Rural and urban employment patterns in Brazil: a dynamic panel data approach

This study applies a quantitative approach in dynamic panel data to capture the determinants of employment quality in Brazil through the model of generalized minimum moments. The results show that the rural universe persists as a more precarious environment than urban areas, although the differences has decreased with time. In addition, the results of this study show that growth of labor income and the level of education of employees are important tools not only to expand the levels of job quality, but also as response to dilemmas of the surveyed labor markets.

Keywords:
labor market; occupation quality; panel data; agricultural and non-agricultural employment

Variable (Percentage of population)		Partial Indicator	Employment Quality Index
A	Over the age of 15 years	FOR = (0.1A+0.2B+0.4C+0.3D) Formality (FOR)	EQI EQI = 0.4(FOR) + 0.6{INC)
B	With weekly journey of up to 44 hours
C	Officially registered
D	Social security contributors
E	Average monthly income in main work (standardized)	INC = 0.6E + 0.4F Income (INC)
F	With minimum remuneration above 1 S.M.	INC = 0.6E + 0.4F Income (INC)

Variable	Variable Code	Source
Natural logarithm of the occupation quality index (EQI)	QE	Authors' construction
Natural logarithm of the outdated occupation quality index	QE _t-1	Endogenous variable
Natural logarithm of value added Agriculture	VA1	Brazilian Institute of Geography and Statistics
Natural logarithm of value added non-agricultural	VA2	Brazilian Institute of Geography and Statistics
Natural logarithm of the average yield of the main work of urban farm workers	IN1	Brazilian Institute of Geography and Statistics
Natural logarithm of the average yield of the main work of urban non-agricultural workers	IN2	Brazilian Institute of Geography and Statistics
Natural logarithm of the average yield of the main work of rural non-agricultural workers	IN3	Brazilian Institute of Geography and Statistics
Natural logarithm of the average yield of the main work of rural agricultural workers	IN4	Brazilian Institute of Geography and Statistics
Natural logarithm of the Gini concentration index for income from work	IG	Authors' construction
Natural logarithm of the average level of schooling of urban workers	ED1	Brazilian Institute of Geography and Statistics
Natural logarithm of the average level of schooling of rural workers	ED2	Brazilian Institute of Geography and Statistics

Variables	Ordinary least squares [A]			Fixed-effects model [B]			Generalized least squares [C]
Variables	Coef.	Stat. (t)	p-value	Coef.	Stat. (t)	p-value	Coef.	Stat. (t)	p-value
E. Q. I._t-1	0.322	5.050	0.000	0.183	2.820	0.011	0.309	16.870	0.000
Agri. value added	-0.008	-0.590	0.556	0.001	0.010	0.989	-0.074	-3.140	0.005
Non-agri. value added	0.012	1.180	0.239	0.080	0.550	0.590	0.141	5.570	0.000
Average income	0.421	8.910	0.000	0.784	4.150	0.000	0.298	8.310	0.000
Gini index	-0.186	-1.060	0.288	-0.188	-0.620	0.541	-0.559	-6.320	0.000
Schooling	1.524	4.960	0.000	-0.377	-0.540	0.597	1.407	8.730	0.000
Constant	-6.699	-8.360	0.000	-5.566	-5.510	0.000	-6.858	-23.950	0.000
Statistical Test	F (6, 182) = 254.92			F(6, 20) = 150.06			F (6, 20) = 152.61
	Prob > F = 0.000			Prob > F = 0.000			Prob > F = 0.000
	R²= 0.9046			Prob > F = 0.000			Prob > F = 0.000
	Number of comments = 189			Number of comments = 189			Number of comments = 189
				Number of groups = 21			Number of groups = 21
							Instruments = 19.4
	H0: the absence of first-order autocorrelation						0.005
	H0: the absence of second-order autocorrelation						0.870
	Hansen test						0.310

Variables	Ordinary least squares [A]			Fixed-effects model [B]			Generalized least squares [C]
Variables	Coef.	Stat. (t)	p-value	Coef.	Stat. (t)	p-value	Coef.	Stat. (t)	p-value
E. Q. I._t-1	0.436	7.020	0.000	-0.101	-1.130	0.273	0.609	4.940	0.000
Agri. value added	0.015	2.750	0.007	-0.047	-3.130	0.005	0.011	1.540	0.139
Non-agri. value added	-0.007	-1.500	0.135	0.101	1.140	0.266	-0.006	-1.070	0.296
Average income	0.205	6.420	0.000	0.797	7.900	0.000	0.145	3.160	0.005
Gini index	-0.053	-0.680	0.498	-0.167	-1.380	0.183	-0.051	-0.340	0.738
Schooling	1.649	7.740	0.000	0.445	1.630	0.118	1.191	3.370	0.003
Constant	-5.420	-8.990	0.000	-7.745	-11.000	0.000	-3.862	-3.460	0.002
Statistical tests	F (6, 182) = 576.65			F(6,20) = 714.39			F(6, 20) = 680.36
	Prob > F = 0.0000			Prob > F = 0.0000			Prob > F = 0.000
	R² = 0.9554			Prob > F = 0.0000			Prob > F = 0.000
	Number of comments = 189			Number of comments = 189			Number of comments = 189
				Number of groups = 21			Number of groups = 21
							Instruments = 19.4
	H0: the absence of first-order autocorrelation						0.006
	H0: the absence of second-order autocorrelation						0.517
	Hansen test						0.152

Variables	Ordinary least squares [A]			Fixed-effects model [B]			Generalized least squares [C]
Variables	Coef.	Stat. (t)	p-value	Coef.	Stat. (t)	p-value	Coef.	Stat. (t)	p-value
E. Q. I._t-1	0.169	2.890	0.004	0.110	2.340	0.030	0.135	3.540	0.002
Agri. value added	0.003	0.320	0.747	0.047	1.520	0.145	-0.001	-0.170	0.869
Non-agri. value added	-0.001	-0.110	0.916	-0.237	-2.750	0.012	0.000	0.020	0.981
Average income	0.501	14.260	0.000	1.003	15.110	0.000	0.525	32.970	0.000
Gini index	-0.309	-2.560	0.011	-0.142	-0.730	0.477	-0.330	-2.460	0.023
Schooling	0.507	5.420	0.000	0.011	0.050	0.959	0.508	6.330	0.000
Constant	-4.827	-12.940	0.000	-4.732	-9.410	0.000	-4.999	-18.740	0.000
Statistical tests	F( 6, 182) = 351.09			F(6,20) = 323.36			F(6, 20) = 2760.56
	Prob > F = 0.000			Prob > F = 0.000			Prob > F = 0.000
	R² = 0.9293			Prob > F = 0.000			Prob > F = 0.000
	Number of comments = 189			Number of comments = 189			Number of comments = 189
				Number of groups = 21			Number of groups = 21
							Instruments = 19.4
	H0: the absence of first-order autocorrelation						0.004
	H0: the absence of second-order autocorrelation						0.884
	Hansen test						0.123

Variables	Ordinary least squares [A]			Fixed-effects Model [B]			Generalized least squares [C]
Variables	Coef.	Stat. (t)	p-value	Coef.	Stat. (t)	p-value	Coef.	Stat. (t)	p-value
E. Q. I._t-1	0.320	3.990	0.000	0.230	3.700	0.001	0.295	3.090	0.006
Agri. value added	-0.005	-0.360	0.719	-0.004	-0.120	0.902	-0.193	-4.260	0.000
Non-agri. value added	0.011	1.140	0.256	-0.141	-1.250	0.225	0.092	3.740	0.001
Average income	0.470	7.140	0.000	1.171	15.600	0.000	0.498	9.090	0.000
Gini index	0.081	0.500	0.614	0.231	1.710	0.102	-0.902	-2.120	0.047
Schooling	0.538	4.400	0.000	-0.534	-1.990	0.061	0.301	2.150	0.044
Constant	-4.360	-7.530	0.000	-4.849	-7.080	0.000	-4.144	-6.100	0.000
Statistical tests	F( 6, 182) = 493.20			F(6,20) = 319.30			F(6, 20) = 1712.22
	Prob > F = 0.000			Prob > F = 0.000			Prob > F = 0.000
	R² = 0.9293			Prob > F = 0.000			Prob > F = 0.000
	Number of comments = 189			Number of comments = 189			Number of comments = 189
				Number of groups = 21			Number of groups = 21
							Instruments = 19.4
	H0: the absence of first-order autocorrelation						0.048
	H0: the absence of second-order autocorrelation						0.170
	Hansen test						0.245

Universidade Católica Dom Bosco Av. Tamandaré, 6000 - Jd. Seminário, 79117-900 Campo Grande- MS - Brasil, Tel./Fax: (55 67) 3312-3373/3377 - Campo Grande - MS - Brazil
E-mail: suzantoniazzo@ucdb.br

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[1] Evânio Mascarenhas Paulo: PhD. Students in Economics at Pontifical Catholic University of Rio Grande do Sul and Master in Rural Economics at Federal University of Ceará. E-mail: evanio.paulo@acad.pucrs.br.