## Servicios Personalizados

## Revista

## Articulo

## Indicadores

- Citado por SciELO
- Accesos

## Links relacionados

- Similares en SciELO

## Compartir

## Revista mexicana de ciencias agrícolas

##
*versión impresa* ISSN 2007-0934

### Rev. Mex. Cienc. Agríc vol.8 no.2 Texcoco feb./mar. 2017

#### https://doi.org/10.29312/remexca.v8i2.63

Investigation Notes

Mexican international migrants immersed in the agricultural sector: a probabilistic analysis

^{1}Economía-Colegio de Postgraduados. Carretera México-Texcoco, km 36.5. Montecillo, Estado de México, México. CP. 56230. (chalita@colpos.mx; gramirez@colpos.mx).

The crisis in the Mexican agricultural sector has, among its many consequences, international emigration. The objective of the present study was to determine the factors influencing the probability that international migrants are immersed in the agricultural sector through a binary logistic regression model, as well as to describe the sociodemographic and labor profile in a historical way from 2005 to 2014 and thus propose agricultural and migratory policies that help to compensate the phenomenon. For this purpose, was used as the main input the information obtained from the construction of panels of the national survey of occupation and employment (ENOE). The results show that the majority of the emigrants belong to the agricultural sector and that when arriving in the United States they tend to engage in non-agricultural activities. Contrasting socio-demographic and labor characteristics among agricultural and non-agricultural emigrants reveals the precariousness in which both populations live, accentuating themselves with greater force in the agricultural sector. The best estimated model shows that being a male, living in localities less than 2 500 inhabitants, having up to 9 years of schooling, being in informal work and receiving up to two minimum wages, are the factors that best describe an emigrant who belonged to the agricultural sector.

**Keywords: **binary logistic regression; ENOE; estimated model; socio-demographic and occupational profile

La crisis en el sector agropecuario mexicano tiene entre sus múltiples consecuencias la emigración internacional. El objetivo del presente estudio fue determinar los factores que influyen en la probabilidad en que los emigrantes internacionales estén inmersos en el sector agropecuario mediante un modelo de regresión logística binaria, además de describir el perfil sociodemográfico y laboral de manera histórica de 2005 a 2014 y así proponer políticas agrícolas y migratorias que ayuden resarcir el fenómeno. Para ello, se utilizó como insumo la información proveniente de la construcción de paneles de la encuesta nacional de ocupación y empleo (ENOE). Los resultados muestran que la mayoría de los emigrantes pertenecen al sector agropecuario y que al llegar a Estados Unidos se ocupan de actividades no agropecuarias. Al contrastar las características sociodemográficas y laborales entre emigrantes agropecuarios y no agropecuarios se revela la precariedad en que viven ambas poblaciones, acentuándose con mayor fuerza en los agropecuarios. El mejor modelo estimado muestra que el varón, vive en localidades menores a los 2 500 habitantes, tener hasta 9 años de escolaridad, encontrarse en la informalidad laboral y recibir hasta dos salarios mínimos, son los factores que mejor describen a un emigrante que perteneció al sector agropecuario.

**Palabras clave: **ENOE; modelo estimado; perfil sociodemográfico y laboral; regresión logística binaria

The migration is a highly complex demographic phenomenon that responds to diverse and very difficult to determine causes. In Mexico as a result of the political, economic and social changes that have taken place during the last decades, they have created a new environment for the agricultural sector. These changes, coupled with a lack of public policies to support the countryside, have favored the widening of the poverty gap, provoking a profound social inequality and having among its many consequences the international emigration, mainly to the United States. The international emigrants from the agricultural sector are one of the less studied and understood subjects, there are no specific statistics or an in-depth analysis of the consequences of this phenomenon. In this context, the objective of this research is to present an analysis that allows to glimpse the general situation in which the international emigrants who were working in the agricultural sector in Mexico, just before their flight to the United States, and from a model of binary logistic regression, provide an approach to the factors that influence the probability that international migrants have been engaged in the agricultural sector.

The source of information for the study comes from the ENOE, which is produced by the National Institute of Statistics and Geography (INEGI), can be obtained from information on sociodemographic, occupational and labor data at nationally. The ENOE information is obtained through overlapping panels in which the same household is monitored for up to five quarters, and in each quarter about 20% of the households participating in the sample are broken. By integrating the information of two contiguous combs of the same household one can identify the absent ones in the home, and with the support of the questions about the country of birth and the destination of the absentee, a subgroup of people can be created who are the international emigrants.

Based on this methodology, it was possible to estimate gross international migration rates from the first quarter of 2005 to the first quarter of 2015. The Figure 1, shows the annual averages. Because the number of mexican-americans (those born in the United States of Mexican parents) is a considerable number, it was decided to omit them and take into account only those born in Mexico. According to ENOE numbers, both the number of migrants and the number of mexican immigrants has decreased in the last decade. Even before the economic crisis of 2008 there was a reduction in the volume of international mexican emigrants, however it is evident that it was from that year that the reduction was greater so that in 2005, for each ten thousand habitual residents in our country counted 109 exits for change of residence abroad, in 2009 the rate was reduced to more than half and by 2014 to less than one third (Figure 1).

What is striking, and the subject of this research, is that during the period 2005-2014, the highest percentage of international emigrants who were employed in Mexico belonged to the agricultural sector (Figure 2).

However, when the migrants arrive in the United States, the vast majority of them are employed in construction, or in the hotel and recreation sector, the agricultural sector only reaches 4.8% and 4.9% in 2013 and 2014 respectively (^{CPS, 2014}) (Figure 3).

Fuente: elaboración a partir de estimaciones de la current population survey (CPS), suplemento de marzo 2005-2014.

Estimates from the ENOE indicate that the majority of international migrants who belonged to the agricultural sector over the period 2005-2014 are men (97.6% on average); in contrast, migrants immersed in another sector the percentage of men (84.1%), the majority of the agricultural migrants are younger than the non-agricultural ones, they have, in average, 29 and 30 years respectively. Likewise, the largest proportion of agricultural emigrants are concentrated mainly in small communities (less than 2 500 inhabitants), with an average of 76.7% of the population, while non-agricultural communities are concentrated in urban communities, 75.4% on average. In terms of marital status, on average 60% of agricultural emigrants are married or in free union, a higher percentage than non-agricultural workers (53%).

Among the characteristics of agricultural emigrants, there is an increase in the level of primary education (6 years of schooling) in 2005 to incomplete secondary education (7 years of schooling) in 2014, in the case of non-agricultural workers, the number of years studied from 9 to 10 years. The agricultural migrants had lower incomes than non-agricultural workers, on average more than 85% had incomes of no more than two minimum wages, of these, on average, 41.2% did not receive income, while non-agricultural migrants only 40.2% earned up to two minimum wages.

As for the labor profile of agricultural emigrants, the majority of them are unpaid subordinate workers (40% on average), followed by self-employed workers (29.2% on average), in contrast, the vast majority of non-agricultural migrants are unpaid subordinate workers (73.3%). It is also observed that the distribution of the employed population by type of employment (formal and informal) has changed relatively little between 2005 and 2014, above 95% of agricultural emigrants are employed in informal activities, while non-agricultural migrants have on average, a lower percentage of labor informality (72%).

In order to determine the factors that determine the probability that an international migrant has been immersed in the agricultural sector, a logistic model was used, because it allows to introduce a mixture of categorical and quantitative variables, it is possible to calculate the parameters of quantification of known risk in the literature as “odds ratio” from the regression coefficients (β) of the independent variables introduced in the model and the dependent variable (the one to be modeled, Y) is categorical, (binary in our case), which simplifies the representation of phenomena.

Logistic regression expresses the probability that the event in question will occur as a function of certain variables, which are presumed relevant or influential. If this fact we want to model or predict is represented by Y (the dependent variable), and the k (independent and control) explanatory variables are designated by X_{1}, X_{2}, X_{3}, ..... X_{k} the general equation (or logistic function) is:

Where: β_{1,} β_{2} β_{3….} β_{k=} = are the parameters of the model; exp= denotes the exponential function. This exponential function is a simplified expression corresponding to raising the number e to the power contained within the parentheses, where e is the Euler number or constant, or base of the neperian logarithms (whose approximate value to the thousandth is 2.718) (^{Jay, 2008}).

In the Table 1 gives a brief description of the variables that were used in the analysis. The effects of the explanatory variables on the probability of belonging to the agricultural sector are analyzed through a logistic model where the independent variable is dichotomic that takes the value equal to one if the migrant belonged to the agricultural sector and zero if it was in another sector. In this way, we try to estimate the probability that an international emigrant has been immersed in the agricultural sector. In order to obtain the data of the emigrants, in the first instance were merged, the bases of the first quarter of 2013 to the first quarter of 2015, after which a primary association or independence exercise was carried out between the explanatory variables and the dependent variable in order to explore the bi-variant associations through an analysis of contingency tables, this allowed to obtain, in a general way, an idea of the independent effects of the different variables on the probability that an emigrant had belonged to the agricultural sector before leaving.

*= la región tradicional comprende: Aguascalientes, Colima, Durango, Guanajuato, Jalisco, Michoacán, Nayarit, San Luis Potosí y Zacatecas; la norte: Baja California, Baja California Sur, Coahuila, Chihuahua, Nuevo León, Sinaloa, Sonora y Tamaulipas; la centro: Distrito Federal, Hidalgo, México, Morelos, Puebla, Querétaro y Tlaxcala; y la sur-sureste: Campeche, Chiapas, Guerrero, Oaxaca, Quintana Roo, Tabasco, Veracruz y Yucatán (CONAPO, 2012).

Subsequently, analysis was performed to detect possible confounding variables, if necessary, adjust or control, and thus prevent the primary relationship between the explanatory variables and dependent is spurious. In the same way an analysis is performed to detect the variables that could be effect modifiers or interaction in terms of increasing or decreasing the effect of the main relation.

Having performed the corresponding analyzes, the logistic regression was performed using the IBM SPSS 19 statistical program (^{Valderrey, 2010}). The method chosen is that of “forward” using the criterion of the likelihood ratio to contrast the new variables to be introduced or drawn from the model. Three measures “abstract model” are given to globally assess its validity: the first is the value of -2LL and the other two are coefficients of determination (R^{2}), similar to that obtained in linear regression, expressing the ratio of the variation explained by the model. A perfect model would have a value of -2LL very small (ideally zero) and R^{2} close to one (ideally one) (Table 2).

The data suggest that the model explained 51% of the variability of the studied phenomenon that is sufficient given the complexity of the phenomenon and the limitations of the source. Subsequently is shown the global adjustment test of the model known as the “Hosmer and Lemeshow test” (Table 3).

What is wanted in this test is that there is no significance (the opposite of what is usually usual) because the null hypothesis is that the proposed model conforms to what is observed. Therefore, a p-value greater than 0.05 implies that what we observe fits sufficiently into reality.

Finally the variables in the equation are presented, their regression coefficients with their corresponding standard errors, the value of the Wald statistic to evaluate the null (β_{t}=0), the statistical significance associated, and the value of the OR (exp (B)) with their confidence intervals (Table 4).

With this data we can construct the logistic regression equation:

With this equation we can predict the probability of having the result (Y= 1) of "agricultural sector". Thus, an individual who is male (sex= 1), the locality in which he or she resides is less than 2 500 inhabitants (locality= 1), the number of years of study are less than nine (years of schooling= 1), their income is not more than two minimum wages (income= 1) and serves on labor informality (formality= 1) would, according to the best estimate model, a likelihood that the migrant has been busy before leaving:

This predicted probability (as is greater than 0.5) is classified as the migrant associated with the agricultural sector.

The significant variables in the best estimated model reflect the precariousness and poor quality of life sustained by people immersed in the agricultural sector before emigrating.

Conclusions

The model suggests that in order to reduce the precariousness of the migrant in the agricultural sector, it is necessary to increase the educational level, the monetary income and reduce the informal work. Likewise, it is essential to create the conditions to accelerate the growth of the economy and create more and better jobs, an indispensable condition to aspire to reduce migratory pressures and discourage emigration in the long term.

Literatura citada

Census Bureau y B LS. 2014. Bureau of labor statistics, current population survey (CPS), suplemento ampliado. 2000-2014.. 55 p. [ Links ]

Escalante, R.; Catalán, H.; Galindo, L. y Reyes, O. 2007. Desagrarización en México: tendencias actuales y retos hacia el futuro. Documento de trabajo, México. 186 p. [ Links ]

Jay, L. D. 2008, Probabilidad y estadística para ingeniería y ciencias, Cengage Learning. 515-517 pp. [ Links ]

Instituto Nacional de Estadística, Geografía e Informática (INEGI). 2012. Migración internacional captada a través de la encuesta nacional de ocupación y empleo 2006-2010 ENOE: metodología y caracterización demográfica. INEGI. 1-9 pp. [ Links ]

Valderrey, S. P. 2010. Paquete estadístico para las ciencias sociales (SPSS) 17. Extracción del conocimiento a partir del análisis de datos, Alfaomega. 319 p. [ Links ]

Received: January 2017; Accepted: February 2017