Spatial determinants of poverty in rural Kenya
Okwi et al. 10.1073/pnas.0611107104.
Supporting Information
Files in this Data Supplement:
Supporting Appendix
Poverty Mapping in Kenya. The poverty mapping g exercise in Kenya makes use of two household data sets: the 1997 Welfare Monitoring Survey and the 1999 Population and Housing Census. Poverty mapping is one application of the method called small area estimation. The method is typically divided into three stages:
• Stage 0 involves identifying variables that describe household characteristics that may be related to income and poverty and that exist in both the household survey and in the census
• Stage 1 estimates a measure of welfare, usually per capita expenditure, as a function of these household characteristics using regression analysis and the household level data.
• Stage 2 applies this regression to the same household characteristics in the census data, generating predicted welfare for each household in the census. This information is then generated up to the desired administrative unit, such as district or Location, to estimate the incidence of poverty and the standard error of the poverty estimate.
Details of this approach and the resulting poverty estimates are explained in CBS and ILRI, 2003.
Diagnostic Tests for Spatial Dependence. The results of the diagnostic tests show the presence of spatial dependence, as they are all highly significant (Table A1). Thus, we chose to use a spatial regression model to control for this spatial autocorrelation.
Table A1. Diagnostics for spatial dependence (OLS model) poverty rate
|
For weight matrix :(row-standardized weights) |
|
|
|
Test |
Value |
Probability |
|
Moran's I |
2.97322 |
0.00295 |
|
Lagrange multiplier (lag) |
0.77618 |
0.37831 |
|
Robust LM (lag) |
6.87594 |
0.00874 |
|
Lagrange multiplier (error) |
5.35721 |
0.02064 |
|
Robust LM (error) |
11.45697 |
0.00071 |
In the spatial error model, a full set of variables hypothesized to have some spatial relationship with community level poverty is included. The model fit increases to 0.54, which is not a huge change from the OLS model, but by removing the nuisance caused by spatial autocorrelation, we can now have more confidence in our parameter estimates, and concentrate on the variables that are showing a strong spatial relationship to poverty prevalence at the location level.
Distance Bands. To assess spatial autocorrelation, we must develop a spatial weights matrix to define exactly the "neighborhood" for each rural aggregated location. There are many ways to assign neighbor weights and the choice depends on the type of spatial application and on the research question. This specification requires a priori knowledge of the range and intensity of the spatial covariance between regions. Common methods include row standardization, length of common boundary and distance functions [Anselin L (2002) Agricult Econ 27:247-267]. In this study, we used inverse distance band weights for each province. Different distance bands (in meters) are used for each region: Central = 11,573, Coast = 46,775, Eastern = 46,775, North Eastern = 50,542.63, Nyanza = 9,160, Rift Valley = 9,160, Western = 9,160. We conducted sensitivity analysis of the results obtained by using different weighting schemes.
Table A2. Description of variables
|
Short description |
Source |
Explanation |
Agroclimatological
|
Annual Rainfall (mm) |
The WorldClim interpolated global terrestrial climate surfaces for the year 2000. Version 1.3. |
The average annual rainfall within the location boundaries, calculated as the sum of all the monthly rainfall figures derived from the original Worldclim1.3 dataset of monthly layers. |
|
Rainfall coefficient of variation |
The WorldClim interpolated global terrestrial climate surfaces for the year 2000. Version 1.3. |
The average coefficient of variation (CV) of rainfall between the months within 1 year within the location boundaries. This variable was derived from the worldclim1.3 dataset of bio-climatic information, which describes the "rainfall seasonality". |
Distance and access to services
|
Travel time to municipality |
- Africover landcover multipurpose database (FAO) - based on LANDSAT TM 1999 - NASA, Shuttle Radar Topography Mission (SRTM) - 2000 - World Database on Protected Areas (WDPA - sea.unep-wcmc.org/wdbpa) - based on data from 1963 - 2002 - Roads - ILRI 1999 - Settlements - 1997 |
This variable represents the average travel time from any place within the location to the nearest municipality (according to definitions of CBS). Travel time is a function of slope, road type and "impediments" (i.e. wetlands, water bodies and natural parks). The table below summarizes the travel times: |
|
Travel time to town |
Idem above |
This variable represents the average travel time from any place within the location to the nearest town (according to definitions of CBS). |
|
Travel time to trade centre |
Idem above |
This variable represents the average travel time from any place within the location to the nearest trade centre (according to definitions of CBS). |
|
Travel time to market centre |
Idem above |
This variable represents the average travel time from any place within the location to the nearest market centre (according to definitions of CBS). |
|
Travel time to type 1 road |
Idem above |
This variable represents the average travel time from any place within the location to the nearest road of type 1. Type 1: Tarmac/All Weather Bound Type 2: Murram/All Weather Loose Type 3: Earth/Dry Weather |
|
Travel time to type 1 or 2 road |
Idem above |
This variable represents the average travel time from any place within the location to the nearest road of type 1 or 2. |
|
Travel time to type 1, 2 or 3 road |
Idem above |
This variable represents the average travel time from any place within the location to the nearest road of type 1, 2 or 3. |
|
Travel time to type 1, 2 or 3 road |
Idem above |
This variable represents the average travel time from any place within the location to the nearest road of type 1, 2 or 3. |
Land use
|
Percent of location under protected area |
World Database on Protected Areas (WDPA - sea.unep-wcmc.org/wdbpa) - based on data from 1963 - 2002 |
This variable represents the percent of location that is under the Protected Area. |
|
Percent of location under wetlands |
Africover landcover multipurpose database (FAO) - based on LANDSAT TM 1999 |
The original land cover was produced from visual interpretation of digitally enhanced LANDSAT TM images (Bands 4,3,2) acquired mainly in 1999. Wetland areas are extracted on the basis of code1 of the original layer (considered to be wetland areas) |
|
Percent of location arable land (I.e. LGP > 60 days) |
Jones P.G., 2004. Report on preparation of growing season days coverages for Hadley 3 scenarios A2 and B2 for the year 2000. Consultant's report, ILRI |
The variable describes the percentage of the location that is arable. Arable land was defined as land with a length of growing period of more than 60 days per year. |
|
Arable land between 30 and 60 % (1=yes ; 0=no) |
Jones P.G., 2004. Report on preparation of growing season days coverages for Hadley 3 scenarios A2 and B2 for the year 2000. Consultant's report, ILRI. |
This variable takes a value of 1 if the arable land is 30-60% of the location's area, and 0 otherwise. Arable land was defined as land with a length of growing period of more than 60 days per year. |
|
Percent of location under water |
Africover landcover multipurpose database (FAO) - based on LANDSAT TM 1999 |
The original land cover has been produced from visual interpretation of digitally enhanced LANDSAT TM images (Bands 4,3,2) acquired mainly in 1999. Water areas extracted on the basis of code1 of the original layer (considered to be water bodies: 7WP, 7WP-Y, 8WFP). |
|
Percent of location that is built up |
Africover landcover multipurpose database (FAO) - based on LANDSAT TM 1999 |
The original land cover has been produced from visual interpretation of digitally enhanced LANDSAT TM images (Bands 4,3,2) acquired mainly in 1999. Build-up areas extracted on the basis of code1 of the original layer (considered to be build-up areas: 5U, 5UC, 5UR, 5I, 5A). |
|
Percent of location under forest |
Africover landcover multipurpose database (FAO) - based on LANDSAT TM 1999 |
The original land cover has been produced from visual interpretation of digitally enhanced LANDSAT TM images (Bands 4,3,2) acquired mainly in 1999. Forest areas extracted on the basis of code1 of the original layer (considered to be forested areas). The resulting shapefile was converted to a raster with the following values: 100 = forest (covering about 100% of the area); 65 = mixed forest (covering approx. 65% of the area; 0 = non-forest |
|
Percent of location under farmland |
Africover landcover multipurpose database (FAO) - based on LANDSAT TM 1999 |
The variable contains the percentage of the location's area that is under agricultural land. The original land cover has been produced from visual interpretation of digitally enhanced LANDSAT TM images (Bands 4,3,2) acquired mainly in 1999. Farming areas were extracted on the basis of code1 of the original layer (considered to be agricultural areas). The resulting shapefile was converted to a raster with the following values: 100 = agriculture (covering about 100% of the area); 65 = mixed agriculture (covering approx. 65% of the area); 0 = non-agriculture |
|
Percent of location under grass |
Africover landcover multipurpose database (FAO) - based on LANDSAT TM 1999 |
The original land cover has been produced from visual interpretation of digitally enhanced LANDSAT TM images (Bands 4,3,2) acquired mainly in 1999. Grass areas extracted on the basis of code1 and code2 of the original layer (considered to be grassland areas) |
|
Natural factors |
|
|
|
Arable land more than 60 % (1=yes ; 0=no) |
Jones P.G., 2004. Report on preparation of growing season days coverages for Hadley 3 scenarios A2 and B2 for the year 2000. Consultant's report, ILRI. |
This variable takes a value of 1 if the arable land is more than 60% of the location's area, and 0 otherwise. Arable land was defined as land with a length of growing period of more than 60 days per year. |
|
Percent of location with Arid or Semi-Arid land (i.e. LGP <= 180 days) |
Jones P.G., 2004. Report on preparation of growing season days coverages for Hadley 3 scenarios A2 and B2 for the year 2000. Consultant's report, ILRI. |
This variable describes the percentage of the location that is arid or semi-arid (ASAL). ASAL was defined as land with a length of growing period of less than 180 days per year. |
|
Elevation (masl) |
NASA, Shuttle Radar Topography Mission (SRTM) - 2000 |
The average elevation in meters above sea level within the location. |
|
Percent of location Steep land (I.e. > 10%) |
NASA, Shuttle Radar Topography Mission (SRTM) - 2000 |
This variable represents the percentage of the location's area that is defined as steep. Steep land was defined as having a slope of more than 10%. The slope was calculated based on the elevation and can be expressed in degrees or percent. |
|
Percent of location with 0 - 4% slope |
NASA, Shuttle Radar Topography Mission (SRTM) - 2000 |
The percentage of the location's area with a slope between 0 and 4 %. |
|
Percent of location with 4 - 8% slope |
NASA, Shuttle Radar Topography Mission (SRTM) - 2000 |
The percentage of the location's area with a slope between 4 and 8 %. |
|
Percent of location with 8 - 15% slope |
NASA, Shuttle Radar Topography Mission (SRTM) - 2000 |
The percentage of the location's area with a slope between 8 and 15 %. |
|
Percent of location with 15 - 30% slope |
NASA, Shuttle Radar Topography Mission (SRTM) - 2000 |
The percentage of the location's area with a slope between 15 and 30 % |
|
Percent of location with over 30% slope |
NASA, Shuttle Radar Topography Mission (SRTM) - 2000 |
The percentage of the location's area with a slope of more than 30 %. |
Soil Classification. The soil suitability was based on the classifications from morphological sequence where soils in the highland areas i.e., Andosols and Nitisols are classified as of good quality whereas those in the low lying areas comprising soils such as Gleysols and solonetz soils are classified as of poor quality due to saturation with base and poor drainage, thus poor in terms of plant growth. The soil suitability refers to the whole land surface within the country and is not restricted only to the arable lands and is based on the dominant type in a location.
Table A3: Descriptive statistics
|
Variable |
Label |
Mean |
Std. Dev. |
Min |
Max |
|
popden |
Population density |
205.64 |
226.05 |
0.12 |
2431.78 |
|
Elevation |
Elevation |
1345.19 |
659.38 |
2.82 |
3087.83 |
|
distance to forest |
Distance to forest (m) |
5658 |
8114 |
0.00 |
46756 |
|
Perc_water |
Percent of location under water |
0.44 |
2.63 |
0.00 |
60.20 |
|
Perc_built |
Percent of location built |
0.14 |
0.79 |
0.00 |
13.85 |
|
Perc_for |
Percent of location under forest |
4.42 |
11.13 |
0.00 |
84.66 |
|
Perc_farmland |
Percent of location under farmland |
28.28 |
27.26 |
0.00 |
97.95 |
|
Perc_grass |
Percent of location under grassland |
17.42 |
14.26 |
0.00 |
82.11 |
|
Perc_wooded |
Percent of location under wooded |
20.35 |
22.24 |
0.00 |
100.00 |
|
Perc_prota |
Percent of location under protected area |
1.60 |
9.39 |
0.00 |
100.00 |
|
Perc_wetlands |
Percent of location under wetlands |
1.60 |
6.13 |
0.00 |
97.43 |
|
Perc0_4slop |
Percent of location with 0_4 slope |
43.60 |
32.87 |
0.00 |
100.00 |
|
Perc4_8slop |
Percent of location with 4_8 slope |
24.08 |
16.18 |
0.00 |
73.76 |
|
Perc8_15slop |
Percent of location with 8_15slop |
17.14 |
14.79 |
0.00 |
59.27 |
|
Perc15_30slop |
Percent of location with 15_30slope |
10.79 |
13.26 |
0.00 |
62.13 |
|
Perc30_abovesl |
Percent of location with 30_aboveslope |
4.40 |
8.79 |
0.00 |
70.87 |
|
t_trav_munic |
Travel time to municipality (minutes) |
296.49 |
383.67 |
11.07 |
4417.17 |
|
t_trav_town |
Travel time to town (minutes) |
201.07 |
295.94 |
7.94 |
4323.98 |
|
t_trav_tcentre |
Travel time to trading centre (minutes) |
167.46 |
284.54 |
7.79 |
4323.98 |
|
t_trav_mrkt |
Travel time to market (minutes) |
128.81 |
254.57 |
7.53 |
3933.49 |
|
t_trav_road1 |
Travel time to road 1(tarmac) (minutes) |
229.44 |
365.34 |
5.46 |
4308.73 |
|
t_trav_road12 |
Travel time to road 1 or 2 ( tarmac or murram) (minutes) |
175.17 |
297.74 |
5.46 |
4275.04 |
|
t_trav_raod123 |
Travel time to 1or 2 or 3 (tarmac, murram or dirt)(minutes) |
116.49 |
251.88 |
3.93 |
3939.50 |
|
t_trav_hc |
Travel time to health centre (minutes) |
131.64 |
136.65 |
8.87 |
1302.03 |
|
Flood |
Flood potential (Dummy) |
0.40 |
0.49 |
0.00 |
1.00 |
|
Cvrain |
Coefficient of variation (rainfall) |
63.96 |
27.98 |
30.00 |
131.58 |
|
NDVI |
Normalized Difference Vegetation Index |
0.70 |
0.10 |
0.37 |
0.86 |
|
av_rainfall |
Average rainfall (mm) |
961.42 |
512.05 |
0.00 |
1987.00 |
|
lgparidsemi180 |
Length of growing period (LGP) 180 days |
20.10 |
38.53 |
0.00 |
100.00 |
|
lgp60days |
Length of growing period (less than 60days) |
95.56 |
19.21 |
0.00 |
100.00 |
|
d_dist_disthosp |
Distance to district hospital (meters) |
24983.52 |
28494.55 |
1508.18 |
160466.00 |
|
d_dist_dispen |
Distance to dispensary (meters) |
7574.55 |
7987.97 |
1022.44 |
64203.69 |
|
Goodsoil |
Good soil (dummy) |
0.44 |
0.50 |
0.00 |
1.00 |
|
d_dist_10k2 |
Distance to nearest town of 10,000 pple (meters) |
29853.15 |
32467.06 |
1407.87 |
234269.70 |
|
d_dist_50k2 |
Distance to nearest town of 50,000 pple (meters) |
70740.03 |
108082.70 |
1756.27 |
547139.10 |
|
d_dist_100k2 |
Distance to nearest town of 100,000 pple (ms) |
92253.60 |
121926.80 |
2366.59 |
638788.10 |
|
d_dist_200k2 |
Distance to nearest town of 200,000 pple(m) |
152382.00 |
152727.90 |
4892.93 |
798293.00 |
Table A4: Ordinary least squares (OLS) estimation
|
Dependent variable: poverty rate |
|
|
|
Variable |
Coefficient |
t-statistic |
|
Population density |
-0.0001 |
(5.0228)** |
|
Average elevation (meters above sea level) |
0.0000 |
(2.5578)* |
|
reg2 (Central) |
-0.1400 |
(14.0785)** |
|
reg3 (Coast) |
0.0622 |
(4.5167)** |
|
reg4 (East) |
0.0983 |
(12.1229)** |
|
reg5 (North Eastern) |
0.1817 |
(13.3528)** |
|
reg6 (Nyanza) |
0.1418 |
(15.8019)** |
|
reg8 (Western) |
0.0998 |
(9.4043)** |
|
Percent of location under grass |
-0.0016 |
(6.0789)** |
|
Percent of location under farmland |
0.0002 |
-1.1856 |
|
Percent of location wooded |
0.0005 |
(3.3277)** |
|
Percent of location that is built up |
-0.0125 |
(4.3257)** |
|
Percent of location with 4-8% slope |
0.0013 |
(6.4337)** |
|
Percent of location with 8-15% slope |
0.0001 |
-0.4054 |
|
Percent of location with 15-30% slope |
-0.0001 |
-0.3696 |
|
Percent of location with >30% slope |
0.0021 |
(5.7023)** |
|
Percent of location with LGP <60 days |
0.0005 |
(3.6689)** |
|
Percent of location with LGP 180 days |
-0.0006 |
(5.9913)** |
|
Rangeland (dummy) |
0.0109 |
-1.6311 |
|
Good soil (dummy) |
-0.0106 |
(2.0023)* |
|
Average travel time to type 1 or 2 road (minutes) |
ff0.0000 |
-1.6445 |
|
Mean distance to district hospital |
-0.0002 |
-1.3807 |