O papel do capital humano no processo de mudança estrutural

Pinto, Michele Aparecida Nepomuceno; Tebaldi, Edinaldo; Cunha, Marina Silva da

Michele Aparecida Nepomuceno Pinto

data collection

data analysis

writing the text

Universidade Federal de Mato Grosso do Sul, Câmpus de Chapadão do Sul, MS, Brasil. michele.nepomuceno@ufms.br

https://orcid.org/0000-0002-6446-4846

Edinaldo Tebaldi

work supervision

writing the text

reviewing the text

Departamento de Economia, Bryant University, Smithfield, RI, Estados Unidos da America. etebaldi@bryant.edu

https://orcid.org/0000-0003-3670-3245

Marina Silva da Cunha

text writing

text review

Programa de Pós-Graduação em Economia, Universidade Estadual de Maringá, Maringá, PR, Brasil. mscunha@uem.br

https://orcid.org/0000-0001-9122-3944

About authors

Michele Aparecida Nepomuceno Pinto - michele.nepomuceno@ufms.br

Universidade Federal de Mato Grosso do Sul, Câmpus de Chapadão do Sul, MS, Brasil.

ORCID: https://orcid.org/0000-0002-6446-4846

Edinaldo Tebaldi - etebaldi@bryant.edu

Departamento de Economia, Bryant University, Smithfield, RI, Estados Unidos da America

ORCID: https://orcid.org/0000-0003-3670-3245

Marina Silva da Cunha - mscunha@uem.br

Programa de Pós-Graduação em Economia, Universidade Estadual de Maringá, Maringá, PR, Brasil.

ORCID: https://orcid.org/0000-0001-9122-3944

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)
L.Employment share	0.792^***	0.837^***	1.076^***	0.990^***	0.674^***	0.871^***	1.025^***	0.798^***	0.842^***	0.919^***
	[11.38]	[10.03]	[25.59]	[13.52]	[9.09]	[10.74]	[11.30]	[17.76]	[15.16]	[28.77]
Human capital index	-0.486	-10.46^**	3.616^**	-0.488^**	1.518^*	2.852^**	-1.107	2.559^**	2.101	0.662
	[-0.22]	[-2.28]	[2.06]	[-2.22]	[1.96]	[2.00]	[-1.62]	[2.15]	[1.37]	[0.55]
Ln physical capital	-0.409	1.158	-2.658^***	0.301^**	-0.529	-1.105	0.328	0.229	0.0386	0.750
	[-0.37]	[0.61]	[-2.72]	[2.36]	[-1.08]	[-1.33]	[1.24]	[0.52]	[0.05]	[0.85]
Ln Population density	0.725	0.403	0.408	0.0353	-0.442^*	-0.234	0.296	-0.0398	-0.191	-0.549^**
	[1.28]	[0.51]	[1.29]	[0.58]	[-1.81]	[-0.72]	[1.52]	[-0.43]	[-1.37]	[-2.48]
Ln Exportation	-1.255	-0.542	0.503	-0.0853	-0.272	1.854^**	-0.446^*	0.182	0.0448	1.038
	[-1.52]	[-0.48]	[1.07]	[-0.87]	[-0.51]	[2.31]	[-1.87]	[0.75]	[0.17]	[1.32]
Time Dummies	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Observations	326	326	336	336	336	336	325	305	235	315
# Instruments	47	49	44	47	46	50	37	38	37	49
p-values for
AR(1)	0	0	0	0	0	0	0	0	0	0
AR(2)	0.583	0.545	0.843	0.299	0.804	0.460	0.836	0.128	0.872	0.953
Sargan Overid	0.152	0.568	0.375	0.127	0.390	0.395	0.378	0.162	0.402	0.367

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)
L.Employment share	1.024^***	0.903^***	0.925^***	0.868^***	0.580^***	1.091^***	1.039^***	0.904^***	0.986^***	1.032^***
	[12.32]	[24.32]	[16.86]	[16.01]	[6.52]	[15.62]	[12.89]	[17.65]	[15.50]	[9.39]
L2.Employment share					0.243^***					-0.147
					[2.75]					[-1.63]
Human capital index	-5.222	-0.151	-0.466	0.178^**	-1.590^*	3.257^***	1.321^**	1.200	2.880^*	-2.455^***
	[-1.64]	[-0.68]	[-0.30]	[2.56]	[-1.89]	[2.97]	[2.15]	[1.20]	[1.81]	[-2.60]
Ln physical capital	2.217	-0.0997	-1.828^**	-0.109^***	0.855^*	-1.750^***	-0.752^**	0.242	-0.267	0.276
	[1.63]	[-0.67]	[-2.11]	[-2.89]	[1.90]	[-3.00]	[-2.15]	[0.41]	[-0.35]	[0.43]
Ln Population density	2.459^***	0.0238	0.224	-0.0145^**	0.0320	-0.794^**	-0.403^*	0.0727^*	-0.237	-0.0746
	[3.32]	[0.38]	[0.73]	[-2.17]	[0.45]	[-2.57]	[-1.72]	[1.87]	[-1.56]	[-0.55]
Ln Exportation	-1.905	0.204	2.295^**	0.0749^***	-0.131	1.644^**	1.075^***	0.182	-0.134	0.0847
	[-1.22]	[1.62]	[2.46]	[3.44]	[-0.53]	[2.41]	[3.34]	[0.68]	[-0.24]	[0.15]
Time Dummies	Yes	Yes	Yes	yes	yes	yes	Yes	Yes	yes	yes
Observations	335	335	335	307	298	335	335	307	251	289
# Instruments	55	63	54	50	49	42	43	40	43	53
p-values for
AR(1)	0	0	0	0	0	0	0	0	0	0
AR(2)	0.853	0.469	0.954	0.449	0.847	0.181	0.304	0.545	0.297	0.336
Sargan Overid	0.530	0	0.932	0	0.181	0.284	0.912	0.332	0.504	0.142

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)
L.Employment share	0.428^**	0.0225	0.538^**	0.0788	-0.0167	0.188	0.172	-0.0539	0.254	0.157
	[2.52]	[0.10]	[2.40]	[0.47]	[-0.11]	[0.99]	[1.26]	[-0.29]	[1.39]	[0.79]
L2.Employment share	0.0647		0.00352	0.0718	0.00259		-0.163^*	0.116
	[0.38]		[0.03]	[0.68]	[0.03]		[-1.88]	[1.08]
Human capital index	-0.137	0.872^***	-0.199^*	0.0575	0.300^*	0.180^*	-0.0970	0.295^**	0.116	-0.158
	[-1.40]	[2.80]	[-1.67]	[0.46]	[1.81]	[1.83]	[-0.88]	[2.07]	[1.40]	[-1.30]
Ln physical capital	0.0949^*	-0.417^***	0.0359	-0.0392	-0.204^**	-0.102^**	-0.00442	-0.0797^*	-0.0244	0.104
	[1.79]	[-2.94]	[0.71]	[-0.58]	[-2.43]	[-2.47]	[-0.08]	[-1.76]	[-0.48]	[1.59]
Ln Population density	0.0340	-0.0330	0.0210	-0.0242	0.00648	-0.0157	0.0372	-0.0141	-0.0119	0.00829
	[1.13]	[-1.31]	[0.87]	[-1.53]	[0.37]	[-1.27]	[1.26]	[-1.24]	[-1.54]	[1.00]
Ln Exportation	-0.0933^**	-0.00483	-0.0407	0.104	0.0431	0.0440	-0.0204	0.0433	0.00931	-0.0881^**
	[-2.56]	[-0.04]	[-1.22]	[1.35]	[0.52]	[0.91]	[-0.55]	[1.52]	[0.42]	[-2.52]
Time Dummies	Yes	Yes	yes	Yes	yes	Yes	yes	Yes	yes	yes
Observations	290	308	289	288	288	308	290	281	235	289
# Instruments	43	40	44	51	50	39	45	42	36	35
p-values for
AR(1)	0	0	0	0	0	0	0	0	0	0
AR(2)	0.273	0.937	0.913	0.664	0.233	0.420	0.955	0.150	0.144	0.143
Sargan Overid	0.346	0.549	0.915	0.240	0.711	0.779	0.113	0.365	0.113	0.680

Notes: Each model refers to the speed of the added value share of a sector: (1) Agriculture; (2) Mining; (3) Manufacturing; (4) Utilities; (5) Construction; (6) Trade; (7) Transportation services; (8) Financial services; (9) Government and (10) Community and personal services.
t statistics in brackets, ^* p< 0.10, ^** p< 0.05, ^*** p< 0.01
All regressions are estimated using a one-step system GMM estimator and include time dummies. Also, GMM-style instruments are replaced with their principal instruments components using the methods developed by Mehrhoff (2009MEHRHOFF, J. A solution to the problem of too many instruments in dynamic panel data GMM. Discussion Paper, Series 1: Economic Studies 31/2009, Deutsche Bundesbank, 2009.); Kapetanios and Marcellino (2010KAPETANIOS, G.; MARCELLINO, M. Factor-GMM estimation with large sets of possibly weak instruments. Computational Statistics & Data Analysis, n. 54(11), pp. 2655-2675. 2010.) and Bai and Ng (2010BAI, J.; NG, S. Instrumental variable estimation in a data rich environment. Econometric Theory, n. 26(6), pp. 1577-1606, 2010.) and are implemented in Stata using the command xtabond2.
Specifications: Models (1) and (7): 2 lags for the share variable, 1 lag for all the explanatory, time variable considered exogenous and with 1 lag. Models (2) and (6): 1 lag for the share variable, 1 lag for the explanatory variables (human capital, physical capital and exportation), time variable and populational density considered exogenous e with 1 lag. Model (3): 2 lags for the share variable, 1 lag for the explanatory variables (human capital and populational density), time variable, physical capital and exportation considered exogenous with 1 lag. Models (4) and (5): 2 lags for the share variable, 1 lag for the explanatory variables (human capital, physical capital and exportation), time variable and populational density considered exogenous with 1 lag. Model (8): 2 lags for the share variable, 1 lag for the explanatory variables (human capital and physical capital), time variable, populational density and exportation considered exogenous e with 1 lag. Models (9) and (10): 1 lag for the share variable, 1 lag for the explanatory variables (human capital and physical capital), time variable, populational density and exportation considered exogenous e with 1 lag.
Source: Author’s elaboration.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)
L.Employment share	0.346	-0.378	0.359^**	0.0550	-0.270	0.0187	0.0517	-0.0499	0.269^**	0.0557
	[1.63]	[0.02]	[2.18]	[0.32]	[-1.55]	[0.12]	[0.47]	[-0.31]	[2.36]	[0.29]
L2.Employment share				0.0877			-0.147^*	0.0832	0.0681	-0.00507
				[0.81]			[-1.67]	[0.76]	[0.73]	[-0.04]
Human capital index	-0.0480	0.013	0.00595	-0.0292	0.0673^*	0.0539^**	-0.0238	0.0529^**	0.0174	-0.0499
	[-1.43]	[0.70]	[0.23]	[-1.09]	[1.74]	[2.06]	[-1.25]	[2.00]	[1.37]	[-1.56]
Ln physical capital	0.0389	-0.122	-0.111^**	-0.0270	-0.209^***	-0.179^***	-0.0301	-0.133^***	-0.0407^*	0.0524
	[0.81]	[0.09]	[-2.46]	[-0.56]	[-3.05]	[-3.25]	[-0.78]	[-3.27]	[-1.74]	[1.12]
Ln Population density	0.0166	-0.005	-0.0142	0.0734^**	-0.00724	-0.00667	-0.00498	-0.00301	0.00315	0.0723^**
	[0.70]	[0.89]	[-1.29]	[2.40]	[-0.47]	[-1.12]	[-0.53]	[-0.23]	[0.52]	[2.51]
Ln Exportation	-0.0527^*	0.004	0.101^**	0.0176	0.189^**	0.0835^***	0.0582	0.105^*	0.0111	-0.109^***
	[0.08]	[0.95]	[0.02]	[0.77]	[0.03]	[0.00]	[0.13]	[0.09]	[0.39]	[0.00]
Time Dummies	yes	yes	yes	yes	Yes	yes	yes	yes	yes	Yes
Observations	291	291	291	282	289	281	273	273	213	266
# Instruments	38	38	46	57	42	34	52	55	46	45
p-values for
AR(1)	0	0	0	0	0	0	0	0	0	0
AR(2)	0.153	0.153	0.975	0.740	0.772	0.701	0.697	0.495	0.547	0.400
Sargan Overid	0.135	0.135	0.318	0.228	0.265	0.148	0.344	0.127	0.119	0.417

Notes: Each model refers to the speed of the employment share of a sector: (1) Agriculture; (2) Mining; (3) Manufacturing; (4) Utilities; (5) Construction; (6) Trade; (7) Transportation services; (8) Financial services; (9) Government and (10) Community and personal services.
t statistics in brackets, ^* p< 0.10, ^** p< 0.05, ^*** p< 0.01
All regressions are estimated using a one-step system GMM estimator and include time dummies. Also, GMM-style instruments are replaced with their principal instruments components using the methods developed by Mehrhoff (2009MEHRHOFF, J. A solution to the problem of too many instruments in dynamic panel data GMM. Discussion Paper, Series 1: Economic Studies 31/2009, Deutsche Bundesbank, 2009.); Kapetanios and Marcellino (2010KAPETANIOS, G.; MARCELLINO, M. Factor-GMM estimation with large sets of possibly weak instruments. Computational Statistics & Data Analysis, n. 54(11), pp. 2655-2675. 2010.) and Bai and Ng (2010BAI, J.; NG, S. Instrumental variable estimation in a data rich environment. Econometric Theory, n. 26(6), pp. 1577-1606, 2010.) and are implemented in Stata using the command xtabond2.
Specifications: Model (1): 1 lag for the share variable, 1 lag for the explanatory variables (human capital, physical capital and populational density), time variable and exportation considered exogenous and with 1 lag. Model (2): 1 lag for the share variable, 1 lag for all the explanatory variables, time variable considered exogenous and with 1 lag. Model (3): 1 lag for the share variable, 1 lag for the explanatory variables (human capital, physical capital and exportation), time variable and populational density considered exogenous and with 1 lag. Model (4): 2 lags for the share variable, 1 lag for all the explanatory variables, time variable considered exogenous and with 1 lag. Model (5): 1 lag for the share variable, 1 lag for the explanatory variables (human capital and exportation), time variable, physical capital and populational density considered exogenous and with 1 lag. Model (6): 1 lag for the share variable, 1 lag for the explanatory variables (human capital and physical capital), time variable, exportation and populational density considered exogenous and with 1 lag. Models (7) and (8): 2 lags for the share variable, 1 lag for all the explanatory variables, time variable considered exogenous and with 1 lag. Model (9): 2 lags for the share variable, 1 lag for the explanatory variables (human capital and physical capital), time variable, exportation and populational density considered exogenous and with 1 lag. Model (10): 2 lags for the share variable, 1 lag for the explanatory variables (human capital and populational density), time variable, exportation and physical capital considered exogenous and with 1 lag.
Source: Author’s elaboration.

[1] About authors

Michele Aparecida Nepomuceno Pinto - michele.nepomuceno@ufms.br

Universidade Federal de Mato Grosso do Sul, Câmpus de Chapadão do Sul, MS, Brasil.

ORCID: https://orcid.org/0000-0002-6446-4846

Edinaldo Tebaldi - etebaldi@bryant.edu

Departamento de Economia, Bryant University, Smithfield, RI, Estados Unidos da America

ORCID: https://orcid.org/0000-0003-3670-3245

Marina Silva da Cunha - mscunha@uem.br

Programa de Pós-Graduação em Economia, Universidade Estadual de Maringá, Maringá, PR, Brasil.

ORCID: https://orcid.org/0000-0001-9122-3944

Brasil

Brasil

O papel do capital humano no processo de mudança estrutural

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