Source: Ideas and Institutions Issue #21
Analysis
The Gloss in the Gross Domestic Product Estimate
On November 30, the National Statistical Office of Government of India released the GDP estimates for the July-September quarter of 2022-23. The year-on-year growth in the quarter was 6.3 percent. GDP growth in the previous quarter (April-June) was 13.5 percent. Considering both these quarters together, the year-on-year growth in April-September this year is an impressive 9.7 percent. These numbers are being cited to suggest that the Indian economy is doing well. Some commentators have also compared these estimates with those of other countries to argue that the Indian economy is in fact doing better than other economies. It is not obvious that either of these conclusions is valid.
The GDP in April-September of 2022 is only 5.7 percent more than that produced in the corresponding period three years ago (April-September of 2019). Looking at this from the production side, the gross value added by the economy during this period was 6.1 percent higher than that in the corresponding period in 2019. However, if we leave out “electricity, gas, water supply and other utility services” and “public administration, defense and other services”, which are essentially under the government’s direct influence, the gross value added in April-September of 2022 was only 4.5 percent higher than that in the corresponding period of 2019.
In April-September of this year, the output in most sectors has been only marginally higher than what they had produced in the corresponding period three years ago. The gross value added in “trade, hotels, transport, communication and broadcasting services” was still well below what it had been in the corresponding period of 2019. Further, the value added in the manufacturing sector in April-September this year is about the same as it was in the corresponding period of last year, and is actually lower in the July-September quarter of this year than the corresponding quarter of last year. This could be a sign that the sector is slowing down.
Due to the pandemic and the government’s response to it, the Indian economy shrank by 6.6 percent in 2020-21. The first half of the year was particularly bad—GDP shrunk by 15.2 percent over the corresponding period of the previous year. The way to see the growth since then should be in terms of recovery from a crisis. The year-on-year growth numbers can therefore be misleading because they hide the fact that the economy is still recovering. If we look back at India’s growth experience in the past, we see this illusory acceleration a few times.
Consider the 1970s. The average growth rate between 1975-1976 and 1978-1979 was 5.8 percent, which was quite high given the general performance of the economy at that time. However, the growth during this period cannot be understood without seeing what happened in the preceding years. The years between 1971-72 and 1974-75 had been the worst four-year period since 1950, with growth averaging only 1.4 percent. GDP per capita in 1974-75 was less than what it had been in 1969-70. From 1975-76, the economy was recovering, and the relatively high year-on-year growth did not necessarily reflect newfound dynamism. Some authors later looked at this time series and concluded that 1975-76 was the year when India transitioned to higher growth. As I have argued in a previous essay, this was a mistake.
Comparisons with other countries and regions should also be made with caution. The growth rates since 2021 have been affected by how much of a decline the economies suffered in 2020. The economies that suffered a sharp decline are more likely to register a higher growth rate because of the rebound. In the EU (comprising twenty-seven countries), GDP in April-September of 2020 was 8.5 percent lower than what it had been in the corresponding period in 2019, while in the U.S., it was 5.2 percent lower. Growth in the corresponding period in 2021 and 2022 has averaged 5.2 percent in the U.S., while that in the EU has been higher—averaging 6 percent. However, the U.S. economy is 4.8 percent bigger than what it was in the same period in 2019, while the EU economy is only 2.8 percent bigger. Therefore, comparing growth rates can be quite misleading after a crisis.
While crises do make international comparisons of growth rates more difficult, such comparisons can be misleading otherwise as well, especially when a developing country is being compared with a developed country. It is often suggested that India being the world’s fastest growing major economy should be a matter of pride. If we consider any economy with more than 2 trillion PPP dollars in 2021 as a major economy, India’s per capita is, by a considerable margin, the lowest among all the major economies in the world. Since the other major economies are at a different stage of development, comparing growth rates is not fair.
Developed countries face different challenges as compared to developing countries. The former are already prosperous and close to the productivity frontier. Consequently, growth for them tends to be slow to moderate. For such countries, the pandemic presented a challenge of preserving their productive capacities while mitigating the impact, and then restoring the economic output. Once they have done so, they can say that they have recovered from the economic impact of the pandemic. Developing countries are catching up, and can therefore grow at a moderate to high rate. For them, the pandemic presented a dual challenge. It was not just about preserving capacities but also to not allow the pandemic to derail the process of catching up. So, for developing countries, the challenge was to continue on their journey while dealing with the pandemic.
One way of understanding this is by comparing the actual GDP with what the GDP would have been had the pandemic (and, of course, any other such shock) not happened. If we draw a counterfactual time series for what the GDP would have been in the years since the pandemic hit, we can then see how far the actual GDP for a country or region is from that counterfactual. We can then compare the recoveries of different countries and regions. There are many ways of constructing such a counterfactual time series. For this essay, I constructed a counterfactual time series for the April-September period since 2020 by projecting the average year-on-year growth rate for the corresponding period in the five years just prior to the pandemic (2015 to 2019).
The actual GDP of the EU and the U.S. in April-September 2020 was 10.5 percent and 7.3 percent lower than their counterfactual GDPs, respectively. This was the period when the economic costs of the pandemic and the policy responses to it were at their highest. In April-September 2022, this gap had been reduced to 3.8 percent for the EU and 2.2 percent for the U.S. In India, this gap was 20.7 percent in April-September 2020 and has been reduced to 13.3 percent in April-September 2022. So, in comparison to India, the U.S. and the EU have closed the gap more quickly and are much closer to where they might have been had the pandemic not happened. Since the developed countries are prosperous, and there is a diminishing marginal utility of income, the loss of welfare in India has been much larger than even what these numbers indicate. It remains to be seen how long it takes for the Indian economy to get close to the pre-pandemic trendline.
Finally, it is also worth considering the slowdown in the Indian economy before the pandemic. From 2016-17 onwards, there was a steady deceleration in the growth rate—from 8.3 percent growth in 2016-17 to 6.8 percent in 2017-18 to 6.4 percent in 2018-19 to 3.7 percent in 2019-20. Even in the first three quarters of 2019-20, when the pandemic had not hit, GDP growth was only 4.1 percent. The growth in 2019-20 was the lowest since 2008-09 and the second lowest since 1991-92. There must have been some causes for this slowdown. Since there was no global crisis at that time, perhaps these causes relate to domestic political economy or institutional or policy issues. It is unclear whether these causes have gone away or been substantially mitigated. The GDP estimates do not support such a conclusion.
It would be a mistake to be complacent about the Indian economy on the basis of post-crisis growth numbers and comparisons with much richer countries’ growth rates.
—By Suyash Rai
Review
Estimating the Impact of Building Height Restrictions on Urban Sprawl
How do urban planning policies affect the spatial distribution of cities? Sprawling Indian cities are plagued by increased costs of living (due to a host of factors like commute costs), increased difficulties in the provision of public goods and infrastructure, and poor management of their populations. A new paper based on research in Brazil highlights the specific link between urban sprawl and one specific urban planning policy—Floor Area Ratio (FAR). FAR is the measurement of the building’s floor area in relation to the size of the plot of land on which the building stands. FAR requirements limit the amount of space or height any given building can occupy in relation to the land. It is used worldwide as a planning tool to limit building heights with the goal of limiting population density and to plan the provision of public utilities.
In their paper “FAR Regulations and the Spatial Size of Brazilian Cities” (September 2022), authors Ricardo Carvalho de Andrade Lima and Leonardo Monasterio study the impact of FAR regulations on urban sprawl in 325 Brazilian cities that are home to more than half of the country’s population. Interestingly, they base their research on similar research done for India in 2012.
The authors use “Annual Mapping Project for Land Use and Coverage in Brazil” to estimate land use coverage from 2020 and employ the ordinary least squares (OLS) method as well as the instrumental variables method to deal with the influence of endogenous factors. The authors pick “the local proportion of homeowners among the high-income households as an instrument for maximum-allowed FAR.” This is because, while homeowners can significantly influence the adoption of land use, especially zoning policies, they have little control over structural factors such as “income, population size, and local geography.”
Their analysis finds that “cities that implement a lower maximum-allowed FAR have larger urban areas.” Specifically, if the maximum allowed FAR is reduced by one standard deviation, this results in an expansion of the city by 12.4 percent. In addition, cities with higher average incomes and higher urban populations have higher areas.
Finally, the authors estimate the welfare costs of stringent FAR requirements along three parameters: transport costs, health costs, and CO2 emission costs. They estimate transportation costs as an aggregate of bus fares and the loss of welfare from commute. In Brazil, this amounts to USD 81 per annum per household, or an average welfare loss of USD 1.9 million per city. To compute health and CO2 costs, the authors use preexisting estimates. For health, the authors estimate that reducing FAR by one standard deviation would cost USD 449,000 for the average city in their sample. Similarly, for CO2 emissions, the authors estimate that “the impact of raising the FAR by one standard deviation on the city radius would result in additional emissions of 0.5 tons of CO2 per year or USD 29,774.”
This research provides a useful framework for thinking about the externalities of urban planning. As is true for all central planning, it has unintended consequences. While FAR limitations can limit urban densities, they may be doing so inefficiently in India, possibly leading to problems similar to those in Brazilian cities. Managing this efficiently is easier said than done. Rapidly urbanizing cities face a significant dilemma—solving the technocratic problem of efficient land use management that makes cities affordable and healthier while managing the political problem of negotiating with incumbents who are unhappy at the rapid densification of their neighborhoods and argue for stringent FAR requirements. Empirical research, like the paper reviewed here, can inform this discussion by highlighting the unintended consequences of the latter and providing policy guidance on the former.
—By Anirudh Burman