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Dual concepts of Yin-Yang, Animal Spirits, and network trajectory phases of innovation in capturing the selected
environments of macroeconomics both in developing and developed countries
Vignes Gopal Krishna Department of Economics
Faculty of Economics and AdministrationUniversity of Malaya50603 Kuala Lumpur
Email address : [email protected]
Outline
a) Introduction b) Theoretical Guide c) Methodology and Data d) Results and Findings e) Conclusion f) References
Introduction
• Transitional thoughts from mono to multi dimension.
• Contradictory power between traditional and assimilated realities.
• Current world speaks out more on the partial equilibrium than full equilibrium
• Mono mode versus combined mode (Pragmatic Approach)
Theoretical Guide
• Duo states versus Multi-states of Animal Spirits
* Flexibility versus Rigidity of thoughts * “Partial win” versus “Full win” * Integration versus Dis-integration * Real complexity & Evolutionary economics * Passive versus active mode
Methodology and Data
• Specification of selected variables a) Adolescents fertility rate(AFR), b) infant mortality rate (IMR), c) Total fertility rate (TFR), d) Annual growth rate (AGR), e) Real gross domestic product (RG), f) Rural population (Rural population), g) Cereal yield (CY), h) CO emissions per capita (COPP),
• i) inflation rate (IR), • J) Fossil Fuel energy consumption (%)(FFEC),• K) Labour force participation rate (LFPR)• l) Female labour force participation rate
(FLFPR) • m)Male labour force participation rate (MLFPR
• 126 countries (2010 & 2011) – World Development Indicators
• Classification of countries based on the four stages of development – Global Competitiveness Report 2012-2013
• Selection of variables – Global Risk Report
Countries FDCAlbania 0Armenia 0Bangladesh 1Benin 1
Bosnia and Herzegovina 0Bulgaria 0Burkina Faso 1Burundi 1Cambodia 1Cameroon 1Cape Verde 0Chad 1China 0Colombia 1Costa Rica 0Cote d'Ivoire 0
Dominican Republic 0Ecuador 0El Salvador 0Ethiopia 1Gambia, The 1Georgia 0Ghana 1
Kenya 1Kyrgyz Republic 1Lesotho 1Liberia 1Macedonia, FYR 0Madagascar 1Malawi 1Malaysia 1Mali 1Mauritania 0Mauritius 1Moldova 0Montenegro 0Morocco 1Mozambique 0Namibia 1Nepal 1Nicaragua 1Nigeria 1Pakistan 0Panama 0Paraguay 0Peru 0Romania 1Rwanda 1Senegal 0Serbia 1
Sierra Leone 0South Africa 0Suriname 0Swaziland 1Tajikistan 1Tanzania 0Thailand 0Timor-Leste 1Uganda 0Ukraine 1Vietnam 1Yemen, Rep. 1Zambia 1Zimbabwe 1
Argentina 1Australia 0Austria 0Bahrain 1Barbados 1Belgium 0Brazil 1Canada 0Chile 1Croatia 1Cyprus 0Czech Republic 0Denmark 0Estonia 1Finland 0France 0Germany 0Greece 0Hong Kong SAR, China 0Hungary 1Iceland 0Ireland 0Israel 0Italy 0Japan 0Kazakhstan 1Korea, Rep. 0Latvia 1Lebanon 1
Lithuania 1Luxembourg 0Malaysia 1Malta 0Mexico 1Netherlands 0New Zealand 0Norway 0Oman 1Poland 1Portugal 0
Puerto Rico 0Russian Federation 1Seychelles 1Singapore 0Slovak Republic 0Slovenia 0Spain 0Sweden 0Switzerland 0Trinidad and Tobago 0Turkey 1United Arab Emirates 1United Kingdom 0United States 0Uruguay 0
Results and Findings
Table 2: Levene’s Two-tailed t-test Scores by the types of classifications (Stages of Factor and Efficiency driven)- FDC = 1, FDC = 0 (Stage of Efficiency driven)
FDC N Mean Standard Deviation
Standard error T-test
AFR 0 33 61.77 36.62 6.38 1 39 72.33 42.70 6.84 -1.13
IMR 0 33 29.58 26.28 4.581 39 47.34 25.07 4.01 -
2.92***
TFR 0 32 2.35 1.72 0.301 39 3.51 1.95 0.31 -
2.66***
AGR 0 30 4.50 3.00 0.551 39 4.74 4.02 0.64 -0.28
RG 0 30 86186000000000 449021000000000
81979600000000
1 39 30326000000000 116103000000000
18591400000000 0.67
RP 0 33 33605442.59 116922000.00 20353465.61Note: ***, ** and * refer to statistical significance at 1%, 5% and 10% respectively; Source: Computed using SPSS 19.0 for Windows package.
1 39 37656166.78 136044000.00 21784487.34 -0.14
CY 0 33 2737.19 1321.65 230.07
1 39 2320.45 1704.37 272.92 1.17
COPP 0 33 2.45 2.43 0.42
1 39 1.12 1.76 0.28 2.62**
IR 0 33 7.01 4.15 0.72
1 39 8.47 6.63 1.06 -1.14
FFEC 0 33 52.46 33.65 5.86
1 39 33.16 34.72 5.56 2.39**
LFPR 0 32 63.73 11.26 1.99
1 39 68.52 11.29 1.81 -1.78*
LFPRF 0 32 51.69 16.02 2.83
1 39 59.89 16.93 2.71 -2.09**
LFPRM 0 32 76.10 10.27 1.81
1 39 61.77 36.62 6.38 -0.60
Note: ***, ** and * refer to statistical significance at 1%, 5% and 10% respectively; Source: Computed using SPSS 19.0 for Windows package.
Table 3: Levene’s Two-tailed t-test Scores by the types of classifications (Stages of transitional and innovation driven)- TSC = 1, TSC = 0 (Stage of Innovation driven)
TSC N Mean Standard Deviation
Standard error T-test
AFR 0 35 13.65 12.73 2.15 1 19 28.48 20.50 4.70 -2.87
***
IMR 0 35 4.02 3.93 0.66 1 20 9.40 5.33 1.19 -3.94***
AGR 0 34 1.55 2.47 0.42 1 18 5.05 2.35 0.55 -5.02***
RG 0 34 47971000000000 202229000000000 34682000000000 1 18 11273000000000 25724300000000 6063270000000 1.04
RP 0 35 5378813.38 10160850.19 1717497.15 1 20 7950872.27 11429124.94 2555630.03 -0.84
CY 0 35 4617.45 2359.76 398.87 1 20 3839.64 3948.57 882.93 0.80
COPP 0 35 9.26 6.17 1.04 1 20 8.21 5.91 1.32 0.62*
Note: ***, ** and * refer to statistical significance at 1%, 5% and 10% respectively; Source: Computed using SPSS 19.0 for Windows package.
IR 0 35 3.15 1.57 0.27
1 20 4.49 2.89 0.65 -1.92
FFEC 0 35 73.78 23.25 3.93
1 19 81.68 23.65 5.43 -1.18
LFPR 0 35 60.87 5.90 1.00
1 19 61.09 8.43 1.94 -0.10
LFPRF 0 35 53.38 7.41 1.25
1 19 46.98 11.90 2.73 2.13**
LFPRM 0 35 68.75 5.26 0.89
1 19 73.29 8.89 2.04 -2.04*
Note: ***, ** and * refer to statistical significance at 1%, 5% and 10% respectively; Source: Computed using SPSS 19.0 for Windows package.
CONTINUOUS FLOW OF NETWORK TRAJECTORY PHASES OF INNOVATION (CFNTPI)
Origination and development
Creative Accumulation (Mark 2)
Information power
Participating Institutions
Number of actors
Behavioural economics
Timing/Duration
Diffusion
Creative Destruction (Mark 1)
Retention
Adaptation
Adoption
Information power
Participating Institutions
Number of actors
Sources : Author, Schumpeter (1934), Akerlof and Shiller (2009), Dopfer et al(2004), and Dopfer (2012).
Conclusion• Stages of pre-diffusion and post-diffusion that have captured the power of
asymmetric information, participating institutions and the number of actors are crucial in maintaining the increasing pace of sustainability from the modes of partial and full capacity through the timing and behavioural aspects of economics
• Contradictory power and the power of relation will determine the pace of patching up and catching up.
• Combined version of the analytical approach is an important tool to capture the overall complexity of economics.
• The discussions on the issues of interest will control the contradictory power in the dual mixture on the patterns of positive and evolutionary.
References• Akerlof, G.A. & Shiller, R.J., Animal Spirits: How Human Psychology drives the
economy, and why it matters for global capitalism. United States: Princeton University Press, 2009
• Dopfer, K., Foster, J. & Potts, J. (2004). Micro-meso-macro. Journal of Evolutionary Economics, 14, 263-279.
• Dopfer, K. (2012).The origins of meso economics Schumpeter's legacy and beyond”. Journal of Evolutionary Economics, 22, 133-160.
• Feenstra, RC, & Hanson, GH. (1996). Globalization, outsourcing, and wage inequality. The American Economic Review, 86(2), 240-245.
• Lall, S. (1992). Technological Capabilities and Industrialization. World Development, 20(2),165-186.
• Nelson, R. (2008). Economic Development from the Perspective of Evolutionary Economic Theory”, Oxford Development Studies, 36(1), 9-21.
• Nelson, R.R & Winter, S.G(1982). An Evolutionary Theory of Economic Change. Harvard University Press
• Schumpeter, J.A (1934) The theory of Economic Development, Cambridge, M.A: Harvard University Press.
• Sturgeon, T. J. (2002). Modular production networks: a new American model of industrial organization. Industrial and corporate change, 11(3), 451-496.
• Suzuki, A., Jarvis, L. S., & Sexton, R. J. (2011). Partial Vertical Integration, Risk Shifting, and Product Rejection in the High-Value Export Supply Chain: The Ghana Pineapple Sector. World Development, 39(9), 1611-1623.
