Famers’ access to credit: Does collateral matter or cash flow matter?—Evidence from Sindh, Pakistan

Figures & data

Figure 1. Theoretical analysis framework.

Table 1. The land utilization statistics in Sindh province of Pakistan

Land utilization	Million hectare	Percentage
Total reported area	14.09	100
Uncultivated area	6.80	48.26
Culturable waste	1.18	8.37
Cultivated area	5.08	36.05
Forest area	1.03	7.31

Source: Agriculture Statistics of Pakistan 2010–2011.

Figure 2. Map of study area.

Table 2. Mean socioeconomic characteristics of the respondents

Variables	Mean	SD
Age of rural household head (in years)	41.16	11.47
Gender (dummy)	0.793	0.406
Education (in years)	7.450	4.425
Household size (in number)	8.853	2.720
Farming experience (in years)	26.58	9.539
Farm size (acres)	12.81	14.40
Off-farm income (in PKR)	23,283.33	12,108.20
Availability of collateral (dummy)	0.677	0.469

Source: Researchers’ own calculations, Field Survey (2016).

Table 3. Estimated correlation matrix results

Variables	Credit access	Age	Gender	Education	Household size	Farming experience	Farm size	Off-income	Availability of collateral
Credit access	1.00
Age	0.03	1.00
Gender	0.10	0.10	1.00
Education	0.04	−0.07	−0.09	1.00
Household size	0.13*	0.35**	−0.01	−0.03	1.00
Farming experience	0.12*	0.60**	−0.04	−0.05	0.23**	1.00
Farm size	0.03	0.21**	0.06	0.04	0.08	0.07	1.00
Off-income	0.16**	−0.06	−0.05	0.02	−0.01	0.06	−0.06	1.00
Availability of collateral	0.42**	−0.01	0.07	0.01	0.04	0.01	−0.05	0.036	1.00

^* Correlation is significant at the 0.05 level (2-tailed).

^** Correlation is significant at the 0.01 level (2-tailed).

Table 4. Results of probit regression analysis

Variables	Coefficient	Robust SE	z	P > |z|
Age	−0.0163912	0.0141206	−1.16	0.246
Gender	0.2519995	0.2381868	1.06	0.290
Education	0.2365701	0.2836081	0.83	0.404
Household size	0.0843538**	0.0443412	1.90	0.057
Farming experience	0.6894329**	0.3526541	1.95	0.051
Farm size	0.168599	0.1687914	1.00	0.318
Income level	0.7005428**	0.3198813	2.19	0.029
Availability of collateral	1.560256***	0.2496934	6.25	0.000
Constant	−7.240896**	3.234158	−2.24	0.025
LR χ^2 = 50.37 Prob > χ^2 0.000
Pseudo R^2 = 0.3079
Log Likelihood = −74.796532

Source: Researchers’ own calculations, Field Survey (2016).

^* p < 0.10 of significance level.

^** p < 0.05 of significance level.

^*** p < 0.01 of significance level.

Table 5. Socioeconomic factors affecting on access to credit (marginal effects analysis)

Variables	dy/dx	Std. Err.	z	P > |z|
Age	−0.0017323	0.00147	−1.18	0.238
Gender	0.0300731	0.02937	1.02	0.306
Education	0.0284702	0.038	0.75	0.454
Household size	0.0089149*	0.00501	1.78	0.075
Farming experience	0.0559083**	0.02598	2.15	0.031
Farm size	0.0178184	0.01807	0.99	0.324
Income level	0.0740369***	0.0258	2.87	0.004
Availability of collateral	0.2665232***	0.04612	5.78	0.000

Source: Researchers’ own calculations, Field Survey (2016).

* p < 0.10 of significance level.

** p < 0.05 of significance level.

*** p < 0.01 of significance level.