دانلود مقاله ISI انگلیسی شماره 51934
ترجمه فارسی عنوان مقاله

روش های برای مطالعات چند کشوری حاکمیت شرکتی: شواهدی از کشورهای BRIC

عنوان انگلیسی
Methods for multicountry studies of corporate governance: Evidence from the BRIKT countries ☆
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
51934 2014 11 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Journal of Econometrics, Volume 183, Issue 2, December 2014, Pages 230–240

ترجمه کلمات کلیدی
برزیل؛ کشور کره؛ هند؛ روسیه؛ ترکیه؛ حاکمیت شرکتی؛ هیئت مدیره؛ افشا؛ حقوق سهامداران؛ مرزهای حساس
کلمات کلیدی انگلیسی
G18; G30; G34; G39; K22; K29Brazil; Korea; India; Russia; Turkey; Corporate governance; Boards of directors; Disclosure; Shareholder rights; Sensitivity bounds
پیش نمایش مقاله
پیش نمایش مقاله  روش های برای مطالعات چند کشوری حاکمیت شرکتی: شواهدی از کشورهای BRIC

چکیده انگلیسی

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.