Local public investment drivers in Morocco: A panel data analysis

This study aims to investigate the relationships between economic development and environmental degradation regarding the emissions of CO₂ and SO₂ in Southeast Asia (SEA). The pooling data consist of 10 countries, Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Singapore, the Philippines, Thailand, and Vietnam, in the period 2003-2012. Furthermore, income elasticity of CO₂ and SO₂ emissions is computed for each country to observe the sensitivity of environmental degradation through the emissions of CO₂ and SO₂ brought by economic development.
The results indicate that CO₂ displays an inverted U-shape pattern, whereas SO₂ has decreased at an increasing rate since 2003. It is expected that SO₂ will increase as the SEA economies further develop. The turning points for both CO₂ and SO₂, indicate that the current SEA income level has not reached the turning point. The income elasticities show that income elasticities for CO₂ are positive for all 10 countries. Both Singapore and Malaysia are classified as countries with high income. However, Singapore, with 0.64%, has the highest income elasticity, and Malaysia, with 0.15%, has the second lowest. There is no indication that wealthy countries have a significant impact on CO₂ through economic development. Income elasticities for SO₂ of each country are all negative. This suggests that SO₂ is an inferior good. Brunei, with 8.41%, has the most sensitivity toward change in SO₂ emissions, whereas Myanmar, with only 0.58%, is the least sensitive to SO₂ emissions.

The study aims to identify the financial performance measures used as a proxy of the firm-level competitiveness dimensions of small and medium-sized enterprises and their competitiveness. By investigating the factors that affect competitiveness in general, those areas will be introduced, related to an identified competitiveness dimension. Financial and non-financial performance indicators will assess these areas. The paper considers competitiveness as an outcome variable, suggests a relationship between financial performance and the identified areas, and searches for the financial performance measures drivers.
A panel data model was tested on Hungarian small and medium-sized enterprises (SMEs) and US SMEs. The collected data cover the period between 2013 and 2017. As a result of the applied panel regression, those variables were successfully identified that drive and could predict financial performance measures related to competitiveness. The research found a significant difference between the two-sample dataset results, which differences can be connected to country, industry, and, in general, to economic development characteristics.
The results provide decision-making support and hint about the managerial tools and techniques aiming to control the firm characteristics, performance, and, eventually, firm-level competitiveness. Based on the results, further research can be dedicated to the development characteristics of firm-level competitiveness and the analysis of the relationship between the competitiveness dimensions and competitiveness itself.

Acknowledgment
The research was financed by the Higher Education Institutional Excellence Programme of the Ministry for Innovation and Technology in Hungary, within the framework of the 4th thematic programme „Enhancing the Role of Domestic Companies in the Reindustrialization of Hungary” of the University of Pécs.

The Working Capital Management (WCM) has an important role for the firm’s success or failure, because it directly affects the overall business health of the firm. This study examined the impact of WCM on profitability and shareholders’ wealth using 50 companies listed in different sectors on the Colombo Stock Exchange (CSE) for the period from 2010 to 2015. This sample represents 47% of the selected sectors of CSE. The profitability of the company is measured using gross operating profit (GOP) and shareholders wealth measured by Tobin’s Q (TQ) ratio. The WCM is measured using five independent variables namely stock holding period (SHP), debtors’ collection period (DCP), creditors’ settlement period (CSP), cash conversion circle (CCC) and current assets ratio (CAR). Further, three additional variables such as firm size (SIZE), leverage (LEV) and earning yield (EY) are employed as controlling variables to capture the impact of other performance of the companies.
The data were analyzed using ordinary least square (OLS) and panel data regression models. These regression models reveal that there is a significant negative relationship between CCC and dependent variables (GOP & TQ). Further, this relationship has been confirmed by the major components of CCC such as SHP, DCP. Firm size also positively and significantly effects on the firm GOP while negatively effects on the TQ. Further, they revealed that there is a significant positive relationship between LEV and TQ. The study finds that the shareholders’ wealth and profitability can be increased through the efficiency of WCM.