Methods for multicountry studies of corporate governance: Evidence from the BRIKT countries

• Published in: Journal of econometrics Vol. 183; no. 2; pp. 230 - 240
• Main Authors: Black, Bernard, de Carvalho, Antonio Gledson, Khanna, Vikramaditya, Kim, Woochan, Yurtoglu, Burcin
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.12.2014; Elsevier Sequoia S.A
• Subjects: Board of directors; Boards of directors; Brazil; Coefficients; Corporate governance; Covariance; Disclosure; Eastern Europe; Econometrics; Economic theory; Emerging markets; India; Korea; Latin America; Market value; Middle East; Regression analysis; Russia; Sensitivity bounds; Shareholder rights; Studies; Turkey; Validation studies; Brazil; South Korea; Turkey; India; Russia; Sensitivity bounds; G30; Shareholder rights; G34; K22; G39; Disclosure; G18; India; Russia; K29; Turkey; Corporate governance; Brazil; Boards of directors; Korea
• ISSN: 0304-4076; 1872-6895
• DOI: 10.1016/j.jeconom.2014.05.013

We discuss empirical challenges in multicountry studies of the effects of firm-level corporate governance on firm value, focusing on emerging markets. We assess the severe data, “construct validity”, and endogeneity issues in these studies, propose methods to respond to those issues, and apply those methods to a study of five major emerging markets—Brazil, India, Korea, Russia, and Turkey. We develop unique time-series datasets on governance in each country. We address construct validity by building country-specific indices which reflect local norms and institutions. These similar-but-not-identical indices predict firm market value in each country, and when pooled across countries, in firm fixed-effects (FE) and random-effects (RE) regressions. In contrast, a “common index”, which uses the same elements in each country, has no predictive power in FE regressions. For the country-specific and pooled indices, FE and RE coefficients on governance are generally lower than in pooled OLS regressions, and coefficients with extensive covariates are generally lower than with limited covariates. These results confirm the value of using FE or RE with extensive covariates to reduce omitted variable bias. We develop lower bounds on our estimates which reflect potential remaining omitted variable bias.