By Tong Zhou
Politicians always argue that their policies will benefit citizens and society. However, we often have little information to assess how right they are about these claims. But how do we know when these claims are correct or not?
To create a standardized approach for objectively assess the economic efficiency of public policies, economists created cost-benefit analysis, a tool for weighing the economic costs and benefits of proposed policies. In this post, I will provide some tips for beginners doing cost benefit analysis.
Firstly, let’s look at the cost benefit analysis as laid out in Boardman et. al’s Cost-Benefit Analysis: Concepts and Practice. The following nine steps summarize the process of cost benefit analysis:
1. Define the policy that we are analyzing and the set of alternative policies that could be adopted in place of the policy.
2. Define who has standing in the study, or whose benefits and costs count for the analysis. Sometimes the standing can be the citizens of a city, sometimes the standing can be broader like all human beings on Earth. Standing depends on the specific policy project.
3. Then, we must determine all the impacts caused by the policy. Any policy will have benefit categories and cost categories just as a coin has two sides. We want to be as objective as possible in this process as biased determination of impacts can make your analysis meaningless. Sometimes we can be guardians and spenders unconsciously. Peer review is a good way to avoid this problem.
4. Now we can quantify the impacts found in the last step. For example, in the study we are doing this summer, we found that the Earned Income Tax Credit (EITC) will increase the number of workers in the labor market. In this step, we want to determine how many more workers will enter the job market. Sometimes it can be hard to translate impact categories into numbers as translation requires serious analysis and literature review to find an accurate number. You will read a lot of literature to find the best sources of data and effect sizes. Reliable sources play a significant role here: we must stand on the shoulders of giants in cost-benefit work.
5. Monetize all impacts. Some people may argue that not everything has a price: things like time and life are often considered “priceless.” However, people make tradeoffs of their time and risks of life all the time. We can survey people to ask them their willingness to pay for these goods. Monetization is a key step in any cost-benefit analysis because it allows us to compare seemingly incomparable outcomes.
6. Even though we have the monetized all impacts, not all benefit or cost are realized today. In order to get an accurate estimation of present value, or how we value these future outcomes now, we will discount all impacts occurring in the future. The discount rate usually ranges from 3% to 7%, though this is a point of contention in the world of cost-benefit analysis.
7. Finally, we have determined the cost and benefits of the policy. Simply subtract costs from benefit to calculate the net benefit. A positive net benefit suggests the policy is economically efficient, while a negative net benefit suggests the policy is economically inefficient.
8. Even though we have determined the results, we still want to know how accurate our results are. To do this, we perform sensitivity analysis such as a Monte Carlo Simulation, the best practice in determining the accuracy of cost-benefit analytical outcomes. Sensitivity analysis is a tool that will tell us the most likely net benefit.
9. The final step is to report your findings in a format that provides guidance to policymakers.
After carrying out all the steps above, we want to double-check that the policy impacts we found that are directly caused by the policy. We do not want to count correlated impacts that would not bear out as causal in a final report. Even though researchers may find that Nobel Prize winners usually eat more chocolate than normal people, eating more chocolates will not necessarily increase your likelihood to win a Nobel Prize.
Although the steps listed above seem pretty straightforward, it is very easy to still encounter uncertainties in the model which may undermine the accuracy of the final results. For example, some impacts are more important than others so we might need to weight the impact for policymakers. Prices may fluctuate over time so we need to use the current price. My suggestion is to spend some time reading how others do cost-benefit analysis. Finding other similar examples can not only teach us innovative ways to deal with uncertainties but also let us compare different ways to find the best one.
My final suggestion for beginners is to keep practicing cost-benefit analysis. We cannot truly master it unless we practice. Practice goes a long way and only by refining our craft will we improve at it.
Tong is a Data Analysis Intern at Scioto Analysis and a data science student at Denison University.