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.

Estimating the causal effect of immigration on the labor market outcomes of native workers has been a major concern in the literature. Because immigrants decide whether and where to migrate, immigrant populations generally consist of individuals with characteristics that differ from those of a randomly selected sample. One solution is to focus on events such as civil wars and natural catastrophes that generate rapid and unexpected flows of refugees into a country unrelated to their personal characteristics, location, and employment preferences. These “natural experiments” yield estimates that find small negative effects on native workers’ employment but not on wages.

Measuring workers’ productivity is important for public policy and private-sector decision-making. Due to the lack of a general measure that captures workers’ productivity, firms often use one- or multi-dimensional performance measures, which can be used, for example, to analyze how different incentive systems affect workers’ behavior. The public sector itself also uses measures to monitor and evaluate personnel, such as teachers. Policymakers and managers need to understand the advantages and disadvantages of the available metrics to select the right performance measures for their purpose.

There are more informal workers than formal workers across the globe, and yet there remains confusion as to what makes workers or firms informal and how to measure the extent of it. Informal work and informal economic activities imply large efficiency and welfare losses, in terms of low productivity, low earnings, sub-standard working conditions, and lack of social insurance coverage. Rather than quibbling over definitions and measures of informality, it is crucial for policymakers to address these correlates of informality in order to mitigate the negative efficiency and welfare effects.

International migration alters the socio-economic conditions of the individuals and families migrating as well as the host and sending countries. The data to study and to track these movements, however, are largely inadequate or missing. Understanding the reasons for these data limitations and recently developed methods for overcoming them is crucial for implementing effective policies. Improving the available information on global migration patterns will result in numerous and wide-ranging benefits, including improved population estimations and providing a clearer picture of why certain migrants choose certain destinations.

Imagine a government confronted with a controversial policy question, like whether it should cut the level of unemployment benefits. Will social welfare rise as a result? Will some groups be winners and other groups be losers? Will the welfare gap between the employed and unemployed increase? “Happiness data” offer a new way to make these kinds of evaluations. These data allow us to track the well-being of the whole population, and also sub-groups like the employed and unemployed people, and correlate the results with relevant policy changes.

Following a decline in employment and participation rates during the 1980s and 1990s, Israel managed to reverse these trends during the last 15 years. This was accompanied by a substantial decrease in unemployment. New labor force participants are mostly from the low end of the education distribution, and many are relatively old. They entered the labor force in response to cuts in welfare payments and increases in the mandatory retirement age. Net household income for all population groups has increased due to growth in labor income; however, inequality between households has increased.

Both the availability and sheer volume of data sets containing individual molecular genetic information are growing at a rapid pace. Many argue that these data can facilitate the identification of genes underlying important socio-economic outcomes, such as educational attainment and fertility. Opponents often counter that the benefits are as yet unclear, and that the threat to individual privacy is a serious one. The initial exploration presented herein suggests that significant benefits to the understanding of socio-economic outcomes and the design of both social and education policy may be gained by effectively and safely utilizing genetic data.