# **Box 2 Is China’s Emergence Putting Pressure on Machinery Prices?**

To what extent is the emergence of China as an important player in the global trade in machinery putting competitive pressure on prices? We estimate a linear regression model through which we test whether the unit value of exports in machinery from Southeast Asian countries is affected by the quantity of China’s exports. The model is given as: lnVijkt = α 0 + α 1*Fjk + α 2 ln Qjkt−1 + α 3 (Fjk*Qjkt−1) + α 4lnVijt−1 + Zijt β + εijt **,** (1) where _V_ is the unit value of exports, _F_ is a dummy variable for the finished good, _Q_ is the volume of exports from China, _Z_ is a matrix of covariates, and _ε_ is the error term. The subscripts _i_ , _j_ , _k_ , and _t_ represent the country (Indonesia, Malaysia, the Philippines, and Thailand), product classification at the 6-digit level of the Harmonized Commodity Description and Coding System, the category (components or finished products), and the time (spanning from 1992 to 2004).

To control for observables characteristics, we include a set of covariates _Z_ such as time, product, country dummies and their interactions, the importation of capital goods, world GDP per-capita (excluding the reporter country _i_ ), the difference between country _i_ GDP per capita and world GDP per capita, and world tariff rates. Note that we also include a lagged dependent variable on the right-hand side of equation (1) to allow for the possibility that a previous period has determined the current period of the unit value (a control for possible rigidity). Had we not included this variable as a regressor, the lagged volume of Chinese exports, _Q_ , might have correlated with the error term. This problem arises because China’s export volume at time _t−1_ might correlate with the world import price at _t−1_ . We also include the share of imports of capital goods from China as a control for the possible existence of cheaper imports of capital goods from China. Assuming that the lagged volume of exports from China and the unit value, _Qjt−1_ and _Vijt−1_ , are predetermined, we estimate equation (1) using ordinary least squares.

Whether or not the increase in China’s presence in the global trade in machinery has caused changes in the market share of exports in machinery from Southeast Asian countries, the exportation of machinery from China is rapidly gaining market share. However, the exports of machinery from countries in Southeast Asia are also increasing the presence of these countries in the global market. The empirical model for our analysis on this question is given as: Δ MSijkt = δ 0 + δ 1*Fjk + δ 2 Δ CMSjt−1 + δ 3(Fjk* Δ CMSjt−1) + δ 4 Δ RCAijkt−1 + Wijt Γ + ξ ijkt (2) On the left-hand side of equation (2), Δ _MS_ is the change in the global market share of product category _k_ at the 4-digit SITC, component, finished, in the exportation of commodity _j_ from country _i_ at time _t_ . On the right-hand side, _F_ is a dummy variable for finished goods, Δ _CMS_ is China’s change in global market share for product _j_ at time _t_ , Δ _RCA_ is the change in the RCA index (scale from 0 to 100), product _j_ , category _k_ , and Γ contains other covariates similar to _Z_ in equation (1). To control for the degree of competitiveness of a particular product produced by a particular country, we include a lagged change in the RCA index in the right-hand side variable of equation (2).

The third objective is to examine the direction of specialization in the context of product fragmentation. The data suggest that several countries are becoming more competitive in producing components. Apart from the situation as regards China, the performance of the countries in Southeast Asia in the exportation of components compares more favorably with that in finished machinery. Therefore, we examine the extent to which China’s shift in specialization in finished machinery is causing Southeast Asia to shift its exportation of components to China. The empirical model for this objective is given as: SCCijt = γ 0 + γ 1CMFjt−1 + γ 3RCACijt−1 + Wijt Π + ηijt (3) On the left-hand side of equation (3), _SCC_ is the share of exports in the components of product _j_ from country _i_ to China at time _t_ (the share of exports going to the world is 100 percent). On the right-hand side, _CMF_ is China’s global market share for finished machinery of product _j_ at time _t−1_ , _RCAC_ is the RCA index in components for country _j_ (scale 0 to 100) at time _t−1_ . Finally, matrix _W_ contains covariates such as dummy variables for industry, location, time, and China’s import tariff.

For equation (3), we reclassify our product into a 3-digit SITC more broadly than we do in equation (2). This relatively more aggregate category reduces the extreme variation in market share relative to a more detailed product category, thereby allowing us to examine the effect of spillover more accurately. In our sample, we also retain products that have components in their classification. Finally, because some countries do not engage in the exportation of particular products, _SCC_ is censored from below. Thus, we estimate equation (3) using Tobit, using zero as the lower censoring point.

We estimate equations (1) to (3) using pooled time series and cross-product data. The time series spans from 1992 to 2004, while the cross product varies depending on the estimated equation. Most of our trade data come from the United Nations Commodity Trade Statistics Database (http://unstats.un.org/unsd/comtrade/), and the rest have been obtained from the World Development Indicators Database (http://www.worldbank.org/data/datapubs/datapubs.html).

_Source:_ Rahardja 2006.

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