The Bon Curve Revisited
The relationship between economic development and the construction industry is generally understood to follow an inverted U-shaped curve. In developing countries the level of construction output as a share of GDP rises as the economy grows, reflecting the investment required to generate that growth. As countries become middle income, construction’s share of GDP levels out, and then declines in high income countries. A high level of capital investment and construction of infrastructure has long been recognised as a characteristic of industrialising countries, and is clearly related to the stage of economic development of a country.
In construction economics this is known as the ‘Bon Curve’, after Ranko Bon (1992), who was a founding editor of the Construction Economics and Management journal with Will Hughes. His inverted U-shaped relationship between the contribution of construction to GDP and economic development has been generally supported by many studies over the last 25 years. Although there are exceptions like micro-states and oil exporters, these studies reach statistically significant conclusions on the path the contribution of construction makes to the economy over a long time period.
It has always been difficult to get data sets for this research. Turin’s pioneering work in the 1960s and 70s used 46 and 78 countries respectively, and the latter found an S-shaped curve for developing countries as the rate of increase of construction’s share was rapid at first but leveled out and stabilized over time. Bon had only six countries is his data set (US, Japan, UK, Finland, Ireland and Italy). Many of the subsequent studies supporting Bon also do not include much data, or an economic model. Most of them are descriptive, typically grouping countries into four categories based on per capita income and then calculating an average of construction's share of GDP in each group. There are many reasons why these average values are likely to be biased, such as non-stationarity of the data, changes in composition of groups over time, omitted variables and outliers.
However, using a data set of 205 countries, which is much larger than the sample in all those previous studies, a 2011 paper found qualified support for the Bon Curve:
Cross-sectional comparison and longitudinal analysis were used to verify Bon’s propositions. The inverted U-shaped relationship between construction activities and level of development was not confirmed when the aggregated data of all countries over time were considered simultaneously. The relationships across countries at a given time were not confirmed in the majority of the yearly aggregated data. The relationships within countries over time were confirmed in 78 economies, mostly from high and upper-middle income countries. Bon’s proposition of ‘volume follows share’ was not confirmed. Declines in construction were found in most of the high income economies. In conclusion, Bon’s curve is to be interpreted as explaining variation within the developed economies over time (Choy 2011: 695).
This sort of variability across countries at similar levels of GDP and income means generalisations about the relationship between construction and economic development should be approached with care. The individual cases are distinct, so there is little help for policy from the aggregate numbers. Researcg finds a significant relationship between the construction industry and economic growth in developing countries, but also suggests that the relationship appears to be more complicated than originally thought.
To get at these complications, one approach taken has been to broaden the number of variables used in the analysis and to expand the macroeconomic reach of the models. As well as GDP or GNI per capita, which may or may not be adjusted for purchasing power parity, construction value added instead of output has been found useful, and variables like life expectancy, population or urbanisation (population density) tested. Other research has looked at sectors like infrastructure and housing, or groups of countries by geography or stage of development.
A 2014 paper using 148 countries found the curve, which Giradi and Mura called the ‘Construction Development Curve’ fits better if economic development is measured by alternative indicators instead of per-capita GDP, using life expectancy and a broad Economic Development Index (EDI). Population density, demographic growth and credit expansion did not explain cross-country variation in the share of construction in output in their model:
We have used panel data for world countries for the period 2000-2011 to provide evidence of a bell-shaped relationship between construction activity and economic development, consistent with the theory proposed by Bon (1992). The relation gets stronger after logarithmic transformation of the data. This implies that the curve is asymmetric with respect to its maximum: the size of the construction sector tends to increase in developing countries, to peak in newly industrialized economies and to decline at a slowing pace afterwards, approaching stabilization in the most advanced economies.
We have also found that the curve fits better when employing alternative indicators to measure the level of economic development instead of per capita GDP. This supports the intuition that the size of the construction sector is not just a function of per capita output, but is related to broader socio-economic trends which are intimately linked with economic development, namely urbanization, industrialization and creation of basic infrastructures. In particular, we have found that the model fits better when economic development is measured through an index (EDI) composed of per capita income, life expectancy, maternal mortality ratio and the share of agriculture in employment. However, and rather interestingly, we have obtained an even better fit to the data when using life expectancy alone as the proxy for development. (Giradi and Mura 2014: 20).
The relationship between construction and the stage of economic development is complex, and unlikely to be explained by only GDP or income per capita. Both broader measures, like an EDI (or perhaps the UN Human Development Index), and more specific ones like construction value added improve the explanatory power of models. The bell-shaped relationship is largely determined by new building, which intuitively makes sense because the built environment in developing countries will be growing rapidly. On the other hand, the level of renewal and maintenance will increase with the size of the built environment and thus is also related to the stage of economic development, and this increase would explain the tendency toward stabilization of construction’s share of GDP in mature economies. The role of repair and maintenance raises the awkward issues of data quality and availability. In many countries this is not measured directly through work done but picked up in the output of the construction trades. Facility management firms are classified as business services, unless also construction contractors, so the R&M work they do is not counted in construction output. Output itself is measured inconsistently across countries, and the reliability of the data in developing counties is often weak. International comparisons of construction are subject to a wide range of factors, and for a review of these see Meikle and Gruneberg (2015).
Bon, R. 1992. The future of international construction: Secular patterns of growth and decline. Habitat International, 16(3), 119–128. Choy, C. F. 2011. Revisiting the ‘Bon curve’, Construction Management and Economics, 29:7, 695-712. Girardi, D. and Mura, A. 2014. The Construction-Development Curve: Evidence from a New International Dataset, The IUP Journal of Applied Economics, Vol. XIII, No.3. Meikle, J. and Gruneberg, S. 2015. Measuring and comparing construction activity internationally. In R. Best, and J. Meikle (eds.), Measuring Construction: Prices, Output and Productivity, Abingdon: Routledge.