New sources of data create challenges that may require new skills
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.
Employers can use laboratory experiments to
structure payment policies and incentive schemes
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
Quantitative policy evaluation can benefit from
a rich set of econometric methods for analyzing count data
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.
Splitting a policy intervention’s effect into
its causal channels can improve the quality of policy analysis
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.
Studies of independent contractors suggest that
workers’ effort may be more responsive to wage incentives than previously
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 gives comparable estimates
of the average monetary returns of one additional year of education
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
Differences in efficiency in public services can
offer clues about good practice
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.
Availability of bilateral data on migratory flows has
renewed interest in using gravity models to identify migration determinants
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.
GDP summarizes only one aspect of a country’s
condition; other measures in addition to GDP would be valuable
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.