Volume 22 (2022): Edition 3 (September 2022)

The main goal of foreign aid should be the support of developing regions; however, due to the rising international requirements on the amount of provided aid, donors start to focus on the benefits aid can bring them as well. Both country-specific and general studies have been conducted to evaluate the return of aid from the donor’s perspective, with the majority of them suggesting that provided aid boosts donor’s exports to the developing countries. As no such analysis exists for the Czech Republic, this paper tries to fill this gap and aims to find out whether there is a positive relationship between the Czech aid and Czech exports. While employing the gravity model of international trade, the study, however, suggests that the Czech aid is not statistically significant for the volume of Czech exports. Unlike other donors, the Czech Republic thus leaves a considerable trade potential arising from the foreign aid untapped. The reasons might be other motives behind the Czech aid (both official and unofficial), poor co-operation of aid and trade policies, or changes in trade patterns.

In this paper, we explore the drawbacks of GDP per capita in purchasing power parity as an indicator of economic development and well-being and evaluate the factors which diminish its ability to represent the level of life. Firstly, we theoretically outline the issues that might be undermining the suitability of GDP per capita as a measure of well-being, and debate other development indicators. Subsequently, we confront GDP per capita with the most well-known development indicator – the Human Development Index HDI – and calculate the deviations between these two indicators for a panel of 141 countries. To empirically evaluate the potential limitations of GDP in measuring development, we regress the computed deviations between development and GDP on an array of economic, social, and political variables employing a heterogeneous panel dataset and robust fixed effects estimators. The results reveal that countries with higher income inequality and level of economic freedom are characterised by lower development than implied by their GDP per capita. Contrarily, the size of the shadow economy is negatively linked to the deviations of HDI from GDP. Certain sociocultural, geographic, and ecological factors, such as higher fertility rates, cold climate, and the depletion of natural resources, are prevalent among nations ranking higher in GDP per capita than in development.

The main purpose of this study is to investigate the best predictor of firm performance among different proxies. A sample of 287 Czech firms was taken from automobile, construction, and manufacturing sectors. Panel data of the firms was acquired from the Albertina database for the time period from 2016 to 2020. Three different proxies of firm performance, return of assets (RoA), return of equity (RoE), and return of capital employed (RoCE) were used as dependent variables. Including three proxies of firm’s performance, 16 financial ratios were measured based on the previous literature. A machine learning-based decision tree algorithm, Chi-squared Automatic Interaction Detector (CHAID), was deployed to gauge each proxy’s efficacy and examine the best proxy of the firm performance. A partitioning rule of 70:30 was maintained, which implied that 70% of the dataset was used for training and the remaining 30% for testing. The results revealed that return on assets (RoA) was detected to be a robust proxy to predict financial performance among the targeted indicators. The results and the methodology will be useful for policy-makers, stakeholders, academics and managers to take strategic business decisions and forecast financial performance.