Import Demand for Milk in China: Dynamics and Market Integration. *Senarath Dharmasena **Jing Wang *David A. Bessler

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1 Import Demand for Milk in China: Dynamics and Market Integration *Senarath Dharmasena **Jing Wang *David A. Bessler *Department of Agricultural Economics, Texas A&M University Phone: (979) ** Northeast Agricultural University of China Selected Paper Proposal to Southern Agricultural Economics Association Annual Meeting, Atlanta, Georgia, January 31-February 3, 2015 Copyright 2015 by Senarath Dharmasena, Jing Wang and David A. Bessler. All rights reserved. Readers may make verbatim copied of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies.

2 Import Demand for Milk in China: Dynamics and Market Integration Senarath Dharmasena (Texas A&M University), Jing Wang (Northeast Agricultural University of China) and David A. Bessler (Texas A&M University) Abstract The consumption of milk in China has been increasing over past two decades. Currently, China does not produce enough milk to meet this increase in demand. Consequently a large volume of milk is imported. In this study, import demand for milk in China is explored using demand systems approach formulated through a vector error correction model (VECM). Additionally, import market integration is explored using contemporaneous causality structures developed through artificial intelligence and Direct Acyclic Graphs (DAGs) applied to innovations of VECM. Objectives of this study are to (1) Develop a VECM and test price homogeneity in the cointegration space; (2) Estimate almost ideal demand system model where prices are expressed in relative prices; (3) Calculate Chinese import demand elasticities for milk; and (4) Model import market integration using DAGs. Annual import volume (lbs) and total value ($US) of milk imported to China from Australia, New Zealand, United States and rest of the world, from are collected from UN COMTRADE database. Calculated milk import demand elasticities shed light on the sensitivity of milk imports to changes in milk price of exporting country. Causality structure based off of innovations of VECM will allow us identify import market integration patterns. Preliminary results from causality structures reveal that the import share from the rest of the world is endogenous, while that of the United States, Australia and New Zealand was found to be weakly exogenous. 1

3 Import Demand for Milk in China: Dynamics and Market Integration Introduction The consumption of milk in China has been increasing over past two decades. Per capita milk consumption in China was 32 kg in 2011; which is 86% more compared to that of Currently, China does not produce enough milk to meet increasing demand. Consequently a large volume of milk is imported. United Nations reported that China imported $484 million worth of milk in International competitiveness of Chinese milk products were explored by Qiao (2003). Liu (2007) showed the effect of trade liberalization on dairy products in China. In this study, we explore import demand for milk in China using demand systems approach formulated through a vector error correction model (VECM). Additionally, import market integration is explored using contemporaneous causality structures developed through artificial intelligence and Direct Acyclic Graphs (DAGs) (Sprites et al, 2000) applied to innovations of VECM. Recent studies in non-stationarity and cointegration opened a new approach for introducing dynamics in demand systems, which has made the almost ideal demand system (AIDS) of Deaton and Muellbauer (1980) in error correction form very attractive (Gil, et al., 2004). Although, other models such as Armington s model and Rotterdam model have been used in the past to study import demand for various products (Babula, 1987; Seale et al., 1992), AIDS model is used due to its ease in estimation and flexibility. We estimate quadratic almost ideal demand system (QUAIDS) of Banks et al., (1997) in error correction form, which nests the AIDS. Engle & Granger (1987) two-step procedure has been used to estimate cointegrated 2

4 demand systems, although this approach has been criticized due to various estimation issues (Gil et al., 2004). Recently, Pesaran and Shin (1999) have used Johansen (1988) approach to specify, estimate and test theoretical restrictions on cointegrated demand systems (Gil et al., 2004). Objectives: Specific objectives of this study are to (1) Develop a VECM and test price homogeneity in the cointegration space (Sanjuan & Gil, 2001) (2) Estimate QUAIDS model where prices are expressed in relative prices (3) Calculate Chinese import demand elasticities for milk (4) Model import market integration using DAGs. Data and Methodology: Annual import volume (lbs) and total value ($US) of milk to China from Australia, New Zealand, United States and rest of the world, from are collected from UN COMTRADE database. Import prices are calculated taking the ratio of value to quantities. Once non-stationarity properties are identified (Dickey & Fuller, 1979), then price homogeneity in the cointegration space is tested. QUAIDS model in error correction form is estimated and import demand elasticities are calculated. In estimating QUAIDS model, it is assumed that imports of milk is separable from other imports as well as demand for milk is separable from that of domestic production (Lin et al., 1991; Honma, 1993; Agcaoili-Sombilla & Rosegrant, 1994; Yang & Koo, 1994). Finally, import market integration is explored using DAGs. Preliminary analysis was carried out to identify relationships between import price of Chinese milk, import shares from United States, Australia New Zealand and rest of the world 3

5 and total and total import value of milk imports to China using artificial intelligence and directed acyclic graphs techniques. Causal relationships come from recent efforts in computer science (Pearl, 1995; Pearl, 2000; Spirtes, Glymour and Scheines, 2000). One reason for studying causal models, represented here as X Y, is to predict the consequences of changing the effect variable (Y) by changing the cause variable (X). The possibility of manipulating Y by way of manipulating X is at the heart of causation. Hausman (1998, page 7) writes: Causation seems connected to intervention and manipulation: one can use causes to wiggle their effects. A manipulation-based definition of causation is generally more in line with a philosophical definition of causality than other recently offered definitions that focus exclusively on predictability. For example, Bunge (1959) argued that causality requires a productive or genetic principle that models how something comes into being. X causes Y if X is productive of Y. Definitions that focus on prediction alone, without distinguishing between intervention (first) and subsequent realization, may mistakenly label as cause variables that are associated only through an omitted variable. For example, Granger-type causality (Granger 1980) focuses solely on prediction, without considering intervention and manipulation. Here we follow work of Spirtes, Glymour and Scheines (2000) and Pearl (2000), in representing causal structures in graphical form and using separation notions to help identify causal flows with observational data. Directed Graphs Essentially, a directed graph is an illustration using arrows and vertices to represent the causal flow among a set of variables, whose values are measured in non-time sequence (e.g., 4

6 cross section data). A graph is an ordered triple <V,M,E> where V is a non-empty set of vertices (variables), M is a non-empty set of marks (symbols attached to the end of undirected edges) and E is a set of ordered pairs. Each member of E is called an edge. Vertices connected by an edge are said to be adjacent. If we have a set of vertices {A,B,C,D} the undirected graph contains only undirected edges (e.g. A B). A directed graph contains only directed edges (e.g. C D). A directed acyclic graph is a directed graph that contains no directed cyclic paths. An acyclic graph has no path that leads away from a variable only to return to that same variable. (The path A B C A is labeled cyclic as here we move from A to B, but then return to A by way of C.) Only acyclic graphs are used in our analysis. It is helpful and valid to use terms from genealogy when referring to variables and their position in a causal structure as, for example, parents, grandparents, children, grandchildren, ancestors or descendents, etc. So in the path A B C D, the variables A, B and D are ancestors of variable C. As such, variable C is a descendent of variables A, B and D. The variable A is a parent of variable B. Variable C has two parents, B and D, and one grandparent, variable A. Directed acyclic graphs are pictures (illustrations) for representing conditional independence as given by the recursive decomposition: (1) Pr( v 1, v 2, v 3,... v n ) = n i= 1 Pr( v i pa i ) where Pr is the probability of vertices (variables) v 1, v 2, v 3,... v n, the notation pa i represents the realization of some subset of the variables that precede (come before in a causal sense) v i in 5

7 order (v 1, v 2, v 3,... v n ) and the symbol Π represents the product operation, with index of operation denoted below (start) and above (finish) the symbol. Pearl (1995) proposed d-separation as a graphical characterization of conditional independence. That is, d-separation characterizes the conditional independence relations given by the above product. If we formulate a directed acyclic graph in which the variables corresponding to pa i are represented as the parents (direct causes) of v i, then the independencies implied by the product given above can be read off the graph using the notion of d-separation as defined in Pearl (1995): Definition: Let X, Y and Z be three disjoint subsets of vertices [variables] in a directed acylic graph G, and let p be any path between a vertex [variable] in X and a vertex [variable] in Y, where by 'path' we mean any succession of edges, regardless of their directions. Z is said to block p if there is a vertex w on p satisfying one of the following: (i) w has converging arrows along p, and neither w nor any of its descendants are on Z or (ii) w does not have converging arrows along p, and w is in Z. Furthermore, Z is said to d-separate X from Y on graph G, written (X Y Z) G, if and only if Z blocks every path from a vertex [variable] in X to a vertex [variable] in Y. Geiger, Verma and Pearl (1990) demonstrated that there is a one-to-one correspondence between the set of conditional independencies, X Y Z, implied by the above factorization and the set of triples, X, Y, Z, that satisfy the d-separation criterion in graph G. If G is a directed acyclic graph with vertex set V, and X, Y and Z are in V, then G linearly implies the correlation between X and Y conditional on Z is zero if and only if X and Y are d-separated given Z. 6

8 Next step of the analysis is to explore the time-series properties of data and develop the demand system model in the cointegration framework (if cointegration was found in the data). If not, the dynamic properties will be captured using a dynamic almost ideal demand system model. Results and Discussion: Preliminary results are as follows. 1 Figure 1: Contemporaneous causality pattern of import share, import price and total import value of Chinese milk market. According to Figure 1, the import prices are related, however model did not identify the direction 1 w_us= import share from United States; w_aus=import share from Australia; w_nz=import share from New Zealand; w_row=import share from the rest of the world; p_us=price paid for milk imports from the United States; p_aus=price paid for milk imports from Australia; p_nz=price paid for milk imports from New Zealand; p_row=price paid for milk imports from the rest of the world; m=total import value of milk. 7

9 of the causality. There can be many reasons for this result (such as possible omitted variables) which warrants further analysis. As far as import shares go, import shares from New Zealand, Australia and the United States determine the import share from the rest of the world. Also, import share from New Zealand causes the import share from Australia, which in terns causes import share from rest of the world. Import price from Australia causes import share from the United States. Import share from the rest of the world is endogenous, while that of the United States, Australia and New Zealand was found to be weakly exogenous. Further analysis is being conducted and results will be available in the near future. 8

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