Financial System Stability and Manufacturing Performance in Nigeria

This study investigates the relationship between the three measures of manufacturing performance [manufacturing production growth rate (MPGR), share of manufacturing in GDP (SMGDP), capacity utilization rate (CUR)] and the variables of financial system stability [exchange rate (EXR), fiscal deficit (FD), lending rate (LR), saving rate (SR), consumer price index (CPI), bank loan to manufacturing sector (BLM)]. The study uses Johansen cointegration and Parsimonious error correction model as the estimation techniques. Findings from the results of the study reveals that there is a long-run relationship between the three measures of manufacturing performance (i.e. MPGR, SMGDP and CUR) and the variables of financial system stability in Nigeria during the period under review. Also, consumer price index (CPI) and lending rate (LR) have negative and significant impacts on manufacturing production growth rate (MPGR) in Nigeria. In addition, fiscal deficit (FD) and lending rate (LR) have significant and negative effect on share of manufacturing in GDP (SMGDP). Lastly, banks loan to manufacturing sector (BLM) has significant negative effect on capacity utilization rate (CUR) while exchange rate (EXR) has positive and significant impact on capacity utilization rate (CUR).

Understanding the Operational Efficiency of Bicycle-Sharing Based on the Influencing Factor Analyses: A Case Study in Nanjing, China

With the expansion of urban scale and the growth of urban population, the bicycle-sharing system has been greatly helping grease the wheels of convenience and diversity of citizens' travel. Nevertheless, there are a set of additional problems, including imbalance of supply and demand at rental stations and low utilization of system operation, which have disrupted the travel experience of consumers, the profitability of businesses, and the coordination of government. In this study, we take Nanjing as an example to measure the operating efficiency of bicycle-sharing by calculating the capacity utilization rate (CUR). Afterwards, based on the IC card data of bicycle-sharing users, we statistically analyzed the traffic inflow and outflow at rental stations. Besides, this paper discusses the factors influencing the use of bicycle-sharing, by introducing the method of sampling stepwise regression into the study of rental situation and geographical environment. The results are as follows: (1) demand for bicycle-sharing is higher on weekdays than on weekends, especially during the morning and evening rush hours. (2) The daily average capacity utilization rate of bicycle-sharing is less than 0.08, indicating that the system is not efficient enough. During morning and evening rush hours, only less than 10% of rental stations have high inflow and outflow, and there is an imbalance of inflow and outflow for the same rental station at different times of the day. (3) The stepwise regression results show that the inflow and outflow of bicycle-sharing rental stations are mainly affected by the distribution of traffic, education, entertainment, medical, and other functional zones near the stations. These findings could provide relevant government departments and enterprises with strategies and suggestions for the efficient and healthy operation of the urban bicycle-sharing system.

The grouping of industrial enterprises by capacity utilization rate and level of debts to banks (III Quarter of 2020)

Russian industrial enterprises, examined by the Russian Economic Barometer are grouped according to two criteria: the level of capacity utilization rate, as well as the level of debts to banks. This grouping allows you to see how key characteristics of enterprises differ depending on how their production capacities are loaded and what level of debts to banks they have. Data are presented for the third quarter of 2020.

The grouping of industrial enterprises by capacity utilization rate and level of debts to banks (IV Quarter of 2020)

Russian industrial enterprises, examined by the Russian Economic Barometer are grouped according to two criteria: the level of capacity utilization rate, as well as the level of debts to banks. This grouping allows you to see how key characteristics of enterprises differ depending on how their production capacities are loaded and what level of debts to banks they have. Data are presented for the fourth quarter of 2020.

The grouping of industrial enterprises by capacity utilization rate and level of debts to banks (II Quarter of 2020)

Russian industrial enterprises, examined by the Russian Economic Barometer are grouped according to two criteria: the level of capacity utilization rate, as well as the level of debts to banks. This grouping allows you to see how key characteristics of enterprises differ depending on how their production capacities are loaded and what level of debts to banks they have. Data are presented for the second quarter of 2020.

The grouping of industrial enterprises by capacity utilization rate and level of debts to banks (I Quarter of 2021)

Russian industrial enterprises, examined by the Russian Economic Barometer are grouped according to two criteria: the level of capacity utilization rate, as well as the level of debts to banks. This grouping allows you to see how key characteristics of enterprises differ depending on how their production capacities are loaded and what level of debts to banks they have. Data are presented for the first quarter of 2021.

The grouping of industrial enterprises by capacity utilization rate and level of debts to banks (I Quarter of 2020)

Russian industrial enterprises, examined by the Russian Economic Barometer are grouped according to two criteria: the level of capacity utilization rate, as well as the level of debts to banks. This grouping allows you to see how key characteristics of enterprises differ depending on how their production capacities are loaded and what level of debts to banks they have. Data are presented for the first quarter of 2020.