Knowledge, Capabilities, and the Poverty Trap: The Complex Interplay Between Technological, Social, and Geographical Factors

Abstract

This chapter explores the suggestion that technological capabilities, if they are to lead to development, need to be accompanied by a broad set of social capabilities reflecting not only such things as the provision of education and good governance, but also the spread of values, beliefs, and institutions that encourage members of society to contribute actively to the development process. The empirical analysis presented in the chapter, based on a large dataset of relevant indicators for countries at different levels of development, lends considerable support to this suggestion. However, the analysis also shows that some countries, mostly tropical, are disadvantaged by a powerful vicious circle of high fertility rates, low education, and high frequency of serious disease, which hamper the building of technological and social capabilities and perpetuate poverty. The authors conclude that there is no easy “technological fix” to the problem of underdevelopment.

This chapter is based on a presentation at the Fifth Interdisciplinary Symposium on Knowledge and Space, “Knowledge and the Economy,” June 25–28, 2008, Villa Bosch Studio, Heidelberg, organized by the Department of Geography, University of Heidelberg, and supported by the Klaus Tschira Foundation. The support from VINNOVA Core Funding of Centers for Innovation Systems Research (Grant agreement 2010–01370) is gratefully acknowledged. Jan Fagerberg would also like to thank International Centre for Economic Research, University of Torino, for its support during the final revision of the chapter. We have benefitted from suggestions from the participants of the symposium and an anonymous referee, retaining sole responsibility for the final version.

Appendices

Appendix A: Data and Sources

A brief overview of definitions, sources, and coverage of the indicators is given in Table 7.2. The main source of data is the World Bank (World Development Indicators), which combines various sources of data for a large sample of countries. The database has been complemented by data from other international organizations and datasets produced by research projects. National sources were used only for Taiwan when necessary and, in a few cases, for R&D data in developing countries.

Although the selected indicators have broad coverage, in some cases there were missing values that had to be estimated. We used the impute procedure in the Stata 9 to fill in the missing values (for details see Stata 2005). In each case we based our estimation on data for other indicators in the dataset. The number of countries with estimated data for each indicator is given in the last column of Table 7.2. We stress that considerable care was taken to check these estimated data against observed figures. If the estimated data exceeded the maximum (or minimum) observed value of an indicator elsewhere, we truncated the data by replacing the estimated values by the maximum (or minimum) observed figure. For some of the governance indicators, we also reversed the scale, though kept the original range, in order to present the indicators in increasing order (with low value signaling weak governance and high value signaling strong governance).

Table 7.2 An overview of the indicators

Appendix B

Table 7.3 Factor scores, by country