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In an evaluation of a job training program, the causal effects of the program on wages are often of more interest to economists than the program's effects on employment or on income. The reason is that the effects on wages reflect the increase in human capital due to the training program, whereas the effects on total earnings or income may be simply reflecting the increased likelihood of employment without any effect on wage rates. Estimating the effects of training programs on wages is complicated by the fact that, even in a randomized experiment, wages are truncated by nonemployment, i.e., are only observed and well-defined for individuals who are employed. We present a principal stratification approach applied to a randomized social experiment that classifies participants into four latent groups according to whether they would be employed or not under treatment and control, and argue that the average treatment effect on wages is only clearly defined for those who would be employed whether they were trained or not. We summarize large sample bounds for this average treatment effect, and propose and derive a Bayesian analysis and the associated Bayesian Markov Chain Monte Carlo computational algorithm. Moreover, we illustrate the application of new code checking tools to our Bayesian analysis to detect possible coding errors. Finally, we demonstrate our Bayesian analysis using simulated data.

We survey banks to construct national estimates of total noncash payments by type, payments fraud and related information. The survey is designed to create aggregate total estimates of all payments in the United States using data from responses returned by a representative, random sample. In 2016, the number of questions in the survey doubled compared with the previous survey, raising serious concerns of smaller bank nonparticipation. To obtain sufficient response data for all questions from smaller banks, we administered a modified survey design which, in addition to randomly sampling banks, also randomly assigned one of several survey forms, subsets of the full survey. This case study illustrates that while several other factors influenced response outcomes, the approach helped ensure sufficient response for smaller banks. Using such an approach may be especially important in an optional-participation survey, when reducing costs to respondents may affect success, or when imputation of unplanned missing items is already needed for estimation. While a variety of factors affected the outcome, we find that the planned missing data approach improved response outcomes for smaller banks. The planned missing item design should be considered as a way of reducing survey burden or increasing unit-level and item-level responses for individual respondents without reducing the full set of survey items collected.

The estimation of the effects of treatments – endogenous variables representing everything from child participation in a pre-kindergarten program to adult participation in a job-training program to national participation in a free trade agreement – has occupied much of the theoretical and applied econometric research literatures in recent years. This volume brings together a diverse collection of papers on this important topic by leaders in the field from around the world. This collection draws attention to several key facets of the recent evolution in this literature.

This paper analysed the motives behind farmers' savings with Rural and Community Banks (RCBs) and the effect of these savings on rice yield in the Hohoe Municipality of the Volta region of Ghana.

A multi-stage sampling approach was used to draw a random sample of 222 rice farmers, and a structured questionnaire was employed to collect cross-sectional data. A Likert scale was used to rank the motive behind farmers' savings while the endogenous switching regression model was used to estimate the effect of savings on rice yield.

The results of the study showed that most farmers mobilise savings to enhance farm investment which is critical to increasing rice productivity. Improved labour and fertiliser use had a positive influence on rice yield, while farm size had an inverse relation with rice yield. Further, the findings show that savings with RCBs help mobilise the necessary finance to enhance rice productivity. In terms of the treatment effect of savings, the results indicate that farmers who patronise saving products of RCBs recorded a statistically significant average yield of 1.41 Mt/ha more than those not patronising saving products from any bank.

While the literature on agricultural finance focuses largely on credit, this study demonstrates that savings hold significant benefits for the development of agriculture through productivity gains. The importance of this demonstration is further shown by the fact that credit access depends on the ability to save in most developing countries.

There is a need to educate farmers about the essence of patronising formal savings products.

This study represents the first attempt at linking farmers' savings to agricultural productivity using an econometric methodology in Ghana. The study serves as a foundation paper and for that matter will serve as a guide to future research on savings mobilisation and agricultural productivity nexus.

When there are few treated clusters in a pure treatment or difference-in-differences setting, t tests based on a cluster-robust variance estimator can severely over-reject. Although procedures based on the wild cluster bootstrap often work well when the number of treated clusters is not too small, they can either over-reject or under-reject seriously when it is. In a previous paper, we showed that procedures based on randomization inference (RI) can work well in such cases. However, RI can be impractical when the number of possible randomizations is small. We propose a bootstrap-based alternative to RI, which mitigates the discrete nature of RI p values in the few-clusters case. We also compare it to two other procedures. None of them works perfectly when the number of clusters is very small, but they can work surprisingly well.

Three themes dominate Hunting Causes. The first is that cause is a plural concept. The methods and metaphysics of causation, Cartwright believes, are context dependent. Different causal accounts seem to be at odds with one another only because the same word means different things in different contexts. Every formal approach to causality uses a conceptual framework that is “thinner” than causal reality. She lists a bewildering variety of approaches to causation: probabilistic and Bayes-net accounts (of, for example, Patrick Suppes, Clive Granger, Wolfgang Spohn, Judea Pearl, and Clark Glymour), modularity accounts (Pearl, James Woodward, and Stephen LeRoy), invariance accounts (Woodward, David Hendry, and Kevin Hoover), natural experiments (Herbert Simon, James Hamilton, and Cartwright), causal process accounts (Wesley Salmon and Philip Dowe), efficacy accounts (Hoover), counterfactual accounts (David Lewis, Hendry, Paul Holland, and Donald Rubin), manipulationist accounts (Peter Menzies and Huw Price), and others. The lists of advocates of various accounts overlap. Nevertheless, she sometimes treats these accounts as if they were so different that it is not clear why they should be the subject of a single book. And she fails to explain what they have in common. If, as she apparently believes, they do not have a common essence, do they have a Wittgensteinian family resemblance? She fails to explore in any systematic way the complementarities among the different approaches – for example, between invariance accounts, Bayes-nets, and natural experiments – that frequently make their advocates allies rather than opponents.

Accounting researchers have performed many studies related to public sector budgeting and financial management. Public sector accounting research seeks to explain the role of accounting and auditing in the public sector. For example, researchers examine issues such as (1) the use of accounting information by elected officials, (2) the demand for auditing, and (3) the determination of bond ratings. This review of the public sector accounting literature describes some of the theoretical foundations utilized in public sector accounting research and reviews a sample of selected empirical studies.