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. 2018 Apr 16;18:104. doi: 10.1186/s12884-018-1734-0

Table 1.

Linear regression analyses to investigate how well relative quintiles, actual mean wealth index scores and absolute income (per quintile) predict SBA coverage (N = 1465 observations)

SBA prevalence (coefficients expressed as percent point)
Analysis level Cross-country analysis Within country analysis
Model 1 Model2 Model 3 Model 4 Model 5 Model 6 Model 7
Asset quintile 1 0 (reference) p < 0.001 0 (reference) p < 0.001 0 (reference) p = 0.139
Asset quintile 2 10.19 (1.05) 10.19 (1.17) 2.18 (3.93)
Asset quintile 3 18.66 (1.80) 18.66 (2.02) 5.36 (6.53)
Asset quintile 4 28.60 (2.23) 28.60 (2.49) 9.98 (9.23)
Asset quintile 5 40.04 (2.56) 40.04 (2.86) 11.79 (14.02)
Mean wealth scores 6.97 (1.82)
P < 0.001
6.85 (3.29)
P < 0.001
Log incomea 19.13 (1.24)
p < 0,001
18.38 (1.31)
p < 0.001
12.78 (6.13)
p = 0.04
Survey specific intercepts NO NO NO YES YES YES YES
R-squared 0.220 0.128 0.516 0.877 0.777 0,879 0,881

Robust standard errors in parentheses are clustered at the country level

aIncome is expressed in 2011 purchasing power parity-adjusted international dollars. Model 1 and model 4: cross-country and within-country prediction of SBA coverage according to wealth quintiles. Model 2 and model 5: cross-country and within-country prediction of SBA coverage according to actual mean wealth scores. Model 3 and model 6: cross-country and within-country prediction of SBA coverage according to household income. Model 7: within-country prediction of SBA coverage according to wealth quintiles and household income