Dataset of companies’ profitability, government debt, financial statements' key indicators and earnings in an emerging market: Developing a panel and time series database of value-added tax rate increase impacts

Introduction

The reasoning and setting behind the creation of this dataset are to help the researcher to perform a comparative analysis across different disciplines to study the role of these variables in a specific phenomenon or the factors enlightening value-added tax (VAT) usefulness and effectiveness. For example, estimate the consequences of a VAT rate increase on profitability, government debt etc. This dataset helps complete two of the few studies investigating the VAT effect in the Kingdom of Saudi Arabia (KSA). There was an encouragement to observe the effect of imposing the new 15% VAT on the profitability of nonfinancial Saudi-listed companies. This dataset is in two types: a firm-level panel dataset sample and a time series sample (Mgammal, 2021; Mgammal, Al-Matari, and Alruwaili, 2023).

Methods

This dataset article contains proxies to measure the economic impacts of VAT as used by several prior researchers (Mgammal, 2021). The representatives for measuring the economic effects of VAT were manually collected and built using secondary data obtained from publicly available data from 2019 to 2020. In Table 1, ProFtEBITDA means company profit measured by earnings before interest, taxes, devaluation, and amortisation (EBITDA) and scaled by total assets and data collected from FinBox database tools. Data were hand collected from companies’ tax reports. We specify companies and identify them with inclusion/exclusion criteria, as mentioned in Table 3. The inclusion/exclusion criteria of all data in this article are as follows: we included nonfinancial companies and excluded finance firms. Then we filtered the sample by excluding companies with annual reports unavailable for two years. The fiscal year-end date is not 31/12/of each year, and the accounting period is over 12 months. Consequently, the final dataset is 494 company-year observations (226-panel data sample and 268-time series data sample) from 2019 to 2020. This final sample is the foundation that can be used for analysis in future research. SIZE2020 and SIZE2019 are the mean company size in 2020 and 2019, measured by the natural logarithm of total assets and data gathered from companies’ annual reports. EAT2020 and EAT2019 are the mean company earnings after tax in 2020 and 2019 and were measured by deducting all expenditures and revenue taxes from the business’s revenues. Following prior research, two variables were added, GvD2020 and GvD2019, which meant government debt in 2020 and 2019 and was measured as government debt over the gross domestic product (GDP). Data for this were extracted from the Trading Economics (https://tradingeconomics.com/) database. Regarding the process for accessing the data, the data was hand collated directly from the Trading Economics website using many Saudi Arabia Indicators reports, especially GDP Indicators reports. In this context, the data collected from the Trading dataset are the same inclusion/exclusion criteria as mentioned above, and we started data collection on 23/01/2021 to 30/03/2021.

As in Table 2 below, an index containing 11 items was included: BALANCE SHEET: total assets, total equity, and liabilities-equity. INCOME STATEMENT: total income, total revenues, total expenses, and net income. CASH FLOW: changes in operation. Activity, changes in investing act, changes in the financing act, and cash at the end of the period. These factors help control for potential impacts when analysing how a VAT increase will affect a firm. We take each into account as each has a component of the likely effect of a VAT increase. Collectively, these classifications were based on data available on the Tadawul market website as we clarify in inclusion/exclusion criteria above. It is a periodic data set of most Saudi registered companies from before the introduction of the new VAT rate of 15% in 2019 to after the introduction of the new VAT rate in 2020. The dataset framework was chosen when the new 15% VAT was introduced due to public access to VAT information for nonfinancial companies. Tadawul requires all public companies to publish their financial statements on the Tadawul website quarterly and annually (https://m5.gs/OXFqam) (Tadawul, 2019-2020). These data in the file can help build additional variables, such as measuring the effect of VAT before and after increasing its rate using unique techniques such as the difference-in-difference (DID) approach and the autoregressive integrated moving average (ARIMA) modelling approach. Nevertheless, since the literature does not provide common definitions or metrics, we leave the creation of these additional variables at the discretion of potential users. Thus, users can use this dataset to create these measurements from their perspective (Baatwah and Aljaaidi, 2021; Mgammal, 2021).