Poor health as a precursor to consumer debt distress in South Africa

Figures & data

Table 1. Detailed variable definition.

Variable	Measurement
Debt service ratio	Continuous: Ratio of total monthly debt repayment to gross monthly income (debt service/Income) × 100.
Over-indebted	Dummy: 1 if wave 3 debt-service ratio is equal to or greater than 25%, 0 otherwise.
Income	Continuous: Total gross monthly income in wave 2 (restricted to 1st and 99th percentiles).
Outstanding debt	Continuous: Total consumer debt outstanding in wave 3 (restricted to 1st and 99th percentiles).
Ill-health	Dummy: 1 if the individual was diagnosed with a serious illness between 2009 and 2011, 0 otherwise.
Commitments	Continuous: Number of consumer debt commitments held in wave 2.
Medical aid	Dummy: 1 if the individual is covered by medical aid, 0 otherwise.
Employed	Dummy: 1 if the individual had a regular employment in wave 2, 0 otherwise.
Income shock	Dummy: 1 if wave 3 gross income is less than wave 2 gross income, 0 otherwise.
Age	Continuous: Age of the respondent in wave 3 (between 20 and 70).
BMI	Continuous: Body Mass index (BMI = weight (kg)/height (m)^2): wave 2 and/or wave 3 (restricted to 1st and 99th percentiles).
Education	Highest formal educational level attained: 7 categories, from 0 (no education) to 6 (University).
IncomeXemployed	Continuous: Interaction of Income and Employment.

Table 2. Distribution of consumer debt (n = 2139).

	Mean	Standard deviation
Debt outstanding	R11 006	R24 838
Debt service ratio (DSR)	39%	109%
If DSR > = 25% (n = 920)	74%	160%

Table 3. Consumer debt performance against selected variables.

	Debt outstanding			Debt-service ratio > = 25%
	Mean	Corr	P*	Mean	Corr	P*
Ill-health    (1)	R10083	−0.035	0.154	.51	0.075	0.001
(0)	R12199			.42
Medical aid (1)	R27382	0.343	0.000	.34	−0.123	0.000
(0)	R6619			.48
Income shock (1)	R14403	0.035	0.242	.55	0.104	0.000
(0)	R12195			.42
Employed (1)	R15262	0.196	0.000	.38	−0.216	0.000
(0)	R4601			.62
Quintiles of gross income		0.432	0.000		−0.123	0.000
1st quintile	R4788			.55
2nd quintile	R4099			.56
3rd quintile	R2858			.48
4th quintile	R5792			.47
5th quintile	R21064			.37
Commitments 1	R5754	0.459	0.000	.38	0.197	0.000
2	R16777			.57
3	R39199			.63
4	R74609			.74
Age.    20–30 years	R7179	0.085	0.000	.39	0.060	0.018
31–40 years	R11043			.47
41–50 years	R16068			.44
51–65 years	R14061			.46
66–70 years	R9902			.57
Education		0.285	0.000		−0.109	0.000
No education	R3531			.51
Grade R – 11	R6096			.49
Grade 12/Matric	R12105			.42
NTC 1–3	R22402			.58
Certificate	R15367			.43
Diploma	R22542			.31
Degree	R33765			.34
BMI quintiles		0.064	0.003		−0.028	0.226
1st quintile	R8255			.41
2nd quintile	R10834			.43
3rd quintile	R10911			.39
4th quintile	R13486			.42
5th quintile	R13592			.46

Notes: Corr refers to bivariate correlation coefficients while P* refers to their significance levels

Table 4. Logistic regression estimation results.

	Dependent variable: 1 if debt service ratio > = 25%         0 if debt service ratio < 25%
	Coefficients	Odds ratios	Marginal effects
Ill-health	0.292*	1.339*	0.072*
	(0.157)	(0.210)	(0.039)
Medical aid	−0.685***	0.504***	−0.168***
	(0.206)	(0.104)	(0.051)
Income shock	1.198***	3.314***	0.294***
	(0.169)	(0.558)	(0.041)
Employed	−3.393**	0.034**	−0.833**
	(1.477)	(0.050)	(0.363)
Log income	−0.782***	0.458***	−0.192***
	(0.208)	(0.095)	(0.051)
Education	−0.100**	0.905**	−0.025**
	(0.046)	(0.042)	(0.011)
Age	0.008	1.008	0.002
	(0.006)	(0.006)	(0.002)
Commitments	1.388***	4.006***	0.341***
	(0.125)	(0.501)	(0.031)
BMI	−0.005	0.995	−0.001
	(0.009)	(0.009)	(0.002)
IncomeXemployed	0.391*	1.478*	0.096*
	(0.216)	(0.319)	(0.053)
Intercept	3.874***	48.116***
	(1.388)	(66.786)
N	1,222	1,222	1,222
Pseudo R^2	0.156
Log likelihood	−708.90
Chi Square	195.37

Notes: Robust standard errors in parentheses.

***p < 0.01; **p < 0.05; *p < 0.10.

Figure 1. Diagnostics for Leverage and Outliers.

Table A1. Pairwise correlation matrix.

	Ill-health	Medical Aid	Income shock	Employed	Income	Education	Age	Commitments	BMI
Ill-health	1 (16229)
Medical Aid	−0.0063 (14261)	1 (14396)
Income shock	−0.0250 (6071)	0.0303 (5627)	1 (6142)
Employed	−0.0459 (15564)	0.2367 (14395)	0.1998 (6067)	1 (17149)
Income	−0.0058 (7132)	0.4833 (6537)	0.3107 (6056)	0.4999 (7787)	1 (7931)
Education	−0.1010 (16177)	0.4067 (14370)	0.0675 (6121)	0.2973 (15726)	0.3856 (7200)	1 (16403)
Age	0.2665 (12562)	0.0763 (11152)	−0.0115 (5187)	−0.0247 (12259)	0.1714 (6125)	−0.1661 (12715)	1 (12759)
Commitments	−0.0169 (2658)	0.3310 (2690)	−0.0079 (1507)	0.2673 (2692)	0.3806 (1596)	0.2615 (2690)	0.0202 (2605)	1 (2692)
BMI	0.1107 (14948)	0.1011 (14002)	0.0198 (5839)	0.0952 (16212)	0.0541 (7373)	0.0765 (15104)	0.2489 (11809)	0.0803 (2618)	1 (16226)

Note: Number of observations in parenthesis.

Table B1. Conditional fixed effects model of debt distress.

	Fixed Effects
	Coefficients
Ill-health	0.060*
	(0.032)
Medical aid	−0.121***
	(0.038)
Income shock	0.239***
	(0.033)
Employment	−0.792***
	(0.277)
Log income	−0.166***
	(0.039)
Age	0.002
	(0.001)
Commitments	0.269***
	(0.020)
BMI	−0.001
	(0.002)
IncomeXemployed	0.092**
	(0.040)
Education	−0.018*
	(0.009)
Intercept	1.351***
	(0.259)
N	1,222
R^2	0.192
Number of Year	3
Year FE	YES

Notes: Standard errors in parentheses.

***p < 0.01, **p < 0.05, *p < 0.10.