Kind or contented? An investigation of the gift exchange hypothesis in a natural field experiment in Colombia

The gift exchange hypothesis postulates that workers reciprocate above market-clearing wages with above-minimum effort. This hypothesis has received mixed support in dyadic employer-worker relationships. We present a field-experimental test to assess this hypothesis in the context of a triadic relationship in which only one out of two workers receives a pay increase. We conjecture that inequality aversion motivations may thwart positive reciprocity motivations and analyze the interaction between such motivations theoretically. Across three treatments, the pay increase is justified to workers based on either relative merit or relative need or was arbitrary as no justification was offered. Two conditions in which either one or both workers receive a bonus serve as the reference. In contrast to the gift exchange hypothesis, we find that pay increases lead to a decrease in productivity. Such a decrease is most sizable in the condition where both workers receive the bonus. A post-diction of this result is that workers interpret the monetary bonus as a signal of the employer’s contentment with their effort, which makes them feel entitled to reduce their effort. In other treatments, receiving the pay increase while the coworker does not has a positive effect on productivity, especially when the pay increase is based on merit. This result is consistent with statusseekingpreferences rather than aversion against advantageous inequality. Conversely, not receiving the pay increase while the coworker does, leads to lower productivity, especially when the pay increase is assigned based on relative needs.

Forecasting of Market Clearing Volume Using Wavelet Packet-Based Neural Networks with Tracking Signals

In order to analyze the nature of electrical demand series in deregulated electricity markets, various forecasting tools have been used. All these forecasting models have been developed to improve the accuracy of the reliability of the model. Therefore, a Wavelet Packet Decomposition (WPD) was implemented to decompose the demand series into subseries. Each subseries has been forecasted individually with the help of the features of that series, and features were chosen on the basis of mutual correlation among all-time lags using an Auto Correlation Function (ACF). Thus, in this context, a new hybrid WPD-based Linear Neural Network with Tapped Delay (LNNTD) model, with a cyclic one-month moving window for a one-year market clearing volume (MCV) forecasting has been proposed. The proposed model has been effectively implemented in two years (2015–2016) and unconstrained MCV data collected from the Indian Energy Exchange (IEX) for 12 grid regions of India. The results presented by the proposed models are better in terms of accuracy, with a yearly average MAPE of 0.201%, MAE of 9.056 MWh, and coefficient of regression (R2) of 0.9996. Further, forecasts of the proposed model have been validated using tracking signals (TS’s) in which the values of TS’s lie within a balanced limit between −492 to 6.83, and universality of the model has been carried out effectively using multiple steps-ahead forecasting up to the sixth step. It has been found out that hybrid models are powerful forecasting tools for demand forecasting.