Most countries implement social protection programs to help individuals manage risks such as unemployment, disability, illness, longevity or death. In many middle income countries, these are often based on a 'Bismarckian model' (named after Otto von Bismarck), where benefits are financed by contributions levied on salaried employment. In countries with a large informal sector, however, only a fraction of the population is covered by this system and non-contributory programs have been added or are planned to increase coverage. This can create distortions in the labor market, and the book is about policies to expand the coverage of social insurance programs to all workers, without reducing incentives to job creation and formal work. While few would argue against the need and social merits of social insurance and social assistance programs there are growing concerns about their unintended consequences on labor markets because of poor design. The programs can distort incentives and individual behaviors in ways that either reduce employment levels and/or promote informality, ultimately affecting productivity and economic performance. For instance, high social security contribution rates can reduce formal employment; badly designed unemployment benefits can reduce incentives to keep, search, and take jobs; and fragmented social assistance programs can become a tax on formal labor and encourage informality. The book reviews the evidence regarding the effects of social insurance and social assistance programs on labor market outcomes and discusses options to improve their design and implementation. The book focuses particularly on middle income countries in Latin America and Asia with a large informal sector and suggests ways to reduce these distortions and better manage and finance the subsidies to make coverage universal, while creating good jobs. The book compiles expert papers from the joint conferences of the World Bank (WB), the Institute for the Study of Labor (IZA) and the Inter-American Development Bank (IDB) on Employment and Development.

Social Insurance, Informality, and Labor Markets: How to Protect Workers While Creating Good Jobs

Business & Money

Kaplan, David

Frölich, Markus

Rigolini, Jamele

Pagés, Carmen

Robalino, David

Economics

Oxford University Press

Impact Evaluation: Treatment Effects and Causal Analysis

In recent years, interest in rigorous impact evaluation has grown tremendously in policy-making, economics, public health, social sciences and international relations. Evidence-based policy-making has become a recurring theme in public policy, alongside greater demands for accountability in public policies and public spending, and requests for independent and rigorous impact evaluations for policy evidence. Frölich and Sperlich offer a comprehensive and up-to-date approach to quantitative impact evaluation analysis, also known as causal inference or treatment effect analysis, illustrating the main approaches for identification and estimation: experimental studies, randomization inference and randomized control trials (RCTs), matching and propensity score matching and weighting, instrumental variable estimation, difference-in-differences, regression discontinuity designs, quantile treatment effects, and evaluation of dynamic treatments. The book is designed for economics graduate courses but can also serve as a manual for professionals in research institutes, governments, and international organizations, evaluating the impact of a wide range of public policies in health, environment, transport and economic development.

Sperlich, Stefan

Cambridge University Press

In recent years, interest in rigorous impact evaluation has grown tremendously in policy-making, economics, public health, social sciences and international relations. Evidence-based policy-making has become a recurring theme in public policy, alongside greater demands for accountability in public policies and public spending, and requests for independent and rigorous impact evaluations for policy evidence. Frölich and Sperlich offer a comprehensive and up-to-date approach to quantitative impact evaluation analysis, also known as causal inference or treatment effect analysis, illustrating the main approaches for identification and estimation: experimental studies, randomization inference and randomized control trials (RCTs), matching and propensity score matching and weighting, instrumental variable estimation, difference-in-differences, regression discontinuity designs, quantile treatment effects, and evaluation of dynamic treatments. The book is designed for economics graduate courses but can also serve as a manual for professionals in research institutes, governments, and international organizations, evaluating the impact of a wide range of public policies in health, environment, transport and economic development.

Policy evaluation and programme choice are important tools for informed decision-making, for the administration of active labour market programmes, training programmes, tuition subsidies, rehabilitation programmes etc. Whereas the evaluation of programmes and policies is mainly concerned with an overall assessment of impact, benefits and costs, programme choice considers an optimal allocation of individuals to the programmes. This book surveys potential evaluation strategies for policies with multiple programmes and discusses evaluation and treatment choice in a coherent framework. Recommendations for choosing appropriate evaluation estimators are derived. Furthermore, a semiparametric estimator of optimal treatment choice is developed to assist in the optimal allocation of participants.

Programme Evaluation and Treatment Choice (Lecture Notes in Economics and Mathematical Systems, 524)

Springer

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