What empirical studies assess the causal impact of the American Rescue Plan versus Trump tax cuts on economic growth and inequality?
Executive summary
Empirical work on the American Rescue Plan (ARPA) and the 2017 Trump tax cuts (TCJA) exists mostly as policy-analyst evaluations, macroeconomic projections, and retrospective empirical studies that infer causal effects using administrative and survey data; there are relatively few randomized or natural‑experiment papers that definitively isolate causal impacts across both policies in a head‑to‑head way [1] [2] [3]. Policy shops and academic commentators converge on one broad pattern: ARPA’s targeted fiscal supports produced measurable short‑run boosts to employment, poverty reduction, and income for low‑income households, while the TCJA and its proposed extensions have been shown to disproportionately benefit higher‑income households and widen income inequality, though claims about growth effects are contested [1] [4] [5] [2].
1. What counts as causal evidence in these debates — and why that matters
Causal assessment requires either a credible counterfactual (difference‑in‑differences, regression discontinuity, instrumental variables) or structural macro models that make explicit behavioral assumptions; many high‑profile studies of ARPA and the TCJA rely on a mix of microdata quasi‑experimental designs for distributional outcomes and macroeconomic models (dynamic scoring or general‑equilibrium) for growth effects, leaving interpretation dependent on methods and assumptions [6] [1] [2].
2. Empirical evidence on the American Rescue Plan’s causal effects
Analysts at the Center on Budget and Policy Priorities and the Economic Policy Institute point to concrete, empirically grounded outcomes tied to ARPA: expanded Child Tax Credit and EITC provisions reduced child poverty and lifted hundreds of thousands of children above poverty thresholds, and ARPA’s aid to states and households supported employment and consumption during the pandemic recovery—claims backed by administrative and Census‑linked estimates cited by CBPP and EPI [1] [4]. Those organizations frame these as causal impacts because changes in benefits and timelines produced discrete before/after comparisons in poverty, insurance take‑up, and employment, but their work is policy‑analysis driven rather than randomized trials [1] [4].
3. Empirical evidence on the Trump tax cuts’ causal effects
Retrospective analyses by nonpartisan budget shops and think tanks find that the 2017 TCJA disproportionately allocated benefits to higher earners and corporations and lowered revenue as a share of GDP—effects linked to rising inequality in the post‑enactment period, with multiple groups arguing the growth payoff touted by proponents did not materialize as promised [2] [3] [5]. Modeling studies that favor TCJA extensions (for example, a CEA‑linked study summarized by Ways and Means) claim gains in jobs and wages under certain scenarios, but these rely on dynamic scoring assumptions about investment and labor supply that critics say overstate causal growth channels relative to distributional harms shown in tax‑return based microstudies [7] [6].
4. Comparative findings, empirical gaps, and methodological limits
Comparative causal claims—saying ARPA caused X while TCJA caused Y—are complicated because ARPA operated as countercyclical, means‑tested transfers in a depressed pandemic economy while TCJA was a structural tax change in a non‑recession context; cross‑policy causal inference therefore requires careful controls for macro conditions and timing, which many policy reports do qualitatively but few academic papers fully satisfy [1] [2] [8]. The dominant empirical consensus in the provided sources is that ARPA produced measurable poverty reduction and short‑run demand effects, whereas TCJA materially favored the wealthy and worsened income concentration; assertions that TCJA “paid for itself” via growth are not borne out in multiple post‑enactment revenue and distribution analyses cited by CBPP, EPI, and others [2] [8] [5].
5. Who’s making the claims, what are the hidden agendas, and what remains unknown
Analyses come from ideologically varied actors: progressive research centers (CBPP, EPI, inequality.org) emphasize distributional and poverty‑erasures from ARPA [1] [8] [5], while industry‑friendly or supply‑side groups (Tax Foundation, CEA summaries) produce model‑based estimates showing average after‑tax income gains and pro‑growth scenarios for TCJA extensions [6] [7]; each side’s methods and incentives shape assumptions about labor supply, investment responses, and dynamic feedbacks, and the literature lacks many long‑run causal studies that track how each policy permanently shifted inequality and productivity beyond immediate fiscal windows [6] [9].