Financial knowledge, attitude towards money and investment decisions: new insights for the farmland market

ABSTRACT

A considerable share of farmland market activity is related to non-agricultural owners. Recent research has shown that those non-agricultural owners are likely to be private persons. However, little is known about potential factors which drive their decision to invest in farmland. This study aims to investigate four groups of factors which could affect the decision of non-agricultural persons to buy farmland: Key investment information, financial knowledge, the attitude towards money, and sociodemographic characteristics. Primary discrete choice data, which is representative for our study region Germany within four categories, has been collected for the purpose of this study. 637 participants were asked to invest in farmland, financial products or save their money in a bank account. A mixed logit approach revealed, that variables from each factor group are related to the decision to invest in farmland. According to the effect sizes, risk (volatility of returns), returns, a university degree and a preference for a secure investment performance are especially important factors for investment decisions. Furthermore, considerable differences exist between people with a high financial literacy score or a high level of self-assessed financial knowledge.

KEYWORDS:

• Investment decisions
• discrete choice modelling
• farmland market
• mixed logit model
• individual attitudes

JEL CLASSIFICATION:

• Q14
• Q15
• C25

Acknowledgement

We thank the editor and two anonymous reviewers for their helpful comments. We gratefully acknowledge financial support from the German Research Foundation (DFG). We thank Stephanie Gallenkämper and Lisa Kappenberg for helpful technical support and discussions.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study is available from the corresponding author upon reasonable request.

Supplemental material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/00036846.2023.2293670

Notes

¹ For reaching representativeness of the sample and to address issues due to the COVID-19 pandemic, an online survey was most appropriate.

² Within the online experiment, we were aware of the fact that not all participants might know the different asset types and understand what they mean. Therefore, we included little questions marks in the choice sets, which were clickable and contain short explanations for the respective asset types..

³ The number of types increased the complexity of the experiment. However, we hypothesize that types of investments can have considerable effects on investment decisions due to differing preferences and potential prejudices of the participants. Therefore, we decided to include the type of investment as an attribute.

⁴ The reasons for setting liquidity and transaction costs as alternative-specific constants are (i) to achieve a moderate complexity of the DCE and (ii) their low variation possibilities. While the difference of those characteristics between the alternatives is remarkable, the variation between different types of farmland and financial products is vanishingly small and is assumed to not affect the investment decision further. This assumption remained also robust over the two pre-tests conducted while designing the experiment.

⁵ Since the federal states are presented in an aggregated form, the quotas are not presented here. Each federal state percentage was also matched to a high degree.

⁶ For additional interpretation possibilities, the odds ratios of the coefficient can be calculated with the following formula: $oddsratio=ex{p}^{\beta }$, where $\beta$ is the respective coefficient. For positive coefficients, the odds ratio is > 1 , for negative coefficients, the odds ratio is < 1. The odds ratio calculations are left out in this paper due to the focus on predictive margins and their contrasts.

⁷ An increase of the returns by 50% means for example that returns of 2% increase to 3%.

Additional information

Funding

This work was supported by the Deutsche Forschungsgemeinschaft.