This paper considers the problem of testing cross-sectional correlation in large panel data models with serially-correlated errors. It finds that existing tests for cross-sectional correlation encounter size distortions with serial correlation in the errors. To control the size, this paper proposes a modification of Pesaran’s Cross-sectional Dependence (CD) test to account for serial correlation of an unknown form in the error term. We derive the limiting distribution of this test as $\left(N,,,T\right)\to \infty$ . The test is distribution free and allows for unknown forms of serial correlation in the errors. Monte Carlo simulations show that the test has good size and power for large panels when serial correlation in the errors is present.

This paper presents new empirical evidence on the effect of population aging on economic growth in China. Aging is widely assumed to impact economic growth by shaping the labor market performance and human capital. Using an interactive fixed effects model and a provincial panel data set during 1990–2015, and also exploiting the predicted province‐by‐year variation in population aging fraction as an instrument, we examine the impact of population aging on the growth rate of gross domestic product (GDP) per capita, labor force ratio, and labor productivity. The results show that a 10% increase in the fraction of the population aged 65+ decreases the growth rate of GDP per capita by approximately 2%. Furthermore, channel decomposition shows that half of the decrease is due to slower growth of the labor force ratio, whereas the rest may be due to slower productivity growth. In addition, the negative effect of aging on GDP growth is more substantial after the year 2000, and the effect of aging on the labor force ratio and productivity shows different patterns during different periods.

Fifteen triads of unacquainted men conversed for ten minutes while stress was measured in real time by pulse rate and thumb blood volume (TBV). Salivary measures of testosterone (T), cortisol (C), and the stress-related enzyme alpha-amylase (AA) were measured at the beginning and end of the session. Fully or partially transitive status hierarchies formed in 14 triads. (Highest ranked man was scored 1, lowest 3, with ties allowed.) Ten of the triads participated in Study 1, where nothing was at stake in the casual conversation. Five additional triads were run in Study 2, intended to introduce competition by offering a $20 reward to the man afterward chosen as having led the conversation. Most results from the two studies are similar, suggesting that the $20 reward had little effect. Combining studies, pulse and TBV show that conversation is more stressful than watching a video beforehand. Within the conversation, speaking turns are more stressful than listening turns, especially among the lowest ranked men, less so among those higher in rank. This supports a stress-based mechanism for status allocation among humans. Apparently, human speech is a form of status signaling, homologous with nonlinguistic status signals used by other primates, as posited by the “biosocial model.” The biosocial model also posits that a physiological substrate (T, C, and AA) is related to dominance or status. Predicted effects are not replicated here, except for an inverse relationship between the stress enzyme AA and status. The mostly null results, obtained from conversations where there was little or nothing at stake, suggest that T and C (and their interaction) are not relevant to emergent status in the absence of serious competition.

The manufacturing sector’s significant contribution to global carbon emissions necessitates effective decarbonization. This study evaluates the causal effect of exposure to China’s “Made in China 2025” (MIC) initiative, an industrial policy promoting intelligent manufacturing, on firms’ estimated carbon emissions by exploiting the policy as a quasi-natural experiment. Employing a Difference-in-Differences (DID) model on a panel of Chinese A-share manufacturing firms from 2011 to 2022, we find that exposure to the MIC policy is associated with an approximately 0.5% reduction in firms’ estimated carbon emissions. Although the reduction appears modest at the firm level, its aggregate effect may be substantial given the large scale of China’s manufacturing sector. Various robustness checks confirm the validity and persistence of this effect. Crucially, the environmental benefits are heterogeneous, showing stronger effects in competitive, technology-intensive, and labor-intensive sectors compared to capital-intensive ones. Mechanism analysis reveals that exposure to the MIC policy promotes substantive green innovation, accelerates digital transition through process optimization, and significantly enhances energy efficiency, thereby contributing to lower estimated firm-level carbon emissions. These findings underscore the potential role of intelligent-manufacturing-oriented industrial policies in reconciling industrial growth with climate mitigation and provide micro-level evidence on the environmental effects of policy-induced intelligent manufacturing transformation, extending a literature that has largely focused on economic outcomes. The results also suggest that policymakers should further promote intelligent manufacturing and design sector-specific green technology support policies, particularly for capital-intensive sectors where carbon-reduction effects appear relatively weaker, to maximize carbon reduction benefits.