Labour Commutation in the Agricultural Sector—An Analysis of Agricultural Workers in Chile
Abstract
:1. Introduction
2. Antecedents and Theorical Framework
2.1. Agricultural Employment: The Case of Chile
2.2. Labour Commutation
3. Materials and Methods
3.1. Data and Variables
3.2. Method: Logit Model
3.3. Method: Matching
4. Results
4.1. Logit Model for Likelihood of Agrarian Workers Commuting: Country-Level and Macro-Areas
- The average probability that an agricultural worker commutes is 15%.
- If an agrarian worker is a man, the probability is 15.07%, while the probability that a woman worker will commute is lower, at 14.79%.
- If agricultural workers have a primary education, the probability of commuting is 10%, which increases to 18.07% if they have secondary education and 29.79% if they have post-secondary education.
- If an agricultural worker is head of a household, the probability of commuting decreases to 13.27%, and increases to 17.26% if not head of a household.
- If a person has a full-time job, the probability increases to 17.13% and decreases to 5.97% with a part-time job.
- The average probability of commuting is 18.31% for persons under 46 years old and decreases to 11.83% if they are 46 years old or older.
- Finally, if they work as a formal worker, this increases the probability of commuting to 18.18% and decreases to 10.86% if they are an informal worker.
4.2. Effects of Socio-Demographic and Job Characteristics on the Probability of Commuting Matching
- While it is clearer that sex (being man or woman) does not affect commuting at the country level, there are two major differences in the macro-zones. In the Central zone, a man is 5.81% less likely to commute than a woman, while in Chilean Patagonia, the probability increases by 3.42% for men.
- The negative effect on commuting for an agricultural worker over 46 years of age is higher in Chilean Patagonia (−9.83%).
- The positive effect of post-secondary studies is higher in the Central zone (18.27%).
- The negative effect of being head of household is strongest in Chilean Patagonia (5.51%) and in the Small North (5.17%).
- A full-time job has a positive effect, and in the South zone, it has the highest impact (16.97%).
- Finally, being an agricultural worker in the formal sector has a positive impact in the South zone (11.26%). However, the effect is negative in the Great North (−4.74%).
5. Discussion and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
- de Abreu, P.; Caetano, C. Participación de los sectores agropecuario y de hidrocarburos y minería en el producto interno bruto (PIB) de los países de América del Sur entre 1960 y 2014. Rev. CEPAL 2019, 129, 29–54. [Google Scholar] [CrossRef]
- García, Z. Agricultura, Expansión del Comercio y Equidad de Género; División de Género y Población de la FAO: Rome, Italy, 2006; pp. 1–54. Available online: http://www.fao.org/3/a-a0493s.pdf (accessed on 1 September 2022).
- Alston, J.M.; Pardey, P.G. Agriculture in the Global Economy. J. Econ. Perspect. 2014, 28, 121–146. [Google Scholar] [CrossRef] [Green Version]
- Ammirato, S.; Felicetti, A.M. The Agritourism As a Means of Sustainable Development for Rural Communities: A Research from the Field. Int. J. Interdiscip. Environ. Stud. 2014, 8, 17–29. [Google Scholar] [CrossRef]
- Charlton, D.; Castillo, M. Potential Impacts of a Pandemic on the US Farm Labor Market. Appl. Econ. Perspect. Policy 2021, 43, 39–57. [Google Scholar] [CrossRef]
- Mancilla, C.; Ferrada, L.M. Labour Reconversion from the Agricultural Sector to Rural Tourism: Analysis of Rural Areas in Chile. Sustainability 2021, 13, 11152. [Google Scholar] [CrossRef]
- Mancilla, C.; Ferrada, L.; Soza-Amigo, S. Labour reconversion of the agricultural sector towards tourism. Challenges for Chilean Patagonia. Econ. Agrar. Recur. Nat. 2019, 19, 175–194. [Google Scholar] [CrossRef] [Green Version]
- Anríquez, G. Desafíos en el Mercado Laboral para el Desarrollo de la Agricultura Chilena; Ministerio de Agricultura, Oficina de Estudios y Políticas Agrarias del Ministerio de Agricultura: Santiado, Chile, 2016; Available online: https://www.odepa.gob.cl/wp-content/uploads/2016/12/EstMercLaboralAgricola.pdf (accessed on 1 September 2022).
- Anríquez, G.; Foster, W.; Melo, O.; Subercaseaux, J.P.; Valdés, A. Evidencia y Desafíos Para el Empleo Estacional en la Fruticultura de Chile; Centro de Políticas Públicas UC: Santiago, Chile, 2016; Available online: https://repositorio.uc.cl/handle/11534/45715 (accessed on 1 September 2022).
- Hertz, T.; Winters, P.; de la O Campos, A.P.; Quiñones, E.J.; Davis, B.; Zezza, A. Wage Inequality in International Perspective. Effects of Location, Sector, and Gender. ESA Working Paper 08-08. 2008, pp. 1–34. Available online: http://www.fao.org/3/a-ak230e.pdf (accessed on 1 September 2022).
- Jamett Sasonov, I.; Paredes Araya, D. Conmutación de Larga Distancia En Chile: Estimando El Premio Por Trabajar Muy Lejos de Casa. Estud. Econ. 2013, 40, 179–209. [Google Scholar] [CrossRef]
- Aroca, P.; Atienza, M. La Conmutación Regional En Chile y Su Impacto En La Región de Antofagasta. Eure 2008, 34, 97–120. [Google Scholar] [CrossRef] [Green Version]
- Pérez-Trujillo, M.; Oyarzo Aguilar, M.; Paredes Araya, D. Long-Distance Commuting and the Effect of Differentiated Salary Expectations in the Commuters’ Place of Living on the Wage Obtained in the Place of Working. Ann. Reg. Sci. 2020, 65, 459–489. [Google Scholar] [CrossRef]
- Pino Arriagada, O.; Concha Murúa, G. Análisis Comparado de Conmutación Interregional En La Nueva Región Del Biobío, Chile (Años 2010–2017). Entramado 2019, 16, 60–74. [Google Scholar] [CrossRef]
- Carriel, V.; Lufin, M.; Pérez-Trujillo, M. Do Workers Negative Self-Select When They Commute? Evidence for the Chilean Case of Long-Distance Commuting. Ann. Reg. Sci. 2022, 69, 255–279. [Google Scholar] [CrossRef] [PubMed]
- Berdegué, J.A.; Hiller, T.; Ramírez, J.M.; Satizábal, S.; Soloaga, I.; Soto, J.; Uribe, M.; Vargas, O. Delineating Functional Territories from Outer Space. Lat. Am. Econ. Rev. 2019, 28, 1–24. [Google Scholar] [CrossRef]
- Mancilla, C.; Soza-Amigo, S.; Ferrada, L.M. Una Propuesta Metodológica Para El Análisis Intertemporal de La Conmutación Laboral: El Caso de La Patagonia Chilena. Estud. Demogr. Urbanos Col. Mex. 2020, 36, 149–192. [Google Scholar] [CrossRef]
- Ferrada, L.M.; Soza-Amigo, S.; Mancilla, C. Chilean Patagonia: Characteristics of the Geographical Space Commutation and Functionality. Rev. Geogr. Venez. 2019, 60, 346–359. [Google Scholar]
- Ferrada, L.M.; Soza Amigo, S.; Mancilla, C. Explicando La Conmutación Laboral: El Caso de La Patagonia Chilena. Rev. Análisis Econ. 2019, 34, 131–151. [Google Scholar] [CrossRef]
- Santibañez, F. El cambio climático y los recursos hídricos de Chile. In Agricultura Chilena, Reflexiones y Desafíos al 2030; Apey, A., Barrera, D., Rivas, T., Eds.; Oficina de Estudios y Políticas Agrarias (ODEPA): Santiago, Chile, 2017; Available online: https://www.opia.cl/601/w3-article-91839.html?_external_redirect=articles-91839_archivo_01.pdf (accessed on 10 September 2021).
- Real Average Income of the Employed Population: National and Regional, by Branch of Economic Activity by Sex, Year. Instituto Nacional de Estadísticas—Chile (National Statistics Institute–Chile). Available online: https://stat.ine.cl/index.aspx?queryid=131 (accessed on 10 September 2022).
- Encuesta Nacional de Empleo 2018 (National Employment Survey 2018). Instituto Nacional de Estadísticas—Chile (National Statistics Institute–Chile). Available online: https://www.ine.cl/docs/default-source/ocupacion-y-desocupacion/bbdd/bases-anuales/stata/ano-2018.dta (accessed on 10 July 2022).
- Instituto Nacional de Estadísticas—Chile (National Statistics Institute–Chile). Separata Técnica Metodología de Estimación Anual Encuesta Nacional de Empleo (ENE); Instituto Nacional de Estadísticas: Santiago, Chile, 2022; pp. 1–14. Available online: https://www.ine.cl/docs/default-source/ocupacion-y-desocupacion/publicaciones-y-anuarios/separatas/tem%C3%A1ticas/separata-tecnica-metodologia-de-estimacion-anual.pdf (accessed on 2 September 2022).
- Said, J. Patagonia; Sudamericana: Buenos Aires, Argentina, 2012; pp. 1–280. [Google Scholar]
- Ministerio de Agricultura—Regiones. Ministerio de Agricultura de Chile (Ministry of Agriculture). Available online: https://www.minagri.gob.cl/regiones (accessed on 1 December 2022).
- Greene, W. Econometric Analysis, 5th ed.; Prentice Hall: Hoboken, NJ, USA, 2003; pp. 1–802. [Google Scholar]
- Wagner, J. What a Difference a Y Makes-Female and Male Nascent Entrepreneurs in Germany. Small Bus. Econ. 2007, 28, 1–21. [Google Scholar] [CrossRef] [Green Version]
- Rosenbaum, P.R.; Rubin, D.B. The Central Role of the Propensity Score in Observational Studies for Causal Effects. Biometrika 1983, 70, 41–55. [Google Scholar] [CrossRef]
- Rosenbaum, P.R.; Rubin, D.B. Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score. Matched Sampl. Causal Eff. 2012, 39, 207–216. [Google Scholar] [CrossRef]
- Arellano, A. Los Efectos de los Servicios de Recolocación en la Duración del Desempleo en España. In Metodología y Diseño de Estudios Para la Evaluación de Políticas Públicas; García, J., Ed.; Antoni Bosch: Barcelona, Spain, 2009; pp. 135–162. [Google Scholar]
- Efron, B.; Tibshirani, R. Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy. Stat. Sci. 1986, 1, 75–77. [Google Scholar] [CrossRef]
- Lacirignola, C.; Adinolfi, F.; Capitanio, F. Food security in the Mediterranean countries. New Medit. 2015, 14, 2–10. [Google Scholar]
- Chavas, J.-P.; Rivieccio, G.; Di Falco, S.; De Luca, G.; Capitanio, F. Agricultural diversification, productivity, and food security across time and space. Agric. Econ. 2022, 00, 1–18. [Google Scholar] [CrossRef]
- De Castro, P.; Adinolfi, F.; Capitanio, F.; Di Falco, S.; Di Mambro, A. The Politics of Land and Food Scarcity; Routledge—Earthscan, Taylor & Francis Group Ltd.: Oxford, UK, 2012; pp. 1–154. [Google Scholar]
- ODEPA. Chilean Agriculture Overview; Oficina de Estudios y Políticas Agrarias (Office of Agricultural Studies and Policies): Santiago, Chile, 2019; Available online: https://www.odepa.gob.cl/wp-content/uploads/2019/09/panorama2019Final.pdf (accessed on 1 December 2022).
Zone | Characteristics |
---|---|
Great North |
|
Small North |
|
Central |
|
South |
|
Patagonia |
|
Variables | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Commutant (proportion) | Male (proportion) | Age (mean) | Primary Studies (proportion) | Secondary Studies (proportion) | Post-Secondary Studies (proportion) | Household Head (proportion) | Full-Time Job | Formal Job (proportion) | Observations | ||
Total Country: Chile | Non-Commutant | 0.0000 | 0.7466 | 46.3498 | 0.5475 | 0.3744 | 0.0780 | 0.5774 | 0.7900 | 0.5447 | 18987 |
Commutant | 1.0000 | 0.7506 | 41.8722 | 0.3445 | 0.4679 | 0.1876 | 0.5008 | 0.9243 | 0.6852 | 2798 | |
Total Sample | 0.1497 | 0.7472 | 45.6796 | 0.5171 | 0.3884 | 0.0945 | 0.5659 | 0.8101 | 0.5657 | 21785 | |
*** | *** | *** | *** | *** | *** | *** | |||||
Great North Zone | Non-Commutant | 0.0000 | 0.7774 | 44.7801 | 0.4900 | 0.4354 | 0.0746 | 0.6346 | 0.6883 | 0.2574 | 1569 |
Commutant | 1.0000 | 0.7951 | 41.2593 | 0.2342 | 0.6440 | 0.1218 | 0.5512 | 0.9315 | 0.2201 | 144 | |
Total Sample | 0.1021 | 0.7792 | 44.4208 | 0.4638 | 0.4567 | 0.0795 | 0.6261 | 0.7131 | 0.2536 | 1713 | |
** | *** | * | *** | ** | |||||||
Small North Zone | Non-Commutant | 0.0000 | 0.7411 | 48.3819 | 0.5876 | 0.3397 | 0.0727 | 0.5827 | 0.7648 | 0.4400 | 2377 |
Commutant | 1.0000 | 0.8072 | 42.1646 | 0.3561 | 0.4615 | 0.1824 | 0.5013 | 0.8472 | 0.5797 | 241 | |
Total Sample | 0.1434 | 0.7506 | 47.4905 | 0.5544 | 0.3572 | 0.0884 | 0.5711 | 0.7766 | 0.4600 | 2618 | |
*** | *** | *** | *** | *** | *** | *** | |||||
Central Zone | Non-Commutant | 0.0000 | 0.7526 | 45.1455 | 0.4825 | 0.4303 | 0.0873 | 0.5550 | 0.8591 | 0.6846 | 4688 |
Commutant | 1.0000 | 0.7207 | 41.7800 | 0.3479 | 0.4715 | 0.1806 | 0.4527 | 0.9153 | 0.7087 | 1077 | |
Total Sample | 0.1840 | 0.7467 | 44.5262 | 0.4577 | 0.4379 | 0.1045 | 0.5362 | 0.8695 | 0.6891 | 5765 | |
*** | *** | *** | *** | *** | *** | *** | |||||
South Zone | Non-Commutant | 0.0000 | 0.7267 | 46.8964 | 0.5907 | 0.3402 | 0.0691 | 0.5790 | 0.7661 | 0.4946 | 6190 |
Commutant | 1.0000 | 0.7466 | 42.0625 | 0.3454 | 0.4552 | 0.1994 | 0.5297 | 0.9471 | 0.7080 | 913 | |
Total Sample | 0.1383 | 0.7295 | 46.2277 | 0.5567 | 0.3561 | 0.0872 | 0.5722 | 0.7912 | 0.5241 | 7103 | |
*** | *** | *** | *** | ** | *** | *** | |||||
Patagonia Zone | Non-Commutant | 0.0000 | 0.7804 | 46.8059 | 0.5611 | 0.3524 | 0.0865 | 0.5984 | 0.7566 | 0.5301 | 4163 |
Commutant | 1.0000 | 0.8160 | 41.5776 | 0.3502 | 0.4574 | 0.1923 | 0.5549 | 0.9258 | 0.7077 | 423 | |
Total Sample | 0.1234 | 0.7848 | 46.1609 | 0.5351 | 0.3654 | 0.0996 | 0.5930 | 0.7775 | 0.5520 | 4586 | |
** | *** | *** | *** | *** | *** | *** | *** |
Dependent Variable (Commutant = 1) | Coefficient | Marginal Effect | P > z |
---|---|---|---|
Sex (1 = male) | 0.1275 | 0.0143 | * |
(0.0709) | (0.0077) | ||
Age | −0.0088 | −0.0010 | *** |
(0.0024) | (0.0002) | ||
Secondary Studies (1 = yes) | 0.4745 | 0.0568 | *** |
(0.0674) | (0.0084) | ||
Post-Secondary Studies (1 = yes) | 1.1263 | 0.1768 | *** |
(0.0972) | (0.0195) | ||
Household Head (1 = yes) | −0.1356 | −0.0157 | ** |
(0.0661) | (0.0077) | ||
Full-time job (1 = yes) | 0.9384 | 0.0877 | *** |
(0.1165) | (0.0081) | ||
Formal Sector (1 = yes) | 0.3095 | 0.0351 | *** |
(0.0626) | (0.007) | ||
Constant | −2.7205 | ||
(0.1578) | |||
Pseudo R2 | 0.0554 | ||
LR Chi2 (7) | 404.92 | ||
Prob > Chi2 | 0.0000 | *** | |
Observations | 21,751 |
Variable | Great North | Small North | Central | South | Patagonia | |||||
---|---|---|---|---|---|---|---|---|---|---|
Dependent Variable (Commutant = 1) | Coef. | Marginal Effect | Coef. | Marginal Effect | Coef. | Marginal Effect | Coef. | Marginal Effect | Coef. | Marginal Effect |
Sex (1 = male) | 0.2004 | 0.0130 | 0.4923 | 0.0498 * | 0.0048 | 0.0007 | 0.1536 | 0.0147 | 0.2138 | 0.0189 |
(0.3201) | (0.0197) | (0.2564) | (0.0233) | (0.1209) | (0.0175) | (0.1081) | (0.0100) | (0.2012) | (0.0168) | |
Age | 0.0012 | 0.0001 | −0.0132 | −0.0015 * | −0.0061 | −0.0009 | −0.0070 | −0.0007 * | −0.0186 | −0.0017 *** |
(0.0098) | (0.0006) | (0.0074) | (0.0008) | (0.0043) | (0.0006) | (0.0038) | (0.0003) | (0.0054) | (0.0005) | |
Secondary Studies (1 = yes) | 1.0978 | 0.0798 *** | 0.5444 | 0.0641 *** | 0.2943 | 0.0433 ** | 0.6108 | 0.0647 *** | 0.3838 | 0.0372 ** |
(0.3370) | (0.0243) | (0.2000) | (0.0244) | (0.1192) | (0.0178) | (0.1041) | (0.0119) | (0.1567) | (0.0157) | |
Post-Secondary Studies (1 = yes) | 1.2243 | 0.1292 * | 1.1459 | 0.1761 ** | 0.9308 | 0.1674 *** | 1.3772 | 0.2050 *** | 0.9682 | 0.1215 *** |
(0.5067) | (0.0740) | (0.3659) | (0.0720) | (0.1704) | (0.0363) | (0.1540) | (0.0312) | (0.2068) | (0.0327) | |
Household Head (1 = yes) | −0.4872 | −0.0351 | −0.1877 | −0.0210 | −0.2746 | −0.0401 ** | 0.0076 | 0.0007 | 0.0572 | 0.0053 |
(0.3190) | (0.0233) | (0.2298) | (0.0259) | (0.1126) | (0.0165) | (0.1097) | (0.0108) | (0.1576) | (0.0145) | |
Full-time Job (1 = yes) | 1.8464 | 0.0976 *** | 0.1977 | 0.0210 | 0.5658 | 0.0716 *** | 1.3454 | 0.1005 *** | 1.0358 | 0.0779 *** |
(0.3479) | (0.0165) | (0.2757) | (0.0280) | (0.2139) | (0.0226) | (0.1744) | (0.0090) | (0.2389) | (0.0142) | |
Formal Sector (1 = yes) | −0.6447 | −0.0386 *** | 0.4582 | 0.0516 *** | 0.0029 | 0.0004 *** | 0.5137 | 0.0503 *** | 0.3295 | 0.0302 *** |
(0.3101) | (0.0170) | (0.1915) | (0.0222) | (0.1133) | (0.0164) | (0.0981) | (0.0094) | (0.1541) | (0.0138) | |
Constant | −4.1943 | −2.2268 | −1.8459 | −3.5467 | −2.7259 | |||||
(0.6875) | (0.5026) | (0.2777) | (0.2505) | (0.3454) | ||||||
Pseudo R2 | 0.0975 | 0.0591 | 0.0282 | 0.0860 | 0.0638 | |||||
LR Chi2 (7) | 37.84 | 36.92 | 64.81 | 252.01 | 85.80 | |||||
Prob > Chi2 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | |||||
Observations | 1709 | 2616 | 5754 | 7093 | 4579 |
Chile | Zone | |||||
---|---|---|---|---|---|---|
Great North | Small North | Central | South | Patagonia | ||
Agrarian Worker | 0.1500 | 0.1024 | 0.1435 | 0.1843 | 0.1388 | 0.1235 |
Agrarian Worker: Male | 0.1507 | 0.1042 | 0.1544 | 0.1780 | 0.1421 | 0.1284 |
Agrarian Worker: Female | 0.1479 | 0.0960 | 0.1108 | 0.2030 | 0.1299 | 0.1057 |
Agrarian Worker: Primary Studies | 0.1000 | 0.0517 | 0.0922 | 0.1401 | 0.0861 | 0.0808 |
Agrarian Worker: Secondary Studies | 0.1807 | 0.1444 | 0.1854 | 0.1985 | 0.1774 | 0.1546 |
Agrarian Worker: Post-Secondary Studies | 0.2979 | 0.1570 | 0.2960 | 0.3187 | 0.3174 | 0.2386 |
Agrarian Worker: Household Head | 0.1327 | 0.0900 | 0.1259 | 0.1555 | 0.1285 | 0.1155 |
Agrarian Worker: Non-Household Head | 0.1726 | 0.1234 | 0.1670 | 0.2176 | 0.1526 | 0.1351 |
Agrarian Worker: Full-time Job | 0.1713 | 0.1339 | 0.1566 | 0.1941 | 0.1663 | 0.1471 |
Agrarian Worker: Part-time Job | 0.0597 | 0.0244 | 0.0980 | 0.1195 | 0.0350 | 0.0411 |
Agrarian Worker: Age under 46 | 0.1831 | 0.1194 | 0.1851 | 0.2113 | 0.1758 | 0.1588 |
Agrarian Worker: Age 46 or more | 0.1183 | 0.0796 | 0.1089 | 0.1554 | 0.1061 | 0.0915 |
Agrarian Worker: Formal Sector | 0.1818 | 0.0889 | 0.1810 | 0.1900 | 0.1874 | 0.1583 |
Agrarian Worker: Informal Sector | 0.1086 | 0.1070 | 0.1116 | 0.1719 | 0.0851 | 0.0806 |
Chile | Zone | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Great North | Small North | Central | South | Patagonia | ||||||||
Coef. | Coef. | Coef. | Coef. | Coef. | Coef. | |||||||
Sex (1 = male) | −0.0114 | 0.0284 | −0.0014 | −0.0581 | *** | 0.0021 | 0.0342 | * | ||||
(0.0086) | (0.0204) | (0.0157) | (0.0134) | (0.0183) | (0.0175) | |||||||
Age 46 or more | −0.0888 | *** | −0.0591 | *** | −0.0933 | *** | −0.0432 | *** | −0.0949 | *** | −0.0983 | *** |
(0.0049) | (0.0160) | (0.0121) | (0.0108) | (0.0085) | (0.0111) | |||||||
Primary Studies (1 = yes) | −0.0882 | *** | −0.0316 | ** | −0.0780 | *** | −0.0692 | *** | −0.1081 | *** | −0.0943 | *** |
(0.0057) | (0.0137) | (0.0119) | (0.0127) | (0.0081) | (0.0092) | |||||||
Secondary Studies (1 = yes) | 0.0551 | *** | 0.0319 | ** | 0.0442 | *** | 0.0622 | *** | 0.0610 | *** | 0.0469 | *** |
(0.0054) | (0.0142) | (0.0121) | (0.0116) | (0.0084) | (0.0103) | |||||||
Post-Secondary Studies (1 = yes) | 0.1367 | *** | 0.0064 | ** | 0.0976 | ** | 0.1827 | *** | 0.1538 | *** | 0.0813 | *** |
(0.0121) | (0.0340) | (0.0418) | (0.0249) | (0.0220) | (0.0229) | |||||||
Household Head (1 = yes) | −0.0438 | *** | −0.0219 | ** | −0.0517 | *** | −0.0471 | *** | −0.0289 | *** | −0.0551 | *** |
(0.0050) | (0.0166) | (0.0110) | (0.0093) | (0.0093) | (0.0104) | |||||||
Full-time Job (1 = yes) | 0.1448 | *** | 0.0227 | ** | 0.0813 | *** | 0.1389 | *** | 0.1697 | *** | 0.1273 | *** |
(0.0068) | (0.0171) | (0.0148) | (0.0306) | (0.0160) | (0.0130) | |||||||
Formal Sector (1 = yes) | 0.0828 | *** | −0.0474 | *** | 0.0361 | ** | 0.0907 | *** | 0.1126 | *** | 0.0765 | *** |
(0.0057) | (0.0155) | (0.0145) | (0.0137) | (0.0089) | (0.0099) |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Mancilla, C.; Ferrada, L.M.; Soza-Amigo, S.; Rovira, A. Labour Commutation in the Agricultural Sector—An Analysis of Agricultural Workers in Chile. Agriculture 2022, 12, 2110. https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture12122110
Mancilla C, Ferrada LM, Soza-Amigo S, Rovira A. Labour Commutation in the Agricultural Sector—An Analysis of Agricultural Workers in Chile. Agriculture. 2022; 12(12):2110. https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture12122110
Chicago/Turabian StyleMancilla, Claudio, Luz María Ferrada, Sergio Soza-Amigo, and Adriano Rovira. 2022. "Labour Commutation in the Agricultural Sector—An Analysis of Agricultural Workers in Chile" Agriculture 12, no. 12: 2110. https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture12122110