Non-Timber Forest Products and the Cosmetic Industry: An Econometric Assessment of Contributions to Income in the Brazilian Amazon
Abstract
:1. Introduction
2. Background
2.1. NTFPs and CCPs for Improving Rural Livelihoods
2.2. Estimating Income from NTFPs
3. Study Region
4. Methodology
- (1)
- Smallholders engaged in NTFP extraction have higher annual incomes than households that do not extract NTFPs.
- (2)
- Smallholders who are members of CCP cooperatives will have higher annual incomes than households that are not members.
4.1. Sampling and Data Sources
4.2. Analytical Approach
4.3. Analytical Process
5. Results
5.1. Household Characteristics and Economic Activities
5.2. Results from OLS Regression, Multicollinearity, and Spatial Statistics
6. Discussion
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Municipality | Community Population | Sample Size | Percent |
---|---|---|---|
Tomé-Açu | 159 | 36 | 22 |
Anajás | 243 | 125 | 51 |
Igarape-Miri | 198 | 48 | 24 |
Breves | 90 | 73 | 81 |
Total | 690 | 282 | 41 |
Variables | Mean (Std. Dev.) | Median | Min | Max |
---|---|---|---|---|
Age of the household head | 43 (14) | 42 | 18 | 81 |
Years of education | 5 (4) | 5 | 0 | 19 |
Years on property | 32 (17) | 33 | 1 | 80 |
Family size | 5 (3) | 5 | 1 | 17 |
Distance nearest river (km) | 6 (14) | 1 | 0 | 46 |
Distance nearest road (km) | 86 (55) | 113 | 318 | 141 |
Distance-Belém (km) | 156 (50) | 170 | 71 | 247 |
# Households | % | |||
Engage in NTFP | 237 | 84 | ||
Membership in Cooperative | 133 | 47 |
Income Source | Mean | SD | Min | Max | Count (%) | Mean | SD |
---|---|---|---|---|---|---|---|
NTFP (oilseeds, açaí) | 3392 | 5107 | 0 | 27,344 | 237 (84) | 4036 | 5098 |
Agriculture | 1365 | 4903 | 0 | 28,625 | 38 (13) | 10,125 | 4894 |
black pepper | 1249 | 4582 | 0 | 27,666 | 25 (9) | 14,090 | 4574 |
passionfruit | 17 | 186 | 0 | 2656 | 4 (2) | 1207 | 186 |
cupuaçu | 51 | 287 | 0 | 2598 | 15 (5) | 953 | 286 |
cocoa | 45 | 260 | 0 | 3228 | 14 (5) | 897 | 260 |
Extractive Activities | 870 | 1733 | 0 | 14,140 | 129 (46) | 1902 | 1730 |
palmheart | 473 | 1305 | 0 | 10,329 | 65 (23) | 2051 | 1303 |
timber | 330 | 933 | 0 | 7082 | 61 (22) | 1527 | 931 |
shrimp | 67 | 292 | 0 | 3836 | 36 (13) | 527 | 291 |
Off-farm Labor | 745 | 1993 | 0 | 11,066 | 67 (24) | 3137 | 1990 |
Sawmill | 109 | 635 | 0 | 7097 | 18 (6) | 1705 | 634 |
Government Transfers | 1591 | 1742 | 0 | 7633 | 223 (79) | 2012 | 1768 |
defeso | 136 | 382 | 0 | 2671 | 36 (13) | 1065 | 381 |
bolsa verde | 62 | 142 | 0 | 738 | 47 (17) | 375 | 141 |
bolsa família | 547 | 543 | 0 | 2287 | 179 (63) | 862 | 543 |
retirement | 846 | 1772 | 0 | 7033 | 59 (21) | 4043 | 1768 |
Total | 8076 | 6276 | 1800 | 29,849 | 282 (100) |
Model | Rho/Lambda | R2 |
---|---|---|
SAR W matrix | 0.15 (0.06) * | 0.3534 |
SEM W matrix | 0.17 (0.05) ** | 0.3641 |
SDM W matrix | 0.14 (0.50) | 0.3740 |
SDM W2 matrix | 0.01 (0.90) | 0.3991 |
SDM W4 matrix | −0.23 (0.01) *** | 0.3943 |
SAR_g W matrix | 0.08 (0.16) | 0.3349 |
SEM_g W matrix | 0.21 (0.03) ** | 0.3621 |
SDM_g W matrix | 0.07 (0.23) | 0.0535 |
SDM_g W2 matrix | 0.50 (0.00) *** | 0.1380 |
SDM_g W4 matrix | 0.50 (0.00) *** | 0.0332 |
Variables | VIF | OLS | SEM_W | SDM_W4 | SEM_g W | SDM_g W2 | SDM_g W4 |
---|---|---|---|---|---|---|---|
b(SE) | b(SE) | b(SE) | b(SE) | b(SE) | b(SE) | ||
% NTFP Income | 1.98 | 33(13) *** | 32(12) *** | 38(12) *** | 33(11) *** | 32(12) *** | 31(11) *** |
Membership | 1.11 | 1321(647) *** | 1389(665) *** | 1916(826) *** | 1244(711) *** | 1090(794) * | 1633(783) *** |
Family size | 1.21 | 52(129) | 78(126) | −7(136) | 101(120) | 79(116) | 68(115) |
Years-Property | 1.5 | 50(21) *** | 48(21) *** | 39(21) * | 39(20) *** | 21(21) | 28(20) * |
Years-Education | 1.43 | 192(89) *** | 201(86) *** | 162(87) * | 197(90) *** | 182(86) *** | 170(85) *** |
Age | 1.81 | 18(30) | 15(29) | 45(30) | 27(29) | 55(27) *** | 40(26) * |
Açai (y/n) | 1.46 | 916(856) | 839(839) | 693(835) | 741(865) | 600(832) | 728(794) |
Distance-Rivers | 2.15 | 111(31) *** | 105(34) *** | −1565(705) *** | 80(0.04) *** | −1374(0.89) ** | −369(0.61) |
Distance-Roads | 3.25 | −9(12) | −9(13) | −1171(598) *** | −12(0.01) | −639(0.84) | −215(0.51) |
Distance-Belém | 3.04 | −34(10) *** | −34(11) *** | −267(343) | −34(0.01) *** | −163(0.42) | −68(0.30) |
Constant | 7340(2431) *** | 7470(2441) *** | 2383(5297) | 7268(2547) *** | 4260(3690) | 280(5118) | |
R2 | 0.353 | 0.364 | 0.390 | 0.362 | 0.138 | 0.033 |
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Antunes, A.; Simmons, C.S.; Veiga, J.P. Non-Timber Forest Products and the Cosmetic Industry: An Econometric Assessment of Contributions to Income in the Brazilian Amazon. Land 2021, 10, 588. https://doi.org/10.3390/land10060588
Antunes A, Simmons CS, Veiga JP. Non-Timber Forest Products and the Cosmetic Industry: An Econometric Assessment of Contributions to Income in the Brazilian Amazon. Land. 2021; 10(6):588. https://doi.org/10.3390/land10060588
Chicago/Turabian StyleAntunes, Aghane, Cynthia S. Simmons, and Joao Paulo Veiga. 2021. "Non-Timber Forest Products and the Cosmetic Industry: An Econometric Assessment of Contributions to Income in the Brazilian Amazon" Land 10, no. 6: 588. https://doi.org/10.3390/land10060588
APA StyleAntunes, A., Simmons, C. S., & Veiga, J. P. (2021). Non-Timber Forest Products and the Cosmetic Industry: An Econometric Assessment of Contributions to Income in the Brazilian Amazon. Land, 10(6), 588. https://doi.org/10.3390/land10060588