BIZweek n°277 14 fév 2020
BIZweek n°277 14 fév 2020
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  • Parution : n°277 de 14 fév 2020

  • Périodicité : hebdomadaire

  • Editeur : Capital Publications Ltd

  • Format : (260 x 370) mm

  • Nombre de pages : 6

  • Taille du fichier PDF : 2,2 Mo

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VENDREDI 14 FÉVRIER 2020 BIZWEEK ÉDITION 277 Which industries benefit (suffer) during credit booms (busts) ? What is the longtermimpact of credit booms on a country’s industrial structure ? Does the sectoral composition of output and employment during a credit boom help us tell good booms apart from bad ones ; that is, those followed by a costly bust down the road ? Key findings are as follows. Credit booms do not lift all boats alike During booms, aggregate value-added and employment growth increase. But the aggregate performance hides substantial industry-level heterogeneity. Industries that are less tradable, more labor-intensive, and more reliant on external finance tend to benefit the most from credit booms. Indeed, most of the extra value-added and employment growth concentrates in a few industries—specifically, construction and, as a distant second, finance. However,the same industries that benefit the most during booms experience the most severe downturns during busts. This implies that credit booms tend to leave few long-termfootprints on a country’s industrial composition. Construction is special Construction is the only sector that consistently overperforms in bad credit booms. On average, output and employment growth in construction are roughly 3 percentage points higher in bad booms than in good ones. In all other sectors, the difference is smaller and not statistically significant. This finding holds in advanced economies and emerging markets and is robust to the exclusion of the post-2003 period. Hence, the uniqueness of the construction sector goes beyond the housing boom-bust cycle that characterized the global financial crisis. An unusually rapid expansion of the construction sector can help discriminate bad from good credit booms An additional percentage point of value-added or employment growth in the construction sector during a boom raises the probability of the boom being bad—followed by subpar economic performance or a systemic financial crisis—by 2 and 5 percentage points, respectively. Construction growth is also a more robust predictor of the economic costs of bad booms than other variables. One percentage point higher value-added growth in the construction sector during a bad boom corresponds to nearly a 0.1 percentage point drop in aggregate output growth during the bust. Such predictive power goes beyond other variables previously identified in the literature as good predictors of troublesome booms (such as the magnitude and duration of the boom, household credit growth, or house price growth). The broad cross-country availability of data on construction (relative, for instance, to household credit growth or house prices) is an additional advantage. Strikingly, in our sample, long-lasting booms that featured rapid construction growth never ended well. Which Sectors Benefit, Which Suffer ? Almost all sectors expand faster during booms than during tranquil times, both in terms of output and employment. However, there is significant heterogeneity across sectors. Construction and finance (a distant second) gain the most during booms. Agriculture and services are at the opposite end of the spectrum. Information, mining, trade, and manufacturing lie somewhere in the middle. As for real estate, employment increases dramatically during booms, but value added appears relatively acyclical. On the flip side, no sector is immune to the bust. All of them contract during this phase, but there is again significant variation across sectors. Construction and finance again top the chart during busts (with finance again a distant second, especially when it comes to employment growth), making them the most procyclical sectors. The real estate sector shows a strong decline in employment but a much smaller decline in terms of value added (confirming the picture from the boom phase). Trade, information, and manufacturing also show procyclicality, but primarily in terms of value added rather than employment. Overall, we find that the sectors that benefit the most during booms also experience the most severe downturn during busts. Sectoral Activity during Good and Bad Booms When good and bad booms are looked at separately, the overall composition of activity is not too different. Specifically, when we rank sectors by their growth rates, three sectors top the chart in both good and bad booms : construction, finance, and information. In general, however, sectors that performbetter than others in good booms also performbetter than others in bad ones. The average industry performance is also close for the two types of booms. One sector seems to behave systematically differently between good and bad booms : construction. Finance, again, is a distant second. Trade also shows strong asymmetry, but only when it comes to growth in value added. The remaining sectors display marginal differences or mixed signs. Overall, construction displays the starkest asymmetry across good and bad booms : value added and employment in the construction sector grow between 2 and 3 percentage points more in bad booms than in good ones, respectively. Peculiarity of the construction sector The unique status of the construction sector in differentiating between good and bad booms ACTA PUBLICA IMF STAFF DISCUSSION NOTE Discerning Good from Bad Credit Booms : The Role of Construction Credit booms are a focal point for policymakers and scholars of financial crises. Yet our understanding of how the real sector behaves during booms, and why some booms may go bad, is limited. Despite a large and growing body of literature, most of the work has focused on aggregate economic activity, and relatively little is known about which industries benefit and which suffer during these episodes. This note aims to fill this gap by analyzing disaggregated output and employment data in a large sample of advanced and emerging market economies between 1970 and 2014 may stem from multiple factors. First, certain intrinsic characteristics of the construction sector could generate an acute resource misallocation problem. Construction produces highly tangible assets that can be pledged as collateral in loans, which in turn helps to raise more funds and increase investment. However, construction does not have the growth potential of many industries that fallunder the broadumbrella of the manufacturing sector. Hence, too much investment in construction due to its high asset tangibility may result in lower TFP and output (Reis 2013 ; Ebrahimy, forthcoming). This type of misallocation of real resources could also make the economy vulnerable to adverse shocks such as a house price drop. Overinvestment may also occur within the corporate sector and by firms in other industries, which may depress productivity growth in their core line of business (Shi, forthcoming). For instance, a rapid increase in house prices could driveup the return in real estate investment, pushing firms with abundant cash to invest in real estate. When profitability is very high, investment in real estate can crowd out a firm’s managerial resources, resulting in fewer productivity-enhancing activities and lower TFP growth. In these scenarios, higher construction growth during a boom may be a sign of misallocation of resources. Disproportionately allocating credit, capital, and labor to construction (as wellas to the rest of the nontradable sector) can lead to low TFP and output growth in subsequent years and an economy more vulnerable to adverse shocks. Second, there may be additional distortions related to the high labor intensity of construction and the relatively low level of skills needed. Exceptional growth in construction employment during booms can generate adverse incentives or mask existing structural problems in the labor market ; for example,job losses in manufacturing (Charles, Hurst, and Notowidigdo 2016). Third, unusually high growth in the construction sector during a boom tends to make an assessment of economic fundamentals more difficult. This is because, relative to manufacturing, measuring the underlying drivers of growth, such as productivity, can be intrinsically more difficult in the construction sector. This can create adverse incentives for governments by obscuring the connection between policy measures taken during the boom and economic performance after the boom (Fernandez Villaverde, Garicano, and Santos 2013). Finally, leverage may play an important role. Construction projects have largeup-front financing needs, and final consumers of the product (either residential or commercial real estate) also tend to use external financing for their purchases. Moreover, credit booms disproportionately benefit industries that have high external finance dependence. As a result, and compared with tranquil times, aggregate leverage may increase significantly more during booms led by industries such as construction. Conclusion Credit booms have been shown to be a harbinger of financial distress and subpar economic performance. Less often, they are associated with financial deepening and sustainable growth bursts. Distinguishing good from bad booms has proved difficult, especially when solely based on aggregate data. We take a first step toward showing how sectoral activity can help in this task. We show that the construction sector seems to play a unique role. First, it displays the strongest acceleration (deceleration) in both value-added and employment growth during booms (busts). Second, among all sectors, construction is the only one that consistently overperforms during bad booms. Finally, the pace of construction activity during the boom phase is a better predictor of the economic costs associated with bad booms than other variables identified in previous studies. Our findings have two important implications. First, they highlight potentially new channels through which some booms might lead to subsequent economic underperformance. Since construction is not a high-TFP-growth sector, booms may result in misallocation of resources in the economy. Alternatively, the role of real estate as collateral may generate amplification mechanisms and credit booms and—by increasing leverage—add to the strength of these mechanisms. Second, monitoring construction activity during a boom can provide a litmus test for policy action. In addition to sectoral value added and employment information, which are often available with a few months’lag, more high-frequency indicators such as construction permit applications could act as valuable signals. Ongoing and future research should continue to expand in the direction of using more granular data to improve our understanding of macro-financial dynamics. A straightforward application could be the introduction of construction sector indicators in the growth-at-risk framework, which links current macro-financial conditions to the distribution of future growth (Adrian and others, forthcoming). Further, effectiveness and possible side effects of policy options to curb excessive developments in the construction sector should be explored. Finally, as longer time series become available, whether the trade-off faced during booms changes in longer horizons should be assessed. Giovanni Dell’Ariccia, Ehsan Ebrahimy, Deniz Igan, and Damien Puy IMF, 12 February 2020 3

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