Political connections and firm performance: Further evidence using a generalised quantile regression approach
We investigate the relationship between political connectedness and firm performance employing the generalised quantile regression (GQR) method. We analysed the annual data of 190 Pakistani firms (including politically connected and non-politically connected firms) that were listed on the Pakistan S...
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2021
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oai:doaj.org-article:b746d1b73bf14710a5b2748b7e60907c2021-11-20T04:55:56ZPolitical connections and firm performance: Further evidence using a generalised quantile regression approach0970-389610.1016/j.iimb.2021.08.005https://doaj.org/article/b746d1b73bf14710a5b2748b7e60907c2021-09-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S0970389621000641https://doaj.org/toc/0970-3896We investigate the relationship between political connectedness and firm performance employing the generalised quantile regression (GQR) method. We analysed the annual data of 190 Pakistani firms (including politically connected and non-politically connected firms) that were listed on the Pakistan Stock Exchange from 2007 to 2014. The analysis reveals that the political connections of the board directors impact firm performance negatively in different performance distributions. When compared to high-performing firms, the magnitude of impact of political connectedness is considerably higher in the case of low-performing firms. Overall, there is a negative correlation between political connectedness and performance after we account for the endogenous selection of the political connections in the board.Faisal ShahzadAsif SaeedGhazanfar Ali AsimFiza QureshiIjaz Ur RehmanSaba QureshiElsevierarticlePolitically connected firmsFirm performanceGeneralised quantile regressionBusinessHF5001-6182ENIIMB Management Review, Vol 33, Iss 3, Pp 205-213 (2021) |
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Politically connected firms Firm performance Generalised quantile regression Business HF5001-6182 |
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Politically connected firms Firm performance Generalised quantile regression Business HF5001-6182 Faisal Shahzad Asif Saeed Ghazanfar Ali Asim Fiza Qureshi Ijaz Ur Rehman Saba Qureshi Political connections and firm performance: Further evidence using a generalised quantile regression approach |
description |
We investigate the relationship between political connectedness and firm performance employing the generalised quantile regression (GQR) method. We analysed the annual data of 190 Pakistani firms (including politically connected and non-politically connected firms) that were listed on the Pakistan Stock Exchange from 2007 to 2014. The analysis reveals that the political connections of the board directors impact firm performance negatively in different performance distributions. When compared to high-performing firms, the magnitude of impact of political connectedness is considerably higher in the case of low-performing firms. Overall, there is a negative correlation between political connectedness and performance after we account for the endogenous selection of the political connections in the board. |
format |
article |
author |
Faisal Shahzad Asif Saeed Ghazanfar Ali Asim Fiza Qureshi Ijaz Ur Rehman Saba Qureshi |
author_facet |
Faisal Shahzad Asif Saeed Ghazanfar Ali Asim Fiza Qureshi Ijaz Ur Rehman Saba Qureshi |
author_sort |
Faisal Shahzad |
title |
Political connections and firm performance: Further evidence using a generalised quantile regression approach |
title_short |
Political connections and firm performance: Further evidence using a generalised quantile regression approach |
title_full |
Political connections and firm performance: Further evidence using a generalised quantile regression approach |
title_fullStr |
Political connections and firm performance: Further evidence using a generalised quantile regression approach |
title_full_unstemmed |
Political connections and firm performance: Further evidence using a generalised quantile regression approach |
title_sort |
political connections and firm performance: further evidence using a generalised quantile regression approach |
publisher |
Elsevier |
publishDate |
2021 |
url |
https://doaj.org/article/b746d1b73bf14710a5b2748b7e60907c |
work_keys_str_mv |
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1718419734123249664 |