The Tax Cuts and Jobs Act (TCJA) of 2017 represents the most significant reform of the U.S. income tax code since the Tax Reform Act of 1986. Previous analyses of the TCJA's economic impact often rely on estimates based on data prior to the enactment of the legislation. This paper leverages plausibly exogenous variations in state-level tax changes brought about by the TCJA and employs local projections with two-way fixed effects to examine its effects on the labor market. Measures of TCJA tax shocks are constructed with the NBER-TAXSIM model using state-level tabulations of individual income tax returns from the Statistics of Income (SOI). Our findings suggest that tax cuts amounting to 1 percent of Adjusted Gross Income (AGI) under the TCJA are associated with a 0.7-1 percentage point increase in the labor force participation rate (LFPR) and a 0.8-1.5 percent increase in payroll employment over the two years following the TCJA's implementation. These results appear broadly robust to assumptions about heterogeneous state responses and the inclusion of interactive fixed effects.

This paper is about the nonparametric regression of a choice variable on a nonlinear budget set under utility maximization with general heterogeneity, i.e. in the random utility model (RUM). We show that utility maximization and convex budget sets make this regression three dimensional with a more parsimonious specification than previously derived. We show that nonconvexities in the budget set will have little effect on these results in important cases. We characterize all the restrictions of utility maximization on the budget set regression and show how to check these restrictions in applications. We formulate budget set effects that can be identified by this regression and give automatic debiased machine learners of these effects. We consider use of control functions to allow for endogeneity. Throughout we take as the main example the effect of taxes on taxable income including accounting for productivity growth. In an application to Swedish data we find the taxable income elasticity of a change in the slope of each segment to be .52, that the regression satisfies the restrictions of utility maximization at the values chosen for over 95% of observations, and that a productivity growth rate we estimate is close to other estimates.

We exploit the 1997 and 2003 constitutional amendments in Texas—allowing home equity loans and lines of credit for nonhousing purposes—as natural experiments to estimate the effect of easier credit access on the labor market. Using state-level as well as micro data, we find that easier access to housing credit led to a notably lower labor force participation rate between 1997 and 2007. Our findings are remarkably robust to improved synthetic control methods based on insights from machine learning. Our research shows that negative labor market effects of easier credit access are important for assessing its stimulative impact on overall growth. (JEL C45, G21, G51, J16, J22)

This paper introduces an estimator for the average of heterogeneous elasticities of taxable income (ETI), addressing key econometric challenges posed by nonlinear budget sets. Building on an isoelastic utility framework, we derive a linear-in-logs taxable income specification that incorporates the entire budget set while allowing for individual-specific ETI and productivity growth. To account for endogenous budget sets, we employ panel data and estimate individual-specific ridge regressions, constructing a debiased average of ridge coefficients to obtain the average ETI.

The Tax Cuts and Jobs Act (TCJA) of 2017 is the most extensive overhaul of the U.S. income tax code since the Tax Reform Act of 1986.Existing estimates of TCJA's economic impact are based on economic projections using pre-TCJA estimates of tax effects.I exploit plausibly exogenous state-level variation in tax changes from TCJA and find that an income tax cut equaling 1 percent of GDP led to a 1.3 percentage point faster job growth and nearly 1.5 percentage points higher GDP growth.The impact on growth was the strongest in the year of the tax change, with much smaller effects in the following two years.The estimates imply a tax cut multiplier of around 1.5 and a cost per job of $105,000.Moreover, they also suggest that TCJA-related income tax cuts of 0.8 percent of GDP led to 1 percentage point stronger job growth in 2018, which translates to about 1.5 million jobs at a cost of nearly $158 billion.

Advocates of Medicaid expansion argue that federal Medicaid assistance to states fosters economic activity, generating positive local multiplier effects. Furthermore, during economic downturns, Congress regularly tweaks federal match rates for state Medicaid spending—including during the COVID-19 public health emergency—in order to assist states. Despite heavy reliance on Medicaid funding formulas, identifying the economic effect of these federal transfers has proved challenging. This is because federal Medicaid assistance (to states) is endogenous since funding levels are correlated with unobserved factors driving state economic activity. To address this concern, we construct an instrument based on a nonlinearity in the federal matching rate for state Medicaid spending. Using state-level panel data from 1990 to 2013, we find that federal Medicaid assistance does stimulate economic activity, but the implied cost per job created is quite high, and the multiplier is well below 1. Despite modest economic effects over the entire sample period, we find that federal Medicaid assistance provided powerful fiscal stimulus to states after the Great Recession when the implied multiplier exceeded 1.

We exploit the 1997 and 2003 constitutional amendments in Texas—allowing home equity loans and lines of credit for non-housing purposes—as natural experiments to estimate the effect of easier credit access on the labor market. Using state-level as well as micro data and the synthetic control approach, we find that easier access to housing credit led to a 1.2 percentage point average decline in the labor force participation rate between 1997 and 2007. We show that our findings are remarkably robust to improved synthetic control methods based on insights from machine-learning. We also find that declines in the labor force participation rate were larger among females, prime age individuals, the college-educated, and homeowners. Our research shows that negative labor market effects of easier credit access should be an important factor when assessing its stimulative impact on overall growth.

This paper is about the nonparametric regression of a choice variable on a nonlinear budget set under utility maximization with general heterogeneity, i.e. in the random utility model (RUM). We show that utility maximization and convex budget sets make this regression three dimensional with a more parsimonious specification than previously derived. We show that nonconvexities in the budget set will have little effect on these results in important cases. We characterize all the restrictions of utility maximization on the budget set regression and show how to check these restrictions in applications. We formulate budget set effects that can be identified by this regression and give automatic debiased machine learners of these effects. We consider use of control functions to allow for endogeneity. Throughout we take as the main example the effect of taxes on taxable income including accounting for productivity growth. In an application to Swedish data we find the taxable income elasticity of a change in the slope of each segment to be .52, that the regression satisfies the restrictions of utility maximization at the values chosen for over 95% of observations, and that a productivity growth rate we estimate is close to other estimates.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

The elasticity of taxable income is vital when predicting the effect of taxes. Bunching at kinks/notches has been used to estimate this elasticity. We show that when the preference distribution is unrestricted, bunching at a kink or a notch is not informative about the size of the elasticity, and neither is the entire distribution of taxable income. Bunching identifies the taxable income elasticity when the preference distribution is correctly specified across the kink and provides bounds under restrictions on the preference distribution. We find wide bounds in an empirical example based on upper and lower bounds for the preference density.

The Tax Cuts and Jobs Act (TCJA) of 2017 is the most extensive overhaul of the U.S. income tax code since the Tax Reform Act of 1986. Existing estimates of TCJA’s economic impact are based on economic projections using pre-TCJA estimates of tax effects. Following recent pioneering work of Zidar (2019), I exploit plausibly exogenous state-level variation in tax changes and find that an income tax cut equaling 1 percent of GDP led to a 1 percentage point higher nominal GDP growth and about 0.3 percentage point faster job growth in 2018.

Almost all recent literature on Medicaid and labor supply has used Affordable Care Act (ACA)-induced Medicaid eligibility expansions in various states as natural experiments. Estimated effects on employment and earnings differ widely due to differences in the scope of eligibility expansion across states and are potentially subject to biases due to policy endogeneity. Using a Regression Kink Design (RKD) framework, this paper takes a new approach to the identification of the effect of Medicaid generosity on household income. Both state-level data and March CPS data from 1980 to 2013 suggest that generous federal funding of state-level Medicaid costs have a negative effect on household income. The negative impact of Medicaid generosity on household income is more pronounced at the lower end of the household income distribution and on the income and earnings of female heads.

We explore the implications of heterogeneity in the elasticity of taxable income (ETI) for tax-reform based estimation methods.We theoretically show that existing methods yield elasticities that are biased and lack policy relevance.We illustrate the empirical importance of our theoretical analysis using the NBER tax panel for 1979-1990.We show that elasticity heterogeneity is the main explanation for large differences between estimates in the previous literature.Our preferred, newly suggested method yields elasticity estimates of approximately 0.7 for taxable income and 0.2 for broad income.

We estimate the impact of the Social Security early entitlement age (EEA) on later-life income, poverty, and mortality by tracing birth cohorts of men who had access to different potential claiming ages from the Social Security Amendments of 1961, which introduced age 62 as the EEA. Based on 1968??? 2001 Current Population Survey data, the average claiming age fell by 1.4 years, and Social Security income fell for male-headed families by 2.4 percent at the mean and 6 percent at the 25th percentile. Total family income fell, and the poverty rate rose by about one percentage point. Finally, mortality rates fell modestly in retirement.

Texas is among the top 10 states in terms of tax stimulus received from the Tax Cuts and Jobs Act of 2017. The law likely played an important role in the state’s stronger subsequent job growth relative to the nation.

Before 1998, Texas was the only state that greatly restricted home equity loans and cash-out refinancing for non-housing consumption. Such borrowing was authorized in Texas, for the first time, through a constitutional amendment in 1998. Using state-level panel data and recently developed synthetic control methods based on machine learning we find that the Texas’ constitutional amendments relaxing credit constraints had an insignificant impact on GDP growth. Our findings have important policy implications for the stimulative effect of easier home equity access on GDP growth.

Texas is the leading destination for companies relocating from other states. The economic benefits of the moves may be best measured in terms of the ancillary activity generated rather than the benefits directly attributable to the relocations.