Modeling the factors of portfolio at risk for microfinance institutions in Palestine

Figures & data

Table 1. Variables measurement

Variable	Notation	Description	Effect on PAR30
(1)Inflation	INF	% change in consumer price index	Negative
(2)Interest rate	I	Interest rate on conventional loans in dollar currency	Positive
(3)Gross domestic product	GDP	% change in gross domestic product on annual basis	Negative
(4)Employee staff	PERSONNEL	Number of individuals who actively employed by MFI.	Positive
(5)Operational efficiency	OE/TA	Operating expenses divided by total assets of each MFI.	Positive
(6)Active borrowers	AB	Natural logarithm of number active borrowers who currently obtained loans from MFI.	Positive
(7)Portfolio at risk	PAR30	Portfolio at risk is loans overdue <30 divided by gross loan portfolio.	––––

Source: Author conceptualization.

Table 2. Descriptive statistics and Pearson correlation

	Mean	S.D	(1)	(2)	(3)	(4)	(5)	(6)	(7)
(1) INF	0.014	0.014	1
(2) I	0.061	0.003	0.607**	1
(3) GDP	0.064	0.076	0.563**	0.605**	1
(4) Personnel	80.20	77.05	−0.317*	−0.280*	−0.214*	1
(5) OE/TA	0.133	0.068	0.219*	0.185	0.059	−0.481**	1
(6) Log AB	3.771	0.392	−0.272*	−0.189	−0.142	0.588**	−0.416**	1
(7) PAR30	0.073	0.055	0.112	−0.100	−0.229*	0.236*	0.519**	−0.407**	1

Note: The table presents the descriptive statics and correlation of research variables and significance. * and ** denote significant level at 5% and 1%, respectively. Source: Author calculation.

Figure 1. Average of Portfolio at risk in Palestine from 2010 to 2020.

Source: based on historical data obtained from Palestinian MFIs annual reports.

Table 3. Results of panel unit roots test (Levin Lin Chu test)

Variable	Level		1st difference		2nd difference
	Statistic	Prob.	Statistic	Prob.	Statistic	Prob.
INF	−2.713	0.003*	−2.336	0.009*	−7.304	0.000*
I	−0.658	0.255	−19.381	0.000*	−26.085	0.000*
GDP	−1.788	0.036*	−3.983	0.000*	−12.139	0.000*
Personnel	−2.144	0.016*	−3.220	0.000*	−3.059	0.001*
OE/TA	−1.474	0.070	−2.210	0.013*	−4.667	0.000*
Log AB	−2.088	0.018*	−1.64	0.050*	−4.613	0.000*
PAR30	2.034	0.979	−1.203	0.114	−12.236	0.000*

Note: Unit root test with intercept. Source: E-views estimation.

Table 4. Optimal panel data estimates

Method	Hypothesis	Statistical test	Statistics	p-Value
Breusch and Pagan	H0: OLS appropriates than fixed effects	Lagrangian multiplier	19.673	0.0325
Correlated random effect	H0: Random effects appropriate than Fixed effects (H0: β1 = 0).	Huasman test	0.6218	0.8914

Source: E-views estimation.

Table 5. Residual diagnostic tests of panel data estimates

Approach	Hypothesis	Diagnostic test	Statistics	p-Value
Normality	H0: Explanatory variables are normally distributed	Jarque–Bera	9.2422	0.0098
Autocorrelation	H0: There is not first order autocorrelation	Wooldridge test	5.4510	0.0655
Heteroskadisity	H0: Disturbance term constant	Breusch Pagan Godfrey	14.834	0.0111

Source: E-view estimation.

Table 6. Panel data regression results

Variable	Pooled (OLS)	Fixed effects	Random effects	Robust GMM
INF	0.9492 (0.0856)	1.7129** (0.0059)	0.9493 (0.0978)	0.9492 (0.0856)
I	−1.2923 (0.3529)	−1.2135 (0.3417)	−1.2924 (0.3713)	−1.2923 (0.3529)
GDP	−0.2906** (0.0356)	−0.3226* (0.0110)	−0.2905** (0.0429)	−0.2923** (0.0356)
Personnel	0.0004* (0.0316)	0.0002 (0.2010)	0.0004* (0.0113)	0.0004** (0.0086)
OE/TA	0.3985** (0.0001)	0.4259* (0.0247)	0.3985** (0.0002)	0.3985* (0.0001)
Log AB	−0.0963** (0.0010)	−0.0484 (0.3228)	−0.0963** (0.0015)	−0.0963* (0.0010)
Constant	0.4285** (0.0009)	−0.1187 (0.5576)	0.4284** (0.0013)	0.4285** (0.009)
R-squared	0.5110	0.6573	0.5110	0.5110
F statistic (Prob.)	8.3605 (0.0005)	8.4417 (0.0000)	8.3605 (0.0000)	—-

Note: * and ** denote significant level at 1% and 5%, respectively. Source: E-views estimation.