While a carbon tax is widely acknowledged as an efficient policy to mitigate climate change, adoption has lagged. Part of the challenge resides in the distributional implications of a carbon tax and a belief that it tends to be regressive. Even when not regressive, poor households could be hurt by a carbon tax, particularly in countries that rely heavily on carbon-intensive energy sources. Using household surveys, we study how a carbon tax may affect households in the Asia Pacific region, the main source of CO2 emissions. We document a wide range of country-specific policies that could be implemented to compensate households, reduce inequality, and build support for adoption.
International Monetary Fund. Strategy, Policy, & Review Department
Over the course of the pandemic, the Fund has made several modifications to the access limits on the use of Fund’s resources to increase the borrowing space under the hard caps on emergency financing and under the annual limits that trigger exceptional access (EA) safeguards under GRA and PRGT. The current temporarily-increased access limits expire at end-December 2021, and absent policy changes, the limits would return to the lower pre-pandemic levels or to the new PRGT annual access limit. Staff proposes to let all access limits return to pre-pandemic levels (or the new PRGT annual access limit), with the exception of the cumulative access limits for emergency financing instruments, which would be extended at the current level for another 18 months.
This technical assistance (TA) report on government finance statistics (GFS) covers the remote TA to the Ministry of Finance (MOF) during September 21–October 2 and December 14–18, 2020 and March 9–13 and April 19–23, 2021 (which was extended to May 2021). These peripatetic activities were conducted remotely due to the travel restrictions resulting from the COVID-19 situation. This report documents the main achievements from these activities. These activities were part of the GFS and Public Sector Debt Statistics (PSDS) project funded by the Government of Japan (JSA3) and implemented by the IMF Statistics Department (STA) and the IMF Capacity Development Office in Thailand (CDOT).
The Financial Action Task Force’s gray list publicly identiﬁes countries with strategic deﬁciencies in their AML/CFT regimes (i.e., in their policies to prevent money laundering and the ﬁnancing of terrorism). How much gray-listing aﬀects a country’s capital ﬂows is of interest to policy makers, investors, and the Fund. This paper estimates the magnitude of the eﬀect using an inferential machine learning technique. It ﬁnds that gray-listing results in a large and statistically signiﬁcant reduction in capital inﬂows.