Chapter

Chapter 3. China’s Rapid Investment, Potential Output, and Output Gap

Author(s):
Anoop Singh, Malhar Nabar, and Papa N'Diaye
Published Date:
November 2013
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Author(s)
Papa N’Diaye and Steven Barnett 

The rapid increase in China’s investment ratio since 2000 has important implications for the potential output of the economy and the output gap. This chapter estimates China’s potential output and provides estimates of the output gap. The results indicate that China has a large output gap that could take some time to close and would require structural reforms beyond standard demand-management tools.

Introduction

China’s rapid investment since 2000 has contributed to a substantial buildup of capacity. Measures of China’s capital stock using the perpetual inventory method show that China’s capital-to-output ratio stood between 2¼ and 2½ times higher than other countries at a similar stage of development (Figure 3.1). Nevertheless, it has been argued that China’s low stock of capital per capita, particularly as compared with advanced economies, means that the country has room to invest further.

Figure 3.1Capital-Stock-to-GDP Ratio and GDP per Capita

(select industrial and emerging market economies, 2006–12 average)

Sources: IMF, World Economic Outlook; and IMF staff estimates.

This argument does not hold for several reasons. First, it is not clear why China’s low per capita capital stock relative to advanced economies would be a justification for it to invest at such a rapid pace. Second, there is no doubt that China has investment needs, particularly in infrastructure. For example, the OECD reports that, notwithstanding having a comparable land area and four times the population of the United States, China’s total length of paved roads is about half that in the United States. Similarly, the total length of the railway network in China is about one-third of that in the United States. However, as shown in Chapter 2, China is investing much more than what fundamentals would suggest and more than what other countries with similar growth strategies were investing during their boom periods (Figure 3.2). It is also not clear whether this investment is being efficiently allocated. Specifically, the increase in investment since 2008 has created problems of underutilized capacity in several key sectors of the economy (Figure 3.3). Average capacity utilization in key industries, such as steel, cement, and automobiles, has declined from just under 80 percent before the crisis to about 60 percent in 2013. The underutilization of capacity partly reflects lower demand from China’s trading partners since the global financial crisis, and partly reflects China’s decision to build capital stock ahead of future domestic demand. With prospects for demand from China’s trading partners weaker than before the crisis, it is important for China to successfully rebalance its economy toward private consumption. This rebalancing will ensure that today’s idle capital stock will be utilized later, making the underlying investment productive.

Figure 3.2Share of Investment in GDP*

Sources: IMF, World Economic Outlook; and IMF staff estimates.

* Gross fixed capital formation.

Figure 3.3China: Average Capacity Utilization Rate

Source: IMF staff calculations.

Estimating China’s Potential Output

China’s rapid growth and the ongoing changes in its economic structure make estimating potential output particularly challenging. Over the years, many methods have been proposed for measuring potential output. One idea that always lies close to the surface is that there is some production function that links output to available inputs of labor, capital, and raw materials, given the current technology, and that one can think of the current level of potential output as what would emerge from this production function, given the current levels of fixed inputs and sustainable levels of variable inputs. Although this idea is useful in a general sense, and indeed motivates the idea that there is some link between conditions in labor markets and conditions in product markets, it has been found that, in practice, not much is added to the precision of measures of excess demand by the structure of the production function. The uncertainty in pinning down potential output is simply transferred into uncertainty about total factor productivity.1 Moreover, for an economy experiencing major structural change, a production function approach may not be reliable because it may not fully account for an increase in the share of capital stock that has become obsolete.

The modern standard methodology for measuring potential output is to use some variant of filtering. Thus, time-series techniques are used to fit trend lines through the data, and these trends provide the measures of the underlying “equilibrium” values.2 It is important to stress that in referring to these values as equilibrium values, the perspective is that of the effect on inflation. The trend lines are used to define gaps—deviations of actual observed values from these trends—that are, in turn, used to describe the dynamics of inflation and the policy control process. These measures are determined, at least in part, by their ability to represent these processes. It is not the case, for example, that the filter-based measure of “potential” output means that this is the best that could be done with the best possible use of all resources without constraints. For example, capital put in place under different conditions may not be malleable or useful at all in new industries. Labor may need new skills or to be relocated to achieve the longer-term production function possibilities frontier. What is measured using this filtering technique represents what can be produced today, given all the constraints, without generating inflationary pressures.

The methodology used to estimate China’s potential output combines the two approaches described above, the production function and a filtering technique, following IMF (2006). It uses information from both the supply side and the demand side to condition the estimates of potential output. The essential idea behind this approach is that one can profit from considering more than just the data on output. In particular, since we know that there is a link between labor input and output, it may be useful to exploit information about the degree of excess demand in the labor market. Similarly, the behavior of inflation informs us about the likely existence of excess demand/supply in the product market.

The methodology treats the filtering problem as a small system, in which the estimates of potential output, trend labor participation, hours worked, capacity utilization, the non-accelerating inflation rate of unemployment (NAIRU), and some of the parameters of the dynamic model are determined simultaneously, allowing the analysis to account for interactions among unemployment, output, variable inputs, and inflation. The resulting trend estimates of output, variable inputs, and the unemployment rate should be seen as the levels that can be used without causing inflation to rise or fall; in other words, potential is defined as how much the economy could produce without generating inflation while fully utilizing available capacity and labor.

The system consists of five structural equations (see Annex 3A)—a production function, a Phillips curve, a NAIRU, an Okun’s law, and a resource utilization relationship—and several identities.

  • The production function, equation (3A.4) in the annex, links output to hours worked, capital, and total factor productivity, with the share of hours worked and capital fixed at their averages of about 0.5 and 0.5, respectively. At potential, hours worked is the product of working-age population, trend participation rate, trend average hours worked, and 1 minus the NAIRU. The trend participation rate and average hours worked as well as the NAIRU are determined simultaneously, consistent with stable inflation. At the same time, the potential capital stock series is the product of the capital stock and the trend capacity utilization rate. Total factor productivity depends on past realizations of total factor productivity.

  • The Phillips curve, equation (3A.2) in the annex, relates inflation to expected inflation, terms of trade shocks (changes in import prices and oil prices), and past values of the output gap. The influence of excess demand is captured through the output gap. This model is a backward-looking autoregressive model that has been employed extensively to estimate the parameters of reduced-form expectations-augmented Phillips curves. Inflation expectations are modeled as a pure distributed lag of past inflation, with a restriction that the coefficients sum to 1. The influence of import price and oil price pass-throughs are also added to the inflation process. It is important to stress that, because it is the inflation expectations series that matters in the Phillips curve, an alternative specification for the inflation expectations process would alter the gap estimates.

  • The NAIRU equation, equation (3A.7) in the annex, relates the unemployment rate to the past unemployment rate, capturing some persistence in the dynamics of unemployment.

  • The Okun’s law equation, equation (3A.8) in the annex, links the movements in unemployment to those in the output gap. Some degree of persistence in the dynamics of the unemployment gap is captured by the presence of the lagged values of the unemployment gap. By the same token, the resource utilization equation (equation (3A.11) in the annex) links the capacity utilization rate to the output gap, with excess demand translating into tight capacity.

Implications for Potential Output Growth and the Output Gap

Estimates of China’s potential growth rate suggest an annual rate of about 10 percent on average for the past decade (Figure 3.4). Potential output has continued to grow at about 10 percent even as actual GDP growth fell to 9.3 percent at the onset of the global financial crisis because the government put in place a massive stimulus package that further boosted investment. The estimates also suggest that capital accumulation accounted for more than 57 percent of potential output growth, total factor productivity growth accounted for about 38 percent, and labor inputs accounted for about 5 percent.

Figure 3.4China: Potential Output Growth

Source: IMF staff estimates.

Note: TFP = total factor productivity.

The consequence of such high potential growth in the face of slowing activity is large slack in the economy. Unlike output gap estimates based on standard filtering techniques such as the Hodrick-Prescott filter, IMF staff estimates of the output gap suggest the Chinese economy was 5 percent below potential at end-2011 (Figure 3.5). Although this is certainly a jarring number, what needs to be remembered is that China has had a structural output gap for the best part of a decade. The gap was closing in the run-up to the financial crisis, but doing so based upon an unsustainable level of external demand. Even in 2007, when growth exceeded 14 percent, nonfood inflation was very low and stable, suggesting the economy was still operating below capacity. Then, in 2008, the government put in place an enormous investment stimulus that propped up growth but built out capacity in a range of areas.

Figure 3.5Estimates of China’s Output Gap

Source: IMF staff estimates.

Looking ahead, it is clear that closing China’s large output gap could take some time and would certainly require more than standard demand-management tools. Indeed, this excess capacity is the reflection of the economy’s heavy reliance on investment—the sustainability of which can be questioned—encouraged by various cost advantages, including low costs of capital, labor, utilities, pollution, energy, land, and tax incentives, as discussed in Chapter 4. Therefore, closing this output gap will require accelerating structural reforms to promote the rebalancing of the economy away from investment and toward consumption, which could mean lower potential output growth.

Annex 3A. Equations in the Model

(3A.1) Output decomposition

(3A.2) Phillips curve

(3A.3) Unemployment rate

(3A.4) Stochastic process for potential output

(3A.5) Potential capital stock

(3A.6) Stochastic process for the output gap

(3A.7) Stochastic process for the NAIRU

(3A.8) Stochastic process for the unemployment gap

(3A.9) Capacity utilization

(3A.10) Stochastic process for trend capacity utilization

(3A.11) Stochastic process for the capacity utilization gap

(3A.12) Potential labor input

(3A.13) Hours worked

(3A.14) Stochastic process for trend hours worked

(3A.15) Stochastic process for the hours worked gap

(3A.16) Participation rate

(3A.17) Stochastic process for trend participation rate

(3A.18) Stochastic process for the participation rate gap

Definition of Variables

y is 100 times the logarithm of real GDP

y is 100 times the logarithm of potential output growth

ygap is the output gap

π is 400 times the quarterly percent change in the consumer price index (using the difference in logarithms as an approximation)

πimp is 400 times the quarterly percent change in the implicit import deflator (using the difference in logarithms as an approximation)

πoil is 400 times the quarterly percent change in the World Economic Outlook crude oil price index defined as a simple average of three spot prices: Dated Brent, West Texas Intermediate, and the Dubai Fateh (in the United States) (using the difference in logarithms as an approximation)

k is the capital stock

k is potential capital stock

h is the average weekly hours worked

h is potential hours worked

part is the participation rate

part is the trend participation rate

u is the unemployment rate

u is the NAIRU

pop is the working-age population

cu is the capacity utilization rate

cugap is the capacity utilization gap

References

This does not mean that production functions are not useful in other ways. In more complex models with stocks, it is essential to have an explicit link between investment spending and the creation of productive capacity.

The phrase “trend lines” is used to describe the series identified as potential output, the non-accelerating inflation rate of unemployment, and so on. They are not necessarily straight lines.

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