Methods

Big Data refers to data sets of much larger size, higher frequency, and often more personalized information. Examples include data collected by smart sensors in homes or aggregation of tweets on Twitter. In small data sets, traditional econometric methods tend to outperform more complex techniques. In large data sets, however, machine learning methods shine. New analytic approaches are needed to make the most of Big Data in economics. Researchers and policymakers should thus pay close attention to recent developments in machine learning techniques if they want to fully take advantage of these new sources of Big Data.

Can a company attract a different type of employee by changing its compensation scheme? Is it sufficient to pay more to increase employees’ motivation? Should a firm provide evaluation feedback to employees based on their absolute or their relative performance? Laboratory experiments can help address these questions by identifying the causal impact of variations in personnel policy on employees’ productivity and mobility. Although they are collected in an artificial environment, the qualitative external validity of findings from the lab is now well recognized.

Often, economic policies are directed toward outcomes that are measured as counts. Examples of economic variables that use a basic counting scale are number of children as an indicator of fertility, number of doctor visits as an indicator of health care demand, and number of days absent from work as an indicator of employee shirking. Several econometric methods are available for analyzing such data, including the Poisson and negative binomial models. They can provide useful insights that cannot be obtained from standard linear regression models. Estimation and interpretation are illustrated in two empirical examples.

Policy evaluation aims at assessing the causal effect of an intervention (for example job-seeker counseling) on a specific outcome (for example employment). Frequently, the causal channels through which an effect materializes can be important when forming policy advice. For instance, it is essential to know whether counseling affects employment through training programs, sanctions, job search assistance, or other dimensions, in order to design an optimal counseling process. So-called “mediation analysis” is concerned with disentangling causal effects into various causal channels to assess their respective importance.

A fundamental question in economic policy is how labor supply responds to changes in remuneration. The responsiveness of labor supply determines the size of the employment impact and efficiency loss of progressive income taxation. It also affects predictions about the impacts of policies ranging from fiscal responses to business cycles to government transfer programs. The characteristics of jobs held by independent contractors provide an opportunity to overcome problems faced by earlier studies and help answer this fundamental question.

The Mincer equation—arguably the most widely used in empirical work—can be used to explain a host of economic, and even non-economic, phenomena. One such application involves explaining (and estimating) employment earnings as a function of schooling and labor market experience. The Mincer equation provides estimates of the average monetary returns of one additional year of education. This information is important for policymakers who must decide on education spending, prioritization of schooling levels, and education financing programs such as student loans.

Efficiency is an important consideration for those who manage public services. Costs vary with output and with a variety of other factors. In the case of higher education, for example, factors include quality, student demographics, the scale and scope of the higher education provider, and the size and character of the real estate. But even when taking all these factors into account, costs vary across providers because of differences in efficiency. Such differences offer clues about good practice that can lead to improvements in the system as a whole. The role of efficiency is illustrated by reference to higher education institutions in England.

Gravity models have long been popular for analyzing economic phenomena related to the movement of goods and services, capital, or even people; however, data limitations regarding migration flows have hindered their use in this context. With access to improved bilateral (country to country) data, researchers can now use gravity models to better assess the impacts of migration policy, for instance, the effects of visa restriction policies on migration flows. The specification, estimation, and interpretation of gravity models are illustrated in different contexts and limitations of current practices are described to enable policymakers to make better informed decisions.

Gross domestic product (GDP) is the key indicator of the health of an economy and can be easily compared across countries. But it has limitations. GDP tells what is going on today, but does not inform about sustainability of growth. The majority of time is spent in home production, yet the value of this time is not included in GDP. GDP does not measure happiness, so residents can be dissatisfied even when GDP is rising. In addition, GDP does not consider environmental factors, reflect what individuals do outside paid employment, or even measure the current or future potential human capital of a country. Hence, complementary measures may help to show a more comprehensive picture of an economy.