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The cyber insurance market in Germany shows a notable gap: while many large corporations are already demanding cyber insurance, small and medium-sized enterprises (SMEs) are still reluctant, despite its benefits. This study aims to analyze the behavioral and informational factors that influence cyber insurance decision-making and uncover the determinants that may inhibit demand among German SMEs.

Using the data from a questionnaire survey of 1,248 German SME executives, the influence of behavioral and informational factors on cyber insurance demand is assessed utilizing logistic regression.

The results reveal that the estimated financial impact and anxiety about a potential cyberattack significantly increase the likelihood of SMEs purchasing cyber insurance. Conversely, the perceived probability of future cyberattacks and prior experience do not significantly influence insurance decisions, probably due to challenges in probability estimation. In addition, confidence in the organization’s cyber risk management has a positive but insignificant influence on cyber insurance demand. External cybersecurity specialists positively impact cyber insurance demand, while internal cyber risk assessment has no significant influence, highlighting the challenges SMEs face in cyber risk assessment. Independent Internet research negatively impacts the purchase of cyber insurance, probably due to information overload.

This study significantly contributes to the literature on corporate (cyber) insurance purchasing by primarily focusing on behavioral influences on SMEs’ insurance decision-making. It is also the first empirical analysis of the key information sources used by SMEs in their insurance decision-making, thereby providing various academic and practical implications.

In India, women encounter twofold challenges – declining labour force participation and a widening gender wage gap (GWG). The study explores the precarious position of women during the third decade of economic reforms. It examines the influence of demographic and job characteristics on wages, uncovering socioeconomic imbalances and disentangling wage disparities attributable to productive and non-productive factors.

Using unit-level data from the NSS EUS (2011–12) and PLFS (2018–19), the study employs OLS and quantile regression methods to estimate the contribution of socioeconomic factors in wage determination. It applied Oaxaca–Blinder decomposition alongside other mean-based decomposition approaches. Furthermore, the counterfactual decomposition proposed by Machado–Mata and Melly is also applied.

Structural and socio-cultural barriers continue to depress women’s LFPR and wages. Women remain concentrated at the lower end of the wage spectrum, earning less than men. However, GWG has slightly narrowed, but discrimination remains substantial, which is primarily driven by employers’ undervaluation of women’s work, rooted in prejudice. Additionally, the sticky-floor phenomenon worsened in 2018–19, further hindering upward mobility.

Leveraging recent survey data helps inform policy discourse to promote gender equality and address workplace disparities. It urges policymakers to re-evaluate anti-discrimination measures to combat socioeconomic challenges.