Discrimination in the rental housing market in the Sunyani Municipality, Ghana

Figures & data

Figure 1. A diagrammatic representation of the study.

Figure 2. Map of the study area.

Source: University of Cape Coast, Cartography Section (University of Cape Coast, Cartography Section, 2018)

Table 1. Explanation of variables used in the analysis

Variable	Description	Measurement	Model 1	Model 2
Dependent variable
Discrimination against tenants	Have you ever been discriminated against	1 = Yes 0 = Otherwise
Discrimination by Landlords	Have you ever discriminated against a tenant	1 = Yes 0 = Otherwise
Independent variables
Household size	Number of people in the household of the tenant	Number	+	+
Gender	Gender of respondent	1 = Male 0 = Female	+	+
Age	Age of landlord	Number		+
Age of tenant	The age of the tenant	Number	+
Nationality	Tenant country of nationality	1 = Ghanaian 0 = Otherwise	+	+
Marital status	Marital status of the tenant	1 = Married 0 = Otherwise	-	-
Disability	Disability status of the tenant	1 = Yes 0 = No	+	+
Ethnic group	The ethnic group of tenant	1 = Akan 0 = Otherwise	+	+

Model 1 represents the hypothesis for the probit regression on tenants, while Model 2 is for landlords.

Table 2. Discriminating factors considered by landlords and tenants

	Landlords			Tenants
Discrimination factors	% in agreement	Mean	SD	% in agreement	Mean	SD
Ethic origin	41.6	0.42	0.494	36.4	0.36	0.482
Nationality	38.2	0.38	0.487	26.0	0.26	0.440
Disability	35.8	0.36	0.481	32.6	0.33	0.470
Age	35.3	0.35	0.479	23.1	0.23	0.423
Marital status	62.4	0.62	0.486	51.2	0.51	0.501
Household size	53.2	0.53	0.500	47.9	0.48	0.501
Gender	41.0	0.41	0.493	16.1	0.16	0.368

Scale: 0.00–0.49 = No; 0.50–1.00 = Yes

Source: Fieldwork (2020).

Table 3. Determinants of discrimination against tenants

Discriminate	Coefficient	Robust Std Err.	Marginal Effect	P-Value
Household size	0.051	0.020	0.015	***0.010
Gender	−0.378	0.234	−0.112	0.106
Ethnic group	−0.371	0.222	−0.109	*0.096
Age	−0.014	0.014	−0.004	0.288
Nationality	−0.425	0.512	−0.126	0.406
Marital status	−0.645	0.385	−0.179	*0.094
Disability	0.107	0.196	0.031	0.584
Constant	1.745	0.791		**0.027
Prob>chi2 = 0.000	PseudoR^2 = 0.235
Wald chi2(9) = 45.41
Log pseudo likelihood = −86.912

Source: Fieldwork (2020).

Table 4. Factors influencing the discrimination against tenants by landlords

Discriminate	Coefficient	Robust Std Err.	Marginal Effect	P-Value
Household size	0.145	0.047	0.009	***0.002
Gender	−1.099	0.512	−0.063	**0.032
Age of tenant	3.988	0.777	0.276	***0.000
Nationality	−1.712	0.528	−0.133	**0.001
Marital status (Married)	−2.742	0.603	−0.254	***0.000
Ethnicity	−3.100	1.079	−0.240	***0.004
Disability	2.427	0.790	0.172	***0.002
Constant	−5.257	1.599		***0.001
Prob>chi2 = 0.000	PseudoR^2 = 0.807
Wald chi2(10) = 53.48
Log pseudo likelihood = −18.760

Source: Fieldwork (2020).

Table A1. Measures of fit of probit regression for tenants

Log-Lik Intercept Only	−113.552	Log-Lik Full Model	−86.912
D(160)	173.825	LR(9)	53.279
		Prob>LR	0.00
McFadden’s R^2	0.235	McFadden’s Adj R^2	0.147
ML (Cox-Snell) R^2	0.269	Cragg-Uhler (Nagelkerke) R^2	0.365
McKelvey and Zavoina’s R^2	0.399	Efron’s R^2	0.293
Variance of y*	1.663	Variance of error	1.000
Count R^2	0.759	Adj Count R^2	0.379
AIC	1.140	AIC*n	193.825
BIC	−647.903	BIC’	−7.057
BIC used by Stata	225.183	AIC used by Stata	193.825
Wald test of Insigma = 0	Chi2(1) = 0.59	Prob>chi2 = 0.444

Table A2. Linktest analysis of probit regression for tenants

Discriminate	Coeff.	Std. Err.	Z	P>|z|	(95% Conf. Interval)
_hat	1.032	0.178	5.78	0.000	0.682	1.382
_hatasq	−0.068	0.195	−0.35	0.729	−0.450	0.315
_cons	0.032	0.148	0.22	0.830	−0.257	0.321
Number of obs	170
LR Chi^2 (2)	53.40
Prob>Chi^2	0.000
Pseudo R^2	0.235
Log-likelihood	−86.852

Table A3. Measures of fit of probit regression for landlords

Log-Lik Intercept Only	−97.008	Log-Lik Full Model	−18.760
D(162)	37.520	LR(10)	156.497
		Prob>LR	0.00
McFadden’s R^2	0.807	McFadden’s Adj R^2	0.693
ML (Cox-Snell) R^2	0.595	Cragg-Uhler (Nagelkerke) R^2	0.883
McKelvey and Zavoina’s R^2	0.957	Efron’s R^2	0.828
Variance of y*	23.302	Variance of error	1.000
Count R^2	0.960	Adj Count R^2	0.837
AIC	0.344	AIC*n	59.520
BIC	−797.313	BIC’	−104.964
BIC used by Stata	94.206	AIC used by Stata	59.520
Wald test of Insigma = 0	Chi2(2) = 3.93	Prob>chi2 = 0.1404

Table A4. Linktest analysis of probit regression for landlords

Discriminate	Coeff.	Std. Err.	Z	P>|z|	(95% Conf. Interval)
_hat	1.016	0.198	5.12	0.000	0.627	1.405
_hatasq	0.051	0.152	0.33	0.738	−0.246	0.348
_cons	0.075	0.320	−0.23	0.815	−0.701	0.552
Number of obs	173
LR Chi^2 (2)	156.61
Prob>Chi^2	0.000
Pseudo R^2	0.807
Log-likelihood	−18.704

University of Cape Coast, Cartography Section. (2018). Map of Ghana. Department of Geography and Regional Planning. Cape Coast: University of Cape Coast.

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