Sequential Matching Estimation of Dynamic Causal Models

This paper proposes sequential matching and inverse selection probability weighting to estimate dynamic casual effects. The sequential matching estimators extend simple, matching estimators based on propensity scores for static causal analysis that have been frequently applied in the evaluation literature. A Monte Carlo study shows that the suggested estimators perform well in small and medium seize samples. Based on the application of the sequential matching estimators to an empirical problem - an evaluation study of the Swiss active labour market policies - some implementational issues are discussed and results are provided.Dynamic treatment effects, nonparametric identification, causal effects, sequential randomisation, programme evaluation, panel data

A comparative assessment of methodologies used to evaluate competition policy

Research by academics and competition agencies on evaluating competition policy has grown rapidly during the last two decades. This paper surveys the literature in order to (i) assess the fitness for purpose of the main quantitative methodologies employed, and (ii) identify the main undeveloped areas and unanswered questions for future research. It suggests that policy evaluation is necessarily an imprecise science and that all existing methodologies have strengths and limitations. The areas where the need is most pressing for further work include: understanding why Article 102 cases are only infrequently evaluated; the need to bring conscious discussion of the counterfactual firmly into the foreground; a wider definition of policy to include success in deterrence and detection. At the heart of the discussion is the impact of selection bias on most aspects of evaluation. These topics are the focus of ongoing work in the CCP

Do Fixed-Term Contracts Increase the Long-Term Employment Opportunities of the Unemployed?

The paper investigates whether (unsubsidised) fixed-term contracts (FTCs) are a means of integration for the unemployed in the West German labour market. This is done by analysing whether entering into an FTC improves the employment opportunities of an unemployed person in terms of the probability of subsequent permanent contracts and subsequent periods of employment and unemployment. The empirical analysis is based on propensity score matching methods, obtaining the effects of FTCs by comparing the future situation of (?treated?) unemployed entering into FTCs after a particular unemployment duration with a suitable control group of ?non-treated? individuals. In principal different counterfactual situations for treated persons entering into FTCs after a certain number of month of unemployment are reasonable. A first counterfactual is never to enter into an FTC. A second counterfactual is not to take up an FTC job in this month but possibly in a later month. These two possible counterfactuals imply different definitions for the group of non-treated individuals and impose different policy questions. Both definitions are analysed in the paper. The propensity score is estimated by a discrete hazard rate model, which seems to be an appropriate way of taking into account the potential endogenous effect of the unemployment duration on the selection into the type of contract. Further insights are gained by comparing the determinants of the transition to FTC and permanent contract jobs. There is some evidence that FTCs may serve as ?stepping stones? towards permanent employment for the unemployed. However, the hypothesis that FTCs lead to dual labour markets cannot be rejected. --Fixed-term Contracts,Propensity Score Matching,Hazard Rate Model,Unemployment Duration,Stepping Stones,Dual Labour Markets

WP 2018-382

Working paperThis paper develops an innovative approach to measuring the effect of health on retirement. The approach elicits subjective probabilities of working at specified time horizons fixing health level. Using a treatment-effect framework, within-individual differences in elicited probabilities of working given health yield individual-level estimates of the causal effect of health (the treatment) on working (the outcome). We call this effect the Subjective ex ante Treatment Effect (SeaTE). The paper then develops a dynamic programming framework for the SeaTE. This framework allows measurement of individual-level value functions that map directly into the dynamic programming model commonly used in structural microeconometric analysis of retirement. The paper analyzes conditional probabilities elicited in the Vanguard Research Initiative (VRI)—a survey of older Americans with positive assets. Among workers 58 and older, a shift from high to low health would on average reduce the odds of working by 28.5 percentage points at a two-year horizon and 25.7 percentage points at a four-year horizon. There is substantial variability across individuals around these average SeaTEs, so there is substantial heterogeneity in taste for work or returns to work. This heterogeneity would be normally unobservable and hard to disentangle from other determinants of retirement in data on realized labor supply decisions and health states. The paper’s approach can overcome the problem that estimates of the effect of health on labor supply based on behavioral (realizations) data can easily overstate the effect of health on retirement whenever less healthy workers tend to retire earlier for reasons other than health.Social Security Administration, RRC08098401-09, R-UM17-06https://deepblue.lib.umich.edu/bitstream/2027.42/146528/1/wp382.pdfDescription of wp382.pdf : Working pape

A Primer on Causality in Data Science

Many questions in Data Science are fundamentally causal in that our objective is to learn the effect of some exposure, randomized or not, on an outcome interest. Even studies that are seemingly non-causal, such as those with the goal of prediction or prevalence estimation, have causal elements, including differential censoring or measurement. As a result, we, as Data Scientists, need to consider the underlying causal mechanisms that gave rise to the data, rather than simply the pattern or association observed in those data. In this work, we review the 'Causal Roadmap' of Petersen and van der Laan (2014) to provide an introduction to some key concepts in causal inference. Similar to other causal frameworks, the steps of the Roadmap include clearly stating the scientific question, defining of the causal model, translating the scientific question into a causal parameter, assessing the assumptions needed to express the causal parameter as a statistical estimand, implementation of statistical estimators including parametric and semi-parametric methods, and interpretation of our findings. We believe that using such a framework in Data Science will help to ensure that our statistical analyses are guided by the scientific question driving our research, while avoiding over-interpreting our results. We focus on the effect of an exposure occurring at a single time point and highlight the use of targeted maximum likelihood estimation (TMLE) with Super Learner.Comment: 26 pages (with references); 4 figure

Econometric analyses of microfinance credit group formation, contractual risks and welfare impacts in Northern Ethiopia

Key words Microfinance, joint liability, contractual risk, group formation, risk-matching, impact evaluation, Panel data econometrics, dynamic panel probit, trend models, fixed-effects, composite counterfactuals, propensity score matching, farm households, Ethiopia. Lack of access to credit is a key obstacle for economic development in poor countries. The underlying problem is related to information asymmetry combined with the poor’s lack of collateral to pledge. New mechanisms in microfinance offer ways to deal with this problem without resorting to collateral requirements. The objective of this thesis is to examine the mechanisms of providing credit through microfinance and assess the long-run borrowing effects on household welfare in Ethiopia. The Ethiopian environment provides a suitable setting to examine these issues. To meet this objective, two unique data sets - a five-wave panel data on 400 and a cross-sectional data on 201 households - from northern Ethiopia are used. Borrowing decision is first conceptualized using a dynamic stochastic theoretical framework. Two types of risks involved in joint liability lending are incorporated, i.e., risk of partner failure and risk of losing future access to credit. Empirical analysis using recent dynamic panel data probit techniques show that these contractual risks indeed impede participation in borrowing. The impediment is higher for the poorer, and for new than repeat participants. Second, group formation is analyzed within the framework of alternative microeconomic theories of joint liability where the commonly held hypothesis that groups formed are homogeneous in risk profiles is tested. Empirical results reject this hypothesis indicating that the formation of heterogeneous risk profiles is an inherent feature in group formation and repayment. In fact, there is evidence that borrowers take advantage of established informal credit and saving, and other social networks, which also suggests that group formation outcomes vary depending on underlying socioeconomic contexts. Third, the impact of long-term borrowing on household welfare is assessed from the dimension of intensity and timing of participation in borrowing. Panel data covering relatively long period enabled to account for duration and timing concerns in program evaluation. Recent parametric and semi-parametric panel data techniques are innovatively employed to mitigate participation selection biases. Results from both approaches indicate that borrowing has increased household welfare significantly: the earlier and more frequent the participation the higher the impact partly due to lasting effects of credit. This also suggests that impact studies that are based on a single-shot observation of outcomes and that do not account for the timing and duration of participation may underestimate microfinance credit impacts. </p