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Geographical concentration of knowledge and technology-intensive industries in India: empirical evidence from establishment-level analysis

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Abstract

This paper investigates the geographical concentration of 35 knowledge and technology-intensive industries (KTI), covering 0.43 million establishments at a district level using Economic Census (2013) data. Empirical results exhibit that the spatial dependence for high and medium–high R&D-intensive industries prevails across various districts of India. Specifically, results demonstrate that the magnitude of the geographical concentration effect differs across high and medium–high R&D-intensive industries and the high-high employment cluster, mainly perceptible in Maharashtra and Telangana states in India. Moreover, the results validate that the substantial evidence of employment concentration of KTI industries has been confined to only a few specific districts of different states in India. Further, we estimate a regression line between the unweighted and weighted Ellison–Glaeser index for a more robust analysis and to capture the neighborhood effect. Empirical results exhibit that for specific KTI industries, the estimated coefficients between these indices exceed one, indicating substantial evidence of the neighborhood effect, which facilitates the geographical concentration of a few KTI industries specific to certain locations in India. Empirical results from this study emerge specific policies to emphasize the districts to increase the employment opportunities where the KTI industry has a higher employment concentration. Further, emphasis should be given to the KTI industries to enhance their value-addition capability for various products and services.

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Fig. 1

Source: Authors' computations using GeoDa software. The number of districts shown in the parentheses and local Moran's I values for these districts is significant at a 5% level. The bright red shows the high–high employment association, bright blue for low–low, light blue for low–high, and pink for the high–low employment association, respectively. Out of 641 districts, 491 districts using queen contiguity and 351 districts using inverse distance matrix in gray color are not statistically significant at the 5% level (Anselin, 1995). Also, the undefined category in the black color represents districts where no workers are employed in high R&D-intensive industries

Fig. 2

Source: Own computations using GeoDa software. The values in parentheses denote the number of districts. Hotspots (High–High) are shown in red, and cold spots (Low–Low) are shown in dark blue. These districts' hotspots and cold spots are significant at a 5% level

Fig. 3

Sources: Own computations using ArcGIS 10.6 by considering the Economic Census (2013) data

Fig. 4

Source: Own computations using ArcGIS 10.6 by considering the Economic Census (2013) data

Fig. 5

Source: Author's computations using Stata 14. We choose only those Indian states where the spatially weighted Ellison-Glaeser index is statistically significant at a 5% level for both queen contiguity and inverse distance weight matrix

Fig. 6

Source: Own computations using ArcGIS 10.6 by considering the Economic Census (2013) data

Fig. 7

Sources: Own computations using ArcGIS 10.6 by considering the Economic Census (2013) data

Fig. 8

Sources: Own computations using ArcGIS 10.6 by considering the Economic Census (2013) data

Fig. 9

Source: Author's computations using Stata 14 . We choose only those Indian states where the estimated value of the spatially weighted Ellison–Glaeser index is statistically significant at a 5% level for both queen contiguity and inverse distance weight matrix

Fig. 10

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Data availability

Data will be available from the corresponding author upon request.

Notes

  1. The R&D intensity implies the ratio of an industry’s business R&D expenditures to its value-added output (Galindo-Rueda & Verger, 2016). The KTI industries include IT and software publishing, scientific research and development, air and spacecraft, pharmaceuticals, computer, electronic and optical products, motor vehicles, medical and dental instruments, railroad, chemicals, and electrical industries. For more details, see Table 7 in the appendix section.

  2. Data source: IHS Market, special tabulations (2019) of Comparative Industry Service.

  3. Data source: Oxford Economics, special tabulations (2019) of Global Trade Databank.

  4. The data can be accessed using the following source: UNESCO Institute for Statistics (UIS).

  5. According to the Economic Census (2013), an establishment is a unit situated in a single location in which predominantly one kind of entrepreneurial activity is carried out such that at least a part of the goods and services produced by the unit goes for sale (i.e., the entire product is not for sole consumption).

  6. Localization of industries implies industries that display significant geographic concentration relative to manufacturing in general (Aleksandrova et al., 2020).

  7. For more details about MAUP, see Briant et al. (2010) and Guimarães et al. (2011).

  8. To get the data, use this link: http://icssrdataservice.in/datarepository/index.php.

  9. For more detailed classifications, see Table 7 in Appendix A.

  10. We use spmat command created by (Drukker et al., 2013) in order to calculate queen contiguity and inverse distance weight matrix in Stata 14.

  11. Spatial outliers depict high-low employment cluster and low–high employment cluster.

  12. Moran’s I index pseudo p-value is 0.001 obtained by doing randomization 9999 times in Geoda software. For more detailed discussion, see Anselin et al. (2010).

  13. To classify the intensity of localization across the industry, Ellison–Glaeser (1997) uses the magnitude of the estimated value of the Ellison–Glaeser (EG) index. When the EG index estimated value is greater than 0.05, it indicates a highly concentrated (HC) industry. Industries with an estimated value between 0.02 and 0.05 indicate somewhat concentrated, and industries with a value less than 0.02 belong to barely concentrated.

  14. While computing the spatially weighted EG index, we use queen contiguity and inverse distance weight matrix.

  15. For the estimated value of unweighted EG and spatially weighted EG index (using queen contiguity and inverse distance weight matrix) for different states and union territories of India, see Tables 8, 9, 10 in the appendix section.

  16. Figure 6 (e and f) clearly shows that on average, the spatially weighted index is more than the unweighted EG index by 9% using queen contiguity weight matrix and 3% using inverse distance weight matrix.

  17. Centripetal forces lead to the agglomeration of industries like buyer–supplier linkages, labor market pooling, knowledge spillover, etc.

  18. For estimated value of unweighted EG and spatially weighted EG index (using queen contiguity and inverse distance weight matrix) for different states and union territories of India, see Tables 11, 12, 13, 14 in the appendix section.

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Acknowledgements

We would like to thank anonymous journal reviewers for their valuable comments and insightful suggestions. We would also like to thank the Editor of the journal, Abhijit Banerji, for allowing us to editing and revise this paper.

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  1. Department of Humanities and Social Sciences, Indian Institute of Technology Ropar, Punjab, India

    Sugam Agarwal & Smruti Ranjan Behera

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Appendix

Appendix

See Tables 7, 8, 9, 10, 11, 12, 13 and 14.

Table 7 Classification of KTI industries.
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Table 8 Computed EG and spatially weighted EG index for software publishing (NIC-582).
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Table 9 Computed EG and spatially weighted EG index for research and experimental development on social sciences and humanities (NIC-722).
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Table 10 Computed EG and spatially weighted EG index for air and spacecraft and related machinery (NIC-303).
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Table 11 Computed EG and spatially weighted EG index for transport equipment n.e.c. (NIC-309).
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Table 12 Computed EG and spatially weighted EG index for computer programming, consultancy and related activities (NIC-620).
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Table 13 Computed EG and spatially weighted EG index for parts and accessories for motor vehicles (NIC-293).
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Table 14 Computed EG and spatially weighted EG index for railway locomotives and rolling stock (NIC-302).
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Agarwal, S., Behera, S.R. Geographical concentration of knowledge and technology-intensive industries in India: empirical evidence from establishment-level analysis. Ind. Econ. Rev. 57, 513–552 (2022). https://doi.org/10.1007/s41775-022-00145-w

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