Regional Labor Market Differences in Brazil and Search Frictions: Some Structural Estimates

Oliveira, Paulo Felipe de; Carvalho, José Raimundo

doi:10.5935/0034-7140.20160005

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Articles • Rev. Bras. Econ. 70 (1) • Jan-Mar 2016 • https://doi.org/10.5935/0034-7140.20160005 copy

Regional Labor Market Differences in Brazil and Search Frictions: Some Structural Estimates¹ * This paper has been circulating under the title "Estimation of an Equilibrium Search Model with Productivity Dispersion: a Regional Analysis for Brazil". We thank participants from the 33rd Meeting of the Brazilian Econometric Society, 2011, and the 6th CAEN-EPGE Meeting of Public Policy and Economic Growth, 2013, for valuable comments and suggestions. The usual disclaimer applies.

Authorship SCIMAGO INSTITUTIONS RANKINGS

We estimate an equilibrium job search model for six metropolitan areas located in different regions of Brazil. Two mechanisms of wage determination are considered: wage posting by monopsony firms and Nash bilateral bargaining. In order to estimate the model, we use the non-parametric method developed by Bontemps, Robin, & van den Berg [2000Bontemps, C., Robin, J.-M., & van den Berg, G. J. (2000). Equilibrium search with continuous productivity dispersion: Theory and nonparametric estimation. International Economic Review, 41(2), 305-358. Retrieved from http://www.jstor.org/stable/2648885
http://www.jstor.org/stable/2648885... . Equilibrium search with continuous productivity dispersion: Theory and nonparametric estimation. International Economic Review, 41(2), 305-358]. There is significant heterogeneity among the estimated structural parameters for these regions. We succeed in rationalizing some well-known regional differences in wages, unemployment rates and productivity prevalent in Brazilian labor markets, thus offering new interpretations. Metropolitan regions in the Northeast have much lower λ₀ (arrival rate of wage offers for unemployed workers) andλ₁ (arrival rate of wage offers for employed workers) vis a vis areas in the South or Southeast regions. This is a new, and much more precise, result worth considering on the regional inequality debate in Brazil. Regional inequality in wages, besides being an outcome of its regional human capital distribution, can be rationalized as inequality of search frictions brought by differences inλ₁. We found a key (indirect) role for search frictions when analysing productivity differences as well. Since search frictions impact simultaneously on monopsony power as well as on productivity, in order to understand better regional productivity differences we must deepen our analysis on how these structural parameters are differentiated by regions. Labor market frictions add important insights into the regional debate, something not captured by more traditional econometric reduced form approaches.

Keywords:
Search Theory; Regional Differences; Structural Econometrics

Region	2004	2005	2006	2007	2008	2009	2011
Brazil	1,007.68	1,043.71	1,127.58	1,170.46	1,197.75	1,231.56	1,357.84
North	780.41	803.42	865.49	934.61	940.49	985.38	1,067.22
Northeast	566.77	590.78	664.76	682.43	726.74	760.33	848.52
Southeast	1,245.6	1,289.6	1,385.48	1,419.14	1,427.75	1,457.84	1,603.35
South	1,119.33	1,151.6	1,222.12	1,286.72	1,332.61	1,380.37	1,468.88
Center-West	1,220.96	1,277.92	1,352.02	1,451.23	1,504.83	1,504.43	1,707.97

	2004	2005	2006	2007	2008	2009	2011
Brazil	6.89	7.01	7.19	7.31	7.47	7.60	8.73
North	6.28	6.47	6.65	6.82	6.97	7.11	7.13
Northeast	5.52	5.67	5.86	6.03	6.24	6.38	6.53
Southeast	7.60	7.71	7.89	8.01	8.11	8.23	8.39
South	7.39	7.50	7.64	7.68	7.90	8.00	8.04
Center-West	7.13	7.29	7.46	7.55	7.76	7.90	8.17

Metropolitan Area	2004	2005	2006	2007	2008	2009	2010	2011
Salvador - Bahia	16.03	15.38	13.57	13.43	11.36	11.37	10.79	9.23
Belo Horizonte - Minas Gerais	10.63	8.98	8.57	7.35	6.43	6.39	5.39	4.68
Recife - Pernambuco	12.68	13.41	13.96	11.78	8.97	9.84	8.46	6.20
Rio de Janeiro - Rio de Janeiro	9.03	7.81	7.83	7.09	6.75	5.96	5.53	5.30
Porto Alegre - Rio Grande do Sul	8.64	7.50	8.12	6.97	5.83	5.42	4.46	4.44
Sao Paulo - Sao Paulo	12.63	10.44	10.60	9.88	8.53	8.91	6.73	6.09
Brazil	11.48	9.83	9.98	9.29	7.89	8.08	6.74	5.98

	Extractive Industry	Transformation Industry
Brazil	145.64	26.97
North	448.44	37.21
Northeast	57.78	19.69
Southeast	156.84	30.81
South	18.15	21.87
Center-West	86.38	24.59

	Total	Recife	Salvador	Belo Horizonte	Rio de Janeiro	São Paulo	Porto Alegre
Number of workers	46,367	3,272	5,380	9,333	8,377	12,564	7,441
Employed (%)	87	83	79	90	88	86	91
Unemployed (%)	13	17	21	10	12	14	9
Men (%)	53.81	53.55	53.75	54.61	53.67	53.38	53.81
Education
Uneducated (%)	1	1	1	1	1	1	0
Literacy (%)	23	21	20	25	24	19	26
Elementary education (%)	20	18	18	22	21	18	24
High School (%)	48	55	54	45	44	50	44
University (%)	9	5	7	7	10	12	6
Age	32.93	32.79	32.87	32.28	34.48	32.35	33.09
(std. dev.)	(10.12)	(9.66)	(9.59)	(10.14)	(2.10)	(10.23)	(10.23)
Unemployed
Left censored (%)	0.78	0.52	1.45	0.00	0.28	1.75	0.94
Right censored (%)	74.21	72.03	86.27	58.18	86.91	72.74	64.59
Incomplete duration; mean	8.00	6.26	25.09	4.66	11.77	7.59	7.09
(std. dev.)	(8.79)	(6.97)	(10.91)	(4.5)	(11.52)	(6.99)	(8.22)
Employed
Left Censored (%)	0.00	0.00	0.00	0.00	0.00	0.00	0.00
Right censored (%)	87.93	74.81	89.33	83.95	94.42	90.13	86.59
Incomplete duration; mean	55.95	57.54	59.9	50.43	62.85	54.77	53.95
(std. dev.)	(67.28)	(65.74)	(71.74)	(63.95)	(72.89)	(65.39)	(64.78)
Transitions to Unemployment	3,364	582	297	898	270	680	637
Transitions to another job	1,504	1,998	159	443	143	386	275
Earnings Distribution G(w)
Minimum	300	300	300	300	300	300	300
P ₁₀	465	415	415	465	465	480	465
Q ₁	500	465	465	470	500	600	550
Q ₂	700	505	550	600	700	800	700
Q ₃	1,000	700	800	915	1,000	1,200	1,000
P ₉₀	1,680	1,000	1,400	1,500	1,700	2,000	1,600
P₉₀/P₁₀	3.61	2.41	3.37	3.23	4.17	3.44	3.66
Mean	926.48	658.51	786.52	867.61	927.54	1,087.44	936.29
(std. dev.)	(751.37)	(406.27)	(647.12)	(699.6)	(765.88)	(879.76)	(681.47)

	λ ₁	λ ₁	δ	k _i
	unemployed arrival rate offer	employed arrival rate offer	separation rate	level of friction
Recife	0.1497	0.0089	0.0196	0.4515
	[0.1361; 0.1618]	[0.007;0.0106]	[0.0186;0.0205]	[0.3467; 0.5571]
Salvador	0.0898	0.0133	0.0153	0.8670
	[0.0849; 0.0942]	[0.0111;0.0159]	[0.0146;0.016]	[0.7283; 1.0824]
Belo Horizonte	0.2277	0.0307	0.0164	1.8683
	[0.2191; 0.2397]	[0.0275;0.0343]	[0.0158;0.0171]	[1.6828; 2.1442]
Rio de Janeiro	0.0915	0.0209	0.0115	1.8182
	[0.086; 0.096]	[0.0183;0.024]	[0.0111;0.012]	[1.536; 2.1304]
São Paulo	0.1284	0.0306	0.0141	2.1697
	[0.1234; 0.1329]	[0.0271;0.0337]	[0.0136;0.0145]	[1.8963; 2.4559]
Porto Alegre	0.1615	0.0311	0.0141	2.2078
	[0.1523; 0.1713]	[0.0271;0.0356]	[0.0135;0.0146]	[1.8941; 2.6102]

	Country	λ ₁	λ ₁	δ	k _i
Our results	Brazil - PME	0.129	0.022	0.015	1.467
Van den Berg & Ridder (1998)van den Berg, G. J., & Ridder, G. (1998). An empirical equilibrium search model of the labor market. Econometrica, 66(5), 1183-1222. doi: 10.2307/2999634 https://doi.org/10.2307/2999634...	Netherlands	0.033	0.047	0.005	9.400
Bontemps et al. (2000)Bontemps, C., Robin, J.-M., & van den Berg, G. J. (2000). Equilibrium search with continuous productivity dispersion: Theory and nonparametric estimation. International Economic Review, 41(2), 305-358. Retrieved from http://www.jstor.org/stable/2648885 http://www.jstor.org/stable/2648885...	France	0.063	0.008	0.006	1.333
Bunzel et al. (2001)Bunzel, H., Christensen, B. J., Jensen, P., Kiefer, N. M., Korsholm, L., Muus, L., ... Rosholm, M. (2001, January). Specification and estimation of equilibrium search models. Review of Economic Dynamics, 4(1), 90-126. doi: 10.1006/redy.1999.0092 https://doi.org/10.1006/redy.1999.0092...	Denmark	0.028	0.010	0.015	0.235
Sulis (2008)Sulis, G. (2008). Wage dispersion and equilibrium search models: Some evidence from Italy. LABOUR, 22(4), 593-627. doi: 10.1111/j.1467-9914.2008.00426.x https://doi.org/10.1111/j.1467-9914.2008...	Italy	0.043	0.006	0.013	0.462
Carvalho (2012)Carvalho, J. R. (2012). Desigualdades salariais entre Nordeste e Sudeste: Explicações "estruturais" através de um modelo de busca por emprego com dados retrospectivos. Revista Econômica do Nordeste, 43(4), 119-140.	Brazil - PPV	0.137	0.005	0.005	1.000

	Minimum	P ₁₀	Q ₁	Q ₂	Q ₃	P ₉₀	P₉₀/P₁₀	Mean
Recife	849.94	851.63	851.63	1,149.04	2,812.47	6,298.05	7.40	5,903.52
Salvador	730.30	748.52	748.52	1,109.06	2,308.59	8,190.00	10.94	5,350.19
Belo Horizonte	642.59	661.69	676.81	941.36	1,993.42	6,159.22	9.31	4,264.10
Rio de Janeiro	694.92	712.04	747.33	1,174.25	2,327.15	8,801.40	12.36	4,424.64
São Paulo	768.79	781.69	883.96	1,268.75	3,139.06	8,156.23	10.43	4,147.55
Porto Alegre	694.14	710.52	788.53	1,068.96	2,070.59	6,424.08	9.04	4,142.61

	Minimum	P ₁₀	Q ₁	Q ₂	Q ₃	P ₉₀	P₉₀/P₁₀	Mean
Recife	300.00	515.61	593.03	656.90	1,018.52	1,591.67	3.09	956.46
Salvador	300.00	507.43	581.71	717.42	1,166.16	2,317.39	4.57	1,174.23
Belo Horizonte	300.00	568.27	574.95	763.98	1,289.42	2,373.75	4.18	1,265.98
Rio de Janeiro	300.00	576.47	625.32	926.82	1,434.59	2,775.68	4.81	1,372.77
São Paulo	300.00	605.46	767.02	1,056.84	1,757.31	3,164.49	5.23	1,617.22
Porto Alegre	300.00	575.30	686.68	904.65	1,398.28	2,504.62	4.35	1,352.76

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[TFN1] Note: R$ of 09/2013 - INPC.

[TFN2] Source: IBGE/PNAD.