Brazilian Import Demand of Dairy Products with Emphasis in the Mercosul Context

Arancibia, Rodrigo García; Guiguet, Edith Depetris

doi:10.1590/0103-6351/4946

Rodrigo García Arancibia

Instituto de Economía Aplicada Litoral, Universidad Nacional del Litoral, Santa Fe, Santa Fe, Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina. Red Nacional de Investigadores en Economía (RedINE, Argentina).

Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina

Red Nacional de Investigadores en Economía, Argentina

Rodrigo García Arancibia - rgarcia@fce.unl.edu.ar Instituto de Economía Aplicada Litoral, Universidad Nacional del Litoral, Santa Fe, Santa Fe, Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina. Red Nacional de Investigadores en Economía (RedINE, Argentina).

https://orcid.org/0000-0003-3999-2097

Edith Depetris Guiguet

Instituto de Economía Aplicada Litoral, Universidad Nacional del Litoral, Santa Fe, Santa Fe, Argentina.

Edith Depetris Guiguet - eguiguet@fce.unl.edu.ar Instituto de Economía Aplicada Litoral, Universidad Nacional del Litoral, Santa Fe, Santa Fe, Argentina.

https://orcid.org/0000-0001-7716-0879

About the authors

Rodrigo García Arancibia - rgarcia@fce.unl.edu.ar

Instituto de Economía Aplicada Litoral, Universidad Nacional del Litoral, Santa Fe, Santa Fe, Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina. Red Nacional de Investigadores en Economía (RedINE, Argentina).

Edith Depetris Guiguet - eguiguet@fce.unl.edu.ar

Instituto de Economía Aplicada Litoral, Universidad Nacional del Litoral, Santa Fe, Santa Fe, Argentina.

Importer (m)	Product (i)	Exporter (h)	w_hⁱ		p_hⁱ
Importer (m)	Product (i)	Exporter (h)	Mean	SD	Mean	SD
Brazil	WMP	Argentina	0.412	0.163	3.074	1.083
	WMP	Uruguay	0.157	0.122	2.852	1.068
	SMP	Argentina	0.087	0.052	3.082	1.009
	SMP	Uruguay	0.08	0.082	2.99	1.15
	Cheese	Argentina	0.122	0.086	3.976	1.187
	Cheese	Uruguay	0.072	0.044	4.031	1.469
	Cheese	Others	0.071	0.043	6.423	2.327

Variables	w_a^WMP	w_u^WMP	w_a^SMP	w_u^SMP	w_a^Ch	w_u^Ch
T ₂	-0.087^* ***** p <0.01%;	0.052^ p <0.05%;	-0.004	0.024	-0.013	-0.002
T ₂	(0.029)	(0.026)	(0.012)	(0.019)	(0.014)	(0.011)
T ₃	-0.087^* ***** p <0.01%;	0.019	0.0001	0.015	0.008	0.015
T ₃	(0.030)	(0.027)	(0.008)	(0.019)	(0.014)	(0.010)
T ₄	-0.051^* * p <0.1%. P-values in brackets.	0.003	0.002	0.008	0.009	0.022^ p <0.05%;
T ₄	(0.028)	(0.022)	(0.017)	(0.018)	(0.014)	(0.009)
*trend*	-0.022^* ***** p <0.01%;	-0.010^* ***** p <0.01%;	0.004^ p <0.05%;	0.003	0.014^* ***** p <0.01%;	0.004^* ***** p <0.01%;
*trend*	(0.004)	(0.003)	(0.002)	(0.002)	(0.002)	(0.001)
*In(E/P)*	-0.060^* ***** p <0.01%;	0.100^* ***** p <0.01%;	-0.026^* ***** p <0.01%;	0.040^* ***** p <0.01%;	0.008	-0.015^ p <0.05%;
*In(E/P)*	(0.019)	(0.017)	(0.009)	(0.013)	(0.009)	(0.006)
In p_a^WMP	-0.084	-0.135	-0.016	0.119	-0.002	0.086^ p <0.05%;
In p_a^WMP	(0.172)	(0.087)	(0.07)	(0.092)	(0.067)	(0.041)
In p_u^WMP	-0.135	0.101	0.045	-0.005	0.052	0.014
In p_u^WMP	(0.087)	(0.08)	(0.039)	(0.051)	(0.036)	(0.024)
In p_a^SMP	-0.016	0.045	0.018	-0.042	0.025	-0.041^* * p <0.1%. P-values in brackets.
In p_a^SMP	(0.07)	(0.039)	(0.058)	(0.048)	(0.030)	(0.022)
In p_u^SMP	0.119	-0.005	-0.042	-0.001	-0.056	-0.047^* * p <0.1%. P-values in brackets.
In p_u^SMP	(0.092)	(0.051)	(0.048)	(0.083)	(0.038)	(0.028)
In p_a^Ch	-0.002	0.052	0.025	-0.056	-0.069^* * p <0.1%. P-values in brackets.	0.058^* ***** p <0.01%;
In p_a^Ch	(0.067)	(0.036)	(0.030)	(0.038)	(0.035)	(0.019)
In p_u^Ch	0.086^ p <0.05%;	0.014	-0.041^* * p <0.1%. P-values in brackets.	-0.047^* * p <0.1%. P-values in brackets.	0.058^* ***** p <0.01%;	-0.075^* ***** p <0.01%;
In p_u^Ch	(0.041)	(0.024)	(0.022)	(0.028)	(0.019)	(0.019)
In p_o^Ch	0.034	-0.071^* ***** p <0.01%;	0.012	0.033	-0.008	0.005
In p_o^Ch	(0.034)	(0.021)	(0.019)	(0.023)	(0.016)	(0.012)
*α_j*	1.503^* ***** p <0.01%;	-1.290^* ***** p <0.01%;	0.459^* ***** p <0.01%;	-0.561^* ***** p <0.01%;	-0.093	0.268^* ***** p <0.01%;
*α_j*	(0.281)	(0.252)	(0.128)	(0.186)	(0.134)	(0.092)
R ²	0.397	0.254	0.1	0.174	0.452	0.214
B-G Test^† † H0: no serial correlation.	0.21	0.018	0.07	0.472	17.531	0.553
B-G Test^† † H0: no serial correlation.	[0.65]	[0.89]	[0.79]	[0.49]	[0.00]	[0.46]
B-P Test^‡ ‡ H0: no heteroscedasticity.	31.761	16.11	10.275	15.719	23.796	12.382
B-P Test^‡ ‡ H0: no heteroscedasticity.	[0.00]	[0.19]	[0.59]	[0.2]	[0.02]	[0.42]
RESET^⁑ ⁑ H0: no functional misspecification error.	3.778	0.272	2.416	0.785	10.432	2.058
RESET^⁑ ⁑ H0: no functional misspecification error.	[0.03]	[0.76]	[0.09]	[0.46]	[0.00]	[0.13]

	Log-Likelihood (𝓁)	-2[𝓁(θ ^R) - 𝓁(θ ^NoR)]	df
No -AR(1)	1499.1
AR(1)	1529.9	61.6	(1)
Homogeneity	1525.7	8.4158	(6)
Symmetry	1520.5	18.692	(15)
Homogeneity & Symmetry	1518.9	21.941	(21)

Product	Exporter	Expenditure Elasticity	Own-Price Elasticity
Product	Exporter	Expenditure Elasticity	Marshallian	Hicksian
WMP	Argentina	0.800^* ***** p <0.01%;	-1.131^ p <0.05%;	-0.802^* * p <0.1%
WMP	Argentina	(0.041)	(0.407)	(0.408)
WMP	Uruguay	1.600^* ***** p <0.01%;	0.074	0.325
WMP	Uruguay	(0.093)	(0.481)	(0.479)
SMP	Argentina	0.761^* ***** p <0.01%;	-1.580^ p <0.05%;	-1.514^ p <0.05%;
SMP	Argentina	(0.090)	(0.676)	(0.674)
SMP	Uruguay	1.517^* ***** p <0.01%;	-1.321	-1.199
SMP	Uruguay	(0.142)	(1.007)	(1.011)
Cheese	Argentina	1.182^* ***** p <0.01%;	-1.434^* ***** p <0.01%;	-1.290^* ***** p <0.01%;
Cheese	Argentina	(0.070)	(0.274)	(0.275)
Cheese	Uruguay	0.847^* ***** p <0.01%;	-2.203^* ***** p <0.01%;	-2.143^* ***** p <0.01%;
Cheese	Uruguay	(0.078)	(0.238)	(0.237)
Cheese	Others	0.387^* ***** p <0.01%;	-0.988^* ***** p <0.01%;	-0.961^* ***** p <0.01%;
Cheese	Others	(0.063)	(0.185)	(0.185)

Quantity	Price
Quantity	WMP -Ar	WMP-Uy	SMP-Ar	SMP-Uy	Ch-Ar	Ch-Uy	Ch-O
	Marshallian
WMP-Ar	-	-0.494^ p <0.05%;	0.077	0.370^* * p <0.1%	-0.059	0.273^* ***** p <0.01%;	0.164^ p <0.05%;
WMP-Ar	-	(0.202)	(0.175)	(0.218)	(0.135)	(0.094)	(0.082)
WMP-Uy	-1.628^* ***** p <0.01%;	-	0.112	-0.17	0.513^ p <0.05%;	0.03	-0.530^* ***** p <0.01%;
WMP-Uy	(0.528)	-	(0.238)	(0.323)	(0.220)	(0.152)	(0.119)
SMP-Ar	0.378	0.331	-	-0.015	0.411	-0.463^* * p <0.1%	0.178
SMP-Ar	(0.818)	(0.427)	-	(0.556)	(0.327)	(0.243)	(0.226)
SMP-Uy	1.602	-0.319	-0.082	-	-0.985^ p <0.05%;	-0.676^ p <0.05%;	0.265
SMP-Uy	(1.128)	(0.638)	(0.603)	-	(0.450)	(0.329)	(0.292)
Ch-Ar	-0.356	0.726^ p <0.05%;	0.259	-0.623^ p <0.05%;	-	0.466^* ***** p <0.01%;	-0.220^* * p <0.1%
Ch-Ar	(0.458)	(0.285)	(0.237)	(0.298)	-	(0.146)	(0.127)
Ch-Uy	1.553^* ***** p <0.01%;	0.183	-0.573^* * p <0.1%	-0.704^* * p <0.1%	0.834^* ***** p <0.01%;	-	0.065
Ch-Uy	(0.54)	(0.33)	(0.296)	(0.365)	(0.245)	-	(0.151)
Ch-O	1.124^ p <0.05%;	-0.983^* ***** p <0.01%;	0.252	0.391	-0.281	0.098	-
Ch-O	(0.471)	(0.276)	(0.276)	(0.329)	(0.216)	(0.150)	-
	Hicksian
WMP-Ar	-	-0.369^* * p <0.1%	0.147	0.434^* * p <0.1%	0.039	0.330^* ***** p <0.01%;	0.221^ p <0.05%;
WMP-Ar	-	(0.202)	(0.174)	(0.219)	(0.136)	(0.094)	(0.081)
WMP-Uy	-0.969^* * p <0.1%	-	0.251	-0.042	0.708^* ***** p <0.01%;	0.144	-0.417^* ***** p <0.01%;
WMP-Uy	(0.530)	-	(0.237)	(0.326)	(0.221)	(0.150)	(0.124)
SMP-Ar	0.691	0.45	-	0.046	0.504	-0.409^* * p <0.1%	0.231
SMP-Ar	(0.820)	(0.424)	-	(0.554)	(0.329)	(0.242)	(0.223)
SMP-Uy	2.227^* * p <0.1%	-0.082	0.05	-	-0.801^* * p <0.1%	-0.567^* * p <0.1%	0.372
SMP-Uy	(1.122)	(0.636)	(0.602)	-	(0.453)	(0.327)	(0.289)
Ch-Ar	0.131	0.911^* ***** p <0.01%;	0.362	-0.528^* * p <0.1%	-	0.551^* ***** p <0.01%;	-0.136
Ch-Ar	(0.456)	(0.284)	(0.236)	(0.299)	-	(0.145)	(0.125)
Ch-Uy	1.901^* ***** p <0.01%;	0.316	-0.499^* * p <0.1%	-0.636^* * p <0.1%	0.937^* ***** p <0.01%;	-	0.125
Ch-Uy	(0.541)	(0.329)	(0.295)	(0.367)	(0.246)	-	(0.15)
Ch-O	1.284^ p <0.05%;	-0.922^* ***** p <0.01%;	0.286	0.422	-0.234	0.126	-
Ch-O	(0.471)	(0.275)	(0.276)	(0.328)	(0.216)	(0.151)	-

\begin{matrix} \ln E_{m} (p, U_{m}) = α_{0} + \sum_{i} \sum_{h} α_{h}^{i} \ln p_{h}^{i} + \frac{1}{2} \sum_{i} \sum_{j} \sum_{h} \sum_{k} λ_{hk}^{ij} \ln p_{h}^{i} \ln p_{k}^{i} + \\ + β_{0} U_{m} \prod_{i} \prod_{h} {(p_{h}^{i})}^{β_{h}^{i}} \end{matrix}

w_{h, t}^{i} = α_{h}^{i} + \sum_{j ɛ N} \sum_{k ɛ K_{j}} γ_{hk}^{ij} \ln p_{k, t}^{j} + β_{h}^{i} \ln (\frac{E_{t}}{{\tilde{P}}_{t}}) + ε_{h, t}^{i}

w_{h, t}^{i} = α_{h, 0}^{i} + \sum_{j ɛ N} \sum_{k ɛ K_{j}} γ_{hk}^{ij} \ln p_{k, t}^{j} + β_{h}^{i} \ln (\frac{E_{t}}{{\tilde{P}}_{t}}) + ς_{h}^{i} t_{y} + \sum_{τ} θ_{h, τ}^{i} T_{τ} + ε_{h, t}^{i}

\begin{matrix} \sum_{i} \sum_{h} α_{h, 0}^{i} = 1, \sum_{i} \sum_{h} ς_{h}^{i} = 0, \sum_{i} \sum_{h} θ_{h, τ}^{i} = 0, \sum_{i} \sum_{h} γ_{hk}^{ij} = 0, \sum_{i} \sum_{h} β_{h}^{i} = 0, \\ \sum_{i} \sum_{h} ς_{h}^{i} = 0, \sum_{i} \sum_{h} θ_{h, τ}^{i} = 0, \sum_{i} \sum_{h} γ_{hk}^{ij} = 0, \sum_{i} \sum_{h} β_{h}^{i} = 0 \end{matrix}

\ln p_{h, t}^{i} = \{\begin{matrix} \ln (E_{h, t}^{i} / q_{h, t}^{i}) & if & E_{h, t}^{i}, q_{h, t}^{i} = 0 \\ x_{t}^{T} {\hat{μ}}_{h}^{i} & otherwise \end{matrix}

\ln {\tilde{P}}_{t} = \{\begin{matrix} \sum_{j} \sum_{k} w_{k, 1}^{j} \ln p_{k, 1}^{j} & if & t = 1 \\ \sum_{j} \sum_{k} w_{k, t - 1}^{j} \ln p_{k, t}^{j} & if & t > 1 \end{matrix}

[1] Rodrigo García Arancibia - rgarcia@fce.unl.edu.ar

Instituto de Economía Aplicada Litoral, Universidad Nacional del Litoral, Santa Fe, Santa Fe, Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina. Red Nacional de Investigadores en Economía (RedINE, Argentina).

Brasil

Brasil

Brazilian Import Demand of Dairy Products with Emphasis in the Mercosul Context

Demanda brasileira de importações de laticínios com ênfase no contexo do Mercosul

Abstract