DOI: 10.7910/dvn/aglejw
title: Dataset for Tax Disclosure, a component of tax savings and Quality Assessment of Tax Planning Information in Malaysia
title: Tax Disclosure, Tax Planning and a component of tax savings

description: &lt;b&gt; Abstract &lt;sub&gt;&lt;br&gt; &lt;i&gt; 
This article is fundamentally a dataset required for analysing some taxation variables. The data describes the raw, combined, and filtered tax information on company-level such as Tax planning, Tax Saving and Tax disclosure in Malaysia. It combines a firm-level panel dataset and seven variables: tax disclosure, tax planning and a component of tax saving (namely: permanent differences, temporary differences, foreign tax rates, tax losses or NOLS and unclassified ETR reconciling). The dataset results from 2,811 company-year observations for the period 2010 to 2012, excluding financial companies (195 Obs), firms whose data needed is not available (48 Obs), firms whose date of fiscal year-end is not excluding 31/12 and Accounting period is more than 12 months (495 Obs), loss-making companies at least in one of the years (894 Obs), firms their annual reports not available for three years (291 Obs), firms with an extreme value of ETR (18 Obs) and firms with outliers and influential observations (12 Obs), resulting in a final sample of 858 company-year observations. The data were extracted from company annual reports. Researchers can smoothly use this dataset as two data types were created to allow individual and compound measurements. The individual determinant is the direct measurement of tax planning. For the compound measurements for tax disclosure, we used the index of ETR reconciling items and the five components of tax saving as undirect measurements of tax planning.
description: &lt;hr&gt; &lt;b&gt; Value of the Data: &lt;sub&gt;&lt;br&gt; 

• This dataset panel is beneficial for at least two reasons: (1) it contains data on tax disclosure collected to measure disclosure of tax information using tax expenses data set; and (2) the dataset has been arranged into 2 sorts: The individual determinant is the direct TP measurement. The multiple measurements are: TD we used ETR reconciling index Items and components of tax-saving (Foreign tax rates, Temporary differences, Permanent differences, Unclassified effective tax rate reconciling and Tax losses or NOLs) as undirect measurement of TP.&lt;br&gt;

• The data allows researchers to scrutinise the influence of tax disclosure, tax planning and components of tax saving on various accounting matters such as corporate governance mechanisms, performance of companies and quality of financial reporting. Furthermore, this dataset is value to related parties, e.g., investors, stakeholders, tax authorities, decision-makers, managers and market regulators in assessing the good TD and TP practices of companies listed on the Bursa Malaysia. This assessment will give them assurance in making different decisions. &lt;br&gt;

• These data can be analysed and/or compared with data in other emerging economies. Because Malaysia could be similar to other countries in Southeast Asia. They can be used to the discussion about tax disclosure regarding which parties are responsible for the verification of tax disclosure by firms, and which parties benefit from this disclosure.&lt;br&gt;

• The data is also beneficial for articles that have been theoretically enclosed on the Scholes-Wolfson framework and the signaling theory.
description: &lt;hr&gt;&lt;b&gt;1. Data &lt;sub&gt; &lt;br&gt; 

The period of this data is selected to reflect the most current data related to public listed companies in Malaysia during data collection. These 3 consecutive years are selected due to several conditions: it is the period of Market upturn economy, 2010 was the time instantly afterward the downturn and tax management actions are further spread. In 2010, the economic in Malaysia practised a strong growth, recording 7.2% an extension after the 2009 decline, good capital expenditure enlargement in all sectors. &lt;br&gt;

Using panel dataset from a sample of visibly-traded registered organisations in Malaysia. The sample framework is nonfinancial public organisations that are recorded in Bursa Malaysia. The sample framework was chosen due to the public access to information about corporate governance and financial of firms, where Malaysian registered firms commit to publish their annual-reports publicly after the year end. The sample selection procedure started with public listed corporations that were registered during the time of 2010 to 2012. &lt;br&gt;

The dataset categorised into 10 sorts classification of industrial: consumer product, construction, infrastructure, hotels, trading services, technology, industrial products, properties and plantation. All of the information is published and has been by archival the organisations. Data on the classification of industry came from Bursa Malaysia classification. &lt;br&gt;

Taxation data, which includes reconciliation and tax expenses information, is hand gathered from the annual-reports of the corporations, particularly from the tax part of the minutes to the accounts. Only one researcher is involved in data collection, thus inconsistency is not an issue. All the items are carefully checked and they are correctly coded. &lt;br&gt;

The sample eliminated corporations making a loss, corporations with incomplete data and financial corporations. The sample size after the screening process for the annual period, the cases of multivariate outliers have been eliminated. The criteria that were applied reduced the sample size to 888 observations. After the screening process for the year periods, the cases of multivariate outliers were detected using a variety of tools, such as univariate, bivariate and multivariate techniques, based on the number of variables and the Cook Distance test . In this context, 18 Obs 2.03 percent of 888 Obs the full sample have excessive ETR rates and 12 Obs 1.35 percent of the full sample were identified as outliers Obs and influential according to studentised residual&gt; | 2 |. The 30 Obs have been identified as outliers and consequently in order to avoid deformation in the outcomes, the 30 outliers were omitted. Considering merely organisations that have strong incentives to carry out the events of TP. &lt;br&gt;

Therefore, the final dataset sample is 858 Obs were specified to be eligible for implication in the analyses and it is the basis for the analysis, i.e. multivariate, bivariate, additional tests and descriptive. We utilises balanced panel data as it is a more sensitive measurement of the modifications that could occur between points in time. Additionally, the outcomes created are more robust, consistent, and stable to enable a generalisation of the population, so that it is more meaningful and representative.
description: &lt;hr&gt;&lt;b&gt; 2. Methods, materials and Experimental design &lt;sub&gt; &lt;br&gt; 

Basically, we collected data to measure the level of company tax disclosure by allocating a score for tax disclosure data according to the number of items revealed in the annual-reports of the companies. Furthermore, we carefully scrutinised all items to replicate regulations of taxation in Malaysia. Thus, we moved “tax benefit from goodwill deduction” to PD section from TDF section as the item is not taxable in scheming taxable income according to Income-Tax Act 1967. In order to allocating the score of tax disclosure items, an unweighted approach has been utilising. The items of data are numerically allocated on a dichotomous basis a corporation is scored “1” for an item disclosed in the annual-report and “0” if it is not disclosed. The summation of tax disclosure items in Mgammal index are considered for each sampled corporation as a ratio of the totality disclosure score to the highest possible disclosure by the corporation. The tax disclosure items for every corporation is subsequently expressed as a percentage. The unweighted tax disclosure items ascertain the disclosure level as the ratio of the total items revealed to the highest possible score appropriate for a specific organisation (Mgammal, 2020).&lt;br&gt;

We collected data to measure Tax planning using this equation: (STR-ETR)*PBT . Further, we collected data to measure the component of tax savings using the published score of the components of tax saving. We disaggregating the total tax-saving into five components abovementioned. Moreover, to aggregate tax saving information, the tax saving is measured according to the values of these items in the financial statement means based on the total of tax-saving components. The value of each component that has been utilised as a measurement of tax-saving was evaluated based on the magnitude of total the items (Mgammal, 2019). The raw and final data are provided in the link to a repository.&lt;br&gt;