How do you pay? The role of incentives at the point-of-sale

This paper uses discrete-choice models to quantify the role of consumer socioeconomic characteristics, payment instrument attributes, and transaction features on the probability of using cash, debit card, or credit card at the point-of-sale. We use the Bank of Canada 2009 Method of Payment Survey, a two-part survey among adult Canadians containing a detailed questionnaire and a three-day shopping diary. We find that cash is still used intensively at low value transactions due to speed, merchant acceptance, and low costs. Debit and credit cards are used more frequently for higher transaction values where safety, record keeping, the ability to delay payment and credit card rewards gain prominence. We present estimates of the elasticity of using a credit card with respect to credit card rewards. Reward elasticities are a key element in understanding the impact of retail payment pricing regulation on consumer payment instrument usage and welfare. JEL Classification: E41, C35, C83credit card rewards, discrete-choice models, Retail payments

© notice, is given to the source. Subjective Health Expectations ∗

Subjective health expectations are derived using data from the Health and Retirement Study (HRS). We first use a Bayesian updating mechanism to correct for focal point responses and reporting errors of the original health expectations variable. We then test the quality of the health expectations measure and describe its correlation with various health indicators and other individual characteristics. Our results indicate that subjective health expectations do contain additional information that is not incorporated in subjective mortality expectations and that the rational expectations assumption cannot be rejected for subjective health expectations. Finally, the data suggest that individuals younger than 70 years of age seem to be more pessimistic about their health than individuals in their 70’s

crs: A package for nonparametric spline estimation in R

crs is a library for R written by Jeffrey S. Racine (Maintainer) and Zhenghua Nie. This add-on package provides a collection of functions for spline-based nonparametric estimation of regression functions with both continuous and categorical regressors. Currently, the crs package integrates data-driven methods for selecting the spline degree, the number of knots and the necessary bandwidths for nonparametric conditional mean, IV and quantile regression. A function for multivariate density spline estimation with mixed data is also currently in the works. As a bonus, the authors have also provided the first simple R interface to the NOMAD (‘nonsmooth mesh adaptive direct search’) optimization solver which can be applied to solve other mixed integer optimization problems that future users might find useful in other settings. Although the crs package shares some of the same functionalities as its kernel-based counterpart—the np package by the same author—it currently lacks some of the features the np package provides, such as hypothesis testing and semiparametric estimation. However, what it lacks in breadth, crs makes up in speed. A Monte Carlo experiment in this review uncovers sizable speed gains compared to its np counterpart, with a marginal loss in terms of goodness of fit. Therefore, the package will be extremely useful for applied econometricians interested in employing nonparametric techniques using large amounts of data with a small number of discrete covariates

Data Science in Stata 16: Frames, Lasso, and Python Integration

Stata is one of the most widely used software for data analysis, statistics, and model fitting by economists, public policy researchers, epidemiologists, among others. Stata's recent release of version 16 in June 2019 includes an up-to-date methodological library and a user-friendly version of various cutting edge techniques. In the newest release, Stata has implemented several changes and additions that include:• Lasso• Multiple data sets in memory• Meta-analysis• Choice models• Python integration• Bayes-multiple chains• Panel-data ERMs• Sample-size analysis for CIs• Panel-data mixed logit• Nonlinear DSGE models• Numerical integrationThis review covers the most salient innovations in Stata 16. It is the first release that brings along an implementation of machine-learning tools. The three innovations we considered are: (1) Multiple data sets in Memory, (2) Lasso for causal inference, and (3) Python integration

Whenever and Wherever: The Role of Card Acceptance in the Transaction Demand for Money

The use of payment cards, either debit or credit, is becoming more and more widespread in developed economies. Nevertheless, the use of cash remains significant. We hypothesize that the lack of card acceptance at the point of sale is a key reason why cash continues to play an important role. We formulate a simple inventory model that predicts that the level of cash demand falls with an increase in card acceptance. We use detailed payment diary data from Austrian and Canadian consumers to test this model while accounting for the endogeneity of acceptance. Our results confirm that card acceptance exerts a substantial impact on the demand for cash. The estimate of the consumption elasticity (0.23 and 0.11 for Austria and Canada, respectively) is smaller than that predicted by the classic Baumol-Tobin inventory model (0.5). We conduct counterfactual experiments and quantify the effect of increased card acceptance on the demand for cash. Acceptance reduces the level of cash demand as well as its consumption elasticity

Does the Proportion of Same-Day and 24-Hour Appointments Impact Patient Satisfaction?

Background: The relationship between open access and patient satisfaction is mixed. Our study is the first to assess the relationship between open access appointment scheduling and patient satisfaction in the Military Health System (MHS). It is also unique in that we examine both same-day and 24-hour access through a relationship with satisfaction. Methods: We conducted a panel time-series analysis with general estimating equations on the Army population of outpatient facilities (N = 32), with 32 364 957 total observations. Our primary independent variables were the proportion of a facility’s appointments within 24 hours and same day from July 2013 to May 2015. Results: We identified that a higher proportion of same-day appointments is associated with increased patient satisfaction with the ability to see their provider when needed. We did not find the same result when examining access within 24 hours. Conclusions: Open access appointment scheduling appears to have a greater impact on patient satisfaction with timeliness of care if that appointment is made the same day the patient presents to the facility. Facilities should consider opening more of their schedule to accommodate same-day appointments. This can result in less costly primary care instead of emergency department usage

Industry shutdown rates and permanent layoffs: evidence from firm-worker matched data

Firm shutdown creates a turbulent situation for workers as it leads directly to layoffs for its workers. An additional consideration is whether a firm’s shutdown within an industry creates turbulence for workers at other continuing firms. Using data drawn from the Longitudinal Worker File, a Canadian firm-worker matched employment database, we investigate the impact of industry shutdown rates on workers at continuing firm. This paper exploits variation in shutdown rates across industries and within an industry over time to explain the rate of permanent layoffs and the growth of workers’ earnings. We find an increase in industry shutdown rates increases the probability of permanent layoffs and decreases earnings growth for workers at continuing firms

A Multiwavelength Study of a Sample of 70 micron Selected Galaxies in the COSMOS Field I: Spectral Energy Distributions and Luminosities

We present a large robust sample of 1503 reliable and unconfused 70microm selected sources from the multiwavelength data set of the Cosmic Evolution Survey (COSMOS). Using the Spitzer IRAC and MIPS photometry, we estimate the total infrared luminosity, L_IR (8--1000 microns), by finding the best fit template from several different template libraries. The long wavelength 70 and 160 micron data allow us to obtain a reliable estimate of L_IR, accurate to within 0.2 and 0.05 dex, respectively. The 70 micron data point enables a significant improvement over the luminosity estimates possible with only a 24 micron detection. The full sample spans a wide range in L_IR, L_IR ~ 10^8-10^14 L_sun, with a median luminosity of 10^11.4 L_sun. We identify a total of 687 luminous, 303 ultraluminous, and 31 hyperluminous infrared galaxies (LIRGs, ULIRGs, and HyLIRGs) over the redshift range 0.01<z<3.5 with a median redshift of 0.5. Presented here are the full spectral energy distributions for each of the sources compiled from the extensive multiwavelength data set from the ultraviolet (UV) to the far-infrared (FIR). Using SED fits we find possible evidence for a subset of cooler ultraluminous objects than observed locally. However, until direct observations at longer wavelengths are obtained, the peak of emission and the dust temperature cannot be well constrained. We use these SEDs, along with the deep radio and X-ray coverage of the field, to identify a large sample of candidate active galactic nuclei (AGN). We find that the fraction of AGN increases strongly with L_IR, as it does in the local universe, and that nearly 70% of ULIRGs and all HyLIRGs likely host a powerful AGN.Comment: 31 pages including 31 figures and 6 tables. Accepted for publication in ApJ. The full resolution version is available here: http://www.ifa.hawaii.edu/~jeyhan/paperI/Kartaltepe_70mic_PaperI.pd

Modelling performed for predictions of fusion power in JET DTE2: overview and lessons learnt

For more than a decade, an unprecedented predict-first activity has been carried in order to predict the fusion power and provide guidance to the second Deuterium–Tritium (D–T) campaign performed at JET in 2021 (DTE2). Such an activity has provided a framework for a broad model validation and development towards the D–T operation. It is shown that it is necessary to go beyond projections using scaling laws in order to obtain detailed physics based predictions. Furthermore, mixing different modelling complexity and promoting an extended interplay between modelling and experiment are essential towards reliable predictions of D–T plasmas. The fusion power obtained in this predict-first activity is in broad agreement with the one finally measured in DTE2. Implications for the prediction of fusion power in future devices, such as ITER, are discussed