EMIUB-Derived Attitudes towards Migration

An (2011) reviews numerous agent-based modelling approaches that have been used in simulating human decisions in coupled human and natural systems (CHANS). The author describes nine types of decision models used in the reviewed articles: microeconomic; space theory based; psychological and cognitive; institution-based; experience- or preference-based;

participatory; empirical or heuristic rule; evolutionary programming and; assumption and/or calibration-based rule models. An concludes that the models range from highly empirically based to more mechanistic or process-based with both extremes of this gradient having both strengths and weaknesses. While broadly falling within the category of psychological and cognitive modelling as a result of the use of a conceptual model of agent cognition, the model developed by this thesis also adopts an empirical rule-based approach. As a result of the availability of the EMIUB data, an empirical approach is chosen as the preferable course of model development. As such, rather than using theories of behaviour gained from either published academic literature or field research, the rules of behaviour that contribute to the cognitive MARC model presented in Chapter 3 are developed from empirical evidence gained from the EMIUB data. While theories presented in the literature relating to tendencies towards migration decisions in the face of environmental change are seen to contrast with variations in location, constructing rules of behaviour on the basis of empirical data collected in the case-study location avoids such potential misrepresentation. Furthermore, the challenge of addressing the impact of different individual attributes such as age, gender, marital status and origin location on tendencies towards migration can be easily represented in a stochastic manner.

Although field interviews conducted across Burkina Faso were undertaken in a manner that permitted focus groups to be conducted in each of the rainfall zones identified by Henry et al.

(2004a) in Figure 4.6, the EMIUB data divides Burkina Faso according to an alternative zonal breakdown. Figure 4.9 displays the zones into which Burkina Faso is divided according to the EMIUB data. While the Sahel and Southwest zones represent those of the 200-499 mm and 900+ mm Henry et al. rainfall zones respectively, the two central zones (500-699 mm and 700-899 mm) are merged into one Centre zone within the EMIUB breakdown. Two further zones are

identified within the EMIUB data, the capital city Ouagadougou and the second largest city, Bobo Dioulasso. For the purposes of this research, migration is defined as the movement of an agent from their model zone of origin to any other model zone within Burkina Faso, or out of the country. This contrasts with the definition used by Henry et al. (2004a) where migration is defined as, “a change of residence involving a departure from the village for a duration of at least three months” but enables clearer zonal separation of agent movements as migration. The spatial resolution used in the definition of migration used by this research is chosen in order to focus further upon the role of changes in rainfall variability in each model zone and reduce the complexity of modelled agents by locating them simply in a geographical zone rather than attributing specific coordinates to their respective origins and destinations.

Figure 4.9: Division of Burkina Faso into five zones according to the EMIUB data breakdown.

With Burkina Faso therefore divided into five separate zones, the probability of an individual within each of those zones migrating to one of the alternatives or out of the country could be calculated. The process of translating field interview outputs into a usable framework (Figure 4.8) identified age, gender, marital status, work, assets and migration experience as the key attributes contributing to an individual’s attitude towards migration behaviour. While all of

these apart from assets are temporally referenced by the EMIUB data only age, gender and marital status were used to calculate behavioural attitude values. By including three different age categories, two gender categories and two marital status categories, a total of twelve combinations of attributes exist for which attitudes towards migration were required.

Furthermore, with five potential destination options available to individuals residing in one of five origin locations, a total of twenty five potential origin-destination combinations exist. As a result, by limiting the individual characteristics identified by the field research to just age, gender and marital status, a total of three hundred individual attitudes towards migration options must already be calculated from the EMIUB data. Including two categories for migration experience and four for work would increase the number of attitude values required to two thousand four hundred.

In order to test the value of agent attributes age, gender and marital status as independent variables to be used in the calculation of migration probabilities, a series of binary logistic regressions were initially undertaken to test the statistical relationship between each variable and an individual deciding to migrate or stay. The results of these independent binary logit models are displayed in Table 4.2. When considered as independent variables, from the data record of 171,209 individual records, components of age, age² (in order to test the non-linearity of the relationship between age and migration), gender and marital status all showed strong statistical significance in determining migration (p < 0.01). Furthermore, the additional variable of origin location, inherently included in migration probability calculations from each of the five model zones, was shown to be independently statistically significant. Using Ouagadougou as the reference category for the logistic regression, the origin zones of Bobo Dioulasso, Sahel and Centre were shown to result in significantly different migration (p < 0.01), while Southwest was less significant (p > 0.10). Finally, using dry conditions as the reference category, rainfall was shown to be independently not significant in determining migration (p = 0.565 and p = 0.977 for average and wet conditions respectively). The full outputs of each binary logit model are displayed as Appendix 3A.

Independent Variable

Odds Ratio P < 95% Confidence Interval

age 1.208 0.000** 1.193 1.223

age2 0.996 0.000** 0.996 0.996

gender_2 0.773 0.000** 0.745 0.802

marital 0.807 0.000** 0.776 0.840

origin_2 1.123 0.000** 1.052 1.198

origin_3 1.147 0.000** 1.078 1.221

origin_4 1.104 0.002** 1.038 1.175

origin_5 1.063 0.098* 0.989 1.144

rainfall_2 0.988 0.565 0.947 1.030

rainfall_3 0.999 0.977 0.954 1.046

Table 4.2: Individual variable binary logit model results from an EMIUB record of 171,209 migrating and non-migrating individuals between 1970 and 1999. ** p = < 0.01. * p = < 0.05.

When used in a multivariate binary logit model however, the significance of some migration determinants changed as a result of the implicit impact of some variables changing the standalone relevance of others. The results of the multivariate binary logit models are displayed in Table 4.3. Individual variables of age and age² remained highly significant (p < 0.01) with the relevant odds ratios showing a U-shaped relationship between age and migration so that migration increases with age but only to a certain point. Using males as the reference category, gender was also seen to retain strong significance (p < 0.01) so that the likelihood of a female migrating was significantly lower than that of a male when considered alongside all other variables. However, when considered in this manner, marital status lost some of its significance (p < 0.05), likely due to the difference in migration behaviour of single and married individuals being significantly affected by gender. Furthermore, the significance of origin locations as a determinant of migration increased with the least significant difference seen from one origin being Southwest (p < 0.05). Finally, the impact of rainfall as a determinant of migration was seen to increase appreciably when considered as part of a multivariate binary logit model (p = 0.268 and p = 0.371 for average and wet conditions respectively) but did not reach overall significance. On this basis, individual variables of age, gender, marital status and origin location were seen to be appropriate determinants of migration for use in the calculation of behavioural attitude probability values. Furthermore, the fact that the significance of rainfall as a determinant was seen to increase when considered alongside these variables suggested that they were appropriate proxies through which the role of changes in rainfall variability in migration decision-making could be considered. The full outputs of each multivariate binary logit model are displayed as Appendix 3B.

Independent

Table 4.3: Multivariate binary logit model results from an EMIUB record of 171,209 migrating and non-migrating individuals between 1970 and 1999. ** p = < 0.01. * p = < 0.05.

The three hundred attitude values calculated therefore represent the probability of an individual recorded by the EMIUB data with specific age, gender, marital status and origin location attributes relocating to another zone. In order to inform the ABM with agent behavioural attitude values that reflect these probabilities during dry, average or wet rainfall years, the number of attitude values that must be calculated increases to nine hundred using age, gender and marital status attributes alone over the 30 year test period from 1970-1999. Due to the necessity of calculating nine hundred behavioural attitude values from the EMIUB data it is not possible to display all the results here. While Chapter 5 displays a small proportion of those values calculated, Appendix 4 displays the full tables of probability values in a series of weight matrices.