Bibliographic record
Abstract
There is evidence that tax rates have varied considerably through time. In the postwar years changes in business taxation in the US have occurred at a pace of approximately every three years. The purpose of this paper is to examine the implications of tax uncertaintyforinvestment. Investment decisions are, at least largely, irreversible and therefore sensitive to both risk and uncertainty (in the Knightian sense). We consider the investment decisions of a monopolistically competitive firm under uncertainty about the investment tax credit and the corporate income tax rate and show that costs of adjustment arise endogenously due to the irreversible nature of investment. We then make different assumptions about the nature of the policy risk and uncertainty facing the firm, and analyse the response of investment. First, we show that greater volatility of the tax credit rate reduces investment. Second, we consider the impact of high policy variability and show that, in the case of a positively serially correlated tax credit, lower persistence in policy leads to greater variabilityininvestment. Third, we assume that the firm is learning about the underlying true tax credit and corporate tax rate, and show that the anticipation of learning leads to an endogenous marginal adjustment cost and depresses investment. We examine the impact of increases in risk with learning, and provide conditions under which increases in tax risk lead to a decline in investment. We also consider learning when the underlying tax process follows a Poisson process. Finally, we use data on the determinants of the user cost of capital and investment for the US economy to show quantitatively the impact of tax uncertainty on US investment for each case.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".