The stakes of this research are considerable. Roughly one in four early care and education teachers leaves their position every year, a turnover rate dramatically higher than the roughly 10 percent annual rate observed among kindergarten teachers. Each departure disrupts the stable relationships young children need for healthy development, degrades classroom quality, increases child-to-adult ratios, and piles additional stress onto the teachers who remain. Worse still, studies show that most early childhood teachers who quit their center do not simply move to another one; they leave the field entirely, compounding a staffing shortage that the U.S. Chamber of Commerce has flagged as a threat to families’ access to affordable childcare.

The new study, published in the Early Childhood Education Journal by Erin G. Fox, Sarah N. Lang, and Kelly M. Purtell, draws on the 2019 National Survey of Early Care and Education Workforce Questionnaire, a federally funded survey designed to capture a nationally representative portrait of the workforce serving children from birth through age five who have not yet entered kindergarten. Because the survey disproportionately sampled low-income areas, the researchers applied statistical sampling weights to ensure their findings generalize to the broader national population of center-based teachers. The analysis was preregistered on the Open Science Framework, a practice that commits researchers to their analysis plan in advance and reduces the risk of cherry-picking results.

To measure the risk of departure, the team constructed a binary indicator of turnover intention from two survey questions: whether respondents had done anything in the past three months to look for a new or additional job, and their main reason for doing so. Teachers searching only for a second job or summer employment, which do not signal intent to quit their current classroom role, were classified as not intending to leave. The three focal predictors were equally carefully measured. Compensation came from the survey’s calculated continuous hourly wage variable. Workplace support was a composite of three items asking teachers whether they and their co-workers were treated with respect day to day, whether teamwork was encouraged, and whether they had help dealing with difficult children or parents, a scale that showed strong internal consistency. Mental health was assessed with the brief seven-item version of the Center for Epidemiological Studies Depression Scale.

The statistical strategy relied on weighted binary logistic regression, a technique suited to predicting a yes-or-no outcome while accounting for the survey’s complex sampling design. In the primary model, teachers who reported greater workplace support had roughly half the odds of intending to leave compared with those who felt less supported, an odds ratio of about 0.51. Each increment of depressive symptoms increased the odds of turnover intention. Hourly wage, by contrast, was not significantly associated with turnover intention at all, a result that held even after controlling for an extensive set of covariates including race and ethnicity, education level, years in the field, possession of a Child Development Associate credential, household income, receipt of government assistance, marital status, and number of children at home. The full model accounted for approximately 14 percent of the variability in turnover intention.

Guided by Cumming and Wong’s holistic framework of early childhood educator well-being, which treats individual, relational, and workplace factors as interconnected, the researchers also tested whether depression changes the game. Prior regional studies had hinted that teachers with poorer mental health may be more sensitive to low pay or weak workplace support, making those conditions more likely to push them toward the door. Yet in this nationally representative sample, neither interaction term reached statistical significance. Depression did not meaningfully amplify the relationship between workplace support and turnover intention, nor between wage and turnover intention, though the interaction with workplace support came relatively close with a p-value of .09.

The null finding on wages is arguably the most provocative result, and the authors offer several plausible explanations. Average hourly pay in the sample was just $14.38, and compensation across centers varies relatively little, meaning a teacher committed to the field is unlikely to find meaningfully better pay simply by switching employers. That structural ceiling may mute wage differences as a motivator for job searching. There is also the question of calling: many early childhood teachers describe their work as a vocation, accepting low wages because they find the work meaningful, a commitment that may paradoxically contribute to their financial fragility. Importantly, the authors caution that the wage null finding does not mean money does not matter. Previous experimental evidence from Virginia showed that financial incentive payments reduced turnover, and low compensation has been linked to worse mental health and diminished capacity to provide high-quality care. Other dimensions of financial well-being, such as benefits and one-time bonuses, may still shape decisions to stay or go.

The findings on workplace support and mental health point toward actionable strategies for center leaders. Cultures of respect, teamwork, and practical help handling challenging interactions appear to function as a genuine retention lever, not merely a nicety. Leadership practices such as open communication, formal recognition of teachers’ accomplishments, investment in professional development, peer mentorship programs, and collaborative team-building can all foster the relational climate that the data suggest keeps teachers in their classrooms. On the mental health front, interventions ranging from social-emotional learning programs designed for teachers to mindfulness training have shown promise in bolstering educators’ resilience and well-being, and the new evidence suggests such supports may pay dividends in workforce stability.

The study also carries instructive caveats. Its cross-sectional design precludes causal conclusions, and the workplace support scale showed a ceiling effect, with responses clustered at the positive end, which may have limited the ability to detect moderation effects. Both workplace support and depression were self-reported, raising the possibility that the cognitive biases associated with depression color how teachers perceive their work environments, making the two constructs difficult to fully disentangle. The data, collected in 2019, predate the COVID-19 pandemic, which evidence suggests intensified both turnover and mental health strains in the early childhood workforce. And with the model explaining only about 14 percent of the variance, much of the story of why teachers stay or leave remains untold.

Even with those limitations, the national scope of the analysis marks a meaningful advance over the small, regional studies that have long dominated this literature, confirming that workplace support and mental well-being are robust correlates of turnover intention across the country rather than artifacts of particular states or programs. The authors conclude that targeting supportive workplace climates and teachers’ mental health through prevention and intervention may be among the most effective ways to reduce early childhood teachers’ intention to leave their jobs. For a field in which one departure can fracture a young child’s first relationships outside the family, that message carries urgency well beyond the staff lounge.

Subject of Research: Predictors of turnover intention among center-based early care and education teachers in the United States

Article Title: Examining Influence on Early Care and Education Teachers’ Intention to Leave: Compensation, Workplace Support, and Depressive Symptoms

Article References: Fox, E. G., Lang, S. N., & Purtell, K. M. (2026). Examining Influence on Early Care and Education Teachers’ Intention to Leave: Compensation, Workplace Support, and Depressive Symptoms. Early Childhood Education Journal. https://doi.org/10.1007/s10643-026-02329-y

Image Credits: AI Generated

DOI: 10.1007/s10643-026-02329-y

Keywords: early care and education, teacher turnover, turnover intention, workplace support, depressive symptoms, compensation, teacher retention, early childhood workforce, mental health, childcare, Examining, Influence

The research team, Erkut Altindağ of Doğuş University and Özge Gül of Istanbul Rumeli University, collected 42,852 public LinkedIn comments from January to December 2023. They targeted discussions on posts tagged with keywords such as compensation, benefits, salary, and total rewards, along with conversations in professional human resources groups and responses to corporate announcements about benefits packages. Rather than relying on the structured questionnaires and interviews that have long dominated compensation research—methods vulnerable to social desirability bias and flawed retrospective recall—the researchers tapped into unfiltered, spontaneous discourse. They are careful, however, to frame social media as a complement to surveys rather than a replacement, acknowledging that LinkedIn participation is shaped by self-selection, vocal minorities, and the performative nature of public posting.

Technically, the study deployed a multi-modal analytical pipeline built on some of the most powerful tools in modern computational linguistics. At its core was a BERT-based sentiment analysis model, fine-tuned on 1,000 manually labeled compensation-related comments to classify each remark as positive, neutral, or negative. The BERT architecture, a deep bidirectional transformer encoder, outperformed simpler baselines including a TF-IDF logistic regression model (F1 = 0.71) and a lexicon-based VADER approach (F1 = 0.65), achieving a validation accuracy of 0.89 and an F1-score of 0.87 on the test set. Class imbalance in the training data was handled through class-weighted cross-entropy loss, and the framework incorporated a custom lexicon of compensation-specific terms to sharpen accuracy in this particular domain.

Alongside sentiment analysis, the researchers applied two complementary techniques for uncovering structure in the discourse. Topic modeling was performed using both Latent Dirichlet Allocation, optimized to 15 topics through coherence scoring, and BERTopic, a neural approach capable of tracking how themes evolved dynamically across the year. In parallel, a custom named entity recognition model built on spaCy’s framework and trained on 2,500 annotated comments extracted specific entities from the text: organizations, benefit types, and job roles. The entity recognition model achieved an F1-score of 0.83, with particularly strong performance in identifying organizations (F1 = 0.86) and benefit types (F1 = 0.83). Data quality was protected through rigorous preprocessing—English-language filtering using langdetect, deduplication via exact and fuzzy matching based on Levenshtein distance, and thorough text cleaning—while validation included inter-rater reliability assessment by three independent coders, who achieved a Cohen’s Kappa of 0.82, and five-fold cross-validation across all models.

The results paint a vivid picture of sector-specific sentiment in the world of work. Technology sector discussions maintained the highest positive sentiment, with a mean score of 0.42, followed by healthcare at 0.31 and financial services at 0.25. A one-way analysis of variance confirmed that these differences between industries were statistically significant (F(2, 42,849) = 23.45, p < 0.001). Sentiment also fluctuated throughout the year, with notable peaks coinciding with major corporate benefits announcements, and the technology sector showed greater volatility than other industries. The authors note that because LinkedIn comments are nested within users, posts, and organizations, the standard ANOVA violates independence assumptions, so the reported statistic should be treated as a first-order approximation, with multilevel modeling recommended for future work.

Perhaps the most striking finding concerns how the themes of employee conversation shifted over the course of a single year. Work-life balance discussions surged from 14.2 percent of the discourse in the first quarter of 2023 to 41.2 percent by the fourth quarter—a 27 percentage-point increase in thematic prevalence. Meanwhile, traditional base compensation discussions declined by 12.0 percent. A chi-square test confirmed the statistical significance of the overall thematic shift (χ²(4) = 156.23, p < 0.001), though the researchers emphasize that the omnibus test does not establish the significance of any single theme’s change; the individual percentage shifts are presented as descriptive magnitudes pending confirmatory pairwise comparisons with multiple-testing correction. Even with those caveats, the direction is clear: employees are talking less about raw salary and more about time, flexibility, and well-being.

The named entity recognition analysis added another layer of insight by linking specific benefits to sentiment outcomes. The model identified 3,427 unique organizations, 892 granular benefit-related entities—which aggregate onto roughly 47 canonical benefit categories—and 1,245 job roles across the dataset. Health insurance emerged as the benefit type most strongly correlated with positive sentiment (r = 0.42, p < 0.001, 95 percent confidence interval 0.38 to 0.46), followed closely by flexible work arrangements (r = 0.38, p < 0.001). These correlations remained robust after controlling for industry sector and temporal variation. Health insurance dominated the discourse with 12,458 mentions, while retirement benefits, despite lower frequency at 7,892 mentions, maintained a moderate positive correlation with sentiment (r = 0.31, p < 0.001).

The theoretical backbone of the study is Social Exchange Theory, the classic framework introduced by Peter Blau in 1964, which holds that workplace relationships operate on reciprocity: employees weigh the benefits they receive against the effort and commitment they contribute. When the exchange feels fair, workers respond with engagement, loyalty, and discretionary effort; when they feel undervalued, dissatisfaction and turnover intentions follow. Viewed through this lens, the findings suggest that employees increasingly interpret non-monetary rewards—flexible schedules, development opportunities, and work-life balance initiatives—as signals of organizational commitment to their well-being, not merely as transactional extras. The observed migration of discourse away from traditional compensation and toward holistic well-being aligns with this relational interpretation and with prior survey-based evidence that workers increasingly prioritize intangible benefits over pay alone.

The authors are candid about the limitations of their approach. The single-platform focus on LinkedIn may miss perspectives prevalent elsewhere; the one-year observation window means the quarter-to-quarter thematic shifts should be read as within-year movements rather than stable longitudinal trends; the English-only filter introduces cultural and linguistic bias; and LinkedIn’s user base skews toward certain professional and demographic groups. The dataset itself, consisting of publicly available comments collected under applicable data protection rules, cannot be shared in raw form, though anonymized and aggregated data are available on reasonable request. Despite these constraints, the study establishes a validated, replicable framework for social media analytics in compensation research—one the authors say achieved robust overall performance (F1 = 0.83) across the pipeline.

The practical implications for employers are considerable. As organizations compete for talent in a tight labor market, the study suggests that total rewards strategies built around pay alone may be increasingly out of step with workforce expectations. Compensation professionals now have evidence that flexible work arrangements and health benefits generate the strongest positive sentiment, that sentiment varies meaningfully by industry, and that the timing of benefits announcements visibly moves the needle on employee discourse. The researchers call for cross-platform validation, longitudinal studies spanning multiple years, multilingual analysis capabilities, and the integration of demographic variables to capture preference variation across employee segments. They even point toward predictive models capable of anticipating emerging compensation trends before they fully materialize in public conversation. In an era when employee voice is amplified, searchable, and machine-readable, the silent signals of the workforce are silent no more—and organizations that learn to listen computationally may gain a decisive edge in designing rewards that resonate.

Beyond its immediate findings, the study sits within a broader methodological turn in organizational research. Computational text analysis has gained traction across management science because it captures behavior in natural settings, sidestepping the artificiality of laboratory tasks and the recall problems of retrospective questionnaires. The choice of BERT is significant in this respect: unlike earlier bag-of-words techniques that ignore word order, transformer models process each word in relation to its surrounding context, allowing them to distinguish, for example, a sarcastic complaint about a benefits package from a sincere endorsement using nearly identical vocabulary. This contextual sensitivity matters greatly in compensation discourse, where negation, hedging, and irony are common.

The domain-specific adaptations the researchers made also illustrate a key lesson for applied text analytics. Off-the-shelf sentiment tools are typically trained on general web text or product reviews, where the language of workplace compensation is underrepresented. By supplementing the model with a custom lexicon of compensation terms and fine-tuning on manually labeled comments, the team addressed the vocabulary gap that often degrades performance when general-purpose models are applied to specialized professional discourse. The reported gap between the BERT model and the lexicon-based VADER baseline underscores how much accuracy can be lost without such adaptation.

The theoretical framing also deserves emphasis. Social Exchange Theory, as elaborated by scholars such as Gould-Williams and Davies, holds that employees interpret rewards not merely as transactional payments but as signals of how much the organization values them. The study’s finding that health insurance and flexible work arrangements carry the strongest positive sentiment fits this account: benefits that touch on security and personal autonomy may function as especially potent signals of organizational care. Conversely, the decline in base-pay discussion suggests that salary, while foundational, may be increasingly treated as a baseline expectation rather than a differentiator among employers.

For researchers, the study also highlights unresolved measurement questions. Because sentiment scores were aggregated across comments nested within users, posts, and organizations, the effective sample size for industry comparisons is smaller than the raw comment count suggests, and future multilevel designs could partition variance at each level. Extending the framework to multilingual corpora would be particularly valuable given that compensation norms and benefit expectations vary substantially across national labor markets. If subsequent work confirms these patterns across platforms and languages, social media analytics could become a routine complement to engagement surveys, giving organizations a near real-time barometer of how their reward strategies are actually landing with the workforce.

Subject of Research: Natural language processing analysis of employee perceptions of total rewards through LinkedIn discourse

Article Title: Employee perceptions of total rewards revealed through natural language processing of LinkedIn discourse

Article References: Altindağ, E., & Gül, Ö. (2026). Employee perceptions of total rewards revealed through natural language processing of LinkedIn discourse. Discover Global Society, 4(1), Article 238. https://doi.org/10.1007/s44282-026-00565-6

Image Credits: AI Generated

DOI: 10.1007/s44282-026-00565-6

Keywords: natural language processing, total rewards, LinkedIn, sentiment analysis, employee benefits, compensation, social media analytics, BERT, topic modeling, named entity recognition, work-life balance, human resource management