Abstract
Audit is an indispensable mechanism for developing and sustaining trust in accounting information and thus in efficient capital markets. In emerging markets, current practices require the auditor to be effective and efficient because users rely heavily and need the timely information certified by auditors. However, these practices do not meet expectations, and more research into the development and strengthening of audit practices is required. Given that audit effectiveness and efficiency are crucial components for most accounting/auditing research models and the lack of readily available data in well-known databases (e.g., DataStream; OSIRIS; Audit Analytics), this dataset consists of longitudinal data for the variables most used in prior research for measuring the effectiveness and efficiency of audit. The dataset includes data for audit report lag, audit fees, auditor type, auditor tenure, and audit opinion for firms listed in the Omani capital market. It also details data for audit firms’ names and industry affiliations to extract further related variables such as industry expertise, client importance, independence, and big4/second-tier audit firms analysis, which are measured from the researcher’s perspective. The collection process identifies all listed firms for the period 2005-2019 (1,865 observations), with 1,117 observations in the final sample. Sources such as audit reports, corporate governance reports, the OSIRIS database, and the capital market website have been used to acquire the data. This dataset is valid for research into audit quality, audit efficiency, financial reporting quality, audit regulation changes, and external corporate governance.