This empirical paper explores trends in innovation activities measured by a countries’ total patent application submission intensity relative to its population, and by analyzing U.S. granted patents data for cohorts of developed countries and developing countries. In addition to tabular and graphical analyses, I use a baseline regression model and a variant model thereof to assess the relative influence of a set of aggregate variables on innovation activities in eight manufacturing industries across two cohorts of countries (developed and developing) where each cohort contains eight individual countries. Eight industries included in this study are: Chemical, Petroleum, electrical and electronics equipment, machinery, pharmaceutical, plastic, computer, and textile. The cohort of developed countries includes Australia, Canada, Czech Republic, France, Italy, Poland, Switzerland, and the United States. The cohort of developing countries includes Brazil, China, India, Malaysia, Mexico, Russia, South Africa, and Turkey. Per regression results, ethnic diversity is a statistically significant positive determinant of innovation for all industry aggregate patent count for both high income and developing countries. Also, per capita electricity usage, R&D expenditure as percent of GDP, and percent of population with internet access are three positive factors of innovation irrespective of industrial subsectors and position of a country in the development echelon. Interestingly, impact of ICT-services export is statistically significant and innovation boosting in developing countries in the cohort relative to countries in the cohort of developed countries. It also appears that trade openness served as a stronger stimulant of innovation activities for developing countries’ but not as much for the cohort of developed or high-income countries. This paper attempts to extend the literature on cross-country comparison of innovation activities by using two measures of innovation activities across developed and developing countries, and by analyzing both aggregate and sector-level data for eight manufacturing industries both graphically and utilizing panel regression models.