Yuswandi A. Temenggung
Ministry of Home Affairs, Republic of Indonesia

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Capital Investment Behavior of Local Government in Regional Development Bank (BPD) in Indonesia M. Yusuf; Sri Hartoyo; Adler H. Manurung; Yuswandi A. Temenggung
Jurnal Bina Praja Vol. 9 No. 2 (2017)
Publisher : Research and Development Agency Ministry of Home Affairs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21787/jbp.09.2017.281-293

Abstract

Capital investment of local government or long-term regional investment is a form of government program and one of the tools of regulating the regional fiscal. Local government investment in the form of direct investment is the capital investment in business entities which aims to increase regional economic growth, increase regional income, and improve the welfare of the community. Regional capital investment is a decision-making behavior to invest or not to invest in business entities to obtain dividends. The behavior of decision making requires Information on bank performance, knowledge of banking governance, and shareholder agreements with bank directors. This study aims to analyze performance behavior and investment of provincial government that affects the profit of regional development banks in Indonesia. The method of analysis is done using descriptive statistic and multiple linear regression. Multiple Linear Regression with dependent variable of regional development bank profit and independent variable consist of Bank size (SIZE), business risk of BPD bank (RISK_Bt), Capital Adequacy Ratio (CAR), Operational Cost to Operating Income (BOPO), Return of Equity (ROE), Interest Rate of Bank (INTEREST), Provincial Capital Investment to Bank BPD (PMD), Regional Minimum Wage (UMR), Initial Public Offering Dummy (DIPO), and Bank Business Target Dummy (DSARBISB). The result of descriptive statistical analysis concluded that the capital investment of 26 provincial governments in 26 regional development banks describes the varying bank performance caused by different bank sizes. The results of multiple regression analysis can conclude that all independent variables are able to both explain the dependent variable at a significant level of level below 1%, as well as individually ten independent variables that are statistically significantly different with zero at levels below 1%, below 5%, and below 10%.