Abstract
Research on barriers to climate change adaptation has, hitherto, disproportionately focused on institutional barriers. Despite the critical importance of personal barriers in shaping the adaptive response of humanity to climate change and variability, the literature on the subject is rather nascent. This study is premised on the hypothesis that place-specific characteristics (where you live) and compositional (both biosocial and sociocultural) factors may be salient to differentials in adaptation to climate change in coastal areas of developing countries. This is because adaptation to climate change is inherently local. Using cross-sectional survey data on 1,253 individuals (606 males and 647 females), barriers to adaptation to climate change were observed to vary with place, indicating that there is inequality in barriers to adaptation. In the multivariate models, the place-specific differences in barriers to adaptation were robust and remained statistically significant even when socio-demographic (compositional) variables were controlled. Observed differences in barriers to adaptation to climate change in coastal Tanzania mainly reflect strong place-specific disparities among groups indicating the need for adaptation policies that are responsive to processes of socio-institutional learning in a specific context, involving multiple people that have a stake in the present and the future of that place. These people are making complex, multifaceted choices about managing and adapting to climate-related risks and opportunities, often in the face of resource constraints and competing agendas.
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Acknowledgments
We acknowledge research funding from ‘the Indian Ocean World: The Making of the First Global Economy in the Context of Human Environment Interaction’ project within the framework of Major Collaborative Research Initiative (MCRI). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Many thanks to Karen Van Kerkoerle, of the Cartographic Unit, Department of Geography, University of Western Ontario, for drawing the map of the study areas.
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Appendix
Appendix
Contingency tables show the distribution of the barriers to adaptation to climate change by Place-specific and compositional (biosocial and sociocultural) variables
Distribution of self-reported barriers to adaptation to climate change: don’t know what steps to take to protect myself (n = 1,130)
Variables | Yes (%) | No (%) | Pearson’s χ² (df) |
---|---|---|---|
Compositional factors | |||
Sex | χ² (1) = 9.9300, Pr = 0.002, Cramer’s V = 0.09 | ||
Male | 45.3 | 54.7 | |
Female | 54.7 | 45.3 | |
Age | χ² (3) = 0.5389, Pr = 0.900, Cramer’s V = 0.02 | ||
18–35 | 49.8 | 50.2 | |
36–50 | 50.9 | 49.1 | |
51–65 | 48.9 | 51.1 | |
More than 65 | 53.2 | 46.8 | |
Marital status | χ² (1) = 0.7202, Pr = 0.396, Cramer’s V = −0.02 | ||
Unmarried | 51.9 | 48.1 | |
Married | 49.2 | 50.8 | |
Ethnicity | χ² (2) = 11.2682, Pr = 0.004, Cramer’s V = 0.10 | ||
Zaramo | 59.8 | 40.2 | |
Sambaa | 45.4 | 54.6 | |
Others | 48.0 | 52.0 | |
Religion | χ² (2) = 32.4003, Pr = 0.000, Cramer’s V = 0.16 | ||
Christian | 39.5 | 60.5 | |
Muslim | 55.8 | 44.2 | |
Traditional | 0.0 | 100.0 | |
Employment | χ² (1) = 3.3447, Pr = 0.067, Cramer’s V = −0.05 | ||
Unemployed | 60.0 | 40.0 | |
Employed | 49.4 | 50.6 | |
Income* | – | – | – |
Educational attainment | χ² (3) = 50.0298, Pr = 0.000, Cramer’s V = 0.21 | ||
No education | 64.4 | 35.6 | |
Primary | 57.8 | 42.2 | |
Secondary | 44.9 | 55.1 | |
Tertiary | 31.4 | 68.6 | |
Place-specific factors | |||
Availability of health facility in the neighbourhood | χ² (1) = 0.6692, Pr = 0.413, Cramer’s V = 0.02 | ||
Yes | 54.2 | 45.8 | |
No | 49.8 | 50.2 | |
Region | χ² (2) = 60.5685, Pr = 0.000, Cramer’s V = 0.23 | ||
Dar es Salaam and Zanzibar | 38.3 | 61.7 | |
Pwani | 63.9 | 36.1 | |
Tanga | 58.6 | 41.4 | |
Distance to nearest health facility* | – | – | – |
Accessibility of health facility in the neighbourhood | χ² (1) = 13.3316, Pr = 0.000, Cramer’s V = 0.11 | ||
Not easy | 43.3 | 56.7 | |
Easy | 54.4 | 45.6 | |
Residential locality | χ² (1) = 41.7254, Pr = 0.000, Cramer’s V = 0.19 | ||
Rural | 61.7 | 38.3 | |
Urban | 42.2 | 57.8 |
Distribution of self-reported barriers to adaptation to climate change: lack the skill needed (n = 1,130)
Variables | Yes (%) | No (%) | Pearson’s χ² (df) |
---|---|---|---|
Compositional factors | |||
Sex | χ² (1) = 8.0164, Pr = 0.005, Cramer’s V = 0.08 | ||
Male | 66.2 | 33.8 | |
Female | 73.9 | 26.1 | |
Age | χ² (3) = 4.2596, Pr = 0.235, Cramer’s V = 0.06 | ||
18–35 | 69.0 | 31.0 | |
36–50 | 68.7 | 31.3 | |
51–65 | 71.8 | 28.2 | |
More than 65 | 79.2 | 20.8 | |
Marital status | χ² (1) = 3.9051, Pr = 0.048, Cramer’s V = 0.06 | ||
Unmarried | 66.6 | 33.4 | |
Married | 72.2 | 27.8 | |
Ethnicity | χ² (2) = 4.7400, Pr = 0.093, Cramer’s V = 0.06 | ||
Zaramo | 75.9 | 24.1 | |
Sambaa | 67.2 | 32.8 | |
Others | 69.1 | 30.9 | |
Religion | χ² (2) = 5.4271, Pr = 0.06, Cramer’s V = 0.07 | ||
Christian | 66.1 | 33.9 | |
Muslim | 72.4 | 27.6 | |
Traditional | 50.0 | 50.0 | |
Employment | χ² (1) = 12.0575, Pr = 0.001, Cramer’s V = −0.09 | ||
Unemployed | 86.2 | 13.8 | |
Employed | 69.1 | 30.9 | |
Income* | – | – | – |
Educational attainment | χ² (3) = 59.7848, Pr = 0.000, Cramer’s V = 0.23 | ||
No education | 83.9 | 16.1 | |
Primary | 77.9 | 22.1 | |
Secondary | 65.3 | 34.7 | |
Tertiary | 51.0 | 49.0 | |
Place-specific factors | |||
Availability of health facility in the neighbourhood | χ² (1) = 0.5051, Pr = 0.477, Cramer’s V = 0.02 | ||
Yes | 67.5 | 32.5 | |
No | 70.6 | 29.4 | |
Region | χ² (2) = 16.9107, Pr = 0.000, Cramer’s V = 0.12 | ||
Dar es Salaam and Zanzibar | 64.5 | 35.5 | |
Pwani | 75.7 | 24.3 | |
Tanga | 75.6 | 24.4 | |
Distance to nearest health facility* | – | – | – |
Accessibility of health facility in the neighbourhood | χ² (1) = 4.5675, Pr = 0.033, Cramer’s V = −0.06 | ||
Not easy | 73.9 | 26.1 | |
Easy | 68.0 | 32.0 | |
Residential locality | χ² (1) = 33.9572, Pr = 0.000, Cramer’s V = 0.17 | ||
Rural | 63.8 | 36.2 | |
Urban | 79.6 | 20.4 |
Distribution of self-reported barriers to adaptation to climate change: lack of personal energy or motivation (n = 1,130)
Variables | Yes (%) | No (%) | Pearson’s χ² (df) |
---|---|---|---|
Compositional factors | |||
Sex | χ² (1) = 3.4068, Pr = 0.065, Cramer’s V = 0.05 | ||
Male | 51.2 | 48.8 | |
Female | 56.7 | 43.3 | |
Age | χ² (3) = 7.9184, Pr = 0.048, Cramer’s V = 0.08 | ||
18–35 | 58.9 | 41.1 | |
36–50 | 52.2 | 47.8 | |
51–65 | 48.5 | 51.5 | |
More than 65 | 57.1 | 42.9 | |
Marital status | χ² (1) = 22.1577, Pr = 0.000, Cramer’s V = −0.14 | ||
Unmarried | 63.5 | 36.5 | |
Married | 49.0 | 51.0 | |
Ethnicity | χ² (2) = 0.8937, Pr = 0.640, Cramer’s V = 0.03 | ||
Zaramo | 56.8 | 43.2 | |
Sambaa | 53.8 | 46.2 | |
Others | 53.3 | 46.7 | |
Religion | χ² (2) = 0.0800, Pr = 0.961, Cramer’s V = 0.0084 | ||
Christian | 53.6 | 46.4 | |
Muslim | 54.3 | 45.7 | |
Traditional | 50.0 | 50.0 | |
Employment | χ² (1) = 0.7637, Pr = 0.382, Cramer’s V = −0.03 | ||
Unemployed | 58.8 | 41.2 | |
Employed | 53.7 | 46.3 | |
Income* | – | – | – |
Educational attainment | χ² (3) = 12.5329, Pr = 0.006, Cramer’s V = 0.10 | ||
No education | 71.3 | 28.7 | |
Primary | 52.0 | 48.0 | |
Secondary | 51.9 | 48.1 | |
Tertiary | 55.7 | 44.3 | |
Place-specific factors | |||
Availability of health facility in the neighbourhood | χ² (1) = 3.3476, Pr = 0.07, Cramer’s V = −0.05 | ||
Yes | 53.1 | 46.9 | |
No | 61.8 | 38.2 | |
Region | χ² (2) = 13.7881, Pr = 0.001, Cramer’s V = 0.11 | ||
Dar es Salaam and Zanzibar | 58.4 | 41.6 | |
Pwani | 55.4 | 44.6 | |
Tanga | 45.3 | 54.7 | |
Distance to nearest health facility* | – | – | – |
Accessibility of health facility in the neighbourhood | χ² (1) = 38.6438, Pr = 0.000, Cramer’s V = 0.18 | ||
Not easy | 42.3 | 57.7 | |
Easy | 61.3 | 38.7 | |
Residential locality | χ² (1) = 13.9949, Pr = 0.000, Cramer’s V = −0.11 | ||
Rural | |||
Urban |
Distribution of self-reported barriers to adaptation to climate change: lack of time (n = 1,130)
Variables | Yes (%) | No (%) | Pearson’s χ² (df) |
---|---|---|---|
Compositional factors | |||
Sex | χ² (1) = 1.9559, Pr = 0.162, Cramer’s V = −0.04 | ||
Male | 16.1 | 83.6 | |
Female | 13.2 | 86.8 | |
Age | χ² (3) = 2.8717, Pr = 0.538, Cramer’s V = 0.05 | ||
18–35 | 14.5 | 85.5 | |
36–50 | 12.7 | 87.3 | |
51–65 | 16.3 | 83.7 | |
More than 65 | 18.2 | 81.8 | |
Marital status | χ² (1) = 0.8748, Pr = 0.350, Cramer’s V = −0.03 | ||
Unmarried | 15.9 | 84.1 | |
Married | 13.9 | 86.1 | |
Ethnicity | χ² (2) = 18.8154, Pr = 0.000, Cramer’s V = 0.12 | ||
Zaramo | 10.6 | 89.4 | |
Sambaa | 5.0 | 95.0 | |
Others | 17.3 | 82.7 | |
Religion | χ² (2) = 5.8896, Pr = 0.053, Cramer’s V = 0.07 | ||
Christian | 18.1 | 81.9 | |
Muslim | 12.8 | 87.2 | |
Traditional | 25.0 | 75.0 | |
Employment | χ² (1) = 1.291, Pr = 0.000, Cramer’s V = −0.03 | ||
Unemployed | 18.8 | 81.2 | |
Employed | 14.3 | 85.7 | |
Income* | – | – | – |
Educational attainment | χ² (3) = 9.6827, Pr = 0.021, Cramer’s V = 0.09 | ||
No education | 14.9 | 85.1 | |
Primary | 11.6 | 88.4 | |
Secondary | 15.9 | 84.1 | |
Tertiary | 20.6 | 79.4 | |
Place-specific factors | |||
Availability of health facility in the neighbourhood | χ² (1) = 3. 9668, Pr = 0.046, Cramer’s V = 0.06 | ||
Yes | 8.9 | 91.1 | |
No | 15.3 | 84.7 | |
Region | χ² (2) = 39.2638, Pr = 0.000, Cramer’s V = 0.18 | ||
Dar es Salaam and Zanzibar | 21.2 | 78.8 | |
Pwani | 10.4 | 89.7 | |
Tanga | 6.8 | 93.2 | |
Distance to nearest health facility* | – | – | – |
Accessibility of health facility in the neighbourhood | χ² (1) = 16.5106, Pr = 0.000, Cramer’s V = 0.11 | ||
Not easy | 9.3 | 90.7 | |
Easy | 17.9 | 82.1 | |
Residential locality | χ² (1) = 37.7121, Pr = 0.000, Cramer’s V = 0.17 | ||
Rural | 7.4 | 92.6 | |
Urban | 19.6 | 80.4 |
Distribution of self-reported barriers to adaptation to climate change: lack of money or resources needed (n = 1,130)
Variables | Yes (%) | No (%) | Pearson’s χ² (df) |
---|---|---|---|
Compositional factors | |||
Sex | χ² (1) = 4.5769, Pr = 0.032, Cramer’s V = 0.06 | ||
Male | 61.6 | 38.4 | |
Female | 67.7 | 32.3 | |
Age | χ² (3) = 6.6403, Pr = 0.084, Cramer’s V = 0.07 | ||
18–35 | 63.3 | 36.7 | |
36–50 | 66.8 | 33.2 | |
51–65 | 61.1 | 38.9 | |
More than 65 | 75.3 | 24.7 | |
Marital status | χ² (1) = 3.9364, Pr = 0.047, Cramer’s V = −0.06 | ||
Unmarried | 68.6 | 31.4 | |
Married | 62.7 | 37.3 | |
Ethnicity | |||
Zaramo | 65.2 | 34.8 | χ² (2) = 0.6799, Pr = 0.712, Cramer’s V = 0.02 |
Sambaa | 61.3 | 38.7 | |
Others | 65.1 | 34.8 | |
Religion | χ² (2) = 6.7887, Pr = 0.034, Cramer’s V = 0.08 | ||
Christian | 59.7 | 40.3 | |
Muslim | 67.4 | 32.6 | |
Traditional | 50.0 | 50.0 | |
Employment | χ² (1) = 6.5310, Pr = 0.011, Cramer’s V = −0.07 | ||
Unemployed | 77.5 | 22.5 | |
Employed | 63.8 | 36.2 | |
Income* | – | – | – |
Educational attainment | χ² (3) = 34.1185, Pr = 0.000, Cramer’s V = 0.17 | ||
No education | 86.2 | 13.8 | |
Primary | 68.0 | 32.0 | |
Secondary | 60.5 | 39.5 | |
Tertiary | 53.1 | 46.9 | |
Place-specific factors | |||
Availability of health facility in the neighbourhood | χ² (1) = 49.8885, Pr = 0.000, Cramer’s V = −0.21 | ||
Yes | 95.0 | 5.0 | |
No | 61.3 | 38.7 | |
Region | χ² (2) = 15.2163, Pr = 0.000, Cramer’s V = 0.12 | ||
Dar es Salaam and Zanzibar | 59.5 | 40.5 | |
Pwani | 72.9 | 27.1 | |
Tanga | 66.8 | 33.2 | |
Distance to nearest health facility* | – | – | – |
Accessibility of health facility in the neighbourhood | χ² (1) = 21. 7830, Pr = 0. 000, Cramer’s V = 0.14 | ||
Not easy | 56.3 | 43.7 | |
Easy | 70.0 | 30.0 | |
Residential locality | χ² (1) = 1.5169, Pr = 0.218, Cramer’s V = 0.04 | ||
Rural | 66.9 | 33.1 | |
Urban | 63.3 | 36.7 |
Distribution of self-reported barriers to adaptation to climate change: lack of help from others (n = 1,130)
Variables | Yes (%) | No (%) | Pearson’s χ² (df) |
---|---|---|---|
Compositional factors | |||
Sex | χ² (1) = 2.3778, Pr = 0.123, Cramer’s V = −0.05 | ||
Male | 65.7 | 34.3 | |
Female | 61.3 | 38.7 | |
Age | χ² (3) = 8.1575, Pr = 0.04, Cramer’s V = 0.08 | ||
18–35 | 63.8 | 36.2 | |
36–50 | 59.7 | 40.3 | |
51–65 | 69.6 | 30.4 | |
More than 65 | 57.1 | 42.9 | |
Marital status | χ² (1) = 1.1464, Pr = 0.284, Cramer’s V = 0.03 | ||
Unmarried | 61.2 | 38.8 | |
Married | 64.5 | 35.5 | |
Ethnicity | |||
Zaramo | 64.8 | 35.2 | χ² (2) = 3.7892, Pr = 0.150, Cramer’s V = 0.06 |
Sambaa | 70.6 | 29.4 | |
Others | 61.8 | 38.2 | |
Religion | χ² (2) = 2.7308, Pr = 0.255, Cramer’s V = 0.05 | ||
Christian | 64.5 | 35.5 | |
Muslim | 62.6 | 37.4 | |
Traditional | 100.0 | 0.0 | |
Employment | χ² (1) = 1.2519, Pr = 0.263, Cramer’s V = 0.03 | ||
Unemployed | 57.5 | 42.5 | |
Employed | 63.8 | 36.2 | |
Income* | – | – | – |
Educational attainment | χ² (3) = 40.0745, Pr = 0.000, Cramer’s V = 0.19 | ||
No education | 37.9 | 62.1 | |
Primary | 60.2 | 39.8 | |
Secondary | 72.3 | 27.7 | |
Tertiary | 69.1 | 30.9 | |
Place-specific factors | |||
Availability of health facility in the neighbourhood | χ² (1) = 15.6198, Pr = 0.000, Cramer’s V = 0.12 | ||
Yes | 46.7 | 53.3 | |
No | 65.3 | 34.7 | |
Region | χ² (2) = 52.2237, Pr = 0.000, Cramer’s V = 0.21 | ||
Dar es Salaam and Zanzibar | 72.9 | 27.1 | |
Pwani | 61.4 | 38.6 | |
Tanga | 48.2 | 51.8 | |
Distance to nearest health facility* | – | – | – |
Accessibility of health facility in the neighbourhood | χ² (1) = 16.3284, Pr = 0.000, Cramer’s V = −0.12 | ||
Not easy | 70.7 | 29.3 | |
Easy | 58.9 | 41.1 | |
Residential locality | χ² (1) = 2.9761, Pr = 0.085, Cramer’s V = −0.05 | ||
Rural | 60.4 | 39.6 | |
Urban | 65.4 | 34.6 |
Distribution of self-reported barriers to adaptation to climate change: feel I don’t make a difference (n = 1,130)
Variables | Yes (%) | No (%) | Pearson’s χ² (df) |
---|---|---|---|
Compositional factors | |||
Sex | χ² (1) = 0.0229, Pr = 0.880, Cramer’s V = −0.004 | ||
Male | 55.1 | 44.9 | |
Female | 54.7 | 45.3 | |
Age | |||
18–35 | 53.4 | 46.6 | χ² (3) = 3.2677, Pr = 0.352, Cramer’s V = 0.05 |
36–50 | 52.8 | 47.2 | |
51–65 | 59.3 | 40.7 | |
More than 65 | 57.1 | 42.9 | |
Marital status | χ² (1) = 20.0352, Pr = 0.000, Cramer’s V = 0.13 | ||
Unmarried | 45.8 | 54.2 | |
Married | 59.7 | 40.3 | |
Ethnicity | |||
Zaramo | 53.8 | 46.2 | χ² (2) = 0.6465, Pr = 0.724, Cramer’s V = 0.02 |
Sambaa | 52.1 | 47.9 | |
Others | 55.6 | 44.4 | |
Religion | χ² (2) = 2.7983, Pr = 0.247, Cramer’s V = 0.05 | ||
Christian | 57.9 | 42.1 | |
Muslim | 53.3 | 46.7 | |
Traditional | 75.0 | 25.0 | |
Employment | χ² (1) = 8.2399, Pr = 0.004, Cramer’s V = −0.08 | ||
Unemployed | 70.0 | 30.0 | |
Employed | 53.7 | 46.3 | |
Income* | – | – | – |
Educational attainment | χ² (3) = 3.9816, Pr = 0.263, Cramer’s V = 0.06 | ||
No education | 44.8 | 55.2 | |
Primary | 55.7 | 44.3 | |
Secondary | 56.4 | 43.6 | |
Tertiary | 54.6 | 45.4 | |
Place-specific factors | |||
Availability of health facility in the neighbourhood | χ² (1) = 22.0902, Pr = 0.000, Cramer’s V = 0.14 | ||
Yes | 35.0 | 65.0 | |
No | 57.3 | 42.7 | |
Region | χ² (2) = 5.2663, Pr = 0.072, Cramer’s V = 0.07 | ||
Dar es Salaam and Zanzibar | 58.4 | 41.6 | |
Pwani | 52.1 | 47.9 | |
Tanga | 51.1 | 48.9 | |
Distance to nearest health facility* | – | – | – |
Accessibility of health facility in the neighbourhood | χ² (1) = 9.5747, Pr = 0.002, Cramer’s V = −0.09 | ||
Not easy | 60.7 | 39.3 | |
Easy | 51.3 | 48.7 | |
Residential locality | χ² (1) = 0.0039, Pr = 0.950, Cramer’s V = 0.001 | ||
Rural | 55.0 | 45.0 | |
Urban | 54.8 | 45.2 |
Distribution of self-reported barriers to adaptation to climate change: I don’t believe in climate change (n = 1,130)
Variables | Yes (%) | No (%) | Pearson’s χ² (df) |
---|---|---|---|
Compositional factors | |||
Sex | χ² (1) = 1.3781, Pr = 0.240, Cramer’s V = 0.03 | ||
Male | 6.3 | 93.7 | |
Female | 8.1 | 91.9 | |
Age | χ² (3) = 4.9499, Pr = 0.176, Cramer’s V = 0.07 | ||
18–35 | 8.9 | 91.1 | |
36–50 | 5.3 | 94.7 | |
51–65 | 6.7 | 93.3 | |
More than 65 | 10.4 | 89.6 | |
Marital status | χ² (1) = 0.0256, Pr = 0.873, Cramer’s V = 0.005 | ||
Unmarried | 7.1 | 92.9 | |
Married | 7.3 | 92.7 | |
Ethnicity | χ² (2) = 12.9632, Pr = 0.002, Cramer’s V = 0.09 | ||
Zaramo | 6.8 | 93.2 | |
Sambaa | 0.8 | 99.2 | |
Others | 8.4 | 91.6 | |
Religion | χ² (2) = 10.4669, Pr = 0.005, Cramer’s V = 0.09 | ||
Christian | 10.4 | 89.6 | |
Muslim | 5.6 | 94.4 | |
Traditional | 25.0 | 75.0 | |
Employment | χ² (1) = 2.9819, Pr = 0.084, Cramer’s V = −0.05 | ||
Unemployed | 12.5 | 87.5 | |
Employed | 6.9 | 93.1 | |
Income* | – | – | – |
Educational attainment | χ² (3) = 8.8704, Pr = 0.031, Cramer’s V = 0.09 | ||
No education | 3.4 | 96.6 | |
Primary | 5.8 | 94.2 | |
Secondary | 8.3 | 91.7 | |
Tertiary | 11.3 | 88.7 | |
Place-specific factors | |||
Availability of health facility in the neighbourhood | χ² (1) = 8.5152, Pr = 0.004, Cramer’s V = 0.09 | ||
Yes | 0.8 | 99.2 | |
No | 8.0 | 92.0 | |
Region | χ² (2) = 64.8263, Pr = 0.000, Cramer’s V = 0.24 | ||
Dar es Salaam and Zanzibar | 13.6 | 86.4 | |
Pwani | 2.9 | 97.1 | |
Tanga | 0.0 | 100.0 | |
Distance to nearest health facility* | – | – | – |
Accessibility of health facility in the neighbourhood | χ² (1) = 6.1195, Pr = 0.013, Cramer’s V = 0.07 | ||
Not easy | 4.9 | 95.1 | |
Easy | 8.7 | 91.3 | |
Residential locality | χ² (1) = 38.2802, Pr = 0.000, Cramer’s V = −0.18 | ||
Rural | 1.5 | 98.5 | |
Urban | 11.2 | 88.8 |
Distribution of self-reported barriers to adaptation to climate change: believe God will protect me (n = 1,130)
Variables | Yes (%) | No (%) | Pearson’s χ² (df) |
---|---|---|---|
Compositional factors | |||
Sex | χ² (1) = 0.1709, Pr = 0.679, Cramer’s V = −0.01 | ||
Male | 40.6 | 59.4 | |
Female | 39.4 | 60.6 | |
Age | χ² (3) = 8.1728, Pr = 0.043, Cramer’s V = 0.08 | ||
18–35 | 34.7 | 65.3 | |
36–50 | 41.6 | 58.4 | |
51–65 | 43.7 | 56.3 | |
More than 65 | 46.8 | 53.2 | |
Marital status | χ² (1) = 1.6277, Pr = 0.202, Cramer’s V = 0.04 | ||
Unmarried | |||
Married | |||
Ethnicity | χ² (2) = 5.7772, Pr = 0.056, Cramer’s V = 0.07 | ||
Zaramo | 42.8 | 57.2 | |
Sambaa | 30.2 | 69.8 | |
Others | 40.7 | 59.4 | |
Religion | χ² (2) = 0.5618, Pr = 0.755, Cramer’s V = 0.02 | ||
Christian | 38.7 | 61.3 | |
Muslim | 40.6 | 59.4 | |
Traditional | 50.0 | 50.0 | |
Employment | χ² (1) = 2.6996, Pr = 0.100, Cramer’s V = −0.05 | ||
Unemployed | 48.8 | 51.2 | |
Employed | 39.3 | 60.7 | |
Income* | – | – | – |
Educational attainment | χ² (3) = 2.2766, Pr = 0.517, Cramer’s V = 0.24 | ||
No education | 34.5 | 65.5 | |
Primary | 39.1 | 60.9 | |
Secondary | 42.7 | 57.3 | |
Tertiary | 40.7 | 59.3 | |
Place-specific factors | |||
Availability of health facility in the neighbourhood | χ² (1) = 3.9549, Pr = 0.047, Cramer’s V = 0.06 | ||
Yes | 31.7 | 68.3 | |
No | 41.0 | 59.0 | |
Region | χ² (2) = 24.4885, Pr = 0.000, Cramer’s V = 0.15 | ||
Dar es Salaam and Zanzibar | 45.3 | 54.7 | |
Pwani | 42.5 | 57.5 | |
Tanga | 28.3 | 71.7 | |
Distance to nearest health facility* | – | – | – |
Accessibility of health facility in the neighbourhood | χ² (1) = 14.0137, Pr = 0.000, Cramer’s V = −0.11 | ||
Not easy | 47.0 | 53.0 | |
Easy | 35.7 | 64.3 | |
Residential locality | χ² (1) = 9.3832, Pr = 0.002, Cramer’s V = −0.09 | ||
Rural | 34.6 | 65.4 | |
Urban | 43.7 | 56.3 |
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Armah, F.A., Luginaah, I., Hambati, H. et al. Assessing barriers to adaptation to climate change in coastal Tanzania: Does where you live matter?. Popul Environ 37, 231–263 (2015). https://doi.org/10.1007/s11111-015-0232-9
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DOI: https://doi.org/10.1007/s11111-015-0232-9