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

Muhammad Salar Khan

PLOS ONE, 2022, vol. 17, issue 10, 1-23

Abstract: Within the national innovation system literature, the low- and middle-income countries (LMICs) eligible for the World Bank’s International Development Association (IDA) support, are rarely part of empirical discourses on growth, development, and innovation. One major issue hindering empirical analyses in LMICs is the lack of complete data availability. This work offers a new full panel dataset with no missing values for IDA-eligible LMICs. I use a standard, widely respected multiple imputation method (specifically, Predictive Mean Matching) developed by Rubin in the 1980s, which conforms to the structure of multivariate continuous panel data at the country level. The incomplete input data consisting of many variables come from publicly available established sources. These variables, in turn, capture six crucial country-level capacities: technological capacity, financial capacity, human capital capacity, infrastructural capacity, public policy capacity, and social capacity. Such capacities are part and parcel of the National Absorptive Capacity Systems (NACS). The dataset (MSK dataset) thus produced contains data on 47 variables for 82 LMICs between 2005 and 2019. The dataset has passed a quality and reliability check and can therefore be used for comparative analyses of national absorptive capacities and development, transition, and convergence among LMICs.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0274402

DOI: 10.1371/journal.pone.0274402

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