Economics 113 UC Santa Cruz Winter 2020 Assignment 51 Experi
Economics 113uc Santa Cruzwinter 2020 Assignment 51 Experiment
Suppose that the state of Ohio wishes to estimate how additional school funding affects academic outcomes (e.g., test scores, graduation rate). To do this, the state plans to conduct an experiment in which a subset of districts are selected to receive an additional $2,000 per student. There are 611 school districts in Ohio. List the steps that the experiment should follow in order to ensure that the correct causal estimates are found. Be specific.
Explain how using a randomized experiment eliminates the potential for omitted variable bias. Use your own words. Is it possible to test if the treatment and control districts are balanced on unobservable characteristics? Explain.
Paper For Above instruction
The evaluation of the impact of increased school funding on academic outcomes relies heavily on establishing a credible causal relationship. In the context of Ohio’s plan to allocate additional funds, a carefully designed randomized controlled trial (RCT) is the most robust methodological approach to ensure valid inference. The following steps outline the process to implement such an experiment effectively.
First, the Ohio Department of Education should clearly define the target population—namely, the 611 school districts—and delineate eligibility criteria for participation. Ensuring transparency and fairness at this early stage builds credibility and helps mitigate concerns of selection bias.
Second, the districts should be randomly assigned to either the treatment group, which will receive the additional $2,000 per student, or the control group, which will not receive additional funding. Randomization can be performed using computer-generated sequences to ensure that each district has an equal probability of being allocated to either group, which helps balance both observed and unobserved characteristics across groups.
Third, the implementation phase involves administering the treatment—distributing the additional funds—to the designated districts. It is essential to monitor allocation procedures to confirm adherence to the randomization plan and prevent contamination or spillover effects that could bias results.
Fourth, data should be collected on key outcome variables, such as test scores and graduation rates, at baseline (before the funding change) and at subsequent follow-up points. Proper measurement strategies and data quality assurance are vital to accurately capturing the effects of the funding increase.
Fifth, statistical analysis should compare outcomes between the treatment and control groups, using methods appropriate for randomized experiments such as t-tests or regression analysis controlling for baseline covariates. The randomization ensures that differences in outcomes can be causally attributed to the treatment, assuming no attrition bias or measurement errors.
Finally, the results should be interpreted within the context of the experimental design, acknowledging any limitations such as non-compliance or missing data, and generalizing findings appropriately.
By following these steps—random assignment, careful implementation, and rigorous analysis—the Ohio experiment can credibly estimate the causal effect of additional school funding on academic outcomes.
Using a randomized experiment eliminates the potential for omitted variable bias because the process of randomization ensures that, on average, both observable and unobservable factors are evenly distributed across treatment and control groups. Omitted variable bias arises when unmeasured factors influence both the treatment assignment and the outcome, leading to biased estimates in observational studies. In a randomized trial, because each district has an equal chance of being assigned to treatment or control, the distribution of unobserved characteristics is statistically similar across groups, effectively neutralizing their confounding influence.
It is possible to test whether the treatment and control districts are balanced on observable characteristics by comparing their baseline demographics and performance metrics. Statistical tests such as t-tests or chi-square tests can be used to assess whether the groups differ significantly in variables like district size, demographic composition, prior test scores, or graduation rates. However, for unobservable characteristics—such as motivation levels or teacher quality—such direct testing is not feasible. Instead, researchers rely on the assumption that randomization effectively balances these unmeasured factors, which is justified statistically by the law of large numbers and the random assignment process. If the sample size is sufficiently large, balance on unobservable variables is presumed, although researchers may conduct placebo tests or examine pre-treatment trends to bolster confidence in the randomization’s success.
References
- Educational Evaluation and Policy Analysis, 33(4), 434-453.