By: Strutt, Anna; Walmsley, Terrie; Barth, James R.. Economics Research International. No. 2011 (2011), pp.1-9, 9 p.
Hindawi Publishing Corporation Economics Research International Volume 2011, Article ID 926484, 9 pages doi:10.1155/2011/926484
Research Article Implications of the Global Financial Crisis for China: A Dynamic CGE Analysis to 2020
Anna Strutt1,2 and Terrie Walmsley3, 4
Department of Economics, Waikato Management School, Hamilton 3240, New Zealand 2 School of Economics, University of Adelaide, Adelaide, SA 5005, Australia 3 Center for Global Trade Analysis, Purdue University, West Lafayette, IN 47906, USA 4 Department of Economics, University of Melbourne, Parkville, VIC 3010, Australia
Correspondence should be addressed to Anna Strutt, firstname.lastname@example.org
Received 2 June 2011; Accepted 4 August 2011
Academic Editor: James R. Barth
Copyright © 2011 A. Strutt and T. Walmsley. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The global financial crisis resulted in a significant downturn in the global economy, with impacts felt throughout the world. In this paper, we use a dynamic global general equilibrium model to explore the longer-term impacts of the financial crisis, with a particular focus on China. The economies of most countries suffered to some extent, with the extent of declines in the long run likely to depend on the extent to which investment declines. Our results suggest that overall the financial crisis leads to international trade falling by approximately 14 percent from the 2020 baseline level. Within this, the composition of trade changes, particularly reflecting changes in demand for construction of investment goods and increasing longer-term demand from economies like China. We also briefly consider the impact of a more protracted recovery from the crisis, which has even more significant impacts on the global economy.
The global financial crisis resulted in a significant downturn in the global economy. Although the worst of the crisis appears over at the time of writing, the global recovery “remains exposed to significant risks” (2011). This paper uses a dynamic computable general equilibrium (CGE) model to explore some potential effects of the global financial crisis for China.
To examine the impacts of the crisis, we use GDyn, a dynamic global CGE model, developed by Ianchovichina and McDougall  and based on the GTAP model . The GDyn model incorporates most features of the GTAP model, including bilateral trade flows, a sophisticated consumer demand function and intersectoral factor mobility. In addition, GDyn tracks foreign ownership of capital and investment behavior. This allows us to include the impacts of endogenous capital accumulation and the movement of investment between countries in response to differing expected rates of return. Use of a dynamic model also allows us to model consecutive periods of the crisis and the con- sequent time-path of adjustment for each economy. While
GDyn has an improved treatment of investment relative to the GTAP model and captures errors in expectations, it does not model debt obligations or money and, therefore, does not purport to explain the financial crisis. Thus, we use the model to mimic key macroeconomic impacts of the financial crisis, in an effort to shed light on the longer-term impacts of the crisis for China.
We first explain the model and develop a baseline scenario for GDyn, which depicts how the global economy might have evolved without the impact of the global financial crisis. This is then compared with results arising with the global financial crisis. We also consider the potential impacts of a slower recovery from the crisis. Finally, we offer some concluding comments, including discussing some limitations of the current study.
- Model and Baseline Scenario
The GDyn model incorporates a treatment of investment that relies on (a) the gradual elimination of errors in expectations, (b) the gradual equalization of rates of return to investment, and (c) the gradual movement of economies towards Economics Research International steady-state growth . We adapt the model for our current purposes to include endogenously determined employment of skilled and unskilled labour, along with capital.
In combination with the GDyn model, we use version 7 of the GTAP Data Base, which has a base year of 2004 . This database is augmented with supplementary data required for the GDyn model . The 113 countries/regions and 57 sectors in the full GTAP Data Base are aggregated to 29 regions and 27 sectors. However, we focus here on results for China, contrasting them with a limited selection of other countries.
2.1. Developing the Baseline Scenario. Before examining the impacts of the global financial crisis, we first develop a baseline for the model which represents how the global economy might have looked in the absence of the crisis. The development of a baseline is an important component of the experimental design when using a dynamic model ; however, building a suitable baseline is a complex task.
Given the difficulties in creating a baseline for the GDyn model, previous baselines have focused on obtaining projections for a few key macroeconomic variables, such as real GDP, population, skilled and unskilled labour, along with implementation of key policies which have already been agreed upon and are expected to affect the regions/sectors being considered . An alternative approach, developed by Dixon and Rimmer  for single country model baselines, uses a series of simulations (historical, decomposition, and forecasting) to develop a baseline scenario. We use a combination of these approaches, focusing on the path of the macrovariables. Previous work with GDyn indicated that the way we model errors in expectation and productivity changes tends to have significant impacts on the baseline ; therefore, we focus particularly on improving the specification of these. Key aspects of our baseline are summarised below.
work, both contribute to structural change over time in the economies we model. We retain the standard GTAP approach to modelling utility and income elasticities [3, 4], therefore, we concentrate here on explaining the approach taken to modelling sectoral productivity growth.
The assumptions we make on sectoral productivity growth broadly follow the approach of Hertel et al.  and Golub et al. , which based nonagricultural productivity growth on economy-wide labour productivity growth rates, adjusted for productivity differences across sectors. We update the labour productivity differentials using the latest available OECD estimates, (which provide estimates from 1995–2003, contrasting with previous estimates based on 1970–90 data) employ greater sectoral differentiation and also apply this approach to agricultural sectors. Sectoral differentials for labour productivity growth rates are derived from the OECD STAN data . These data provide estimates of labour productivity in terms of the amount of value added per unit of input. Following the approach of Kets and Lejour (15), these indexes of sectoral labour productivity growth are averaged across countries. (We use a simple average as recommended by Kets and Lejour for this kind of labour productivity data.) To estimate sectoral differentials, the average growth in labour productivity growth for each sector is assessed relative to average labour productivity growth. Other studies have employed different assumptions for land-using sectors, taking into account the productivity of all inputs, rather than just the value added (12, 13, 16).
Table 1 summarises our estimated sectoral productivity differentials, following the approach outlined above. Consistent with previous studies, a relatively high level of productivity growth is indicated for agricultural sectors. In addition, certain other sectors also experience relatively high productivity growth, particularly some of the manufacturing sectors such as electronics, machinery, and motor vehicles. These findings are broadly consistent with the earlier work of Kets and Lejour , including the finding of particularly high growth rates in the transport and communication sector. Since we define these sectoral differentials as the ratio of labour productivity growth in each sector to the economywide average, they are implemented for each region by multiplying by the base factor productivity growth rate for each region. Thus, although we only present sectoral differentials in Table 1, the effective impact of these differentials will vary significantly by regions, with the relatively rapidly growing economy of China having a high base factor productivity growth rate.
2.1.1. Data Sources. Historical data were collected primarily from the World Development Indicators for available countries . Some additional data for the Asian economies were also collected. (We are grateful to Ginalyn Komoto and Susan Stone, previously of ADBI, for their assistance in collating this additional data.) Historical data were generally found to be particularly good prior to 2006. We use the available historical data to find average annual growth rates and construct an historical baseline. We then include the following variables in the baseline: real GDP, investment, consumption, government spending, population, and skilled and unskilled labour.
2.1.2. Structural Change and Sectoral Productivity. A range of alternative assumptions are possible with respect to sectoral productivity differentials over time. Ngai and Pissarides  discuss two competing explanations for structural change: the technological explanation attributes it to differences in sectoral rates of total factor productivity, while the utilitybased explanation relies on different income elasticities for different goods leading to structural change. However, these two explanations may coexist , and, in the current
2.1.3. Calibration of the Baseline. As outlined in Walmsley , historical investment can be accommodated in one of two ways: (a) by introducing an additional risk premium to explain the difference between actual and model determined investment or (b) by introducing errors in expectations. The difference between the two alternatives stems from assumptions regarding their permanence into the long run. In the first case, large risk premiums may be created by low investment in some countries, and these risk premiums are permanent, and; therefore, low investment remains in the long run. This method would be appropriate for a country……..