{"id":6131,"date":"2019-07-24T08:30:36","date_gmt":"2019-07-23T22:30:36","guid":{"rendered":"https:\/\/testblogs.griffith.edu.au\/asiainsights\/?p=6131"},"modified":"2024-09-19T05:04:35","modified_gmt":"2024-09-18T19:04:35","slug":"monetary-policy-transmission-in-png","status":"publish","type":"post","link":"https:\/\/testblogs.griffith.edu.au\/asiainsights\/monetary-policy-transmission-in-png\/","title":{"rendered":"Monetary policy transmission in PNG"},"content":{"rendered":"\n

MARK OFOI<\/a>, TARLOK SINGH<\/a>, JEN-JE SU<\/a> AND PARMENDRA SHARMA<\/a>\u00a0 | <\/p>\n\n\n\n

Literature on monetary policy transmission channels is vast\u2014it is one of the most extensively investigated issues in social sciences. Academics, researchers, and policy-makers alike have been profoundly interested in how monetary policy decisions and actions transmit anticipated outcomes. Justifiably so\u2014monetary policy, historically and in the present day, continues to be one of the most talked about central banking functions and tools\u2014regardless of the exchange rate regime. To exemplify, at the time of drafting this paper, one of the hottest and broadest conversations in Australia has been around the \u201ccash rate\u201d\u2014the central bank\u2019s pivotal monetary policy tool. These conversations on the back of softening and worrying inflationary and GDP trends, coupled with public pressure, have recently led to two consecutive cuts to the rate (25 basis points each), bringing it down to historically low levels of 1%. Political and public pressure and expectations have in turn compelled financial institutions to immediately pass on the cut to customers and clients via mostly an equivalent cut to their lending rates such as home loan rates, which is expected to boost private sector credit, investments and employment opportunities, despite some skepticism from some quarters.<\/p>\n\n\n\n

Notwithstanding the outcomes, economies like Australia have tools such as the cash rate or other options available to stimulate the economy, including indeed \u201cstimulus\u201d packages. And, generally, they seem to have some desired effects, at least in the past. But, what about the transmission and available options to the open, vulnerable, socio-economically disadvantaged economies of the Pacific island Countries (PICs)? How effective has monetary policy transmission been? What has the influence been on inflation, the supply of loanable funds and indeed on GDP? Despite the vastness of the literature, the relationships in the case of the PICs appear to remain little known. Across Papua New Guinea (PNG), Fiji, Solomon Islands and Vanuatu\u2014the largest of the PICs\u2014monetary policy studies are but only a handful.<\/p>\n\n\n\n

Of these PICs, PNG is by far the largest in terms of population, land area and GDP. The country\u2019s land area of 462,840 km2 is equivalent to almost 80 percent of the PICs total land area; similarly, its 8 million population accounts for over 70 percent of the PICs total population. PNG\u2019s GDP has been more than US$20 billion annually. Yet, in the case of PNG, monetary policy transmission and influences appear not at all to be well documented, scientifically. This prohibits the central bank from designing and implementing appropriate policy responses for accomplishing its price stability and other enacted mandates. This study attempts to fill that research gap in the literature. In doing so, the study focusses on the money multiplier as the transmission mechanism and private sector credit as the transmitted outcome. Essentially, we ask if the money multiplier notion holds in the case of PNG and what is the effect on private sector credit.<\/p>\n\n\n\n

The paper uses a structural vector autoregressive (SVAR) technique comprising six variables (interest rate, inflation, loans, deposits, reserve money and real GDP) and quarterly data covering sample periods 1980q1 to 2017q4 and 2000q1 to 2017q4. An adaptation to the model is made using styled facts to capture the relevant policy variables currently in use and the expected chain of causality to test the robustness of the model.<\/p>\n\n\n\n

The key findings of the paper are:<\/p>\n\n\n\n