Scarcity, Cognition, and Health Decision-Making: Evidence from a Lab-in-the-Field Experiment in Rural China

Scarcity, Cognition, and Health Decision-Making: Evidence from a Lab-in-the-Field Experiment in Rural China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Scarcity, Cognition, and Health Decision-Making: Evidence from a Lab-in-the-Field Experiment in Rural China Haiou Zhu, Fangzhou You, E Liu, Thorsten Gruber, Hua Dong, Cees Bont This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7321300/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Understanding how financial scarcity impacts health-related decision-making is vital for designing interventions to break the cycle between poverty and poor health. We conducted a lab-in-the-field experiment with 479 rural adults in two low-income regions of China, Yunnan and Shaanxi, to examine the cognitive and behavioural consequences for health-related preferences and choices. Participants were randomly assigned to a scarcity prime (Hard vs. Easy scenario) and completed tasks measuring cognitive function (Raven’s matrices), time and risk preferences in both monetary and health domains, and budget allocations across necessities (groceries), health, and temptation goods. In Yunnan, a moderately poor region, scarcity significantly reduced cognitive performance and increased health-related risk aversion, whereas no such effects were observed in Shaanxi, where poverty was more severe. Time preferences were largely unaffected by the prime in both regions. Spending allocations showed limited shifts under scarcity, with some subgroups reallocating more toward health. Our findings suggest that scarcity’s psychological effects are not universal but context-specific, with implications for health interventions and poverty alleviation strategies. Choice architecture interventions (e.g., nudges) may be effective for moderately poor populations, while choice infrastructure with structural supports remains essential for those facing chronic deprivation. Earth and environmental sciences/Environmental social sciences Health sciences/Health care Biological sciences/Psychology Social science/Psychology Scarcity Psychology of Poverty Cognitive Function Present Bias Risk Aversion Health Decision-Making Health Inequality Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Health-related decision-making often requires individuals to plan for the future and invest resources in preventive or long-term benefits 1 , 2 . However, the reality of scarcity, having insufficient resources for one’s needs, can undermine such forward-looking health behaviours 3 , 4 . Households facing tight budgets tend to prioritise immediate necessities like food or shelter over expenditures such as medications or preventive services (annual check-ups, immunizations) 5 . Living in poverty has also been linked to certain unhealthy behaviours, for example choosing cheaper, calorie-dense foods or spending on “temptation goods” (like alcohol or cigarettes) despite long-term health costs 6 . Indeed, poverty contributes to a vicious cycle in which poor health outcomes both result from and exacerbate economic hardship in low-income communities 7 . This is evident in rural China, as in many rural areas globally, that illness is a leading cause of poverty 8 . Rural regions face stark disparities in healthcare access compared to cities, and vulnerable groups such as the elderly, women, and left-behind children (those with migrant worker parents) suffer disproportionately from unmet health needs 9 , 10 . Understanding the relationship and underlying mechanisms between health decision-making and poverty is essential for designing interventions to break these traps and reduce health inequities 11 , 12 . Scarcity theory provides an integrative framework for how poverty impacts decision-making 13 , 14 . Classical economics traditionally focuses on objective scarcity, a shortfall in material resources, and how it constrains consumption and investment 2 . By contrast, behavioural economics and psychology, particularly in poverty literature, examine subjective scarcity, the feeling of having less than one needs 14 . The psychology of poverty describes a scarcity mindset, where both monetary concerns (e.g. paying for rent, school fees, utility bills) and non-monetary hardships (poor healthcare access, malnutrition, lack of sleep) preoccupy one’s thoughts, making everyday decisions more difficult and stressful 15 , 16 . This perspective emphasises that poverty entails psychological and cognitive scarcity as much as material deprivation 15 . Even among higher-income individuals, acute worries, such as during an economic crisis, a pandemic lockdown, or concerning climate change, may impair cognitive performance and decision-making 17 , 18 . The key idea of scarcity theory is that scarcity itself induces a distinctive mindset that influences how people think and decide, and subsequently affect human preferences and behaviours 13 . Two core psychological mechanisms have been proposed to explain decisions under scarcity: cognitive load and tunneling 13 , 19 , 20 . First, poverty-related concerns can occupy a portion of one’s mental bandwidth, leaving fewer cognitive resources available for other tasks 21 , 22 . While some experiments show that financial worries directly impairs cognitive performance 21 , 22 , others find no significant cognitive decline under scarcity conditions 23 – 25 . A recent meta-analysis found a moderate negative effect of scarcity on cognitive function, though effect sizes varied with context and individual differences (e.g., education level, severity, and timing of scarcity) 26 . Second, feeling resource-constrained can lead to tunnelling, a narrowing of focus and attention 15 , 27 . Some evidence suggests scarcity-induced tunnelling may improve decision-making by eliminating distractions 28 , increase consistency in how people value products 29 , or even increase cognitive performance when paired with incentives 30 . On the other hand, studies have linked tunneling to counter-productive choices such as over-borrowing 31 , impulse purchase 32 , and poor budgeting (failing to plan expenditures) 33 . Whether scarcity leads to more efficient decisions or worse ones likely depends on which psychological mechanism (taxing cognitive load vs. enhancing focus) dominates in a given context. Scarcity may also influence basic preferences, such as temporal discounting and risk-taking, which in turn drive various behaviours 16 , 19 . Scarcity might intuitively be expected to foster present bias (favouring immediate smaller rewards over future larger rewards) and risk aversion (opting for safer choices), as those under financial strain prioritise immediate needs and security 34 , 35 . Indeed, some studies report that individuals exhibited more present bias before payday compared with after payday 36 and poorer villagers were more loss-averse and less patient than wealthier ones 34 . However, other experiments paint a more complex picture. A study that primed low-income participants with financial worries found it could reduce risk aversion in some cases 24 . Some field studies found no significant change in time preferences due to short-term financial fluctuations 25 , 37 . These mixed results indicate that scarcity does not uniformly heighten time-discounting or risk aversion 13 . One possible explanation for the mixed findings on how scarcity affects decision-making is that the effects may differ depending on the decision domain involved 38 – 40 . People often behave differently when the consequences involve health rather than money. For example, individuals tend to be more risk-averse for outcomes that affect health (such as choosing medical treatments) than for financially framed outcomes 41 . Likewise, individuals sometimes display a higher impatience (steeper time discounting) for health benefits compared to monetary gains, perhaps because immediate health improvements are valued particularly strongly 42 . These domain differences suggest the need to explicitly study health decisions under scarcity, rather than assume findings from financial decisions can be generalised. However, existing research on scarcity has predominantly emphasized financial decision-making 13 , 19 , 43 , or explored scarcity’s impacts on behaviours related to productivity 44 , parenting and investment in children’s education 45 , 46 and savings 47 . There is a lack of research examining how scarcity influences health-related decisions, despite the central role that health decisions play in quality of life and long-term economic outcomes. Drawing on scarcity theory and given the mixed evidence on mechanisms and their consequences, the present study aimed to examine the effects of financial scarcity on (i) cognitive function, (ii) time and risk preferences in both monetary and health domains, (iii) spending trade-off decisions, among low-income adults in rural China. We implemented a lab-in-the-field experiment in two distinct rural regions, Yunnan and Shaanxi providences of China. We randomly assigned participants to either an urgent high-cost expenses scenario (“Hard” scarcity prime) or minor expenses scenario (“Easy” control), following methods used by prior studies 21 , 24 , 25 . Both regions are low-income and predominantly agricultural, but they differ in important socio-economic aspects (see details in Results). This two-site design allowed us to test whether scarcity effects are general or context-specific. We also adopted a multi-dimensional perspective on scarcity, examining three different sources of scarcity: (1) immediate financial worry (induced scarcity via a financial priming task), (2) objective scarcity (household annual income), and (3) liquidity fluctuation (the number of days since participants’ last payment). These factors have each been identified in prior work as important predictors of subjective or objective scarcity, but most previous studies examine them in isolation 22 , 48 . By considering them jointly, we can explore whether multiple scarcity pressures interact or moderate each other’s effects. In terms of outcomes, we measured cognitive functioning using the Standard Progressive Matrices (a Raven’s matrices test) 49 . Time and risk preferences were measured in both monetary and health contexts using non-incentivized choice tasks (the multiple price list task based on the quasi-hyperbolic discounting model for time 50 , and the Eckel & Grossman lottery task for risk 51 ). Finally, we included a budget allocation task to observe how participants prioritise spending between groceries (basic needs), health, and temptation goods when faced with a constrained budget. We extend prior research by comparing scarcity’s cognitive and behavioural effects across two socioeconomically distinct poor regions and to include health-related decision measures and highlight the context-dependent nature of scarcity effects on decision-making. Results Sample Characteristics and Balance Check A sample of 479 adults from rural Yunnan (N = 264) and Shaanxi (N = 215) was included in the main analysis (see data exclusion details in Methods). Summary demographics and a balance check for the random assignment are provided in Supplementary Table S1 . In Yunnan, 53.0% of participants were male, compared to 39.5% in Shaanxi. Over half of all participants (53.8% in Yunnan; 66.1% in Shaanxi) were aged 50 or above. A notable proportion (18.6% in Yunnan; 13.5% in Shaanxi) self-reported as illiterate. Annual household incomes differed starkly between the regions, reflecting different levels of scarcity: Yunnan participants reported a mean income of ¥28,462 ( $ 4,233 USD), more than double the Shaanxi sample’s mean of ¥11,122 ( $ 1,654 USD). Additionally, about half of the participants relied on small-scale farming or migrant labour as their primary income source (48.9% in Yunnan; 64.7% in Shaanxi). In Shaanxi, the majority (71.6%) reported having no fixed or regular income, versus 44.3% in Yunnan. Correspondingly, the time since last payday was much longer on average in Shaanxi (mean = 95 days) than in Yunnan (mean = 31 days), indicating more irregular cash flows in the Shaanxi communities. Despite these between-region differences, the randomisation to Hard vs. Easy prime conditions was generally successful within each region. The groups assigned to the Hard vs. Easy scenario did not significantly differ on most background characteristics. Two modest imbalances were noted in Shaanxi: participants in the Easy group had slightly higher education levels on average than those in the Hard group ( P = 0.002), and a greater proportion of the Easy group had a recent physical health exam ( P = 0.04). Manipulation Check The scarcity prime was effective in inducing greater subjective financial concern in the Hard condition compared to the Easy condition in both regions (Fig. 1 ). As intended, participants presented with the Hard scenarios reported substantially higher levels of worry and stress than those given the Easy versions. This pattern held true for all three vignettes and in both regions. Across all scenarios, the differences between Hard and Easy groups are statistically significant ( P < 0.001 by t-tests; see SI Table S2 for the exact stress ratings and statistics). Thus, our priming manipulation successfully created an acute sense of financial scarcity in Hard-condition participants relative to controls, which serves as a foundation for examining its causal effects on cognitive and choice outcomes. Cognitive Function Inducing a scarcity mindset impaired cognitive performance in Yunnan but not in Shaanxi. In Yunnan, the Easy group scored 61.5% on average on the 12-item Raven’s task, compared to 54.6% in the Hard group. Regression analysis (see SI Table S3) confirmed a significant negative effect of the Hard prime, an average drop of 8.4 percentage points ( P < 0.01). In contrast, Shaanxi participants showed no meaningful difference (60.2% Hard vs. 58.4% Easy). In other words, prompting thoughts of urgent financial needs reduced cognitive performance in Yunnan but had no effect in Shaanxi. We tested whether this effect varied by annual income or days since last payment (Fig. 2 ), but neither interaction was significant in either region (see SI Table S4). Time Preference (Present Bias) We examined whether scarcity influenced time preferences using multiple price list tasks for monetary and health choices. Only participants who made consistent choices, i.e., defined as switching their preference no more than once in each price list, were included in the analysis. Present bias was defined as switching to a delay choice in the second price list, indicating hyperbolic discounting. In Yunnan, the Hard prime group was slightly less likely to show present bias in monetary choices (48.1%) than the Easy group (55.2%), with a marginally significant effect (P = 0.065). No difference was found in health choices (40.2% vs. 39.5%). In Shaanxi, present bias rates were similar across conditions in both domains, with no significant effects (see SI Tables S4–S5). Risk Preference (Risk Aversion) We measured risk-taking behaviour through two parallel tasks: one involving a monetary lottery game and one involving a vaccination benefits choice. For monetary risk, the Hard prime did not significantly change risk preference in either region (Fig. 4 A and 4 C). Ordered logistic regressions confirmed no meaningful prime effect on financial risk-taking (see SI Table S6). For health-related risk, participants under the Hard prime became significantly more risk averse in Yunnan sample (Fig. 4 B). The Hard-primed individuals were more likely to opt for the safer vaccine option rather than a riskier option. In the Easy condition, 64.6% of Yunnan participants chose the safest vaccine, whereas under the Hard condition this percentage increased to 82.1%. An ordered logistic regression confirmed a significant effect of the prime ( P = 0.008) after controlling for covariates. By contrast, no significant prime effect on health risk-taking was found in Shaanxi (Fig. 4 D). Spending Allocation Decisions A key question in our study was whether scarcity affects how people allocate spending across essential (groceries), health-related, and temptation goods. Across both samples, the largest budget share went to groceries, followed by health and temptation goods (see SI Table S4.1–4.2). We used seemingly unrelated regression (SUR) models to assess the effects of scarcity priming and its interaction with annual income and days since last pay (see SI Tables S7.1–S7.2). In Yunnan, there were no significant main effects of scarcity on any spending category. Income positively predicted health spending (β = 0.234, P = 0.025), but the interaction with scarcity was non-significant, as were all interactions with liquidity. In Shaanxi, scarcity priming also showed no main effects. However, higher income reduced grocery spending (Group × Income: β = − 0.63, P = 0.046) and increased temptation spending (β = 0.394, P = 0.046) under the Hard prime. Interactions involving days since last payment were not significant in either region. Subgroup analysis We ran subgroup analyses to explore heterogeneity in the effects of scarcity priming (Hard vs. Easy) on cognitive function and spending allocations across grocery, health, and temptation goods in Yunnan. We focused on Yunnan because this sample have more variations in effects on outcome variables. Each model included socio-demographic controls, excluding those overlapping with the subgroup variable. Exposure to the Hard condition significantly reduced cognitive scores across several subgroups, especially those with no income (β = − 25.58, P = 0.01), unfixed income (β = − 10.25, P = 0.03), lower income (β = − 9.64, P = 0.02), longer delay since last payment (β = − 12.35, P < 0.001), and smaller last payment (β = − 16.77, P < 0.001). Similar effects were found among women (β = − 10.7, P = 0.01), older adults (β = − 9.43, P = 0.02), and those without recent health exams (β = − 10.72, P = 0.05). In spending allocation, scarcity reduced grocery shares among participants with lower last payments (β = − 0.07, P = 0.04) and women (β = − 0.06, P = 0.09). Health spending increased under scarcity among those with lower income (β = 0.05, P = 0.09), longer delay since last pay (β = 0.09, P = 0.02), lower last payment (β = 0.07, P = 0.03), and women (β = 0.06, P = 0.08). Temptation spending remained largely unchanged, except for a decline among lower-income participants (β = − 0.03, P = 0.09). Discussion This study set out to examine how scarcity affects cognitive functioning and decision-making across both economic and health domains. We investigates three facets of scarcity (a financial worry prime, chronic income level, and liquidity fluctuation) and compared outcomes in two distinct low-income populations. We found that scarcity induced via a financial prime significantly impaired cognitive performance and increased health-related risk aversion in the relatively less impoverished region (Yunnan), whereas no such effects were observed in the much poorer region (Shaanxi). Moreover, time-discounting behaviour was largely unaffected by the acute scarcity prime in either region. These findings indicate that the cognitive and behavioural impacts of scarcity are highly context dependent. We discuss the policy implications of our findings, arguing that policies must combine both behavioural and structural interventions to improve financial well-being across different poverty contexts. One of the salient results was the cognitive tax of scarcity observed in Yunnan. Participants from Yunnan who were primed with hard financial scenarios performed significantly worse on the Raven’s matrices, consistent with the idea that even momentary financial anxiety can sap mental bandwidth. This aligns with the findings that worrying about economic problems impairs cognitive capacity 21 , 22 . At the same time, the absence of any cognitive decline in the Shaanxi sample is equally noteworthy. Notably, our manipulation check confirmed that the Hard prime raised worry and stress levels in both regions, so the lack of cognitive impairment in Shaanxi was not due to the prime failing in that group. One explanation is that a ceiling effect occurred because Shaanxi participants, being significantly poorer, may be so accustomed to financial hardship that an additional hypothetical challenge does not influence their thinking. Our results resonate with other mixed findings in the literature, for example, studies in American samples have failed to reproduce Mani et al.’s cognitive effects, suggesting that certain populations or experimental conditions are more susceptible than others 25 . In term of temporal discounting, Yunnan participants in the Hard condition showed a marginal decrease in monetary choices but not in health choices. Shaanxi participants showed no change in time preference with the prime in either domain. Our results add to the growing consensus that poverty does not straightforwardly create impulsivity in intertemporal choice 36 . With respect to risk-taking, we found a domain-specific effect that immediate financial worries made Yunnan participants more conservative in health choices but not in monetary choices. This is consistent with prior observations that people are naturally more risk-averse for health than money, and our results indicate scarcity amplifies that tendency, at least in the Yunnan context 42 . Meanwhile, in Shaanxi, risk preferences were essentially flat across conditions in both domains. The budget allocation task provided insight into how scarcity influences spending priorities and trade-offs among necessities (groceries), health, and temptation goods. In the full sample, the Hard prime did not produce statistically significant reallocations on average. We observed some suggestive patterns that Yunnan participants who were primed to feel scarce allocated slightly more of their hypothetical budget to health expenses and slightly less to food, compared to those in the Easy condition (control). Notably, our subgroup analyses in Yunnan sample revealed a more nuanced pattern. It was the participants with the scarcest real resources, for example, those with lower income or those who were farthest from their last payday, who showed the largest adjustments under the Hard prime. Generally, our results are consistent with scarcity theory that the poor often make consistent consumption decisions focusing on necessities 28 . Interestingly, in Shaanxi we observed a small, counterintuitive interaction: participants with relatively higher incomes (within the extremely poor Shaanxi sample) who were under the Hard prime allocated slightly less of their budget to food and more to temptation goods. One speculative interpretation is that those few individuals who had a bit more financial cushion might have coped with the induced stress by forgoing some necessities and indulging in a small “treat.” They also indicate that the ability to adjust spending in response to a scarcity cue depends on having some slack or discretionary spending to begin with. However, given the borderline significance and the fact that this effect was isolated to one subgroup, we interpret it with caution. Our results inform a range of intervention strategies aimed at improving decision-making and well-being for people living under scarcity. A key insight is that in contexts of moderate poverty (like our Yunnan sample), light-touch behavioural interventions have potential to yield benefits, especially in shifting attention and choices toward long-term health. In such settings, policymakers can leverage choice architecture, the design of how options are presented, to ease cognitive burdens on the poor 52 . For example, simplifying the process of accessing healthcare (e.g. providing assistance with paperwork, sending reminders for check-ups, or using default enrolment in basic insurance programs) can reduce the mental effort required to make a healthy choice. Another strategy is behavioural targeting, which involves timing and tailoring interventions to moments and groups where they will have the greatest impact 53 . Our data suggest that right after an income payment, when liquidity is temporarily higher and minds are less preoccupied with scarcity, could be a window of opportunity for engagement. Interventions such as encouraging people to set aside savings for health or enrol in insurance might be more successful if they occur just after harvest or payday. Targeting also means identifying vulnerable subgroups who are most affected by scarcity. We found, for example, that women and those with irregular income (long gaps since last pay) experienced larger cognitive deficits and made bigger spending adjustments under scarcity. These groups might benefit from extra support, such as providing financial counselling, budgeting tools, or priority access to healthcare services. For populations in more financial constraints (like our Shaanxi sample), our results imply that purely psychological interventions (e.g. scarcity primes or minor nudges) might not be enough to change behaviour. Policies must combine both behavioural and structural interventions to improve financial well-being across populations 54 . Choice infrastructure, which encompasses the broader systems, policies, and structures that affect the accessibility and support of public health solutions, might be needed 55 . Conditional cash transfer programs, which provide payments to low-income households for behaviours like attending health check-ups or keeping children in school, have proven effective in many developing contexts 56 , 57 . Such incentives can be implemented along with the above choice architecture techniques. Scholars have pointed out that effective behavioural policies often integrate nudges with economic and material measures to sustain long-term change 55 , 56 . For instance, timing the incentive payout to match with periods when families typically run low on cash, or framing the incentive as reward for good health behaviour to increase its salience. Our observation that Shaanxi participants didn’t further reduce their already minimal health spending under strain suggests they highly value health but simply lack the means, a scenario where subsidizing health via conditional grants or vouchers can have high impact. In designing all these interventions, it is crucial to adopt an inclusive and context-sensitive approach. Our findings, where different subgroups responded differently to scarcity, suggest that multiple channels or variations of an intervention may be needed. Policymakers and practitioners should involve target communities (like rural villagers) in co-creating solutions, acknowledging local realities such as literacy levels, cultural beliefs, and social support networks. For example, if women in these villages shoulder most financial and healthcare responsibilities (as is often the case), interventions should be gender inclusive, for instance, training local women as community health finance facilitators who can help families navigate options and lighten cognitive burdens 58 . It is important to acknowledge the limitations of this study. First, while our sample covers two distinct regions, it is not representative of all rural populations. Caution is needed in generalising these results beyond the specific context of rural China. Since our two regions differed on multiple dimensions (age, income, culture), investigating which aspect was key could be done with a larger sample across more varied communities. Second, our outcome measures might simplify complex constructs. Cognitive function was measured with Raven’s matrices, which capture fluid intelligence but not other aspects like memory or attention span that scarcity might also affect. Time and risk preferences were measured through choice tasks that, although standard, are still hypothetical and may not predict real-world behaviour (especially in the health domain). Third, although we tried to control for key covariates and used random assignment for the prime, causal interpretation should be cautious. Future research should build on these findings by exploring interventions and moderating factors. Finally, we assessed multiple outcomes and conducted numerous subgroup tests, which raises the possibility of mixed findings. We mitigated this concern by focusing on the strongest and most consistent effects (notably, the cognitive and health-risk changes in Yunnan), and by treating the more marginal findings (e.g. certain spending interactions or subgroup differences) as exploratory. Replication in future studies will be important to confirm which of these effects are robust. Methods Study Design and Participants This study employed a lab-in-the-field experimental design 59 in two rural regions of China: Yunnan Province in the southwest and Shaanxi Province in the northwest. We conducted the study between May and September 2022, and collected data from local villages within each region, in collaboration with 2 local community leaders, 2 senior researchers, and 9 research assistants (4 from Yunnan and 5 from Shaanxi). By including both regions, we aimed to compare findings and to examine potential contextual moderators of scarcity effects. Prior to the main study, we conducted pilot tests in each region (16 participants in Yunnan; 14 in Shaanxi) to verify that the study materials were culturally appropriate and understood by participants with low literacy. Feedback from the pilots was used to refine the study design, such as the financial scenarios and items selected for spending allocation task. The study protocol was approved by the Ethics Committee of the first author’s institution. Participation was voluntary and anonymous, and respondents received a small compensation (such as a towel) upon completion of the tasks. All participants provided informed consent prior to participation. For individuals with limited literacy, the consent information and study instructions were explained verbally in the local dialect, in accordance with the approved ethical procedures. Within each region, participants were randomly assigned to one of the two experimental conditions (between-subject): a Hard scarcity prime or an Easy control prime. Random assignment was done in the field by alternating survey forms. Research assistants, who were well-acquainted with the local communities, visited various villages and weekend markets to recruit participants. The inclusion criteria were: (1) 18 years old or above; (2) living in rural areas; and (3) capable of understanding the instructions and performing the tasks accordingly. Experimental Procedure and Measurements After providing informed consent, all participants completed the same set of tasks, which were administered by trained research assistants. The tasks were administered in the local language and were presented orally to any participants who could not read, to ensure comprehension. The survey typically took 30–45 minutes to complete. Details of the tasks, constructs and measurements are presented in Supplementary Information. We adopted the method of priming, frequently used in psychology and economics 60 , and designed three different sets of financial scenarios: raising money in one week, a decline of annual income and an increase in medical costs. The first two sets were adapted from Mani et al. 21 , whereas the third set focused on financial shocks brought on by illness. The scenarios and questions were identical for the two groups except that the magnitude of money varies. For example, the ‘hard’ scenario described an unforeseen event requiring an immediate ¥20000 expense, whereas the ‘easy’ scenario only required ¥1000 expense. Participants were asked to respond both open-ended and closed-ended questions. The open-ended questions asked participants how they would handle the financial shocks in order to elicit their feelings and financial concerns. The closed-ended questions were coded with five-item Likert scales to measure the levels of worries. Immediately after the priming task, participants completed the easiest set (12 items) of the 60-item Standard Progressive Matrices, a widely used measure of fluid intelligence that does not require reading or mathematical skills—an important consideration given the low literacy levels in our study population 49 . This task assessed participants’ capacity for logical reasoning and problem-solving in novel situations 61 . Participants were encouraged to do their best, and while there was no strict time limit, they were asked to complete the task as efficiently as possible. We then used non-incentivised methods to elicit time and risk preferences, as prior evidence suggests hypothetical monetary choices yield results comparable to incentivised ones, offering a lower-cost alternative in field experiments 62 – 64 . We designed two intertemporal choice tasks, one framed as receiving a cash payment and the other as receiving a health subsidy (for a physical examination), using a multiple price list (MPL) format based on the quasi-hyperbolic discounted model 50 . Each task included two sets of eight money-sooner versus money-later choices: one for today vs. one month later, and another for six months vs. seven months later (e.g., ¥200 today vs. ¥300 in one month, or ¥200 in sixth month vs. ¥300 in seventh month). The two choice scenarios were identical in terms of amount of money. Choosing the earlier, smaller payoff indicated greater impatience. Participants were considered consistent if they switched their preference at most once in each MPL (which is the pattern expected of a rational time-consistent or present-biased decision-maker), and inconsistent if they switched multiple times (which could indicate confusion or lack of stable preference). As described in the Results, we defined a participant as exhibiting present bias if they chose the smaller-sooner over the larger-later reward in the today vs. next month comparison but did not do so in the six-month vs. seven-month future comparison. The primary outcome from these tasks was the percentage of present-biased participants in Hard vs. Easy groups within each region. We measured risk preference using the Eckel & Grossman lottery task 51 . In the monetary risk task, participants chose one among six lotteries, each lottery offering a 50/50 chance of winning either a lower or a higher monetary amount (with higher expected values for riskier lotteries). In the health-framed risk task, participants chose among six hypothetical vaccines, each offering different probabilities of short-term vs. long-term protection (analogous to the risk-return trade-offs in the monetary lotteries). Choosing a safer lottery or vaccine (one with a lower payoff range but more certain outcome) indicates greater risk aversion, whereas choosing a riskier option indicates greater risk tolerance. We recorded each participant’s choice and analysed whether the distribution of choices differed by prime condition in each region. Next, we included a budget allocation task where participants were asked to allocate a hypothetical budget of ¥100 across three categories: daily groceries (necessities), medicine and health-related goods, and temptation goods (non-necessities such as snacks, toys or leisure items). This task was designed to mimic the experience of budgeting in a shopping scenario, aiming to make the decision process feel as realistic as possible. The specific items listed under each category were popular, familiar items in the local markets (refined based on the pilot study feedback). After participants made their allocations, we converted the amounts into percentages of the ¥100 budget for analysis. Finally, we collected demographic and economic background information. This included age, gender, education, job, annual income, sources of income, payment frequency, physical examination, household size, household annual income and any outstanding debt. Notably, we asked participants to report the date and amount of the last payment they had received (salary, pension, remittance, etc.). Research assistants in the field helped clarify these questions and ensure accurate responses, especially for income, which some participants could only estimate. Statistical Analysis A sample of 301 participants were recruited from Yunnan, of which 150 were randomly assigned to the ‘hard’ scenarios and 151 to the ‘easy’ scenarios, and a sample of 245 participants were recruited from Shaanxi, of which 127 were randomly assigned to the ‘hard’ scenarios and 118 to the ‘easy’ scenarios. The sample size was determined by practical feasibility, including constraints of time and resources. A power analysis adjusted for multiple comparisons (α ≈ 0.0045) with 80% power across five primary outcomes indicated a minimum of 111 participants per group. A total of 37 participants from the Yunnan sample and 30 from the Shaanxi sample were excluded for one or more of the following reasons: incomplete survey submissions, unengaged responses, or absent information. This resulted in final samples of 264 in Yunnan (130 in the ‘hard’ group and 134 in the ‘easy’ group) and 215 in Shaanxi (110 in the ‘hard’ group and 105 in the ‘easy’ group) for the main analyses. Data analysis was performed using STATA 17. We used Pearson chi square tests for categorical variables and t-tests for continuous variables for balancing checks comparing the demographic variations between the ‘hard’ and ‘easy’ groups. We ran separate models for Yunnan and Shaanxi and used varied regression models to examine the treatment effects on key outcome variables, including ordinary least squares (OLS) regressions for continuous variables (priming worries and cognitive function), logistic regression for binary variables (time preferences), ordered logistic regression for ordered variables (risk preferences) and seemingly unrelated regressions (SUR) for proportional variables (spending proportions). We examined the prime × income and prime × days-since-last-pay interaction terms to explore moderation by objective and fluctuation of scarcity. All regressions models used robust standard errors and controlled for demographic and income-related information. We also conducted subgroup analysis to examine the heterogeneity in the key treatment effects in Yunnan. Declarations Competing interests The authors declare no competing interests. Author Contribution H.Z. conceptualised the study and designed the experiment. F.Y. and E.L. coordinated data collection in the field. H.Z. performed the statistical analysis. T.G., H.D., and C.D.B. advised on study design and data interpretation. H.Z. wrote the manuscript with input from all authors. All authors reviewed and approved the final manuscript. Acknowledgement This work was supported by the University Research Studentship from Loughborough University. Data Availability Data will be made available in a public repository upon publication. References Dupas, P. & Miguel, E. Impacts and Determinants of Health Levels in Low-Income Countries. in Handbook of Economic Field Experiments, Volume 2 vol. 2 3–93 (North Holland, (2017). Kremer, M., Rao, G. & Schilbach, F. Behavioral development economics. in Handbook of Behavioral Economics: Applications and Foundations 1 vol. 2 345–458 (North-Holland, (2019). Sommet, N. & Spini, D. Financial scarcity undermines health across the globe and the life course. Soc. Sci. Med. 292 , 114607 (2022). 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The Raven’s Progressive Matrices: Change and Stability over Culture and Time. Cogn. Psychol. 41 , 1–48 (2000). Laibson, D. Golden eggs and hyperbolic discounting. Q. J. Econ. 112 , 443–478 (1997). Eckel, C. C. & Grossman, P. J. Sex differences and statistical stereotyping in attitudes toward financial risk. Evol. Hum. Behav. 23 , 281–295 (2002). Thaler, R. H. & Sunstein, C. R. Nudge: Improving Decisions About Health, Wealth and Happiness (Penguin, 2009). Cohen, J., Dupas, P. & Schaner, S. Price subsidies, diagnostic tests, and targeting of malaria treatment: Evidence from a randomized controlled trial. Am. Econ. Rev. 105 , 609–645 (2015). Ruggeri, K. et al. The persistence of cognitive biases in financial decisions across economic groups. Sci. Rep. 13 , 10329 (2023). Schmidt, R. A model for choice infrastructure: looking beyond choice architecture in Behavioral Public Policy. Behav. Public. Policy . 1–26 10.1017/bpp.2021.44 (2022). McGuire, J., Kaiser, C. & Bach-Mortensen, A. M. A systematic review and meta-analysis of the impact of cash transfers on subjective well-being and mental health in low- and middle-income countries. Nat. Hum. Behav. 6 , 359–370 (2022). Haushofer, J. & Shapiro, J. The Short-term Impact of Unconditional Cash Transfers to the Poor: ExperimentalEvidence from Kenya*. Q. J. Econ. 131 , 1973–2042 (2016). Ma, X. et al. A study on the factors influencing the vulnerability of women of childbearing age to health poverty in rural western China. Sci. Rep. 14 , 13219 (2024). Harrison, G. W. & List, J. A. Field Experiments. Source: J. Economic Literature . 42 , 1009–1055 (2004). Cohn, A. & Maréchal, M. A. Priming in economics. Curr. Opin. Psychol. 12 , 17–21 (2016). Dean, E. B., Schilbach, F. & Schofield, H. Poverty and cognitive function. in The economics of poverty traps 57–118 (University of Chicago Press, (2017). Dohmen, T. et al. Individual risk attitudes: Measurement, determinants, and behavioral consequences. J. Eur. Econ. Assoc. 9 , 522–550 (2011). Madden, G. J. et al. Delay discounting of potentially real and hypothetical rewards: II. Between-and within-subject comparisons. Exp. Clin. Psychopharmacol. 12 , 251 (2004). Ubfal, D. How general are time preferences? Eliciting good-specific discount rates. J. Dev. Econ. 118 , 150–170 (2016). Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 11 Aug, 2025 Editor assigned by journal 11 Aug, 2025 Submission checks completed at journal 08 Aug, 2025 First submitted to journal 07 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7321300","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":497912971,"identity":"325b9dd6-9ffe-4753-910d-75631e9bd3f6","order_by":0,"name":"Haiou Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYBACAwbGBhCdwMAOpg8gyRDUwnOAaC0QkMAgkUCkFnP2w42PeRjq8gxuvj38mafmDgO/RALjhx8Mh41xabHsSWw25mFgKza4nZcmzXPsGYNkzwFmyR6Gw2Y4HXYgsU2ah4EnccPtHDNm3obDDAbHGxikGRgO2+DUcv4hSItE4oabZ4w/g7UcZmD+jVfLDbAtBokbbvAYSENtYQPZgtthNx42G84xSEiceSYvTXLOscM8kj0H2yx7DNJxet/gfPrDB28q6hL7jp89/OFNzWE5fonkwzd+VFgbNuDSA9EIIngYmHhAJDhyccYKCuBhYPxBlMJRMApGwSgYaQAA15pWGynVi4AAAAAASUVORK5CYII=","orcid":"","institution":"University of Oxford","correspondingAuthor":true,"prefix":"","firstName":"Haiou","middleName":"","lastName":"Zhu","suffix":""},{"id":497912972,"identity":"5e2e280f-febd-4fe0-9589-0f5140d86127","order_by":1,"name":"Fangzhou You","email":"","orcid":"","institution":"University of York","correspondingAuthor":false,"prefix":"","firstName":"Fangzhou","middleName":"","lastName":"You","suffix":""},{"id":497912973,"identity":"71b0797a-3582-403e-8bb1-40c3d4b764ef","order_by":2,"name":"E Liu","email":"","orcid":"","institution":"Yunnan Minzu University","correspondingAuthor":false,"prefix":"","firstName":"E","middleName":"","lastName":"Liu","suffix":""},{"id":497912974,"identity":"70078888-c689-48b1-b340-f3be120ce644","order_by":3,"name":"Thorsten Gruber","email":"","orcid":"","institution":"Loughborough University","correspondingAuthor":false,"prefix":"","firstName":"Thorsten","middleName":"","lastName":"Gruber","suffix":""},{"id":497912975,"identity":"e8fca859-1f9d-46df-a595-ea27153dbea9","order_by":4,"name":"Hua Dong","email":"","orcid":"","institution":"Brunel University London","correspondingAuthor":false,"prefix":"","firstName":"Hua","middleName":"","lastName":"Dong","suffix":""},{"id":497912976,"identity":"3fec4f64-8ef8-4651-8040-9e53f4a80cdb","order_by":5,"name":"Cees Bont","email":"","orcid":"","institution":"National University of Singapore","correspondingAuthor":false,"prefix":"","firstName":"Cees","middleName":"","lastName":"Bont","suffix":""}],"badges":[],"createdAt":"2025-08-07 18:23:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7321300/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7321300/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88777216,"identity":"61f83977-8e3d-4002-ad01-3f7fe29c0594","added_by":"auto","created_at":"2025-08-11 10:11:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":142466,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eManipulation check. \u003c/strong\u003eParticipants’ self-reported stress or difficulty in response to three hypothetical financial scenarios: (1) an urgent need to raise money (¥20000/¥1000) within one week for an unexpected expense, (2) a sudden decrease (50%/5%) in monthly income, and (3) an unforeseen medical expense (¥24000/¥1200), comparing Hard vs. Easy conditions, by region. Error bars show 95% confidence intervals. All Hard–Easy differences are significant at \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001 (See SI Table S2 for statistics).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7321300/v1/4d3a31f8687ea16a860c04b4.png"},{"id":88777911,"identity":"6addecb4-f201-4b72-a9cb-1dc2a8aaa882","added_by":"auto","created_at":"2025-08-11 10:19:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":173363,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of priming, annual income and days since last payday on cognitive function. Raven’s cognitive test scores by condition (left), and interaction of the prime with annual income and days since last payday (right). In Yunnan, Hard-primed participants scored significantly lower on Raven’s than Easy-primed participants. In Shaanxi, scores did not differ by prime. The interactions were not significant (95% confidence intervals from linear regressions). All regressions controlled demographic variablesand employed robust standard errors. Error bars are 95% confidence intervals.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7321300/v1/cd39469cc0cf7c8f0618cf70.png"},{"id":88777214,"identity":"7f6b5550-2cf8-4eed-b03c-699db8824346","added_by":"auto","created_at":"2025-08-11 10:11:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":104560,"visible":true,"origin":"","legend":"\u003cp\u003eProportions of present bias in monetary and health decisions, by condition and region. The sample for present bias was limited to participants who consistently chose either the earlier or later payment in both monetary and health time-discounting tasks, switching no more than once between options (see SI Table S5 for statistics). Error bars represent ±1 standard error and are 95% confidence intervals. All regressions controlled demographic variables and employed robust standard errors.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7321300/v1/4dc25ca79437b4e314dc11c8.png"},{"id":88777219,"identity":"15382012-c867-4242-b9c5-f2677417c05d","added_by":"auto","created_at":"2025-08-11 10:11:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":280600,"visible":true,"origin":"","legend":"\u003cp\u003eRisk preferences in monetary-framed (A, C) and health-framed (B, D) lottery tasks in Yunnan (A, B) and Shaanxi (C, D). Bars show the percentage of participants choosing from six lotteries in each condition. In Yunnan, the Hard prime substantially increased the preference for the safest vaccine (B), indicating higher health-related risk aversion under scarcity. No such change was seen for monetary lotteries (A) or for either task in Shaanxi (C, D). Statistical results are presented in SI Table S6.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7321300/v1/6ed9a9c2816a534ab364efcd.png"},{"id":88779046,"identity":"4d4d56eb-e700-4a66-91b9-4680b3ed9f59","added_by":"auto","created_at":"2025-08-11 10:27:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":116574,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of scarcity priming and interactions on spending allocation decisions. Each estimate was obtained from SUR regression. The dependent variables are the proportion of money allocated to each of the three categories of goods. Error bars represent ±1 standard error and are 95% confidence intervals.All regressions controlled demographic variablesand employed robust standard errors.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7321300/v1/68a474a39f4014ab4ac780fb.png"},{"id":88777223,"identity":"8e5780a5-983d-46e8-b0c9-31fcf0b29a04","added_by":"auto","created_at":"2025-08-11 10:11:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":84544,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResults of subgroup analysis of Yunnan sample\u003c/strong\u003e. The plotted coefficients represent the effect of the Hard prime (vs. Easy) on Raven’s scores or spending shares within each subgroup, with 95% confidence intervals. For cognitive outcomes (A), negative values indicate worse performance under scarcity. For spending outcomes (B, C, D), positive values indicate a higher budget share under scarcity for the given category. Subgroups include: income source (none vs. any), income level (above vs. below median), payment frequency (no fixed income vs. regular income), time since last payment (above vs. below median), last payment amount (below vs. above median), gender, age group, and recent health exam (yes vs. no). Descriptive statistics, standard errors, and \u003cem\u003eP \u003c/em\u003evalues are reported in the supplementary Table S8.1- S8.4.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7321300/v1/d90366844fddf925fa3f1f92.png"},{"id":88780284,"identity":"c9604c0c-3b62-473b-9278-d3d120d83b36","added_by":"auto","created_at":"2025-08-11 10:43:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1579670,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7321300/v1/09558e54-2ed7-40e4-838a-7a859dcfad53.pdf"},{"id":88779045,"identity":"d3b7b46c-cd29-4ab5-8bbd-9c3d098eabfb","added_by":"auto","created_at":"2025-08-11 10:27:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1271357,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7321300/v1/7c9111f473d99bebcb989521.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Scarcity, Cognition, and Health Decision-Making: Evidence from a Lab-in-the-Field Experiment in Rural China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHealth-related decision-making often requires individuals to plan for the future and invest resources in preventive or long-term benefits\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. However, the reality of scarcity, having insufficient resources for one\u0026rsquo;s needs, can undermine such forward-looking health behaviours\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Households facing tight budgets tend to prioritise immediate necessities like food or shelter over expenditures such as medications or preventive services (annual check-ups, immunizations)\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Living in poverty has also been linked to certain unhealthy behaviours, for example choosing cheaper, calorie-dense foods or spending on \u0026ldquo;temptation goods\u0026rdquo; (like alcohol or cigarettes) despite long-term health costs\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Indeed, poverty contributes to a vicious cycle in which poor health outcomes both result from and exacerbate economic hardship in low-income communities\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. This is evident in rural China, as in many rural areas globally, that illness is a leading cause of poverty\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Rural regions face stark disparities in healthcare access compared to cities, and vulnerable groups such as the elderly, women, and left-behind children (those with migrant worker parents) suffer disproportionately from unmet health needs\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Understanding the relationship and underlying mechanisms between health decision-making and poverty is essential for designing interventions to break these traps and reduce health inequities\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eScarcity theory provides an integrative framework for how poverty impacts decision-making\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Classical economics traditionally focuses on objective scarcity, a shortfall in material resources, and how it constrains consumption and investment\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. By contrast, behavioural economics and psychology, particularly in poverty literature, examine subjective scarcity, the feeling of having less than one needs\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. The psychology of poverty describes a scarcity mindset, where both monetary concerns (e.g. paying for rent, school fees, utility bills) and non-monetary hardships (poor healthcare access, malnutrition, lack of sleep) preoccupy one\u0026rsquo;s thoughts, making everyday decisions more difficult and stressful\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. This perspective emphasises that poverty entails psychological and cognitive scarcity as much as material deprivation\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Even among higher-income individuals, acute worries, such as during an economic crisis, a pandemic lockdown, or concerning climate change, may impair cognitive performance and decision-making\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. The key idea of scarcity theory is that scarcity itself induces a distinctive mindset that influences how people think and decide, and subsequently affect human preferences and behaviours\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTwo core psychological mechanisms have been proposed to explain decisions under scarcity: cognitive load and tunneling\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. First, poverty-related concerns can occupy a portion of one\u0026rsquo;s mental bandwidth, leaving fewer cognitive resources available for other tasks\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. While some experiments show that financial worries directly impairs cognitive performance\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, others find no significant cognitive decline under scarcity conditions\u003csup\u003e\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. A recent meta-analysis found a moderate negative effect of scarcity on cognitive function, though effect sizes varied with context and individual differences (e.g., education level, severity, and timing of scarcity)\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Second, feeling resource-constrained can lead to tunnelling, a narrowing of focus and attention\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Some evidence suggests scarcity-induced tunnelling may improve decision-making by eliminating distractions\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, increase consistency in how people value products\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, or even increase cognitive performance when paired with incentives\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. On the other hand, studies have linked tunneling to counter-productive choices such as over-borrowing\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, impulse purchase\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, and poor budgeting (failing to plan expenditures)\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Whether scarcity leads to more efficient decisions or worse ones likely depends on which psychological mechanism (taxing cognitive load vs. enhancing focus) dominates in a given context.\u003c/p\u003e\u003cp\u003eScarcity may also influence basic preferences, such as temporal discounting and risk-taking, which in turn drive various behaviours\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Scarcity might intuitively be expected to foster present bias (favouring immediate smaller rewards over future larger rewards) and risk aversion (opting for safer choices), as those under financial strain prioritise immediate needs and security\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Indeed, some studies report that individuals exhibited more present bias before payday compared with after payday\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e and poorer villagers were more loss-averse and less patient than wealthier ones\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. However, other experiments paint a more complex picture. A study that primed low-income participants with financial worries found it could reduce risk aversion in some cases\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Some field studies found no significant change in time preferences due to short-term financial fluctuations\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. These mixed results indicate that scarcity does not uniformly heighten time-discounting or risk aversion\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOne possible explanation for the mixed findings on how scarcity affects decision-making is that the effects may differ depending on the decision domain involved\u003csup\u003e\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. People often behave differently when the consequences involve health rather than money. For example, individuals tend to be more risk-averse for outcomes that affect health (such as choosing medical treatments) than for financially framed outcomes\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Likewise, individuals sometimes display a higher impatience (steeper time discounting) for health benefits compared to monetary gains, perhaps because immediate health improvements are valued particularly strongly\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. These domain differences suggest the need to explicitly study health decisions under scarcity, rather than assume findings from financial decisions can be generalised. However, existing research on scarcity has predominantly emphasized financial decision-making\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, or explored scarcity\u0026rsquo;s impacts on behaviours related to productivity\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, parenting and investment in children\u0026rsquo;s education\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e and savings\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. There is a lack of research examining how scarcity influences health-related decisions, despite the central role that health decisions play in quality of life and long-term economic outcomes.\u003c/p\u003e\u003cp\u003eDrawing on scarcity theory and given the mixed evidence on mechanisms and their consequences, the present study aimed to examine the effects of financial scarcity on (i) cognitive function, (ii) time and risk preferences in both monetary and health domains, (iii) spending trade-off decisions, among low-income adults in rural China. We implemented a lab-in-the-field experiment in two distinct rural regions, Yunnan and Shaanxi providences of China. We randomly assigned participants to either an urgent high-cost expenses scenario (\u0026ldquo;Hard\u0026rdquo; scarcity prime) or minor expenses scenario (\u0026ldquo;Easy\u0026rdquo; control), following methods used by prior studies\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Both regions are low-income and predominantly agricultural, but they differ in important socio-economic aspects (see details in Results). This two-site design allowed us to test whether scarcity effects are general or context-specific.\u003c/p\u003e\u003cp\u003eWe also adopted a multi-dimensional perspective on scarcity, examining three different sources of scarcity: (1) immediate financial worry (induced scarcity via a financial priming task), (2) objective scarcity (household annual income), and (3) liquidity fluctuation (the number of days since participants\u0026rsquo; last payment). These factors have each been identified in prior work as important predictors of subjective or objective scarcity, but most previous studies examine them in isolation\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. By considering them jointly, we can explore whether multiple scarcity pressures interact or moderate each other\u0026rsquo;s effects. In terms of outcomes, we measured cognitive functioning using the Standard Progressive Matrices (a Raven\u0026rsquo;s matrices test)\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Time and risk preferences were measured in both monetary and health contexts using non-incentivized choice tasks (the multiple price list task based on the quasi-hyperbolic discounting model for time\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, and the Eckel \u0026amp; Grossman lottery task for risk\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e). Finally, we included a budget allocation task to observe how participants prioritise spending between groceries (basic needs), health, and temptation goods when faced with a constrained budget. We extend prior research by comparing scarcity\u0026rsquo;s cognitive and behavioural effects across two socioeconomically distinct poor regions and to include health-related decision measures and highlight the context-dependent nature of scarcity effects on decision-making.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSample Characteristics and Balance Check\u003c/h2\u003e\u003cp\u003eA sample of 479 adults from rural Yunnan (N\u0026thinsp;=\u0026thinsp;264) and Shaanxi (N\u0026thinsp;=\u0026thinsp;215) was included in the main analysis (see data exclusion details in Methods). Summary demographics and a balance check for the random assignment are provided in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. In Yunnan, 53.0% of participants were male, compared to 39.5% in Shaanxi. Over half of all participants (53.8% in Yunnan; 66.1% in Shaanxi) were aged 50 or above. A notable proportion (18.6% in Yunnan; 13.5% in Shaanxi) self-reported as illiterate. Annual household incomes differed starkly between the regions, reflecting different levels of scarcity: Yunnan participants reported a mean income of \u0026yen;28,462 (\u003cspan\u003e$\u003c/span\u003e4,233 USD), more than double the Shaanxi sample\u0026rsquo;s mean of \u0026yen;11,122 (\u003cspan\u003e$\u003c/span\u003e1,654 USD). Additionally, about half of the participants relied on small-scale farming or migrant labour as their primary income source (48.9% in Yunnan; 64.7% in Shaanxi). In Shaanxi, the majority (71.6%) reported having no fixed or regular income, versus 44.3% in Yunnan. Correspondingly, the time since last payday was much longer on average in Shaanxi (mean\u0026thinsp;=\u0026thinsp;95 days) than in Yunnan (mean\u0026thinsp;=\u0026thinsp;31 days), indicating more irregular cash flows in the Shaanxi communities. Despite these between-region differences, the randomisation to Hard vs. Easy prime conditions was generally successful within each region. The groups assigned to the Hard vs. Easy scenario did not significantly differ on most background characteristics. Two modest imbalances were noted in Shaanxi: participants in the Easy group had slightly higher education levels on average than those in the Hard group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), and a greater proportion of the Easy group had a recent physical health exam (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eManipulation Check\u003c/h3\u003e\n\u003cp\u003eThe scarcity prime was effective in inducing greater subjective financial concern in the Hard condition compared to the Easy condition in both regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). As intended, participants presented with the Hard scenarios reported substantially higher levels of worry and stress than those given the Easy versions. This pattern held true for all three vignettes and in both regions. Across all scenarios, the differences between Hard and Easy groups are statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 by t-tests; see SI Table S2 for the exact stress ratings and statistics). Thus, our priming manipulation successfully created an acute sense of financial scarcity in Hard-condition participants relative to controls, which serves as a foundation for examining its causal effects on cognitive and choice outcomes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eCognitive Function\u003c/h3\u003e\n\u003cp\u003eInducing a scarcity mindset impaired cognitive performance in Yunnan but not in Shaanxi. In Yunnan, the Easy group scored 61.5% on average on the 12-item Raven\u0026rsquo;s task, compared to 54.6% in the Hard group. Regression analysis (see SI Table S3) confirmed a significant negative effect of the Hard prime, an average drop of 8.4 percentage points (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In contrast, Shaanxi participants showed no meaningful difference (60.2% Hard vs. 58.4% Easy). In other words, prompting thoughts of urgent financial needs reduced cognitive performance in Yunnan but had no effect in Shaanxi. We tested whether this effect varied by annual income or days since last payment (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), but neither interaction was significant in either region (see SI Table S4).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eTime Preference (Present Bias)\u003c/h3\u003e\n\u003cp\u003eWe examined whether scarcity influenced time preferences using multiple price list tasks for monetary and health choices. Only participants who made consistent choices, i.e., defined as switching their preference no more than once in each price list, were included in the analysis. Present bias was defined as switching to a delay choice in the second price list, indicating hyperbolic discounting. In Yunnan, the Hard prime group was slightly less likely to show present bias in monetary choices (48.1%) than the Easy group (55.2%), with a marginally significant effect (P\u0026thinsp;=\u0026thinsp;0.065). No difference was found in health choices (40.2% vs. 39.5%). In Shaanxi, present bias rates were similar across conditions in both domains, with no significant effects (see SI Tables S4\u0026ndash;S5).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eRisk Preference (Risk Aversion)\u003c/h3\u003e\n\u003cp\u003eWe measured risk-taking behaviour through two parallel tasks: one involving a monetary lottery game and one involving a vaccination benefits choice. For monetary risk, the Hard prime did not significantly change risk preference in either region (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Ordered logistic regressions confirmed no meaningful prime effect on financial risk-taking (see SI Table S6). For health-related risk, participants under the Hard prime became significantly more risk averse in Yunnan sample (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The Hard-primed individuals were more likely to opt for the safer vaccine option rather than a riskier option. In the Easy condition, 64.6% of Yunnan participants chose the safest vaccine, whereas under the Hard condition this percentage increased to 82.1%. An ordered logistic regression confirmed a significant effect of the prime (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008) after controlling for covariates. By contrast, no significant prime effect on health risk-taking was found in Shaanxi (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eSpending Allocation Decisions\u003c/h2\u003e\u003cp\u003eA key question in our study was whether scarcity affects how people allocate spending across essential (groceries), health-related, and temptation goods. Across both samples, the largest budget share went to groceries, followed by health and temptation goods (see SI Table S4.1\u0026ndash;4.2). We used seemingly unrelated regression (SUR) models to assess the effects of scarcity priming and its interaction with annual income and days since last pay (see SI Tables S7.1\u0026ndash;S7.2). In Yunnan, there were no significant main effects of scarcity on any spending category. Income positively predicted health spending (β\u0026thinsp;=\u0026thinsp;0.234, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025), but the interaction with scarcity was non-significant, as were all interactions with liquidity. In Shaanxi, scarcity priming also showed no main effects. However, higher income reduced grocery spending (Group \u0026times; Income: β = \u0026minus;\u0026thinsp;0.63, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046) and increased temptation spending (β\u0026thinsp;=\u0026thinsp;0.394, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046) under the Hard prime. Interactions involving days since last payment were not significant in either region.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSubgroup analysis\u003c/h3\u003e\n\u003cp\u003eWe ran subgroup analyses to explore heterogeneity in the effects of scarcity priming (Hard vs. Easy) on cognitive function and spending allocations across grocery, health, and temptation goods in Yunnan. We focused on Yunnan because this sample have more variations in effects on outcome variables. Each model included socio-demographic controls, excluding those overlapping with the subgroup variable. Exposure to the Hard condition significantly reduced cognitive scores across several subgroups, especially those with no income (β = \u0026minus;\u0026thinsp;25.58, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01), unfixed income (β = \u0026minus;\u0026thinsp;10.25, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03), lower income (β = \u0026minus;\u0026thinsp;9.64, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02), longer delay since last payment (β = \u0026minus;\u0026thinsp;12.35, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and smaller last payment (β = \u0026minus;\u0026thinsp;16.77, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similar effects were found among women (β = \u0026minus;\u0026thinsp;10.7, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01), older adults (β = \u0026minus;\u0026thinsp;9.43, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02), and those without recent health exams (β = \u0026minus;\u0026thinsp;10.72, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05). In spending allocation, scarcity reduced grocery shares among participants with lower last payments (β = \u0026minus;\u0026thinsp;0.07, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04) and women (β = \u0026minus;\u0026thinsp;0.06, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.09). Health spending increased under scarcity among those with lower income (β\u0026thinsp;=\u0026thinsp;0.05, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.09), longer delay since last pay (β\u0026thinsp;=\u0026thinsp;0.09, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02), lower last payment (β\u0026thinsp;=\u0026thinsp;0.07, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03), and women (β\u0026thinsp;=\u0026thinsp;0.06, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.08). Temptation spending remained largely unchanged, except for a decline among lower-income participants (β = \u0026minus;\u0026thinsp;0.03, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.09).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study set out to examine how scarcity affects cognitive functioning and decision-making across both economic and health domains. We investigates three facets of scarcity (a financial worry prime, chronic income level, and liquidity fluctuation) and compared outcomes in two distinct low-income populations. We found that scarcity induced via a financial prime significantly impaired cognitive performance and increased health-related risk aversion in the relatively less impoverished region (Yunnan), whereas no such effects were observed in the much poorer region (Shaanxi). Moreover, time-discounting behaviour was largely unaffected by the acute scarcity prime in either region. These findings indicate that the cognitive and behavioural impacts of scarcity are highly context dependent. We discuss the policy implications of our findings, arguing that policies must combine both behavioural and structural interventions to improve financial well-being across different poverty contexts.\u003c/p\u003e\u003cp\u003eOne of the salient results was the cognitive tax of scarcity observed in Yunnan. Participants from Yunnan who were primed with hard financial scenarios performed significantly worse on the Raven’s matrices, consistent with the idea that even momentary financial anxiety can sap mental bandwidth. This aligns with the findings that worrying about economic problems impairs cognitive capacity\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. At the same time, the absence of any cognitive decline in the Shaanxi sample is equally noteworthy. Notably, our manipulation check confirmed that the Hard prime raised worry and stress levels in both regions, so the lack of cognitive impairment in Shaanxi was not due to the prime failing in that group. One explanation is that a ceiling effect occurred because Shaanxi participants, being significantly poorer, may be so accustomed to financial hardship that an additional hypothetical challenge does not influence their thinking. Our results resonate with other mixed findings in the literature, for example, studies in American samples have failed to reproduce Mani et al.’s cognitive effects, suggesting that certain populations or experimental conditions are more susceptible than others\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn term of temporal discounting, Yunnan participants in the Hard condition showed a marginal decrease in monetary choices but not in health choices. Shaanxi participants showed no change in time preference with the prime in either domain. Our results add to the growing consensus that poverty does not straightforwardly create impulsivity in intertemporal choice\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. With respect to risk-taking, we found a domain-specific effect that immediate financial worries made Yunnan participants more conservative in health choices but not in monetary choices. This is consistent with prior observations that people are naturally more risk-averse for health than money, and our results indicate scarcity amplifies that tendency, at least in the Yunnan context\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Meanwhile, in Shaanxi, risk preferences were essentially flat across conditions in both domains.\u003c/p\u003e\u003cp\u003eThe budget allocation task provided insight into how scarcity influences spending priorities and trade-offs among necessities (groceries), health, and temptation goods. In the full sample, the Hard prime did not produce statistically significant reallocations on average. We observed some suggestive patterns that Yunnan participants who were primed to feel scarce allocated slightly more of their hypothetical budget to health expenses and slightly less to food, compared to those in the Easy condition (control). Notably, our subgroup analyses in Yunnan sample revealed a more nuanced pattern. It was the participants with the scarcest real resources, for example, those with lower income or those who were farthest from their last payday, who showed the largest adjustments under the Hard prime. Generally, our results are consistent with scarcity theory that the poor often make consistent consumption decisions focusing on necessities\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Interestingly, in Shaanxi we observed a small, counterintuitive interaction: participants with relatively higher incomes (within the extremely poor Shaanxi sample) who were under the Hard prime allocated slightly less of their budget to food and more to temptation goods. One speculative interpretation is that those few individuals who had a bit more financial cushion might have coped with the induced stress by forgoing some necessities and indulging in a small “treat.” They also indicate that the ability to adjust spending in response to a scarcity cue depends on having some slack or discretionary spending to begin with. However, given the borderline significance and the fact that this effect was isolated to one subgroup, we interpret it with caution.\u003c/p\u003e\u003cp\u003eOur results inform a range of intervention strategies aimed at improving decision-making and well-being for people living under scarcity. A key insight is that in contexts of moderate poverty (like our Yunnan sample), light-touch behavioural interventions have potential to yield benefits, especially in shifting attention and choices toward long-term health. In such settings, policymakers can leverage choice architecture, the design of how options are presented, to ease cognitive burdens on the poor\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. For example, simplifying the process of accessing healthcare (e.g. providing assistance with paperwork, sending reminders for check-ups, or using default enrolment in basic insurance programs) can reduce the mental effort required to make a healthy choice. Another strategy is behavioural targeting, which involves timing and tailoring interventions to moments and groups where they will have the greatest impact\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Our data suggest that right after an income payment, when liquidity is temporarily higher and minds are less preoccupied with scarcity, could be a window of opportunity for engagement. Interventions such as encouraging people to set aside savings for health or enrol in insurance might be more successful if they occur just after harvest or payday. Targeting also means identifying vulnerable subgroups who are most affected by scarcity. We found, for example, that women and those with irregular income (long gaps since last pay) experienced larger cognitive deficits and made bigger spending adjustments under scarcity. These groups might benefit from extra support, such as providing financial counselling, budgeting tools, or priority access to healthcare services.\u003c/p\u003e\u003cp\u003eFor populations in more financial constraints (like our Shaanxi sample), our results imply that purely psychological interventions (e.g. scarcity primes or minor nudges) might not be enough to change behaviour. Policies must combine both behavioural and structural interventions to improve financial well-being across populations\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Choice infrastructure, which encompasses the broader systems, policies, and structures that affect the accessibility and support of public health solutions, might be needed\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Conditional cash transfer programs, which provide payments to low-income households for behaviours like attending health check-ups or keeping children in school, have proven effective in many developing contexts\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Such incentives can be implemented along with the above choice architecture techniques. Scholars have pointed out that effective behavioural policies often integrate nudges with economic and material measures to sustain long-term change\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. For instance, timing the incentive payout to match with periods when families typically run low on cash, or framing the incentive as reward for good health behaviour to increase its salience. Our observation that Shaanxi participants didn’t further reduce their already minimal health spending under strain suggests they highly value health but simply lack the means, a scenario where subsidizing health via conditional grants or vouchers can have high impact.\u003c/p\u003e\u003cp\u003eIn designing all these interventions, it is crucial to adopt an inclusive and context-sensitive approach. Our findings, where different subgroups responded differently to scarcity, suggest that multiple channels or variations of an intervention may be needed. Policymakers and practitioners should involve target communities (like rural villagers) in co-creating solutions, acknowledging local realities such as literacy levels, cultural beliefs, and social support networks. For example, if women in these villages shoulder most financial and healthcare responsibilities (as is often the case), interventions should be gender inclusive, for instance, training local women as community health finance facilitators who can help families navigate options and lighten cognitive burdens\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIt is important to acknowledge the limitations of this study. First, while our sample covers two distinct regions, it is not representative of all rural populations. Caution is needed in generalising these results beyond the specific context of rural China. Since our two regions differed on multiple dimensions (age, income, culture), investigating which aspect was key could be done with a larger sample across more varied communities. Second, our outcome measures might simplify complex constructs. Cognitive function was measured with Raven’s matrices, which capture fluid intelligence but not other aspects like memory or attention span that scarcity might also affect. Time and risk preferences were measured through choice tasks that, although standard, are still hypothetical and may not predict real-world behaviour (especially in the health domain). Third, although we tried to control for key covariates and used random assignment for the prime, causal interpretation should be cautious. Future research should build on these findings by exploring interventions and moderating factors. Finally, we assessed multiple outcomes and conducted numerous subgroup tests, which raises the possibility of mixed findings. We mitigated this concern by focusing on the strongest and most consistent effects (notably, the cognitive and health-risk changes in Yunnan), and by treating the more marginal findings (e.g. certain spending interactions or subgroup differences) as exploratory. Replication in future studies will be important to confirm which of these effects are robust.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Methods","content":"\u003ch2\u003eStudy Design and Participants\u003c/h2\u003e\u003cp\u003eThis study employed a lab-in-the-field experimental design\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e in two rural regions of China: Yunnan Province in the southwest and Shaanxi Province in the northwest. We conducted the study between May and September 2022, and collected data from local villages within each region, in collaboration with 2 local community leaders, 2 senior researchers, and 9 research assistants (4 from Yunnan and 5 from Shaanxi). By including both regions, we aimed to compare findings and to examine potential contextual moderators of scarcity effects. Prior to the main study, we conducted pilot tests in each region (16 participants in Yunnan; 14 in Shaanxi) to verify that the study materials were culturally appropriate and understood by participants with low literacy. Feedback from the pilots was used to refine the study design, such as the financial scenarios and items selected for spending allocation task. The study protocol was approved by the Ethics Committee of the first author’s institution. Participation was voluntary and anonymous, and respondents received a small compensation (such as a towel) upon completion of the tasks. All participants provided informed consent prior to participation. For individuals with limited literacy, the consent information and study instructions were explained verbally in the local dialect, in accordance with the approved ethical procedures.\u003c/p\u003e\u003cp\u003eWithin each region, participants were randomly assigned to one of the two experimental conditions (between-subject): a Hard scarcity prime or an Easy control prime. Random assignment was done in the field by alternating survey forms. Research assistants, who were well-acquainted with the local communities, visited various villages and weekend markets to recruit participants. The inclusion criteria were: (1) 18 years old or above; (2) living in rural areas; and (3) capable of understanding the instructions and performing the tasks accordingly.\u003c/p\u003e\u003ch2\u003eExperimental Procedure and Measurements\u003c/h2\u003e\u003cp\u003e After providing informed consent, all participants completed the same set of tasks, which were administered by trained research assistants. The tasks were administered in the local language and were presented orally to any participants who could not read, to ensure comprehension. The survey typically took 30–45 minutes to complete. Details of the tasks, constructs and measurements are presented in Supplementary Information.\u003c/p\u003e\u003cp\u003eWe adopted the method of priming, frequently used in psychology and economics\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e, and designed three different sets of financial scenarios: raising money in one week, a decline of annual income and an increase in medical costs. The first two sets were adapted from Mani et al.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, whereas the third set focused on financial shocks brought on by illness. The scenarios and questions were identical for the two groups except that the magnitude of money varies. For example, the ‘hard’ scenario described an unforeseen event requiring an immediate ¥20000 expense, whereas the ‘easy’ scenario only required ¥1000 expense. Participants were asked to respond both open-ended and closed-ended questions. The open-ended questions asked participants how they would handle the financial shocks in order to elicit their feelings and financial concerns. The closed-ended questions were coded with five-item Likert scales to measure the levels of worries.\u003c/p\u003e\u003cp\u003eImmediately after the priming task, participants completed the easiest set (12 items) of the 60-item Standard Progressive Matrices, a widely used measure of fluid intelligence that does not require reading or mathematical skills—an important consideration given the low literacy levels in our study population\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. This task assessed participants’ capacity for logical reasoning and problem-solving in novel situations\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. Participants were encouraged to do their best, and while there was no strict time limit, they were asked to complete the task as efficiently as possible.\u003c/p\u003e\u003cp\u003eWe then used non-incentivised methods to elicit time and risk preferences, as prior evidence suggests hypothetical monetary choices yield results comparable to incentivised ones, offering a lower-cost alternative in field experiments\u003csup\u003e\u003cspan additionalcitationids=\"CR63\" citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e–\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. We designed two intertemporal choice tasks, one framed as receiving a cash payment and the other as receiving a health subsidy (for a physical examination), using a multiple price list (MPL) format based on the quasi-hyperbolic discounted model\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Each task included two sets of eight money-sooner versus money-later choices: one for today vs. one month later, and another for six months vs. seven months later (e.g., ¥200 today vs. ¥300 in one month, or ¥200 in sixth month vs. ¥300 in seventh month). The two choice scenarios were identical in terms of amount of money. Choosing the earlier, smaller payoff indicated greater impatience. Participants were considered \u003cb\u003econsistent\u003c/b\u003e if they switched their preference at most once in each MPL (which is the pattern expected of a rational time-consistent or present-biased decision-maker), and \u003cb\u003einconsistent\u003c/b\u003e if they switched multiple times (which could indicate confusion or lack of stable preference). As described in the Results, we defined a participant as exhibiting present bias if they chose the smaller-sooner over the larger-later reward in the \u003cem\u003etoday vs. next month\u003c/em\u003e comparison but did not do so in the \u003cem\u003esix-month vs. seven-month\u003c/em\u003e future comparison. The primary outcome from these tasks was the percentage of present-biased participants in Hard vs. Easy groups within each region.\u003c/p\u003e\u003cp\u003eWe measured risk preference using the Eckel \u0026amp; Grossman lottery task\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. In the monetary risk task, participants chose one among six lotteries, each lottery offering a 50/50 chance of winning either a lower or a higher monetary amount (with higher expected values for riskier lotteries). In the health-framed risk task, participants chose among six hypothetical vaccines, each offering different probabilities of short-term vs. long-term protection (analogous to the risk-return trade-offs in the monetary lotteries). Choosing a safer lottery or vaccine (one with a lower payoff range but more certain outcome) indicates greater risk aversion, whereas choosing a riskier option indicates greater risk tolerance. We recorded each participant’s choice and analysed whether the distribution of choices differed by prime condition in each region.\u003c/p\u003e\u003cp\u003eNext, we included a budget allocation task where participants were asked to allocate a hypothetical budget of ¥100 across three categories: daily groceries (necessities), medicine and health-related goods, and temptation goods (non-necessities such as snacks, toys or leisure items). This task was designed to mimic the experience of budgeting in a shopping scenario, aiming to make the decision process feel as realistic as possible. The specific items listed under each category were popular, familiar items in the local markets (refined based on the pilot study feedback). After participants made their allocations, we converted the amounts into percentages of the ¥100 budget for analysis.\u003c/p\u003e\u003cp\u003eFinally, we collected demographic and economic background information. This included age, gender, education, job, annual income, sources of income, payment frequency, physical examination, household size, household annual income and any outstanding debt. Notably, we asked participants to report the date and amount of the last payment they had received (salary, pension, remittance, etc.). Research assistants in the field helped clarify these questions and ensure accurate responses, especially for income, which some participants could only estimate.\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eA sample of 301 participants were recruited from Yunnan, of which 150 were randomly assigned to the ‘hard’ scenarios and 151 to the ‘easy’ scenarios, and a sample of 245 participants were recruited from Shaanxi, of which 127 were randomly assigned to the ‘hard’ scenarios and 118 to the ‘easy’ scenarios. The sample size was determined by practical feasibility, including constraints of time and resources. A power analysis adjusted for multiple comparisons (α ≈ 0.0045) with 80% power across five primary outcomes indicated a minimum of 111 participants per group. A total of 37 participants from the Yunnan sample and 30 from the Shaanxi sample were excluded for one or more of the following reasons: incomplete survey submissions, unengaged responses, or absent information. This resulted in final samples of 264 in Yunnan (130 in the ‘hard’ group and 134 in the ‘easy’ group) and 215 in Shaanxi (110 in the ‘hard’ group and 105 in the ‘easy’ group) for the main analyses.\u003c/p\u003e\u003cp\u003eData analysis was performed using STATA 17. We used Pearson chi square tests for categorical variables and t-tests for continuous variables for balancing checks comparing the demographic variations between the ‘hard’ and ‘easy’ groups. We ran separate models for Yunnan and Shaanxi and used varied regression models to examine the treatment effects on key outcome variables, including ordinary least squares (OLS) regressions for continuous variables (priming worries and cognitive function), logistic regression for binary variables (time preferences), ordered logistic regression for ordered variables (risk preferences) and seemingly unrelated regressions (SUR) for proportional variables (spending proportions). We examined the \u003cem\u003eprime × income\u003c/em\u003e and \u003cem\u003eprime × days-since-last-pay\u003c/em\u003e interaction terms to explore moderation by objective and fluctuation of scarcity. All regressions models used robust standard errors and controlled for demographic and income-related information. We also conducted subgroup analysis to examine the heterogeneity in the key treatment effects in Yunnan.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eH.Z. conceptualised the study and designed the experiment. F.Y. and E.L. coordinated data collection in the field. H.Z. performed the statistical analysis. T.G., H.D., and C.D.B. advised on study design and data interpretation. H.Z. wrote the manuscript with input from all authors. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis work was supported by the University Research Studentship from Loughborough University.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData will be made available in a public repository upon publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDupas, P. \u0026amp; Miguel, E. Impacts and Determinants of Health Levels in Low-Income Countries. in Handbook of Economic Field Experiments, Volume 2 vol. 2 3\u0026ndash;93 (North Holland, (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKremer, M., Rao, G. \u0026amp; Schilbach, F. 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We conducted a lab-in-the-field experiment with 479 rural adults in two low-income regions of China, Yunnan and Shaanxi, to examine the cognitive and behavioural consequences for health-related preferences and choices. Participants were randomly assigned to a scarcity prime (Hard vs. Easy scenario) and completed tasks measuring cognitive function (Raven\u0026rsquo;s matrices), time and risk preferences in both monetary and health domains, and budget allocations across necessities (groceries), health, and temptation goods. In Yunnan, a moderately poor region, scarcity significantly reduced cognitive performance and increased health-related risk aversion, whereas no such effects were observed in Shaanxi, where poverty was more severe. Time preferences were largely unaffected by the prime in both regions. Spending allocations showed limited shifts under scarcity, with some subgroups reallocating more toward health. Our findings suggest that scarcity\u0026rsquo;s psychological effects are not universal but context-specific, with implications for health interventions and poverty alleviation strategies. Choice architecture interventions (e.g., nudges) may be effective for moderately poor populations, while choice infrastructure with structural supports remains essential for those facing chronic deprivation.\u003c/p\u003e","manuscriptTitle":"Scarcity, Cognition, and Health Decision-Making: Evidence from a Lab-in-the-Field Experiment in Rural China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-11 10:11:15","doi":"10.21203/rs.3.rs-7321300/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-11T09:16:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-11T08:17:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-09T01:10:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-08-07T18:11:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9a1e5006-7b49-4e79-ad1a-7be1394ea4fb","owner":[],"postedDate":"August 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":52895517,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":52895518,"name":"Health sciences/Health care"},{"id":52895519,"name":"Biological sciences/Psychology"},{"id":52895520,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-03-26T11:38:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-11 10:11:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7321300","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7321300","identity":"rs-7321300","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}