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Trade musings for the week - 2

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There's no hedge here: Zero-hedge, the perpetual pessimist website, claims that India is creating a massive global rice shortage through the ban on rice exports from India . Zerohedge is wrong when he says that India accounts for 40% of the world's rice exports. That's not the usual case. Here are the actual figures for last 10 years:  India was a large supplier to the extent of 40% only in the year 2021. During other years, it was a supplier of around 15% of global supplies. There was a big jump in exports from India in 2021, not at the cost of other exporting countries, but to bridge the demand that went up by around 32% in that year. The same continued over to 2022 till the exports were banned on the 9th of September. While India has reasons to ban the exports of rice, it won't lead to a catastrophe as Zerohedge predicts.  Broken rice in USA mostly goes into animal feed. And making ethanol at many places. The main importers of broken rice are China, Senegal, Belgium,

Thoughts that mattered for trade this week - 1

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 - A delay in filing a response by an Indian exporter (Pokarna Engineered Stone Ltd) to a regulatory enquiry by the US Dept of Commerce led to imposition of a whooping 323% anti-dumping duty. India is the chief supplier to US market for quartz countertops, and this particular exporter is a major player there. The delay in filing was 5 hours, something that could have been condoned in the larger interest of trade. The prices of quartz countertops in US market would now go up, hurting Indian exporters and US consumers while benefiting the complainant importer in the USA who imports from some place other than India.  Here's the link for more info.  - Two things may hurt dollar dominance as per The Economist in this post . One, the digital-currency systems that may evolve out of central banks from China, India or Brazil. UPI is specifially mentioned in the post, probably due to the resilence it has shown in the face of huge number of transactions. Two, the evolving decentralized finan

The Indian Agarbatti exports case study - effect of import control

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What happens if we restrict cheap imports coming into the country in an employment intensive sector? While a free trade economist would squirm in agony, policymakers are faced with such choices on daily basis. Navigating multilaterally agreed rules in order to create space for domestic industry to fight and survive is a tight balancing act. I present a case study where the policymakers made a choice under similar circumstances.  Agarbatti (Incense sticks) industry in India is small in terms of turnover at around Rs 7000 crores (under USD 1 Billion). However,  it is large in terms of employment per Rupee it generates. It employs close to 400,000 people, around 80 percent of whom are women (https://bit.ly/3oyNysO). Agarbatti industry also exports a significant amount of production and maintains leadership position in the world market. The leading competitor is China.  Agarbatti manufacturing needs raw material - a premix powder popularly known as 'masala' - and bamboo sticks. Ove

Framework for Artificial Intelligence Regulation – Questionnaire (FAIR-Q)

  Introduction: Artificial Intelligence (AI) tools used in governance require a great degree of scrutiny before they are launched. This is due to the potential of AI tools to create a disproportionate impact when used to achieve a public policy goal.  To mitigate the risks, a Framework for AI Regulation – Questionnaire (FAIR-Q) is proposed, which can be used by regulators or government departments implementing these AI based solutions. Recommendation: FAIR-Q should be adopted by any department that proposes use of any AI tool for governance. FAIR-Q covers questions related to the need for AI, algorithms, processes, fairness, accountability, and ethical issues. The government department and the AI team should be able to satisfactorily answer these before the tool is allowed into public service.  The questions are designed to remain simple, yet effective. Relevance: AI tools can embed and exacerbate biases and inequalities found in the data that is used to train the tool. This raises imp