The United States Congress approved an emergency bailout package of $700 billion during that fateful week in September 2008. This was just days after the spectacular crash of Lehman Brothers.
[wptelegram-join-channel link=”https://t.me/s/upsctree” text=”Join @upsctree on Telegram”]
That bailout package was part of what became the Troubled Asset Relief Program, or TARP, which was used to buy off mortgage-backed securities from banks, hedge funds and pension funds to avert further Lehman-type bankruptcies. These mortgages had turned into toxic assets that nobody wanted to buy, and government funds were used for purchases of last resort.
As a result, fresh money was injected into the banking system for it to resume normal credit operations and clean up balance sheets. Some people believe that the then Treasury secretary Henry Paulson practically spooked the Congress into approving a gigantic package at short notice, without adequate debate. Such was the state of panic and fear of domino failures in the financial system that the package was de facto viewed as fiscal support, even though it was a monetary tool.
Subsequent actions of the US government and Federal Reserve blurred the distinction between fiscal and monetary policy. A fancy term was coined to describe unorthodox measures like a central bank buying off mortgages and loans, and thus taking credit risk onto its balance sheet. It was called ‘quantitative easing’and was merrily pursued by all the major central banks of the developed world, from New York and Washington to London, Frankfurt and Tokyo.
Central banks embarked upon an aggressive money-printing spree. Assets on their books ballooned. The chairman of the European Central Bank (ECB) famously said that he would do whatever it took to revive the economy. This meant buying even junk bonds to push the envelope. Nothing seemed untouchable to a central bank. While the US economy did stabilize and its unemployment rate halved, the monetary effort seemed excessive for the limited success it was achieving.
On the other side of the Atlantic, European growth did not pick up significantly, hampered as it was by sovereign debt crises in addition to the mortgage crisis. But a recession was averted and some tepid growth was achieved.
During the pandemic year more than a decade later, the West’s monetary spigots have been opened even more. A liquidity glut has ensued. The size of the Federal Reserve’s balance sheet has grown by seven times since its pre-Lehman days, which amounts to a compound annual growth rate of about 21% over a 12-year period. While the rate of monetary expansion over this period has been torrid, neither employment nor economic output grew by even a fraction of that rate.
Still, the US economy stayed afloat and stock markets rallied, while wealth inequality worsened. How does a central bank exit this chakravyuh, or maze? Any hint of reducing the rate of money expansion threatens to cause panic and burst the bubble it blew. Just a mention of the word ‘taper’, which means reducing the pace of money expansion, caused a big shock to the world economy in 2013. The shock was such that the T-word has got seared into the collective memory of global financial markets.
The Reserve Bank of India (RBI) too finds itself in a similar predicament, where the way out of its liquidity glut is hazy. By last August, RBI’s balance sheet had ballooned by more than 30% over the previous year, thanks to purchases of foreign exchange externally and of government bonds domestically. That pace has been sustained. RBI has injected liquidity through long-term repo operations, which essentially provide long-term money at low overnight rates.
The Indian central bank has also provided implicit liquidity support to mutual funds, which is like an Indian version of unorthodox monetary policy. It has not quite ventured into taking credit risk onto its books, nor has it signalled a readiness to buy toxic assets, but that day may not be far off when it is asked to defrost frozen credit markets. To enhance market liquidity through money infusion or through open market operations is nothing but money creation.
As a result of India’s liquidity glut, money is flowing in and out of the central bank to the tune of ₹7 trillion on a daily basis. This has resulted in an anomaly: market lending rates have gone below RBI’s reverse repo rate, which is supposed to be the de facto floor. Cheap money is an invitation to do foolish and risky things, which, if done widely and voluminously enough, can spell disaster for financial stability. So RBI has tentatively tried to nudge market rates higher by announcing a reverse repo auction. This is our own mild version of policy normalization (sans the dreaded T-word).
But the market reaction was one of panic all the same, and there was a spike in interest rates, causing the central bank to rethink its strategy. To calm nervous bond traders, the governor has categorically said that liquidity support will continue as long as necessary, but surely we need to plan an exit from the current glut?
Why not simply loan ₹5 trillion to the central government against shares of public sector undertakings, on a bilateral basis at a low rate of 3% for a period of five years to fund its huge deficit? That will bypass markets and not cause any disruption to interest rates. Whatever the way out of this whirlpool of liquidity, it’s not going to be easy.
Darknet, also known as dark web or darknet market, refers to the part of the internet that is not indexed or accessible through traditional search engines. It is a network of private and encrypted websites that cannot be accessed through regular web browsers and requires special software and configuration to access.
The darknet is often associated with illegal activities such as drug trafficking, weapon sales, and hacking services, although not all sites on the darknet are illegal.
Examples of darknet markets include Silk Road, AlphaBay, and Dream Market, which were all shut down by law enforcement agencies in recent years.
These marketplaces operate similarly to e-commerce websites, with vendors selling various illegal goods and services, such as drugs, counterfeit documents, and hacking tools, and buyers paying with cryptocurrency for their purchases.
Anonymity: Darknet allows users to communicate and transact with each other anonymously. Users can maintain their privacy and avoid being tracked by law enforcement agencies or other entities.
Access to Information: The darknet provides access to information and resources that may be otherwise unavailable or censored on the regular internet. This can include political or sensitive information that is not allowed to be disseminated through other channels.
Freedom of Speech: The darknet can be a platform for free speech, as users are able to express their opinions and ideas without fear of censorship or retribution.
Secure Communication: Darknet sites are encrypted, which means that communication between users is secure and cannot be intercepted by third parties.
Illegal Activities: Many darknet sites are associated with illegal activities, such as drug trafficking, weapon sales, and hacking services. Such activities can attract criminals and expose users to serious legal risks.
Scams: The darknet is a hotbed for scams, with many fake vendors and websites that aim to steal users’ personal information and cryptocurrency. The lack of regulation and oversight on the darknet means that users must be cautious when conducting transactions.
Security Risks: The use of the darknet can expose users to malware and other security risks, as many sites are not properly secured or monitored. Users may also be vulnerable to hacking or phishing attacks.
Stigma: The association of the darknet with illegal activities has created a stigma that may deter some users from using it for legitimate purposes.
AI, or artificial intelligence, refers to the development of computer systems that can perform tasks that would normally require human intelligence, such as recognizing speech, making decisions, and understanding natural language.
Virtual assistants: Siri, Alexa, and Google Assistant are examples of virtual assistants that use natural language processing to understand and respond to users’ queries.
Recommendation systems: Companies like Netflix and Amazon use AI to recommend movies and products to their users based on their browsing and purchase history.
Efficiency: AI systems can work continuously without getting tired or making errors, which can save time and resources.
Personalization: AI can help provide personalized recommendations and experiences for users.
Automation: AI can automate repetitive and tedious tasks, freeing up time for humans to focus on more complex tasks.
Job loss: AI has the potential to automate jobs previously performed by humans, leading to job loss and economic disruption.
Bias: AI systems can be biased due to the data they are trained on, leading to unfair or discriminatory outcomes.
Safety and privacy concerns: AI systems can pose safety risks if they malfunction or are used maliciously, and can also raise privacy concerns if they collect and use personal data without consent.