Govt backed Social security program ,There is little doubt that Coronavirus will leave an anemic economy. There has ,however, been little plans afoot to bring life to vital sectors of the economy with the help of others. Government initiated some local level social security programs without bringing aboard the non governmental organizations. Government-backed social security programs seldom trickle down to target group of people, a view pretty popular among ordinary citizens.
Seldom reach village & slum.
Micro lenders doing great job,
Helping borrowers hit by disaster.
Setting shining example in the globe.
Yet calamity hobbles their operation,
Hampering the process of resource creation.
There may be some truth in it. Back in November and December last year, I interviewed 40 rickshaw pullers to grasp well spending and savings behavior of Dhaka rickshaw pullers. When I asked them whether they received any social security benefits from the government, I got three affirmative responses. One got rice from vulnerable group feeding program, another's father received old age benefit and one rickshaw puller's wife got lactating mother's benefit. All three had closer ties with local government representatives: one's relative was union parishod member, the other two had links with ruling elites. So three out of forty rickshaw pullers received government sponsored social security benefits. The forty-respondent strong study may not representative but is an indication how ineffective the government social security program is.
Meanwhile, micro-lending institutions are far more helpful in post disaster periods. In that study, one rickshaw puller, hailing from Borishal, told me one leading microcredit institution gave him, an existing borrower, a calf to recuperate the loss he sustained in Sidr. It is also important to note that a huge section of beneficiaries are women and bottom rung of the society, who generate resources in the economy by raising livestock and sharecropping.
Despite a sustained effort to cast aspersions on microcredit institutions, they are doing a commendable job to scale up the have-nots and accelerate the economy. However, disasters also hobble their operations and seriously harm empowerment of the poor. Microcredit institutions are vital to provide any economic support to the marginalized and vulnerable population.
Recently, I embarked upon to look at disaster year's influence on microcredit operations of three leading microcredit institutions. Data for the period 2005-2018 gathered from Bangladesh Economic Review. Disaster years were political or international or natural disasters that hurt the economy.
Durbin-Watson statistic for ASA and Grameen reported no autocorrelation. For 14 observations and 1 explanatory variable, ASA had a statistic d= 1.695 and Grameen's statistic was d = 1.716. However, for BRAC, it fell into indecisive zone (d = 1.048). Modified d test was found to have positive autocorrelation (d= 1.084< du= 1.35).
For ASA and Grameen , following regression functions were constructed:
lnDisbt = b1 + b2Rect + b3Dt
Where lnDisbt = log natural of disbursement at t,
Rect = Recovery at t,
D= 1 for disaster years,
= 0 for calm years.
And for BRAC, following regression function was chosen:
Disbt = a + b Dt + c Rect + d Dt Rect
+ ut
where Disbt = Disbursement at t,
Rect = Recovery at t,
D= 1 for disaster years,
= 0 for calm years.
and ut was generated through AR(1) scheme:
ut = ut-1 + e
Since BRAC data had positive correlation, transformed function was constructed. From known P, I derived (Disbt - PDisbt-1) as regressand and (Rect - PRect-1) as regressor. This approach was resulted in loss of 1 observation for both. Prais-Winsten transformation (√1-P2 Disbt and √1-P2 Rect ) was done to prevent it. For calm years, values of D were zero. But first observation of disaster years was set 1/(1-P) and 1 for the rest of the disaster years. Dt Rect
was zero for the calm years. First observation for disaster years was assigned Dt Rect
= Rect and all observations were assigned (Dt Rect - Dt Rect-1) = ( Rect - P Rect-1)
The resulting functions appeared to be:
ASA: lnDisbt = 8.99 + 0.0000067Rect - 0.144Dt (F= 2.588, p= 0.12) (t=35.70, p=0) (t=2.27, p= 0.043) (t= -0.42, p= 0.67)
Grameen: lnDisbt = 9.083 + 0.00012Rect - 0.0038Dt (F= 67.85, p= 0.0)
(t=73.07, p=0) (t=11.53, p= 0.00) (t= -0.038, p= 0.96)
BRAC: Disbt = -0.506 - 37.214 Dt + 1.112 Rect + 0.0037 Dt Rect (F= 506.9, p= 0)
(t= -0.001, p= 0.99) (t=-0.125, p=902) (t=18.63, p=0.00) (t=0.061, p=0.95)
The model fit well for BRAC. However, differential intercept and differential slope coefficients were found to be insignificant. Had the differential intercept coefficient been significant, we would say that BRAC's disbursement of microcredit during disaster years was lower by Tk 37.214 crores.
Regression model for Grameen fit well at 5% level of significance, as reported by F. Meanwhile, it did not fit well for ASA at that level of significance. However, slope coefficient for dummy variable for both the model did not appear to be significant. Had it been significant, we would say Grameen's disbursement for disaster years was 0.38% lower than that of normal years and ASA's disbursement for disruptive years was 13.45% lower than that of normal years.
It is evident that disruption resulting from various forms of disasters has harming effect on microcredit operations. This may affect economic activities of have-not communities.
Since microcredit institutions scattered all across country and they have a large borrowers' base, post disaster efforts will be better implemented with their help. Misappropriation will be lower if the fund is distributed through them. At the same time, more government money should be injected into microcredit institutions so that microcredit operations, or economic activities of the have-nots, do not get affected by Coronavirus. Money at the hands of Have -nots and marginalized groups means resource creation, leading to increase of GDP. And microcredit institutions are leading the way in terms of resource creation.
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