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The effects of the investment decisions of telecommunications firms on their financial performance during the COVID-19 pandemic

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Abstract

In this paper we examine how the financial performance of telecommunications firms is affected by the COVID-19 pandemic, and investigate the role of capital expenditures in this relationship. The full sample consists of 383 unique telecommunications firms from 72 countries. Empirical models are estimated using ordinary least squares regression with the Driscoll–Kraay standard errors method. We find that the financial performance of telecommunications firms, on average, decreased slightly during the pandemic period. However, firms with higher capital expenditures have increased their financial performance in the pandemic era. We attribute this evidence to fewer agency problems and managerial myopia, since managers investing to meet demand surge in uncertainty may prioritize the long-term sustainability of their firms. We further find that telecommunications firms that have been most adversely affected by the repercussions of COVID-19 are those with lower capital expenditures operating in countries with less economic development and weaker institutional environments. Our main findings are robust to potential endogeneity issues, reverse causality, and alternative dependent variables. The findings have implications for company managers, investors, regulatory bodies, and policymakers.

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Notes

  1. The most up-to-date data is available at: https://covid19.who.int/.

  2. https://www2.telegeography.com/hubfs/assets/product-tear-sheets/product-page-content-samples/global-internet-geography/telegeography-global-internet-geography-executive-summary.pdf.

  3. https://corporate.comcast.com/covid-19.

  4. https://www.reuters.com/article/us-health-coronavirus-vodafone-group-dat-idUSKBN2151MP.

  5. https://news.un.org/en/story/2023/05/1136367.

  6. We consider net income after taxes as an income measure for ROA. However, to ease concerns on measurement, we also use EBITDA (earnings before ınterest, taxes, depreciation, and amortization) as an income measure. The unreported results indicate that our findings are unchanged. These results are available upon request.

  7. We did not consider any market-based performance indicator as our main measure of performance since these indicators reflect the market point of view and are related to the future expectations of the firms. However, in this paper, we are interested in the current performance of firms in the COVID period. Moreover, very volatile price movements occurred during the pandemic period and investors made too much emotional pricing, this may be led to measurement errors.

  8. We thank anonymous reviewers for highlighting this important data.

  9. We obtained these numbers by adding the coefficient of the STRINGENCY (− 0.0004) to the minimum (0.001) or maximum (0.264) value of CAPEX in Table 3 times the interaction coefficient (0.003).

  10. While calculating the ROE, firms with negative total equity value were eliminated, since the ROE values of the companies with both negative total equity value and loss in the same year would be false-positive.

  11. In unreported results, the eigenvalues of PCA show that the first principal component captures over 63 percent of the variance. The overall KMO measure of sampling adequacy is 0.68, which is above the recommended threshold level of 0.50. Thus, this measure contains many attributes of the financial performance of firms.

  12. Standard errors are computed using the robust estimator of the covariance matrix of the parameter estimates which ensures robust standard errors for heteroscedasticity and autocorrelation within panels. Lagged ROA is treated as predetermined and it is instrumented by their own one- to five-period lags. COVID measures and other fixed effect dummy variables are treated as exogenous and they are instrumented by their own instrument. CAPEX, COVID × CAPEX, and all remaining firm‐level variables are treated as endogenous and they are instrumented by their own two- to nine-period lags. We collapse the matrix of instruments. Columns (1)–(3) of Table 8 show that the p-values of Hansen tests are 0.204, 0.139, and 0.144, respectively and the p values of AR (1)/AR (2) tests are 0.00/0.83 and 0.00/0.77, and 0.00/0.76 respectively. These statistics confirm that the models are correctly specified and the instruments are valid.

  13. We rerun our model by using GSMA Infrastructure Index, instead of Mobile Connectivity Index of GSMA. The unreported results aren't changed. These results are available upon request.

  14. We thank an anonymous reviewer for bringing attention to this significant matter.

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Authors and Affiliations

  1. Informatics Institute, Istanbul Technical University, Istanbul, Turkey

    İlhan Çam, Nisa Özge Önal Tuğrul, Kevser Şimşek & Ertuğrul Karaçuha

  2. Department of Business Administration, Gebze Technical University, Kocaeli, Turkey

    İlhan Çam & Gökhan Özer

  3. Electrical Engineering, Istanbul Technical University, Istanbul, Turkey

    Kamil Karaçuha

  4. Department of Management, Kyrgyz-Turkish Manas University, Bishkek, Kyrgyzstan

    Gökhan Özer

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  1. İlhan Çam
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Çam, İ., Önal Tuğrul, N.Ö., Şimşek, K. et al. The effects of the investment decisions of telecommunications firms on their financial performance during the COVID-19 pandemic. Empir Econ 66, 2007–2047 (2024). https://doi.org/10.1007/s00181-023-02525-4

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