To test evidence for/ against existing economic theories- For example, what is the evidence for the existence of the Philip’s curve in India in the last ten years? Or, how elastic is demand for fruits in Crawford market to changes in their prices, keeping everything else constant?
To evaluate economic policies- As economists, we are ultimately interested in influencing people’s economic lives, which can be done by implementing the right policies. Economists are hence interested in testing how various economic policies work. This requires collecting the relevant data, testing them and presenting evidence for or against a certain economic policy.
To forecast- As economists, we are interested in forecasting or predicting the future economic scenario. Such predictions are made based on available data and fitting appropriate statistical models on them.
Methods of data collection
– There are multiple ways of collecting the relevant data.
The statistical models that we run can be based on secondary data (such as data collected from the World Bank, RBI, national household surveys, etc.) or primary data collected through surveys or field experiments (Randomized Control Trials)
What do economists do with the data?
Run statistical models to make sense of the data- For example, on the data collected on prices and demand for footwear, we can test how sensitive demand is to changes in prices by running a simple regression model.
For the specific task of forecasting economic phenomena, economists generally use time series data; for instance, we can forecast GDP for India two quarters from now, based on regressions of GDP on variables such as investment, interest rates and so on.
Data visualization- The results are finally presented in a graphical format so that it is interesting and easily understood by the general audience.
Big data and machine learning– Big data and machine learning have further enhanced the role of economists as data analysts and opened more opportunities for economists to work as data scientists. The advent of big data has given us access to a large pool of data, which firms and agencies are interested in making sense of. Because economists are naturally trained in making sense of data, they make good data analysts and data scientists.
A word of caution– Even as economists work with data, the quality of data at hand and the underlying assumptions of the statistical models need to be taken note of. The interpretation of the results should be done after taking these factors into account.