The Unassuming Economist

From The Statesman:

“India’s economic growth, celebrated in aggregate terms, hides an uncomfortable truth: much of this progress has bypassed the majority of the population, particularly in rural areas. Despite being one of the world’s largest economies, India ranks poorly in per capita income, a stark reminder that GDP growth alone does not equate to widespread prosperity. This widening economic gap has real implications, most notably in terms of demand ~ the very engine of sustainable growth. Recent analyses point to stagnant rural wages, a persisting issue that has kept the purchasing power of millions at a minimum.

Surveys show that the average income for a rural earning person is less than Rs 35,000 a year, or about Rs 2,886 per month. This is barely enough to cover basic needs for an earning person’s family, leaving little room for discretionary spending that could stimulate demand in other sectors of the economy. In fact, a large part of household expenditure in rural India goes towards food ~ nearly 40 per cent ~ reflecting the immense burden of basic costs. Even as the urban middle class begins to feel the strain of stagnant wages, the situation in rural areas is far more concerning. This demand deficit is now reverberating across the broader economy.

Consumption data reveals that rural and low-income urban households spend less than Rs 3,000 and Rs 5,000 per capita monthly, respectively, underscoring a sharp divide in consumer power. Even among salaried urban workers, incomes are modest, with casual labourers faring far worse. Without income growth to support rising aspirations, spending power remains subdued, creating a cycle of low demand that impacts businesses and, ultimately, economic growth. India’s challenge, therefore, is not just about growth, but about inclusive growth. Job creation, particularly in rural areas, is essential. The current trend toward increased informalisation ~ with more people in unstable jobs or self-employment ~ further erodes income stability and social mobility.”

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From a paper by Luciano Vereda, Helder Ferreira de Mendonça, and George Morcerf:

“Our study advances the modelling of forecast revisions by accounting for the nuanced impact of informational shocks across different time horizons. Specifically, we introduce modifications to the error structure of regression models used to detect biases in macroeconomic forecasts. Drawing on consensus forecasts of inflation and output growth from the central banks of Brazil, Chile, and Mexico, our approach offers a nuanced understanding of bias estimation uncertainty, leading to a more robust rejection of the null hypothesis of no biases. By elucidating the differential effects of informational shocks on forecast accuracy across time periods, our findings not only contribute to the refinement of forecasting methodologies but also have implications for policymakers and economic analysts striving for more accurate and reliable predictions in dynamic economic environments.”

From a paper by Hagen Kruse:

“This study documented for the first-time patterns in export specialization from an activity perspective, and showed how it can generate additional insights beyond those based on the product perspective. First, export incomes from production activities decline and engineering, managerial, and services support activities grow as countries develop. Second, countries initially specialize along the extensive margin (shifting activities across industries) but later along the intensive margin (shifting activities across occupational classes). Third, new activity specialization is strongly related to the proximity of this activity to the initial export basket, in particular for specializations along the extensive margin and in routine intensive occupations. Fourth, countries that defy proximity and diversify quicker appear to grow faster in GDP per capita. This is correlation however and no claim for causation is made.

We see at least two promising avenues for further research. One avenue is in the modelling of structural change and the role of international trade. The canonical macro-structural change framework focuses on the sectoral composition of the economy in terms of employment and value-added. Trade can shape the sectoral composition in various ways (Alessandria, Yi and Johnson 2021). Lower trade barriers facilitate specialization for example through shifting comparative advantage and promoting economies of scale. Sectoral specialization will consequently affect the sectoral composition of the economy. And given a set of trade barriers, policy changes or technology shocks to the economy may also affect specialization patterns and consequently sector composition. Trade barriers are typically related to products whereas technological change such as automation affects particular activities rather than products or sectors. Modelling the composition of the economy in terms of activities in addition to sectors appears therefore to be a promising way forward as for example in Bárány and Siegel (2018) and Duernecker and Herrendorf (2022).”

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From a paper by Feng Wang and Mengdie Qu:

“Income inequality and energy poverty are critical obstacles to the worldwide low-carbon transformation and deeply affect human behavior. Applying a dynamic panel data model, this study investigates the effect of income inequality and energy poverty on global carbon emissions. We determine the effect of the interaction between income inequality and energy poverty on the global low-carbon transformation based on a panel data set of 193 countries from 1990 to 2019. A one standard deviation decrease in the Gini coefficient causes a 2.98 % decrease in carbon emissions per capita, with the median value of energy poverty. However, in poor countries where the proportion of population with access to electricity is less than 86.0 %, reducing income inequality will increase carbon emissions. The role of energy poverty on carbon emissions per capita is also affected by income inequality. When the Gini coefficient is lower than 0.461, increasing access to electricity will reduce carbon emissions. In contrast, when the Gini coefficient is higher than the critical value of 0.461, increased access to electricity will raise carbon emissions. These findings indicate a new strategy for advancing low-carbon transformation based on the interrelationship between income equality and energy poverty eradication.”

From the World Economic Forum:

“Imagine a city in Asia and the Pacific, in 2030, where sustainability flourishes, public services are accessible to all, and green spaces abound amid bustling urban life. In several cities across the region, local governments are moving towards achieving such a vision, but progress is slow.

But time is of the essence, and the cities of tomorrow will be shaped by decisions made today. The building of resilient, equitable and sustainable urban environments requires a new type of city leadership that champions a transformative approach that goes way beyond traditional urban management.

In today’s world, cities account for more than 70% of global carbon dioxide (CO2) emissions, mainly from transportation and buildings. By 2050, rapid urbanization will add 1.2 billion people to cities in Asia and the Pacific, putting immense pressure on already strained infrastructure and services, compounded by the impacts of climate change.

Without rapid transformation, many cities in Asia Pacific will see rising inequalities and be even more vulnerable to climate-induced disasters.

Six of the 10 countries most affected by climate-related events over the past two decades lie in Asia and the Pacific, compounding to the vulnerability of some of the growing cities. This unfolds as many of them already struggle to provide basic services like clean water, affordable housing and reliable energy for their growing populations.”

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