Integration, Contagion, and Income Distribution

Abstract

The 2008/2009 global financial crisis and the unprecedented policy response in advanced economies have a worldwide impact. The episode also led many to question the standard framework of economic thinking on regional integration, financial liberalization and their repercussions on income disparity. The paper argues that one needs to take a balanced view on integration—not just the benefits but also the risks. If regional integration leads to greater inequality, the expected growth and prospect of improved welfare can be diminished. Utilizing a general equilibrium framework, it is also shown how financial liberalization and the surge of capital inflows can produce not only financial instability but also worsening income disparity. By combining model-based results and theory-based ranking applied to the Asian case, and considering the benefits, opportunities, costs and risks of alternative policies, it is revealed that imposing levy on bank-led flows can be used to reduce instability and inequality. This type of macro-prudential policy reflects a departure from the ‘First Best’ to the ‘Second Best’ approach of liberalization, where the frictionless outcome of the former is seriously questioned.

Appendices

Appendix

The presence of feedback influences in a network model requires a large matrix—known as supermatrix—that contains a set of sub-matrixes. The supermatrix captures the influence of elements in a network on other elements in that network. Denoting a cluster by C _h , h = 1, ……m, and assuming that it has n_h elements e _h1 , e _h2 , e _h3 ……., e _hmh , and laying out all the clusters and all the elements in each cluster both vertically on the left and horizontally at the top, we have the following supermatrix:

Supermatrix of a Network

The typical entry of this supermatrix is

Entries in Supermatrix of a Network

$$ {\mathrm{W}}_{\mathrm{i}\mathrm{j}}=\left[\begin{array}{cccc}\hfill {\mathrm{W}}_{\mathrm{i}1}^{\left({\mathrm{j}}_1\right)}\hfill & \hfill {\mathrm{W}}_{\mathrm{i}1}^{\left({\mathrm{j}}_2\right)}\hfill & \hfill \bullet \bullet \bullet \hfill & \hfill {\mathrm{W}}_{\mathrm{i}1}^{\left(\mathrm{j}{\mathrm{n}}_{\mathrm{j}}\right)}\hfill \\ {}\hfill {\mathrm{W}}_{\mathrm{i}2}^{\left({\mathrm{j}}_1\right)}\hfill & \hfill {\mathrm{W}}_{\mathrm{i}2}^{\left({\mathrm{j}}_2\right)}\hfill & \hfill \bullet \bullet \bullet \hfill & \hfill {\mathrm{W}}_{\mathrm{i}1}^{\left(\mathrm{j}{\mathrm{n}}_{\mathrm{j}}\right)}\hfill \\ {}\hfill \begin{array}{l}\bullet \\ {}\bullet \\ {}\bullet \end{array}\hfill & \hfill \begin{array}{l}\bullet \\ {}\bullet \\ {}\bullet \end{array}\hfill & \hfill \operatorname{}\operatorname{}\operatorname{}\hfill & \hfill \begin{array}{l}\bullet \\ {}\bullet \\ {}\bullet \end{array}\hfill \\ {}\hfill {\mathrm{W}}_{\mathrm{i}{\mathrm{n}}_{\mathrm{i}}}^{\left({\mathrm{j}}_1\right)}\hfill & \hfill {\mathrm{W}}_{\mathrm{i}{\mathrm{n}}_{\mathrm{i}}}^{\left({\mathrm{j}}_2\right)}\hfill & \hfill \bullet \bullet \bullet \hfill & \hfill {\mathrm{W}}_{\mathrm{i}{\mathrm{n}}_{\mathrm{i}}}^{\left(\mathrm{j}{\mathrm{n}}_{\mathrm{j}}\right)}\hfill \end{array}\right] $$

The entries of sub-matrixes in W _ij are the ratio scales derived from paired comparisons performed on the elements within the clusters themselves according to their influence on each element in another cluster (outer dependence) or elements in their own cluster (inner dependence). Judgments are elicited from which ratio scales are derived. The resulting unweighted supermatrix is then transformed into a matrix where each column sums to unity to generate a stochastic supermatrix. The derived weights are used to weight the elements of the corresponding column blocks (cluster) of the supermatrix, resulting in a weighted supermatrix which is also stochastic. The final ranking is derived from the limiting supermatrix, obtained by raising the stochastic supermatrix to large powers to read off final priorities, in which all matrix columns are identical. Each gives the relative priorities of the elements from which the priorities of the elements in each cluster are normalized to one (the powers of the supermatrix do not converge unless it is stochastic, ensuring that its largest eigenvalue is one). Using the example of the EXPAND component under the BENEFIT cluster in Fig. 9.10, the resulting limiting supermatrix is

Limiting super matrix

		Alternatives	Goal	EXPAND
		Encourage Outflows	Assign Levy	Reg Safety Nets	EXPAND	CD	FIN INC	FINTM	ID
Alternatives	Encourage Outflows	0.18477	0.18477	0.18477	0.18477	0.18477	0.18477	0.18477	0.1848
	Assign Levy	0.23605	0.23605	0.23605	0.23605	0.23605	0.23605	0.23605	0.2361
	Reg Safety Nets	0.07918	0.07918	0.07918	0.07918	0.07918	0.07918	0.07918	0.0792
Goal	EXPAND	0	0	0	0	0	0	0	0
EXPAND	CD	0.0745	0.0745	0.0745	0.0745	0.0745	0.0745	0.0745	0.0745
	FIN INC	0.15758	0.15758	0.15758	0.15758	0.15758	0.15758	0.15758	0.1576
	FINTM	0.13292	0.13292	0.13292	0.13292	0.13292	0.13292	0.13292	0.1329
	ID	0.135	0.135	0.135	0.135	0.135	0.135	0.135	0.135