Older workforce management through active aging frameworks: case studies of challenges and implications in Thailand and China Open Access

Aging populations are a central labor and governance challenge, particularly in Thailand and China, which are rapidly becoming super-aged societies. In this context, older workforce management describes policy frameworks aimed at extending productive aging through institutional governance, skill building and behavior regulation (Walker, 2002; Phillipson, 2013). The active aging index (AAI) and localized adaptations evaluate older adult health as well as social participation; they help governments to understand, mobilize and manage aging labor forces (UNECE, 2019; Fernandes et al., 2021). In 2023, Thailand became a completely aged society, with over 20% of the population aged 60 or above. In 2024, China surpassed 300 million people aged 60 and over, comprising 22% of the population (Yao, 2025). In both countries, prolonging older adult working life, especially in sectors such as agriculture, has become a strategic policy objective (Wang et al., 2020; Spiceworks, 2021). These challenges are exacerbated locally by poverty, digital exclusion and structural inequalities. In Talat Mai, a rural subdistrict in Ang Thong Province, over 25% of residents are aged over 60, many still active in agriculture; these workers are characterized by limited income, declining health and low digital literacy. A parallel situation exists in Qixian County, Henan Province, where older rural workers engage in farming without institutional support or reskilling opportunities. These regions show how aging and labor remain linked in lived experience and policy gaps.

This paper investigates how active aging frameworks, particularly the AAI, function as aging metrics and governance instruments to forecast continued older productivity and self-responsibility. Foucault's (1991) governmentality theory is employed to analyze how normative aging ideals are embedded in local policy to shape older laborer identities. Such frameworks operate across rural contexts, indicating governance friction, cultural incongruity and limitations in applying global aging indicators locally. Integrating survey data from Thailand with secondary information from China, this study shows how aging is managed through statistical instruments and behavioral scripts, and how such management is a form of older workforce governance. It highlights the policy potential of AAI-based approaches and their role in making aging a governed labor and economic value category.

Previous studies have highlighted the growing importance of active aging frameworks in addressing demographic change and workforce sustainability in aging societies. Recent international research has emphasized that as populations rapidly age in many regions, governments increasingly promote policies to extend working lives and prolong older adult participation in labor markets (Katiraee et al., 2024; García-Pereiro et al., 2025). Active aging discussions also emphasize its role in supporting labor market sustainability. Walker (2006) argues that an active aging policy may extend working life while improving aging worker health, well-being and productivity. Aging societies must prepare governmentally for demographic change by budgeting and providing other services, especially to integrate and manage older laborers. Organizationally, research suggests the importance of workplace conditions in supporting active aging. Zacher et al. (2018) showed that supportive work environments and attention to employee well-being may enhance engagement and productivity among older workers. The World Health Organization (WHO, 2001, 2002) inclusively defines geriatric health beyond physical needs to include ongoing engagement in socially productive, meaningful work with continued involvement in social, economic, spiritual, cultural and civic affairs. Recent policy frameworks, including the United Nations Decade of Healthy Ageing (2021–2030), focus on multidimensional indicators capturing health, participation, security and environmental enablement for active aging across the life course (WHO, 2024). The World Health Organization (WHO) AAI has four domains with 22 indicators: employment, societal participation, independent healthy and secure living, active aging, environmental capacity and enablement (UNECE, 2019). This model appears inclusive, but De São José et al. (2017) argue that AAI promotion of normative aging centered on productivity and measurable participation may ignore diverse aging experiences. Similarly, Principi et al. (2023) note that although the AAI supports evidence-based policymaking, its implementation may risk reinforcing productivist interpretation of aging or excluding disadvantaged older populations if local diversity and stakeholder participation are underestimated. Through standardized metrics, the AAI frames aging as a condition to be managed through continuous self-optimization, following Foucault's (1977) concept of disciplinary power. Older persons are not coerced but are incentivized by measurement and comparison to conform to idealized active aging standards. In Thailand, the WHO (2002) AAI motivated Dr. Fahumnuayphol (2020) to create a localized active aging assessment instrument, implemented by the Foundation of Thai Gerontology Research and Development Institute (TGRI). In 2017, the Active Aging Scale(AAS) was first localized in China by Zhang Jiange and colleagues with institutional backing from gerontology and geriatrics researchers. Retaining core AAI domains, the Chinese version incorporates culturally specific elements such as filial piety and moral conduct, aligning with global aging frameworks and domestication by Confucian values. In both countries, the instruments monitor health and shape behavioral expectations with what Foucault (1991) terms technologies of the self.

In Thailand, AAI employment indicators are adapted to local contexts to evaluate labor participation of older adults of diverse ages. Employment involves four indicators targeting working rates for four age ranges: 55 to 59, 60 to 64, 65 to 69 and 70 to 74. Societal participation likewise covers four indicators: volunteer activities, childcare, helping the infirm and disabled and political participation. Independent, healthy and secure living concerns eight indicators: physical exercise, health service access, independent life, three financial security measures, physical safety and lifelong learning. Active aging capacity and environmental enablement feature six factors: life expectancy at age 55, healthy life expectancy at age 55, psychological well-being, information and communication technology use, social connectedness and educational level. Scores vary internationally. The main goal of AAI is to provide a flexible framework to match local and national contexts to show conditions and needs, inspiring policies and measures (United Nations Economic Commission for Europe, 2019).

Unlike Thailand's community-based model, China's national AAI, state-led since the 13th Five-Year Plan for Economic and Social Development of the People's Republic of China (Zaidi et al., 2019), adapts the European framework to Confucian norms, underscoring family support, moral duty and public service access (Wang et al., 2020). Field data from Henan show strong family-based interaction but ongoing gaps in financial independence and civic participation, especially among rural older women and low-income groups (Li et al., 2020). China's AAI is administratively coherent, but may overlook local needs and cultural diversity. To address this, the AAS was later translated into Chinese and validated for use among community-dwelling older adults. This localized instrument complements China's top-down model with a multidimensional, culturally responsive aging assessment (Wu et al., 2025). Although reliable, its regional bias and dependence on self-reporting require redesign and wider validation (Wu et al., 2025). Recent policy-oriented research also indicates that governments should adapt labor market strategies to age demographics. Nagarajan and Sixsmith (2023) note that many nations are reforming their policy to extend older adult workforce participation, combining labor market policy with technological and organizational innovation to address aging society issues. These studies demonstrate that active aging frameworks are increasingly linked with labor market participation, organizational management and public policy strategy in aging societies.

Despite the increasing global use of active aging frameworks and related indices, most extant research compares national-level policy or measures demography in developed or urban contexts. Recent comparative studies have explored new aging indices internationally, such as Australia's Aging Well Index, which evaluates the well-being and social participation of older populations (Du et al., 2025). However, how these frameworks operate in rural and low-income agrarian settings, where older adults often remain economically active in informal labor systems, has been relatively overlooked. This study addresses this gap by examining how active aging frameworks, particularly the AAI and its localized adaptations, work in rural Thailand and China and how they cohere with local governance structures, cultural norms and rural labor systems.

This research combines participatory action research in Thailand with secondary data analysis from rural China to discern the role of active aging frameworks in managing rural workforces. Primary data are collected in the Talat Mai Subdistrict by localized AAI assessment and participatory field engagement. Comparative insights for China are obtained through secondary data analysis and a systematic literature search of rural aging scholarship. This enables a cross-context comparison of how active aging frameworks operate in different governance and rural labor environments. Combining primary field data with secondary empirical evidence, this study captures localized community experience and broader structural patterns of active aging governance in rural contexts.

Participatory action research (PAR) is the primary approach in Thailand, with a quantitative survey based on localized AAI assessment. By contrast, Chinese data are gathered through secondary data analysis from Li et al. (2020), investigating active aging status and impact factors among rural older people. PAR is used to collaborate between academic and community partners to pose problems and examine evidence (von Unger et al., 2022). This participatory approach enables researchers and community stakeholders to jointly identify local challenges and develop context-sensitive interventions, strengthening the relevance of research findings. Following Foucault, in this context, PAR operates by a structured reflexivity regime, with community voices mediated by disciplinary instruments such as questionnaires and standard domains (Foucault, 1991).

Three study phases comprise:

1. A holistic view of health conditions by active aging survey or assessment;

2. Development project design and implementation;

3. Evaluation based on a qualitative questionnaire.

The first AAI quantitative survey data collection phase included 400 participants aged over 60 years in the Talat Mai Subdistrict. The second phase collected qualitative information from 33 older residents and stakeholders, including community leaders, local government officials and business owners. 25 key informants provide qualitative data for evaluation, including older residents, local government officials, stakeholders and experts.

AAI quantitative assessment in the first research phase is followed by qualitative questionnaires in the second and third stages for project evaluation and implementation in Thailand. Talat Mai joins the Thai Gerontology Research and Development Institute (TGRI) development program research team to initiate intervention. Local government officials facilitate contact with older residents. Before collecting data, the research scope and key details are explained as well as the approximate time required for potential participants, with permissions and consent requested for each quantitative and more time-consuming qualitative data collection. On 17 February 2023, the project ethics were approved and data gathering began on 20 February 2023, ending in January 2024.

To identify a dataset comparable to the Thai AAI-based study, a systematic literature search on the Scopus database is done with the keywords active aging, China and rural. Scopus was chosen as the primary database because of its broad coverage of peer-reviewed international journals, ensuring reliability and academic quality of the comparatively analyzed literature. The search yielded 25 academic articles published between 2014 and 2025, categorized by focus and methodology. Health-centric studies account for much of the literature on biomedical conditions or health outcomes, including frailty, sarcopenia, depression and physical disability (Chen and Zheng, 2025; Liu et al., 2022a, b; Huang et al., 2025a, b; Xue et al., 2023; Meng et al., 2025; Zeng et al., 2022). Some papers claim to focus on rural aging but use data from mixed or peri-urban settings or fail to disaggregate urban and rural conclusions (Chen et al., 2021a, b; Lin and Xu, 2025; Xiao et al., 2021; Luo et al., 2025). These are excluded to maintain consistency with the case study of Thailand, which examines rural populations. Thematic studies with non-index structures study significant aging-related topics such as intergenerational support, family dynamics, migration, leisure and digital participation (Liu, 2014a, b; Shen et al., 2020; Zhang et al., 2023a, b; Li et al., 2018a, b; Zhao et al., 2022a, b; Tang et al., 2020).

Only a few articles demonstrate potential index-based framing. For example, Wang et al. (2021) offer a scoring mechanism for digital participation and Liu et al. (2023) propose a health promotion model based on socioecological factors. But these are often limited in scope (focused on a single domain like digital access) or lack clear rural sample separation, making them inappropriate for multidimensional policy comparison. In sum, the Chinese literature on rural aging presents valuable thematic and sectoral insights, but none provides a comprehensive, index-based dataset aligned with the AAI structure for rural Thailand. A detailed classification and exclusion rationale is provided in Table 1, highlighting methodological gaps in the current literature on rural AAI comparison.

A case study by Li et al. (2020), although limited in scale, incorporates a structured approach to measuring multiple aspects of active aging in a rural Chinese context. This choice enables a clearer, if imperfect, cross-country comparison. In China, data were obtained between November 2018 and January 2019 in Qixian County, Hebi City, Henan Province, by secondary analysis of a cross-sectional survey. Two townships are randomly selected from the five under county administration, and three administrative villages are likewise sampled from each. Six hundred and seventy rural older adults were recruited based on age, location, health condition and consent. The study follows standard ethical protocols, and all participants are briefed on the study purpose, procedures and confidentiality measures before data collection (Li et al., 2020).

Mixed-method analysis based on the active aging assessment device developed by Dr. Fahumnuayphol (2020) comprises 25 questions from five domains. This multidimensional assessment framework enables comprehensive evaluation of older adult physical, social and economic capacities, which are central components of the active aging concept.

1. Physical;

2. Mental;

3. Intellectual;

4. Social;

5. Security.

Individual sample vitality is categorized into four levels: low, slightly low, slightly high and high. Data analysis includes frequency and percentage as well as total individual scores for each area, followed by categorization into four levels based on measurement criteria and interpretation. These quantitative indicators provide a structured overview of vitality levels among older participants and support identification of groups that may require targeted intervention or further community support. Vitality interpretation and assessment are divided into assessment areas at levels of one to four:

1. Physical;

2. Mental;

3. Intellectual;

4. Social;

5. Security;

6. All four areas (excluding security): with equal weight for each area;

7. And all five areas: with equal weight for each area.

In each area and overall, sample vitality is categorized into four levels:

1. Low (0.00%–25.00% of total score)

2. Slightly low (25.01%–50.00% of total score)

3. Slightly high (50.01%–75.00% of total score)

4. High (75.01%–100.00% of total score)

For China, secondary data were analyzed by a cross-sectional survey of 625 rural older adults in Qixian County. The sample comprises 49.1% males and 50.9% females, with most aged between 60 and 79. Educational levels are generally low, with over 78% having only primary education or less. Marital and living arrangements vary, with 19% living alone and 39% residing with children. The total Active Aging Score is 70.00 (SD = 30.00), with the highest average score in self-care (M = 3.00, SD = 1.71) and the lowest in economic security (M = 1.00, SD = 1.00). Spearman's rank correlation indicates that gender, age, religious belief, education, marital status, living arrangement, monthly income, sleep quality, weekly exercise frequency, family support for social participation, labor participation and fitness facility availability are associated with active aging (p < 0.05). Multiple linear regression analysis further identified eight predictors: age, marital status, living condition, income, sleep quality, exercise frequency, family support and fitness facility access (p < 0.05) (Li et al., 2020).

Qualitatively, reflective analysis or reflexivity is a PAR technique used to reflect project content and collaboration. It also involves reflecting on what is happening momentarily, which is continuous, dynamic and fluctuates according to researcher's perception (von Unger et al., 2022). Mentimeter software analyzes qualitative data.

Empirical findings from the Thailand field study and secondary dataset from rural China.

The quantitative survey in the Talat Mai subdistrict involved 400 respondents aged 60 and above. Most were between 60 and 69, a comparatively young, physically active segment of the geriatric population. Active aging assessment indicates that many respondents maintain relatively good physical and mental vitality, due to continued engagement in agricultural activities such as growing rice and other farm tasks. For social participation, respondents reported frequent interaction with family members and neighbors, reflecting strong community networks typical of rural Thai communities. However, the security dimension is weaker, particularly in financial stability. Many older adults report limited, irregular income, suggesting that although they remain economically active through informal agricultural labor, their financial security is low.

In China, secondary data from a cross-sectional survey of 625 rural older adults in Qixian County were analyzed (Li et al., 2020). The sample comprises 49.1% males and 50.9% females, with most aged between 60 and 79. Educational attainment is generally low, with over 78% of respondents having primary education or less (Li et al., 2020). The overall Active Aging Score is 70.00 (SD = 30.00), indicating a moderate level of active aging among the rural older population (Li et al., 2020). Among different dimensions, self-care ability has the highest average score (M = 3.00, SD = 1.71), suggesting that many older adults maintain everyday functional independence. In contrast, economic security has the lowest score (M = 1.00, SD = 1.00), showing financial vulnerability among rural geriatric populations (Li et al., 2020).

Statistical analysis further indicates that factors associated with active aging outcomes include age, marital status, living conditions, income, sleep quality, exercise frequency, family support and fitness facility access (p < 0.05) (Li et al., 2020).

Overall, the findings from Thailand and China reveal similar structural patterns. Older adults in both rural contexts show personal independence through family-based support networks, helping active aging outcomes. However, economic security remains a major challenge in both settings. Although older individuals continue to participate in local labor systems, particularly through informal agricultural work, their financial stability is limited, indicating the need for policy interventions addressing aging and rural workforce participation.

An instrument must be developed to measure health and aging. Holistically, AAI assessment may gauge the ability for independent living, paid work, social engagement and active aging (UNECE, 2019). Since 2001, international governments and medical professionals have recommended AAI (WHO, 2002) for national adaptation. It took 2 decades for the AAI to be introduced to Ang Thong Province. Rigorous measurement of aging and health is enabled by expanding the AAI framework in a rural Thai context. This application demonstrates how active aging indicators may generate locally grounded evidence to inform policies related to older workforce participation and community-based employment. Unlike Thailand's community-based and participatory use of the AAI, China's approach is policy-driven and institutional. While Thailand emphasizes local engagement and flexible assessment, China has adapted the AAI to align with national priorities, focusing on family support, moral responsibility and service access (Wang et al., 2020). These contrasting governance approaches prove that active aging frameworks are implemented differently according to institutional structure and policy priority.

The findings from Thailand suggest that many rural older adults remain economically active through informal labor sectors such as agriculture, caregiving and community-based activities. This indicates that active aging policies should support flexible employment opportunities, community work programs and age-friendly local economic initiatives that enable older adults to remain engaged in productive roles while maintaining well-being. Similarly, the Chinese dataset indicates how demographic and socioeconomic factors including age, income, living arrangements and health status can be used by local governments to identify vulnerability and labor capacity among rural older populations. Such data allow policymakers to design targeted programs, including community services, retraining opportunities and social participation initiatives, to boost continued workforce engagement.

From a policy perspective, these findings suggest that standardized indicators such as the AAI may support evidence-based planning, but should be implemented with sensitivity to local cultural practices, informal labor systems and rural social structures. Thus, policies to manage older workforce participation should combine quantitative indicators with community-based knowledge and participatory approaches.

Quantitative surveys are rapid and generic, potentially leading to a lack of motivation, explanation and trust-building between researchers and participants. The 25 questions from the four domains based on the AAI outline broadly characterize aging, but cannot explain the rationale behind respondent viewpoints. Additionally, interviewers may assume that respondents process information identically. For example, the question about how often samples engage in physically demanding tasks such as farming, moving heavy things, running, or aerobics for at least 10 min requires interpretation that may differ depending on the individual. Older farmers in Talat Mai may have physical lifestyles considered strenuous by urban older residents, potentially affecting data accuracy. Another research objective is to provide appropriate interventions, calling for an understanding of geriatric preferences, thought processes and lifestyles. Trust building is essential in researcher-participant interactions. However, the survey design impedes establishing trust by validating dependability or insights from older participants' experience. This may restrict the implementation of appropriate interventions.

In China, similar limitations apply. Large-scale active aging assessments, such as those conducted in rural Henan Province, rely on structured questionnaires that favor coverage over depth. Although variables like income, exercise frequency and sleep quality are statistically linked to active aging score, the instruments are insensitive to cultural value, personal narrative and individual meaning-making. For instance, high scores in self-care suggest functional independence, but reveal little about how older adults perceive autonomy or intergenerational expectations. Relying on self-reported data also raises concerns about social desirability bias, particularly in rural communities where older respondents may answer according to perceived norms. Without complementary qualitative engagement, such as interviews or participatory observation, these surveys risk misrepresenting the lived realities of older populations. As in Thailand, the absence of trust-building and interpretive dialog may hinder the design of culturally relevant interventions.

Apart from constraints pertaining to primary informants, active aging assessment has only limited accommodation to topographical variety. Results also imply that samples seek more income because of low security levels and associate places of employment with hometowns. Samples value hereditary land customs and consider farming to be a lifestyle. Job opportunity programs must align with agricultural values, as assessment may not yield sufficient participant and spatial insights. Finally, potential impediments relate to health and digital literacy. For China, similar challenges exist in aligning active aging assessment with regional and spatial diversity. Although instruments like the AAI and AAS have been adapted to national contexts, their application in rural counties such as Qixian is deficient. As in Thailand, older adults in rural China are attached to land, seeing farming as a lifestyle tied to identity, stability and intergenerational duty more than mere labor. Economic insecurity is pronounced, yet older adults may prefer informal or agricultural work near their hometowns over formal employment programs.

Active aging assessment generates data on health and aging, which require electronic devices to complete. Online data collection may be challenging for older farmers lacking technological literacy and unreceptive to online surveys. Although each participant owns a smartphone, many are unable to access the Internet and network connections are often unstable and inconsistent during data collection. Completing assessments is obstructed by issues from vision impairment to illiteracy.

Due to budget constraints, all nations should prioritize programs suitable for specific areas. Active aging assessment implications contribute to the availability of health and aging data comprehensively reflecting geriatric needs. This demonstrates the value of widespread health measurement, establishing local government databases and contributing to policy source data. By following new public management principles (Promberger et al., 2003; Spiceworks, 2021), local government administration may provide efficient public facilities and collaborate with non-governmental organizations to meet community requirements. AAI data may support these efforts as local governments strengthen management to sustain policies to assist older residents. The AAI might be interpreted by Michel Foucault as a disciplinary instrument ruling older individuals by self-monitoring, normalization and administrative classification mechanisms (Foucault, 1977) rather than overt coercion. This perspective explains findings from Thailand and China, where older adults are encouraged through assessment devices, policy programs and institutional expectations to behave according to normative ideals of active and productive aging.

Despite the benefits of active aging assessment and its implications for rural Thailand and China, restrictions remain. These are evident when gathering data and determining how to maximize its potential. The quantitative survey was designed to gather basic systematic data on older residents' health and well-being. Participants apparently fail to justify, explain, or fully comprehend their requirements or demands. Hence, administrators hesitate over choosing the most effective intervention. Talat Mai is populated by farmers with a specific value system for growing rice as a lifestyle. They have additional household responsibilities and cultivate land that has been bequeathed to them. Many farmers were generationally socialized to grow rice according to this culture, ideology and habit (Thongsawang et al., 2024), despite physical debility, labor shortage, low productivity, rising production cost due to inadequate irrigation and rainfall, as well as the high cost of agricultural chemicals. These informal agricultural roles demonstrate how the older rural population remains economically active, and why workforce-sensitive aging assessments are necessary to tailor support programs and vocational intervention. In terms of governmentality, such assessments function as mechanisms through which older individuals are guided to perceive continued labor participation as a social expectation and a form of responsible aging.

Older regional residents are connected to traditional Thai values by the rice-growing area topography and the belief that the subdistrict is designated for rice crops and cannot be altered. These assumptions, combined with the aging process, make older persons less adaptable to change. Despite challenges and setbacks, many old farmers continue to cultivate rice in paddies, leading to ongoing poverty. These convictions must guide interventions, with a thorough and insightful understanding of the processes involved. In Thailand and China, infrastructural and perceptual barriers affect health equity as well as the effectiveness of workforce planning and policy implementation for older populations. Alternative techniques and approaches for active aging assessment should be combined to develop programs. In China, these disciplinary effects are more explicitly institutionalized. Including active aging (积极老龄化; jījí lǎolínghuà) in the Thirteenth Five-Year Plan expresses a state-level ambition to govern the subject as a matter of national stability. Through the AAI and localized AAS, older adults are encouraged to conform to roles defined by state values, such as being family-supportive, socially useful and morally upright. Hence, active aging policies are instruments of governance, shaping how older individuals are categorized, evaluated and integrated into demographic and labor management strategies.

The PAR technique may strengthen researcher-participant rapport, offer hands-on field insight and boost local knowledge synthesis. Cooperating with diverse stakeholders is needed to promote well-being (Ansell and Gash, 2008), following PAR principles. In-depth interviews, focus groups and observations may yield useful data. For older participants with limited digital access, paper-based assessment forms and research assistance should be provided to collect data. To ensure effective interventions, these qualitative approaches may complement the AAI and reinforce topographical and contextual findings. While the AAI and AAS offer valuable entry points for understanding aging, their impact depends on implementation.

Comparing the two cases highlights similarities and institutional differences in how active aging frameworks operate rurally. In Thailand, the AAI is implemented through participatory community-based initiatives identifying local labor potential among older adults. Conversely, the Chinese approach reflects a centralized governance model, with active aging indicators integrated into broad demographic management and policy planning. Despite differences, both contexts show that older adults remain economically active with informal or community-based labor, while facing ongoing financial security and structural inequality challenges. These differences illustrate how governing aging is shaped by distinct institutional contexts, reinforcing Foucault's claim that policy instruments and statistical indicators may be social regulation mechanisms.

Comprehensive interventions are proposed for older Talat Mai Subdistrict residents, where population aging, poverty and development assistance requirements are increasing. To inform the local government about the vitality levels of geriatric constituents, active aging assessment is used to illustrate health and aging issues. Yet, AAI assessment survey results may provide general health information without identifying current or historical causes of health issues. To lessen these constraints, additional PAR-related techniques and resources are needed: participant observation, focus groups, in-depth interviews, memos and qualitative evaluation questionnaires. In addition to supporting general well-being, the AAI also identifies older individuals who remain economically active, especially in rural settings where informal labor survives. This function allows local governments to align aging policy with workforce planning, such as designing training, reemployment, or support programs befitting the capabilities of older adults.

The AAI is a globally accepted model for aging governance, promoting ideals of self-reliance, productivity and social engagement. But its application in rural Thailand and China reveals how aging can be subject to normalization, discipline and comparison by metrics and benchmarks. Thailand's AAI appears community-driven but reproduces institutional assumptions favoring urban aging ideals. In contrast, the Chinese version reflects a state-driven population management strategy, uniform standards and centralized data use. In both contexts, the AAI facilitates governance through self-regulation, illustrating how individuals are invited to measure their lives according to externally defined ideals. Yet beyond this regulatory role, AAI-based systems are also emerging as policy infrastructures that bridge aging demographics with labor market needs, positioning older people as contributors to local development as well as care recipients. This dynamic reveals the growing role of older workforce management, encompassing policy and institutional instruments designed to extend older adult economic participation, especially through informal or community-based labor frameworks.