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Within the national innovation system literature, empirical analyses areseverely lacking for developing economies. Particularly, the low- andmiddle-income countries (LMICs) eligible for the World Bank's InternationalDevelopment Association (IDA) support, are rarely part of any empiricaldiscourse on growth, development, and innovation. One major issue hinderingpanel analyses in LMICs, and thus them being subject to any empiricaldiscussion, is the lack of complete data availability. This work offers a newcomplete panel dataset with no missing values for LMICs eligible for IDA'ssupport. I use a standard, widely respected multiple imputation technique(specifically, Predictive Mean Matching) developed by Rubin (1987). Thistechnique respects the structure of multivariate continuous panel data at thecountry level. I employ this technique to create a large dataset consisting ofmany variables drawn from publicly available established sources. Thesevariables, in turn, capture six crucial country-level capacities: technologicalcapacity, financial capacity, human capital capacity, infrastructural capacity,public policy capacity, and social capacity. Such capacities are part andparcel of the National Absorptive Capacity Systems (NACS). The dataset (MSKdataset) thus produced contains data on 47 variables for 82 LMICs between 2005and 2019. The dataset has passed a quality and reliability check and can thusbe used for comparative analyses of national absorptive capacities anddevelopment, transition, and convergence analyses among LMICs.

Estimating a new panel MSK dataset for comparative analyses of national  absorptive capacity systems, economic growth, and development in low and  middle income economies