Risk attitude, innovativeness, and financial literacy—A cross-occupational comparison of German farmers and foresters

Abstract

Factors such as risk attitude, innovativeness, and financial literacy are crucial in agricultural and forest economics, especially amidst weather and market risks. Hence, understanding them is central for fostering a resilient primary sector. We surveyed 371 German farmers and 215 foresters in 2022 online. Both groups were financial literate, reported a neutral generalized risk attitude, leaned toward risk aversion in their professional context, were open to innovation, and showed statistically significant differences between contextualized and generalized risk attitude. Unlike foresters, farmers displayed statistically significant differences between their self-reported general and contextualized innovativeness. Among other things, the results highlight the value of context-specific methods in primary sector research. The study showcases a commitment to open science by using a synthetic dataset to make the analysis transparent and allowing for replication while ensuring participant privacy through the differential privacy framework.

1. Introduction

The European Unions’ Green Deal highlights the central role of the primary sector, notably agriculture and forestry, in sustaining local rural economies, conserving biodiversity, and combating climate change (European Commission 2019). Both farmers and foresters depend on living organisms’ growth, but exhibit distinct working conditions. Farmers mainly focus on yearly cycles of producing food and raw materials. In contrast, foresters operate on longer timelines and primarily produce only raw materials. Farmers are predominantly self-employed, managing both the production and sale of their output with direct effects on their income. In contrast, foresters often work within larger forestry organizations (Conway et al. 2003; Govorushko 2012; Sauter et al. 2018). The primary sector faces production and market risks amplified by climate change challenges (Menapace et al. 2016; Brunette et al. 2017; Finger et al. 2022; Nabuurs et al. 2022; Wang et al. 2022). Weather-related events pose distinct financial challenges for these two groups. A hail event, for example, might impose a liquidity constraint on farmers due to immediate crop loss. In contrast, foresters may experience a cash glut following such events, as they often lead to unplanned timber harvests. These events can force foresters to sell large volumes of timber prematurely, temporarily boosting their revenue. Thus, the perceptions of the effects of similar risk events may differ between farmers, who face liquidity constraints, and foresters, who might temporarily have increased liquidity.

Against this background, it is crucial to gain insights into farmers’ and foresters’ decision-making. These insights can help to design sound policies and education strategies to strengthen the economic and environmental sustainability of the primary sector. Recent studies emphasize behavioral factors affecting these decisions (e.g., Deuffic et al. 2018; Degnet et al. 2022; Schaub et al. 2023; Wuepper et al. 2023). Factors such as risk attitude and innovativeness are linked to farmers’ (e.g., Schaub et al. 2023) and foresters’ decision-making (e.g., Sikora and Nybakk 2012; Sauter and Musshoff 2018). Furthermore, financial literacy is essential in the decision-making process. Financial literacy is the comprehension of economic phenomena such as inflation, risk diversification, and compound interest. In short, financial literacy refers to an individuals’ ability to comprehend financial information and make informed decisions (Hogarth and Hilgert 2002; Lusardi and Mitchell 2008; Cole and Shastry 2009) regarding investments or savings (Klapper and Lusardi 2020; Pitthan and Witte 2021) and evaluate the decisions’ profitability, which is a central determinant in both agricultural (e.g., Cary and Wilkinson 1997; Wang et al. 2023) and forest (e.g., Lähdesmäki and Matilainen 2014; Kupčák and Šmída 2015) management. Drawing valid conclusions from scientific findings within the primary sector requires an understanding of these factors. Yet, comprehensive analyses comparing risk attitudes, innovativeness, and financial literacy across farmers and foresters are scarce. This paper seeks to address this research gap by comparing named factors for both groups. We further explore the interrelationship between risk attitude, innovativeness, and financial literacy. We present each factor and associated literature gaps.

Given the role of primary sector policies in either mitigating or exacerbating risks in the primary sector, it is crucial to understand farmers’ and foresters’ risk attitudes. Risk attitudes mediate the policy effects on farmers’ (Bar‐Shira et al. 1997; Koundouri et al. 2009; Wuepper et al. 2023) and foresters’ (Sauter et al. 2018) decisions. Beyond policy design, understanding risk attitudes helps forecast policy reception in order to inform and ensure effective communication. Various methods, from multiple-price lists (e.g., lottery tasks) to multi-item scales, help elicit risk attitudes (Iyer et al. 2020). To the best of our knowledge, only Sauter et al. (2018) compared farmers’ and foresters’ risk attitude using lottery tasks (Eckel and Grossman 2002; Holt and Laury 2002) and a general self-assessment statement based on Dohmen et al. (2011). The literature suggests that the risk attitude is context-specific (Weber 2010), and for instance, that contextualized elicitation methods have a higher explanatory power (e.g., Menapace et al. 2016). Apart from Andersson (2012) as well as Andersson and Gong (2010), who focused on forestry financial risk attitudes, no study contrasted generalized and contextualized elicitation methods in forest economics or compared risk attitudes of farmers and foresters using both approaches.

Innovativeness also plays a central role in primary sector decision-making and is closely tied to risk-taking, as new ideas and practices often involve uncertainties. Adopting innovative approaches is a risk that farmers and foresters embrace in pursuit of future-proof practices (EIP-AGRI 2020). This factor is pivotal for both farmers (Pennings and Smidts 2000) and foresters (Sikora and Nybakk 2012). Though some research, such as Kreft et al. (2021), investigated the relationship between risk attitude and innovativeness for farmers, this area remains largely unexplored, particularly for foresters. Moreover, the distinction between contextualized and generalized statements in this context has not been in focus yet.

Financial literacy emerges as a third factor, alongside risk attitude and innovativeness, that is crucial to primary producers’ decision-making. While studies have illuminated financial literacy variations across countries and groups (Lusardi and Mitchell 2011; Klapper et al. 2015) and also assessed farmers’ financial literacy (e.g., Gaurav and Singh 2012; Aggarwal et al. 2014; Liu et al. 2023), foresters’ financial literacy has not been investigated yet. Furthermore, there is a lack of research comparing financial literacy among primary sector producers.

To address the identified research gaps, our study compared and contrasted the risk attitude, innovativeness, and financial literacy of farmers and foresters. Beyond this, we analyzed the relationships between risk attitude, innovativeness, and financial literacy. Using an online survey conducted in the first half of 2022, we gathered responses from 371 German farmers and 215 German foresters. Our methodological approach mirrors and extended the study by Sauter et al. (2018) by using the generalized risk attitude self-assessment statement of Dohmen et al. (2011)¹ and adding a contextualized statement. Notably, our research marks the first comparison of both farmer and forester innovativeness via generalized and contextualized statements and examines their relation to risk attitude. Furthermore, this research is the first to compare the financial literacy of these two groups. Lastly, our work is the first to explore how financial literacy influences risk attitudes among primary producers.

Understanding the differences and similarities between farmers’ and foresters’ decision-making is crucial for scholars, policy-makers, and extension services to effectively translate academic insights into practical applications. Thus, potential inferences from results in agricultural to forest economics research and vice versa can be evaluated. To be specific, the results and conclusions could lead to improved communication and implementation of primary sector policies and other measures. For extension services, the results could be of special interest for consulting in risk management and finance. Furthermore, understanding financial literacy could also foster the creation of financial systems tailored to primary producers. The systematic application of different multi-item-based elicitation methods and the examination of inter-relationships among the factors add value to future research in the field. The interplay between risk attitude, innovativeness, and financial literacy observed in our study also opens new avenues for research.

Lastly, by partly replicating the study of Sauter et al. (2018) we also contribute to the replication crisis in (agricultural) economic research (Camerer et al. 2016; Finger et al. 2023). In recognition of the growing movement toward open science, we generated a synthetic dataset from the collected survey data. Synthetic data mimics the statistical properties of the original dataset without containing any raw data points (Wimmer and Finger 2023). It enabled us to share the data openly for secondary analyses while ensuring that the participants’ data are protected. In addition to the recommended use of synthetic data by Wimmer and Finger (2023), we also checked for privacy, which was not subject to an analysis by the aforementioned authors (Wimmer and Finger 2023, p. 322). Ganev and DeCristofaro (2023) recently highlighted that under the differential privacy (DP) framework (Dwork 2006; Dwork and Roth 2014) robust privacy guarantees for synthetic data can be achieved. Hence, by using a synthetic dataset generated under a DP framework, we are not only protecting the privacy of the farmers and foresters with mathematical guarantees, but we are also making our findings more transparent and reproducible in line with the philosophy of open science. Thus, this paper presents the first combined application of synthetic data and the DP framework in agricultural and forestry economics.

2. Literature review and expected results

Farmers and foresters are typically considered risk-averse (Brunette et al. 2017; Iyer et al. 2020). A meta-analysis by Brunette et al. (2015) supports this, including both groups under the umbrella of risk-averse natural resource managers. Sauter et al. (2018) find foresters to be more risk-averse than farmers, attributing this to their distinct work environments. Using the same general self-assessment statement as Sauter et al. (2018) derived from Dohmen et al. (2011), we expected our results to align with their findings. By examining both general and contextualized self-assessment, we expect foresters to report a statistically significant higher risk aversion than farmers.

Innovativeness can be defined as the tendency to deviate from established technologies and practices (Kangogo et al. 2021). For both agriculture and forestry, embracing new technologies and management practices is pivotal for productivity and sustainability (Chavas and Nauges 2020; EIP-AGRI 2020; Weiss et al. 2021). Innovation requires an elevated level of risk-seeking since deviating from established technologies and practices (e.g., innovativeness) requires taking a higher risk in pursuit of new ideas and opportunities. Pennings and Smidts (2000) find risk-averse farmers to be statistically significantly less innovative. However, Kreft et al. (2021) did not find a statistically significant link between farmers’ risk aversion and innovativeness. Sikora and Nybakk (2012) find a statistically significant positive correlation between foresters’ innovativeness and risk-seeking behavior. Given the policy emphasis on innovation in both sectors (EIP-AGRI 2020), it is important to understand how risk attitudes affect innovation uptake. Based on the aforementioned literature, we expect the following: Though both farmers and foresters are expected to be innovative, foresters’ expected stronger risk aversion might lead to observing a statistically significant greater innovativeness for farmers. Furthermore, our research expectation is a statistically significant positive association between the innovativeness and risk-seeking behavior in both groups.

Foresters face long time horizons between planting and harvesting, requiring proficiency in financial calculations over extended periods (Sauter and Musshoff 2018). Likewise, financial literate farmers exhibit greater capabilities of decoding information which results in higher adoption rates of risk management instruments like insurances (Liu et al. 2023). Given the importance of financial literacy in both fields and their distinct financial challenges, it is plausible that their financial literacy levels might be comparable. Accordingly, our expectation is no statistically significant differences in financial literacy between farmers and foresters.

Research indicates that lower cognitive abilities (e.g., numeracy) correlates with self-reported risk aversion (Falk et al. 2018). Risk, defined as an outcome with a quantifiable probability (Knight 1921), can be misjudged due to deficits in risk and probability comprehension. This misunderstanding, often rooted in limited numeracy, could skew an individuals’ risk attitude, comprehension, and behavior (Reyna et al. 2009; Garcia-Retamero et al. 2019; Meraner and Finger 2019). Numeracy only provides a measure of basic mathematical skills. Financial literacy extends this view by embedding comprehension of economic phenomena such as risk diversification (Klapper and Lusardi 2020), which is vital for primary producers to understand the economic feasibility and profitability of their decisions and risk management. For instance, the ability to comprehend the intricacies of insurance contracts or loans can affect how these individuals perceive and manage risk. Considering the relationship between financial literacy, risk understanding, and consequent risk behavior, we expect a statistically significant negative correlation between financial literacy and a risk-averse attitude for farmers and foresters.

3. Material and methods

3.1 Structure of the questionnaire

In the first half of 2022, parallel online surveys were conducted among German farmers and foresters. Participants were ensured that their responses would be kept anonymous and that they could discontinue participation in the survey at any time without facing any consequences. By starting the survey, participants agreed to our data protection declaration.²

We reached out through newsletters and e-mail lists from institutions like the Chambers of Agriculture and forest owners’ associations of various Federal states. Additionally, Facebook was used to access potential participants active in online communities. It is important to note that, due to the nature of this sampling method, especially on platforms like Facebook, the exact reach and response rate of our survey could no determined. In spite of these limitations, the multi-faceted approach aimed to capture a comprehensive snapshot of these occupational groups. The survey was not incentivized.

The questionnaire has three stages. The first stage asked about participants’ socio-demographic characteristics and farm or forest enterprise. In the second stage, their risk attitudes and innovativeness were assessed using a generalized and contextualized 11-point self-assessment scale adapted from Dohmen et al. (2011). Furthermore, a test question to identify inconsistent response behavior was included. The third stage featured an assessment of financial literacy based on Klapper et al. (2015). The self-assessment statements and financial literacy tasks used in the survey are presented below.

Risk Attitude

“Are you generally a risk-seeking person or do you try to avoid risks?” 1 – risk-averse/ 11 – risk-seeking

“In terms of your behavior in your farm (forest enterprise), would you consider yourself a risk-seeking person or do you try to avoid risk?” 1 – risk-averse/ 11 – risk-seeking

Innovativeness

“In general, would you consider yourself to be an innovative person?” 1 – innovation-averse/ 11 – innovation-seeking

“In terms of your behavior on your farm (forest enterprise), would you consider yourself to be an innovative person?” 1 – innovation-averse/ 11 – innovation-seeking

Financial Literacy

The four questions regarding risk diversification (1), inflation (2), numeracy (3), and compound interest (4) read as follows:

1. What is in your view the safer option?

   • Investment of money in one investment

   • Investment of money in various investments (correct)

   • I do not know

2. Assume that over the next 10 years the prices of the products you buy will double. If your income doubles at the same time, how many products could you buy?

   • Fewer products than today

   • Same amount of products as today (correct)

   • More products than today

   • I do not know

3. Suppose you borrow €1000. What is the lower amount to be repaid?

   • €1050

   • €1000 + 3 per cent (correct)

   • I do not know

4. Suppose you have invested €1000 in your bank account. You receive 5 per cent interest per year. How much money can you withdraw after 5 years if you do not withdraw any money in the meantime?

   • More than €1250 (correct)

   • Exactly €1250

   • Less than €1250

   • I do not know

3.2 Differentially private synthetic data

DP is a mathematical criterion for privacy-preserving computations. It provides a robust privacy guarantee, which limits the impact of release of a result (e.g., a statistic) based on private input on the privacy of any individuals’ specific input. Intuitively, DP relies on statistical noise to protect individuals’ data. The basic DP criterion can be fulfilled by any randomized computation F and is defined as follows: F is differentially private, if for all adjacent datasets |${{{\rm{D}}}_1}$| and |${{{\rm{D}}}_2}$| (meaning that they differ in at most one record) and S  |$\in$| Range (F) the following holds:

The amount of noise is steered with the parameter epsilon (ε), often called privacy budget. Accordingly, higher values indicate less privacy and at the same time allowing for more accuracy in the given analysis and vice versa (Dwork 2006). A recommended ε value is ln(3), as suggested by Dwork and Smith (2010), balances privacy with reasonable precision. Our data synthesis utilized the Private Aggregation of Teacher Ensemble (PATE-GAN) framework (Jordon et al. 2018) combined with the conditional Generalized Adversary Network (GAN) architecture for tabular data (Xu et al. 2019). The process continues until the ε-budget, indicative of the privacy loss during synthetic data creation, is achieved.

The choice of synthetic data generated under DP guarantees was due to our commitment to participant confidentiality. To be specific, our data protection declaration, in strict alignment with GDPR principles, underscores our dedication to maintaining the highest standards of data privacy and integrity, particularly through our use of differentially privacy synthetic data (DPSD). In addition, even though individual attributes like age or gender might not explicitly risk participant anonymity, unique combinations, especially in closely-knit communities like agriculture and forestry, might. Furthermore, information about farm size for farmers and forest size for foresters is economically sensitive. It can reveal a lot about the financial status and business scale of individuals and can be linked to individuals especially together with demographic information in closely-knit communities like agriculture and forestry. The challenge is not only about hiding direct identifiers but ensuring that variable combinations would not lead to unintended identification, especially when the data could potentially be combined with other available datasets. Furthermore, it needs to be highlighted in detail why DPSD was chosen over just synthetic data. While synthetic data mirrors the original dataset's statistical properties, it is not always immune to re-identification techniques. DP enhances data protection by introducing tailored noise, making reverse engineering or specific participant inference virtually impossible. In fact, current research has shown that under the DP framework robust privacy guarantees can be given for synthetic data (Ganev and De Cristofaro 2023). DPSD strikes a balance, safeguarding privacy while preserving data utility. We regard DPSD as an innovative solution, though not a panacea, in the ongoing debate on open science and data privacy, which we aim to introduce to agricultural and forestry economics with our study.

4. Results and discussion

4.1 Descriptive results

Table 1 offers the descriptive statistics of participants, aligning closely with findings from Sauter et al. (2018). Of the 1,185 farmers who initiated the survey, 454 completed it. After discarding questionnaires with inconsistent answers, 371 records from farmers remained for the analysis. This sample size for farmers aligns with other studies on their risk attitudes (Iyer et al. 2020). Additionally, the descriptive statistics are comparable with previous findings (e.g., Maart-Noelck and Musshoff 2014; Vollmer et al. 2017; Rommel et al. 2019). The typical farmer in our dataset is 47 years old, predominantly male (86 per cent), with 47 per cent holding a higher education degree. The average farm spans 462 hectares of arable land. In total, 620 foresters started the survey of which 244 foresters completed the survey. For the analysis, 215 usable records of foresters remained after removing inconsistent questionnaires. The descriptive statistics of the foresters are comparable to other studies investigating foresters, but our sample size is larger (e.g., Musshoff and Maart-Noelck 2014; Brunette et al. 2020). Foresters in our sample average 49 years in age, with 88 per cent being male. Many, 47 per cent of the foresters have a higher education degree, and the average forest area is 10,594 hectares.

In total, our sample size exceeds that of the only comparable study by Sauter et al. (2018) for both occupational groups. There are no statistically significant differences between the socio-demographic characteristics of farmers and foresters. However, the reader should be cautioned that our sample also exhibits a slight bias toward participants with higher educational degrees and larger enterprises.

Table 2 shows the descriptive results for farmers’ and foresters’ risk attitude and innovativeness. Notably, over 50 per cent of farmers identify as risk-seeking in both contexts. This contradicts many prior studies that characterized farmers as predominantly risk-averse using multi-item scales. However, a lack of standardized scales across studies complicates direct comparisons (Iyer et al. 2020). The studies that employed the same scale for the general self-assessment exhibited partially conflicting results. Some find farmers to be risk-neutral or even risk-seeking, while others find them to be risk-averse. For instance, our findings are in contrast to Meraner et al. (2018) finding that farmers assess themselves in general as risk-averse. Both Maart-Noelck and Musshoff (2014) as well as Rommel et al. (2019) find farmers asses themselves to be risk-averse with a tendency to be risk-neutral. Still, our results align with Meraner and Finger (2019) suggesting risk-neutrality in business related contexts.

Foresters in our study also report being risk-neutral in a general sense, but slightly risk-averse when considering a specific decision for their forestry enterprise. Past research on foresters’ risk attitudes varies: For instance, Musshoff and Maart-Noelck (2014); Brunette et al. (2017); Kang et al. (2019) as well as Sauter and Musshoff (2018) find foresters to be risk-averse using a lottery task. While Andersson and Gong (2010) as well as Andersson (2012) identify foresters as risk-neutral to risk prone using a financial task. Sikora and Nybakk (2012) classify foresters as risk-seeking based on self-assessment using a 7-point item. Our results only partly resemble Sauter et al. (2018), who classify both farmers and foresters as risk-averse.

In terms of innovativeness, both farmers and foresters report being innovation-seeking, as expected. Less than 5 per cent in both occupational groups indicate they are innovation-averse in general and regarding their business decisions. Likewise, the lack of standardization in the measures and statements used to assess innovativeness complicates comparisons between previous studies. For instance, Michels et al. (2020) and Mann (2018) use a 5-point item and find farmers to be less innovative. In contrast, Slijper et al. (2020) as well as Kangogo et al. (2021) find farmers to self-assess as innovative using two or three 7-point Likert scales respectively. For foresters, Sikora and Nybakk (2012) classify them as being open to innovation using a 7-point Likert scales.

4.2 Results of the comparison

In the following, results are presented and discussed based on the literature discourse in Section 2. The results shown in Table 3, support our expectation that foresters display a statistically significant higher degree of risk aversion compared to farmers. The results partly resemble the findings of Sauter et al. (2018), who find foresters to be more risk-averse. Additionally, we evaluate if there are any statistically significant differences in the risk attitudes measured with generalized and contextualized self-assessment statements. For both occupational groups, results show that there are statistically significant differences, with farmers and foresters being relatively more risk-averse when considering contextualized statements compared to generalized statements. These findings are consistent with the broader consensus in risk research, emphasizing the context-dependent nature of risk attitudes (e.g., Menapace et al. 2016).

Figure 1 plots the empirical cumulative distribution functions of the generalized (left) and contextualized (right) risk attitude coefficients. These distributions offer additional insights into the differences in risk preferences between farmers and foresters. Note that a greater proportion of foresters lean toward the lower end of the 11-point Likert scale, indicating a more risk-averse tendency than farmers.

In alignment with findings presented in Table 4, our expectation that farmers report a statistically significant higher innovativeness is not met: both occupational groups showcase analogous levels of innovativeness. Likewise, no statistically significant difference is found for the contextualized self-assessment between both occupational groups applying the same statistical test.

In detail, the results show that there is a statistically significant difference in farmers’ self-assessed innovativeness based on the generalized or contextualized statement. In contrast, no statistically significant difference is found in foresters’ innovativeness based on these two measures. The observed results may be attributable to the difference in employment structures between farmers and foresters. Predominantly, farmers operate as self-employed individuals, whereas foresters are typically employed by, for instance, public forestry. In the context of forestry employment, individual innovativeness may have limited influence on job performance, hence the lack of distinction of their general innovativeness.

Turning our attention to Table 5, we delve into the relationship between innovation-seeking and risk-seeking tendencies which we expected to be statistically significantly positively correlated. The upper part of Table 5 shows the results for farmers, while the lower part of Table 5 displays the results for foresters, both based on a Spearmans’ rank correlation coefficient. For both subsamples, positive statistically significant correlation coefficients with moderate effect sizes are found. Furthermore, a positive statistically significant correlation is found between the generalized and contextualized measures of innovativeness and risk attitude for both farmers and foresters, as expected. The findings indicate that farmers and foresters displaying greater innovativeness are also more inclined to take risks, a contrast to Kreft et al. (2021), who find no statistically significant relationship among farmers. This finding is consistent with previous research by Sikora and Nybakk (2012) for foresters and Pennings and Smidts (2000) for farmers.

Table 6 presents the results of the financial literacy assessment. On average, farmers score 2.81 and foresters achieve 3.01 out of 4 points. An individual is classified as financially literate if they answer at least three out of the four finanical concepts correctly (Klapper et al. 2015), which applies to both primary producers on average. Drawing from the results in Table 6, our anticipation of no statistically significant differences between the financial literacy scores of farmers and foresters holds true. When examining the questions in detail, a statistically significant higher percentage of foresters correctly answer the question on risk diversification, while both groups have the lowest percentage of correct answers for the question on inflation. In addition to Table 6, Fig. 2 shows the distribution of correct answers for farmers and foresters individually with respect to financial literacy.

Table 7 shows the results on the expected statistically significant negative correlation between financial literacy and a risk-averse attitude. For this purpose, Spearmans’ rank correlation coefficients are estimated. Notably, while three out of the four correlation coefficients mirror the anticipated negative sign, they lack the statistical significance and adequate effect sizes requisite to validate our initial expectation based on the literature (e.g., Lilleholt 2019).

As suggested by one anonymous reviewer, we used a regression model to account for potential confounding variables. This allowed us to differentiate whether observed differences in risk preferences could be attributed to occupation (either as a farmer or a forester) or other influencing factors (e.g., age and financial literacy). Following again the approach of Sauter et al. (2018), we estimated interval regressions on the self-assessment values of farmers’ and foresters’ risk attitude and innovativeness and included collected comparable control variables. Results are shown in Table 8. The selection of control variables was constrained by the availability of comparable data across both farmer and forester samples. This led to the exclusion of variables such as farm size and forest enterprise size, which were not directly comparable between the two groups. In the regression of risk preferences, financial literacy was included as an independent variable based on existing literature (referenced in Section 2), which suggests a link between financial literacy and risk attitude. This rationale guided our decision to include financial literacy in the risk preference regression while excluding it from the innovativeness analysis as this is not and cannot be derived from the literature. A more risk-seeking attitude in both domains is statistically significantly associated with being a farmer even when controlling for other variables. However, this does not hold true for both innovativeness measurements. Hence, the differences in self-reported risk preferences between farmers and foresters persist, indicating that these are intrinsic to the groups and not driven by confounding factors. The risk attitude in both domains is not statistically significantly associated with participants’ age. However, participants’ age is statistically significantly negatively correlated with the contextualized innovativeness. Lastly, financial literacy is not statistically significantly associated with the risk attitude. The regression results reinforce our previously presented results and conclusions.

4.3 Discussion of the results and research outlook

Discrepancies between our results in Table 2 and Sauter et al. (2018), despite using the same self-assessment methodology, may be attributed to temporal shifts in risk preferences (Finger et al. 2022) or varied recruitment methods. While Sauter et al. (2018) also engaged agricultural and forestry associations, our study expanded recruitment to social media. For clearer insights, future research should consider temporal examinations and more representative sampling.

Combining results of Tables 3–5 present an intriguing landscape of behavioral tendencies among farmers and foresters. Results of Table 3 indicate that farmers demonstrate a statistically significant higher risk-seeking behavior compared to the foresters. Hence, one could suggest they are also more open-minded regarding new practices or technologies. However, no statistically significant differences between farmers’ and foresters’ innovativeness are found. The observed statistically significant differences in risk attitudes between foresters and farmers, as well as their analogous levels of innovativeness, can be explained through the lens of self-selection and inherited roles. Individuals with inherent risk-averse tendencies may be more likely to choose forestry, attracted by its long-term planning periods and the less entrepreneurial risk of not being self-employed. In contrast, those who inherit farming roles may not initially align with the profession's risk profile but adapt over time. Despite these differences in risk attitudes, both professions demand a certain level of innovativeness, driven by the need to adapt to environmental and market challenges. For further research it could be of interest, to examine how inheriting a farming role influences individuals’ risk attitude and innovativeness compared to those who have actively chosen agriculture or being a farmer as a career.

This complex interplay between risk attitude and innovativeness is further accentuated by Table 5. The results underline that risk-seeking behavior and innovativeness are statistically significant and positively correlated. Farmers (ρ = 0.32) and foresters (ρ = 0.29) exhibit a moderate association between generalized risk attitude and innovativeness. Similarly, for contextualized risk attitude and innovativeness, farmers (ρ = 0.34) and foresters (ρ = 0.29) present comparable strengths in these relationships. This brings forth an intriguing question: Why, despite their higher risk-seeking tendencies, are farmers not statistically significantly more innovative than foresters? Even with a correlation between innovativeness and risk, innovation types and contexts could vary. Foresters, despite being risk-averse, might have avenues promoting innovation without substantial risk. This is underscored by the statistically significant differences in generalized and contextualized innovativeness among farmers, but not foresters. The perception and nature of risks might differ too, with foresters focused on long-term considerations like forest health, while farmers tackle short-term market uncertainties.

Our findings from Tables 3 and 4 emphasize the importance of contextual framing when assessing risk attitudes and innovativeness across occupational groups. Contrasting results from general and contextualized lottery tasks with foresters could be illuminating. As seen in agricultural economics (e.g., Menapace et al. 2016), it would be insightful to check if contextualized methods better explain foresters’ decision-making. While foresters’ innovativeness levels are not statistically significantly different, farmers show statistically significant varied innovativeness in their professional setting. This disparity could be explained by the self-employment of farmers compared to foresters’ employment status. Therefore, future studies should adopt contextualized prompts to gauge farmers’ innovative tendencies in different management domains and delve deeper into foresters’ professional roles when assessing their innovativeness specifically and decision-making in general. It would also be worthwhile to test the proposed 11-point scale's validity by linking it with the adoption of innovative practices in forestry and agriculture, such as precision farming or sustainable forest management.

Table 5 results reveal a positive statistically significant correlation between risk-seeking behavior and innovativeness in the primary sector. When formulating financial strategies or policies to support innovation adoption in this sector, integrating individual risk attitudes is recommended. However, correlation does not equate to causality. To understand whether risk-seeking individuals genuinely lean toward innovation, a more in-depth examination, perhaps using multi-period business management games, is needed.

While the results from Table 6 indicate that farmers and foresters are relatively financially literate, further research could be valuable in deepening our understanding of their financial literacy. This could include applying a self-assessment numeracy item (Meraner and Finger 2019) in addition to the financial literacy tasks by Klapper et al. (2015). This would allow for an examination of the correlation between self-assessment and objective measures of financial literacy. In light of this, there could be value in applying context-specific financial literacy tasks, mirroring the already recommended adoption of contextualized elicitation methods. Lastly, it would be valuable to understand how this literacy translates into real-world (financial) decisions.

Tables 3, 6, 7, and 8 interweave an interesting narrative. From Table 3, we observe that farmers are statistically significantly more risk-seeking compared to foresters. Findings from Table 6 reveal that there are no statistically significant differences in the financial literacy scores of farmers and foresters. The results in Tables 7 and 8 add a layer of complexity to this scenario as there is no statistically significant association between risk attitude and financial literacy, further underscored by negligible effect sizes. Hence, with equivalent financial literacy levels, the differing risk attitudes of farmers and foresters might stem from unique occupational challenges and perspectives—potentially the long-term planning in forestry versus farming's more immediate decision-making.

Nevertheless, the results are perplexing since the broader literature states that individuals with more cognitive abilities (e.g., financial literacy) tend to be less risk-averse because they exhibit more reflective patterns and succumb less to behavioral biases (e.g., Lilleholt 2019). In contrast to Grüner (2022), who finds a statistically significant influence of cognitive abilities on farmers’ risk attitudes elicited through a lottery task, our results, on initial inspection, suggest that multi-item scales might not be influenced by the financial literacy of farmers and foresters. Yet, our finding is at odds with previous research that has demonstrated links between self-reported risk preferences, cognitive abilities (Dohmen et al. 2018), (self-reported) numeracy (Falk et al. 2018; Andreoni et al. 2020), and financial literacy (Le Fur and Outreville 2022). Dohmen et al. (2018) suggest that individuals with lower cognitive abilities might perceive themselves as more risk-averse, suggesting that this perception might stem from inaccuracies in self-assessment rather than true risk aversion. Similarly, Mudzingiri and Koumba (2021) as well as Mudzingiri et al. (2019) argue that, while financial literacy is not directly linked to self-reported risk attitudes, it does assist individuals in more precisely assessing their risk preferences. They highlight that financial literacy could help bridge the gap between risk attitudes elicited from lotteries and those that are self-reported. Moreover, Riepe et al. (2022) note that financial literacy can moderate the effect of risk preferences on decision-making, indicating that improved financial literacy might mitigate the inhibitory influences of risk aversion on certain decisions, like initiating entrepreneurial activities. These insights imply that the broader notion of financial literacy, extending beyond just numeracy, might not directly influence the self-reported risk attitudes of farmers and foresters. Instead, its’ role seems to be more like a moderator, aiding individuals in aligning their self-reported risk attitudes with their experimental displayed risk attitude. An interesting avenue for future research might be examining the potential of integrating financial literacy treatments in surveys and experiments to enhance the consistency between self-reported and experimentally elicited risk attitudes. Consequently, exploring the role of financial literacy in determining farmers’ and foresters’ risk attitudes from lotteries—an aspect not explored in our study—becomes essential. Nevertheless, it cannot be ruled out that the discrepancy with prior research may arise from the characteristics of our study's sample. A replication involving a more representative sample of farmers and foresters could also offer additional insights.

4.4 Replication of the key results using a differentially private synthetic data set

Tables 9–14 present the results of our study based on the ε = DPSD under the conditional PATE-GAN framework with DP applying the common of ln(3) compared to the original data set. In general, the findings from the study are successfully replicated utilizing the DPSD. It should be noted that one might arrive at different conclusions when using the DPSD for the results presented in Table 11 regarding farmers’ and foresters’ innovativeness, which could be off-set using a higher ε-level.³

5. Concluding remarks

This research aimed to provide a comprehensive comparison of risk attitudes, innovativeness, and financial literacy among primary sector producers in Germany, namely farmers and foresters. The interrelationships between these elements were also examined. Through two parallel online surveys, we gathered data from 371 farmers and 215 foresters in the first half of 2022. Risk attitudes and innovativeness were gauged using generalized and contextualized self-assessment statements based on Dohmen et al. (2011), while financial literacy was evaluated through four questions following Klapper et al. (2015).

The results allow several implications for different groups of interest of which some are highlighted in the following: Partly contrary to the findings of the replicated study by Sauter et al. (2018), both farmers and foresters identify themselves as risk-neutral but exhibit risk-averse tendencies when decisions are framed to their enterprises. The apparent contradiction to the previous literature underscores the dynamic nature of risk attitudes over time, thus advocating for longitudinal studies to capture this dynamism. Additionally, the observed differences highlight again the importance of using comparable elicitation methods for individuals risk attitude and innovativeness across studies to ensure consistency in results and interpretations. This would enable more accurate comparisons and facilitate the applicability of previous findings in the future. Moreover, considering the application of contextualized risk attitude measurements in forest economic research could yield more nuanced insights. Notwithstanding the identified similarities in risk attitudes among farmers and foresters, any variations should not be overlooked, especially when designing policy interventions within the primary sector.

Both farmers and foresters rate themselves as open to innovation, and no statistically significant differences are found between the two groups using the generalized and contextualized statements. For farmers, a statistically significant distinction is observed between generalized and context-specific measures of innovativeness. The results also imply that, in future studies, context-specific innovativeness should receive more attention as has already been established for risk attitudes. A statistically significant positive correlation is identified between innovativeness and risk-seeking behavior.

No statistically significant differences are observed between the financial literacy scores of farmers and foresters; both groups can be considered on average financially literate. Yet, given their low performance on the inflation and compound interest tasks, reinforcing basic skills in this context could be useful during risk management or finance training. Furthermore, the anticipated correlation between financial literacy and risk-seeking attitude was not statistically significant. At first glance, this finding suggests that multi-item scales, due to their simple nature, might be less influenced by financial literacy than lottery tasks. However, existing literature hints that enhancing financial literacy could improve the accuracy of self-reported risk attitudes, aligning them more closely with observed risk behaviour—a field ripe for further exploration. Extending such studies also to emerging and developing countries, where financial literacy training might be needed, remains a crucial next step.

Finally, by employing a DPSD mechanism with a privacy budget of the ln(3), we successfully replicated our findings substantially. This method has been largely unexplored in the field of agricultural and forestry economics. However, it is important to note that we do not regard DPSD as an ultimate solution to the debate on data privacy (Reiting and Deshpande 2023). It represents a promising compromise that warrants further investigation. Researchers should evaluate its appropriateness on a case-by-case basis, factoring in the context, sensitivity of the data at hand and confidentiality promised to participants. Wimmer and Finger (2023) highlight regarding the use of synthetic data, that they should not be used for new results and policy implications, but rather for replication. Furthermore, original data should be published where possible. This holds also true for DPSD.

Acknowledgments

The authors thank Jennifer Haines for assistance with language revisions.

We gratefully thank two anonymous reviewers and the editor for their helpful comments.

Funding

Financial support by the German Research Foundation (DFG) is gratefully acknowledged.

Conflict of interest

The authors report there are no competing interests to declare.

Data availability

Differentially private synthetic data set available.

Footnotes

References