we should expect this are relatively narrow. The paper then examines the example of the low income housing tax credit, concluding that the credit should be replaced with a direct expenditure in the form of increased tenant vouchers.
Tax credits, Housing vouchers, Agency, Agency (Law), Moral hazard, Housing Law, Law, Tax expenditures, Low-income housing
Tax credits, Housing vouchers, Agency, Agency (Law), Moral hazard, Housing Law, Law, Tax expenditures, Low-income housing
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 25 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
