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This chapter develops an alternative theory about welfare state development, which posits that reformers try to root their legislation in cross‐class alliances intersecting the internally diverse interests of capital and labor. Reformers appeal to interests in progressive reform deriving from capitalists’ desire for regulation of markets. The theory about the politics of the welfare state draws on labor economics, arguing that macroeconomic shocks to systems of labor market governance (various forms of collective bargaining and managerial relations) create an opportunity for politicians to enlist capitalist support for regulatory assistance through social insurance and labor reform.

This chapter starts by making a brief portrait of labor economics in the decades preceding Mincer's work. When Jacob Mincer started his academic career, labor economics was finishing a long process of change that marked the postwar years. This transformation, crucial for understanding the emergence of human capital theory, was generally characterized by the increasing pervasiveness of neoclassical economic theory in a field traditionally more attached to an empirical and institutionalist research agenda. Under the stimulus of some prominent labor researchers, Mincer became increasingly interested in labor issues, namely in labor supply behavior. This chapter analyzes some of his major pieces of research around the theme of labor supply and lifetime labor income, and the way these differed from previous labor research. This work is also presented in a way that shows the close links between Mincer's research on human capital and the long-term behavior of labor supply.

This chapter presents some concluding thoughts. The aim of this book has been to present the main econometric techniques used by labour economists. It aims to serve as a platform for adapting material already encountered in econometrics classes and textbooks to the empirical analysis of labour market phenomena. There is no conventional wisdom on how to conduct empirical analysis in labour economics. Several approaches coexist, running from structural models with very tight links to economic theory, to so-called ‘model-free’ approaches based on emulating the experimental approach. In practice, most studies in empirical labour economics lie somewhere in between these two benchmarks, and consist of estimating models which are loosely based on theoretical reasoning and specified in a flexible manner so that the data can ‘talk’.

This introductory chapter sets out the purpose of the book, which is to provide a practical guide to understanding and applying the standard econometric tools that are used in labour economics. Emphasis is placed on both the input and the output of empirical analysis, rather than the understanding of the origins and properties of estimators and tests, topics which are more than adequately covered in recent textbooks on microeconometrics. The basic idea developed in this book is that linear regression is an important starting point for empirical analysis in labour economics.

The idea that education can provide benefits, even economic ones, is certainly an old one. At least by the late 18th century, some people started thinking about skilled individuals as a kind of expensive instrument and machine, a kind of capital whose long-term benefit would compensate the efforts and expenditures of years of early personal and intellectual development. These metaphors about the economic potential of education and training coalesced in the late 1950s becoming one of the most popular research programs of contemporary economics. It is nevertheless rather peculiar that it took so much time for economists to pick-up on those metaphors and turn them into a fully-fledged theoretical framework. Likewise, it is quite interesting to investigate the way prior resistances or lack of attention gave way to subsequent popularity within the discipline. This chapter analyzes the early lack of attention and the increasing attention in the postwar period to the economic value of education.

A defined-contribution fund is a pension fund that provides benefits that are determined by the returns on invested assets that are often based on the regular contribution of a salary of fixed proportion. On the other hand, a defined-benefit fund is one that offers benefits that are determined by a formula that was previously fixed and whose basis usually relies on the years of service or the final or average salary. The economic effects brought about by the developments of pension funds rely on the types of fund and on both types of fund dominating only in particular areas. While the type of fund also influences the significance of major regulatory issues, this chapter attempts to look into and further investigate some of the fundamental economic issues regarding the choice of type of fund particularly in terms of capital markets, labour economics, agency, insurance, information, and corporate finance.

While econometric techniques have become increasingly sophisticated, regression analysis in one form or another continues to be a major tool in empirical studies. Linear regression is also important in the way it serves as a reference for other techniques — it is usually the failure of the conditions that justify the application of linear regression that give rise to alternative methods. Furthermore, many more complicated techniques often contain elements of linear regression or modifications of it. This chapter discusses the use of linear regression and related methods in labour economics. It begins with a review of some useful results on the linear regression model. It then covers specification issues in the linear model and the Mincer earnings equation.

A large part of empirical analysis in labour economics continues to be based on non-experimental (that is, survey) data including studies seeking to estimate the impact of a programme or policy measure. Applying an experimental approach to estimating this impact will generally be made difficult on the one hand by self-selection mechanisms and on the other by the absence of a well-defined control group. This chapter presents different methods of undertaking policy evaluation using these kinds of situations. It first sets out the basic framework for analysing pure experimental data, since this provides the key to the popularity of such an approach. It shows how the experimental estimates obtained can be viewed as regression estimates, that is, comparing the mean outcomes for the treated group and the control group. This provides a link with material presented in earlier chapters as well as a means of adapting econometric tools in order undertake policy evaluation.

In many countries, the rise and persistence of unemployment from the 1970s onwards led to labour economists paying special attention to the duration of a spell of unemployment. The econometric analysis of spell durations is based on methods developed in other disciplines (especially biometrics and the analysis of survival times after surgery or medical treatment). Furthermore, while the focus was initially on unemployment duration, other issues in labour economics can be approached in a similar manner, such as job mobility, strike duration, and time out of the labour force for maternity leave. Essentially, the aim is to determine the factors that influence the length of a spell in a given state, and the likelihood of leaving that state for another. This chapter presents key elements in modelling durations relevant to empirical labour economics.

In many situations, the question addressed in labour economics is of a binary nature. An individual decides whether to participate or not in the labour force. He or she is either in or out. Sometimes, due to the way in which a survey is undertaken, data are only available for discrete binary outcomes — we do not know how many hours a person works, but we know that it is either part-time or full-time. In these circumstances, the variable that is being modelled is dichotomous and it is customary to treat such a variable as a dummy variable, sometimes referred to as a ‘(0,1)-dummy’ or an indicator variable. In terms of the notation for the dependent variable (y_i ) of the previous chapters, for each individual in the sample either y_i = 0 or y_i = 1. This type of data has given rise to the use of logit and probit models due to the discrete nature of the dependent variable. These are both nonlinear models and are estimated using maximum likelihood methods rather than by least squares. This chapter compares the results obtained by least squares and the logit/probit methods. It also examines how these methods can be adapted for use with more than two alternatives. In the latter context, there is an important distinction to be made between ordered alternatives and straightforward, non-hierarchical multinomial outcomes.

Selection mechanisms and the bias they introduce into econometric estimation constitute one of the principal concerns in empirical labour economics. Economic behaviour is based to a large extent on incentives and the existence of comparative advantage. This means that individuals' labour force participation, job choice, enrolment in the labour market, and educational programmes and mobility will all be influenced by the potential gains that exist (or are thought to exist). People self-select into different situations and types of status. This chapter presents econometric approaches developed by econometricians specifically to analyse these features of labour market behaviour. The major worry associated with these methods is that the models are constructed on the basis that the unobserved characteristics and factors captured by the error terms are normally distributed.

The economics of artificial intelligence (AI) is commanding growing attention, but not all aspects of the problem have been considered adequately. For instance, in addition to its immediate effects on real wages, AI also has distributional effects through consumer surplus. Those effects may vary greatly, depending on whether there are constant or increasing returns to scale. AI also will have distributional consequences in the international sphere, for instance by making it harder for lower-wage nations to build their export capacities by offering lower wages to investors. Finally, AI will interact with political economy factors. Society may need to redistribute status as well as income, and furthermore the possibility of corruption may render large-scale redistribution impractical.

This chapter introduces the framework underlying the 2013 World Development Report: Jobs, explains how it extends traditional approaches to employment, and summarizes how it has been applied to the country cases in this volume. The innovation of the framework is to propose that the social value of jobs may differ from the value they have in terms of earnings or output. An analytical framework is presented to illustrate these spillovers, which can apply to living standards, aggregate productivity, and social cohesion. Summarizing some of the main messages from the case studies, the chapter illustrates how types of jobs that can have significant development payoffs differ depending on the particular challenges that countries face because of their level of development, demography, or institutions. The chapter also summarizes the range of policies that can encourage the creation of “good jobs for development,” depending on the country context.