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We investigate discrepancies between willingness to pay (WTP) and willingness to accept (WTA) in the context of a stated choice experiment. Using data on customer preferences for water services where respondents were able to both ‘sell’ and ‘buy’ the choice experiment attributes, we find evidence of non-linearity in the underlying utility function even though the range of attribute levels is relatively small. Our results reveal the presence of significant loss aversion in all the attributes, including price. We find the WTP–WTA schedule to be asymmetric around the current provision level and that the WTP–WTA ratio varies according to the particular provision change under consideration. Such reference point findings are of direct importance for practitioners and decision-makers using choice experiments for economic appraisal such as cost–benefit analysis, where failure to account for non-linearity in welfare estimates may significantly over- or under-state individual's preferences for gains and avoiding losses respectively.

This chapter investigates how international trade affects pollution using annual data from 34 Asian countries for the period 1970–2019. Following the work of Antweiler, Copeland, and Taylor (2001), the authors divide the impact into three effects – scale, technique, and composition effects. The scale of economic activity drives pollution demand. The technique effect reflects increased willingness to bear the costs of abating pollution as a country gets more prosperous because of increased international trade. International trade changes the composition of output in a country and therefore the level of pollution as different goods are produced with different pollution intensities. This is called the composition effect. This chapter measures pollution using carbon dioxide emissions (metric tons per capita) obtained from the United States Energy Information Administration. This study estimates a regression model that provides estimates of the magnitudes of trade’s impact on pollution as per the aforesaid three effects. The authors find that the scale and the composition effects of pollution are positive, but the technique effect is negative, and that the net effect is negative (international trade leads to a lower level of emission) when the underlying model is linear, but it is positive (international trade leads to a higher level of emission when non-linearities are considered).

The relationship between CEO compensation and firm performance is a field of intense theoretical and empirical research. The purpose of this study is to gain additional insights into the nature of this relationship by examining empirically the relatively unexplored areas of its non-linearity. The findings of this study show strong evidence that supports the view that the relationship between executive compensation and firm performance is non-linear and asymmetric. Additionally, the structure of asymmetry is found to be dependent upon the measure of performance. Convexity characterizes the asymmetry of the relationship between executive compensation and market returns, while concavity distinguishes the asymmetry of the relationship between executive compensation and accounting returns.

Are the learning procedures of genetic algorithms (GAs) able to generate optimal architectures for artificial neural networks (ANNs) in high frequency data? In this experimental study, GAs are used to identify the best architecture for ANNs. Additional learning is undertaken by the ANNs to forecast daily excess stock returns. No ANN architectures were able to outperform a random walk, despite the finding of non-linearity in the excess returns. This failure is attributed to the absence of suitable ANN structures and further implies that researchers need to be cautious when making inferences from ANN results that use high frequency data.

We examine a large sample of some 100 economies worldwide to study the impact of financial sector size expansion on labor market performance. Simple linear dynamic panel data models inspired by the well-developed finance-growth literature suggest that (on average) a larger financial sector is beneficial for the labor market as it reduces unemployment rates. However, estimating country- and period-specific benchmark levels of financial sector size, we document that the relative contribution of finance vanishes with excessive levels of finance, and excessive levels of credit may actually be detrimental to employment. These non-linearities in the finance-unemployment nexus are more pronounced within developed economies. Overall, our study sheds new light on the ongoing controversy about the impact of the financial sector on societal well-being and highlights the importance of monitoring the expansion of the financial sector, in particular when it comes to credit markets.

It has long been challenged that the distributions of empirical returns do not follow the log-normal distribution upon which many celebrated results of finance are based including the Black–Scholes Option-Pricing model. Borland (2002) succeeds in obtaining alternate closed form solutions for European options based on Tsallis distribution, which allow for statistical feedback as a model of the underlying stock returns. Motivated by this, we simulate two distinct time series based on initial data from NIFTY daily close values, one based on the Gaussian return distribution and the other on non-Gaussian distribution. Using techniques of non-linear dynamics, we examine the underlying dynamic characteristics of both the simulated time series and compare them with the characteristics of actual data. Our findings give a definite edge to the non-Gaussian model over the Gaussian one.

The authors introduce a new approach to estimate high-dimensional factor-augmented vector autoregressive models (FAVAR) where the loadings are subject to idiosyncratic regime-switching dynamics. Our Bayesian estimation method alleviates computational challenges and makes the estimation of high-dimensional FAVAR with heterogeneous regime-switching straightforward to implement. The authors perform extensive simulation experiments to study the finite sample performance of our estimation method, demonstrating its relevance in high-dimensional settings. Next, the authors illustrate the performance of the proposed framework for studying the impact of credit market disruptions on a large set of macroeconomic variables. The results of this study underline the importance of accounting for non-linearities in factor loadings when evaluating the propagation of aggregate shocks.