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Purpose – This chapter investigates the role that mandatory genetically modified (GM) labeling versus voluntary labeling has played in the split between those countries with small GM markets and those with large GM markets.

Methodology/approach – Data on product introductions and other market evidence are used to examine market outcomes and identify the likely drivers of GM market bifurcation.

Findings – Labeling has negligible effects on consumer choice or on GM differentiation costs and therefore does not explain the split in GM market outcomes. Other factors have driven market outcomes: namely, consumer confidence in government and the safety of the food supply, competition among manufacturers and retailers, market momentum, and most importantly, the affordability of a non-GM strategy. Ultimately, a non-GM market strategy is feasible only if consumers are willing to cover the additional costs associated with non-GM production and marketing. The two elements composing the cost/price wedge between GM and non-GM products – the cost-reducing benefits of the GM technology and the costs of differentiating non-GM products – therefore play an important role in market outcomes. In the mid-1990s, when producers, manufacturers, and retailers were determining their strategies, neither element was very large. As a result, both GM and non-GM marketing strategies were economically feasible.

Practical implication – Regardless of the labeling regime, changes in the cost/price wedge between GM and non-GM products could change the mix of GM and non-GM products on the market.

Originality/value of paper – This analysis extends the literature by focusing on the impact of labeling regime on both consumer behavior and the cost/price wedge between GM and non-GM products.

Market sentiment has shown to influence housing prices in the global north, but in emerging economies, the nexus is rare to chance on in the current state of science for policy direction. More importantly in the recent decade where policymakers are yet to conclude on the myriad of factors confronting the housing market in sub-Saharan Africa inhibiting affordability. This paper therefore examines the impact of market sentiment on house prices in South Africa.

The study used the Autoregressive Distributed Lag (ARDL) approach with quarterly data spanning from 2005Q1 to 2020Q4.

In all, it was established that market sentiment plays a minimal role in the property market in South Africa. But there was enough evidence of cointegration from the bound test between sentiment and house prices. Nevertheless, the lag values of sentiment pointed to a rise in house prices. Exchange rate volatilities and inflation had a statistically significant effect on prices in both the long and short term, respectively.

Policymakers could still monitor market sentiment in the housing market due to the strong chemistry between house prices and sentiment, as evidenced from the bound test, but focus on economic fundamentals as the main policy tool for house price reduction.

The findings and the creation of the sentiment index make an invaluable contribution to the paper and add to the paucity of literature on the study of market sentiment in the housing market.

The purpose of this paper is to focus on the relationship between attitudinal data from the long-running Michigan Surveys of Consumers and US real GDP growth. One survey question asks, “Generally speaking, do you think now is a good time or a bad time to buy a house?” with the follow-up question “Why do you say so?” There are several factors for consumers to choose as reasons. Given the strong link between US housing market activity and business cycles, the authors ask whether the responses to the follow-up question explain the behavior of output growth.

The authors employ an augmented autoregressive model to investigate the relationship between output growth and the responses to the follow-up question for 1986–2007 and for 1986–2018, which includes the 2008 financial crisis. The authors follow the general-to-specific approach to obtain the final model estimates for interpretation. For a deeper analysis, the authors estimate the model using the responses of survey participants in the bottom 33 percent, middle 33 percent and upper 33 percent income categories, separately. While avoiding aggregation bias, this approach helps reveal important information embodied in the cross-sectional distribution of the data.

The follow-up question focuses on such factors as home prices, mortgage rates, houses as a good/bad investment, timing, uncertain future and affordability. The authors find that the majority of these factors chosen as reasons by consumers in the middle and upper 33 percent income categories explain the behavior of output growth. Among the factors chosen as reasons by consumers in the bottom 33 percent income category, only the mortgage rate and uncertain future explain output growth.

This study provides new insights into the usefulness of detailed consumer survey data in explaining the behavior of output growth and further underlines the usefulness of such measures across different income categories for revealing important information contained in the cross-sectional distribution of the data.