The Unassuming Economist

“The (International Monetary) Fund’s stance on equity has changed in parallel with external circumstances and the demands of its members, driven, sometimes forcefully, by its MDs. Poverty featured prominently in the Fund’s discourse in its early years when the institution began to take into account the voice and needs of its most vulnerable members. The 1980s and 1990s saw the consolidation of concessional financing, which broadened its focus towards equity between individuals and the “high-quality growth” championed by management and, at first, also by member countries. In the research conducted by the institution’s staff, inequality, social factors, and gender issues gradually gained prominence. These factors were included only to a limited extent and temporarily in the Fund’s activity, given the absence of strong support from the Board.

At the beginning of this century, there was growing disaffection with the Fund among developing countries, which demanded to be treated on a more equal footing. The far-reaching institutional and cultural reform of the IMF in the first decade of this millennium helped to put inequality and other macro-critical issues firmly on the Fund’s agenda. It is possible that the fallout from this century’s two major crises is contributing to consolidate inequality, gender, and the environment in the Fund’s activity and discourse. This may also have been assisted by the fact that the IMF has been led by two women in the recent past.

This paper has analysed how equity issues have been incorporated into the debate and design of the Fund’s policies and, through its texts, what stance each agent (management, member countries and staff) has adopted at each point in time. As a possible follow-up to this analysis, it is worth exploring the extent to which the Fund has put this “declaration of intent” into effect in its surveillance and lending activity and whether its implementation has been consistent with the general guidelines on equity. A text-mining analysis of the content of Article IV reports and IMF programmes could help assess the effective implementation of these issues, for which the annual reports provide only an approximation, and to verify the consistency between discourse, policy design and implementation.”

Source: Banco de España. 2021. The International Monetary Fund’s View of Social Equity Throughout Its 75 Years of Existence (p. 18)

Click here to read the full report.

Policymaking and research on perhaps some of the most pressing social issues in the contemporary world today, like poverty, inequality, access to resources, and related matters, is both blessed and plagued with the idea that additional evidence on people’s identities and information sets can radically transform the rate of success or failure of policies. 

Among other things, one such question has also been the irony of demand for redistributive and poverty alleviation programs not rising commensurately or even remotely as much with the ever-rising level of inequalities in the world. Many studies have attempted to explain this phenomenon by presenting the idea that poor people often have only limited knowledge about their relative deprivation viz other people in the economy. They also believe their income levels to approximately coincide with the average income level of the country, thus convincing themselves of the non-usefulness of any redistribution programs. 

This study, by Hoy and Mager, empirically tests some of these theories using randomized surveys and churns out some insightful observations. It redefines the idea of ‘benchmarking’ incomes for designing redistribution programs and explains the importance of information sets in shaping poor people’s preferences for accepting aid. 

Click here to read more.

Will this time be different? https://voxeu.org/article/covid-19-will-raise-inequality-if-past-pandemics-are-guide.  See presentation, which also discusses other factors that could affect the evolution of inequality.

“Reducing movements within countries has been effective in developed economies – averting about 650,000 deaths – but not in developing ones,” according to new research. “Countries that acted fast fared better” and “closing borders has had no appreciable effect, even after 50 days.” The authors studied “various types of lockdowns implemented around the world mitigated the surge in infections and reduced mortality related to the Covid-19, and whether their effectiveness  differed in developing versus developed countries.” Their data cover 184 countries from December 31st 2019 to May 4th 2020, and identifies when lockdowns were adopted, along with confirmed cases and deaths. Link to paper: fast and local.

AI Economist is a new portal devised by Salesforce to develop ‘ a new line of research that studies how to improve economic design using AI with the goal of optimizing productivity and social equality for everyone‘. Interesting piece in the FT on how the AI Economist can help in the Covid recovery.

Excerpts from the Salesforce interview  with the creators of the AI Economist:

“How does the AI Economist work? 

Taxes and subsidies are important tools governments use to reduce inequality and redistribute wealth. However, we still haven’t quite figured out how to implement optimal tax policies for a wide range of social objectives, such as the trade-off between equality and productivity. Economic theory cannot fully model the complexities of the real world, and careful real-world experimentation with taxes is almost impossible. 

Through the AI Economist, we’re bringing reinforcement learning (RL) to tax research for the first time to provide a simulation and data-driven solution to defining optimal taxes for a given socio-economic objective.

The AI Economist uses a collection of AI agents designed to simulate how real people might react to different taxes. In the simulation, each AI agent earns money by collecting and trading resources and building houses. The agents learn to maximize their utility (or happiness) by adjusting their movement, trading, and building behavior. One way to do this is to maximize income while minimizing effort, for example, making as high of an hourly wage as possible.  

Simultaneously, the AI Economist learns to optimize taxes and subsidies to promote global objectives.”