The negative and stable relationship between an economy’s aggregate demand conditions and overall unemployment is well-documented. We show that there is a large degree of heterogeneity in the cyclical sensitivities of unemployment across worker and economy groups. First, unemployment is more than twice as sensitive to aggregate demand in advanced as in emerging market and developing economies. Second, youth’s unemployment is twice as sensitive as that of adults’. Third, women’s unemployment is significantly less sensitive to demand than men’s in advanced economies. We also present evidence that the cyclical sensitivity of unemployment during the 2020 COVID-19 recession decreased in advanced economies, but not emerging market and developing economies. Together, these findings point to the highly unequal impacts of the business cycle across worker and economy groups.

This paper aims to empirically study the short-term relationship between robot adoption and the labor market across a diverse set of advanced and emerging economies. Additionally, it seeks to analyze the impact of macroeconomic and institutional factors on this relationship. This study reveals robot adoption promotes employment growth in advanced economies, while it has a negative effect on employment in emerging economies. This heterogeneity can be attributed to both direct and indirect linkages between robots and labor in production. Directly, robots can either substitute or complement human labor. Indirectly, robot adoption stimulates output growth, leading to increased labor demand. We also show that the robot–labor relationship is influenced by macroeconomic variables such as development stage, unemployment rate, and education level, as well as institutional variables such as business regulation and structural reforms. These findings suggest the need for a more inclusive and sustainable approach to the advancement of robot adoption and automation.

The climate factors are the main driving forces of variability of renewable energy sources. In facing the safety challenges of the large-scale grid integration of renewable energy, it is important to understand the effects of climate shocks on the generation of the renewables as well as if the complementarity among different types of renewable energy exist and could be employed to smooth out the production and better match with the load. We employ a Bayesian vector autoregressive (BVAR) model along with impulse response function and forecast error variance decomposition to investigate the dynamic complementarity among renewable energy in the face of different climate shocks. The proposed methodology is applied over Qinghai employing the power generation data as well as meteorological data. The results show that there is greater general complementarity between solar power and hydropower. Wind power and solar power, and wind power and hydropower, exhibit dynamic complementarity when there are fluctuations in precipitation and wind speed, respectively. China should make efficient use of the natural complementarity advantages between renewable energy sources, carry out joint optimal dispatching, and accelerate the construction of a clean, low-carbon, safe and efficient energy system.