price-related risks would originate from the international markets, information on
import policy and World Trade Organisation (WTO) related measures plays an
important role. However, the data shows that the share of households that are aware
of WTO stipulations is as low as is only 8 % at All India level. Karnataka’s score is
one percent less than the All India figure, i.e. 7 %. Kerala is the only state where
44 % of the farmer households are aware of it. Punjab stands second with 23 % of
households being aware of WTO related norms. The rest of the Indian States have
negligible share of households being aware of WTO (see Table 2).
Moving on to the aspect of crop insurance, it is found that only 4 % of the
household had insured their crop at All India level while in case of Karnataka the
figure is slightly higher revealing 8 % of the households obtaining crop insurance.
The main reason for not being insured is the lack of awareness about this programme. It is found that 57 % of households at All India level were not aware of
insurance facility and Karnataka again roughly shows the same picture, i.e. 54 % of
the households were not aware of it. Expectedly, Punjab on the other hand shows a
different picture where, only 20 % of the households are not aware of the programme; but surprisingly, even though majority of the households had awareness,
only 1.25 % of the households had insured their crop. What actually is the reason for
such low insurance coverage of farmer households even when they are aware of the
facility? Is it because the programme is not user friendly? If so the usefulness of the
crop insurance programme and how viable it is for different sections of the farmers
need to be studied with the aid of primary and secondary data as also intensive field
visits to find answers to the above questions. The next section provides a regression
analysis based on Situation Assessment Survey of Farmers data.
3 Regression Analysis
To identify the factors that determine adoption of crop insurance by the farmer
households in the state of Karnataka, a probit regression has been carried out. The
dependent variable is a dichotomous variable, assuming a value 1 if a farmer
household has insured crop, zero value has been assigned otherwise. We have
considered the following set of independent variables.
3.1 Selection of Explanatory Variables
Having crop insurance depends on both demand and supply side factors. Demand
for crop insurance may depend on the need to have crop insurance and on
awareness or information about crop insurance. Need or desire to have crop
insurance may depend on the risk faced by a household. A household is expected to
have higher demand for crop insurance if risk faced by the household is high. As
already mentioned in Sect. 1, there are three types of risk that a farmer may face,
246
M. Rajeev et al.
import policy and World Trade Organisation (WTO) related measures plays an
important role. However, the data shows that the share of households that are aware
of WTO stipulations is as low as is only 8 % at All India level. Karnataka’s score is
one percent less than the All India figure, i.e. 7 %. Kerala is the only state where
44 % of the farmer households are aware of it. Punjab stands second with 23 % of
households being aware of WTO related norms. The rest of the Indian States have
negligible share of households being aware of WTO (see Table 2).
Moving on to the aspect of crop insurance, it is found that only 4 % of the
household had insured their crop at All India level while in case of Karnataka the
figure is slightly higher revealing 8 % of the households obtaining crop insurance.
The main reason for not being insured is the lack of awareness about this programme. It is found that 57 % of households at All India level were not aware of
insurance facility and Karnataka again roughly shows the same picture, i.e. 54 % of
the households were not aware of it. Expectedly, Punjab on the other hand shows a
different picture where, only 20 % of the households are not aware of the programme; but surprisingly, even though majority of the households had awareness,
only 1.25 % of the households had insured their crop. What actually is the reason for
such low insurance coverage of farmer households even when they are aware of the
facility? Is it because the programme is not user friendly? If so the usefulness of the
crop insurance programme and how viable it is for different sections of the farmers
need to be studied with the aid of primary and secondary data as also intensive field
visits to find answers to the above questions. The next section provides a regression
analysis based on Situation Assessment Survey of Farmers data.
3 Regression Analysis
To identify the factors that determine adoption of crop insurance by the farmer
households in the state of Karnataka, a probit regression has been carried out. The
dependent variable is a dichotomous variable, assuming a value 1 if a farmer
household has insured crop, zero value has been assigned otherwise. We have
considered the following set of independent variables.
3.1 Selection of Explanatory Variables
Having crop insurance depends on both demand and supply side factors. Demand
for crop insurance may depend on the need to have crop insurance and on
awareness or information about crop insurance. Need or desire to have crop
insurance may depend on the risk faced by a household. A household is expected to
have higher demand for crop insurance if risk faced by the household is high. As
already mentioned in Sect. 1, there are three types of risk that a farmer may face,
246
M. Rajeev et al.