Complexidade Econômica e Desenvolvimento Humano: uma análise a partir do DEA

Ferraz, Diogo; Moralles, Herick Fernando; Campoli, Jessica Suárez; Oliveira, Fabíola Cristina Ribeiro de; Rebelatto, Daisy Aparecida do Nascimento

doi:10.1590/0104-530X3925-18

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Original Article • Gest. Prod. 25 (4) • Oct-Dec 2018 • https://doi.org/10.1590/0104-530X3925-18 copiar

Complexidade Econômica e Desenvolvimento Humano: uma análise a partir do DEA

Autoria SCIMAGO INSTITUTIONS RANKINGS

Resumo

O crescimento econômico não é o único fator que explica o desenvolvimento humano. Nesse aspecto, índices sobre a qualidade de vida têm sido recorrentes na literatura. Entretanto, esses índices não analisam como a complexidade econômica é convertida em desenvolvimento humano. O objetivo deste artigo é mensurar a eficiência de países latino-americanos e asiáticos na conversão de complexidade econômica em desenvolvimento humano, entre 2010 e 2014. O método utilizado foi o Data Envelopment Analysis (DEA), por meio do Modelo de Retornos Variáveis à Escala (BCC) e Análise de Janela. Os resultados demonstraram que, em 2014, todos os países asiáticos foram eficientes, exceto China e Filipinas. Nesse ano, Cuba foi o país que mais serviu de benchmark para os países ineficientes. A análise em janela demonstrou que apenas Japão, Coréia do Sul e Singapura se mantiveram eficientes ao longo do tempo. Esse resultado confirma a hipótese inicial deste artigo: economias mais complexas são mais eficientes na geração de desenvolvimento humano.

Palavras-chave:
Análise por Envoltória de Dados (DEA); Análise em janela; Complexidade econômica; Economic Complexity Index (ECI); Desenvolvimento humano

Model	Input Oriented	Output Oriented
CCR	$\begin{array}{l} MAX \sum_{i = 1}^{m} u_{i} {.y}_{i0} \\ S u b j e c t t o : \\ \sum_{j = 1}^{n} v_{j} {.x}_{j0} = 1 \\ \sum_{i = 1}^{m} u_{i} {.y}_{ik} - \sum_{j = 1}^{n} v_{j} {.x}_{jk} \leq 0, for k = 1,2,..., h \end{array}$	$\begin{array}{l} MIN \sum_{j = 1}^{n} v_{j} {.x}_{j0} \\ S u b j e c t t o : \\ \sum_{i = 1}^{m} u_{i} {.y}_{i0} = 1 \\ \sum_{i = 1}^{m} u_{i} {.y}_{ik} - \sum_{j = 1}^{n} v_{j} {.x}_{jk} \leq 0, for k = 1,2,..., h \end{array}$
BCC	$\begin{array}{l} MAX \sum_{i = 1}^{m} u_{i} {.y}_{i0} + w \\ S u b j e c t t o : \\ \sum_{j = 1}^{n} v_{j} {.x}_{j0} = 1 \\ \sum_{i = 1}^{m} u_{i} {.y}_{ik} - \sum_{j = 1}^{n} v_{j} {.x}_{jk} + w \leq 0, for k = 1,2,..., h \\ w w i t h o u t s i g n a l r e s t r i c t i o n \end{array}$	$\begin{array}{l} M I N \sum_{j = 1}^{n} v_{j} . x_{j 0} - w \\ S u b j e c t t o : \\ \sum_{i = 1}^{m} u_{i} . y_{i 0} = 1 \\ \sum_{i = 1}^{m} u_{i} . y_{i k} - \sum_{j = 1}^{n} v_{j} . x_{j k} + w \leq 0, for k = 1,2,..., h \\ w w i t h o u t s i g n a l r e s t r i c t i o n \end{array}$

Variable	Source	Type	Previous Studies
Economic Complexity Index (ECI)	Economic Complexity Observatory	Input	Proposal of this article
Life Expectancy at Birth (LEB)	World Bank	Output	Despotis (2005a) Despotis, D. (2005a). A reassessment of the human development index via data envelopment analysis. The Journal of the Operational Research Society, 56(8), 969-980. http://dx.doi.org/10.1057/palgrave.jors.2601927. http://dx.doi.org/10.1057/palgrave.jors...
Mean Years of Schooling (MYS)	World Bank	Output	Despotis (2005b) Despotis, D. (2005b). Measuring human development via data envelopment analysis: the case of Asia and the Pacific. Omega, 33(5), 385-390. http://dx.doi.org/10.1016/j.omega.2004.07.002. http://dx.doi.org/10.1016/j.omega.2004....
Unemployment Rate (UR)	United Nations Develop. Prog. UNDP	Output	Morais & Camanho (2011) Morais, P., & Camanho, A. S. (2011). Evaluation of performance of European cities with the aim to promote quality of life improvements. Omega, 39(4), 398-409. http://dx.doi.org/10.1016/j.omega.2010.09.003. http://dx.doi.org/10.1016/j.omega.2010....
Sanitation Rate (SR)	World Bank	Output	Mariano & Rebelatto (2014) Mariano, E. B., & Rebelatto, D. A. D. (2014). Transformation of wealth produced into quality of life: analysis of the social efficiency of nation-states with the DEA’s triple index approach. The Journal of the Operational Research Society, 65(11), 1664-1681. http://dx.doi.org/10.1057/jors.2013.132. http://dx.doi.org/10.1057/jors.2013.132...

Variables	ECI	MYS	LEB	SR	ER
ECI	1
AME	0.4661***	1
LE	0.5107***	0.6808***	1
BS	0.4191***	0.6298***	0.7612***	1
ER	0.2259***	0.1984 ^ at the level of 5%.	0.1341 ^ at the level of 5%.	-0.0173	1

Variables	(1)	(2)	(3)	(4)
Variables	MYS	LEB	SR	ER
Economic Complexity Index	0.363 ^* ***** p<0.01;	0.774 ^* ***** p<0.01;	1.693 ^ p<0.05;	0.608 ^* ***** p<0.01;
	(0.0780)	(0.179)	(0.847)	(0.227)
GDP per capita	0.0000437 ^* ***** p<0.01;	0.000151 ^* ***** p<0.01;	0.000220 ^* ***** p<0.01;	0.0000236 ^* * p<0.1.
	(-0.00000646)	(-0.0000171)	(-0.0000515)	(-0.000013)
Constant	7.767 ^* ***** p<0.01;	73.57 ^* ***** p<0.01;	82.80 ^* ***** p<0.01;	94.31 ^* ***** p<0.01;
	(0.0932)	(0.200)	(1.111)	(0.230)
Observations	130	130	130	130
Number of paisnum	26	26	26	26

Ranking	Country	CCR Model	BCC Model	Scale Efficiency	∑λk	Return of Scale
1	Bolivia	1.0000	1.0000	1.0000	1.0000	Constant
2	Chile	0.9788	1.0000	0.9788	1.1012	Decreasing
3	Cuba	1.0000	1.0000	1.0000	1.0000	Constant
4	Ecuador	1.0000	1.0000	1.0000	1.0000	Constant
5	Venezuela	1.0000	1.0000	1.0000	1.0000	Constant
6	Japan	0.7445	1.0000	0.7445	1.4402	Decreasing
7	Korea Rep.	0.7822	1.0000	0.7822	1.3690	Decreasing
8	Singapore	0.7989	1.0000	0.7989	1.3501	Decreasing
9	Thailand	0.7979	1.0000	0.7979	1.3218	Decreasing
10	Malaysia	0.8171	1.0000	0.8171	1.2750	Decreasing
11	Guatemala	0.8911	0.9925	0.8978	1.1286	Decreasing
12	Honduras	0.9277	0.9898	0.9373	1.0842	Decreasing
13	Peru	0.9644	0.9878	0.9763	1.0309	Decreasing
14	Paraguay	0.9352	0.9859	0.9486	1.0806	Decreasing
15	Nicaragua	0.9575	0.9825	0.9746	1.0355	Decreasing
16	Panama	0.8409	0.9824	0.8560	1.1922	Decreasing
17	Uruguay	0.8769	0.9796	0.8951	1.1798	Decreasing
18	Argentina	0.9146	0.9749	0.9381	1.1169	Decreasing
19	Mexico	0.7614	0.9719	0.7834	1.3132	Decreasing
20	Costa Rica	0.9002	0.9719	0.9263	1.1747	Decreasing
21	China	0.7418	0.9711	0.7638	1.3467	Decreasing
22	El Salvador	0.8309	0.9588	0.8665	1.1786	Decreasing
23	Brazil	0.8375	0.9557	0.8763	1.1665	Decreasing
24	Philippines	0.7900	0.9475	0.8337	1.2182	Decreasing
25	Colombia	0.8522	0.9281	0.9181	1.1441	Decreasing
26	Dom. Rep.	0.8647	0.9038	0.9568	1.1226	Decreasing

Inefficient country	Benchmarks
Argentina	Chile, Cuba, Republico of Korea e Singapore
Brazil	Cuba, Singapore and Thailand
China	Cuba, Singapore and Thailand
Colombia	Cuba, Singapore and Thailand
Costa Rica	Chile, Cuba and Singapore
Domin. Rep.	Chile, Cuba and Singapore
El Salv.	Bolívia, Cuba and Thailand
Guatemala	Bolívia, Cuba and Thailand
Honduras	Bolívia, Cuba and Thailand
Mexico	Cuba, Singapore and Thailand
Nicarágua	Bolívia, Cuba and Ecuador
Panama	Cuba, Singapore and Thailand
Paraguay	Bolívia, Cuba and Thailand
Peru	Bolívia, Cuba and Thailand
Philippines	Cuba, Thailand and Malasya
Uruguay	Chile, Cuba and Singapore

Ranking	Country	Windows			Average	Stand. Deviation
Ranking	Country	2010-2012	2011-2013	2012-2014	Average	Stand. Deviation
1	Singapore	100.00%	100.00%	100.00%	100.00%	0.00%
2	Korea, Rep.	100.00%	100.00%	100.00%	100.00%	0.00%
3	Japan	100.00%	100.00%	100.00%	100.00%	0.00%
4	Thailand	100.00%	100.00%	99.98%	99.99%	0.02%
5	Cuba	100.00%	99.96%	99.90%	99.95%	0.07%
6	Chile	99.39%	99.78%	99.65%	99.61%	0.43%
7	Bolivia	99.25%	99.67%	99.49%	99.47%	0.52%
8	Malaysia	99.28%	99.35%	99.44%	99.36%	0.29%
9	Ecuador	98.40%	99.08%	98.70%	98.73%	1.07%
10	Peru	98.61%	98.80%	98.58%	98.66%	0.66%
11	Guatemala	98.19%	98.53%	98.74%	98.49%	0.44%
12	Honduras	98.07%	98.62%	98.54%	98.41%	0.96%
13	Venezuela	97.98%	98.94%	98.21%	98.38%	1.52%
14	Paraguay	97.55%	98.07%	97.92%	97.84%	1.29%
15	Panama	97.31%	98.02%	98.07%	97.80%	0.64%
16	Nicaragua	96.61%	98.46%	97.55%	97.54%	2.42%
17	Uruguay	97.13%	97.63%	97.14%	97.30%	0.71%
18	China	97.30%	97.15%	97.06%	97.17%	0.16%
19	Argentina	96.87%	97.38%	96.87%	97.04%	0.80%
20	Costa Rica	97.16%	97.15%	96.64%	96.99%	0.52%
21	Mexico	96.87%	96.89%	97.02%	96.93%	0.13%
22	El Salvador	95.11%	95.51%	95.58%	95.40%	0.43%
23	Brazil	94.93%	95.44%	95.43%	95.27%	0.46%
24	Philippines	94.40%	94.58%	94.56%	94.51%	0.16%
25	Colombia	91.50%	92.17%	92.37%	92.01%	0.65%
26	Dominican Rep.	90.50%	90.57%	90.24%	90.44%	0.85%

Universidade Federal de São Carlos Departamento de Engenharia de Produção , Caixa Postal 676 , 13.565-905 São Carlos SP Brazil, Tel.: +55 16 3351 8471 - São Carlos - SP - Brazil
E-mail: gp@dep.ufscar.br

Acompanhe os números deste periódico no seu leitor de RSS

[1] Where: xjk represents the amount of input j of DMU k; yik represents the amount of output i of DMU k; xj0 represents the amount of input j of the DMU under analysis; yi0 represents the amount of output i of the DMU under analysis; vj represents the weight of input j for the DMU under analysis; ui represents the weight of output i for the DMU under analysis; m is the quantity of analyzed outputs; n is the quantity of inputs analyzed and; w represents the scale factor. Source: Mariano & Rebelatto (2014 Mariano, E. B., & Rebelatto, D. A. D. (2014). Transformation of wealth produced into quality of life: analysis of the social efficiency of nation-states with the DEA’s triple index approach. The Journal of the Operational Research Society, 65(11), 1664-1681. http://dx.doi.org/10.1057/jors.2013.132.
http://dx.doi.org/10.1057/jors.2013.132... , p. 5).