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This dissertation studies fiscal policy, specifically the efficiency and electoral constraints on tax policy, and the measurement of public sector health to increase the effectiveness of government spending. Chapter 1 studies high-income taxpayer responses through the tax base channel to changes in marginal income tax rates in the United States. Prior research that has used bunching methods to estimate the taxable income response of high earners has presented no evidence of bunching at the top kink in the regular, federal income tax schedule. I argue that at the federal level, a combination of the regular and Alternative Minimum Tax schedules identifies the actual tax-related incentives that apply to high earners. I use annual income tax codes and publicly available samples of Internal Revenue Service individual income tax return data from 1993-2011 to characterize the combined schedule for each taxpayer. I discover previously undetected bunching at the top kink in this schedule and use it to estimate the elasticity of taxable income with respect to the net-of-tax rate for high earners to be between 0.15 to 0.28. This implies an upper bound on the efficiency cost of income taxation of 45 cents to a dollar, and a lower bound on the optimal top marginal tax rate of 70 percent, suggesting an optimal rate that is higher than prevailing top rates. I also mitigate an emerging endogeneity concern with bunching estimators that use kink points fixed in taxable income. By using effective top kinks that vary across taxable income for each taxpayer, I separate variation in marginal tax rates from variation in taxable income, making my bunching estimates more methodologically robust than earlier estimates. Chapter 2 estimates the impact of tax reforms on citizens’ voting behavior. We examine the effect of changing income tax burdens on voting behavior in presidential and House elections across the United States. To do so, we use a novel simulated instrumental variable approach in conjunction with survey, administrative, and voting data for the years 2010 to 2020 to isolate changes in tax burdens that arise purely due to variation in tax policy from changes caused by demographic shifts. We estimate that an increase in tax burdens by about half a standard deviation increases the vote share for the Republican party by one to six percentage points. This relationship is strongest, both statistically and in terms of magnitude, for presidential elections. For House elections, we find suggestive, but not definitive evidence that this relationship holds. Our analysis shows that contrary to popular belief, taxpayers continue to vote in their economic self-interest. In Chapter 3, I develop a tool for measuring the multidimensional performance of the public sector in the spirit of multidimensional measures of poverty. The framework allows fiscally constrained policymakers to measure a sector’s resource base, assess it over time, and optimize spending. The measure's decompositional properties provide for easy identification of the sources of deprivation along various dimensions and across subgroups, such as geographical areas and subsectors. In an application to the public education sector in Sindh province, Pakistan, I show that 27 percent of public schools are multidimensionally deprived and the weakest dimensions are physical infrastructure and facilities. Single-sex, rural schools, where instruction is in the native Sindhi language contribute the most to the overall measurement of sectoral weakness.

The Next Generation Science Standards propose a multidimensional model of science learning, comprised of Core Disciplinary Ideas, Science and Engineering Practices, and Crosscutting Concepts (NGSS Lead States, 2013). Accordingly, there is a need for student assessment aligned with the new standards. Creating assessments that validly and reliably measure multidimensional science ability is a challenge for the measurement community (Pellegrino, et al., 2014). Multidimensional assessment tasks may need to go beyond typical item designs of standalone multiple-choice and short-answer items. Furthermore, scoring and modeling of student performance should account for the multidimensionality of the construct. This research contributes to knowledge about best practices for multidimensional science assessment by exploring three areas of interest: 1) item design, 2) scoring rubrics, and 3) measurement models. This study investigated multidimensional scaffolding and response format by comparing alternative item designs on an elementary assessment of matter. Item variations had a different number of item prompts and/or response formats. Observations about student cognition and performance were collected during cognitive interviews and a pilot test. Items were scored using a holistic rubric and a multidimensional rubric, and interrater agreement was examined. Assessment data was scaled with multidimensional scores and holistic scores, using unidimensional and multidimensional Rasch models, and model-data fit was compared. Results showed that scaffolding is associated with more thorough responses, especially among low ability students. Students tended to utilize different cognitive processes to respond to selected-response items and constructed-response items, and were more likely to respond to selected-response arguments. Interrater agreement was highest when the structure of the item aligned with the structure of the scoring rubric. Holistic scores provided similar reliability and precision as multidimensional scores, but item and person fit was poorer. Multidimensional subscales had lower reliability, less precise student estimates than the unidimensional model, and interdimensional correlations were high. However, the multidimensional rubric and model provide nuanced information about student performance and better fit to the response data. Recommendations about optimal combinations of scaffolding, rubric, and measurement models are made for teachers, policymakers, and researchers.