3.1. Descriptive results
Table 3.1 shows the summary statistics for population, household attributes, and occupational profile based on migration status.
Table 3.1 –Descriptive Statistics for Socio-economic and Demographic attributes
|
%/Average
|
SD
|
|
Male
|
Female
|
|
Individual Attribute
|
|
|
|
Gender
Married
Education level
Primary
Secondary
College
University
Household Relationship
Household head
Spouse
Child
|
52%
58%
93%
69%
51%
44%
60%
11%
70%
|
36.4%
48%
59%
90%
65%
43%
36%
43%
69%
76%
|
NS
NS
***
NS
***
NS
NS
NS
NS
|
Job Status (Before Migration)
Employed
Unemployed
Occupational Choices
|
71%
20%
|
59%
29%
|
NS
NS
|
Self-employment
Farmstead labour
Education/research
Manufacturing
Civil Service
Cottage Industry
|
38%
13%
71%
67%
54%
47%
|
67%
51%
49%
43%
36%
33%
|
***
***
NS
NS
NS
NS
|
Sample size (n=49)
|
25
|
24
|
|
On average, male 71% and female 59% confirmed that they had jobs before reverse migration. Unemployment rate for male was at 20% while females were at 29%. More females than males were working in the cottage industry, personal businesses (entrepreneurship) and farms after migration. By contrast, the males surpassed the females in industries such as civil service, education and research as well as manufacturing as presented in Table 3-1.
Table 3-2: Respondents Household and Income
|
Mean value
|
Min
|
Max SD
|
Household attributes
Age
|
42.2
|
25
|
68 15.95
|
Family size
male dependants
female dependants
the aged members (70+ years)
Land per individual in rural area (Acre)
Family income
Monthly Income before migration
Monthly income after Migration
|
3.00
3.00
2.00
1.00
0.99
37,400
39,750
|
1.00
1.00
1.00
1.00
0.25
15,500
17,100
|
7.00 2.051
5.00 1.269
4.00 0.991
2.00 0.471
5.00 ***
50,600 18972.62
53,100 21391.51
|
Sample size
|
49
|
49
|
|
On average, the respondent's household size was 3 with 5 maximum members. 59% of families had between one and two elderly people aged 70 years and above. The average size of land owned was 0.99acres. Before migration, the average family income ranged between 17100 and 3 100. After migration, the average income of the respondents ranged between 15500 and 50600. Intraregional migration rate in Kenya for the year 2012, Nyamira recorded the highest rates of immigration while Kisumu and Nyandarua had the lowest. More females than males migrated into these two counties. In Murang’a, there migration rates of males and females was almost similar. The counties that had more males than females migrating were Uasin Gishu, Nakuru and Narok [29].
The percentage of those involved in the agricultural sector increased from 5% to 19%.In education and research, the percentage dropped from 20% to 15%. The number of those in the civil service rose from 19% to 36% and represents the highest change. The percentage of people working in the cottage industry reduced from 20% to about 6%. The largest decline was experienced in the manufacturing sector. Those in the sector fell from 30% to about 3%. That said, the number of those working as small business owners rise from 15% to 30%. The average incomes for those working in the agricultural sector rose from Ksh 39,000 to Ksh 49,000. Contrariwise, the employees working in the educational sector experienced a growth of income from Ksh39000 to Ksh40000. For those working in the civil service, the incomes rose from slightly lower to slightly higher than 50,000. The incomes for those working in the cottage industry increased from 19000 to 20000. The workers in the manufacturing sector experienced a growth in the incomes from 40000 to 50000. A similar income growth rate was experienced among those that were self-employed. In table 3.2, gains and losses of labor across sectors are demonstrated.
3.2 Empirical Results
3.2.1 Key determinants for Income and Occupational Change
The study tested the association between return migrants’ demographic and socioeconomic characteristics and income change as presented in table 3-3 cross-tabulation.
Table 3-3: Demographic Socio-economic attributes and Income change
Attribute
|
Group
|
Mean monthly Income(Ksh)
|
Difference
|
Gamma
|
Phi (Φ)
|
Before return
|
After return
|
Gender
|
Male
|
37469
|
45174
|
7705
|
.148
|
.766
|
Female
|
39900
|
44280
|
4380
|
Age
|
25-34 Years
|
30475
|
40080
|
9605
|
.307
|
3.563
|
35-44 Years
|
45600
|
57000
|
11400**
|
45-59 years
|
63000
|
69000
|
6000
|
60 and Above
|
54600
|
37600
|
-17000**
|
Education
|
Postgraduate
|
70000
|
98000
|
28000**
|
-.074
|
1.919
|
primary
|
56000
|
44000
|
-12000**
|
undergraduate
|
29083
|
35667
|
6584
|
Some College
|
39364
|
45364
|
6000
|
secondary
|
39000
|
44118
|
5118
|
Relationship
|
Relative
|
33571
|
39700
|
6129
|
-.109
|
1.436
|
Spouse
|
43088
|
48800
|
5712
|
Family head
|
29667
|
39000
|
9333
|
Son/daughter
|
38875
|
43154
|
4279
|
Land size
|
> 2.5acres
|
55000
|
74000
|
19000**
|
0.4785
|
1.518
|
0.5acre
|
38500
|
43000
|
4500
|
|
|
0.25acre
|
67000
|
60000
|
-7000
|
|
|
Note: The asterisk indicates a statistically significant change at the p<.01 level
Migrants with more 2.5 acres of land (mean= Ksh19, 000, Gamma=.4785, Φ=1.518). Migrants between 34-44years (mean=Ksh11400, Gamma=.307, Φ=3.563) have significant income raise as .01 significance level. Though, returnees who were 60+ years, less than 0.25acres of land, and primary leavers had a significant income reduction (Mean Ksh -17,00, -12,000 & -7,000) respectively. Possible loss of source of income in the city either retirement or layoff could have forced them to relocate to rural areas. Land is the most essential means of production. As such, it largely determined income change as observed from the results. The association and difference among groups were significant at chi-square p<.01 level. Table 3-4 cross-tabulation presents career change based on multiple socio-economic attributes.
Table 3-4: Demographic Socio-economic attributes and career change
Characteristic
|
Group
|
Change in career (%)
|
Phi
(Φ)
|
Cramer’s V
|
No
|
Yes
|
Gender
|
female
|
29
|
71**
|
1.016
|
.718
|
male
|
67
|
33
|
|
|
Age
|
25-34 Years
|
71
|
50.
|
1.324
|
.765
|
35-44 Years
|
0.0
|
14
|
|
|
45-59 years
|
14
|
17
|
|
|
60 and Above
|
17
|
83**
|
|
|
Education
|
postgraduate
|
0.0
|
4
|
1.149
|
.663
|
primary
|
14
|
0.0
|
|
|
secondary
|
0.0
|
21
|
|
|
vocational
|
28
|
32
|
|
|
undergraduate
|
57
|
28
|
|
|
Note: The asterisk indicates a statistically significant difference at the p<.01 level
It is observed that females in contrast with males were more exposed to career change (71% and 33% respectively, Φ=1.016, Cramer’s V= .718). However, the results indicated that the returnee’s education was not a significant determiner of career change. Males and people aged between 25-34 years were the least likely to change careers based on our results.
3.2.2. Urban-rural migration impacts on migrants’ income
Parameter estimates and maximum likelihood estimates across the measured independent variables as presented in Table 3-5.
Table 3-5: Probit Model Bivariate estimates for income change on returned migrants
Variables (Determinants)
|
Coefficients
|
Marginal effect
|
Std Error
|
-individual attributes
|
|
|
|
Female
|
0.087
|
-0.123
|
0.057
|
Male
|
0.096
|
0.093
|
0.089
|
25-34years
|
0.178
|
0.041
|
0.097
|
35-59years
|
0.399**
|
0. 421
|
0.213
|
60 and Above
|
-0.369**
|
-0.312
|
0.224
|
-Education
|
|
|
|
primary
|
-0.459**
|
-0.226
|
0.327
|
secondary
|
0.197
|
0.083
|
0.019
|
college
|
0.241
|
0.135
|
0.184
|
Undergraduate degree
|
0.232
|
0.234
|
0.195
|
postgraduate degree
|
0.513**
|
0.591
|
0.333
|
-Employment
|
|
|
|
self-employed/business
|
0.068
|
0.102
|
0.034
|
employed in government
|
0.372**
|
0.220
|
0.138
|
part time Job (Yes=1)
experience more than 7 years
experience less than 2years
|
0.083
0.161
0.097
|
o.105
0.161
0.217
|
0.069
0.031
0.044
|
self-employed/business
|
0.068
|
0.102
|
0.034
|
employed in government
|
0.372**
|
0.220
|
0.138
|
-Land Size in the Rural Area
|
|
|
|
Less than 2 acres
|
0.274
|
0.170
|
0.191
|
Above 2.5 acres
|
0.507**
|
0.473
|
0.474
|
Sample size
|
49
|
|
|
Wald Chi-square
|
3.891
|
|
|
Wald test of ρ=0.00
|
0.014
|
|
|
Log pseudolikelihood
|
9.701
|
|
|
Note: The asterisk indicates a statistically significant difference at the p<.01 level
Table 3.5 reveals the coefficients of the income change model and their significance. The results reveal a significant change in income for returning migrants across different socio-economic and demographic features. Age 35-59years (R2=0.399, p<.001) and postgraduate degree (R2=0.513, p<.001) land above 2.5acres (R2=0.507, p<.001), and returnees employed in government (R2=0.372, p<.001) predicted a significant and positive change in returnee’s income. By contrast, returnees above 60 years of age (R2=-0.369, p<.001), primary school leavers (R2=-0.459, p<.001) significantly predicted a negative monthly income change. The Wald chi-square value 3.891 and test of p-value 0.014 imply that the null hypothesis of significant income change across models is rejected at at p≥0.01 levels. For marginal effect, when all the other socio-economic predictor variables are held constant varies; those who had more than 2.5acres of land obtained a marginal income rise of 47.3%, part-time jobs, and the self-employed, the marginal income rise was 10.5% and 10.2% respectively. Among returnees with more than 7 years of experience, the marginal probability of income rise was 16.1%. Besides, returnees that left the city to assume government-related jobs in the rural areas were at 22% likelihood of income increment. Individuals aged 35-59years experienced a 42.1% marginal increase in income after migration. Females were susceptible to marginal decline of 12.3%.
3.2.3. Urban-rural migration implications on migrants’ career
Maximum likelihood estimates across the measured predictor variables. The results establish a significant likelihood for occupational change upon reallocating to the rural area for migrants aged 60 year and above, primary school, and farmstead labourers.
Table 3-6: Probit Model Bivariate estimates of migrant and career change
Variables (Determinants)
|
Coefficients
|
Marginal effect
|
Std Error
|
-individual attributes
|
|
|
|
Female
|
0.326**
|
0.310
|
0.241
|
Male
|
0.219
|
0.119
|
0.107
|
25-34years
|
0.113
|
0.108
|
o.068
|
35-59years
|
0.256
|
0.193
|
0.143
|
60 and Above
|
0.797**
|
0.651
|
0.688
|
- Education
|
|
|
|
education (primary)
|
0.348**
|
0.312
|
o.068
|
secondary
|
0.268
|
0.168
|
0.019
|
college
|
0.207
|
0.151
|
0.184
|
Undergraduate degree
|
0.113
|
0.130
|
0.195
|
postgraduate degree
|
0.092
|
0.294
|
0.333
|
- Marital status
|
|
|
|
separated/divorced=3
|
0.129
|
0.137
|
0.067
|
married = 2
|
0.288
|
0.069
|
0.187
|
Employment
|
|
|
|
Self-employed/business
|
0. 269
|
o. 096
|
0.934
|
employed in government
|
0.196
|
0. 125
|
0.125
|
experience more than 7years
|
0.082
|
0.167
|
0.023
|
experience less than 2 years
|
0. 416**
|
o. 520
|
0.391
|
-Land size in rural area
|
|
|
|
Less than 2acres
|
0.203
|
0.216
|
0.183
|
Above 2.5 acres
|
0.517**
|
0.527
|
0.479
|
Sample size
|
49
|
|
|
Wald Chi-square
|
3.891
|
|
|
Wald test of ρ=0.00
|
0.0314
|
|
|
Log pseudolikelihood
|
7.362
|
|
|
Note: The asterisk indicates a statistically significant difference at the p<.01 level
Based on these results, female gender (R2 =0.326), primary level of education (R2 =0.348), land size above 2.5acres in the rural areas (R2 =0.517) and working experience less than 2 years (R2 =0.416). Also migrants aged 60 years and above (R2=0.797) were significant predictors of occupational change. Conversely, marital status (R2 =0.129 divorced, R2 =-0.288 married), degree holders (R2=0.113 and 0.092 respectively), experience more than 7years (R2=0.082) were weak determinants towards occupational change. Returnees with less than 2 years of working experience (R2 =0.269). It is essential to note that having returnees that were either employed in government or those with more than 7years of experience were reluctant to occupational change (R2=0.196, R2=0.082) respectively. The elderly migrants could have reached the decision to return back home for retirement. Those with low educational attainment, might have been susceptible to unforeseeable layoff, hence decided to return to their rural homes. The marginal effect of migration reveals that returnees of above 60+years had 65.1%. Also, the size of land owned by the migrants above 2.5 acres in the rural area predicted a chance of 52.7% career change after migration. Migrants with less than 2year of working experience and returnees of female gender (52.0% and 34.8% respectively). Land size in rural areas was a prime factor associated with occupational change since the likelihood of joining agriculture after assigning dummy variables, and setting the baseline at two years, especially for returnees with more than 2.5acres of land size was noteworthy to change career at 52.5%. Wald test of p-value =0.00314. The null hypothesis for occupational change across models is rejected at p≥0.01.