Data and methods

Information from longitudinal surveys transforms snapshots of a given moment into something with a time dimension. It illuminates patterns of events within an individual’s life and records mobility and immobility between older and younger generations. It can track the different pathways of men and women and people of diverse socio-economic background through the life course. It can join up data on aspects of a person’s life, health, education, family, and employment and show how these domains affect one another. It is ideal for bridging the different silos of policies that affect people’s lives.

Many measures of job satisfaction have been trending downward. Because jobs are a key part of most people’s lives, knowing what makes a good job (job quality) is vital to knowing how well society is doing. Integral to worker well-being, job quality also affects the labor market through related decisions on whether to work, whether to quit, and how much effort to put into a job. Empirical work on what constitutes a good job finds that workers value more than wages; they also value job security and interest in their work. Policy to affect job quality requires information on the cost of the different aspects of job quality and how much workers value them.

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.

In the context of growing worldwide inequality, it is important to know what happens when the demand for low-skilled workers changes. Because natural resource shocks are global in nature, but have highly localized impacts on labor prospects in resource extraction areas, they offer a unique opportunity to evaluate low-skilled men's behavior when faced with extreme variations in local labor market conditions. This situation can be utilized to evaluate a broad range of outcomes, from education and income, to marital and fertility status, to voting behavior.

Linear regression is a powerful tool for investigating the relationships between multiple variables by relating one variable to a set of variables. It can identify the effect of one variable while adjusting for other observable differences. For example, it can analyze how wages relate to gender, after controlling for differences in background characteristics such as education and experience. A linear regression model is typically estimated by ordinary least squares, which minimizes the differences between the observed sample values and the fitted values from the model. Multiple tools are available to evaluate the model.

Randomized control trials are often considered the gold standard to establish causality. However, in many policy-relevant situations, these trials are not possible. Instrumental variables affect the outcome only via a specific treatment; as such, they allow for the estimation of a causal effect. However, finding valid instruments is difficult. Moreover, instrumental variables estimates recover a causal effect only for a specific part of the population. While those limitations are important, the objective of establishing causality remains; and instrumental variables are an important econometric tool to achieve this objective.

The open science and research transparency movement aims to make the research process more visible and to strengthen the credibility of results. Examples of open research practices include open data, pre-registration, and replication. Open science proponents argue that making data and codes publicly available enables researchers to evaluate the truth of a claim and improve its credibility. Opponents often counter that replications are costly and that open science efforts are not always rewarded with publication of results.

Non-experimental evaluations of programs compare individuals who choose to participate in a program to individuals who do not. Such comparisons run the risk of conflating non-random selection into the program with its causal effects. By randomly assigning individuals to participate in the program or not, experimental evaluations remove the potential for non-random selection to bias comparisons of participants and non-participants. In so doing, they provide compelling causal evidence of program effects. At the same time, experiments are not a panacea, and require careful design and interpretation.

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.

Stability and change are essential elements of social reality and economic progress. Cross-sectional surveys are a means of providing information on specific issues at a particular point in time, though without providing any information about the prevailing stability. Limited information on change can be obtained by retrospective questioning, but this is often impaired by “recall bias.” However, valid information on change is essential for assessing whether phenomena such as poverty are permanent or only temporary. Panel data analyses can address these problems as well as provide an essential tool for effective policy design.