Infrastructure Investment and Economic Growth

 Growth in real GDP per worker and five types of infrastructure per worker

 

There is a new paper from three World Bank researchers on the relationship between infrastructure and economic growth, a difficult topic they tackle with some sophisticated econometric techniques using data from the World Bank and the Penn tables. Disentangling the economic effects of infrastructure from the effects of other macroeconomic factors requires long time periods and a method to extract estimates from the data.  The researchers use a pooled mean group estimator to compare differences between countries in growth of real GDP per worker and investment in five types of infrastructure per worker between 1992 and 2017.

 

Because other factors like population growth, education levels, openness to trade and type of exports have significant effects on economic growth, any measured effect of infrastructure investment will be small by comparison. This research estimated the strength of the relationship between real GDP per worker and infrastructure investment by the size of the infrastructure coefficients, shown in the table below. While the coefficient values are indeed small they also show clearly that higher investment in each of the five types of infrastructure leads to higher real GDP per worker, as shown in the figures below.  

 

This is an important result. Their model credibly finds larger effects for infrastructure investment on economic output than previous studies, and found the effects of infrastructure were higher in the three decades after 1991 than the two before. There are separate estimates for a group of low- and middle-income countries and another group of high-income countries. Infrastructure has more effect in the group of developing countries compared to industrialised countries.

 

The paper starts by reviewing previous research on the impacts of infrastructure investment on economic growth and development. Some studies showed a strong positive relationship between infrastructure development and economic growth, others found a mildly positive relationship or no relationship. The author’s note “Many factors are responsible for these varying results, such as differences in methods, differing approaches to measuring infrastructure development, the varying development stages of countries included in the sample, varying time periods, and geographical factors such as high or low population density.” 

 

Their study evaluates the contributions to growth in a panel of 87 countries over the period 1992 to 2017 of three main categories of infrastructure: transport, electricity, and telecommunications. The main estimate uses a pooled mean group estimator to estimate their effect on growth, and finds larger effects for infrastructure investment on economic output than found by previous studies. They also find the effects of infrastructure are higher in the three decades after 1991 than the two before. 

 

Although other studies have shown a strong positive relationship between infrastructure and economic growth in less developed countries lacking adequate infrastructure, whether this finding holds for industrialised economies remains an open question because other research has not found a significant effect. Is there a threshold level of economic development (measured in terms of per capita GDP or human development indicator) below which the relationship between the infrastructure and economic growth is stronger, and is the relationship is weak or absent above the threshold? 

 

The paper has separate estimates for 48 low- and middle-income countries and 39 high-income economies. Infrastructure has larger effects in the group developing economies compared to industrialised economies. Theinfrastructure coefficients that measure the effect are smaller in the developed country sample than the developing country sample, in Table 11. Railways essentially have a zero effect on both groups, unlike their effect in the earlier period 1970-91.  Compared to 1970-91 developing country coefficients for roads, electricity and mobile phones and particularly telephones are all higher.

Their Figures plot the relationship between real GDP per worker and the different infrastructure indicators in the 87-country panel from 1992 to 2017, with higher infrastructure per worker associated with higher real GDP per worker. This relationship is notably strong for electricity generation capacity (r = 0.77) and the telecommunications variables (r = 0.52 and r = 0.67 for mobile and fixed line telephones, respectively).

Figures 1 - 5. Real GDP per worker and various infrastructure variables, 1992-2017 country means

Timilsina,Govinda R.; Stern,David S.; Das,Debasish Kumar. How Much Does Physical Infrastructure Contribute to Economic Growth An Empirical Analysis. Policy Research working paper, WPS 9888 Washington, D.C.: World Bank Group. 

 

https://documents.worldbank.org/en/publication/documents-reports/documentdetail/553061639760111979/how-much-does-physical-infrastructure-contribute-to-economic-growth-an-empirical-analysis

Economic Development and Construction

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.

The Bon Curve

 

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).  

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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.